causal comparative research articles in education

Action Research: Designing Causal-Comparative Studies for Assessing Educational Situations in the Philippine Context

Action research has emerged as a crucial methodology for educators and education professionals to enhance their practice and address real-world challenges in the classroom. The Department of Education (DepEd) in the Philippines acknowledges the significance of action research in improving the quality of education and tackling various issues faced by teachers and students. This article focuses on designing causal-comparative studies as a component of action research, specifically for assessing situations within the DepEd context.

The purpose of this article is to provide a comprehensive guide for DepEd personnel on how to design and conduct causal-comparative studies effectively. By understanding and implementing this research methodology, educators can gain valuable insights into educational practices and their effects on student outcomes, ultimately contributing to evidence-based decision-making and continuous improvement in the Philippine education system.

Table of Contents

Understanding Action Research in the Philippine Educational Context

Action research is a systematic inquiry conducted by educators to gather information about their schools, teaching methods, and student learning. It aims to improve educational practices and solve specific problems encountered in the classroom or school environment. The process involves identifying a problem, collecting and analyzing data, and implementing changes based on the findings.

In the Philippine context, action research has gained significant traction in recent years. DepEd Order No. 16, s. 2017, titled “Research Management Guidelines,” emphasizes the importance of research in informing policy and practice within the education sector. This order provides a framework for managing research initiatives at various levels of the education system, from schools to the central office.

The Philippine Basic Education Research Agenda (PBERA), established by DepEd Order No. 39, s. 2016, further underscores the significance of research in addressing key educational challenges. The PBERA identifies priority research areas, including teaching and learning, child protection, human resource development, and governance. Causal-comparative studies can be particularly useful in exploring these priority areas and generating evidence-based recommendations for improvement.

Learning Action Cells (LACs)

An important aspect of action research in the Philippine context is the implementation of Learning Action Cells (LACs). As outlined in DepEd Order No. 35, s. 2016, LACs are school-based continuing professional development strategies that aim to improve teaching practices and student learning outcomes. LACs provide a collaborative platform for teachers to engage in action research, share best practices, and address common challenges in their classrooms.

When conducting causal-comparative studies, researchers can utilize LACs as a means of:

• Identifying research problems relevant to local school contexts
• Collaborating with colleagues in data collection and analysis
• Discussing and interpreting research findings
• Developing and implementing action plans based on research results

Causal-Comparative Research Design: An Overview

Causal-comparative research, also known as ex post facto research, is a non-experimental research design used to explore possible cause-and-effect relationships between variables. In this design, researchers examine how independent variables affect dependent variables by comparing groups of individuals or situations where the independent variable is present or absent.

Key characteristics of causal-comparative research include:

• Non-manipulation of variables: Unlike experimental research, causal-comparative studies do not manipulate the independent variable. Instead, they examine existing groups or situations.
• Retrospective approach: Researchers study events or conditions that have already occurred, attempting to identify potential causes for observed differences between groups.
• Group comparison: The design involves comparing two or more groups that differ on the independent variable to determine if there are significant differences in the dependent variable.
• Hypothesis testing: Researchers formulate hypotheses about the relationship between variables and test these hypotheses through statistical analysis.
• Limitations in establishing causality: While causal-comparative studies can suggest causal relationships, they cannot definitively prove causation due to the lack of experimental control.

Designing a Causal-Comparative Study for DepEd Personnel

When designing a causal-comparative study for assessing situations within DepEd, researchers should follow these steps:

• Identify the research problem
• Formulate research questions and hypotheses
• Define variables
• Select participants
• Choose data collection methods
• Analyze data
• Draw conclusions and make recommendations

1. Identify the Research Problem

The first step in designing a causal-comparative study is to identify a specific problem or situation that needs assessment within the DepEd context. This problem should align with the priorities outlined in the PBERA and address relevant challenges in the Philippine education system.

Example: A researcher might want to investigate the impact of a new teaching method, such as the use of mother tongue-based multilingual education (MTB-MLE), on student performance in elementary schools.

2. Formulate Research Questions and Hypotheses

Based on the identified problem, researchers should develop clear research questions and hypotheses. These should be specific, measurable, and aligned with the goals of the study.

Research Question: Does the implementation of MTB-MLE in elementary schools affect student achievement in reading comprehension compared to schools using English-only instruction?

Hypothesis: Students in schools implementing MTB-MLE will show higher achievement scores in reading comprehension compared to students in schools using English-only instruction.

3. Define Variables

In causal-comparative research, it is crucial to clearly define the independent and dependent variables:

• Independent Variable: The factor that is observed to potentially cause a change (e.g., language of instruction: MTB-MLE vs. English-only)
• Dependent Variable: The outcome or effect being measured (e.g., student achievement scores in reading comprehension)

It is also important to identify potential confounding variables that may influence the relationship between the independent and dependent variables. In this example, confounding variables might include socioeconomic status, teacher qualifications, or school resources.

4. Select Participants

Researchers should carefully select participants for the study, ensuring that the sample is representative of the target population and sufficiently large to yield meaningful results. In the DepEd context, this might involve choosing schools, teachers, or students based on specific criteria.

• Group 1: Grade 3 students in schools implementing MTB-MLE
• Group 2: Grade 3 students in schools using English-only instruction

Considerations for participant selection:

• Sample size: Determine an appropriate sample size based on statistical power analysis and practical constraints.
• Matching: Attempt to match participants in both groups based on relevant characteristics (e.g., age, gender, socioeconomic status) to minimize the impact of confounding variables.
• Geographical representation: Include schools from various regions to ensure a diverse and representative sample.
• School type: Consider including both public and private schools, if relevant to the research question.

5. Choose Data Collection Methods

Select appropriate methods for collecting data that will help answer the research questions. Common data collection methods in causal-comparative studies include:

• Surveys or questionnaires
• Standardized tests
• Observations
• Document analysis

For the MTB-MLE example, researchers might use:

• Standardized reading comprehension tests administered in the students’ mother tongue and in English
• Classroom observations to assess implementation fidelity of MTB-MLE or English-only instruction
• Teacher surveys to gather information on instructional practices and perceptions of the language of instruction
• Student interviews to explore their experiences with the language of instruction
• Analysis of student records to gather demographic information and past academic performance

When selecting data collection methods, consider:

• Validity and reliability of instruments
• Cultural appropriateness for the Philippine context
• Language considerations (e.g., translation and back-translation of instruments)
• Feasibility of implementation given time and resource constraints

6. Analyze Data

Once data is collected, researchers should use appropriate statistical methods to analyze the results. For causal-comparative studies, common analytical techniques include:

• t-tests (independent samples or paired samples)
• Analysis of Variance (ANOVA)
• Analysis of Covariance (ANCOVA)
• Chi-square tests
• Multiple regression analysis

In the MTB-MLE example, researchers might use:

• Independent samples t-test to compare the mean reading comprehension scores of students in MTB-MLE schools versus English-only schools.
• ANCOVA to control for potential confounding variables such as socioeconomic status or prior academic achievement.
• Multiple regression analysis to examine the relative influence of various factors (e.g., language of instruction, teacher qualifications, school resources) on reading comprehension scores.

When conducting statistical analyses, researchers should:

• Check assumptions of statistical tests (e.g., normality, homogeneity of variance)
• Use appropriate effect size measures to complement significance testing
• Consider using statistical software such as SPSS, R, or Stata for complex analyses
• Consult with a statistician if needed, especially for more advanced analytical techniques

7. Draw Conclusions and Make Recommendations

Based on the data analysis, researchers should interpret the results and draw conclusions about the possible cause-and-effect relationship between variables. It is important to consider limitations of the study and potential alternative explanations for the findings.

When interpreting results:

• Address each research question and hypothesis systematically
• Discuss the practical significance of findings, not just statistical significance
• Compare results to previous research in the field
• Acknowledge limitations of the study and potential threats to internal and external validity

Finally, researchers should make recommendations for practice or further research based on their conclusions. These recommendations should be specific, actionable, and relevant to the DepEd context.

Example recommendations for the MTB-MLE study:

• If results show positive effects of MTB-MLE on reading comprehension, recommend expanding the program to more schools and providing additional resources for implementation.
• If results are mixed or inconclusive, suggest further research to examine the long-term effects of MTB-MLE or to investigate factors that may moderate its effectiveness.
• Propose professional development programs for teachers to enhance their skills in implementing MTB-MLE or in supporting students’ language development.
• Recommend policy changes to support effective language instruction based on the study’s findings.

Considerations for DepEd Personnel

When conducting causal-comparative studies within the DepEd system, researchers should keep the following considerations in mind:

1. Ethical Considerations

Research ethics are of paramount importance, especially when conducting studies involving children or vulnerable populations. DepEd Order No. 16, s. 2017 outlines specific guidelines for ethical research practices:

• Obtain necessary approvals from DepEd authorities at appropriate levels (central, regional, division, or school)
• Secure informed consent from participants or their guardians
• Ensure confidentiality and protection of participants’ personal information
• Consider potential risks and benefits to participants and schools
• For studies involving Indigenous Peoples (IP) learners or Indigenous Cultural Communities (ICCs), adhere to the principle of Free, Prior, and Informed Consent (FPIC) and respect indigenous knowledge systems and practices (IKSPs)

2. Cultural Sensitivity

• Respect the diverse cultural backgrounds of students and teachers in the Philippines
• Adapt research instruments and procedures to be culturally appropriate
• Be mindful of language differences and provide translations when necessary
• When researching in areas with Indigenous Peoples, collaborate with community elders and leaders to ensure cultural appropriateness of the study

3. Resource Constraints

• Be aware of potential limitations in terms of time, funding, and available resources within the DepEd system
• Develop realistic timelines and budget plans
• Consider applying for the Basic Education Research Fund (BERF) to support research activities
• Explore possibilities for cost-sharing or in-kind contributions from participating schools or divisions

4. Generalizability

• Consider the extent to which findings can be generalized to other schools or regions within the Philippines
• Discuss factors that may limit generalizability, such as unique characteristics of the sample or specific contextual factors
• Consider including diverse school types, such as those in the Alternative Learning System (ALS), to enhance the applicability of findings

5. Policy Implications

• Reflect on how the study’s findings might inform DepEd policies and practices
• Consider the feasibility of implementing recommendations within the current education system
• Align recommendations with existing DepEd initiatives and priorities
• Discuss potential implications for both formal education and alternative learning systems

6. Capacity Building

• Use the research process as an opportunity to build research skills among DepEd personnel
• Encourage collaboration between experienced researchers and novice teacher-researchers
• Provide training and support for data collection, analysis, and report writing
• Utilize Learning Action Cells (LACs) to foster a culture of research and continuous improvement among teachers

7. Research Partnerships

DepEd encourages collaboration with external partners to enhance the quality and impact of educational research. Potential research partners include:

• State universities and colleges
• Private academic institutions
• Non-governmental organizations (NGOs)
• International development agencies
• Other government agencies

When engaging in research partnerships, ensure that:

• There is a clear Memorandum of Agreement (MOA) outlining roles, responsibilities, and expectations
• The research aligns with DepEd’s priorities and ethical guidelines
• There are provisions for capacity building of DepEd personnel
• Intellectual property rights and data ownership are clearly defined

8. Research Committees

DepEd has established research committees at various levels to oversee and support research activities:

• National Research Committee (NRC)
• Regional Research Committee (RRC)
• Schools Division Research Committee (SDRC)

Researchers should familiarize themselves with the roles and responsibilities of these committees and seek their guidance and approval as necessary throughout the research process.

Dissemination and Utilization of Research Findings

Effective dissemination and utilization of research findings are crucial for improving educational practices and policies. DepEd researchers should consider the following strategies:

• Present findings at school-level Learning Action Cells (LACs) to facilitate immediate application of insights in the classroom
• Share results with relevant stakeholders, including school administrators, teachers, and policymakers through research forums and conferences
• Publish findings in academic journals or DepEd research bulletins
• Develop user-friendly summaries or infographics to make results accessible to a wider audience
• Incorporate research findings into school improvement plans and professional development programs
• Use research results to inform policy reviews and development at various levels of the DepEd system
• Collaborate with the Policy Research and Development Division (PRD) to ensure that research findings are considered in national policy formulation

Monitoring and Evaluation of Research Projects

To ensure the quality and relevance of research projects, DepEd has established monitoring and evaluation mechanisms:

• Progress monitoring: Research committees at various levels track the progress of approved research projects
• Technical assistance: The Policy Research and Development Division (PRD) and regional counterparts provide support to researchers throughout the research process
• Quality assurance: Research outputs are evaluated based on established criteria before acceptance and dissemination
• Impact assessment: DepEd periodically assesses the impact of completed research projects on educational practices and policies

Researchers should actively engage with these monitoring and evaluation processes to ensure the success and relevance of their studies.

Designing causal-comparative studies for assessing situations within the DepEd context can provide valuable insights into educational practices and their effects on student outcomes. By following a systematic approach to research design and considering the unique aspects of the Philippine education system, DepEd personnel can conduct meaningful action research that contributes to the improvement of teaching and learning in their schools.

As educators and researchers continue to engage in action research, it is essential to share findings and best practices within the DepEd community. This collaborative approach can lead to a culture of continuous improvement and evidence-based decision-making in Philippine education. By building a strong foundation of research-based knowledge, DepEd can enhance its capacity to address educational challenges and provide high-quality education to all Filipino students, including those in alternative learning systems and indigenous communities.

Through the combined efforts of teachers, administrators, and policymakers, research-driven improvements in the Philippine education system can help realize the vision of accessible, equitable, and high-quality education for all learners.

Copyright Notice :

This article, “Action Research: Designing Causal-Comparative Studies for Assessing Educational Situations in the Philippine Context,” was authored by Mark Anthony Llego and published on August 9, 2024.

Mark Anthony Llego

Mark Anthony Llego, a visionary from the Philippines, founded TeacherPH in October 2014 with a mission to transform the educational landscape. His platform has empowered thousands of Filipino teachers, providing them with crucial resources and a space for meaningful idea exchange, ultimately enhancing their instructional and supervisory capabilities. TeacherPH's influence extends far beyond its origins. Mark's insightful articles on education have garnered international attention, featuring on respected U.S. educational websites. Moreover, his work has become a valuable reference for researchers, contributing to the academic discourse on education.

Leave a Comment Cancel reply

Can't find what you're looking for.

We are here to help - please use the search box below.

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Causal Comparative Research: Definition, Types & Benefits

Within the field of research, there are multiple methodologies and ways to find answers to your needs, in this article we will address everything you need to know about Causal Comparative Research, a methodology with many advantages and applications.

What Is Causal Comparative Research?

Causal-comparative research is a methodology used to identify cause-effect relationships between independent and dependent variables.

Researchers can study cause and effect in retrospect. This can help determine the consequences or causes of differences already existing among or between different groups of people.

When you think of Casual Comparative Research, it will almost always consist of the following:

• A method or set of methods to identify cause/effect relationships
• A set of individuals (or entities) that are NOT selected randomly – they were intended to participate in this specific study
• Variables are represented in two or more groups (cannot be less than two, otherwise there is no differentiation between them)
• Non-manipulated independent variables – *typically, it’s a suggested relationship (since we can’t control the independent variable completely)

Types of Casual Comparative Research

Casual Comparative Research is broken down into two types:

• Retrospective Comparative Research
• Prospective Comparative Research

Retrospective Comparative Research: Involves investigating a particular question…. after the effects have occurred. As an attempt to see if a specific variable does influence another variable.

Prospective Comparative Research: This type of Casual Comparative Research is characterized by being initiated by the researcher and starting with the causes and determined to analyze the effects of a given condition. This type of investigation is much less common than the Retrospective type of investigation.

LEARN ABOUT: Quasi-experimental Research

Causal Comparative Research vs Correlation Research

The universal rule of statistics… correlation is NOT causation! 

Casual Comparative Research does not rely on relationships. Instead, they’re comparing two groups to find out whether the independent variable affected the outcome of the dependent variable

When running a Causal Comparative Research, none of the variables can be influenced, and a cause-effect relationship has to be established with a persuasive, logical argument; otherwise, it’s a correlation.

Another significant difference between both methodologies is their analysis of the data collected. In the case of Causal Comparative Research, the results are usually analyzed using cross-break tables and comparing the averages obtained. At the same time, in Causal Comparative Research, Correlation Analysis typically uses scatter charts and correlation coefficients.

Advantages and Disadvantages of Causal Comparative Research

Like any research methodology, causal comparative research has a specific use and limitations to consider when considering them in your next project. Below we list some of the main advantages and disadvantages.

• It is more efficient since it allows you to save human and economic resources and to do it relatively quickly.
• Identifying causes of certain occurrences (or non-occurrences)
• Thus, descriptive analysis rather than experimental

Disadvantages

• You’re not fully able to manipulate/control an independent variable as well as the lack of randomization
• Like other methodologies, it tends to be prone to some research bias , the most common type of research is subject- selection bias , so special care must be taken to avoid it so as not to compromise the validity of this type of research.
• The loss of subjects/location influences / poor attitude of subjects/testing threats….are always a possibility

Finally, it is important to remember that the results of this type of causal research should be interpreted with caution since a common mistake is to think that although there is a relationship between the two variables analyzed, this does not necessarily guarantee that the variable influences or is the main factor to influence in the second variable.

LEARN ABOUT: ANOVA testing

QuestionPro can be your ally in your next Causal Comparative Research

QuestionPro is one of the platforms most used by the world’s leading research agencies, thanks to its diverse functions and versatility when collecting and analyzing data.

With QuestionPro you will not only be able to collect the necessary data to carry out your causal comparative research, you will also have access to a series of advanced reports and analyses to obtain valuable insights for your research project.

We invite you to learn more about our Research Suite, schedule a free demo of our main features today, and clarify all your doubts about our solutions.

LEARN MORE         SIGN UP FREE

Author : John Oppenhimer

MORE LIKE THIS

Customer Experience Lessons from 13,000 Feet — Tuesday CX Thoughts

Aug 20, 2024

Insight: Definition & meaning, types and examples

Aug 19, 2024

Employee Loyalty: Strategies for Long-Term Business Success 

Jotform vs SurveyMonkey: Which Is Best in 2024

Aug 15, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

• Translators
• Graphic Designers

Please enter the email address you used for your account. Your sign in information will be sent to your email address after it has been verified.

Causal Comparative Research: Insights and Implications

Diving into the realm of research methodologies, one encounters a variety of approaches tailored for specific inquiries. Causal Comparative Research, at its core, refers to a research design aimed at identifying and analyzing causal relationships between variables, specifically when the researcher does not have control over active manipulation of variables. Instead of manipulating variables as in experimental research, this method examines existing differences between or among groups to derive potential causes.

Its significance in the academic and research arena is multifaceted. For scenarios where experimental designs are either not feasible or ethical, Causal Comparative Research provides an alternative pathway to glean insights. This approach bridges the gap between mere observational studies and those requiring strict control, offering researchers a valuable tool to unearth potential causal links in a myriad of contexts. By understanding these causal links, scholars, policymakers, and professionals can make more informed decisions and theories, further enriching our collective knowledge base.

Background and evolution

Causal Comparative Research, while not as old as some other research methodologies, has roots deeply embedded in the quest for understanding relationships without direct manipulation. The method blossomed in fields such as education, sociology, and psychology during times when researchers confronted questions of causality. Over time, as the academic community acknowledged the need to investigate causal relationships in naturally occurring group differences, this method gained traction.

What sets Causal Comparative Research apart from other methodologies is its unique stance on causality without direct interference. Experimental research, often hailed as the gold standard for identifying causal relationships, involves deliberate manipulation of independent variables to gauge their effect on dependent variables . This controlled setting allows for clearer cause-and-effect assertions. On the other hand, observational studies, which are purely descriptive, steer clear from making any causal inferences and focus primarily on recording and understanding patterns or phenomena as they naturally occur.

Yet, nestled between these two methodologies, Causal Comparative Research carves its niche. It aims to identify potential causes by examining existing differences between or among groups. While it doesn't offer the direct control of an experiment, it delves deeper than a mere observational approach by trying to understand the "why" behind observed differences. In doing so, it offers a unique blend of retrospective investigation with a pursuit for causality, providing researchers with a versatile tool in their investigative arsenal.

Key characteristics

Causal Comparative Research is distinguished by a unique set of features that demarcate its approach from other research methodologies. These characteristics not only define its operational dynamics but also guide its potential applications and insights. By understanding these foundational traits, researchers can effectively harness the method's strengths and navigate its nuances.

Non-manipulation of variables

One of the foundational attributes of Causal Comparative Research is the non-manipulation of variables. Rather than actively intervening or changing conditions, researchers in this paradigm focus on studying groups as they naturally present themselves . This means the intrinsic differences between groups, which have already emerged, become the central focus.

Such a non-interventionist approach allows for real-world applicability and reduces the artificiality sometimes present in controlled experiments. However, this comes at the cost of being less definitive about causal relationships since the conditions aren't being manipulated directly by the researcher.

By studying pre-existing conditions and group differences, researchers aim to unearth potential causative factors or trends that may have otherwise gone unnoticed in a more controlled setting.

Retrospective in nature

Causal Comparative Research is inherently retrospective. Instead of setting up conditions and predicting future outcomes, researchers using this method look backward, aiming to identify what might have caused the current differences between groups .

This backward-looking approach offers a distinct vantage point. It allows researchers to harness historical data, past events, and already established patterns to discern potential causal relationships. While this method doesn't provide as concrete causative conclusions as prospective studies, it provides crucial insights into historical causative factors.

Understanding the past is vital in many academic fields. This retrospective nature provides a pathway to delve into historical causality, offering insights that can guide future investigations and decisions.

Relies on existing differences between or among groups

The very essence of Causal Comparative Research is rooted in the examination of existing differences. Instead of creating distinct groups through manipulation, researchers study naturally occurring group differences .

These existing distinctions can arise from a multitude of factors, be it cultural, environmental, socio-economic, or even genetic. The goal is to discern whether these differences can hint at underlying causal relationships or if they are mere coincidences.

The reliance on pre-existing differences is both a strength and a limitation. It ensures genuine applicability to real-world scenarios but also introduces potential confounding variables that researchers must be cautious of while interpreting results.

Advantages of causal comparative research

Offering a unique blend of observational and experimental techniques, Causal Comparative Research is tailored for situations demanding flexibility without compromising on the search for causal insights. Here is why many researchers consider it a crucial tool in their investigative arsenal.

Useful when experimental research is not feasible

Causal Comparative Research emerges as a strong alternative in scenarios where experimental research is unfeasible. Experimental research, while robust, often requires conditions or manipulations that might not be viable or ethical , especially in fields like psychology, sociology, or education.

In such situations, relying on naturally occurring differences provides researchers a viable avenue to still investigate potential causal relationships without directly intervening or risking harm. Thus, it offers a middle ground between pure observation and controlled experimentation, allowing for causal inquiries in challenging contexts.

Provides valuable insights in a short amount of time

One of the standout attributes of Causal Comparative Research is its efficiency. Given that it focuses on pre-existing differences, there's no need to wait for conditions to develop or results to manifest over extended periods.

This means that researchers can glean valuable insights in a relatively shorter time frame compared to longitudinal or prospective experimental designs. For pressing questions or time-sensitive scenarios, this method offers timely data and conclusions. Its swiftness does not compromise depth, ensuring that the insights derived are both timely and profound.

Can offer preliminary evidence before experimental designs are implemented

Before diving into a full-fledged experimental design, researchers often seek preliminary evidence or hints to justify their hypotheses or the feasibility of the experiment. Causal Comparative Research serves this purpose aptly.

By examining existing differences and drawing potential causal links, it provides an initial layer of evidence. This preliminary data can guide the structuring of more elaborate, controlled experiments, ensuring they're grounded in prior findings. Thus, it acts as a stepping stone, paving the way for more rigorous research designs by providing an initial overview of potential causal links.

Limitations and challenges

Every research methodology, regardless of its strengths, comes with its set of limitations and challenges. Causal Comparative Research, while flexible and versatile, is no exception. Before embracing its advantages, it's imperative for researchers to be acutely aware of its potential pitfalls and the nuances that might influence their findings.

Cannot definitively establish cause-and-effect relationships

While Causal Comparative Research offers valuable insights into potential causal relationships, it does not provide definitive cause-and-effect conclusions. Without direct manipulation of variables, it's challenging to ascertain a clear causative link. This inherent limitation means that, at best, findings can suggest probable causes but cannot confirm them with the same certainty as experimental research.

Potential for confounding variables

Given the reliance on naturally occurring group differences, there's a heightened risk of confounding variables influencing the outcomes . These are external factors that might affect the dependent variable , clouding the clarity of potential causal links. Researchers must remain vigilant, identifying and accounting for these variables to ensure the study's findings remain as untainted as possible by external influences.

Difficulty in ensuring group equivalency

Ensuring that the groups under study are equivalent is paramount in Causal Comparative Research. Any intrinsic group differences, other than the ones being studied, can skew results and interpretations. This challenge underscores the importance of careful selection and meticulous analysis to minimize the impact of non-equivalent groups on the research findings.

Steps in conducting causal comparative research

The process of Causal Comparative Research demands a systematic progression through specific stages to ensure that the research is comprehensive, accurate, and valid. Below is a step-by-step breakdown of this research methodology:

• Identification of the Research Problem: This initial stage involves recognizing and defining the specific research problem or research question . It forms the foundation upon which the entire research process will be built, making it crucial to be clear, concise, and relevant.
• Selection of Groups: Once the problem is identified, researchers need to select the groups they wish to compare. These groups should have existing differences relevant to the research question. The accuracy and relevance of group selection directly influence the research's validity.
• Measurement of the Dependent Variable(s): In this phase, researchers decide on the dependent variables they'll measure. These are the outcomes or effects potentially influenced by the groups' differences. Proper operationalization and measurement scales are essential to ensure that the data collected is accurate and meaningful.
• Data Collection and Analysis: With everything set up, the actual data collection begins. This could involve surveys, observations, or any other relevant data collection method. Post collection, the data undergoes rigorous analysis to identify patterns, differences, or potential causal links.
• Interpretation and Reporting of Results: Once the analysis is complete, researchers need to interpret the results in the context of the research problem. This interpretation forms the basis of the research's conclusions. Finally, findings are reported, often in the form of academic papers or reports, ensuring that the insights can be shared and critiqued by the broader academic community.

By meticulously following these steps, researchers can navigate the complexities of Causal Comparative Research, ensuring that their investigations are both methodologically sound and academically valuable.

Key considerations for validity

When conducting Causal Comparative Research, validity remains at the forefront. Ensuring that the research accurately captures and represents the phenomena under study is pivotal for its credibility and utility. Delving into the intricacies of validity, two primary considerations emerge: internal and external validity.

Internal validity concerns

Internal validity pertains to the degree to which the research accurately establishes a cause-and-effect relationship between variables. However, several threats can compromise it, especially in a causal-comparative setup, for instance:

• Maturation: Refers to changes occurring naturally over time within participants, which could be misconstrued as effects of the studied variable.
• Testing: Concerns the effects of taking a test multiple times. Participants might improve not because of the variable of interest, but due to familiarity with the test.
• Instrumentation: Issues arise when the tools or methods used to collect data change or are inconsistent, potentially skewing results.

Addressing these concerns and others is crucial to maintain the research's integrity and ensure that the findings genuinely reflect the causal relationships under scrutiny.

External validity considerations

While internal validity focuses on the research's accuracy within its confines, external validity revolves around the generalizability of the findings. It assesses whether the study's conclusions can be applied to broader contexts, populations, or settings.

One major concern here is the representativeness of the groups studied. If they are too niche or specific, generalizing findings becomes problematic. Additionally, the conditions under which the research is conducted can influence its applicability elsewhere. If the environment, time, or setting is too unique, the findings might not hold true in different scenarios.

Ensuring robust external validity means that the research doesn't just hold academic value, but can also inform real-world practices, policies, and decisions, making its implications far-reaching and impactful.

Illustrative examples of causal comparative research

Across varied disciplines, Causal Comparative Research has been employed to address pressing questions, providing insights into causal factors without the need for direct manipulation. Let's explore a few examples that encapsulate its breadth and significance.

Comparing traditional and online learning outcomes

With the rise of digital platforms, online learning has rapidly grown as a popular alternative to traditional classroom settings . However, discerning the effectiveness of both mediums in terms of student performance and engagement is essential for educators and institutions. Causal Comparative Research provides an apt approach to explore this, without altering the learning environments, but rather examining the existing outcomes.

• Identification of the Research Problem: The primary concern here is understanding the potential causal factors behind the differing success rates or engagement levels of students in traditional classrooms versus online learning platforms.
• Selection of Groups: Two primary groups would be selected for this study: students who have primarily undergone traditional classroom learning and those who have predominantly experienced online learning. It would be essential to ensure these groups are as comparable as possible in other aspects, such as age, educational level, and background.
• Measurement of the Dependent Variable(s): The dependent variables might include academic performance (grades or test scores), engagement metrics (participation in class discussions or assignments turned in), and possibly even feedback or satisfaction surveys from students regarding their learning experience.
• Data Collection and Analysis: Data would be gathered from institutional records, online learning platforms, and potentially direct surveys. Once collected, statistical analyses would be employed to compare the performance and engagement metrics between the two groups, adjusting for any potential confounding variables.
• Interpretation and Reporting of Results: After analysis, researchers would interpret the data to understand any significant differences in outcomes between traditional and online learners. It's crucial to report findings with the acknowledgment that the research indicates correlation and not necessarily direct causation. Recommendations could be made for educators based on the insights gathered.

In conclusion, while both traditional and online learning environments offer unique benefits, utilizing Causal Comparative Research allows institutions and educators to glean vital insights into their relative effectiveness. Such understanding can guide curriculum development, teaching methodologies, and even future educational investments.

Analysis of lifestyle factors in disease prevalence

In contemporary health studies, lifestyle factors like diet, exercise, and stress have often been cited as potential determinants of disease prevalence . With diverse populations adhering to varied lifestyles, understanding the potential influence of these factors on disease rates becomes pivotal for healthcare professionals and policymakers. Causal Comparative Research offers a path to delve into these influences by analyzing existing health outcomes against different lifestyle patterns.

• Identification of the Research Problem: The primary goal is to determine whether specific lifestyle factors (e.g., sedentary behavior, dietary habits, tobacco use) have a significant influence on the prevalence of certain diseases, such as heart disease, diabetes, or hypertension.
• Selection of Groups: Groups can be categorized based on distinct lifestyle patterns. For example, groups might consist of individuals who are sedentary versus those who exercise regularly, or those who adhere to a vegetarian diet versus those who consume meat regularly.
• Measurement of the Dependent Variable(s): The dependent variable would be the prevalence or incidence of specific diseases in each group. This can be measured using health records, self-reported incidents, or clinical diagnoses.
• Data Collection and Analysis: Data can be sourced from health databases, patient surveys, or direct health check-ups. Statistical tools can then be applied to identify any significant disparities in disease rates between the varied lifestyle groups, accounting for potential confounders like age, genetics, or socio-economic status.
• Interpretation and Reporting of Results: After the data analysis, findings would elucidate any notable correlations between lifestyle factors and disease prevalence. It's vital to emphasize that this research would indicate associations, not direct causation. Still, such insights could be invaluable for health promotion campaigns and policy formulation.

To conclude, by leveraging Causal Comparative Research in analyzing lifestyle factors and their potential influence on disease rates, healthcare stakeholders can be better equipped with knowledge that informs public health strategies and individual lifestyle recommendations.

Resilience levels in trauma survivors vs. non-trauma individuals

Resilience—the capacity to recover quickly from difficulties and maintain mental health— has piqued the interest of psychologists , especially when comparing trauma survivors to those who haven't experienced trauma. The ability to understand the underlying factors contributing to resilience can pave the way for better therapeutic approaches and interventions.

• Identification of the Research Problem: Determining whether individuals who have experienced trauma have different resilience levels compared to those who haven't.
• Selection of Groups: One group would consist of individuals who have experienced significant traumatic events (such as natural disasters, personal assaults, or wartime experiences), and the second group would comprise individuals with no history of significant trauma.
• Measurement of the Dependent Variable(s): Resilience levels would be the primary dependent variable, measured using standardized resilience scales like the Connor-Davidson Resilience Scale (CD-RISC) .
• Data Collection and Analysis: Participants from both groups would complete the chosen resilience scale. Data would then be analyzed to determine if there are significant differences in resilience scores between the two groups. Covariates like age, gender, socioeconomic status, and mental health history might be controlled for to enhance the study's validity.
• Interpretation and Reporting of Results: The findings would indicate whether trauma survivors, on average, have higher, lower, or comparable resilience levels to their non-trauma counterparts. This would provide valuable insights into the potential protective factors or coping strategies that trauma survivors might develop.

The outcomes of this study can significantly influence therapeutic strategies and post-trauma interventions, ensuring that individuals who've faced traumatic events receive tailored care that acknowledges their unique psychological landscape.

Impact of family structure on child development outcomes

Family structures have undergone significant evolution over the decades . With varying family setups—from nuclear families to single-parent households to extended family living arrangements—the question arises: How do these different structures impact child development? Delving into this query provides insights crucial for educators, therapists, and policymakers.

• Identification of the Research Problem: Investigate the potential differences in child development outcomes based on varying family structures.
• Selection of Groups: Children would be categorized based on their family structure: nuclear families, single-parent households, extended family households, and other non-traditional structures.
• Measurement of the Dependent Variable(s): Child development outcomes, which could include academic performance, socio-emotional development, and behavioral patterns. These would be measured using standardized tests, behavioral assessments, and teacher or caregiver reports.
• Data Collection and Analysis: Data would be collected from schools, families, and relevant institutions. Statistical methods would then be used to determine significant differences in developmental outcomes across the different family structures, controlling for factors like socio-economic status, parental education, and location.
• Interpretation and Reporting of Results: Findings would detail whether and how family structures play a pivotal role in shaping child development. Results could reveal, for instance, if children from extended family structures exhibit better socio-emotional skills due to increased interactions with varied age groups within the family.

Understanding the nuances of how family structure affects child development can guide interventions, curricula designs, and policies to cater better to the diverse needs of children, ensuring every child receives the support they require to thrive.

Impact of organizational structures on employee productivity

As businesses evolve, they experiment with different organizational structures , from traditional hierarchies to flat structures to matrix setups. How do these varying structures influence employee productivity and satisfaction? Exploring this can provide businesses valuable insights to optimize performance and employee morale.

• Identification of the Research Problem: Determine the effect of different organizational structures on employee productivity.
• Selection of Groups: Employees from diverse firms, categorized based on their company's organizational structure: hierarchical, flat, matrix, and hybrid structures.
• Measurement of the Dependent Variable(s): Employee productivity could be gauged through metrics like task completion rate, project delivery timelines, and output quality. Additionally, employee satisfaction surveys might be incorporated as secondary data.
• Data Collection and Analysis: Data would be collected from employee performance metrics and satisfaction surveys across different companies. Advanced statistical methods would be employed to analyze potential variations in productivity and satisfaction across organizational structures, accounting for potential confounders.
• Interpretation and Reporting of Results: Findings might indicate, for instance, that flat structures promote higher employee autonomy and satisfaction but might face challenges in larger teams due to potential communication breakdowns.

By discerning the relationship between organizational structure and employee productivity, businesses can make informed decisions on organizational design, ensuring optimal output while fostering a conducive work environment.

Best practices

Ensuring the validity and reliability of your Causal Comparative Research findings is paramount. Implementing best practices not only adds rigor to the research but also increases the trustworthiness of the results. Below are some practices to uphold when conducting Causal Comparative Research.

Ensuring representative samples

One of the primary pillars of credible research is the selection of a representative sample. A sample that genuinely mirrors the larger population ensures that findings can be more confidently generalized. In Causal Comparative Research, the groups being compared should ideally capture the broader dynamics and diversity of the populations they represent.

To ensure a representative sample, researchers should be wary of biases during selection. This includes avoiding convenience sampling unless it's justified. Stratified random sampling or quota sampling can help in ensuring that different subgroups within the population are adequately represented.

Furthermore, the size of the sample plays a crucial role. While a larger sample can often yield more reliable results, it's imperative to ensure that it remains manageable and aligns with the study's logistical and financial constraints.

Controlling for extraneous variables

Extraneous variables can introduce noise into the research, obscuring the clarity of potential causal relationships. It's essential to identify potential confounders and control for them, ensuring that they don't unduly influence the outcome.

In Causal Comparative Research, since there's no direct manipulation of variables, the risk of uncontrolled extraneous variables affecting the outcome is heightened. One way to control for these variables is through matching, where participants in different groups are matched based on certain criteria, ensuring that these criteria do not interfere with the results.

Another technique involves statistical control, where advanced analytical methods, such as covariance analysis , are employed to account for the variance caused by extraneous variables.

Choosing appropriate statistical tools and techniques

The analysis phase is the heart of the research, where data comes alive and starts narrating a story. Selecting the appropriate statistical tools and techniques is pivotal in ensuring that this story is accurate and meaningful.

In Causal Comparative Research, the choice of statistical analysis largely depends on the nature of the data and the research question . For instance, if you're comparing means of two groups, a t-test might be appropriate. However, for more than two groups, an ANOVA could be the preferred choice.

Advanced statistical models, such as regression analysis or structural equation modeling , might be employed for more complex research questions. Regardless of the chosen method, it's crucial to ensure that assumptions of the tests are met, and the data is adequately prepared for analysis.

In the landscape of research methodologies, Causal Comparative Research stands out as a compelling blend of observational and quasi-experimental approaches. While it offers the advantage of examining naturally occurring differences without the need for direct manipulation, it comes with its own set of challenges and considerations. As with all research methods , its efficacy lies in the meticulous application of its principles, and the conscious effort to uphold best practices. When executed with rigor, this method provides invaluable insights, bridging the gap between observation and direct experimentation, and helping researchers navigate the complex webs of causality in varied fields.

Header image by Tom Wang .

Causal Comparative Research: Methods And Examples

Ritu was in charge of marketing a new protein drink about to be launched. The client wanted a causal-comparative study…

Ritu was in charge of marketing a new protein drink about to be launched. The client wanted a causal-comparative study highlighting the drink’s benefits. They demanded that comparative analysis be made the main campaign design strategy. After carefully analyzing the project requirements, Ritu decided to follow a causal-comparative research design. She realized that causal-comparative research emphasizing physical development in different groups of people would lay a good foundation to establish the product.

What Is Causal Comparative Research?

Examples of causal comparative research variables.

Causal-comparative research is a method used to identify the cause–effect relationship between a dependent and independent variable. This relationship is usually a suggested relationship because we can’t control an independent variable completely. Unlike correlation research, this doesn’t rely on relationships. In a causal-comparative research design, the researcher compares two groups to find out whether the independent variable affected the outcome or the dependent variable.

A causal-comparative method determines whether one variable has a direct influence on the other and why. It identifies the causes of certain occurrences (or non-occurrences). It makes a study descriptive rather than experimental by scrutinizing the relationships among different variables in which the independent variable has already occurred. Variables can’t be manipulated sometimes, but a link between dependent and independent variables is established and the implications of possible causes are used to draw conclusions.

In a causal-comparative design, researchers study cause and effect in retrospect and determine consequences or causes of differences already existing among or between groups of people.

Let’s look at some characteristics of causal-comparative research:

• This method tries to identify cause and effect relationships.
• Two or more groups are included as variables.
• Individuals aren’t selected randomly.
• Independent variables can’t be manipulated.
• It helps save time and money.

The main purpose of a causal-comparative study is to explore effects, consequences and causes. There are two types of causal-comparative research design. They are:

Retrospective Causal Comparative Research

For this type of research, a researcher has to investigate a particular question after the effects have occurred. They attempt to determine whether or not a variable influences another variable.

Prospective Causal Comparative Research

The researcher initiates a study, beginning with the causes and determined to analyze the effects of a given condition. This is not as common as retrospective causal-comparative research.

Usually, it’s easier to compare a variable with the known than the unknown.

Researchers use causal-comparative research to achieve research goals by comparing two variables that represent two groups. This data can include differences in opportunities, privileges exclusive to certain groups or developments with respect to gender, race, nationality or ability.

For example, to find out the difference in wages between men and women, researchers have to make a comparative study of wages earned by both genders across various professions, hierarchies and locations. None of the variables can be influenced and cause-effect relationship has to be established with a persuasive logical argument. Some common variables investigated in this type of research are:

• Achievement and other ability variables
• Family-related variables
• Organismic variables such as age, sex and ethnicity
• Variables related to schools
• Personality variables

While raw test scores, assessments and other measures (such as grade point averages) are used as data in this research, sources, standardized tests, structured interviews and surveys are popular research tools.

However, there are drawbacks of causal-comparative research too, such as its inability to manipulate or control an independent variable and the lack of randomization. Subject-selection bias always remains a possibility and poses a threat to the internal validity of a study. Researchers can control it with statistical matching or by creating identical subgroups. Executives have to look out for loss of subjects, location influences, poor attitude of subjects and testing threats to produce a valid research study.

Harappa’s Thinking Critically program is for managers who want to learn how to think effectively before making critical decisions. Learn how leaders articulate the reasons behind and implications of their decisions. Become a growth-driven manager looking to select the right strategies to outperform targets. It’s packed with problem-solving and effective-thinking tools that are essential for skill development. What more? It offers live learning support and the opportunity to progress at your own pace. Ask for your free demo today!

Explore Harappa Diaries to learn more about topics such as Objectives Of Research Methodology , Types Of Thinking , What Is Visualisation and Effective Learning Methods to upgrade your knowledge and skills.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Sustainability as a resilience factor in the agri-food supply chain.

1. Introduction

• Healthcare crisis: The global pandemic of COVID-19 has had serious consequences in the operation of the global logistics gear, with mismatches that have particularly affected demand forecasting, productive capacity and transport reliability [ 2 ].
• Political crisis: The Russian invasion of Ukraine has also had important effects on supply chains since the territories in conflict are producers of essential raw materials in sectors such as food and energy [ 3 ]. On the other hand, the war in Gaza, in the Middle East, has also caused large shipping companies to avoid passing through the Red Sea, having to chart new routes that increase journey time and associated costs [ 4 ].
• Climate crisis: Extreme weather phenomena, such as the Gloria storm of 2019 (which mainly affected the United States and Canada) or the Filomena storm of 2021 (with serious consequences in southern Europe), as well as the episodes of extreme drought that many areas of the planet are suffering from, have a full impact on the productive and logistical processes since they are disruptions that add high complexity to the strategic planning of the entire value chain [ 5 ].
• Is the resilience of agri-food supply chains positively conditioned by holistic sustainability management (social, environmental and governance)?
• What specific sustainable management variables are related to greater logistics resilience?

2. Literature Review

3. materials and methods.

• Dependent Variable: ○ Y: supply chain resilience.
• Explanatory Variables: Environmental Sustainability ○ X1: The company reduces GHG emissions (GRI 305-5). ○ X2: The company reduces energy consumption (GRI 302-4). Social Sustainability ○ X3: The company selects suppliers with social criteria (GRI 414-1). ○ X4: The company ensures the safety and health of its workers (GRI 403-1). Governance Sustainability ○ X5: The company promotes diversity and equal opportunities in management and employees (GRI 405-1). ○ X6: The company fights against corruption (GRI 205-2).

5. Discussion

• The reduction in pollutant emissions (environmental sustainability).
• The safety and health of workers (social sustainability).
• Diversity in management bodies and employees (sustainability of governance).

6. Conclusions

• Sustainability can be considered a factor that has a positive impact on the resilience of agri-food supply chains.
• To build a resilient supply chain, comprehensive sustainability management is needed, embracing the environmental, social and governance spheres.
• The sustainable management variables most shared by companies with solid value chains are as follows: the reduction in pollutant emissions, the safety and health of their workers, and the commitment to diversity in management bodies and teams.
• There are three more variables that also have a notable link to resilience: the reduction in energy consumption, the selection of suppliers with social criteria, and the fight against corruption.
• All the logical correlations that lead to the resilience of supply chains account for more than 60% of the sustainability variables analyzed.
• The most frequent logical correlation in the companies in the sample is the one that includes all explanatory variables of environmental, social and governance sustainability.

Author Contributions

Institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Elver, H. Global Food Systems in Crisis: A Human Rights Approach. In Global Governance and International Cooperation: Managing Global Catastrophic Risks in the 21st Century ; Taylor & Francis: Milton Park, UK, 2024; pp. 332–347. [ Google Scholar ] [ CrossRef ]
• Rivera-Ferre, M.G.; López-i-Gelats, F.; Ravera, F.; Oteros-Rozas, E.; Di Masso, M.; Binimelis, R.; El Bilali, H. The two-way relationship between food systems and the COVID19 pandemic: Causes and consequences. Agric. Syst. 2021 , 191 , 103134. [ Google Scholar ] [ CrossRef ]
• Behnassi, M.; El Haiba, M. Implications of the Russia–Ukraine war for global food security. Nat. Hum. Behav. 2022 , 6 , 754–755. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Notteboom, T.; Haralambides, H.; Cullinane, K. The Red Sea Crisis: Ramifications for vessel operations, shipping networks, and maritime supply chains. Marit. Econ. Logist. 2024 , 26 , 1–20. [ Google Scholar ] [ CrossRef ]
• Tchonkouang, R.D.; Onyeaka, H.; Nkoutchou, H. Assessing the vulnerability of food supply chains to climate change-induced disruptions. Sci. Total Environ. 2024 , 920 , 171047. [ Google Scholar ] [ CrossRef ]
• Fan, S.; Yang, Z.; Wang, J.; Marsland, J. Shipping accident analysis in restricted waters: Lesson from the Suez Canal blockage in 2021. Ocean Eng. 2022 , 266 , 113119. [ Google Scholar ] [ CrossRef ]
• Jia, F.; Shahzadi, G.; Bourlakis, M.; John, A. Promoting resilient and sustainable food systems: A systematic literature review on short food supply chains. J. Clean. Prod. 2024 , 435 , 140364. [ Google Scholar ] [ CrossRef ]
• Scholten, K.; Schilder, S. The role of collaboration in supply chain resilience. Supply Chain Manag. 2015 , 20 , 471–484. [ Google Scholar ] [ CrossRef ]
• Montanyà, O.; Amat, O. The resilience factors of the agri-food supply chain: An integrative review of the literature in the context of the COVID-19 pandemic. Intang. Cap. 2023 , 19 , 379–392. [ Google Scholar ] [ CrossRef ]
• Paul, S.K.; Moktadir, M.A.; Ahsan, K. Key supply chain strategies for the post-COVID-19 era: Implications for resilience and sustainability. Int. J. Logist. Manag. 2023 , 34 , 1165–1187. [ Google Scholar ] [ CrossRef ]
• López-Castro, L.F.; Solano-Charris, E.L. Integrating Resilience and Sustainability Criteria in the Supply Chain Network Design. A Systematic Literature Review. Sustainability 2021 , 13 , 10925. [ Google Scholar ] [ CrossRef ]
• Mondoux, A.; Christinet, B.; Fenal, R.; Viret, O. Simulation of the impact of a wine supply management tool: A potential implementation of a climatic reserve in Switzerland. Int. J. Wine Bus. Res. 2023 , 35 , 614–631. [ Google Scholar ] [ CrossRef ]
• Ivanov, D. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E Logist. Transp. Rev. 2020 , 136 , 101922. [ Google Scholar ] [ CrossRef ]
• Patidar, A.; Sharma, M.; Agrawal, R.; Sangwan, K.S.; Sarkar, P. Building supply chain resilience in developing economies: A weighted Ishikawa diagram approach. Oper. Manag. Res. 2024 , 17 , 363–386. [ Google Scholar ] [ CrossRef ]
• Xu, W.; Lu, Y.; Proverbs, D. An evaluation of factors influencing the vulnerability of emergency logistics supply chains. Int. J. Logist. Res. Appl. 2023 , 1–34. [ Google Scholar ] [ CrossRef ]
• Al-Refaie, A.; Al-Tahat, M.; Lepkova, N. Modelling relationships between agility, lean, resilient, green practices in cold supply chains using ISM approach. Technol. Econ. Dev. Econ. 2020 , 26 , 675–694. [ Google Scholar ] [ CrossRef ]
• Bernabei, M.; Colabianchi, S.; Costantino, F. Actions and Strategies for Coronavirus to Ensure Supply Chain Resilience: A Systemic Review. Sustainability 2022 , 14 , 13243. [ Google Scholar ] [ CrossRef ]
• Ivanov, D. Lead time sensitivity of product availability in supply chain resilience analysis. Int. J. Integr. Supply Manag. 2024 , 17 , 101–119. [ Google Scholar ] [ CrossRef ]
• Camur, M.C.; Ravi, S.K.; Saleh, S. Enhancing supply chain resilience: A machine learning approach for predicting product availability dates under disruption. Expert. Syst. Appl. 2024 , 247 , 123226. [ Google Scholar ] [ CrossRef ]
• Neboh, N.D.; Mbhele, T.P. Supply chain design dimensions for supply chain resilience in the South African fast-moving consumer goods retail industry. Afr. J. Manag. 2021 , 7 , 58–81. [ Google Scholar ] [ CrossRef ]
• Negri, M.; Cagno, E.; Colicchia, C.; Sarkis, J. Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda. Bus. Strategy Environ. 2021 , 30 , 2858–2886. [ Google Scholar ] [ CrossRef ]
• Nenavani, J.; Prasuna, A.; Kumar, S.N.V.S.; Kasturi, A. ESG measures and financial performance of logistics companies. Lett. Spat. Resour. Sci. 2024 , 17 , 5. [ Google Scholar ] [ CrossRef ]
• Işık, C.; Ongan, S.; Islam, H.; Menegaki, A.N. A roadmap for sustainable global supply chain distribution: Exploring the interplay of ECON-ESG factors, technological advancement and SDGs on natural resources. Resour. Policy 2024 , 95 , 105114. [ Google Scholar ] [ CrossRef ]
• Kumar, S.; Sharma, R.; Malik, F.A. Strategic sustainability investigation towards resilient supply chains post-pandemic COVID-19. In VUCA and Other Analytics in Business Resilience, Part B ; Emerald Publishing Limited: Bradford, UK, 2024; pp. 113–125. [ Google Scholar ] [ CrossRef ]
• Cimino, A.; Longo, F.; Mirabelli, G.; Solina, V. A cyclic and holistic methodology to exploit the Supply Chain Digital Twin concept towards a more resilient and sustainable future. Clean. Logist. Supply Chain. 2024 , 11 , 100154. [ Google Scholar ] [ CrossRef ]
• Hassna, B.; Namany, S.; Alherbawi, M.; Elomri, A.; Al-Ansari, T. Multi-Objective Optimization for Food Availability under Economic and Environmental Risk Constraints. Sustainability 2024 , 16 , 4336. [ Google Scholar ] [ CrossRef ]
• Cotta, D.; Klink, L.; Alten, T.; Al Madhoon, B. How do supply chain managers perceive the relationship between resilience and sustainability practices? An exploratory study. Bus. Strategy Environ. 2023 , 32 , 3737–3751. [ Google Scholar ] [ CrossRef ]
• Wang, M.; Wang, B.; Zhu, X.; Wu, Y.J. How Does Supply Chain Resilience Affect Supply Chain Performance? The Mediating Effect of Sustainability. Sustainability 2022 , 14 , 14626. [ Google Scholar ] [ CrossRef ]
• Seekell, D.; Carr, J.; Dell, J.; Gomez, M.; Grady, C. A balancing act: The interplay of food supply chain resilience and environmental sustainability in American cities. Environ. Res. Lett. 2023 , 18 , 124022. [ Google Scholar ] [ CrossRef ]
• Zhang, B.; Mohammad, J. The effects of sustainability innovation and supply chain resilience on sustainability performance: Evidence from China’s cold chain logistics industry. Cogent Bus. Manag. 2024 , 11 , 2353222. [ Google Scholar ] [ CrossRef ]
• Silva, M.E.; Pereira, M.M.O.; Hendry, L.C. Embracing change in tandem: Resilience and sustainability together transforming supply chains. Int. J. Oper. Prod. Manag. 2023 , 43 , 166–196. [ Google Scholar ] [ CrossRef ]
• Kumar, G.; Meena, P.; Difrancesco, R.M. How do collaborative culture and capability improve sustainability? J. Clean. Prod. 2021 , 291 , 125824. [ Google Scholar ] [ CrossRef ]
• Hsieh, C.-C.; Chen, S.-L.; Huang, C.-C. Investigating the Role of Supply Chain Environmental Risk in Shaping the Nexus of Supply Chain Agility, Resilience, and Performance. Sustainability 2023 , 15 , 15003. [ Google Scholar ] [ CrossRef ]
• Shekarabi, H.; Vali-Siar, M.M.; Mozdgir, A. Food supply chain network design under uncertainty and pandemic disruption. Oper. Res. 2024 , 24 , 26. [ Google Scholar ] [ CrossRef ]
• Gallo, A.; Operato, L.; Masoero, G.; Panzeri, A.; Rondoni, N. The Integrated ESG Approach to Assess Sustainability: An Application in the Food and Beverage Sector. In Production Processes and Product Evolution in the Age of Disruption ; Lecture Notes in Mechanical Engineering; Springer: Berlin/Heidelberg, Germany, 2023; pp. 423–431. [ Google Scholar ] [ CrossRef ]
• Hassen, T.B.; El Bilali, H. Impacts of the Russia-Ukraine War on Global Food Security: Towards More Sustainable and Resilient Food Systems? Foods 2022 , 11 , 2301. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Raman, R.; Sreenivasan, A.; Ma, S.; Patwardhan, A.; Nedungadi, P. Green Supply Chain Management Research Trends and Linkages to UN Sustainable Development Goals. Sustainability 2023 , 15 , 15848. [ Google Scholar ] [ CrossRef ]
• Többen, J.; Stöver, B.; Reuschel, S.; Distelkamp, M.; Lutz, C. Sustainability implications of the EU’s bioeconomy transition along global supply chains. J. Clean. Prod. 2024 , 461 , 142565. [ Google Scholar ] [ CrossRef ]
• Mostaghim, N.; Gholamian, M.R.; Arabi, M. Designing a resilient-sustainable integrated broiler supply chain network using multiple sourcing and backup facility strategies dealing with uncertainties in a disruptive network: A real case of a chicken meat network. Comput. Chem. Eng. 2024 , 188 , 108772. [ Google Scholar ] [ CrossRef ]
• Sezer, M.D.; Kazancoglu, Y.; Mangla, S.K.; Lafçı, Ç. Smart, sustainable, and resilient food supply chains in disruptive events context. Bus. Strategy Environ. 2024 . [ Google Scholar ] [ CrossRef ]
• Langreo, A.; Germán, L. Transformations in the food system and the role of industrial and food distribution changes in the Spanish diet during the twentieth century. Hist. Agrar. 2018 , 74 , 167–200. [ Google Scholar ] [ CrossRef ]
• Alikhani, R.; Torabi, S.A.; Altay, N. Retail supply chain network design with concurrent resilience capabilities. Int. J. Prod. Econ. 2021 , 234 , 108042. [ Google Scholar ] [ CrossRef ]
• El Sherif, R.; Pluye, P.; Hong, Q.N.; Rihoux, B. Using qualitative comparative analysis as a mixed methods synthesis in systematic mixed studies reviews: Guidance and a worked example. Res. Synth. Methods 2024 , 15 , 450–465. [ Google Scholar ] [ CrossRef ]
• Shalahuddin, M.; Sunindyo, W.D.; Effendi, M.R.; Surendro, K. Fuzzy-set qualitative comparative analysis (fsQCA) for validating causal relationships in system dynamics models. Eng. Rep. 2024 , e12855. [ Google Scholar ] [ CrossRef ]
• Dangla, R.R. CANDO A CRISE CHE EXIME. O SECTOR DA DISTRIBUCIÓN E O COMERCIO AO POR MENOR ESPAÑOL: PEQUENAS Vs GRANDES EMPRESAS. Rev. Galega Econ. 2016 , 25 , 23–34. [ Google Scholar ] [ CrossRef ]
• On-Shelf Availability Barometer—NIQ. Available online: https://nielseniq.com/global/en/solutions/on-shelf-availability-barometer/ (accessed on 8 July 2024).
• Stone, J.; Rahimifard, S. Resilience in agri-food supply chains: A critical analysis of the literature and synthesis of a novel framework. Supply Chain Manag. 2018 , 23 , 207–238. [ Google Scholar ] [ CrossRef ]
• AECOC BARÓMETRO OSA|AECOC. Available online: https://www.aecoc.es/servicios/estudios/barometro-osa/ (accessed on 8 July 2024).
• Vilalta, O.M.; Serrat, N.A. Analysis of technology as a factor of resilience in the agri-food supply chain. In Increasing Supply Chain Performance in Digital Society ; IGI Global: Hershey, PA, USA, 2022; pp. 59–77. [ Google Scholar ] [ CrossRef ]
• Buallay, A. Sustainability reporting and retail sector performance: Worldwide evidence. Int. Rev. Retail Distrib. Consum. Res. 2022 , 32 , 311–330. [ Google Scholar ] [ CrossRef ]
• Mayorova, E. Corporate social responsibility disclosure: Evidence from the European retail sector. Entrep. Sustain. Issues 2019 , 7 , 891–905. [ Google Scholar ] [ CrossRef ]
• Anguiano-Santos, C.; Salazar-Ordóñez, M. Sustainability reporting as a tool for fostering sustainable growth in the agri-food sector: The case of Spain. J. Environ. Plan. Manag. 2024 , 67 , 426–453. [ Google Scholar ] [ CrossRef ]
• Muhammad, A.; Rushdi, A. Utilization of Karnaugh Maps in Multi-Value Qualitative Comparative Analysis. Int. J. Math. Eng. Manag. Sci. 2018 , 3 , 28–46. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Arimany-Serrat, N.; Montanyà, O.; Amat, O. Sustainability as a Resilience Factor in the Agri-Food Supply Chain. Sustainability 2024 , 16 , 7162. https://doi.org/10.3390/su16167162

Arimany-Serrat N, Montanyà O, Amat O. Sustainability as a Resilience Factor in the Agri-Food Supply Chain. Sustainability . 2024; 16(16):7162. https://doi.org/10.3390/su16167162

Arimany-Serrat, Núria, Oriol Montanyà, and Oriol Amat. 2024. "Sustainability as a Resilience Factor in the Agri-Food Supply Chain" Sustainability 16, no. 16: 7162. https://doi.org/10.3390/su16167162

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 21 August 2024

Serum proteomics reveal APOE-ε4 -dependent and APOE-ε4 -independent protein signatures in Alzheimer’s disease

• Elisabet A. Frick 1 ,
• Valur Emilsson   ORCID: orcid.org/0000-0001-9982-0524 1 , 2 ,
• Thorarinn Jonmundsson   ORCID: orcid.org/0000-0001-9158-0087 2 ,
• Anna E. Steindorsdottir   ORCID: orcid.org/0000-0002-2386-5934 2 ,
• Erik C. B. Johnson   ORCID: orcid.org/0000-0002-0604-2944 3 , 4 ,
• Raquel Puerta   ORCID: orcid.org/0000-0002-1191-5893 5 ,
• Eric B. Dammer   ORCID: orcid.org/0000-0003-2947-7606 3 , 6 ,
• Anantharaman Shantaraman   ORCID: orcid.org/0000-0003-1384-941X 3 , 6 ,
• Amanda Cano 5 , 7 ,
• Mercè Boada   ORCID: orcid.org/0000-0003-2617-3009 5 , 7 ,
• Sergi Valero 5 , 7 ,
• Pablo García-González   ORCID: orcid.org/0000-0003-0125-5403 5 , 7 ,
• Elias F. Gudmundsson   ORCID: orcid.org/0000-0002-7661-4872 1 ,
• Alexander Gudjonsson 1 ,
• Rebecca Pitts   ORCID: orcid.org/0000-0003-4733-414X 8 ,
• Xiazi Qiu   ORCID: orcid.org/0000-0002-8397-2168 8 ,
• Nancy Finkel 8 ,
• Joseph J. Loureiro   ORCID: orcid.org/0000-0001-7222-9160 8 ,
• Anthony P. Orth   ORCID: orcid.org/0009-0005-9865-0494 9 ,
• Nicholas T. Seyfried   ORCID: orcid.org/0000-0002-4507-624X 3 , 4 , 6 ,
• Allan I. Levey   ORCID: orcid.org/0000-0002-3153-502X 3 , 4 ,
• Agustin Ruiz   ORCID: orcid.org/0000-0003-2633-2495 5 , 7 ,
• Thor Aspelund   ORCID: orcid.org/0000-0002-7998-5433 1 , 2 ,
• Lori L. Jennings   ORCID: orcid.org/0000-0001-5130-8417 8 ,
• Lenore J. Launer   ORCID: orcid.org/0000-0002-3238-7612 10 ,
• Valborg Gudmundsdottir   ORCID: orcid.org/0000-0002-7459-1603 1 , 2 &
• Vilmundur Gudnason   ORCID: orcid.org/0000-0001-5696-0084 1 , 2  

Nature Aging ( 2024 ) Cite this article

11 Altmetric

Metrics details

• Alzheimer's disease
• Functional genomics

A deeper understanding of the molecular processes underlying late-onset Alzheimer’s disease (LOAD) could aid in biomarker and drug target discovery. Using high-throughput serum proteomics in the prospective population-based Age, Gene/Environment Susceptibility–Reykjavik Study (AGES) cohort of 5,127 older Icelandic adults (mean age, 76.6 ± 5.6 years), we identified 303 proteins associated with incident LOAD over a median follow-up of 12.8 years. Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status, were implicated in neuronal processes and overlapped with LOAD protein signatures in brain and cerebrospinal fluid. We identified 17 proteins whose associations with LOAD were strongly dependent on APOE- ε 4 carrier status, with mostly consistent associations in cerebrospinal fluid. Remarkably, four of these proteins (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE- ε 4 yet upregulated due to LOAD, a finding replicated in external cohorts and possibly reflecting a response to disease onset. These findings highlight dysregulated pathways at the preclinical stages of LOAD, including those both independent of and dependent on APOE- ε 4 status.

Similar content being viewed by others

Unbiased proteomics and multivariable regularized regression techniques identify SMOC1, NOG, APCS, and NTN1 in an Alzheimer’s disease brain proteomic signature

CSF proteome profiling across the Alzheimer’s disease spectrum reflects the multifactorial nature of the disease and identifies specific biomarker panels

Plasma proteomic profiles predict future dementia in healthy adults

Alzheimer’s disease (AD) is the most common cause of dementia, accounting for up to 80% of all dementia cases 1 , of which late-onset Alzheimer’s disease (LOAD) is most common 2 . As of 2022, approximately 55 million individuals worldwide had dementia, representing one out of nine people aged 65 years or older 3 . Although promising advances have been made in amyloid-targeting therapeutic options for early-stage LOAD 4 , 5 , they still have limited benefit, and identification of additional risk pathways that can be used for early detection and intervention is highly needed. To meet these demands, a variety of biologically relevant circulating molecules have been broadly associated with LOAD risk. The proteome in particular has the potential to reveal circulating markers of disease-related molecular pathways from different tissues, and studies assessing the circulating proteomic signatures between older adults without dementia and individuals suffering from LOAD have been described 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 . Modest sample sizes, low-throughput proteomics and lack of longitudinal information have, however, been limiting factors in these studies. A recent large-scale longitudinal study identified promising blood-based markers for all-cause incident dementia, although it is unknown how specific the results are to LOAD 17 . Information on the global circulating proteomic profile preceding the onset of LOAD, and how well it reflects AD-related processes in brain and cerebrospinal fluid (CSF), is, thus, scarce.

AD has a considerable genetic component, and both common 18 and rare risk variants have been identified 19 , of which the strongest effects are conferred by variants in the well-known APOE (apolipoprotein E) gene. Approximately 25% of the general population carries the APOE-ε4 variant, whereas it is present in over 50% of AD cases 20 , 21 . The APOE-ε4 allele increases the risk of LOAD by three-fold in heterozygous carriers and by up to 12-fold in homozygous carriers 22 . Although the link between the ε4 allele and LOAD has been extensively researched, light has yet to be shed on the precise mechanism by which the APOE gene affects LOAD onset and/or progression. Importantly, recent large-scale proteogenomic studies have consistently established the APOE locus as a protein-regulatory hotspot, regulating levels of hundreds of proteins in both circulation 23 , 24 , 25 , 26 and CSF 27 , 28 . However, it remains unknown to what extent these proteins relate to LOAD and if they can provide new information on the mechanisms through which APOE-ε4 mediates its risk. Identifying LOAD-associated circulatory proteins and whether their association is APOE-ε4 dependent or independent is crucial for the understanding of AD more generally as well as for gaining insight into potential pathways suitable for targeting in personalized treatment.

The current study tests the hypotheses that specific proteomic signatures in the circulation precede LOAD diagnosis and can reflect dysregulated biological pathways in the brain and CSF. Furthermore, we expect that some of these protein signatures may be affected by the APOE- ε 4 genotype and can, thus, provide molecular readout of pathways directly affected by APOE- ε 4 . To address these hypotheses, we used a high-throughput aptamer-based platform to characterize 4,137 serum proteins in 5,294 participants of the population-based Age, Gene/Environment Susceptibility–Reykjavik Study (AGES) 29 to identify protein signatures of incident LOAD (events occurring during follow-up) and prevalent LOAD, taking an unbiased, longitudinal and cross-sectional approach to the discovery of potential biomarkers for LOAD (Fig. 1 ). Considering the protein-regulatory influence of APOE and how it may impact the way that serum proteins are associated with LOAD, we disentangled the LOAD protein signature into APOE- ε 4 -dependent and APOE- ε 4 -independent components, by identifying proteins whose LOAD association is largely attenuated upon conditioning on APOE- ε 4 carrier status. We compared the serum protein signature of LOAD to those observed in CSF and brain and, finally, used genetic variation as anchors to determine the potential causal direction between serum proteins and disease state.

a , Overview of the AGES cohort and study participants. Prevalent non-AD dementia cases were excluded from the analysis. b , Overview of the aptamers tested and their associations with LOAD. Serum measurements of 4,782 aptamers were tested for associations with prevalent and incident LOAD status, using logistic and Cox proportional hazards regression models, respectively. From the proteins associated with incident LOAD, sets of 140 proteins with an APOE-ε4 -independent association and 17 proteins with an APOE-ε4 -dependent association were defined. The APOE-ε4 -dependent proteins were further expanded to first-degree PPI partners. All sets of proteins were subjected to functional enrichment analysis and bidirectional MR analysis. c , Overview of the replication cohorts used in the study, which include proteins measured in the circulation (ACE) as well as in brain and CSF (Emory). This figure was created with BioRender.

The AGES study cohort

This prospective population-based study was based on 5,127 participants free of dementia at baseline, after the exclusion of 163 individuals with prevalent non-AD dementia and 167 individuals with prevalent LOAD. During a potential follow-up of 12.8 years (median using reverse Kaplan–Meier, 95% confidence interval (CI): 12.6–13.2), 655 individuals were diagnosed with incident LOAD, with the last individual being diagnosed 16 years from baseline. Of those, 115 were diagnosed at the AGES 5-year follow-up study visit, whereas the remaining cases were based on clinical diagnosis of LOAD from linked records ( Methods ). Participants with incident LOAD were older at entry, were more likely to carry an APOE-ε4 allele, had lower body mass index (BMI) and had lower education levels compared to healthy individuals (Supplementary Table 1 ). See Fig. 1 for the study overview.

Serum protein profile of incident LOAD in AGES

To investigate the LOAD-associated circulatory proteomic patterns that occur before disease onset, we used Cox proportional hazards (Cox PH) models and found 320 aptamers (303 proteins) to be significantly (false discovery rate (FDR) < 0.05) associated with incident LOAD diagnosis after adjusting for age and sex (model 1), with hazard ratios (HRs) ranging from 0.78 for TBCA to 1.47 for NTN1 per standard deviation increase of protein levels (Fig. 2a and Supplementary Table 2 ). To account for variability related to APOE- ε 4 carrier status, we adjusted for the genotype in an additional model (model 2; Supplementary Table 2 ), which resulted in 140 significant aptamers (130 unique proteins, HR: 0.79 (CD4)–1.25 (CGA/FSHB), FDR < 0.05) (Fig. 2b ), all of which overlapped with model 1 (Fig. 2c ). When comparing the two models, 43% of the serum proteins remained significant after APOE-ε4 adjustment, indicating that their LOAD association is independent of the APOE-ε4 genotype (Table 1 and Supplementary Table 2 ). Adjusting for additional AD risk factors and estimated glomerular filtration rate (eGFR) ( Methods ) retained 38 significant LOAD-associated aptamers (35 proteins, HR: 0.80 (CD4)–1.26 (SMOC1), FDR < 0.05) (model 3; Supplementary Table 2 ), which may reflect specific processes affecting risk of LOAD that are not captured by currently established risk factors.

a , b , Volcano plots showing the protein association profile for incident LOAD with the HR for incident LOAD from the Cox PH models ( x axis) and −log 10 of Benjamini–Hochberg FDRs ( y axis) across two models: without APOE- ε 4 adjustment (model 1) ( a ) and with APOE- ε 4 adjustment (model 2) ( b ). c , Venn diagram for the overlap between models 1 and 2 for incident LOAD. d , e , Enrichment of top GO terms from GSEA analysis for incident LOAD (model 1) shown as a dot plot stratified by ontology ( d ) and gene-concept network ( e ). f , g , Comparison of effect sizes (HR) from Cox PH models for incident LOAD between the AGES and the ACE ( n  = 719) cohorts for all proteins reaching nominal significance ( P  < 0.05) in the Cox PH in ACE for model 1 ( f ) and model 2 ( g ). Protein associations with Benjamini–Hochberg FDR < 0.05 are denoted in red. BP, biological process; CC, cellular component; MF, molecular function.

As HR variability can arise with lengthy follow-up time, secondary analyses were implemented with a 10-year follow-up cutoff, which revealed mostly overlapping results (Supplementary Note 1 , Supplementary Tables 3 and 4 and Supplementary Fig. 1 ). We did, however, detect protein associations specific to the shorter follow-up time, which potentially reflect processes that take place closer to the LOAD diagnosis. As there may be further differences in proteomic profiles depending on whether protein sampling occurred before or after LOAD diagnosis, we additionally considered the protein profile of 167 AGES participants with prevalent LOAD at baseline (Supplementary Note 2 , Supplementary Fig. 2a–c and Supplementary Tables 5 – 7 ). Interestingly, many of the proteins associated with increased risk of incident LOAD showed the opposite direction of effect for prevalent LOAD although generally not statistically significant (Supplementary Fig. 2d ). These contrasting results suggest an important temporal element in the LOAD-associated proteome. In total, 346 aptamers (329 unique proteins) were associated with LOAD when all outcomes (incident and prevalent LOAD), follow-up times and models were considered (Supplementary Tables 2 , 3 and 5 ).

To evaluate which biological processes are reflected by the overall incident LOAD-associated protein signature in AGES, we performed a gene set enrichment analysis (GSEA). The strongest enrichment for protein associations in model 1 was observed for Gene Ontology (GO) terms related to axon development and neuron morphogenesis (Fig. 2d,e and Supplementary Table 8 ). The proteins driving the enrichment included neural cell adhesion molecules 1 and 2 (NCAM1 and NCAM2), netrin 1 (NTN1), contactin 1 (CNTN1), neuropilin 1 (NRP1), fibronectin leucine-rich transmembrane protein 2 (FLRT2), matrix metallopeptidase 2 (MMP2) and cell adhesion molecule L1-like (CHL1). GSEA of the protein profiles of model 2, where APOE- ε 4 carrier status was adjusted for, showed similar enrichment results (Supplementary Table 8 ), demonstrating that these terms were mainly driven by the APOE- ε 4 -independent component of the LOAD-associated protein profile (Supplementary Note 3 ). No tissue-elevated gene expression was significantly enriched among the LOAD-associated proteins, except for adipose tissue (Supplementary Table 8 ). Nevertheless, seven (35%) of the top 20 APOE-ε4- independent LOAD-associated proteins had elevated expression in brain or choroid plexus compared to other tissues (Table 1 ).

Proteins with APOE-ε4 -dependent association with incident LOAD

As previously mentioned, 43% of the protein associations with incident LOAD were independent of APOE- ε 4 . Of the remaining 57% that were affected by APOE-ε4 adjustment, we identified 17 proteins whose associations with incident LOAD were particularly strongly affected by APOE- ε 4 carrier status (Table 2 , Fig. 3a , Supplementary Fig. 3 and Supplementary Table 2 ). These proteins, hereafter referred to as APOE-ε4 -dependent proteins, were defined as proteins significantly (FDR < 0.05) associated with incident LOAD in model 1 but whose nominal significance was attenuated ( P  > 0.05) or whose direction of effect changed upon APOE- ε 4 adjustment in model 2. These APOE-ε4 -dependent proteins included those with the strongest associations with LOAD before adjusting for the APOE- ε 4 allele (Fig. 2a ). The levels of the APOE protein (targeted by four aptamers) were not significantly associated with incident or prevalent LOAD (FDR > 0.19 for all models, both outcomes). However, lower levels were observed in prevalent LOAD at a nominal significance ( P  < 0.05) that was unaffected by adjustment for APOE-ε4 carrier status (Supplementary Table 9 ). Figure 3b shows the intra-correlations among the 17 APOE-ε4 -dependent proteins. All the 17 APOE-ε4 -dependent proteins were strongly regulated by the APOE- ε 4 allele (Fig. 3c , Table 2 , Supplementary Fig. 4 and Supplementary Table 10 ), with the ε 4 allele increasing the levels of five of the proteins and decreasing the levels of the other 12. Accordingly, we observed that increased levels of the five APOE-ε4 upregulated proteins and decreased levels of the 12 APOE-ε4 downregulated proteins were also associated with higher risk of LOAD, yielding an HR above and below 1, respectively (Fig. 3d ). As per definition, most of the APOE-ε4 -dependent proteins lost significance upon APOE-ε4 adjustment, yet, interestingly, the direction of effect inverted for five proteins after APOE-ε4 adjustment (ARL2, IRF6, NEFL, S100A13 and TBCA) (Fig. 3e ). A previous study using the Simoa assay (Quanterix) reported increased NEFL levels in APOE-ε4 compared to APOE-ε3 carriers 30 , whereas we observed the opposite. We, thus, compared NEFL measurements from SOMAscan and the Simoa assay in a subset of AGES and observed differences in the associations with both APOE-ε4 and LOAD, indicating that they potentially measure different NEFL species 31 (Supplementary Note 4 and Supplementary Fig. 5 ).

a , Spaghetti plot showing the statistical significance as Benjamini–Hochberg FDR of protein associations with incident LOAD across the three Cox PH models, highlighting a set of 17 unique proteins (green) whose association with incident LOAD is attenuated upon APOE- ε 4 adjustment. The horizontal lines indicate Benjamini–Hochberg FDR < 0.05 (dashed) and P  < 0.05 (dot-dashed). The total number of significantly associated proteins (FDR < 0.05) for each model is shown above. b , Pairwise Pearson’s correlation among the 17 APOE-ε4 -dependent proteins. c , Forest plot showing the effect (beta coefficient) of the APOE genotype on the 17 APOE-ε4 -dependent proteins in AGES. The beta coefficients indicate the change in protein levels per ε4 allele count and are shown with 95% CIs. d , e , Forest plots showing the HR for incident LOAD per standard deviation increase in level for each of the 17 APOE-ε4 -dependent proteins in AGES without APOE- ε 4 adjustment (model 1) ( d ) and with APOE- ε 4 adjustment (model 2) ( e ). The LOAD HRs are shown with 95% CIs. Proteins that change direction of effect between the two models are highlighted in red. f – h , Replication analyses for c – e were performed in the ACE cohort ( n  = 719) in the same manner as in the AGES cohort. FAM159B (in gray) was not measured in the ACE SOMAscan assay.

The HR conferred by APOE-ε4 for incident LOAD in the AGES cohort was 2.1 (Cox PH P  = 1.23 × 10 −27 ) per copy of the ε4 allele. To evaluate if any of the 17 APOE-ε4 -dependent proteins might mediate the effect of APOE - ε4 on incident LOAD, we performed a regression-based mediation analysis. The overall proportion of the effect mediated was non-significant (estimate = −0.05, P  = 1; Supplementary Fig. 6a ), thus suggesting that these proteins do not mediate the LOAD risk conferred by APOE-ε4 . However, although not direct mediators, the 17 proteins could be blood-based readouts of a true mediator within tissue-specific pathological processes occurring before LOAD diagnosis. We additionally considered the change in HR for APOE-ε4 on risk of incident LOAD when adjusting for individual LOAD-associated proteins and found that adjustment for most proteins resulted in a minor effect decrease (Supplementary Fig. 6b ). Intriguingly, however, the adjustment for four APOE-ε4 -dependent proteins (NEFL, ARL2, TBCA and S100A13) caused an increase of approximately 10% in APOE-ε4 effect size (Supplementary Fig. 6b,c ). Thus, the effect of APOE-ε4 on LOAD is partly masked by secondary opposing associations between these proteins and LOAD, which are further explored below. Although the 17 APOE-ε4 -dependent proteins were not significantly enriched for tissue-elevated gene expression (Supplementary Table 8 ), we observed that four (LRRN1, FAM159B, NEFL and HBQ1) had elevated gene expression in brain compared to other tissues, and one (TMCC3) clustered with oligodendrocyte-related genes (Table 2 ). Of the remaining APOE-ε4 -dependent proteins, eight were ubiquitously expressed, including in brain tissue, and four were elevated in other tissues. We did not detect any significantly enriched molecular signatures or GO terms for the 17 APOE-ε4 -dependent proteins (Supplementary Table 8 ). However, a network analysis of measured and inferred physical protein–protein interactions (PPIs) 32 revealed that the APOE-ε4 -dependent proteins interact directly with proteins involved in microtubule and centromeric functions, neuronal response and development, neuroinflammation and AD (Extended Data Fig. 1 , Supplementary Tables 11 – 13 and Supplementary Note 5 ).

Given the well-established relationship between APOE and cholesterol 33 , we explored the potential effect of serum lipid levels on the association between LOAD and the 17 APOE-ε4 -dependent proteins (Supplementary Table 14 , Supplementary Figs. 7 and 8 and Supplementary Note 6 ). Our findings suggest that, although many of the APOE-ε4 -dependent proteins are associated with cholesterol levels, it is not the driver of their link to LOAD.

Finally, given the observed APOE-ε4 -dependent and APOE-ε4 -independent proteomic associations with LOAD in the full cohort, we additionally investigated if any proteins were differentially associated with LOAD within APOE-ε4 carriers versus non-carriers by stratification via an interaction analysis. We found differential associations between the strata for several proteins, potentially suggesting that different pathways vary in their contribution to the development of LOAD depending on APOE-ε4 carrier status (Supplementary Note 7 and Supplementary Table 15 ).

External validation of protein associations with LOAD

We evaluated the protein associations with incident LOAD from our APOE-ε4 -dependent/independent analyses in an external cohort, the Alzheimer Center Barcelona (ACE) ( n  = 1,341), with SOMAscan platform (v4.1-7K) measurements from plasma of individuals who were referred to the center. The longitudinal component of ACE consists of individuals who had been diagnosed with mild cognitive impairment (MCI) at the center and had been followed up. A total of 719 participants had follow-up information and 266 converted to LOAD over a median follow-up of 3.14 years (reverse Kaplan–Meier, 95% CI: 3.04–3.28) (Supplementary Table 16 ). Despite the fundamentally different cohorts, with AGES being population based and using the V3-5K SOMAscan platform and ACE based on individuals with established symptoms and the v4.1-7K SOMAscan platform, we replicated 36 protein associations with LOAD at nominal significance ( P  < 0.05) in the smaller ACE cohort (Table 3 , Supplementary Table 10 and Fig. 2f,g ). Of those, 30 proteins were nominally significant in model 1, with 97% being directionally consistent with the observations in AGES (Fig. 2f ). In model 2, 21 proteins were nominally significant, 86% of which were directionally consistent (Fig. 2g ). After multiple testing correction, seven proteins remained statistically significant (FDR < 0.05), all of which were directionally consistent (Table 3 , Supplementary Table 10 and Fig. 2f,g ). Six were statistically significant (FDR < 0.05) in model 1 (NEFL, LRRN1, TBCA, CTF1, C1orf56 and TIMP4) and one in model 2 (S100A13) (Supplementary Table 10 ). Of all 332 tested aptamers, 213 (64%) were directionally consistent regardless of significance in model 1 (two-sided exact binomial test P  = 2.0 × 10 −5 ), and 202 (61%) were directionally consistent in model 2 (two-sided exact binomial test P  = 0.002), demonstrating an enrichment for consistency in direction of effect. The protein associations replicated in the ACE cohort are of particular interest as they represent potentially clinically relevant candidates for LOAD that are consistent in two different contexts: in both a general population and a clinically derived symptomatic sample set. However, our results suggest that many of the proteins that associate with long-term LOAD risk are not strongly associated with the conversion from MCI to AD, which is further into the AD trajectory and may also explain the limited overlap between the proteins associated with prevalent and incident LOAD in AGES.

Validation of reversed association conditional on APOE-ε4

Specifically considering the APOE-ε4 -dependent proteins, the association between the APOE- ε 4 allele and the proteins was replicated for 13 of 17 proteins in the ACE cohort (Fig. 3f and Supplementary Fig. 4b ). Furthermore, the change in direction of effect for incident LOAD upon APOE- ε 4 adjustment was replicated in the ACE cohort for four of five proteins (ARL2, NEFL, S100A13 and TBCA) (Fig. 3g,h and Supplementary Table 10 ), with even larger effects observed in the ACE cohort compared to AGES in the APOE- ε 4 adjusted model and three proteins (ARL2, S100A13 and TBCA) becoming statistically significant ( P  < 0.05). Thus, the attenuation of the primary LOAD associations for these proteins upon APOE- ε 4 adjustment meet the criteria of APOE- ε 4 dependence ( Methods ). No significant interaction between these proteins and APOE- ε 4 carrier status on AD risk was observed in either the AGES or ACE cohorts. Taken together, our results show that these proteins are strongly downregulated by APOE- ε 4 and, consequently, show an inverse relationship with incident LOAD; but when adjusting for the APOE-ε4 allele, their association with LOAD is still significant but reversed, suggesting a secondary non- APOE-ε4- mediated process affecting these same proteins in relation to LOAD in the opposite direction that is more strongly observed in a cohort of individuals with MCI than in the population-based AGES cohort.

Potential causal associations between proteins and LOAD

The proteins associated with LOAD could include proteins causally related to the disease or proteins whose serum level changes reflect a response to prodromal or genetic liability to LOAD. To test this hypothesis, we performed a bidirectional two-sample Mendelian randomization (MR) analysis, including the targets of all 346 aptamers associated with LOAD in our study. Genetic variant associations for serum protein levels were obtained from a catalog of cis -protein quantitative trait loci (pQTLs) from AGES 23 , whereas variant associations with LOAD were extracted from a recent GWAS of 39,106 clinically diagnosed LOAD cases, 46,828 proxy-LOAD and dementia cases and 401,577 controls of European ancestry 18 . In total, 117 (34%) of the LOAD-associated serum aptamers had cis -pQTLs that were suitable as genetic instruments and were included in the protein-LOAD MR analysis (Supplementary Table 17 ).

In the forward MR analysis, two proteins—integrin binding sialoprotein (IBSP) and amyloid precursor protein (APP)—had support for causality (Supplementary Table 18 ). IBSP had a risk-increasing effect for LOAD in both the causal analysis (odds ratio (OR) = 1.26, FDR = 0.03) and observational analysis (incident LOAD full follow-up, HR = 1.13, FDR = 0.04). APP had a protective effect for LOAD in both the causal analysis (OR = 0.76, FDR = 0.03) and the observational analysis (incident LOAD full follow-up, HR = 0.88, FDR = 0.047). Notably, although not statistically significant, we observed suggestive support for a protective effect of genetically determined serum levels of acetylcholinesterase (ACHE; OR = 0.92, P  = 0.061), a target of clinically used therapeutic agent for dementia 34 (Supplementary Table 18 and Supplementary Fig. 9 ). In a forward MR analysis of the APOE - ε4 -dependent protein interaction partners, two proteins, APP and MAPK3, had support for causality (Supplementary Table 13 and Supplementary Note 5 ).

As most of the observational protein associations in the current study were detected for incident LOAD and, thus, reflect changes that take place before the onset of clinically diagnosed disease, it is unlikely that their levels and effects are direct downstream consequences of the disease after it reaches a clinical stage. However, they may reflect a response to a prodromal stage of the disease. We, therefore, performed a reverse MR to test if the observed changes in serum protein levels are likely to occur downstream of the genetic liability to LOAD, which may capture processes both at the prodromal and clinical stage. The APOE locus is likely to have a dominant pleiotropic effect in the reverse MR analysis (Supplementary Table 19 , Supplementary Fig. 10 and Supplementary Note 8 ), as it has a disproportionately strong effect on LOAD risk compared to all other common genetic variants while also being a well-established pQTL trans -hotspot, affecting circulating levels of up to hundreds of proteins 23 , 24 , 25 , 26 . We, therefore, performed the primary reverse MR analysis using only LOAD-associated genetic variants outside of the APOE locus as instruments. We found two proteins (S100A13 and ARL2) that were significantly (FDR < 0.05) increased by LOAD or its genetic liability (Supplementary Table 19 and Supplementary Figs. 10 and 11 ). Interestingly, both were among the 17 previously identified APOE - ε4 -dependent LOAD proteins, together with two additional proteins that were nominally significant in the reverse MR (TBCA, P  = 4.4 × 10 −4 , FDR = 0.051 and IRF6, P  = 7.9 × 10 −4 , FDR = 0.055). Thus, intriguingly, these findings suggest that these four proteins are upregulated by LOAD, in contrast to the observed APOE-ε4 downregulation of the same proteins (Fig. 4 ). This supports our findings of competing biological effects described above (Fig. 3e and Supplementary Fig. 6 ), and, collectively, our results indicate that simultaneous opposing effects of APOE-ε4 on the one hand and LOAD on the other result in differential regulation of these proteins in serum (Fig. 4b ).

a , Comparison of HRs for incident LOAD with and without APOE- ε 4 adjustment in the observational analysis (Cox PH) ( n  = 5,127), the effects of APOE- ε 4 on protein levels in AGES ( n  = 5,332) and reverse MR ORs (excluding the APOE locus) shown for the four APOE-ε4 -dependent proteins that change direction of effect in both observational and causal analyses when APOE is accounted for. All effects are shown with 95% CIs. b , c , Visual summaries of the observed data. b , Mediation diagrams showing three possible hypotheses that could explain the relationship among APOE-ε4 , LOAD and the four proteins shown in a . Our analyses do not support the hypothesis that LOAD mediates the effect of APOE- ε 4 on proteins (hypothesis 1) or the other way around (hypothesis 2). However, our results from both the observational and causal analyses support the hypothesis that two mechanisms are at play that affect the same proteins in the opposite direction (hypothesis 3). c , The APOE- ε 4 genotype leads to increased risk of LOAD by its effects in brain tissue. The same genotype results in a downregulation of serum levels of four proteins that are consequently themselves negatively associated with incident LOAD. Additionally, other non- APOE LOAD risk variants lead to upregulation of the same proteins in the reverse MR analysis, possibly reflecting a response to LOAD or its genetic liability. This figure was created with BioRender.

We performed a replication analysis of the effect of APOE- ε 4 on protein levels and the reverse MR results for these four proteins using published protein GWAS summary statistics from two recent studies 24 , 35 . In the external datasets, the downregulation of all four proteins by APOE- ε 4 (as determined by the rs429358 C allele) was replicated (Supplementary Fig. 12 ). In the reverse MR analysis (excluding the APOE locus), the upregulation of protein levels by LOAD liability observed in AGES was also detected for two proteins (S100A13 and TBCA) in both validation cohorts, reaching significance ( P  < 0.05) in the study by Ferkingstad et al. 35 (Supplementary Fig. 12 and Supplementary Table 20 ). Although the two proteins changed direction in a similar manner as in AGES, the effect size was considerably smaller in the validation cohorts. Notably, however, individuals in these two cohorts are much younger than those in AGES, with mean ages of 55 years and 48 years for the Ferkingstad et al. 35 and Sun et al. 24 studies, respectively, compared to 76 years in AGES. Therefore, we conducted an age-stratified reverse MR analysis in AGES that showed a strong age-dependent effect, with a much larger effect of LOAD genetic liability on protein levels in individuals over 80 years of age compared to those younger than 80 years (Supplementary Fig. 12 ). The effect size in AGES individuals younger than 80 years was in line with the effect observed in the validation cohorts. Thus, if the upregulation of these proteins reflects a response to prodromal or preclinical LOAD, an older cohort may be needed to detect an association of the same degree as we found in AGES. However, the observed support in the validation cohorts for the discordant effects of APOE versus non- APOE LOAD-associated genetic variants on the same serum proteins strongly implicates these proteins as directly relevant to LOAD, potentially as readouts of biological processes that are both disrupted by APOE-ε4 and modulated in the opposite manner as a response to genetic predisposition to LOAD or the disease onset in general.

Together, these results indicate that LOAD or its general genetic liability causally affects the levels of some APOE-ε4 -dependent proteins, but this effect is simultaneously masked by the strong effects of the APOE locus in the other direction (Fig. 4a ). These outcomes strengthen the results described above, showing that the levels of these four proteins are strongly downregulated in APOE- ε 4 carriers, and lower levels of these proteins are, therefore, associated with increased risk of LOAD in an APOE-ε4 -dependent manner (Fig. 4b ). Simultaneously, the reverse MR analysis shows that the collective effect of the other non- APOE LOAD risk variants is to upregulate the serum levels of these same proteins, possibly reflecting a response mechanism to LOAD pathogenesis (Fig. 4c ). Again, this is in line with the observational analysis, where all four proteins changed direction of effect when adjusting for APOE-ε4 (Figs. 3d,e,g,h and 4a ).

Overlap with the AD brain and CSF proteome

To evaluate to what extent our LOAD-associated serum proteins reflect the proteomic profile of AD in relevant tissues, we queried data from recent proteomic studies of AD in CSF 36 and brain 37 , which also describe tissue-specific co-regulatory modules. We observed that, of our LOAD-associated serum proteins, 51 were also associated with AD in brain as measured by mass spectrometry (MS), with 32 (63%) being directionally consistent (Fig. 5a,b and Supplementary Tables 21 and 22 ). Higher directional consistency was observed within the APOE-ε4 -independent protein group, or 15 (71%) of 21 proteins associated with AD in brain tissue. Additionally, 60 proteins were directly associated with AD in CSF as measured with SOMAscan (7K) (Fig. 5a ), with 46 (77%) being directionally consistent (Fig. 5b ). The proportion of directionally consistent associations between serum and CSF was higher in both the APOE-ε4 -independent and APOE-ε4 -dependent protein groups, or 88% (22 of 25 and seven of eight for APOE-ε4 -independent and APOE-ε4 -dependent proteins, respectively) (Fig. 5b and Supplementary Table 21 ). However, directional inconsistency between plasma and CSF AD proteomic profiles was reported previously in a similar comparison 38 . Fourteen proteins overlapped among all three tissues in the context of AD (Fig. 5a and Supplementary Table 21 ). Many of these proteins have established links or are highly relevant to LOAD, such as spondin 1 (SPON1), involved in the processing of APP 39 ; secreted modular calcium-binding protein 1 (SMOC1), previously proposed as a biomarker of LOAD in postmortem brains and CSF 40 ; NTN1, an interactor of APP and regulator of amyloid beta (Aβ) production 41 ; NEFL, previously proposed as a plasma biomarker for LOAD and axon injury 42 , 43 ; and Von Willebrand factor (VWF), known for its role in blood clotting and associations with LOAD 44 (Supplementary Table 21 ). Notably, some of the APOE-ε4 -dependent proteins were associated with AD across all three tissues, such as TBCA and TP53I11.

a , Venn diagram showing the overlap of AD-associated proteins in serum, brain and CSF. b , Comparison of the effect sizes for AD-associated proteins that overlap between serum and brain (top) and serum and CSF (bottom). The proteins are stratified based on the APOE-ε4 dependence in AGES for incident LOAD. The effect size in AGES is shown for incident LOAD model 1 (Cox PH), except for proteins that were uniquely identified using the shorter 10-year follow-up (Cox PH) or prevalent LOAD (logistic regression), in which case the respective effect size from the significant association is shown. c – e , Heatmap showing the enrichment (two-sided Fisher’s test) of AD-associated proteins by tissue type ( x axis) in the AGES serum protein modules ( c ), Emory CSF protein modules ( d ) and Emory brain protein modules ( e ) ( y axis). Modules that are enriched for AD associations in more than one tissue are highlighted with red squares.

We previously described the co-regulatory structure of the serum proteome, which can broadly be defined as 27 modules of correlated proteins 25 (Supplementary Table 23 ). In the current study, we found that, among the 346 aptamers (329 proteins) associated with LOAD (prevalent or incident, any model), five serum protein modules (M27, M3, M11, M2 and M24) were overrepresented (Fig. 5c and Supplementary Table 24 ). In particular, the 140 APOE-ε4- independent proteins were specifically overrepresented in module M27, enriched for proteins involved in neuron development and the extracellular matrix (ECM), and in module M3, associated with growth factor signaling pathways (Supplementary Table 24 ). By contrast, the 17 APOE-ε4- dependent proteins were specifically enriched in protein module M11 (Supplementary Table 24 ), which is strongly enriched for lipoprotein-related pathways and is under strong genetic control of the APOE locus 25 . Serum modules M27, M24 and M11 were all enriched for AD associations in CSF (Fig. 5c ). We next sought to understand to what extent our LOAD-associated proteins identified in serum might reflect AD protein signatures in CSF and brain tissue. Among the LOAD-associated proteins measured in serum, we found the APOE-ε4 -dependent and APOE-ε4 -independent proteins to be enriched in different CSF modules, most of which were also linked to AD (Fig. 5d and Supplementary Table 24 ). In brain tissue, the serum APOE-ε4- independent LOAD proteins were particularly enriched in brain module M42 (Matrisome), which is enriched for ECM proteins 37 (Fig. 5e and Supplementary Table 24 ). Strikingly, M42 was strongly enriched for the AD proteomic profiles of all three tissues (Fig. 5e and Supplementary Table 24 ). Interestingly, members of this module (SMOC1, SPON1, NTN1, GPNMB and APP), with some of the strongest association with AD in brain (Fig. 5b and Supplementary Table 24 ), overlapped with some of the strongest associations in serum to incident LOAD in our study (Fig. 2a,b and Supplementary Table 2 ).

This module has furthermore been demonstrated to be correlated with Aβ deposition in the brain, and some of its protein constituents (for example, MDK, NTN1 and SMOC1) have been shown to co-localize with and bind to Aβ 37 . Additionally, the APOE locus regulates M42 levels in the brain (mod-QTL), and, although the APOE protein is a member of module M42, this regulation was found to not be solely driven through the levels of the APOE protein itself 37 . Our results simultaneously show that other members of the module, such as SPON1 and SMOC1, exhibit an APOE-ε4 -independent association to incident LOAD in serum. Interestingly, these same two proteins are increased in CSF 30 years before symptom onset in autosomal dominant early-onset AD 45 . In summary, we demonstrate significant overlaps in LOAD-associated protein expression across blood, CSF and brain on both an individual protein level and on a protein module level.

We describe a comprehensive mapping of the serum protein profile of LOAD that provides insight into processes that are independent of or dependent on the genetic control of APOE - ε 4 (Supplementary Fig. 13 ). We identified 329 proteins in total that differed in the incident or prevalent LOAD cases compared to non-LOAD participants in a population-based cohort with long-term follow-up. Among these, we identified a grouping of proteins based on their primary LOAD association being statistically independent of (140 proteins), or dependent on (17 proteins) APOE - ε 4 carrier status. Many of the APOE-ε4 -independent proteins are implicated in neuronal pathways and are shared with the LOAD-associated CSF and brain proteome. The 17 APOE-ε4 -dependent proteins overlap with AD-associated protein modules in CSF and interact directly with protein partners involved in LOAD, including APP. Another key finding is that, among these 17 proteins, four proteins (ARL2, S100A13, TBCA and IRF6) change LOAD-associated direction of effect both observationally and genetically when taking APOE- ε 4 carrier status into account. Notably, we replicated this directional change both observationally for three proteins (ARL2, S100A13 and TBCA) and genetically for two proteins (S100A13 and TBCA) in external cohorts. Collectively, our results suggest that, although their primary association with LOAD reflects the risk conferred by APOE - ε 4 , there exists a secondary causal effect of LOAD itself on the protein levels in the reverse direction as supported by the MR analysis, possibly reflecting a response to the disease onset.

Previous studies identifying proteins associated with LOAD were limited to cross-sectional cohorts or were based on all-cause dementia 6 , 17 , 46 , 47 . Here, we extend those findings by distinguishing LOAD cases from other types of dementia based on a clinical diagnosis criterion in a prospective cohort study to identify LOAD-specific serum protein signatures preceding clinical onset. A recent study of UK Biobank participants using the Olink platform identified several proteins associated with incident dementia, including AD 48 . Their top AD-associated proteins differed from those prioritized in the current study; thus, future work is required to determine how proteomic platform and cohort differences, such as age, influence protein associations with LOAD, both of which we found to directly affect the results in our extended analyses of NEFL (Supplementary Note 4 ). Furthermore, our comparative approach of statistical models with and without APOE- ε 4 adjustment provides a compartmentalized view of the LOAD serum protein profile and demonstrates how protein effects can differ depending on genetic confounders, which are imperative to take into consideration. We found that the proteins associated with incident LOAD in our study, in particular those independently of APOE- ε 4 , such as GPNMB, NTN1, SMOC1 and SPON1, overlap with the proteomic profile of LOAD in CSF 38 and brain 37 ; are enriched for neuronal pathways; and have been functionally implicated with LOAD (Table 1 ), which may reflect an altered abundance of neuronal proteins in the circulation during the prodromal stage of LOAD. These overlaps that we found across independent cohorts and different proteomics technologies suggest that the serum levels of some proteins have a direct link to the biological systems involved in LOAD pathogenesis and may even provide a peripheral readout of neurodegenerative processes before clinical diagnosis of LOAD. In particular, the proteins that show directionally consistent effect sizes suggest exceptional AD-specific robustness as the measurements vary by tissue, methodology and populations.

We identified 17 proteins with a particularly strong APOE-ε4 -dependent association with incident LOAD, of which eight were also associated with prevalent AD in CSF. The association between APOE- ε 4 and circulating levels of these proteins was reported by our group 23 , 25 , 26 and others 49 , but their direct association with incident LOAD has, to our knowledge, not been previously described. Interestingly, we previously observed multiple independent genetic signals in the APOE–APOC1–APOC1P1 region affecting these same proteins to a varying degree, some of which co-localize with GWAS signals for LOAD 23 , which necessitate further investigation for better understanding of the complex regulatory effects in this genetic region that converge on the same set of proteins. The proteins with an APOE-ε4 -dependent association with LOAD may point directly to the processes through which APOE- ε 4 mediates its risk and provide a readout of the pathogenic process in the circulation for the approximately 50% of patients with LOAD worldwide carrying the variant 20 , 21 . Although our data do not provide information on the tissue origin of the APOE-ε4 -dependent proteins, some exhibit brain-elevated gene expression 50 or have been associated with LOAD at the transcriptomic or protein level in brain tissue or CSF (Table 2 ). At the genetic level, a lookup in the GWAS catalog 50 shows that an intron variant in the IRF6 gene has a suggestive GWAS association with LOAD via APOE-ε4 carrier status interaction 51 . In addition, variants in the TMCC3 gene have been linked to LOAD 52 , educational attainment 53 and caudate volume change rate 54 , and variants in the TBCA gene have been suggestively associated with reaction time 55 and PHF-tau levels 56 . Collectively, the gene expression patterns for these proteins in the brain, interactions with proteins involved in neuronal processes and suggestive associations between genetic markers in or near these genes and brain-related outcomes suggest that these APOE-ε4 -dependent proteins may reflect brain-specific processes affected by APOE- ε 4 carrier status that affect the risk of developing LOAD. Notably, the association patterns for ARL2, S100A13 and TBCA suggest the presence of a pathway that is downregulated by APOE-ε4 already in midlife, given the consistent effect of APOE-ε4 on the same proteins in younger cohorts, but upregulated at the onset of LOAD, as supported by the larger observed effects in the APOE- ε 4 adjusted analysis in the ACE cohort of individuals who are closer to diagnosis on the AD trajectory than those in AGES. Additional studies are required to expand on these interpretations and dissect the complex mechanisms at playages to determine if the modulation of the process represented by these proteins has therapeutic potential.

Conflicting results have been observed for the relationship between serum or plasma levels of the APOE protein and LOAD 57 . Although serum levels of the APOE protein, as measured by the SOMAscan platform, were not strongly associated with LOAD in AGES, our results support a relationship between lower APOE levels and prevalent LOAD. We furthermore observed conflicting results in the association between APOE- ε4 and NEFL compared to a previous study 30 . Our results comparing different methods for measuring NEFL in AGES (Supplementary Note 4 ) highlight the importance of considering proteomic platforms and their potential differences in protein species detection, as noted by Budelier et al. 31 and others 58 .

Two proteins, IBSP and APP, were identified to potentially have a causal role in LOAD. IBSP was previously associated with plasma Aβ and incident dementia 59 , and APP is the precursor protein for Aβ 60 . Based on the MR analysis for the third of LOAD-associated proteins that could be tested, most do not appear to be causal in and of themselves, but their association with incident LOAD may still reflect changes that occur years before the onset of LOAD that could be of interest to target before irreversible damage accumulates.

A major strength of our study is the high-quality data from a prospective longitudinal population-based cohort study with detailed follow-up, broad coverage of circulating proteins and a comprehensive comparison to the AD proteome in CSF and brain. The limitations of our study include that our results are based on a Northern European cohort and cannot necessarily be transferred directly to other populations or ethnicities. Additionally, although we partly replicated our overall findings in an external cohort, a greater replication proportion could be anticipated in a more comparable cohort as discussed above. The ACE cohort consists of clinically referred individuals with MCI and proteomic measurements performed on a different version of the SOMAscan platform (version 4.1 versus version 3 in AGES). Additionally, different normalization procedures were applied by SomaLogic for the two SOMAscan versions, which may have an effect on the LOAD associations 47 . Regardless of these differences, we did replicate most of the APOE-ε4 -dependent LOAD associations, including the APOE-ε4 -dependent change in effect for ARL2, S100A13 and TBCA. We could not test all LOAD-associated proteins for causality, including most of the APOE-ε4 -dependent proteins, due to lack of significant cis -pQTLs for two-thirds of the proteins; thus, we cannot exclude the possibility that some could be causal but missed by our analysis. Finally, although a clinical LOAD diagnosis criterion was used for classifying cases, it is possible that some individuals were misclassified, and some of our findings may, thus, reflect processes related to dementia in general. As a result, it is critical to validate these findings in individuals with established Aβ and tau deposits as well as in experimental settings.

The proteins highlighted in this study and the mechanisms they point to may be used as a source of biomarkers or therapeutic targets that can be modulated for the prevention or treatment of LOAD. This large prospective cohort study, using both a longitudinal and a cross-sectional design, represents a unified and comprehensive reference analysis with which past and future serum protein biomarkers and drug targets can be considered, compared and evaluated.

AGES study population

Participants aged 66–96 years were from the AGES cohort. AGES is a single-center prospective population-based study of deeply phenotyped individuals ( n  = 5,764; mean age, 76.6 ± 5.6 years; 58% women) and survivors of the 40-year-long prospective Reykjavik study, an epidemiologic study aimed to understand aging in the context of gene/environment interaction by focusing on four biologic systems: vascular, neurocognitive (including sensory), musculoskeletal and body composition/metabolism 29 . The AGES study was approved by the Nation Bioethics Committee in Iceland (approval number VSN-00-063), by the National Institute on Aging Intramural Institutional Review Board and by the Data Protection Authority in Iceland. All participants provided informed consent for their participation in the study and did not receive compensation.

Of the AGES participants, 3,411 attended a 5-year follow-up visit, and all participants were followed up for incident dementia through medical and nursing home reports (Resident Assessment Instrument (RAI)) and death certificates. The follow-up time was up to 16.9 years, with the last individual being diagnosed 16 years from baseline. LOAD diagnosis at AGES baseline and follow-up visits was carried out using a three-step procedure as previously described 29 . In brief, cognitive assessment was carried out on all participants. Neuropsychological testing was performed on individuals with suspected dementia. Individuals remaining suspect for dementia underwent further neurologic and proxy examinations in the second diagnosis step. Third, a panel comprising a neurologist, a geriatrician, a neuroradiologist and a neuropsychologist assessed the positive-scoring participants according to international guidelines and gave a dementia diagnosis. Diagnoses for all-cause dementia and LOAD from nursing home reports were based on intake examinations upon entry or standardized procedures carried out in all Icelandic nursing homes 61 . Diagnosis of LOAD was established according to National Institute of Neurological and Communicative Diseases and Stroke–Alzheimerʼs Disease and Related Disorders Association (NINCDS-ADRDA) criteria or according to International Classification of Diseases, 10th revision (ICD-10) code F00 criteria. The participants diagnosed at baseline were defined as prevalent LOAD cases, whereas individuals diagnosed with LOAD during the follow-up period (either at the AGESII follow-up visit or through linked records) were defined as incident LOAD cases. All prevalent non-AD dementia cases ( n  = 163) were excluded from analyses.

Age, sex, education and lifestyle variables were assessed using questionnaires at baseline. Education was categorized as primary, secondary, college or university degree. Smoking was characterized as current, former or never smoker. APOE genotyping was assessed using microplate array diagonal gel electrophoresis (MADGE) 62 . BMI and hypertension were assessed at baseline. BMI was calculated as weight (kg) divided by height squared (m 2 ), and hypertension was defined as antihypertensive treatment or blood pressure (BP) > 140/90 mmHg. Type 2 diabetes was defined from self-reported diabetes, diabetes medication use or fasting plasma glucose ≥7 mmol L −1 . Serum creatinine was measured using a Roche Hitachi 912 instrument, and eGFR was derived with the four-variable MDRD study equation 63 .

Proteomic measurements

The proteomic measurements in AGES were described in detail elsewhere 26 , 64 and were available for 5,457 participants. In brief, a custom version of the SOMAscan platform (Novartis V3-5K) was applied based on SOMAmer protein profiling technology 65 , 66 including 4,782 aptamers that bind to 4,137 human proteins. Serum was prepared using a standardized protocol 67 from blood samples collected after an overnight fast by the same personnel who were specifically trained in protocols for sample collection and handling. Special care was taken to minimize the time between blood draw and sample centrifugation. Bench time was minimized at all times, and samples were stored in 0.5-ml aliquots at −80 °C under constant surveillance. Serum samples that had not been previously thawed were used for the protein measurements. All samples were randomized and run as a single set at SomaLogic, Inc., blinded to any phenotypic outcomes. Hybridization controls were used to adjust for systematic variability in detection, and calibrator samples of three dilution sets (40%, 1% and 0.005%) were included so that the degree of fluorescence was a quantitative reflection of protein concentration. All aptamers that passed quality control had median intra-assay and inter-assay coefficient of variation (CV) < 5%. Finally, intra-plate median signal normalization was applied to individual samples by SomaLogic instead of normalization to an external reference of healthy individuals, as is done for later versions of the SOMAscan platform ( https://somalogic.com/wp-content/uploads/2022/07/SL00000048_Rev-3_2022-01_-Data-Standardization-and-File-Specification-Technical-Note-v2.pdf ).

Of the 37 APOE-ε4 -independent and APOE-ε4 -dependent proteins highlighted in Tables 1 and 2 , respectively, orthogonal MS verified the specificity of eight aptamers (seven proteins) in previous studies 25 . Twelve additional aptamers were profiled (CD4 (3143_3_1), BRD4 (10043_31_3), SPON1 (5496_49_3), SMOC1 (13118_5_3), LRRN1 (11293_14_3), S100A13 (7223_60_3), CTF1 (13732_79_3), ARL2 (12587_65_3), C1orf56 (5744_12_3), MSN (5009_11_1), IRF6 (9999_1_3) and NEFL (10082_251_3)), and two additional aptamers (C1orf56 (5744_12_3) and MSN (5009_11_1)) were confirmed (Table 2 ) with SOMAmer pulldown mass spectrometry (SP-MS) using patient serum samples (>65 years) purchased from BioIVT. The new confirmations’ methodology is consistent with previous publications 25 , but the instrumentation was updated. Data-dependent analysis was performed on an Orbitrap Eclipse operated in positive ionization mode, with electrospray voltage of 1,500 V and ion transfer tube temperature of 275 °C applied. Full MS scans with quadrupole isolation were acquired in the Orbitrap mass analyzer using a scan range of 375–1,500  m / z , standard AGC target and automatic maximum injection time. Data-dependent scans were acquired in the Orbitrap with a 0.7- m / z quadrupole isolation window, 50,000 resolution, 50% normalized AGC target, 200-ms maximum injection time and 38% HCD collision energy over a 2-s cycle time. Dynamic exclusion of 45 s relative to ±10 p.p.m. reference mass tolerance was applied. The peptides were eluted with Aurora Ultimate 25 cm × 75 µm ID, 1.7 µm C18 nano columns over a 90-min gradient on the Vanquish Neo UHPLC system (Thermo Fisher Scientific). Raw data files were processed in Proteome Discoverer version 2.5 with SequestHT database search using a canonical human FASTA database (20,528 sequences, updated 8 April 2022).

The proteomic measurements for NEFL in a subset of AGES using the Simoa assay from Quanterix were described elsewhere 68 .

ACE Alzheimer Center Barcelona was founded in 1995 and has collected and analyzed roughly 18,000 genetic samples, diagnosed over 8,000 patients and participated in nearly 150 clinical trials to date. For more details, visit https://www.fundacioace.com/en . The syndromic diagnosis of all individuals of the ACE cohort was established by a multidisciplinary group of neurologists, neuropsychologists and social workers. Healthy controls (HCs), including individuals with a diagnosis of subjective cognitive decline (SCD), were assigned a Clinical Dementia Rating (CDR) of 0, and individuals with MCI were assigned a CDR of 0.5. For MCI diagnoses, the classification of López et al. and Petersen’s criteria were used 69 , 70 , 71 , 72 . The 2011 National Institute on Aging and Alzheimer’s Association (NIA-AA) guidelines were used for AD diagnosis 73 . All ACE clinical protocols were previously published 74 , 75 , 76 . All ACE cohort participants provided written informed consent for their participation in the study and did not receive compensation 74 . Paired plasma and CSF samples 77 , following consensus recommendations, were stored at −80 °C. A subset of the ACE cohort was analyzed with the SOMAscan 7K proteomic platform 78 ( n  = 1,370) (SomaLogic, Inc.). The proteomic data underwent standard quality control procedures at SomaLogic and were median normalized to reference using the adaptive normalization by maximum likelihood (ANML) method ( https://somalogic.com/wp-content/uploads/2022/07/SL00000048_Rev-3_2022-01_-Data-Standardization-and-File-Specification-Technical-Note-v2.pdf ). Additionally, APOE genotyping was assessed using TaqMan genotyping assays for rs429358 and rs7412 single-nucleotide polymorphisms (SNPs) (Thermo Fisher Scientific). Genotypes were furthermore extracted from the Axiom 815K Spanish Biobank Array (Thermo Fisher Scientific) performed by the Spanish National Center for Genotyping (CeGen).

Statistics and reproducibility

Protein measurement data were Box-Cox transformed and then centered and scaled. Extreme outliers (>4.3 s.d.) were excluded as previously described 64 . The assumption of normal distribution for the transformed protein measurements was visually inspected but not formally tested. Sample size was not predetermined by any statistical method but, rather, by available data. The associations of serum protein profiles with prevalent AD ( n  = 167) were examined cross-sectionally by logistic regression at baseline. The associations of serum protein profiles with incident LOAD ( n  = 655) were examined longitudinally using Cox proportional hazards models after excluding all participants with prevalent dementia. Participants who died or were diagnosed with incident non-AD dementia were censored at date of death or diagnosis. To account for HR variability that may arise with lengthy follow-up periods, a secondary analysis using a 10-year follow-up cutoff of incident LOAD was performed ( n LOAD  = 432). All individuals who had not experienced an event by the end of the 10-year follow-up were considered as not having an event. These individuals were not excluded from the analysis and were, thus, treated as ‘healthy’ controls. To compare the fits of the two follow-up times and to test for time dependence of the coefficients, we used ANOVA and the ‘survsplit’ function from the ‘survival’ R package 79 . For both prevalent and incident LOAD, we examined three covariate-adjusted models. The primary model (model 1) included the covariates sex and age. Model 2 included as an additional covariate the APOE- ε 4 allele count (ε2/ε4 genotypes excluded). The third model (model 3) included additional adjustment for cardiovascular and lifestyle risk factors (BMI, type 2 diabetes, education, hypertension and smoking history) that have been associated with risk of LOAD 80 and kidney function (eGFR) that may influence circulating protein levels. When performing the APOE -ε4 stratification analysis via interaction for incident LOAD, we added an interaction term between each aptamer and APOE-ε4 carrier status (no, not carrying ε4; yes, either ε34 or ε44) in model 2. We then used the ‘glht’ function from the ‘multcomp’ package (version 1.4.20) to obtain a recalculated HR and P value per strata. We extracted the effect sizes and P values for the interaction term directly from the summary of the Cox model. To assess the associations between APOE genotype (0, 1 or 2 copies of APOE - ε4 ) and the LOAD-associated proteins, a multiple linear regression was performed adjusting for sex and age, where the beta coefficient indicates the change in protein levels per ε4 allele count. Benjamini–Hochberg FDR was used to account for multiple hypothesis testing. ACE SomaLogic proteomics data were similarly Box-Cox transformed, and association analysis was performed in the same manner as in AGES. Mediation analysis was conducted using the ‘cmest’ function from the ‘CMAverse’ (version 0.1.0) R package with APOE - ε4 as exposure, incident LOAD as outcome and the 17 APOE-ε4 -dependent proteins as potential mediators. The proportion mediated was calculated with direct counterfactual imputation estimation and 95% CIs based on 1,000 bootstrap repetitions.

APOE- ε 4 dependence criteria of the proteins were defined as serum proteins that met FDR significance of less than 0.05 in association with incident LOAD in model 1, thus unadjusted for the APOE- ε 4 allele, but whose nominal significance was abolished upon APOE- ε 4 correction in model 2 ( P  > 0.05). Serum proteins that remained nominally significantly associated with incident LOAD ( P  < 0.05) upon APOE- ε 4 correction but changed direction of effect were also considered to meet the APOE-ε4 dependence criteria, as a reversal of the effect indicates that the primary association is driven by APOE- ε 4 .

Functional enrichment analyses were performed using overrepresentation analysis (ORA) and GSEA using the R packages ‘clusterProfiler’ and ‘fgsea’ 81 , 82 . The association significance cutoff for inclusion in ORA was FDR < 0.05. Background for both methods was specified as all proteins tested from the analysis leading up to enrichment testing. The investigated gene sets were the following: GO, Human Phenotype Ontology, KEGG, Wikipathways, Reactome, Pathway Interaction Database (PID), microRNA targets (MIRDB and Legacy), transcription factor targets (GTRD and Legacy), ImmuneSigDB and the vaccine response gene set 83 . Finally, we included tissue gene expression signatures via the same methods (ORA and GSEA) using data from GTEx 84 and the Human Protein Atlas 85 , where gene expression patterns across tissues were categorized in the same manner as described by Uhlen et al. 85 , and tissue-elevated expression was considered as gene expression in any of the categories ‘tissue-specific’, ’tissue-enriched’ or ‘group-enriched’. minGSSize was set at 2 when investigating the LOAD-associated serum proteins directly. Before running the GSEA, the average effect size from the appropriate observational analysis was computed for each protein detected by multiple aptamers to eliminate duplicates from the protein list. Duplicate protein annotations were removed before executing the ORA. The effect sizes from the observational analyses were used for GSEA ranking. For the PPI network analysis, PPIs from InWeb 32 ( n  = 14,448, after Entrez ID filtering) were used to obtain the first-degree interaction partners of the APOE-ε4 -dependent proteins. For GSEA of the APOE-ε4 -dependent protein interaction partners, minGSSize was set to 15, and maxGSSize was set to 500. Gene expression patterns based on a consensus dataset combining GTEx and Human Protein Atlas gene expression data were obtained from the Human Protein Atlas (version 23) for the top LOAD-associated proteins (Tables 1 and 2 ) as well as single-cell sequencing cluster membership 85 . Analyses were conducted using R versions 4.2.1. and 4.2.3.

Protein comparisons across serum, CSF and brain

To compare protein modules and AD associations across tissues, protein modules and protein associations with AD were obtained from brain 37 and CSF 36 . The brain data, from the Banner Sun Health Research Institute 86 and ROSMAP 87 , included tandem mass tag (TMT)-MS-based quantitative proteomics for 106 controls, 200 asymptomatic AD cases and 182 AD cases. The CSF samples were collected under the auspices of the Emory Goizueta Alzheimer’s Disease Research Center (ADRC) and the Emory Healthy Brain Study (EHBS) 36 . The cohort consisted of 140 healthy controls and 160 patients with AD as defined by the National Institute on Aging research framework 73 . Protein measurements were performed using TMT-MS and SOMAScan (7K). Only SomaLogic protein measurements were included in the comparison between CSF and serum, which were median normalized. Proteins were matched on SomaLogic aptamer ID when possible but otherwise by Entrez gene symbol. Overlaps between modules and AD-associated (FDR < 0.05) proteins across tissues were evaluated with Fisher’s exact test.

A two-sample bidirectional MR analysis was performed, first, to evaluate the potential causal effects of serum protein levels on AD (forward MR) and, second, to evaluate the potential causal effects of AD or its genetic liability on serum protein levels (reverse MR). All aptamers significantly (FDR < 0.05) associated with LOAD (incident or prevalent) were included in the MR analyses, or a total of 346 unique aptamers (Supplementary Tables 2 , 3 and 5 ), of which 320 aptamers were significant in the full follow-up incident LOAD analysis (models 1–3); 106 aptamers were significant in the 10-year follow-up incident LOAD analysis (models 1–3); and 10 aptamers were significant in the prevalent LOAD analysis (models 1–3). Genetic instruments for serum protein levels were obtained from a GWAS of serum protein levels in AGES 23 and defined as follows. All variants within a 1-Mb (±500-kb) cis -window for the protein-encoding gene were obtained for a given aptamer. A cis -window-wide significance level Pb = 0.05/N, where N equals the number of SNPs within a given cis -window, was computed, and variants within the cis -window for each aptamer were clumped ( r 2  ≥ 0.2, P  ≥ Pb). All aptamers included in the MR analysis had instruments with F -statistic > 10 (Supplementary Table 17 ). The effect of the genetic instruments for serum protein levels on LOAD risk was obtained from a GWAS of 39,106 clinically diagnosed LOAD cases, 46,828 proxy-LOAD and dementia cases and 401,577 controls of European ancestry 18 . Genetic instruments for the serum protein levels not found in the LOAD GWAS dataset were replaced by proxy SNPs ( r 2  > 0.8) when possible, to maximize SNP coverage. Genetic instruments for LOAD in the reverse causation MR analysis were obtained from the same LOAD GWAS 18 , where genome-wide significant variants were extracted ( P  < 5 × 10 −8 ) and clumped at a more stringent linkage disequilibrium (LD) threshold ( r 2  ≥ 0.01) than for the protein instruments to limit overrepresentation of SNPs from any given locus across the genome. In the reverse causation MR analysis, cis- variants (±500 kb) for the given protein were excluded from the analysis to avoid including pleiotropic instruments affecting the outcome (protein levels) through other mechanisms than the exposure (LOAD). The primary reverse causation MR analysis was performed excluding any variants in the APOE locus (chr19:45,048,858–45,733,201, genome build GRCh37). Causal estimate in the forward MR for each protein was obtained by the generalized weighted least squares (GWLS) method 88 , which accounts for correlation between instruments. Causality for proteins with single cis -acting variants was assessed with the Wald ratio estimator. For the reverse MR analysis, the inverse variance weighted method was applied due to a more stringent LD filtering of the instruments. Instrument heterogeneity was evaluated with Cochran’s Q test and horizontal pleiotropy with the MR Eggerʼs test using the ‘TwoSampleMR’ R package.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data from AGES are available through collaboration ([email protected]) under a data usage agreement with the Icelandic Heart Association. Data from the ACE cohort are available at the Global Neurodegeneration Proteomics Consortium ( https://www.neuroproteome.org/ ) via the Alzheimer Disease Data Initiative portal or by direct request to ACE Alzheimer Center Barcelona (contact: [email protected]). GWAS summary statistics were obtained from the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession numbers GCST90027158 , GCST90243133 , GCST90242506 and GCST90241568 ), the Global Lipids Genetics Consortium ( https://csg.sph.umich.edu/willer/public/glgc-lipids2021/ ) and deCode Genetics ( https://www.decode.com/summarydata/ ). Tissue specificity information was obtained from Human Protein Atlas version 23 ( https://v23.proteinatlas.org/ ). The MS-based validation data for aptamers included on the custom SOMAscan panel used in this study are available from ProteomeXchange via the PRIDE partner repository ( https://www.ebi.ac.uk/pride/ ) 89 under accession numbers PXD008819 – PXD008823 and PXD054671 , and from the PASSEL repository ( https://peptideatlas.org/passel/ ) under accession number PASS01145 . All other data supporting the conclusions of this study are presented in the main text and are freely available as a supplement to this manuscript.

Code availability

Statistical analyses were performed in R ( https://www.r-project.org/ ). The code used in this study will be made available by the corresponding author, V. Gudmundsdottir, upon reasonable request.

Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63 , 168–174 (2006).

Article   PubMed   Google Scholar  

Reitz, C., Rogaeva, E. & Beecham, G. W. Late-onset vs nonmendelian early-onset Alzheimer disease: a distinction without a difference? Neurol. Genet. 6 , e512 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Rajan, K. B. et al. Population estimate of people with clinical AD and mild cognitive impairment in the United States (2020–2060). Alzheimers Dement. 17 , 1966–1975 (2021).

van Dyck, C. H. et al. Lecanemab in early Alzheimer’s disease. N. Engl. J. Med. 388 , 9–21 (2023).

Mintun, M. A. et al. Donanemab in early Alzheimer’s disease. N. Engl. J. Med. 384 , 1691–1704 (2021).

Article   CAS   PubMed   Google Scholar  

Sattlecker, M. et al. Longitudinal protein changes in blood plasma associated with the rate of cognitive decline in Alzheimer’s disease. J. Alzheimers Dis. 49 , 1105–1114 (2016).

Kiddle, S. J. et al. Candidate blood proteome markers of Alzheimer’s disease onset and progression: a systematic review and replication study. J. Alzheimers Dis. 38 , 515–531 (2014).

Sattlecker, M. et al. Alzheimer’s disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimers Dement. 10 , 724–734 (2014).

O’Bryant, S. E. et al. A serum protein-based algorithm for the detection of Alzheimer’s disease. Arch. Neurol. 67 , 1077–1081 (2010).

Ijsselstijn, L. et al. Serum levels of pregnancy zone protein are elevated in presymptomatic Alzheimer’s disease. J. Proteome Res. 10 , 4902–4910 (2011).

Ray, S. et al. Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling proteins. Nat. Med. 13 , 1359–1362 (2007).

Henkel, A. W. et al. Multidimensional plasma protein separation technique for identification of potential Alzheimer’s disease plasma biomarkers: a pilot study. J. Neural Transm. 119 , 779–788 (2012).

Choi, J., Malakowsky, C. A., Talent, J. M., Conrad, C. C. & Gracy, R. W. Identification of oxidized plasma proteins in Alzheimer’s disease. Biochem. Biophys. Res. Commun. 293 , 1566–1570 (2002).

Cutler, P. et al. Proteomic identification and early validation of complement 1 inhibitor and pigment epithelium-derived factor: two novel biomarkers of Alzheimer’s disease in human plasma. Proteomics Clin. Appl. 2 , 467–477 (2008).

Hye, A. et al. Proteome-based plasma biomarkers for Alzheimer’s disease. Brain 129 , 3042–3050 (2006).

Doecke, J. D. et al. Blood-based protein biomarkers for diagnosis of Alzheimer disease. Arch. Neurol. 69 , 1318–1325 (2012).

Walker, K. A. et al. Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk. Nat. Aging 1 , 473–489 (2021).

Bellenguez, C. et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nat. Genet. 54 , 412–436 (2022).

Khani, M., Gibbons, E., Bras, J. & Guerreiro, R. Challenge accepted: uncovering the role of rare genetic variants in Alzheimer’s disease. Mol. Neurodegener. 17 , 3 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Frisoni, G. B. et al. The prevalence of apoE-ε4 in Alzheimer’s disease is age dependent. J. Neurol. Neurosurg. Psychiatry 65 , 103 (1998).

Gharbi-Meliani, A. et al. The association of APOE ε4 with cognitive function over the adult life course and incidence of dementia: 20 years follow-up of the Whitehall II study. Alzheimers Res. Ther. 13 , 5 (2021).

Corder, E. H. et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261 , 921–923 (1993).

Gudjonsson, A. et al. A genome-wide association study of serum proteins reveals shared loci with common diseases. Nat. Commun. 13 , 480 (2022).

Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558 , 73–79 (2018).

Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361 , 769–773 (2018).

Emilsson, V. et al. Coding and regulatory variants are associated with serum protein levels and disease. Nat. Commun. 13 , 481 (2022).

Cruchaga, C. et al. Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and informs causal proteins for Alzheimer’s disease. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-2814616/v1 (2023).

Yang, C. et al. Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders. Nat. Neurosci. 24 , 1302–1312 (2021).

Harris, T. B. et al. Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics. Am. J. Epidemiol. 165 , 1076–1087 (2007).

Malek-Ahmadi, M. et al. Plasma NfL is associated with the APOE ε4 allele, brain imaging measurements of neurodegeneration, and lower recall memory scores in cognitively unimpaired late-middle-aged and older adults. Alzheimers Res. Ther. 15 , 74 (2023).

Budelier, M. M. et al. A map of neurofilament light chain species in brain and cerebrospinal fluid and alterations in Alzheimer’s disease. Brain Commun. 4 , fcac045 (2022).

Li, T. et al. A scored human protein–protein interaction network to catalyze genomic interpretation. Nat. Methods 14 , 61–64 (2016).

Postmus, I. et al. Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins. Nat. Commun. 5 , 5068 (2014).

Marucci, G. et al. Efficacy of acetylcholinesterase inhibitors in Alzheimer’s disease. Neuropharmacology 190 , 108352 (2021).

Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53 , 1712–1721 (2021).

Dammer, E. B. et al. Proteomic analysis of Alzheimer’s disease cerebrospinal fluid reveals alterations associated with APOE ε4 and atomoxetine treatment. Sci. Transl. Med. 16 , eadn3504 (2024).

Johnson, E. C. B. et al. Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level. Nat. Neurosci. 25 , 213–225 (2022).

Dammer, E. B. et al. Multi-platform proteomic analysis of Alzheimer’s disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome. Alzheimers Res. Ther. 14 , 174 (2022).

Porter, T. et al. Cognitive gene risk profile for the prediction of cognitive decline in presymptomatic Alzheimer’s disease. Pers. Med. Psychiatry 7–8 , 14–20 (2018).

Google Scholar  

Zhou, M. et al. Targeted mass spectrometry to quantify brain-derived cerebrospinal fluid biomarkers in Alzheimer’s disease. Clin. Proteomics 17 , 19 (2020).

Lourenço, F. C. et al. Netrin-1 interacts with amyloid precursor protein and regulates amyloid- β production. Cell Death Differ. 16 , 655–663 (2009).

Zetterberg, H. Neurofilament light: a dynamic cross-disease fluid biomarker for neurodegeneration. Neuron 91 , 1–3 (2016).

Graham, N. S. N. et al. Axonal marker neurofilament light predicts long-term outcomes and progressive neurodegeneration after traumatic brain injury. Sci. Transl. Med. 13 , eabg9922 (2021).

Wolters, F. et al. Von Willebrand factor and the risk of dementia: a population-based study. Neurology 8 , 5474 (2018).

Johnson, E. C. B. et al. Cerebrospinal fluid proteomics define the natural history of autosomal dominant Alzheimer’s disease. Nat. Med. 29 , 1979–1988 (2023).

Lindbohm, J. V. et al. Plasma proteins, cognitive decline, and 20‐year risk of dementia in the Whitehall II and Atherosclerosis Risk in Communities studies. Alzheimers Dement. 18 , 612–624 (2022).

Eldjarn, G. H. et al. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature 622 , 348–358 (2023).

Guo, Y. et al. Plasma proteomic profiles predict future dementia in healthy adults. Nat. Aging 4 , 247–260 (2024).

Sebastiani, P. et al. A serum protein signature of APOE genotypes in centenarians. Aging Cell 18 , e13023 (2019).

Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47 , D1005–D1012 (2019).

Jun, G. et al. A novel Alzheimer disease locus located near the gene encoding tau protein. Mol. Psychiatry 21 , 108–117 (2016).

Chen, H.-H. et al. Genetically regulated expression in late-onset Alzheimer’s disease implicates risk genes within known and novel loci. Transl. Psychiatry 11 , 618 (2021).

Okbay, A. et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat. Genet. 54 , 437–449 (2022).

Brouwer, R. M. et al. Genetic variants associated with longitudinal changes in brain structure across the lifespan. Nat. Neurosci. 25 , 421–432 (2022).

Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9 , 2098 (2018).

Wang, H. et al. Genome-wide interaction analysis of pathological hallmarks in Alzheimer’s disease. Neurobiol. Aging 93 , 61–68 (2020).

Rasmussen, K. L. Plasma levels of apolipoprotein E, APOE genotype and risk of dementia and ischemic heart disease: a review. Atherosclerosis 255 , 145–155 (2016).

Leckey, C. A. & Zetterberg, H. Neurofilament light chain: defining the analyte. Brain Commun. 4 , fcac070 (2022).

Tin, A. et al. Proteomic analysis identifies circulating proteins associated with plasma amyloid β and incident dementia. Biol. Psychiatry Glob. Open Sci. 3 , 490–499 (2022).

Tanzi, R. E. et al. Amyloid β protein gene: cDNA, mRNA distribution, and genetic linkage near the Alzheimer locus. Science 235 , 880–884 (1987).

Jørgensen, L. M., El Kholy, K., Damkjær, K., Deis, A. & Schroll, M. [‘RAI’— an international system for assessment of nursing home residents]. Ugeskr. Læger 159 , 6371–6376 (1997).

Gudnason V, S. J. S. L. H. S. S. G. Association of apolipoprotein E polymorphism with plasma levels of high density lipoprotein and lipoprotein(a), and effect of diet in healthy men and women. NUTRITION METABOLISM AND CARDIOVASCULAR DISEASES 3 , 136–141 (1993).

Levey, A., Greene, T., Kusek, J. & Beck, G. A simplified equation to predict glomerular filtration rate from serum creatinine. J. Am. Soc. Nephrol. 11 , 155A (2000).

Gudmundsdottir, V. et al. Circulating protein signatures and causal candidates for type 2 diabetes. Diabetes 69 , 1843–1853 (2020).

Lamb, J. R., Jennings, L. L., Gudmundsdottir, V., Gudnason, V. & Emilsson, V. It’s in our blood: a glimpse of personalized medicine. Trends Mol. Med. 27 , 20–30 (2021).

Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5 , e15004 (2010).

Tuck, M. K. et al. Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J. Proteome Res. 8 , 113–117 (2009).

van Gennip, A. C. E. et al. Associations of plasma NfL, GFAP, and t-tau with cerebral small vessel disease and incident dementia: longitudinal data of the AGES-Reykjavik Study. Geroscience 46 , 505–516 (2024).

Jessen, F. et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimers Dement. 10 , 844–852 (2014).

Lopez, O. L. et al. Risk factors for mild cognitive impairment in the Cardiovascular Health Study Cognition Study: part 2. Arch. Neurol. 60 , 1394–1399 (2003).

PubMed   Google Scholar  

Petersen, R. C. et al. Mild cognitive impairment: a concept in evolution. J. Intern. Med. 275 , 214–228 (2014).

Petersen, R. C. et al. Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56 , 303–308 (1999).

Jack, C. R. et al. NIA‐AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14 , 535–562 (2018).

Orellana, A. et al. Establishing In-house cutoffs of CSF Alzheimer’s disease biomarkers for the AT(N) stratification of the Alzheimer Center Barcelona cohort. Int. J. Mol. Sci. 23 , 6891 (2022).

Rodriguez-Gomez, O. et al. FACEHBI: a prospective study of risk factors, biomarkers and cognition in a cohort of individuals with subjective cognitive decline. study rationale and research protocols. J. Prev. Alzheimers Dis. 4 , 100–108 (2016).

Moreno-Grau, S. et al. Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer’s disease and three causality networks: the GR@ACE project. Alzheimers Dement. 15 , 1333–1347 (2019).

Vanderstichele, H. et al. Standardization of preanalytical aspects of cerebrospinal fluid biomarker testing for Alzheimer’s disease diagnosis: a consensus paper from the Alzheimer’s Biomarkers Standardization Initiative. Alzheimers Dement. 8 , 65–73 (2012).

Candia, J., Daya, G. N., Tanaka, T., Ferrucci, L. & Walker, K. A. Assessment of variability in the plasma 7k SomaScan proteomics assay. Sci Rep. 12 , 17147 (2022).

Therneau, T. & Crowson, C. Using time dependent covariates and time dependent coefficients in the Cox model. Survival Vignettes http://lynchjim.com/doc/r-recommended/library/survival/doc/timedep.pdf (2014).

Gottesman, R. F. et al. Associations between midlife vascular risk factors and 25-year incident dementia in the Atherosclerosis Risk in Communities (ARIC) cohort. JAMA Neurol. 74 , 1246–1254 (2017).

Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16 , 284–287 (2012).

Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).

Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102 , 15545–15550 (2005).

Aguet, F. et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369 , 1318–1330 (2020).

Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347 , 1260419 (2015).

Beach, T. G. et al. Arizona Study of Aging and Neurodegenerative Disorders and Brain and Body Donation Program. Neuropathology 35 , 354–389 (2015).

Bennett, D. A. et al. Religious Orders Study and Rush Memory and Aging Project. J. Alzheimers Dis. 64 , S161–S189 (2018).

Burgess, S., Dudbridge, F. & Thompson, S. G. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat. Med. 35 , 1880–1906 (2016).

Perez-Riverol, Y. et al. The PRIDE database resources in 2022: A hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50 , D543–D552 (2022).

Zhang, S. et al. Degradation and inhibition of epigenetic regulatory protein BRD4 exacerbate Alzheimer’s disease-related neuropathology in cell models. J. Biol. Chem. 298 , 101794 (2022).

Korb, E., Herre, M., Zucker-Scharff, I., Darnell, R. B. & Allis, C. D. BET protein Brd4 activates transcription in neurons and BET inhibitor Jq1 blocks memory in mice. Nat. Neurosci. 18 , 1464–1473 (2015).

Ho, A. & Südhof, T. C. Binding of F-spondin to amyloid-β precursor protein: a candidate amyloid-β precursor protein ligand that modulates amyloid-β precursor protein cleavage. Proc. Natl Acad. Sci. USA 101 , 2548–2553 (2004).

Quinn, D. M. Acetylcholinesterase: enzyme structure, reaction dynamics, and virtual transition states. Chem. Rev. 87 , 955–979 (1987).

Article   CAS   Google Scholar  

Luís, J. P., Simões, C. J. V. & Brito, R. M. M. The therapeutic prospects of targeting IL-1R1 for the modulation of neuroinflammation in central nervous system disorders. Int. J. Mol. Sci . 23 , 1731 (2022).

Letronne, F. et al. ADAM30 downregulates APP-linked defects through cathepsin D activation in Alzheimer’s disease. EBioMedicine 9 , 278–292 (2016).

Drummond, E. et al. The amyloid plaque proteome in early onset Alzheimer’s disease and Down syndrome. Acta Neuropathol. Commun. 10 , 53 (2022).

Leshchyns’ka, I. & Sytnyk, V. Synaptic cell adhesion molecules in Alzheimer’s disease. Neural Plast. 2016 , 6427537 (2016).

Wennström, M. & Nielsen, H. M. Cell adhesion molecules in Alzheimer’s disease. Degener. Neurol. Neuromuscul. Dis. 2 , 65–77 (2012).

PubMed   PubMed Central   Google Scholar  

Kim, W., Watanabe, H., Lomoio, S. & Tesco, G. Spatiotemporal processing of neural cell adhesion molecules 1 and 2 by BACE1 in vivo. J. Biol. Chem. 296 , 100372 (2021).

Camporesi, E. et al. Neuroligin-1 in brain and CSF of neurodegenerative disorders: investigation for synaptic biomarkers. Acta Neuropathol. Commun. 9 , 19 (2021).

Dinamarca, M. C., Di Luca, M., Godoy, J. A. & Inestrosa, N. C. The soluble extracellular fragment of neuroligin-1 targets Aβ oligomers to the postsynaptic region of excitatory synapses. Biochem. Biophys. Res. Commun. 466 , 66–71 (2015).

Martin Flores, N. et al. Downregulation of Dickkopf-3, a Wnt antagonist elevated in Alzheimer’s disease, restores synapse integrity and memory in a disease mouse model. eLife 12 , RP89453 (2024).

Taylor, H. A., Przemylska, L., Clavane, E. M. & Meakin, P. J. BACE1: more than just a β‐secretase. Obesity Rev. 23 , e13430 (2022).

Wang, D. et al. Cardiotrophin-1 (CTF1) ameliorates glucose-uptake defects and improves memory and learning deficits in a transgenic mouse model of Alzheimer’s disease. Pharmacol. Biochem. Behav. 107 , 48–57 (2013).

Rayaprolu, S. et al. Flow-cytometric microglial sorting coupled with quantitative proteomics identifies moesin as a highly-abundant microglial protein with relevance to Alzheimer’s disease. Mol. Neurodegener. 15 , 28 (2020).

Zetterberg, H. et al. Association of cerebrospinal fluid neurofilament light concentration with Alzheimer disease progression. JAMA Neurol. 73 , 60–67 (2016).

Download references

Acknowledgements

The authors acknowledge the contribution of the Icelandic Heart Association (IHA) staff to AGES-Reykjavik as well as the involvement of all study participants. National Institute on Aging (NIA) contracts N01-AG-12100 and HHSN271201200022C (for V. Gudnason) and Althingi (the Icelandic Parliament) financed the AGES-Reykjavik study. IHA and Novartis have collaborated on proteomics research since 2012. This study was also funded by NIA grants 1R01AG065596-01A1 (to V. Gudnason), 1K08AG068604 (to E.J.) and P30AG066511 and U01AG061357 (to A.I.L.) and Icelandic Research Fund grant 206692-051 (to V. Gudmundsdottir). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The Genome Research @ Ace Alzheimer Center Barcelona (GR@ACE) project is supported by Grifols SA, Fundación bancaria ‘La Caixa’, Ace Alzheimer Center Barcelona and CIBERNED. Additional support was received from the ADAPTED and MOPEAD projects (European Union/EFPIA Innovative Medicines Initiative Joint (grant numbers 115975 and 115985, respectively)), from the HARPONE project, from the Agency for Innovation and Entrepreneurship (VLAIO) grant number PR067/21 and from Janssen. A.C. received support from the National Institute of Health Carlos III under the Sara Borrell grant (CD22/00125). The funders had no control over the publication.

Author information

Authors and affiliations.

Icelandic Heart Association, Kopavogur, Iceland

Elisabet A. Frick, Valur Emilsson, Elias F. Gudmundsson, Alexander Gudjonsson, Thor Aspelund, Valborg Gudmundsdottir & Vilmundur Gudnason

Faculty of Medicine, University of Iceland, Reykjavik, Iceland

Valur Emilsson, Thorarinn Jonmundsson, Anna E. Steindorsdottir, Thor Aspelund, Valborg Gudmundsdottir & Vilmundur Gudnason

Goizueta Alzheimer’s Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA

Erik C. B. Johnson, Eric B. Dammer, Anantharaman Shantaraman, Nicholas T. Seyfried & Allan I. Levey

Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA

Erik C. B. Johnson, Nicholas T. Seyfried & Allan I. Levey

Research Center and Memory Clinic. Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya, Barcelona, Barcelona, Spain

Raquel Puerta, Amanda Cano, Mercè Boada, Sergi Valero, Pablo García-González & Agustin Ruiz

Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA

Eric B. Dammer, Anantharaman Shantaraman & Nicholas T. Seyfried

CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain

Amanda Cano, Mercè Boada, Sergi Valero, Pablo García-González & Agustin Ruiz

Novartis, Cambridge, MA, USA

Rebecca Pitts, Xiazi Qiu, Nancy Finkel, Joseph J. Loureiro & Lori L. Jennings

Novartis, San Diego, CA, USA

Anthony P. Orth

Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA

Lenore J. Launer

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization: E.A.F., V. Gudmundsdottir., V.E. and V. Gudnason. Formal analysis: E.A.F., V. Gudmundsdottir, T.J., A.E.S., E.F.G., A.G., T.A., E.B.D. and A.S. Resources: V. Gudnason, V.E., A.I.L., A.R., A.P.O., J.J.L., L.L.J., N.T.S., E.C.B.J. and L.J.L. Data curation: R.P., A.C., M.B., P.G.-G., S.V., V. Gudmundsdottir, T.A. and E.F.G. Writing original draft: E.A.F. and V. Gudmundsdottir. Writing review and editing: all authors Visualization: E.A.F. and V. Gudmundsdottir. Supervision: V. Gudmundsdottir and V. Gudnason. Funding acquisition: V. Gudnason and L.J.L.

Corresponding authors

Correspondence to Valborg Gudmundsdottir or Vilmundur Gudnason .

Ethics declarations

Competing interests.

R.P., X.Q., N.F., L.L.J., A.P.O. and J.J.L. are employees and stockholders of Novartis. N.T.S. and A.I.L. are co-founders of Emtherapro. No other potential conflicts of interest relevant to this article were reported.

Peer review

Peer review information.

Nature Aging thanks Suzanne Schindler and Jin-Tai Yu for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended data fig. 1 functional enrichment analysis of apoe-ε4 -dependent protein-protein interaction partners..

a ) A scheme of the PPI partners selection, where first degree partners of the APOE-ε4- dependent proteins were extracted from the InWeb database. b-c ) Enrichment of selected GO terms for the PPI partner proteins shown as b ) dotplot and c ) gene-concept network. d-e ) Enrichment of top seven unique Wikipathways shown as d ) dotplot and e ) gene-concept network. BP, biological process; CC, cellular component; MF, molecular function.

Supplementary information

Supplementary information.

Supplementary Notes 1–8, Supplementary Figs. 1–13, Supplementary Table 25 and Supplementary References.

Reporting Summary

Supplementary tables 1–24., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ .

Reprints and permissions

About this article

Cite this article.

Frick, E.A., Emilsson, V., Jonmundsson, T. et al. Serum proteomics reveal APOE-ε4 -dependent and APOE-ε4 -independent protein signatures in Alzheimer’s disease. Nat Aging (2024). https://doi.org/10.1038/s43587-024-00693-1

Download citation

Received : 11 December 2023

Accepted : 22 July 2024

Published : 21 August 2024

DOI : https://doi.org/10.1038/s43587-024-00693-1

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.