Importance of integrating biological sex and age analyses in health research
Clinical Snapshot
PICO Framework
| P — Population | Health researchers and research methodologists conducting human, animal, and cell population studies |
| I — Intervention | Primary analysis by sex and age variables rather than controlling for them as confounders |
| C — Comparator | Traditional approach of treating sex and age as confounding variables to be controlled |
| O — Outcomes | Improved accuracy of research findings, prevention of mechanistic insight loss, enhanced data interpretation |
Bottom Line
This editorial makes a compelling case for treating biological sex and age as primary analytical variables rather than confounders in health research. The authors argue that traditional approaches of controlling for sex and age may mask important mechanistic insights and lead to inaccurate findings. Their recommendation to conduct sex- and age-stratified analyses as primary investigations, with pooled analyses as secondary, has particular relevance given increasing use of large datasets and machine learning in healthcare research. While the editorial format limits systematic evidence presentation, the methodological recommendations are sound and practically implementable. For Australian clinicians and researchers, this approach aligns with NHMRC research guidelines and could enhance the precision and applicability of research findings across diverse patient populations. The recommendations are especially relevant for cardiovascular, endocrine, and aging-related research where sex and age differences are well-established but often inadequately analyzed.
Key Findings
P Value: Not applicable - editorial format
Effect Size: Not applicable - editorial format
Primary Outcome: Recommendation to analyze data by sex and age as primary analysis rather than controlling for these variables
Nnt Or Sensitivity: Not applicable - methodological recommendation rather than clinical intervention
Confidence Interval: Not applicable - editorial format
Clinical Application
Highly feasible - requires analytical approach modification rather than additional resources Directly applicable to Australian research institutions, NHMRC-funded studies, and clinical research networks. Aligns with NHMRC guidelines on sex and gender considerations in research All health researchers conducting human, animal, and cellular studies across medical specialties
Abstract
Research findings in human, animal and cell populations may be influenced by biological sex and age. The traditional statistical approach to sex and/or age differences that are discovered in research data is to consider them as confounders that interfere with the ability to make accurate estimations and thus need to be controlled for. This article provides examples of how combining sexes or controlling for sex and/or age may lead to inaccurate findings and overlook important insights. Rather than treating these variables as confounding factors, they should be viewed as variables of importance to the research question. We contend that data should be analyzed according to sex/age as the primary analysis, while controlling for sex and age should be conducted as a secondary analysis. This topic is increasingly important as data are made publicly available and deep learning models/artificial intelligence are used to analyze large volumes of data. Mechanistic insights will continue to be lost if prompt action is not taken to report data according to sex and age and to analyze data by sex and age in health research.
References
- 1.Fairweather, D., & Kararigas, G. (2026). Importance of integrating biological sex and age analyses in health research. Biology of Sex Differences. https://doi.org/10.1038/s41746-024-01196-4
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