Biomarkers for aging identified in cross-sectional studies tend to be non-causative

Abstract

Biomarkers are important tools for diagnosis, prognosis, and identification of the causal factors of physiological conditions. Biomarkers are typically identified by correlating biological measurements with the status of a condition in a sample of subjects. Cross-sectional studies sample subjects at a single timepoint, while longitudinal studies follow a cohort through time. Identifying biomarkers of aging is subject to unique challenges. Individuals who age faster have intrinsically higher mortality rates and so are preferentially lost over time, in a phenomenon known as cohort selection. In this paper, we use simulations to show that cohort selection biases cross-sectional analysis away from identifying causal loci of aging, to the point where cross-sectional studies are less likely to identify loci that cause aging than if loci had been chosen at random. We go on to show this bias can be corrected by incorporating correlates of mortality identified from longitudinal studies, allowing cross sectional studies to effectively identify the causal factors of aging.

A roadmap to build a phenotypic metric of ageing: insights from the Baltimore Longitudinal Study of Aging

“…To date, most models designed to capture phenotypic/biological ageing were developed by selecting dimensions cross‐sectionally associated with age, and primarily validated using predictive models of multimorbidity and/or mortality as the reference outcomes, and only rarely using longitudinal change in phenotypes . Although these models have contributed substantially to our clinical understanding of the ageing process, our work shows that the validity, precision and robustness of these models can be further improved by considering longitudinal data and perform better especially later in the age spectrum when nonlinear trends are more likely to occur.…”

“…Ideally, these measures should be constructed using parameters that capture essential characteristics of ageing, collected longitudinally to avoid the biasing effect of secular trends and selective attrition and organized according to the major domains that mediate the relationships between ageing/diseases and physical and cognitive limitations. Recently, it has been recognized that although risk factors for development of specific medical conditions, clinical progression and mortality can be reliably identified from cross‐sectional studies, biomarkers that convey unbiased information on the pace of ageing are best elaborated using longitudinal data . Furthermore, given that most age‐related changes begin to manifest in early‐to‐mid life and accelerate in later decades, observations should cover a substantial proportion of the lifespan rather than being limited to the final stages of life.…”

An integrative study of five biological clocks in somatic and mental health

“…Since no previously published algorithms were used, we trained our own clocks using ridge regression with cross-validation. This approach relies on the assumption that the determined cross-sectional correlation between the omics patterns and chronological age arise mainly as a consequence of biological aging, and is independent from potential secular trends ( Nelson et al, 2020 ; Belsky, 2015 ; Belsky et al, 2020 ). As common to cross-sectional studies, it is, however, impossible to completely rule out potential cohort effects or uncontrolled individual differences and results should be interpreted in light of this limitation.…”

Accelerated aging in the brain, epigenetic aging in blood, and polygenic risk for schizophrenia

“…First, the sample size of this study, whilst large for a longitudinal neuroimaging study, is very modest for a genetic or epigenetic study. Secondly, the cross-sectional design for epigenetics limits our ability to detect a possible age acceleration rate in the blood and its correlation to accelerating brain age (Nelson et al, 2019). Depending on the time lag between illness onset and accelerated epigenetic aging, and because of the fact that most of the blood sample were acquired at baseline, the effects of the disease on DNA methylation may have yet to occur, especially in the younger adolescent population when onset of psychosis typically occurs (Paus et al, 2008).…”