Sociogenomic studies aimed at investigating biological pathways for frequently observed differences in disease outcomes across groups aggregated by socioeconomic status (SES) have commonly opted for either agnostic genome-wide or targeted candidate-gene approaches. A recent paper by Levine et al. (1) rightly calls attention to a third, perhaps preferred way: a priori selection of candidate gene expression pathways, here “conserved transcriptional response to adversity” (CTRA), to focus and provide biological meaning to observed differences. A focus on CTRA led to a priori hypotheses about the types of genes and directions of association that should be expected between adversity groups, by way of aggregating findings by transcription factor binding motifs. Levine et al. are careful in their discussion (and title) to avoid suggesting that observed associations lead to direct inferences about causal life-course mechanisms, even while improving our understanding of physiologically relevant differences between SES groups. Trivially, identified differences in expression may be a product of disease states or subclinical pathologies correlated with SES. More importantly, their findings are consistent with any number of causal exposures that occur prior to the observation period, which may in turn be mediated by or simply correlated with adult SES indicators. As described in their earlier work (2), associations between childhood SES and putative genomic function are often stronger than those observed with adult SES—intuitive because social attainment and behaviors are, at least partially, due to past stressors (3). However, evidence for early-life associations with later (epi)genomics are not themselves sufficient to imply early programming or “embedding,” because associations may be transient or noncausal (4) or, again, a result of correlated life-course exposures (5). Unfortunately, the accompanying commentary by Belsky and Snyder-Mackler (6) is less restrained in inferring mechanistic conclusions from these findings, particularly implications for “socially disadvantaged children” in a study using only adult data. Most importantly, observed associations between SES and differences in gene expression are conflated with inferences about social embodiment mechanisms that may lead to such physiologic differences. Their commentary notes: “The present study by Levine et al.…suggests differential regulation of gene expression as a potential mechanism in this social gradient” (6, p. 511). The correspondence between the acute, reversible gene expression patterns from manipulation of social status in rhesus monkeys, described by the authors, and programmed effects is by no means assured. It may be important to note here that CTRA derives from evolutionary or interstressor “conservation,” while mechanistic evidence for biological programming within a single life course remains limited. To investigate hypotheses regarding embedding or physiological processes acting as pathways for SES disparities, there is no substitute for actual longitudinal investigations including mediating mechanisms. Others have well described the correspondence between critical periods hypotheses and causal mediation models and intuitions for applying them (7). While Belsky and Snyder-Mackler rightly highlight certain causal assumptions proximal to adult differences in gene expression (e.g., mediation by cell type and reverse causation) with respect to social embedding hypotheses that span the life cycle, these concerns are relatively minor. Ultimately, the findings from Levine et al. are quite useful for informing future causal investigations of biological embedding mechanisms by identifying specific pathways to target. For example, the strongest signal they detected was for the family of cyclic adenosine monophosphate (cAMP) response element binding proteins (CREB), which has a role in mammalian circadian clocks (8). Investigations of the role of shift work during early and middle adulthood in explaining these associations would seem a natural extension of this work (9). In such studies, specific hypotheses regarding social mechanisms and relevant life-course causal exposures should be given primary attention. Acknowledgments J.H. is supported by the Canadian Institutes of Health (Research Operating Grant #343015). J.K. was supported by a Canada Research Chair in Health Disparities. The authors’ sources of support had no control over the preparation, review, or approval of this letter. Conflict of interest: none declared. References 1 Levine ME , Crimmins EM , Weir DR , et al. . Contemporaneous social environment and the architecture of late-life gene expression profiles . Am J Epidemiol . 2017 ; 186 ( 5 ): 503 – 509 . Google Scholar CrossRef Search ADS PubMed 2 Levine ME , Cole SW , Weir DR , et al. . Childhood and later life stressors and increased inflammatory gene expression at older ages . Soc Sci Med . 2015 ; 130 : 16 – 22 . Google Scholar CrossRef Search ADS PubMed 3 Liu RT , Alloy LB . Stress generation in depression: a systematic review of the empirical literature and recommendations for future study . Clin Psychol Rev . 2010 ; 30 ( 5 ): 582 – 593 . Google Scholar CrossRef Search ADS PubMed 4 Sharp GC , Salas LA , Monnereau C , et al. . Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium . Hum Mol Genet . 2017 ; 26 ( 20 ): 4067 – 4085 . Google Scholar CrossRef Search ADS PubMed 5 Huang JY , Gavin AR , Richardson TS , et al. . Accounting for life-course exposures in epigenetic biomarker association studies: early life socioeconomic position, candidate gene DNA methylation, and adult cardiometabolic risk . Am J Epidemiol . 2016 ; 184 ( 7 ): 520 – 531 . Google Scholar CrossRef Search ADS PubMed 6 Belsky DW , Snyder-Mackler N . Invited commentary: integrating genomics and social epidemiology—analysis of late-life low socioeconomic status and the conserved transcriptional response to adversity . Am J Epidemiol . 2017 ; 186 ( 5 ): 510 – 513 . Google Scholar CrossRef Search ADS PubMed 7 Howe LD , Smith AD , Macdonald-Wallis C , et al. . Relationship between mediation analysis and the structured life course approach . Int J Epidemiol . 2016 ; 45 ( 4 ): 1280 – 1294 . Google Scholar PubMed 8 Gau D , Lemberger T , von Gall C , et al. . Phosphorylation of CREB Ser142 regulates light-induced phase shifts of the circadian clock . Neuron . 2002 ; 34 ( 2 ): 245 – 253 . Google Scholar CrossRef Search ADS PubMed 9 Burgard SA , Lin KY . Bad jobs, bad health? How work and working conditions contribute to health disparities . Am Behav Sci . 2013 ; 57 ( 8 ): 1105 – 1127 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)
American Journal of Epidemiology – Oxford University Press
Published: Apr 11, 2018
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