Person:

Patel, Chirag

Abstract Background: It is hypothesized that environmental exposures and behaviour influence telomere length, an indicator of cellular ageing. We systematically associated 461 indicators of environmental exposures, physiology and self-reported behaviour with telomere length in data from the US National Health and Nutrition Examination Survey (NHANES) in 1999–2002. Further, we tested whether factors identified in the NHANES participants are also correlated with gene expression of telomere length modifying genes. Methods: We correlated 461 environmental exposures, behaviours and clinical variables with telomere length, using survey-weighted linear regression, adjusting for sex, age, age squared, race/ethnicity, poverty level, education and born outside the USA, and estimated the false discovery rate to adjust for multiple hypotheses. We conducted a secondary analysis to investigate the correlation between identified environmental variables and gene expression levels of telomere-associated genes in publicly available gene expression samples. Results: After correlating 461 variables with telomere length, we found 22 variables significantly associated with telomere length after adjustment for multiple hypotheses. Of these varaibales, 14 were associated with longer telomeres, including biomarkers of polychlorinated biphenyls([PCBs; 0.1 to 0.2 standard deviation (SD) increase for 1 SD increase in PCB level, P < 0.002] and a form of vitamin A, retinyl stearate. Eight variables associated with shorter telomeres, including biomarkers of cadmium, C-reactive protein and lack of physical activity. We could not conclude that PCBs are correlated with gene expression of telomere-associated genes. Conclusions: Both environmental exposures and chronic disease-related risk factors may play a role in telomere length. Our secondary analysis found no evidence of association between PCBs/smoking and gene expression of telomere-associated genes. All correlations between exposures, behaviours and clinical factors and changes in telomere length will require further investigation regarding biological influence of exposure.

Objective: Better identification of at-risk groups could benefit HIV-1 care programmes. We systematically identified HIV-1 risk factors in two nationally representative cohorts of women in the Demographic and Health Surveys. Methods: We identified and replicated the association of 1415 social, economic, environmental, and behavioral factors with HIV-1 status. We used the 2007 and 2013–2014 surveys conducted among 5715 and 15 433 Zambian women, respectively (688 shared factors). We used false discovery rate criteria to identify factors that are strongly associated with HIV-1 in univariate and multivariate models of the entire population, as well as in subgroups stratified by wealth, residence, age, and past HIV-1 testing. Results: In the univariate analysis, we identified 102 and 182 variables that are associated with HIV-1 in the two surveys, respectively (79 factors were associated in both). Factors that were associated with HIV-1 status in full-sample analyses and in subgroups include being formerly married (adjusted OR 2007, 2.8, P < 10−16; 2013–2014 2.8, P < 10−29), widowhood (aOR 3.7, P < 10−12; and 4.2, P < 10−30), genital ulcers within 12 months (aOR 2.4, P < 10−5; and 2.2, P < 10−6), and having a woman head of the household (aOR 1.7, P < 10−7; and 2.1, P < 10−26), while owning a bicycle (aOR 0.6, P < 10−6; and 0.6, P < 10−8) and currently breastfeeding (aOR 0.5, P < 10−9; and 0.4, P < 10−26) were associated with decreased risk. Area under the curve for HIV-1 positivity was 0.76–0.82. Conclusion: Our X-wide association study identifies under-recognized factors related to HIV-1 infection, including widowhood, breastfeeding, and gender of head of the household. These features could be used to improve HIV-1 identification programs.