Jonathan Emberson

The relationship between alcohol consumption and cause-specific mortality in adults aged ≥ 70 years remain unclear. This study assessed the associations between alcohol consumption with risks of cause-specific mortality in older men. This study included 4,582 London Civil Servants from the 1997–98 Whitehall-I Study resurvey with no prior cardiovascular disease or cancer. Alcohol consumption was self-reported by postal questionnaires and classified as abstainers (lifelong non-drinkers), ex-drinkers, or current drinkers. Current drinkers were further categorised by weekly intake: <7, 7–13, 14–27, 28–41, and ≥ 42 units/week. During a mean follow up of 11.8 years, 4194 deaths (91%) occurred. Compared with light drinkers (< 7 units/week), all-cause mortality risk was highest among ex-drinkers (HR: 1.21, 95% CI 1.11–1.32) and those consuming ≥ 42 units/week (HR: 1.38, 95% CI 1.15–1.67). Among current drinkers, increased alcohol consumption was associated with a higher risk of all-cause mortality (HR: 1.05, 95% CI 1.01–1.08 per 10 units/week) and non-vascular mortality (HR: 1.05, 95% CI 1.01–1.09), but not vascular mortality (HR: 1.04, 95% CI 0.99–1.10). The findings were unaltered by excluding deaths in the first five years of follow-up. The study distinguished ex-drinkers from never-drinkers and excluded participants with major disease to minimise the potential for reverse-causation bias. Alcohol consumption in older men was associated with a higher risk of all-cause mortality, with heavy drinkers experiencing a 38% higher mortality risk than light drinkers.

Background and hypothesis: The associations of polygenic scores (PGSs) for eGFR with clinical outcomes in prevalent CKD populations have not been well evaluated. Collider bias may distort associations of exposures (including PGS) with outcomes in populations with prevalent disease. Methods: Analyses included European ancestry participants from UK Biobank and three CKD cohorts (SHARP, CRIC, and EMPA-KIDNEY) without ESKD at baseline. The exposure was a PGS for low eGFR derived from a genome-wide association study (GWAS) of eGFR in the FinnGen cohort. The primary outcomes were eGFR slope (ml/min/1.73m2/year) and kidney disease progression (KDP; defined as first occurrence of incident dialysis, transplantation, sustained eGFR decline by ≥40%, or sustained eGFR <10 ml/min/1.73 m2). Associations between the PGS and eGFR slope were assessed by linear regression and meta-analysed across studies. Associations with KDP were assessed using Cox proportional hazards models. Results: A total of 107 536 participants in UKB and 6846 participants from CKD cohorts were included in analyses. In UKB, higher PGS was associated with lower baseline eGFR (−5.09 [−5.31; −4.86] ml/min/1.73 m2 for those in the top 10% of the PGS vs. the rest), with greater risk of KDP (HR 1.36 [1.20–1.55]) and faster eGFR decline (−0.12 [−0.14; −0.09] ml/min/1.73m2/year). However, in CKD cohorts, higher PGS was not associated with baseline eGFR (0.15 [−0.73; 1.03] ml/min/1.73 m2) but was associated with lower risk of KDP (HR 0.76 [0.66–0.89]), with a directionally consistent (though non-significant) effect on eGFR decline (+0.19 [−0.01; 0.39] ml/min/1.73m2/year). Conclusions: A PGS for low eGFR is associated with faster eGFR decline and higher risk of KDP in a population unselected for CKD, but when applied in populations with CKD, non-causal associations are introduced which artificially reverse the associations seen. Applying PGS in populations selected for prevalent disease may lead to qualitatively misleading conclusions on the direction of associations with clinical outcomes.

To fully understand the causes of human disease worldwide, large blood based prospective studies are needed in a range of populations with different healthcare settings and exposures to environmental and genetic risk factors. The Mexico City Prospective Study, updated in this cohort profile, was established in the late 1990s to investigate the sociodemographic, lifestyle, physical, and biological (including genetic) determinants of disease among around 150 000 adults in Mexico City. The study has over 20 years of mortality follow-up, cohort-wide biochemical and genetic data, repeat survey data from a subset of surviving participants, and ongoing ascertainment of non-fatal diseases. All Mexico City Prospective Study data are available for open access academic research within a trusted research environment, with preferential access for researchers based in Mexico and no data access fees for researchers in low income or middle income countries. Beyond representing a genetically diverse Latin American population, a key distinguishing feature of the study is the high baseline prevalence of obesity and diabetes. Earlier findings showed that diabetes had a substantially greater impact on mortality than suggested by evidence from high income countries, with a third of all deaths at ages 35-74 years attributed to diabetes. These results contributed to rapid changes in national disease prevention policies. By combining a genetically admixed (ie, individuals with ancestry from multiple ancestral populations) Latin American population, family based recruitment, a high burden of metabolic disease, availability of biochemical and genetic data, long term follow-up, and open access data availability, the Mexico City Prospective Study offers unique opportunities for health related discoveries within Mexico and internationally.

Human populations differ in disease prevalence and phenotypes, but the extent to which differences are caused by genetic factors is unknown for most complex traits. Comparing phenotypic means across populations is confounded by environmental differences and using polygenic predictors can lead to biased inference ^1,2 . Family-based analyses of people of genetically admixed ancestry enable estimation of ancestry effects unconfounded by ancestry–environment correlations. Here we leverage genetic data from admixed adults in the Mexico City Prospective Study ^3,4 to estimate within-family ancestry effects ⁵ . We assessed genetic ancestry and 15 complex traits in 52,583 unrelated people and 39,714 relatives from 17,627 families. At the population level, relative to European ancestry, the effect of Indigenous American ancestry was −1.98 s.d. ( P < 2 × 10 ⁻¹⁶ ) for height and a natural log odds ratio of 1.73 (95% confidence interval, 1.54–1.92) for type 2 diabetes. Within families, the effect of Indigenous American ancestry was −1.51 s.d. ( P = 10 ⁻⁸ ) for height and natural log odds ratio of 5.13 (95% confidence interval, 2.48–7.78) for type 2 diabetes. These effects are supported by between-ancestry differences in trait-increasing allele counts and evidence of selection at trait-associated loci. We found no within-family ancestry effect on educational attainment or other traits despite significant associations at the population level, implying environmental causes or confounding. Overall, this study provides an experimental design to study between-ancestry genetic effects and identifies significant ancestry differences for height, type 2 diabetes and metabolic traits in a genetically diverse population from Mexico City.

Despite metabolomics transforming our understanding of risk factors and aetiology of metabolic diseases, profiling is rarely performed for people of non-European ancestries, on whom much of metabolic disease burden falls. Metabolome-wide association studies (MWAS) can be performed using genetic scores to predict metabolomic traits, helping address these inequities; however, their performance in populations of admixed American (AMR) ancestries is unexplored. We evaluated 141 genetic scores, developed in an INTERVAL Study sample of European (EUR) genetic ancestries, in the Mexico City Prospective Study (MCPS; n =132,336), obtaining a median predictive R ² of 0.027. Training Bayesian ridge models within MCPS substantially improved performance, with a median R ² of 0.083 on a withheld 20% subset. MCPS-trained models also outperformed INTERVAL-trained models among UK Biobank participants of AMR ancestries ( n =600; median R ² : 0.070 vs. 0.046). Finally, among AMR participants of the All of Us cohort, using MCPS-trained (vs. INTERVAL-trained) models to predict metabolomic traits yielded five times as many significant associations (FDR-corrected P <0.05) across three cardiometabolic diseases: ischaemic heart disease, type 2 diabetes, and chronic kidney disease. The genetic scores are openly available at the OmicsPred portal ( www.OmicsPred.org ), enabling better-powered analyses in diverse AMR cohorts and helping reduce global inequities in omics research.

Background: The causal relevance of different adiposity markers to infection-related death remains uncertain. We estimated the lifelong associations of genetically predicted adiposity traits with infection-related mortality among 126 491 Mexican adults. Methods: Mendelian randomization (MR) using trans-ancestry instruments for body mass index (BMI), fat-mass percentage (FM%), and waist and hip circumference was used to estimate causal effects on infection-related mortality at ages 35–74 years, overall and across six infection subtypes. Multivariable MR was used to assess independent associations. Results: Genetic instruments explained 1.6%–3.5% of the variance in adiposity markers and the strengths of their associations with the markers were similar in men and women. Over 20 years, 2592 infection-related deaths occurred at ages 35–74 years. Genetically predicted adiposity was strongly associated with infection mortality, with the strongest association for FM% [hazard ratio (HR) per 1-SD higher level: 2.54; 95% confidence interval (CI) 1.91–3.38] and the weakest for hip circumference (HR 1.61; 95% CI 1.30–2.01). Associations were consistent across six subtypes: respiratory; urinary tract; gastrointestinal; sepsis; skin, bone, and connective tissue; and other infections. For BMI and FM%, mutual adjustment for waist and hip circumference reduced the associations by nearly one-third. For waist circumference, adjustment for BMI and hip circumference reduced the association by more than half, while, for hip circumference, mutual adjustment for BMI and waist circumference almost eliminated its association. Sensitivity analyses supported the main conclusions. Conclusion: Higher adiposity is causally associated with a higher risk of infection-related mortality in Mexican adults. Strategies to reduce the population levels of adiposity in Mexico could contribute to lowering mortality from infection.

Type 2 diabetes (T2D) is a highly heritable, polygenic disease with over 600 loci identified through genome-wide association studies (GWAS). However, despite possessing unique genetic variation shaped by demographic history and admixture, Latin American populations remain markedly underrepresented in global genomic research. To address this gap, we conducted genome- and exome-wide analyses of 19,431 T2D cases and 105,611 controls from the Mexico City Prospective Study (MCPS). We identified 86 independent GWAS associations, including 21 novel signals, 15 of which replicated in external cohorts. Risk alleles at novel loci were enriched in individuals with Indigenous American ancestry. Exome analyses revealed rare and ultra-rare missense variants with substantial risk effects at HNF1A and GCK , as well as a protein-damaging variant in SLC30A8 that reduced T2D risk by 45% in carriers. Integrative analyses indicate that T2D genetic architecture in Mexico is predominantly driven by common regulatory variation acting in the endocrine pancreas. Polygenic risk scores strongly stratified T2D risk and transferred to Indigenous Mexican populations. These findings demonstrate the power of large-scale genetic discovery in diverse populations to refine disease architecture and identify loci with potential therapeutic relevance.