Search

One of the promises of precision medicine is to understand and act on inter-individual genetic differences in drug responses. SNPdrug3D contains the complete genomic landscape of missense single nucleotide variants (SNV) across the human proteome and at population-wide level that could affect drug binding. In the Singapore SG10K Health and global gnomAD cohorts, comprising variations in over 80,000 individuals, we identified ~ 1.17 million variants mapped to residues near bound drug molecules in protein-drug complexes relative to ~ 6000 drug molecules and experimentally verified effects of selected SNVs, including previously uncharacterized variants, on drug binding in relevant proteins ranging from kinases to cytochrome P450s (CYPs). The latter led to a specific predictor for interpreting variants in the CYP family that vastly outperforms existing tools in the prediction of pharmacogenetic effects. By placing variants and drugs in structural context, SNPdrug3D aids drug development by early flagging of resistance potential through population-specific variability.

Background: Obesity and related metabolic disturbances including diabetes, hypertension and hyperlipidemia predict future cognitive decline. Asia has a high prevalence of both obesity and metabolic disease, potentially amplifying the future burden of dementia in the region. We aimed to investigate the impact of adiposity and metabolic risk on cognitive function in Asian populations, using an epidemiological analysis and a two-sample Mendelian Randomization (MR) study. Methods: The Health for Life in Singapore (HELIOS) Study is a population-based cohort of South-East-Asian men and women in Singapore, aged 30–84 years. We analyzed 8769 participants with metabolic and cognitive data collected between 2018 and 2021. Whole-body fat mass was quantified with Dual X-Ray Absorptiometry (DEXA). Cognition was assessed using a computerized cognitive battery. An index of general cognition ‘g’ was derived through factor analysis. We tested the relationship of fat mass indices and metabolic measures with ‘g’ using regression approaches. We then performed inverse-variance-weighted MR of adiposity and metabolic risk factors on ‘g’, using summary statistics for genome-wide association studies of BMI, visceral adipose tissue (VAT), waist-hip-ratio (WHR), blood pressure, HDL cholesterol, triglycerides, fasting glucose, HbA1c, and general cognition. Findings: Participants were 58.9% female, and aged 51.4 (11.3) years. In univariate analysis, all 29 adiposity and metabolic measures assessed were associated with ‘g’ at P < 0.05. In multivariable analyses, reduced ‘g’ was consistently associated with increased visceral fat mass index and lower HDL cholesterol (P < 0.001), but not with blood pressure, triglycerides, or glycemic indices. The reduction in ‘g’ associated with 1SD higher visceral fat, or 1SD lower HDL cholesterol, was equivalent to a 0.7 and 0.9-year increase in chronological age respectively (P < 0.001). Inverse variance MR analyses showed that reduced ‘g’ is associated with genetically determined elevation of VAT, BMI and WHR (all P < 0.001). In contrast, MR did not support a causal role for blood pressure, lipid, or glycemic indices on cognition. Interpretation: We show an independent relationship between adiposity and cognition in a multi-ethnic Asian population. MR analyses suggest that both visceral adiposity and raised BMI are likely to be causally linked to cognition. Our findings have important implications for preservation of cognitive health, including further motivation for action to reverse the rising burden of obesity in the Asia–Pacific region. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Ministry of Health (MOH) Nanyang Technological University National Medical Research Council (NMRC) Published version This work was supported by intramural funding from Nanyang Technological University, Lee Kong Chian School of Medicine, and the National Healthcare Group. J.C. is supported by: the Singapore Ministry of Health and National Medical Research Council STaR funding scheme [NMRC/StaR/0028/2017], LCG funding [MOH-000271], and Phase 2 National Precision Medicine Programme (Research Platform and Data Enablers) [NMRC/PRECISE/2020]; and the Singapore Agency for Science Technology and Research IAF-PP National Precision Medicine Program Phase 1A (A Population Level Genomic Infrastructure) [H17/ 01/a0/007] and IAF-PP Asian Skin Microbiome Programme [H18/01/ a0/016]. T.M. received postdoctoral fellowship from the Singapore Ministry of Education – Research Scholarship Block and Lee Kong Chian School of Medicine.

Polygenic risk scores (PRS), commonly referred to as genetic or genomic risk scores, aggregate the effects of multiple genetic variants into a single composite estimate of genetic risk. PRS scores are typically used to predict the risk of developing a disease or to explain the phenotypic variation, and are derived from the effect sizes observed in large-scale genome-wide association studies (GWAS). Unlike rare monogenic diseases such as cystic fibrosis, which are attributable to genetic variants in single genes, with large effects on disease status, common complex diseases such as type 2 diabetes mellitus (T2DM) are polygenic, with risk contributed by a panel of genetic variants present throughout the genome. The concept of integrating information from these multiple genetic variants into a single metric of genetic risk was initially proposed in the shape of genetic risk scores, which generally limited the score to include single-nucleotide polymorphisms (SNPs) that were common and reached genome-wide significance in the initial GWASs. In contrast, PRS incorporates information from a much larger set of genetic variants, typically hundreds of thousands, including SNPs below the threshold for genome-wide statistical significance, and often with much more modest effect sizes. Indeed, recent findings have pointed to how polygenic background could also increase the accuracy of risk estimation for individuals with monogenic risk variant in conditions such as familial hypercholesterolaemia, hereditary breast and ovarian cancer, and Lynch syndrome. Published version

ObjectiveTo ascertain the accuracy of 2 upper-arm blood pressure monitors, GE Carescape V100 Vital Signs and Omron HEM-9210T, used in a large, South-East Asian epidemiological cohort by the AAMI/ESH/ISO 81060-2:2018.Methods149 participants were recruited from the ongoing Health for Life in Singapore (HELIOS) Study in multi-ethnic Singapore.Results110 datasets were analysed. For criterion 1, the mean ± SD of differences for GE Carescape V100 was -3.5 ± 7.4/ -3.5 ± 7.4 mmHg (systolic/diastolic), and -4.2 ± 6.9/ -4.2 ± 6.9 mmHg (systolic/diastolic) for Omron HEM-9210T. For criterion 2, GE Carescape V100was -3.5 ± 5.6 / -3.5 ± 5.6 mmHg (systolic/diastolic), and -4.2 ± 4.5 / - 4.2 ± 4.6 mmHg (systolic/diastolic) for Omron HEM-9210T.ConclusionBoth GE Carescape V100 Vital Signs and Omron HEM-9210T monitor passed the AAMI/ESH/ISO 81060-2:2018 validation standard.

Human blood is conventionally considered sterile. Recent studies have challenged this, suggesting the presence of a blood microbiome in healthy humans. We present the largest investigation to date of microbes in blood, based on shotgun sequencing libraries from 9,770 healthy subjects. Leveraging the availability of data from multiple cohorts, we stringently filtered for laboratory contaminants to identify 117 microbial species detected in the blood of sampled individuals, some of which had signatures of DNA replication. These primarily comprise of commensals associated with human body sites such as the gut (n=40), mouth (n=32), and genitourinary tract (n=18), which are species that are distinct from common pathogens detected in clinical blood cultures based on more than a decade of records from a tertiary hospital. Contrary to the expectations of a shared blood microbiome, no species were detected in 84% of individuals, while only a median of one microbial species per individual was detected in the remaining 16%. Futhermore, microbes of the same species were detected in

Aims/hypothesis Type 2 diabetes is a complex metabolic disease with increasing prevalence worldwide. Improving the prediction of incident type 2 diabetes using epigenetic markers could help tailor prevention efforts to those at the highest risk. The aim of this study was to identify predictive methylation markers for incident type 2 diabetes by combining epigenome-wide association study (EWAS) results from five prospective European cohorts. Methods We conducted a meta-analysis of EWASs in blood collected 7–10 years prior to type 2 diabetes diagnosis. DNA methylation was measured with Illumina Infinium Methylation arrays. A total of 1250 cases and 1950 controls from five longitudinal cohorts were included: Doetinchem, ESTHER, KORA1, KORA2 and EPIC-Norfolk. Associations between DNA methylation and incident type 2 diabetes were examined using robust linear regression with adjustment for potential confounders. Inverse-variance fixed-effects meta-analysis of cohort-level individual CpG EWAS estimates was performed using METAL. The methylGSA R package was used for gene set enrichment analysis. Confirmation of genome-wide significant CpG sites was performed in a cohort of Indian Asians (LOLIPOP, UK). Results The meta-analysis identified 76 CpG sites that were differentially methylated in individuals with incident type 2 diabetes compared with control individuals (p values −7). Sixty-four out of 76 (84.2%) CpG sites were confirmed by directionally consistent effects and p values 1c) showed no improvement (AUC 0.757 vs 0.753). Gene set enrichment analysis of the full epigenome-wide results clearly showed enrichment of processes linked to insulin signalling, lipid homeostasis and inflammation. Conclusions/interpretation By combining results from five European cohorts, and thus significantly increasing study sample size, we identified 76 CpG sites associated with incident type 2 diabetes. Replication of 64 CpGs in an independent cohort of Indian Asians suggests that the association between DNA methylation levels and incident type 2 diabetes is robust and independent of ethnicity. Our data also indicate that BMI partly explains the association between DNA methylation and incident type 2 diabetes. Further studies are required to elucidate the underlying biological mechanisms and to determine potential causal roles of the differentially methylated CpG sites in type 2 diabetes development. Graphical abstract

We determined the relationships between DNA sequence variation and DNA methylation using blood samples from 3,799 Europeans and 3,195 South Asians. We identify 11,165,559 SNP-CpG associations (methylation quantitative trait loci (meQTL), P

BackgroundThere are few data to support accurate interpretation of spirometry data in South Asia, a major global region with a high reported burden of chronic respiratory disease.MethodWe measured lung function in 7453 healthy men and women aged ≥18 years, from Bangladesh, North India, South India, Pakistan and Sri Lanka, as part of the South Asia Biobank study. First, we assessed the accuracy of existing equations for predicting normal forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1) and FEV1/FVC ratio. Then, we used our data to derive (n=5589) and internally validate (n=1864) new prediction equations among South Asians, with further external validation among 339 healthy South Asians living in Singapore.ResultsThe Global Lung Initiative (GLI) and National Health and Nutrition Examination Survey consistently overestimated expiratory volumes (best fit GLI-African American, mean±sdz-score: FEV1−0.94±1.05, FVC −0.91±1.10; n=7453). Age, height and weight were strong predictors of lung function in our participants (p10.03±0.99, FVC 0.04±0.97, FEV1/FVC −0.03±0.99) and external validation (z-scores: FEV10.31±0.99, FVC 0.24±0.97, FEV1/FVC 0.16±0.91). Further adjustment for study regions improves the model fit, with highest accuracy for estimation of region-specific lung function in South Asia.ConclusionWe present improved equations for predicting lung function in South Asians. These offer the opportunity to enhance diagnosis and management of acute and chronic lung diseases in this major global population.

IntroductionSouth Asians are at high risk of type 2 diabetes (T2D). We assessed whether intensive family-based lifestyle intervention leads to significant weight loss, improved glycaemia and blood pressure in adults at elevated risk for T2D.MethodsThis cluster randomised controlled trial (iHealth-T2D) was conducted at 120 locations across India, Pakistan, Sri Lanka and the UK. We included 3684 South Asian men and women, aged 40–70 years, without T2D but with raised haemoglobin A1c (HbA1c) and/or waist circumference. Participants were randomly allocated either to the family-based lifestyle intervention or control group by location clusters. Participants in the intervention received 9 visits and 13 telephone contacts by community health workers over 1-year period, and the control group received usual care. Reductions in weight (aim >7% reduction), waist circumference (aim ≥5 cm reduction), blood pressure and HbA1C at 12 months of follow-up were assessed. Our linear mixed-effects regression analysis was based on intention-to-treat principle and adjusted for age, sex and baseline values.ResultsThere were 1846 participants in the control and 1838 in the intervention group. Between baseline and 12 months, mean weight of participants in the intervention group reduced by 1.8 kg compared with 0.4 kg in the control group (adjusted mean difference −1.10 kg (95% CI −1.70 to −1.06), pConclusionAn intensive family-based lifestyle intervention adopting low-resource strategies led to effective reduction in weight and waist circumference at 12 months, which has potential long-term benefits for preventing T2D. A higher number of attended sessions increased the effect on waist circumference, blood pressure and HbA1c.Trial registration numberEudraCT: 2016-001350-18; ClinicalTrials.gov: NCT02949739.