Browsing by Author "Loh, M."

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Data resource profile: understanding the patterns and determinants of health in South Asians-the South Asia Biobank
(Oxford University Press., 2021) Song, P.; Gupta, A.; Goon, I.Y.; Hasan, M.; Mahmood, S.; Pradeepa, R.; Siddiqui, S.; Frost, G.S.; Kusuma, D.; Miraldo, M.; Sassi, F.; Wareham, N.J.; Ahmed, S.; Anjana, R.M.; Brage, S.; Forouhi, N.G.; Jha, S.; Kasturiratne, A.; Katulanda, P.; Khawaja, K.I.; Loh, M.; Mridha, M.K.; Wickremasinghe, A.R.; Kooner, J.S.; Chambers, J.C.
No abstract available
Effects of a lifestyle intervention programme after 1 year of follow-up among South Asians at high risk of type 2 diabetes: a cluster randomised controlled trial
(BMJ Publishing Group Ltd, 2021) Muilwijk, M.; Loh, M.; Siddiqui, S.; Mahmood, S.; Palaniswamy, S.; Shahzad, K.; Athauda, L.K.; Jayawardena, R.; Batool, T.; Burney, S.; Glover, M.; Bamunuarachchi, V.; Panda, M.; Madawanarachchi, M.; Rai, B.; Sattar, I.; Silva, W.; Waghdhare, S.; Jarvelin, M.R.; Rannan-Eliya, R.P.; Wijemunige, N.; Gage, H.M.; Valabhji, J.; Frost, G.S.; Wickremasinghe, R.; Kasturiratne, A.; Khawaja, K.I.; Ahmad, S.; Valkengoed, I.G.V.; Katulanda, P.; Jha, S.; Kooner, J.S.; Chambers, J.C.
Introduction South 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.Methods This 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. Results There 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), p<0.001). The adjusted mean difference for waist circumference was −1.9 cm (95% CI −2.5; to 1.3), p<0.001). No overall difference was observed for blood pressure or HbA1c. People who attended multiple intervention sessions had a dose-dependent effect on waist circumference, blood pressure and HbA1c, but not on weight. Conclusion An 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.
Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study
(The Lancet, Diabetes & Endocrinology, 2015) Chambers, J.C.; Loh, M.; Lehne, B.; Drong, A.; Kriebel, J.; Motta, V.; Wahl, S.; Elliott., H.R; Rota, F.; Scott, W.R.; Zhang, W.; Tan, S.T.; Campanella, G.; Chadeau-Hyam, M.; Yengo, L.; Richmond, R.C.; Adamowicz-Brice, M.; Afzal, U.; Bozaoglu, K.; Mok, Z.Y.; Ng, H.K.; Pattou, F.; Prokisch, H.; Rozario, M.A.; Tarantini, L.; Abbott, J.; Ala-Korpela, M.; Albetti, B.; Ammerpohl, O.; Bertazzi, P.A.; Blancher, C.; Caiazzo, R.; Danesh, J.; Gaunt, T.R.; de Lusignan, S.; Gieger, C.; Illig, T.; Jha, S.; Jones, S.; Jowett, J.; Kangas, A.J.; Kasturiratne, A.; Kato, N.; Kotea, N.; Kowlessur, S.; Pitkäniemi, J.; Punjabi, P.; Saleheen, D.; Schafmayer, C.; Soininen, P.; Tai, E.S.; Thorand, B.; Tuomilehto, J.; Wickremasinghe, A.R.; Kyrtopoulos, S.A.; Aitman, T.J.; Herder, C.; Hampe, J.; Cauchi, S.; Relton, C.L.; Froguel, P.; Soong, R.; Vineis, P.; Jarvelin, M.R.; Scott, J.; Grallert, H.; Bollati, V.; Elliott, P.; McCarthy, M.I.; Kooner, J.S. J
BACKGROUND: Indian Asians, who make up a quarter of the world's population, are at high risk of developing type 2 diabetes. We investigated whether DNA methylation is associated with future type 2 diabetes incidence in Indian Asians and whether differences in methylation patterns between Indian Asians and Europeans are associated with, and could be used to predict, differences in the magnitude of risk of developing type 2 diabetes. METHODS: We did a nested case-control study of DNA methylation in Indian Asians and Europeans with incident type 2 diabetes who were identified from the 8-year follow-up of 25 372 participants in the London Life Sciences Prospective Population (LOLIPOP) study. Patients were recruited between May 1, 2002, and Sept 12, 2008. We did epigenome-wide association analysis using samples from Indian Asians with incident type 2 diabetes and age-matched and sex-matched Indian Asian controls, followed by replication testing of top-ranking signals in Europeans. For both discovery and replication, DNA methylation was measured in the baseline blood sample, which was collected before the onset of type 2 diabetes. Epigenome-wide significance was set at p<1 × 10(-7). We compared methylation levels between Indian Asian and European controls without type 2 diabetes at baseline to estimate the potential contribution of DNA methylation to increased risk of future type 2 diabetes incidence among Indian Asians. FINDINGS: 1608 (11•9%) of 13 535 Indian Asians and 306 (4•3%) of 7066 Europeans developed type 2 diabetes over a mean of 8•5 years (SD 1•8) of follow-up. The age-adjusted and sex-adjusted incidence of type 2 diabetes was 3•1 times (95% CI 2•8-3•6; p<0•0001) higher among Indian Asians than among Europeans, and remained 2•5 times (2•1-2•9; p<0•0001) higher after adjustment for adiposity, physical activity, family history of type 2 diabetes, and baseline glycemic measures. The mean absolute difference in methylation level between type 2 diabetes cases and controls ranged from 0•5% (SD 0•1) to 1•1% (0•2). Methylation markers at five loci were associated with future type 2 diabetes incidence; the relative risk per 1% increase in methylation was 1•09 (95% CI 1•07-1•11; p=1•3 × 10(-17)) for ABCG1, 0•94 (0•92-0•95; p=4•2 × 10(-11)) for PHOSPHO1, 0•94 (0•92-0•96; p=1•4 × 10(-9)) for SOCS3, 1•07 (1•04-1•09; p=2•1 × 10(-10)) for SREBF1, and 0•92 (0•90-0•94; p=1•2 × 10(-17)) for TXNIP. A methylation score combining results for the five loci was associated with future type 2 diabetes incidence (relative risk quartile 4 vs quartile 1 3•51, 95% CI 2•79-4•42; p=1•3 × 10(-26)), and was independent of established risk factors. Methylation score was higher among Indian Asians than Europeans (p=1 × 10(-34)). INTERPRETATION: DNA methylation might provide new insights into the pathways underlying type 2 diabetes and offer new opportunities for risk stratification and prevention of type 2 diabetes among Indian Asians. FUNDING: The European Union, the UK National Institute for Health Research, the Welcome Trust, the UK Medical Research Council, Action on Hearing Loss, the UK Biotechnology and Biological Sciences Research Council, the Oak Foundation, the Economic and Social Research Council, Helmholtz Zentrum Munchen, the German Research Center for Environmental Health, the German Federal Ministry of Education and Research, the German Center for Diabetes Research, the Munich Center for Health Sciences, the Ministry of Science and Research of the State of North Rhine-Westphalia, and the German Federal Ministry of Health. Copyright © 2015 Elsevier Ltd. All rights reserved.
Epigenome-wide association study of body mass index, and the adverse outcomes of adiposity
(Nature Publishing Group, 2017) Whal, S.; Drong, A.; Lehne, B.; Loh, M.; Scott, W.R.; Kunze, S.; Tsai, P.C.; Ried, J.S.; Zhang, W.; Yang, Y.; Tan, S.; Fiorito, G.; Franke, L.; Guarrera, S.; Kasela, S.; Kriebel, J.; Richmond, R.C.; Adamo, M.; Afzal, U.; Ala-Korpela, M.; Albeetti, B.; Ammerpohl, O.; Apperley, J.F.; Beekman, M.; Bertazzi, P.A.; Black, S.L.; Blancher, C.; Bonder, M.J.; Brosch, M.; Carstensen-Kirberg, M.; de Craen, A.J.; de Lusignan, S.; Dehghan, A.; Elkalaawy, M.; Fischer, K.; Franco, O.H.; Gaunt, T.R.; Hampe, J.; Hashemi, M.; Isaacs, A.; Jenkinson, A.; Jha, S.; Kato, N.; Krogh, V.; Laffan, M.; Meisinger, C.; Meitinger, T.; Mok, Z.Y.; Motta, V.; Ng, H.K.; Nikolakopoulou, Z.; Nteliopoulos, G.; Panico, S.; Pervjakova, N.; Prokisch, H.; Rathmann, W.; Roden, M.; Rota, F.; Rozario, M.A.; Sandling, J.K.; Schafmayer, C.; Schramm, K.; Siebert, R.; Slagboom, P.E.; Soininen, P.; Stolk, L.; Strauch, K.; Tai, E.S.; Tarantini, L.; Thorand, B.; Tigchelaar, E.F.; Tumino, R.; Uitterlinden, A.G.; van Duijn, C.; van Meurs, J.B.; Vineis, P.; Wickremasinghe, A.R.; Wijmenga, C.; Yang, T.P.; Yuan, W.; Zhernakova, A.; Batterham, R.L.; Smith, G.D.; Deloukas, P.; Heijmans, B.T.; Herder, C.; Hofman, A.; Lindgren, C.M.; Milani, L.; van der Harst, P.; Peters, A.; Illig, T.; Relton, C.L.; Waldenberger, M.; Järvelin, M.R.; Bollati, V.; Soong, R.; Spector, T.D.; Scott, J.; McCarthy, M.I.; Elliott, P.; Bell, J.T.; Matullo, G.; Gieger, C.; Kooner, J.S.; Grallert, H.; Chambers, J.C.
Approximately 1.5 billion people worldwide are overweight or affected by obesity, and are at risk of developing type 2 diabetes, cardiovascular disease and related metabolic and inflammatory disturbances. Although the mechanisms linking adiposity to associated clinical conditions are poorly understood, recent studies suggest that adiposity may influence DNA methylation, a key regulator of gene expression and molecular phenotype. Here we use epigenome-wide association to show that body mass index (BMI; a key measure of adiposity) is associated with widespread changes in DNA methylation (187 genetic loci with P < 1 × 10-7, range P = 9.2 × 10-8 to 6.0 × 10-46; n = 10,261 samples). Genetic association analyses demonstrate that the alterations in DNA methylation are predominantly the consequence of adiposity, rather than the cause. We find that methylation loci are enriched for functional genomic features in multiple tissues (P < 0.05), and show that sentinel methylation markers identify gene expression signatures at 38 loci (P < 9.0 × 10-6, range P = 5.5 × 10-6 to 6.1 × 10-35, n = 1,785 samples). The methylation loci identify genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Finally, we show that the disturbances in DNA methylation predict future development of type 2 diabetes (relative risk per 1 standard deviation increase in methylation risk score: 2.3 (2.07-2.56); P = 1.1 × 10-54). Our results provide new insights into the biologic pathways influenced by adiposity, and may enable development of new strategies for prediction and prevention of type 2 diabetes and other adverse clinical consequences of obesity.
Identification of genetic effects underlying type 2 diabetes in South Asian and European populations
(Nature Publishing Group UK, 2022) Loh, M.; Zhang, W.; Ng, H.K.; Schmid, K.; Lamri, A.; Tong, L.; Ahmad, M.; Lee, J.J.; Ng, M.C.Y.; Petty, L.E.; Spracklen, C.N.; Takeuchi, F.; Islam, M.T.; Jasmine, F.; Kasturiratne, A.; Kibriya, M.; Mohlke, K.L.; Paré, G.; Prasad, G.; Shahriar, M.; Chee, M.L.; de Silva, H.J.; Engert, J.C.; Gerstein, H.C.; Mani, K.R.; Sabanayagam, C.; Vujkovic, M.; Wickremasinghe, A.R.; Wong, T.Y.; Yajnik, C.S.; Yusuf, S.; Ahsan, H.; Bharadwaj, D.; Anand, S.S.; Below, J.E.; Boehnke, M.; Bowden, D.W.; Chandak, G.R.; Cheng, C.Y.; Kato, N.; Mahajan, A.; Sim, X.; McCarthy, M.I.; Morris, A.P.; Kooner, J.S.; Saleheen, D.; Chambers, J.C.
South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.
The iHealth-T2D study, prevention of type 2 diabetes amongst South Asians with central obesity and prediabetes: study protocol for a randomised controlled trial
(BioMed Central, London, 2021) Kasturiratne, A.; Khawaja, K.I.; Ahmad, S.; Siddiqui, S.; Shahzad, K.; Athauda, L.K.; Jayawardena, R.; Mahmood, S.; Muilwijk, M.; Batool, T.; Burney, S.; Glover, M.; Palaniswamy, S.; Bamunuarachchi, V.; Panda, M.; Madawanarachchi, S.; Rai, B.; Sattar, I.; Silva, W.; Waghdhare, S.; Jarvelin, M.R.; Rannan-Eliya, R.P.; Gage, H.M.; van Valkengoed, I.G.M.; Valabhji, J.; Frost, G.S.; Loh, M.; Wickremasinghe, A.R.; Kooner, J.S.; Katulanda, P.; Jha, S.; Chambers, J.C.
Background: People from South Asia are at increased risk of type 2 diabetes (T2D). There is an urgent need to develop approaches for the prevention of T2D in South Asians that are cost-effective, generalisable and scalable across settings.Hypothesis: Compared to usual care, the risk of T2D can be reduced amongst South Asians with central obesity or raised HbA1c, through a 12-month lifestyle modification programme delivered by community health workers.Design: Cluster randomised clinical trial (1:1 allocation to intervention or usual care), carried out in India, Pakistan, Sri Lanka and the UK, with 30 sites per country (120 sites total). Target recruitment 3600 (30 participants per site) with annual follow-up for 3 years.Entry criteria: South Asian, men or women, age 40-70 years with (i) central obesity (waist circumference ≥ 100 cm in India and Pakistan; ≥90 cm in Sri Lanka) and/or (ii) prediabetes (HbA1c 6.0-6.4% inclusive).Exclusion criteria: known type 1 or 2 diabetes, normal or underweight (body mass index < 22 kg/m2); pregnant or planning pregnancy; unstable residence or planning to leave the area; and serious illness.Endpoints: The primary endpoint is new-onset T2D at 3 years, defined as (i) HbA1c ≥ 6.5% or (ii) physician diagnosis and on treatment for T2D. Secondary endpoints at 1 and 3 years are the following: (i) physical measures: waist circumference, weight and blood pressure; (ii) lifestyle measures: smoking status, alcohol intake, physical activity and dietary intake; (iii) biochemical measures: fasting glucose, insulin and lipids (total and HDL cholesterol, triglycerides); and (iv) treatment compliance. Intervention: Lifestyle intervention (60 sites) or usual care (60 sites). Lifestyle intervention was delivered by a trained community health worker over 12 months (5 one-one sessions, 4 group sessions, 13 telephone sessions) with the goal of the participants achieving a 7% reduction in body mass index and a 10-cm reduction in waist circumference through (i) improved diet and (ii) increased physical activity. Usual care comprised a single 30-min session of lifestyle modification advice from the community health worker. Results: We screened 33,212 people for inclusion into the study. We identified 10,930 people who met study entry criteria, amongst whom 3682 agreed to take part in the intervention. Study participants are 49.2% female and aged 52.8 (SD 8.2) years. Clinical characteristics are well balanced between intervention and usual care sites. More than 90% of follow-up visits are scheduled to be complete in December 2020. Based on the follow-up to end 2019, the observed incidence of T2D in the study population is in line with expectations (6.1% per annum). Conclusion: The iHealth-T2D study will advance understanding of strategies for the prevention of diabetes amongst South Asians, use approaches for screening and intervention that are adapted for low-resource settings. Our study will thus inform the implementation of strategies for improving the health and well-being of this major global ethnic group.
The iHealth-T2D study: a cluster randomised trial for the prevention of type 2 diabetes amongst South Asians with central obesity and prediabetes-a statistical analysis plan
(BioMed Central, London, 2022) Muilwijk, M.; Loh, M.; Mahmood, S.; Palaniswamy, S.; Siddiqui, S.; Silva, W.; Frost, G.S.; Gage, H.M.; Jarvelin, M.R.; Rannan-Eliya, R.P.; Ahmad, S.; Jha, S.; Kasturiratne, A.; Katulanda, P.; Khawaja, K.I.; Kooner, J.S.; Wickremasinghe, A.R.; van Valkengoed, I.G.M.; Chambers, J.C.
Background: South Asians are at high risk of type 2 diabetes (T2D). Lifestyle modification is effective at preventing T2D amongst South Asians, but the approaches to screening and intervention are limited by high costs, poor scalability and thus low impact on T2D burden. An intensive family-based lifestyle modification programme for the prevention of T2D was developed. The aim of the iHealth-T2D trial is to compare the effectiveness of this programme with usual care. Methods: The iHealth-T2D trial is designed as a cluster randomised controlled trial (RCT) conducted at 120 sites across India, Pakistan, Sri Lanka and the UK. A total of 3682 South Asian men and women with age between 40 and 70 years without T2D but at elevated risk for T2D [defined by central obesity (waist circumference ≥ 95 cm in Sri Lanka or ≥ 100 cm in India, Pakistan and the UK) and/or prediabetes (HbA1c ≥ 6.0%)] were included in the trial. Here, we describe in detail the statistical analysis plan (SAP), which was finalised before outcomes were available to the investigators. The primary outcome will be evaluated after 3 years of follow-up after enrolment to the study and is defined as T2D incidence in the intervention arm compared to usual care. Secondary outcomes are evaluated both after 1 and 3 years of follow-up and include biochemical measurements, anthropometric measurements, behavioural components and treatment compliance. Discussion: The iHealth-T2D trial will provide evidence of whether an intensive family-based lifestyle modification programme for South Asians who are at high risk for T2D is effective in the prevention of T2D. The data from the trial will be analysed according to this pre-specified SAP. Ethics and dissemination: The trial was approved by the international review board of each participating study site. Study findings will be disseminated through peer-reviewed publications and in conference presentations.
A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure
(University of Chicago Press, 2018) Sung, Y.J.; Winkler, T.W.; de Las Fuentes, L.; Bentley, A.R.; Brown, M.R.; Kraja, A.T.; Schwander, K.; Ntalla, I.; Guo, X.; Franceschini, N.; Lu, Y.; Cheng, C.Y.; Sim, X.; Vojinovic, D.; Marten, J.; Musani, S.K.; Li, C.; Feitosa, M.F.; Kilpelainen, T.O.; Richard, M.A.; Noordam, R.; Aslibekyan, S.; Aschard, H.; Bartz, T.M.; Dorajoo, R.; Liu, Y.; Manning, A.K.; Rankinen, T.; Smith, A.V.; Tajuddin, S.M.; Tayo, B.O.; Warren, H.R.; Zhao, W.; Zhou, Y.; Matoba, N.; Sofer, T.; Alver, M.; Amini, M.; Boissel, M.; Chai, J.F.; Chen, X.; Divers, J.; Gandin, I.; Gao, C.; Giulianini, F.; Goel, A.; Harris, S.E.; Hatwig, F.P.; Horimoto, A.R.V.R.; Hsu, F.C.; Jackson, A.U.; Kahonen, M.; Kasturiratne, A.; Kuhnel, B.; Leander, K.; Lee, W.J.; Lin, K.H.; an Luan, J.; McKenzie, C.A.; Meian, H.; Nelson, C.P.; Rauramaa, R.; Schupf, N.; Scott, R.A.; Sheu, W.H.H.; Stancakova, A.; Takeuchi, F.; van der Most, P.J.; Varga, T.V.; Wang, H.; Wang, Y.; Ware, E.B.; Weiss, S.; Wen, W.; Yanek, L.R.; Zhang, W.; Zhao, J.H.; Afag, S.; Alfred, T.; Amin, N.; Arking, D.; Aung, T.; Barr, R.G.; Bielak, L.F.; Boerwincle, E.; Bottinger, E.P.; Braund, P.S.; Brody, J.A.; Broeckel, U.; Cabrera, C.P.; Cade, B.; Caizheng, Y.; Campbell, A.; Canouil, M.; Chakravarti, A.; CHARGE Neurology Working Group; Chauhan, G.; Christensen, K.; Cocca, M.; COGENT-Kidney Consortium; Collins, F.S.; Connel, J.M.; de Mutsert, R.; de Silva, H.J.; Debette, S.; Dorr, M.; Duan, Q.; Eaton, C.B.; Ehret, G.; Evangelou, E.; FAul, J.D.; Fisher, V.A.; Forouhi, N.G.; Franco, O.H.; Friedlander, Y.; Gao, H.; GIANT Consortium; Gigante, B.; Graff, M.; Gu, C.C.; Gu, D.; Gupta, P.; Hagenaars, S.P.; Harris, T.B.; He, J.; Heikkinen, S.; Heng, C.K.; Hirata, M.; Hofman., A.; Howard, B.V.; Hunt, S.; Irvin, M.R.; Jia, Y.; Joehanes, R.; Justice, A.E.; Katsuya, T.; Kaufman, J,; Kerrison, N.D.; Khor, C.C.; Koh, W.P.; Koistinen, H.A.; Komulainen, P.; Kooperberg, C.; Krieger, J.E.; Kubo, M.; Kuusisto, J.; Lanefeld, C.D.; Langenberg, C.; Launer, L.J.; Lehne, B.; Lewis, C.E.; Li, Y.; Lifelines Cohort Study; Lim, S.H.; Lin, S.; Liu, C.T.; Liu, J.; Liu, J.; Liu, K.; Liu, Y.; Loh, M.; Lohmann, K.K.; Long, J.; Louie, T.; Magi, R.; Mahajan, A.; Meitinger, T.; Metspalu, A.; Milani, L.; Momozawa, Y.; Morris, A.P.; Mosley, T.H.Jr.; Munson, P.; Murray, A.D.; Nalls, M.A.; Nasri, U.; Norris, J.M.; North, K.; Ogunniyi, A.; Padmanabhan, S.; Palmas, W.R.; Palmer, N.D.; Pankow, J.S.; Pedersen, N.L.; Peters, A.; Peyser, P.A.; Polasek, O.; Raitakari, O.T.; Renstrom, F.; Rice, T.K.; Ridker, P.M.; Robino, A.; Robinson, J.G.; Rose, L.M.; Rudan, I.; Salako, B.L.; Sandow, K.; Schmidt, C.O.; Schreiner, P.J.; Scott, W.R.; Seshadri, S.; Sever, P.; Sitlani, C.M.; Smith, J.A.; Snieder, H.; Starr, J.M.; Strauch, K.; Tang, H.; Taylor, K.D.; Teo, Y.Y.; Tham, Y.C.; Uitterlineden, A.G.; Waldenberger, M.; Wang, L.; Wang, Y.X.; Wei, W.B.; Williams, C.; Wilson, G.; Wojczynski, M.K.; Yao, J.; Yuan, J.M.; Zonderman, A.B.; Becker, D.M.; Boehnke, M.; Bowden, D.W.; Chambers, J.C.; Chen, Y.I.; de Faire, U.; Deary, I.J.; Esco, T.; Farrall, M.; Forrester, T.; Franks, P.W.; Freedman, B.I.; Froguel, P.; Gasparini, P.; Gieger, C.; Horta, B.L.; Hung, Y.J.; Jonas, J.B.; Kato, N.; Kooner, J.S.; Laakso, M.; Lehtimaki, T.; Liang, K.W.; Magnusson, P.K.E.; Newman, A.B.; Oldehinkel, A.J.; Pereira, A.C.; Redline, S.; Rettig, R.; Samani, N.J.; Scott, J.; Shu, X.O.; van der Harst, P.; Wagenknecht, L.E.; Wareham, N.J.; Watkins, H.; Weir, D.R.; Wickremasinghe, A.R.; Wu, T.; Zheng, W.; Kamatani, Y.; Laurie, C.C.; Bouchard, C.; Cooper, R.S.; Evans, M.K.; Gudnason, V.; Kardia, S.L.R.; Kritchevsky, S.B.; Levy, D.; O'Connell, J.R.; Psaty, B.M.; van Dam, R.M.; Sims, M.; Arnett, D.K.; Mook-Kanamori, D.O.; Kelly, T.N.; Fox, E.R.; Hayward, C.; Fornage, M.; Rotimi, C.N.; Province, M.A.; van Dujin, C.M.; Tai, E.S.; Wong, T.Y.; Loos, R.J.F.; Reiner, A.P.; Rotter, J.I.; Zhu, X.; Bierut, L.J.; Gauderman, W.J.; Caulfield, M.J.; Elliott, P.; Rice, K.; Munroe, P.B.; Morrison, A.C.; Cupples, L.A.; Rao., D.C.; Chasman, D.I.
Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10-8) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10-8). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).
Low uptake of COVID-19 prevention behaviours and high socioeconomic impact of lockdown measures in South Asia: Evidence from a large-scale multi-country surveillance programme
(Elsevier Science, 2021) Kusuma, D.; Pradeepa, R.; Khawaja, K.I.; Hasan, M.; Siddiqui, S.; Mahmood, S.; Ali Shah, S.M.; de Silva, C.K.; de Silva, L.; Gamage, M.; Loomba, M.; Rajakaruna, V.P.; Hanif, A.A.; Kamalesh, R.B.; Kumarendran, B.; Loh, M.; Misra, A.; Tassawar, A.; Tyagi, A.; Waghdhare, S.; Burney, S.; Ahmad, S.; Mohan, V.; Sarker, M.; Goon, I.Y.; Kasturiratne, A.; Kooner, J.S.; Katulanda, P.; Jha, S.; Anjana, R.M.; Mridha, M.K.; Sassi, F.; Chambers, J.C.; NIHR Global Health Research Unit for diabetes and cardiovascular disease in South Asia.
BACKGROUND: South Asia has become a major epicentre of the COVID-19 pandemic. Understanding South Asians' awareness, attitudes and experiences of early measures for the prevention of COVID-19 is key to improving the effectiveness and mitigating the social and economic impacts of pandemic responses at a critical time for the Region. METHODS: We assessed the knowledge, behaviours, health and socio-economic circumstances of 29,809 adult men and women, at 93 locations across four South Asian countries. Data were collected during the national lockdowns implemented from March to July 2020, and compared with data collected prior to the pandemic as part of an ongoing prospective surveillance initiative. RESULTS: Participants were 61% female, mean age 45.1 years. Almost half had one or more chronic disease, including diabetes (16%), hypertension (23%) or obesity (16%). Knowledge of the primary COVID-19 symptoms and transmission routes was high, but access to hygiene and personal protection resources was low (running water 63%, hand sanitisers 53%, paper tissues 48%). Key preventive measures were not widely adopted. Knowledge, access to, and uptake of COVID-19 prevention measures were low amongst people from disadvantaged socio-economic groups. Fifteen percent of people receiving treatment for chronic diseases reported loss of access to long-term medications; 40% reported symptoms suggestive of anxiety or depression. The prevalence of unemployment rose from 9.3% to 39.4% (P < 0.001), and household income fell by 52% (P < 0.001) during the lockdown. Younger people and those from less affluent socio-economic groups were most severely impacted. Sedentary time increased by 32% and inadequate fruit and vegetable intake increased by 10% (P < 0.001 for both), while tobacco and alcohol consumption dropped by 41% and 80%, respectively (P < 0.001), during the lockdown. CONCLUSIONS: Our results identified important knowledge, access and uptake barriers to the prevention of COVID-19 in South Asia, and demonstrated major adverse impacts of the pandemic on chronic disease treatment, mental health, health-related behaviours, employment and household finances. We found important sociodemographic differences for impact, suggesting a widening of existing inequalities. Our findings underscore the need for immediate large-scale action to close gaps in knowledge and access to essential resources for prevention, along with measures to safeguard economic production and mitigate socio-economic impacts on the young and the poor. KEYWORDS: COVID-19; Preventative measures; Socioeconomic impact; South Asia; Surveillance system.
Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation
(Nature Publishing Company, New York, 2022) Mahajan, A.; Spracklen, C.N.; Zhang, W.; Ng, M.C.Y.; Petty, L.E.; Kitajima, H.; Yu, G.Z.; Rüeger, S.; Speidel, L.; Kim, Y.J.; Horikoshi, M.; Mercader, J.M .; Taliun, D.; Moon, S.; Kwak, S.H.; Robertson, N.R.; Rayner, N.W.; Loh, M.; Kim, B.; Chiou, J.; Miguel-Escalada, I.; Parolo, P.D.B.; Lin, K.; Bragg, F.; Preuss, M.H.; Takeuchi, F.; Nano, J.; Guo, X.; Lamri, A.; Nakatoch, M.; Scott, R.A.; Lee, J.J.; Huerta-Chagoya, A.; Graff, M.; Chai, J.F.; Parra, E. J.; Yao, J.; Bielak, L.F.; Tabara, Y.; Hai, Y.; Steinthorsdottir, V.; Cook, J.P.; Kals, M.; Grarup, N.; Schmidt, E.M.; Pan, I.; Sofer, T.; Wuttke, M.; Sarnowski, C.; Gieger, C.; Nousome, D.; Trompet, S.; Long, J.; Sun, M.; Tong, L.; Chen, W.M.; Ahmad, M.; Noordam, R.; Lim, V.J.Y.; Tam, C.H.T.; Joo, Y.Y.; Chen, C.H.; Raffield, L.M.; Lecoeur, C.; Prins, B.P.; Nicolas, A.; Yanek, L.R.; Chen, G.; Jensen, R.A.; Tajuddin, S.; Kabagambe, E.K.; An, P.; Xiang, A.H.; Choi, H.S.; Cade, B.E.; Tan, J.; Flanagan, J.; Abaitua, F.; Adair, L.S.; Adeyemo, A.; Aguilar-Salinas, C.A.; Akiyama, M.; Anand, S.S.; Bertoni, A.; Bian, Z.; Bork-Jensen, J.; Brandslund, I.; Brody, J.A.; Brummett, C.M.; Buchanan, T.A.; Canouil, M.; Chan, J.C.N.; Chang, L.C.; Chee, M.L.; Chen, J.; Chen, S.H.; Chen, Y.T.; Chen, Z.; Chuang, L.M.; Cushman, M.; Das, S.K.; de Silva, H.J.; Dedoussis, G.; Dimitrov, L.; Doumatey, A.P.; Du, S.; Duan, Q.; Eckardt, K.U.; Emery, L.S.; Evans, D.S.; Evans, M.K.; Fischer, K.; Floyd, J.S.; Ford, I.; Fornage, M.; Franco, O.H.; Frayling, T.M.; Freedman, B.I.; Fuchsberger, C.; Genter, P.; Gerstein, H.C.; Giedraitis, V.; Villalpando, C.G.; Villalpando, M.E.G.; Goodarzi, M.O.; Larsen, P.G.; Gorkin, D.; Gross, M.; Guo, Y.; Hackinger, S.; Han, S.; Hattersley, A.T.; Herder, C.; Howard, A.G.; Hsueh, W.; Huang, M.; Huang, W.; Hung, Y.; Hwang, M.Y.; Hwu, C.; Ichihara, S.; Ikram, M.A.; Ingelsson, M.; Islam, M.T.; Isono, M.; Jang, H.M.; Jasmine, F.; Jiang, G.; Jonas, J.B.; Jørgensen, M.E.; Jørgensen, T.; Kamatani, Y.; Kandeel, F.R.; Kasturiratne, A.; Katsuya, T.; Kaur, V.; Kawaguchi, T.; Keaton, J.M.; Kho, A.N.; Khor, C.C.; Kibriya, M.G.; Kim, D.H.; Kohara, K.; Kriebel, J.; Kronenberg, F.; Kuusisto, J.; Läll, K.; Lange, L.A.; Lee, M.; Lee, N.R.; Leong, A.; Li, L.; Li, Y.; Li-Gao, R.; Ligthart, S.; Lindgren, C.M.; Linneberg, A.; Liu, C.; Liu, J.; Locke, A.E.; Louie, T.; Luan, J.; Luk, A.O.; Luo, X.; Lv, J.; Lyssenko, V.; Mamakou, V.; Mani, K.R.; Meitinger, T.; Metspalu, A.; Morris, A.D.; Nadkarni, G.N.; Nadler, J.L.; Nalls, M.A.; Nayak, U.; Nongmaithem, S.S.; Ntalla, I.; Okada, Y.; Orozco, L.; Patel, S.R.; Pereira, M.A.; Peters, A.; Pirie, F.J.; Porneala, B.; Prasad, G.; Preissl, S.; Rasmussen-Torvik, L.J.; Reiner, A.P.; Roden, M.; Rohde, R.; Roll, K.; Sabanayagam, C.; Sander, M.; Sandow, K.; Sattar, N.; Schönherr, S.; Schurmann, C.; Shahriar, M.; Shi, J.; Shin, D.M.; Shriner, D.; Smith, J.A.; So, W.Y.; Stančáková, A.; Stilp, A.M.; Strauch, K.; Suzuki, K.; Takahashi, A.; Taylor, K.D.; Thorand, B.; Thorleifsson, G.; Thorsteinsdottir, U.; Tomlinson, B.; Torres, J.M.; Tsai, F.; Tuomilehto, J.; Tusie-Luna, T.; Udler, M.S.; Salgado, A.V.; Dam, R.M.; Klinken, J.B.; Varma, R.; Vujkovic, M.; Wacher-Rodarte, N.; Wheeler, E.; Whitsel, E.A.; Wickremasinghe, A.R.; Dijk, K.W.; Witte, D.R.; Yajnik, C.S; Yamamoto, K.; Yamauchi, T.; Yengo, L.; Yoon, K.; Yu, C.; Yuan, J.M.; Yusuf, S.; Zhang, L.; Zheng, W.; FinnGen; eMERGE Consortium; Leslie J Raffel; Igase, M.; Ipp, E.; Redline, S.; Cho, Y.S.; Lind, L.; Province, M.A.; Hanis, C.L.; Peyser, P.A.; Ingelsson, E.; Zonderman, A.B.; Psaty, B.M.; Wang, Y.; Rotimi, C.N.; Becker, D.M.; Matsuda, F.; Liu, Y.; Zeggini, E.; Yokota, M.; Rich, S.S.; Kooperberg, C.; Pankow, J.S.; Engert, J.C.; Chen, Y.I.; Froguel, P.; Wilson, J.G.; Sheu, W.H.H.; Kardia, S.L.R.; Wu, J.Y.; Hayes, M.G.; Ma, R.C.W.; Wong, T.Y.; Groop, L.; Mook-Kanamori, D.O.; Chandak, G.R.; Collins, F.S.; Bharadwaj, D.; Paré, G.; Sale, M.M.; Ahsan, H.; Motala, A.A.; Shu, X.O.; Park, K.S.; Jukema, J.W.; Cruz, M.; Cowdin, R.M.; Grallert, H.; Cheng, C.Y.; Bottinger, E.P.; Dehghan, A.; Tai, E.S.; Dupuis, J.; Kato, N.; Laakso, M.; Köttgen, A.; Koh, W.P.; Palmer, C.N.A.; Liu, S.; Abecasis, G.; Kooner, J.S.; Loos, R.J.F.; North, K.E.; Haiman, C.A.; Florez, J.C.; Saleheen, D.; Hansen, T.; Pedersen, O.; Mägi, R.; Langenberg, C.; Wareham, N.J.; Maeda, S.; Kadowaki, T.; Lee, J.; Millwood, I.Y.; Walters, R.G.; Stefansson, K.; Myers, S.R.; Ferrer, J.; Gaulton, K.J.; Meigs, J.B.; Mohlke, K.L.; Gloyn, A.L.; Bowden, D.W.; Below, J.E.; Chambers, J.C.; Sim, X.; Boehnke, M.; Rotter, J.I.; McCarthy, M.I.; Morris, A.P.
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.
Multi-ancestry genome-wide association study of lipid levels incorporating gene-alcohol interactions.
(School of Hygiene and Public Health of Johns Hopkins University,Baltimore., 2019) de Vries, P. S.; Brown, M. R.; Bentley, A. R.; Sung, Y. J.; Winkler, T. W.; Ntalla, I.; Schwander, K.; Kraja, A. T.; Guo, X.; Franceschini, N.; Cheng, C. Y.; Sim, X.; Vojinovic, D.; Huffman, J. E.; Musani, S. K.; Li, C.; Feitosa, M.F.; Richard, M.A.; Noordam, R.; Aschard, H.; Bartz, T. M.; Bielak, L. F.; Deng, X.; Dorajoo, R.; Lohman, K.K.; Manning, A. K.; Rankinen, T.; Smith, A. V.; Tajuddin, S. M.; Evangelou, E.; Graff, M.; Alver, M.; Boissel, M.; Chai, J. F.; Chen, X.; Divers, J.; Gandin, I.; Gao, C.; Goel, A.; Hagemeijer, Y.; Harris, S. E.; Hartwig, F. P.; He, M.; Horimoto, A. R. V. R.; Hsu, F. C.; Jackson, A. U.; Kasturiratne, A.; Komulainen, P.; Kühnel, B.; Laguzzi, F.; Lee, J. H.; Luan, J.; Lyytikäinen, L. P.; Matoba, N.; Nolte, I. M.; Pietzner, M.; Riaz, M.; Said, M. A.; Scott, R. A.; Sofer, T.; Stancáková, A.; Takeuchi, F.; Tayo, B. O.; van der Most, P. J.; Varga, T. V.; Wang, Y.; Ware, E. B.; Wen, W.; Yanek, L. R.; Zhang, W.; Zhao, J. H.; Afaq, S.; Amin, N.; Amini, M.; Arking, D. E.; Aung, T.; Ballantyne, C.; Boerwinkle, E.; Broeckel, U.; Campbell, A.; Canouil, M.; Charumathi, S.; Chen, Y. I.; Connell, J. M.; de Faire, U.; de Las Fuentes, L.; de Mutsert, R.; de Silva, H.J.; Ding, J.; Dominiczak, A. F.; Duan, Q.; Eaton, C. B.; Eppinga, R.N.; Faul, J. D.; Fisher, V.; Forrester, T.; Franco, O. H.; Friedlander, Y.; Ghanbari, M.; Giulianini, F.; Grabe, H. J.; Grove, M. L.; Gu, C. C.; Harris, T. B.; Heikkinen, S.; Heng, C. K.; Hirata, M.; Hixson, J. E.; Howard, B. V.; Ikram, M. A.; InterAct Consortium; Jr. Jacobs, D. R.; Johnson, C.; Jonas, J. B.; Kammerer, C. M.; Katsuya, T.; Khor, C. C.; Kilpeläinen, T. O.; Koh, W. P.; Koistinen, H. A.; Kolcic, I.; Kooperberg, C.; Krieger, J. E.; Kritchevsky, S. B.; Kubo, M.; Kuusisto, J.; Lakka, T. A.; Langefeld, C. D.; Langenberg, C.; Launer, L. J.; Lehne, B.; Lemaitre, R. N.; Li, Y.; Liang, J.; Liu, J.; Liu, K.; Loh, M.; Louie, T.; Mägi, R.; Manichaikul, A. W.; McKenzie, C. A.; Meitinger, T.; Metspalu, A.; Milaneschi, Y.; Milani, L.; Mohlke, K. L.; Jr. Mosley, T. H.; Nelson, C. P.; Mukamal, K. J.; Nalls, M. A.; Nauck, M.; Sotoodehnia, N.; O'Connell, J. R.; Palmer, N. D.; Pazoki, R.; Pedersen, N. L.; Peters, A.; Peyser, P. A.; Polasek, O.; Poulter, N.; Raffel, L. J.; Raitakari, O. T.; Reiner, A. P.; Rice, T. K.; Rich, S. S.; Robino, A.; Robinson, J. G.; Rose, L. M.; Rudan, I.; Schmidt, C. O.; Schreiner, P. J.; Scott, W. R.; Sever, P.; Shi, Y.; Sidney, S.; Sims, M.; Smith, B. H.; Smith, J. A.; Snieder, H.; Starr, J. M.; Strauch, K.; Tan, N.; Taylor, K. D.; Teo, Y. Y.; Tham, Y. C.; Uitterlinden, A. G.; van Heemst, D.; Vuckovic, D.; Waldenberger, M.; Wang, L.; Wang, Y.; Wang, Z.; Wei, W. B.; Williams, C.; Sr Wilson, G.; Wojczynski, M. K.; Yao, J.; Yu, B.; Yu, C.; Yuan, J. M.; Zhao, W.; Zonderman, A. B.; Becker, D. M.; Boehnke, M.; Bowden, D. W.; Chambers, J. C.; Deary, I. J.; Esko, T.; Farrall, M.; Franks, P. W.; Freedman, B. I.; Froguel, P.; Gasparini, P.; Gieger, C.; Horta, B. L.; Kamatani, Y.; Kato, N.; Kooner, J. S.; Laakso, M.; Leander, K.; Lehtimäki, T.; Lifelines Cohort, Groningen,; The Netherlands (Lifelines Cohort Study); Magnusson, P. K. E.; Penninx, B.; Pereira, A. C.; Rauramaa, R.; Samani, N.J.; Scott, J.; Shu, X. O.; van der Harst, P.; Wagenknecht, L. E.; Wang, Y. X.; Wareham, N. J.; Watkins, H.; Weir, D. R.; Wickremasinghe, A.R.; Zheng, W.; Elliott, P.; North, K. E.; Bouchard, C.; Evans, M. K.; Gudnason, V.; Liu, C. T.; Liu, Y.; Psaty, B. M.; Ridker, P. M.; van Dam, R. M.; Kardia, S. L. R.; Zhu, X.; Rotimi, C. N.; Mook-Kanamori, D. O.; Fornage, M.; Kelly, T. N.; Fox, E. R.; Hayward, C.; van Duijn, C. M.; Tai, E. S.; Wong, T. Y.; Liu, J.; Rotter, J. I.; Gauderman, W. J.; Province, M. A.; Munroe, P. B.; Rice, K.; Chasman, D. I.; Cupples, L. A.; Rao, D. C.; Morrison, A. C.
An individual's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multi-ancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in Stage 1 (genome-wide discovery) and 66 studies in Stage 2 (focused follow-up), for a total of 394,584 individuals from five ancestry groups. Genetic main and interaction effects were jointly assessed by a 2 degrees of freedom (DF) test, and a 1 DF test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in Stage 1 and were evaluated in Stage 2, followed by combined analyses of Stage 1 and Stage 2. In the combined analysis of Stage 1 and Stage 2, 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2 DF tests, of which 18 were novel. No genome-wide significant associations were found testing the interaction effect alone. The novel loci included several genes (PCSK5, VEGFB, and A1CF) with a putative role in lipid metabolism based on existing evidence from cellular and experimental models.
Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids.
(Nature Publishing Group, 2019) Bentley, A.R.; Chasman, D. I.; Schwander, K.; Ntalla, I.; Kraja, A.T.; Winkler, T.W.; Brown, M. R.; Sung, Y. J.; Lim, E.; Huffman, J.E.; Vojinovic, D.; Sim, X.; Cheng, C.Y.; Lu, Y.; Liu, J.; Guo, X.; Deng, X.; Musani, S.K.; Li, C.; Feitosa, M.F.; Richard, M.A.; Noordam, R.; Baker, J.; Chen, G.; Aschard, H.; Bartz, T.M.; Ding, J.; Dorajoo, R.; Manning, A.K.; Rankinen, T.; Smith, A. V.; Tajuddin, S.M.; Zhao, W.; Graff, M.; Alver, M.; Boissel, M.; Chai, J. F.; Chen, X.; Divers, J.; Evangelou, E.; Gao, C.; Goel, A.; Hagemeijer, Y.; Harris, S. E.; Hartwig, F. P.; He, M.; Horimoto, A.R.V. R.; Hsu, F.C.; Hung, Y. J.; Jackson, A. U.; Kasturiratne, A.; Komulainen, P.; Kühnel, B.; Leander, K.; Lin, K. H.; Luan, J.; Lyytikäinen, L.P.; Matoba, N.; Nolte, I. M.; Pietzner, M.; Prins, B.; Riaz, M.; Robino, A.; Said, M. A.; Schupf, N.; Scott, R. A.; Sofer, T.; Stancáková, A.; Takeuchi, F.; Tayo, B. O.; van der Most, P. J.; Varga, T. V.; Wang, T. D.; Wang, Y.; Ware, E. B.; Wen, W.; Xiang, Y. B.; Yanek, L. R.; Zhang, W.; Zhao, J. H.; Adeyemo, A.; Afaq, S.; Amin, N.; Amini, M.; Arking, D.E.; Arzumanyan, Z.; Aung, T.; Ballantyne, C.; Barr, R. G.; Bielak, L. F.; Boerwinkle, E.; Bottinger, E.P.; Broeckel, U.; Chen, Y. I.; Charumathi, S.; Canouil, M.; Campbell, A.; Cade, B. E.; Brown, M.; Christensen, K.; de Las Fuentes, L.; Connell, J. M.; Concas, M. P.; COGENT-Kidney Consortium; de Silva, H.J.; de Vries, P. S.; Doumatey, A.; Duan, Q.; Eaton, C. B.; Eppinga, R.N.; Faul, J. D.; Floyd, J.S.; Gigante, B.; Gharib, S. A.; Forouhi, N.G.; Ghanbari, M.; Gao, H.; Gandin, I.; Friedlander, Y.; Forrester, T.; Hixson, J. E.; Hirata, M.; Justice, A. E.; Jonas, J. B.; Johnson, C.; Joehanes, R.; Jia, Y.; EPIC-InterAct Consortium; Ikram, M.A.; Katsuya, T.; Khor, C.C.; Kilpeläinen, T.O.; Koh, W. P.; Kolcic, I.; Kooperberg, C.; Krieger, J.E.; Kritchevsky, S.B.; Kubo, M.; Kuusisto, J.; Lakka, T. A.; Langefeld, C.D.; Langenberg, C.; Launer, L. J.; Lehne, B.; Lewis, C. E.; Li, Y.; Liang, J.; Lin, S.; Liu, C.T.; Liu, J.; Liu, K.; Loh, M.; Lohman, K.K.; Louie, T.; Luzzi, A.; Mägi, R.; Mahajan, A.; Manichaikul, A.W.; McKenzie, C.A.; Meitinger, T.; Metspalu, A.; Milaneschi, Y.; Milani, L.; Mohlke, K. L.; Momozawa, Y.; Morris, A. P.; Murray, A. D.; Nalls, M. A.; Nauck, M.; Nelson, C. P.; North, K. E.; O'Connell, J.R.; Palmer, N.D.; Papanicolau, G.J.; Pedersen, N. L.; Peters, A.; Peyser, P. A.; Polasek, O.; Poulter, N.; Raitakari, O.T.; Reiner, A. P.; Renström, F.; Rice, T.K.; Rich, S.S.; Robinson, J.G.; Rose, L. M.; Rosendaal, F. R.; Rudan, I.; Schmidt, C.O.; Schreiner, P. J.; Scott, W.R.; Sever, P.; Shi, Y.; Sidney, S.; Sims, M.; Smith, J. A.; Snieder, H.; Starr, J. M.; Strauch, K.; Stringham, H. M.; Tan, N. Y. Q.; Tang, H.; Taylor, K. D.; Teo, Y. Y.; Tham, Y. C.; Tiemeier, H.; Turner, S. T.; Uitterlinden, A. G.; Understanding Society Scientific Group; van Heemst, D.; Waldenberger, M.; Wang, H.; Wang, L.; Wang, L.; Wei, W. B.; Williams, C. A.; Wilson, G. Sr.; Wojczynski, M. K.; Yao, J.; Young, K.; Yu, C.; Yuan, J. M.; Zhou, J.; Zonderman, A. B.; Becker, D. M.; Boehnke, M.; Bowden, D. W.; Chambers, J. C.; Cooper, R. S.; de Faire, U.; Deary, I. J.; Elliott, P.; Esko, T.; Farrall, M.; Franks, P. W.; Freedman, B. I.; Froguel, P.; Gasparini, P.; Gieger, C.; Horta, B. L.; Juang, J. J.; Kamatani, Y.; Kammerer, C. M.; Kato, N.; Kooner, J. S.; Laakso, M.; Laurie, C. C.; Lee, I. T.; Lehtimäki, T.; Lifelines Cohort; Magnusson, P. K. E.; Oldehinkel, A. J.; Penninx, B. W. J. H.; Pereira, A. C.; Rauramaa, R.; Redline, S.; Samani, N. J.; Scott, J.; Shu, X. O.; van der Harst, P.; Wagenknecht, L. E.; Wang, J. S.; Wang, Y. X.; Wareham, N. J.; Watkins, H.; Weir, D. R.; Wickremasinghe, A.R.; Wu, T.; Zeggini, E.; Zheng, W.; Bouchard, C.; Evans, M. K.; Gudnason, V.; Kardia, S. L. R.; Liu, Y.; Psaty, B. M.; Ridker, P. M.; van Dam, R. M.; Mook-Kanamori, D. O.; Fornage, M.; Province, M. A.; Kelly, T. N.; Fox, E. R.; Hayward, C.; van Duijn, C. M.; Tai, E. S.; Wong, T. Y.; Loos, R. J. F.; Franceschini, N.; Rotter, J. I.; Zhu, X.; Bierut, L. J.; Gauderman, W. J.; Rice, K.; Munroe, P. B.; Morrison, A. C.; Rao, D. C.; Cupples, L. A.; Rotimi, C. N.
The concentrations of high- and low-density-lipoprotein cholesterol and triglycerides are influenced by smoking, but it is unknown whether genetic associations with lipids may be modified by smoking. We conducted a multi-ancestry genome-wide gene-smoking interaction study in 133,805 individuals with follow-up in an additional 253,467 individuals. Combined meta-analyses identified 13 new loci associated with lipids, some of which were detected only because association differed by smoking status. Additionally, we demonstrate the importance of including diverse populations, particularly in studies of interactions with lifestyle factors, where genomic and lifestyle differences by ancestry may contribute to novel findings.
A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure
(IRL Press at Oxford University Press., 2019) Sung, Y.J.; de Las Fuentes, L.; Winkler, T.W.; Chasman, D.I.; Bentley, A.R.; Kraja, A.T.; Ntalla, I.; Warren, H.R.; Guo, X.; Schwander, K.; Manning, A.K.; Brown, M.R.; Aschard, H.; Feitosa, M.F.; Franceschini, N.; Lu, Y.; Cheng, C.Y.; Sim, X.; Vojinovic, D.; Marten, J.; Musani, S.K.; Kilpeläinen, T.O.; Richard, M.A.; Aslibekyan, S.; Bartz, T.M.; Dorajoo, R.; Li, C.; Liu, Y.; Rankinen, T.; Smith, A.V.; Tajuddin, S.M.; Tayo, B.O.; Zhao, W.; Zhou, Y.; Matoba, N.; Sofer, T.; Alver, M.; Amini, M.; Boissel, M.; Chai, J.F.; Chen, X.; Divers, J.; Gandin, I.; Gao, C.; Giulianini, F.; Goel, A.; Harris, S.E.; Hartwig, F.P.; He, M.; Horimoto, A.R.V.R.; Hsu, F.C.; Jackson, A.U.; Kammerer, C.M.; Kasturiratne, A.; Komulainen, P.; Kühnel, B.; Leander, K.; Lee, W.J.; Lin, K.H.; Luan, J.; Lyytikäinen, L.P.; McKenzie, C.A.; Nelson, C.P.; Noordam, R.; Scott, R.A.; Sheu, W.H.H.; Stančáková, A.; Takeuchi, F.; van der Most, P.J.; Varga, T.V.; Waken, R.J.; Wang, H.; Wang, Y.; Ware, E.B.; Weiss, S.; Wen, W.; Yanek, L.R.; Zhang, W.; Zhao, J.H.; Afaq, S.; Alfred, T.; Amin, N.; Arking, D.E.; Aung, T.; Barr, R.G.; Bielak, L.F.; Boerwinkle, E.; Bottinger, E.P.; Braund, P.S.; Brody, J.A.; Broeckel, U.; Cade, B.; Campbell, A.; Canouil, M.; Chakravarti, A.; Cocca, M.; Collins, F.S.; Connell, J.M.; de Mutsert, R.; de Silva, H.J.; Dörr, M.; Duan, Q.; Eaton, C.B.; Ehret, G.; Evangelou, E.; Faul, J.D.; Forouhi, N.G.; Franco, O.H.; Friedlander, Y.; Gao, H.; Gigante, B.; Gu, C.C.; Gupta, P.; Hagenaars, S.P.; Harris, T.B.; He, J.; Heikkinen, S.; Heng, C.K.; Hofman, A.; Howard, B.V.; Hunt, S.C.; Irvin, M.R.; Jia, Y.; Katsuya, T.; Kaufman, J.; Kerrison, N.D.; Khor, C.C.; Koh, W.P.; Koistinen, H.A.; Kooperberg, C.B.; Krieger, J.E.; Kubo, M.; Kutalik, Z.; Kuusisto, J.; Lakka, T.A.; Langefeld, C.D.; Langenberg, C.; Launer, L.J.; Lee, J.H.; Lehne, B.; Levy, D.; Lewis, C.E.; Li, Y.; Lifelines Cohort Study; Lim, S.H.; Liu, C.T.; Liu, J.; Liu, J.; Liu, Y.; Loh, M.; Lohman, K.K.; Louie, T.; Mägi, R.; Matsuda, K.; Meitinger, T.; Metspalu, A.; Milani, L.; Momozawa, Y.; Mosley, T.H. Jr; Nalls, M.A.; Nasri, U.; O'Connell, J.R.; Ogunniyi, A.; Palmas, W.R.; Palmer, N.D.; Pankow, J.S.; Pedersen, N.L.; Peters, A.; Peyser, P.A.; Polasek, O.; Porteous, D.; Raitakari, O.T.; Renström, F.; Rice, T.K.; Ridker, P.M.; Robino, A.; Robinson, J.G.; Rose, L.M.; Rudan, I.; Sabanayagam, C.; Salako, B.L.; Sandow, K.; Schmidt, C.O.; Schreiner, P.J.; Scott, W.R.; Sever, P.; Sims, M.; Sitlani, C.M.; Smith, B.H.; Smith, J.A.; Snieder, H.; Starr, J.M.; Strauch, K.; Tang, H.; Taylor, K.D.; Teo, Y.Y.; Tham, Y.C.; Uitterlinden, A.G.; Waldenberger, M.; Wang, L.; Wang, Y.X.; Wei, W.B.; Wilson, G.; Wojczynski, M.K.; Xiang, Y.B.; Yao, J.; Yuan, J.M.; Zonderman, A.B.; Becker, D.M.; Boehnke, M.; Bowden, D.W.; Chambers, J.C.; Chen, Y.I.; Weir, D.R.; de Faire, U.; Deary, I.J.; Esko, T.; Farrall, M.; Forrester, T.; Freedman, B.I.; Froguel, P.; Gasparini, P.; Gieger, C.; Horta, B.L.; Hung, Y.J.; Jonas, J.B.; Kato, N.; Kooner, J.S.; Laakso, M.; Lehtimäki, T.; Liang, K.W.; Magnusson, P.K.E.; VOldehinkel, A.J.; Pereira, A.C.; Perls, T.; Rauramaa, R.; Redline, S.; Rettig, R.; Samani, N.J.; Scott, J.; Shu, X.O.; van der Harst, P.; Wagenknecht, L.E.; Wareham, N.J.; Watkins, H.; Wickremasinghe, A.R.; Wu, T.; Kamatani, Y.; Laurie, C.C.; Bouchard, C.; Cooper, R.S.; Evans, M.K.; Gudnason, V.; Hixson, J.; Kardia, S.L.R.; Kritchevsky, S.B.; Psaty, B.M.; van Dam, R.M.; Arnett, D.K.; Mook-Kanamori, D.O.; Fornage, M.; Fox, E.R.; Hayward, C.; van Duijn, C.M.; Tai, E.S.; Wong, T.Y.; Loos, R.J.F.; Reiner, A.P.; Rotimi, C.N.; Bierut, L.J.; Zhu, X.; Cupples, L.A.; Province, M.A.; Rotter, J.I.; Franks, P.W.; Rice, K.; Elliott, P.; Caulfield, M.J.; Gauderman, W.J.; Munroe, P.B.; Rao, D.C.; Morrison, A.C.
ABSTRACT: Elevated blood pressure (BP), a leading cause of global morbidity and mortality, is influenced by both genetic and lifestyle factors. Cigarette smoking is one such lifestyle factor. Across five ancestries, we performed a genome-wide gene-smoking interaction study of mean arterial pressure (MAP) and pulse pressure (PP) in 129 913 individuals in stage 1 and follow-up analysis in 480 178 additional individuals in stage 2. We report here 136 loci significantly associated with MAP and/or PP. Of these, 61 were previously published through main-effect analysis of BP traits, 37 were recently reported by us for systolic BP and/or diastolic BP through gene-smoking interaction analysis and 38 were newly identified (P < 5 × 10-8, false discovery rate < 0.05). We also identified nine new signals near known loci. Of the 136 loci, 8 showed significant interaction with smoking status. They include CSMD1 previously reported for insulin resistance and BP in the spontaneously hypertensive rats. Many of the 38 new loci show biologic plausibility for a role in BP regulation. SLC26A7 encodes a chloride/bicarbonate exchanger expressed in the renal outer medullary collecting duct. AVPR1A is widely expressed, including in vascular smooth muscle cells, kidney, myocardium and brain. FHAD1 is a long non-coding RNA overexpressed in heart failure. TMEM51 was associated with contractile function in cardiomyocytes. CASP9 plays a central role in cardiomyocyte apoptosis. Identified only in African ancestry were 30 novel loci. Our findings highlight the value of multi-ancestry investigations, particularly in studies of interaction with lifestyle factors, where genomic and lifestyle differences may contribute to novel findings.
Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries
(Public Library of Science, 2018) Feitosa, M.F.; Kraja, A.T.; Chasman, D.I.; Sung, Y.J.; Winkler, T.W.; Ntalla, I.; Guo, X.; Franceschini, N.; Cheng, C.Y.; Sim, X.; Vojinovic, D.; Marten, J.; Musani, S.K.; Li, C.; Bentley, A.R.; Brown, M.R.; Scwander, K.; Richard, M.A.; Noordam, R.; Aschard, H.; Bartz, T.M.; Bielak, L.F.; Dorajoo, R.; Fishaer, V.; Hartwig, F.P.; Horimoto, A.R.V.R.; Lohman, K.K.; Manning, A.K.; Rankinen, T.; Smith, A.V.; Tajiddin, S.M.; Wojczynski, M.K.; Alver, M.; Boissel, M.; Cai, Q.; Campbell, A.; Chai, J.F.; Chen, X.; Divers, J.; Gao, C.; Goel, A.; Hagemeijer, Y.; Harris, S.E.; He, M.; Hsu, F.C.; Jackson, A.U.; Kahonen, M.; Kasturiratne, A.; Komulainen, P.; Kuhnel, B.; Laguzzi, F.; Luan, J.; Matoba, N.; Nolte, I.M.; Padmanabhan, S.; Riaz, M.; Rueedi, R.; Robino, A.; Said, M.A.; Scott, R.A.; Soffer, T.; Stancakova, A.; Takeuchi, F.; Tayo, B.O.; van de Most, P.J.; Varga, T.V.; Vitart, V.; Wang, Y.; Ware, E.B.; Warren, H.R.; Weiss, S.; Wen, W.; Yanek, L.R.; Zhang, W.; Zhao, J.H.; Afaq, S.; Amin, N.; Amini, M.; Arking, D.E.; Aung, T.; Boerwinkle, E.; Borecki, I.; Broecki, I.; Broeckel, U.; Brown, M.; Brumat, M.; Burke, G.L.; Canouil, M.; Chakravarthi, A.; Charumathi, S.; Ida Chen, Y.D.; Connel, J.M.; Correa, A.; de Las Fuentes, L.; de Mutsert, R.; de Silva, H.J.; Deng, X.; Ding, J.; Duan, Q.; Eaton, C.B.; Ehret, G.; Eppinga, R.N.; Evangelou, E.; Faul, J.D.; Felix, S.B.; Forouhi, N.G.; Forrester, T.; Franco, O.H.; Friedlander, Y.; Gandin, I.; Gao, H.; Ghanbari, M.; Gigante, B.; Gu, C.C.; Gu, D.; Hagenaars, S.P.; Halmans, G.; Harris, T.B.; He, J.; Heikkinen, S.; Heng, C.K.; Hirata, M.; Howard, B.V.; Ikram, M.A.; InterAct Consortium; John, U.; Katsuya, T.; Lakka, T.A.; Langefeld, C.D.; Langenberg, C.; Launer, L.J.; Lehne, B.; Lewis, C.E.; Li, Y.; Lin, S.; Lin, U.; Liu, J.; Liu, J.; Loh, M.; Louie, T.; Magi, R.; McKenzie, C.A.; Meitinger, T.; Metspalu, A.; Milaneschi, Y.; Milani, L.; mohlke, K.L.; Momozawa, Y.; Nalls, M.A.; Nelson, C.P.; Sotoodehnia, N.; Norris, J.M.; O'Connel, J.R.; Palmer, N.D.; Perls, T.; Pedersen, N.L.; Peters, A.; Peyser, P.A.; Poulter, N.; Raffel, L.J.; Raitakari, O.T.; Roll, K.; Rose, L.M.; Rosendaal, F.R.; Rotter, J.I.; Schimidit, C.O.; Schreiner, P.J.; Schupf, N.; Scott, W.R.; Sever, P.S.; Shi, Y.; Sidney, S.; Sims, M.; Sitlani, C.M.; Smith, J.A.; Snieder, H.; Starr, J.M.; Strauch, K.; Stringham, H.M.; Tan, N.Y.Q.; Tang, H.; Taylor, K.D.; Teo, Y.Y.; Tham, Y.C.; Turner, S.C.; Uitterlinden, A.G.; Vollenweider, P.; Waldenberger, M.; Wang, L.; Wang, Y.X.; Wei, W.B.; Williams, C.; Yao, J.; Yuan, J.M.; Zhao, W.; Zonderman, A.B.; Becker, D.M.; Boehnke, M.; Bowden, D.W.; Chambers, J.C.; Deary, I.J.; Esco, T.; Farall, M.; Frankd, P.W.; Freedman, B.I.; Froguel, P.; Gasparini, P.; Gieger, C.; Jonas, J.B.; Kamatani, Y.; Kato, N.; Kooner, J.S.; Kutalik, Z.; Laakso, M.; Laurie, C.C.; Leander, K.; Lehtimaki, T.; Study, L.C.; Magnusson, P.K.E.; Olderhinkel, A.J.; Penninx, B.W.J.H.; Polasek, O.; Porteous, D.J.; Rauramaa, R.; Ssamani, N.J.; Scott, J.; Shu, X.O.; van der Harst, P.; Wagenknecht, L.E.; Wareham, N.J.; Watkins, H.; Weir, D.R.; Wickremasinghe, A.R.; Wu, T.; Zheng, W.; Bouchard, C.; Christensen, K.; Evans, M.K.; Gudnason, V.; Horta, B.L.; Kardia, S.L.R.; Liu, Y.; Pereira, A.C.; Psaty, B.M.; Ridker, P.M.; van Dam, R.M.; Gauderman, W.J.; Zhu, X.; Mook-Kanamori, D.O.; Fornage, M.; Rotimi, C.N.; Cupples, L.A.; Kelly, T.N.; Fox, E.R.; Hayward, C.; van Duijn, C.M.; Tai, E.S.; Wong, T.Y.; Kooperberg, C.; Palmas, W.; Rice, K.; Morrison, A.C.; Elliott, P.; Caulfield, M.J.; Munroe, P.B.; Rao, D.C.; Province, M.A.; Levy, D.
Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension
Reference equations for evaluation of spirometry function tests in South Asia, and amongst south asians living in other countries
(European Respiratory Society, 2022) Leong, W.Y.; Gupta, A.; Hasan, M.; Mahmood, S.; Siddiqui, S.; Ahmed, S.; Goon, I.Y.; Loh, M.; Mina, T.H.; Lam, B.; Yew, Y.W.; Ngeow, J.; Lee, J.; Lee, E.S.; Riboli, E.; Elliott, P.; Tan, G.P.; Chotirmall, S.H.; Wickremasinghe, A.R.; Kooner, J.S.; Khawaja, K.I.; Katulanda, P.; Mridha, M.K.; Jha, S.; Ranjit, M.A.; Pradeepa, G.; Kasturiratne, A.; Chambers, J.C.
Background: There is little data to accurate interpretation of spirometry data in South Asia, a major global region with high reported burden for chronic respiratory disease. Method: We measured lung function in 7,453 healthy men and women aged over 18 years, from Bangladesh, North India, South India, Pakistan and Sri Lanka, as part of the South Asia Biobank study. We first assessed the accuracy of existing equations for predicting normal forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and FEV1/FVC ratio. We then used our data to derive (N=5589) and internally validate (N=1864) new prediction equations amongst South Asians, with further external validation amongst 339 healthy South Asians living in Singapore. Results: GLI2012 and NHANESIII consistently overestimated expiratory volumes (best fit GLI-African American, mean [sd] z-score (n=7453): FEV1 -0.94 [1.05]; FVC -0.91 [1.10]). Age, height and weight were strong predictors of lung function in our participants (p<0.001), and sex specific reference equations using these three variables were highly accurate in both internal validation (z-scores: FEV1 0.03 [0.99]; FVC 0.04 [0.97]; FEV1/FVC -0.03 [0.99]) and external validation (z-scores: FEV1 0.31 [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. Conclusion: We 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.
Smokeless and combustible tobacco use among 148,944 South Asian adults: a cross-sectional study of South Asia Biobank
(Springer, 2023) Xie, W.; Mridha, M.K.; Gupta, A.; Kusuma, D.; Butt, A.M.; Hasan, M.; Brage, S.; Loh, M.; Khawaja, K.I.; Pradeepa, R.; Jha, V.; Kasturiratne, A.; Katulanda, P.; Anjana, R.M.; Chambers, J.C.
INTRODUCTION Tobacco use, in both smoking and smokeless forms, is highly prevalent among South Asian adults. The aims of the study were twofold: (1) describe patterns of SLT and combustible tobacco product use in four South Asian countries stratified by country and sex, and (2) assess the relationships between SLT and smoking intensity, smoking quit attempts, and smoking cessation among South Asian men. METHODS Data were obtained from South Asia Biobank Study, collected between 2018 and 2022 from 148,944 men and women aged 18 years and above, living in Bangladesh, India, Pakistan, or Sri Lanka. Mixed effects multivariable logistic and linear regression were used to quantify the associations of SLT use with quit attempt, cessation, and intensity. RESULTS Among the four South Asian countries, Bangladesh has the highest rates of current smoking (39.9% for male, 0.4% for female) and current SLT use (24.7% for male and 23.4% for female). Among male adults, ever SLT use was associated with a higher odds of smoking cessation in Bangladesh (OR, 2.88; 95% CI, 2.65, 3.13), India (OR, 2.02; 95% CI, 1.63, 2.50), and Sri Lanka (OR, 1.36; 95% CI, 1.14, 1.62). Ever SLT use and current SLT use was associated with lower smoking intensity in all countries. CONCLUSIONS In this large population-based study of South Asian adults, rates of smoking and SLT use vary widely by country and gender. Men who use SLT products are more likely to abstain from smoking compared with those who do not.
The Trans-ancestral genomic architecture of glycemic traits
(Nature Pub. Co., 2021) Chen, J.; Spracklen, C.N.; Marenne, G.; Varshney, A.; Corbin, L.J.; Luan, J.; Willems, S.M.; Wu, Y.; Zhang, X.; Horikoshi, M.; Boutin, T.S.; Mägi, R.; Waage, J.; Li-Gao, R.; Chan, K.H.K; Yao, J.; Anasanti, M.D.; Chu, A.Y.; Claringbould, A.; Heikkinen, J.; Hong, J.; Hottenga, J.J.; Huo, S.; Kaakinen, M.A.; Louie, T.; März, W.; Moreno-Macias, H.; Ndungu, A.; Nelson, S.C.; Nolte, I.M.; North, K.E.; Raulerson, C.K.; Ray, D.; Rohde, R.; Rybin, D.; Schurmann, C.; Sim, X.; Southam, L.; Stewart, I.D.; Wang, C.A.; Wang, Y.; Wu, P.; Zhang, W.; Ahluwalia, T.S.; Appel, E.V.R.; Bielak, L.F.; Brody, J.A.; Burtt, N.P.; Cabrera, C.P.; Cade, B.E.; Chai, J.F.; Chai, X.; Chang, L.C.; Chen, C.H.; Chen, B.H.; Chitrala, K.N.; Chiu, Y.F.; De Haan, H.G.; Delgado, G.E.; Demirkan, A.; Duan, Q.; Engmann, J.; Fatumo, S.A.; Gayán, J.; Giulianini, F.; Gong, J.H.; Gustafsson, S.; Hai, Y.; Hartwig, F.P.; He, J.; Heianza, Y.; Huang, T.; Huerta-Chagoya, A.; Hwang, M.Y.; Jensen, R.A.; Kawaguchi, T.; Kentistou, K.A.; Kim, Y.J.; Kleber, M.E.; Kooner, I.K.; Lai, S.; Lange, L.A.; Langefeld, C.D.; Lauzon, M.; Li, M.; Ligthart, S.; Liu, J.; Loh, M.; Long, J.; Lyssenko, V.; Mangino, M.; Marzi, C.; Montasser, M.E.; Nag, A.; Nakatochi, M.; Noce, D.; Noordam, R.; Pistis, G.; Preuss, M.; Raffield, L.; Rasmussen-Torvik, L.J.; Rich, S.S.; Robertson, N.R.; Rueedi, R.; Ryan, K.; Sanna, S.; Saxena, R.; Schraut, K.E.; Sennblad, B.; Setoh, K.; Smith, A.V.; Sparsø, T.; Strawbridge, R.J.; Takeuchi, F.; Tan, J.; Trompet, S.; Van den Akker, E.; Van der Most, P.J.; Verweij, N.; Vogel, M.; Wang, H.; Wang, C.; Wang, N.; Warren, H.R.; Wen, W.; Wilsgaard, T.; Wong, A.; Wood, A.R.; Xie, T.; Zafarmand, M.H.; Zhao, J.H.; Zhao, W.; Amin, N.; Arzumanyan, Z.; Astrup, A.; Bakker, S.J.L.; Baldassarre, D.; Beekman, M.; Bergman, R.N.; Bertoni, A.; Blüher, M.; Bonnycastle, L.L.; Bornstein, S.R.; Bowden, D.W.; Cai, Q.; Campbell, A.; Campbell, H.; Chang, Y.C.; de Geus, E.J.C.; Dehghan, A.; Du, S.; Eiriksdottir, G.; Farmaki, A.E.; Frånberg, M.; Fuchsberger, C.; Gao, Y.; Gjesing, A.P.; Goel, A.; Han, S.; Hartman, C.A.; Herder, C.; Hicks, A.A.; Hsieh, C.H.; Hsueh, W.A.; Ichihara, S.; Igase, M.; Ikram, M.A.; Johnson, W.C.; Jørgensen, M.E.; Joshi, P.K.; Kalyani, R.R.; Kandeel, F.R.; Katsuya, T.; Khor, C.C.; Kiess, W.; Kolcic, I.; Kuulasmaa, T.; Kuusisto, J.; Läll, K.; Lam, K.; Lawlor, D.A.; Lee, N.R.; Lemaitre, R.N.; Li, H.; Lifelines Cohort Study; Lin, S.Y.; Lindström, J.; Linneberg, A.; Liu, J.; Lorenzo, C.; Matsubara, T.; Matsuda, F.; Mingrone, G.; Mooijaart, S.; Moon, S.; Nabika, T.; Nadkarni, G.N.; Nadler, J.L.; Nelis, M.; Neville, M.J.; Norris, J.M.; Ohyagi, Y.; Peters, A.; Peyser, P.A.; Polasek, O.; Qi, Q.; Raven, D.; Reilly, D.F.; Reiner, A.; Rivideneira, F.; Roll, K.; Rudan, I.; Sabanayagam, C.; Sandow, K.; Sattar, N.; Schürmann, A.; Shi, J.; Stringham, H.M.; Taylor, K.D.; Teslovich, T.M.; Thuesen, B.; Timmers, P.R.H.J.; Tremoli, E.; Tsai, M.Y.; Uitterlinden, A.; van Dam, R.M.; van Heemst, D.; van Hylckama Vlieg, A.; van Vliet-Ostaptchouk, J.V.; Vangipurapu, J.; Vestergaard, H.; Wang, T.; Willems van Dijk, K.; Zemunik, T.; Abecasis, G.R.; Adair, L.S.; Aguilar-Salinas, C.A.; Alarcón-Riquelme, M.E.; An, P.; Aviles-Santa, L.; Becker, D.M.; Beilin, L.J.; Bergmann, S.; Bisgaard, H.; Black, C.; Boehnke, M.; Boerwinkle, E.; Böhm, B.O.; Bønnelykke, K.; Boomsma, D.I.; Bottinger, E.P.; Buchanan, T.A.; Canouil, M.; Caulfield, M.J.; Chambers, J.C.; Chasman, D.I.; Chen, Y.I.; Cheng, C.Y.; Collins, F.S.; Correa, A.; Cucca, F.; de Silva, H.J.; Dedoussis, G.; Elmståhl, S.; Evans, M.K.; Ferrannini, E.; Ferrucci, L.; Florez, J.C.; Franks, P.W.; Frayling, T.M.; Froguel, P.; Gigante, B.; Goodarzi, M.O.; Gordon-Larsen, P.; Grallert, H.; Grarup, N.; Grimsgaard, S.; Groop, L.; Gudnason, V.; Guo, X.; Hamsten, A.; Hansen, T.; Hayward, C.; Heckbert, S.R.; Horta, B.L.; Huang, W.; Ingelsson, E.; James, P.S.; Jarvelin, M.R.; Jonas, J.B.; Jukema, J.W.; Kaleebu, P.; Kaplan, R.; Kardia, S.L.R.; Kato, N.; Keinanen-Kiukaanniemi, S.M.; Kim, B.J.; Kivimaki, M.; Koistinen, H.A.; Kooner, J.S.; Körner, A.; Kovacs, P.; Kuh, D.; Kumari, M.; Kutalik, Z.; Laakso, M.; Lakka, T.A.; Launer, L.J.; Leander, K.; Li, H.; Lin, X.; Lind, L.; Lindgren, C.; Liu, S.; Loos, R.J.F.; Magnusson, P.K.E.; Mahajan, A.; Metspalu, A.; Mook-Kanamori, D.O.; Mori, T.A.; Munroe, P.B.; Njølstad, I.; O'Connell, J.R.; Oldehinkel, A.J.; Ong, K.K.; Padmanabhan, S.; Palmer, C.N.A.; Palmer, N.D.; Pedersen, O.; Pennell, C.E.; Porteous, D.J.; Pramstaller, P.P.; Province, M.A.; Psaty, B.M.; Qi, L.; Raffel, L.J.; Rauramaa, R.; Redline, S.; Ridker, P.M.; Rosendaal, F.R.; Saaristo, T.E.; Sandhu, M.; Saramies, J.; Schneiderman, N.; Schwarz, P.; Scott, L.J.; Selvin, E.; Sever, P.; Shu, X.O.; Slagboom, P.E.; Small, K.S.; Smith, B.H.; Snieder, H.; Sofer, T.; Sørensen, T.I.A.; Spector, T.D.; Stanton, A.; Steves, C.J.; Stumvoll, M.; Sun, L.; Tabara, Y.; Tai, E.S.; Timpson, N.J.; Tönjes, A.; Tuomilehto, J.; Tusie, T.; Uusitupa, M.; van der Harst, P.; van Duijn, C.; Vitart, V.; Vollenweider, P.; Vrijkotte, T.G.M.; Wagenknecht, L.E.; Walker, M.; Wang, Y.X.; Wareham, N.J.; Watanabe, R.M.; Watkins, H.; Wei, W.B.; Wickremasinghe, A.R.; Willemsen, G.; Wilson, J.F.; Wong, T.Y.; Wu, J.Y.; Xiang, A.H.; Yanek, L.R.; Yengo, L.; Yokota, M.; Zeggini, E.; Zheng, W.; Zonderman, A.B.; Rotter, J.I.; Gloyn, A.L.; McCarthy, M.I.; Dupuis, J.; Meigs, J.B.; Scott, R.A.; Prokopenko, I.; Leong, A.; Liu, C.T.; Parker, S.C.J.; Mohlke, K.L.; Langenberg, C.; Wheeler, E.; Morris, A.P.; Barroso, I.; Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC) Collaborators.
ABSTRACT: Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation
(Nature Publishing Company, 2015) Kato, N.; Loh, M.; Takeuchi, F.; Verweij, N.; Wang, X.; Zhang, W.; Kelly, T.N.; Saleheen, D.; Lehne, B.; Leach, I.M.; Drong, A.W.; Abbott, J.; Wahl, S.; Tan, S.T.; Scott, W.R.; Campanella, G.; Chadeau-Hyam, M.; Afzal, U.; Ahluwalia, T.S.; Bonder, M.J.; Chen, P.; Dehghan, A.; Edwards, T.L.; Esko, T.; Go, M.J.; Harris, S.E.; Hartiala, J.; Kasela, S.; Kasturiratne, A.; Khor, C.C.; Kleber, M.E.; Li, H.; Mok, Z.Y.; Nakatochi, M.; Sapari, N.S.; Saxena, R.; Stewart, A.F.; Stolk, L.; Tabara, Y.; Teh, A.L.; Wu, Y.; Wu, J.Y.; Zhang, Y.; Aits, I.; Da Silva Couto Alves, A.; Das, S.; Dorajoo, R.; Hopewell, J.C.; Kim, Y.K.; Koivula, R.W.; Luan, J.; Lyytikäinen, L.P.; Nguyen, Q.N.; Pereira, M.A.; Postmus, I.; Raitakari, O.T.; Bryan, M.S.; Scott, R.A.; Sorice, R.; Tragante, V.; Traglia, M.; White, J.; Yamamoto, K.; Zhang, Y.; Adair, L.S.; Ahmed, A.; Akiyama, K.; Asif, R.; Aung, T.; Barroso, I.; Bjonnes, A.; Braun, T.R.; Cai, H.; Chang, L.C.; Chen, C.H.; Cheng, C.Y.; Chong, Y.S.; Collins, R.; Courtney, R.; Davies, G.; Delgado, G.; Do, L.D.; Doevendans, P.A.; Gansevoort, R.T.; Gao, Y.T.; Grammer, T.B.; Grarup, N.; Grewal, J.; Gu, D.; Wander, G.S.; Hartikainen, A.L.; Hazen, S.L.; He, J.; Heng, C.K.; Hixson, J.E.; Hofman, A.; Hsu, C.; Huang, W.; Husemoen, L.L.; Hwang, J.Y.; Ichihara, S.; Igase, M.; Isono, M.; Justesen, J.M.; Katsuya, T.; Kibriya, M.G.; Kim, Y.J.; Kishimoto, M.; Koh, W.P.; Kohara, K.; Kumari, M.; Kwek, K.; Lee, N.R.; Lee, J.; Liao, J.; Lieb, W.; Liewald, D.C.; Matsubara, T.; Matsushita, Y.; Meitinger, T.; Mihailov, E.; Milani, L.; Mills, R.; Mononen, N.; Müller-Nurasyid, M.; Nabika, T.; Nakashima, E.; Ng, H.K.; Nikus, K.; Nutile, T.; Ohkubo, T.; Ohnaka, K.; Parish, S.; Paternoster, L.; Peng, H.; Peters, A.; Pham, S.T.; Pinidiyapathirage, M.J.; Rahman, M.; Rakugi, H.; Rolandsson, O.; Rozario, M.A.; Ruggiero, D.; Sala, C.F.; Sarju, R.; Shimokawa, K.; Snieder, H.; Sparso, T.; Spiering, W.; Starr, J.M.; Stott, D.J.; Stram, D.O.; Sugiyama, T.; Szymczak, S.; Tang, W.H.; Tong, L.; Trompet, S.; Turjanmaa, V.; Ueshima, H.; Uitterlinden, A.G.; Umemura, S.; Vaarasmaki, M.; van Dam, R.M.; van Gilst, W.H.; van Veldhuisen, D.J.; Viikari, J.S.; Waldenberger, M.; Wang, Y.; Wang, A.; Wilson, R.; Wong, T.Y.; Xiang, Y.B.; Yamaguchi, S.; Ye, X.; Young, R.D.; Young, T.L.; Yuan, J.M.; Zhou, X.; Asselbergs, F.W.; Ciullo, M.; Clarke, R.; Deloukas, P.; Franke, A.; Franks, P.W.; Franks, S.; Friedlander, Y.; Gross, M.D.; Guo, Z.; Hansen, T.; Jarvelin, M.R.; Jorgensen, T.; Jukema, J.W.; Kähönen, M.; Kajio, H.; Kivimaki, M.; Lee, J.Y.; Lehtimäki, T.; Linneberg, A.; Miki, T.; Pedersen, O.; Samani, N.J.; Sorensen, T.I.; Takayanagi, R.; Toniolo, D.; BIOS-consortium; CARDIo GRAMplusCD; LifeLines Cohort Study; InterAct Consortium; Ahsan, H.; Allayee, H.; Chen, Y.T.; Danesh, J.; Deary, I.J.; Franco, O.H.; Franke, L.; Heijman, B.T.; Holbrook, J.D.; Isaacs, A.; Kim, B.J.; Lin, X.; Liu, J.; März, W.; Metspalu, A.; Mohlke, K.L.; Sanghera, D.K.; Shu, X.O.; van Meurs, J.B.; Vithana, E.; Wickremasinghe, A.R.; Wijmenga, C.; Wolffenbuttel, B.H.; Yokota, M.; Zheng, W.; Zhu, D.; Vineis, P.; Kyrtopoulos, S.A.; Kleinjans, J.C.; McCarthy, M.I.; Soong, R.; Gieger, C.; Scott, J.; Teo, Y.Y.; He, J.; Elliott, P.; Tai, E.S.; van der Harst, P.; Kooner, J.S.; Chambers, J.C.
We carried out a trans-ancestry genome-wide association and replication study of blood pressurephenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10(-11) to 5.0 × 10(-21)). The sentinel blood pressure SNPs are enriched for association with DNAmethylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNAmethylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10(-6)). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.