Journal/Magazine Articles
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This collection contains original research articles, review articles and case reports published in local and international peer reviewed journals by the staff members of the Faculty of Medicine
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Item 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.Item 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.Item 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.Item 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.Item 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.Item 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 availableItem 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.Item 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.Item 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.Item 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.