Medicine
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This repository contains the published and unpublished research of the Faculty of Medicine by the staff members of the faculty
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Item A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids(American Society of Human Genetics., 2022) Ramdas, S.; Judd, J.; Graham, S.E.; Kanoni, S.; Wang, Y.; Surakka, I.; Wenz, B.; Clarke, S.L.; Chesi, A.; Wells, A.; Bhatti, K.F.; Vedantam, S.; Winkler, T.W.; Locke, A.E.; Marouli, E.; Zajac, G.J.M.; Wu, K.H.; Ntalla, I.; Hui, Q.; Klarin, D.; Hilliard, A.T.; Wang, Z.; Xue, C.; Thorleifsson, G.; Helgadottir, A.; Gudbjartsson, D.F.; Holm, H.; Olafsson, I.; Hwang, M.Y.; Han, S.; Akiyama, M.; Sakaue, S.; Terao, C.; Kanai, M.; Zhou, W.; Brumpton, B.M.; Rasheed, H.; Havulinna, A.S.; Veturi, Y.; Pacheco, J.A.; Rosenthal, E.A.; Lingren, T.; Feng, Q.; Kullo, I.J.; Narita, A.; Takayama, J.; Martin, H.C.; Hunt, K.A.; Trivedi, B.; Haessler, J.; Giulianini, F.; Bradford, Y.; Miller, J.E.; Campbell, A.; Lin, K.; Millwood, I.Y.; Rasheed, A.; Hindy, G.; Faul, J.D.; Zhao, W.; Weir, D.R.; Turman, C.; Huang, H.; Graff, M.; Choudhury, A.; Sengupta, D.; Mahajan, A.; Brown, M.R.; Zhang, W.; Yu, K.; Schmidt, E.M.; Pandit, A.; Gustafsson, S.; Yin, X.; Luan, J.; Zhao, J.H.; Matsuda, F.; Jang, H.M.; Yoon, K.; Gomez, C.M.; Pitsillides, A.; Hottenga, J.J.; Wood, A.R.; Ji, Y.; Gao, Z.; Haworth, S.; Mitchell, R.E.; Chai, J.F.; Aadahl, M.; Bjerregaard, A.A.; Yao, J.; Manichaikul, A.; JaneLee, W.; Hsiung, C.A.; Warren, H.R.; Ramirez, J.; Jensen, J.B.; Kårhus, L.; Goel, A.; Lleal, M.S.; Noordam, R.; Mauro, P.; Matteo, F.; McDaid, A.F.; Marques-Vidal, P.; Wielscher, M.; Trompet, S.; Sattar, N.; Møllehave, L.T.; Munz, M.; Zeng, L.; Huang, J.; Yang, B.; Poveda, A.; Kurbasic, A.; Schönherr, S.; Forer, L.; Scholz, M.; Galesloot, T.E.; Bradfield, J.P.; Ruotsalainen, S.E.; Daw, E.W.; Zmuda, J.M; Mitchell, J.S.; Fuchsberger, C.; Christensen, H.; Brody, J.A.; Le, P.; Feitosa, M.F.; Wojczynski, M.K.; Hemerich, D.; Preuss, M.; Mangino, M.; Christofidou, P.; Verweij, N.; Benjamins, J.W.; Engmann, J.; Noah, T.L.; Verma, A.; Slieker, R.C.; Lo, K.S.; Zilhao, N.R.; Kleber, M.E.; Delgado, G.E.; Huo, S.; Ikeda, D.D.; Iha, H.; Yang, J.; Liu, J.; Demirkan, A.; Leonard, H.L.; Marten,J.; Emmel, C.; Schmidt, B.; Smyth, L.J.; Cañadas-Garre, M.; Wang, C.; Nakatochi, M.; Wong, A.; Hutri-Kähönen , N.; Sim, X.; Xia, R.; Huerta-Chagoya, A.; Fernandez-Lopez, J.C.; Lyssenko, V; Nongmaithem, S.S.; Sankareswaran, A.; Irvin, M.R.; Oldmeadow, C.; Kim, H.N.; Ryu, S.; Timmers, P.R.H.J; Arbeeva, L.; Dorajoo, R.; Lange, L.A.; Prasad, G.; Lorés-Motta, L.; Pauper, M.; Long, J.; Li, X.; Theusch, E.; Takeuchi, F.; Spracklen, C.N.; Loukola, A.; Bollepalli, S.; Warner, S.C.; Wang, Y.X.; Wei, W.B.; Nutile, T.; Ruggiero, D.; Sung,Y.J.; Chen, S.; Liu, F.; Yang, J.; Kentistou, K.A.; Banas, B.; Morgan, A.; Meidtner, K.; Bielak, L.F.; Smith, J.A.; Hebbar, P.; Farmaki, A.E.; Hofer, E.; Lin, M.; Concas, M.P.; Vaccargiu, S.; Most, P.J.; Pitkänen, N.; Cade, B.E.; Laan, S.W.; Chitrala, K.N.; Weiss, S.; Bentley, A.R.; Doumatey, A.P.; Adeyemo, A.A.; Lee, J.Y.; Petersen, E.R.B.; Nielsen, A.A.; Choi, H.S.; Nethander, M.; Nethander, M.; Freitag-Wolf, S.; Southam, L.; Rayner, N.W.; Wang, C.A.; Lin, S.; Wang, J.S.; Couture, C.; Lyytikäinen, L.P.; Nikus, K.; Partida, G.C.; Vestergaard, H.; Hidalgo, B.; Giannakopoulou, O.; Cai, Q.; Obura, M.O.; Setten, J.; He, K.Y.; Tang, H.; Terzikhan, N.; Shin, J.H.; Jackson, R.D.; Reiner, A.P.; Martin, L.W.; Chen, Z.; Li, L.; Kawaguchi, T.; Thiery, J.; Bis, J.C.; Launer, L.J.; Li, H.; Nalls, M.A.; Raitakari, O.T.; Ichihara, S.; Wild, S.H.; Nelson, C.P.; Campbell, H.; Jäger, S.; Nabika, T.; Al-Mulla, F.; Niinikoski, H.; Braund, P.S.; Kolcic, I.; Kovacs, P.; Giardoglou, T.; Katsuya, T.; Kleijn, D.; Borst, G.J.; Kim, E.K.; Adams, H.H.H.; Ikram, M.A.; Zhu, X.; Asselbergs, F.W.; Kraaijeveld, A.O.; Beulens, J.W.J.; Shu, X.O.; Rallidis, L.S.; Pedersen, O.; Hansen, T.; Mitchell, P.; Hewitt, A.W.; Kähönen, M.; Pérusse, L.; Bouchard, C.; Tönjes, A.; Chen, Y.D.I; Pennell, C.E.; Mori, T.A.; Lieb, W.; Franke, A.; Ohlsson, C.; Mellström, D.; Cho, Y.S.; Lee, H.; Yuan, J.M.; Koh, W.P.; Rhee, S.Y.; Woo, J.T.; Heid, I.M.; Stark, K.J.; Zimmermann, M.E.; Völzke, H.; Homuth, G.; Homuth, G.; Evans, M.K.; Zonderman, A.B.; Polasek, O.; Pasterkamp, G.; Hoefer, I.E.; Redline, S.; Pahkala, K.; Oldehinkel, A.J.; Snieder, H.; Biino, G.; Schmidt, R.; Schmidt, H.; Bandinelli , S; Dedoussis, G.; Thanaraj, T.A.; Peyser, P.A.; Kato, N.; Schulze, M.B.; Girotto, G.; Böger, C.A.; Jung, B.; Joshi, P.K.; Bennett, D.A.; Jager, P.L.D.; Lu, X.; Mamakou, V.; Brown, M.; Caulfield, M.J.; Munroe, P.B.; Guo, X.; Ciullo, M.; Jonas, J.B.; Samani, N.J.; Kaprio, J.; Pajukanta, P.; Luna, T.T.; Salinas, C.A.A.; Adair, L.S.; Bechayda, S.A.; de Silva, H.J.; Wickremasinghe, A.R.; Krauss, R.M.; Wu, J.Y.; Zheng,W.; Hollander, A.I.; Bharadwaj, D.; Correa, A,; Wilson, J.G.; Lind, L.; Heng, C.K.; Nelson, A.E.; Golightly, Y.M.; Wilson, J.F.; Penninx, B.; Kim, H.L.; Attia, J.; Scott, R.J.; Rao, D.C.; Arnett, D.K.; Walker, M.; Scott, L.J.; Koistinen, H.A.; Chandak, G.R.; Mercader, J.M.; Villalpando, C.G.; Orozco, L.; Fornage, M.; Tai, E.S.; Dam, R.M.; Lehtimäki, T.; Chaturvedi, N.; Yokota, M.; Liu, J.; Reilly, D.F.; McKnight, A.J.; Kee, F.; Jöckel, K.H.; McCarthy, M.I.; Palmer, C.N.A.; Vitart, V.; Hayward, C.; Simonsick, E.; Duijn, C.M; Jin, Z.B.; Jin, Z.B.; Lu, F.; Hishigaki, H.; Lin, X.; März, W.; Gudnason, V.; Tardif, J.C.; Lettre, G.; Hart, L.M.T.; Elders, P.J.M.; Rader, D.J.; Loos, S.M.; Province, M.A.; Parra, E.J.; Cruz, M.; Psaty, B.M.; Brandslund, I.; Pramstaller, P.P.; Rotimi, C.N.; Christensen, K.; Ripatti, S.; Widén, E.; Hakonarson, H.; Grant, S.F.A.; Kiemeney, L.; de Graaf, J.; Loeffler, M.; Kronenberg, F.; Gu, D.; Erdmann, J.; Schunkert, H.; Franks,P.W.; Linneberg, A.; Jukema, J.W.; Khera, A.V.; Männikkö, M.; Jarvelin, M.R.; Kutalik, Z.; Francesco, C.; Kanamori, D.O.M.; Dijk, K.W.; Watkins, H.; Strachan, D.P.; Grarup, N.; Sever, P.; Poulter, N.; Sheu, W.H.H.; Rotter, J.I.; Dantoft, T.M.; Karpe, F.; Neville, M.J.; Timpson, N.J.; Cheng, C.Y.; Wong, T.Y.; Khor, C.C.; Li, H.; Sabanayagam, C.; Peters, A.; Gieger, C.; Hattersley, A.T.; Pedersen, N.L.; Magnusson, P.K.E.; Boomsma, D.I.; de Geus, E.J.C.; Cupples, L.A.; Meurs, J.B.J.; Ikram, A.; Ghanbari, M.; Larsen, P.G.; Huang, W.; Kim, Y.J.; Tabara, Y.; Wareham, N.J.; Langenberg, C.; Zeggini, E.; Tuomilehto, J.; Kuusisto, J.; Laakso, M.; Ingelsson, E.; Abecasis, G.; Chambers, J.C.; Kooner, J.S.; de Vries, P.S.; Morrison, A.C.; Hazelhurst, S.; Ramsay, M.; North, K.E.; Daviglus, M.; Kraft, P.; Martin, N.G.; Whitfield, J.B.; Abbas, S.; Saleheen, D.; Walters, R.G.; Holmes, M.V.; Black, C.; Smith, B.H.; Baras, A.; Justice, A.E.; Buring, J.E.; Ridker, P.M.; Chasman, D.I.; Kooperberg, C.; Tamiya, G.; Yamamoto, M.; Heel, D.A.; Trembath, R.C.; Wei, W.Q.; Jarvik, G.P.; Namjou, B.; Hayes, M.G.; Ritchie, M.D.; Jousilahti, P.; Salomaa, V.; Hveem, K.; Åsvold, B.O.; Kubo, M.; Kamatani, Y.; Okada, Y.; Murakami, Y.; Kim, B.J.; Thorsteinsdottir, U.; Stefansson, K.; Zhang, J.; Chen, Y.E.; Ho, Y.L.; Lynch, J.A.; Tsao, P.S.; Chang, K.M.; Cho, K.; O'Donnell, C.J.; Gaziano, J.M.; Wilson, P.; Mohlke, K.L.; Frayling, T.M.; Hirschhorn, J.N.; Kathiresan, S.; Boehnke, M.; Million Veterans Program; Global Lipids Genetics Consortium; Grant, S.; Natarajan, P.; Sun, Y.V.; Morris, A.P.; Deloukas, P.; Peloso, G.; Assimes, T.L.; Willer, C.J.; Zhu, X.; Brown, C.D.A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.Item 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.Item Prevalence, predictors and outcome of chronic kidney disease in a cohort of aging urban, adult Sri Lankans(Sri Lanka Medical Association, 2021) de Silva, S.T.; Ediriweera, D.D.; Niriella, M.A.; Kasturiratne, A.; Kato, N.; Wickremasinghe, A.R.; de Silva, H.J.Introduction and Objectives Chronic kidney disease (CKD) affects 700 million people worldwide. In 2017, deaths due to CKD accounted for 4.6% of mortality globally. Prevalence of CKD is increasing worldwide, with increasing diabetes and hypertension in aging populations. Our objective was to determine prevalence of CKD in an urban, adult cohort, since such data is scarce in Sri Lanka. Methods The study population was selected by stratified random sampling from Ragama MOH area initially in 2007 (aged 35-64 years) and re-assessed in 2014. On both occasions, participants were assessed by structured interview, anthropometric measurements and biochemical tests. CKD was assessed in 2014. Estimated glomerular filtration rate (eGFR) was calculated using CKD-EPI formula. CKD was defined as eGFR<60ml/min/1.72m2, using KDIGO/KDOQI classification. The cohort was further followed up in 2017 when all-cause and cardiovascular (CV) mortality and CV morbidity were also assessed. Results Of 2985 individuals recruited in 2007, 2148(71.6%) attended follow-up in 2014. 2032/2148 (94.6%) had CKD data [57.0% women, mean age 52.3(SD 7.7) years]. Age adjusted prevalence of CKD was 3.03(1.98–4.11) per 100 population in 2014; 41/61(67.2%) were in CKD Stage-3A, 14/61(23%) in Stage-3B, 6/61(9.8%) in Stage-4 and none in end-stage. Independent predictors of CKD in 2014 were older age (p<0.001), male gender (P<0.05) and presence of diabetes (p<0.001) and hypertension (p<0,001) in 2007. CKD was not associated with all-cause mortality, CV mortality or morbidity (p>0.05) in 2017.Conclusion Diabetes and hypertension were associated with CKD. Most with CKD were in Stage 3, where early recognition and better control of co-morbidities retard progression of CKD.Item 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.Item Physical activity tracking among Sri Lankan adults: findings from a 7-year follow-up of the Ragama Health Study(SAGE Publications, 2021) Pinidiyapathirage, J.; Kasturiratne, A.; Bennie, J.A.; Pathmeswaran, A.; Biddle, S.J.H.; de Silva, H.J.; Chackrewarthy, S.; Dassanayake, A.S.; Ranawaka, U.; Kato, N.; Wickremasinghe, A.R.ABSTRACT: Limited data are available on physical activity tracking among adults in low- and middle-income countries. Using a longitudinal design, we assessed trends and correlates of physical activity among Sri Lankan adults. Individuals selected through age-stratified random sampling, were screened initially in 2007 (n = 2986) and reevaluated in 2014 (n = 2148). On both occasions, structured interviews and clinical measurements were completed. Approximately 40% of the participants engaged in recommended levels of physical activity both at baseline and follow-up. One-fifth reported increased physical activity at follow-up, a similar proportion reported being persistently inactive or a reduction in physical activity. In the adjusted analysis, being persistently active was associated with male sex, a lower educational level and income, being free of any chronic disease conditions, better self-rated health, and sitting time <8 hours. Our findings support public health interventions to help maintain recommended physical activity levels over time, particularly for subgroups at high-risk of physical inactivity. KEYWORDS: Sri Lanka; lower middle-income countries; non-communicable diseases; physical activity; population studies.Item The clinical utility of accurate NAFLD ultrasound grading: Results from a community-based, prospective cohort study(Elsevier Science Ireland Ltd, 2021) Niriella, M.A.; Ediriweera, D.S.; Kasturiratne, A.; Gunasekara, D.; de Silva, S.T.; Dassanayake, A.S.; de Silva, A.P.; Kato, N.; Pathmeswaran, A.; Wickremasinghe, A.R.; de Silva, H.J.OBJECTIVES: Despite its widespread use there is no consensus on ultrasound criteria to diagnose fatty liver. METHOD: In an ongoing, cohort-study, participants were initially screened in 2007 and reassessed in 2014 by interview, anthropometric measurements, liver ultrasonography, and blood tests. We evaluated utility of increased hepatic echogenicity alone (intermediate) compared to using additional criteria which included signal attenuation and/or vascular blunting along with increase of hepatic echogenicity (moderate-severe), to diagnose fatty liver in NAFLD. We made a comparison of the two radiologically defined groups, in order to choose a classification method for NAFLD, which may better predict baseline adverse metabolic traits (MT), and adverse metabolic and cardiovascular events (CVE) after 7-year of follow-up. RESULTS: Of 2985 recruited in 2007, 940 (31.5 %) had moderate-severe NAFLD, 595 (19.9 %) intermediate NAFLD, and 957 (32.1 %) were controls (no fatty liver). 2148 (71.9 %) attended follow-up in 2014; they included 708 who had moderate-severe NAFLD, 446 intermediate NAFLD and 674 controls, at baseline (in 2007). At baseline, adverse anthropometric indices and MTs were significantly higher in both moderate-severe NAFLD and intermediate NAFLD compared to controls, except for low HDL. They were commoner in moderate-severe NAFLD than in intermediate NAFLD. After seven years, the odds of developing new-onset metabolic traits and CVEs were significantly higher compared to controls only in moderate-severe NAFLD. CONCLUSIONS: Only moderate-severe NAFLD predicted risk of incident adverse MTs and CVEs. However, both moderate-severe and intermediate NAFLD were associated with higher prevalence of adverse anthropometric and metabolic traits, thereby identifying individuals who need medical intervention even among those with milder degrees of fatty liver. We therefore recommend using increased hepatic echogenicity, and not only the more stringent criteria (which include signal attenuation and/or vascular blunting), for the diagnosis of fatty liver in individuals with NAFLD. KEYWORDS: Cardiovascular events; Fatty liver; NAFLD; Outcomes; Ultrasonography; Ultrasound criteria.Item Predictors of mortality in a cohort of adult Sri Lankans(Sri Lanka Medical Association., 2019) Kasturiratne, A.; Beddage, T.; de Silva, S.T.; Niriella, M.A.; Pathmeswaran, A.; Kato, N.; Wickremasinghe, A.R.; de Silva, H.J.INTRODUCTION & OBJECTIVES: There is limited knowledge on early predictors of mortality among Sri Lankans. We investigated predictors of mortality in the Ragama Health Study cohort 10 years after recruitment. METHODS: The study population (35-64-year-olds selected by age-stratified random sampling from the Ragama Medical Officer of Health area) was initially screened in 2007 for socio-demographic, lifestyle, anthropometric and clinical risk factors with informed written consent. Their vital status was assessed in 2017 and the causes of death of the deceased were retrieved from death certificates available with the next-of-kin or declaration of death records from the hospital where the death occurred. RESULTS: Of the 2986 enrolled in 2007, 169 [Males: 107 (63.3%)] had died over the 10-year period, giving an overall mortality rate of 5.7% (95% CI: 5.0 % - 6.4%). The number of deaths due to cardiovascular and cancer related causes were 73/169 and 31/169, respectively. On multivariate analysis using Cox's proportional hazards model, advancing age, male sex, lower educational level, unsafe alcohol consumption and the presence of metabolic syndrome at baseline were independent predictors of all-cause mortality. Advancing age, male sex, unsafe alcohol consumption and the presence of metabolic syndrome at baseline were independent predictors of cardiovascular mortality, while advancing age and unsafe alcohol consumption were the only independent predictors of cancer-related mortality. CONCLUSION: Unsafe alcohol consumption and the presence of metabolic syndrome were important modifiable risks for mortality in this population. Addressing these risks in early adulthood will help to improve longevity.Item Incidence and predictors of Diabetes Mellitus: A 7- year community cohort follow-up of urban, adult Sri Lankans(Sri Lanka Medical Association., 2019) de Silva, S.T.; Ediriweera, D.; Beddage, T.; Kasturiratne, A.; Niriella, M.A.; de Silva, A.P.; Dassanayake, A.S.; Pathmeswaran, A.; Kato, N.; Wickremasinghe, A.R.INTRODUCTION & OBJECTIVES: There is limited data on incidence of type 2 diabetes mellitus (T2DM) from South Asia. We investigated incidence and predictors ofT2DM in an urban, adult population after seven-years of follow-up. METHODS: The study population (42-71 year-olds in 2014, selected by age-stratified random sampling from the Ragama MOH area) was initially screened in 2007 and re-evaluated in 2014 with informed written consent. On both occasions they were assessed by structured interview, anthropometric measurements, liver ultrasound, biochemical and serological tests. RESULTS: Of the 2986 enrolled in 2007, 737 had established T2DM giving a baseline prevalence of 24.7% (95% CI: 23.1%-26.2%). 2148/2984 (71.6%) of the original cohort attended follow-up [1237 (57.5%) women; median (IQR) 60 (54-66) years]. 1650 participants who did not have T2DM in 2007 presented for follow up; 436 (27.6%) of them had developed new T2DM by 2014, giving an annual incidence of 3.9% (95% CI: 3.0%-4.9%). Of 525 participants with pre-diabetes (HbA1c 5.7-6.4%) in 2007, 364 attended follow up and 201/364 (55.1%) had developed T2DM by 2014, giving an annual conversion rate of pre-diabetes to T2DM of 7.9%. On logistic regression, pre-diabetes (OR:4.4;95%CI:3.3%-6.0%), central obesity (OR: 1.8;95%CI: 1.3%-2.4%), dyslipidemia (OR: l.5;95%CI: 1.1 %-2.1 %) and non-alcoholic fatty liver disease (NAFLD) (OR:1.5;95%CI: 1.1 %-2.1%) showed significant association with incident T2DM. CONCLUSION: In this urban cohort, the annual incidence of T2DM was 3.9% and the annual conversion rate of pre-diabetes to T2DM was 7.9%. Our findings emphasize the need for targeted and intensive lifestyle interventions for individuals with high metabolic risk to prevent T2DM.Item Gene-educational attainment interactions in a multi-ancestry genome-wide meta-analysis identify novel blood pressure loci(Stockton Press., 2021) de Las Fuentes, L.; Sung, Y. J.; Noordam, R.; Winkler, T.; Feitosa, M.F.; Schwander, K.; Bentley, A.R.; Brown, M.R.; Guo, X.; Manning, A.; Chasman, D.I.; Aschard, H.; Bartz, T. M.; Bielak, L.F.; Campbell, A.; Cheng, C.Y.; Dorajoo, R.; Hartwig, F. P.; Horimoto, A.R.V.R.; Li, C.; Li-Gao, R.; Liu, Y.; Marten, J.; Musani, S.K.; Ntalla, I.; Rankinen, T.; Richard, M.; Sim, X.; Smith, A.V.; Tajuddin, S.M.; Tayo, B.O.; Vojinovic, D.; Warren, H.R.; Xuan, D.; Alver, M.; Boissel, M.; Chai, J.F.; Chen, X.; Christensen, K.; Divers, J.; Evangelou, E.; Gao, C.; Girotto, G.; Harris, S.E.; He, M.; Hsu, F.C.; Kühnel, B.; Laguzzi, F.; Li, X.; Lyytikäinen, L. P.; Nolte, I. M.; Poveda, A.; Rauramaa, R.; Riaz, M.; Rueedi, R.; Shu, X.O.; Snieder, H.; Sofer, T.; Takeuchi, F.; Verweij, N.; Ware, E.B.; Weiss, S.; Yanek, L.R.; Amin, N.; Arking, D.E.; Arnett, D.K.; Bergmann, S.; Boerwinkle, E.; Brody, J.A.; Broeckel, U.; Brumat, M.; Burke, G.; Cabrera, C.P.; Canouil, M.; Chee, M.L.; Chen, Y. I.; Cocca, M.; Connell, J.; de Silva, H.J.; de Vries, P. S.; Eiriksdottir, G.; Faul, J.D.; Fisher, V.; Forrester, T.; Fox, E.F.; Friedlander, Y.; Gao, H.; Gigante, B.; Giulianini, F.; Gu, C.C.; Gu, D.; Harris, T. B.; He, J.; Heikkinen, S.; Heng, C. K.; Hunt, S.; Ikram, M. A.; Irvin, M.R.; Kähönen, M.; Kavousi, M.; Khor, C.C.; Kilpeläinen, T.O.; Koh, W.P.; Komulainen, P.; Kraja, A.T.; Krieger, J.E.; Langefeld, C. D.; Li, Y.; Liang, J.; Liewald, D.C.M.; Liu, C.T.; Liu, J.; Lohman, K.K.; Mägi, R.; McKenzie, C.A.; Meitinger, T.; Metspalu, A.; Milaneschi, Y.; Milani, L.; Mook-Kanamori, D.O.; Nalls, M.A.; Nelson, C.P.; Norris, J. M.; O'Connell, J.; Ogunniyi, A.; Padmanabhan, S.; Palmer, N.D.; Pedersen, N. L.; Perls, T.; Peters, A.; Petersmann, A.; Peyser, P. A.; Polasek, O.; Porteous, D. J.; Raffel, L. J.; Rice, T. K.; Rotter, J.I.; Rudan, I.; Rueda-Ochoa, O.L.; Sabanayagam, C.; Salako, B. L.; Schreiner, P.J.; Shikany, J.M.; Sidney, S.S.; Sims, M.; Sitlani, C.M.; Smith, J. A.; Starr, J. M.; Strauch, K.; Swertz, M. A.; Teumer, A.; Tham, Y. C.; Uitterlinden, A.G.; Vaidya, D.; van der Ende, M.Y.; Waldenberger, M.; Wang, L.; Wang, Y. X.; Wei, W.B.; Weir, D.R.; Wen, W.; Yao, J.; Yu, B.; Yu, C.; Yuan, J. M.; Zhao, W.; Zonderman, A.B.; Becker, D.M.; Bowden, D.W.; Deary, I. J.; Dörr, M.; Esko, T.; Freedman, B. I.; Froguel, P.; Gasparini, P.; Gieger, C.; Jonas, J.B.; Kammerer, C.M.; Kato, N.; Lakka, T. A.; Leander, K.; Lehtimäki, T.; Lifelines Cohort Study; Magnusson, P. K. E.; Marques-Vidal, P.; Penninx, B. W. J. H.; Samani, N. J.; van der Harst, P.; Wagenknecht, L. E.; Wu, T.; Zheng, W.; Zhu, X.; Bouchard, C.; Cooper, R. S.; Correa, A.; Evans, M. K.; Gudnason, V.; Hayward, C.; Horta, B. L.; Kelly, T. N.; Kritchevsky, S. B.; Levy, D.; Palmas, W. R.; Pereira, A. C.; Province, M. M.; Psaty, B. M.; Ridker, P. M.; Rotimi, C. N.; Tai, E. S.; van Dam, R. M.; van Duijn, C. M.; Wong, T. Y.; Rice, K.; Gauderman, W. J.; Morrison, A. C.; North, K. E.; Kardia, S. L. R.; Caulfield, M. J.; Elliott, P.; Munroe, P. B.; Franks, P. W.; Rao, D. C.; Fornage, M.ABSTRACT:Educational attainment is widely used as a surrogate for socioeconomic status (SES). Low SES is a risk factor for hypertension and high blood pressure (BP). To identify novel BP loci, we performed multi-ancestry meta-analyses accounting for gene-educational attainment interactions using two variables, "Some College" (yes/no) and "Graduated College" (yes/no). Interactions were evaluated using both a 1 degree of freedom (DF) interaction term and a 2DF joint test of genetic and interaction effects. Analyses were performed for systolic BP, diastolic BP, mean arterial pressure, and pulse pressure. We pursued genome-wide interrogation in Stage 1 studies (N = 117 438) and follow-up on promising variants in Stage 2 studies (N = 293 787) in five ancestry groups. Through combined meta-analyses of Stages 1 and 2, we identified 84 known and 18 novel BP loci at genome-wide significance level (P < 5 × 10-8). Two novel loci were identified based on the 1DF test of interaction with educational attainment, while the remaining 16 loci were identified through the 2DF joint test of genetic and interaction effects. Ten novel loci were identified in individuals of African ancestry. Several novel loci show strong biological plausibility since they involve physiologic systems implicated in BP regulation. They include genes involved in the central nervous system-adrenal signaling axis (ZDHHC17, CADPS, PIK3C2G), vascular structure and function (GNB3, CDON), and renal function (HAS2 and HAS2-AS1, SLIT3). Collectively, these findings suggest a role of educational attainment or SES in further dissection of the genetic architecture of BP.Item Metabolic syndrome, but not non-alcoholic fatty liver disease, increases 10-year mortality: A prospective, community-cohort study(Wiley-Blackwell, 2020) Niriella, M.A.; Kasturiratne, A.; Beddage, T.U.; Withanage, S. A.; Goonatilleke, D.C.; Abeysinghe, C.P.; de Mel, R.T.; Balapitiya, T.L.; de Silva, S.T.; Pathmeswaran, A.; Wickremasinghe, A.R.; Kato, N.; de Silva, H.J.BACKGROUND:Data on outcomes of non-alcoholic fatty liver disease (NAFLD) from South Asia are lacking. We compared mortality, among those with- and without-NAFLD, after 10-years follow-up among urban, adult Sri Lankans.METHOD:Participants (aged 35-64 years), selected by age-stratified random sampling, were screened by structured-interview in 2007. Anthropometric measurements, liver ultrasonography and biochemical/serological tests were done. NAFLD was diagnosed on ultrasound criteria, safe-alcohol consumption (Asian-standards) and absence of hepatitis B/C. Subjects without NAFLD were those without any ultrasound criteria of fatty liver, safe-alcohol consumption and absence of hepatitis B/C. The cohort was re-evaluated to assess mortality in 2017. Participants or their households were contacted by telephone/post, and deaths confirmed by home-visits and death certificate review. Cox-regression was used to determine predictors of all-cause mortality (ACM) and cardiovascular mortality (CVM) in those with- and without-NAFLD.RESULTS:2724 (91.2%) of 2985 original participants were contacted (851-with NAFLD and 1072-without NAFLD). Overall there were 169 (6.2%) deaths [41-deaths among NAFLD (17-cardiovascular; 9-cancer-related; 4-liver-specific; 11-other) and 79-deaths among no-NAFLD (28-cardiovascular; 17-cancer-related; 1-liver-specific; 33-other)]. Metabolic syndrome (MetS), low-education level, higher age and male-gender independently predicted ACM. MetS, increasing age and male-gender independently predicted CVM. NAFLD did not predict either ACM or CVM. In those with NAFLD, MetS and age >55-years were independently associated with ACM, while MetS and male-gender were associated with CVM.CONCLUSION:In this community-based study, increasing age, male-gender and MetS, but not NAFLD, predicted 10-year ACM and CVM. Among those with NAFLD, only those metabolically abnormal were at a higher risk for mortality.