Browsing by Author "Islam, M.T."

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology
(Nature Publishing Group, 2024) Suzuki, K.; Hatzikotoulas, K.; Southam, L.; Taylor, H.J.; Yin, X.; Lorenz, K.M.; Mandla, R.; Huerta-Chagoya, A.; Melloni, G.E.M.; Kanoni, S.; Rayner, N.W.; Bocher, O.; Arruda, A.L.; Sonehara, K.; Namba, S.; Namba, S.S.K.; Preuss, M.H.; Petty, L.E.; Schroeder, P.; Vanderwerff, B.; Kals, M.; Bragg. F.; Lin, K.; Guo, X.; Zhang, W.; Yao, J.; Kim, Y.J.; Graff, M.; Takeuchi, F.; Nano, J.; Lamri, A.; Nakatochi, M.; Moon, S.; Scott, R.A.; Cook, J.P.; Lee, J.J.; Pan, I.; Taliun, D.; Parra, E.J.; Chai. J.F.; Bielak, L.F.; Tabara, Y.; Hai, Y.; Thorleifsson, G.; Grarup, N.; Sofer, T.; Wuttke, M.; Sarnowski, C.; Gieger, C.; Nousome, D.; Trompet, S.; Kwak, S.H.; Long, J.; Sun, M.; Tong, L.; Chen, W.M.; Nongmaithem, S.S.; Noordam, R.; Lim, V.J.Y.; Tam, C.H.T.; Joo, Y.Y.; Chen, C.H.; Raffield, L.M.; Prins, B.P.; Nicolas, A.; Yanek, L.R.; Chen, G.; Brody, J.A.; Kabagambe, E.; An, P.; Xiang, A.H.; Choi, H.S.; Cade, B.E.; Tan, J.; Broadaway, K.A.; Williamson, A.; Kamali, Z.; Cui, J.; Thangam, M.; Adair, L.S.; Adeyemo, A.; Aguilar-Salinas, C.A.; Ahluwalia, T.S.; Anand, S.S.; Bertoni, A.; Bork-Jensen, J.; Brandslund, I.; Buchanan, T.A.; Burant, C.F.; Butterworth, A.S.; 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.; Danesh, J.; 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.; Franco, O.H.; Frayling, T.M.; Freedman, B.I.; Genter, P.; Gerstein, H.C.; Giedraitis, V.; González-Villalpando, C.; González-Villalpando, M.E.; Gordon-Larsen, P.; Gross, M.; Guare, L.A.; Hackinger, S.; Hakaste, L.; Han, S.; Hattersley, A.T.; Herder, C.; Horikoshi, M.; Howard, A.; 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.; Jasmine, F.; Jiang, G.; Jonas, J.B.; Jørgensen, T.; Kamanu, F.K.; Kandeel, F.R.; Kasturiratne, A.; Katsuya, T.; Kaur, V.; Kawaguchi,T.; Keaton, J.M.; Kho, A.N.; Khor, C.; Kibriya, M.G.; Kim, D.; Kronenberg, F.; Kuusisto , J.; Läll, K.; Lange, L.A.; Lee, K.M.; 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.; Lynch, J.A.; Lyssenko, V.; Maeda, S.; Mamakou, V.; Mansuri, S.R.; Matsuda, K.; Meitinger, T.; Melander, O.; Metspalu, A.; Mo, H.; Morris, A.D.; Moura, F.A.; Nadler, J.L.; Nalls, M.A.; Nayak, U.; Ntalla, I.; Okada, Y.; Orozco, L.; Patel, S.R.; Patil, S.; Pei, P.; Pereira, M.A.; Peters, A.; Pirie, F.J.; Polikowsky, H.G.; Porneala, B.; Prasad, G.; Rasmussen-Torvik, L.J.; Reiner, A.P.; Roden, M.; Rohde, R.; Roll, K.; Sabanayagam, C.; Sandow, K.; Sankareswaran , A.; Sattar,N.; Schönherr, S.; Shahriar, M.; Shen , B.; Shi, J.; Shin, D.M.; Shojima, N.; Smith, J.A.; So, W.Y.; Stančáková, A.; Steinthorsdottir, V.; Stilp, A.M.; Strauch, K.; Taylor, K.D.; Thorand, B.; Thorsteinsdottir, U.; Tomlinson, B.; Tran, T.C.; Tsai, F.; Tuomilehto, J.; Tusie-Luna, T.; Udler , M.S.; Valladares-Salgado, A.; Dam, R.M.V.; Klinken, J.B.V.; Varma, R.; Wacher-Rodarte, N.; Wheeler,E.; Wickremasinghe, A.R.; Dijk, K.W.V.; Witte, D.R.; Yajnik, C.S.; Yamamoto, K.; Yamamoto, K.; Yoon, K.; Yu, C.; Yuan, J.; Yusuf, S.; Zawistowski, M.; Zhang, L.; Zheng, W.; Raffel, L.J.; Igase, M.; Ipp, E.; Redline, S.; Cho, Y.S.; Lind, L.; Province, M.A.; Fornage, .M.; Hanis, C.L.; Ingelsson, E.; Zonderman, A.B.; Psaty, B.M.; Wang, Y.; Rotimi, C.N.; Becker,D.M.; Matsuda,F.; Liu, Y.; Yokota,M.; Kardia, S.L.R.; Peyser, P.A.; Pankow, J.S.; Engert, J.C.; Bonnefond, A.; Froguel, P.; Wilson, J.G.; Sheu, W.H.H.; Wu, J.; Hayes, M.G.; Ma, R.C.W.; Wong, T.; Mook-Kanamori, D.O.; Tuomi, T.; Chandak, G.R.; Collins, F.S.; Bharadwaj, D.; Paré, G.; Sale, M.M.; Ahsan, H.; Motala, A.A.; Shu , X.; Park, K.; Jukema, J.W.; Cruz, M.; Chen, Y.I.; Rich, S.S.; McKean-Cowdin, R.; Grallert, H.; Cheng, C.; Ghanbari,M.; Tai , E.; Dupuis, J.; Kato, N.; Laakso, M.; Köttgen, A.; Koh, W.; Bowden, D.W.; Palmer, C.N.A.; Kooner, J.S.; Kooperberg, C.; Liu, S.; North, K.E.; Saleheen, D.; Hansen, T.; Pedersen, O.; Wareham, N.J.; Lee, J.; Kim, B.; Millwood , I.Y.; Walters, R.G.; Stefansson, K.; Ahlqvist, E.; Goodarzi, M.O.; Mohlke, K.L.; Langenberg, C.; Haiman, C.A.; Loos, R.J.F.; Florez, J.C.; Rader, D.J.; Ritchie, M.D.; Zöllner, S.; Mägi, R.; Marston, N.A.; Ruff, C.T.; Heel , D.A.V.; Finer, S.; Denny, J.C.; Yamauchi, T.; Kadowaki, T.; Chambers, J.C.; Ng, M.C.Y.; Sim, X.; Below, J.E.; Tsao, P.S.; Chang, K.; McCarthy, M.I.; Meigs, J.B.; Mahajan, A.; Spracklen, C.N.; Mercader, J.M.; Boehnke, M.; Rotter, J.I.; Vujkovic, M.; Voight, B.F.; Morris, A.P.; Zeggini, E.
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
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.
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.
Prevalence and correlates of cardiometabolic multimorbidity among hypertensive individuals: a cross-sectional study in rural South Asia-Bangladesh, Pakistan and Sri Lanka.
(BMJ Publishing Group Ltd, 2019) Feng, L.; Jehan, I.; de Silva, H.A.; Naheed, A.; Farazdaq, H.; Hirani, S.; Kasturiratne, A.; Ranasinha, C.D.; Islam, M.T.; Siddiquee, A.T.; Jafar, T.H.
OBJECTIVE: To determinate the prevalence and correlates of cardiometabolic multimorbidity (CMM), and their cross-country variation among individuals with hypertension residing in rural communities in South Asia. DESIGN: A cross-sectional study. SETTING: Rural communities in Bangladesh, Pakistan and Sri Lanka. PARTICIPANTS: A total of 2288 individuals with hypertension aged ≥40 years from the ongoing Control of Blood Pressure and Risk Attenuation- Bangladesh, Pakistan and Sri Lanka clinical trial. MAIN OUTCOME MEASURES: CMM was defined as the presence of ≥2 of the conditions: diabetes, chronic kidney disease, heart disease and stroke. Logistic regression was done to evaluate the correlates of CMM. RESULTS: About 25.4% (95% CI 23.6% to 27.2%) of the hypertensive individuals had CMM. Factors positively associated with CMM included residing in Bangladesh (OR 3.42, 95% CI 2.52 to 4.65) or Sri Lankan (3.73, 95% CI 2.48 to 5.61) versus in Pakistan, advancing age (2.33, 95% CI 1.59 to 3.40 for 70 years and over vs 40-49 years), higher waist circumference (2.15, 95% CI 1.42 to 3.25) for Q2-Q3 and 2.14, 95% CI 1.50 to 3.06 for Q3 and above), statin use (2.43, 95% CI 1.84 to 3.22), and higher levels of triglyceride (1.01, 95% CI 1.01 to 1.02 per 5 mg/dL increase). A lower odds of CMM was associated with being physically active (0.75, 95% CI 0.57 to 0.97). A weak inverted J-shaped association between International Wealth Index and CMM was found (p for non-linear=0.058), suggesting higher risk in the middle than higher or lower socioeconomic strata. CONCLUSIONS: CMM is highly prevalent in rural South Asians affecting one in four individuals with hypertension. There is an urgent need for strategies to concomitantly manage hypertension, cardiometabolic comorbid conditions and associated determinants in South Asia.
Regional Variation in Comorbid Prediabetes and Diabetes and Associated Factors among Hypertensive Individuals in Rural Bangladesh, Pakistan, and Sri Lanka
(Hindawi Publishing Corporation, 2019) Feng, L.; Naheed, A.; de Silva, H.A.; Jehan, I.; Raqib, R.; Islam, M.T.; Luke, N.; Kasturiratne, A.; Farazdaq, H.; Senan, S.; Jafar, T.H.; Cobra-Bps Study Group
We aimed to explore the cross-country variation in the prevalence of comorbid prediabetes or diabetes and determine the sociodemographic, lifestyle, and clinical factors, especially body mass index (BMI) and waist circumference, associated with comorbid diabetes in individuals with hypertension in rural South Asia. We analyzed cross-sectional data of 2426 hypertensive individuals of ≥40 years from 30 randomly selected rural communities in Bangladesh, Pakistan, and Sri Lanka. Prediabetes was defined as fasting plasma glucose (FPG) between 100 and 125 mg/dL without use of antidiabetic treatment and diabetes as FPG ≥126 mg/dL or use of antidiabetic medication. The prevalence (95% CI) of prediabetes or diabetes (53.5% (51.5%, 55.5%)) and diabetes (27.7% (25.9%, 29.5%)) was high in the overall hypertensive study population in rural communities in 3 countries. Rural communities in Sri Lanka had the highest crude prevalence of prediabetes or diabetes and diabetes (73.1% and 39.3%) with hypertension, followed by those in Bangladesh (47.4% and 23.1%) and Pakistan (39.2% and 20.5%). The factors independently associated with comorbid diabetes and hypertension were residing in rural communities in Sri Lanka, higher education, international wealth index, waist circumference, pulse pressure, triglyceride, and lower high-density lipoprotein. The association of diabetes with waist circumference was stronger than with BMI in hypertensive individuals. Prediabetes or diabetes are alarmingly common among adults with hypertension and vary among countries in rural South Asia. The high prevalence of comorbid diabetes in Sri Lanka among hypertensives is not fully explained by conventional risk factors and needs further etiological research. Urgent public health efforts are needed to integrate diabetes control within hypertension management programs in rural South Asia, including screening waist circumference.