<p>Article Title Adiposity has a greater impact on hypertension in lean than not-lean populations: a systematic review and meta-analysis </p><p>Journal name European Journal of Epidemiology</p><p>Authors Simin Arabshahi*, Doreen Busingye, Asvini K. Subasinghe, Roger G. Evans, </p><p>Michaela A. Riddell, Amanda G. Thrift</p><p>*Corresponding author. Epidemiology and Prevention Unit, Stroke and Ageing Research </p><p>Department of Medicine, Southern Clinical School, Monash University, Australia. Tel: +61 </p><p>395-947-524; Fax: +61 399-029-791; Email address: [email protected] </p><p>Supplementary Fig. 1 a</p><p>Survey Country year Sex ES (95% CI) %Weight</p><p>Lean Raipur Rani, India 1994-95 M 1.14 (1.02, 1.26) 28.20 Rishi Valley, India 2006 M 1.20 (1.13, 1.29) 71.80 Subtotal (I-squared = 0.0%, p = 0.420) 1.18 (1.12, 1.25) 100.00</p><p>Not-lean South Western China 1989 M 1.21 (1.11, 1.30) 22.59 Rajasthan, India 1994 M 1.11 (1.07, 1.15) 36.51 Moradabad, India <1997 M 1.19 (1.17, 1.22) 40.91 Subtotal (I-squared = 82.4%, p = 0.003) 1.16 (1.10, 1.23) 100.00</p><p>NOTE: Weights are from random effects analysis</p><p>HTN more frequent in lower.8 BMI  1|  HTN more1.4 frequent in higher BMI b</p><p>Survey Country year Sex ES (95% CI) %Weight</p><p>Lean Raipur Rani, India 1994-95 F 1.23 (1.17, 1.29) 20.56 Moradabad, India <1997 F 1.19 (1.16, 1.22) 70.08 Rishi Valley, India 2006 F 1.16 (1.08, 1.25) 9.36 Subtotal (I-squared = 4.1%, p = 0.353) 1.20 (1.17, 1.22) 100.00</p><p>Not-lean South Western China 1989 F 1.09 (0.97, 1.23) 19.32 Rajasthan, India 1994 F 1.01 (0.98, 1.02) 80.68 Subtotal (I-squared = 35.0%, p = 0.215) 1.02 (0.97, 1.09) 100.00</p><p>NOTE: Weights are from random effects analysis</p><p>HTN more frequent in lower.8 BMI  1|  HTN more1.4 frequent in higher BMI c</p><p>Survey BMI Country year Category Sex ES (95% CI) %Weight</p><p>Lean Evodoula, Cameroon 1994&2003 25-30 M 1.30 (0.60, 2.70) 12.55 Evodoula, Cameroon 1994&2003 30+ M 9.40 (1.60, 54.00) 3.81 Assam, India <2004 25-30 M 1.84 (1.08, 3.11) 16.94 Assam, India <2004 30+ M 4.04 (0.64, 25.39) 3.53 Tamil Nadu, India 2005-07 23-27.5 M 2.95 (2.48, 3.50) 24.40 Tamil Nadu, India 2005-07 27.5+ M 5.14 (3.89, 6.80) 22.48 South western, Uganda 2009 25+ M 3.15 (1.80, 5.50) 16.29 Subtotal (I-squared = 74.1%, p = 0.001) 2.97 (2.05, 4.31) 100.00</p><p>Not-lean Nationwide, Mexico 2002 25-30 M 1.85 (1.54, 2.23) 16.91 Nationwide, Mexico 2002 30+ M 3.55 (2.51, 5.02) 13.10 Fuxin, Liaoning, China 2004-06 24-28 M 2.01 (1.79, 2.26) 18.18 Fuxin, Liaoning, China 2004-06 28+ M 4.22 (3.35, 5.32) 15.88 Zhangqiu, China 2007 25-30 M 2.27 (2.05, 2.51) 18.40 Zhangqiu, China 2007 30+ M 3.17 (2.63, 3.58) 17.53 Subtotal (I-squared = 91.4%, p = 0.000) 2.66 (2.13, 3.33) 100.00 . NOTE: Weights are from random effects analysis</p><p>.8 1 10 HTN more frequent in lower BMI  |  HTN more frequent in higher BMI d</p><p>Survey BMI Country year Category Sex ES (95% CI) %Weight Lean Evodoula, Cameroon 1994&2003 25-30 F 1.30 (0.70, 2.30) 10.76 Evodoula, Cameroon 1994&2003 30+ F 3.40 (1.30, 8.70) 6.00 Assam, India <2004 25-30 F 2.06 (1.28, 3.32) 13.18 Assam, India <2004 30+ F 2.66 (0.84, 8.49) 4.44 Tamil Nadu, India 2005-07 23-27.5 F 2.32 (1.94, 2.78) 20.13 Tamil Nadu, India 2005-07 27.5+ F 3.25 (2.57, 4.11) 18.96 West Bengal, India 2007-11 25-30 F 3.94 (2.65, 5.86) 15.04 West Bengal, India 2007-11 30+ F 5.78 (3.31, 10.09) 11.48 Subtotal (I-squared = 69.6%, p = 0.002) 2.83 (2.16, 3.72) 100.00 Not-lean Nationwide, Mexico 2002 25-30 F 1.81 (1.36, 2.41) 13.18 Nationwide, Mexico 2002 30+ F 3.44 (2.60, 4.55) 13.30 Fuxin, Liaoning, China 2004-06 24-28 F 1.88 (1.67, 2.11) 15.84 Fuxin, Liaoning, China 2004-06 28+ F 4.24 (3.52, 5.12) 14.90 Zhangqiu, China 2007 25-30 F 2.04 (1.86, 2.25) 16.06 Zhangqiu, China 2007 30+ F 2.81 (2.41, 3.28) 15.38 South western, Uganda2009 25+ F 2.25 (1.53, 3.30) 11.35 Subtotal (I-squared = 92.1%, p = 0.000) 2.52 (2.00, 3.17) 100.00 . NOTE: Weights are from random effects analysis</p><p>.8 1 10 HTN more frequent in lower BMI  |  HTN more frequent in higher BMI</p><p>Supplementary Fig. 1 Forest plots of the association between body mass index (BMI) and hypertension in populations in two categories of BMI in rural areas of low-to-middle income countries; BMI as (a) continuous variable in males, (b) continuous variable in females, (c) categorical variable in males, and (d) categorical variable in females</p><p>The center of each square indicates the effect size of that study and the horizontal lines indicate </p><p>95% CIs; the area of the square is proportional to the weight of the study; diamonds indicate pooled estimates in each category of BMI </p><p>Abbreviations: M males, F females, ES effect size, CI confidence interval, HTN hypertension</p><p>Definitions: (a) lean: mean BMI ≤ 19.9 kg/m2, not-lean: mean BMI ≥ 20.6 kg/m2; (b) lean: mean </p><p>BMI ≤ 20.2 kg/m2, not-lean: mean BMI ≥ 21.0 kg/m2; (c) lean: mean BMI ≤ 21.4 kg/m2 or BMI ≥</p><p>25 kg/m2 <17%, not-lean: mean BMI ≥ 23.0 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%; and (d) lean: mean</p><p>BMI ≤ 22.6 kg/m2 or BMI ≥ 25 kg/m2 <17%, not-lean: mean BMI ≥ 23.4 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17% Supplementary Fig. 2 a</p><p>Survey BMI Country year Category Sex ES (95% CI) %Weight Lean Evodoula, Cameroon 1994&2003 30+ M 9.40 (1.60, 54.00) 1.55 Fuxin, Liaoning, China 2004-06 28+ M 4.22 (3.35, 5.32) 24.22 Tamil Nadu, India 2005-07 27.5+ M 5.14 (3.89, 6.80) 21.68 Evodoula, Cameroon 1994&2003 30+ F 3.40 (1.30, 8.70) 4.77 Tamil Nadu, India 2005-07 27.5+ F 3.25 (2.57, 4.11) 24.03 Punial Valley, Pakistan 1995 >30 M&F 2.11 (1.30, 3.40) 13.08 Farafenni, Gambia 1996-97 30+ M&F 3.50 (1.20, 10.70) 3.74 Assam, India <2004 30+ M&F 3.10 (1.17, 8.22) 4.57 Amasaman, Ghana <2006 30+ M&F 6.86 (1.67, 28.20) 2.35 Subtotal (I-squared = 45.4%, p = 0.066) 3.75 (2.99, 4.69) 100.00 Not-lean Nationwide, Mexico 2002 30+ M 3.55 (2.51, 5.02) 11.15 Nationwide, Mexico 2002 30+ F 3.44 (2.60, 4.55) 12.63 Fuxin, Liaoning, China 2004-06 28+ F 4.24 (3.52, 5.12) 14.65 West Bengal, India 2007-11 >30 F 5.78 (3.31, 10.09) 7.26 Yarumal, Colombia 1994 30+ M&F 3.36 (1.48, 7.60) 4.38 Gonbad, Golestan, Iran 2004-08 30+ M&F 3.05 (2.88, 3.24) 16.59 Zhangqiu, China 2007 30+ M&F 2.91 (2.56, 3.38) 15.56 Yunnan, China 2008-10 28+ M&F 2.03 (1.84, 2.24) 16.17 NakhonRatchasima, Thailand 2010 30+ M&F 7.43 (1.68, 32.87) 1.62 Subtotal (I-squared = 89.9%, p = 0.000) 3.28 (2.69, 4.00) 100.00 . NOTE: Weights are from random effects analysis</p><p>.8 1 10 HTN more frequent in lower BMI  |  HTN more frequent in higher BMI b</p><p>Survey BMI Country year Category Sex ES (95% CI) %Weight</p><p>Lean Assam, India <2004 30+ M 4.04 (0.64, 25.39) 2.20 Evodoula, Cameroon 1994&2003 30+ M 9.40 (1.60, 54.00) 2.40 Tamil Nadu, India 2005-07 27.5+ M 5.14 (3.89, 6.80) 95.40 Subtotal (I-squared = 0.0%, p = 0.774) 5.19 (3.95, 6.81) 100.00</p><p>Not-lean Fuxin, Liaoning, China 2004-06 28+ M 4.22 (3.35, 5.32) 33.39 Nationwide, Mexico 2002 30+ M 3.55 (2.51, 5.02) 20.75 Zhangqiu, China 2007 30+ M 3.17 (2.63, 3.58) 45.86 Subtotal (I-squared = 51.1%, p = 0.130) 3.57 (2.95, 4.32) 100.00</p><p>NOTE: Weights are from random effects analysis</p><p>HTN more frequent in lower.1 BMI  1|  HTN more10 frequent in higher BMI c</p><p>Survey BMI Country year Category Sex ES (95% CI) %Weight</p><p>Lean Assam, India <2004 30+ F 2.66 (0.84, 8.49) 6.15 Evodoula, Cameroon 1994&2003 30+ F 3.40 (1.30, 8.70) 8.85 Tamil Nadu, India 2005-07 27.5+ F 3.25 (2.57, 4.11) 62.85 West Bengal, India 2007-11 >30 F 5.78 (3.31, 10.09) 22.15 Subtotal (I-squared = 19.2%, p = 0.294) 3.66 (2.72, 4.92) 100.00</p><p>Not-lean Fuxin, Liaoning, China 2004-06 28+ F 4.24 (3.52, 5.12) 34.62 Nationwide, Mexico 2002 30+ F 3.44 (2.60, 4.55) 28.84 Zhangqiu, China 2007 30+ F 2.81 (2.41, 3.28) 36.54 Subtotal (I-squared = 82.0%, p = 0.004) 3.43 (2.61, 4.51) 100.00</p><p>NOTE: Weights are from random effects analysis</p><p>.1 1 10 HTN more frequent in lower BMI  |  HTN more frequent in higher BMI</p><p>Supplementary Fig. 2 Forest plots of the association between obesity (measured by BMI) and hypertension in populations in two categories of BMI in rural areas of low-to-middle income countries; (a) total population, (b) males, and (c) females</p><p>The center of each square indicates the effect size of that study and the horizontal lines indicate </p><p>95% CIs; the area of the square is proportional to the weight of the study; diamonds indicate pooled effect size in each category of BMI</p><p>Abbreviations: M males, F females, ES effect size, CI confidence interval, HTN hypertension</p><p>Definitions: (a) lean: mean BMI < 23.3 kg/m2 or BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ </p><p>23.3 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%; (b) lean: mean BMI ≤ 22.1 kg/m2 or BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ 23.0 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%; (c) lean: mean BMI ≤ 22.6 kg/m2 or BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ 23.4 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17% Supplementary Tables</p><p>Supplementary Table 1 Summary of studies of multivariable association between </p><p> mean body mass index and hypertension in rural areas of low-to-middle income countries </p><p>(RLMICs), according to rank order of population and BMI and type of analysis</p><p>BMI (kg/m2)a Location T M F BMI in the analysis: Continuous Lean South West, China [17] 20.8b 20.6 21.0 Rajasthan, India [19] 21.1b Raipur Rani, Haryana, India [20] Hypertensive – 20.5 22 Normotensive – 18.6 18.9 Total 18.9b 18.7b 19.1b Moradabad, India [25] Hypertensive – 23.0 22.7 Normotensive – 20.3 20.0 Total 20.4b 20.6b 20.2b Rishi Valley, India [40] 19.5 19.9c 19.2c Not- Lean Rajasthan, India [19] 21.4b JingNing County, China [27], She 22.2 – – JingNing County, China [27], Han 22.5 – – Lucknow, Uttar Pradesh, India [31] Hypertensive 24.5 – – Normotensive 20.4 – – Total 22.9b – – Fuxin, Liaoning, China [35] 23.0 – – Sub-Saharan Africa [41] 21.3 – – Hadinaru, Karanataka, India [44] 21.4 – – BMI in the analysis: Categorical Lean Farafenni, Gambia [24] 20.2b 19.8 20.5 Nationwide Vietnam [32] 19.5 – – Abhoynagar, Bangladesh [37] 21.0 – – Matlab, Bangladesh [37] 20.9 – – Mirsarai, Bangladesh [37] 20.7 – – WATCH Bangladesh [37] 20.1 – – Vadu, India [37] 21.2 – – BMI (kg/m2)a Location T M F Purworejo, Indonesia [37] 21.9 – – Chililab, Vietnam [37] 20.9 – – Filabavi, Vietnam [37] 19.5 – – Tamil Nadu, India [38] 21.7b 21.4 21.9 Sub-Saharan Africa [41] 21.3 – – Maharashtra, India [43] 19.7 – – Chirai Goan, Varanasi, India [23] – 7.8%c 8.0%c Assam, India [33] 6.9% – – South Western Uganda [48] – 4.1% West Bengal, India [45] – – 16.8% Punial Valley, Ghizar, Pakistan [22] 14.8% – – Not-Lean Evodoula, Cameroon [21] 1994 – 21.9 22.2 2003 – 22.5 23.4 Total 22.4b 22.1b 22.6b San Antonio, Nueva Ecija, Philippines [26] 23.1d – – Nationwide, Mexico [28] 26.8d 26.0d 27.3d Chachoengsao, Thailand [30] 25.0 – – Fuxin, Liaoning, China [34] 23.2 23.0 23.4 Gonbad, Golestan, Iran [36] 26.3d – – Kanchanaburi, Thailand [37] 23.8 – – Amasaman, Ga, Ghana [39] 23.2 – – Nandan County, China [46], Bai Ku Yao 22.2 – – Nandan County, China [46], Han Chinese 22.7 – – Hubei, China [49] 22.1 – – Zhejiang, China [51] 23.1 – – Yunnan, China [47] 23.3b 23.1 23.4 Yarumal, Colombia [18] 55% – – Inner Mongolia, China [29] 20.2%b 15.4% 23.6% Zhangqiu, Shandong, China [42] 59% – – South Western Uganda [48] – 18.4% NakhonRatchasima, Thailand [50] 44.0% – –</p><p>Abbreviations: T total, M males, F females.</p><p>Definitions: BMI as (1) continuous variable: lean: mean BMI < 21.4 kg/m2; not-lean:</p><p> mean BMI ≥ 21.4 kg/m2. (2) as categorical variable: lean: mean BMI < 22.0 kg/m2 or </p><p>BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ 22.0 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%. a Values are mean or percentage of overweight (BMI: ≥ 25 kg/m2). b Calculated based on weighting the number of study population in each group. c Percentage of overweight (BMI: > </p><p>25 kg/m2). d Reported by the author through personal contact. Supplementary Table 2 Studies of body mass index and hypertension in rural areas of low-to middle income countries (RLMICs), across categories of mean BMI and type of analysis</p><p>Survey BMI OR (95% CI) 2 Location year (kg/m ) Total Males Females Adjustments BMI in the analysis: Continuous Lean South Western, China [17] 1989 Cont. – 1.21 (1.11,1.30) 1.09 (0.97,1.23) Age, smoking status, alcohol consumption, physical activity, and heart rate Rajasthan, India [19] 1994 Cont. – 1.01 (0.98,1.02)a Age, education, physical activity, and smoking status Raipur Rani, India [20] 1994–95 Cont. – 1.14 (1.02,1.26) 1.23 (1.17,1.29) Age, education, SES, physical activity, smoking status, and alcohol consumption Moradabad, India [25] < 1997 Cont. – 1.19 (1.17,1.22) 1.19 (1.16,1.22) Age, WHR, SES, energy expenditure, 2 hour plasma insulin, 2 hour blood glucose, and salt intake Rishi Valley, South India [40] 2006 Cont. – 1.20 (1.13,1.29) 1.16 (1.08,1.25) M: Age, alcohol consumption, physical activity, education, and cholesterol F: Age, smokeless tobacco, education, and triglyceride Not-lean Rajasthan, India [19] 1994 Cont. – 1.11 (1.07,1.15)a Age, education, physical activity, and smoking status JingNing County, China [27], She 2002 Cont. 1.22 (1.16,1.28) – – Age, sex, and alcohol consumption JingNing County, China [27], Han 2002 Cont. 1.10 (1.03,1.18) – – Age, sex, and alcohol consumption Survey BMI OR (95% CI) 2 Location year (kg/m ) Total Males Females Adjustments Lucknow, Uttar Pradesh, India [31] 2003–04 Cont. 1.04 (0.88,1.23) – – Age, sex, WC, physical activity, smoking status, and diabetes Fuxin, Liaoning, China [35] 2004–06 Cont. 1.07 (1.06,1.08) – – Age, sex, smoking status, alcohol consumption, salt intake, and race Sub-Saharan Africa [41] 2006–07 Cont. 1.03 (1.00,1.07) – – Age, sex, village, sampling variables, television ownership, and work-related vigorous physical activity Hadinaru, Karanataka, India [44] 2007–08 Cont. 1.27 (1.19,1.35) – – Age, occupation, physical activity, fat intake, and diabetes BMI in the analysis: Categorical Lean Punial Valley, Ghizar, Pakistan [22] 1995 < 25 1 – – Age, smoking status, use of snuff, 25–30 2.01 (1.51,2.67) – – family history of hypertension, use of salted tea, and wine > 30 2.11 (1.30,3.40) – – consumption Chirai Goan, Varanasi, India [23] < 1996 > 25 1.71 – – Salt intake, fat intake, and energy (1.13,31.86) intake Farafenni, Gambia [24] 1996–97 ≥ 30 3.5 (1.2,10.7) – – Age and sex Nationwide Vietnam [32] 2003–04 < 18.5 1 – – Age, sex, education, and occupation 18.5–25 1.33 (0.92, 1.89) ≥ 25 2.67 (1.75, 4.08) Assam, India [33] < 2004 18.5–25 1 1 1 Age, sex, WHR, type of work, < 18.5 0.65 (0.53,0.78)b 0.54 (0.39,0.74)b 0.71 (0.55,0.91)b smoking status, tobacco chewing, alcohol consumption, extra salt 25–30 1.95 (1.37,2.78)b 1.84 (1.08,3.11)b 2.06 (1.28,3.32)b intake, and marital status ≥ 30 3.10 (1.17,8.22)b 4.04 (0.64,25.39)b 2.66 (0.84,8.49)b Abhoynagar, Bangladesh [37] 2005 ≥ 25 2.67 (1.89,3.77) – – Age, sex, and education Survey BMI OR (95% CI) 2 Location year (kg/m ) Total Males Females Adjustments Matlab, Bangladesh [37] 2005 ≥ 25 3.21 (2.28,4.53) – – Age, sex, and education Mirsarai, Bangladesh [37] 2005 ≥ 25 2.11 (1.53,2.92) – – Age, sex, and education WATCH, Bangladesh [37] 2005 ≥ 25 3.44 (2.12,5.58) – – Age, sex, and education Vadu, India [37] 2005 ≥ 25 3.29 (2.49,4.36) – – Age, sex, and education Purworejo, Indonesia [37] 2005 ≥ 25 2.51 (1.86,3.37) – – Age, sex, and education Chililab, Vietnam [37] 2005 ≥ 25 2.03 (1.39,2.96) – – Age, sex, and education Filabavi, Vietnam [37] 2005 ≥ 25 4.94 (2.57,9.5) – – Age, sex, and education Tamil Nadu, India [38] 2005–07 < 23 – 1 1 Age 23–27.50 – 2.95 (2.48,3.50) 2.32 (1.94,2.78) ≥ 27.50 – 5.14 (3.89,6.80) 3.25 (2.57,4.11) Sub-Saharan Africa [41] 2006–07 < 18 1 – – Age, sex, village, sampling 18–25 1.17 (0.75,1.80) – – variables, television ownership, and work-related vigorous > 25 1.64 (0.95,2.85) – – physical activity Maharashtra, India [43] 2007–08 ≥ 25 2.59 (1.87,3.58) – – Age, sex, education, smoking status, alcohol consumption, hypercholesterolemia, and diabetes West Bengal, India [45] 2007–11 <18.5 – – 1.56 (1.14,2.14) Age, education, income, kitchen 18.5–24.9 – – 1 location, fuel type, and food habit 25–30 – – 3.94 (2.65,5.86) > 30 – – 5.78 (3.31,10.09) South western Uganda [48] 2009 < 18.5 – 1 M: Age, education, and glucose ≥ 18.5–25 – 2.27 (1.69,3.04) 7 mmol/L; ≥ 25 – 3.15 (1.80,5.50) Not-lean Yarumal, Colombia [18] 1994 < 25 1 – – Age, sex, smoking status, Survey BMI OR (95% CI) 2 Location year (kg/m ) Total Males Females Adjustments 25–30 2.41 (1.32,4.40) – – diabetes, stress, family history of ≥ 30 3.36 (1.48,7.60) – – hypertension, and family history of myocardial infarction Evodoula, Cameroon [21] 1994 & 18.5–25 – 1 1 Age, education, smoking status, 2003 < 18.5 – 0.9 (0.4,2.3) 0.4 (0.1,0.9) alcohol consumption, and year of study 25–30 – 1.3 (0.6,2.7) 1.3 (0.7,2.3) ≥ 30 – 9.4 (1.6,54.0) 3.4 (1.3,8.7) San Antonio, Nueva Ecija, 1998 ≥ 25 2.38 (1.34,4.22) – – Age and family history of Philippines [26] hypertension Nationwide, Mexico [28] 2002 < 25 – 1 1 Age, education, and early life 25–30 – 1.85 (1.54,2.23) 1.81 (1.36,2.41) experiences ≥ 30 – 3.55 (2.51,5.02) 3.44 (2.60,4.55) Inner Mongolia, China [29] 2002–03 ≥ 25 2.37(1.82, 3.08) – – Age, sex, WC, alcohol consumption, hyperlipidemia, and hyperglycemia Chachoengsao, Thailand [30] 2003 > 23 3.41 (1.80,6.45) – – Age, family history of hypertension, and marital status Fuxin, Liaoning, China [34] 2004–06 < 24 – 1 1 Age, education, smoking status, 24–28 – 2.01 (1.79,2.26) 1.88 (1.67,2.11) alcohol consumption, lipid disorder, salt intake, and ethnicity ≥ 28 – 4.22 (3.35,5.32) 4.24 (3.52,5.12) Gonbad, Golestan, Iran [36] 2004–08 <18.5 0.59 (0.53,0.66) Age, sex, education, wealth score, 18.5–25 1 smoking status, physical activity, black tea consumption, green tea 25–30 1.89 (1.79,1.99) consumption, and ethnicity ≥ 30 3.05 (2.88,3.24) Kanchanaburi, Thailand [37] 2005 ≥ 25 2.42 (1.95,3) – – Age, sex, and education Amasaman, Ga, Ghana [39] < 2006 18.5–25 1 – – Age, sex, education, occupation, 25–30 5.80 (1.39,24.3) – – job-related physical activity, Survey BMI OR (95% CI) 2 Location year (kg/m ) Total Males Females Adjustments ≥ 30 6.86 (1.67,28.2) – – smoking status, alcohol consumption, contraceptive use, and diabetes Zhangqiu, Shandong, China [42] 2007 < 25 1 1 1 Age, sex, education, physical 25–30 2.13 (1.99,2.28) 2.27 (2.05,2.51) 2.04 (1.86,2.25) activity, smoking status, and alcohol consumption ≥ 30 2.91 (2.56,3.38) 3.17 (2.63,3.58) 2.81 (2.41,3.28) Nandan County, China [46], Bai < 2008 > 24 2.01 (1.15,3.48) – – Age, WC, education, physical Ku Yao activity, alcohol consumption, Nandan County, China [46], Han < 2008 > 24 1.81 (1.07,2.72) – – triglycerides, total energy intake, Chinese total fat intake, sodium intake, total dietary fibre intake, and ethnic group Yunnan, China [47] 2008–10 ≥ 28 2.03 (1.84,2.24) – – Age, sex, education, household income, smoking status, alcohol consumption, family history of chronic diseases, and ethnicity South western Uganda [48] 2009 < 18.5 – 1 F: Age, WC, and glucose ≥ 7 18.5–25 – 1.74 (1.29,2.33) mmol/L ≥ 25 – 2.25 (1.53,3.30) Hubei, China [49] < 2010 ≥ 24 1.53 (1.18,1.97) – – Educational level, sedentary work, physical exercise, alcohol consumption, positive family history of hypertension, salty diet, vegetable and fruit intake, animal insides intake, and dysarteriotony (pressure difference > 60mmHg or <20mmHg between SBP and DBP) Zhejiang, China [51] 2010 < 23 1 – – Age, education, income, alcohol 23–25 1.67(1.48,1.89) – – consumption, diabetes, and Survey BMI OR (95% CI) 2 Location year (kg/m ) Total Males Females Adjustments ≥ 25 3.29(2.94,3.69) – – triglycerides NakhonRatchasima, Thailand [50] 2010 ≥ 30 7.43 – – Age, education, occupation, (1.68,32.87) presence of high cholesterol, having mild to high stress</p><p>Abbreviations: OR odds ratio, CI confidence interval, Cont. continuous, SBP systolic blood pressure, DBP diastolic blood pressure, BMI body </p><p> max index, WC waist circumference, WHR waist-to-hip ratio, SES socioeconomic status.</p><p>Definitions: BMI as (1) continuous variable: lean: mean BMI < 21.4 kg/m2; not-lean: mean BMI ≥ 21.4 kg/m2. (2) as categorical variable: lean: </p><p> mean BMI < 22.0 kg/m2 or BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ 22.0 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%.</p><p> a Relative risk (RR). b Participants who used antihypertensive medication were excluded from this analysis. Supplementary Table 3 Summary of studies of multivariable association between waist circumference and hypertension in rural areas of low-to-middle income countries (RLMICs), according to mean body mass index and type of analysis</p><p>BMI (kg/m2)a WC (cm)b Location T M F T M F WC in the analysis: Continuous Lean Nandan County, China [46], Bai Ku 22.2 – – 71.5 – – Yao Nandan County, China [46], Han 22.7 – – 74.4 – – Chinese Lucknow, Uttar Pradesh, India [31] Hypertensive 24.5 – – 85.9 – – Normotensive 20.4 – – 71.6 – – Not-lean Fuxin, Liaoning, China [53], Han 23.1 – – 80.4 – – Fuxin, Liaoning, China [53], Mongolian 23.6 – – 81.9 – – WC in the analysis: Categorical Lean Tamil Nadu, India [38] 21.6 21.4 21.9 78.7 71.5 South western Uganda [48] – 4.1% WC: F: ≥ 80 cm; M: ≥ 94 cm – 1% Thiruvananthapuram, Kerala, India [55], 16.8% – – LS WC: F: ≥ 80 cm; M: ≥ 85 cm 46.8% – – Not-lean Yunnan, China [47] 23.3c 23.1 23.4 – 81.2 79.5 Fuxin, Liaoning, China [54] – – 23.4 – – 80.0 Inner Mongolia, China [29] 20.2%c 15.4% 23.6% WC:F: ≥ 80 cm; M: ≥ 85 cm – 31.9% 47.8% Virgem das Graças, Minas Gerais, Brazil 31%c 12.9% 48.3% – – – [52] South western Uganda [48] 18.4% WC: F: ≥ 80 cm; M: ≥ 94 cm – 31.2%</p><p>Abbreviations: T total, M males, F females, LS longitudinal study.</p><p>Definitions: WC as (1) continuous variable: lean: mean BMI < 23.1 kg/m2; not-lean: mean </p><p>BMI ≥ 23.1 kg/m2. (2) as categorical variable: lean: mean BMI < 23.3 kg/m2 or BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ 23.3 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%.</p><p>16 a Values are mean or percentage of overweight (BMI: ≥ 25 kg/m2). b Values are mean or percentage of abdominal overweight. c Calculated based on weighting the number of study population in each group.</p><p>17 Supplementary Table 4 Studies of waist circumference and hypertension in rural areas of low-to-middle income countries (RLMICs), across categories of mean body mass index and type of analysis </p><p>Survey OR (95% CI) Location year WC (cm) Total Males Females Adjustments WC in the analysis: Continuous Lean Lucknow, Uttar Pradesh, India [31] 2003–04 Cont. 1.09 (1.02,1.18) – – Age, sex, BMI, physical activity, smoking status, and diabetes Nandan County, China [46], Bai < 2008 Cont. 1.73 (1.12,2.12) – – Age, BMI, education, physical Ku Yao Chinese activity, alcohol consumption, Nandan County, China [46], Han < 2008 Cont. 1.35 (1.20,2.46) triglycerides, total energy intake, Chinese total fat intake, sodium intake, total dietary fibre intake, and ethnic group Not- lean China [53], Mongolian Chinese 2004–06 Cont. 1.018 (1.012,1.023) – – Age, sex, BMI, education, smoking status, alcohol consumption, lipid China [53], Han Chinese 2004–06 Cont. 1.018 (1.015,1.020) – – disorder, diabetes, family history of hypertension, and salt intake WC in the analysis: Categorical Lean Tamil Nadu, India [38] 2005–07 M: ≥ 90 – 3.34 (2.80,3.98) 2.51 (2.12,2.98) Age F: ≥ 80 South western Uganda [48] 2009 M: ≥ 0.94 – 1.41 (0.47,4.19) M: Age, BMI, education, and glucose ≥ 7 mmol/L; Thiruvananthapuram, Kerala, India 2010 M: ≥ 85; 2.45 (1.45,3.70)a – – Age, sex, BMI, education, physical [55], LS F: ≥ 80 activity, smoking status, alcohol consumption, Total cholesterol to </p><p>18 Survey OR (95% CI) Location year WC (cm) Total Males Females Adjustments HDL ratio, LDL, fruit and vegetable intake, and BP Not-lean Inner Mongolia, China [29] 2002–03 M: ≥ 85; 1.34 (1.07, 1.68) – – Age, sex, BMI, alcohol F: ≥ 80 consumption, hyperlipidemia, and hyperglycemia Virgem das Graças, Brazil [52] 2004 M: < 94; 1 – – Age, sex, BMI, WHR, education, F: < 80 alcohol consumption, total M: 94– 2.63 (0.98,7.02) – cholesterol, HDL, LDL, 102; triglycerides, body fat percentage, F: 80–88 and skin colour M: ≥ 102; 3.84 (1.50,9.85) – F: ≥ 88 Fuxin, Liaoning, China [54] 2004–06 < 80 – – 1 Age, education, physical activity, b smoking status, alcohol 80–88 – – 1.31 (1.26,1.37) consumption, and diet ≥ 88 – – 1.78 (1.64,1.94)b Yunnan China [47] 2008–10 M: >90; 3.01 (2.59,3.48) – – Age, sex, education, household F: > 80 income, smoking status, alcohol consumption, family history of chronic diseases, and ethnicity South western Uganda [48] 2009 F: ≥ 80 – 1.37 (1.07,1.74) F: Age, BMI, and glucose ≥ 7 mmol/L </p><p>Abbreviations: OR odds ratio, CI confidence interval, Cont. continuous, M male, F female; Cont., continuous; Cont. continuous, SBP systolic blood pressure, DBP diastolic blood pressure, BMI body max index, WHR waist-to-hip ratio, SES socioeconomic status.</p><p>19 Definitions: WC as (1) continuous variable: lean: mean BMI < 23.1 kg/m2; not-lean: mean BMI ≥ 23.1 kg/m2. (2) as categorical variable: lean: mean BMI < 23.3 kg/m2 or BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ 23.3 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%. a Participants did not take antihypertensive medications the last two weeks before they were examined. b Prevalence ratio (PR).</p><p>20 Supplementary Table 5 Studies of prevalence of hypertension in rural areas of </p><p>Low-to-middle income countries (RLMICs), according to mean body mass index</p><p>(BMI) and type of analysis for the association between BMI and hypertension</p><p>Location Survey year No HTN % BMI in the analysis: Continuous Lean South Western, China [17], T 1989 8,241 2.6a Rajasthan, India [19], F 1994 1,166 17 Raipur Rani, Haryana, India [20], T 1994–95 2,559 4.5 Moradabad, India [25], M < 1997 140 11.2 Moradabad, India [25], F < 1997 115 8.6 Rishi Valley, India [40], T 2006 1,474 11.4a Pooled Total 13,695 5.3b Not- Lean Rajasthan, India [19], M 1994 1,982 24 JingNing County, China [27], She, T 2002 1,168 31.9 JingNing County, China [27], Han, T 2002 520 21.9 Lucknow, Uttar Pradesh, India [31], T 2003–04 400 14.5 Fuxin, Liaoning, China [35] , T 2004–06 45,925 37.8 Sub-Saharan Africa [41], T 2006–07 1,485 22 Hadinaru, Karanataka, India [44], T 2007–08 1,423 15.0 Pooled Totalb 52,903 35.8 P-valuec 0.0001 BMI in the analysis: Categorical Lean Punial Valley, Ghizar, Pakistan [22], T 1995 4,203 14.3a Nationwide Vietnam [32], T 2003–04 2,020 14.3a Assam, India [33], T < 2004 3,180 33.3 Abhoynagar, Bangladesh [37], T 2005 1,983 16.8 Mirsarai, Bangladesh [37], T 2005 2,028 24.1 WATCH Bangladesh [37], T 2005 1,997 9.3 Vadu, India [37], T 2005 2,074 23.6 Purworejo, Indonesia [37], T 2005 1,946 24.1 Filabavi, Vietnam [37], T 2005 2,000 15.1 Matlab, Bangladesh [37], T 2005 2,061 17.1 Chililab, Vietnam [37], T 2005 2,194 18.3 Tamil Nadu, India [38], T 2005–07 10,463 21.5 Sub-Saharan Africa [41], T 2006–07 1,485 22 Maharashtra, India [43], T 2007–08 3,591 25.3 West Bengal, India [45], F 2007–11 1,186 26.3 South western Uganda [48], M 2009 2,719 22.5 Pooled Totalb 45,130 20.8</p><p>21 Location Survey year No HTN % Not-Lean Evodoula, Cameroon [21], T 1994 & 2003 1,132 26.7 San Antonio, Nueva Ecija, Philippines [26], T 1998 336 23.0 Nationwide, Mexico [28], T 2002 6,035 37.7a Inner Mongolia, China [29], T 2002–03 2,532 37.2 Chachoengsao, Thailand [30], T 2003 527 17.8 Fuxin, Liaoning, China [34], T 2004–06 45,925 37.8 Gonbad, Golestan, Iran [36], T 2004–08 39,399 42.9 Kanchanaburi, Thailand [37], T 2005 2,146 27.7 Amasaman, Ga, Ghana [39], T < 2006 362 25.4 Zhangqiu, Shandong, China [42], T 2007 20,167 30.6 Nandan County, China [46], Bai Ku Yao, < 2008 485 21.9 T Nandan County, China [46], Han Chinese, < 2008 501 28.9 T Yunnan, China [47], T 2008–10 10,007 32.8 South western Uganda [48], F 2009 3,959 22.6 Hubei, China [49], T < 2010 2,438 26.9 Zhejiang, China [51], T 2010 10,525 27.0 Pooled Totalb 146,476 36.0 P-valuec 0.0001</p><p>Abbreviations: HTN hypertension, M male, F female, T total.</p><p>Definitions: Hypertension: SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg or using antihypertensive medication. BMI as (1) continuous variable: lean: mean BMI < 21.4 kg/m2; not-lean: mean BMI ≥ 21.4 kg/m2; (2) as categorical variable: lean: mean BMI < 22.0 kg/m2 or BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ 22.0 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%. a Calculated based on weighting the numbers of participants in the study. b Calculated based on weighting the numbers in each study. c P-value from chi-square test for comparison of the prevalence of hypertension with the first group of BMI category.</p><p>22 Supplementary Table 6 Studies of prevalence of hypertension in rural areas of low-to-middle income countries (RLMICs), according to mean body mass index and type of analysis for the association between waist circumference and hypertension </p><p>Location Survey year No HTN % WC in the analysis: Continuous Lean Lucknow, Uttar Pradesh, India [31], T 2003–04 400 14.5 Nandan County, China [46], Bai Ku Yao, <2008 485 21.9 T Nandan County, China [46], Han Chinese, <2008 501 28.9 T Pooled Totala 1,386 18.1 Not- Lean Fuxin, Liaoning, China [53], Mongolian, T 2004–06 9,236 42.0 Fuxin, Liaoning, China [53], Han, T 2004–06 36,154 36.7 Pooled Totala 45,390 37.8 P-valueb 0.0001 WC in the analysis: Categorical Lean Tamil Nadu, India [38], T 2005–07 10,463 21.5 South western Uganda [48], M 2009 2,719 22.5 Pooled Totala 13,182 21.7 Not-Lean Inner Mongolia, China [29], T 2002–03 2,532 37.2 Virgem das Graças, Minas Gerais, Brazil [52], 2004 287 47.0 T Fuxin, Liaoning, China [54], F 2004–06 23,178 38.6 Yunnan, China [47], T 2008–10 10,007 32.8 South western Uganda [48], F 2009 3,959 22.6 Pooled Totala 39,963 35.5 P-valueb 0.0001</p><p>Abbreviations: BMI body mass index, WC waist circumference, T total, M males, F females, LS longitudinal study.</p><p>Definitions: Hypertension: SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg or using antihypertensive medication. b WC as (1) continuous variable: lean: mean BMI < 23.1 kg/m2; not-lean: mean BMI ≥ 23.1 kg/m2; (2) as categorical variable: lean: mean BMI < 23.3 kg/m2 or BMI ≥ 25 kg/m2 <17%; not-lean: mean BMI ≥ 23.3 kg/m2 or BMI ≥ 25 kg/m2 ≥ 17%.</p><p>23 a Calculated based on weighting the numbers in each study. b P-value from chi-square test for comparison of the prevalence of hypertension with the first group of BMI category.</p><p>24</p>