European Journal of Epidemiology

, Volume 29, Issue 5, pp 311–324 | Cite as

Adiposity has a greater impact on hypertension in lean than not-lean populations: a systematic review and meta-analysis

  • Simin Arabshahi
  • Doreen Busingye
  • Asvini K. Subasinghe
  • Roger G. Evans
  • Michaela A. Riddell
  • Amanda G. Thrift


More than 75 % of people with hypertension live in low-to-middle income countries (LMICs). Based on the mismatch theory of developmental origins of disease, we hypothesised that the impact of adiposity on hypertension is augmented in lean compared with not-lean populations in rural areas of LMICs (RLMICs). We reviewed studies from RLMICs in which the association between body mass index (BMI) or waist circumference (WC) and hypertension was assessed using multivariable models. Applying random effect models, we conducted separate meta-analyses, depending on whether BMI/WC was assessed as a continuous or categorical variable. In each analysis, the studies were ranked by the mean BMI of the total population. Those populations with a mean BMI below the median were categorised as lean and those above the median as not-lean. We identified 46 studies of BMI and 12 of WC. The risk of hypertension was greater in lean than in not-lean populations. Obese males in lean populations were 45 % more likely to be hypertensive compared to obese males in not-lean populations, ratio of the two effect sizes: 1.45 (95 % CI 1.04, 2.03), p = 0.027. Also, individuals with WC above normal in lean populations were 52 % more likely to be hypertensive than their counterparts in not-lean populations, ratio of the two effect sizes: 1.52 (95 % CI 1.06, 2.17), p = 0.021. We conclude that the risk of hypertension associated with adiposity is greater in lean than in not-lean populations. This provides further evidence for the mismatch theory and highlights the need for strategies to improve nutrition in disadvantaged RLMICs.


Hypertension Low-to-middle income countries Meta-analysis Body mass index Waist circumference Developmental origins of health and diseases 



We gratefully acknowledge Cielito C. Reyes-Gibby for providing access to their data, Hiram Beltran-Sanchez, Hao Wang, Hoang Van Minh, Arash Etemadi, Prabhdeep Kaur, Yan Li, B. Madhu, and JesusVioque Lopez for providing further details about their studies. This work was supported by a Project Grant from the National Health & Medical Research Council of Australia (NHMRC, 1005740). AGT was supported by a Senior Research Fellowship from the NHMRC (1042600).

Conflict of interest


Supplementary material

10654_2014_9911_MOESM1_ESM.doc (124 kb)
Caption for ESM 1 Supplementary MOOSE and PRISMA checklists (DOC 124 kb)
10654_2014_9911_MOESM2_ESM.doc (62 kb)
Caption for ESM 2 Supplementary methods and results (DOC 62 kb)
10654_2014_9911_MOESM3_ESM.doc (468 kb)
Caption for ESM 3 Supplementary Figures and Tables (DOC 468 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Simin Arabshahi
    • 1
  • Doreen Busingye
    • 1
  • Asvini K. Subasinghe
    • 1
  • Roger G. Evans
    • 2
  • Michaela A. Riddell
    • 1
  • Amanda G. Thrift
    • 1
    • 3
  1. 1.Epidemiology and Prevention Unit, Stroke and Ageing Research, Department of Medicine, Southern Clinical SchoolMonash UniversityMelbourneAustralia
  2. 2.Department of PhysiologyMonash UniversityMelbourneAustralia
  3. 3.Florey Neuroscience InstitutesHeidelbergAustralia

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