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<p>Type 2 diabetes is rising globally, with sub-Saharan Africa facing the steepest projected increase. In low-resource settings, limited screening contributes to high rates of undiagnosed disease, delaying intervention. Traditional risk models often rely on single-factor thresholds or population averages, which can miss combinations of demographic, anthropometric, and lifestyle factors relevant in African contexts. However, emerging evidence suggests that interactions between multiple risk factors provide more accurate, region-specific characterizations of risk. We applied a multidimensional subgroup discovery algorithm to cross-sectional data from three African populations and identified combinations of risk factor cutoffs that define high-risk subgroups. The most consistent profile, such as waist-to-hip ratio greater than 0.9, physical activity less than or equal to 2448 metabolic equivalent-minutes per week, and family history, shows elevated risk across regions and outperforms guideline-based definitions. Here we show that core anthropometric and familial factors are stable across populations, while others are region-specific, improving predictive performance for screening.</p>

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A Stratification Method for Identifying Subgroups at High-Risk for Type 2 Diabetes in sub-Saharan Africa

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月19日

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  • Kayode E. Adetunji,
  • Theophilous Mathema,
  • Isaac Kisiangani,
  • Okechinyere Achilonu,
  • Daniel Nderitu Maina,
  • Samuel Iddi,
  • Nigel John Crowther,
  • Alisha N. Wade,
  • Gershim Asiki,
  • Palwendé Romuald Boua,
  • Michelle Kamp,
  • Given Mashaba,
  • Stephen Tollman,
  • Michèle Ramsay,
  • Skyler Speakman,
  • Scott Hazelhurst

摘要

Type 2 diabetes is rising globally, with sub-Saharan Africa facing the steepest projected increase. In low-resource settings, limited screening contributes to high rates of undiagnosed disease, delaying intervention. Traditional risk models often rely on single-factor thresholds or population averages, which can miss combinations of demographic, anthropometric, and lifestyle factors relevant in African contexts. However, emerging evidence suggests that interactions between multiple risk factors provide more accurate, region-specific characterizations of risk. We applied a multidimensional subgroup discovery algorithm to cross-sectional data from three African populations and identified combinations of risk factor cutoffs that define high-risk subgroups. The most consistent profile, such as waist-to-hip ratio greater than 0.9, physical activity less than or equal to 2448 metabolic equivalent-minutes per week, and family history, shows elevated risk across regions and outperforms guideline-based definitions. Here we show that core anthropometric and familial factors are stable across populations, while others are region-specific, improving predictive performance for screening.