4.6 Article

Anthropometric markers and their association with incident type 2 diabetes mellitus: which marker is best for prediction? Pooled analysis of four German population-based cohort studies and comparison with a nationwide cohort study

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BMJ OPEN
卷 6, 期 1, 页码 -

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BMJ PUBLISHING GROUP
DOI: 10.1136/bmjopen-2015-009266

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资金

  1. Competence Network Diabetes Mellitus of the German Federal Ministry of Education and Research (BMBF) [01GI1110C]
  2. Competence Network Obesity (BMBF) [01GI1121B]
  3. Deutsche Forschungsgemeinschaft as part of the Collaborative Research Center at the Medical Faculty of the Martin-Luther-University Halle-Wittenberg [598]
  4. Wilhelm-Roux Programme of the Martin-Luther-University Halle-Wittenberg
  5. Ministry of Education and Cultural Affairs of Saxony-Anhalt
  6. Federal Employment Office
  7. German Federal Ministry of Education and Research (BMBF) [01ZZ0403]
  8. Ministry for Education, Research, and Cultural Affairs
  9. Ministry for Social Affairs of the Federal State of Mecklenburg-West Pomerania
  10. Heinz Nixdorf Foundation (Germany)
  11. German Ministry of Education and Science
  12. German Federal Ministry of Education and Research
  13. State of Bavaria

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Objective: To compare the association between different anthropometric measurements and incident type 2 diabetes mellitus (T2DM) and to assess their predictive ability in different regions of Germany. Methods: Data of 10 258 participants from 4 prospective population-based cohorts were pooled to assess the association of body weight, body mass index (BMI), waist circumference (WC), waist-to-hip-ratio (WHR) and waist-to-height-ratio (WHtR) with incident T2DM by calculating HRs of the crude, adjusted and standardised markers, as well as providing receiver operator characteristic (ROC) curves. Differences between HRs and ROCs for the different anthropometric markers were calculated to compare their predictive ability. In addition, data of 3105 participants from the nationwide survey were analysed separately using the same methods to provide a nationally representative comparison. Results: Strong associations were found for each anthropometric marker and incidence of T2DM. Among the standardised anthropometric measures, we found the strongest effect on incident T2DM for WC and WHtR in the pooled sample (HR for 1 SD difference in WC 1.97, 95% CI 1.75 to 2.22, HR for WHtR 1.93, 95% CI 1.71 to 2.17 in women) and in female DEGS participants (HR for WC 2.24, 95% CI 1.91 to 2.63, HR for WHtR 2.10, 95% CI 1.81 to 2.44), whereas the strongest association in men was found for WHR among DEGS participants (HR 2.29, 95% CI 1.89 to 2.78). ROC analysis showed WHtR to be the strongest predictor for incident T2DM. Differences in HR and ROCs between the different markers confirmed WC and WHtR to be the best predictors of incident T2DM. Findings were consistent across study regions and age groups (< 65 vs >= 65 years). Conclusions: We found stronger associations between anthropometric markers that reflect abdominal obesity (ie, WC and WHtR) and incident T2DM than for BMI and weight. The use of these measurements in risk prediction should be encouraged.

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