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Background/Objectives <p>Tracking excess adiposity at population scale is essential for managing the obesity pandemic in human populations. New formulas based on weight, height, waist and hip measurements have been suggested as better alternatives to the classic body mass index and waist-hip ratio, but the lack of systematic benchmarking on how these formulas reflect adiposity, metabolic dysfunction and clinical sequelae causes confusion on how to best monitor the health of populations.</p> Subjects/Methods <p>Participants from the Northern Finland Birth Cohort 1966 were included based on data availability at the 46-year visit (2511 women and 1908 men). Cross-sectional sex-adjusted Spearman correlations with clinical biomarkers and serum and urine NMR metabolomics were calculated for body mass index (BMI), waist-hip ratio (WHR), waist-height ratio (WHER), abdominal volume index, body adiposity index, body roundness index, body shape index, conicity index and impedance-based body fat. UK biobank participants were selected based on available data at initial visit (244,947 women and 205,949 men). Prevalent and incident cases of type 2 diabetes, hypertension, liver disease and heart disease were ascertained through register linkage. Prevalent cases were predicted from adiposity measures by age- and sex-adjusted logistic regression and incident cases by age- and sex-adjusted Cox regression.</p> Results <p>Adiposity measures were highly collinear and exhibited low biomolecular specificity. BMI and WHR together captured almost all body shape information related to cardiometabolic diseases. For instance, the c-statistic of the BMI &amp; WHR model for diabetes (0.8012; CI95: 0.7963, 0.8061) was near the theoretical maximum of 0.8047. Diabetes was also predicted by WHER (0.7951; CI95: 0.7903, 0.8000). Other adiposity measures showed equal or worse prediction accuracy. This pattern repeated across multiple disease diagnoses.</p> Conclusions <p>We did not observe sufficient benefits from the more recent body adiposity formulas over body mass index, waist-hip or waist-height ratio to warrant their widespread application in cardiometabolic epidemiology.</p>

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Epidemiological associations between obesity, metabolism and disease risk: are body mass index and waist-hip ratio all you need?

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月19日

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  • Ville-Petteri Mäkinen,
  • Siyu Zhao,
  • Andrei Ihanus,
  • Tuulia Tynkkynen,
  • Mika Ala-Korpela

摘要

Background/Objectives

Tracking excess adiposity at population scale is essential for managing the obesity pandemic in human populations. New formulas based on weight, height, waist and hip measurements have been suggested as better alternatives to the classic body mass index and waist-hip ratio, but the lack of systematic benchmarking on how these formulas reflect adiposity, metabolic dysfunction and clinical sequelae causes confusion on how to best monitor the health of populations.

Subjects/Methods

Participants from the Northern Finland Birth Cohort 1966 were included based on data availability at the 46-year visit (2511 women and 1908 men). Cross-sectional sex-adjusted Spearman correlations with clinical biomarkers and serum and urine NMR metabolomics were calculated for body mass index (BMI), waist-hip ratio (WHR), waist-height ratio (WHER), abdominal volume index, body adiposity index, body roundness index, body shape index, conicity index and impedance-based body fat. UK biobank participants were selected based on available data at initial visit (244,947 women and 205,949 men). Prevalent and incident cases of type 2 diabetes, hypertension, liver disease and heart disease were ascertained through register linkage. Prevalent cases were predicted from adiposity measures by age- and sex-adjusted logistic regression and incident cases by age- and sex-adjusted Cox regression.

Results

Adiposity measures were highly collinear and exhibited low biomolecular specificity. BMI and WHR together captured almost all body shape information related to cardiometabolic diseases. For instance, the c-statistic of the BMI & WHR model for diabetes (0.8012; CI95: 0.7963, 0.8061) was near the theoretical maximum of 0.8047. Diabetes was also predicted by WHER (0.7951; CI95: 0.7903, 0.8000). Other adiposity measures showed equal or worse prediction accuracy. This pattern repeated across multiple disease diagnoses.

Conclusions

We did not observe sufficient benefits from the more recent body adiposity formulas over body mass index, waist-hip or waist-height ratio to warrant their widespread application in cardiometabolic epidemiology.