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Introduction <p>Obesity is a multifactorial condition that is not well captured by conventional anthropometry. Emerging frameworks, including the Lancet Commission approach, objectively stratify obesity and provide a more clinically informative characterization of the burden. The present study compares obesity estimates based on conventional BMI criteria with those derived using the revised framework and examines the distribution and determinants of pre-clinical and clinical obesity (CO) among older Indian adults.</p> Methods <p>We used data from the first wave of the Longitudinal Aging Study in India (2017–2018), including 59,854 adults aged ≥ 45&#xa0;years. A high body mass index (BMI; &gt; 25&#xa0;kg/m<sup>2</sup>) was used to estimate the conventional obesity burden. Pre-clinical obesity and CO were defined using framework guidance and based on variables available in datasets. Weighted prevalence and multivariable logistic regression estimates were calculated using Stata version 16.0.</p> Results <p>Weighted prevalence of high BMI was 27.1% (95% confidence interval: CI 21.5–33.5), while the prevalence of pre-clinical obesity and CO was 27.0% (21.4–33.4) and 15.1% (10.9–20.6), respectively. CO showed marked geographic variation, with the lowest prevalence in Central India (7.77%) and the highest in South India (24.33%); Meghalaya had the lowest state-level prevalence (4.52%) and Chandigarh the highest (37.88%). Women, urban residents, and participants with higher education and wealth had higher odds of both obesity states. Participants with pre-clinical obesity more often reported excellent self-rated health, whereas poor self-rated health was more common among those with CO.</p> Conclusion <p>The revised framework meaningfully differentiates high BMI burden into pre-clinical and CO and identified sociodemographic and spatial disparities. Such classification may help distinguish excess adiposity from obesity with morbidity and functional limitation, although interpretation should remain cautious given the proxy operationalization possible within survey data.</p>

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Novel Clinical and Pre-clinical Obesity Estimates in Older Indian Adults: Insights from a Nationally Representative Dataset

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Madhur Verma,
  • Pritam Halder,
  • Aditi Aditi,
  • Nitin Kapoor,
  • Vandana Esht,
  • Waseem Mumtaz Ahamed,
  • Rakesh Kakkar,
  • Sanjay Kalra

摘要

Introduction

Obesity is a multifactorial condition that is not well captured by conventional anthropometry. Emerging frameworks, including the Lancet Commission approach, objectively stratify obesity and provide a more clinically informative characterization of the burden. The present study compares obesity estimates based on conventional BMI criteria with those derived using the revised framework and examines the distribution and determinants of pre-clinical and clinical obesity (CO) among older Indian adults.

Methods

We used data from the first wave of the Longitudinal Aging Study in India (2017–2018), including 59,854 adults aged ≥ 45 years. A high body mass index (BMI; > 25 kg/m2) was used to estimate the conventional obesity burden. Pre-clinical obesity and CO were defined using framework guidance and based on variables available in datasets. Weighted prevalence and multivariable logistic regression estimates were calculated using Stata version 16.0.

Results

Weighted prevalence of high BMI was 27.1% (95% confidence interval: CI 21.5–33.5), while the prevalence of pre-clinical obesity and CO was 27.0% (21.4–33.4) and 15.1% (10.9–20.6), respectively. CO showed marked geographic variation, with the lowest prevalence in Central India (7.77%) and the highest in South India (24.33%); Meghalaya had the lowest state-level prevalence (4.52%) and Chandigarh the highest (37.88%). Women, urban residents, and participants with higher education and wealth had higher odds of both obesity states. Participants with pre-clinical obesity more often reported excellent self-rated health, whereas poor self-rated health was more common among those with CO.

Conclusion

The revised framework meaningfully differentiates high BMI burden into pre-clinical and CO and identified sociodemographic and spatial disparities. Such classification may help distinguish excess adiposity from obesity with morbidity and functional limitation, although interpretation should remain cautious given the proxy operationalization possible within survey data.