The study, led by Saurav Basu of ESI-PGIMSR and ESIC Medical College in Kolkata together with Shubhanjali Roy of the Indian Institute of Public Health-Delhi, drew on Wave 1 of the Longitudinal Ageing Study in India, or LASI, a nationally representative survey coordinated by the International Institute for Population Sciences in Mumbai. The researchers analyzed data from 60,640 adults aged 45 years or older, classifying each participant into one of three categories: no obesity, preclinical obesity, or clinical obesity. The classification operationalized the 2025 Lancet consensus definition of clinical obesity at a population scale for the first time, distinguishing people who carry excess adiposity but have not yet developed obesity-related illness from those whose excess fat is already manifesting as diagnosed disease.

Technically, the framework moved beyond the conventional reliance on body mass index alone. The researchers integrated Asian-specific BMI thresholds, which recognize that harmful levels of body fat occur at lower BMI values in South Asian populations than in European ones, with Asian-specific waist circumference cut-offs that capture central or abdominal adiposity, the pattern of fat deposition most strongly linked to metabolic risk. These anthropometric measures were then combined with self-reported physician-diagnosed conditions, including hypertension, diabetes, chronic heart disease, chronic bone or joint disease, and high cholesterol, to determine whether an individual’s excess adiposity had crossed the threshold into clinical obesity.

The headline numbers are striking. Overall, 37.53 percent of older Indian adults had either preclinical or clinical obesity. Of these, 15.00 percent fell into the preclinical category, carrying excess adiposity without a diagnosed obesity-related condition, with a 95 percent confidence interval of 14.27 to 15.75 percent. The remaining 22.53 percent had clinical obesity, meaning their excess body fat was accompanied by one or more physician-diagnosed comorbidities, with a confidence interval of 21.71 to 23.37 percent. The remaining 62.47 percent of participants, with a confidence interval of 61.55 to 63.39 percent, were classified as not having obesity. In other words, for every two older Indians with obesity-related disease already diagnosed, roughly one more carries excess fat that has not yet translated into illness, a hidden reservoir of future risk.

Perhaps more revealing than the prevalence figures are the determinants that emerged from the study’s multivariable regression models, which adjusted for a wide range of sociodemographic, behavioral, and psychosocial factors. Women had nearly double the odds of moving up the obesity severity spectrum compared with men, with an adjusted odds ratio of 1.97. Urban residents had 88 percent higher odds than their rural counterparts, with an adjusted odds ratio of 1.88, a finding consistent with the well-documented role of urban environments in promoting sedentary lifestyles, altered diets, and cardiometabolic risk. Individuals in the richest quintile of household monthly per capita expenditure had 91 percent higher odds than those in the poorest, an adjusted odds ratio of 1.91, underscoring that in India the obesity epidemic currently tracks socioeconomic advantage rather than deprivation.

One of the most intriguing findings concerns the social dimension of obesity. Individuals classified as socially isolated, measured using validated social network assessment tools, had 44 percent higher odds of belonging to a higher obesity category, with an adjusted odds ratio of 1.44. This aligns with a growing international literature linking loneliness and weak social ties to weight gain, poorer health behaviors, and worse chronic disease outcomes. Whether social isolation drives obesity through reduced physical activity, altered eating patterns, chronic stress, or some combination of these pathways remains an open question, but the association persisted even after adjustment for depression scores measured with the CES-D scale and other confounders. The finding suggests that obesity interventions in ageing populations may need to consider social connection as seriously as diet and exercise.

Age itself told a more complex story. Adults aged 70 and above had lower odds of being in a higher obesity category compared with younger participants, with an adjusted odds ratio of 0.80. This pattern may partly reflect survivor effects, as individuals with the most severe obesity-related disease may not survive into the oldest age groups, as well as natural changes in body composition with advancing age. It also reflects the known limitations of BMI in very old adults, where sarcopenia, the age-related loss of muscle mass, can mask adiposity behind a deceptively normal weight. The phenotype-based approach used in this study, which incorporates waist circumference rather than relying on BMI alone, is designed to be more sensitive to these age-related shifts in body composition.

The morbidity burden associated with the obesity phenotypes was substantial. Compared with participants without obesity, those with preclinical or clinical obesity had higher prevalence of hypertension, diabetes, chronic heart disease, chronic bone or joint disease, high cholesterol, and multimorbidity, the co-occurrence of two or more chronic conditions. The gradient across phenotypes is clinically meaningful: preclinical obesity identifies people with excess adiposity who have not yet accumulated diagnosed disease, while clinical obesity identifies those in whom the disease process has already declared itself. This distinction, the authors argue, adds diagnostic value beyond BMI by enabling refined risk stratification at the population level, allowing health systems to target intensive management toward those with established disease while directing early identification and prevention toward those at the preclinical stage.

The biological rationale for this phenotyping is well established. Excess adipose tissue, particularly visceral fat, is not a passive energy store but an active endocrine and inflammatory organ. Chronic low-grade inflammation originating in adipose tissue is a recognized mechanism linking obesity to insulin resistance, type 2 diabetes, fatty liver disease, and cardiovascular disease. In older adults, the interaction between obesity and declining muscle strength further accelerates mobility loss and functional impairment. By distinguishing between adiposity that has and has not yet produced clinical manifestations, the new framework attempts to resolve long-standing debates over the heterogeneous health significance of elevated BMI, including the contested concept of metabolically healthy obesity.

For India, the stakes are considerable. The country is undergoing a well-documented nutrition transition, with rising consumption of ultra-processed foods, declining physical activity, and expanding urbanization driving an epidemic of noncommunicable diseases. Routine obesity diagnosis in Indian clinical practice and national surveys has traditionally leaned heavily on BMI, which fails to capture the metabolic heterogeneity that this study reveals. The authors argue that public health strategies should strengthen early identification of high-risk individuals, integrate waist circumference measurement and morbidity assessment into existing noncommunicable disease screening programs for older adults, and address the social and structural factors, from urban design to social isolation, that shape the obesity burden.

The study’s scale and representativeness lend its conclusions unusual weight, though the authors note that it is based on a secondary analysis of de-identified, publicly available LASI Wave 1 data, with diagnoses relying on self-reported physician diagnoses rather than direct clinical examination. Even with those limitations, the message is clear: obesity in ageing India is common, socially patterned, and already deeply entangled with chronic disease, and recognizing the difference between carrying excess fat and being made sick by it could transform how the country, and other nations facing similar transitions, approach the epidemic. As populations worldwide grow older and heavier, the Indian findings offer both a warning about the scale of hidden preclinical risk and a practical template for identifying, stratifying, and ultimately reducing the morbidity that excess adiposity imposes on ageing societies.

Subject of Research: Prevalence, determinants, and morbidity burden of clinical and preclinical obesity phenotypes among older adults in India

Article Title: Clinical obesity phenotypes among older adults: prevalence, determinants, and morbidity burden from the Longitudinal Ageing Study in India

Article References: Basu, S., & Roy, S. (2026). Clinical obesity phenotypes among older adults: prevalence, determinants, and morbidity burden from the Longitudinal Ageing Study in India. International Journal of Obesity. https://doi.org/10.1038/s41366-026-02226-9

Image Credits: AI Generated

DOI: 10.1038/s41366-026-02226-9

Keywords: obesity, older adults, India, LASI, clinical obesity, preclinical obesity, BMI, waist circumference, multimorbidity, social isolation, noncommunicable diseases, public health