ReviewNature reviews. Endocrinology2026
Clinical obesity in Asian people: bridging the gap between adiposity and disease.
Review in Nature reviews. Endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Body roundness index as a novel marker of periodontitis in US adults: NHANES 2009-2014 analysis.Medicine · 2026Article
- Development of Thai-Specific Gestational Weight Gain Targets Using Asian BMI Cut-Points: Implications for Nursing and Midwifery Practice in Preventing Gestational Diabetes Mellitus.Nursing reports (Pavia, Italy) · 2026Article
- The lancet commission obesity categories, excess adiposity, and BMI discordance in urban Chinese adults: a cross-sectional survey in Beijing.Frontiers in nutrition · 2026Article
- Metabolically unhealthy normal weight is associated with subclinical carotid atherosclerosis: cross-sectional and longitudinal evidence from a Chinese health examination cohort.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This Perspective argues for a reconceptualization of clinical obesity in Asian populations, moving beyond BMI-centric definitions towards an adiposity-based and function-based framework. Asian populations exhibit a distinctive obesity phenotype, characterized by excess visceral and ectopic adipose tissue accumulation, reduced β-cell reserve, sarcopenic obesity and heightened cardiometabolic risk at lower BMI thresholds than for white European and North American populations, leading to systematic under-recognition of obesity-related disease when conventional criteria are applied. Building on the Lancet Commission's framework for defining and diagnosing clinical obesity, we propose an integrated approach that combines anthropometric measures, body composition assessment, metabolic and organ-specific markers and emerging biomarkers to distinguish preclinical obesity from clinical obesity on the basis of organ dysfunction and functional impairment. This approach improves risk stratification, supports earlier and more precise diagnosis and informs stage-specific management, including lifestyle intervention, pharmacotherapy and metabolic surgery, particularly in Asian populations. We discuss implications for clinical practice, prevention strategies and public health policy, emphasizing the need to align clinical guidelines, reimbursement systems and education with a function-based definition of obesity. Finally, we highlight key research priorities, including validation of biomarker-driven classifications, assessment of long-term clinical and economic outcomes and development of scalable diagnostic tools, to advance precision care and metabolic health equity across diverse Asian populations.
Indexed as
Identifiers
42045672What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.