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Living South Asians have low lean tissue mass relative to height, which contributes to their elevated type 2 diabetes susceptibility, particularly when accompanied by obesity. While ongoing lifestyle transitions account for rising obesity, the origins of low lean mass remain unclear. We analysed proxies for lean mass and stature among South Asian skeletons spanning the last 11,000 years (n = 197) to investigate the origins of South Asian low lean mass. Compared with a worldwide sample (n = 2,003), South Asian skeletons indicate low lean mass. Stature-adjusted lean mass increased significantly over time in South Asia, but to a very minor extent (0.04 z-score units per 1,000 years, adjusted R 2  = 0.01). In contrast stature decreased sharply when agriculture was adopted. Our results indicate that low lean mass has characterised South Asians since at least the early Holocene and may represent long-term climatic adaptation or neutral variation. This phenotype is therefore unlikely to change extensively in the short term, so other strategies to address increasing non-communicable disease rates must be pursued.

Objectives: Stature estimation from the skeleton is a classic anthropological problem, and recent years have seen the proliferation of population-specific regression equations. Many rely on the anatomical reconstruction of stature from archaeological skeletons to derive regression equations based on long bone lengths, but this requires a collection with very good preservation. In some regions, for example, South Asia, typical environmental conditions preclude the sufficient preservation of skeletal remains. Large-scale epidemiological studies that include medical imaging of the skeleton by techniques such as dual-energy X-ray absorptiometry (DXA) offer new potential datasets for developing such equations., Materials and Methods: We derived estimation equations based on known height and bone lengths measured from DXA scans from the Andhra Pradesh Children and Parents Study (Hyderabad, India). Given debates on the most appropriate regression model to use, multiple methods were compared, and the performance of the equations was tested on a published skeletal dataset of individuals with known stature., Results: The equations have standard errors of estimates and prediction errors similar to those derived using anatomical reconstruction or from cadaveric datasets. As measured by the number of significant differences between true and estimated stature, and the prediction errors, the new equations perform as well as, and generally better than, published equations commonly used on South Asian skeletons or based on Indian cadaveric datasets., Conclusions: This study demonstrates the utility of DXA scans as a data source for developing stature estimation equations and offer a new set of equations for use with South Asian datasets., (© 2018 Wiley Periodicals, Inc.)