Background Malnutrition is a major contributor to disease burden in India. To inform subnational action, we aimed to assess the disease burden due to malnutrition and the trends in its indicators in every state of India in relation to Indian and global nutrition targets. Methods We analysed the disease burden attributable to child and maternal malnutrition, and the trends in the malnutrition indicators from 1990 to 2017 in every state of India using all accessible data from multiple sources, as part of Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. The states were categorised into three groups using their Socio-demographic Index (SDI) calculated by GBD on the basis of per capita income, mean education, and fertility rate in women younger than 25 years. We projected the prevalence of malnutrition indicators for the states of India up to 2030 on the basis of the 1990-2017 trends for comparison with India National Nutrition Mission (NNM) 2022 and WHO and UNICEF 2030 targets. Findings Malnutrition was the predominant risk factor for death in children younger than 5 years of age in every state of India in 2017, accounting for 68•2% (95% UI 65•8-70•7) of the total under-5 deaths, and the leading risk factor for health loss for all ages, responsible for 17•3% (16•3-18•2) of the total disability-adjusted life years (DALYs). The malnutrition DALY rate was much higher in the low SDI than in the middle SDI and high SDI state groups. This rate varied 6•8 times between the states in 2017, and was highest in the states of Uttar Pradesh, Bihar, Assam, and Rajasthan. The prevalence of low birthweight in India in 2017 was 21•4% (20•8-21•9), child stunting 39•3% (38•7-40•1), child wasting 15•7% (15•6-15•9), child underweight 32•7% (32•3-33•1), anaemia in children 59•7% (56•2-63•8), anaemia in women 15-49 years of age 54•4% (53•7-55•2), exclusive breastfeeding 53•3% (51•5-54•9), and child overweight 11•5% (8•5-14•9). If the trends estimated up to 2017 for the indicators in the NNM 2022 continue in India, there would be 8•9% excess prevalence for low birthweight, 9•6% for stunting, 4•8% for underweight, 11•7% for anaemia in children, and 13•8% for anaemia in women relative to the 2022 targets. For the additional indicators in the WHO and UNICEF 2030 targets, the trends up to 2017 would lead to 10•4% excess prevalence for wasting, 14•5% excess prevalence for overweight, and 10•7% less exclusive breastfeeding in 2030. The prevalence of malnutrition indicators, their rates of improvement, and the gaps between projected prevalence and targets vary substantially between the states. Interpretation Malnutrition continues to be the leading risk factor for disease burden in India. It is encouraging that India has set ambitious targets to reduce malnutrition through NNM. The trends up to 2017 indicate that substantially higher rates of improvement will be needed for all malnutrition indicators in most states to achieve the Indian 2022 and the global 2030 targets. The state-specific findings in this report indicate the...

Lean body mass (LBM) and muscle mass remain difficult to quantify in large epidemiological studies due to the unavailability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual-energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n = 2,220; 36% women; age 18-79 yr), representing a wide range of body mass index (14–44 kg/m2), participated in this study. Their LBM, including ALST, was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA-measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height, and weight explained >90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of skinfold thicknesses) increased the explained variation by 5–8% in the fully adjusted models predicting LBM and ALST. More complex equations using all of the above anthropometric variables could predict the DXA-measured LBM and ALST accurately, as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively), as well as good agreement by Bland-Altman analyses (Bland JM, Altman D. Lancet 1: 307–310, 1986). These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition.

As there are limitations expressed for both the methods in accurately estimating Hb, it is difficult to decide whether one is an overestimate or the other an underestimate. By virtue of the principle involved in estimating Hb, cyanmethemoglobin method may be taken as an indirect indicator of iron status. However, it is not clear whether such a principle is involved in estimating Hb by Hemocue. Therefore, these two methods need to be further validated against a sensitive and specific indicator for iron status like circulating transferrin receptor to decide which of the methods can be used to accurately determine the prevalence of iron deficiency anemia in the community.

Stunting has been negatively associated with children's development. We examined the range of height by testing hypotheses: (a) height is positively associated with children's development, with associations moderated by inflammation and (b) home environments characterized by nurturance and early learning opportunities is positively associated with children's development over time and attenuate associations with height. Data included 513 infants (mean age 8.6 months) and 316 preschoolers (mean age 36.6 months) in rural India from a randomized controlled trial of multiple micronutrient powders (MNPs). Measures included height (height‐for‐age z‐scores based on WHO standards), inflammation (C‐reactive protein concentration >5 mg/L), nurturance (HOME Inventory), child development (Mullens Scales of Early Learning), and inhibitory control (preschoolers). Linear mixed effects models accounting for repeated measures, clustering, and confounders were used to assess associations between height and child development over time (infants: enrollment, 6 and 12 months; preschoolers: enrollment and 8 months). Moderating effects of inflammation and nurturance were tested with interaction terms. Among infants and preschoolers, height and nurturance were positively associated with all domains of child development over time, with the exception of inhibitory control. Among preschoolers, in the presence of inflammation, height was not associated with child development. Among infants, but not preschoolers, a nurturant home environment attenuated significant associations between height with fine motor and receptive language development. The mechanisms associated with children's development over time are multifactorial and include direct and indirect associations among nutrition, health, and the home environment, as supported by the Nurturing Care Framework.