Cohort characteristics at 18, 12, 6 and 2 years, and newborn, infant and parent measurements 3-7 2Supplemental table S2a:Lifecourse data in participants with NGT, IFG, IGT and both IFG+IGT groups (Males) 8-10 3 Supplemental table S2b:Lifecourse data in participants with NGT, IFG, IGT and both IFG+IGT groups (Females)

<i>Objective</i> <p>India is a double world capital for early life undernutrition and type 2 diabetes. We aimed to characterise lifecourse growth and metabolic trajectories in those developing glucose intolerance as young adults, in the Pune Maternal Nutrition Study (PMNS). </p> <p><i>Research design and Methods</i></p> <p>PMNS is a community-based intergenerational birth cohort established in 1993, with serial information on parents and children through pregnancy, childhood and adolescence. We compared normal glucose tolerant and glucose intolerant participants for serial growth, estimates of insulin sensitivity and secretion (HOMA and dynamic indices) and beta cell compensation accounting for prevailing insulin sensitivity. <b><i></i></b></p> <p><i>Results</i></p> <p>At 18 years (N=619) 37% men and 20% women were glucose intolerant (184 prediabetes, 1 diabetes) despite 48% being underweight (BMI<18.5 kg/m²). Glucose intolerant participants had higher fasting glucose from childhood. Mothers of glucose intolerant participants had higher glycemia in pregnancy. Glucose intolerant participants were shorter at birth. Insulin sensitivity decreased with age in all participants, and the glucose intolerant had consistently lower compensatory insulin secretion from childhood. Participants in the highest quintile of fasting glucose at 6 and 12 years had a 2.5- and 4.0-fold higher risk respectively of 18-year glucose intolerance; this finding was replicated in two other cohorts. <b><i></i></b></p> <p><i>Conclusion</i></p> Inadequate compensatory insulin secretory response to decreasing insulin sensitivity from early life is the major pathophysiology underlying glucose intolerance in thin rural Indians. Smaller birth size, maternal pregnancy hyperglycemia, and higher glycemia in childhood herald future glucose intolerance, mandating a strategy for diabetes prevention from early life, preferably intergenerationally.

Response surface methodology (RSM) is a collection of techniques useful for analyzing and optimizing problems where several explanatory covariates influence a response. Although this technique is extensively used in various mixture experiments, its application in standardization of micropropagation protocols is limited. The theoretical developments of RSM are usually concerned with continuous data; hence, linear model theory becomes relevant. In plant tissue culture, in which the response variables are mostly numerical data, the development of RSM in a generalized linear model (GLM) setup is of interest from both a theoretical as well as an application perspective. In the present paper, RSM, as applicable for count data, has been used for modeling, analyzing, and optimizing in vitro regeneration of multiple shoots of Basilicum polystachyon, an important medicinal plant. The specific issues addressed herein are the determination of the optimum concentration of plant growth regulators (i.e., the range of variation in dosages of each covariate) at which the regeneration potential of shoot tip explants is expected to increase, selection of the appropriate growth function (response function) of shoot tip, and determination of the optimum levels of the explanatory variables (i.e., the different combination of dosages of various control factors) for experimentation. According to the present analysis, the optimum level combinations of growth regulators for regeneration of multiple shoots from shoot tip explants of B. polystachyon is 8.19 μM benzyladenine and 2.36 μM naphthalene acetic acid, with a response of approximately 12 regenerated shoots.

BackgroundIndia is the world’s paradoxical double capital for early life undernutrition and type 2 diabetes. The Pune Maternal Nutrition Study (PMNS) birth cohort offered a unique opportunity to investigate childhood growth and glucose-insulin metabolism as precursors to glucose intolerance in young adulthood.MethodsPMNS is a community-based pre-conceptional birth cohort established in 1993, with serial information on parents, and on their children through pregnancy, childhood and adolescence. We compared the children’s growth and glucose-insulin indices between those who were and were not glucose intolerant at age 18 years (ADA criteria). We developed a prediction model for 18-year glucose intolerance and replicated it in two other cohorts (Extended PMNS and Pune Children’s Study).FindingsAt age 18 years (N=619) 37% men and 20% women were glucose intolerant even though 48% were underweight (BMI<18.5 kg/m2). Glucose intolerant participants were shorter at birth, and had lower insulin secretion (both sexes) and insulin insensitivity (men) in childhood than those with normal glucose tolerance. Fasting plasma glucose (FPG) concentrations at 6- and 12-years of age strongly predicted glucose intolerance at 18 years. The risk was 2.5 times higher in the highest compared to the lowest quintile at 6 years, and 4.5 times at 12 years. Comparable findings were seen in the other cohorts. Mothers of glucose intolerant participants had higher glycemia in pregnancy.InterpretationGlucose intolerance in young rural Indians can be traced to linear growth faltering in-utero, reduced beta-cell secretion and higher glycemia since childhood. Our findings mandate a strategy for diabetes prevention starting much earlier than the current practice.

Introduction Big data and growth in telecommunications have increased the enormous promise of an informatics approach to health care. India and the United Kingdom are two countries facing these challenges of implementing learning health systems and big data health research. Analysis At present, these opportunities are more likely to be exploited in the private sector or in public‐private partnerships (eg, Public Health Foundation of India [PHFI]) than public sector ventures alone. In both India and the United Kingdom, the importance of health informatics (HIs), a relatively new discipline, is being recognised and there are national initiatives in academic and health sectors to fill gaps in big data health research. The challenges are in many ways greater in India but outweighed by three potential benefits in health‐related scientific research: (a) increased productivity; (b) a learning health system with better use of data and better health outcomes; and (c) to fill workforce gaps in both research and practice. Conclusions Despite several system‐level obstacles, in India, big data research in health care can improve the status quo, whether in terms of patient outcomes or scientific discovery. Collaboration between India and the United Kingdom in HI can result in mutual benefits to academic and health care delivery organisations in both countries and can serve as examples to other countries embracing the promises and the pitfalls of health care research in the digital era.