Economic Development, the Nutrition Trap and Metabolic Disease

Luke, Nancy; Munshi, Kaivan; Oommen, Anu Mary; Singh, Swapnil

doi:10.3386/w29132

Cited by 11 publications

(11 citation statements)

References 64 publications

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“…Malnutrition is linked throughout the life cycle, and an early age growth faltering and rapid weight gain may lead to excess weight and other risks in the adulthood [12,13]. Undernutrition in foetal and childhood contributes to both immediate (such as stunted physical and cognitive achievement) and long-term illhealth leading to cardiometabolic diseases (diabetes, hypertension, cardiovascular disease) [14]. Malnutrition also increases economic costs in the form of higher expenditure on healthcare and loss of human capital.…”

Section: Introductionmentioning

confidence: 99%

Does maternal overnutrition carry child undernutrition in India?

Kumar

Mohanty

2022

PLoS ONE

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Background and objectives Studies in low-and middle-income countries where nutrition transition is underway provides mixed evidence of double burden of maternal overnutrition and child undernutrition among mother-child pairs. Shifting dietary pattern and rapid increase in overweight/obesity among adults with persistent child undernutrition indicate that India is experiencing nutrition transition and double burden of malnutrition. Hence, the study explores the presence of and the factors associated with mother-child dyads of over- and undernutrition in India. Methods and materials The study uses National Family Health Survey 2015–16 data. The analytic sample consists of 28,817 weighted mother-child pairs where an overweight/obese mother is paired with an undernourished child. The nutritional status of children is defined according to WHO 2006 child growth standards as underweight (i.e., low weight-for-age), stunting (i.e., low height-for-age) and wasting (i.e., low weight-for-height). Maternal overweight/obesity (i.e., BMI ≥ 25 kg/m2) is defined using adult BMI criterion. Descriptive, bivariate, and adjusted multivariable logistic regression analysis are conducted. Results Of the overweight/obese mothers, 21.3%, 26.5%, and 14% have underweight, stunted, and wasted children respectively. In adjusted models, maternal short stature (aOR: 2.94, 95% CI: 2.30–3.75), age of child (aOR: 3.29, 95% CI: 2.76–3.92), and poorest wealth status (aOR: 2.01, 95% CI: 1.59–2.54) are significant predictors of overweight/obese mothers and stunted child pairs. Similarly, poor wealth status (aOR: 1.68, 95% CI:1.32–2.14), maternal stature (aOR: 2.70, 95% CI: 2.08–3.52), and child aged 2–5 years (aOR: 1.77, 95% CI:1.51–2.08) are also significantly associated with higher occurrence of overweight/obese mother and-underweight child pairs. Conclusion Findings of the study are consistent with the phase of nutrition transition and double burden of malnutrition. The paper concludes with suggestions to improve the socioeconomic condition, more strategic nutrition specific investments and policy interventions to eliminate all forms of malnutrition for achieving SDGs.

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Section: Introductionmentioning

confidence: 99%

Does maternal overnutrition carry child undernutrition in India?

Kumar

Mohanty

2022

PLoS ONE

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“…48 One pathophysiologic hypothesis that remains under-studied in India and globally is the relationship between early-life undernutrition and adult development of cardiometabolic diseases such as diabetes, referred to as the “double burden of malnutrition.” 49 Early-life undernutrition, which remains highly prevalent in India, may establish metabolic set points that make these individuals susceptible to diabetes as the nutrition transition generates a more obesogenic food environment. 1,50 LASI’s future waves of data collection have the potential to substantially advance the understanding of diabetes and its complications, including cognitive dysfunction, given LASI’s large national sample including many participants recruited from rural areas, which differs from most prior epidemiologic studies of diabetes in India that have focused on urban populations.…”

Section: Discussionmentioning

confidence: 99%

Diabetes and Cognitive Health in India: A Nationally Representative Survey of Adults Aged 45 Years and Older

Flood

Green

Gross

et al. 2022

Preprint

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Background: Few studies have assessed the relationship between diabetes and cognitive health outside of high-income countries. We described this relationship in India, a middle-income country of 1.4 billion people. Methods: Data were analyzed from 58,027 adults aged 45 years or older in the baseline wave of the nationally representative Longitudinal Aging Study in India, from 2017 to 2019. Outcomes were scaled cognitive scores (mean of 0 and standard deviation [SD] of 1) and cognitive impairment, defined as a cognitive score 1.5 standard deviations or below the age- and education-matched mean. Diabetes was defined as a self-report of a prior diabetes diagnosis made by a health professional or having a measured hemoglobin A1c ≥6.5% at the time of the LASI survey. To describe the cross-sectional relationship between cognitive health and diabetes, we constructed survey-weighted multivariable linear and logistic regression models controlling for prespecified covariates, including age, sex, sociodemographic characteristics, and rural versus urban residence. Results: In age- and sex-adjusted models, people with diabetes had cognitive scores that were 0.25 SD higher (95% CI: 0.23 to 0.27) and had a 1.3% (95% CI: 0.7 to 1.8) lower prevalence of cognitive impairment than people without diabetes. Differences persisted even when adjusting for demographic, socioeconomic, and geographic characteristics. Rural versus urban residence modified the relationships of diabetes with cognitive score (P = 0.001) and cognitive impairment (P = 0.004). In fully adjusted models, rural respondents with diabetes had 0.05 SD (95% CI: 0.03 to 0.07) greater cognitive scores and 1.7% (95% CI: 0.9 to 2.5) lower prevalence of cognitive impairment than those without diabetes. In urban areas, respondents with and without diabetes had similar cognitive scores and prevalence of cognitive impairment. Conclusion: Urban-rural health differences, the nutrition transition, and social conditions likely influence the observed cross-sectional relationship between diabetes and cognitive dysfunction in India, leading to different associations than previously reported in many high-income countries.

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“…Luke and colleagues [35] provide an example of a mismatch test for evolutionary PAR that illustrates this last point. They regress adult body mass index ( y 1 ) on ancestral ( e −∞ ) and adult incomes ( e 1 ), with local crop productivity serving as a proxy for ancestral income.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Conceptual and analytical approaches for modeling the developmental origins of inequality

Malani

Rosenbaum

Alberts

et al. 2022

Preprint

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Early life conditions can have profound effects on individual health, longevity, and biological fitness. Two classes of hypotheses are used to explain the evolutionary origins of these effects: developmental constraints (DC) hypotheses, which focus on the deleterious effects of low-quality early-life environments, and predictive adaptive response (PAR) models, which focus on organisms predictions about their adult environment, phenotypic adaptations based on that prediction, and the deleterious consequences of incorrect predictions. Despite their popularity, these ideas remain poorly defined. To remedy this, we provide mathematical definitions for DC, PARs, and related concepts, and develop statistical tests derived from these definitions. We use simulations to demonstrate that PARs are more readily detected by tests based on quadratic regressions than by tests based on more commonly used interaction regression models. Specifically, quadratic regression-based tests on simulated data yield 90.7% sensitivity and 71.5% specificity in detecting PARs, while interaction-based tests yield sensitivity and specificity roughly equal to chance. We demonstrate that the poor performance of interaction models stems from two problems: first, they are mathematically incapable of detecting a central prediction of PAR, and second, they conceptually conflate PARs with DC. Our results emphasize the value of formal statistical modeling to reveal the theoretical underpinnings of verbal and visual models, and their importance for helping resolve conflicting and ambiguous results in this field of research. We conclude by providing recommendations for how researchers can make use of explicit definitions and properly-aligned visualizations and statistical tests to make progress in this important research area.

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Economic Development, the Nutrition Trap and Metabolic Disease

Cited by 11 publications

References 64 publications

Does maternal overnutrition carry child undernutrition in India?

Does maternal overnutrition carry child undernutrition in India?

Diabetes and Cognitive Health in India: A Nationally Representative Survey of Adults Aged 45 Years and Older

Conceptual and analytical approaches for modeling the developmental origins of inequality

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