Nutritional Status and Body Composition at Diagnosis, of South Indian Children with Acute Lymphoblastic Leukaemia (ALL).

BACKGROUND: Accurate estimation of body composition, particularly, Body Cell Mass (BCM), which is independent of hydration status is important in children with cancer. This study aimed to accurately measure the anthropometry and body composition of children with Acute Lymphoblastic Leukaemia (ALL) at diagnosis and compare them with healthy children from South India. METHODS: This was a cross-sectional study in children aged 2 to 8 y with ALL from St. John's Medical College Hospital, Bengaluru, and age and sex-matched, normal-weight children recruited as controls from communities. Anthropometry (weight, height, circumferences), skinfolds and body composition measurements using a whole-body potassium counter were performed. Body mass index-for-age, weight and height for age z-scores were calculated using WHO child growth standards. Biochemical markers, dietary intake and physical activity details were recorded. Categorical and continuous variables were analyzed by chi-square and independent t-tests respectively.     Results: The mean age of the children with ALL (n = 39) was 4.6±1.9 y and control group (n=39) was 4.7±1.9 y; 61.5% were boys. The prevalence of underweight, overweight/obesity and stunting were 17.9%, 7.7%, and 10.3% respectively. The mean weight and height, of children with ALL and children in the control group were 16.8±6.2 kg and 16.4±4.1 kg, 104.3±14.9 cm and 105.1±12.2 cm, respectively with no statistical difference. Children with ALL showed lower body cell mass index kg/m2 (4.6± 0.8), compared to children in the control group (4.7±0.9) p=0.527, but higher fat mass index kg/m2 (3.6±1.1 vs. 3.4±0.8) p=0.276. CONCLUSION: At diagnosis, anthropometric and body composition measurements were similar between children with ALL and children in the control group. The BCM showed a non-significant trend of being lower in children with ALL, which requires close monitoring during treatment. Evaluating early-stage nutritional status and body composition can help in planning appropriate interventions during treatment to prevent long term non-communicable diseases.

Comparison of growth patterns in the first year of life between term small for gestational age and appropriate for gestational age South Indian infants.

BACKGROUND: Early life growth trajectories of Indian small for gestational age (SGA) infants are sparse. This study aimed to compare longitudinal growth in appropriate for gestational age (AGA) and SGA infants during their first year of life. METHODS: Apparently healthy term infants (52 SGA, 154 AGA) were recruited at birth and followed up till 1 year. Parental, sociodemographic characteristics and feeding patterns were recorded. Anthropometric measurements were assessed at birth, 3, 6, 9 and 12 months of age; Z scores and growth velocity at 3-month intervals were computed. Longitudinal measurements were compared between the two groups, using the two-way Friedmans test. Median regression with mixed effects was used to adjust covariates; p value <0.05 was considered statistically significant. RESULT: AGA infants had significantly higher median weight (kg) (2.87 (2.67, 3.04) vs 2.39 (2.25, 2.54)) at birth, (7.08 (6.50, 7.54) vs 6.49 (6.13, 6.78)) at 6 months, (8.64 (7.92, 9.14) vs 7.90 (7.36, 8.54)) at 12 months, median length (cm) ((48.10 (47.20, 49.30) vs 46.75 (45.43, 47.50)) at birth, (65.50 (64.23, 66.98) vs 63.33 (62.26, 65.28)) at 6 months, (73.30 (71.58, 74.66) vs 71.55 (70.00, 73.30)) at 12 months. SGA infants had comparable weight velocity at all intervals except 9-12 months (6.62 (6.45, 6.79) vs (6.70 (6.51, 6.85)), being significantly higher than AGA infants. Differences in skinfold thicknesses between groups were observed only at birth. Exclusivity of breast feeding was significantly higher at 3 months in AGA, compared to SGA infants (80.9% vs 57.8%). Length velocity was comparable at all ages between groups. Sexual dimorphism was observed in the growth velocities of both groups. CONCLUSION: SGA infants grew in parallel to AGA infants, having significantly lower anthropometric measurements at all time points. However, growth velocities were similar; SGA infants had significantly higher weight velocity from 9 to 12 months. Longitudinal studies beyond 1 year of age, using body composition are needed to determine the quality of growth in Indian infants.

Local Sources of Protein in Low- and Middle-Income Countries: How to Improve the Protein Quality?

Protein inadequacy is a major contributor to nutritional deficiencies and adverse health outcomes of populations in low- and middle-income countries (LMICs). People in LMICs often consume a diet predominantly based on staple crops, such as cereals or starches, and derive most of their daily protein intakes from these sources. However, plant-based sources of protein often contain low levels of indispensable amino acids (IAAs). Inadequate intake of IAA in comparison with daily requirements is a limiting factor that results in protein deficiency, consequently in the long-term stunting and wasting. In addition, plant-based sources contain factors such as antinutrients that can diminish protein digestion and absorption. This review describes factors that affect protein quality, reviews dietary patterns of populations in LMICs and discusses traditional and novel small- and large-scale techniques that can improve the quality of plant protein sources for enhanced protein bioavailability and digestibility as an approach to tackle malnutrition in LMICs. The more accessible small-scale food-processing techniques that can be implemented at home in LMICs include soaking, cooking, and germination, whereas many large-scale techniques must be implemented on an industrial level such as autoclaving and extrusion. Limitations and considerations to implement those techniques locally in LMICs are discussed. For instance, at-home processing techniques can cause loss of nutrients and contamination, whereas limitations with larger scale techniques include high energy requirements, costs, and safety considerations. This review suggests that combining these small- and large-scale approaches could improve the quality of local sources of proteins, and thereby address adverse health outcomes, particularly in vulnerable population groups such as children, adolescents, elderly, and pregnant and lactating women.

Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment.

BACKGROUND: Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development. METHODS: Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects). RESULTS: Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities. CONCLUSION: Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.

Potential Mechanisms of Precision Nutrition-Based Interventions for Managing Obesity.

Precision nutrition (PN) considers multiple individual-level and environmental characteristics or variables to better inform dietary strategies and interventions for optimizing health, including managing obesity and metabolic disorders. Here, we review the evidence on potential mechanisms-including ones to identify individuals most likely to respond-that can be leveraged in the development of PN interventions addressing obesity. We conducted a review of the literature and included laboratory, animal, and human studies evaluating biochemical and genetic data, completed and ongoing clinical trials, and public programs in this review. Our analysis describes the potential mechanisms related to 6 domains including genetic predisposition, circadian rhythms, physical activity and sedentary behavior, metabolomics, the gut microbiome, and behavioral and socioeconomic characteristics, i.e., the factors that can be leveraged to design PN-based interventions to prevent and treat obesity-related outcomes such as weight loss or metabolic health as laid out by the NIH 2030 Strategic Plan for Nutrition Research. For example, single nucleotide polymorphisms can modify responses to certain dietary interventions, and epigenetic modulation of obesity risk via physical activity patterns and macronutrient intake have also been demonstrated. Additionally, we identified limitations including questions of equitable implementation across a limited number of clinical trials. These include the limited ability of current PN interventions to address systemic influences such as supply chains and food distribution, healthcare systems, racial or cultural inequities, and economic disparities, particularly when designing and implementing PN interventions in low- and middle-income communities. PN has the potential to help manage obesity by addressing intra- and inter-individual variation as well as context, as opposed to "one-size fits all" approaches though there is limited clinical trial evidence to date.

Infant growth and body composition from birth to 24 months: are infants developing the same?

BACKGROUND: Given the importance of infancy for establishing growth trajectories, with later-life health consequences, we investigated longitudinal body composition among infants from six economically and ethnically diverse countries. METHODS: We recruited mother-infant dyads using the WHO Multicenter Growth Reference Study criteria. We measured fat-free mass (FFM) in 1393 (49% female) infants from birth to 6 months of age (Australia, India, and South Africa; n = 468), 3-24 months of age (Brazil, Pakistan, South Africa, and Sri Lanka; n = 925), and derived fat mass (FM), fat mass index (FMI), and fat-free mass index (FFMI). Height-for-age (HAZ), weight-for-age (WAZ), and weight-for-length (WHZ) Z-scores were computed. Sex differences were assessed using a t-test, and country differences using a one-way analysis of covariance. We further compared subsamples of children with average (-0.25 > HAZ < +0.25), below-average (≤-0.25) and above-average (≥+0.25) HAZ. RESULTS: HAZ performed well between 0 and 6 months, but less so between 3 and 24 months. The stunting prevalence peaked at 10.3% for boys and 7.8% for girls, at 24 months. By 24 months, girls had greater FMI (10%) than boys. There were significant differences in FFM (both sexes in all countries) and FM (Brazilian boys, Pakistani and South African girls) by 24 months of age between infants with average, above-average, and below-average HAZ. CONCLUSION: In a multi-country sample representing more ideal maternal conditions, body composition was heterogeneous even among infants who exhibited ideal length. Having a mean HAZ close to the median of the WHO standard for length reduced FFM between-country heterogeneity but not FM, suggesting that other factors may influence adiposity.