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1.
BMJ Paediatr Open ; 8(1)2024 May 07.
Article in English | MEDLINE | ID: mdl-38719565

ABSTRACT

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.


Subject(s)
Infant, Small for Gestational Age , Humans , Infant, Small for Gestational Age/growth & development , India , Female , Male , Infant, Newborn , Infant , Gestational Age , Child Development/physiology , Longitudinal Studies , Birth Weight/physiology , Anthropometry
2.
Indian Pediatr ; 60(12): 985-990, 2023 Dec 15.
Article in English | MEDLINE | ID: mdl-37700583

ABSTRACT

BACKGROUND: Small for gestational age (SGA) neonates are prone to growth deficits in early life, which may be associated with later life metabolic abnormalities. OBJECTIVES: To compare anthropometry and body composition using air-displacement plethysmography (ADP) in term SGA and appropriate for gestational age (AGA) neonates, and assess if sexual dimorphism existed in estimates of body composition. STUDY DESIGN: Cross-sectional analytical study. PARTICIPANTS: 413 term neonates (91 SGA and 322 AGA) at birth (≤7days). METHODS: Neonatal anthropometry and body composition were measured using ADP. Length corrected fat mass index (FMI) and fat free mass index (FFMI) were calculated. OUTCOME: Anthropometry and body composition estimates of SGA and AGA neonates, segregated by sex. RESULTS: The mean (SD) birth weight of SGA and AGA neonates was 2.5 (0.2) kg and 3.1 (0.3) kg, respectively. SGA neonates had significantly lower % body fat (BF) (2.0%), fat mass (94.4 g), fat free mass (FFM) (349.7 g), FMI (0.34 kg/m2), and FFMI (0.76 kg/m2), but higher %FFM (2.0%) compared to AGA neonates (P<0.001). Males had significantly higher %FFM [91.2 (3.1) vs 90.2 (3.5); P=0.001], FFM [2604 (280) vs 2442 (233) g; P<0.001], and FFMI [11.1 (0.8) vs 10.8 (0.8) kg/m2; P=0.005], but lower % BF [8.8 (3.1) vs 9.8 (3.5); P=0.001] and FMI [1.1(0.4) vs 1.2 (0.5) kg/m2; P=0.008], compared to females. CONCLUSIONS: Accurate estimates of body composition in neonates at birth suggest significantly lower body fat and fat free mass in SGA compared to AGA, with sexual dimorphism.


Subject(s)
Body Composition , Infant, Small for Gestational Age , Infant, Newborn , Male , Female , Humans , Gestational Age , Cross-Sectional Studies , Anthropometry , Birth Weight , Plethysmography
3.
Asia Pac J Clin Nutr ; 31(2): 157-166, 2022.
Article in English | MEDLINE | ID: mdl-35766551

ABSTRACT

The prevalence of the double burden of malnutrition in society is well known with the coexistence of undernutrition with an increase in overweight/obesity; this has been increasing globally with nutritional imbalances and infectious diseases being the major etiological factors. However, there is also the coexistence of inappropriate adiposity or metabolic dysfunction in an individual who appears currently undernourished by anthropometric standards (stunted or underweight); this is the intraindividual double burden of malnutrition. It could also occur in temporal sequence, as anthropometric overweight in an individual who has previously endured childhood under-nutrition. IIDBM has increased the risk for diet-related non-communicable diseases over the past few decades, as it tracks into adulthood, warranting an urgent need for intervention and prevention. While gut dysbiosis has been associated with various forms of malnutrition, the early life gut microbiome composition and its related metabolites and regulatory factors, are possibly linked to the development of inflammatory and metabolic conditions in IIDBM. The possible underlying physiological mechanisms are reviewed here, working through host dietary influences, gut microbial metabolites, host inflammation and metabolic dysregulation. When validated experimentally and tested through appropriately designed randomised, controlled trials, these mechanistic insights will likely lead to development of preventive strategies.


Subject(s)
Gastrointestinal Microbiome , Malnutrition , Adult , Child , Humans , Infant , Malnutrition/epidemiology , Malnutrition/etiology , Nutritional Status , Obesity/epidemiology , Overweight/epidemiology , Prevalence , Socioeconomic Factors
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