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Hills AP, Norris SA, Byrne NM, Jayasinghe S, Murphy-Alford AJ, Loechl CU, Ismail LIC, Kurpad AV, Kuriyan R, Nyati LH, Santos IS, Costa CS, Wickramasinghe VP, Lucas MN, Slater C, Yameen A, Ariff S. Body composition from birth to 2 years. Eur J Clin Nutr 2023:10.1038/s41430-023-01322-7. [PMID: 37563231 DOI: 10.1038/s41430-023-01322-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 05/08/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023]
Abstract
Providing all infants with the best start to life is a universal but challenging goal for the global community. Historically, the size and shape of infants, quantified by anthropometry and commencing with birthweight, has been the common yardstick for physical growth and development. Anthropometry has long been considered a proxy for nutritional status during infancy when, under ideal circumstances, changes in size and shape are most rapid. Developed from data collected in the Multicentre Growth Reference Study (MGRS), WHO Child Growth Standards for healthy infants and children have been widely accepted and progressively adopted. In contrast, and somewhat surprisingly, much less is understood about the 'quality' of growth as reflected by body composition during infancy. Recent advances in body composition assessment, including the more widespread use of air displacement plethysmography (ADP) across the first months of life, have contributed to a progressive increase in our knowledge and understanding of growth and development. Along with stable isotope approaches, most commonly the deuterium dilution (DD) technique, the criterion measure of total body water (TBW), our ability to quantify lean and fat tissue using a two-compartment model, has been greatly enhanced. However, until now, global reference charts for the body composition of healthy infants have been lacking. This paper details some of the historical challenges associated with the assessment of body composition across the first two years of life, and references the logical next steps in growth assessments, including reference charts.
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Affiliation(s)
| | - Shane A Norris
- University of the Witwatersrand, Johannesburg, South Africa
- University of Southampton, Southampton, United Kingdom
| | | | | | | | | | - Leila I Cheikh Ismail
- University of Sharjah, Sharjah, United Arab Emirates
- University of Oxford, Oxford, United Kingdom
| | | | | | | | | | | | | | | | | | - Ayesha Yameen
- Pakistan Institute of Nuclear Science and Technology (PINSTECH), Nilore, Pakistan
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Fortin-Miller S, Plonka B, Gibbs H, Christifano D, Hull H. Prenatal interventions and the development of childhood obesity. Pediatr Obes 2023; 18:e12981. [PMID: 36104864 DOI: 10.1111/ijpo.12981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 08/08/2022] [Accepted: 08/15/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND Excess maternal adiposity during pregnancy has lasting effects on child outcomes including increased risk of overweight/obesity, which drives disease development. Prenatal interventions are a potential avenue to curb childhood obesity rates, but little is known on their long-term influence on offspring adiposity. OBJECTIVE Review the evidence for lasting effects of prenatal interventions on child adiposity. METHODS Three databases were searched for follow-up studies of completed prenatal RCTs that involved a diet, exercise, or combined (diet and exercise) intervention, and assessed offspring adiposity-related outcomes beyond birth. RESULTS A total of 18 follow-up studies describing 4277 offspring were included. Anthropometrics were collected in all studies while body composition was measured in 15 of the studies. Diet or exercise interventions did not have a consistent significant effect on child adiposity. Three combined interventions resulted in lower levels of child adiposity at 3, 6, and 12 months. CONCLUSIONS No strong association was found between prenatal diet, exercise, or combined interventions and child adiposity. Data are limited due to 63.6% overall retention rate for the follow-up studies and heterogeneity of intervention approach and child adiposity measures. Findings suggest combined interventions initiated early in pregnancy may decrease offspring adiposity in the first year of life.
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Affiliation(s)
- Sara Fortin-Miller
- Department of Dietetics and Nutrition, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Brendel Plonka
- Department of Dietetics and Nutrition, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Heather Gibbs
- Department of Dietetics and Nutrition, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Danielle Christifano
- Department of Dietetics and Nutrition, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Holly Hull
- Department of Dietetics and Nutrition, University of Kansas Medical Center, Kansas City, Kansas, USA
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Qin R, Ding Y, Lu Q, Jiang Y, Du J, Song C, Lv H, Lv S, Tao S, Huang L, Xu X, Liu C, Jiang T, Wang Z, Ma H, Jin G, Xia Y, Hu Z, Zhang F, Lin Y. Associations of maternal dietary patterns during pregnancy and fetal intrauterine development. Front Nutr 2022; 9:985665. [PMID: 36185689 PMCID: PMC9520705 DOI: 10.3389/fnut.2022.985665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Dietary pattern is excellent in reflecting an individual's eating conditions. Longitudinal data on fetal growth can reflect the process of intrauterine growth. We aimed to evaluate the associations between maternal dietary patterns and intrauterine parameters in middle and late pregnancy. The present study was conducted within Jiangsu Birth Cohort (JBC) study. Dietary information was assessed with a food frequency questionnaire (FFQ) in the second and third trimester of gestation. B-ultrasound scans were performed to obtain fetal intrauterine parameters, including head circumference (HC), femur length (FL), abdominal circumference (AC), and estimated fetal weight (EFW). Exploratory factor analysis was used to extract dietary patterns. Multiple linear regression and linear mixed-effects model (LMM) were used to investigate the association between maternal dietary patterns and fetal growth. A total of 1,936 pregnant women were eligible for the study. We observed inverse associations of maternal “Vegetables and fish” and “Snack and less eggs” patterns during mid-pregnancy with fetal HC Z-score, respectively (“Vegetables and fish”: β = −0.09, 95% CI −0.12, −0.06; “Snack and less eggs”: β = −0.05, 95% CI −0.08, −0.02). On the contrary, “Animal internal organs, thallophyte and shellfish” pattern in the second trimester was associated with increased HC Z-scores (β = 0.04, 95% CI 0.02, 0.06). Consistently, score increase in “Vegetables and fish” pattern in the third trimester was inversely associated with the Z-scores of HC (β = −0.05, 95% CI −0.09, −0.02), while “Meat and less nuts” pattern was positively correlated with the Z-scores of HC (β = 0.04, 95% CI 0.02, 0.07). As compared to the fetus whose mothers at the lowest tertile of “Snack and less eggs” pattern in both trimesters, those whose mothers at the highest tertile demonstrated 1.08 fold (RR = 2.10, 95% CI 1.34–3.28) increased risk of small HC for gestational age (GA). No correlation was observed between maternal dietary patterns and other intrauterine parameters. Our results suggested the effects of maternal dietary patterns on fetal growth, particularly HC. These findings highlighted the adverse impact of unhealthy dietary pattern on fetal growth, might provide evidence for strategies to prevent intrauterine dysplasia and dietary guidelines during pregnancy.
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Affiliation(s)
- Rui Qin
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Ye Ding
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Qun Lu
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yangqian Jiang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jiangbo Du
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Ci Song
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hong Lv
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Siyuan Lv
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Toxicology and Nutritional Science, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shiyao Tao
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Lei Huang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xin Xu
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Cong Liu
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Tao Jiang
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhixu Wang
- Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Guangfu Jin
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Yankai Xia
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhibin Hu
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
| | - Feng Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Obstetrics and Gynecology Hospital, National Health Commission (NHC) Key Laboratory of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Fudan University, Shanghai, China
- Feng Zhang
| | - Yuan Lin
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
- Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China
- *Correspondence: Yuan Lin
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Gopalakrishnamoorthy M, Whyte K, Horowitz M, Widen E, Toro-Ramos T, Johnson J, Gidwani S, Paley C, Rosenn B, Lin S, Thornton J, Pi-Sunyer X, Gallagher D. Anthropometric models to estimate fat mass at 3 days, 15 and 54 weeks. Pediatr Obes 2022; 17:e12855. [PMID: 34558804 PMCID: PMC8821135 DOI: 10.1111/ijpo.12855] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/19/2021] [Accepted: 09/01/2021] [Indexed: 02/01/2023]
Abstract
BACKGROUND Currently available infant body composition measurement methods are impractical for routine clinical use. The study developed anthropometric equations (AEs) to estimate fat mass (FM, kg) during the first year using air displacement plethysmography (PEA POD® Infant Body Composition System) and Infant quantitative magnetic resonance (Infant-QMR) as criterion methods. METHODS Multi-ethnic full-term infants (n = 191) were measured at 3 days, 15 and 54 weeks. Sex, race/ethnicity, gestational age, age (days), weight-kg (W), length-cm (L), head circumferences-cm (HC), skinfold thicknesses mm [triceps (TRI), thigh (THI), subscapular (SCP), and iliac (IL)], and FM by PEA POD® and Infant-QMR were collected. Stepwise linear regression determined the model that best predicted FM. RESULTS Weight, length, head circumference, and skinfolds of triceps, thigh, and subscapular, but not iliac, significantly predicted FM throughout infancy in both the Infant-QMR and PEA POD models. Sex had an interaction effect at 3 days and 15 weeks for both the models. The coefficient of determination [R2 ] and root mean square error were 0.87 (66 g) at 3 days, 0.92 (153 g) at 15 weeks, and 0.82 (278 g) at 54 weeks for the Infant-QMR models; 0.77 (80 g) at 3 days and 0.82 (195 g) at 15 weeks for the PEA POD models respectively. CONCLUSIONS Both PEA POD and Infant-QMR derived models predict FM using skinfolds, weight, head circumference, and length with acceptable R2 and residual patterns.
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Affiliation(s)
| | - Kathryn Whyte
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center
| | - Michelle Horowitz
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center
| | - Elizabeth Widen
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center,Institute of Human Nutrition, Vagelos College of Physicians and Surgeons, Columbia University,Department of Nutritional Sciences, The University of Texas at Austin
| | - Tatiana Toro-Ramos
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University, New York
| | - Jill Johnson
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University, New York
| | - Sonia Gidwani
- Department Pediatrics, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine
| | - Charles Paley
- Department Pediatrics, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine
| | - Barak Rosenn
- Department of Obstetrics and Gynecology, Mount Sinai West Hospital, Mount Sinai Health System, Icahn School of Medicine
| | - Susan Lin
- Center for Family and Community Medicine, Columbia University
| | | | - Xavier Pi-Sunyer
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center,Institute of Human Nutrition, Vagelos College of Physicians and Surgeons, Columbia University
| | - Dympna Gallagher
- New York Nutrition Obesity Research Center, Division of Endocrinology, Department of Medicine, Columbia University Irving Medical Center,Institute of Human Nutrition, Vagelos College of Physicians and Surgeons, Columbia University
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