1
|
Pilkay S, Riffer A, Carroll A. Trauma context exerts intergenerational effects on child mental health via DNA methylation. Epigenetics 2024; 19:2333654. [PMID: 38577817 PMCID: PMC11000619 DOI: 10.1080/15592294.2024.2333654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 03/18/2024] [Indexed: 04/06/2024] Open
Abstract
Many people experience traumatic or negative events, but few develop mental health issues as a result. This study investigated whether newborn DNA methylation (DNAm) previously associated with maternal childhood physical abuse by her father affected the child's mental health and physical growth, as well as whether it mediated or moderated developmental outcomes. METHODS Study sample (N = 903) and data came from Bristol University's Avon Longitudinal Study of Parents and Children. DNAm was measured in cord blood at birth. DNAm data was preprocessed, normalized, and quality controlled before subsetting to 60 CpG sites of interest from previous research. Linear regression analysis examined newborn DNAm and child development outcome associations. Sobel test examined the mediating relationship between mother's history of childhood abuse by father, newborn targeted gene DNAm of significant CpG sites, and child's mental health and physical growth. Moderation analyses examined the interaction effects between the significant CpG sites and mothers' physical abuse by their fathers on child's mental health and physical growth. RESULTS Full cohort analyses showed that newborn DNAm of several different CpG sites associates with separation anxiety, fear, and unhappy/tearful presentations in children aged 6-7 y. Sex-specific associations emerged with boys showing associations with anxiety and fear, and girls showing associations with fear and unhappiness. In boys only, cord blood DNAm mediates the effect of maternal childhood trauma on offspring mental health. No moderation effects emerged. CONCLUSION Intergenerational effects of mother's relationship to her abuser present in newborn DNAm associate with 7-year-old child's mental health, show sex-specific effects, and newborn DNAm does mediate maternal childhood trauma effects on offspring mental health in early-life.
Collapse
Affiliation(s)
- Stefanie Pilkay
- Falk College of Sport and Human Dynamics, School of Social Work, Syracuse University, Syracuse, NYUSA
| | - Andie Riffer
- Jane Addams College of Social Work, University of Illinois at Chicago, Chicago, IL, USA
| | - Andrew Carroll
- Falk College of Sport and Human Dynamics, School of Social Work, Syracuse University, Syracuse, NYUSA
| |
Collapse
|
2
|
Jones BL, Orton AL, Tindall SW, Christensen JT, Enosakhare O, Russell KA, Robins AM, Larriviere-McCarl A, Sandres J, Cox B, Thomas C, Reynolds C. Barriers to Healthy Family Dinners and Preventing Child Obesity: Focus Group Discussions with Parents of 5-to-8-Year-Old Children. CHILDREN (BASEL, SWITZERLAND) 2023; 10:952. [PMID: 37371184 DOI: 10.3390/children10060952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 05/24/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND Although numerous physical and mental health benefits for children have been linked to family dinners, many families still do not have regular family meals together. This study sought to identify the barriers that keep families from having dinners together. METHODS We interviewed 42 parents of 5-to-8-year-old children in small focus groups to identify barriers and challenges that keep families from having healthy and consistent dinners together. RESULTS Parents reported the main barriers were time (e.g., time strain and overscheduling, mismatched schedules, long work hours, etc.), lack of meal planning or failure to follow plans, lack of skills (e.g., cooking skills or nutritional awareness), external factors (e.g., daycare, schools, or extended family, and competing with advertising), and food-related challenges (e.g., picky eating, food allergies). Parents also suggested potential solutions to overcome these barriers. CONCLUSIONS Overall, parents had a desire to have family dinners with their children, but they felt that there are many barriers keeping them from establishing or maintaining consistent family mealtimes. Future research, as well as child obesity prevention and intervention efforts, should consider these barriers and suggested solutions in efforts to promote healthy and consistent family meals as a means of lowering the prevalence of childhood obesity.
Collapse
Affiliation(s)
- Blake L Jones
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | - Adam L Orton
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | - Spencer W Tindall
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | | | | | - Keeley A Russell
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | - Anne-Marie Robins
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | | | - Joseph Sandres
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | - Braden Cox
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | - Connor Thomas
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | - Christina Reynolds
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| |
Collapse
|
3
|
Martínez-Arroyo A, Cantor E, Fisberg RM, Corvalán C. Lower adherence to a prudent dietary pattern is associated with earlier age at menarche in adolescents from the Growth and Obesity Chilean Cohort Study. Front Public Health 2023; 10:995593. [PMID: 36793361 PMCID: PMC9922868 DOI: 10.3389/fpubh.2022.995593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 12/28/2022] [Indexed: 01/31/2023] Open
Abstract
Introduction Early menarche is associated with obesity, and metabolic and mental health risks, among other diseases. Thus, it is relevant to identify modifiable risk factors of early menarche. Some nutrients and foods have been linked to pubertal timing, but how menarche relates to overall dietary patterns is unclear. Methods The aim of this study was to analyze the association between dietary patterns and age at menarche in a prospective cohort of Chilean girls from low and middle-income families. We conducted a survival analysis of 215 girls (median = 12.7 years, IQR = 12.2-13.2) from the Growth and Obesity Cohort Study (GOCS) who had been followed prospectively since 4 years of age (2006). Age at menarche and anthropometric measurements were recorded every 6 months since 7 years of age while diet (24-hour dietary recall) was collected for 11 years. Dietary patterns were obtained from exploratory factor analysis. Accelerated Failure Time models adjusted for potential confounding variables were used to study the association between dietary patterns and age at menarche. Results Girls' median age at menarche was 12.7 years. Three dietary patterns were identified: "Breakfast/Light Dinner," "Prudent" and "Snacking" which explained 19.5% of the diet variation. Girls in the lowest tertile of the "Prudent" pattern had menarche 3 months earlier than girls in the highest tertile (β: 0.022; 95% CI: 0.003; 0.041). "Breakfast/Light Dinner" and "Snacking" patterns were not associated with age at menarche. Conclusion Our results suggest that healthier dietary patterns during puberty might be associated with menarche timing. Nevertheless, further studies are required to confirm this result and to clarify the association between diet and puberty.
Collapse
Affiliation(s)
- Angela Martínez-Arroyo
- Food Behavior Research Center (CEIC), Faculty of Pharmacy, School of Nutrition and Dietetics, University of Valparaíso, Valparaíso, Chile
| | - Erika Cantor
- Institute of Statistics, University of Valparaiso, Valparaíso, Chile
| | - Regina Mara Fisberg
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Camila Corvalán
- Institute of Nutrition and Food Technology (INTA), University of Chile, Santiago, Chile,*Correspondence: Camila Corvalán ✉
| |
Collapse
|
4
|
Zhang S, Zhou J, Yang M, Zhang F, Tao X, Tao F, Huang K. Sex-specific association between elective cesarean section and growth trajectories in preschool children: A prospective birth cohort study. Front Public Health 2022; 10:985851. [PMID: 36203696 PMCID: PMC9530938 DOI: 10.3389/fpubh.2022.985851] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/25/2022] [Indexed: 01/25/2023] Open
Abstract
Background Elective cesarean section (ECS) primarily contributes to the rising cesarean section (CS) rate, and much attention has been attracted to its health consequences. The association between ECS and overweight and obesity in children has been controversial, and few studies distinguished ECS with medical indications from those without indications. Based on a large sample birth cohort, we aim to examine the association of ECS with or without medical indications on children's physical development by using repeated anthropometric data from birth to 6 years of age. Methods A total of 2304 mother-child pairs with complete data on delivery mode and children's anthropometric measurements were recruited from the Ma'anshan-Anhui Birth Cohort (MABC) in China. ECS was the main exposure in this study, and the primary outcomes were children's growth trajectories and early adiposity rebound (AR). Children's BMI trajectories were fitted by using group-based trajectory models and fractional polynomial mixed-effects models. The association between ECS and children's growth trajectories and early AR was performed using multiple logistic regression models. Results Among 2,304 mother-child pairs (1199 boys and 1105 girls), 1088 (47.2%) children were born by CS, including 61 (5.6%) emergency CS, 441 (40.5%) ECS with medical indications, and 586 (53.9%) ECS without medical indications. After adjusting for potential confounders, it was found that ECS with medical indications was associated with a "high level" of BMI trajectory (OR = 1.776; 95% CI: 1.010-3.123), and ECS without medical indications was associated with early AR (OR = 1.517; 95% CI: 1.123-2.050) in girls. In boys, we found that ECS without medical indications was unlikely to experience an accelerated growth trajectory (OR = 0.484; 95%CI: 0.244-0.959). Conclusions ECS may be related to girls' "high level" BMI trajectories and early AR. If causal, the findings will provide an evidence-based reference for early life interventions for childhood obesity.
Collapse
Affiliation(s)
- Shanshan Zhang
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, NHC Key Laboratory of Study on Abnormal gametes and Reproductive Tract, Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, China
| | - Jixing Zhou
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, NHC Key Laboratory of Study on Abnormal gametes and Reproductive Tract, Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, China
| | - Mengting Yang
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, NHC Key Laboratory of Study on Abnormal gametes and Reproductive Tract, Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, China
| | - Fu Zhang
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, NHC Key Laboratory of Study on Abnormal gametes and Reproductive Tract, Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, China
| | - Xingyong Tao
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, NHC Key Laboratory of Study on Abnormal gametes and Reproductive Tract, Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, China
| | - Fangbiao Tao
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, NHC Key Laboratory of Study on Abnormal gametes and Reproductive Tract, Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, China
| | - Kun Huang
- Department of Maternal, Child & Adolescent Health, School of Public Health, Anhui Medical University, Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, NHC Key Laboratory of Study on Abnormal gametes and Reproductive Tract, Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, China,Scientific Research Center in Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, China,*Correspondence: Kun Huang
| |
Collapse
|
5
|
Ortega-Avila JG, García-Muñoz H, Segura Ordoñez A, Salazar Contreras BC. Sexual dimorphism of leptin and adiposity in children between 0 and 10 years: a systematic review and meta-analysis. Biol Sex Differ 2022; 13:47. [PMID: 36064746 PMCID: PMC9446796 DOI: 10.1186/s13293-022-00454-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 07/20/2022] [Indexed: 11/30/2022] Open
Abstract
Background Differences in adolescents and adults by sex in blood levels of leptin and adiposity have been described; however, it is not yet clear if these differences arise from the prepubertal stage in subjects with a normal-weight. Therefore, we examine whether there are differences by sex in levels of blood leptin and adiposity in children with a normal-weight between 0 and 10 years old. Methods Search strategy: eligible studies were obtained from three electronic databases (Ovid, Embase and LILACS) and contact with experts. Selection criteria: healthy children up to 10 years of age with normal-weight according to age. Data collection and analyses: data were extracted by four independent reviewers using a predesigned data collection form. For the analysis, we stratified according to age groups (newborns, 0.25–0.5 years, 3–5.9 years, 6–7.9 years, 8–10 years). The statistical analysis was performed in the R program. Results Of the initially identified 13,712 records, 21 were selected in the systematic review and meta-analysis. The sex was associated with the overall effect on blood leptin (pooled MD = 1.72 ng/mL, 95% CI: 1.25–2.19) and body fat percentage (pooled MD = 3.43%, 95% CI: 2.53–4.33), being both higher in girls. This finding was consistent in the majority of age groups. Conclusion The results of our meta-analyses support the sexual dimorphism in circulating blood leptin and body fat percentage between girls and boys with normal-weight from prepuberty. Supplementary Information The online version contains supplementary material available at 10.1186/s13293-022-00454-y. Differences by sex in blood leptin and body fat percentage in children are present before 10 years old Girls have higher circulating leptin and body fat percentage than boys The sex differences in blood leptin and adiposity in children increase progressively with the age
Collapse
Affiliation(s)
- Jose Guillermo Ortega-Avila
- Grupo de Investigación de Ciencias Básicas y Clínicas de la Salud, Departamento de Ciencias Básicas de la Salud, Pontificia Universidad Javeriana, Seccional-Cali, Cali, Colombia. .,Grupo de investigación Salud y Movimiento, Facultad de Salud, Universidad Santiago de Cali, Cali, Colombia.
| | - Harry García-Muñoz
- Grupo de investigación Salud y Movimiento, Facultad de Salud, Universidad Santiago de Cali, Cali, Colombia.,Grupo de Nutrición, Departamento de Ciencias Fisiológicas, Facultad de Salud, Universidad del Valle, Cali, Colombia
| | - Alejandro Segura Ordoñez
- Grupo de investigación Salud y Movimiento, Facultad de Salud, Universidad Santiago de Cali, Cali, Colombia.,Grupo de Nutrición, Departamento de Ciencias Fisiológicas, Facultad de Salud, Universidad del Valle, Cali, Colombia
| | - Blanca C Salazar Contreras
- Grupo de investigación Salud y Movimiento, Facultad de Salud, Universidad Santiago de Cali, Cali, Colombia.,Programa de Medicina, Facultad de Salud, Universidad Icesi, Cali, Colombia
| |
Collapse
|
6
|
Weisstaub G, Gonzalez Bravo MA, García-Hermoso A, Salazar G, López-Gil JF. Cross-sectional association between physical fitness and cardiometabolic risk in Chilean schoolchildren: the fat but fit paradox. Transl Pediatr 2022; 11:1085-1094. [PMID: 35958004 PMCID: PMC9360814 DOI: 10.21037/tp-22-25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 04/25/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Previous studies have examined the "fat but fit" paradox, revealing that greater levels of physical fitness may diminish the harmful consequences of excess weight on cardiometabolic risk. Despite the above, specific information about the "fat but fit" paradox in prepuberal population is scarce. The aim of this study was to determine the relationship between cardiometabolic risk across (individual and combined) physical fitness and excess weight status and whether the "fat but fit" paradox is met in the sample of schoolchildren analyzed. METHODS A cross-sectional study was conducted including 452 children (59.1% girls), aged 7-9 years from Santiago (Chile). Physical fitness was assessed as cardiorespiratory fitness and muscular fitness. Cardiorespiratory fitness was determined by the 6-minute-walk-test and muscle strength was assessed by the handgrip and standing long jump tests. Excess weight (overweight and obesity) was computed through body mass index (z-score). Cardiometabolic risk was established by summing the z-score of the serum glucose, triglycerides, high-density lipoprotein, insulin and waist-to-height ratio. RESULTS Schoolchildren with high physical fitness (individual or combined) showed the lowest cardiometabolic risk mean scores (P for trend <0.001 for all physical fitness groups). Conversely, schoolchildren with low physical fitness (individual or combined) showed the highest cardiometabolic risk mean scores (P for trend <0.001 for all categories). Additionally, schoolchildren without excess weight and with high individual or combined physical fitness status exhibits lower cardiometabolic risk mean scores compared to schoolchildren with excess weight and low physical fitness status (individual or combined) (P for trend <0.001 for all physical fitness groups). A lower odd of having high cardiometabolic risk was found in schoolchildren without excess weight and with both high physical fitness (both cardiorespiratory fitness and muscular fitness) [odds ratio (OR) =0.08; 95% confidence interval (CI): 0.04 to 0.16] in comparison to those with excess weight and low physical fitness. CONCLUSIONS Our results suggest that improvements in both fatness and aerobic fitness could be associated with lower cardiometabolic risk.
Collapse
Affiliation(s)
- Gerardo Weisstaub
- Institute of Nutrition and Food Technology (INTA), University of Chile, Santiago, Chile
| | | | - Antonio García-Hermoso
- Navarrabiomed, Hospital Universitario de Navarra (HUN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Navarra, Spain
| | - Gabriela Salazar
- Institute of Nutrition and Food Technology (INTA), University of Chile, Santiago, Chile
| | | |
Collapse
|
7
|
Lin D, Chen DD, Huang J, Li Y, Wen XS, Shi HJ. Longitudinal association between the timing of adiposity peak and rebound and overweight at seven years of age. BMC Pediatr 2022; 22:215. [PMID: 35439975 PMCID: PMC9016949 DOI: 10.1186/s12887-022-03190-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/04/2022] [Indexed: 11/23/2022] Open
Abstract
Background The timing of adiposity peak (AP) or adiposity rebound (AR) is a determinant of overweight or obesity in adolescence and adulthood. However, limited studies have reported the association in young school-age children. We aimed to evaluate this association and explore the role of health behaviours in it. Methods Routinely collected, sequential, anthropometric data from the 1st to 80th months of age were used to estimate AP and AR timings in 2330 children born in Shanghai between 2010 and 2013. Multivariate regression analyses were applied to identify the associations between the AP or AR timings and the risk of developing overweight or obesity in first-grade school children. The roles of health behaviours, including dietary patterns, physical activity level, sleep and snacking habits, and screen time, were also evaluated. Results Children with a late AP or an early AR were at higher risk of overweight but not obesity or central obesity in their first grade. A high physical activity level was associated with a lower risk of having overweight in children with a late AP, and limited screen time was associated with a decreased risk of having overweight or obesity in children with an early AR. The absence of a late-night snacking habit in children with a non-early AR indicated a decreased risk of having overweight. However, this association was not observed among children with an early AR. Conclusion The timings of AP and AR are tied to overweight in middle childhood. Prevention strategies are suggested to move forward to control late AP and early AR. Supplementary Information The online version contains supplementary material available at 10.1186/s12887-022-03190-9.
Collapse
Affiliation(s)
- Dan Lin
- Department of Maternal, Child and Adolescent Health, School of Public Health, Fudan University, Shanghai, China
| | - Di-di Chen
- Minhang District Centre of Disease Control and Prevention, Shanghai, China.,Minhang Branch, School of Public Health, Fudan University, Shanghai, China
| | - Jun Huang
- Minhang Maternal and Child Health Centre, Shanghai, China
| | - Yun Li
- Minhang Maternal and Child Health Centre, Shanghai, China
| | - Xiao-Sa Wen
- Minhang District Centre of Disease Control and Prevention, Shanghai, China.,Minhang Branch, School of Public Health, Fudan University, Shanghai, China
| | - Hui-Jing Shi
- Department of Maternal, Child and Adolescent Health, School of Public Health, Fudan University, Shanghai, China.
| |
Collapse
|
8
|
Why do humans undergo an adiposity rebound? Exploring links with the energetic costs of brain development in childhood using MRI-based 4D measures of total cerebral blood flow. Int J Obes (Lond) 2022; 46:1044-1050. [PMID: 35136192 PMCID: PMC9050592 DOI: 10.1038/s41366-022-01065-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/30/2021] [Accepted: 01/07/2022] [Indexed: 11/08/2022]
Abstract
Background Individuals typically show a childhood nadir in adiposity termed the adiposity rebound (AR). The AR serves as an early predictor of obesity risk, with early rebounders often at increased risk; however, it is unclear why this phenomenon occurs, which could impede understandings of weight gain trajectories. The brain’s energy requirements account for a lifetime peak of 66% of the body’s resting metabolic expenditure during childhood, around the age of the AR, and relates inversely to weight gain, pointing to a potential energy trade-off between brain development and adiposity. However, no study has compared developmental trajectories of brain metabolism and adiposity in the same individuals, which would allow a preliminary test of a brain-AR link. Methods We used cubic splines and generalized additive models to compare age trajectories of previously collected MRI-based 4D flow measures of total cerebral blood flow (TCBF), a proxy for cerebral energy use, to the body mass index (BMI) in a cross-sectional sample of 82 healthy individuals (0–60 years). We restricted our AR analysis to pre-pubertal individuals (0–12 years, n = 42), predicting that peak TCBF would occur slightly after the BMI nadir, consistent with evidence that lowest BMI typically precedes the nadir in adiposity. Results TCBF and the BMI showed inverse trajectories throughout childhood, while the estimated age at peak TCBF (5.6 years) was close but slightly later than the estimated age of the BMI nadir (4.9 years). Conclusions The timing of peak TCBF in this sample points to a likely concordance between peak brain energetics and the nadir in adiposity. Inverse age trajectories between TCBF and BMI support the hypothesis that brain metabolism is a potentially important influence on early life adiposity. These findings also suggest that experiences influencing the pattern of childhood brain energy use could be important predictors of body composition trajectories.
Collapse
|
9
|
Infant feeding practices associated with adiposity peak and rebound in the EDEN mother-child cohort. Int J Obes (Lond) 2022; 46:809-816. [PMID: 34980907 DOI: 10.1038/s41366-021-01059-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 12/01/2021] [Accepted: 12/21/2021] [Indexed: 11/08/2022]
Abstract
BACKGROUND/OBJECTIVE High magnitude of adiposity peak and early adiposity rebound are early risk markers of later obesity. Infant diet represents one of the main modifiable determinants of early growth. This study aimed to investigate the association between infant feeding practices and age and magnitude of adiposity peak and rebound. SUBJECTS/METHODS Analyses were based on data from the French EDEN mother-child cohort. Data on breastfeeding and complementary feeding were collected at birth and 4, 8, and 12 months. From clinical examinations and measurements collected in the child's health booklet up to 12 years, individual growth curves were modeled, and ages and magnitudes of adiposity peak and rebound were estimated. Associations between infant feeding practices and growth were investigated by multivariable linear regression in children after testing a child-sex interaction. RESULTS In the studied population (n = 1225), adiposity peak occurred at a mean of 9.9 ± 2 months and adiposity rebound at 5.5 ± 1.4 years. Associations between infant feeding practices and adiposity peak or rebound were moderated by child sex. For girls, each additional month of breastfeeding was related to a 2-day increase in the age at adiposity peak (p < 0.001), and an 18-day increase in the age at adiposity peak (p = 0.004). Whereas for boys, each additional month for the age at complementary food introduction was associated with a 29-day increase in the age at adiposity rebound (p = 0.02). For boys, long breastfeeding duration was only related to reduced body mass index at adiposity peak. CONCLUSIONS Child sex has a moderating effect on the association between infant feeding practices and adiposity peak or rebound. The well-known association between breastfeeding duration and early growth seems stronger in girls than boys. The association found for complementary feeding in boys may give new insights into preventing obesity.
Collapse
|
10
|
Handgrip strength cut-off points for early detection of cardiometabolic risk in Chilean children. Eur J Pediatr 2021; 180:3483-3489. [PMID: 34105001 DOI: 10.1007/s00431-021-04142-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/28/2021] [Accepted: 06/02/2021] [Indexed: 10/21/2022]
Abstract
The aim of this study was to determine whether handgrip strength is associated with cardiometabolic risk in children. The secondary aim was to establish sex-specific handgrip strength cut-off points for early detection of cardiometabolic risk. A total sample of 452 Chilean children (267 girls and 185 boys) aged 7-9 years old was analyzed. Muscle fitness was measured by an adjustable dynamometer and normalized by body mass (i.e., handgrip strength/body mass). Sex-specific cardiometabolic risk scores were computed as the sum of the waist-to-height ratio (Equation 1) or waist circumference (Equation 2) and insulin, triglycerides, high-density lipoproteins, and glycemia levels. Receiver operating curve (ROC) analyses were performed to identify those with cardiometabolic risk scores > 1 standard deviation above the mean. ROC analyses showed a significant discriminating accuracy of normalized handgrip strength in identifying cardiometabolic risk in boys (≤ 0.33) and girls (≤ 0.40) using both equations. The highest sensitivity was offered by Equation 2 for boys [46%; 95% CI (32-59%)] and for girls [71%; 95% CI (60-80)]. The greatest specificity was also offered by Equation 2 for boys [82%; 95% CI (74-88)] and girls [63%; 95% CI (55-70)]. Since the values obtained by ROC analyses are low (especially in boys), caution is warranted regarding the strength of the existing evidence base.Conclusion: These specific cut-off points according to sex for possible cardiometabolic risk could be used by Chilean health professionals and school staff as an initial assessment in the field setting. What is known • There is strong evidence for the importance of muscular fitness during childhood and adolescence for cardiometabolic risk. • There has been no research to establish minimum handgrip strength capacity levels to predict cardiometabolic risk among Chilean children. What is new • Cut-off points for handgrip strength relative to body mass to identify cardiometabolic risk in Chilean children are 0.33 in boys and 0.40 in girls. • The early use of these cut-off points and its appropriate identification could have benefits of preventive and diagnostic therapeutic intervention and as a starting point to define adequate levels of handgrip strength.
Collapse
|
11
|
Raspini B, Prosperi M, Guiducci L, Santocchi E, Tancredi R, Calderoni S, Morales MA, Morelli M, Simione M, Fiechtner L, Muratori F, Cena H. Dietary Patterns and Weight Status in Italian Preschoolers with Autism Spectrum Disorder and Typically Developing Children. Nutrients 2021; 13:4039. [PMID: 34836294 PMCID: PMC8617730 DOI: 10.3390/nu13114039] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/06/2021] [Accepted: 11/09/2021] [Indexed: 12/18/2022] Open
Abstract
Atypical eating habits are more common in children with autism spectrum disorders (ASD) than typically developing (TD) peers. Feeding problems may lead to the double burden of specific nutrient deficiencies and excessive weight gain, with a consequent increase in obesity prevalence. The dietary intake of Italian preschoolers with ASD compared to their TD peers and the impact of their dietary choices on their weight status and relationship to food selectivity (FS) were investigated. Dietary patterns and their associations with body mass index (BMI) were evaluated in 65 children with ASD and 82 peers with TD aged 1.3-6.4 years. Eating habits were assessed with a modified version of a parent-rated semi-quantitative Food Frequency Questionnaire. Moreover, the prevalence of FS and possible links with dietary patterns and BMI were investigated in the ASD group. Children with ASD consumed significantly higher amounts of simple sugars, processed and ultra-processed carbohydrates, both low- and high-fat animal proteins, and lower amounts of vegetables and fruits compared to peers with TD. The obesity rate was 1.5% in children with TD and more than fourfold (6.2%) in children with ASD, although the difference between groups was not statistically significant. FS was significantly more frequent in children with ASD than in peers with TD. Children with ASD and FS showed significantly lower annual intakes of vegetable proteins and fiber (considered essential nutrients for a healthy diet) than children with ASD without FS. Our results showed that children with ASD showed different dietary habits than those with TD, with the higher consumption of energy-dense foods and lower amounts of food-sourced fibers, which could place them at increased risk to develop overweight, obesity, and micronutrient deficiencies later in life.
Collapse
Affiliation(s)
- Benedetta Raspini
- Department of Public Health, Neurosciences, Experimental and Forensic Medicine, Section of Human Nutrition, University of Pavia, Via Agostino Bassi, 21, 27100 Pavia, Italy
| | - Margherita Prosperi
- Developmental Psychiatry Unit, IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Via Savi 10, 56126 Pisa, Italy
| | - Letizia Guiducci
- Institute of Clinical Physiology, National Research Council, Via Moruzzi 1, 56124 Pisa, Italy
| | - Elisa Santocchi
- Developmental Psychiatry Unit, IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy
| | - Raffaella Tancredi
- Developmental Psychiatry Unit, IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy
| | - Sara Calderoni
- Developmental Psychiatry Unit, IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Via Savi 10, 56126 Pisa, Italy
| | - Maria Aurora Morales
- Institute of Clinical Physiology, National Research Council, Via Moruzzi 1, 56124 Pisa, Italy
| | - Mariangela Morelli
- Department of Public Health, Neurosciences, Experimental and Forensic Medicine, Section of Human Nutrition, University of Pavia, Via Agostino Bassi, 21, 27100 Pavia, Italy
| | - Meg Simione
- Department of Pediatrics, Massachusetts General Hospital for Children, 55 Fruit Street, Boston, MA 02114, USA
| | - Lauren Fiechtner
- Division of Gastroenterology and Nutrition, Massachusetts General Hospital for Children, 55 Fruit Street, Boston, MA 02114, USA
- Division of General Academic Pediatrics, Department of Pediatrics, Massachusetts General Hospital for Children, 55 Fruit Street, Boston, MA 02114, USA
| | - Filippo Muratori
- Developmental Psychiatry Unit, IRCCS Stella Maris Foundation, Viale del Tirreno 331, 56128 Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Via Savi 10, 56126 Pisa, Italy
| | - Hellas Cena
- Department of Public Health, Neurosciences, Experimental and Forensic Medicine, Section of Human Nutrition, University of Pavia, Via Agostino Bassi, 21, 27100 Pavia, Italy
- Clinical Nutrition and Dietetics Service, Unit of Internal Medicine and Endocrinology, ICS Maugeri IRCCS, Via S. Maugeri 10, 27100 Pavia, Italy
| |
Collapse
|
12
|
Anthropometric and bioimpedance equations for fat and fat-free mass in Chilean children 7-9 years of age. Br J Nutr 2021; 126:37-42. [PMID: 33028443 DOI: 10.1017/s0007114520003906] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Assessing children's growth adequately is important due to the necessary prevention of adequate body composition, especially at pre-pubertal age. Simpler measurements such as anthropometry or bioimpedance, using equations validated in Caucasian children, have been demonstrated to overestimate or underestimate fat mass percentage (FM%) or fat-free mass (FFM) in Chilean children. In a sample of 424 children (198 boys and 226 girls) of 7-9 years old, the three component (3C) model was assessed, where total body water was determined by 2H dilution and body volume by air displacement plethysmography, in order to design and validate anthropometry and bioimpedance equations. The FM (%) equation specific for Chilean children was validated as (1·743 × BMI z-score) + (0·727 × triceps skinfold) + (0·385 × biceps skinfold) + 15·985, against the 3C model (R2 0·79). The new FFM equation (kg) generated was (log FFM = (0·018 × age) + (0·047 × sex) + (0·006 × weight) + (0·027 × resistance) + 2·071), with an R2 0·93 (female = 1 and male = 2). The Bland-Altman analysis shows a mean difference of 0·27 (sd 3·5) for the FM% in the whole group as well as 0·004 (sd 0·9) kg is the mean difference for the bioelectrical impedance analysis (BIA) FFM (kg) equation. The new equations for FM (%) and FFM (kg) in Chilean children will provide a simple and valid tool for the assessment of body composition in cohort studies or to assess the impact of nutritional programmes or public policies.
Collapse
|
13
|
Aris IM, Sarvet AL, Stensrud MJ, Neugebauer R, Li LJ, Hivert MF, Oken E, Young JG. Separating Algorithms From Questions and Causal Inference With Unmeasured Exposures: An Application to Birth Cohort Studies of Early Body Mass Index Rebound. Am J Epidemiol 2021; 190:1414-1423. [PMID: 33565574 DOI: 10.1093/aje/kwab029] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 02/04/2021] [Accepted: 02/05/2021] [Indexed: 12/19/2022] Open
Abstract
Observational studies reporting on adjusted associations between childhood body mass index (BMI; weight (kg)/height (m)2) rebound and subsequent cardiometabolic outcomes have often not paid explicit attention to causal inference, including definition of a target causal effect and assumptions for unbiased estimation of that effect. Using data from 649 children in a Boston, Massachusetts-area cohort recruited in 1999-2002, we considered effects of stochastic interventions on a chosen subset of modifiable yet unmeasured exposures expected to be associated with early (<age 4 years) BMI rebound (a proxy measure) on adolescent cardiometabolic outcomes. We considered assumptions under which these effects might be identified with available data. This leads to an analysis where the proxy, rather than the exposure, acts as the exposure in the algorithm. We applied targeted maximum likelihood estimation, a doubly robust approach that naturally incorporates machine learning for nuisance parameters (e.g., propensity score). We found a protective effect of an intervention that assigns modifiable exposures according to the distribution in the observational study of persons without (vs. with) early BMI rebound for fat mass index (fat mass (kg)/ height (m)2; -1.39 units, 95% confidence interval: -1.63, -0.72) but weaker or no effects for other cardiometabolic outcomes. Our results clarify distinctions between algorithms and causal questions, encouraging explicit thinking in causal inference with complex exposures.
Collapse
|
14
|
Fang J, Gong C, Su P, Wan Y, Zhang Z, Tao F, Sun Y. Polygenic interactions with adiposity rebound in the prediction of thelarche. Pediatr Res 2021; 89:1026-1031. [PMID: 32512580 DOI: 10.1038/s41390-020-1001-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 04/18/2020] [Accepted: 05/14/2020] [Indexed: 11/09/2022]
Abstract
BACKGROUND The earliest onset of puberty had shifted downward, which may be due to the role of early growth and development factors in childhood. METHODS All of 1575 Kindergarten Two (K2) children from Anhui province, China were followed up to elementary school. Girls (n = 342) with available data on AR and breast development were included for this analysis. Polygenic risk score (PRS) was computed based on 17 single nucleotide polymorphisms for early puberty. Accelerate failure time (AFT) model was used to describe thelarche timing by early AR among girls with different polygenic susceptibility. RESULTS After adjustment for perinatal anthropometric, household income, parental education and prepuberty BMI-Z score, puberty started 4.12-month earlier in early AR girls compared with normal AR girls (TR: 0.96; 95% CI: 0.95, 0.98, p < 0.001). Furthermore, this puberty-accelerating effect was observed among girls with high (6.06-month earlier, TR: 0.94; 95% CI: 0.90, 0.99) and moderate PRS (4.20-month earlier, TR: 0.96; 95% CI: 0.93, 0.98). No similar results were observed in the low PRS groups (TR: 1.00; 95% CI: 0.96, 1.04). CONCLUSIONS Girls with early AR displayed younger age at thelarche; however, this accelerating effect was only observed among those with genetic susceptibility to early puberty. IMPACT Early AR plays a more important role in predicting earlier thelarche among girls with high and moderate PRS. This study combined with the hot topics of pubertal-related polygenic risk score (PRS) for pubertal timing to examine the longitudinal association between early AR with accelerated pubertal onset. Our results mean that accelerating growth in the early childhood years after birth might forecast early puberty only among girls with genetic predisposition to early puberty. Prevention strategies and management options should be emphasized to target early childhood to address secular trend for early puberty observed in the past decades in China.
Collapse
Affiliation(s)
- Jiao Fang
- Department of Maternal, Child & Adolescent Health, School of Public Health, Hefei, Anhui Province, China
| | - Chun Gong
- Department of Maternal, Child & Adolescent Health, School of Public Health, Hefei, Anhui Province, China
| | - Puyu Su
- Department of Maternal, Child & Adolescent Health, School of Public Health, Hefei, Anhui Province, China.,Anhui Provincial Key Laboratory of Population Health & Aristogenics, Hefei, Anhui Province, China
| | - Yuhui Wan
- Anhui Provincial Key Laboratory of Population Health & Aristogenics, Hefei, Anhui Province, China
| | - Zhihua Zhang
- Department of Epidemiology and Biostatistics, Anhui Medical University, Hefei, Anhui Province, China
| | - Fangbiao Tao
- Department of Maternal, Child & Adolescent Health, School of Public Health, Hefei, Anhui Province, China.,Anhui Provincial Key Laboratory of Population Health & Aristogenics, Hefei, Anhui Province, China
| | - Ying Sun
- Department of Maternal, Child & Adolescent Health, School of Public Health, Hefei, Anhui Province, China. .,Anhui Provincial Key Laboratory of Population Health & Aristogenics, Hefei, Anhui Province, China.
| |
Collapse
|
15
|
Fonseca MJ, Moreira C, Santos AC. Adiposity rebound and cardiometabolic health in childhood: results from the Generation XXI birth cohort. Int J Epidemiol 2021; 50:1260-1271. [PMID: 33523213 DOI: 10.1093/ije/dyab002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 01/08/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND We aimed to evaluate the association of adiposity rebound (AR) timing on cardiometabolic health in childhood. METHODS Participants were part of the Generation XXI birth cohort, enrolled in 2005/2006 in Porto. All measurements of the child's weight and height performed by health professionals as part of routine healthcare were collected. Individual body mass index (BMI) curves were fitted for 3372 children, using mixed-effects models with smooth spline functions for age and random effects. The AR was categorized into very early (<42 months), early (42-59 months), normal (60-83 months) and late (≥84 months). At age 10 years, cardiometabolic traits were assessed and age- and sex-specific z-scores were generated. Adjusted regression coefficients and 95% confidence intervals [β (95% CI)] were computed. RESULTS The mean age at AR was 61.9 months (standard deviations 15.7). Compared with children with normal AR, children with very early or early AR had higher z-scores for BMI [β = 0.40 (95% CI: 0.28; 0.53); β = 0.21 (95% CI: 0.12; 0.30)], waist circumference [β = 0.33 (95% CI: 0.23; 0.43); β = 0.18 (95% CI: 0.10; 0.25)], waist-height ratio [β = 0.34 (95% CI: 0.24; 0.44); β = 0.14 (95% CI: 0.07; 0.22)], fat mass index [β = 0.24 (95% CI: 0.15; 0.33); β = 0.14 (95% CI: 0.08; 0.21)], fat-free mass index [β = 0.25 (95% CI: 0.14; 0.35); β = 0.11 (95% CI: 0.03; 0.19)], systolic blood pressure [β = 0.10 (95% CI: 0.01; 0.20); β = 0.08 (95% CI: 0.01; 0.15)], insulin [β = 0.16 (95% CI: 0.04; 0.29); β = 0.10 (95% CI: 0.01; 0.19)], HOMA-IR [β = 0.17 (95% CI: 0.04; 0.29); β = 0.10 (95% CI: 0.03; 0.19)] and C-reactive protein [β = 0.14 (95% CI: 0.02; 0.26); β = 0.10 (95% CI: 0.01; 0.19)]. Children with very early AR also had worse levels of diastolic blood pressure [β = 0.09 (95% CI: 0.02; 0.16)], triglycerides [β = 0.21 (95% CI: 0.08; 0.34)] and high-density lipoprotein cholesterol [β=-0.18 (95% CI: -0.31; -0.04)]. When analysed continuously, each additional month of age at the AR was associated with healthier cardiometabolic traits. CONCLUSION The earlier the AR, the worse the cardiometabolic health in late childhood, which was consistently shown across a wide range of outcomes and in the categorical and continuous approach.
Collapse
Affiliation(s)
- Maria João Fonseca
- EPIUnit-Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
| | - Carla Moreira
- EPIUnit-Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,CMAT, Departamento de Matemática e Aplicações, Universidade do Minho, Braga, Portugal
| | - Ana Cristina Santos
- EPIUnit-Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal.,Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina, Universidade do Porto, Portugal
| |
Collapse
|
16
|
Cissé AH, Lioret S, de Lauzon-Guillain B, Forhan A, Ong KK, Charles MA, Heude B. Association between perinatal factors, genetic susceptibility to obesity and age at adiposity rebound in children of the EDEN mother-child cohort. Int J Obes (Lond) 2021; 45:1802-1810. [PMID: 33986455 PMCID: PMC8310796 DOI: 10.1038/s41366-021-00847-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 04/21/2021] [Accepted: 04/27/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND Early adiposity rebound (AR) has been associated with increased risk of overweight or obesity in adulthood. However, little is known about early predictors of age at AR. We aimed to study the role of perinatal factors and genetic susceptibility to obesity in the kinetics of AR. METHODS Body mass index (BMI) curves were modelled by using mixed-effects cubic models, and age at AR was estimated for 1415 children of the EDEN mother-child cohort study. A combined obesity risk-allele score was calculated from genotypes for 27 variants identified by genome-wide association studies of adult BMI. Perinatal factors of interest were maternal age at delivery, parental education, parental BMI, gestational weight gain, maternal smoking during pregnancy, and newborn characteristics (sex, prematurity, and birth weight). We used a hierarchical level approach with multivariable linear regression model to investigate the association between these factors, obesity risk-allele score, and age at AR. RESULTS A higher genetic susceptibility to obesity score was associated with an earlier age at AR. At the most distal level of the hierarchical model, maternal and paternal educational levels were positively associated with age at AR. Children born to parents with higher BMI were more likely to exhibit earlier age at AR. In addition, higher gestational weight gain was related to earlier age at AR. For children born small for gestational age, the average age at AR was 88 [±39] days lower than for children born appropriate for gestational age and 91 [±56] days lower than for children born large for gestational age. CONCLUSION The timing of AR seems to be an early childhood manifestation of the genetic susceptibility to adult obesity. We further identified low birth weight and gestational weight gain as novel predictors of early AR, highlighting the role of the intrauterine environment in the kinetics of adiposity.
Collapse
Affiliation(s)
| | - Sandrine Lioret
- Université de Paris, CRESS, INSERM, INRAE, F-75004, Paris, France
| | | | - Anne Forhan
- Université de Paris, CRESS, INSERM, INRAE, F-75004, Paris, France
| | - Ken K. Ong
- grid.5335.00000000121885934MRC Epidemiology Unit and Department of Paediatrics, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | | | - Barbara Heude
- Université de Paris, CRESS, INSERM, INRAE, F-75004, Paris, France
| |
Collapse
|
17
|
Arisaka O, Ichikawa G, Koyama S, Sairenchi T. Childhood obesity: rapid weight gain in early childhood and subsequent cardiometabolic risk. Clin Pediatr Endocrinol 2020; 29:135-142. [PMID: 33088012 PMCID: PMC7534524 DOI: 10.1297/cpe.29.135] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 06/06/2020] [Indexed: 12/19/2022] Open
Abstract
Dynamic changes in body weight have long been recognized as important indicators of risk
for human health. Many population-based observational studies have shown that rapid weight
gain during infancy, including a catch-up growth phenomenon or adiposity rebound in early
childhood, predisposes a person to the development of obesity, type 2 diabetes, and
cardiovascular diseases later in life. However, a consensus has not been established
regarding which period of weight gain contributes to future risks. This review evaluates
recent evidence on the relationship between early rapid growth and future obesity and
cardiometabolic risk, with a focus on the differential significance of rapid weight gain
in infancy and early childhood. Although there is a need for attention to childhood growth
during early infancy before 1 yr of age as it may be related to future obesity, emerging
evidence strongly suggests that toddlers showing an increase in body mass index (BMI)
before 3 yr of age, a period normally characterized by decreased BMI, are prone to
developing later cardiometabolic risk.
Collapse
Affiliation(s)
- Osamu Arisaka
- Department of Pediatrics, Nasu Red Cross Hospital, Tochigi, Japan.,Department of Pediatrics, Dokkyo Medical University School of Medicine, Tochigi, Japan
| | - Go Ichikawa
- Department of Pediatrics, Nasu Red Cross Hospital, Tochigi, Japan.,Department of Pediatrics, Dokkyo Medical University School of Medicine, Tochigi, Japan
| | - Satomi Koyama
- Department of Pediatrics, Dokkyo Medical University School of Medicine, Tochigi, Japan
| | - Toshimi Sairenchi
- Department of Public Health, Dokkyo Medical University School of Medicine, Tochigi, Japan
| |
Collapse
|
18
|
Comparison between type A and type B early adiposity rebound in predicting overweight and obesity in children: a longitudinal study. Br J Nutr 2020; 124:501-512. [PMID: 32174289 DOI: 10.1017/s0007114520000987] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Early adiposity rebound (EAR) predicts paediatric overweight/obesity, but current approaches do not consider both the starting point of EAR and the BMI trajectory. We compared the clinical characteristics at birth, age 3-5 and 6-8 years of children, according to the EAR and to its type (type A/type B-EAR). We assessed the children's odds of being classified as overweight/obese at age 6-8 years, according to the type of EAR as defined at age 3-5 years. As part of this two-wave observational study, 1055 children were recruited and examined at age 3-5 years. Antenatal and postnatal information was collected through interviews with parents, and weight and height from the health records. Type A and type B-EAR were defined in wave 1 according to the BMI nadir and the variation of BMI z-score between the starting point of the adiposity rebound and the last point on the curve. At 6-8 years (wave 2), 867 children were followed up; 426 (40·4 %) children demonstrated EAR. Among them, 172 had type A-EAR, higher rates of parental obesity (P < 0·05) and greater birth weight compared with other children (P < 0·001). Odds for overweight/obesity at 6-8 years, when adjusting for antenatal and postnatal factors, was 21·35 (95 % CI 10·94, 41·66) in type A-EAR children and not significant in type B-EAR children (OR 1·76; 95 % CI 0·84, 3·68) compared with children without EAR. Classification of EAR into two subtypes provides physicians with a reliable approach to identify children at risk for overweight/obesity before the age of 5 years.
Collapse
|
19
|
Perng W, Rahman ML, Aris IM, Michelotti G, Sordillo JE, Chavarro JE, Oken E, Hivert MF. Metabolite Profiles of the Relationship between Body Mass Index (BMI) Milestones and Metabolic Risk during Early Adolescence. Metabolites 2020; 10:E316. [PMID: 32751947 PMCID: PMC7464362 DOI: 10.3390/metabo10080316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/24/2020] [Accepted: 07/28/2020] [Indexed: 11/16/2022] Open
Abstract
Early growth is associated with future metabolic risk; however, little is known of the underlying biological pathways. In this prospective study of 249 boys and 227 girls, we sought to identify sex-specific metabolite profiles that mark the relationship between age and magnitude of the infancy body mass index (BMI) peak, and the childhood BMI rebound with a metabolic syndrome z-score (MetS z-score) during early adolescence (median age 12.8 years). Thirteen consensus metabolite networks were generated between male and female adolescents using weighted correlation network analysis. In girls, none of the networks were related to BMI milestones after false discovery rate (FDR) correction at 5%. In boys, age and/or magnitude of BMI at rebound were associated with three metabolite eigenvector (ME) networks comprising androgen hormones (ME7), lysophospholipids (ME8), and diacylglycerols (ME11) after FDR correction. These networks were also associated with MetS z-score in boys after accounting for age and race/ethnicity: ME7 (1.43 [95% CI: 0.52, 2.34] units higher MetS z-score per 1 unit of ME7), ME8 (-1.01 [95% CI: -1.96, -0.07]), and ME11 (2.88 [95% CI: 2.06, 3.70]). These findings suggest that alterations in sex steroid hormone and lipid metabolism are involved in the relationship of early growth with future metabolic risk in males.
Collapse
Affiliation(s)
- Wei Perng
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Mohammad L. Rahman
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (M.L.R.); (I.M.A.); (J.E.S.); (E.O.); (M.-F.H.)
| | - Izzuddin M. Aris
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (M.L.R.); (I.M.A.); (J.E.S.); (E.O.); (M.-F.H.)
| | | | - Joanne E. Sordillo
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (M.L.R.); (I.M.A.); (J.E.S.); (E.O.); (M.-F.H.)
| | - Jorge E. Chavarro
- Department of Nutrition, T. H. Chan Harvard School of Public Health, Boston, MA 02115, USA;
| | - Emily Oken
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (M.L.R.); (I.M.A.); (J.E.S.); (E.O.); (M.-F.H.)
- Department of Nutrition, T. H. Chan Harvard School of Public Health, Boston, MA 02115, USA;
| | - Marie-France Hivert
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (M.L.R.); (I.M.A.); (J.E.S.); (E.O.); (M.-F.H.)
- Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| |
Collapse
|
20
|
Vereen RJ, Dobson NR, Olsen CH, Raiciulescu S, Kuehn D, Stokes TA, Hunt CE. Longitudinal growth changes from birth to 8-9 years in preterm and full term births. J Neonatal Perinatal Med 2020; 13:223-230. [PMID: 31796687 DOI: 10.3233/npm-190219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
BACKGROUND The aim of this study is to assess the effect of age at adiposity rebound (AR) and changes in growth between birth and 6 months on growth status at 8-9 years in children born term and preterm. Age at AR is inversely correlated with risk for later obesity in children born full term, but has not been analyzed in children born preterm. METHODS Birth anthropometrics, and weight and length/height data from age 6 months through 8-9 years were recorded for 175 children born in 2008 in the military health system. Calculated variables include body mass index (BMI, kg/m2), Z-scores, and age at AR. RESULTS Age at AR could be calculated for 150 children (32% preterm); average age was 5.4 years and 5.3 years for children born term and preterm, respectively (NS). For children born term and preterm, there was a significant correlation between younger age at AR and higher BMI Z-score at 8-9 years (r = - 0.685), and a direct relationship between weight Z-score change from birth to 6 months and weight Z-scores at 8-9 years (p = 0.034). CONCLUSIONS Younger age at AR correlates with higher BMI Z-score at 8-9 years in children born both term and preterm. Weight gain from birth to 6 months correlates with weight Z-score at 8-9 years. These results emphasize the importance of younger age at AR in addition to greater early weight gain as an indicator of later obesity.
Collapse
Affiliation(s)
- Rasheda J Vereen
- National Capital Consortium Pediatrics Residency (Walter Reed National Military Medical Center), Bethesda, MD, USA
| | - Nicole R Dobson
- Department of Pediatrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Cara H Olsen
- Department Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Sorana Raiciulescu
- Department Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Devon Kuehn
- Department of Pediatrics, East Carolina University, Greenville, NC, USA
| | - Theophil A Stokes
- Department of Pediatrics, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Carl E Hunt
- Department of Pediatrics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| |
Collapse
|
21
|
Late Adiposity Rebound and the Probability of Developing and Reversing Childhood Obesity. J Pediatr 2020; 216:128-135.e3. [PMID: 31676030 DOI: 10.1016/j.jpeds.2019.09.065] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 09/17/2019] [Accepted: 09/19/2019] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To evaluate the associations between late adiposity rebound (at or after 7.0 years of age) and the probability of developing and reversing obesity during elementary school years. STUDY DESIGN Using nationally representative cohorts from Early Childhood Longitudinal Studies, Kindergarten Class of 1998-1999 and 2010-2011, weighted extended Cox hazard models were used to assess the probability of developing and reversing obesity (cut-offs for extended models were 6 and 12 months after kindergarten entry, respectively). Measurements used in the study were collected 6 times between kindergarten and fifth grade (Early Childhood Longitudinal Studies, Kindergarten Class of 1998-1999) and 8 times between kindergarten through fourth grade (Early Childhood Longitudinal Studies, Kindergarten Class of 2010-2011). RESULTS Among children with obesity at kindergarten entry, within 6 months, the risk of developing obesity was 73% and 76% lower for boys with late adiposity rebound than their classmates without late adiposity rebound (hazard ratio 0.27 and 0.24). Six months after entering kindergarten, similar association was observed for both boys and girls. Among children without obesity at kindergarten entry, within 12 months, the probability of reversing obesity was 52% and 54% higher for boys with late adiposity rebound than their peers without late adiposity rebound (hazard ratio 1.52 and 1.54). Twelve months after entering kindergarten, the probability of reversing obesity among both sexes with late adiposity rebound was 6-8 times that among children without late adiposity rebound. CONCLUSIONS Late adiposity rebound was significantly associated with a decreased risk of developing obesity and an increased probability of reversing obesity among kindergarteners.
Collapse
|
22
|
Bhullar G, Tanawattanacharoen VK, Yeh MY, Kim WS, Vidmar AP, Geffner ME, Hwang DH, Kim MS. Early Adiposity Rebound Predicts Obesity and Adiposity in Youth with Congenital Adrenal Hyperplasia. Horm Res Paediatr 2020; 93:609-615. [PMID: 33691315 PMCID: PMC8159886 DOI: 10.1159/000514130] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 12/23/2020] [Indexed: 12/24/2022] Open
Abstract
INTRODUCTION Youth with classical congenital adrenal hyperplasia (CAH) have higher prevalence of cardiometabolic risk factors such as obesity, abdominal adiposity, and hypertension. Patients with CAH also exhibit an earlier adiposity rebound (AR) compared to normative populations. However, the predictive relationship between AR and cardiometabolic risk factors needs to be better understood. METHODS We performed a retrospective cohort study at a US tertiary pediatric center in youth with classical CAH due to 21-hydroxylase deficiency. AR was determined by cubic polynomial modeling. A subset of participants had fasting analytes, whole-body dual-energy X-ray absorptiometry, and magnetic resonance imaging as adolescents. RESULTS In 42 youth with CAH (45.2% female, 54.8% Hispanic, and 90.5% salt-wasting form), the average age at AR was 3.4 ± 1.3 years. AR differed by BMI-z, with youth with obesity having an earlier AR (2.8 ± 1.0 years) compared to lean youth (4.1 ± 1.3 years, p = 0.001). However, AR did not differ by either CAH form or sex. Earlier AR predicted higher BMI-z at 7 and 12 years of age. In addition, earlier AR predicted increased central obesity (as measured by waist circumference, subcutaneous adipose tissue, and trunk fat) and total body fat in adolescence. AR was negatively correlated with bone age, and its relationships with HDL and hypertension were trending towards significance. CONCLUSIONS AR in youth with classical CAH could serve as a useful clinical marker to identify those patients who are at higher risk for developing cardiometabolic risk factors during childhood and adolescence.
Collapse
Affiliation(s)
- Gagandeep Bhullar
- Center for Endocrinology, Diabetes, and Metabolism, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | | | - Mei Y. Yeh
- Center for Endocrinology, Diabetes, and Metabolism, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - William S. Kim
- Center for Endocrinology, Diabetes, and Metabolism, Children’s Hospital Los Angeles, Los Angeles, CA, USA
| | - Alaina P. Vidmar
- Center for Endocrinology, Diabetes, and Metabolism, Children’s Hospital Los Angeles, Los Angeles, CA, USA,Keck School of Medicine of University of Southern California, Los Angeles, CA, USA,The Saban Research Institute, Los Angeles, CA, USA
| | - Mitchell E. Geffner
- Center for Endocrinology, Diabetes, and Metabolism, Children’s Hospital Los Angeles, Los Angeles, CA, USA,Keck School of Medicine of University of Southern California, Los Angeles, CA, USA,The Saban Research Institute, Los Angeles, CA, USA
| | - Darryl H. Hwang
- Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Mimi S. Kim
- Center for Endocrinology, Diabetes, and Metabolism, Children’s Hospital Los Angeles, Los Angeles, CA, USA,Keck School of Medicine of University of Southern California, Los Angeles, CA, USA,The Saban Research Institute, Los Angeles, CA, USA
| |
Collapse
|
23
|
Couto Alves A, De Silva NMG, Karhunen V, Sovio U, Das S, Taal HR, Warrington NM, Lewin AM, Kaakinen M, Cousminer DL, Thiering E, Timpson NJ, Bond TA, Lowry E, Brown CD, Estivill X, Lindi V, Bradfield JP, Geller F, Speed D, Coin LJM, Loh M, Barton SJ, Beilin LJ, Bisgaard H, Bønnelykke K, Alili R, Hatoum IJ, Schramm K, Cartwright R, Charles MA, Salerno V, Clément K, Claringbould AAJ, van Duijn CM, Moltchanova E, Eriksson JG, Elks C, Feenstra B, Flexeder C, Franks S, Frayling TM, Freathy RM, Elliott P, Widén E, Hakonarson H, Hattersley AT, Rodriguez A, Banterle M, Heinrich J, Heude B, Holloway JW, Hofman A, Hyppönen E, Inskip H, Kaplan LM, Hedman AK, Läärä E, Prokisch H, Grallert H, Lakka TA, Lawlor DA, Melbye M, Ahluwalia TS, Marinelli M, Millwood IY, Palmer LJ, Pennell CE, Perry JR, Ring SM, Savolainen MJ, Rivadeneira F, Standl M, Sunyer J, Tiesler CMT, Uitterlinden AG, Schierding W, O’Sullivan JM, Prokopenko I, Herzig KH, Smith GD, O'Reilly P, Felix JF, Buxton JL, Blakemore AIF, Ong KK, Jaddoe VWV, Grant SFA, Sebert S, McCarthy MI, Järvelin MR. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI. SCIENCE ADVANCES 2019; 5:eaaw3095. [PMID: 31840077 PMCID: PMC6904961 DOI: 10.1126/sciadv.aaw3095] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Accepted: 08/06/2019] [Indexed: 05/29/2023]
Abstract
Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.
Collapse
Affiliation(s)
- Alexessander Couto Alves
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
| | - N. Maneka G. De Silva
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Ulla Sovio
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Shikta Das
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - H. Rob Taal
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Paediatrics, Erasmus MC, Sophia Children’s Hospital, Rotterdam, Netherlands
| | - Nicole M. Warrington
- Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Western Australia, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
| | - Alexandra M. Lewin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Marika Kaakinen
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK
- Centre for Pharmacology and Therapeutics, Division of Experimental Medicine, Department of Medicine, Imperial College London, Hammersmith Hospital, London, UK
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Surrey, UK
| | - Diana L. Cousminer
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Institute of Biomedicine, Department of Physiology, University of Eastern Finland, Kuopio, Finland
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Elisabeth Thiering
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich Neuherberg, Germany
- Division of Metabolic Diseases and Nutritional Medicine, Dr von Hauner Children’s Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom A. Bond
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Estelle Lowry
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Christopher D. Brown
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xavier Estivill
- Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), Barcelona, Catalonia, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Catalonia, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Virpi Lindi
- Institute of Biomedicine, Department of Physiology, University of Eastern Finland, Kuopio, Finland
| | - Jonathan P. Bradfield
- Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Frank Geller
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Doug Speed
- Aarhus Institute of Advanced Studies (AIAS), Aarhus University, Aarhus, Denmark
- UCL Genetics Institute, University College London, London, UK
| | - Lachlan J. M. Coin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Marie Loh
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR) Singapore, Singapore
| | - Sheila J. Barton
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Lawrence J. Beilin
- Medical School, Royal Perth Hospital, University of Western Australia, Perth, Western Australia, Australia
| | - Hans Bisgaard
- COPSAC, The Copenhagen Prospective Studies on Asthma in Childhood, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Klaus Bønnelykke
- COPSAC, The Copenhagen Prospective Studies on Asthma in Childhood, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rohia Alili
- CRNH Ile de France, Hôpital Pitié-Salpêtrière, Paris, France
| | - Ida J. Hatoum
- CRNH Ile de France, Hôpital Pitié-Salpêtrière, Paris, France
- Obesity, Metabolism, and Nutrition Institute and Gastrointestinal Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Katharina Schramm
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, München, Germany
| | - Rufus Cartwright
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Institute for Reproductive and Developmental Biology, Imperial College London, London, UK
| | - Marie-Aline Charles
- Inserm, UMR 1153 (CRESS), Paris Descartes University, Villejuif, Paris, France
| | - Vincenzo Salerno
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Karine Clément
- CRNH Ile de France, Hôpital Pitié-Salpêtrière, Paris, France
- Inserm, UMR 1153 (CRESS), Paris Descartes University, Villejuif, Paris, France
| | - Annique A. J. Claringbould
- University Medical Centre Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV Groningen, Netherlands
| | - BIOS Consortium
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Paediatrics, Erasmus MC, Sophia Children’s Hospital, Rotterdam, Netherlands
- Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Western Australia, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK
- Centre for Pharmacology and Therapeutics, Division of Experimental Medicine, Department of Medicine, Imperial College London, Hammersmith Hospital, London, UK
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Surrey, UK
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Institute of Biomedicine, Department of Physiology, University of Eastern Finland, Kuopio, Finland
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich Neuherberg, Germany
- Division of Metabolic Diseases and Nutritional Medicine, Dr von Hauner Children’s Hospital, Ludwig-Maximilians University Munich, Munich, Germany
- MRC Integrative Epidemiology Unit at the University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), Barcelona, Catalonia, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Catalonia, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Sidra Medical and Research Center, Doha, Qatar
- Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Aarhus Institute of Advanced Studies (AIAS), Aarhus University, Aarhus, Denmark
- UCL Genetics Institute, University College London, London, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR) Singapore, Singapore
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Medical School, Royal Perth Hospital, University of Western Australia, Perth, Western Australia, Australia
- COPSAC, The Copenhagen Prospective Studies on Asthma in Childhood, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- CRNH Ile de France, Hôpital Pitié-Salpêtrière, Paris, France
- Obesity, Metabolism, and Nutrition Institute and Gastrointestinal Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, München, Germany
- Institute for Reproductive and Developmental Biology, Imperial College London, London, UK
- Inserm, UMR 1153 (CRESS), Paris Descartes University, Villejuif, Paris, France
- University Medical Centre Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV Groningen, Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
- Department of General Practice and Primary Health Care, University of Helsinki, and Helsinki University Hospital, Helsinki, Finland
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, UK
- National Institute for Health Research, Imperial College Biomedical Research Centre, London, UK
- Health Data Research UK London, Imperial College London, London, UK
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- School of Psychology, College of Social Science, University of Lincoln Brayford Pool Lincoln, Lincolnshire, UK
- Human Genetics and Medical Genomics, Faculty of Medicine, University of Southampton, Southampton, UK
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Great Ormond Street Hospital Institute of Child Health, University College London, London, UK
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, North Terrace, Adelaide, South Australia, Australia
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institute, Stockholm, Sweden
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Medicine, Stanford University Medical School, Stanford, CA, USA
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), University of Oxford, Old Road Campus, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Oxford, UK
- School of Public Health and Robinson Research Institute, University of Adelaide, Adelaide, Australia
- Avon Longitudinal Study of Parents and Children, School of Social and Community Medicine, University of Bristol, Bristol, UK
- Division of Internal Medicine, and Biocenter of Oulu, Faculty of Medicine, Oulu University, Oulu, Finland
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Liggins Institute, University of Auckland, Auckland, New Zealand
- A Better Start—National Science, Challenge, University of Auckland, Auckland, New Zealand
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, UK
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Research Unit of Biomedicine, University Oulu, Oulu, Finland
- Medical Research Center and Oulu University Hospital, University of Oulu, Oulu, Finland
- Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, De Crespigny Park, London, UK
- School of Life Sciences, Pharmacy and Chemistry, Kingston University, Kingston upon Thames, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
- Section of Investigative Medicine, Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Elena Moltchanova
- Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Johan G. Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, and Helsinki University Hospital, Helsinki, Finland
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
| | - Cathy Elks
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Bjarke Feenstra
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | - Claudia Flexeder
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich Neuherberg, Germany
| | - Stephen Franks
- Institute for Reproductive and Developmental Biology, Imperial College London, London, UK
| | - Timothy M. Frayling
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, UK
| | - Rachel M. Freathy
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- National Institute for Health Research, Imperial College Biomedical Research Centre, London, UK
- Health Data Research UK London, Imperial College London, London, UK
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Hakon Hakonarson
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew T. Hattersley
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, UK
| | - Alina Rodriguez
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- School of Psychology, College of Social Science, University of Lincoln Brayford Pool Lincoln, Lincolnshire, UK
| | - Marco Banterle
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Joachim Heinrich
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich Neuherberg, Germany
| | - Barbara Heude
- Inserm, UMR 1153 (CRESS), Paris Descartes University, Villejuif, Paris, France
| | - John W. Holloway
- Human Genetics and Medical Genomics, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Albert Hofman
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Elina Hyppönen
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Great Ormond Street Hospital Institute of Child Health, University College London, London, UK
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, North Terrace, Adelaide, South Australia, Australia
| | - Hazel Inskip
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Lee M. Kaplan
- Obesity, Metabolism, and Nutrition Institute and Gastrointestinal Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Asa K. Hedman
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Esa Läärä
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Holger Prokisch
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, München, Germany
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Timo A. Lakka
- Institute of Biomedicine, Department of Physiology, University of Eastern Finland, Kuopio, Finland
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | - Debbie A. Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mads Melbye
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Medicine, Stanford University Medical School, Stanford, CA, USA
| | - Tarunveer S. Ahluwalia
- COPSAC, The Copenhagen Prospective Studies on Asthma in Childhood, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marcella Marinelli
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Catalonia, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Iona Y. Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), University of Oxford, Old Road Campus, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Oxford, UK
| | - Lyle J. Palmer
- School of Public Health and Robinson Research Institute, University of Adelaide, Adelaide, Australia
| | - Craig E. Pennell
- Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Western Australia, Australia
| | - John R. Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Susan M. Ring
- MRC Integrative Epidemiology Unit at the University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Avon Longitudinal Study of Parents and Children, School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Markku J. Savolainen
- Division of Internal Medicine, and Biocenter of Oulu, Faculty of Medicine, Oulu University, Oulu, Finland
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Marie Standl
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich Neuherberg, Germany
| | - Jordi Sunyer
- Pompeu Fabra University (UPF), Barcelona, Catalonia, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Catalonia, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Carla M. T. Tiesler
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich Neuherberg, Germany
- Division of Metabolic Diseases and Nutritional Medicine, Dr von Hauner Children’s Hospital, Ludwig-Maximilians University Munich, Munich, Germany
| | - Andre G. Uitterlinden
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | | | - Justin M. O’Sullivan
- Liggins Institute, University of Auckland, Auckland, New Zealand
- A Better Start—National Science, Challenge, University of Auckland, Auckland, New Zealand
| | - Inga Prokopenko
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Surrey, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, UK
| | - Karl-Heinz Herzig
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Research Unit of Biomedicine, University Oulu, Oulu, Finland
- Medical Research Center and Oulu University Hospital, University of Oulu, Oulu, Finland
- Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
| | - Paul O'Reilly
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, De Crespigny Park, London, UK
| | - Janine F. Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Paediatrics, Erasmus MC, Sophia Children’s Hospital, Rotterdam, Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Jessica L. Buxton
- School of Life Sciences, Pharmacy and Chemistry, Kingston University, Kingston upon Thames, UK
| | - Alexandra I. F. Blakemore
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
- Section of Investigative Medicine, Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, UK
| | - Ken K. Ong
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Vincent W. V. Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Struan F. A. Grant
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sylvain Sebert
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Mark I. McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
| | - Marjo-Riitta Järvelin
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Medical Research Center and Oulu University Hospital, University of Oulu, Oulu, Finland
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| | - Early Growth Genetics (EGG) Consortium
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Department of Obstetrics and Gynaecology, University of Cambridge, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Paediatrics, Erasmus MC, Sophia Children’s Hospital, Rotterdam, Netherlands
- Division of Obstetrics and Gynaecology, The University of Western Australia, Perth, Western Australia, Australia
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Queensland, Australia
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
- Department of Genomics of Common Disease, School of Public Health, Imperial College London, Hammersmith Hospital, London, UK
- Centre for Pharmacology and Therapeutics, Division of Experimental Medicine, Department of Medicine, Imperial College London, Hammersmith Hospital, London, UK
- Department of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey, Surrey, UK
- Division of Human Genetics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Institute of Biomedicine, Department of Physiology, University of Eastern Finland, Kuopio, Finland
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
- Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich Neuherberg, Germany
- Division of Metabolic Diseases and Nutritional Medicine, Dr von Hauner Children’s Hospital, Ludwig-Maximilians University Munich, Munich, Germany
- MRC Integrative Epidemiology Unit at the University of Bristol and NIHR Bristol Biomedical Research Center, Bristol, UK
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Genomics and Disease Group, Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG), Barcelona, Catalonia, Spain
- Pompeu Fabra University (UPF), Barcelona, Catalonia, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Catalonia, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Madrid, Spain
- Sidra Medical and Research Center, Doha, Qatar
- Center for Applied Genomics, Abramson Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
- Aarhus Institute of Advanced Studies (AIAS), Aarhus University, Aarhus, Denmark
- UCL Genetics Institute, University College London, London, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
- Translational Laboratory in Genetic Medicine (TLGM), Agency for Science, Technology and Research (A*STAR) Singapore, Singapore
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Medical School, Royal Perth Hospital, University of Western Australia, Perth, Western Australia, Australia
- COPSAC, The Copenhagen Prospective Studies on Asthma in Childhood, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
- CRNH Ile de France, Hôpital Pitié-Salpêtrière, Paris, France
- Obesity, Metabolism, and Nutrition Institute and Gastrointestinal Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Institute of Human Genetics, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technische Universität München, München, Germany
- Institute for Reproductive and Developmental Biology, Imperial College London, London, UK
- Inserm, UMR 1153 (CRESS), Paris Descartes University, Villejuif, Paris, France
- University Medical Centre Groningen, Department of Genetics, Antonius Deusinglaan 1, 9713 AV Groningen, Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Department of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
- Department of General Practice and Primary Health Care, University of Helsinki, and Helsinki University Hospital, Helsinki, Finland
- Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, University of Exeter, Royal Devon and Exeter Hospital, Exeter, UK
- National Institute for Health Research, Imperial College Biomedical Research Centre, London, UK
- Health Data Research UK London, Imperial College London, London, UK
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- School of Psychology, College of Social Science, University of Lincoln Brayford Pool Lincoln, Lincolnshire, UK
- Human Genetics and Medical Genomics, Faculty of Medicine, University of Southampton, Southampton, UK
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Great Ormond Street Hospital Institute of Child Health, University College London, London, UK
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, North Terrace, Adelaide, South Australia, Australia
- NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Cardiovascular Medicine Unit, Department of Medicine, Karolinska Institute, Stockholm, Sweden
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Medicine, Stanford University Medical School, Stanford, CA, USA
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), University of Oxford, Old Road Campus, Oxford, UK
- Medical Research Council Population Health Research Unit (MRC PHRU) at the University of Oxford, Oxford, UK
- School of Public Health and Robinson Research Institute, University of Adelaide, Adelaide, Australia
- Avon Longitudinal Study of Parents and Children, School of Social and Community Medicine, University of Bristol, Bristol, UK
- Division of Internal Medicine, and Biocenter of Oulu, Faculty of Medicine, Oulu University, Oulu, Finland
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands
- Liggins Institute, University of Auckland, Auckland, New Zealand
- A Better Start—National Science, Challenge, University of Auckland, Auckland, New Zealand
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Headington, Oxford, UK
- Biocenter Oulu, University of Oulu, Oulu, Finland
- Research Unit of Biomedicine, University Oulu, Oulu, Finland
- Medical Research Center and Oulu University Hospital, University of Oulu, Oulu, Finland
- Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King’s College London, De Crespigny Park, London, UK
- School of Life Sciences, Pharmacy and Chemistry, Kingston University, Kingston upon Thames, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, London, UK
- Section of Investigative Medicine, Division of Diabetes, Endocrinology and Metabolism, Imperial College London, London, UK
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK
- Unit of Primary Care, Oulu University Hospital, Oulu, Finland
| |
Collapse
|
24
|
Abstract
Evidence suggests that both high and low birth weight children have increased the risk for obesity and the metabolic syndrome in adulthood. Previously we have found altered feeding behaviour and food preferences in pre-school children and adults born with low birth weight. In this study, we investigated if birth weight was associated with different intake of fat, carbohydrate and/or protein at 6-12 years of age. This is a cross-sectional study where 255 guardians answered online and telephone questions including anthropometrics and demographic data, parental family food rules (food control, encouragement and restriction) and a complete web-based FFQ for their children (130 boys and 125 girls). Baseline demographic and parental food rules characteristics did not differ accordingly to sex. Linear regression models were conducted separately for each sex, adjusted for income, age and maternal age. There were no differences in total energy intake, but energy density (ED, energy content/g) was negatively associated with birth weight in boys. Macronutrient analysis showed that ED intake was from a greater intake of fat. Birth weight was not a significant predictor of protein and carbohydrate intake in boys. In girls, we saw a positive correlation between fat intake and cholesterol intake v. birth weight, but no association with ED intake (results did not remain after adjustment). The study shows that low birth weight is associated with altered fat intake in childhood in a sex-specific manner. It is likely that biological factors such as fetal programming of homoeostatic and/or hedonic pathways influencing food preferences are involved in this process.
Collapse
|
25
|
Age at adiposity rebound in childhood is associated with PCOS diagnosis and obesity in adulthood-longitudinal analysis of BMI data from birth to age 46 in cases of PCOS. Int J Obes (Lond) 2019; 43:1370-1379. [PMID: 30718819 PMCID: PMC6760596 DOI: 10.1038/s41366-019-0318-z] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 11/21/2018] [Accepted: 12/20/2018] [Indexed: 11/24/2022]
Abstract
Background: Adiposity rebound (AR), the second BMI rise in childhood at around the age of 6 years, is associated with obesity and metabolic alteration in later life. Given that polycystic ovary syndrome (PCOS) has a strong metabolic component, early life growth patterns could reveal a risk of PCOS. Thus, we aimed to investigate the associations between age at AR and PCOS diagnosis and BMI later in life. Materials and methods: This study is part of a prospective, population-based longitudinal study, where women with PCOS diagnosis by age 46 (n = 280) were compared with asymptomatic women (CTRLs, n = 1573). Weight and height data from birth to age 13 years, at age at menarche, and at ages 31 and 46 years were analyzed Results: Women with PCOS had lower birth weight (3357 ± 477 vs. 3 445 ± 505 g, p < 0.001), earlier age at AR (5.2 ± 1.0 vs. 5.6 ± 0.90 years, p < 0.001) and higher BMI from AR onwards compared with controls. Early timing of AR was associated with PCOS diagnosis independently of BMI (OR 1.62, 95% Cl 1.37–1.92). Women with PCOS and early AR had higher BMI at 31 and 46 years when compared to controls with early AR. The age at AR did not associate with T levels at ages 31 or 46 years. Conclusions: Early AR was associated with PCOS diagnosis and high BMI in adulthood. Adolescent girls with early AR and persisting obesity should be screened for PCOS symptoms, such as persistent irregular cycles and hirsutism.
Collapse
|
26
|
Aris IM, Rifas-Shiman SL, Li LJ, Kleinman KP, Coull BA, Gold DR, Hivert MF, Kramer MS, Oken E. Patterns of body mass index milestones in early life and cardiometabolic risk in early adolescence. Int J Epidemiol 2019; 48:157-167. [PMID: 30624710 PMCID: PMC6380298 DOI: 10.1093/ije/dyy286] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/18/2018] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Few studies have examined the independent and combined relationships of body mass index (BMI) peak and rebound with adiposity, insulin resistance and metabolic risk later in life. We used data from Project Viva, a well-characterized birth cohort from Boston with repeated measures of BMI, to help fill this gap. METHODS Among 1681 children with BMI data from birth to mid childhood, we fitted individual BMI trajectories using mixed-effects models with natural cubic splines and estimated age, and magnitude of BMI, at peak (in infancy) and rebound (in early childhood). We obtained cardiometabolic measures of the children in early adolescence (median 12.9 years) and analysed their associations with the BMI parameters. RESULTS After adjusting for potential confounders, age and magnitude at infancy BMI peak were associated with greater adolescent adiposity, and earlier adiposity rebound was strongly associated with greater adiposity, insulin resistance and metabolic risk score independently of BMI peak. Children with a normal timing of BMI peak plus early rebound had an adverse cardiometabolic profile, characterized by higher fat mass index {β 2.2 kg/m2 [95% confidence interval (CI) 1.6, 2.9]}, trunk fat mass index [1.1 kg/m2 (0.8, 1.5)], insulin resistance [0.2 units (0.04, 0.4)] and metabolic risk score [0.4 units (0.2, 0.5)] compared with children with a normal BMI peak and a normal rebound pattern. Children without a BMI peak (no decline in BMI after the rise in infancy) also had adverse adolescent metabolic profiles. CONCLUSIONS Early age at BMI rebound is a strong risk factor for cardiometabolic risk, independent of BMI peak. Children with a normal peak-early rebound pattern, or without any BMI decline following infancy, are at greatest risk of adverse cardiometabolic profile in adolescence. Routine monitoring of BMI may help to identify children who are at greatest risk of developing an adverse cardiometabolic profile in later life and who may be targeted for preventive interventions.
Collapse
Affiliation(s)
- Izzuddin M Aris
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
| | - Sheryl L Rifas-Shiman
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Ling-Jun Li
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Division of Obstetrics and Gynecology, KK Women’s and Children’s Hospital, Singapore, Singapore
- Obstetrics and Gynecology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Ken P Kleinman
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
| | - Brent A Coull
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Diane R Gold
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Environmental Medicine, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marie-France Hivert
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Michael S Kramer
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Departments of Pediatrics
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University Faculty of Medicine, Montreal, QC, Canada
| | - Emily Oken
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
27
|
Kamel M, Smith BT, Wahi G, Carsley S, Birken CS, Anderson LN. Continuous cardiometabolic risk score definitions in early childhood: a scoping review. Obes Rev 2018; 19:1688-1699. [PMID: 30223304 DOI: 10.1111/obr.12748] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 06/25/2018] [Accepted: 07/06/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND Cardiometabolic risk (CMR) in young children has been measured using various approaches, including a continuous summary score that incorporates components such as adiposity, lipids, metabolic factors and blood pressure. OBJECTIVES The objective of this study was to comprehensively review definitions of continuous CMR scores in children <10 years of age. METHODS A scoping review was conducted using a systematic search of four scientific databases up to June 2016. Inclusion criteria were children <10 years of age and report of a continuous CMR score. RESULTS Ninety-one articles were included. Most studies were published from 2007 to 2016 (96%). Nearly all continuous CMR scores (90%) were calculated using the sum or the mean of z-scores, and many articles age-standardized and sex-standardized components within their own population. The mean number of variables included in the risk scores was 5 with a range of 3-11. The most commonly included score components were waist circumference (52%), triglycerides (87%), high-density lipoprotein cholesterol (67%), glucose (43%) and systolic blood pressure (52%). IMPORTANCE Continuous CMR scores are emerging frequently in the child health literature and are calculated using numerous methods with diverse components. This heterogeneity limits comparability across studies. A harmonized definition of CMR in childhood is needed.
Collapse
Affiliation(s)
- M Kamel
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - B T Smith
- Public Health Ontario, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - G Wahi
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
| | - S Carsley
- Public Health Ontario, Toronto, Ontario, Canada.,Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - C S Birken
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - L N Anderson
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.,Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| |
Collapse
|
28
|
Binder AM, Stiemsma LT, Keller K, van Otterdijk SD, Mericq V, Pereira A, Santos JL, Shepherd J, Michels KB. Inverse association between estrogen receptor-α DNA methylation and breast composition in adolescent Chilean girls. Clin Epigenetics 2018; 10:122. [PMID: 30286806 PMCID: PMC6172836 DOI: 10.1186/s13148-018-0553-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 09/14/2018] [Indexed: 12/21/2022] Open
Abstract
Background Estrogen receptor-α (ER-α) is a transcriptional regulator, which mediates estrogen-dependent breast development, as well as breast tumorigenesis. The influence of epigenetic regulation of ER-α on adolescent breast composition has not been previously studied and could serve as a marker of pubertal health and susceptibility to breast cancer. We investigated the association between ER-α DNA methylation in leukocytes and breast composition in adolescent Chilean girls enrolled in the Growth and Obesity Cohort Study (GOCS) in Santiago, Chile. Breast composition (total breast volume (BV; cm3), fibroglandular volume (FGV; cm3), and percent fibroglandular volume (%FGV)) was measured at breast Tanner stage 4 (B4). ER-α promoter DNA methylation was assessed by pyrosequencing in blood samples collected at breast Tanner stages 2 (B2; n = 256) and B4 (n = 338). Results After adjusting for fat percentage at breast density measurement, ER-α methylation at B2, and cellular heterogeneity, we observed an inverse association between B4 average ER-α DNA methylation and BV and FGV. Geometric mean BV was 15% lower (95% CI: − 28%, − 1%) among girls in the highest quartile of B4 ER-α methylation (6.96–23.60%) relative to the lowest (0.78–3.37%). Similarly, FGV was 19% lower (95% CI: − 33%, − 2%) among girls in the highest quartile of B4 ER-α methylation relative to the lowest. The association between ER-α methylation and breast composition was not significantly modified by body fat percentage and was not influenced by pubertal timing. Conclusions These findings suggest that the methylation profile of ER-α may modulate adolescent response to estrogen and breast composition, which may influence breast cancer risk in adulthood. Electronic supplementary material The online version of this article (10.1186/s13148-018-0553-5) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Alexandra M Binder
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, 90095, USA
| | - Leah T Stiemsma
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, 90095, USA
| | - Kristen Keller
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, 90095, USA
| | - Sanne D van Otterdijk
- Institute for Prevention and Cancer Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany
| | - Verónica Mericq
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - José L Santos
- Department of Nutrition, Diabetes and Metabolism, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - John Shepherd
- Population Sciences in the Pacific Program, University of Hawaii Cancer Center, Honolulu, HI, 96813, USA
| | - Karin B Michels
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, 90095, USA.
| |
Collapse
|
29
|
Di Gravio C, Krishnaveni GV, Somashekara R, Veena SR, Kumaran K, Krishna M, Karat SC, Fall CHD. Comparing BMI with skinfolds to estimate age at adiposity rebound and its associations with cardio-metabolic risk markers in adolescence. Int J Obes (Lond) 2018; 43:683-690. [PMID: 30006579 PMCID: PMC6230257 DOI: 10.1038/s41366-018-0144-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 04/26/2018] [Accepted: 05/18/2018] [Indexed: 12/03/2022]
Abstract
Background Body mass index (BMI) reaches a nadir in mid-childhood, known as the adiposity rebound (AR). Earlier AR is associated with a higher risk of cardio-vascular diseases in later life. Skinfolds, which are a more direct measure of adiposity, may give better insight into the relationship between childhood adiposity and later obesity and cardio-metabolic risk. Objective We aimed to assess whether AR corresponds to a rebound in skinfolds, and compare associations of BMI-derived AR and skinfold-derived AR with cardio-metabolic risk markers in adolescence. Methods We used penalised splines with random coefficients to estimate BMI and skinfold trajectories of 604 children from the Mysore Parthenon Birth Cohort. Age at AR was identified using differentiation of the BMI and skinfold growth curves between 2 and 10 years of age. At 13.5 years, we measured blood pressure, and glucose, insulin and lipid concentrations. Results BMI and skinfolds had different growth patterns. Boys reached BMI-derived AR earlier than skinfold-derived AR (estimated difference: 0.41 years; 95% CI:[0.23, 0.56]), whereas the opposite was observed in girls (estimated difference: −0.71 years; 95% CI:[−0.90, −0.54]). At 13.5 years, children with earlier BMI-derived AR had higher BMI (−0.58 SD per SD increase of AR; 95%CI:[−0.65, −0.52]), fat mass (−0.44; 95%CI:[−0.50, −0.37]), insulin resistance (HOMA-IR: −0.20; 95%CI:[−0.28, −0.12]) and systolic blood pressure (−0.20; 95%CI:[−0.28, −0.11]), and lower HDL-cholesterol (0.12; 95%CI:[0.04, 0.21]). The associations were independent of BMI at time of rebound, but were fully explained by fat mass at 13.5 years. Similar associations were found for skinfold-derived AR. Conclusion BMI-derived adiposity rebound predicts later cardio-metabolic risk markers similarly to that derived from skinfolds, a direct measure of adiposity.
Collapse
Affiliation(s)
- Chiara Di Gravio
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.
| | | | | | - S R Veena
- CSI Holdsworth Memorial Hospital, Mysore, India
| | - K Kumaran
- CSI Holdsworth Memorial Hospital, Mysore, India
| | | | - S C Karat
- CSI Holdsworth Memorial Hospital, Mysore, India
| | - Caroline H D Fall
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| |
Collapse
|
30
|
Aris IM, Bernard JY, Chen LW, Tint MT, Pang WW, Lim WY, Soh SE, Saw SM, Godfrey KM, Gluckman PD, Chong YS, Yap F, Kramer MS, Lee YS. Infant body mass index peak and early childhood cardio-metabolic risk markers in a multi-ethnic Asian birth cohort. Int J Epidemiol 2018; 46:513-525. [PMID: 27649801 DOI: 10.1093/ije/dyw232] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2016] [Indexed: 12/14/2022] Open
Abstract
Background : Infant body mass index (BMI) peak has received much interest recently as a potential predictor of future obesity and metabolic risk. No studies, however, have examined infant BMI peak in Asian populations, in whom the risk of metabolic disease is higher. Methods : We utilized data among 1020 infants from a mother-offspring cohort, who were Singapore citizens or permanent residents of Chinese, Malay or Indian ethnicity with homogeneous parental ethnic backgrounds, and did not receive chemotherapy, psychotropic drugs or have diabetes mellitus. Ethnicity was self-reported at recruitment and later confirmed using genotype analysis. Subject-specific BMI curves were fitted to infant BMI data using natural cubic splines with random coefficients to account for repeated measures in each child. We estimated characteristics of the child's BMI peak [age and magnitude at peak, average pre-peak velocity (aPPV)]. Systolic (SBP) and diastolic blood pressure (DBP), BMI, sum of skinfolds (SSF) and fat-mass index (FMI) were measured during a follow-up visit at age 48 months. Weighted multivariable linear regression was used to assess the predictors (maternal BMI, gestational weight gain, ethnicity, infant sex, gestational age, birthweight-for-gestational age and breastfeeding duration) of infant BMI peak and its associations with outcomes at 48 months. Comparisons between ethnicities were tested using Bonferroni post-hoc correction. Results : Of 1020 infants, 80.5% were followed up at the 48-month visit. Mean (SD) BMI, SSF and FMI at 48 months were 15.6 (1.8) kg/m 2 , 16.5 (5.3) mm and 3.8 (1.3) kg/m 2 , respectively. Mean (SD) age at peak BMI was 6.0 (1.6) months, with a magnitude of 17.2 (1.4) kg/m 2 and pre-peak velocity of 0.7 (0.3) kg/m 2 /month. Compared with Chinese infants, the peak occurred later in Malay {B [95% confidence interval (CI): 0.64 mo (0.36, 0.92)]} and Indian infants [1.11 mo (0.76, 1.46)] and was lower in magnitude in Indian infants [-0.45 kg/m 2 (-0.69, -0.20)]. Adjusting for maternal education, BMI, gestational weight gain, ethnicity, infant sex, gestational age, birthweight-for-gestational-age and breastfeeding duration, higher peak and aPPV were associated with greater BMI, SSF and FMI at 48 months. Age at peak was positively associated with BMI at 48 months [0.15 units (0.09, 0.22)], whereas peak magnitude was associated with SBP [0.17 units (0.05, 0.30)] and DBP at 48 months [0.10 units (0.01, 0.22)]. Older age and higher magnitude at peak were associated with increased risk of overweight at 48 months [Relative Risk (95% CI): 1.35 (1.12-1.62) for age; 1.89 (1.60-2.24) for magnitude]. The associations of BMI peak with BMI and SSF at 48 months were stronger in Malay and Indian children than in Chinese children. Conclusions : Ethnic-specific differences in BMI peak characteristics, and associations of BMI peak with early childhood cardio-metabolic markers, suggest an important impact of early BMI development on later metabolic outcomes in Asian populations.
Collapse
Affiliation(s)
- Izzuddin M Aris
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research Singapore
| | - Jonathan Y Bernard
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research Singapore
| | - Ling-Wei Chen
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mya Thway Tint
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Wei Wei Pang
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Wai Yee Lim
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Shu E Soh
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research Singapore
| | - Seang-Mei Saw
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Keith M Godfrey
- MRC Lifecourse Epidemiology Unit and NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research Singapore.,Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap-Seng Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research Singapore.,Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Fabian Yap
- Department of Paediatrics, KK Women's and Children's Hospital, Singapore.,Duke-NUS Medical School, Singapore, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Michael S Kramer
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Pediatrics and of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Yung Seng Lee
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research Singapore.,Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.,Department of Khoo Teck Puat-National University Children's Medical Institute, National University Health System, Singapore, Singapore
| |
Collapse
|
31
|
Mustila T, Raitanen J, Keskinen P, Luoto R. A pragmatic controlled trial to prevent childhood obesity within a risk group at maternity and child health-care clinics: results up to six years of age (the VACOPP study). BMC Pediatr 2018; 18:89. [PMID: 29486763 PMCID: PMC5828437 DOI: 10.1186/s12887-018-1065-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 02/15/2018] [Indexed: 02/02/2023] Open
Abstract
Background Obesity in childhood appears often during the toddler years. The prenatal environment influences obesity risk. Maternal gestational diabetes, the child’s diet, and physical activity in the first few years have an important role in subsequent weight gain. A study was conducted to evaluate effectiveness of a primary health-care lifestyle counselling intervention in prevention of childhood obesity up to 6 years of age. Methods The study was a controlled pragmatic trial to prevent childhood obesity and was implemented at maternity and child health-care clinics. The participants (n = 185) were mothers at risk of gestational diabetes mellitus with their offspring born between 2008 and 2010. The prenatal intervention, started at the end of the first trimester of pregnancy, consisted of counselling on diet and physical activity by municipal health-care staff. The intervention continued at yearly appointments with a public health-nurse at child health-care clinics. The paper reports the offspring weight gain results for 2–6 years of age. Weight gain up to 6 years of age was assessed as BMI standard deviation scores (SDS) via a mixed-effect linear regression model. The proportion of children at 6 years with overweight/obesity was assessed as weight-for-height percentage and ISO-BMI. Priority was not given to power calculations, because of the study’s pragmatic nature. Results One hundred forty seven children’s (control n = 76/85% and intervention n = 71/56%) weight and height scores were available for analysis at 6 years of age. There was no significant difference in weight gain or overweight/obesity proportions between the groups at 6 years of age, but the proportion of children with obesity in both groups was high (assessed as ISO-BMI 9.9% and 11.8%) relative to prevalence in this age group in Finland. Conclusion As the authors previously reported, the intervention-group mothers had lower prevalence of gestational diabetes mellitus, but a decrease in obesity incidence before school age among their offspring was not found. The authors believe that an effective intervention should start before conception, continuing during pregnancy and the postpartum period through the developmentally unique child’s first years. Trial registration ClinicalTrials.gov NCT00970710. Registered 1 September 2009. Retrospectively registered.
Collapse
Affiliation(s)
- Taina Mustila
- Seinäjoki Central Hospital, Hanneksenrinne 7, 60220, Seinäjoki, Finland.
| | - Jani Raitanen
- UKK Institute for Health Promotion, Tampere, Finland.,Faculty of Social Sciences, University of Tampere, Tampere, Finland
| | - Päivi Keskinen
- Pediatric Research Centre, 33014 University of Tampere, Tampere, Finland.,Tampere University Hospital, 33521, Tampere, Finland
| | - Riitta Luoto
- Faculty of Social Sciences, University of Tampere, Tampere, Finland
| |
Collapse
|
32
|
Page LC, Shi M, Freemark M. Early-Onset Obesity Caused by Monogenic Disorders. CURRENT PEDIATRICS REPORTS 2017. [DOI: 10.1007/s40124-017-0132-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
33
|
Ip EH, Marshall SA, Saldana S, Skelton JA, Suerken CK, Arcury TA, Quandt SA. Determinants of Adiposity Rebound Timing in Children. J Pediatr 2017; 184:151-156.e2. [PMID: 28242030 PMCID: PMC5404387 DOI: 10.1016/j.jpeds.2017.01.051] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 12/02/2016] [Accepted: 01/20/2017] [Indexed: 02/08/2023]
Abstract
OBJECTIVE Adiposity rebound (AR) or BMI (body mass index) rebound refers to the increase in BMI following the minimum BMI in early childhood. Early AR (before age 5) is predictive of adult obesity. To determine how 4 domains - demographics, maternal BMI, food security, and behavioral characteristics - may affect timing of AR. STUDY DESIGN A total of 248 children, ages 2.5-3.5 years, in Latino farmworker families in North Carolina were examined at baseline and every 3 months for 2 years. BMI was plotted serially for each child and the onset of BMI rebound was determined by visual inspection of the graphs. Given the ages of the children, all rebounds were detected before age 5 years and were deemed "early," whereas other children were classified as "nonrebounders." Classes were then compared in terms of the 4 domains with the use of bivariate analyses and linear mixed models. RESULTS A total of 131 children demonstrated early rebound, 59 children were nonrebounders, and a further 35 had inconclusive data. Parents of early rebounders were less likely to have documentation permitting legal residence in the US. Mothers of early rebounders were on average 3 BMI units heavier. Sex, household food security, diet quality, caloric intake, and daily activity did not differ between classes. In multivariable analysis, female sex, limited maternal education, increased maternal BMI, and increased caloric intake were significant predictors of early rebound. CONCLUSION High maternal BMI was the strongest predictor of early BMI rebound, but increased caloric intake also was significant. Limiting excess calories could delay premature AR and lower the risk of future obesity.
Collapse
Affiliation(s)
- Edward H. Ip
- Department of Biostatistical Sciences, Wake Forest School of
Medicine
| | - Sarah A. Marshall
- Department of Biostatistical Sciences, Wake Forest School of
Medicine
| | - Santiago Saldana
- Department of Biostatistical Sciences, Wake Forest School of
Medicine
| | | | | | - Thomas A. Arcury
- Department of Family and Community Medicine, Wake Forest School of
Medicine
| | - Sara A. Quandt
- Department of Epidemiology and Prevention, Wake Forest School of
Medicine
| |
Collapse
|
34
|
Mo-suwan L, McNeil E, Sangsupawanich P, Chittchang U, Choprapawon C. Adiposity rebound from three to six years of age was associated with a higher insulin resistance risk at eight-and-a-half years in a birth cohort study. Acta Paediatr 2017; 106:128-134. [PMID: 27759899 DOI: 10.1111/apa.13639] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 10/03/2016] [Accepted: 10/17/2016] [Indexed: 01/21/2023]
Abstract
AIM The association between adiposity rebound and insulin resistance in middle childhood has seldom been studied. We examined the effect of body mass index (BMI) velocity and early adiposity rebound on the insulin resistance of prepubertal children. METHODS BMI data from a longitudinal follow-up of a birth cohort in Thailand were used. The homoeostatic model assessment of insulin resistance (HOMA-IR) was calculated from 12-hour fasting plasma glucose and serum insulin at eight-and-a-half years of age. BMI velocity was calculated from four periods: zero to one, one to three, three to six and six to eight-and-a-half years of age. A multiple linear regression model was used to assess the association of BMI velocity during these four periods and insulin resistance at the age of eight-and-a-half years. RESULTS In 814 children - 76.7% of the initial cohort - BMI velocities between years one to three, three to six and six to eight-and-a-half years were positively associated with HOMA-IR levels after adjusting for demographic, behavioural and socio-economic factors. Children who had BMI gains between three and six years had mean HOMA-IR values that were 43% higher than those who did not. CONCLUSION BMI velocity during early and middle childhood, and early adiposity rebound between three and six years, was associated with a higher insulin resistance risk at eight-and-a-half years.
Collapse
Affiliation(s)
- L Mo-suwan
- Department of Paediatrics; Faculty of Medicine; Prince of Songkla University; Songkhla Thailand
| | - E McNeil
- Epidemiology Unit; Faculty of Medicine; Prince of Songkla University; Songkhla Thailand
| | - P Sangsupawanich
- Department of Paediatrics; Faculty of Medicine; Prince of Songkla University; Songkhla Thailand
| | - U Chittchang
- Institute of Nutrition; Mahidol University; Phutthamonthon Nakhon Pathom Thailand
| | - C Choprapawon
- Thammasat Secondary School; Faculty of Learning Sciences and Education; Thammasat University; Pratumtani Thailand
| |
Collapse
|
35
|
Potential selection effects when estimating associations between the infancy peak or adiposity rebound and later body mass index in children. Int J Obes (Lond) 2016; 41:518-526. [PMID: 27899810 DOI: 10.1038/ijo.2016.218] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2016] [Revised: 09/24/2016] [Accepted: 11/13/2016] [Indexed: 12/23/2022]
Abstract
INTRODUCTION This study aims to evaluate a potential selection effect caused by exclusion of children with non-identifiable infancy peak (IP) and adiposity rebound (AR) when estimating associations between age and body mass index (BMI) at IP and AR and later weight status. SUBJECTS AND METHODS In 4744 children with at least 4 repeated measurements of height and weight in the age interval from 0 to 8 years (37 998 measurements) participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants)/I.Family cohort study, fractional polynomial multilevel models were used to derive individual BMI trajectories. Based on these trajectories, age and BMI at IP and AR, BMI values and growth velocities at selected ages as well as the area under the BMI curve were estimated. The BMI growth measures were standardized and related to later BMI z-scores (mean age at outcome assessment: 9.2 years). RESULTS Age and BMI at IP and AR were not identifiable in 5.4% and 7.8% of the children, respectively. These groups of children showed a significantly higher BMI growth during infancy and childhood. In the remaining sample, BMI at IP correlated almost perfectly (r⩾0.99) with BMI at ages 0.5, 1 and 1.5 years, whereas BMI at AR correlated perfectly with BMI at ages 4-6 years (r⩾0.98). In the total study group, BMI values in infancy and childhood were positively associated with later BMI z-scores where associations increased with age. Associations between BMI velocities and later BMI z-scores were largest at ages 5 and 6 years. Results differed for children with non-identifiable IP and AR, demonstrating a selection effect. CONCLUSIONS IP and AR may not be estimable in children with higher-than-average BMI growth. Excluding these children from analyses may result in a selection bias that distorts effect estimates. BMI values at ages 1 and 5 years might be more appropriate to use as predictors for later weight status instead.
Collapse
|
36
|
Bandera EV, Fay SH, Giovannucci E, Leitzmann MF, Marklew R, McTiernan A, Mullee A, Romieu I, Thune I, Uauy R, Wiseman MJ. The use and interpretation of anthropometric measures in cancer epidemiology: A perspective from the world cancer research fund international continuous update project. Int J Cancer 2016; 139:2391-7. [PMID: 27352197 DOI: 10.1002/ijc.30248] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Accepted: 05/17/2016] [Indexed: 03/25/2024]
Abstract
Anthropometric measures relating to body size, weight and composition are increasingly being associated with cancer risk and progression. Whilst practical in epidemiologic research, where population-level associations with disease are revealed, it is important to be aware that such measures are imperfect markers of the internal physiological processes that are the actual correlates of cancer development. Body mass index (BMI), the most commonly used marker for adiposity, may mask differences between lean and adipose tissue, or fat distribution, which varies across individuals, ethnicities, and stage in the lifespan. Other measures, such as weight gain in adulthood, waist circumference and waist-to-hip ratio, contribute information on adipose tissue distribution and insulin sensitivity. Single anthropometric measures do not capture maturational events, including the presence of critical windows of susceptibility (i.e., age of menarche and menopause), which presents a challenge in epidemiologic work. Integration of experimental research on underlying dynamic genetic, hormonal, and other non-nutritional mechanisms is necessary for a confident conclusion of the overall evidence in cancer development and progression. This article discusses the challenges confronted in evaluating and interpreting the current evidence linking anthropometric factors and cancer risk as a basis for issuing recommendations for cancer prevention.
Collapse
Affiliation(s)
- Elisa V Bandera
- Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey, USA
| | | | - Edward Giovannucci
- Harvard TH Chan School of Public Health, Harvard University, Boston, Massachusetts, USA
| | - Michael F Leitzmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany
| | | | - Anne McTiernan
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Amy Mullee
- International Agency for Research on Cancer, Lyon, France
| | | | - Inger Thune
- Institute of Clinical Medicine, Oslo University Hospital and University of Tromsø, Tromsø, Norway
| | - Ricardo Uauy
- Instituto De Nutrición Y Tecnología De Los Alimentos, University of Chile, Santiago, Chile; London, UK School of Hygiene and Tropical Medicine
| | - Martin J Wiseman
- NIHR Southampton Biomedical Research Centre and Southampton General Hospital, Southampton, UK
| |
Collapse
|
37
|
Prevalence and sociodemographic correlates of metabolic syndrome in school-aged children and their parents in nine Mesoamerican countries. Public Health Nutr 2016; 20:255-265. [PMID: 27609776 DOI: 10.1017/s1368980016002342] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE To ascertain the prevalence and sociodemographic correlates of cardiometabolic risk factors in adults and school-aged children from Mesoamerica. DESIGN Cross-sectional study with convenience sampling. In adults, metabolic syndrome was defined according to the National Cholesterol Education Program's Adult Treatment Panel III (ATP III) criteria. In children, we calculated a continuous sex- and age-standardized metabolic risk score using variables corresponding to adult ATP III criteria. Metabolic syndrome prevalence in adults and risk score distribution in children were compared across levels of sociodemographic characteristics with use of Poisson and linear regression, respectively. SETTING Capital cities of Guatemala, El Salvador, the Dominican Republic, Honduras, Nicaragua, Panama, Costa Rica, the Mexican State of Chiapas (Tuxtla Gutiérrez city) and Belize. SUBJECTS Families (n 267), comprising one child aged 7-12 years and their biological parents. RESULTS The prevalence of metabolic syndrome was 37·9 % among women and 35·3 % among men. The most common component was low HDL cholesterol, 83·3 % in women and 78·9 % in men. Prevalence was positively associated with age. In women, metabolic syndrome was inversely related to education level whereas in men it was positively associated with household food security and height, after adjustment. The metabolic risk score in children was inversely related to parental height, and positively associated with height-for-age and with having parents with the metabolic syndrome. CONCLUSIONS Metabolic syndrome is highly prevalent in Mesoamerica. The burden of metabolic risk factors disproportionately affects women and children of lower socio-economic status and men of higher socio-economic status.
Collapse
|
38
|
Cediel G, Corvalán C, López de Romaña D, Mericq V, Uauy R. Prepubertal Adiposity, Vitamin D Status, and Insulin Resistance. Pediatrics 2016; 138:peds.2016-0076. [PMID: 27335379 DOI: 10.1542/peds.2016-0076] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/06/2016] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To evaluate the following from prepuberty to the puberty-onset: (1) changes in serum 25-hydroxyvitamin-D (25[OH]D), adiposity, and insulin resistance (IR); (2) the effect of prepubertal adiposity on serum 25(OH)D changes; and (3) the combined effect of prepubertal obesity and suboptimal-25(OH)D on IR at puberty-onset. METHODS A total of 426 prepubertal children (∼54% girls) were followed during pubertal-onset assessing before and after puberty-onset serum 25(OH)D, adiposity (BMI and waist circumference) and IR indicators (homeostasis-model-assessment of IR [HOMA-IR]). Associations were tested using multiple and logistic regression models adjusted by age, gender, and seasonality. RESULTS At puberty-onset, mean serum 25(OH)D decreased (32.2 ± 8.9 Tanner I vs 25.2 ± 8.3 ng/mL Tanner II) and total and central obesity increased (BMI-for-age-z-score ≥2 SD [%]: 16.4 vs 22.1; waist-circumference ≥75th percentile [%]: 27.2 vs 37.1, all P < .05). Children with higher adiposity before puberty onset had higher risk of suboptimal-25(OH)D (<30 ng/mL) in Tanner II (ie, odds ratio = 2.7 [1.1-6.7] for obesity and 2.7 [1.4-5.5] for central-obesity) after adjusting for relevant covariates. Children with higher adiposity and suboptimal-25(OH)D before puberty-onset had higher HOMA-IR compared with their counterparts in Tanner II (HOMA-IR: 2.8 [2.5-3.1] if central-obese and suboptimal-25[OH]D vs 2.1 [1.9-2.3] no central-obesity and optimal-25[OH]D). CONCLUSIONS We found that serum 25(OH)D declined with puberty-onset, likely because of adiposity increase. Moreover, children with the combined condition of central-obesity and suboptimal-25(OH)D before puberty-onset had higher pubertal IR. These results highlight the need of ensuring adequate-25(OH)D status before pubertal-onset, particularly in obese children.
Collapse
Affiliation(s)
| | | | - Daniel López de Romaña
- Institutes of Nutrition and Food Technology, and Nutrition Research Institute, Lima, Peru; and
| | - Verónica Mericq
- Maternal and Child Research, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Ricardo Uauy
- Institutes of Nutrition and Food Technology, and Department of Nutrition and Public Health Intervention Research, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| |
Collapse
|
39
|
Linares J, Corvalán C, Galleguillos B, Kain J, González L, Uauy R, Garmendia ML, Mericq V. The effects of pre-pregnancy BMI and maternal factors on the timing of adiposity rebound in offspring. Obesity (Silver Spring) 2016; 24:1313-9. [PMID: 27086475 DOI: 10.1002/oby.21490] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 01/06/2016] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To assess the effect of pre-pregnancy body mass index (BMI), gestational weight gain (GWG), and other maternal factors on the timing of adiposity rebound (AR). METHODS In this study, 594 mothers (mothers who do not have diabetes and not underweight) from the longitudinal Growth and Obesity Chilean Cohort Study self-reported their weights at the beginning and end of their pregnancies, and their heights were measured. Pre-pregnancy BMI was categorized as normal weight, overweight, or obesity, and GWG was assessed according to Institute of Medicine guidelines. For children, weight and height measurements from 0 to 3 years were retrieved from records, and they were measured from age 4 to 7 years. BMI curves from 0 to 7 years were used to estimate the age at AR, which was categorized as early (<5 years), intermediate (5-7 years), or late (>7 years). The associations between pre-pregnancy BMI and GWG and early AR were tested using logistic regression models. RESULTS In total, 33% of the mothers had excess pre-pregnancy weight, 31.2% exceeded Institute of Medicine recommendations, and 45% of children had early AR. The pre-pregnancy BMI and parity were associated with earlier AR (OR = 1.07, 95% CI = 1.02-1.11; OR = 0.86; 95% CI = 0.74-0.99, respectively), but GWG was unrelated. CONCLUSIONS These results suggest that preventive strategies for promoting normal pre-pregnancy BMI, especially in women's first pregnancies, could delay the timing of AR, with protective metabolic effects on offspring.
Collapse
Affiliation(s)
- Jeannette Linares
- IDIMI, Institute of Maternal and Child Research, University of Chile, Santiago, Chile
| | - Camila Corvalán
- INTA, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Bárbara Galleguillos
- INTA, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Juliana Kain
- INTA, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Laura González
- INTA, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Ricardo Uauy
- INTA, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - María Luisa Garmendia
- INTA, Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | - Verónica Mericq
- IDIMI, Institute of Maternal and Child Research, University of Chile, Santiago, Chile
| |
Collapse
|
40
|
Haga C, Kondo N, Okamoto R. Factors Associated with Changes in Body Mass Trajectories during Infancy: A Longitudinal Analysis in Japan. Health (London) 2016. [DOI: 10.4236/health.2016.814149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
41
|
Kain J, Martínez M, Close M, Uauy R, Corvalán C. The association of excessive growth with development of general and central obesity at 7 years of age in every period after birth in Chilean children. Nutrition 2015; 32:426-31. [PMID: 26743976 DOI: 10.1016/j.nut.2015.09.018] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Revised: 09/25/2015] [Accepted: 09/26/2015] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To determine whether overweight or obesity between 0 and 6 y increases the probability of developing general/central obesity at 7 y. METHODS Weight, height, and waist circumference were assessed between 0 and 6 y in 628 Chilean children. Body mass index, incidence of overweight (IOW), incidence of obesity (IOB), and prevalence at birth and 0 to 6, 6 to 24, 24 to 48, and 48 to 72 mo and odds ratio (OR) and 95% confidence interval (95% CI) for developing obesity/central obesity at 84 mo were calculated by sex. RESULTS IOW was highest at 6 to 24 mo (20.3% and 21.1% for girls and boys, respectively); IOB was highest at 6 to 24 and 24 to 48 mo (11% and 10%, respectively). OR for developing obesity at 7 y was significant if overweight was present 24 to 48 mo in girls and 6 to 24 mo in boys OR 2.47 [95% CI, 1.5-4] and OR 2.26 [95% CI, 1.30-3.92], respectively, and much higher for children who were obese, OR 6.1 (95% CI, 3.03-12.21) for girls 6 to 24 mo and OR 6.57 (95% CI, 2.55-16.86) for boys 0 to 6 mo. IOW or IOB was not associated with obesity at 84 mo, except for the previous period. Overweight also increased the risk of central obesity very early on, after 6 mo in girls and after birth in boys OR 3.8 [95% CI, 2.3-6.2] and OR 2.5 [95% CI, 2.04-3] at 6 to 24 and 0 to 6 mo, respectively, whereas obesity in all periods was associated with a significantly higher OR of presenting central obesity at 7 y. CONCLUSION There is tracking not only of obesity but also of overweight after birth for developing general or central obesity at 7 y. Prevention should begin very early on.
Collapse
Affiliation(s)
- Juliana Kain
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Región Metropolitana, Chile.
| | - Mario Martínez
- Statistics, University of Manchester, Manchester, United Kingdom
| | - Michael Close
- Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ricardo Uauy
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Región Metropolitana, Chile
| | - Camila Corvalán
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Región Metropolitana, Chile
| |
Collapse
|
42
|
Associations between early body mass index trajectories and later metabolic risk factors in European children: the IDEFICS study. Eur J Epidemiol 2015; 31:513-25. [PMID: 26297214 DOI: 10.1007/s10654-015-0080-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Accepted: 08/13/2015] [Indexed: 10/23/2022]
Abstract
Faster growth seems to be a common factor in several hypotheses relating early life exposures to subsequent health. This study aims to investigate the association between body mass index (BMI) trajectories during infancy/childhood and later metabolic risk in order to identify sensitive periods of growth affecting health. In a first step, BMI trajectories of 3301 European children that participated in the multi-centre Identification and Prevention of Dietary and Lifestyle-induced Health Effects in Children and Infants (IDEFICS) study were modelled using linear-spline mixed-effects models. The estimated random coefficients indicating initial subject-specific BMI and rates of change in BMI over time were used as exposure variables in a second step and related to a metabolic syndrome (MetS) score and its single components based on conditional regression models (mean age at outcome assessment: 8.5 years). All exposures under investigation, i.e. BMI at birth, rates of BMI change during infancy (0 to <9 months), early childhood (9 months to <6 years) and later childhood (≥6 years) as well as current BMI z-score were significantly associated with the later MetS score. Associations were strongest for the rate of BMI change in early childhood (1.78 [1.66; 1.90]; β estimate and 99 % confidence interval) and current BMI z-score (1.16 [0.96; 1.38]) and less pronounced for BMI at birth (0.62 [0.47; 0.78]). Results slightly differed with regard to the single metabolic factors. Starting from birth rapid BMI growth, especially in the time window of 9 months to <6 years, is significantly related to later metabolic risk in children. Much of the associations of early BMI growth may further be mediated through the effects on subsequent BMI growth.
Collapse
|
43
|
Adipose tissue dysregulation and metabolic consequences in childhood and adolescent obesity: potential impact of dietary fat quality. Proc Nutr Soc 2014; 74:67-82. [PMID: 25497038 DOI: 10.1017/s002966511400158x] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Evidence suggests that at a population level, childhood and adolescent obesity increase the long-term risk of chronic diseases such as type 2 diabetes and CVD. At an individual level, however, the metabolic consequences of obesity in youth vary immensely. Despite comparable BMI, some adolescents develop impaired glucose tolerance while others maintain normal glucose homeostasis. It has been proposed that the variation in the capacity to store lipid in the subcutaneous adipose tissue (SAT) may partially discriminate metabolically healthy from unhealthy obesity. In positive energy balance, a decreased capacity to expand SAT may drive lipid accumulation to visceral adipose tissue, liver and skeletal muscle. This state of lipotoxicity is associated with chronic low-grade inflammation, insulin resistance and dyslipidaemia. The present review examines the differential adipose tissue development and function in children and adolescents who exhibit metabolic dysregulation compared with those who are protected. Additionally, the role of manipulating dietary fat quality to potentially prevent and treat metabolic dysfunction in obesity will be discussed. The findings of the present review highlight the need for further randomised controlled trials to establish the effect of dietary n-3 PUFA on the metabolic phenotype of obese children and adolescents. Furthermore, using a personalised nutrition approach to target interventions to those at risk of, or those with established metabolic dysregulation may optimise the efficacy of modifying dietary fat quality.
Collapse
|