Objective To examine associations of demographic, perinatal, and infant feeding characteristics with offspring body composition at approximately 5 months of age. Study design We collected data on 640 ...mother/offspring pairs from early pregnancy through approximately 5 months of age. We assessed offspring body composition with air displacement plethysmography at birth and approximately 5 months of age. Linear regression analyses examined associations between predictors and fat-free mass, fat mass, and percent fat mass (adiposity) at approximately 5 months. Secondary models further adjusted for body composition at birth and rapid infant growth. Results Greater prepregnant body mass index and gestational weight gain were associated with greater fat-free mass at approximately 5 months of age, but not after adjustment for fat-free mass at birth. Greater gestational weight gain was also associated with greater fat mass at approximately 5 months of age, independent of fat mass at birth and rapid infant growth, although this did not translate into increased adiposity. Greater percent time of exclusive breastfeeding was associated with lower fat-free mass (-311 g; P < .001), greater fat mass (+224 g; P < .001), and greater adiposity (+3.51%; P < .001). Compared with offspring of non-Hispanic white mothers, offspring of Hispanic mothers had greater adiposity (+2.72%; P < .001) and offspring of non-Hispanic black mothers had lower adiposity (-1.93%; P < .001). Greater adiposity at birth predicted greater adiposity at approximately 5 months of age, independent of infant feeding and rapid infant growth. Conclusions There are clear differences in infant body composition by demographic, perinatal, and infant feeding characteristics, although our data also show that increased adiposity at birth persists through approximately 5 months of age. Our findings warrant further research into implications of differences in infant body composition.
Objective
To explore the long‐term impact of intrauterine growth restriction (IUGR) among a diverse, contemporary cohort of US children.
Design and Methods
A retrospective cohort of 42 children ...exposed to IUGR and 464 unexposed who were members of Kaiser Permanente of Colorado. Height and weight measurements since birth and measures of abdominal adiposity and insulin‐resistance were measured at an average age of 10.6 (±1.3) years.
Results
Infants born IUGR experienced “catch‐up growth” in the first 12 months of life at a rate of 3.58 kg/m2 compared to 2.36 kg/m2 in unexposed infants (P = 0.01). However, after 1 year of age, no differences in BMI growth velocity were observed. Nevertheless children exposed to IUGR had higher waist circumference (67.0 vs. 65.3 cm, P = 0.03), higher insulin (15.2 vs. 11.0 μU/ml, P = 0.0002), higher HOMA‐IR (2.8 vs. 2.3, P = 0.03), and lower adiponectin levels (9.0 vs. 12.0 μg/ml, P = 0.003) in adolescence, independent of other childhood and maternal factors.
Conclusions
Our data from a contemporary US cohort suggests that children exposed to IUGR have increased abdominal fat and increased insulin resistance biomarkers despite no differences in BMI growth patterns beyond 12 months of age. These data provide further support for the fetal programming hypothesis.
Maternal metabolism during pregnancy shapes offspring health via in utero programming. In the Healthy Start study, we identified five subgroups of pregnant women based on conventional metabolic ...biomarkers: Reference (n = 360); High HDL-C (n = 289); Dyslipidemic–High TG (n = 149); Dyslipidemic–High FFA (n = 180); Insulin Resistant (IR)–Hyperglycemic (n = 87). These subgroups not only captured metabolic heterogeneity among pregnant participants but were also associated with offspring obesity in early childhood, even among women without obesity or diabetes. Here, we utilize metabolomics data to enrich characterization of the metabolic subgroups and identify key compounds driving between-group differences. We analyzed fasting blood samples from 1065 pregnant women at 18 gestational weeks using untargeted metabolomics. We used weighted gene correlation network analysis (WGCNA) to derive a global network based on the Reference subgroup and characterized distinct metabolite modules representative of the different metabolomic profiles. We used the mummichog algorithm for pathway enrichment and identified key compounds that differed across the subgroups. Eight metabolite modules representing pathways such as the carnitine–acylcarnitine translocase system, fatty acid biosynthesis and activation, and glycerophospholipid metabolism were identified. A module that included 189 compounds related to DHA peroxidation, oxidative stress, and sex hormone biosynthesis was elevated in the Insulin Resistant–Hyperglycemic vs. the Reference subgroup. This module was positively correlated with total cholesterol (R:0.10; p-value < 0.0001) and free fatty acids (R:0.07; p-value < 0.05). Oxidative stress and inflammatory pathways may underlie insulin resistance during pregnancy, even below clinical diabetes thresholds. These findings highlight potential therapeutic targets and strategies for pregnancy risk stratification and reveal mechanisms underlying the developmental origins of metabolic disease risk.
Genetic risk scores (GRS) have been developed that differentiate individuals with type 1 diabetes from those with other forms of diabetes and are starting to be used for population screening; ...however, most studies were conducted in European-ancestry populations. This study identifies novel genetic variants associated with type 1 diabetes risk in African-ancestry participants and develops an African-specific GRS.
We generated single nucleotide polymorphism (SNP) data with the ImmunoChip on 1,021 African-ancestry participants with type 1 diabetes and 2,928 control participants. HLA class I and class II alleles were imputed using SNP2HLA. Logistic regression models were used to identify genome-wide significant (
< 5.0 × 10
) SNPs associated with type 1 diabetes in the African-ancestry samples and validate SNPs associated with risk in known European-ancestry loci (
< 2.79 × 10
).
African-specific (HLA-
*03:01-HLA-
*02:01) and known European-ancestry HLA haplotypes (HLA-
*03:01-HLA-
*05:01-HLA-
*02:01, HLA-
*04:01-HLA-
*03:01-HLA-
*03:02) were significantly associated with type 1 diabetes risk. Among European-ancestry defined non-HLA risk loci, six risk loci were significantly associated with type 1 diabetes in subjects of African ancestry. An African-specific GRS provided strong prediction of type 1 diabetes risk (area under the curve 0.871), performing significantly better than a European-based GRS and two polygenic risk scores in independent discovery and validation cohorts.
Genetic risk of type 1 diabetes includes ancestry-specific, disease-associated variants. The GRS developed here provides improved prediction of type 1 diabetes in African-ancestry subjects and a means to identify groups of individuals who would benefit from immune monitoring for early detection of islet autoimmunity.
IMPORTANCE: Asthma is the leading chronic illness in US children, but most descriptive epidemiological data are focused on prevalence. OBJECTIVE: To evaluate childhood asthma incidence rates across ...the nation by core demographic strata and parental history of asthma. DESIGN, SETTING, AND PARTICIPANTS: For this cohort study, a distributed meta-analysis was conducted within the Environmental Influences on Child Health Outcomes (ECHO) consortium for data collected from May 1, 1980, through March 31, 2018. Birth cohort data of children from 34 gestational weeks of age or older to 18 years of age from 31 cohorts in the ECHO consortium were included. Data were analyzed from June 14, 2018, to February 18, 2020. EXPOSURES: Caregiver report of physician-diagnosed asthma with age of diagnosis. MAIN OUTCOME AND MEASURES: Asthma incidence survival tables generated by each cohort were combined for each year of age using the Kaplan-Meier method. Age-specific incidence rates for each stratum and asthma incidence rate ratios by parental family history (FH), sex, and race/ethnicity were calculated. RESULTS: Of the 11 404 children (mean SD age, 10.0 0.7 years; 5836 boys 51%; 5909 White children 53%) included in the primary analysis, 7326 children (64%) had no FH of asthma, 4078 (36%) had an FH of asthma, and 2494 (23%) were non-Hispanic Black children. Children with an FH had a nearly 2-fold higher incidence rate through the fourth year of life (incidence rate ratio IRR, 1.94; 95% CI, 1.76-2.16) after which the rates converged with the non-FH group. Regardless of FH, asthma incidence rates among non-Hispanic Black children were markedly higher than those of non-Hispanic White children during the preschool years (IRR, 1.58; 95% CI, 1.31-1.86) with no FH at age 4 years and became lower than that of White children after age 9 to 10 years (IRR, 0.67; 95% CI, 0.50-0.89) with no FH. The rates for boys declined with age, whereas rates among girls were relatively steady across all ages, particularly among those without an FH of asthma. CONCLUSIONS AND RELEVANCE: Analysis of these diverse birth cohorts suggests that asthma FH, as well as race/ethnicity and sex, were all associated with childhood asthma incidence rates. Black children had much higher incidences rates but only during the preschool years, irrespective of FH. To prevent asthma among children with an FH of asthma or among Black infants, results suggest that interventions should be developed to target early life.
•The contribution of POPs to type 1 diabetes (T1D) remains poorly known.•We investigated the link between POPs and T1D in youth and the effects of POPs on β-cell.•p,p’-DDE, p,p’-DDT, trans-nonachlor, ...and PCB-153 were associated with T1D with normal insulin sensitivity.•p,p’-DDE and PCB-153 cause dysfunction and destruction of β-cells in vitro.•Our findings suggest that POPs may play a role in T1D.
Diabetes affects millions of people worldwide with a continued increase in incidence occurring within the pediatric population. The potential contribution of persistent organic pollutants (POPs) to diabetes in youth remains poorly known, especially regarding type 1 diabetes (T1D), generally the most prevalent form of diabetes in youth.
We investigated the associations between POPs and T1D in youth and studied the impacts of POPs on pancreatic β-cell function and viability in vitro.
We used data and plasma samples from the SEARCH for Diabetes in Youth Case Control Study (SEARCH-CC). Participants were categorized as Controls, T1D with normal insulin sensitivity (T1D/IS), and T1D with insulin resistance (T1D/IR). We assessed plasma concentrations of polychlorinated biphenyls (PCBs) and organochlorine pesticides and estimated the odds of T1D through multivariable logistic regression. In addition, we performed in vitro experiments with the INS-1E pancreatic β-cells. Cells were treated with PCB-153 or p,p’-DDE at environmentally relevant doses. We measured insulin production and secretion and assessed the mRNA expression of key regulators involved in insulin synthesis (Ins1, Ins2, Pdx1, Mafa, Pcsk1/3, and Pcsk2), glucose sensing (Slc2a2 and Gck), and insulin secretion (Abcc8, Kcnj11, Cacna1d, Cacna1b, Stx1a, Snap25, and Sytl4). Finally, we assessed the effects of PCB-153 and p,p’-DDE on β-cell viability.
Among 442 youths, 112 were controls, 182 were classified with T1D/IS and 148 with T1D/IR. The odds ratios (OR) of T1D/IS versus controls were statistically significant for p,p’-DDE (OR 2.0, 95% confidence interval (CI) 1.0, 3.8 and 2.4, 95% CI 1.2, 5.0 for 2nd and 3rd tertiles, respectively), trans-nonachlor (OR 2.5, 95% CI 1.3, 5.0 and OR 2.3, 95% CI 1.1, 5.1 for 2nd and 3rd tertiles, respectively), and PCB-153 (OR 2.3, 95% CI 1.1, 4.6 for 3rd tertile). However, these associations were not observed in participants with T1D/IR. At an experimental level, treatment with p,p’-DDE or PCB-153, at concentrations ranging from 1 × 10-15 M to 5 × 10-6 M, impaired the ability of pancreatic β-cells to produce and secrete insulin in response to glucose. These failures were paralleled by impaired Ins1 and Ins2 mRNA expression. In addition, among different targeted genes, PCB-153 significantly reduced Slc2a2 and Gck mRNA expression whereas p,p’-DDE mainly affected Abcc8 and Kcnj11. While treatment with PCB-153 or p,p’-DDE for 2 days did not affect β-cell viability, longer treatment progressively killed the β-cells.
These results support a potential role of POPs in T1D etiology and demonstrate a high sensitivity of pancreatic β-cells to POPs.
In the United States, one in five adolescents are obese. Index-based dietary patterns are measures of the overall diet that have the potential to serve as valuable obesity risk stratification tools. ...However, little is known about the association between adherence to index-based dietary patterns in childhood and BMI during the transition from childhood to adolescence.
To prospectively examine the relationship between adherence to three index-based dietary patterns in childhood and BMI trajectory during the transition to adolescence.
The study included 581 children enrolled in a Colorado prospective cohort study conducted between 2006 and 2015. Dietary intake was assessed with the Block Kids Food Frequency Questionnaire at age 10 years. Scores were calculated for the Healthy Eating Index-2010 (HEI-2010), the alternate Mediterranean (aMED) diet, and the Dietary Approaches to Stop Hypertension (DASH) diet. Weight and height were assessed via anthropometry at two research visits (ages 10 and 16 years), with interim clinical measurements extracted from Kaiser Permanente medical records. Separate mixed models were used to assess the association between each diet index score and BMI over a 6-year period. Models were stratified by sex and adjusted for age, race/ethnicity, income, and exposure to gestational diabetes.
Median (IQR) number of BMI assessments was 14 (10-18). Among girls, for every ten-unit increase in HEI-2010 score, there was an average 0.64 kg/m
decrease (p = 0.007) in BMI over time, after adjustment for covariates. Among girls, there was no association between BMI and aMED (β = -0.19, p = 0.24) or DASH (β = 0.28, p = 0.38). Among boys, there was no statistically significant association between BMI and HEI-2010 (0.06, p = 0.83), aMED (0.07, p = 0.70), or DASH (0.42, p = 0.06).
Efforts to prevent adolescent obesity could benefit from considering the degree of adherence to federal dietary guidance, as assessed by the HEI, in the period preceding adolescence, especially among girls.
The purpose of this work was to determine the prevalence and predictors of diabetic ketoacidosis at the diagnosis of diabetes in a large sample of youth from the US population.
The Search for ...Diabetes in Youth Study, a multicenter, population-based registry of diabetes with diagnosis before 20 years of age, identified 3666 patients with new onset of diabetes in the study areas in 2002-2004. Medical charts were reviewed in 2824 (77%) of the patients in a standard manner to abstract the results of laboratory tests and to ascertain diabetic ketoacidosis at the time of diagnosis. Diabetic ketoacidosis was defined by blood bicarbonate <15 mmol/L and/or venous pH < 7.25 (arterial/capillary pH < 7.30), International Classification of Diseases, Ninth Revision, code 250.1, or listing of diabetic ketoacidosis in the medical chart.
More than half (54%) of the patients were hospitalized at diagnosis, including 93% of those with diabetic ketoacidosis and 41% without diabetic ketoacidosis. The prevalence of diabetic ketoacidosis at the diagnosis was 25.5%. The prevalence decreased with age from 37.3% in children aged 0 to 4 years to 14.7% in those aged 15 to 19 years. Diabetic ketoacidosis prevalence was significantly higher in patients with type 1 (29.4%) rather than in those with type 2 diabetes (9.7%). After adjusting for the effects of center, age, gender, race or ethnicity, diabetes type, and family history of diabetes, diabetic ketoacidosis at diagnosis was associated with lower family income, less desirable health insurance coverage, and lower parental education.
At the time of diagnosis, 1 in 4 youth presents with diabetic ketoacidosis. Those with diabetic ketoacidosis were more likely to be hospitalized. Diabetic ketoacidosis was a presenting feature of <10% of youth with type 2. Young and poor children are disproportionately affected.
When evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different ...trajectories of the environmental exposure over time. Power analyses for longitudinal mixed models require multiple inputs, including clinically significant differences, standard deviations, and correlations of measurements. Further, methods for power analyses of longitudinal mixed models are complex and often challenging for the non-statistician. We discuss methods for extracting clinically relevant inputs from literature, and explain how to conduct a power analysis that appropriately accounts for longitudinal repeated measures. Finally, we provide careful recommendations for describing complex power analyses in a concise and clear manner.
For longitudinal studies of health outcomes from environmental exposures, we show how to 1 conduct a power analysis that aligns with the planned mixed model data analysis, 2 gather the inputs required for the power analysis, and 3 conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists.
As an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis.
This paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications.
Exposure to beryllium may lead to granuloma formation and fibrosis in those who develop chronic beryllium disease (CBD). Although disease presentation varies from mild to severe, little is known ...about CBD phenotypes. This study characterized CBD disease phenotypes using longitudinal measures of lung function.
Using a case-only study of 207 CBD subjects, subject-specific trajectories over time were estimated from longitudinal pulmonary function and exercise-tolerance tests. To estimate linear combinations of the 30-year values that define underlying patterns of lung function, we conducted factor analysis. Cluster analysis was then performed on all the predicted lung function values at 30 years. These estimates were used to identify underlying features and subgroups of CBD.
Two factors, or composite measures, explained nearly 70% of the co-variation among the tests; one factor represented pulmonary function in addition to oxygen consumption and workload during exercise, while the second factor represented exercise tests related to gas exchange. Factors were associated with granulomas on biopsy, exposure, steroid use and lung inflammation. Three clusters of patients (n = 53, n = 59 and, n = 95) were identified based on the collection of test values. Lower levels of each of the factor composite scores and cluster membership were associated with baseline characteristics of patients.
Using factor analysis and cluster analysis, we identified disease phenotypes that were associated with baseline patient characteristics, suggesting that CBD is a heterogeneous disease with varying severity. These clinical tools may be used in future basic and clinical studies to help define the mechanisms and risk factors for disease severity.
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