Developmental origins of psycho-cardiometabolic multimorbidity in adolescence and their underlying pathways through methylation markers: a two-cohort study.

Understanding the biological mechanisms behind multimorbidity patterns in adolescence is important as they may act as intermediary risk factor for long-term health. We aimed to explore relationship between prenatal exposures and adolescent's psycho-cardiometabolic intermediary traits mediated through epigenetic biomarkers, using structural equation modeling (SEM). We used data from mother-child dyads from pregnancy and adolescents at 16-17 years from two prospective cohorts: Northern Finland Birth Cohort 1986 (NFBC1986) and Raine Study from Australia. Factor analysis was applied to generate two different latent factor structures: (a) prenatal exposures and (b) adolescence psycho-cardiometabolic intermediary traits. Furthermore, three types of epigenetic biomarkers were included: (1) DNA methylation score for maternal smoking during pregnancy (DNAmMSS), (2) DNAm age estimate PhenoAge and (3) DNAm estimate for telomere length (DNAmTL). Similar factor structure was observed between both cohorts yielding three prenatal factors, namely BMI (Body Mass Index), SOP (Socio-Obstetric-Profile), and Lifestyle, and four adolescent factors: Anthropometric, Insulin-Triglycerides, Blood Pressure, and Mental health. In the SEM pathways, stronger direct effects of F1prenatal-BMI (NFBC1986 = ß: 0.27; Raine = ß: 0.39) and F2prenatal-SOP (ß: -0.11) factors were observed on adolescent psycho-cardiometabolic multimorbidity. We observed an indirect effect of prenatal latent factors through epigenetic markers on a psycho-cardiometabolic multimorbidity factor in Raine study (P < 0.05). The present study exemplifies an evidence-based approach in two different birth cohorts to demonstrate similar composite structure of prenatal exposures and psycho-cardiometabolic traits (despite cultural, social, and genetic differences) and a common plausible pathway between them through underlying epigenetic markers.

Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies.

BACKGROUND: Preterm birth is the leading cause of perinatal morbidity and mortality and is associated with adverse developmental and long-term health outcomes, including several cardiometabolic risk factors and outcomes. However, evidence about the association of preterm birth with later body size derives mainly from studies using birth weight as a proxy of prematurity rather than an actual length of gestation. We investigated the association of gestational age (GA) at birth with body size from infancy through adolescence. METHODS AND FINDINGS: We conducted a two-stage individual participant data (IPD) meta-analysis using data from 253,810 mother-child dyads from 16 general population-based cohort studies in Europe (Denmark, Finland, France, Italy, Norway, Portugal, Spain, the Netherlands, United Kingdom), North America (Canada), and Australasia (Australia) to estimate the association of GA with body mass index (BMI) and overweight (including obesity) adjusted for the following maternal characteristics as potential confounders: education, height, prepregnancy BMI, ethnic background, parity, smoking during pregnancy, age at child's birth, gestational diabetes and hypertension, and preeclampsia. Pregnancy and birth cohort studies from the LifeCycle and the EUCAN-Connect projects were invited and were eligible for inclusion if they had information on GA and minimum one measurement of BMI between infancy and adolescence. Using a federated analytical tool (DataSHIELD), we fitted linear and logistic regression models in each cohort separately with a complete-case approach and combined the regression estimates and standard errors through random-effects study-level meta-analysis providing an overall effect estimate at early infancy (>0.0 to 0.5 years), late infancy (>0.5 to 2.0 years), early childhood (>2.0 to 5.0 years), mid-childhood (>5.0 to 9.0 years), late childhood (>9.0 to 14.0 years), and adolescence (>14.0 to 19.0 years). GA was positively associated with BMI in the first decade of life, with the greatest increase in mean BMI z-score during early infancy (0.02, 95% confidence interval (CI): 0.00; 0.05, p < 0.05) per week of increase in GA, while in adolescence, preterm individuals reached similar levels of BMI (0.00, 95% CI: -0.01; 0.01, p 0.9) as term counterparts. The association between GA and overweight revealed a similar pattern of association with an increase in odds ratio (OR) of overweight from late infancy through mid-childhood (OR 1.01 to 1.02) per week increase in GA. By adolescence, however, GA was slightly negatively associated with the risk of overweight (OR 0.98 [95% CI: 0.97; 1.00], p 0.1) per week of increase in GA. Although based on only four cohorts (n = 32,089) that reached the age of adolescence, data suggest that individuals born very preterm may be at increased odds of overweight (OR 1.46 [95% CI: 1.03; 2.08], p < 0.05) compared with term counterparts. Findings were consistent across cohorts and sensitivity analyses despite considerable heterogeneity in cohort characteristics. However, residual confounding may be a limitation in this study, while findings may be less generalisable to settings in low- and middle-income countries. CONCLUSIONS: This study based on data from infancy through adolescence from 16 cohort studies found that GA may be important for body size in infancy, but the strength of association attenuates consistently with age. By adolescence, preterm individuals have on average a similar mean BMI to peers born at term.

Correction: Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies.

[This corrects the article DOI: 10.1371/journal.pmed.1004036.].

Associations of Maternal Educational Level, Proximity to Greenspace During Pregnancy, and Gestational Diabetes With Body Mass Index From Infancy to Early Adulthood: A Proof-of-Concept Federated Analysis in 18 Birth Cohorts.

International sharing of cohort data for research is important and challenging. We explored the feasibility of multi-cohort federated analyses by examining associations between three pregnancy exposures (maternal education, exposure to green vegetation and gestational diabetes) with offspring BMI from infancy to 17 years. We used data from 18 cohorts (n=206,180 mother-child pairs) from the EU Child Cohort Network and derived BMI at ages 0-1, 2-3, 4-7, 8-13 and 14-17 years. Associations were estimated using linear regression via one-stage IPD meta-analysis using DataSHIELD. Associations between lower maternal education and higher child BMI emerged from age 4 and increased with age (difference in BMI z-score comparing low with high education age 2-3 years = 0.03 [95% CI 0.00, 0.05], 4-7 years = 0.16 [95% CI 0.14, 0.17], 8-13 years = 0.24 [95% CI 0.22, 0.26]). Gestational diabetes was positively associated with BMI from 8 years (BMI z-score difference = 0.18 [CI 0.12, 0.25]) but not at younger ages; however associations attenuated towards the null when restricted to cohorts which measured GDM via universal screening. Exposure to green vegetation was weakly associated with higher BMI up to age one but not at older ages. Opportunities of cross-cohort federated analyses are discussed.

Measures of Early-life Behavior and Later Psychopathology in the LifeCycle Project - EU Child Cohort Network: A Cohort Description.

BACKGROUND: The EU LifeCycle Project was launched in 2017 to combine, harmonize, and analyze data from more than 250,000 participants across Europe and Australia, involving cohorts participating in the EU-funded LifeCycle Project. The purpose of this cohort description is to provide a detailed overview of the major measures within mental health domains that are available in 17 European and Australian cohorts participating in the LifeCycle Project. METHODS: Data on cognitive, behavioral, and psychological development has been collected on participants from birth until adulthood through questionnaire and medical data. We developed an inventory of the available data by mapping individual instruments, domain types, and age groups, providing the basis for statistical harmonization across mental health measures. RESULTS: The mental health data in LifeCycle contain longitudinal and cross-sectional data from birth throughout the life course, covering domains across a wide range of behavioral and psychopathology indicators and outcomes, including executive function, depression, ADHD, and cognition. These data span a unique combination of qualitative data collected through behavioral/cognitive/mental health questionnaires and examination, as well as data from biological samples and indices in the form of imaging (MRI, fetal ultrasound) and DNA methylation data. Harmonized variables on a subset of mental health domains have been developed, providing statistical equivalence of measures required for longitudinal meta-analyses across instruments and cohorts. CONCLUSION: Mental health data harmonized through the LifeCycle project can be used to study life-course trajectories and exposure-outcome models that examine early life risk factors for mental illness and develop predictive markers for later-life disease.

Childhood sleep health and epigenetic age acceleration in late adolescence: Cross-sectional and longitudinal analyses.

AIM: Investigate if childhood measures of sleep health are associated with epigenetic age acceleration in late adolescence. METHODS: Parent-reported sleep trajectories from age 5 to 17, self-reported sleep problems at age 17, and six measures of epigenetic age acceleration at age 17 were studied in 1192 young Australians from the Raine Study Gen2. RESULTS: There was no evidence for a relationship between the parent-reported sleep trajectories and epigenetic age acceleration (p ≥ 0.17). There was a positive cross-sectional relationship between self-reported sleep problem score and intrinsic epigenetic age acceleration at age 17 (b = 0.14, p = 0.04), which was attenuated after controlling for depressive symptom score at the same age (b = 0.08, p = 0.34). Follow-up analyses suggested this finding may represent greater overtiredness and intrinsic epigenetic age acceleration in adolescents with higher depressive symptoms. CONCLUSION: There was no evidence for a relationship between self- or parent-reported sleep health and epigenetic age acceleration in late adolescence after adjusting for depressive symptoms. Mental health should be considered as a potential confounding variable in future research on sleep and epigenetic age acceleration, particularly if subjective measures of sleep are used.

Associations of early-life pet ownership with asthma and allergic sensitization: A meta-analysis of more than 77,000 children from the EU Child Cohort Network.

BACKGROUND: Studies examining associations of early-life cat and dog ownership with childhood asthma have reported inconsistent results. Several factors could explain these inconsistencies, including type of pet, timing, and degree of exposure. OBJECTIVE: Our aim was to study associations of early-life cat and dog ownership with asthma in school-aged children, including the role of type (cat vs dog), timing (never, prenatal, or early childhood), and degree of ownership (number of pets owned), and the role of allergic sensitization. METHODS: We used harmonized data from 77,434 mother-child dyads from 9 birth cohorts in the European Union Child Cohort Network when the child was 5 to 11 years old. Associations were examined through the DataSHIELD platform by using adjusted logistic regression models, which were fitted separately for each cohort and combined by using random effects meta-analysis. RESULTS: The prevalence of early-life cat and dog ownership ranged from 12% to 45% and 7% to 47%, respectively, and the prevalence of asthma ranged from 2% to 20%. There was no overall association between either cat or dog ownership and asthma (odds ratio [OR] = 0.97 [95% CI = 0.87-1.09] and 0.92 [95% CI = 0.85-1.01], respectively). Timing and degree of ownership did not strongly influence associations. Cat and dog ownership were also not associated with cat- and dog-specific allergic sensitization (OR = 0.92 [95% CI = 0.75-1.13] and 0.93 [95% CI = 0.57-1.54], respectively). However, cat- and dog-specific allergic sensitization was strongly associated with school-age asthma (OR = 6.69 [95% CI = 4.91-9.10] and 5.98 [95% CI = 3.14-11.36], respectively). There was also some indication of an interaction between ownership and sensitization, suggesting that ownership may exacerbate the risks associated with pet-specific sensitization but offer some protection against asthma in the absence of sensitization. CONCLUSION: Our findings do not support early-life cat and dog ownership in themselves increasing the risk of school-age asthma, but they do suggest that ownership may potentially exacerbate the risks associated with cat- and dog-specific allergic sensitization.

Relationships between intrauterine fetal growth trajectories and markers of adiposity and inflammation in young adults.

BACKGROUND: There is now good evidence that events during gestation significantly influence the developmental well-being of an individual in later life. This study aimed to investigate the relationships between intrauterine growth trajectories determined by serial ultrasound and subsequent markers of adiposity and inflammation in the 27-year-old adult offspring from the Raine Study, an Australian longitudinal pregnancy cohort. METHODS: Ultrasound fetal biometric measurements including abdominal circumference (AC), femur length (FL), and head circumference (HC) from 1333 mother-fetal pairs (Gen1-Gen2) in the Raine Study were used to develop fetal growth trajectories using group-based trajectory modeling. Linear mixed modeling investigated the relationship between adult body mass index (BMI), waist circumference (WC), and high-sensitivity C-reactive protein (hs-CRP) of Gen2 at 20 (n = 485), 22 (n = 421) and 27 (n = 437) years and the fetal growth trajectory groups, adjusting for age, sex, adult lifestyle factors, and maternal factors during pregnancy. RESULTS: Seven AC, five FL and five HC growth trajectory groups were identified. Compared to the average-stable (reference) group, a lower adult BMI was observed in two falling AC trajectories: (ß = -1.45 kg/m2, 95% CI: -2.43 to -0.46, P = 0.004) and (ß = -1.01 kg/m2, 95% CI: -1.96 to -0.05, P = 0.038). Conversely, higher adult BMI (2.58 kg/m2, 95% CI: 0.98 to 4.18, P = 0.002) and hs-CRP (37%, 95% CI: 9-73%, P = 0.008) were observed in a rising FL trajectory compared to the reference group. A high-stable HC trajectory associated with 20% lower adult hs-CRP (95% CI: 5-33%, P = 0.011). CONCLUSION: This study highlights the importance of understanding causes of the unique patterns of intrauterine growth. Different fetal growth trajectories from early pregnancy associate with subsequent adult adiposity and inflammation, which predispose to the risk of diabetes and cardiometabolic disease.

Identifying young adults at high risk of cardiometabolic disease using cluster analysis and the Framingham 30-yr risk score.

BACKGROUND AND AIMS: Current strategies to reduce cardiovascular disease (CVD) risk in young adults are largely limited to those at extremes of risk. In cohort studies we have shown cluster analysis identified a large sub-group of adolescents with multiple risk factors. This study examined if individuals classified at 'high-risk' by cluster analysis could also be identified by their Framingham risk scores. METHODS AND RESULTS: Raine Study data at 17- (n = 1048) and 20-years (n = 1120) identified high- and low-risk groups by cluster analysis using continuous measures of systolic BP, BMI, triglycerides and insulin resistance. We assessed:- CVD risk at 20-years using the Framingham 30 yr-risk-score in the high- and low-risk clusters, and cluster stability from adolescence to adulthood. Cluster analysis at 17- and 20-years identified a high-risk group comprising, 17.9% and 21.3%, respectively of the cohort. In contrast, only 1.2% and 3.4%, respectively, met the metabolic syndrome criteria, all of whom were within the high-risk cluster. Compared with the low-risk cluster, Framingham scores of the high-risk cluster were elevated in males (9.4%; 99%CI 8.3, 10.6 vs 6.0%; 99%CI 5.7, 6.2) and females (4.9%; 99%CI 4.4, 5.4 vs 3.2%; 99%CI 3.0, 3.3) (both P < 0.0001). A score >8 for males and >4 for females identified those at high CVD risk with 99% confidence. CONCLUSION: Cluster analysis using multiple risk factors identified ∼20% of young adults at high CVD risk. Application of our Framingham 30 yr-risk cut-offs to individuals allows identification of more young people with multiple risk factors for CVD than conventional metabolic syndrome criteria.

The EU Child Cohort Network's core data: establishing a set of findable, accessible, interoperable and re-usable (FAIR) variables.

The Horizon2020 LifeCycle Project is a cross-cohort collaboration which brings together data from multiple birth cohorts from across Europe and Australia to facilitate studies on the influence of early-life exposures on later health outcomes. A major product of this collaboration has been the establishment of a FAIR (findable, accessible, interoperable and reusable) data resource known as the EU Child Cohort Network. Here we focus on the EU Child Cohort Network's core variables. These are a set of basic variables, derivable by the majority of participating cohorts and frequently used as covariates or exposures in lifecourse research. First, we describe the process by which the list of core variables was established. Second, we explain the protocol according to which these variables were harmonised in order to make them interoperable. Third, we describe the catalogue developed to ensure that the network's data are findable and reusable. Finally, we describe the core data, including the proportion of variables harmonised by each cohort and the number of children for whom harmonised core data are available. EU Child Cohort Network data will be analysed using a federated analysis platform, removing the need to physically transfer data and thus making the data more accessible to researchers. The network will add value to participating cohorts by increasing statistical power and exposure heterogeneity, as well as facilitating cross-cohort comparisons, cross-validation and replication. Our aim is to motivate other cohorts to join the network and encourage the use of the EU Child Cohort Network by the wider research community.

The LifeCycle Project-EU Child Cohort Network: a federated analysis infrastructure and harmonized data of more than 250,000 children and parents.

Early life is an important window of opportunity to improve health across the full lifecycle. An accumulating body of evidence suggests that exposure to adverse stressors during early life leads to developmental adaptations, which subsequently affect disease risk in later life. Also, geographical, socio-economic, and ethnic differences are related to health inequalities from early life onwards. To address these important public health challenges, many European pregnancy and childhood cohorts have been established over the last 30 years. The enormous wealth of data of these cohorts has led to important new biological insights and important impact for health from early life onwards. The impact of these cohorts and their data could be further increased by combining data from different cohorts. Combining data will lead to the possibility of identifying smaller effect estimates, and the opportunity to better identify risk groups and risk factors leading to disease across the lifecycle across countries. Also, it enables research on better causal understanding and modelling of life course health trajectories. The EU Child Cohort Network, established by the Horizon2020-funded LifeCycle Project, brings together nineteen pregnancy and childhood cohorts, together including more than 250,000 children and their parents. A large set of variables has been harmonised and standardized across these cohorts. The harmonized data are kept within each institution and can be accessed by external researchers through a shared federated data analysis platform using the R-based platform DataSHIELD, which takes relevant national and international data regulations into account. The EU Child Cohort Network has an open character. All protocols for data harmonization and setting up the data analysis platform are available online. The EU Child Cohort Network creates great opportunities for researchers to use data from different cohorts, during and beyond the LifeCycle Project duration. It also provides a novel model for collaborative research in large research infrastructures with individual-level data. The LifeCycle Project will translate results from research using the EU Child Cohort Network into recommendations for targeted prevention strategies to improve health trajectories for current and future generations by optimizing their earliest phases of life.

Evaluating Engagement in a Digital and Dietetic Intervention Promoting Healthy Weight Gain in Pregnancy: Mixed Methods Study.

BACKGROUND: Early excess and inadequate gestational weight gain (GWG) have been associated with negative outcomes for mother and child. The use of digital media to deliver pregnancy lifestyle interventions is increasing, but there is little data on participant engagement. The Pregnancy Lifestyle Activity and Nutrition (PLAN) intervention pilot study was an electronic health and dietetic-delivered intervention program promoting healthy GWG in early pregnancy. OBJECTIVE: This study aims to explore the interactions of participants with the program and to assess its acceptability. METHODS: This study uses both quantitative and qualitative methods using data from parent randomized controlled trial (ACTRN12617000725369). Quantitative data from 22 participants in the intervention arm who completed the study provided measures of the interactions participants had with the digital components of the program and with dietetic consultations. A descriptive qualitative analysis employed semistructured interviews with 9 participants to elicit views on the acceptability of the intervention and its components. RESULTS: The electronic delivery of information and recording of weight from 8 to 20 weeks of gestation were universally accepted. Component (face-to-face dietitian, weight tracker, website information delivery, and SMS goal prompting) acceptability and engagement differed between individuals. A total of 4 key themes emerged from the qualitative analysis: supporting lifestyle change, component acceptability and value, delivery platforms, and engagement barriers. CONCLUSIONS: The PLAN intervention and its delivery via a blend of personal dietetic consultations and digital program delivery was found to be acceptable and valuable to pregnant women. Individuals responded differently to various components, emphasizing the importance of including women in the development of lifestyle interventions and allowing participants to choose and tailor programs. Larger randomized controlled trials using these insights in a broader section of the community are needed to inform the iterative development of practical, time-efficient, and cost-effective ways of supporting optimal GWG with the potential to optimize outcomes for pregnant women and their child.

Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood: An individual participant data meta-analysis.

BACKGROUND: Maternal obesity and excessive gestational weight gain may have persistent effects on offspring fat development. However, it remains unclear whether these effects differ by severity of obesity, and whether these effects are restricted to the extremes of maternal body mass index (BMI) and gestational weight gain. We aimed to assess the separate and combined associations of maternal BMI and gestational weight gain with the risk of overweight/obesity throughout childhood, and their population impact. METHODS AND FINDINGS: We conducted an individual participant data meta-analysis of data from 162,129 mothers and their children from 37 pregnancy and birth cohort studies from Europe, North America, and Australia. We assessed the individual and combined associations of maternal pre-pregnancy BMI and gestational weight gain, both in clinical categories and across their full ranges, with the risks of overweight/obesity in early (2.0-5.0 years), mid (5.0-10.0 years) and late childhood (10.0-18.0 years), using multilevel binary logistic regression models with a random intercept at cohort level adjusted for maternal sociodemographic and lifestyle-related characteristics. We observed that higher maternal pre-pregnancy BMI and gestational weight gain both in clinical categories and across their full ranges were associated with higher risks of childhood overweight/obesity, with the strongest effects in late childhood (odds ratios [ORs] for overweight/obesity in early, mid, and late childhood, respectively: OR 1.66 [95% CI: 1.56, 1.78], OR 1.91 [95% CI: 1.85, 1.98], and OR 2.28 [95% CI: 2.08, 2.50] for maternal overweight; OR 2.43 [95% CI: 2.24, 2.64], OR 3.12 [95% CI: 2.98, 3.27], and OR 4.47 [95% CI: 3.99, 5.23] for maternal obesity; and OR 1.39 [95% CI: 1.30, 1.49], OR 1.55 [95% CI: 1.49, 1.60], and OR 1.72 [95% CI: 1.56, 1.91] for excessive gestational weight gain). The proportions of childhood overweight/obesity prevalence attributable to maternal overweight, maternal obesity, and excessive gestational weight gain ranged from 10.2% to 21.6%. Relative to the effect of maternal BMI, excessive gestational weight gain only slightly increased the risk of childhood overweight/obesity within each clinical BMI category (p-values for interactions of maternal BMI with gestational weight gain: p = 0.038, p < 0.001, and p = 0.637 in early, mid, and late childhood, respectively). Limitations of this study include the self-report of maternal BMI and gestational weight gain for some of the cohorts, and the potential of residual confounding. Also, as this study only included participants from Europe, North America, and Australia, results need to be interpreted with caution with respect to other populations. CONCLUSIONS: In this study, higher maternal pre-pregnancy BMI and gestational weight gain were associated with an increased risk of childhood overweight/obesity, with the strongest effects at later ages. The additional effect of gestational weight gain in women who are overweight or obese before pregnancy is small. Given the large population impact, future intervention trials aiming to reduce the prevalence of childhood overweight and obesity should focus on maternal weight status before pregnancy, in addition to weight gain during pregnancy.

Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: findings from the pregnancy and childhood epigenetics (PACE) consortium.

Pre-pregnancy maternal obesity is associated with adverse offspring outcomes at birth and later in life. Individual studies have shown that epigenetic modifications such as DNA methylation could contribute. Within the Pregnancy and Childhood Epigenetics (PACE) Consortium, we meta-analysed the association between pre-pregnancy maternal BMI and methylation at over 450,000 sites in newborn blood DNA, across 19 cohorts (9,340 mother-newborn pairs). We attempted to infer causality by comparing the effects of maternal versus paternal BMI and incorporating genetic variation. In four additional cohorts (1,817 mother-child pairs), we meta-analysed the association between maternal BMI at the start of pregnancy and blood methylation in adolescents. In newborns, maternal BMI was associated with small (<0.2% per BMI unit (1 kg/m2), P < 1.06 × 10-7) methylation variation at 9,044 sites throughout the genome. Adjustment for estimated cell proportions greatly attenuated the number of significant CpGs to 104, including 86 sites common to the unadjusted model. At 72/86 sites, the direction of the association was the same in newborns and adolescents, suggesting persistence of signals. However, we found evidence for acausal intrauterine effect of maternal BMI on newborn methylation at just 8/86 sites. In conclusion, this well-powered analysis identified robust associations between maternal adiposity and variations in newborn blood DNA methylation, but these small effects may be better explained by genetic or lifestyle factors than a causal intrauterine mechanism. This highlights the need for large-scale collaborative approaches and the application of causal inference techniques in epigenetic epidemiology.

Differential SLC6A4 methylation: a predictive epigenetic marker of adiposity from birth to adulthood.

BACKGROUND: The early life environment may influence susceptibility to obesity and metabolic disease in later life through epigenetic processes. SLC6A4 is an important mediator of serotonin bioavailability, and has a key role in energy balance. We tested the hypothesis that methylation of the SLC6A4 gene predicts adiposity across the life course. METHODS: DNA methylation at 5 CpGs within the SLC6A4 gene identified from a previous methyl binding domain array was measured by pyrosequencing. We measured DNA methylation in umbilical cord (UC) from children in the Southampton Women's Survey cohort (n = 680), in peripheral blood from adolescents in the Western Australian Pregnancy Cohort Study (n = 812), and in adipose tissue from lean and obese adults from the UK BIOCLAIMS cohort (n = 81). Real-time PCR was performed to assess whether there were corresponding alterations in gene expression in the adipose tissue. RESULTS: Lower UC methylation of CpG5 was associated with higher total fat mass at 4 years (p = 0.031), total fat mass at 6-7 years (p = 0.0001) and % fat mass at 6-7 years (p = 0.004). Lower UC methylation of CpG5 was also associated with higher triceps skinfold thickness at birth (p = 0.013), 6 months (p = 0.038), 12 months (p = 0.062), 2 years (p = 0.0003), 3 years (p = 0.00004) and 6-7 years (p = 0.013). Higher maternal pregnancy weight gain (p = 0.046) and lower parity (p = 0.029) were both associated with lower SLC6A4 CpG5 methylation. In adolescents, lower methylation of CpG5 in peripheral blood was associated with greater concurrent measures of adiposity including BMI (p ≤ 0.001), waist circumference (p = 0.011), subcutaneous fat (p ≤ 0.001) and subscapular, abdominal and suprailiac skinfold thicknesses (p = 0.002, p = 0.008, p = 0.004, respectively). In adipose tissue, methylation of both SLC6A4 CpG5 (p = 0.019) and expression of SLC6A4 (p = 0.008) was lower in obese compared with lean adults. CONCLUSIONS: These data suggest that altered methylation of CpG loci within SLC6A4 may provide a robust marker of adiposity across the life course.

Gestational weight gain charts for different body mass index groups for women in Europe, North America, and Oceania.

BACKGROUND: Gestational weight gain differs according to pre-pregnancy body mass index and is related to the risks of adverse maternal and child health outcomes. Gestational weight gain charts for women in different pre-pregnancy body mass index groups enable identification of women and offspring at risk for adverse health outcomes. We aimed to construct gestational weight gain reference charts for underweight, normal weight, overweight, and grades 1, 2 and 3 obese women and to compare these charts with those obtained in women with uncomplicated term pregnancies. METHODS: We used individual participant data from 218,216 pregnant women participating in 33 cohorts from Europe, North America, and Oceania. Of these women, 9065 (4.2%), 148,697 (68.1%), 42,678 (19.6%), 13,084 (6.0%), 3597 (1.6%), and 1095 (0.5%) were underweight, normal weight, overweight, and grades 1, 2, and 3 obese women, respectively. A total of 138, 517 women from 26 cohorts had pregnancies with no hypertensive or diabetic disorders and with term deliveries of appropriate for gestational age at birth infants. Gestational weight gain charts for underweight, normal weight, overweight, and grade 1, 2, and 3 obese women were derived by the Box-Cox t method using the generalized additive model for location, scale, and shape. RESULTS: We observed that gestational weight gain strongly differed per maternal pre-pregnancy body mass index group. The median (interquartile range) gestational weight gain at 40 weeks was 14.2 kg (11.4-17.4) for underweight women, 14.5 kg (11.5-17.7) for normal weight women, 13.9 kg (10.1-17.9) for overweight women, and 11.2 kg (7.0-15.7), 8.7 kg (4.3-13.4) and 6.3 kg (1.9-11.1) for grades 1, 2, and 3 obese women, respectively. The rate of weight gain was lower in the first half than in the second half of pregnancy. No differences in the patterns of weight gain were observed between cohorts or countries. Similar weight gain patterns were observed in mothers without pregnancy complications. CONCLUSIONS: Gestational weight gain patterns are strongly related to pre-pregnancy body mass index. The derived charts can be used to assess gestational weight gain in etiological research and as a monitoring tool for weight gain during pregnancy in clinical practice.

Dietary patterns, body mass index and inflammation: Pathways to depression and mental health problems in adolescents.

BACKGROUND: Observational studies suggest that dietary patterns may impact mental health outcomes, although biologically plausible pathways are yet to be tested. We aimed to elucidate the longitudinal relationship between dietary patterns, adiposity, inflammation and mental health including depressive symptoms in a population-based cohort of adolescents. METHODS: Data were provided from 843 adolescents participating in the Western Australian Pregnancy Cohort (Raine) Study at 14 and 17 years (y) of age. Structural equation modelling was applied to test our hypothesised models relating dietary patterns, energy intake and adiposity (body mass index) at 14 y to adiposity and the pro-inflammatory adipokine (leptin) and inflammation (high sensitivity C-reactive protein - hs-CRP) at 17 y, and these inflammatory markers to depressive symptoms (Beck Depression Inventory) and Internalising and Externalising Behavioral Problems (Child Behavior Check List Youth Self- Report) at 17 y. We further tested a reverse hypothesis model, with depression at 14 y as a predictor of dietary patterns at the same time-point. RESULTS: The tested models provided a good fit to the data. A 'Western' dietary pattern (high intake of red meat, takeaway, refined foods, and confectionary) at 14 y was associated with higher energy intake and BMI at 14 y, and with BMI and biomarkers of inflammation at 17 y (all p < .05). A 'Healthy' dietary pattern (high in fruit, vegetables, fish, whole-grains) was inversely associated with BMI and inflammation at 17 y (p < .05). Higher BMI at 14 y was associated with higher BMI (p < .01), leptin (p < .05), hs-CRP (p < .05), depressive symptoms (p < .05) and mental health problems (p < .05), all at 17 y. CONCLUSION: A 'Western' dietary pattern associates with an increased risk of mental health problems including depressive symptoms in adolescents, through biologically plausible pathways of adiposity and inflammation, whereas a 'Healthy' dietary pattern appears protective in these pathways. Longitudinal modelling into adulthood is indicated to confirm the complex associations of dietary patterns, adiposity, inflammation and mental health problems, including depressive symptoms.

Effects of prenatal n-3 fatty acid supplementation on offspring resolvins at birth and 12 years of age: a double-blind, randomised controlled clinical trial.

Resolution of inflammation is an active process involving specialised pro-resolving mediators (SPM) generated from the n-3 fatty acids EPA and DHA. n-3 Fatty acid supplementation during pregnancy may provide an intervention strategy to modify these novel SPM. This study aimed to assess the effect of n-3 fatty acid supplementation in pregnancy on offspring SPM at birth and 12 years of age (12 years). In all, ninety-eight atopic pregnant women were randomised to 3·7 g daily n-3 fatty acids or a control (olive oil), from 20 weeks gestation until delivery. Blood was collected from the offspring at birth and at 12 years. Plasma SPM consisting of 18-hydroxyeicosapentaenoic acid (18-HEPE), E-series resolvins, 17-hydroxydocosahexaenoic acid (17-HDHA), D-series resolvins, 14-hydroxydocosahexaenoic acid (14-HDHA), 10 S,17S-dihydroxydocosahexaenoic acid, maresins and protectin 1, were measured by liquid chromatography-tandem MS. We identified the resolvins RvE1, RvE2, RvE3, RvD1, 17R-RvD1 and RvD2 for the first time in human cord blood. n-3 Fatty acids increased cord blood 18-HEPE (P<0·001) derived from EPA relative to the control group. DHA-derived 17-HDHA at birth was significantly increased in the n-3 fatty acid group relative to the controls (P=0·001), but other SPM were not different between the groups. n-3 Fatty acid supplementation during pregnancy was associated with an increase in SPM precursors in the offspring at birth but the effects were not sustained at 12 years. The presence of these SPM, particularly at birth, may have functions relevant in the newborn that remain to be established, which may be useful for future investigations.

Serum 25-hydroxyvitamin D concentrations and cardiometabolic risk factors in adolescents and young adults.

Evidence associating serum 25-hydroxyvitamin D (25(OH)D) concentrations and cardiometabolic risk factors is inconsistent and studies have largely been conducted in adult populations. We examined the prospective associations between serum 25(OH)D concentrations and cardiometabolic risk factors from adolescence to young adulthood in the West Australian Pregnancy Cohort (Raine) Study. Serum 25(OH)D concentrations, BMI, homoeostasis model assessment for insulin resistance (HOMA-IR), TAG, HDL-cholesterol and systolic blood pressure (SBP) were measured at the 17-year (n 1015) and 20-year (n 1117) follow-ups. Hierarchical linear mixed models with maximum likelihood estimation were used to investigate associations between serum 25(OH)D concentrations and cardiometabolic risk factors, accounting for potential confounders. In males and females, respectively, mean serum 25(OH)D concentrations were 73·6 (sd 28·2) and 75·4 (sd 25·9) nmol/l at 17 years and 70·0 (sd 24·2) and 74·3 (sd 26·2) nmol/l at 20 years. Deseasonalised serum 25(OH)D3 concentrations were inversely associated with BMI (coefficient -0·01; 95 % CI -0·03, -0·003; P=0·014). No change over time was detected in the association for males; for females, the inverse association was stronger at 20 years compared with 17 years. Serum 25(OH)D concentrations were inversely associated with log-HOMA-IR (coefficient -0·002; 95 % CI -0·003, -0·001; P<0·001) and positively associated with log-TAG in females (coefficient 0·002; 95 % CI 0·0008, 0·004; P=0·003). These associations did not vary over time. There were no significant associations between serum 25(OH)D concentrations and HDL-cholesterol or SBP. Clinical trials in those with insufficient vitamin D status may be warranted to determine any beneficial effect of vitamin D supplementation on insulin resistance, while monitoring for any deleterious effect on TAG.

Importance of cardiometabolic risk factors in the association between nonalcoholic fatty liver disease and arterial stiffness in adolescents.

UNLABELLED: Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide and is regarded as the hepatic manifestation of the metabolic syndrome. In adults, NAFLD is a determinant of arterial stiffness and cardiovascular risk, independent of the metabolic syndrome. Our aim was to ascertain if NAFLD is associated with arterial stiffness, independent of cardiometabolic factors in a population-based cohort of adolescents. The 17-year-olds (n = 964) from an Australian birth cohort had measures of anthropometry, blood pressure, fasting insulin, glucose, lipids, and NAFLD by ultrasound. Two-step cluster analysis identified youth at high metabolic risk. Measures of arterial stiffness (pulse wave velocity [PWV] and augmentation index corrected for heart rate [AI@75]) were obtained using applanation tonometry. The overall prevalence of NAFLD was 13.3%. The "high risk" metabolic cluster at age 17 years included 16% males and 19% females. Compared to "low risk," the "high risk" cluster participants had greater waist circumference, triglycerides, insulin, systolic blood pressure, and lower high-density lipoprotein (HDL) cholesterol (all P < 0.0001). Those who had NAFLD but were not in the "high risk" metabolic cluster did not have increased PWV or AI@75. However, males and females who had NAFLD in the presence of the metabolic cluster had greater PWV (b = 0.20, 95% confidence interval [CI] 0.01 to 0.38, P = 0.037). Males who had NAFLD in the presence of the metabolic cluster had greater AI@75 (b = 6.3, 95% CI 1.9 to 10.7, P = 0.005). CONCLUSION: NAFLD is only associated with increased arterial stiffness in the presence of the "high risk" metabolic cluster. This suggests that arterial stiffness related to the presence of NAFLD is predicated on the presence of an adverse metabolic profile in adolescents.