Search

Background: Early growth, stress, and socioeconomic factors are associated with future risk of individual chronic diseases. It is uncertain whether they also affect the rate of multimorbidity accumulation later in life. This study aimed to explore whether early life factors are associated with the rate at which chronic diseases are accumulated across older age. Methods: In this national birth cohort study, we studied people born at Helsinki University Central Hospital, Helsinki, Finland between Jan 1, 1934, and Dec 31, 1944, who attended child welfare clinics in the city, and were living in Finland in 1971. Individuals who had died or emigrated from Finland before 1987 were excluded, alongside participants without any registry data and who died before the end of the registry follow-up on Dec 31, 2017. Early anthropometry, growth, wartime parental separation, and socioeconomic factors were recorded from birth, child welfare clinic, or school health-care records, and Finnish National Archives. International Classification of Diseases codes of diagnoses for chronic diseases were obtained from the Care Register for Health Care starting from 1987 (when participants were aged 42-53 years) until 2017. Linear mixed models were used to study the association between early-life factors and the rate of change in the number of chronic diseases over 10-year periods. Findings: From Jan 1, 1934, to Dec 31, 2017, 11 689 people (6064 [51 center dot 9%] men and 5625 [48 center dot 1%] women) were included in the study. Individuals born to mothers younger than 25 years (beta 0 center dot 09; 95% CI 0 center dot 06-0 center dot 12), mothers with a BMI of 25-30 kg/m2 (0 center dot 08; 0 center dot 05-0 center dot 10), and mothers with a BMI more than 30 kg/m2 (0 center dot 26; 0 center dot 21-0 center dot 31) in late pregnancy accumulated chronic diseases faster than those born to older mothers (25-30 years) and those with a BMI of less than 25 kg/m2. Individuals with a birthweight less

Background While individuals who were separated from their biological family and placed into the care of the state during childhood (out-of-home care) are more prone to developing selected adverse health problems in adulthood, their risk of cardiovascular disease is uncertain. Our aim was to explore this association by pooling published and unpublished results from prospective cohort studies. Methods We used two approaches to identifying relevant data on childhood care and adult cardiovascular disease (PROSPERO registration CRD42021254665). First, to locate published studies, we searched PubMed (Medline) until November 2023. Second, with the objective of identifying unpublished studies with the potential to address the present research question, we scrutinised retrieved reviews on childhood out-of-home care and other adult health outcomes. Included studies were required to satisfy three criteria: a cohort study in which the assessment of care was made prospectively pre-adulthood (in the avoidance of recall bias); data on an unexposed comparator group were available (for the computation of relative risk); and a diagnosis of adult cardiovascular disease events (coronary heart disease, stroke, or their combination) had been made (as opposed to risk factors only). Collaborating investigators provided study-specific estimates which were aggregated using random-effects meta-analysis. The Newcastle-Ottawa Scale was used to assess individual study quality. Findings Twelve studies (2 published, 10 unpublished) met the inclusion criteria, and investigators from nine provided viable results, including updated analyses of the published studies. Studies comprised 611,601 individuals (301,129 women) from the US, UK, Sweden, Finland, and Australia. Five of the nine studies were judged to be of higher methodological quality. Relative to the unexposed, individuals with a care placement during childhood had a 51% greater risk of cardiovascular disease in adulthood (summary rate ratio

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.

Background: Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. Methods: We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years). Results: Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10−17) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10−7), with some evidence of persis

Background Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. Methods We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years). Results Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10−17) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10−7), with some evidence of pers, Background: Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. Methods: We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years). Results: Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10−17) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10−7), with some evidence of persis

BACKGROUND: The EU LifeCycle Project was launched in 2017 to combine, harmonise, and analyse 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 over 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, behavioural 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 for ages 0-18+ years, covering domains across a wide range of behavioural and psychopathology indicators and outcomes (including executive function, depression, ADHD and cognition). These data span a unique combination of qualitative data collected through behavioural/cognitive/mental health questionnaires and examination, as well as data from biological samples and indices in the form of brain imaging (MRI, foetal 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.

Background Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. Methods We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years). Results Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10−17) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10−7), with some evidence of pers, Background: Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age. Methods: We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1–1.6 years), childhood (4.2–7.5 years); adolescence (12.0–16.0 years), and adulthood (22.0–67.8 years). Results: Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of −0.89 standard deviation (SD) units for albumin with PTB (95% CI: −1.10 to −0.69, P=1.3×10−17) and −0.41 SD for total lipids in medium HDL with SGA (95% CI: −0.56 to −0.25, P=2.6×10−7), with some evidence of persis

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 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 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.

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 p

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 p

The loss of estrogen during menopause causes changes in the female body, with wide-ranging effects on health. Estrogen-containing hormone replacement therapy (HRT) leads to a relief of typical menopausal symptoms, benefits bone and muscle health, and is associated with tissue-specific gene expression profiles. As gene expression is controlled by epigenetic factors (including DNA methylation), many of which are environmentally sensitive, it is plausible that at least part of the HRT-associated gene expression is due to changes in DNA methylation profile. We investigated genome-wide DNA methylation and gene expression patterns of white blood cells (WBCs) and their associations with body composition, including muscle and bone measures of monozygotic (MZ) female twin pairs discordant for HRT. We identified 7,855 nominally significant differentially methylated regions (DMRs) associated with 4,044 genes. Of the genes with DMRs, five (ACBA1, CCL5, FASLG, PPP2R2B, and UHRF1) were also differentially expressed. All have been previously associated with HRT or estrogenic regulation, but not with HRT-associated DNA methylation. All five genes were associated with bone mineral content (BMC), and ABCA1, FASLG, and UHRF1 were also associated with body adiposity. Our study is the first to show that HRT associates with genome-wide DNA methylation alterations in WBCs. Moreover, we show that five differentially expressed genes with DMRs associate with clinical measures, including body fat percentage, lean body mass, bone mass, and blood lipids. Our results indicate that at least part of the known beneficial HRT effects on body composition and bone mass may be regulated by DNA methylation associated alterations in gene expression in circulating WBCs.