Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding.

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

NMR metabolomic modeling of age and lifespan: A multicohort analysis.

Metabolomic age models have been proposed for the study of biological aging, however, they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. Ninety-eight metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈31,000 individuals, age range 24-86 years). We used nonlinear and penalized regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with aging risk factors and phenotypes. Within the UK Biobank (N ≈102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, and chronic obstructive pulmonary disease), and all-cause mortality. Seven-fold cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47 and 0.65 in the training cohort set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with CA were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06/metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.

A framework for conducting time-varying genome-wide association studies: An application to body mass index across childhood in six multiethnic cohorts.

Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

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.

Do compassion and self-compassion moderate the relationship between childhood socioeconomic position and adulthood body composition?

The study aims to investigate the associations of compassion and self-compassion with body composition, and whether adulthood compassion and self-compassion moderate the relationship between childhood SEP and adulthood body composition. The participants came from the Northern Finland Birth Cohort 1986 Study (n = 789, 52.1% women), with a mean age of 34.0 years. Compassion and self-compassion were measured with the Dispositional Positive Emotions Scale and Self-Compassion Scale-Short Form, respectively. Body composition was assessed using anthropometric and body fat measurements at a clinic. Childhood SEP included parental occupation, education, and employment. The results showed that high compassion was associated with three out of the five body composition measurements, namely lower waist circumference (B = -0.960, p = 0.039, 95% CI: -1.870; -0.498), body fat percentage (B = -0.693, p = 0.030, 95% CI: -1.317; -0.069), and fat mass index (B = -0.325, p = 0.023, 95% CI: -0.605; -0.044) (adjusted for sex, and childhood and adulthood SEP) but not with body mass index or waist-to-hip ratio. Self-compassion was not associated with body composition. Neither compassion nor self-compassion moderated the association between childhood SEP and adulthood body composition, as the interaction effects were not significant. Therefore, the dispositions did not protect against the negative effects of childhood SEP on adulthood body composition. High other-directed compassion may be, however, associated with healthier body composition.

Cognition, mental health and quality of life amongst siblings of preterm born children: A systematic review.

AIM: Globally, 1 in 10 babies are born preterm. Families with preterm born infants may suffer strains related to the presence of a preterm child. To date, most evidence focuses on the outcome of children born preterm and of their parents. Our objective was to investigate the evidence on the impact of having a preterm born sibling on cognitive function, mental health and quality of life of term-born siblings and critically appraise the evidence. METHODS: We searched five electronic databases, Google Scholar and reference lists. Two reviewers independently conducted screening, data extraction and critical appraisal. RESULTS: We retrieved 9121 articles. After duplicates, titles, abstract and full text review, seven studies met the inclusion criteria. One study reported higher anxiety and depression scores on index cases in the term born comparison group, compared to the index cases in the preterm born sibling group. Another study reported more feelings of reduced parental attention, and more interpersonal problems in the preterm born sibling group, than the comparison group. CONCLUSIONS: Although two studies reported a difference in outcomes between index cases in preterm born sibling groups and comparison groups, the scarce evidence did not allow us to delineate an effect or lack of it.

A Pregnancy and Childhood Epigenetics Consortium (PACE) meta-analysis highlights potential relationships between birth order and neonatal blood DNA methylation.

Higher birth order is associated with altered risk of many disease states. Changes in placentation and exposures to in utero growth factors with successive pregnancies may impact later life disease risk via persistent DNA methylation alterations. We investigated birth order with Illumina DNA methylation array data in each of 16 birth cohorts (8164 newborns) with European, African, and Latino ancestries from the Pregnancy and Childhood Epigenetics Consortium. Meta-analyzed data demonstrated systematic DNA methylation variation in 341 CpGs (FDR adjusted P < 0.05) and 1107 regions. Forty CpGs were located within known quantitative trait loci for gene expression traits in blood, and trait enrichment analysis suggested a strong association with immune-related, transcriptional control, and blood pressure regulation phenotypes. Decreasing fertility rates worldwide with the concomitant increased proportion of first-born children highlights a potential reflection of birth order-related epigenomic states on changing disease incidence trends.

NMR metabolomic modelling of age and lifespan: a multi-cohort analysis.

Metabolomic age models have been proposed for the study of biological aging, however they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. 98 metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈ 31,000 individuals, age range 24-86 years). We used non-linear and penalised regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with ageing risk factors and phenotypes. Within the UK Biobank (N≈ 102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, chronic obstructive pulmonary disease) and all-cause mortality. Cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47-0.65 in the training set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with chronological age were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06 / metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.

A bidirectional Mendelian randomisation study to evaluate the relationship between body constitution and hearing loss.

Hearing loss and hearing disorders represent possible mediating pathways in the associations between noise exposures and non-auditory health outcomes. In this context, we assessed whether the noise-obesity associations should consider hearing functions as possible mediators and applied Mendelian randomisation (MR) to investigate causal relationships between body constitution and hearing impairments. We obtained genetic associations from publicly available summary statistics from genome-wide association studies in European ancestry adult populations (N= from 210,088 to 360,564) for (i) body constitution: body mass index (BMI), waist circumference (WC) and body fat percentage (BFP), and (ii) hearing loss: sensorineural hearing loss, noise-induced hearing loss, and age-related hearing impairment (ARHI). We employed colocalisation analysis to investigate the genetic associations for BMI and ARHI liability within an FTO locus. We conducted bi-directional MR for the 'forward' (from body constitution to hearing) and 'reverse' directions. We applied the random effects inverse variance-weighted method as the main MR method, with additional sensitivity analyses. Colocalisation analysis suggested that BMI and ARHI shared a causal variant at the FTO gene. We did not find robust evidence for causal associations from body constitution to hearing loss and suggested that some associations may be driven by FTO variants. In the reverse analyses, ARHI was negatively associated with BMI [effect size - 0.22 (95% CI - 0.44 to - 0.01)] and BFP [effect size - 0.23 (95% CI - 0.45 to 0.00)], supporting the notion that ARHI may diminish body constitution. Finally, our data suggest that there is no strong evidence that hearing explains the association between noise exposure and body constitution.

Epigenome-Wide Meta-analysis Reveals Associations Between Dietary Glycemic Index and Glycemic Load and DNA Methylation in Children and Adolescents of Different Body Sizes.

OBJECTIVE: Dietary glycemic index (GI) and glycemic load (GL) are associated with cardiometabolic health in children and adolescents, with potential distinct effects in people with increased BMI. DNA methylation (DNAm) may mediate these effects. Thus, we conducted meta-analyses of epigenome-wide association studies (EWAS) between dietary GI and GL and blood DNAm of children and adolescents. RESEARCH DESIGN AND METHODS: We calculated dietary GI and GL and performed EWAS in children and adolescents (age range: 4.5-17 years) from six cohorts (N = 1,187). We performed stratified analyses of participants with normal weight (n = 801) or overweight or obesity (n = 386). We performed look-ups for the identified cytosine-phosphate-guanine (CpG) sites (false discovery rate [FDR] <0.05) with tissue-specific gene expression of 832 blood and 223 subcutaneous adipose tissue samples from children and adolescents. RESULTS: Dietary GL was positively associated with DNAm of cg20274553 (FDR <0.05), annotated to WDR27. Several CpGs were identified in the normal-weight (GI: 85; GL: 17) and overweight or obese (GI: 136; GL: 298; FDR <0.05) strata, and none overlapped between strata. In participants with overweight or obesity, identified CpGs were related to RNA expression of genes associated with impaired metabolism (e.g., FRAT1, CSF3). CONCLUSIONS: We identified 537 associations between dietary GI and GL and blood DNAm, mainly in children and adolescents with overweight or obesity. High-GI and/or -GL diets may influence epigenetic gene regulation and thereby promote metabolic derangements in young people with increased BMI.

Cardiometabolic risk estimation using exposome data and machine learning.

BACKGROUND: The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. OBJECTIVE: Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. METHODS: From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method. RESULTS: The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. CONCLUSIONS: We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.

A multivariate genome-wide association study of psycho-cardiometabolic multimorbidity.

Coronary artery disease (CAD), type 2 diabetes (T2D) and depression are among the leading causes of chronic morbidity and mortality worldwide. Epidemiological studies indicate a substantial degree of multimorbidity, which may be explained by shared genetic influences. However, research exploring the presence of pleiotropic variants and genes common to CAD, T2D and depression is lacking. The present study aimed to identify genetic variants with effects on cross-trait liability to psycho-cardiometabolic diseases. We used genomic structural equation modelling to perform a multivariate genome-wide association study of multimorbidity (Neffective = 562,507), using summary statistics from univariate genome-wide association studies for CAD, T2D and major depression. CAD was moderately genetically correlated with T2D (rg = 0.39, P = 2e-34) and weakly correlated with depression (rg = 0.13, P = 3e-6). Depression was weakly correlated with T2D (rg = 0.15, P = 4e-15). The latent multimorbidity factor explained the largest proportion of variance in T2D (45%), followed by CAD (35%) and depression (5%). We identified 11 independent SNPs associated with multimorbidity and 18 putative multimorbidity-associated genes. We observed enrichment in immune and inflammatory pathways. A greater polygenic risk score for multimorbidity in the UK Biobank (N = 306,734) was associated with the co-occurrence of CAD, T2D and depression (OR per standard deviation = 1.91, 95% CI = 1.74-2.10, relative to the healthy group), validating this latent multimorbidity factor. Mendelian randomization analyses suggested potentially causal effects of BMI, body fat percentage, LDL cholesterol, total cholesterol, fasting insulin, income, insomnia, and childhood maltreatment. These findings advance our understanding of multimorbidity suggesting common genetic pathways.

Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants.

MOTIVATION: Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium patterns. RESULTS: We present "FiniMOM" (fine-mapping using a product inverse-moment prior), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a nonlocal inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the linkage disequilibrium reference. The results of simulations studies aimed to mimic a typical GWAS on circulating protein levels show improved credible set coverage and power of the proposed method over current state-of-the-art fine-mapping method SuSiE, especially in the case of multiple causal variants within a locus. AVAILABILITY AND IMPLEMENTATION: https://vkarhune.github.io/finimom/.

Vitamin D Status in Women with a History of Infertility and Decreased Fecundability: A Population-Based Study.

BACKGROUND: Infertility and fecundability problems have been linked with lower 25-hydroxyvitamin D (25(OH)D) concentrations, but studies conducted with small, heterogenous or selected populations have shown inconsistent results. METHODS: This study included women at age 31 from prospective population-based Northern Finland Birth Cohort 1966. Serum 25(OH)D concentrations were evaluated between women with or without previous infertility examinations or treatments (infertility group, n = 375, reference group, n = 2051) and time to pregnancy (TTP) of over 12 months (decreased fecundability group, n = 338) with a wide range of confounders. Furthermore, 25(OH)D concentrations were also compared among reproductive outcomes. RESULTS: The mean 25(OH)D concentration was lower and 25(OH)D < 30 nmol/L was more frequent in women with a history of infertility compared to reference group. Moreover, 25(OH)D > 75 nmol/L was more frequent in the reference group. The mean 25(OH)D concentration was lower in women who had had multiple miscarriages. Both history of infertility (ß = -2.7, 95% confidence interval (CI) -4.6, -0.7) and decreased fecundability associated with lower 25(OH)D concentration (ß = -4.1, 95% CI -7.4, -0.8) after adjustments. In conclusion, this population-based study demonstrated that previous infertility and decreased fecundability were associated with lower 25(OH)D.

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.].

Clinical-Grade Patches as a Medium for Enrichment of Sweat-Extracellular Vesicles and Facilitating Their Metabolic Analysis.

Cell-secreted extracellular vesicles (EVs), carrying components such as RNA, DNA, proteins, and metabolites, serve as candidates for developing non-invasive solutions for monitoring health and disease, owing to their capacity to cross various biological barriers and to become integrated into human sweat. However, the evidence for sweat-associated EVs providing clinically relevant information to use in disease diagnostics has not been reported. Developing cost-effective, easy, and reliable methodologies to investigate EVs' molecular load and composition in the sweat may help to validate their relevance in clinical diagnosis. We used clinical-grade dressing patches, with the aim being to accumulate, purify and characterize sweat EVs from healthy participants exposed to transient heat. The skin patch-based protocol described in this paper enables the enrichment of sweat EVs that express EV markers, such as CD63. A targeted metabolomics study of the sweat EVs identified 24 components. These are associated with amino acids, glutamate, glutathione, fatty acids, TCA, and glycolysis pathways. Furthermore, as a proof-of-concept, when comparing the metabolites' levels in sweat EVs isolated from healthy individuals with those of participants with Type 2 diabetes following heat exposure, our findings revealed that the metabolic patterns of sweat EVs may be linked with metabolic changes. Moreover, the concentration of these metabolites may reflect correlations with blood glucose and BMI. Together our data revealed that sweat EVs can be purified using routinely used clinical patches, setting the foundations for larger-scale clinical cohort work. Furthermore, the metabolites identified in sweat EVs also offer a realistic means to identify relevant disease biomarkers. This study thus provides a proof-of-concept towards a novel methodology that will focus on the use of the sweat EVs and their metabolites as a non-invasive approach, in order to monitor wellbeing and changes in diseases.

The interplay between inflammatory cytokines and cardiometabolic disease: bi-directional mendelian randomisation study.

Objective: To leverage large scale genetic association data to investigate the interplay between circulating cytokines and cardiometabolic traits, and thus identifying potential therapeutic targets. Design: Bi-directional Mendelian randomisation study. Setting: Genome-wide association studies from three Finnish cohorts (Northern Finland Birth Cohort 1966, Young Finns Study, or FINRISK study), and genetic association summary statistics pooled from observational studies for expression quantitative trait loci and cardiometabolic traits. Participants: Data for 47 circulating cytokines in 13 365 individuals from genome-wide association studies, summary statistic data for up to 21 735 individuals on circulating cytokines, summary statistic gene expression data across 49 tissues in 838 individuals, and summary statistic data for up to 1 320 016 individuals on cardiometabolic traits. Interventions: Relations between circulating cytokines and cardiovascular, anthropometric, lipid, or glycaemic traits (coronary artery disease, stroke, type 2 diabetes mellitus, body mass index, waist circumference, waist to hip ratio, systolic blood pressure, glycated haemoglobin, high density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, triglycerides, C reactive protein, glucose, fasting insulin, and lifetime smoking). Main outcome methods: Genetic instrumental variables that are biologically plausible for the circulating cytokines were generated. The effects of cardiometabolic risk factors on concentrations of circulating cytokines, circulating cytokines on other circulating cytokines, and circulating cytokines on cardiometabolic outcomes were investigated. Results: Genetic evidence (mendelian randomisation P<0.0011) suggests that higher body mass index, waist circumference, smoking, higher concentrations of lipids, and systolic blood pressure increase circulating concentrations of several inflammatory cytokines and C reactive protein. Evidence for causal relations (mendelian randomisation P<0.0011) were noted between circulating cytokines, including a key role of vascular endothelial growth factor on influencing the concentrations of 10 other cytokines. Both mendelian randomisation (P<0.05) and colocalisation (posterior probability >0.5) suggested that coronary artery disease risk is increased by higher concentrations of circulating tumour necrosis factor related apoptosis-inducing ligand (TRAIL), interleukin-1 receptor antagonist (IL1RA), and macrophage colony-stimulating factor (MCSF). Conclusion: This study offers insight into inflammatory mediators of cardiometabolic risk factors, cytokine signalling cascades, and effects of circulating cytokines on different cardiometabolic outcomes.

Depression mediates the relationship between alexithymia and obesity in the Northern Finland Birth Cohort 1966 (NFBC1966).

BACKGROUND: The fact that a complex relationship exists between alexithymia and body mass index (BMI) is well established, but the underlying mechanisms remain poorly understood. Here, we explore the relationship between alexithymia and depressive symptoms in relation to adiposity measures, including the direct and indirect effect of alexithymia and depressive symptoms on obesity over a 15-year time-period, in the Northern Finland Birth Cohort 1966 (NFBC1966). METHODS: The study included individuals from the Northern Finland Birth Cohort 1966 (NFBC1966) who had available data for adiposity measures (body mass index and waist-to-hip ratio), alexithymia (measured by the 20-Item Toronto Alexithymia Scale: TAS-20), depressive symptoms (measured by the 13-item depression subscale of Hopkins Symptom Checklist: HSCL-13) at age of 31 years (n = 4773) and 46 years (n = 4431). Pearson's (r) correlation, and multiple linear regression were used to investigate the relationships between alexithymia, depressive symptoms, and adiposity measures. The potential mediating role of depressive symptoms was examined via Hayes' procedure (PROCESS). RESULTS: Positive correlations were confirmed between adiposity measures (BMI and WHR) and the TAS-20 score (and its subscale), but not between obesity and HSCL-13 score. The strongest correlation was between the DIF (difficulty identifying feelings) subscale of the TAS-20 and HSCL-13 at both time points (31 y: r(3013) = 0.41, p < 0.01, 46 y: r(3013) = 0.43, p < 0.01). Depressive symptoms completely (z = 2.55 (±0.00003), p = 0.01) and partly (z = 2.16 (±0.0001), p = 0.03) mediated the alexithymia-obesity relationship over the 15-year time-period. LIMITATIONS: Other psychological and environmental factors such as interoception, dietary intake and physical activities may also play a role as a potential mediating factor in alexithymia-obesity relationship. CONCLUSIONS: Our findings provide additional insights of theoretical framework of depressive symptoms mediation effect in the relationship between alexithymia and obesity. Alexithymia and depression should, therefore, be considered in the design of future clinical obesity research.

Maternal prenatal cholesterol levels predict offspring weight trajectories during childhood in the Norwegian Mother, Father and Child Cohort Study.

BACKGROUND: Numerous intrauterine factors may affect the offspring's growth during childhood. We aimed to explore if maternal and paternal prenatal lipid, apolipoprotein (apo)B and apoA1 levels are associated with offspring weight, length, and body mass index from 6 weeks to eight years of age. This has previously been studied to a limited extent. METHODS: This parental negative control study is based on the Norwegian Mother, Father and Child Cohort Study and uses data from the Medical Birth Registry of Norway. We included 713 mothers and fathers with or without self-reported hypercholesterolemia and their offspring. Seven parental metabolites were measured by nuclear magnetic resonance spectroscopy, and offspring weight and length were measured at 12 time points. Data were analyzed by linear spline mixed models, and the results are presented as the interaction between parental metabolite levels and offspring spline (age). RESULTS: Higher maternal total cholesterol (TC) level was associated with a larger increase in offspring body weight up to 8 years of age (0.03 ≤ Pinteraction ≤ 0.04). Paternal TC level was not associated with change in offspring body weight (0.17 ≤ Pinteraction ≤ 0.25). Higher maternal high-density lipoprotein cholesterol (HDL-C) and apoA1 levels were associated with a lower increase in offspring body weight up to 8 years of age (0.001 ≤ Pinteraction ≤ 0.005). Higher paternal HDL-C and apoA1 levels were associated with a lower increase in offspring body weight up to 5 years of age but a larger increase in offspring body weight from 5 to 8 years of age (0.01 ≤ Pinteraction ≤ 0.03). Parental metabolites were not associated with change in offspring height or body mass index up to 8 years of age (0.07 ≤ Pinteraction ≤ 0.99). CONCLUSIONS: Maternal compared to paternal TC, HDL-C, and apoA1 levels were more strongly and consistently associated with offspring body weight during childhood, supporting a direct intrauterine effect.

Influence of Farm Environment on Asthma during the Life Course: A Population-Based Birth Cohort Study in Northern Finland.

We investigated the influence of a farming environment on asthma at three time points from birth to 46 years using the Northern Finland Birth Cohort 1966 (n = 10,926). The prevalence of asthma was investigated by postal questionnaires at 14, 31 and 46 years of age. Exposure to a farming environment was assessed by a postal questionnaire at birth and at 31 and 46 years of age. Odds ratios (ORs) and their 95% confidence intervals (95% CIs) for the prevalence of asthma were obtained from multinomial logistic regression, stratified by sex. Being born in a farmer family was potentially causally associated with lower risk of asthma in males at 31 years of age (OR 0.56, 95% CI 0.37, 0.85) and in females at 46 years of age (OR 0.64, 95% CI 0.44, 0.95). Working as a farmer was not associated with asthma. Exposure to a farming environment in childhood may have a lifelong impact on developing asthma from birth through young adulthood and until middle age, indicating that 'immune deviation' may persist throughout life.