The Obesity Epidemic: From the Environment to Epigenetics – Not Simply a Response to Dietary Manipulation in a Thermoneutral Environment

The prevalence of obesity continues to increase particularly in developed countries. To establish the primary mechanisms involved, relevant animal models which track the developmental pathway to obesity are required. This need is emphasized by the substantial rise in the number of overweight and obese children, of which a majority will remain obese through adulthood. The past half century has been accompanied with unprecedented transitions in our lifestyle. Each of these changes substantially contributes to enhancing our capacity to store energy into adipose tissues. The complex etiology of adiposity is critical as a majority of models investigating obesity utilize a simplistic high-fat/low-carbohydrate diet, fed over a short time period to comparatively young inbred animals maintained in fixed environment. The natural history of obesity is much more complex involving many other mechanisms and this type of challenge may not be the optimal experimental intervention. Such processes include changes in adipose tissue composition with time and the transition from brown to white adipose tissue. Brown adipose tissue, due its unique ability to rapidly produce large amounts of heat could have a pivotal role in energy balance and is under epigenetic regulation mediated by the histone H3k9-specific demethylase Jhdma2a. Furthermore, day length has a potential role in determining endocrine and metabolic responses in brown fat. The potential to utilize novel models and interventions across a range of animal species in adipose tissue development may finally start to yield sustainable strategies by which excess fat mass can, at last, be avoided in humans

Cognitive Function, Mental Health, and Quality of Life in Siblings of Preterm Born Children : Protocol for a Systematic Review

Background: Children and adults born preterm are at increased risk of cognitive impairments, mental health disorders, and poorer quality of life. Epidemiological studies have shown that the impact of preterm birth extends to the immediate family members; however, existing research have focused on parents, and little attention has been given to siblings. Objective: The aim of the systematic review described in this protocol is to synthesize currently available evidence on the impact of exposure to preterm birth (ie, having a sibling born preterm) on cognition, mental health, and quality of life of term born siblings (index child) of preterm born children, and to critically appraise the evidence. Methods: This protocol outlines a systematic review designed in accordance with the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) checklist. We will include all studies that assess outcomes in siblings of children born preterm. Quantitative and qualitative studies will be eligible for the systematic review, and only studies in English will be included. Firstly, search will be conducted electronically on PubMed, Scopus, Embase, Mednar, and opengrey.eu databases and, secondly, manually in Google Scholar and reference lists. The search strategy will include keywords and synonyms, Boolean operators, and text words (ie, within title and abstract). The team of reviewers will screen the search results, extract data from eligible studies, and critically appraise the studies. Analysis will involve both descriptive and quantitative approaches. Meta-analysis will be conducted if appropriate. Results: This systematic review was registered on PROSPERO (International Prospective Register of Systematic Reviews) on December 18, 2020, and it is currently in progress. The findings will be synthesized to determine the effect of preterm birth on full-term siblings and the quality of the available evidence. Conclusions: The evidence derived from this study will shed light on gaps and limitations in the field of preterm birth, more specifically, the effect of preterm birth on full-term siblings. In addition, we hope that understanding the impact of preterm birth on family members will inform targeted interventions and policies for those identified at high risk and how to mitigate health risks. International Registered Report Identifier (IRRID): DERR1-10.2196/34987Peer reviewe

Cognitive Function, Mental Health, and Quality of Life in Siblings of Preterm Born Children : Protocol for a Systematic Review

Background: Children and adults born preterm are at increased risk of cognitive impairments, mental health disorders, and poorer quality of life. Epidemiological studies have shown that the impact of preterm birth extends to the immediate family members; however, existing research have focused on parents, and little attention has been given to siblings. Objective: The aim of the systematic review described in this protocol is to synthesize currently available evidence on the impact of exposure to preterm birth (ie, having a sibling born preterm) on cognition, mental health, and quality of life of term born siblings (index child) of preterm born children, and to critically appraise the evidence. Methods: This protocol outlines a systematic review designed in accordance with the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) checklist. We will include all studies that assess outcomes in siblings of children born preterm. Quantitative and qualitative studies will be eligible for the systematic review, and only studies in English will be included. Firstly, search will be conducted electronically on PubMed, Scopus, Embase, Mednar, and opengrey.eu databases and, secondly, manually in Google Scholar and reference lists. The search strategy will include keywords and synonyms, Boolean operators, and text words (ie, within title and abstract). The team of reviewers will screen the search results, extract data from eligible studies, and critically appraise the studies. Analysis will involve both descriptive and quantitative approaches. Meta-analysis will be conducted if appropriate. Results: This systematic review was registered on PROSPERO (International Prospective Register of Systematic Reviews) on December 18, 2020, and it is currently in progress. The findings will be synthesized to determine the effect of preterm birth on full-term siblings and the quality of the available evidence. Conclusions: The evidence derived from this study will shed light on gaps and limitations in the field of preterm birth, more specifically, the effect of preterm birth on full-term siblings. In addition, we hope that understanding the impact of preterm birth on family members will inform targeted interventions and policies for those identified at high risk and how to mitigate health risks. International Registered Report Identifier (IRRID): DERR1-10.2196/34987Peer reviewe

Maternal hemoglobin associates with preterm delivery and small for gestational age in two Finnish birth cohorts

Objective: To test whether maternal hemoglobin during pregnancy associates with offspring perinatal outcomes in a developed country. Changes in maternal hemoglobin concentration during pregnancy are partly physiological phenomena reflecting alterations of maternal blood volume. Especially hemoglobin measures outside the physiological range may influence maternal health and fetal growth with long-lasting consequences. Study design: We studied an unselected sample drawn from two regional birth cohorts born 20 years apart: The Northern Finland Birth Cohorts 1966 and 1986. These are two mother-and-child population-based birth cohorts together comprising 21,710 mothers and their children. After exclusions, the sample size of the current study was 20,554. Concentrations of maternal hemoglobin at first and last antenatal visits were categorized as low (lowest 10%), medium (reference) or high (highest 10%). Multinomial logistic regression analyses for categories of maternal hemoglobin and perinatal outcomes such as preterm delivery and full-term small and large for gestational age were conducted with adjustments for maternal cofactors. Results: Low maternal hemoglobin at early pregnancy associated with decreased risk of full-term small for gestational age (adjusted OR 0.73, 95% CI [0.58, 0.93], p = 0.010). At late pregnancy, low maternal hemoglobin associated with increased risk of preterm delivery (adjusted OR 1.60, 95% CI [1.26, 2.02], p <0.0005) whereas high maternal hemoglobin associated with increased risk of full-term small for gestational age (adjusted OR 1.29, 95% CI [1.07, 1.56], p=0.009). Maternal hemoglobin did not show constant association with risk of large for gestational age. Conclusion: The results from this study support evidence that both low and high maternal hemoglobin associate with adverse perinatal outcomes. Low maternal hemoglobin associated with preterm delivery and high with full-term small for gestational age. Association was mainly present when maternal hemoglobin was measured during the third trimester. These results indicate that it is important to monitor both extremes of maternal hemoglobin throughout the pregnancy. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

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.</p

An optimal growth pattern during pregnancy and early childhood associates with better fertility in men

Peer reviewe

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.</p

Polycystic ovary syndrome and leukocyte telomere length: cross-sectional and longitudinal changes

Peer reviewe

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

Early exposure to social disadvantages and later life body mass index beyond genetic predisposition in three generations of Finnish birth cohorts

BackgroundThe study aimed to explore the association between early life and life-course exposure to social disadvantage and later life body mass index (BMI) accounting for genetic predisposition and maternal BMI.MethodsWe studied participants of Helsinki Birth Cohort Study born in 1934-1944 (HBCS1934-1944, n=1277) and Northern Finland Birth Cohorts born in 1966 and 1986 (NFBC1966, n=5807, NFBC1986, n=6717). Factor analysis produced scores of social disadvantage based on social and economic elements in early life and adulthood/over the life course, and was categorized as high, intermediate and low. BMI was measured at 62years in HBCS1934-1944, at 46years in NFBC1966 and at 16years in NFBC1986. Multivariable linear regression analysis was used to explore associations between social disadvantages and BMI after adjustments for polygenic risk score for BMI (PRS BMI), maternal BMI and sex.ResultsThe association between exposure to high early social disadvantage and increased later life BMI persisted after adjustments (beta =0.79, 95% CI, 0.33, 1.25, p 0.22, 95% CI -0.91,1.35, p=0.700). In HBCS1934-1944 and NFBC1966, participants who had reduced their exposure to social disadvantage during the life-course had lower later life BMI than those who had increased their exposure (beta -1.34, [-2.37,-0.31], p=0.011; beta -0.46, [-0.89,-0.03], p=0.038, respectively).ConclusionsHigh social disadvantage in early life appears to be associated with higher BMI in later life. Reducing exposure to social disadvantage during the life-course may be a potential pathway for obesity reduction.Peer reviewe