-
Frontiers in Nutrition 2023Food parenting practices are associated with child weight. Such associations may reflect the effects of parents' practices on children's food intake and weight. However,...
BACKGROUND
Food parenting practices are associated with child weight. Such associations may reflect the effects of parents' practices on children's food intake and weight. However, longitudinal, qualitative, and behavioral genetic evidence suggests these associations could, in some cases, reflect parents' response to children's genetic risk for obesity, an instance of gene-environment correlation. We tested for gene-environment correlations across multiple domains of food parenting practices and explored the role of parent-reported child appetite in these relationships.
MATERIALS AND METHODS
Data on relevant variables were available for = 197 parent-child dyads (7.54 ± 2.67 years; 44.4% girls) participating in RESONANCE, an ongoing pediatric cohort study. Children's body mass index (BMI) polygenic risk score (PRS) were derived based on adult GWAS data. Parents reported on their feeding practices (Comprehensive Feeding Practices Questionnaire) and their child's eating behavior (Child Eating Behavior Questionnaire). Moderation effects of child eating behaviors on associations between child BMI PRS and parental feeding practices were examined, adjusting for relevant covariates.
RESULTS
Of the 12 parental feeding practices, 2 were associated with child BMI PRS, namely, restriction for weight control (β = 0.182, = 0.011) and teaching about nutrition (β = -0.217, = 0.003). Moderation analyses demonstrated that when children had high genetic obesity risk and showed moderate/high (vs. low) food responsiveness, parents were more likely to restrict food intake to control weight.
CONCLUSION
Our results indicate that parents may adjust their feeding practices in response to a child's genetic propensity toward higher or lower bodyweight, and the adoption of food restriction to control weight may depend on parental perceptions of the child's appetite. Research using prospective data on child weight and appetite and food parenting from infancy is needed to further investigate how gene-environment relationships evolve through development.
PubMed: 37324730
DOI: 10.3389/fnut.2023.1174441 -
Clinical and Experimental Pediatrics Dec 2023Recent advances in molecular genetics have advanced our understanding of the molecular mechanisms involved in pediatric endocrine disorders and now play a major role in...
Recent advances in molecular genetics have advanced our understanding of the molecular mechanisms involved in pediatric endocrine disorders and now play a major role in mainstream medical practice. The spectrum of endocrine genetic disorders has 2 extremes: Mendelian and polygenic. Mendelian or monogenic diseases are caused by rare variants of a single gene, each of which exerts a strong effect on disease risk. Polygenic diseases or common traits are caused by the combined effects of multiple genetic variants in conjunction with environmental and lifestyle factors. Testing for a single gene is preferable if the disease is phenotypically and/or geneically homogeneous. Next-generation sequencing (NGS) can be applied to phenotypically and genetically heterogeneous conditions. Genome-wide association studies (GWASs) have examined genetic variants across the entire genome in a large number of individuals who have been matched for population ancestry and assessed for a disease or trait of interest. Common endocrine diseases or traits, such as type 2 diabetes mellitus, obesity, height, and pubertal timing, result from the combined effects of multiple variants in various genes that are frequently found in the general population, each of which contributes a small individual effect. Isolated founder mutations can result from a true founder effect or an extreme reduction in population size. Studies of founder mutations offer powerful advantages for efficiently localizing the genes that underlie Mendelian disorders. The Korean population has settled in the Korean peninsula for thousands of years, and several recurrent mutations have been identified as founder mutations. The application of molecular technology has increased our understanding of endocrine diseases, which have impacted on the practice of pediatric endocrinology related to diagnosis and genetic counseling. This review focuses on the application of genomic research to pediatric endocrine diseases using GWASs and NGS technology for diagnosis and treatment.
PubMed: 37321569
DOI: 10.3345/cep.2022.00948 -
The Journal of Adolescent Health :... Sep 2023Overweight in youth is influenced by genes and environment. Gene-environment interaction (G×E) has been demonstrated in twin studies and recent developments in genetics...
PURPOSE
Overweight in youth is influenced by genes and environment. Gene-environment interaction (G×E) has been demonstrated in twin studies and recent developments in genetics allow for studying G×E using individual genetic predispositions for overweight. We examine genetic influence on trajectories of overweight during adolescence and early adulthood and determine whether genetic predisposition is attenuated by higher socioeconomic status and having physically active parents.
METHODS
Latent class growth models of overweight were fitted using data from the TRacking Adolescents' Individual Lives Survey (n = 2720). A polygenic score for body mass index (BMI) was derived using summary statistics from a genome-wide association study of adult BMI (N = ∼700,000) and tested as predictor of developmental pathways of overweight. Multinomial logistic regression models were used to examine effects of interactions of genetic predisposition with socioeconomic status and parental physical activity (n = 1675).
RESULTS
A three-class model of developmental pathways of overweight fitted the data best ("non-overweight", "adolescent-onset overweight", and "persistent overweight"). The polygenic score for BMI and socioeconomic status distinguished the persistent overweight and adolescent-onset overweight trajectories from the non-overweight trajectory. Only genetic predisposition differentiated the adolescent-onset from the persistent overweight trajectory. There was no evidence for G×E.
DISCUSSION
Higher genetic predisposition increased the risk of developing overweight during adolescence and young adulthood and was associated with an earlier age at onset. We did not find that genetic predisposition was offset by higher socioeconomic status or having physically active parents. Instead, lower socioeconomic status and higher genetic predisposition acted as additive risk factors for developing overweight.
Topics: Adult; Adolescent; Humans; Young Adult; Longitudinal Studies; Genetic Predisposition to Disease; Genome-Wide Association Study; Overweight; Body Mass Index; Pediatric Obesity; Risk Factors; Seizures
PubMed: 37318409
DOI: 10.1016/j.jadohealth.2023.04.028 -
Diabetologia Sep 2023Low birthweight is a risk factor for type 2 diabetes but it is unknown whether low birthweight is associated with distinct clinical characteristics at disease onset. We...
AIMS/HYPOTHESIS
Low birthweight is a risk factor for type 2 diabetes but it is unknown whether low birthweight is associated with distinct clinical characteristics at disease onset. We examined whether a lower or higher birthweight in type 2 diabetes is associated with clinically relevant characteristics at disease onset.
METHODS
Midwife records were traced for 6866 individuals with type 2 diabetes in the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) cohort. Using a cross-sectional design, we assessed age at diagnosis, anthropomorphic measures, comorbidities, medications, metabolic variables and family history of type 2 diabetes in individuals with the lowest 25% of birthweight (<3000 g) and highest 25% of birthweight (>3700 g), compared with a birthweight of 3000-3700 g as reference, using log-binomial and Poisson regression. Continuous relationships across the entire birthweight spectrum were assessed with linear and restricted cubic spline regression. Weighted polygenic scores (PS) for type 2 diabetes and birthweight were calculated to assess the impact of genetic predispositions.
RESULTS
Each 1000 g decrease in birthweight was associated with a 3.3 year (95% CI 2.9, 3.8) younger age of diabetes onset, 1.5 kg/m (95% CI 1.2, 1.7) lower BMI and 3.9 cm (95% CI 3.3, 4.5) smaller waist circumference. Compared with the reference birthweight, a birthweight of <3000 g was associated with more overall comorbidity (prevalence ratio [PR] for Charlson Comorbidity Index Score ≥3 was 1.36 [95% CI 1.07, 1.73]), having a systolic BP ≥155 mmHg (PR 1.26 [95% CI 0.99, 1.59]), lower prevalence of diabetes-associated neurological disease, less likelihood of family history of type 2 diabetes, use of three or more glucose-lowering drugs (PR 1.33 [95% CI 1.06, 1.65]) and use of three or more antihypertensive drugs (PR 1.09 [95% CI 0.99, 1.20]). Clinically defined low birthweight (<2500 g) yielded stronger associations. Most associations between birthweight and clinical characteristics appeared linear, and a higher birthweight was associated with characteristics mirroring lower birthweight in opposite directions. Results were robust to adjustments for PS representing weighted genetic predisposition for type 2 diabetes and birthweight.
CONCLUSION/INTERPRETATION
Despite younger age at diagnosis, and fewer individuals with obesity and family history of type 2 diabetes, a birthweight <3000 g was associated with more comorbidities, including a higher systolic BP, as well as with greater use of glucose-lowering and antihypertensive medications, in individuals with recently diagnosed type 2 diabetes.
Topics: Humans; Diabetes Mellitus, Type 2; Birth Weight; Cross-Sectional Studies; Risk Factors; Genetic Predisposition to Disease; Glucose
PubMed: 37303007
DOI: 10.1007/s00125-023-05936-1 -
Journal of Korean Medical Science May 2023Osteoporosis develops in the elderly due to decreased bone mineral density (BMD), potentially increasing bone fracture risk. However, the BMD is not regularly measured...
BACKGROUND
Osteoporosis develops in the elderly due to decreased bone mineral density (BMD), potentially increasing bone fracture risk. However, the BMD is not regularly measured in a clinical setting. This study aimed to develop a good prediction model for the osteoporosis risk using a machine learning (ML) approach in adults over 40 years in the Ansan/Anseong cohort and the association of predicted osteoporosis risk with a fracture in the Health Examinees (HEXA) cohort.
METHODS
The 109 demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8,842 participants were manually selected in an Ansan/Anseong cohort and included in the ML algorithm. The polygenic risk score (PRS) of osteoporosis was generated with a genome-wide association study and added for the genetic impact of osteoporosis. Osteoporosis was defined with < -2.5 T scores of the tibia or radius compared to people in their 20s-30s. They were divided randomly into the training (n = 7,074) and test (n = 1,768) sets-Pearson's correlation between the predicted osteoporosis risk and fracture in the HEXA cohort.
RESULTS
XGBoost, deep neural network, and random forest generated the prediction model with a high area under the curve (AUC, 0.86) of the receiver operating characteristic (ROC) with 10, 15, and 20 features; the prediction model by XGBoost had the highest AUC of ROC, high accuracy and k-fold values (> 0.85) in 15 features among seven ML approaches. The model included the genetic factor, genders, number of children and breastfed children, age, residence area, education, seasons to measure, height, smoking status, hormone replacement therapy, serum albumin, hip circumferences, vitamin B6 intake, and body weight. The prediction models for women alone were similar to those for both genders, with lower accuracy. When the prediction model was applied to the HEXA study, the correlation between the fracture incidence and predicted osteoporosis risk was significant but weak (r = 0.173, < 0.001).
CONCLUSION
The prediction model for osteoporosis risk generated by XGBoost can be applied to estimate osteoporosis risk. The biomarkers can be considered for enhancing the prevention, detection, and early therapy of osteoporosis risk in Asians.
Topics: Adult; Child; Humans; Female; Male; Aged; Bone Density; Genome-Wide Association Study; Osteoporosis; Fractures, Bone; Machine Learning
PubMed: 37270917
DOI: 10.3346/jkms.2023.38.e162 -
Nature Communications Jun 2023We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare...
We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.
Topics: Male; Female; Humans; Blood Pressure; Population Health; Risk Factors; Multifactorial Inheritance; Ethnicity; Genome-Wide Association Study; Genetic Predisposition to Disease
PubMed: 37268629
DOI: 10.1038/s41467-023-38990-9 -
PLoS Genetics Jun 2023The 16p11.2 and 22q11.2 copy number variants (CNVs) are associated with neurobehavioral traits including autism spectrum disorder (ASD), schizophrenia, bipolar disorder,...
The 16p11.2 and 22q11.2 copy number variants (CNVs) are associated with neurobehavioral traits including autism spectrum disorder (ASD), schizophrenia, bipolar disorder, obesity, and intellectual disability. Identifying specific genes contributing to each disorder and dissecting the architecture of CNV-trait association has been difficult, inspiring hypotheses of more complex models, such as multiple genes acting together. Using multi-tissue data from the GTEx consortium, we generated pairwise expression imputation models for CNV genes and then applied these elastic net models to GWAS for: ASD, bipolar disorder, schizophrenia, BMI (obesity), and IQ (intellectual disability). We compared the variance in these five traits explained by gene pairs with the variance explained by single genes and by traditional interaction models. We also modeled polygene region-wide effects using summed predicted expression ranks across many genes to create a regionwide score. We found that in all CNV-trait pairs except for bipolar disorder at 22q11.2, pairwise effects explain more variance than single genes. Pairwise model superiority was specific to the CNV region for all 16p11.2 traits and ASD at 22q11.2. We identified novel individual genes over-represented in top pairs that did not show single-gene signal. We also found that BMI and IQ have significant regionwide association with both CNV regions. Overall, we observe that genetic architecture differs by trait and region, but 9/10 CNV-trait combinations demonstrate evidence for multigene contribution, and for most of these, the importance of combinatorial models appears unique to CNV regions. Our results suggest that mechanistic insights for CNV pathology may require combinational models.
Topics: Humans; Chromosomes, Human, Pair 16; Chromosomes, Human, Pair 22; DNA Copy Number Variations; Behavior; Nervous System Diseases
PubMed: 37267418
DOI: 10.1371/journal.pgen.1010780 -
PLoS Genetics May 2023Females with polycystic ovary syndrome (PCOS), the most common endocrine disorder in women, have an increased risk of developing cardiometabolic disorders such as...
Females with polycystic ovary syndrome (PCOS), the most common endocrine disorder in women, have an increased risk of developing cardiometabolic disorders such as insulin resistance, obesity, and type 2 diabetes (T2D). While only diagnosable in females, males with a family history of PCOS can also exhibit a poor cardiometabolic profile. Therefore, we aimed to elucidate the role of sex in the cardiometabolic comorbidities observed in PCOS by conducting bidirectional genetic risk score analyses in both sexes. We first conducted a phenome-wide association study (PheWAS) using PCOS polygenic risk scores (PCOSPRS) to identify potential pleiotropic effects of PCOSPRS across 1,380 medical conditions recorded in the Vanderbilt University Medical Center electronic health record (EHR) database, in females and males. After adjusting for age and genetic ancestry, we found that European (EUR)-ancestry males with higher PCOSPRS were significantly more likely to develop hypertensive diseases than females at the same level of genetic risk. We performed the same analysis in an African (AFR)-ancestry population, but observed no significant associations, likely due to poor trans-ancestry performance of the PRS. Based on observed significant associations in the EUR-ancestry population, we then tested whether the PRS for comorbid conditions (e.g., T2D, body mass index (BMI), hypertension, etc.) also increased the odds of a PCOS diagnosis. Only BMIPRS and T2DPRS were significantly associated with a PCOS diagnosis in EUR-ancestry females. We then further adjusted the T2DPRS for measured BMI and BMIresidual (regressed on the BMIPRS and enriched for the environmental contribution to BMI). Results demonstrated that genetically regulated BMI primarily accounted for the relationship between T2DPRS and PCOS. Overall, our findings show that the genetic architecture of PCOS has distinct sex differences in associations with genetically correlated cardiometabolic traits. It is possible that the cardiometabolic comorbidities observed in PCOS are primarily explained by their shared genetic risk factors, which can be further influenced by biological variables including sex and BMI.
Topics: Humans; Female; Male; Polycystic Ovary Syndrome; Diabetes Mellitus, Type 2; Risk Factors; Body Mass Index; Phenotype; Cardiovascular Diseases
PubMed: 37256887
DOI: 10.1371/journal.pgen.1010764 -
The Lancet. Digital Health Jun 2023Machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and...
BACKGROUND
Machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and presentations, or with clinical and non-clinical validation by different machine learning methods. Using our published framework, we aimed to discover heart failure subtypes and validate them upon population representative data.
METHODS
In this external, prognostic, and genetic validation study we analysed individuals aged 30 years or older with incident heart failure from two population-based databases in the UK (Clinical Practice Research Datalink [CPRD] and The Health Improvement Network [THIN]) from 1998 to 2018. Pre-heart failure and post-heart failure factors (n=645) included demographic information, history, examination, blood laboratory values, and medications. We identified subtypes using four unsupervised machine learning methods (K-means, hierarchical, K-Medoids, and mixture model clustering) with 87 of 645 factors in each dataset. We evaluated subtypes for (1) external validity (across datasets); (2) prognostic validity (predictive accuracy for 1-year mortality); and (3) genetic validity (UK Biobank), association with polygenic risk score (PRS) for heart failure-related traits (n=11), and single nucleotide polymorphisms (n=12).
FINDINGS
We included 188 800, 124 262, and 9573 individuals with incident heart failure from CPRD, THIN, and UK Biobank, respectively, between Jan 1, 1998, and Jan 1, 2018. After identifying five clusters, we labelled heart failure subtypes as (1) early onset, (2) late onset, (3) atrial fibrillation related, (4) metabolic, and (5) cardiometabolic. In the external validity analysis, subtypes were similar across datasets (c-statistics: THIN model in CPRD ranged from 0·79 [subtype 3] to 0·94 [subtype 1], and CPRD model in THIN ranged from 0·79 [subtype 1] to 0·92 [subtypes 2 and 5]). In the prognostic validity analysis, 1-year all-cause mortality after heart failure diagnosis (subtype 1 0·20 [95% CI 0·14-0·25], subtype 2 0·46 [0·43-0·49], subtype 3 0·61 [0·57-0·64], subtype 4 0·11 [0·07-0·16], and subtype 5 0·37 [0·32-0·41]) differed across subtypes in CPRD and THIN data, as did risk of non-fatal cardiovascular diseases and all-cause hospitalisation. In the genetic validity analysis the atrial fibrillation-related subtype showed associations with the related PRS. Late onset and cardiometabolic subtypes were the most similar and strongly associated with PRS for hypertension, myocardial infarction, and obesity (p<0·0009). We developed a prototype app for routine clinical use, which could enable evaluation of effectiveness and cost-effectiveness.
INTERPRETATION
Across four methods and three datasets, including genetic data, in the largest study of incident heart failure to date, we identified five machine learning-informed subtypes, which might inform aetiological research, clinical risk prediction, and the design of heart failure trials.
FUNDING
European Union Innovative Medicines Initiative-2.
Topics: Humans; Prognosis; Electronic Health Records; Atrial Fibrillation; Heart Failure; Machine Learning
PubMed: 37236697
DOI: 10.1016/S2589-7500(23)00065-1