Dataset Information

Genetic variants of increased waist circumference in psychosis.

ABSTRACT: We examined whether established metabolic risk genetic variants in the population confer a risk for increased waist circumference in patients with schizophrenia spectrum disorders and also an association with schizophrenia spectrum disorders irrespective of waist circumference.We analyzed the association in (i) a case-case model in which patients with schizophrenia spectrum disorder with increased waist circumference (?80?cm for women and ?94?cm for men) (n=534) were compared with patients with normal waist circumference (<80?cm for women; <94?cm for men) (n=124), and in (ii) a case-control model in which schizophrenia spectrum disorder patients with increased waist circumference or irrespective of waist circumference were compared with population-derived controls (n=494) adjusted for age, sex, fasting glucose, smoking, and family history of diabetes.Genetic variants in five genes (MIA3, MRAS, P2RX7, CAMKK2, and SMAD3) were associated with increased waist circumference in patients with schizophrenia spectrum disorder (P<0.046). Genetic variants in three other genes (PPARD, MNTR1B, and NOTCH2) were associated with increased waist circumference in patients when compared with control individuals (P<0.037). Genetic variants in the PPARD, MNTR1B, NOTCH2, and HNF1B were nominally associated with schizophrenia spectrum disorder irrespective of waist circumference (P<0.027). No differences in waist circumference between specific psychosis diagnoses were detected.Increased waist circumference in patients with schizophrenia spectrum disorder may be explained, in part, by increased metabolic risk gene burden, and it indicates a shared genetic susceptibility to metabolic disorder and psychosis per se. Along these lines, common metabolic risk genetic variants confer a risk for increased waist circumference in patients with schizophrenia spectrum disorders.

SUBMITTER: Hukic DS

PROVIDER: S-EPMC5662154 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic variants of increased waist circumference in psychosis.

Hukic Dzana S DS Ösby Urban U Olsson Eric E Hilding Agneta A Östenson Claes-Göran CG Gu Harvest F HF Ehrenborg Ewa E Edman Gunnar G Schalling Martin M Lavebratt Catharina C Frisén Louise L

Psychiatric genetics 20171201 6

<h4>Objective</h4>We examined whether established metabolic risk genetic variants in the population confer a risk for increased waist circumference in patients with schizophrenia spectrum disorders and also an association with schizophrenia spectrum disorders irrespective of waist circumference.<h4>Patients and methods</h4>We analyzed the association in (i) a case-case model in which patients with schizophrenia spectrum disorder with increased waist circumference (≥80 cm for women and ≥94 cm for ...[more]

PMID: 28737528

Similar Datasets

Project description:BACKGROUND:Studies have suggested a link between alcohol intake and adiposity. However, results from longitudinal studies have been inconsistent, and a possible interaction with genetic predisposition to adiposity measures has often not been taken into account. OBJECTIVE:To examine the association between alcohol intake recorded at baseline and subsequent annual changes in body weight (?BW), waist circumference (?WC) and WC adjusted for BMI (?WCBMI), and to test for interaction with genetic predisposition scores based on single nucleotide polymorphisms (SNPs) associated with various forms of adiposity. METHOD:This study included a total of 7028 adult men and women from MONICA, the Diet, Cancer and Health cohort (DCH), and the Inter99 studies. We combined 50 adiposity-associated SNPs into four scores indicating genetic predisposition to BMI, WC, WHRBMI and all three traits combined. Linear regression was used to examine the association of alcohol intake (drinks of 12 g (g) alcohol/day) with ?BW, ?WC, and ?WCBMI, and to examine possible interactions with SNP-scores. Results from the analyses of the individual cohorts were combined in meta-analyses. RESULTS:Each additional drink/day was associated with a ?BW/year of -18.0 g (95% confidence interval (CI): -33.4, -2.6, P = 0.02) and a ?WC of -0.3 mm/year (-0.5, -0.0, P = 0.03). In analyses of women only, alcohol intake was associated with a higher ?WCBMI of 0.5 mm/year (0.2, 0.9, P = 0.002) per drink/day. Overall, we found no statistically significant interactions between the four SNP-scores and alcohol intake in relation to changes in adiposity measures. However in analyses of women separately, we found interaction between the complete score of all 50 SNPs and alcohol intake in relation to ?BW (P for interaction = 0.03). No significant interaction was observed among the men. CONCLUSION:Alcohol intake was associated with a decrease in BW and WC among men and women, and an increase in WCBMI among women only. We found no strong indication that these associations depend on a genetic predisposition to adiposity. TRIAL REGISTRATION:Registry: ClinicalTrials.gov Trial number: CT00289237 , Registered: 19 September 2005 retrospectively registered.

Project description:Mexican Americans are at an increased risk of both thyroid dysfunction and metabolic syndrome (MS). Thus it is conceivable that some components of the MS may be associated with the risk of thyroid dysfunction in these individuals. Our objective was to investigate and replicate the potential association of MS traits with thyroid dysfunction in Mexican Americans.We conducted association testing for 18 MS traits in two large studies on Mexican Americans - the San Antonio Family Heart Study (SAFHS) and the National Health and Nutrition Examination Survey (NHANES) 2007-10. A total of 907 participants from 42 families in SAFHS and 1633 unrelated participants from NHANES 2007-10 were included in this study. The outcome measures were prevalence of clinical and subclinical hypothyroidism and thyroid function index (TFI) - a measure of thyroid function. For the SAFHS, we used polygenic regression analyses with multiple covariates to test associations in setting of family studies. For the NHANES 2007-10, we corrected for the survey design variables as needed for association analyses in survey data. In both datasets, we corrected for age, sex and their linear and quadratic interactions.TFI was an accurate indicator of clinical thyroid status (area under the receiver-operating-characteristic curve to detect clinical hypothyroidism, 0.98) in both SAFHS and NHANES 2007-10. Of the 18 MS traits, waist circumference (WC) showed the most consistent association with TFI in both studies independently of age, sex and body mass index (BMI). In the SAFHS and NHANES 2007-10 datasets, each standard deviation increase in WC was associated with 0.13 (p?<?0.001) and 0.11 (p?<?0.001) unit increase in the TFI, respectively. In a series of polygenic and linear regression models, central obesity (defined as WC???102 cm in men and ?88 cm in women) was associated with clinical and subclinical hypothyroidism independent of age, sex, BMI and type 2 diabetes in both datasets. Estimated prevalence of hypothyroidism was consistently high in those with central obesity, especially below 45y of age.WC independently associates with increased risk of thyroid dysfunction. Use of WC to identify Mexican American subjects at high risk of thyroid dysfunction should be investigated in future studies.

Project description:BackgroundWaist circumference is becoming recognized as a useful predictor of health risks in clinical research. However, clinical datasets tend to lack this measurement and self-reported values tend to be inaccurate. Predicting waist circumference from standard physical features could be a viable method for generating this information when it is missing or mitigating the impact of inaccurate self-reports. This study determined the degree to which the XGBoost advanced machine learning algorithm could build models that predict waist circumference from height, weight, calculated Body Mass Index, age, race/ethnicity and sex, whether they perform better than current models based on linear regression, and the relative importance of each feature in this prediction.MethodsWe trained tree-based models (via XGBoost gradient boosting) and linear models (via regression) to predict waist circumference from height, weight, Body Mass Index, age, race/ethnicity and sex (n = 60,740 participants). We created 10 iterations of each model, each using 90% of the dataset for training and the remaining 10% for testing performance (this group was different for each iteration). We calculated model performance and feature importance as an average across 10 iterations. We then externally validated the ensembled version of the top model.ResultsThe XGBoost model predicted waist circumference with a mean bias ± standard deviation of 0.0 ± 0.04 cm and a root mean squared error of 4.7 ± 0.05 cm, with performance varying slightly by sex and race/ethnicity. The XGBoost model showed varying degrees of improvement over linear regression models. The top 3 predictors were Body Mass Index, weight and race (Asian). External validation found that on average this model overestimated waist circumference by 4.65 cm in the United Kingdom population (mainly due to overprediction in females) and underestimated waist circumference by 1.7 cm in the Chinese population. The respective root mean squared errors were 7.7 cm and 7.1 cm.ConclusionsXGBoost-based models accurately predict waist circumference from standard physical features. Waist circumference prediction using this approach would be valuable for epidemiological research and beyond.

Dataset Information

Genetic variants of increased waist circumference in psychosis.

Publications

Genetic variants of increased waist circumference in psychosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure