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This study aimed to explore the relationship between dietary electrolyte intake and the prevalence of hypertensive disorders of pregnancy (HDP) subtypes. Our analysis included 19 914 pregnant women from the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. A food frequency questionnaire was used to estimate dietary calcium, potassium, sodium, and magnesium intakes. HDP was determined based on the medical records during regular antenatal care. Logistic regression analysis assessed the relationship between dietary electrolytes intake quintiles, and HDP subtypes with adjustment for basic characteristics. Dietary electrolyte intakes were applied for the prediction model. Of the cohort, 547 participants delivered with pre-eclampsia (PE), 278 with superimposed PE (SP), and 896 with gestational hypertension (GH). PE was associated with low crude calcium intake (odds ratio of the first quintile [251 mg/day] to the fifth quintile [623 mg/day] and 95% confidence interval, 1.31 [1.00-1.70]) and P for trend was .02. SP was not associated with any nutritional intake; however, the combined outcome of PE and SP was related to low crude calcium and potassium and energy-adjusted calcium, potassium, and magnesium intakes (P for trend, .01, .048, .02, .04, and .02, respectively). The same tendency was observed for GH. A prediction model that included crude calcium and potassium intakes performed better than a model without them. In conclusion, low dietary calcium, potassium, and magnesium were associated with higher HDP subtypes prevalence. The prediction model implied that crude calcium and potassium intakes might play a critical role in PE and SP pathogenesis.

BackgroundMany loci associated with obesity have been reported in previous genome-wide association studies (GWASs). However, it remains unclear whether variants at all these loci contributed to onset of obesity or whether one or a few variants cause obesity when obesity is a genetically heterogeneous population.ObjectiveTo investigate the genetic architecture of obesity by clustering a population with obesity into clusters using obesity-related factors.MethodsThis study was based on the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study and the Community-Based Cohort Study. As the Step-1, a GWAS with body mass index (BMI) as an outcome was performed for all 48,365 eligible participants. As the Step-2, we then assigned the 13,067/48,365 participants with obesity (BMI ≥ 25 kg/m2) using the k-prototype to 5 clusters. Obesity-related factors (such as age, nutrient intake, physical activity, sleep duration, difference between weight at age 20 and current weight, smoking, alcohol drinking, psychological distress, and birth weight) were used for clustering. Subsequently, participants in each cluster and those with a BMI < 25 kg/m2were combined, and GWASs were performed according to the 5 clusters. Additionally, a sub-analysis using data from the UK Biobank was conducted to compare the results.ResultsThe Step-1 detected 18 genes, most of which were reportedly associated with obesity or obesity-related topics in previous studies. The result of Step-2, of the 18 genes detected in Step-1,LINC01741, CRYZL2P-SEC16B, andSEC16Bwere significantly related to Cluster 2,FTO, PMAIP1, andMC4Rto Cluster 3, andBDNF, BDNF-AS, LINC00678, andKIF18Ato Clusters 4 and 5. In the sub-analysis, a similar phenomenon was observed in which separate obesity-related genes were detected for each cluster.ConclusionsOur data support the notion that a decreased sample size with increased homogeneity may reveal insights into the genetic architecture of obesity.

Autism spectrum disorder (ASD) has heterogeneous phenotypic and genetic characteristics. Results from previous genome-wide association studies (GWAS) have identified numerous genetic variants associated with ASD. The association of these genetic variants with a single disease may be explained by a polygenic model in which the effects of each genetic variant are weak, yet contribute to the disease onset. When conducting GWAS with a certain sample size where no significant signal is produced, the signal will become increasingly difficult to identify if the sample size is reduced by dividing the patients. However, if ASD includes a subtype in which a small number of genes have a relatively strong influence, a subtype described by the oligogenic model, then it may be possible to identify some signals by dividing patients into phenotypically similar clusters and exploring genetic factors. To overcome ASD heterogeneity, we previously conducted cluster analyses of phenotypic variables from the Simons Simplex Collection dataset using k-means algorithm. In the present study, using a deep embedded clustering algorithm, we conducted cluster analyses of Simons Foundation Powering Autism Research for Knowledge (SPARK) datasets and performed GWAS of the clusters. We observed no significant associations in the conventional GWAS comparing all patients to all controls. However, in the GWAS, comparing patients divided into clusters with similar phenotypes to controls (cluster-based GWAS), we identified 90 chromosomal loci that satisfied the P < 5.0 × 10−8, several of which were located within or near previously reported candidate genes for ASD. Our findings suggest that ASD is an aggregation of etiologically heterogeneous subtypes, each of which has its own genetic architecture involving a smaller number of genetic loci than previously thought in addition to that explained by polygenic model.

ObjectiveThe aim of our study was to develop prediction model for preeclampsia (PE) using routinely examined items in early pregnancy especially dipstick test for proteinuria.MethodThe Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study recruited pregnant women and we included 9,086 of them in analysis. Maternal basic characteristics were obtained by self-report, and blood pressure and dipstick test of proteinuria were obtained by medical record at regular antenatal care. The outcome was defined as PE including superimposed preeclampsia. We developed prediction model without dipstick test of proteinuria (model 1) and model with it (model 2), and we compared them by the mean of area under the receiver operating characteristic curve (mAUROC) using five-fold cross validation.ResultsmAUROC of model 1 was 0.769 (95% CI; 0.741 to 0.797) and that of model 2 was 0.785 (95% CI; 0.758 to 0.812). The difference of two mAUROCs was 0.016 (95% CI; 0.004 to 0.028). In model 2, detection rates at false-positive rate of 5%, 10% and 20% were 40%, 49% and 64%, respectively.ConclusionsWe could develop prediction model for PE using routine antenatal care items and it was improved by including dipstick test for proteinuria.

BackgroundAutism spectrum disorder (ASD) has phenotypically and genetically heterogeneous characteristics. A simulation study demonstrated that attempts to categorize patients with a complex disease into more homogeneous subgroups could have more power to elucidate hidden heritability.MethodsWe conducted cluster analyses using the k-means algorithm with a cluster number of 15 based on phenotypic variables from the Simons Simplex Collection (SSC). As a preliminary study, we conducted a conventional genome-wide association study (GWAS) with a dataset of 597 ASD cases and 370 controls. In the second step, we divided cases based on the clustering results and conducted GWAS in each of the subgroups vs controls (cluster-based GWAS). We also conducted cluster-based GWAS on another SSC dataset of 712 probands and 354 controls in the replication stage.ResultsIn the preliminary study, we observed no significant associations. In the second step of cluster-based GWASs, we identified 65 chromosomal loci, which included 30 intragenic loci located in 21 genes and 35 intergenic loci that satisfied the threshold of P−8. Some of these loci were located within or near previously reported candidate genes for ASD: CDH5, CNTN5, CNTNAP5, DNAH17, DPP10, DSCAM, FOXK1, GABBR2, GRIN2A5, ITPR1, NTM, SDK1, SNCA and SRRM4. Of these 65 significant chromosomal loci, rs11064685 located within the SRRM4 gene had a significantly different distribution in the cases vs. controls in the replication cohort.ConclusionsThese findings suggest that clustering may successfully identify subgroups with relatively homogeneous disease etiologies. Further cluster validation and replication studies are warranted in larger cohorts.

Objective Metabolic syndrome and the presence of metabolic syndrome components are risk factors for cardiovascular disease (CVD). However, the association between personality traits and metabolic syndrome remains controversial, and few studies have been conducted in East Asian populations. Methods We measured personality traits using the Japanese version of the Eysenck Personality Questionnaire (Revised Short Form) and five metabolic syndrome components—elevated waist circumference, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure, and elevated fasting glucose—in 1322 participants aged 51.1 ± 12.7 years old from Kakegawa city, Japan. Metabolic syndrome score (MS score) was defined as the number of metabolic syndrome components present, and metabolic syndrome as having the MS score of 3 or higher. We performed multiple logistic regression analyses to examine the relationship between personality traits and metabolic syndrome components and multiple regression analyses to examine the relationship between personality traits and MS scores adjusted for age, sex, education, income, smoking status, alcohol use, and family history of CVD and diabetes mellitus. We also examine the relationship between personality traits and metabolic syndrome presence by multiple logistic regression analyses. Results “Extraversion” scores were higher in those with metabolic syndrome components (elevated waist circumference: P = 0.001; elevated triglycerides: P = 0.01; elevated blood pressure: P = 0.004; elevated fasting glucose: P = 0.002). “Extraversion” was associated with the MS score (coefficient = 0.12, P = 0.0003). No personality trait was significantly associated with the presence of metabolic syndrome. Conclusions Higher “extraversion” scores were related to higher MS scores, but no personality trait was significantly associated with the presence of metabolic syndrome.