Returning genetic risk information for hereditary cancers to participants in a population-based cohort study in Japan.

Large-scale population cohort studies that collect genomic information are tasked with returning an assessment of genetic risk for hereditary cancers to participants. While several studies have applied to return identified genetic risks to participants, comprehensive surveys of participants' understanding, feelings, and behaviors toward cancer risk remain to be conducted. Here, we report our experience and surveys of returning genetic risks to 100 carriers of pathogenic variants for hereditary cancers identified through whole genome sequencing of 50 000 individuals from the Tohoku Medical Megabank project, a population cohort study.

Identification of risk loci for postpartum depression in a genome-wide association study.

Aim: Genome-wide association studies (GWAS) of postpartum depression (PPD) based on accumulated cohorts with multiple ethnic backgrounds have failed to identify significantly associated loci. Herein, we conducted a GWAS of Japanese perinatal women along with detailed confounding information to uncover PPD-associated loci.

Methods: The first and second cohorts (n = 9260 and n = 8582 perinatal women enrolled in the Tohoku Medical Megabank Project) and the third cohort (n = 997), recruited at Nagoya University, underwent genotyping.

Prospective associations of screen time at age 2 with specific behavioral subscales at age 3: a cohort study.

Background: We aim to discover which, if any, of the subscales of internalizing and externalizing behavioral problems at age 3 are still associated with screen time (ST) at age 2 after adjusting for behavioral problems scores at age 2.

Methods: This study was conducted under the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. Information was gathered prospectively, with 7207 mother-child pairs included in the analysis.

Association of olfactory and cognitive function test scores with hippocampal and amygdalar grey matter volume: a cross-sectional study.

Few population-based studies including younger adults have examined the potential of olfactory function tests to capture the degree of atrophy in memory-associated brain regions, which cannot be adequately explained by cognitive function tests screening for cognitive impairment. This population-based study investigated associations between high-resolution olfactory test data with few odours and grey matter volumes (GMVs) of the left and right hippocampi, amygdala, parahippocampi, and olfactory cortex, while accounting for differences in cognitive decline, in 1444 participants (aged 31-91 years). Regression analyses included intracranial volume (ICV)-normalised GMVs of eight memory-related regions as objective variables and age, sex, education duration, smoking history, olfaction test score, and the Montreal Cognitive Assessment-Japanese version (MoCA-J) score as explanatory variables.

How Healthy Lifestyle Habits Have Interacted with SARS-CoV-2 Infection and the Effectiveness of COVID-19 Vaccinations: Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study.

Introduction: To examine the interaction between lifestyle habits and the COVID-19 vaccinations for preventing SARS-CoV-2 infection, we analyzed 11,016 adult participants registered in the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study.

Methods: Lifestyle variables, including regular exercise, smoking and drinking habits, sleep status, body mass index, and daily breakfast consumption, were assessed from 2014 to 2019 using baseline questionnaires. Information on SARS-CoV-2 infection and the COVID-19 vaccination were also collected from March 2020 to May 2023.

Urgent Notification Intervention of Home Blood Pressure in Cohort Studies of the Tohoku Medical Megabank Project.

Introduction: The Tohoku Medical Megabank (TMM) was established for creative reconstruction from the Great East Japan Earthquake and tsunami in 2011. Two prospective genome cohort studies in Miyagi prefecture have successfully recruited approximately 127,000 participants. The health status of these individuals was evaluated at the initial recruitment, and follow-up health checkups have been conducted every 5 years.

The Health History of First-Degree Relatives' Dyslipidemia Can Affect Preferences and Intentions following the Return of Genomic Results for Monogenic Familial Hypercholesterolemia.

Genetic testing is key in modern healthcare, particularly for monogenic disorders such as familial hypercholesterolemia. This Tohoku Medical Megabank Project study explored the impact of first-degree relatives' dyslipidemia history on individual responses to familial hypercholesterolemia genomic results. Involving 214 participants and using Japan's 3.

Stakeholder Perception of the Implementation of Genetic Risk Testing for Twelve Multifactorial Diseases.

Genome-wide association studies have been employed to develop numerous risk prediction models using polygenic risk scores (PRSs) for multifactorial diseases. However, healthcare providers lack confidence in their understanding of PRS risk stratification for multifactorial diseases, which underscores the need to assess the readiness of PRSs for clinical use. To address this issue, we surveyed the perceptions of healthcare providers as stakeholders in the clinical implementation of genetic-based risk prediction for multifactorial diseases.

Association Between Olfactory Test Data with Multiple Levels of Odor Intensity and Suspected Cognitive Impairment: A Cross-Sectional Study.

Background: Olfactory function decline has recently been reported to be associated with a risk of cognitive impairment. Few population-based studies have included younger adults when examining the association between olfactory test data with multiple odor intensities and suspected cognitive impairment.

Objective: We investigated the association between high-resolution olfactory test data with fewer odors and suspected cognitive impairments.

Visualization of estimated prevalence of CES-D positivity accounting for background factors and AIS scores.

Development of methods for population screening is necessary to improve the efficiency of secondary prevention of diseases. Until now, a common cutoff has been used for all people in the data set. However, if big data for health information can be used to modify individual cutoffs according to background factors, it may avoid wasting medical resources.

Returning individual genomic results to population-based cohort study participants with BRCA1/2 pathogenic variants.

Background: Recent advances in human genome research have provided evidence for genotype-phenotype associations, pathogenicity, and clinical actionability of variants and genomic risk prediction of disease. However, the return of individual genomic results to healthy individuals is fraught with ethical and practical complexity.

Methods: Individual genomic results were returned to BRCA1/2 pathogenic variant (PV) carriers of the Tohoku Medical Megabank cohort study participants with an information on hereditary breast and ovarian cancer syndrome (HBOC).

A Pilot Study for Return of Individual Pharmacogenomic Results to Population-Based Cohort Study Participants.

Introduction: Pharmacogenomic (PGx) testing results provide valuable information on drug selection and appropriate dosing, maximization of efficacy, and minimization of adverse effects. Although the number of large-scale, next-generation-sequencing-based PGx studies has recently increased, little is known about the risks and benefits of returning PGx results to ostensibly healthy individuals in research settings.

Methods: Single-nucleotide variants of three actionable PGx genes, namely, , , and , were returned to 161 participants in a population-based Tohoku Medical Megabank project.

Heart Rate Information-Based Machine Learning Prediction of Emotions Among Pregnant Women.

In this study, the extent to which different emotions of pregnant women can be predicted based on heart rate-relevant information as indicators of autonomic nervous system functioning was explored using various machine learning algorithms. Nine heart rate-relevant autonomic system indicators, including the coefficient of variation R-R interval (CVRR), standard deviation of all NN intervals (SDNN), and square root of the mean squared differences of successive NN intervals (RMSSD), were measured using a heart rate monitor (MyBeat) and four different emotions including "happy," as a positive emotion and "anxiety," "sad," "frustrated," as negative emotions were self-recorded on a smartphone application, during 1 week starting from 23rd to 32nd weeks of pregnancy from 85 pregnant women. The k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR), random forest (RF), naïve bayes (NB), decision tree (DT), gradient boosting trees (GBT), stochastic gradient descent (SGD), extreme gradient boosting (XGBoost), and artificial neural network (ANN) machine learning methods were applied to predict the four different emotions based on the heart rate-relevant information.

dbTMM: an integrated database of large-scale cohort, genome and clinical data for the Tohoku Medical Megabank Project.

To reveal gene-environment interactions underlying common diseases and estimate the risk for common diseases, the Tohoku Medical Megabank (TMM) project has conducted prospective cohort studies and genomic and multiomics analyses. To establish an integrated biobank, we developed an integrated database called "dbTMM" that incorporates both the individual cohort/clinical data and the genome/multiomics data of 157,191 participants in the Tohoku Medical Megabank project. To our knowledge, dbTMM is the first database to store individual whole-genome data on a variant-by-variant basis as well as cohort/clinical data for over one hundred thousand participants in a prospective cohort study.

The return of individual genomic results to research participants: design and pilot study of Tohoku Medical Megabank Project.

Certain large genome cohort studies attempt to return the individual genomic results to the participants; however, the implementation process and psychosocial impacts remain largely unknown. The Tohoku Medical Megabank Project has conducted large genome cohort studies of general residents. To implement the disclosure of individual genomic results, we extracted the potential challenges and obstacles.

Machine learning for effectively avoiding overfitting is a crucial strategy for the genetic prediction of polygenic psychiatric phenotypes.

The accuracy of previous genetic studies in predicting polygenic psychiatric phenotypes has been limited mainly due to the limited power in distinguishing truly susceptible variants from null variants and the resulting overfitting. A novel prediction algorithm, Smooth-Threshold Multivariate Genetic Prediction (STMGP), was applied to improve the genome-based prediction of psychiatric phenotypes by decreasing overfitting through selecting variants and building a penalized regression model. Prediction models were trained using a cohort of 3685 subjects in Miyagi prefecture and validated with an independently recruited cohort of 3048 subjects in Iwate prefecture in Japan.

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection.

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Center for Epidemiologic Studies-Depression Scale (CES-D). The nested fivefold cross-validation was used for developing and evaluating the prediction models.

Public Relations and Communication Strategies in Construction of Large-Scale Cohorts and Biobank: Practice in the Tohoku Medical Megabank Project.

The Tohoku Medical Megabank Project was designed as part of the national reconstruction project for addressing the damage from the 2011 Great East Japan Earthquake. It is an integrated project involving the genome cohort study of 150,000 participants, integrated biobank construction, and multi-omics analyses. Public relations and communication activities emerged to be extremely important in the successful development of this project.