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 3.5KJPN genome reference panel, the study assessed preferences and intentions regarding familial hypercholesterolemia genetic testing results. The data revealed a significant inclination among participants with a family history of dyslipidemia to share their genetic test results, with more than 80% of participants intending to share positive results with their partners and children and 98.1% acknowledging the usefulness of positive results for personal health management. The study underscores the importance of family health history in genetic-testing perceptions, highlighting the need for family-centered approaches in genetic counseling and healthcare. Notable study limitations include the regional scope and reliance on questionnaire data. The study results emphasize the association between family health history and genetic-testing attitudes and decisions.

Progress report of the Tohoku Medical Megabank Community-Based Cohort Study: Study profile of the repeated center-based survey during second period in Miyagi Prefecture.

BACKGROUND: The purpose of this study was to report the basic profile of the Miyagi Prefecture part of a repeated center-based survey during the second period (2nd period survey) of the Tohoku Medical Megabank Community-Based Cohort Study (TMM CommCohort Study), as well as the participants' characteristics based on their participation type in the baseline survey. METHODS: The 2nd period survey, conducted from June 2017 to March 2021, included participants of the TMM CommCohort Study (May 2013 to March 2016). In addition to the questionnaire, blood, urine, and physiological function tests were performed during the 2nd period survey. There were three main ways of participation in the baseline survey: Type 1, Type 1 additional, or Type 2 survey. The 2nd period survey was conducted in the same manner as the Type 2 survey, which was based on the community support center (CSC). RESULTS: In Miyagi Prefecture, 29,383 (57.7%) of 50,967 participants participated in the 2nd period survey. The participation rate among individuals who had visited the CSC was approximately 80%. Although some factors differed depending on the participation type in the baseline survey, the 2nd period survey respondents in the Type 1 and Type 2 survey groups at baseline had similar traits. CONCLUSIONS: The 2nd period survey of the TMM CommCohort Study provided detailed follow-up information. Following up on the health conditions of the participants will clarify the long-term effects of disasters and contribute to personalized prevention.

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. We also examined the differences between older and younger adults in this association. METHODS: The Japanese version of the Montreal Cognitive Assessment (MoCA-J) was administered to 1,450 participants, with three odor-intensity-level olfactometry using six different odors. Logistic regressions to discriminate suspected cognitive impairment were conducted to examine the association, adjusted for age, sex, education duration, and smoking history. Data were collected from the Program by Tohoku University Tohoku Medical Megabank Organization, with an additional olfactory test conducted between 2019 and 2021. RESULTS: We generally observed that the lower the limit of distinguishable odor intensity was, the higher the MoCA-J score was. The combination of spearmint and stuffy socks contributed most to the distinction between suspected and unsuspected cognitive impairment. Furthermore, the association was significant in women aged 60-74 years (adjusted odds ratio 0.881, 95% confidence interval [0.790, 0.983], p = 0.024). CONCLUSIONS: The results indicate an association between the limit of distinguishable odor intensity and cognitive function. The olfactory test with multiple odor intensity levels using fewer odors may be applicable for the early detection of mild cognitive impairment, especially in older women aged 60-74 years.

Tohoku Medical Megabank Brain Magnetic Resonance Imaging Study: Rationale, Design, and Background.

The Tohoku Medical Megabank Brain Magnetic Resonance Imaging Study (TMM Brain MRI Study) was established to collect multimodal information through neuroimaging and neuropsychological assessments to evaluate the cognitive function and mental health of residents who experienced the Great East Japan Earthquake (GEJE) and associated tsunami. The study also aimed to promote advances in personalized healthcare and medicine related to mental health and cognitive function among the general population. We recruited participants for the first (baseline) survey starting in July 2014, enrolling individuals who were participating in either the TMM Community-Based Cohort Study (TMM CommCohort Study) or the TMM Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study). We collected multiple magnetic resonance imaging (MRI) sequences, including 3D T1-weighted sequences, magnetic resonance angiography (MRA), diffusion tensor imaging (DTI), pseudo-continuous arterial spin labeling (pCASL), and three-dimensional fluid-attenuated inversion recovery (FLAIR) sequences. To assess neuropsychological status, we used both questionnaire- and interview-based rating scales. The former assessments included the Tri-axial Coping Scale, Impact of Event Scale in Japanese, Profile of Mood States, and 15-item Depression, Anxiety, and Stress Scale, whereas the latter assessments included the Mini-Mental State Examination, Japanese version. A total of 12,164 individuals were recruited for the first (baseline) survey, including those unable to complete all assessments. In parallel, we returned the MRI results to the participants and subsequently shared the MRI data through the TMM Biobank. At present, the second (first follow-up) survey of the study started in October 2019 is underway. In this study, we established a large and comprehensive database that included robust neuroimaging data as well as psychological and cognitive assessment data. In combination with genomic and omics data already contained in the TMM Biobank database, these data could provide new insights into the relationships of pathological processes with neuropsychological disorders, including age-related cognitive impairment.

Comprehensive evaluation of machine learning algorithms for predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability.

Introduction: Perinatal women tend to have difficulties with sleep along with autonomic characteristics. This study aimed to identify a machine learning algorithm capable of achieving high accuracy in predicting sleep-wake conditions and differentiating between the wake conditions before and after sleep during pregnancy based on heart rate variability (HRV). Methods: Nine HRV indicators (features) and sleep-wake conditions of 154 pregnant women were measured for 1 week, from the 23rd to the 32nd weeks of pregnancy. Ten machine learning and three deep learning methods were applied to predict three types of sleep-wake conditions (wake, shallow sleep, and deep sleep). In addition, the prediction of four conditions, in which the wake conditions before and after sleep were differentiated-shallow sleep, deep sleep, and the two types of wake conditions-was also tested. Results and Discussion: In the test for predicting three types of sleep-wake conditions, most of the algorithms, except for Naïve Bayes, showed higher areas under the curve (AUCs; 0.82-0.88) and accuracy (0.78-0.81). The test using four types of sleep-wake conditions with differentiation between the wake conditions before and after sleep also resulted in successful prediction by the gated recurrent unit with the highest AUC (0.86) and accuracy (0.79). Among the nine features, seven made major contributions to predicting sleep-wake conditions. Among the seven features, "the number of interval differences of successive RR intervals greater than 50 ms (NN50)" and "the proportion dividing NN50 by the total number of RR intervals (pNN50)" were useful to predict sleep-wake conditions unique to pregnancy. These findings suggest alterations in the vagal tone system specific to pregnancy.

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). One hundred and eighty participants, including 9 BRCA1/2 PV carriers, were asked about their willingness to receive individual genomic results, without revealing the gene name and related disorders, prior to the study. Of the 142 participants who responded, 103 showed willingness to know their genomic information. Each of the six BRCA1/2 PV carriers who consented to participate in the study received information about HBOC in person and underwent validation testing with blood resampling. RESULTS: All participants were in their 60s or 70s; of the four females and two males, two had a history of breast cancer and five had a family history of HBOC-related cancers. All participants appreciated the information, without remarkable negative psychological impact of the return, and intended to undergo clinical risk surveillance. Five participants were accompanied by family members while receiving the results, and three first-degree female relatives wished to undergo genomic testing at the hospital. CONCLUSIONS: Our results suggest that returning actionable genomic information to participants in a population-based genome cohort study is beneficial for preventing or providing early-stage intervention for associated diseases.

Design and Progress of Child Health Assessments at Community Support Centers in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank Project.

The Tohoku Medical Megabank Project (TMM) has been conducting a birth and three-generation cohort study (the BirThree Cohort Study). We recruited 73,529 pregnant women and their family members for this cohort study, which included 23,143 newborns and 9,459 of their siblings. We designed and are in the process of conducting three-step health assessments for each newborn at approximately ages of 5, 10 and 16. These health assessments are administered at seven community support centers. Trained genome medical research coordinators conduct physical examinations of and collect biological specimens from each participant. The Sendai Children's Health Square has been established as the headquarters for these child health assessments and is utilized to accumulate knowledge that can facilitate the proper practice of child health assessments. We designed all the relevant health assessments facilities to allow parents and their children to participate in the health assessments concomitantly. Our centers serve as places where child participants and their parents can feel at ease as a result of the implementation of safety measures and child hospitality measures. The TMM BirThree Cohort Study is in the process of conducting strategically detailed health assessments and genome analysis, which can facilitate studies concerning the gene-environment interactions relevant to noncommunicable diseases. Through these operations, our study allows for a significant depth of data to be collected in terms of the number of biospecimens under study and the comprehensiveness of both basic and clinical data alongside relevant family information.

Reduced antiviral seropositivity among patients with inflammatory bowel disease treated with immunosuppressive agents.

BACKGROUND: Although live-attenuated vaccines are contraindicated under immunosuppression, the immune status of patients with inflammatory bowel disease (IBD) has not been fully assessed prior to immunosuppressive therapy. AIMS: To investigate antiviral serostatus against viruses requiring live vaccines for prevention in IBD patients undergoing immunosuppressive therapy. METHODS: This multicenter study included IBD patients who were aged <40 years and were treated with thiopurine monotherapy, molecular-targeted monotherapy, or combination therapy. Gender- and age-matched healthy subjects (HS) living in the same areas were included as control group. Antibody titers against measles, rubella, mumps, and varicella were measured by enzyme-linked immunosorbent assays. RESULTS: A total of 437 IBD patients (163 ulcerative colitis [UC] and 274 Crohn's disease [CD]) and 225 HS were included in the final analysis. Compared with HS, IBD patients had lower seropositivity rates for measles (IBD vs. HS = 83.91% vs. 85.33%), rubella (77.55% vs. 84.89%), mumps (37.50% vs. 37.78%), and varicella (91.26% vs. 96.44%). Gender- and age-adjusted seropositivity rates were lower in UC patients than in both CD patients and HS for measles (UC, CD, and HS = 81.60%, 85.29%, and 85.33%), rubella (76.40%, 78.23%, and 84.89%), mumps (27.16%, 43.70%, and 37.78%), and varicella (90.80%, 91.54%, and 96.44%); the difference was significant for all viruses except measles. Divided by the degree of immunosuppression, there were no significant differences in seropositivity rates among IBD patients. CONCLUSIONS: IBD patients, especially those with UC, exhibit reduced seropositivity rates and may benefit from screening prior to the initiation of immunosuppressive therapy.

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. We conducted a web-based study on the need for risk prediction based on genetic information and the appropriate timing of testing for 12 multifactorial diseases. Responses were obtained from 506 stakeholders. Positive perceptions of genetic risk testing were found for adult-onset chronic diseases. As per participant opinion, testing for adult-onset diseases should be performed after the age of 20 years, whereas testing for psychiatric and allergic disorders that manifest during childhood should be performed from birth to 19 years of age. The stakeholders recognized the need for genetic risk testing for diseases that develop in adulthood, believing that the appropriate testing time is after maturity. This study contributes to the discussion on the clinical implementation of the PRS for genetic risk prediction of multifactorial diseases.

Genome-wide Association Study of Axial Length in Population-based Cohorts in Japan: The Tohoku Medical Megabank Organization Eye Study.

Purpose: To elucidate the differences in ocular biometric parameters by generation and gender and to identify axial length (AL)-associated genetic variants in Japanese individuals, we analyzed Tohoku Medical Megabank Organization (ToMMo) Eye Study data. Design: We designed the ToMMo Eye Study, examined AL variations, and conducted genome-wide association studies (GWASs). Participants: In total, 33 483 participants aged > 18 years who were recruited into the community-based cohort (CommCohort) and the birth and three-generation cohort (BirThree Cohort) of the ToMMo Eye Study were examined. Methods: Each participant was screened with an interview, ophthalmic examinations, and a microarray analysis. The GWASs were performed in 22 379 participants in the CommCohort (discovery stage) and 11 104 participants in the BirThree Cohort (replication stage). We evaluated the associations of single nucleotide polymorphisms (SNPs) with AL using a genome-wide significance threshold (5 × 10-8) in each stage of the study and in the subsequent meta-analysis. Main Outcome Measures: We identified the association of SNPs with AL and distributions of AL in right and left eyes and individuals of different sexes and ages. Results: In the discovery stage, the mean AL of the right eye (23.99 mm) was significantly greater than that of the left eye (23.95 mm). This difference was reproducible across sexes and ages. The GWASs revealed 703 and 215 AL-associated SNPs with genome-wide significance in the discovery and validation stages, respectively, and many of the SNPs in the discovery stage were replicated in the validation stage. Validated SNPs and their associated loci were meta-analyzed for statistical significance (P < 5 × 10-8). This study identified 1478 SNPs spread over 31 loci. Of the 31 loci, 5 are known AL loci, 15 are known refractive-error loci, 4 are known corneal-curvature loci, and 7 loci are newly identified loci that are not known to be associated with AL. Of note, some of them shared functional relationships with previously identified loci. Conclusions: Our large-scale GWASs exploiting ToMMo Eye Study data identified 31 loci linked to variations in AL, 7 of which are newly reported in this article. The results revealed genetic heterogeneity and similarity in SNPs related to ethnic variations in AL.

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. Here we show that the estimated prevalence of the Center for Epidemiologic Studies Depression Scale positivity can be visualized by a heatmap using background factors from epidemiological big data and scores from the Athens Insomnia Scale. We also show that cutoffs based on the estimated prevalence can be used to decrease the number of people screened without decreasing the number of prevalent cases detected. Since this method can be applied to the screening of different outcomes, we believe our work can contribute to the development of efficient screening methods for various diseases.

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, MT-RNR1, CYP2C19, and NUDT15, were returned to 161 participants in a population-based Tohoku Medical Megabank project. Informed consent was obtained from the participants after a seminar on the outline of this study. The results were sent by mail alongside sealed information letter intended for clinicians. As an exception, genetic counseling was performed for the MT-RNR1 m.1555A > G variant carriers by a medical geneticist, and consultation with an otolaryngologist was encouraged. Questionnaire surveys (QSs) were conducted five times to evaluate the participants' understanding of the topic, psychological impact, and attitude toward the study. Results: Whereas the majority of participants were unfamiliar with the term PGx, and none had undergone PGx testing before the study, more than 80% of the participants felt that they could acquire basic PGx knowledge sufficient to understand their genomic results and were satisfied with their potential benefit and use in future prescriptions. On the other hand, some felt that the PGx concepts or terminology was difficult to fully understand and suggested that in-person return of the results was desirable. Conclusions: These results collectively suggest possible benefits of returning preemptive PGx information to ostensibly healthy cohort participants in a research setting.

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. Major challenges include the determination of genes/disorders based on the current medical system in Japan, the storage of results, prevention of misunderstanding, and collaboration of medical professionals. To overcome these challenges, we plan to conduct multilayer pilot studies, which deal with different disorders/genes. We finally chose familial hypercholesterolemia (FH) as a target disease for the first pilot study. Of the 665 eligible candidates, 33.5% were interested in the pilot study and provided consent after an educational "genetics workshop" on the basic genetics and medical facts of FH. The genetics professionals disclosed the results to the participants. All positive participants were referred to medical care, and a serial questionnaire revealed no significant psychosocial distress after the disclosure. Return of genomic results to research participants was implemented using a well-prepared protocol. To further elucidate the impact of different disorders, we will perform multilayer pilot studies with different disorders, including actionable pharmacogenomics and hereditary tumor syndromes.

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. dbTMM enables us to stratify our cohort by both genome-wide genetic factors and environmental factors, and it provides a research and development platform that enables prospective analysis of large-scale data from genome cohorts.

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. To predict four different emotions, RF also showed a modest area under the receiver operating characteristic curve (AUC-ROC) of 0.70. CVRR, RMSSD, SDNN, high frequency (HF), and low frequency (LF) mostly contributed to the predictions. GBT displayed the second highest AUC (0.69). Comprehensive analyses revealed the benefits of the prediction accuracy of the RF and GBT methods and were beneficial to establish models to predict emotions based on autonomic nervous system indicators. The results implicated SDNN, RMSSD, CVRR, LF, and HF as important parameters for the predictions.

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. Genotyping was performed using HumanOmniExpressExome BeadChip Arrays. We used the target phenotype of depressive symptoms and simulated phenotypes with varying complexity and various effect-size distributions of risk alleles. The prediction accuracy and the degree of overfitting of STMGP were compared with those of state-of-the-art models (polygenic risk scores, genomic best linear-unbiased prediction, summary-data-based best linear-unbiased prediction, BayesR, and ridge regression). In the prediction of depressive symptoms, compared with the other models, STMGP showed the highest prediction accuracy with the lowest degree of overfitting, although there was no significant difference in prediction accuracy. Simulation studies suggested that STMGP has a better prediction accuracy for moderately polygenic phenotypes. Our investigations suggest the potential usefulness of STMGP for predicting polygenic psychiatric conditions while avoiding overfitting.

Design and Progress of Oral Health Examinations in the Tohoku Medical Megabank Project.

In order to assess the long-term impact of the Great East Japan Earthquake on the oral health of disaster victims and to evaluate gene-environmental interactions in the development of major oral diseases and oral-systemic associations, the oral part of two large-scale genome cohort studies by the Tohoku Medical Megabank Organization (ToMMo), including the Community-based cohort (CommCohort) study and the Birth and Three-Generation cohort (BirThree) study, have been conducted. The study population comprised 32,185 subjects, including 16,886 participants in the CommCohort study and 15,299 participants in the BirThree cohort study, recruited from 2013 to 2017. The oral studies consist of a questionnaire regarding oral hygiene behavior, clinical examinations by dentists, and oral plaque and saliva sampling for microbiome analyses, which were carried out at seven community support centers in Miyagi prefecture. The median age of all participants was 55.0 years, and 66.1% of participants were women. Almost all participants reported that they brushed their teeth more than once a day. The median number of present teeth was 27.0, and the decayed, missing and filled tooth number was 16.0, with a significant difference according to age and sex. The median periodontal pocket and clinical attachment level was 2.48 mm and 4.00 mm, respectively. Periodontal parameters increased significantly according to age, except for the accumulation of dental calculus. The oral part of these extensive cross-sectional studies provides a unique and important platform for future studies on oral health and diseases that elicit through interactions with systemic diseases, lifestyles, life events and genetic backgrounds, and contributes to researches clarifying the long-term effects of disasters on oral health.

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. The HSIC Lasso-based prediction model showed better predictive power than the other prediction models, including Lasso, support vector machine, partial least squares, random forest, and neural network. L-leucine, 3-hydroxyisobutyrate, and gamma-linolenyl carnitine frequently contributed to the prediction. We have demonstrated that the HSIC Lasso-based prediction model integrating nonlinear feature selection showed improved predictive power for depressive symptoms based on metabolome data as well as on risk metabolites based on nonlinear statistics in the Japanese population. Further studies should use HSIC Lasso-based prediction models with different ethnicities to investigate the generality of each risk metabolite for predicting depressive symptoms.

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. To gain insights into the contributions of these activities, we divided the public relations and communication activities for the project into three phases based on the situations surrounding the project. Prior to the start of the cohort study (Phase I), a cooperative relationship was established with a focus on concluding cooperation agreements with local governments. Until the participants reached the target number (Phase II), we actively communicated with the media to publicize the project. During the phase in which use of the constructed biobank is promoted (Phase III), for ensuring the industrial utilization of the biobank, visits from the industry are promoted. Throughout the execution of these activities, we explored the best strategies for building relationships with multiple stakeholders like local government, media and industry. By paying attention to these phases that have been changing according to the project's progress, we were able to adapt the strategies and methods of public relations and communication. The success of these activities has enabled the overall project to progress smoothly. We hope that the process of designing our project's public relations and communication activities will be useful for other similar initiatives.