Dataset Information

Associations of Nutrient Patterns with the Prevalence of Metabolic Syndrome: Results from the Baseline Data of the Japan Multi-Institutional Collaborative Cohort Study.

ABSTRACT: : The association between nutrient patterns and metabolic syndrome (MetS) has not been examined in a Japanese population. A cross-sectional study was performed on 30,108 participants (aged 35-69 years) in the baseline survey of the Japan Multi-Institutional Collaborative Cohort Study. Dietary intake was assessed using a 46-item food frequency questionnaire. MetS was diagnosed according to the Joint Interim Statement Criteria of 2009, using body mass index instead of waist circumference. Factor analysis was applied to energy-adjusted intake of 21 nutrients, and three nutrient patterns were extracted: Factor 1 (fiber, potassium and vitamins pattern); Factor 2 (fats and fat-soluble vitamins pattern); and Factor 3 (saturated fatty acids, calcium and vitamin B₂ pattern). In multiple logistic regression analysis adjusted for sex, age, and other potential confounders, Factor 1 scores were associated with a significantly reduced odds ratio (OR) of MetS and all five components. Factor 2 scores were associated with significantly increased prevalence of MetS, obesity, and high blood pressure. Factor 3 scores were significantly associated with lower OR of MetS, high blood pressure, high serum triglycerides and low HDL cholesterol levels. Analysis of nutrient patterns may be useful to assess the overall quality of diet and its association with MetS.

SUBMITTER: Iwasaki Y

PROVIDER: S-EPMC6566447 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Associations of Nutrient Patterns with the Prevalence of Metabolic Syndrome: Results from the Baseline Data of the Japan Multi-Institutional Collaborative Cohort Study.

Iwasaki Yuki Y Arisawa Kokichi K Katsuura-Kamano Sakurako S Uemura Hirokazu H Tsukamoto Mineko M Kadomatsu Yuka Y Okada Rieko R Hishida Asahi A Tanaka Keitaro K Hara Megumi M Takezaki Toshiro T Shimatani Keiichi K Ozaki Etsuko E Koyama Teruhide T Suzuki Sadao S Nakagawa-Senda Hiroko H Kuriki Kiyonori K Miyagawa Naoko N Kadota Aya A Ikezaki Hiroaki H Furusyo Norihiro N Oze Isao I Ito Hidemi H Mikami Haruo H Nakamura Yohko Y Wakai Kenji K

Nutrients 20190430 5

<b>:</b> The association between nutrient patterns and metabolic syndrome (MetS) has not been examined in a Japanese population. A cross-sectional study was performed on 30,108 participants (aged 35-69 years) in the baseline survey of the Japan Multi-Institutional Collaborative Cohort Study. Dietary intake was assessed using a 46-item food frequency questionnaire. MetS was diagnosed according to the Joint Interim Statement Criteria of 2009, using body mass index instead of waist circumference. F ...[more]

PMID: 31052301

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Project description:ObjectiveThe aim of the present study was to investigate the associations between breastfeeding and the prevalence of metabolic syndrome in community-dwelling parous women and to clarify whether the associations depend on age.MethodsThe present cross-sectional study included 11,118 women, aged 35-69 years. Participants' longest breastfeeding duration for one child and their number of breastfed children were assessed using a self-administered questionnaire, and their total breastfeeding duration was approximated as a product of the number of breastfed children and the longest breastfeeding duration. The longest and the total breastfeeding durations were categorized into none and tertiles above 0 months. Metabolic syndrome and cardiovascular risk factors (obesity, hypertension, dyslipidemia, and hyperglycemia) were defined as primary and secondary outcomes, respectively. Associations between breastfeeding history and metabolic syndrome or each cardiovascular risk factor were assessed using multivariable unconditional logistic regression analysis.ResultsAmong a total of 11,118 women, 10,432 (93.8%) had ever breastfed, and 1,236 (11.1%) had metabolic syndrome. In participants aged <55 years, an inverse dose-response relationship was found between the number of breastfed children and the prevalence of metabolic syndrome; multivariable-adjusted odds ratios for 1, 2, 3, and ≥4 breastfed children were 0.60 (95% confidence interval [CI]: 0.31 to 1.17), 0.50 (95% CI: 0.29 to 0.87), 0.44 (95% CI: 0.24 to 0.84), and 0.35 (95% CI: 0.14 to 0.89), respectively. The longest and total breastfeeding durations of longer than 0 months were also associated with lower odds of metabolic syndrome relative to no breastfeeding history in participants aged <55 years. In contrast, all measures of breastfeeding history were not significantly associated with metabolic syndrome and cardiovascular risk factors in participants aged ≥55 years old.ConclusionsBreastfeeding history may be related to lower prevalence of metabolic syndrome in middle-aged parous women.

Project description:BACKGROUND:Mental health has become a major public health issue worldwide. Biological and epidemiological studies suggest diet has a role in the prevention or cure of mental disorders. However, further research is required to elucidate the relationship between diet and mental health. This study aimed to investigate associations between dietary intake of nutrients (macronutrients, vitamins, calcium, and fatty acids) and food groups (fish, meat and chicken, dairy products, and vegetables) and mental health among middle-aged Japanese in cross-sectional and prospective studies. METHODS:In total, 9298 men and women that participated in two areas of the Japan Multi-Institutional Collaborative Cohort Study were eligible for analysis at the baseline (cross-sectional) survey. Of these, 4701 participants were followed for about 5?years and included in the follow-up (prospective) analysis. The 12-item General Health Questionnaire (GHQ) was used to assess participants' general mental health status over the past several weeks. The average intake of 46 foods over the past year was assessed by a validated food frequency questionnaire. We also evaluated lifestyle and medical factors using a self-administered questionnaire. A cross-sectional logistic regression analysis was performed to estimate odds ratios for a GHQ score???4 (poor mental health) according to dietary intake of foods/nutrients at baseline. The prospective study used baseline dietary and lifestyle factors and GHQ scores at follow-up. RESULTS:The cross-sectional logistic regression analysis showed vegetables, protein, calcium, vitamin D, carotene and n-3 highly-polyunsaturated fatty acids were inversely associated with a GHQ score???4. On the other hand, mono-unsaturated fatty acids showed a positive association with higher GHQ score. The prospective logistic regression analysis found dairy products, calcium, vitamin B2, and saturated fatty acids were inversely correlated with a GHQ score???4. Calcium was associated with GHQ scores in both the cross-sectional and follow-up studies. In the follow-up study, the multivariable-adjusted odds ratio for a GHQ score???4 was 0.71 (95% confidence interval, 0.55-0.92) for the highest versus lowest quartiles of calorie-adjusted dietary calcium intake. CONCLUSION:Consuming particular nutrients and foods, especially calcium and dairy products, may lead to better mental health in Japanese adults.

Project description:PurposeAttenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images.MethodsNon-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center).ResultsIn terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21-14.81%) and FL-PL (CI:11.82-13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32-12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34-26.10%). Furthermore, the Mann-Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed.ConclusionDeep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.

Dataset Information

Associations of Nutrient Patterns with the Prevalence of Metabolic Syndrome: Results from the Baseline Data of the Japan Multi-Institutional Collaborative Cohort Study.

Publications

Associations of Nutrient Patterns with the Prevalence of Metabolic Syndrome: Results from the Baseline Data of the Japan Multi-Institutional Collaborative Cohort Study.

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