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Abstract The association between serum uric acid (UA) levels and left ventricular hypertrophy (LVH) remains unclear. We aimed to investigate this association using electrocardiographic findings. Health examination data from Kagoshima Kouseiren Hospital included 79,200 participants without cardiovascular diseases. Hypertension was defined as a blood pressure ≥ 140/90 mmHg. A Sokolow–Lyon criterion of ≥ 3.5 mm was used to define LVH. Sex-specific associations between UA and LVH were adjusted for age, body mass index, systolic blood pressure, serum triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting plasma glucose, estimated glomerular filtration rate, and lifestyle factors. The mean UA levels were 4.5 ± 1.1 and 6.1 ± 1.4 mg/dL in women and men, respectively (LVH prevalences, 4.0%, and 11.8%, respectively). Individuals without and with hypertension had mean UA levels of 5.2 ± 1.4 and 5.7 ± 1.5 mg/dL, respectively (LVH prevalences, 5.5%, and 14.4%, respectively). UA levels were significantly associated with LVH in women aged 40–49 and 50–59 years and in men aged 50–59 years. Compared with the first UA quartile, the fourth quartile showed a more significant association with LVH in individuals without hypertension. Furthermore, UA was associated with LVH in individuals without obesity, dyslipidemia, reduced kidney function, and diabetes in both sexes. Serum UA levels are associated with LVH in middle-aged women and men without cardiovascular disease risk, suggesting the potential role of UA as an LVH marker. Anwar Ahmed Salim and Shin Kawasoe contributed equally to this work.

Abstract Chronic kidney disease (CKD) is a risk factor for end-stage renal disease and contributes to increased risk of cardiovascular disease morbidity and mortality. We aimed to develop a risk prediction score and equation for future CKD using health checkup data. This study included 58,423 Japanese participants aged 30–69 years, who were randomly assigned to derivation and validation cohorts at a ratio of 2:1. The predictors were anthropometric indices, life style, and blood sampling data. In derivation cohort, we performed multivariable logistic regression analysis and obtained the standardized beta coefficient of each factor that was significantly associated with new-onset CKD and assigned scores to each factor. We created a score and an equation to predict CKD after 5 years and applied them to validation cohort to assess their reproducibility. The risk score ranged 0–16, consisting of age, sex, hypertension, dyslipidemia, diabetes, hyperuricemia, and estimated glomerular filtration rate (eGFR), with area under the curve (AUC) of 0.78 for the derivation cohort and 0.79 for the validation cohort. The CKD incidence gradually and constantly increased as the score increased from ≤ 6 to ≥ 14. The equation consisted of the seven indices described above, with AUC of 0.88 for the derivation cohort and 0.89 for the validation cohort. We developed a risk score and equation to predict CKD incidence after 5 years in Japanese population under 70 years of age. These models had reasonably high predictivity, and their reproducibility was confirmed through internal validation.

BackgroundPredicting metabolic syndrome (MetS) is important for identifying high-risk cardiovascular disease individuals and providing preventive interventions. We aimed to develop and validate an equation and a simple MetS score according to the Japanese MetS criteria.MethodsIn total, 54,198 participants (age, 54.5±10.1 years; men, 46.0%), with baseline and 5-year follow-up data were randomly assigned to 'Derivation' and 'Validation' cohorts (ratio: 2:1). Multivariate logistic regression analysis was performed in derivation cohort and scores were assigned to factors corresponding to β-coefficients. We evaluated predictive ability of the scores using area under the curve (AUC), then applied them to validation cohort to assess reproducibility.ResultsThe primary model ranged 0-27 points had an AUC of 0.81 (sensitivity: 0.81, specificity: 0.81, cut-off score: 14), and consisted of age, sex, blood pressure (BP), body mass index (BMI), serum lipids, glucose measurements, tobacco smoking, and alcohol consumption. The simplified model (excluding blood tests) ranged 0-17 points with an AUC of 0.78 (sensitivity: 0.83, specificity: 0.77, cut-off score: 15) and included: age, sex, systolic BP, diastolic BP, BMI, tobacco smoking, and alcohol consumption. We classified individuals with a score ConclusionWe developed a primary score, an equation model, and a simple score. The simple score is convenient, well-validated with acceptable discrimination, and could be used for early detection of MetS in high-risk individuals.