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Aims/Introduction: Fat mass and fat‐free mass affect glycated hemoglobin A1c (HbA1c) levels and blood glucose levels, respectively. The aim of the present study was to examine the association between the fat mass index and fat‐free mass index with HbA1c. Materials and Methods: We carried out a cross‐sectional study that included 3,731 men and 9,191 women aged ≥20 years, living in Miyagi Prefecture, Japan, who were not treated for diabetes. The fat mass index and fat‐free mass index were calculated as fat mass and fat‐free mass divided by the height squared, respectively. The indices were classified into sex‐specific quartiles and combined into 16 groups. An analysis of covariance was used to assess associations between the combined fat mass index and fat‐free mass index with HbA1c adjusted for potential confounders. The linear trend test was carried out by stratifying the fat mass index and fat‐free mass index, entering the number as a continuous term in the regression model. Results: In multivariable models, a higher fat mass index was related to higher HbA1c levels in men and women in all fat‐free mass index subgroups (P < 0.001 for linear trend). When we excluded the participants who had been identified as having diabetes, the fat‐free mass index was also related to higher HbA1c levels in most fat mass index subgroups (P < 0.05 for linear trend). Conclusions: Fat mass index was positively related to HbA1c levels. The fat‐free mass index was also related to HbA1c levels when we excluded participants who had been identified as having have diabetes. [ABSTRACT FROM AUTHOR]

Objective: To investigate the inter-relationships among genetic risk, healthy lifestyle adherence, and hyperuricaemia susceptibility., Methods: This prospective cohort study was conducted with 7,241 hyperuricaemia-free individuals aged ≥ 20 years from the Tohoku Medical Megabank Community-based cohort study. A comprehensive lifestyle score included body mass index, smoking, drinking, and physical activity, and a polygenic risk score (PRS) was constructed based on uric acid loci from a previous genome-wide association study meta-analysis. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and hyperuricaemia incidence and calculate the area under the receiver operating characteristic curve (AUROC). Hyperuricaemia was defined as a uric acid level ≥7.0 mg/dl or a self-reported history of hyperuricaemia., Results: Of the 7,241 adults (80.7% females; mean [SD] age: 57.7 [12.6] years), 217 (3.0%) developed hyperuricaemia during 3.5 years of follow-up. Genetic risk correlated with hyperuricaemia development (P for interaction = 0.287), and lifestyle risks were independently associated. Those with a high genetic risk and poor lifestyle had the highest risk (odds ratio: 5.34; 95% confidence interval [CI]: 2.61-12.10). Although not statistically significant, incorporating the PRS in the model with lifestyle information improved predictive ability (AUROC = 0.771, 95% CI: 0.736-0.806 for lifestyle; AUROC = 0.785, 95% CI: 0.751-0.819 for lifestyle and PRS; p = 0.07)., Conclusion: : A healthy lifestyle to prevent hyperuricaemia, irrespective of genetic risk, may mitigate the genetic risk. Genetic risk may complement lifestyle factors in identifying individuals at a heightened hyperuricaemia risk., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.)

Aim: This study examined the relationship between genetic risk, healthy lifestyle, and risk of developing diabetes., Methods: This prospective cohort study included 11,014 diabetes-free individuals ≥ 20 years old from the Tohoku Medical Megabank Community-based cohort study. Lifestyle scores, including the body mass index, smoking, physical activity, and gamma-glutamyl transferase (marker of alcohol consumption), were assigned, and participants were categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score (PRS) was constructed based on the type 2 diabetes loci from the BioBank Japan study. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and diabetes incidence and to calculate the area under the receiver operating characteristic curve (AUROC)., Result: Of the 11,014 adults included (67.8% women; mean age [standard deviation], 59.1 [11.3] years old), 297 (2.7%) developed diabetes during a mean 4.3 (0.8) years of follow-up. Genetic and lifestyle score is independently associated with the development of diabetes. Compared with the low genetic risk and ideal lifestyle groups, the odds ratio was 3.31 for the low genetic risk and poor lifestyle group. When the PRS was integrated into a model including the lifestyle and family history, the AUROC significantly improved to 0.719 (95% confidence interval [95% CI]: 0.692-0.747) compared to a model including only the lifestyle and family history (0.703 [95% CI, 0.674-0.732])., Conclusion: Our findings indicate that adherence to a healthy lifestyle is important for preventing diabetes, regardless of genetic risk. In addition, genetic risk might provide information beyond lifestyle and family history to stratify individuals at high risk of developing diabetes.

Aims: Although fat mass (FM) and fat-free mass (FFM) have an impact on lipid metabolism, the relationship between different body composition phenotypes and lipid profiles is still unclear. By dividing the FM and FFM by the square of the height, respectively, the fat mass index (FMI) and fat-free mass index (FFMI) can be used to determine the variations in body composition. This study aimed to investigate the relationship of combined FMI and FFMI with low-density lipoprotein cholesterol (LDL-C) levels., Methods: This cross-sectional study comprised 5,116 men and 13,630 women without cardiovascular disease and without treatment for hypertension, and diabetes. Following sex-specific quartile classification, FMI and FFMI were combined into 16 groups. Elevated LDL-C levels were defined as LDL-C ≥ 140 mg/dL and/or dyslipidemia treatment. Multivariable logistic regression models were used to examine the relationships between combined FMI and FFMI and elevated LDL-C levels., Results: Overall, elevated LDL-C levels were found in 1,538 (30.1%) men and 5,434 (39.9%) women. In all FFMI subgroups, a higher FMI was associated with elevated LDL-C levels. Conversely, FFMI was inversely associated with elevated LDL-C levels in most FMI subgroups. Furthermore, the groups with the highest FMI and lowest FFMI had higher odds ratios for elevated LDL-C levels than those with the lowest FMI and highest FFMI., Conclusions: Regardless of FFMI, FMI was positively associated with elevated LDL-C levels. Conversely, in the majority of FMI subgroups, FFMI was inversely associated with elevated LDL-C levels.

Background: Although fat mass index (FMI) and fat-free mass index (FFMI) affect lung function, FMI and FFMI are not independent of each other, since FMI and FFMI were calculated as fat mass and fat-free mass divided by height squared, respectively. We aimed to examine the association of combined FMI and FFMI with lung function., Methods: In this cross-sectional study, lung function was evaluated using forced expiratory volume at 1 s (FEV 1 ) and forced vital capacity (FVC) measured using spirometry. Both FMI and FFMI were classified into sex-specific quartiles (16 groups). Analysis of covariance was used to assess the associations of combined FMI and FFMI with lung function. The trend test was conducted by stratifying the FMI and FFMI, scoring the categories from 1-4 (lowest-highest), and entering the number as a continuous term in the regression model., Results: This study included 3,736 men and 8,821 women aged ≥20 years living in Miyagi Prefecture, Japan. The mean FEV 1 was 3.0 (standard deviation [SD], 0.7) L for men and 2.3 (SD, 0.5) L for women. The mean FVC was 3.8 (SD, 0.7) L for men and 2.8 (SD, 0.5) L for women. FMI was inversely associated with lung function among all FFMI subgroups in both sexes. Conversely, FFMI was positively associated with lung function in all FMI subgroups in both sexes., Conclusion: Higher FMI was associated with lower lung function independent of FFMI; higher FFMI was associated with higher lung function independent of FMI. Reducing FMI and maintaining FFMI might be important for respiratory health.

Aim: People with high normal blood pressure (BP) have a higher risk of cardiovascular events than those with normal BP; therefore, progression to hypertension (HT) should be prevented. We aimed to assess the HT risk using central BP and carotid intima media thickness (CIMT) in people with high normal BP., Methods: This prospective cohort study used the Tohoku Medical Megabank Community-Based Project Cohort Study (conducted from 2013 in Miyagi Prefecture in Japan). The participants had a high normal BP, defined as a systolic BP of 120-139 mmHg and diastolic BP ＜90 mmHg using brachial BP measurement during the baseline survey. The outcome was new-onset HT during the secondary survey, conducted four years after the baseline survey., Results: Overall, 4,021 participants with high normal BP during the baseline survey, with an average age of 58.7 years, were included; 1,030 (26%) were diagnosed with new-onset HT during the secondary survey, 3.5±0.7 years after the baseline survey. The multivariable odds ratio (95% confidence interval) for HT in the highest versus lowest quartile of central BP was 1.7 (1.2-2.4, p=0.0030), and that of CIMT was 1.8 (1.4-2.4, p＜0.001). Subgroup analysis according to age (＜60 and ≥ 60 years) and sex revealed that the central BP was influential in groups with younger age and female individuals; CIMT was influential in all groups., Conclusions: Higher central BP and thicker CIMT at the baseline were correlated with new-onset HT in individuals with high normal BP, independent of brachial systolic BP and other cardiovascular risk factors.