The association between sarcopenia and cardiovascular disease (CVD) in elderly people has not been adequately assessed. The aim of this study was to investigate whether CVD is more prevalent in subjects with sarcopenia independent of other well-established cardiovascular risk factors in older Korean adults. This study utilized the representative Korean population data from the Korea National Health and Nutrition Examination Survey (KNHANES) which was conducted in 2009. Subjects older than 65 years of age with appendicular skeletal muscle mass (ASM) determined by dual energy X-ray absorptiometry were selected. The prevalence of sarcopenia in the older Korean adults was investigated, and it was determined whether sarcopenia is associated with CVD independent of other well-known risk factors. 1,578 subjects aged 65 years and older with the data for ASM were selected, and the overall prevalence of sarcopenia was 30.3% in men and 29.3% in women. Most of the risk factors for CVD such as age, waist circumference, body mass index, fasting plasma glucose and total cholesterol showed significant negative correlations with the ratio between appendicular skeletal muscle mass and body weight. Multiple logistic regression analysis demonstrated that sarcopenia was associated with CVD independent of other well-documented risk factors, renal function and medications (OR, 1.768; 95% CI, 1.075–2.909, P = 0.025). Sarcopenia was associated with the presence of CVD independent of other cardiovascular risk factors after adjusting renal function and medications.

Background: A proportion of obese subjects appear metabolically healthy (MHO) but little is known about the natural history of MHO and factors predicting its future conversion to metabolically unhealthy obese (MUO). Objectives: The aim was to determine prospectively the frequency of conversion of MHO to MUO and the clinical variables that independently predicted this conversion, with a particular focus on the role of body composition. Methods: We identified 85 Japanese Americans with MHO (56 men, 29 women), aged 34–73 years (mean age 49.8 years) who were followed at 2.5, 5 and 10 years after enrollment with measurements of metabolic characteristics, lifestyle and abdominal and thigh fat areas measured by computed tomography. Obesity was defined using the Asian body mass index criterion of ⩾25 kg m⁻². Metabolically healthy was defined as the presence of ⩽2 of 5 metabolic syndrome components proposed by the National Cholesterol Education Program Adult Treatment Panel III, while metabolically unhealthy was defined as ⩾3 components. Results: Over 10 years of follow-up, 55 MHO individuals (64.7%) converted to MUO. Statistically significant univariate predictors of conversion included dyslipidemia, greater insulin resistance and greater visceral abdominal (VAT) and subcutaneous abdominal fat area (SAT). In multivariate analysis, VAT (odds ratio per 1-s.d. increment (95% confidence interval) 2.04 (1.11–3.72), P=0.021), high-density lipoprotein (HDL) cholesterol (0.24 (0.11–0.53), P<0.001), fasting plasma insulin (2.45 (1.07–5.62), P=0.034) and female sex (5.37 (1.14–25.27), P=0.033) were significantly associated with future conversion to MUO. However, SAT was not an independent predictor for future conversion to MUO. Conclusions: In this population, MHO was a transient state, with nearly two-thirds developing MUO over 10 years, with higher conversion to MUO independently associated with VAT, female sex, higher fasting insulin level and lower baseline HDL cholesterol level.

OBJECTIVE: We investigated the efficacy of an integrated digital health care platform with artificial intelligence (AI)–based dietary management in adults with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: In this 48-week, open-label, randomized, multicenter clinical trial, overweight or obese adults with T2D were randomly assigned to one of three groups in a 1:1:1 ratio: group A received routine diabetes care; group B used the digital integrated health care platform by themselves; and group C used the platform with feedback from medical staff and intermittently applied personal continuous glucose monitoring. The primary end point was the difference of change in HbA1c from baseline to 24 weeks between groups A and B, while secondary end points included changes in HbA1c from baseline to 48 weeks and changes in body weight during follow-up. RESULTS: A total of 294 participants were randomly assigned to group A (n = 99), B (n = 97), or C (n = 98). The decreases in HbA1c from baseline to 24 and 48 weeks in group B (−0.32 ± 0.58% to 24 weeks and −0.28 ± 0.56% to 48 weeks) and group C (−0.49 ± 0.57% to 24 weeks and −0.44 ± 0.62% to 48 weeks) were significantly larger than those in group A (−0.06 ± 0.61% to 24 weeks and 0.07 ± 0.78% to 48 weeks). Groups B and C exhibited greater weight loss than group A from baseline to 24 weeks, and group C demonstrated more weight loss than group A from baseline to week 48. CONCLUSIONS: Among adults with T2D, use of an integrated digital health care platform with AI-driven dietary management resulted in better glycemia and more weight loss.