We aimed to develop a multiple logistic regression model for predicting the occurrence of metabolic syndrome (MetS) using homeostasis model assessment of insulin resistance (HOMA-IR) levels, gender, age, and Diabetes Mellitus (DM) status, and to evaluate its predictive accuracy. Data from 6134 participants in the 2019 Korea National Health and Nutrition Examination Survey were analyzed. MetS was diagnosed using the Adult Treatment Panel III criteria. A logistic regression model was developed based on the regression coefficients of each variable. Model performance was evaluated through a receiver operating characteristic analysis, revealing an overall area under the curve (AUC) of 0.819, a sensitivity of 80%, and a specificity of 68.9%. Age-specific analysis showed that the model’s predictive power was highest among those aged 20–29 years (AUC: 0.864). Conversely, the AUC progressively decreased in individuals aged ≥50 years, indicating reduced predictive power in older adults. These findings suggest the importance of adopting a multidimensional approach that considers HOMA-IR, age, gender, and DM status for predicting MetS. The developed prediction model can be used as a valuable tool for the early diagnosis of MetS and the development of tailored MetS prevention programs. It also provides foundational data for shaping public health policies related to MetS.

Background/Objectives: This study investigated the relationships among the Landing Error Scoring System (LESS), markerless 3D (OpenCap)-derived knee valgus, and surface electromyography (EMG) of quadriceps and hamstrings. Methods: Thirty-two healthy male university students (26 athletes, 6 non-athletes) completed a drop-landing task. LESS was video-scored; knee valgus at peak knee flexion was computed with OpenCap; and bilateral rectus femoris (RF) and biceps femoris (BF) EMG was recorded. Phase-specific EMG was normalized to peak dynamic activity. Results: LESS showed an independent negative association with left knee valgus (p = 0.001). In the regression model, bilateral BF acceleration-phase activity was a significant predictor of knee valgus (p < 0.05). Exploratory comparisons indicated that athletes exhibited lower RF deceleration activity and smaller left-side valgus than non-athletes. Conclusions: These findings suggest that hamstring activity during the transition phase is linked to knee alignment. This study demonstrates the complementary value of integrating LESS with markerless 3D motion capture, although caution is warranted when generalizing group differences due to the unequal sample size.

Insulin resistance is closely associated with metabolic diseases such as type 2 diabetes and cardiovascular disease, and the resting heart rate (RHR) is an important marker of the autonomic nervous system’s status. This study aimed to investigate the association between the RHR and the homeostatic model assessment of insulin resistance (HOMA-IR) in Korean adults. Using data from the 2019 Korea National Health and Nutrition Examination Survey (KNHANES 2019), we analyzed 6118 adults and classified the participants into four groups based on their RHR (≤67, 68–71, 72–79, and ≥80 beats/min [bpm]). Logistic regression analysis considering demographic, lifestyle, and biochemical factors revealed that the odds of having a HOMA-IR of 2.5 or higher were significantly increased in the higher RHR groups. Specifically, compared to the ≤67 bpm group, the 68–71, 72–79, and ≥80 bpm groups had increased odds of 1.277, 1.599, and 1.919 times, respectively. These results are expected to contribute to the development of strategies for the early diagnosis and management of metabolic diseases through the RHR, and further research is needed to deepen the understanding of the physiological mechanisms of this relationship, including the management of the RHR through lifestyle modification and regular exercise and the effect of reducing insulin resistance.

Background/Objectives: Ashwagandha (Withania somnifera) is increasingly marketed as an adjunct that amplifies the strength and muscle-size gains produced by resistance training. Prior meta-analyses pooled trials across mixed exercise contexts rather than trials confined to structured resistance training. We therefore quantified adjunctive ashwagandha effects on muscular strength, chest circumference and body composition within such trials. Methods: We searched twelve bibliographic sources (including PubMed/MEDLINE, Europe PMC, OpenAlex and Cochrane CENTRAL) and two trial registers (ClinicalTrials.gov, WHO ICTRP) without date restriction to 23 August 2026, with reference-list, included-study-list and forward-citation screening, for randomized controlled trials (RCTs) combining a standardized ashwagandha extract (root or root-and-leaf) with structured resistance training in healthy adults (PRISMA 2020). Screening, extraction and risk-of-bias assessment were duplicated independently. Change-from-baseline standardized mean differences (SMDs; Hedges g) were pooled with restricted maximum-likelihood random-effects models, with RoB 2, leave-one-out and Hartung–Knapp–Sidik–Jonkman (HKSJ) sensitivity analyses, and GRADE. Embase, Scopus, Web of Science and CINAHL were unavailable to us, but Embase- and CINAHL-derived records were retrieved indirectly through CENTRAL. Results: Four RCTs were eligible; three (n = 161) provided poolable data, and one preprint was reported narratively because its bench-press values were not interpretable as absolute loads. Ashwagandha was associated with greater gains than placebo in 1-RM bench press (SMD 0.60, 95% CI 0.20–1.01), lower-body 1-RM (0.52, 0.19–0.86) and chest circumference (0.86, 0.41–1.31), but not body fat (−0.32, −0.72–0.08). Heterogeneity was low to moderate (I² = 7–34%); all three pooled trials raised at least some concerns on RoB 2. Under HKSJ adjustment both strength estimates lost statistical significance and each depended on one influential trial; for chest circumference (k = 2), the HKSJ interval was too imprecise to be informative. GRADE certainty was Low for both strength outcomes and body fat, and Very Low for chest circumference. Conclusions: Current evidence does not establish that adjunctive ashwagandha meaningfully enhances resistance-training adaptations; it indicates possible benefits for maximal strength and, indirectly, upper-body girth, but the evidence base is small, statistically fragile and of low to very low certainty, with no dependable effect on adiposity. Adequately powered, independently funded RCTs using direct measures of hypertrophy are required. This review received no external funding and was not registered.

Background: As a risk factor for diabetes, there are many evidences for physical characteristics and lifestyle habits. There are studies showing that resting heart rate is also related, but evidence is still lacking. Therefore, this study attempted to determine the effect of resting heart rate on diabetes.Methods: The subject of this study was analyzed with 6,622 people who had fasting blood glucose test data out of a total of 7,993 people who participated in the 7th National Health and Nutrition Survey. Fasting blood glucose above 126 mg/dL was defined as a diabetic group and a multiple logistic regression analysis was used. General characteristics, lifestyle and blood chemistry test results were adjusted to investigate the association with diabetes according to the resting heart rate level in the subjects.Results: The risk of diabetes according to the resting heart rate level was 1.434 times in the 2nd quartile, 1.714 times in the 3rd and 1.785 times in the 4th, compared to the 1st quartile. In model 4, except for the 2nd, which was not significant the analysis with all related variables adjusted for the risk rate in the 3rd and 4th order showed that the risk rate increased to 2.364 times and 3.477 times, respectively as the resting heart rate increased.Conclusions: This study shows that high resting heart rate is associated with increased diabetes, and suggests that high heart rate, along with other known risk factors, may be a useful tool in predicting diabetes risk.