Metabolic Syndrome and Related Disorders

Triglyceride-glucose (TyG) index and its relationship with metabolic syndrome (MetS), physical activity (PA), and physical fitness in the pediatric population remain unclear. This cross-sectional study explored the mediating effect of central obesity and PA on the relationship between the TyG index and physical fitness in a pediatric population.

A total of 614 Korean children (320 boys and 294 girls) aged 7–12 years participated in this study. MetS was defined as the continuous MetS risk value in the 4th quartile obtained by adding standardized scores for the syndrome components. PA was quantified using an accelerometer, and physical fitness was evaluated using composite scores for the endurance, strength, power, and flexibility domains.

Receiver operating characteristic curve analysis revealed that the TyG index outperformed body mass index (z = 3.005, P = 0.003) and the homeostasis assessment model for insulin resistance (z = 3.543, P = 0.001) in detecting the presence of MetS. Mediation analysis revealed that while the TyG index has a direct effect on composite physical fitness scores (β = −0.3832 and SE = −2.0942, 95% confidence interval, CI = −0.7426 to −0.0239), there was an indirect effect of the TyG index on physical fitness via vigorous PA (β = −0.0802 and SE = 0.0377) and waist-to-hip ratio (β = −0.1318, SE = 0.0509).

The TyG index has a significant impact on physical fitness in the presence of the two mediators (β = - 0.3832, SE = −2.0942, 95% CI = −0.7426 to - 0.0239).

The relationship between regional fat and obstructive sleep apnea (OSA) remains poorly understood. This study seeks to explore the link between regional fat and OSA, utilizing data from the National Health and Nutrition Examination Survey (NHANES).

This cross-sectional analysis used NHANES 2015–2018 data. OSA symptoms were assessed through sleep questionnaires. Regional fat mass (FM) was measured using dual-energy X-ray absorptiometry, including trunk, arm, leg, android, gynoid, and abdominal FM. The fat mass index (FMI) was calculated by dividing FM by the square of height. Logistic regression evaluated the association between regional FMI and OSA, with univariate and stratified analyses to identify potential effect modifiers.

A total of 3,099 participants were included, with 1,595 classified into the OSA group. Significant associations were found between OSA and several regional FMIs, including trunk, arm, leg, android, gynoid, and abdomen. These associations were consistent in males, and in females, leg and gynoid FMI were not linked to OSA. Stratified analyses by race revealed significant associations between OSA and regional FMI indices (trunk, arm, leg, android, gynoid, and abdominal FMI) in non-Hispanic Whites and between OSA and trunk, android, and abdominal FMI in other Hispanics. No associations were observed in the Mexican American or non-Hispanic Black groups. Stratification by body mass index (BMI) indicated distinct profiles: obese individuals (BMI ≥30) showed associations limited to trunk, arm, android, and abdominal FMIs, while nonobese participants (BMI <30) displayed broader associations encompassing all regional FMIs. Both univariate and stratified analyses highlighted abdominal FMI as the strongest predictor of OSA.

Higher regional FMI, particularly abdominal fat, is associated with an increased risk of OSA, with stronger associations observed in male, White, and nonobese populations.

Based on the high prevalence of kidney stone disease (KSD) and its possible relationship with metabolic components, the aim of this study was to examine the associations of metabolic syndrome (MetS) and its components with KSD.

This is a cross-sectional assessment of the Prospective Epidemiological Research Studies of Iranian Adults (PERSIAN) Guilan cohort study (PGCS), which includes 10,520 participants aged between 35 and 70 in northern Iran from 2014 to 2017. Demographic data and clinical characteristics were filled out. MetS was determined by the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) with the following criteria: hypertriglyceridemia, low high-density lipoprotein cholesterol (HDL-C), hypertension, abdominal obesity, and hyperglycemia. The association of self-reported KSD with MetS was examined using logistic regression analysis. Odds ratio (OR) and 95% confidence interval (CI) were calculated.

The prevalence of MetS and KSD was 41.8% and 15.6%, respectively. In the unadjusted model, MetS was associated with 18% increased odds of KSD (OR = 1.18, 95% CI: 1.06–1.31). This association remained significant after adjustment for some demographic characteristics (aOR = 1.30, 95% CI: 1.16–1.46). All MetS components except for low HDL-C were also associated with increased odds of KSD, after adjusting for some demographic variables. In addition, the odds of KSD increased with the number of MetS components, up to an almost 2.2-fold odds among subjects with all five MetS components.

This study found that the risk of KSD increases with MetS as a whole, all MetS components except for low HDL-C, and the number of MetS components. Our study might provide evidence for individualized management of MetS for preventing KSD.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor often accompanied by various metabolic abnormalities both before and after clinical diagnosis. Diagnosis at advanced stages significantly increases mortality risk. This study aimed to investigate the potential relationship between metabolic and inflammatory parameters at the time of diagnosis and disease stage, and their impact on prognosis in patients with pancreatic cancer.

A total of 89 patients (43.8% male, 56.2% female) diagnosed with PDAC were included in the retrospective, single-center study. Disease stages at diagnosis that were categorized as stages 1–2 (resectable) and stages 3–4 (locally advanced and metastatic), height, weight, accompanying diseases, body mass index (BMI), inflammatory parameters (C-reactive protein (CRP), neutrophil/lymphocyte ratio (NLR), and metabolic parameters (glucose, HbA1c %, triglyceride, High-Density Lipoprotein [HDL]-cholesterol, Low-Density Lipoprotein [LDL]-cholesterol) were recorded. These parameters were compared between early and advanced stage patients, and their effect on one-year survival was evaluated.

The mean age of the patients was 62.7 years; 45.1% of the patients were in stages 1–2, while 48.3% were in stage 4. The one-year overall mortality rate was 32.6%, and the mortality rate was 52.1% in advanced stage (stages 3–4) patients and 9.8% in early stage (stages 1–2) patients. Mean CRP and NLR levels were significantly higher in patients who died compared to those who survived (7.4 vs. 4.4, p = 0.001; p = 0.000, respectively). Mean HDL-cholesterol level was lower in patients who died compared to survivors (34 mg/dL vs. 47.2 mg/dL, p = 0.001). Mean fasting blood glucose and HbA1c levels were higher in patients who died compared to survivors (167 mg/dL vs. 135 mg/dL, 7.5% vs. 6.6%; p = 0.008; p = 0.037, respectively). There were no significant differences in gender, BMI, presence of co-morbidities, triglyceride, or LDL-cholesterol levels between the groups.

Recognizing clinical biomarkers predicting prognosis in PDAC could significantly contribute to disease management.

Cardiovascular disease (CVD) will be the leading cause of mortality in Africa by 2030. Yet, little is known about the key drivers of CVD risk in the region.

To examine the risk factors associated with CVD risk in a sample of rural midlife and elderly Kenyans.

Cross-sectional study design. Data were collected following established protocols and included physical activity (PA), body mass index (BMI), waist circumference, blood pressure (BP), and self-reported medical history. Absolute CVD risk scores [Framingham risk scores (FRS)] were computed using non-lab-based Framingham algorithm. Descriptive and inferential statistics were used to evaluate factors associated with CVD risk scores and related sex-specific differences.

The sample (N = 102; mean age 59.8 ± 7.3 years; 57.8% female) was on average highly active (median 8891 steps/day) with 61.8% hypertension prevalence. Females versus males had higher BMI (29.2 vs. 24.8 kg/m²; P < 0.001) and central adiposity (84.8 vs. 18.6%; P < 0.001). However, they had lower systolic BP (129.3 vs. 138.3 mmHg; P = 0.032) and didn’t smoke (0.0 vs. 11.6%; P = 0.012). Females also were 6.6 years younger (P < 0.001) and had fewer years of education (P < 0.001) and less PA (P = 0.046). Overall, 34.3% of the sample was at high risk of CVD (FRS ≥20%), but females had lower risk compared with males (median FRS 7.4 vs. 25.0%; P < 0.001). Higher CVD risk was associated with higher education (P < 0.001) and having adequate income (P = 0.048). When considering females separately, none of the sociodemographic characteristics or PA measures were associated with CVD risk, but for males, higher CVD risk was associated with higher education (P = 0.025) and lower PA (P = 0.009).

Age, BMI, BP, and smoking partially explain sex differences in CVD risk burden. However, sex differences also exist with males being older with higher education—factors associated with higher CVD risk. More research is needed to examine factors associated with absolute CVD risk in females.