Roles of serum uric acid on the association between arsenic exposure and incident metabolic syndrome in an older Chinese population.

Growing evidences showed that heavy metals exposure may be associated with metabolic diseases. Nevertheless, the mechanism underlying arsenic (As) exposure and metabolic syndrome (MetS) risk has not been fully elucidated. So we aimed to prospectively investigate the role of serum uric acid (SUA) on the association between blood As exposure and incident MetS. A sample of 1045 older participants in a community in China was analyzed. We determined As at baseline and SUA concentration at follow-up in the Yiwu Elderly Cohort. MetS events were defined according to the criteria of the International Diabetes Federation (IDF). Generalized linear model with log-binominal regression model was applied to estimate the association of As with incident MetS. To investigate the role of SUA in the association between As and MetS, a mediation analysis was conducted. In the fully adjusted log-binominal model, per interquartile range increment of As, the risk of MetS increased 1.25-fold. Compared with the lowest quartile of As, the adjusted relative risk (RR) of MetS in the highest quartile was 1.42 (95% confidence interval, CI: 1.03, 2.00). Additionally, blood As was positively associated with SUA, while SUA had significant association with MetS risk. Further mediation analysis demonstrated that the association of As and MetS risk was mediated by SUA, with the proportion of 15.7%. Our study found higher As was remarkably associated with the elevated risk of MetS in the Chinese older adults population. Mediation analysis indicated that SUA might be a mediator in the association between As exposure and MetS.

The effect of a nurse-led low carbohydrate regimen on anthropometric and laboratory parameters of patients with metabolic syndrome: a quasi-experimental study.

Introduction: Metabolic syndrome is a global health concern. It is a condition that includes a cluster of various risk factors for type 2 diabetes and cardiovascular disease. This quasi-experimental study investigates the effect of a nurse-led low-carbohydrate regimen on anthropometric and laboratory parameters in metabolic syndrome patients. Methods: The study used a quasi-experimental design conducted at the University of Mosul; 128 participants meeting the metabolic syndrome criteria were recruited and divided into the intervention and control groups. The intervention group received personalized counseling and support in implementing a low-carb regime, while the control group received standard advice. The study participants were assessed by anthropometry, and laboratory parameters were evaluated pre- and post-intervention. Statistical data analysis was conducted using IBM-SPSS 27, including chi-square, Fisher's exact test, t-tests, and the Mcnemar test, which were performed to compare the changes within and between groups. Results: The mean age of the participants in the intervention and control groups was 50.72 ± 6.43 years and 49.14 ± 6.89 years, respectively. Compared to the control group, the intervention group experienced a significant positive reduction in anthropometric measures and laboratory parameters, including weight, body mass index (BMI), waist circumference, lipid profiles, and HbA1c. Conclusion: A tangible effect of nurse-led interventions based on low-carbohydrate regimens in managing metabolic syndrome was empirically authenticated. Positive changes were observed in the intervention group regarding anthropometric measures and laboratory parameters. However, future research may require a larger sample size and a longer follow-up to confirm these effects and evaluate long-term metabolic impacts.

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data.

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabetes. Early and accurate identification of individuals at risk for MetS is essential. Various machine learning approaches have been employed to predict MetS, such as logistic regression, support vector machines, and several boosting techniques. However, these methods use MetS as a binary status and do not consider that MetS comprises five components. Therefore, a method that focuses on these characteristics of MetS is needed. In this study, we propose a multi-task deep learning model designed to predict MetS and its five components simultaneously. The benefit of multi-task learning is that it can manage multiple tasks with a single model, and learning related tasks may enhance the model's predictive performance. To assess the efficacy of our proposed method, we compared its performance with that of several single-task approaches, including logistic regression, support vector machine, CatBoost, LightGBM, XGBoost and one-dimensional convolutional neural network. For the construction of our multi-task deep learning model, we utilized data from the Korean Association Resource (KARE) project, which includes 352,228 single nucleotide polymorphisms (SNPs) from 7729 individuals. We also considered lifestyle, dietary, and socio-economic factors that affect chronic diseases, in addition to genomic data. By evaluating metrics such as accuracy, precision, F1-score, and the area under the receiver operating characteristic curve, we demonstrate that our multi-task learning model surpasses traditional single-task machine learning models in predicting MetS.

Association between polychlorinated biphenyl (PCB) and dioxin with metabolic syndrome (METS): a systematic review and meta-analysis.

Polychlorinated biphenyls (PCBs) and dioxin are persistent endocrine disrupting chemicals (EDCs) and have been associated with an increased risk of metabolic syndrome (MetS). The aim of this systematic review and meta-analysis was to assess the associations of PCBs and dioxin with MetS and its risk factors, including obesity, hypertriglyceridaemia (HTG), hypertension (HTN) and diabetes mellitus (DM). We searched three electronic databases for epidemiological studies concerning PCBs and dioxin with MetS published up to the end of 2023. Meta-analysis was performed for MetS itself and each of the MetS risks based on a random-effects meta-analysis model, and odds ratios (ORs) with 95% confidence intervals (CIs) were obtained. Publication bias was assessed based on Egger's test. Eleven studies were included from three databases up to 2023. There were 40,528 participants aged 18-89, where 18-100% of them were males, included in our meta-analysis. The meta-analysis results showed a strong association between PCB exposure and DM (OR = 3.593, 95% CI 2.566, 5.031), while most of the risk factors for MetS, including obesity (OR = 1.875, 95% CI 0.883, 3.979), HTN (OR = 1.335, 95% CI 0.902, 1.976) and HTG (OR = 1.611, 95% CI 0.981, 2.643), were weakly associated with PCB. Furthermore, both PCBs (OR = 1.162, 95% CI 0.994, 1.357) and dioxin (OR = 2.742, 95% CI 1.936, 3.883) were found to be weakly and strongly associated with MetS, respectively. Meta-regression analysis showed that DM in the Asian population is associated with PCB exposure, while HTG in the Northern American population is associated with PCB exposure. Our meta-analysis has demonstrated a strong relationship between DM and PCBs, while the relationship between PCBs with MetS and other risk factors is less pronounced. Additionally, MetS is weakly associated with dioxin exposure. To improve primary care outcomes, healthcare providers should consider incorporating the assessment of patients' risk of exposure to PCBs and dioxins into their evaluation procedures for more targeted medical interventions.

Prevalence and contributing factors of metabolic syndrome in rheumatoid arthritis patients.

BACKGROUND: Promoting prevalence of metabolic syndrome (MetS) in Rheumatoid arthritis (RA) patients might occur secondary to RA therapy as well as sedentary life style. However, conflicting observations have been reported on the correlation between MetS and RA. This study aimed to determine the frequency of MetS and association of its components in RA. METHODS: In this study, 500 RA patients and 500 age- and gender-matched healthy controls were enrolled. MetS was fulfilled through the International Diabetes Federation (IDF) criteria. A multivariate regression model was used to control for variables independently associated with the risk of MetS in RA patients. RESULTS: The prevalence of MetS was 58.8% on IDF criteria in RA patients that was higher than controls (20.4%). Higher incidence of cardiovascular disease (CVD), the familial history of CVD, hypertension, type 2 diabetes mellitus (T2DM), smoking, dyslipidemia, and higher levels of body mass index (BMI), waist circumference (WC), total cholesterol level, fasting blood sugar (FBS), triglyceride (TG) level, low-density lipoprotein (LDL) level, while lower levels of high-density lipoprotein (HDL) were associated with an increased risk of MetS in RA patients. Multivariate regression analysis indicated that age, WC, dyslipidemia, LDL, and DAS28 were independent predictors of MetS in the RA patients. CONCLUSIONS: The prevalence of MetS is higher in RA patients. Our findings suggest an association between cardiovascular risk factors and the increased prevalence of MetS in RA patients.

Dietary fatty acid pattern and its association with metabolic profile among overweight and obese adults.

BACKGROUND: Numerous studies have revealed the role of dietary fatty acids in human health. However, few studies have evaluated dietary fatty acid patterns and their association with metabolic parameters. The current study aimed to explore the association between dietary fatty acid patterns and risk factors for metabolic syndrome (MetS) among overweight and obese adults. METHODS: This cross-sectional study involved 340 participants who were overweight or obese. The study included assessments of body composition and anthropometric measurements. Dietary fatty acid consumption was evaluated using a validated Food Frequency Questionnaire (FFQ) containing 168 items. Additionally, biochemical parameters, including serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), fasting serum glucose (FSG), and insulin levels, were measured using enzymatic methods. Fatty acid patterns were determined by principal component analysis (PCA), and the association between these dietary FA patterns and risk factors related to MetS components was assessed using logistic regression. RESULTS: Factor analysis conducted in this study explored three dietary fatty acid patterns: saturated fatty acids (SFA), polyunsaturated fatty acids (PUFA), and long-chain combined fatty acids (LC-CFA). Those at the highest tertile of the SFA pattern had lower diastolic blood pressure (DBP) (P = 0.03). Low-density lipoprotein cholesterol (LDL) was lower in the second and third tertiles (P ≤ 0.05). Also, higher fasting blood glucose (FBS) was observed in the second and third tertiles (P < 0.05), and the homeostatic model assessment of insulin resistance (HOMA-IR) was higher in the third tertile (P = 0.049). In the PUFA pattern, FBS was lower in the third tertile (P = 0.03). In the LC-CFA pattern, lower TC was achieved in higher tertiles (P = 0.04). CONCLUSION: Our findings demonstrated that consuming high and moderate SFA patterns is associated with higher FBS and HOMA-IR. Also, increased consumption of SCFAs is related to lower DPB and LDL. Individuals who consumed more PUFA, especially linoleic acid, had lower FBS. These outcomes might be beneficial in managing MetS and leading to a new field of research.

Associations of dietary pattern, insulin resistance and risk of developing metabolic syndrome among Chinese population.

Evidence regarding the role of dietary patterns in metabolic syndrome (MetS) is limited. The mechanistic links between dietary patterns, insulin resistance, and MetS are not fully understood. This study aimed to evaluate the associations between dietary patterns and the risk of MetS in a Chinese population using a longitudinal design. Data from the China Health and Nutrition Survey, a nationally representative survey, were analyzed. MetS cases were identified based on biomarker data collected in 2009. Factor analysis was employed to identify dietary patterns, while logistic regression models were utilized to examine the association between dietary patterns and MetS. Mediation models were applied to assess multiple mediation effects. Two dietary patterns were revealed by factor analysis. Participants in the higher quartiles of the traditional Chinese dietary pattern had lower odds of MetS than those in the lowest quartile (Q1) (OR = 0.58, 95%CI: 0.48, 0.69 for Q4; OR = 0.75, 95%CI: 0.63, 0.89 for Q3). Conversely, participants in the higher quartiles of the modern Chinese dietary pattern had higher odds of MetS compared to those in the lowest quartile (Q1) (OR = 1.40, 95%CI: 1.17, 1.68 for Q4; OR = 1.27, 95%CI: 1.06, 1.52 for Q3). Significant associations between dietary patterns and MetS were mediated by insulin resistance. Therefore, dietary patterns in Chinese adults are associated with MetS, and these associations appear to be mediated through insulin resistance. These findings underscore the critical role of dietary patterns in the development of MetS and establish a foundation for culturally tailored dietary interventions aimed at reducing rates the prevalence of MetS among Chinese adults.

Metabolic characteristics of different phenotypes in reproductive-aged women with polycystic ovary syndrome.

Objective: Polycystic ovary syndrome (PCOS) is an endocrine metabolic disorder in reproductive-aged women. The study was designed to investigate the metabolic characteristics of different phenotypes in women with PCOS of reproductive age. Methods: A total of 442 women with PCOS were recruited in this cross-sectional study. According to different phenotypes, all women were divided into three groups: the chronic ovulatory dysfunction and hyperandrogenism group (OD-HA group, n = 138), the chronic ovulatory dysfunction and polycystic ovarian morphology group (OD-PCOM group, n = 161), and the hyperandrogenism and polycystic ovarian morphology group (HA-PCOM group, n = 143). The metabolic risk factors and prevalence rates of metabolic disorders among the three groups were compared. Results: The body mass index (BMI), waist circumference, and waist-to-hip ratio (WHR) of women from the OD-HA group and HA-PCOM group were significantly higher than those of women from the OD-PCOM group (p < 0.05). The serum insulin concentration and homeostasis model assessment of insulin resistance (HOMA IR) at 2 h and 3 h after oral glucose powder in women from the OD-HA group and HA-PCOM group were significantly higher than those from the OD-PCOM group (p < 0.05). The serum total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) in women from the OD-HA group and HA-PCOM group were significantly higher than those in women from the OD-PCOM group (p < 0.05). The prevalence rates of impaired glucose tolerance (IGT), type 2 diabetes mellitus (T2DM), insulin resistance (IR), metabolic syndrome (MS), nonalcoholic fatty liver disease (NAFLD), and dyslipidemia of women with PCOS were 17.9%, 3.6%, 58.4%, 29.4%, 46.6%, and 43.4%, respectively. The prevalence rates of IGT, IR, MS, NAFLD, and dyslipidemia of women in the OD-HA group and HA-PCOM group were significantly higher than those of women in the OD-PCOM group (p < 0.05). T concentration (>1.67 nmol/L) and Ferriman-Gallwey (F-G) score (>3) significantly increased the risk of metabolic disorders in women with PCOS (p < 0.05). Conclusion: The phenotypes of OD-HA and HA-PCOM in women with PCOS were vulnerable to metabolic disorders compared to OD-PCOM. Thus, the metabolic disorders in women with PCOS especially those with the HA phenotype should be paid more attention in order to reduce long-term complications.

Effect of metabolic syndrome on testosterone levels in patients with metastatic prostate cancer: a real-world retrospective study.

Background: Metabolic syndrome (MetS) has been shown to have a negative impact on prostate cancer (PCa). However, there is limited research on the effects of MetS on testosterone levels in metastatic prostate cancer (mPCa). Objective: This study aims to investigate the influence of MetS, its individual components, and composite metabolic score on the prognosis of mPCa patients, as well as the impact on testosterone levels. Additionally, it seeks to identify MetS-related risk factors that could impact the time of decline in testosterone levels among mPCa patients. Methods: A total of 212 patients with mPCa were included in the study. The study included 94 patients in the Non-MetS group and 118 patients in the combined MetS group. To analyze the relationship between MetS and testosterone levels in patients with mPCa. Additionally, the study aimed to identify independent risk factors that affect the time for testosterone levels decline through multifactor logistic regression analysis. Survival curves were plotted by the Kaplan-Meier method. Results: Compared to the Non-MetS group, the combined MetS group had a higher proportion of patients with high tumor burden, T stage ≥ 4, and Gleason score ≥ 8 points (P < 0.05). Patients in the combined MetS group also had higher lowest testosterone values and it took longer for their testosterone to reach the lowest level (P < 0.05). The median progression-free survival (PFS) time for patients in the Non-MetS group was 21 months, while for those in the combined MetS group it was 18 months (P = 0.001). Additionally, the median overall survival (OS) time for the Non-MetS group was 62 months, whereas for the combined MetS group it was 38 months (P < 0.001). The median PFS for patients with a composite metabolic score of 0-2 points was 21 months, 3 points was 18 months, and 4-5 points was 15 months (P = 0.002). The median OS was 62 months, 42 months, and 29 months respectively (P < 0.001). MetS was found to be an independent risk factor for testosterone levels falling to the lowest value for more than 6 months. The risk of testosterone levels falling to the lowest value for more than 6 months in patients with MetS was 2.157 times higher than that of patients with Non-MetS group (P = 0.031). Patients with hyperglycemia had a significantly higher lowest values of testosterone (P = 0.015). Additionally, patients with a BMI ≥ 25 kg/m2 exhibited lower initial testosterone levels (P = 0.007). Furthermore, patients with TG ≥ 1.7 mmol/L experienced a longer time for testosterone levels to drop to the nadir (P = 0.023). The lowest value of testosterone in the group with a composite metabolic score of 3 or 4-5 was higher than that in the 0-2 group, and the time required for testosterone levels to decrease to the lowest value was also longer (P < 0.05). Conclusion: When monitoring testosterone levels in mPCa patients, it is important to consider the impact of MetS and its components, and make timely adjustments to individualized treatment strategies.

Prevalence and metabolic risk factors of chronic kidney disease among a Mexican adult population: a cross-sectional study in primary healthcare medical units.

Introduction: The intricate relationship between obesity and chronic kidney disease (CKD) progression underscores a significant public health challenge. Obesity is strongly linked to the onset of several health conditions, including arterial hypertension (AHTN), metabolic syndrome, diabetes, dyslipidemia, and hyperuricemia. Understanding the connection between CKD and obesity is crucial for addressing their complex interplay in public health strategies. Objective: This research aimed to determine the prevalence of CKD in a population with high obesity rates and evaluate the associated metabolic risk factors. Material and Methods: In this cross-sectional study conducted from January 2017 to December 2019 we included 3,901 participants of both sexes aged ≥20 years who were selected from primary healthcare medical units of the Mexican Social Security Institute (IMSS) in Michoacan, Mexico. We measured the participants' weight, height, systolic and diastolic blood pressure, glucose, creatinine, total cholesterol, triglycerides, HDL-c, LDL-c, and uric acid. We estimated the glomerular filtration rate using the Collaborative Chronic Kidney Disease Epidemiology (CKD-EPI) equation. Results: Among the population studied, 50.6% were women and 49.4% were men, with a mean age of 49 years (range: 23-90). The prevalence of CKD was 21.9%. Factors significantly associated with an increased risk of CKD included age ≥60 years (OR = 11.70, 95% CI [9.83-15.93]), overweight (OR = 4.19, 95% CI [2.88-6.11]), obesity (OR = 13.31, 95% CI [11.12-15.93]), abdominal obesity (OR = 9.25, 95% CI [7.13-11.99]), AHTN (OR = 20.63, 95% CI [17.02-25.02]), impaired fasting glucose (IFG) (OR = 2.73, 95% CI [2.31-3.23]), type 2 diabetes (T2D) (OR = 14.30, 95% CI [11.14-18.37]), total cholesterol (TC) ≥200 mg/dL (OR = 6.04, 95% CI [5.11-7.14]), triglycerides (TG) ≥150 mg/dL (OR = 5.63, 95% CI 4.76-6.66), HDL-c <40 mg/dL (OR = 4.458, 95% CI [3.74-5.31]), LDL-c ≥130 mg/dL (OR = 6.06, 95% CI [5.12-7.18]), and serum uric acid levels ≥6 mg/dL in women and ≥7 mg/dL in men (OR = 8.18, 95% CI [6.92-9.68]), (p < 0.0001). These factors independently contribute to the development of CKD. Conclusions: This study underscores the intricate relationship between obesity and CKD, revealing a high prevalence of CKD. Obesity, including overweight, abdominal obesity, AHTN, IFG, T2D, dyslipidemia, and hyperuricemia emerged as significant metabolic risk factors for CKD. Early identification of these risk factors is crucial for effective intervention strategies. Public health policies should integrate both pharmacological and non-pharmacological approaches to address obesity-related conditions and prevent kidney damage directly.

Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.

BACKGROUND: Atherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases. METHODS: Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes. RESULTS: A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein-Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples. CONCLUSION: We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS.

Prediction of visceral adipose tissue magnitude using a new model based on simple clinical measurements.

Aims: Waist circumference (WC) is a reliable obesity surrogate but may not distinguish between visceral and subcutaneous adipose tissue. Our aim was to develop a novel sex-specific model to estimate the magnitude of visceral adipose tissue measured by computed tomography (CT-VAT). Methods: The model was initially formulated through the integration of anthropometric measurements, laboratory data, and CT-VAT within a study group (n=185), utilizing the Multivariate Adaptive Regression Splines (MARS) methodology. Subsequently, its correlation with CT-VAT was examined in an external validation group (n=50). The accuracy of the new model in estimating increased CT-VAT (>130 cm2) was compared with WC, body mass index (BMI), waist-hip ratio (WHR), visceral adiposity index (VAI), a body shape index (ABSI), lipid accumulation product (LAP), body roundness index (BRI), and metabolic score for visceral fat (METS-VF) in the study group. Additionally, the new model's accuracy in identifying metabolic syndrome was evaluated in our Metabolic Healthiness Discovery Cohort (n=430). Results: The new model comprised WC, gender, BMI, and hip circumference, providing the highest predictive accuracy in estimating increased CT-VAT in men (AUC of 0.96 ± 0.02), outperforming other indices. In women, the AUC was 0.94 ± 0.03, which was significantly higher than that of VAI, WHR, and ABSI but similar to WC, BMI, LAP, BRI, and METS-VF. It's demonstrated high ability for identifying metabolic syndrome with an AUC of 0.76 ± 0.03 (p<0.001). Conclusion: The new model is a valuable indicator of CT-VAT, especially in men, and it exhibits a strong predictive capability for identifying metabolic syndrome.

The Gender Gap in the Relationship between Metabolic Syndrome and Restrictive Ventilatory Defects.

BACKGROUND: Given the fundamental physiological differences between the sexes, this study aimed to investigate the effect of metabolic syndrome on ventilatory defects stratified by sex. METHODS: We conducted a nationwide, pooled, cross-sectional study. Data from 45,788 participants (men, n = 15,859; women, n = 29,929) aged 30 years or more were obtained from the Taiwan Biobank. Age-sex-adjusted and multivariate logistic regression models were used to estimate the risk of developing impaired pulmonary function (restrictive or obstructive ventilatory defects) in individuals with or without metabolic syndromes. Separate models were also used to estimate the effect of metabolic syndrome scores and the effect of individual metabolic abnormalities on the risk of restrictive ventilatory defects. RESULTS: The overall prevalence of metabolic syndrome was estimated to be 15.9% in Taiwan, much higher in men than in women (18.6% versus 14.4%). A significant association was observed between metabolic syndromes and the risk of restrictive ventilatory defects. The risk of developing a restrictive ventilator defect was 35% higher in participants with metabolic syndromes (odds ratio, 1.35; 95% confidence interval, 1.26-1.45) than in those without metabolic syndromes. Elevated blood pressure and a triglycerides abnormality were important predictors of restrictive ventilator defects. Sex-stratified subgroup analyses of the individual metabolic abnormalities indicated that men with abdominal obesity and women with dysglycemia were more likely to develop restrictive ventilatory defects. CONCLUSIONS: Our study's evidence suggested that metabolic syndromes were important predictors of impaired pulmonary function and an increased risk of developing restrictive ventilatory defects, and its risk increased with increasing numbers of metabolic abnormalities.

Association between the triglyceride glucose-body mass index and future cardiovascular disease risk in a population with Cardiovascular-Kidney-Metabolic syndrome stage 0-3: a nationwide prospective cohort study.

BACKGROUND: The American Heart Association (AHA) has recently introduced the concept of Cardiovascular-Kidney-Metabolic (CKM) syndrome, which is the result of an increasing emphasis on the interplay of metabolic, renal and cardiovascular diseases (CVD). Furthermore, there is substantial evidence of a correlation between the triglyceride glucose-body mass index (TyG-BMI ) and CVD as an assessment of insulin resistance (IR). However, it remains unknown whether this correlation exists in population with CKM syndrome. METHODS: All data for this study were obtained from the China Health and Retirement Longitudinal Study (CHARLS). The exposure was the participants' TyG-BMI at baseline, which was calculated using a combination of triglycerides (TG), fasting blood glucose (FBG) and body mass index (BMI). The primary outcome was CVD, which were determined by the use of a standardised questionnaire during follow-up. To examine the relationship between TyG-BMI and CVD incidence in population with CKM syndrome, both Cox regression analyses and restricted cubic spline (RCS) regression analyses were performed. RESULTS: A total of 7376 participants were included in the final analysis. Of these, 1139, 1515, 1839, and 2883 were in CKM syndrome stages 0, 1, 2, and 3, respectively, at baseline. The gender distribution was 52.62% female, and the mean age was 59.17 ± 9.28 (years). The results of the fully adjusted COX regression analyses indicated that there was a 6.5% increase in the risk of developing CVD for each 10-unit increase in TyG-BMI,95% confidence interval (CI):1.041-1.090. The RCS regression analyses demonstrated a positive linear association between TyG-BMI and the incidence of CVD in the CKM syndrome population (P for overall < 0.001, P for nonlinear = 0.355). CONCLUSIONS: This cohort study demonstrated a positive linear association between TyG-BMI index and increased CVD incidence in a population with CKM syndrome stage 0-3. This finding suggests that enhanced assessment of TyG-BMI index may provide a more convenient and effective tool for individuals at risk for CVD in CKM syndrome stage 0-3.

New Advances in Metabolic Syndrome.

It is with great anticipation and pride that we present a Special Issue entitled "New Advances in Metabolic Syndrome", which provides a compendium of high-quality original papers written on novel aspects of metabolic syndrome (MetS) [...].

Prevalence of Pancreatic Steatosis and Its Associated Factors in Turkey: A Nation-Wide Multicenter Study.

BACKGROUND/AIMS:  Pancreatic steatosis (PS) is a pathology associated with metabolic syndrome (MS), endocrin and exocrine disfunctions of the pancreas, and fatty liver. The data on the frequency of PS are very limited. We aimed to evaluate the frequency of PS detected by transabdominal ultrasonography (TAU) in gastroenterology clinics located in different geographical regions of Turkey and the factors associated with it. MATERIALS AND METHODS:  Volunteers were evaluated by TAU for PS and hepatosteatosis (HS), and its degree. Pancreatic stiffness was evaluated by ultrasonographic shear wave elastography (SWE). All demographic, physical, and biochemical parametres were measured. RESULTS:  A total of 1700 volunteers from 14 centers throughout Turkey were included in the study. Mean age was 48.03 ± 20.86 years (56.9% female). Prevalance of PS was detected in 68.9%. In the PS group, age, body mass index (BMI), waist circumference, systolic blood pressure, fasting blood glucose (FBG), lipid levels, insulin resistance, diabetes mellitus, hypertension, MS frequency, and pancreatic SWE score were increasing, and fecal elastase level was decreasing in correlation with the degree of PS. The frequency of HS was 55.5%. Hepatosteatosis [odds ratio (OR): 9.472], increased age (OR: 1.02), and BMI (OR: 1.089) were independent risk factors for the occurrence of PS. Lean-PS rate was 11.8%. The lean-PS group was predominantly female and younger than non-lean PS. Also it has lower blood pressure, FBG, liver enzymes, lipid levels, and HS rates. CONCLUSION:  The frequency of PS was found 68.9% in Turkey. Its relationship was determined with age, BMI, HS, MS (and its components), pancreatic stiffness, and fecal elastase level.

Cardiovascular-Kidney-Metabolic Syndrome: Association with Adverse Events After Major Noncardiac Surgery.

BACKGROUND: The American Heart Association (AHA) recently defined the cardiovascular-kidney-metabolic syndrome (CKM) as a new entity to address the complex interactions between heart, kidneys, and metabolism. The aim of this study was to assess the outcome impact of CKM syndrome in patients undergoing noncardiac surgery. METHODS: This is a secondary analysis of a prospective international cohort study including patients aged ≥45 years with increased cardiovascular risk undergoing noncardiac surgery. Main exposure was CKM syndrome according to the AHA definition. The primary end point was a composite of major adverse cardiovascular events (MACE) 30 days after surgery. Secondary end points included all-cause mortality and non-MACE complications (Clavien-Dindo class ≥3). RESULTS: This analysis included 14,634 patients (60.8% male, mean age = 72±8 years). MACE occurred in 308 patients (2.1%), and 335 patients (2.3%) died. MACE incidence by CKM stage was as follows: CKM 0: 5/367 = 1.4% (95% confidence interval [CI], 0.4%-3.2%); CKM 1: 3/367 = 0.8% (95% CI, 0.2%-2.4%); CKM 2: 102/7440 = 1.4% (95% CI, 1.1%-1.7%); CKM 3: 27/953 = 2.8% (95% CI, 1.9%-4.1%); CKM 4a: 164/5357 = 3.1% (95% CI, 2.6%-3.6%); CKM 4b: 7/150 = 4.7% (95% CI, 1.9%-9.4%). In multivariate logistic regression, CKM stage ≥3 was independently associated with MACE, mortality, and non-MACE complications, respectively (MACE: OR 2.26 [95% CI, 1.78-2.87]; mortality: OR 1.42 [95% CI: 1.13 -1.78]; non-MACE complications: OR 1.11 [95% CI: 1.03-1.20]). CONCLUSION: The newly defined CKM syndrome is associated with increased morbidity and mortality after non-cardiac surgery. Thus, cardiovascular, renal, and metabolic disorders should be regarded in mutual context in this setting.

Association between systemic immune-inflammation index and cardiovascular-kidney-metabolic syndrome.

This study aims to explore the relationship between the Systemic Immune-Inflammation Index (SII) and Cardiovascular-Kidney-Metabolic (CKM) Syndrome and its components. Data from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2018 were analyzed. CKM Syndrome is defined as the coexistence of Cardiometabolic Syndrome (CMS) and Chronic Kidney Disease (CKD). The SII is calculated using the formula: SII = (Platelet count × Neutrophil count)/Lymphocyte count. Weighted logistic regression models were used to examine the associations between SII and CKM, as well as its specific components. Restricted cubic splines explored non-linear relationships, and piecewise linear regression models assessed threshold effects. A consistent positive correlation was observed between elevated SII levels and the likelihood of CKM and its related diseases. In the fully adjusted Model 3, an increase of 1000 units in SII was associated with a 1.48-fold increase in the odds of CKM (95% CI 1.20-1.81, p < 0.001). Quartile analysis revealed a dose-response relationship, with the highest quartile of SII (Q4) showing the strongest association with CKM and its components. Nonlinear analyses revealed inflection points for waist circumference, triglycerides, low HDL-C, and cardiometabolic syndrome at specific SII levels, indicating a change in the direction or strength of associations beyond these points. Conversely, a linear relationship was observed between SII and chronic kidney disease. The SII is positively correlated with the risk of CKM Syndrome and its individual components, with evidence of non-linear relationships and threshold effects for some components.

Dietary niacin Intake and its association with all-cause and cardiovascular mortality rates in individuals with metabolic syndrome.

BACKGROUND: Individuals with metabolic syndrome face elevated cardiovascular and mortality risks, and there is ongoing debate regarding the cardiovascular effects of niacin and its impact on the prognosis of metabolic syndrome. EXPOSURE: Levels of dietary niacin intake based on 24-hour dietary recall. METHODS: Kaplan-Meier survival curves were used to compare survival status among quartiles of dietary niacin intake. Weighted Cox proportional hazards models and restricted cubic splines were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of all-cause and CVD mortality associated with the exposure. RESULTS: This cohort study included 8,744 participants, and during a median follow-up period of 106 months, 1,552 (17.7%) deaths were recorded, with 511 attributed to cardiovascular disease. Kaplan-Meier curves comparing quartiles of dietary niacin intake showed significant differences in both all-cause and cardiovascular mortality rates (log-rank p < 0.001). In the fully adjusted model, the highest quartile of dietary niacin intake was associated with HRs of 0.68 (95% CI: 0.54, 0.87, P = 0.002) for all-cause mortality and 0.63 (95% CI: 0.39, 0.78, P < 0.001) for cardiovascular mortality. CONCLUSION: The results of this cohort study suggest that higher dietary niacin intake is associated with reduced cardiovascular and all-cause mortality risks in the metabolic syndrome population. Furthermore, there appears to be a dose-response relationship between dietary niacin intake and the risks of all-cause and cardiovascular mortality.