Predictor Of MetS Research Articles

The Investigation of Metabolic Syndrome Among Middle-Aged Women, Kamyaran, Iran: A Cross-sectional Study

Objectives: Physical activity and nutrition can affect some serum markers associated with metabolic syndrome (MetS). We aimed to ascertain the prevalence and predictors of MetS among middle-aged women in this study. Materials and Methods: This cross-sectional descriptive-analytical study was performed on 164 eligible middle-aged women. Sampling was performed by two-stage cluster random sampling among all the health care centers in Kamyaran, Iran. Data were collected through anthropometrics, Food Records, and International Physical Activity Questionnaires. Serum lipid profile and glycemic control indexes, calcium, and 25-hydroxy vitamin D3 were assayed. Blood pressure was measured. The National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria were used to detection of MetS. Results: Forty-one women (25.0%; 95% CI: 18.4% to 31.6%) had MetS. Fasting blood sugar (FBS) (adjusted odds ratio (aOR): 0.020; 95% CI: 1.001 to 1.039), triglyceride (aOR: 1.024; 95% CI: 1.015-1.033), low physical activity: walking (aOR: 0.999; 95% CI: 0.998 0.999), dietary iron (aOR: 1.059; 95% CI: 0.989- 1.134) and waist circumference (aOR: 1.062: 95% CI; 1.020 1.105) have been detected as the MetS predictors in this study. Conclusions: Higher serum levels of FBS and triglyceride, higher iron intake and waist circumference, and low physical activity enhanced the chance of getting MetS.

Prediction of metabolic and pre-metabolic syndromes using machine learning models with anthropometric, lifestyle, and biochemical factors from a middle-aged population in Korea

BackgroundMetabolic syndrome (MetS) is a complex condition that appears as a cluster of metabolic abnormalities, and is closely associated with the prevalence of various diseases. Early prediction of the risk of MetS in the middle-aged population provides greater benefits for cardiovascular disease-related health outcomes. This study aimed to apply the latest machine learning techniques to find the optimal MetS prediction model for the middle-aged Korean population.MethodsWe retrieved 20 data types from the Korean Medicine Daejeon Citizen Cohort, a cohort study on a community-based population of adults aged 30–55 years. The data included sex, age, anthropometric data, lifestyle-related data, and blood indicators of 1991 individuals. Participants satisfying two (pre-MetS) or ≥ 3 (MetS) of the five NECP-ATP III criteria were included in the MetS group. MetS prediction used nine machine learning models based on the following algorithms: Decision tree, Gaussian Naïve Bayes, K-nearest neighbor, eXtreme gradient boosting (XGBoost), random forest, logistic regression, support vector machine, multi-layer perceptron, and 1D convolutional neural network. All analyses were performed by sequentially inputting the features in three steps according to their characteristics. The models’ performances were compared after applying the synthetic minority oversampling technique (SMOTE) to resolve data imbalance.ResultsMetS was detected in 33.85% of the subjects. Among the MetS prediction models, the tree-based random forest and XGBoost models showed the best performance, which improved with the number of features used. As a measure of the models’ performance, the area under the receiver operating characteristic curve (AUC) increased by up to 0.091 when the SMOTE was applied, with XGBoost showing the highest AUC of 0.851. Body mass index and waist-to-hip ratio were identified as the most important features in the MetS prediction models for this population.ConclusionsTree-based machine learning models were useful in identifying MetS with high accuracy in middle-aged Koreans. Early diagnosis of MetS is important and requires a multidimensional approach that includes self-administered questionnaire, anthropometric, and biochemical measurements.

Self-rated health may be a predictor for metabolic syndrome and high hs-CRP prevalences in healthy adults in South Korea: Based on the 2015 Korea National Health and Nutrition Examination Survey

Self-rated health (SRH), affected by sociodemographic and health-related behavioral factors, is related to metabolic syndrome (MetS) and high-sensitivity C-reactive protein (hs-CRP) levels. We hypothesized that SRH would have an independent effect on MetS and high hs-CRP incidence in healthy adults after adjusting for sociodemographic and health-related behavioral confounding factors. Data of 1545 healthy participants (aged 19-65 years; 654 men), selected from the 2015 Korean National Health and Nutritional Examination Survey, were cross-sectionally analyzed. The SRH levels significantly increased with higher income (P < .05) and educational levels (P < .01) and were associated with sex, job, marital status, smoking, physical activity, perceived body image, and weight change (all P < .05). The percentage of participants with “very high or high” perceived stress were significantly lower in the “very healthy” group (18.5%) than in the “unhealthy” group (49.7%). Dietary protein, calcium, and phosphorus intakes were significantly higher with better SRH levels. MetS prevalence was related to sex, educational level, job, marital status, smoking, high-intensity exercise, and aerobic exercise (all P < .05), with a 2.776 times higher risk in the “unhealthy” than in the “very healthy” group after adjusting for these confounders. High hs-CRP levels were related to marital status, smoking, weekly walking, and aerobic exercise (all P < .05), with its risk being 2.093 times higher in the “unhealthy” than in the “very healthy” group after adjusting for these confounders. Thus, SRH may be an independent predictor of MetS and high hs-CRP levels and could be used for the development of a health promotion program in healthy adults.

Relationships between Bone Turnover Markers and Factors Associated with Metabolic Syndrome in Prepubertal Girls and Boys

The associations between individual components of metabolic syndrome (MetS) and bone health in children are complex, and data on this topic are sparse and inconsistent. We assessed the relationship between bone turnover markers and markers of the processes underlying MetS (insulin resistance and inflammation) in a group of presumably healthy children aged 9–11 years: 89 (51 girls, 38 boys) presenting without any features of MetS and 26 (10 girls, 16 boys) with central obesity and two features of MetS. Concentrations of glucose, triglycerides (TG), HDL cholesterol (HDL-C), C-reactive protein (CRP), HbA1c, total 25-hydroxyvitamin D (25(OH)D), intact-P1NP (N-terminal propeptide of type I procollagen), CTX-1 (C-terminal telopeptide of type I collagen) were assayed and insulin resistance was assessed (HOMA-IR). BMI centile, waist circumference (WC) and blood pressure were measured. The presence of MetS in girls resulted in significantly lower concentrations of CTX-1 and a trend to lower CTX-1 in boys. The concentrations of bone formation marker i-P1NP were not affected. Among the features associated with MetS, HOMA-IR appeared as the best positive predictor of MetS in girls, whereas CRP was the best positive predictor in boys. A significant influence of HOMA-IR on the decrease in CTX-1 in girls was independent of BMI centile and WC, and the OR of having CTX-1 below the median was 2.8-fold higher/1SD increased in HOMA-IR (p = 0.003). A weak relationship between CTX-1 and CRP was demonstrated in girls (r = −0.233; p = 0.070). Although TG, as a MetS component, was the best significant predictor of MetS in both sexes, there were no correlations between bone markers and TG. We suggest that dyslipidemia is not associated with the levels of bone markers in prepubertal children whereas CRP is weakly related to bone resorption in girls. In prepubertal girls, insulin resistance exerts a dominant negative impact on bone resorption, independent of BMI centile and waist circumference.

Relationship Analysis of Inorganic Arsenic Exposure and Metabolic Syndrome Based on Propensity Score Matching in Xinjiang, China.

PurposeThe role of inorganic arsenic (iAs) in the risk of metabolic syndrome (MetS) remains unclear. This investigation focused on the effect of iAs exposure on MetS and whether the results are consistent in different subgroups.Patients and MethodsThe present study was conducted on 629 men and 616 women aged 35–70 years and living in Xinjiang Uygur Autonomous Region, China. The 1:1 propensity score matching (PSM) was adopted to regulate the confounding factors, and the multivariate logistic regression was performed to assess the relationship between urinary iAs and MetS.ResultsThe median content of urinary iAs was examined as 2.20 μg/dL (interquartile range: 1.30–3.20 μg/dL), and the MetS prevalence reached 23.69% (295 cases/950 participants). After the confounding factors were adjusted, the ORs (95% CIs) for MetS from the minimal to the maximum urinary iAs quartiles reached 1.171 (0.736,1.863), 1.568 (1.008, 2.440) and 2.011 (1.296, 3.120), respectively (referencing 1.00) (P for trend=0.001). After the PSM, the urinary iAs content still plays a potential prediction role in MetS (P for trend=0.011). In addition, as revealed from the subgroup analysis, the urinary iAs content was a predictor of MetS in the female patients, whereas it did not serve as a significant predictor of MetS in the male patients (P for interaction<0.05).ConclusionThe increased urinary iAs content was associated with the increased prevalence of MetS in Chinese population. More attention should be paid to female urinary iAs content to avoid the high prevalence of MetS.

The Prevalence of Metabolic Syndrome, Scoring, and Comparison in People With and Without COPD: Evidence from Shahrekord PERSIAN Cohort Study.

Background: Comorbidities are common in patients with chronic obstructive pulmonary diseases (COPDs), including metabolic syndrome (MetS). This study aimed to determine the prevalence of MetS and its components in people with and without COPD. Methods: This population-based study was performed on 6961 adults in the Shahrekord Prospective Epidemiological Research Studies in Iran. Data collection, spirometry, and COPD diagnosis were performed according to the cohort protocol from 2015 to 2019. The data were analyzed by two independent sample t-tests, analysis of covariance, chi-square, and odds ratio (OR) with 95% confidence interval (95% CI) in logistic regression models. P value <0.05 was considered statistically significant. All analyses were conducted using Stata Statistical Software Release 16 (Stata Corp., College Station, TX). Results: The prevalence of MetS in patients with and without COPD was 28.4% and 31%, respectively. The most common components of MetS in people with COPD were low high-density lipoprotein cholesterol (47.4%), waist circumference (43.9%), and high fasting plasma glucose (39.3%). There was a statistically significant difference in the frequency of respiratory dysfunction between people with and without MetS. Being older than 60 (OR = 2.20, 95% CI: 1.72-2.80), female (OR = 1.36, 95% CI: 1.49-1.97), obese (OR = 11.17, 95% CI: 9.02-13.62), and uneducated (OR = 1.80, 95% CI: 1.49-2.17), and living in urban areas (OR = 1.96, 95% CI: 1.64-2.35) are stronger predictors of MetS in this population. Conclusion: There was no significant difference in the prevalence of MetS between patients with and without COPD. Spirometry parameters and respiratory problems were significant in subjects with and without MetS.

Machine learning-aided risk prediction for metabolic syndrome based on 3 years study

Metabolic syndrome (MetS) is a group of physiological states of metabolic disorders, which may increase the risk of diabetes, cardiovascular and other diseases. Therefore, it is of great significance to predict the onset of MetS and the corresponding risk factors. In this study, we investigate the risk prediction for MetS using a data set of 67,730 samples with physical examination records of three consecutive years provided by the Department of Health Management, Nanfang Hospital, Southern Medical University, P.R. China. Specifically, the prediction for MetS takes the numerical features of examination records as well as the differential features by using the examination records over the past two consecutive years, namely, the differential numerical feature (DNF) and the differential state feature (DSF), and the risk factors of the above features w.r.t different ages and genders are statistically analyzed. From numerical results, it is shown that the proposed DSF in addition to the numerical feature of examination records, significantly contributes to the risk prediction of MetS. Additionally, the proposed scheme, by using the proposed features, yields a superior performance to the state-of-the-art MetS prediction model, which provides the potential of effective prescreening the occurrence of MetS.

Prevalence of Cardiometabolic Syndrome and its Association With Body Shape Index and A Body Roundness Index Among Type 2 Diabetes Mellitus Patients: A Hospital-Based Cross-Sectional Study in a Ghanaian Population.

Cardiometabolic syndrome (MetS) is closely linked to type 2 diabetes mellitus (T2DM) and is the leading cause of diabetes complications. Anthropometric indices could be used as a cheap approach to identify MetS among T2DM patients. We determined the prevalence of MetS and its association with sociodemographic and anthropometric indices among T2DM patients in a tertiary hospital in the Ashanti region of Ghana. A comparative cross-sectional study was conducted among 241 T2DM outpatients attending the Komfo Anokye Teaching Hospital (KATH) and the Kumasi South Hospital for routine check-up. Sociodemographic characteristics, clinicobiochemical markers, namely, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), and glycated hemoglobin (HbA1C) were measured. Anthropometric indices, namely, body mass index (BMI), Conicity index (CI), body adiposity index (BAI), A body shape index (ABSI), body roundness index (BRI), Waist-to-hip ratio (WHR), and Waist-to-height ratio (WHtR) were computed based on either the Height, Weight, Waist circumference (WC) or Hip circumference (HC) of the patients. Metabolic syndrome (MetS) was classified using the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria. Data entry and analysis were done using Excel 2016 and SPSS version 25.0 respectively. Of the 241 T2DM patients, 99 (41.1%) were males whereas 144 (58.9%) were females. The prevalence of cardiometabolic syndrome (MetS) was 42.7% with dyslipidemia and hypertension recording a prevalence of 6.6 and 36.1%, respectively. Being a female T2DM patient [aOR = 3.02, 95%CI (1.59-5.76), p = 0.001] and divorced [aOR = 4.05, 95%CI (1.22-13.43), p = 0.022] were the independent sociodemographic predictors of MetS among T2DM patients. The 4th quartile for ABSI and 2nd to 4th quartiles for BSI were associated with MetS on univariate logistic regression (p <0.05). Multivariate logistic regression identified the 3rd quartile (aOR = 25.15 (2.02-313.81), p = 0.012) and 4th quartile (aOR = 39.00, 95%CI (2.68-568.49), p = 0.007) for BRI as the independent predictors of MetS among T2DM. The prevalence of cardiometabolic syndrome is high among T2DM patients and this was influenced by female gender, being divorced, and increased BRI. Integration of BRI as part of routine assessment could be used as early indicator of cardiometabolic syndrome among T2DM patients.

Total Testosterone to Estradiol Ratio as a Predictor Marker of Metabolic Syndrome in Males.

Nowadays, metabolic syndrome (MetS) represents a global health challenge in developed and developing countries. The sex hormones disorders in males are related to many clinical co-morbidities. This study aimed to evaluate the total testosterone (TT) to estradiol (E2) ratio as a predictor marker of MetS. This case-control study included 88 MetS patients and 88 healthy individuals (control), in the age range of 18-69 years who were selected among patients who were referring to an outpatient clinic, using a convenience sampling method. The study participants were selected based on their medical history and physical examination, which included waist circumference, blood pressure, serum E2, TT, fasting blood glucose (FBG), triglyceride (TG), and high-density lipoprotein-cholesterol (HDL-C). Diagnosis of MetS was confirmed according to the National Cholesterol Education Program Adult Treatment Panel III guidelines. The findings revealed that the mean TT level was significantly lower among patients with MetS (P<0.001), while the mean E2 level was significantly higher among patients with MetS (P<0.001). The mean TT to E2 ratio was significantly lower among patients with MetS (OR=9.6, P<0.001). There was a significant correlation between MetS components and TT to E2 ratio and waist circumference (WC) (r = - 0.49, P<0.0001). The means of weight, WC, blood pressure, and FBG levels were significantly higher in patients with MetS (P<0.001, P<0.001, P<0.001, P=0.04, respectively), and the lipid profile of patients with MetS was abnormal (TG, P<0.001, HDL-C, P<0.001). Eventually, it can be concluded that the TT to E2 ratio can be regarded as a significant predictor of MetS in males.

Interplay of serum hepcidin with female sex hormones, metabolic syndrome, and abdominal fat distribution among premenopausal and postmenopausal women

Background and purposeHepcidin is the central regulatory molecule of systemic iron homeostasis. Serum ferritin, insulin resistance (IR) and metabolic syndrome (MetS), female sex hormones, and abdominal fat distribution are related to each other and all are linked to menopausal state. Our study was the first to assess the impact of these parameters on hepcidin level among premenopausal women (group I) during the early follicular phase (group I-F) and mid-luteal-phase (group I-L) of the same reproductive cycle and among postmenopausal women (group II). Serum iron parameters, estrogen, progesterone and hepcidin, and plasma insulin were assessed. Abdominal subcutaneous fat (SCF) and peritoneal visceral fat (PVF) thickness were measured by unenhanced- CT. Group I and group II were divided into MetS and non-MetS subgroups.ResultsThe entire group II and MetS-stratified subgroups had significant higher hepcidin level than corresponding group I-F and group I-L. Group I-L had significant higher hepcidin than group I-F. Among group I-F, group I-L, and group II, MetS subgroups had higher hepcidin but not hepcidin/ ferritin ratio (H/F) than corresponding non-MetS; and hepcidin had positive correlations with ferritin, insulin, IR, and SCF. In group I-F and group II, hepcidin had positive correlations with estrogen and progesterone; hepcidin levels increase significantly and linearly with increasing number of MetS features; and cut off values of hepcidin for prediction of MetS were 5.8 ≥ and ≥ 10.3 ng/ml respectively. Main contributors to hepcidin were iron and ferritin in all groups, SCF and progesterone in group I-F, and insulin, progesterone, and MetS in group II. H/F ratio was higher in group II.ConclusionPostmenopausal state (postMS), MetS, and luteal phase are independently associated with high hepcidin level. Serum iron parameters (iron and ferritin) as main regulators of hepcidin are preserved regardless of menopausal state. Its regulation differs based on menopausal state: IR, MetS, and progesterone in postMS meanwhile abdominal SCF and progesterone in premenopausal states. Despite positive associations of estrogen and progesterone with hepcidin, they do not explain its higher level in postMS. Hepcidin levels linearly increase with number of Mets feature and it had high sensitivity for diagnosis of MetS.

Lipid Accumulation Product: An Effective Obesity Index to Predict Metabolic Syndrome

Background: Obesity is a significant risk factor in the development of metabolic syndrome (MetS). Lipid Accumulation Product (LAP) is an obesity index and has been proposed to be a predictor of metabolic syndrome. The present study aims to see the effectiveness of Lipid Accumulation Product as an obesity index to predict metabolic syndrome in a Bangladeshi population. Methods: This cross-sectional study was conducted in Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh, between March 2016 and February 2017. A total of 200 apparently healthy subjects (127 men and 73 women) were selected for the study, attending out-patient-departments of the same institution. Anthropometric measurements were recorded, which included their height, weight, waist circumference (WC) and body mass index (BMI). Overnight fasting blood samples were collected to estimate fasting serum glucose and lipid profile. Then LAP was calculated and evaluated as a tool in prediction of MetS in the study subjects. Receiver operating characteristic (ROC) curves were plotted to assess the performance of LAP in MetS prediction by gender. The power of MetS prediction was quantified by the area under the curve (AUC) with 95% confidence intervals. Results: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NVP) of LAP in predicting MetS were 70.45%, 78.57%, 72.09% and 77.19% in male study subjects and 20.45%, 96.43%, 81.82% and 60.67% in female subjects respectively. ROC curve analysis showed that the optimal cutoff value of LAP in male study subjects was 40.72, while area under the curve was 0.92; in female study subjects, the values were 51.69 and 0.91 respectively (P&lt;0.001). Conclusion: Lipid Accumulation Product was found simple, accessible and effective obesity index to predict metabolic syndrome in apparently healthy adults. J Bangladesh Coll Phys Surg 2022; 40: 5-9

Early Postpartum Glucose Intolerance, Metabolic Syndrome and Gestational Diabetes Mellitus Determinants after Assisted Conception: A Prospective Cohort Study.

Background:This study aimed to determine the prevalence of postpartum metabolic syndrome (MetS), glucose intolerance, and the determinants, 6-12 weeks postpartum in women with assisted reproduction technology conception gestational diabetes mellitus diagnosis (ART-GDM) compared to women with spontaneous conception and GDM diagnosis (SC-GDM).Materials and Methods:In this prospective cohort study, two groups consisting of 62 ART-GDM and 64 SC-GDM singleton pregnant women were followed 6-12 weeks after delivery for postpartum MetS. Fasting glucose, 75-g 2-h OGTT, and lipid profile were assessed. Waist and hip circumference, and systolic and diastolic blood pressures (BP) were measured at postpartum. Clinical, paraclinical, and obstetric data were recorded from registry offices. The prevalence of MetS and glucose intolerance were determined. Predictors of MetS and glucose intolerance were evaluated by logistic regression.Results:The prevalence of postpartum MetS was 20.8% in ART-GDM women and 10.9% in SC-GDM (P=0.123). Mean postpartum BMI and systolic BP were significantly higher in the ART-GDM group (P=0.016 and P=0.027 respectively). Adverse pregnancy outcomes were significantly higher in the ART-GDM group. Postpartum glucose intolerance prevalence did not vary significantly between the groups. Family history of diabetes was a predictive factor for postpartum MetS and glucose intolerance 6-12 weeks after delivery.Conclusion:Early postpartum MetS and glucose intolerance prevalence after assisted conception did not vary significantly; however, postpartum body mass index (BMI) and systolic BP were significantly higher in the ART-GDM group. Lifestyle modification programs and long-term health care of ART women with GDM diagnosis can be recommended. Further studies with larger sample size and longer follow-up are necessary to verify our findings.

Neck circumference is independently associated with metabolic syndrome in women with polycystic ovary syndrome.

Recent compelling evidence has shown that neck circumference (NC), as a reliable and convenient anthropometric index, has better predictive values of hyperuricemia and insulin resistance in women with polycystic ovary syndrome (PCOS) compared with traditional anthropometric measurements. Since both PCOS and metabolic syndrome (MetS) share similar characteristics and affect long-term health of women, we conducted this cross-sectional study to explore the correlation of NC with MetS and metabolic risk factors. Anthropometric parameters, blood pressure, glycemic and lipid profile of 633 PCOS and 2,172 non-PCOS women from January 2018 to June 2021 were analyzed. The results showed that the prevalence of MetS was 28.0% and 9.4% in PCOS and non-PCOS women, respectively. The prevalence of MetS, hypertention, obesity, central obesity, hyperglycemia and dyslipidaemia was also significantly higher in both PCOS and non-PCOS women with larger NC. Additionally, logistic regression analysis showed that PCOS women in the highest quartile of NC had the highest prevalence of MetS (RR = 9.94, 95%CI: 2.41-40.99) after adjusting for confounding factors, while the association between NC and MetS was much attenuated after adjusting for confounding factors in non-PCOS women. Furthermore, we also identified that the optimal NC cutoff value was 33 cm in PCOS women for the prediction of MetS. The potential mechanism could be attributed to the increased release of adipokines and excessive free fatty acids release from subcutaneous adipose tissue, which consequently precipitate the development of MetS. In conclusion, NC was found to be positively and independently correlated with the prevalence of MetS.

National trends in metabolic syndrome among adults in Mongolia from three cross-sectional surveys in 2009, 2013 and 2019

Background and aimMetabolic syndrome (MetS) may increase the risk of cardiometabolic diseases. The study aimed to estimate the proportion and associated factors of MetS among individuals (≥15 years) across three cross-sectional surveys in 2009, 2013 and 2019 in Mongolia. MethodCross-sectional data were analysed from 6,321 individuals (15–64/69 years, median age = 42 years) that participated in the 2009, 2013 or 2019 Mongolia STEPS surveys, and had complete MetS (harmonized definition) measurements. ResultsAlmost two in five participants (35.6%) had MetS, 33.3% in 2009, 35.3% in 2013, ad 37.0% in 2019 (p-for trend 0.023). In adjusted logistic regression analyses, older age was associated with MetS in 2009 and in 2019, male sex and higher education were associated with MetS in 2013, and urban residence was associated with MetS in 2009. Current tobacco use was associated with MetS in 2009 and 2019, moderate or high physical activity was protective against MetS in 2013 and 2019, while general overweight and obesity increased the odds of MetS in 2009, 2013, and 2019. ConclusionThe study showed that almost two in five adults were identified as having MetS, which significantly increased from 2009 to 2019, and that several predictors of MetS were found.

Relationship between circulating serum omentin-1 levels and nascent metabolic syndrome in patients with hypertension

The prevalence of metabolic syndrome (MetS) is more common in patients with hypertension and is associated with an increased risk of target organ damage and/or cardiovascular disease (CVD). Omentin-1 is...

Determinants of Exercise Capacity Following ST-Elevation Myocardial Infarction (STEMI).

Background: Abnormal left ventricular systolic and diastolic function and reduced exercise capacity are associated with worse prognosis following ST-elevation myocardial infarction (STEMI). However, evidence is lacking on the determinants of exercise capacity following STEMI. We sought to determine the impact of systolic and diastolic dysfunction on exercise capacity and outcomes following first-ever STEMI. Methods: In a retrospective analysis of 139 consecutive STEMI patients who had a transthoracic echocardiogram following STEMI and completed exercise treadmill testing, the primary outcome was to identify clinical and echocardiographic determinants of exercise capacity, and the secondary outcome was to identify determinants of major adverse cardiac events (MACEs). Results: Mean number of metabolic equivalents (METs > 8) was used as a cut-off. Age, female sex, anterior infarction, abnormal diastolic function, minimum left atrial indexed volume (LAVImin) ≥ 18 mL/m2, average e’, and E/e’ were associated with METs ≤ 8, but not left ventricular ejection fraction (LVEF). On multivariate analysis, LAVImin (OR 4.3, 95%CI 1.3–14.2; p = 0.017), anterior infarction (OR 2.6, 95%CI 1.2–5.9; p = 0.022), and abnormal diastolic function (OR 3.73, 95%CI 1.7–8.4; p = 0.001) were independent predictors of METs ≤ 8. On Kaplan–Meier analysis, METs ≤ 8 (p = 0.01) and abnormal diastolic function (p = 0.04) were associated with MACEs (median follow-up 2.3 years). METs ≤ 8 was an independent predictor of MACEs (HR 3.4, 95%CI 1.2–9.8; p = 0.02). Conclusions: Following first-ever STEMI, increased LAVImin, anterior infarction, and abnormal diastolic function were independent predictors of reduced exercise capacity. Furthermore, reduced exercise capacity was an independent predictor of MACEs. These results highlight important prognostic and therapeutic implications related to abnormal diastolic function in STEMI patients that are distinct from those with LV systolic impairment.

Comparison of the metabolic syndrome risk factors in antipsychotic naïve and chronic schizophrenia patients

Aim of the studyThis study aimed to compare the prevalence of MetS and cardiovascular risk factors in antipsychotic naïve schizophrenia (AN-SZ) and chronic schizophrenia (C-SZ) patients. Also, the effects of lifestyle, physical activity and clinical characteristics of these patients on metabolic syndrome were explored.Subject or material and methodsIn this cross-sectional study, 150 patients, 16-65 aged years were included. All subjects were recruited from the Psychiatric clinic of a tertiary hospital, ---, ---. The severity of symptoms was assessed by the Positive and Negative Syndrome Scale. Physical activity and lifestyle were evaluated by the Baecke and Lifestyle questionnaires.ResultsFifty AN-SZ patients and 100 C-SZ patients participated. The rate of abdominal obesity was 29.2% for females and 10.3% for males. The C-SZ patients had significantly fewer healthy habitual physical activity and lifestyle, compared with AN-SZ patients. The prevalence of MetS in the AN-SZ and C-SZ groups was 8% and 23%, respectively (odds ratio [OR] 3.13). Binary logistic regression revealed age and unhealthy lifestyle to be significant predictors of MetS (adjusted OR 1.09 and 0.65, respectively).DiscussionWe found with increasing each 10 years, the odds of MetS to increase 2.37 times. There was a significant negative association between a healthy lifestyle of SZ patients and MetS. The results showed A decrease in the score of the Lifestyle questionnaire by each one-point to increase the odds of MetS by 45%.ConclusionsFuture studies are recommended to explore the importance of weight management and nutrition control for reducing the rate of MetS.

Individual and joint effects of metal exposure on metabolic syndrome among Chinese adults

Growing evidence suggests that metal exposure contributes to metabolic syndrome (MetS), but little is known about the effects of combined exposure to metal mixtures. This cross-sectional study included 3748 adults who were recruited from the Medical Physical Examination Center of Tongji Hospital, Wuhan, China. The levels of 21 metal(loid)s in urine were measured by inductively coupled plasma mass spectrometry. MetS was diagnosed according to National Cholesterol Education Program's Adult Treatment Panel III recommendations. Multivariate logistic regression model was uesd to explore the effects of single-metal and multi-metal exposures. The elastic net (ENET) regularization with an environmental risk score (ERS) was performed to estimate the joint effects of exposure to metal mixtures. A total of 636 participants (17%) were diagnosed with MetS. In single metal models, MetS was positively associated with zinc (Zn) and negatively associated with nickel (Ni). In multiple metal models, the associations remained significant after adjusting for the other metals. In the joint association analysis, the ENET models selected Zn as the strongest predictor of MetS. Compared to the lowest quartile, the highest quartile of ERS was associated with an elevated risk of MetS (OR = 3.72; 95% CI: 2.77, 5.91; P-trend < 0.001). Overall, we identified that the combined effect of multiple metals was related to an increased MetS risk, with Zn being the major contributor. These findings need further validation in prospective studies.

Optimal waist circumference cut-off points and prediabetic state as predictors of metabolic syndrome prevalence among Saudi adults in Jeddah city

With increasing prevalence of metabolic syndrome and absence of local waist circumference cut-off point, it is important to determine the local cut-off point of waist circumference to predict metabolic syndrome. The aim of this study is to estimate the prevalence of metabolic syndrome among Saudi adults who attend primary health care centers (PHC) in Jeddah city in 2017, and to determine the appropriate waist circumference cut-off value for identifying a person at risk for the metabolic syndrome. A cross-sectional analytic study was conducted. Results shows; mean age of participants was 30.94±9.70 and waist circumference was 96.87±17.40 in males and 86.51±15.30 in females. The prevalence (CI: 95%) of pre-diabetes was 16.3% (12.9%; 20.1%), while the prevalence of metabolic syndrome was {(95% CI = 10.6%) (7.80%; 13.8%)}. Abdominal obesity was the most frequent component of MetS, detected in 60.6%, and followed by high blood pressure in 25.0%. In males, a waist circumference≥93.5 cm is likely to indicate MetS with 100% sensitivity and 47.2% specificity; whereas, in females, optimal waist circumference cut-off was determined as ≥83.5 cm, showing 92.3% sensitivity and 46.4% specificity. In multivariate logistic regression, extended waist circumference was the strongest predictor of MetS {OR (95%CI) =3.75 (1.30; 10.81); p=0.014}, followed by the presence pre-diabetes {OR (95%CI) =2.31 (1.06; 5.04); p=0.035}. Further, high educational level was a significant predictor for MetS {OR (95%CI) = 0.34 (0.12; 0.99); p=0.047}; while age and marital status were not significant predictors.

Positive correlation of serum angiopoietin-like protein 3 levels with metabolic syndrome in patients with coronary artery disease.

Objectives:Angiopoietin-like protein 3 (ANGPTL3) regulates triglyceride metabolism by reversibly inhibiting the lipoprotein lipase activity. Metabolic syndrome (MetS) is an independent risk factor for further cardiovascular disease. This study evaluated the relationship between the fasting serum ANGPTL3 levels and MetS in patients with coronary artery disease (CAD).Materials and Methods:Fasting blood samples were obtained from 90 patients with CAD. Serum ANGPTL3 levels were measured using a commercial enzyme-linked immunosorbent assay kit. MetS and its components were defined using the diagnostic criteria of the International Diabetes Federation.Results:Fifty-three patients (58.9%) had MetS. The hypertension (P = 0.001), diabetes (P < 0.001), body weight (P = 0.027), body mass index (P = 0.001), waist circumference (P < 0.001), systolic blood pressure (P = 0.001), fasting glucose (P < 0.001), triglycerides (P < 0.001), blood urea nitrogen (P = 0.044), C-reactive protein (P = 0.010), insulin (P = 0.040), homeostasis model assessment of insulin resistance (P = 0.002), and ANGPTL3 level (P = 0.001) of CAD patients who had MetS were higher, and the high-density lipoprotein cholesterol (P = 0.001) and estimated glomerular filtration rate (P = 0.016) were lower. A binary logistic regression analysis of the significant variables also revealed that the ANGPTL3 level (odds ratio: 1.023, 95% confidence interval: 1.008–1.038, P = 0.002) was an independent predictor of MetS in patients with CAD.Conclusion:The results of our study indicated that the fasting ANGPTL3 level was positively associated with MetS among patients with CAD.