Predictors Of Metabolic Syndrome Research Articles

Dapagliflozin added to metformin reduces perirenal fat layer in type 2 diabetic patients with obesity

Sodium-glucose co-transporters type 2 inhibitors (SLGT2i) are highly effective in controlling type 2 diabetes, but reported beneficial cardiovascular effects suggest broader actions on insulin resistance. Weight loss may be initially explained by glycosuria-induced net caloric output and secondary volumetric reduction, but its maintenance could be due to loss of visceral fat mass. Structured ultrasound (US) imaging of abdominal adipose tissue (“eco-obesity”) is a recently described methodology used to measure 5 consecutive layers of abdominal fat, not assessable by DEXA or CT scan: superficial subcutaneous (SS), deep subcutaneous (DS), preperitoneal (PP), omental (Om) and right perirenal (RK). PP, Om and RK are predictors of metabolic syndrome (MS) with defined cut-off points. To assess the effect of SLGT2i on every fat depot we enrolled 29 patients with type 2 Diabetes (HbA1c 6.5–9%) and Obesity (IMC > 30 kg/m2) in an open-label, randomized, phase IV trial (EudraCT: 2019-000979-16): the Omendapa trial. Diabetes was diagnosed < 12 months before randomization and all patients were treatment naïve. 14 patients were treated with metformin alone (cohort A) and 15 were treated with metformin + dapaglifozin (cohort B). Anthropometric measures and laboratory tests for glucose, lipid profile, insulin, HOMA, leptin, ultrasensitive-CRP and microalbuminuria (MAL) were done at baseline, 3rd and 6th months. At 6th month, weight loss was −5.5 ± 5.2 kg (5.7% from initial weight) in cohort A and −8.4 ± 4.4 kg (8.6%) in cohort B. Abdominal circumference showed a −2.7 ± 3.1 cm and −5.4 ± 2.5 cm reduction, respectively (p = 0.011). Both Metformin alone (−19.4 ± 20.1 mm; −21.7%) or combined with Dapaglifozin (−20.5 ± 19.4 mm; −21.8%) induced significant Om fat reduction. 13.3% of cohort A patients and 21.4% of cohort’s B reached Om thickness below the cut-off for MS criteria. RK fat loss was significantly greater in cohort B group compared to cohort A, at both kidneys. Only in the Met + Dapa group, we observed correlations between Om fat with leptin/CRP/MAL and RK fat with HOMA-IR. US is a useful clinical tool to assess ectopic fat depots. Both Metformin and Dapaglifozin induce fat loss in layers involved with MS but combined treatment is particularly effective in perirenal fat layer reduction. Perirenal fat should be considered as a potential target for cardiovascular dapaglifozin beneficial effects.

Resting metabolic rate in children with obesity: a predictor of metabolic syndrome

Resting metabolic rate in children with obesity: a predictor of metabolic syndrome

Baseline urinary osteopontin levels are associated with the improvement of metabolic syndrome

Background and aimsWhile serum osteopontin (OPN)'s established role in cardiometabolic risk is recognized, its potential as a predictor of metabolic syndrome (MetS) improvement through a urine assay has not yet been demonstrated. In this study, we propose its potential predictive role over a 12-month period of standard care, with the ability to complement anthropometric measures. Methods and resultsHierarchical clustering revealed a notable association of urinary OPN (uOPN) with MetS criteria and overcame anthropometric measures in predicting the improvement at 12 months (OR of 2.74 [95% CI 1.32 to 6.29]). uOPN significantly contributed to the homogeneity of the nodes in the random forest and ultimately enhanced the performance of anthropometric measures when assessed for accuracy and area under the curve (AUC). ConclusionOur findings offer insights into potential applications in cardiometabolic medicine for uOPN, which is easily detectable in non-invasive biological samples through an affordable assay.

Machine learning analysis for the association between breast feeding and metabolic syndrome in women

This cross-sectional study aimed to develop and validate population-based machine learning models for examining the association between breastfeeding and metabolic syndrome in women. The artificial neural network, the decision tree, logistic regression, the Naïve Bayes, the random forest and the support vector machine were developed and validated to predict metabolic syndrome in women. Data came from 30,204 women, who aged 20 years or more and participated in the Korean National Health and Nutrition Examination Surveys 2010–2019. The dependent variable was metabolic syndrome. The 86 independent variables included demographic/socioeconomic determinants, cardiovascular disease, breastfeeding duration and other medical/obstetric information. The random forest had the best performance in terms of the area under the receiver-operating-characteristic curve, e.g., 90.7%. According to random forest variable importance, the top predictors of metabolic syndrome included body mass index (0.1032), medication for hypertension (0.0552), hypertension (0.0499), cardiovascular disease (0.0453), age (0.0437) and breastfeeding duration (0.0191). Breastfeeding duration is a major predictor of metabolic syndrome for women together with body mass index, diagnosis and medication for hypertension, cardiovascular disease and age.

Sagittal Abdominal Diameter as the Best Predictor of Metabolic Syndrome in HIV-Infected Men on Antiretroviral Therapy.

The interaction of human immunodeficiency virus (HIV), host and antiretroviral therapy causes a range of metabolic disorders that can be characterized as a metabolic syndrome (MetS) that increases the cardiovascular risk. MetS involves central obesity, which can be detected using different anthropometric parameters. To assess the abilities of different anthropometric parameters in the prediction of MetS in HIV-infected men on ART. The study involved 92 male participants (mean age 44.46±10.38 years), divided into two groups: with and without MetS. All subjects underwent biochemical evaluation (triglycerides, HDL-cholesterol, fasting glucose), blood pressure measurement and anthropometric assessment: body mass, body height, body mass index (BMI), body fat mass, body circumferences (chest, upper arm, forearm, waist, hip, proximal and middle thigh and calf), sagittal abdominal diameter (SAD), skinfold thicknesses (subscapular, anterior and posterior upper arm, anterior and lateral forearm, abdominal, supraspinal, thigh and calf), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), waist-to-thigh ratio (WTR), sagittal abdominal diameter-to-body height ratio (SADH), body adiposity index (BAI) and conicity index. MetS was specified according to IDF criteria. Subjects with MetS had statistically significant higher values of all anthropometric parameters except middle thigh circumference, calf skinfold and body height. According to ROC analysis and Binary Logistic Regression, SAD has been shown as the best predictor of MetS with a predictive value of 21.40 cm (AUC:0.91), followed by WHR with a predictive value of 0.93. Sagittal abdominal diameter is the strongest anthropometric indicator of MetS in HIV-infected patients on ART.

A Novel Clinical Predictor of Metabolic Syndrome: Vascular Risk Age

A Novel Clinical Predictor of Metabolic Syndrome: Vascular Risk Age

Dietary Patterns and Metabolic Syndrome Risk in Adults Living with HIV: A Cross-Sectional Study in Lusaka District, Zambia

Background: Metabolic syndrome, a cluster of cardiovascular risk factors, affects approximately 25% of the global population, with a significant impact on the 37,900,000 people living with HIV in 2017. Objective: This cross-sectional study (April-July 2020) in Lusaka District aimed to assess dietary patterns and metabolic syndrome risk in 180 randomly sampled HIV-positive individuals (aged 18 and above) from three health facilities. Methods: Participants underwent anthropometric measurements, blood pressure checks, and biochemical assessments. High prevalence rates were observed for factors like high waist-hip ratio (39%), high total cholesterol (52%), high triglycerides (46%), and high blood pressure (33%). Results: Three dietary clusters (omnivorous, vegetarian, unclassified) showed no significant association with metabolic syndrome or its components (p &gt; 0.05). Conclusion: While metabolic syndrome prevalence among HIV-positive individuals is low, there are notable occurrences of high blood pressure and predictors of non-communicable diseases. Commonly consumed foods did not show a significant association with metabolic syndrome predictors (p &gt; 0.05).

Clinical features and predictors of metabolic syndrome among obstructive sleep apnea patients

BackgroundObstructive sleep apnea (OSA) and metabolic syndrome (MetS) are two diseases associated with an increased risk of cardio-metabolic complications and both share the same risk factors such as obesity and smoking.ObjectivesTo study the clinical features and predictors of MetS in OSA patients.MethodologyA cross-sectional study was conducted in the sleep lab of the Pulmonology Department at Kasr Al-Ainy Hospital, Cairo University, during the period from September 2022 to March 2023. Eighty OSA patients were included, and each patient was subjected to history taking, body measurements, blood pressure measurements, Epworth sleepiness scale, STOP-BANG score, polysomnography, and laboratory investigations including lipid profile and (HBA1C), MetS was defined based on the International Diabetes Federation (IDF) guidelines. Then patients were classified into two groups; OSA with MetS and OSA without MetS, we compared both groups using a one-way (ANOVA) test.ResultsMetS was diagnosed in 66.3% of the study group. OSA with MetS had higher BMI (44.76 ± 9.55) with significant p values (< 0.001), lower average O2 saturation % (90.23 ± 5.5, p value = 0.013), and longer SPO2 time < 90% (37.39 ± 34.14, p value = 0.034) than the other group. BMI, waist, hip, and neck circumferences and SPO2 < 90% were predictors of MetS among OSA patients with significant p values. Females had significantly higher BMI (46.64 ± 9.58), p value = 0.015. Males had significantly higher AHI than females (32.99 ± 24.02 versus 19.83 ± 14.74 respectively), p value = 0.031.ConclusionMetS was diagnosed in 66.3% of OSA. BMI, neck, hip, waist circumferences, and SPO2 < 90% were predictors of MetS among OSA.Trial registrationRetrospectively registered, date of registration is 18/09/2023, and number of registration is NCT06051097. The link to the study on clinicaltrials.gov.

Serum Gamma Glutamyl Transferase: Understanding its Contribution as a Potential Predictor of the Occurrence of Type 2 Diabetes.

The liver and kidneys are the primary locations of the glutathione metabolism enzyme gamma-glutamyl transferase (GGT). The two main factors contributing to an increase are hepatic illnesses and excessive alcohol use. This study set out to test a theory on the predictive importance of the association between GGT and Type 2 diabetes mellitus. (T2DM). In order to do this, we combed through PubMed, Google Scholar, Medline, and Science Direct for a wide range of information from previous studies. Attributes were established at the outset and compared to GGT concentration. GGT, present in most cells, absorbs glutathione for intracellular antioxidant defences. This study links GGT to hepatic enzymes including HDL, LDL, and triglyceride. LDL, triglycerides, AST, and ALT increased with GGT concentration, but LDL decreased. Because of obesity, GGT production rises with BMI. We found that greater GGT levels were associated with more T2DM after analysing data from multiple sources. This literature review concludes that GGT is related to other factors such as BMI, HDL, AST, and triglycerides in the development of diabetes mellitus. Serum GGT was found to be a potential predictor of metabolic syndrome and T2DM.

Lipid accumulation product is an effective predictor of metabolic syndrome in non-obese women with polycystic ovary syndrome.

To explore the correlation of lipid accumulation product (LAP) with metabolic syndrome (MS) and to assess the predictive value of LAP for MS risk in polycystic ovary syndrome (PCOS) with different body mass index (BMI). A total of 242 PCOS patients and 150 controls were recruited and divided into normal-weight, overweight, and obese groups, then further divided into MS and without MS subgroups. Clinical and anthropometric variables and laboratory results were recorded. LAP was calculated from waist circumference (WC) and triglyceride using sex-specific formulae. Logistic regression analysis and receiver operating characteristic (ROC) curve were applied to determine and analyze the predictive value of LAP for MS. The prevalence of MS among PCOS patients was 45.04%, which was significantly higher than that of the controls (10%). Stratified by BMI, the incidence of MS in the normal-weight, overweight, and obese PCOS groups were 15.58%, 41.43%, and 71.58%, respectively. Logistic regression analysis indicated that LAP was an independent risk factor for MS in both normal-weight and overweight groups; however, the results were not significant in the obese group. ROC curve analysis showed that LAP had an outstanding discrimination index for MS in normal-weight (AUC=0.960, cut-off value=42.5) and overweight (AUC=0.937, cut-off value=47.93) PCOS patients, with a sensitivity of 0.917/0.931 (normal-weight/overweight) and a specificity of 0.969/0.854 (normal-weight/overweight), respectively. Normal-weight and overweight PCOS patients also have a fairly high incidence of MS and should receive as much attention as obese patients. Compared to applying multiple clinical indicators, LAP is more convenient and facilitates acquiring early and accurate diagnoses of MS among non-obese PCOS patients using fewer MS markers.

Complete blood count inflammation derived indexes as predictors of metabolic syndrome in type 2 diabetes mellitus.

Metabolic syndrome (MetS) is a group of comorbidities related to regulating hyperglycemia and acute cardiovascular incidents and complications. With the increasing prevalence in individuals with type 2 diabetes mellitus (T2DM), MetS represents an increasing public health problem and clinical challenge, and early diagnosis is necessary to avoid the accelerated development of diabetic complications. To investigate the role of Complete Blood Count-derived Inflammation Indexes (CBCIIs) in predicting MetS in T2DM individuals. The study was designed as a two-year prospective study and included 80 T2DM individuals divided into MetS and non-MetS groups based on MetS development over two years. The sera samples were analyzed for complete blood count parameters and C-reactive protein (CRP). Based on the laboratory test results, 13 CBCIIs were calculated and analyzed. The receiver operating characteristic (ROC) curve and their corresponding areas under the curve (AUC) were used to determine prognostic accuracy. There were significant differences between T2DM participants with Mets and those without MetS concerning Neutrophil to Platelet Ratio (NPR) values (p< 0.001), Neutrophil to Lymphocyte and Platelet Ratio (NLPR) (p< 0.001), Platelet to Lymphocyte Ratio (PLR) (p< 0.001), Lymphocyte to C-reactive protein Ratio (LCR) (p< 0.001), C-reactive protein to Lymphocyte Ratio (CRP/Ly) (p< 0.001), Systemic immune inflammation index (SII) (< 0.001), and Aggregate Index of Systemic Inflammation (AISI) (p= 0.005). The results of ROC curve analysis have shown that the LCR (AUC of 0.907), CRP/Ly (AUC of 0.907) can serve as excellent predictors, but NPR (AUC of 0.734), NLRP (AUC of 0.755), PLR (AUC of 0.823), SII (AUC of 0.745), and AISI (AUC of 0.688) as good predictors of MetS in T2 DM individuals. This study confirms the reliability of the CBCIIs as novel, simple, low cost and valuable predictors of MetS developing in T2DM.

MODERN VIEWS ON THE PATHOGENESIS OF IMMUNE DYSFUNCTION AGAINST THE BACKGROUND OF THE METABOLIC SYNDROME IN ISCHEMIC HEART DISEASE

Introduction. Today, the pathology of the cardiovascular system is one of the most common and fatal diseases. Cardiovascular diseases are the cause of disability among the younger and younger population. Taking into account the frequency of cardiovascular diseases, the severity of the course and their lethality, the study of this topic remains one of the most urgent problems of medicine, in particular cardiology.&#x0D; The aim of the study. Consider modern views on the pathogenesis of coronary heart disease against the background of metabolic syndrome and the role of the immune system.&#x0D; Conclusions. Ischemic heart disease is the leading cause of mortality in Ukraine and the world. In recent years, there has been convincing evidence of a significant prevalence of cardiovascular disease in patients with metabolic syndrome. The presence of concomitant metabolic syndrome in patients with coronary heart disease worsens the course of the underlying disease and has an unfavorable prognosis, and even fatal cases.&#x0D; Therefore, the detection of an increase in the level of body mass index, dyslipidemia, hyperglycemia, arterial hypertension in a patient strengthens the effects of each other, that is, they have a synergistic effect, and in general, the risk of developing CHD becomes quite high.&#x0D; IL-6 is one of the cytokines released by both macrophages and adipocytes and its levels have been shown to be increased in insulin resistance and obesity. In fact, IL-6 is known to regulate fat and glucose metabolism, mediating insulin resistance through various complex mechanisms. This cytokine acts on various tissues, leading to the metabolic effects of obesity. In the liver, IL-6 increases the production of acute phase reactants, including CRP. Several studies have demonstrated that high CRP levels have the strongest correlation with cardiac events, T2DM, and MS. IL-6 also contributes to a prothrombotic state by increasing the level of fibrinogen, another acute phase reactant. In addition, IL-6 targets other tissues, such as endothelial cells, to promote the expression of vascular cell adhesion molecules, leading to vascular wall atherosclerosis, inflammation, and dysfunction.&#x0D; These data support the role of IL-6 in the development of insulin resistance, but do not support the hypothesis that IL-6 is involved in β-cell failure.&#x0D; IL-18 is a pro-inflammatory cytokine associated with insulin resistance and T2DM risk. IL-18 stimulates the production of gamma interferon (IFN-γ), which, in turn, is probably involved in the pathogenesis of atherosclerosis. IL-18 is a cytokine that is a predictor of metabolic syndrome.&#x0D; TNFα is another cytokine produced in adipose tissue, mainly from local macrophages, and its production also varies with adipose tissue mass and correlates with insulin resistance, both hallmarks of MS. TNFα exerts its pathogenic effects by disrupting insulin signaling in adipocytes and hepatocytes through serine phosphorylation and inactivation of insulin receptors and downstream signaling molecules, leading to decreased metabolic effects of insulin. TNFα also contributes to insulin resistance by inducing hepatic lipolysis.

Predictors of metabolic syndrome and its components in patients with type 2 diabetes: A cross-sectional study in the Ho municipality, Ghana

BackgroundMetabolic syndrome refers to a group of risk factors that individually or synergistically increase the risk of cardiovascular disease. The predictors of metabolic syndrome are thought to vary between populations and ethnicities. Hence, this study aimed to investigate the factors predicting metabolic syndrome and its components among patients with type 2 diabetes in the Ho municipality. MethodsA hospital-based cross-sectional study was designed to include 197 patients with type 2 diabetes at the Ho Municipal Hospital. A semi-structured questionnaire was used to obtain data on demography and lifestyle variables. Anthropometric, haemodynamic, and biochemical parameters were measured. Metabolic syndrome was defined according to the harmonised criteria. Renal function was evaluated based on the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI)] and the evidence of proteinuria. Chi-square test and logistic regression analyses were performed to determine the predictors of metabolic syndrome and its components. ResultsOverall, the prevalence of metabolic syndrome was 59.90 %. Females had higher odds of metabolic syndrome [OR: 2.64, 95 % CI: 1.48–4.71; p = 0.001], abdominal obesity [OR: 11.49, 95 % CI: 5.18–25.49, p < 0.001], and low HDL-C levels [OR: 17.67, 95 % CI: 4.73–65.99, p < 0.001]. Having a family history of diabetes increased the risk of metabolic syndrome [OR: 1.94, 95 % CI: 1.09–3.54, p = 0.023] and abdominal obesity [OR: 2.60, 95 % CI: 1.25–5.42, p = 0.011]. Having diabetes for 5 years or more was associated with uncontrolled FBG [OR: 4.75, 95 % CI: 1.25–17.98, p = 0.022] and low HDL-C levels [OR: 4.52, 95 % CI: 1.64–12.45, p = 0.004]. Attainment of secondary education increased the risk of abdominal obesity [OR: 5.73, 95 % CI: 1.46 – 22.49, p = 0.012], while the attainment of tertiary education increased the risk of low HDL-C [OR: 4.20, 95 % CI: 1.09 – 16.24, p = 0.038]. Physical inactivity was associated with metabolic syndrome [OR: 2.19, 95 % CI: 1.12 – 4.28, p = 0.022]. Being older than 50 years had a two-fold increased risk of elevated BP [OR: 2.53, 95 % CI: 1.36–4.69, p = 0.003]. However, females were about 56 % unlikely to be associated with elevated BP [OR: 0.44, 95 % CI: 0.23–0.84, p = 0.013]. ConclusionThe female gender, family history of diabetes, and longer duration of diabetes as well as attainment of secondary and tertiary level education and physical inactivity were significant predictors of metabolic syndrome and its components in patients with type 2 diabetes.

Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease.

Metabolic syndrome is experiencing a concerning and escalating rise in prevalence today. The link between metabolic syndrome and periodontal disease is a highly relevant area of research. Some studies have suggested a bidirectional relationship between metabolic syndrome and periodontal disease, where one condition may exacerbate the other. Furthermore, the existence of periodontal disease among these individuals significantly impacts overall health management. This research focuses on the relationship between periodontal disease and metabolic syndrome, while also incorporating data on general health status and overall well-being. We aimed to develop advanced machine learning models that efficiently identify key predictors of metabolic syndrome, a significant emphasis being placed on thoroughly explaining the predictions generated by the models. We studied a group of 296 patients, hospitalized in SCJU Sibiu, aged between 45-79 years, of which 57% had metabolic syndrome. The patients underwent dental consultations and subsequently responded to a dedicated questionnaire, along with a standard EuroQol 5-Dimensions 5-Levels (EQ-5D-5L) questionnaire. The following data were recorded: DMFT (Decayed, Missing due to caries, and Filled Teeth), CPI (Community Periodontal Index), periodontal pockets depth, loss of epithelial insertion, bleeding after probing, frequency of tooth brushing, regular dental control, cardiovascular risk, carotid atherosclerosis, and EQ-5D-5L score. We used Automated Machine Learning (AutoML) frameworks to build predictive models in order to determine which of these risk factors exhibits the most robust association with metabolic syndrome. To gain confidence in the results provided by the machine learning models provided by the AutoML pipelines, we used SHapley Additive exPlanations (SHAP) values for the interpretability of these models, from a global and local perspective. The obtained results confirm that the severity of periodontal disease, high cardiovascular risk, and low EQ-5D-5L score have the greatest impact in the occurrence of metabolic syndrome.

Relationship between Abdominal Volume Index and Body Adiposity Index and Scales of Insulin Resistance and Metabolic Syndrome.

A descriptive, cross-sectional study was carried out on 193,462 workers from different Spanish regions and work groups between January 2019 and September 2021. Abdominal volume index (AVI) and body adiposity index (BAI) were evaluated to assess obesity and its association with insulin resistance using three risk scales (TyG index, Triglycerides/HDL, and METS-IR), while their association with metabolic syndrome was determined using the NCEP ATP III, IDF, and JIS models. The results of the ROC curves to determine the predictive value of BAI and AVI in relation to the three criteria evaluated to calculate MetS in all instances presented a higher area under the curve (AUC) for AVI. The high values of AVI stand out for predicting MetS when applying the IDF criteria. The cut-off point in women was 13.70 with a Youden index of 0.802, whereas in men, the cut-off point was set at 17.59 with a Youden index of 0.672. Regarding the relationship of BAI and AVI with insulin resistance risk scales for both sexes, the AUC only revealed high values when using the METS-IR formula for both AVI and BAI. The AVI cut-off points to predict high values of insulin resistance risk scales in women were established at 13.12 with a Youden index of 0.722. In men, the cut-off point was 17.59, with a Youden index of 0.626. The BAI cut-off points in women were set at 33.88 with a Youden index of 0.748. In men, the cut-off point was 27.91, with a Youden index of 0.598. AVI demonstrated its value as a predictor of metabolic syndrome while exclusively applying the IDF criteria. AVI and BAI demonstrated their value as predictors of high values of insulin resistance risk scales only in the case of METS-IR. This predictive value is also higher in women.

Age-related determinants of the metabolic syndrome in women of reproductive age of the main ethnic groups of the Baikal Region

The aim of the study. To determine the age limit of the initial manifestations of the metabolic syndrome in women of reproductive age of the Caucasian and Asian ethnic groups. Materials and methods. The study included women of reproductive age of the Russian and Buryat ethnic groups (n = 1231). We carried out general clinical examination, studied glycemia levels and lipid metabolism indicators. The analysis of the components of the metabolic syndrome was performed taking into account age characteristics and ethnicity. Threshold values for the age of metabolic disorders manifestation were determined. Results. It was found that the main components of the metabolic syndrome in women of reproductive age of the Russian and Buryat ethnic groups were a decrease in  high-density lipoprotein (HDL), an increase in waist circumference, and an increase in blood pressure. The age-related determinants of the metabolic syndrome in the  population of women of reproductive age in the Baikal region were determined. It was shown that the age-related determinants of the metabolic syndrome in Caucasian and Asian women do not differ significantly and amount to 33.5 years (95% confidence interval (95% CI): 32.5; 38.5) and 36.5 years (95% CI: 27.5; 52.5), respectively. A universal critical age value was determined as 34.5 years (95% CI: 32.5; 38.5), which is associated with a significant increase in the prevalence of the metabolic syndrome in accordance with the ATP  III criteria. The ranking of metabolic syndrome criteria in the studied groups revealed the following order of their occurrence: the decrease in HDL index prevailed, followed by the increase in waist circumference, blood pressure, glucose and triglycerides; no dependence on the ethnicity was found. Conclusion. When assessing the age-related determinants of the metabolic syndrome in the mixed Caucasoid-Asian group, it is advisable to use universal approaches, taking into account the cut-off point of age, which is 34.5 years. The determination of age criteria for the correct classification of patients with respect to the manifestations of the metabolic syndrome is necessary to analyze the contribution of the age factor to the implementation of various metabolic syndrome predictors, to  develop prognostic models, and to  use effective age-determined markers of metabolic disorders.

Visceral Adiposity Index: An Effective Tool for Predicting Metabolic Syndrome in Bangladeshi Adult Population

Background: Obesity induces adipocyte dysfunction with secretion of adipokines, which results in a cascade of chemical reaction which triggers the atherogenic process and insulin resistance leading to a set of metabolic abnormalities i.e., metabolic syndrome. The Visceral Adiposity Index (VAI) is a simple, gender-based obesity index and has been proposed to be a predictor of metabolic syndrome. Objective: Our study aims to evaluate the effectiveness of the Visceral Adiposity Index (VAI) in prediction metabolic syndrome in Bangladeshi adult population. Methods: This cross-sectional study was carried out in Department of Biochemistry and Molecular Biology, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh, from January to June of 2017. A total of 200 apparently healthy subjects (108 men and 92 women) were selected for the study, who attended the out-patient-departments of the same institution. Anthropometric measurements were recorded, e.g., height, weight, waist circumference (WC). Body mass index (BMI) was calculated. Overnight fasting blood samples were collected to estimate plasma glucose and serum lipid profile. Then VAI was calculated for men and women separately and evaluated as a tool in diagnosis of metabolic syndrome among the participants. Receiver operating characteristic (ROC) curves were plotted to assess the performance of VAI in metabolic syndrome prediction by gender. The power of metabolic syndrome prediction was quantified by the area under the curve (AUC) with 95% confidence intervals. Results: The mean age of the study participants was 42.4±5.2 years. Among 108 men, 63 had metabolic syndrome, while among 92 women, 31 had metabolic syndrome. Sensitivity and specificity of VAI in predicting metabolic syndrome in male study subjects were 83.2% and 70.3%, respectively, while in female study subjects 80.1% and 70.5% respectively. Receiver operating characteristic (ROC) curve analysis showed that the optimal cutoff value of VAI in male study subjects was 2.16 and area under the curve (AUC) value was 0.907, while in female study subjects, the values were 2.25 and 0.918 respectively (P&lt;0.001). Conclusion: The Visceral Adiposity Index (VAI) was found simple, accessible, and effective obesity index to predict metabolic syndrome in apparently healthy adults. Mugda Med Coll J. 2022; 5(2): 88-92

The Relationship between Insulin Resistance and Thyroid Volume in Georgia.

413 patients with a mean age of 37.3 ± and 11.4 years were included in this study. Out of those, 120 were males, and 293 were females who were studied retrospectively. They had hyperinsulinemia and were referred to the clinic from 2017 to 2019. The factors studied were age, sex, body mass index (BMI), clinical signs, thyroid ultrasound, alanine aminotransferase (ALT), aspartate aminotransferase (AST), lipids, fasting insulin, fasting glucose, thyroid stimulating hormone (TSH), Free thyroxine (FT4), and Zinc (Zn). IR was detected in 252 individuals. The frequency of men with insulin resistance was significantly higher than in the control group - 72.50%, and 56.31%, respectively (F = 9.55, p= 0.0021). Mean thyroid volume in the patients with IR was significantly higher compared to the controls 20.52 + 6.39 cm3 and 15.25 + 6.55 cm3, respectively (p < 0.001). Hyperinsulinemia had a significant positive correlation with Goiter r = 0.445, p < 0.0001. The associated factors for hyperinsulinemia are: Goiter (1) - OR = 5.12 (95% CI:3.02-8.69); Cholesterol - OR = OR = 3.31 (95% CI: 1.54-7.14); Triglycerides - OR = 3.23 (95% CI:1.02-10.28); Obesity (1)- OR = 3.94 (95% CI: 2.23-6.98); Thyroid structural changes (1) - OR = 2.01 (95% CI: 1.12-3.60); ALT/AST-OR = 4.53 (95% CI: 2.33-8.80); Zn decreased Odds Ratio hyperinsulinemia - OR = 0.95 (95% CI: 0.94-0.97). Hyperinsulinemia is the most common cause of diffuse goiter and the heterogeneous structure of the thyroid. The volume of the thyroid gland shows a significant positive association with the characteristics of metabolic syndrome and increased thyroid volume predictors of metabolic syndrome.

Clinical utility of lipid ratios as potential predictors of metabolic syndrome among the elderly population: Birjand Longitudinal Aging Study (BLAS)

BackgroundElderly adults are at higher risk of developing metabolic syndrome (MetS). The present study aims to investigate the relationship between lipid ratios and MetS in the elderly population.MethodsThis study was conducted on elderly population of Birjand during 2018–2019. The data of this study was driven from Birjand Longitudinal Aging Study (BLAS). The participants were selected based on multistage stratified cluster sampling. Patients were categorized into quartiles according to the lipid ratios (TG/HDL-C, LDL-C/HDL-C, non-HDL/HDL-C), and the relationship between lipid ratio quartiles and MetS was determined by Logistic Regression using Odds Ratio. Finally, the optimal cut-off for each lipid ratio in MetS diagnosis was calculated according to the Area Under the Curve (AUC).ResultsThis study included 1356 individuals, of whom 655 were men and 701 were women. In our study, the crude prevalence of MetS was 792 (58%), including 543 (77.5%) women and 249 (38%) men. Increasing trends were observed in quartiles of all lipid ratios for TC, LDL-C, TG, and DBP. TG/HDL was also the best lipid ratio to diagnose the MetS, based on NCEP ATP III criteria. One unit increased in level of TG/HDL resulted in 3.94 (OR: 3.94; 95%CI: 2.48–6.6) and 11.56 (OR: 11.56; 95%CI: 6.93–19.29) increasing risk of having MetS in quartile 3 and 4 compared to quartile 1, respectively. In men and women, the cutoff for TG/HDL was 3.5 and 3.0, respectively.ConclusionsOur results showed that the TG/HDL-C is superior to the LDL-C/HDL-C and the non-HDL /HDL-C to predict MetS among the elderly adults.

Visceral Adiposity and Lipid Accumulation Product Indices as Valuable Predictors of Metabolic Syndrome in the Elderly: Bushehr Elderly Health (BEH) Program

Metabolic Syndrome (MetS) encompasses a cluster of cardiometabolic risk factors and plays a significant role in elderly patients long-term complications