Metabolic Syndrome Factors Research Articles

Physical activity and body composition risk for metabolic syndrome

Metabolic syndrome is one of the main risk factors for various diseases such as cardiovascular disease and diabetes. The effect of physical activity has been widely studied regarding its benefits in reducing the development of metabolic syndrome. However, there are still underlying shortcomings in the results of previous research. Objective: to assess the influence of physical activity and metabolic syndrome components on the risk of metabolic syndrome in workers. Cross-sectional research was conducted on employees of PT. PLN. Variables assessed include waist circumference, arm circumference, body mass index (BMI), blood pressure, blood glucose and total cholesterol. Physical activity assessment was carried out using the Global Physical Activity Questionnaire (GPAQ). A total of 40 employees were included in this study. A total of 18 respondents (45%) were at risk of metabolic syndrome. The majority of metabolic syndrome components in employees are overweight (50%), excessive arm and waist circumference (85% and 65%, respectively). The majority of respondents had moderate physical activity (57.5%) and high activity (27.5%). Excessive arm and waist circumference had a significant relationship with the risk of metabolic syndrome in respondents (p < 0.05), however, physical activity did not affect the risk of metabolic syndrome. It is hoped that further research in broader populations will be needed to assess risk factors for metabolic syndrome in workers.

Identifying the Risk Factors for Metabolic Syndrome in Bangladesh: Documented from a Nationwide Survey

Summary Metabolic syndrome is a complex of interrelated health conditions that pose a significant risk of developing cardiovascular disease, stroke, and type 2 diabetes. Resistance to insulin, genetic predisposition, high blood pressure, inflammation, and excess abdominal fat are the main stimuli of this syndrome. Metabolic syndrome is becoming more widespread due to fast and unplanned urbanization causing changes in lifestyle, such as poor dietary habits and sedentary behavior, that decrease the metabolic rate in the human body. A developing South Asian country like Bangladesh is most vulnerable to components of metabolic syndrome such as obesity, hypertension and diabetes. Consequently, it has become one of the major public health concerns. Prediction of disease status is a key component of community and health service policymaking. A nationally representative cross-sectional survey, the Bangladesh Demographic and Health Survey (BDHS), is used to find statistically significant variables for metabolic syndrome. BDHS datasets do not contain any direct data regarding metabolic syndrome. A binary variable is generated by utilizing the available data on blood pressure, blood glucose level, and body mass index (BMI). Overall, 34.33% of the population has metabolic syndrome. Primarily, bivariate analysis is performed using chi-square testing to find variables that are correlated with metabolic syndrome. Results of binary logistic analysis are presented in terms of coefficients and odds ratios (OR) with 95% confidence intervals (CI). Age, gender, education, division (province), occupation type, and wealth index are found to be important covariates for the syndrome. Age especially is seen as one of the most influential factors, since the prevalence of metabolic syndrome is only 12.17% for the age group younger than 18 years, while for the group older than 65 years it is 62.18%. Residents of Barishal have the highest rate of metabolic syndrome (38.58%). The rate in the country’s capital Dhaka is 34.48%. Individuals whose employment primarily involves manual labor are 11.1% less likely to suffer from metabolic syndrome than those doing non-manual work.

To compare the HOMA-IR and metabolic profile in lean and obese subjects with non-alcoholic fatty liver disease

Background and objectiveNon-alcoholic fatty liver disease (NAFLD) is primarily perceived as a condition prevalent among obese individuals. Its pathogenesis is closely intertwined with metabolic syndrome components. However, the association between insulin resistance and NAFLD in nonobese individuals remains ambiguous. Observational studies have scrutinized the prevalence of insulin resistance and metabolic syndrome in lean NAFLD patients.Materials and methodsThis is an observational study, and NAFLD screening was carried out among inpatient and outpatient attendees at SRM Medical College’s General Medicine Department. Out of 200 screened patients meeting inclusion and exclusion criteria, 80 were diagnosed with non-alcoholic fatty liver disease (NAFLD). The assessment of metabolic syndrome was performed using the NCEP-ATP III criteria, allowing for comparison between groups concerning insulin resistance and metabolic parameters.ResultsAmong NAFLD patients, those in the obese age group exhibited a higher prevalence of hypertension (57.8%; p < 0.001) and metabolic syndrome (75.6%; p < 0.0001). The lean NAFLD group showed elevated HOMA-IR levels (4.16) compared to obese NAFLD patients (2.92), with a significant statistical disparity (p < 0.0001). Additionally, the HSI value significantly increased in obese NAFLD patients (p < 0.00001).ConclusionInsulin resistance, a key factor in metabolic syndrome, is prevalent in lean individuals with non-alcoholic fatty liver disease (NAFLD), playing a pivotal role in its development. This resistance, linked to metabolic syndrome, promotes hepatic triglyceride and fatty acid accumulation, leading to NAFLD. Moreover, insulin resistance correlates significantly with weight gain in NAFLD patients.

Metabolic syndrome and pharmacotherapy outcomes in patients with type 2 diabetes mellitus

BackgroundMetabolic syndrome is a group of metabolic abnormalities that increase predisposition to several diseases including ischemic heart disease and diabetes mellitus. The study aimed to investigate metabolic syndrome among patients with type-2 diabetes mellitus (DM), and its impact on pharmacotherapy outcomes.MethodsAn observational cross-sectional study was performed on 910 patients with type-2 DM between June and December 2023. Fasting blood sugar, triglycerides, high-density lipoproteins (HDL), blood pressure, and abdominal obesity were measured. Metabolic syndrome was identified according to the National Cholesterol Education Program Adult Treatment Panel III criteria. Pharmacotherapy outcomes were assessed according to American Association of Clinical Endocrinologists and American Diabetes Association guidelines using the ability to achieve adequate glycemic control and normal levels of blood pressure and fasting plasma lipoproteins.ResultsIn total, 87.5% of type-2 DM patients had metabolic syndrome; the prevalence increased with age and was higher among females. Metabolic syndrome showed the following distribution of risk factors: insulin resistance (100%), low HDL (95.3%), elevated blood pressure (83%), triglycerides dyslipidemia (80.1%), and abdominal obesity (62.5%). Majority of the patients had either 5 or 4 risk factors of metabolic syndrome. The most common comorbidities were dyslipidemia (97.7%) and hypertension (83%). Treatment outcomes were insufficient where adequate glycemic control was only achieved in 12% of type-2 DM patients, and proper management of comorbid dyslipidemia and hypertension was achieved in 29% and 40.9% of patients, respectively. Adequate blood pressure control was less achieved in patients with metabolic syndrome (34.4%) than those without metabolic syndrome (77.2%). Similarly, dyslipidemia was less controlled in patients with metabolic syndrome (26.9%) than in those without metabolic syndrome (47.3%).ConclusionPharmacotherapy outcomes were inadequate for most patients with type-2 diabetes mellitus. Adopting early preventive and therapeutic interventions for metabolic syndrome is advised to improve treatment outcomes of the comorbid dyslipidemia and hypertension.

Latest Findings on the Role of α-Ketoglutarate in Metabolic Syndrome

Alpha-ketoglutarate (α-KG), an endogenous intermediate of the tricarboxylic acid cycle, is involved in a variety of cellular metabolic pathways. It serves as an energy donor, a precursor of amino acid biosynthesis, and an epigenetic regulator. α-KG plays physiological functions in immune regulation, oxidative stress, and anti-aging as well. In recent years, it has been reported that the level of α-KG in the body is closely associated with metabolic syndrome, including obesity, hyperglycemia, and other pathological factors. Exogenous supplementation of α-KG improves obesity, blood glucose levels, and cardiovascular disease risks associated with metabolic syndrome. Furthermore, α-KG regulates the common pathological mechanisms of metabolic syndrome, suggesting the potential application prospect of α-KG in metabolic syndrome. In order to provide a theoretical basis for further exploration of the application of α-KG in metabolic syndrome, we focused on α-KG and metabolic syndrome in this article and summarized the latest research progress in the role of α-KG in improving the pathological condition and disease progression of metabolic syndrome. For the next step, researchers may focus on the co-pathogenesis of metabolic syndrome and investigate whether α-KG can be used to achieve the therapeutic goal of "homotherapy for heteropathy" in the treatment of metabolic syndrome.

Metabolic syndrome in children and adolescents

Background: Metabolic syndrome is a serious condition associated with multiple diseases. The prevalence of metabolic syndrome is increasing caused by an increase in obesity rates among children and adolescents.Current Concepts: Owing to the various definitions used, there is lack of consensus regarding the diagnostic criteria for metabolic syndrome in children and adolescents. Although the exact pathogenesis remains unclear, insulin resistance and subsequent inflammation are key mechanistic contributors. Metabolic syndrome is characterized by overweight and obesity accompanied by abnormalities in glucose metabolism, dyslipidemia, and hypertension and is associated with other conditions such as fatty liver, polycystic ovary syndrome, and a pro-inflammatory state. Prevention and management of metabolic syndrome can be achieved through lifestyle modifications, behavioral interventions, pharmacotherapy, and surgical interventions.Discussion and Conclusion: Childhood and adolescent obesity serves as a risk factor for metabolic syndrome during later life. Early identification and intervention are important to prevent the progression of metabolic syndrome. Clinicians must identify at-risk children and adolescents and provide guidance for the prevention and management of metabolic syndrome.

The potential association between metabolic disorders and pulmonary tuberculosis: a Mendelian randomization study

BackgroundMetabolic disorders (MetDs) have been demonstrated to be closely linked to numerous diseases. However, the precise association between MetDs and pulmonary tuberculosis (PTB) remains poorly understood.MethodSummary statistics for exposure and outcomes from genome-wide association studies (GWASs) for exposures and outcomes were obtained from the BioBank Japan Project (BBJ) Gene–exposure dataset. The 14 clinical factors were categorized into three groups: metabolic laboratory markers, blood pressure, and the MetS diagnostic factors. The causal relationship between metabolic factors and PTB were analyzed using two-sample Mendelian Randomization (MR). Additionally, the direct effects on the risk of PTB were investigated through multivariable MR. The primary method employed was the inverse variance-weighted (IVW) model. The sensitivity of this MR analysis was evaluated using MR-Egger regression and the MR-PRESSO global test.ResultsAccording to the two-sample MR, HDL-C, HbA1c, TP, and DM were positively correlated with the incidence of active TB. According to the multivariable MR, HDL-C (IVW: OR 2.798, 95% CI 1.484–5.274, P = 0.001), LDL (IVW: OR 4.027, 95% CI 1.140–14.219, P = 0.03) and TG (IVW: OR 2.548, 95% CI 1.269–5.115, P = 0.009) were positively correlated with the occurrence of PTB. TC (OR 0.131, 95% CI 0.028–0.607, P = 0.009) was negatively correlated with the occurrence of PTB. We selected BMI, DM, HDL-C, SBP, and TG as the diagnostic factors for metabolic syndrome. DM (IVW, OR 1.219, 95% CI 1.040–1.429 P = 0.014) and HDL-C (IVW, OR 1.380, 95% CI 1.035–1.841, P = 0.028) were directly correlated with the occurrence of PTB.ConclusionsThis MR study demonstrated that metabolic disorders, mainly hyperglycemia, and dyslipidemia, are associated with the incidence of active pulmonary tuberculosis.

Metabolic Syndrome in people treated with Antipsychotics (RISKMet): A multimethod study protocol investigating genetic, behavioural, and environmental risk factors.

The RISKMet project aims to: (1) identify risk factors for metabolic syndrome (MetS) by comparing patients with and without MetS; (2) characterise patients treated with second-generation antipsychotics (SGAs) about MetS diagnosis; (3) study behavioural patterns, including physical activity (PA) and dietary habits, in patients and healthy individuals using a prospective cohort design. The RISKMet project investigates MetS in individuals treated with SGAs, focusing on both adult and paediatric populations. The study utilizes a case-control design to examine potential risk factors for MetS, categorizing participants as MetS+ considered as "Cases" and MetS- considered as "Controls" matched by sex and age. The evaluation of factors such as MetS, lifestyle habits, and environmental influences is conducted at two time points, T0 and T3, after 3 months. Subsequently, the project aims to assess body parameters, including physical examinations, and blood, and stool sample collection, to evaluate metabolic markers and the impact of SGAs. The analysis includes pharmacological treatment data and genetic variability. Behavioural markers related to lifestyle, eating behaviour, PA, and mood are assessed at both T0 and T3 using interviews, accelerometers, and a mobile app. The study aims to improve mental and physical well-being in SGA-treated individuals, establish a biobank for MetS research, build an evidence base for physical health programs, and develop preventive strategies for SGA-related comorbidities. This project innovates MetS monitoring in psychiatry by using intensive digital phenotyping, identifying biochemical markers, assessing familial risks, and including genetically similar healthy controls. ISRCTN18419418 at www.isrctn.com.

Prevalence of Metabolic Syndrome in Primary Health Care, Riyadh, Saudi Arabia

Background: Metabolic Syndrome (MS) is identified as a cluster of risk factors that significantly increase the risk of diabetes, cardiovascular disease, and stroke. It is characterized by obesity, hyperglycemia, dyslipidemia, and hypertension. Multiple definitions and criteria of MS exist. The most widely used set of criteria are the US National Cholesterol Education Program (NCEP) ATP III and the International Diabetes Federation (IDF), both of which are used in this study. Due to different defining criteria and population demographics, the prevalence of MS varies widely across countries, regions, and continents.Objective: This study investigates the prevalence and risk factors of metabolic syndrome among primary healthcare attendees in Riyadh, Saudi Arabia.Methods: A cross-sectional study was conducted over 12 months at the Prince Sultan Military Medical City (PSMMC) Wazzarat Center in Riyadh, Saudi Arabia, involving 262 participants. The study aimed to determine the prevalence and predictors of MS among primary healthcare attendees using ATP III and IDF criteria. Participants were assessed for sociodemographic features, comorbidities, vital signs, and laboratory data related to MS. Statistical analysis utilized R v 4.3 for descriptive statistics, Pearson correlation, Chi-square tests for categorical variables, and the Mann-Whitney test for continuous variables.Results: Among the study participants, 20.2% met the criteria for MS according to ATP III, and 19.9% according to IDF criteria, with a strong concordance between the two (Kappa statistic = 0.92, P &lt; 0.001). The median age of individuals with MS was significantly higher (45 years) than those without (35 years, P &lt; 0.001). Significant predictors included age, with BMI and fasting blood glucose showing a strong positive correlation (r &gt; 0.5, P &lt; 0.001). Notably, 60.7% of the cohort were medically free from conditions contributing to MS.Conclusion: The prevalence of MS among primary healthcare attendees in Riyadh, as defined by ATP III and IDF criteria, underscores the importance of age and lifestyle factors as predictors. The findings advocate for targeted preventive strategies focusing on lifestyle modifications to mitigate the risk of MS. Further research is suggested to explore the long-term impact of these interventions.

Aminotransferases as causal factors for metabolic syndrome: A bidirectional Mendelian randomization study.

Circulating aminotransferases (ALT and AST) have been used as biomarkers for liver injury. The causal relationships between aminotransferases and metabolic syndrome remain ambiguous. We conducted bidirectional and multivariable Mendelian randomization (MR) analyses between aminotransferases and traits related to metabolic syndrome using genetic variants obtained from genome-wide association studies (GWASs). MR-PRESSO tests were adopted to remove outliers and eliminate pleiotropy. MR steiger tests were conducted to ensure the correct direction of the causal effects. Both aminotransferases were risk factors for essential hypertension. ALT is a risk factor for type 2 diabetes. The bidirectional causal relationship between ALT and hyperglycemia, serum lipids, and obesity was demonstrated. The effect of fasting glucose on AST was demonstrated, while type 2 diabetes did not affect AST. The effect of HDL-C on ALT and the effect of triglycerides on AST were found in multivariable MR analyses. Our bidirectional MR analyses suggest that ALT and AST are causally associated with several metabolic syndrome-related traits, especially hypertension and type 2 diabetes. These findings highlight the potential role of aminotransferases as biomarkers and therapeutic targets for metabolic syndrome.

A meta-analysis of the association between RBP4 rs3758539 genotype and metabolic syndrome factors.

To explore the link between the RBP4 rs3758539 genotype and metabolic syndrome risk factors and whether the impact of this genetic variation displays any potential race discrepancy. This meta-analysis followed the PRISMA guidelines and was registered with PROSPERO (registration no. CRD42023407999). PubMed, Web of Science, Embase, Cochrane Library, Google Scholar, Airiti Library and CINAHL databases were used for the study search until October 2023. We evaluated the methodological quality using the Joanna Briggs Institute checklist and determined the correlation using a random-effects meta-analysis. The results indicated that individuals with the rs3758539 GA/AA genotype had a higher risk profile, including lower high-density lipoprotein levels [correlation: -0.045, 95% confidence interval (CI): -0.080 to -0.009, p = .015, I2 = 46.9%] and higher body mass index (correlation: 0.117, 95% CI: 0.036-0.197, p = .005, I2 = 82.0%), body fat (correlation: 0.098, 95% CI: 0.004-0.191, p = .041, I2 = 64.0%), and low-density lipoprotein levels (correlation: 0.074, 95% CI: 0.010-0.139, p = .024, I2 = 0%), of developing metabolic syndrome than those with the GG genotype. The subgroup analysis maintained a significantly positive correlation between the rs3758539 GA/AA genotype and body mass index (correlation: 0.163, 95% CI: 0.031-0.289, p = .016, I2 = 88.9%) but a negative correlation with high-density lipoprotein levels (correlation: -0.047, 95% CI: -0.087 to -0.006, p = .025, I2 = 65.7%) in the Asian group only. The current meta-analysis supports a significant link between the RBP4 rs3758539 GA/AA genotype and the metabolic syndrome.

Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b.

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

The relationship between heavy metals and metabolic syndrome using machine learning.

Exposure to high levels of heavy metals has been widely recognized as an important risk factor for metabolic syndrome (MetS). The main purpose of this study is to assess the associations between the level of heavy metal exposure and Mets using machine learning (ML) method. The data used in this study are from the national health and nutrition examination survey 2003-2018. According to the demographic information and heavy metal exposure level of participants, a total of 22 variables were included. Lasso was used to screen out the key variables, and 9 commonly used ML models were selected to establish the associations with the 5-fold cross validation method. Finally, we choose the SHapley Additive exPlanations (SHAP) method to explain the prediction results of Adaboost model. 11,667 eligible individuals were randomly divided into two groups to train and verify the prediction model. Through lasso, characteristic variables were selected from 24 variables as predictors. The AUC (area under curve) of the models selected in this study were all greater than 0.7, and AdaBoost was the best model. The AUC value of AdaBoost was 0.807, the accuracy was 0.720, and the sensitivity was 0.792. It is noteworthy that higher levels of cadmium, body mass index, cesium, being female, and increasing age were associated with an increased probability of MetS. Conversely, lower levels of cobalt and molybdenum were linked to a decrease in the estimated probability of MetS. Our study highlights the AdaBoost model proved to be highly effective, precise, and resilient in detecting a correlation between exposure to heavy metals and MetS. Through the use of interpretable methods, we identified cadmium, molybdenum, cobalt, cesium, uranium, and barium as prominent contributors within the predictive model.

New paradigms in the study of the cholinergic system and metabolic diseases: Acetyl-and-butyrylcholinesterase.

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are enzymes that belong to the neuromuscular cholinergic system, their main function is to hydrolyze the neurotransmitter acetylcholine (ACh), through their hydrolysis these enzymes regulate the neuronal and neuromuscular cholinergic system. They have recently attracted considerable attention due to the discovery of new enzymatic and nonenzymatic functions. These discoveries have aroused the interest of numerous scientists, consolidating the relevance of this group of enzymes. Recent investigations have revealed a positive correlation between several risk factors for metabolic syndrome (MetS) and the expression of cholinesterases (ChE's), which underscore the impact of high ChE's activity on the pro-inflammatory state associated with MetS. In addition, the excessive hydrolysis of ACh and other choline esters (succinylcholine, propionylcholine, butyrylcholine, etc.) by both ChE's results in the overproduction of fatty acid precursor metabolites, which facilitate the synthesis of very low-density lipoproteins and triacylglycerols. Participation in these processes may represent the link between ChE's and metabolic disorders. However, further scientific research is required to fully elucidate the involvement of ChE's in metabolic diseases. This review aims to collect recent research studies that contribute to understanding the association between the cholinergic system and metabolic diseases.

Prevalence of cardiometabolic risk factors according to urbanization level, gender and age, in apparently healthy adults living in Gabon, Central Africa.

The prevalence of cardiometabolic risk factors (CMRFs) is increasing in sub-Saharan Africa and represents a serious public health issue. Accurate data are required to implement adapted prevention programs and healthcare strategies. Thus, the aim of this study was to estimate the prevalence rates of CMRFs according to the level of urbanization, age and gender in Gabon. A cross-sectional study was conducted in northern (Bitam), western coast (Libreville, Melen) and southeast (Koulamoutou) areas of Gabon using the World Health Organization's (WHO) stepwise approach for the surveillance of chronic disease risk factors. Participants over 18 years of age, without known underlying disease, living in rural and urban areas of Gabon were included. Sociodemographic, biological, and behavioral data were collected. Univariate and multivariate analysis were used to identify the CMRFs. Of the 978 participants, 499 lived in urban and 479 in rural areas. Their median age was 38[28-50] years. Tobacco (26.1% vs 6.2%; p < 0.01) and excessive alcohol consumption (19.4% vs 9.6%; p < 0.01) predominated in rural than in urban areas, respectively. Urban dwellers had more often insufficient physical activity than rural people (29.5% vs 16.3%; p < 0.01). In total, 79.9% of participants aged under 54 years had a high blood pressure;10.6% of the younger participants had pre-hypertension. Metabolic syndrome was more frequent in women (21.7%) than in men (10.0%) (p < 0.01); 6.4% of men and 2.5% of women had a high Framingham score (p = 0.03). Finally, 54.0% of the participants had three or four CMRFs. The multivariate analysis showed that men were more likely to be smokers and to be at risk of pre-hypertension or high blood pressure (p < 0.01). Women were more likely to be obese or to have a metabolic syndrome (p < 0.01). Living in urban areas was also a risk factor for hypertension, diabetes, metabolic syndrome and high LDL cholesterol level. The prevalence of CMRFs was high in the study population. Disparities were observed according to urban and rural areas, gender and age. National prevention and healthcare strategies for cardiometabolic diseases in Gabon should consider these observed differences.

Association between metabolic phenotypes of overweight/obesity and cardiovascular diseases in postmenopausal women

Background and aimObesity and metabolic abnormalities were associated with an increased risk of cardiovascular disease. However, it is unclear how metabolic weight phenotypes relate to cardiovascular diseases in postmenopausal women. This study aimed to explore the relationships in postmenopausal women. Methods and resultsWe included 15,575 postmenopausal women aged 35–75 years (median age, 60.6) without cardiovascular disease at baseline from a subcohort of the China Patient-centered Evaluative Assessment of Cardiac Events Million Persons Project. Metabolically unhealthy phenotype was defined as having ≥2 risk factors of metabolic syndrome: blood pressure ≥130/85 mm Hg or current use of antihypertensive drugs, fasting glucose ≥5.6 mmol/L or current use of antidiabetic agents, triglycerides ≥1.7 mmol/L, and high-density lipoprotein cholesterol <1.3 mmol/L. Cox regression analysis was used to evaluate the risks of cardiovascular diseases. Over a median follow-up period of 3.55 (interquartile range, 2.59–4.44) years, a total of 1354 cardiovascular events occurred. Compared to metabolically healthy normal weight, the multivariate-adjusted hazard ratios and their 95% confidence intervals were 1.41 (1.16–1.72) for metabolically unhealthy normal weight, 1.42 (1.16–1.73) for metabolically healthy overweight/obesity, and 1.75 (1.48–2.08) for metabolically unhealthy overweight/obesity. Subdividing overweight/obesity into separate groups revealed higher total cardiovascular disease risk only in metabolically unhealthy individuals across body mass index categories. ConclusionIn postmenopausal women, both metabolically healthy overweight/obesity and metabolically unhealthy normal weight were associated with a higher risk of cardiovascular disease compared to metabolically healthy normal weight, and the greatest risk was observed in the metabolically unhealthy overweight/obesity category.

Association between dental scaling and metabolic syndrome and lifestyle.

Periodontal disease is a risk factor for diabetes and metabolic syndrome, and non-surgical periodontal treatment has been shown to help maintain stable blood sugar in diabetic patients. Determining the level of preventive scaling in patients with metabolic syndrome will help manage the disease. The purpose of this study was to investigate the extent to which people with metabolic syndrome or bad lifestyle performed scaling and the association between preventive scaling and metabolic syndrome or lifestyle in a large population. This study was conducted on adults aged 20 years or older from January 2014 to December 2017 in the National Health Insurance System (NHIS) database. Among 558,067 people who underwent health checkups, 555,929 people were included. A total of 543,791 people were investigated for preventive scaling. Metabolic syndrome components were abdominal obesity, lower high density lipoprotein cholesterol (HDL)-C, high triglycerides, high blood pressure and hyperglycemia. Unhealthy lifestyle score was calculated by assigning 1 point each for current smokers, drinkers, and no performing regular exercise. When multiple logistic regression analysis was performed after adjusting for age, sex, income, body mass index (BMI), smoking, drinking and regular exercise, the Odds ratios (OR) and 95% confidence intervals (CI) of the group with 5 metabolic syndrome components were 0.741 (0.710, 0.773) (p<0.0001). After adjustment for age, sex, income, BMI, smoking, drinking, regular exercise, diabetes, hypertension and dyslipidemia, the OR (95% CI) of the group with unhealthy lifestyle score = 3 was 0.612 (0.586, 0.640) (p<0.0001). The more metabolic syndrome components, and the higher unhealthy lifestyle score, the less scaling was performed.

Effects of hydroethanolic garlic extract on oxidative stress, lipolysis, and glycogenesis in high-fat diet-fed Drosophila melanogaster

Introduction: Metabolic syndrome is multifaceted health condition associated with metabolism, which leads to the accumulation of oxidation products and impairment of the antioxidant system. Medicinal, edible plant alternatives are being sought to prevent the predisposing factors of metabolic syndrome, one of which is obesity. We studied the effects of hydroethanolic garlic extract (HGE) on oxidative stress, lipolysis, and glycogenesis in high-fat diet-fed Drosophila melanogaster. Methods: The HGE was processed and antioxidant assays (in vitro and in vivo) coupled with metabolic biomarkers were assayed following standard procedures. Results: HGE inhibited ABTS (2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), hydroxyl, and hydrogen peroxide radicals in high-fat-fed D. melanogaster. The HGE also reduced the activity of superoxide dismutase and increased catalase activity significantly. The HGE promoted lipolysis and glycogenesis by reducing cholesterol, triglycerides, and total sugar concentrations while glycogen content was increased. Conclusion: Our results revealed that HGE improved the antioxidant defense system and promoted lipolysis and glycogenesis in metabolic deranged D. melanogaster. This suggests the therapeutic usefulness of garlic in the management of metabolic diseases.

Cost-effectiveness of behavioural counselling intervention compared with non-intervention for adult patients with metabolic syndrome to prevent cardiovascular diseases and type 2 diabetes in Japan: a microsimulation modelling study

ObjectivesNationwide lifestyle intervention—specific health guidance (SHG) in Japan—employs counselling and education to change unhealthy behaviours that contribute to metabolic syndrome, especially obesity or abdominal obesity. We aimed to perform a...

The effects of raja banana (Musa acuminata) peel extract on body weight, body mass index, body fat percentage, and visceral fat mass in male rats with obesity

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Background: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;Obesity is a risk factor for metabolic syndrome which can increase mortality. Obese sufferers often fail to lose body weight (BW) through non-pharmacological therapy. Obesity synthetic drugs can cause side effects, so natural ingredients are needed as alternative therapies.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Objectives: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;This study aims to determine the effect of raja banana peel extract (RBPE) on BW, body mass index (BMI), body fat percentage (BFP), and visceral fat mass in obese male rats.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Methods: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;The research subjects were 30 male Wistar rats weighing 125-200 g, aged eight weeks. Obesity was induced by being given high-fat high fructose (HFHFr) feed for 28 days. Rats were randomized and divided into five groups: the negative control group (C-) was given standard feed and distilled water, the positive control (C+) was given standard feed and orlistat, treatment 1 (T1), treatment 2 (T2), and treatment 3 (T3). Which were given standard feed and RBPE doses of 200 mg/kgBW/day, 400 mg/kgBW/day, and 800 mg/kgBW/day. Data before and after the induction of obesity were analyzed using paired t-tests. BW, BMI, and visceral fat mass were analyzed using the One-Way Analysis of Variance (ANOVA) and Repeated Measure ANOVA tests. BFP was analyzed using the Kruskal-Wallis test and the Friedman test.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Results: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;RBPE can significantly reduce BW (p=0.026), BMI (p&amp;lt;0.001), and BFP (p&amp;lt;0.001). However, all groups had no significant difference in visceral fat mass (p=0.187). T3 was the group with the lowest average BW, BMI, BFP, and visceral fat mass although the highest weight loss during the intervention period occurred in C+.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;strong&gt;&lt;em&gt;Conclusions: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;RBPE has the potential as an alternative therapy for obesity because it can reduce BW, BMI, and BFP. Future studies can investigate the effect of RBPE on other obesity parameters such as lipid profiles.&lt;/em&gt;