Risk models and scores for metabolic syndrome: systematic review protocol

Does Serum Uric Acid Mediate Relation between Healthy Lifestyle and Components of Metabolic Syndrome?

A healthy lifestyle is related to metabolic syndrome (MetS), but the mechanism is not fully understood. This study aimed to examine the association of components of MetS with lifestyle in a Chinese population and potential mediation role of serum uric acid (SUA) in the association between lifestyle behaviors and risk of components of MetS. Data were derived from a baseline survey of the Shaanxi urban cohort in the Regional Ethnic Cohort Study in northwest China. The relationship between components of MetS, healthy lifestyle score (HLS), and SUA was investigated by logistic or linear regression. A counterfactual-based mediation analysis was performed to ascertain whether and to what extent SUA mediated the total effect of HLS on components of MetS. Compared to those with 1 or less low-risk lifestyle factors, participants with 4-5 factors had 43.6% lower risk of impaired glucose tolerance (OR = 0.564; 95%CI: 0.408~0.778), 60.8% reduction in risk of high blood pressure (OR = 0.392; 95%CI: 0.321~0.478), 69.4% reduction in risk of hypertriglyceridemia (OR = 0.306; 95%CI: 0.252~0.372), and 47.3% lower risk of low levels of HDL cholesterol (OR = 0.527; 95%CI: 0.434~0.641). SUA mediated 2.95% (95%CI: 1.81~6.16%) of the total effect of HLS on impaired glucose tolerance, 14.68% (95%CI: 12.04~18.85%) on high blood pressure, 17.29% (95%CI: 15.01~20.5%) on hypertriglyceridemia, and 12.83% (95%CI: 10.22~17.48%) on low levels of HDL cholesterol. Increased HLS tends to reduce risk of components of MetS partly by decreasing the SUA level, which could be an important mechanism by which lifestyle influences MetS.

Serum Uric Acid to Creatinine Ratio and Risk of Metabolic Syndrome in Patients with Overweight/Obesity

Purpose

Metabolic syndrome (MetS) is a rising global concern with an increasing prevalence. This study aimed to evaluate the relationship between serum uric acid to creatinine ratio (SUA/Cr) and MetS in adults with overweight/obesity in China.

Patients and Methods

We conducted a cross-sectional study comprising 4699 participants with overweight/obesity who underwent physical examinations. Their serum levels of various components, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), fasting plasma glucose (FPG), creatinine (Cr), and uric acid (UA) were measured. Renal function-normalized SUA was calculated using SUA/Cr. Logistic regression analysis was employed to investigate the association between SUA/Cr and MetS in adults with overweight/obesity.

Results

SUA/Cr levels were lower in non-MetS participants (OR: 2.159, 95% CI: 1.82 to 2.56; p < 0.001), and tended to rise with the increasing number of MetS components. Additionally, elevated SUA/Cr levels were associated with a higher risk of hypertension, hyperglycemia, and dyslipidemia.

Conclusion

SUA/Cr levels were significantly associated with MetS and its components in Chinese adults with overweight/obesity.

Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

Purpose

The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population.

Patients and Methods

The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People's Hospital in 2009 and 2019, to determine MetS based on criteria established by the Chinese Diabetes Society. Non-invasive characteristics such as gender, age, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected and used as input variables to train and evaluate ML models for MetS identification. Several ML models were used for MetS identification, including logistic regression (LR), k-nearest neighbors algorithm (k-NN), naive bayesian (NB), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM).

Results

Our ML models all showed good performance in the 10-fold cross-validation except for the SVM model. In the external validation, the NB model exhibited the best performance with an AUC of 0.976, accuracy of 0.923, sensitivity of 98.32%, and specificity of 91.32%.

Conclusion

This study proposed a new non-invasive method for early and low-cost identification of MetS by using ML models. This approach has the potential to serve as a highly sensitive, convenient, and cost-effective tool for large-scale MetS screening.

Prevalence and correlates of metabolic syndrome among women living in urban slums, Mysore, India

Metabolic Syndrome (MetSyn) is a predictor of cardiovascular disease (CVD). About a third of urban Indians suffer from MetSyn. This study examined the prevalence of MetSyn among women living in urban slums. A cross-sectional survey was carried out between October 2017 and May 2018 among a non-probability sample of slum-dwelling women, 40-64 years of age, in six government-designated slums in Mysore, India. Data were collected on demographics, diet, behavioral risks, anthropometry, blood pressure, serum glucose, hemoglobin A1c, and serum lipids. The study used a definition of MetSyn from the International Diabetes Federation Task Force on Epidemiology and Prevention with an HbA1c measure for average blood glucose. About two-fifths of the 607 participants had MetSyn (41.5%; 95% CI: 37.7-45.5). Of those, 40.9% met three criteria, 38.1% four, and 25.0%, all five criteria. Elevated BP was the most prevalent MetSyn factor (79.6%), followed by increased waist circumference (54.5%), low HDL (50.1%), elevated Hb A1c (37.1%), and elevated triglycerides (36.1%). Odds for MetSyn were 1.52 times greater for those who were 50-59 years of age compared with those 40-49 years of age (adjusted odds ratio[AOR]:1.52; 95% CI:0.96-2.40). Women with mobility issues had 1.29 times higher odds of MetSyn than those without it (AOR: 0.76, 95% CI: 0.96, 1.75). Housewives had 1.29 times greater odds of MetSyn (AOR: 1.29, 95% CI: 1.00, 1.67). There is a high prevalence of MetSyn among urban slum-dwelling women in Mysore. There is a need for interventions aimed at reducing CVD risk factors in this population.

Visceral obesity measured using computed tomography scans: No significant association with mortality in critically ill patients

INTRODUCTION

The association between obesity and outcome in critical illness is unclear. Since the amount of visceral adipose tissue(VAT) rather than BMI mediates the health effects of obesity we aimed to investigate the association between visceral obesity, BMI and 90-day mortality in critically ill patients.

METHOD

In 555 critically ill patients (68% male), the VAT Index(VATI) was measured using Computed Tomography scans on the level of vertebra L3. The association between visceral obesity, BMI and 90-day mortality was investigated using univariable and multivariable analyses, correcting for age, sex, APACHE II score, sarcopenia and muscle quality.

RESULTS

Visceral obesity was present in 48.1% of the patients and its prevalence was similar in males and females. Mortality was similar amongst patients with and without visceral obesity (27.7% vs 24.0%, p = 0.31). The corrected odds ratio of 90-day mortality for visceral obesity was 0.667 (95%CI 0.424-1.049, p = 0.080). Using normal BMI as reference, the corrected odds ratio for overweight was 0.721 (95%CI 0.447-1.164 p = 0.181) and for obesity 0.462 (95%CI 0.208-1.027, p = 0.058).

CONCLUSION

No significant association of visceral obesity and BMI with 90-day mortality was observed in critically ill patients, although obesity and visceral obesity tended to be associated with improved 90-day mortality.

Exposure to air pollution and prevalence of metabolic syndrome: A nationwide study in China from 2011 to 2015

BACKGROUND

Evidence concerning the influence of air pollution on metabolic syndrome (MetS) is still limited. We aimed to investigate whether sustained exposure to air pollutants are associated with increased prevalence of MetS and its individual components.

METHODS

We conducted a cross-sectional study comprised of 14,097 individuals participated in the first or third survey of the CHARLS. The personal cumulative (3-year averaged) exposure concentrations of nitrogen dioxide (NO₂), particulate matter (PM) with a diameter of 1.0 μm or less (PM₁), PM with a diameter of 10 μm or less (PM₁₀) and PM with a diameter of 2.5 μm or less (PM_2.5) were estimated using a spatiotemporal random forest model at 0.1° × 0.1° spatial resolution based on residential address of each participant provided. We utilized logistic regression models to estimate the associations of the four air pollutants with the prevalence of MetS and its individual components, and performed interaction analyses to evaluate potential effect modifications by gender, health status, age and drinking status.

RESULTS

Sustained exposure to air pollutants is associated with increased prevalence of MetS. For every 10 μg/m³ increase in NO₂, PM₁, PM₁₀ and PM_2.5, the adjusted odds ratio (OR) of MetS was 2.276 (95 % CI: 2.148, 2.412), 1.207 (95 % CI: 1.155, 1.263), 1.027 (95 % CI: 1.006, 1.048) and 1.027 (95 % CI: 0.989, 1.066), respectively. For MetS components, we observed significant associations between NO₂, PM₁, PM₁₀ and central obesity, high blood pressure, elevated fasting glucose and low high-density lipoprotein cholesterol. For example, the adjusted OR of low high-density lipoprotein cholesterol for every 10 μg/m³ increase in NO₂ was 1.855 (95 % CI: 1.764, 1.952). We also identified that age could significantly modified the association between NO₂ and prevalence of MetS.

CONCLUSIONS

Chinese adults sustained exposure to higher concentrations of air pollutants are associated with increased prevalence of MetS and its components.

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.

Construction of Xinjiang metabolic syndrome risk prediction model based on interpretable models

Background

We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS.

Methods

This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model.

Results

Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise.

Conclusions

Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.

Risk assessment of metabolic syndrome prevalence involving sedentary occupations and socioeconomic status

OBJECTIVES

To determine whether occupation type, distinguished by socioeconomic status (SES) and sedentary status, is associated with metabolic syndrome (MetS) risk.

METHODS

We analysed two data sets covering 73 506 individuals. MetS was identified according to the criteria of the modified Adult Treatment Panel III. Eight occupational categories were considered: professionals, technical workers, managers, salespeople, service staff, administrative staff, manual labourers and taxi drivers; occupations were grouped into non-sedentary; sedentary, high-SES; and sedentary, non-high-SES occupations. A multiple logistic regression was used to determine significant risk factors for MetS in three age-stratified subgroups. R software for Windows (V.3.5.1) was used for all statistical analyses.

RESULTS

MetS prevalence increased with age. Among participants aged ≤40 years, where MetS prevalence was low at 6.23%, having a non-sedentary occupation reduced MetS risk (OR=0.88, p<0.0295). Among participants aged >60 years, having a sedentary, high-SES occupation significantly increased (OR=1.39, p<0.0247) MetS risk.

CONCLUSIONS

The influence of occupation type on MetS risk differs among age groups. Non-sedentary occupations and sedentary, high-SES occupations decrease and increase MetS risk, respectively, among younger and older adults, respectively. Authorities should focus on individuals in sedentary, high-SES occupations.

Effects of Lifestyle Intervention on Inflammatory Markers and Waist Circumference in Overweight/Obese Adults With Metabolic Syndrome: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Physical inactivity and an imbalanced diet could lead to some cardio metabolic risk factors.

OBJECTIVE

The objective of this meta-analysis was to investigate the effects of lifestyle modification on inflammatory indicators and waist circumference (WC) in overweight/obese subjects with metabolic syndrome (MS).

DATA SOURCES

A systematic search was conducted in PubMed, CINAHL, MEDLINE, Cochrane, Google Scholar, and Web of Science.

STUDY SELECTION

The selection criteria were randomized controlled trials (RCTs) investigating the effects of lifestyle interventions on inflammation and WC from inception to 20 December 2020. The weighted mean difference (WMD) and 95% confidence interval (CI) between interventions were computed using a random or fixed-effects model.

RESULTS

Six RCTs (including 1246 MS patients who had, on average, overweight/obesity) met all inclusion criteria. Interventions lasted 6 to 12 months (2-5 sessions per week). Lifestyle intervention significantly reduced C-reactive protein (WMD: -0.52 mg/ml, 95% CI: -0.72, -0.33), IL-6 (WMD: -0.50 pg/ml, 95% CI: -0.56, -0.45), and increased adiponectin (WMD: 0.81 µg/ml, 95% CI, 0.64, 0.98). Moreover, lifestyle modification significantly decreased WC (WMD: -3.12 cm, 95% CI, -4.61, -1.62).

CONCLUSION

Our findings provide evidence that lifestyle alterations, including physical activity and diet, can lead to significant improvement in abdominal obesity, measured by WC and some inflammation markers among overweight/obese individuals with MS. Further high-quality research is needed to clarify the mechanisms underlying the effect of such interventions on this population's inflammatory markers.

Prevalence of metabolic syndrome and associated factors among patients with chronic Chagas disease

The increase in life expectancy and the migration of individuals with Chagas disease (ChD) from rural to urban centers exposes them to the development of chronic-degenerative abnormalities that may increase the prevalence of metabolic syndrome (MetS). The present study aimed to identify the prevalence of MetS and its components in individuals with chronic ChD. This is a cross-sectional study with 361 patients of both sexes, aging >18 years, followed at a national reference center (Rio de Janeiro, Brazil). MetS diagnosis followed the International Diabetes Federation 2005 criteria. The association between the variables was determined through logistic regression models. The mean age was and 60.7±10.8 years. About half (56.2%) were female and the majority self-reported their race as mulatto (59.8%). The percentage of individuals with MetS was 40.4%. The variables independently associated with MetS were age (OR 1.06; 95%CI 1.04-1.09), high education levels (OR 0.36; 95%CI 0.17-0.79) and cardiac form with heart failure (OR 0.34; 95%CI 0.17-0.68). Therefore, a high prevalence of MetS was found in this Brazilian chronic ChD cohort. The identification of the associated factors can facilitate the development of effective approaches for preventing and managing MetS in ChD patients.

Psychological Continuum of Elderly Patients Suffering from Arterial Hypertension with Metabolic Syndrome, Against the Background of Chronotherapy with a Fixed Combination of Amlodipine, Lisinopril and Rosuvastatin

Aim        To study the psychological continuum in elderly patients with arterial hypertension associated with metabolic syndrome during the chronotherapy with a fixed combination (FC) of amlodipine, lisinopril, and rosuvastatin.Material and methods        In the inpatient conditions, 63 patients aged 60-74 years with arterial hypertension associated with metabolic syndrome were treated with chronotherapy with a FC of amlodipine, lisinopril, and rosuvastatin (5 / 10 / 10 mg/day in the evening). These patients composed the main group. The control group (58 patients aged 60-74 years with arterial hypertension associated with metabolic syndrome) was treated with the FC of amlodipine, lisinopril, and rosuvastatin at the same dose of 5 / 10 / 10 mg/day in the morning.Results   At one year, the disorders of psychological continuum were significantly decreased with the chronotherapy (evening dosing) with the antihypertensive FC of amlodipine, lisinopril, and rosuvastatin compared to the traditional treatment (morning dosing) at the same dose of 5 / 10 / 10 mg/day in both groups. With the chronotherapeutic approach, the dynamic of cognitive disorders in patients aged 60-74 years with arterial hypertension associated with metabolic syndrome was characterized by a significant increase in the Mini-Mental-State-Examination scale score from 17.8±0.3 at baseline to 23.5±0.4 with the evening dosing (р&lt;0.001) vs. the increase from 16.9±0.3 to 20.4±0.4 (р&lt;0.001) with the morning dosing. The situational anxiety score decreased from 40.0±2.2 to 30.6±1.8 (р&lt;0.05) and from 40.8±2.5 to 33.5±1.9  (р&lt;0.05), and the trait anxiety score decreased from 48.8±2.0 to 26.4±1.9 (р&lt;0.001) and from 44.9±1.9 to 30.7±1.7  (р&lt;0.01) with the evening and morning dosing, respectively. Depressive disorders slightly decreased with the chronotherapy by 14.1 % vs. 7.7 % with the traditional regimen; nevertheless, they were consistent with depressive spectrum disorders in both groups.Conclusion            The study results showed a higher effectiveness of the chronotherapeutic treatment compared to the traditional treatment with FC of amlodipine, lisinopril, and rosuvastatin in arterial hypertension with metabolic syndrome.

Metabolic Syndrome Triggered by Fructose Diet Impairs Neuronal Function and Vascular Integrity in ApoE-KO Mouse Retinas: Implications of Autophagy Deficient Activation

Metabolic syndrome is a disorder characterized by a constellation of clinical findings such as elevated blood glucose, hyperinsulinemia, dyslipidemia, hypertension, and obesity. A positive correlation has been found between metabolic syndrome or its components and retinopathy, mainly at microvascular level, in patients without a history of diabetes. Here, we extend the investigations beyond the vascular component analyzing functional changes as well as neuronal and glial response in retinas of Apolipoprotein E knockout (ApoE-KO) mice fed with 10% w/v fructose diet. Given that autophagy dysfunction is implicated in retinal diseases related to hyperglycemia and dyslipidemia, the activation of this pathway was also analyzed. Two months of fructose intake triggered metabolic derangements in ApoE-KO mice characterized by dyslipidemia, hyperglycemia and hyperinsulinemia. An increased number of TUNEL positive cells, in addition to the ganglion cell layer, was observed in the inner nuclear layer in retina. Vascular permeability, evidenced by albumin–Evans blue leakage and extravasation of albumin was also detected. Furthermore, a significant decrease of the glial fibrillary acidic protein expression was confirmed by Western blot analysis. Absence of both Müller cell gliosis and pro-angiogenic response was also demonstrated. Finally, retinas of ApoE-KO FD mice showed defective autophagy activation as judged by LC3B mRNA and p62 protein levels correlating with the increased cell death. These results demonstrated that FD induced in ApoE-KO mice biochemical alterations compatible with metabolic syndrome associated with neuronal impairment and mild vascular alterations in the retina.

The potential of artemisinins as anti-obesity agents via modulating the immune system

Artemisinin and its derivatives are the most effective antimalarial drugs. Besides anti-malarial activity, artemisinin and its derivatives have displayed wide-spectrum bioactivities such as anti-parasite, anti-tumor, and anti-obesity effects. Obesity is an epidemic worldwide which is a big threat to human health, but there are only a few approved anti-obesity drugs in the world. Also, these drugs are efficient to limited patients partly because their safety and efficacy are questioned. Anti-inflammatory therapies may be valuable in obesity treatment since growing evidence shows chronic metabolic inflammation is implicated in metabolic disease pathogenesis. As artemisinin and its derivatives display effective anti-inflammatory and immunoregulatory properties with less toxicity, it provides an insight for novel drug development in obesity therapeutic strategies via immune-regulatory mechanisms. In this review, the potential of artemisinin and its derivatives to treat various metabolic diseases such as obesity and diabetes is discussed.

Reporting and Methods in Developing Prognostic Prediction Models for Metabolic Syndrome: A Systematic Review and Critical Appraisal

PURPOSE

A prognostic prediction model for metabolic syndrome can calculate the probability of risk of experiencing metabolic syndrome within a specific period for individualized treatment decisions. We aimed to provide a systematic review and critical appraisal on prognostic models for metabolic syndrome.

MATERIALS AND METHODS

Studies were identified through searching in English databases (PubMed, EMBASE, CINAHL, and Web of Science) and Chinese databases (Sinomed, WANFANG, CNKI, and CQVIP). A checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) and the prediction model risk of bias assessment tool (PROBAST) were used for the data extraction process and critical appraisal.

RESULTS

From the 29,668 retrieved articles, eleven studies meeting the selection criteria were included in this review. Forty-eight predictors were identified from prognostic prediction models. The c-statistic ranged from 0.67 to 0.95. Critical appraisal has shown that all modeling studies were subject to a high risk of bias in methodological quality mainly driven by outcome and statistical analysis, and six modeling studies were subject to a high risk of bias in applicability.

CONCLUSION

Future model development and validation studies should adhere to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement to improve methodological quality and applicability, thus increasing the transparency of the reporting of a prediction model study. It is not appropriate to adopt any of the identified models in this study for clinical practice since all models are prone to optimism and overfitting.