metabolic syndrome
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2022 ◽  
Vol 13 ◽  
pp. 100198
Charles Oluwaseun Adetunji ◽  
Olugbenga Samuel Michael ◽  
Shweta Rathee ◽  
Kshitij RB Singh ◽  
Olulope Olufemi Ajayi ◽  

Mohsen Azimi-Nezhad ◽  
Nayyereh Aminisani ◽  
Ahmad Ghasemi ◽  
Azam Rezaei Farimani ◽  
Fatemeh Khorashadizadeh ◽  

İsmail Dündar ◽  
Ayşehan Akıncı

Abstract Objectives The aim of the study was to determine the prevalence of metabolic syndrome (MetS), type 2 diabetes mellitus (T2DM), and other comorbidities in overweight and obese children in Malatya, Turkey. Methods Retrospective cross-sectional study. We studied 860 obese and overweight children and adolescents (obese children Body mass index (BMI) >95th percentile, overweight children BMI >85th percentile) aged between 6 and 18 years. The diagnosis of MetS, impaired glucose tolerance (IGT), impaired fasting glucose (IFG), and T2DM were defined according to modified the World Health Organization criteria adapted for children. Other comorbidities were studied. Results Subjects (n=860) consisted of 113 overweight and 747 obese children of whom 434 (50.5%) were girls. MetS was significantly more prevalent in obese than overweight children (43.8 vs. 2.7%, p<0.001), and in pubertal than prepubertal children (41.1 vs. 31.7%, p<0.001). Mean homeostasis model assessment for insulin ratio (HOMA-IR) was 3.6 ± 2.0 in the prepubertal and 4.9 ± 2.4 in pubertal children (p<0.001). All cases underwent oral glucose tolerance test and IGT, IFG, and T2DM were diagnosed in 124 (14.4%), 19 (2.2%), and 32 (3.7%) cases, respectively. Insulin resistance (IR) was present in 606 cases (70.5%). Conclusions Puberty and obesity are important risk factors for MetS, T2DM, and IR. The prevalence of MetS, T2DM, and other morbidities was high in the study cohort. Obese children and adolescents should be carefully screened for T2DM, insulin resistance, hyperinsulinism, dyslipidemia, hypertension, IGT, and IFG. The prevention, early recognition, and treatment of obesity are essential to avoid associated morbidities.

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 212
Sunmin Park ◽  
Chaeyeon Kim ◽  
Xuangao Wu

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

2022 ◽  
Vol 8 (1) ◽  
pp. 310-317
Debasish Dutta

Background: NAFLD is a condition defined by excessive fat accumulation in the form of triglycerides (steatosis) in the liver (> 5% of hepatocytes histologically). Non-alcoholic fatty liver disease is increasingly being recognized as a major cause of liver-related morbidity and mortality among 15-40% of the general population. Aim of the study: To evaluate the clinical profile of patients with non-alcoholic fatty liver disease and its association with metabolic syndrome.Methods:The present cross-sectional, retro-spective study was conducted as outdoor patient basis in the Department of Medicine, Jashore medical college hospital & a private diagnostic centre, Jashore.. A total of 74 cases were included for the study. All patients in the study underwent routine investigations including complete blood counts, blood sugar, liver function tests, HBsAg, anti-HCV, lipid profile andUSG of whole abdomen. The data was collected during OPD treatment and was recorded in predesigned and pretested proforma and analyzed.Results:Mean age of the patient was 53.70±7.22 years. On physical examination findings showed the mean BMI was 27.6±4.39 kg/m2, mean waist circumference was 74.22±7.44 cm. Mean diastolic blood pressure (mm Hg) was 92.87±6.25 and mean systolic blood pressure (mm Hg) 132.0±18.17. Maximum 52% patients had triglycerides >150 mg/dl while low serum HDL level was seen in 37% patients and increased waist circumference was found in 32% patients. Altered ALT ≥41 IU was observed in 10 (62.50%) of Grade II of patients with NAFLD with metabolic syndrome. Central obesity was observed in 12 (75.00%) of Grade II patients with NAFLD with metabolic syndrome. While 14 (87.50%) Grade II of patients with NAFLD with metabolic syndrome showed impaired fasting glucose (>110 mg/dl). Hypertriglyceridemia (>150 mg/dl) in 12 (70.58%) seen in Grade I of patients with NAFLD without metabolic syndrome.Conclusion:Higher prevalence of all the components of metabolic syndrome in cases of NAFLD was observed. It can be concluded that symptoms and signs of NAFLD are non-specific and occur later in the course of the disease hence the physician should have a high index of suspicion in order to detect NAFLD early in the course of the disease.

2022 ◽  
Vol 8 (4) ◽  
pp. 308-311
Farah Ahsan ◽  
Naeem Qureshi ◽  
Sumera Samreen ◽  
Sonali Kukreti

We aimed to provide correlation of HbA1c & Microalbumin in urine in patients of metabolic syndrome.: 100 patients coming to OPD of Medicine department in Shri Mahant Indresh Hospital. Plasma samples taken for Hba1c and urine for microalbumin and run on VITROS 5600/7600 and reported for Hba1c & microalbumin. : 51 were males and 49 were females out of 100 total patients. For males age mean & SD was 55.84±13.52 & for females was 57.56±10.08.For raised and unraised HbA1c 10.42±+9.628 & 5.066±.0.216 for raised and unraised microalbumin 412.±1133 & 11.97±7.129.When we compared both HbA1c and microalbumin in both males and females then mean and SD came out to for HbA1C for males 8.56±2.663 and females were 11.62±12.86 with t value 2.327 and p value 0.021 that states it was significant. And for micralbumin for male 391.5±1184 & for females 60.37±116.6 t value was 2.7832 and p value was 0.0059 it also states it was significant. Therefore both the parameters were significant in patients of metabolic syndrome.

2022 ◽  
Vol 8 (4) ◽  
pp. 304-307
Jayshri Sadashiv Jankar

Serum ferritin, an acute phase reactant, is an indicator of the body's iron reserves. Increased body iron reserves and subclinical hemochromatosis have been linked to the development of hyperglycaemia, type 2 diabetes, metabolic syndrome, and potentially diabetic retinopathy, nephropathy, and vascular dysfunction, according to recent research. The objective of this study was to see if there was a link between Serum Ferritin and Type 2 diabetes and metabolic syndrome, as well as to see if there was a link between S. ferritin and HbA1c.The present study included 50 diagnosed cases of type 2 diabetes mellitus (males: 32, females: 18) and 50 healthy controls of same age (males: 28, females: 22). Serum ferritin levels, glycated hemoglobin were measured and compared. : When diabetic patients were compared to controls, serum ferritin was considerably greater, and serum ferritin had a positive correlation with the duration of diabetes and glycated hemoglobin. Positive correlation was found between serum ferritin levels and glycated hemoglobin and duration of disease.

2022 ◽  
Vol 14 (1) ◽  
Jingya Wang ◽  
Yang Bai ◽  
Zihang Zeng ◽  
Jun Wang ◽  
Ping Wang ◽  

Abstract Background The relation between cigarette smoking and metabolic syndrome (MetS) remains unclear, and previous studies focusing on MetS are limited in sample size. We investigated the association between life-course smoking and MetS with independent discovery and replication samples. Methods Preliminary analysis utilized data from an annual cross-sectional survey of 15,222 participants aged ≥ 60 years in Tianjin, China. Suggestive associations were followed-up in 8565 adults from the China Health and Nutrition Survey. MetS was identified according to the criteria of the Chinese Diabetes Society in 2013. Life-course smoking was assessed by a comprehensive smoking index (CSI), based on information on smoking intensity, duration, and time since cessation across life-course, collected through standard questionnaires. Participants were divided into four groups: non-smokers; and the tertiles of CSI in ever smokers. Multivariable logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between life-course smoking and MetS. Results In the discovery sample, ORs of MetS were 2.01 (95%CI: 1.64–2.47) and 1.76 (95%CI: 1.44–2.16) for smokers in the highest and second tertile of CSI compared with never smokers. Potential interaction was shown for age, with increased ORs for MetS associated with smoking limited to individuals who aged < 70 years (Pinteraction = 0.015). We were able to replicate the association between cigarette smoking and MetS in an independent adult sample (second tertile vs. never: OR = 1.30, 95%CI: 1.04–1.63). The interaction of smoking with age was also replicated. Conclusions Life-course cigarette smoking is associated with an increased odds of MetS, especially among individuals who aged < 70 years.

2022 ◽  
Vol 14 (1) ◽  
Keiko Kabasawa ◽  
Michihiro Hosojima ◽  
Yumi Ito ◽  
Kazuo Matsushima ◽  
Junta Tanaka ◽  

Abstract Background Although metabolic syndrome traits are risk factors for chronic kidney disease, few studies have examined their association with urinary biomarkers. Methods Urinary biomarkers, including A-megalin, C-megalin, podocalyxin, albumin, α1-microglobulin, β2-microglobulin, and N-acetyl-β-D-glucosaminidase, were cross-sectionally assessed in 347 individuals (52.7% men) with a urine albumin-to-creatinine ratio (ACR)  < 300 mg/g in a health checkup. Metabolic syndrome traits were adopted from the National Cholesterol Education Program (third revision) of the Adult Treatment Panel criteria modified for Asians. Results Participants had a mean body mass index, estimated glomerular filtration rate (eGFR), and median ACR of 23.0 kg/m2, 74.8 mL/min/1.73 m2, and 7.5 mg/g, respectively. In age- and sex-adjusted logistic regression analysis, A-megalin and albumin were significantly associated with the clustering number of metabolic syndrome traits (3 or more). After further adjustment with eGFR, higher quartiles of A-megalin and albumin were each independently associated with the clustering number of metabolic syndrome traits (adjusted odds ratio for A-megalin: 1.30 per quartile, 95% CI 1.03–1.64; albumin: 1.42 per quartile, 95% CI 1.12–1.79). Conclusions Both urinary A-megalin and albumin are associated with the clustering number of metabolic syndrome traits. Further research on urinary A-megalin is warranted to examine its role as a potential marker of kidney damage from metabolic risk factors.

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