Diabetes, Metabolic Syndrome and Obesity最新文献

{"title":"Prevalence and Determinants of Obesity and Overweight Among Children and Adolescents in the Middle East and North African Countries: An Updated Systematic Review","authors":"B. Alruwaili, Duaa Bayyumi, Ohud Alruwaili, Razan Alsadun, Amal Alanazi, Amal Hadi, Nada Alruwaili, Ashokkumar Thirunavukkarasu, Norah Aldaghmani, Aljawharah Alrayes","doi":"10.2147/dmso.s458003","DOIUrl":"https://doi.org/10.2147/dmso.s458003","url":null,"abstract":": In recent years, the countries of the Middle East and North Africa (MENA) region have experienced alarming increases in the prevalence of childhood overweight and obesity. This updated systematic review sought to measure the prevalence and determinants of obesity and overweight among children and adolescents in MENA countries. A literature search for relevant observational studies published in English was conducted using PubMed, Web of Science, Google Scholar, and Saudi Digital Library. The risk of bias was evaluated using the Newcastle-Ottawa Scale. Twenty-one published articles during the past five years were included in the systematic review. Varied approaches were used to diagnose childhood overweight and obesity, including the International Obesity Taskforce (IOTF), Centre for Disease Control (CDC), World Health Organization (WHO), and Saudi Growth Pattern Curves. We found that the combined prevalence of childhood overweight and obesity in the Middle East is up to 49.4%, depending on the methods applied in their studies. Risk factors identified were age, male gender, lack of sufficient physical activity, consumption of fried food, perceived stress level, number of family members, family size, mother’s occupation, education level, family history of obesity, high energy consumption from carbohydrates, ≥2 hours spent on watching television on weekend days with overweight, and always eating breakfast while watching television with obesity. The results of this review indicate that the issue of childhood and adolescent overweight and obesity in the Middle East is substantial and concerning. Most of the risk factors identified are modifiable and, if given appropriate attention, could significantly reduce the burden of associated chronic complications.","PeriodicalId":504951,"journal":{"name":"Diabetes, Metabolic Syndrome and Obesity","volume":"47 S226","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141135236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation Between Serum Vitamin E and HOMA-IR in Patients with T2DM","authors":"Jing Zhang, Yilin Hou, Zhaozi Zhang, Yihe Shi, Zhimin Wang, Guangyao Song","doi":"10.2147/dmso.s450738","DOIUrl":"https://doi.org/10.2147/dmso.s450738","url":null,"abstract":"Background: Peroxidation is one of the important causes of insulin resistance (IR), and vitamin E is a natural antioxidant, and there may be some correlation between serum vitamin E levels and insulin resistance. Purpose: The correlation between serum vitamin E and insulin resistance in type 2 diabetes mellitus (T2DM) population. Methods: Two hundred and forty-two people (119 with T2DM) were included. One hundred and nineteen patients with T2DM were selected as the case group, and 123 people with non-T2DM were selected as the control group. People insulin resistance was detected by the homeostasis model assessment method (HOMA-IR) greater than 2.69 were included in the diabetic insulin resistance group, and those with HOMA-IR less than 2.69 were included in the diabetic non-insulin resistance group. Record the general body indicators, biochemical indicators, hepatic function indicators, vitamin E, and other indicators. Correlation analysis, logistic regression, trend analysis, and restricted cubic spline (RCS) were performed using SPSS 25.0 and R 4.1.1 software. Correlation analysis, logistic regression, trend analysis, restricted cubic spline (RCS) analysis were conducted on general body indicators, biochemical indicators, hepatic function indicators, vitamin E, and other indicators. Results: The logistic regression results showed that after adjusting for confounding factors, vitamin E was an independent influencing factor for insulin resistance in T2DM patients (P < 0.001). The trend analysis results show that with the decrease of serum vitamin E levels, the risk of insulin resistance in T2DM patients gradually increases. The RCS results showed that the risk of insulin resistance was significantly increased when the serum vitamin E level was lower than 10,575.23 ng/mL. Conclusion: Serum vitamin E levels are lower in T2DM patients than in healthy populations; Vitamin E is an independent influencing factor for HOMA-IR in T2DM patients. The risk of insulin resistance gradually increases in T2DM patients as serum vitamin E levels decrease. Vitamin E is a risk factor for insulin resistance at serum vitamin E levels below 10,575.23 ng/mL. At higher serum vitamin E levels than 10,575.23 ng/mL, vitamin E is a protective factor for insulin resistance.","PeriodicalId":504951,"journal":{"name":"Diabetes, Metabolic Syndrome and Obesity","volume":"67 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140764571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Relationship Between Small Dense Low-Density Lipoprotein Cholesterol and Metabolic Syndrome","authors":"Xiaoli Zhu, Yijun Chen, Mingan Zhu, Jinxi Hu","doi":"10.2147/DMSO.S450783","DOIUrl":"https://doi.org/10.2147/DMSO.S450783","url":null,"abstract":"Background Prior research has established an association between small dense low-density lipoprotein cholesterol (sdLDL-C) and dyslipidemia, serving as a significant marker for predicting cardiovascular diseases. Nevertheless, the connection between sdLDL-C and metabolic syndrome (MetS) remains unclear. Methods This study retrospectively analyzed 23,187 individuals who underwent health checkups at Taizhou Hospital’s health management center. Here, we investigated the relationship between sdLDL-C and MetS, along with its components, utilizing Spearman correlation analysis, receiver operating characteristic (ROC) curve analysis, logistic regression, and mediation analysis. Results The MetS group exhibited significantly higher level of sdLDL-C compared to the non-MetS group (P<0.001). We observed a strong correlation between sdLDL-C and several key factors: TG (r = 0.711), TC (r = 0.672), LDL-C (r = 0.781), GGT (r = 0.420), and HDL-C (r = −0.417). After adjusting for age and gender, the odds ratio (OR) (95% confidence interval [CI]) for MetS incidence in the second, third, and fourth quartiles versus the first quartile of sdLDL-C concentration were 2.264 (95% CI: 1.851, 2.770), 4.053 (95% CI: 3.350, 4.903), and 9.034 (95% CI: 7.531, 10.837). The optimal cut-off value for diagnosing MetS using sdLDL-C was determined to be 0.98 mmol/L, with an area under the ROC curve (AUC) of 0.716 (95% CI: 0.705, 0.726). Additionally, mediation analysis revealed that sdLDL-C mediated a 12.8% correlation between GGT and TG concentration. Conclusion The sdLDL-C is correlated with MetS and it can successfully mediate the relationship between GGT and TG. Our data suggests that sdLDL-c and GGT are suitable parameters for preventing and monitoring MetS.","PeriodicalId":504951,"journal":{"name":"Diabetes, Metabolic Syndrome and Obesity","volume":"42 11-12","pages":"1523 - 1532"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140398833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}