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Background: Oxidative stress (OS) is associated with a variety of non-communicable diseases, including MetS, diabetes mellitus, metabolic syndrome, and cardiovascular disease through increased production of reactive oxygen species (ROS) and impairment of antioxidant defense mechanisms. Antioxidants can protect cells against free radical damage, so it seems important to determine the relationship between the quality of dietary antioxidants intake and chronic diseases. The Dietary Antioxidant Quality Score (DAQS) is obtained by adding the daily intake of known dietary vitamins and minerals, including selenium, zinc, vitamin A, vitamin C, and vitamin E, compared to the recommended daily intake (RDI). Therefore, this study aims to determine the relationship between DAQS, serum lipids, markers of glucose homeostasis, blood pressure and anthropometric features among obese adults. Methods: In the present cross-sectional study, 338 individuals who were obese (BMI ≥ 30 kg/m2) aged 20-50 years were recruited from Tabriz and Tehran, Iran. A validated semi-quantitative Food Frequency Questionnaire (FFQ) with 168 food items was used to quantify dietary consumption; accordingly, DAQS was computed. Blood biomarkers were measured using enzyme-linked immunosorbent assay (ELISA) kits. A standard mercury sphygmomanometer was used to assess blood pressure, and bioelectrical impedance analysis (BIA) was performed to determine body composition. The association between the DAQS tertiles and biochemical variables was investigated using multinomial logistic regression. Results: Participants in the highest tertile of DAQS have a lower diastolic blood pressure (DBP) values in all of the adjusted models [odds ratio (OR) = 0.920; confidence interval (CI)= 0.852-0.993, P-value = 0.03] in the analysis of co-variance (ANCOVA) model. Similarly, subjects at the second tertile of DAQS had lower DBP compared with the first tertile in age and sex-adjusted model [OR= 0.937; CI= 0.882-0.997]. There was no statistically significant difference for other metabolic parameters in different DAQS tertiles. Conclusion: According to our findings, higher DAQS was associated with lower DBP among obese adults with obesity in two major cities of Iran (Tehran and Tabriz). Other studies with interventional design are needed to better elucidate these associations and underlying mechanisms. [ABSTRACT FROM AUTHOR]

Background: Several previous studies revealed the role of dietary energy density (DED) in developing obesity and related disorders. However, the possible role of DED in triggering cardiometabolic risk factors of individuals with obesity has not been studied yet. This study aimed to evaluate the association between DED and anthropometric parameters, blood pressure, and components of metabolic syndrome (MetS) (such as glycemic markers, lipid profile, and blood pressure) among individuals with obesity. Methods: In this cross-sectional study, we included 335 adults with obesity (BMI ≥ 30 kg/m2) aged 20–50 years in Tabriz and Tehran, Iran. Dietary intake was assessed by a validated semi-quantitative Food Frequency Questionnaire (FFQ), including 168 food items; then, DED was calculated. MetS was defined based on the guidelines of the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III). Enzymatic methods were used to assess serum lipids, glucose, and insulin concentrations. Blood pressure was measured by sphygmomanometer and body composition by bioelectrical impedance analysis (BIA). Results: Participants in the higher tertiles of DED had more intake of carbohydrate, dietary fat, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), and meat, fish, poultry (MFP). Increasing the DED in both methods had no association with systolic blood pressure (SBP), fasting blood sugar (FBS), low-density lipoprotein cholesterol (LDL-C), insulin, triglyceride (TG), and homeostatic model assessment of insulin resistance (HOMA-IR) even after adjustment for confounders. However, diastolic blood pressure (DBP) decreased in second tertile of DED I (β = 0.921, P = 0.004). The quantitative insulin-sensitivity check index (QUICKI) in second tertile of both DED methods had significant positive association with DED. In the second tertile of DED II, while total cholesterol (TC) significantly decreased (P crude = 0.036, P adjusted = 0.024), high-density lipoprotein cholesterol (HDL-C) increased (β = 1.096, p = 0.03). There was no significant changes in biochemical parameters in third tertile of DED I and II even after adjustment for covariates. Also, higher tertiles of DED was associated with reduced prevalence of MetS. Conclusion: High DED was associated with lower levels of blood pressure and TC but elevated levels of HDL and QUICKI independent of such confounders as age, BMI, sex, and physical activity. Further longitudinal studies are warranted to better elucidate casual associations. [ABSTRACT FROM AUTHOR]

Introduction: Regarding the increased prevalence of obesity among children and adolescents, and the impact of obesity on insulin resistance (IR) and other metabolic disorders, this study was performed to determine the association of cardiometabolic risk factors (CMRFs) with IR in overweight and obese children. Method: In this cross-sectional study 150 overweight and obese children (BMI ≥ 85th and BMI ≥ 95th age-sex specific percentile) and adolescents were selected via convenient sampling method from Endocrinology clinic in Karaj; Iran in 2020. Anthropometric indices, lipid profile, fasting blood glucose (FBG), and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) were evaluated. IR was defined as HOMA-IR ≥ 2.6. Multivariable linear and logistic regression model was used to assess the association of CMRFs with insulin level and IR respectively. Results: The mean age of children was 10.37 (± 2.6) years. Fifty-four percent of the participants were girls. IR was increased through increasing age (P < 0.001). In the multivariate logistic regression model, by increasing each unit increment in waist circumference (OR: 1.03, 95% CI: 1.01–1.06), wrist circumference (OR: 1.47, 95% CI: 1.06–2.02) total cholesterol (OR: 1.01, 95% CI: 1.003–1.03) and FBG (OR: 1.11, 95% CI: 1.05–1.18) the odds of IR increased significantly. Moreover, in the adjusted linear regression model, HOMA-IR was associated significantly with waist to height ratio (β: 2.45), and FBG (β: 0.02). Conclusion: There was a significant association between some CMRFS with IR in overweight and obese children. [ABSTRACT FROM AUTHOR]

Background: Features of metabolic syndrome such as abdominal obesity, insulin resistance, hypertension and dyslipidemia are commonly encountered in polycystic ovary syndrome (PCOS). Recent evidence has suggested an association between high serum uric acid/creatinine (UA/Cr) ratio and metabolic syndrome however, no studies have investigated this association in PCOS. The current study was conducted to investigate the relationship between UA/Cr ratio and PCOS and to identify whether UA/Cr ratio and free androgen index (FAI) have an additive interaction for detection of PCOS risk in obese women. Methods: This study enrolled 40 obese women with PCOS and 40 control women with regular menstrual cycles matched for age and body mass index (BMI). Anthropometric measurements, fasting glucose, fasting insulin, homeostasis model assessment of insulin resistance (HOMA-IR), lipids profile, luteinizing hormone (LH), follicle stimulating hormone (FSH), estradiol, dehydroepiandrosterone sulfate (DHEAS), sex hormone binding globulin (SHBG), total testosterone, free androgen index (FAI), UA/Cr ratio were assessed. Results: Serum UA/Cr ratio was significantly higher in obese women with PCOS than in non-PCOS women. UA/Cr ratio was correlated with BMI, waist and neck circumferences, blood pressure, fasting insulin, HOMA-IR, lipids, LH/FSH, estradiol, DHEAS, total testosterone, FAI and SHBG. UA/Cr ratio and FAI were independent risk factors for PCOS in obese women however, the addictive interaction between UA/Cr ratio and FAI had a higher fold risk (OR: 4.3, 95% CI, 3.4–7.58) and a more significance (P = 0.002) for determination of PCOS. Conclusion: Serum UA/Cr ratio combined with FAI can exert an additive or synergistic impact on prediction of PCOS in obese women. [ABSTRACT FROM AUTHOR]

Background: Obesity is a major public health concern in developed and even developing countries worldwide. Adiponectin is a protein secreted by adipose tissue that modulates many metabolic processes and plays a vital role in obesity. This study aimed to determine the association of four variants of the ADIPOQ gene with serum adiponectin, cortisol levels and obesity status. Methods: This case-control study was performed on 164 obese individuals compared by 156 control from the Tehran Lipid and Glucose Study (TLGS). Standard procedures obtained anthropometric measures and metabolic parameters. Cortisol and adiponectin levels were measured by ELISA method. rs1501299, rs266729, rs17300539, and rs17366743 on the ADIPOQ gene were genotyped using the PCR-RFLP. The correlation between adiponectin gene SNPs and obesity were calculated by Additive, dominant, and recessive genetic models. Pearson's or Spearman's found correlations between adiponectin levels and metabolic and anthropometric variables. Data were analyzed using SPSS software Version 20. Results: Adiponectin and cortisol levels were significantly lower in obese subjects compared to the control group (p < 0.05). There was a significant negative correlation between serum adiponectin level and BMI, waist circumference (WC), waist-hip ratio, hip circumference (HC), Fasting blood sugar (FBS) Triglyceride (TG), Total cholesterol (TC), Systolic blood pressure (SBP), Diastolic blood pressure (DBP) (r = − 0.147, r = − 0.324, r = 0.371, r = − 0.179, r = − 0.299, r = − 0.277, r = − 0.041, r = − 0.134, and r = − 0.149, respectively). A positive correlation was found between adiponectin and high-density lipoprotein cholesterol (HDL-C) (r = 0.29), but no significant correlations were found between adiponectin and Low-density lipoprotein cholesterol(LDL-C) and cortisol. ADIPOQ variant rs1501299 was significantly associated with cortisol levels in subjects with BMI ≥ 25 (P-value =0.039). Conclusions: Adiponectin and cortisol levels were associated with obesity. No ADIPOQ gene variants and haplotypes were associated with cortisol, Adiponectin, and obesity. [ABSTRACT FROM AUTHOR]

Introduction: The relationship between dyslipidemia and obesity has been widely reported, but the global lipid profiles associated with the development of obesity still need to be clarified. An investigation into the association between the lipidomic plasma profile and adolescent obesity may provide new insights into the development of obesity. Methods: Mass spectrometry coupled with liquid chromatography was applied to detect the global lipidome in the fasting plasma from 90 Chinese adolescents, including 34 obese adolescents, 26 overweight adolescents, and 30 adolescents with a normal body mass index (BMI). All participants underwent anthropometric measurements by using InBody. Clinical biochemical indicators were measured by Cobas Elecsys. Results: Both qualitative and quantitative analyses revealed a gradual change in plasma lipid features among obese students, exhibiting characteristics close to overweight students, but differing significantly from normal students. Compared with normal and overweight students, levels of triglyceride (TG), 18-hydroxycortisol, isohumulinone A, and 11-dihydro-12-norneoquassin were up-regulated in the obese group, while phosphatidylcholine (PC), phosphatidylethanolamine (PE), lysoPC (LPC), lysoPE (LPE), and phosphatidylinositol (PI) were significantly down-regulated in the obese group. Then, we conducted Venn diagrams and selected 8 significant metabolites from the 3 paired comparisons. Most of the selected features were significantly correlated with the anthropometric measurements. Conclusions: This study demonstrated evidence for a relationship between the eight significant metabolites with obese adolescents. These lipid features may provide a basis for evaluating risk and monitoring the development of obesity. [ABSTRACT FROM AUTHOR]