Metabolic syndrome and related disorders最新文献

Background: Household food insecurity (HFI) refers to the lack of access to safe and nutritious food, and this condition may be associated with the occurrence of metabolic syndrome (MetS). Thus, this study aimed to conduct a quantitative synthesis (meta-analysis) to summarize the evidence from epidemiological studies on the association between HFI and MetS. Methods: A systematic search was conducted in the PubMed, Embase, Web of Science, and Latin American and Caribbean Health Sciences Information Center databases to retrieve epidemiological studies published until October 2023. The entire process of selection, data extraction, and assessment of article quality was independently performed by two reviewers. The quality of the studies was evaluated using the criteria proposed by the National Institutes of Health instrument. The random-effects model was used to report the quantitative synthesis of combined data. The Q-test and I² index were used to assess heterogeneity. Egger's and Begg's tests were employed to evaluate publication bias. Results: A total of 10 articles meeting the eligibility criteria were selected and included in this meta-analysis. High heterogeneity was observed among the studies (I² > 70), along with a low risk of publication bias. Considering all ten included studies, no statistically significant association was found between HFI and MetS (odds ratio = 1.17; 95% confidence interval: 0.89-1.55; I² = 79.9%). Conclusions: The findings of this meta-analysis did not reveal a statistically significant association between HFI and MetS, indicating the need for further studies aimed at exploring and expanding the scientific evidence on this relationship.

Aims: Identifying participants with type 2 diabetes (T2D) based only on electronic health record (EHR) or self-reported data has limited accuracy. Therefore, the objective of the study was to develop an algorithm using EHR and self-reported data to identify participants with and without T2D. Methods: We included participants enrolled in the Mayo Clinic Biobank. At enrollment, participants completed a baseline questionnaire on health conditions, including T2D, and provided access to their EHR data. T2D status was based on self-report and EHR data (International Classification of Diseases codes, hemoglobin A1c [HbA1c], plasma glucose, and glucose-regulating medications) within 5 years prior to and 2 months after enrollment. Participants who self-reported T2D but lacked corroborating EHR data were categorized separately ("only self-reported T2D"). After identifying participants with T2D, we identified participants without T2D based on normal HbA1c and plasma glucose. Participants who self-reported the absence of T2D but lacked corroborating EHR data were categorized separately ("only self-reported no T2D"). Using manual chart reviews (gold standard), we calculated the positive and negative predictive values (NPV) to identify T2D. Results: Of 57,000 participants, the algorithm classified participants as having T2D (n = 6,238), no T2D (n = 38,883), "only self-reported T2D" (n = 757), and "only self-reported no-T2D" (n = 9,759). The algorithm had a high positive predictive value (96.0% [91.5%-98.5%]), NPV (100% [98.0%-100%]), and accuracy (99.5% [98.3%-99.8%]). Participant age (median [range]) ranged from 52 (18-98) years (only self-reported T2D) to 67 (19-99) years (T2D) (P < 0.0001), and the proportion of women ranged from 45.3% (T2D) to 69.6% (only self-reported no T2D) (P < 0.0001). Most participants were of the White race (84.0%-92.7%) and non-Hispanic ethnicity (97.6%-98.6%). Conclusions: In this study, we developed an algorithm to accurately identify participants with and without T2D, which may be generalizable to cohorts with linked EHR data.

Introduction: Hashimoto's thyroiditis is a common endocrinological disorder that often coexists with obesity. Thyroid hormones interact with the regulation of sex steroids, and thyroid autoimmunity has a negative impact on female fertility. There are studies showing when euthyroid state is achieved with hormone replacement therapy (HRT), the reproductive hormone profile is improved but they usually compare the reproductive hormones before and after HRT in the same individuals. Studies comparing patients with Hashimoto's thyroiditis in an euthyroid state receiving HRT with individuals having normal thyroid function are limited. Here, it was aimed to search the impact of euthyroid Hashimoto's thyroiditis on reproductive hormone profile in women living with obesity. Materials and Methods: Sixty-one randomly selected female patients with Hashimoto's thyroiditis were included as the case group and 60 patients without Hashimoto's thyroiditis were included as the control group, from our obesity center. The case group included patients who had menstrual cycles and were euthyroid under l-thyroxine treatment for at least 6 months. Data on weight, height, body mass index (BMI), waist circumference (WC), free thyroxine (fT4), thyroid stimulating hormone (TSH), thyroid peroxidase antibody (anti-TPO), cortisol, insulin, prolactin (PRL), follicular stimulating hormone (FSH), luteinizing hormone (LH), estradiol (E₂), progesterone (prog), testosterone (T), and dehydroepiandrosterone sulfate (DHEAS) levels, l-thyroxine treatment dosage (for case group), and accompanying diseases were recorded. The results were evaluated using SPSS. Results: A total of 121 patients were included in the study. Mean age was 41.8 ± 8.5 years in case and 38.6 ± 8.9 years in control group. There was no significant difference in weight, height, BMI, WC, or accompanying diseases between Hashimoto's thyroiditis and control group. fT4, anti-TPO, cortisol levels were higher in Hashimoto's thyroiditis group when compared with control group, but there was no significant difference for TSH, insulin, FSH, LH, E₂, prog, T, DHEAS, or PRL. Conclusion: In women living with obesity, it is important to screen for Hashimoto's thyroiditis and achieve euthyroidism through effective LT4 treatment to promote a healthy reproductive system and improve fertility rates.

Background and Objective: Obesity is a global health issue intricately linked to metabolic syndrome (MetS), insulin resistance, and dyslipidemia. Anthropometric indices, particularly those measuring central obesity, have emerged as more reliable predictors of these metabolic disorders than general obesity indices such as body mass index (BMI). However, the relative predictive power of these indices remains debated, particularly across sexes. This study aimed to evaluate the discriminative performance of various anthropometric measures, including lipid accumulation product (LAP), BMI, waist circumference (WC), and visceral adiposity index (VAI), in predicting insulin sensitivity, β-cell function, MetS, and dyslipidemia using National Health and Nutritional Evaluation Survey III (NHANES III) data. Methods: A cross-sectional analysis of 3,706 adults from the NHANES III database was conducted. Anthropometric indices were compared against insulin sensitivity Homeostasis Model Assessment (HOMA)-S, β-cell function (HOMA-B), metabolic syndrome (MetS) status, and dyslipidemia. Receiver-operating characteristic (ROC) curves and linear regression models were used to identify thresholds for predicting metabolic abnormalities. Results: LAP emerged as the most discriminative index across all outcomes, outperforming BMI and WC, particularly in predicting insulin sensitivity and β-cell function in males. In females, BMI was superior in predicting β-cell function. VAI demonstrated the strongest association with dyslipidemia but was less effective in predicting insulin resistance. Conclusion: LAP significantly outperforms conventional anthropometric indices in identifying insulin resistance and MetS, highlighting its potential as a screening tool for cardiometabolic risk. Gender differences in the predictive abilities of these measures suggest that BMI may retain value in assessing β-cell function in females. VAI should be considered when screening for dyslipidemia but is less effective for insulin resistance.

Objectives: There has been discussion over the association between vitamin C intake and the risk of metabolic syndrome (MetS). This study examined the relationship between dietary vitamin C intake and the risk of MetS in a sizable adult American population. Methods: We examined the relationship between dietary vitamin C intake and the risk of MetS in 12,943 persons from the 2007 to 2018 National Health and Nutrition Examination Survey (NHANES). This association was then evaluated using logistic regression and restricted cubic spline models. Sex and age-based subgroup analyses were carried out. Results: According to the results of the multiple regression model, the risk of MetS was inversely correlated with dietary vitamin C intake, vitamin C intake derived from fruits and vegetables. The adjusted results (odds ratios with 95% confidence intervals) for the highest versus lowest tertile were 0.80 (0.68-0.93), 0.86 (0.75-0.98), and 0.80 (0.69-0.93). Subgroup analyses further showed that the negative correlation of dietary vitamin C intake with the risk of MetS was particularly pronounced among females, those in the 20-39 age group, and those in the ≥60 age group. The dose-response relationship's findings indicated that vitamin C from diet and fruits had a nonlinear correlation with the risk of MetS, whereas vitamin C from vegetables had a linear correlation. Conclusions: The risk of MetS in adult Americans was found to be negatively correlated with dietary vitamin C intake, particularly from fruits and vegetables.

Background: Women with gestational diabetes mellitus (GDM) and their offspring have an increased risk of adverse perinatal and long-term health outcomes, which may be attributable to epigenetic modification of diabetes and obesity susceptibility genes. We aimed to investigate the methylation patterns of eight genes in GDM and normoglycemic (NG) mothers, and their respective offspring. Methods: This cross-sectional study, conducted at Aga Khan University from August 2019 to December 2022, recruited pregnant women in the first trimester of gestation from the outpatient obstetrics clinic. Participants were classified as NG or GDM based on the Society of Obstetricians and Gynecologists Pakistan. Venous blood samples were collected from mothers and cord blood from neonates. Peripheral blood mononuclear cells were used for DNA extraction and methylation analysis using methylation-specific PCR. Maternal and neonatal clinical data were recorded. Statistical analysis was performed using R, including binary logistic regression to assess the association between various gene methylation levels and GDM. Results: The study found that GDM mothers had significantly higher fasting blood glucose, 2-hr OGTT, and serum carboxymethyl lysine (CML) levels compared to NG mothers, but no significant differences in neonatal birth weight or serum CML levels. Chemerin methylation was significantly lower in GDM mothers and their babies, while NAMPT, MTNR1B, FNDC5, FAT4, and FTO methylation levels were higher in GDM offspring compared to NG offspring. GDM mothers also had higher methylation levels of brain-derived neurotrophic factor gene (BDNF). Multivariable binary logistic regression identified methylation levels of maternal BDNF and neonatal MTNR1B to be independently associated with GDM. Conclusions: Our study shows a trend of epigenetic modifications in both GDM mothers and their offspring in various genes related to metabolism and inflammation, suggesting an intergenerational transmission of increased risk of developing metabolic disorders. These findings emphasize the need for high throughput studies, early screening, tight glucose control during pregnancy, and postnatal follow-up to mitigate long-term health risks.

Background: Previous studies suggested a relationship between obesity and a high risk of thyroid cancer. However, the association between high body mass index (BMI) and the aggressiveness of papillary thyroid carcinoma (PTC) is controversial. In this study, we aimed to investigate the impact of excess BMI on histopathologic aggressiveness of PTC in a Chinese population. Methods: Between January 2015 and September 2020, 4369 PTC patients who were tested for BRAF mutation at Jiangyuan Hospital were enrolled. Logistic regression analyses were used to evaluate the associations between BMI and clinicopathological features of PTC as well as tumor BRAF mutational status. Results: Of 4369 PTC patients, the mean BMI was 24.06 ± 3.49 kg/m², and BRAF^V600E mutations were detected in 3528 (80.8%) patients. BMI ≥24.0 at initial surgery was associated with tumor multifocality and bilaterality, but not with advanced tumor stage, extrathyroidal extension (ETE), ratio of positive lymph nodes >0.3, distant metastasis, or BRAF^V600E mutation. Conclusion: Our present study suggested that compared to patients with a normal BMI, overweight and obese patients had a greater risk of multifocality and bilaterality of PTC. No significant associations were observed between higher BMI and the more advanced tumor-node-metastasis stage or BRAF^V600E mutational status.

Background: This study aimed to explore the association of cardiometabolic index (CMI), CMI-age, visceral adiposity index (VAI), and VAI-age with heart failure (HF) and to compare those indicators for early identification of HF. Methods: Drawing from the National Health and Nutrition Examination Survey (NHANES) for 2011-2018, we enrolled 8999 participants in a cross-sectional study. The association of different visceral obesity indicators (CMI, CMI-age, VAI, and VAI-age) with HF was estimated by multivariable regression analysis. Receiver operating characteristic (ROC) curves were used to examine the predictive ability of CMI, CMI-age, VAI, and VAI-age on patients with HF. Results: CMI, CMI-age, VAI and VAI-age showed positive correlations with HF. When indicators were analyzed as continuous variables, CMI, CMI-age, VAI, and VAI-age showed positive correlations with HF in both the crude and adjusted models (all P < 0.05). When indicators were analyzed as categorical variables, it was found that in all four models, the ORs of group Q4 was significantly different compared to Q1 (all P < 0.05), suggesting the risk of HF is significantly increased with higher CMI, CMI-age, VAI, or VAI-age. The association between those indicators (CMI, CMI-age, VAI, and VAI-age) and HF was similar in all stratified populations (P for interaction >0.05).The areas under the ROC curve (AUCs) of four indicators in predicting HF were significantly different (CMI: 0.610, 95% CI, 0.578-0.643; CMI-age: 0.700, 95% CI, 0.669-0.726; VAI: 0.593, 95% CI, 0.561-0.626; VAI-age: 0.689, 95% CI, 0.661-0.718), suggesting that CMI-age was significantly better than the other three indicators in predicting HF (P < 0.001). Conclusions: CMI, CMI-age, VAI, and VAI-age were all independently correlated with the risk of HF. In four indicators, the CMI-age was significantly better than the other three indicators in predicting HF, which provides new insights into the prevention and management of HF.