BMC Endocrine Disorders最新文献

Background: Central obesity is a well-recognized risk factor for diabetes, yet the potential role of lipids in the diabetes risk associated with central obesity remains unclear. This study aimed to explore the possible mediating role of 11 lipid parameters [high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), non-high-density lipoprotein cholesterol (Non-HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), remnant cholesterol (RC), and ratios of Non-HDL-C/HDL-C, RC/HDL-C, LDL/HDL-C, TG/HDL-C, TC/HDL-C] in the association of central obesity with diabetes risk.

Methods: We utilized data from 15,453 participants in the NAGALA longitudinal cohort to assess the association of baseline central obesity indicators [waist-height ratio (WHtR), waist circumference (WC)] and the 11 lipid parameters with diabetes risk. Mediation analysis models were constructed to explore the mediating role of lipid parameters in the association of WC/WHtR with diabetes.

Results: Confirmatory associative analysis using multivariable Cox regression showed that, except for Non-HDL-C, TC and LD-C, the remaining eight lipid parameters were significantly associated with WC/WHtR and diabetes risk. Mediation analysis indicated that TG, RC, HDL-C, and lipid ratios such as Non-HDL-C/HDL-C ratio, RC/HDL-C ratio, TG/HDL-C ratio, TC/HDL-C ratio and LDL/HDL-C ratio are potential lipids affecting the diabetes risk related to central obesity. Among these, the RC/HDL-C ratio seemed to contribute the most in the WC/WHtR-related diabetes risk association, with a mediation percentage of about 37%. Additionally, lipid ratio parameters appeared to play a more mediating role in the association of central obesity-related diabetes risk than individual lipids.

Conclusions: In central obesity-related diabetes risk, most lipids, especially lipid ratio parameters, play a significant mediating role. Given these findings, we advocate for increased efforts in multifactorial risk monitoring and joint management of diabetes. The evaluation of lipids, particularly lipid ratio parameters, may be holds substantial value in the prevention and management of diabetes risk under close monitoring of central obesity.

Objective: To analyse the correlation between urinary albumin excretion rate (UAER) and estimated glomerular filtration rate (eGFR) and the risk factors for reducing eGFR in patients with type 2 diabetes mellitus (T2DM).

Methods: A total of 431 T2DM patients admitted between January 2019 and March 2020 were selected and divided into two groups according to eGFR level. Comparing the differences between baseline data and clinical indicators, multivariate logistic regression was used to analyse the risk factors of eGFR reduction and to analyse the association between UAER and eGFR.

Results: In total, 167 patients were included in the study group and 264 patients were included in the conventional group. The study group participants were older, had longer diabetes duration, and had higher fatty liver, peripheral vascular disease (PVD), hypertension prevalence, and mean body mass index (P < 0.05). The levels of various indicators were lower than those of the conventional group (P < 0. 05). Additionally, PVD, nocturnal systolic blood pressure, fatty liver, and beta-2-microglobulin (β 2-MG) were independent risk factors for eGFR decline, with high density lipoprotein (HDL) and fasting C-peptide (CP) as protective factors. There was no obvious correlation between UAER and eGFR.

Conclusion: Peripheral vascular disease, systolic blood pressure, fatty liver, and beta-2-microglobulin are risk factors for decreased eGFR levels in patients with T2DM, which should be applied for control DKD. HDL and fasting CP have important effects on maintaining eGFR, and blood pressure and fasting CP can be used as new targets for subsequent diabetic kidney disease treatment.

Background: Metabolic syndrome (MetS) imposes a significant health burden on patients globally. Chronic low-grade inflammation is pivotal in the onset and progression of this condition. However, the role of the novel inflammatory marker, red cell distribution width to albumin ratio (RAR), in the development of MetS remains unclear.

Methods: This population-based cross-sectional study utilized data from the 2011-2020 National Health and Nutrition Examination Survey (NHANES). Participants included individuals over 18 years old with complete data on serum albumin concentration, red cell distribution, and MetS and its components. MetS was defined using the criteria established by the National Cholesterol Education Program Adult Treatment Panel III. The calculation formula for RAR is: RAR = Red cell distribution width (%)/serum albumin (g/dL). Study participants were stratified into four quartiles based on RAR levels. Logistic regression analysis and subgroup analysis were employed to explore the independent interaction between RAR and MetS, as well as investigate the relationship between RAR levels and the specific components of MetS. Finally, the receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of RAR for MetS.

Results: A total of 4899 participants were included in this study, comprising 2450 males and 2449 females; 1715 individuals (35.01%) were diagnosed with MetS. As the quartile of RAR increased, the proportion of individuals with MetS also increased. Spearman correlation analysis indicated a positive correlation between RAR and the insulin resistance index HOMA-IR. Logistic regression analysis, adjusting for multiple confounding factors, showed that each standard deviation increase in RAR was associated with a significant 1.665-fold increase (95% CI, 1.404-1.975; P < 0.001) in the odds of MetS prevalence. In logistic regression analysis stratified by quartiles of RAR, the risks of MetS in Q1-Q4 were 1.372 (95% CI, 1.105-1.704; P = 0.004), 1.783 (95% CI, 1.434-2.216; P < 0.001), and 2.173 (95% CI, 1.729-2.732; P < 0.001), respectively. Subgroup analyses and interaction tests demonstrated that gender, age, race, education, smoking status, and physical activity modified the positive association between RAR and MetS (p for interaction < 0.05). Additionally, analysis of the area under the receiver operating characteristic (ROC) curve showed that the optimal cutoff value for predicting MetS using RAR was 3.1348 (sensitivity: 59.9%; specificity: 60.6%; and AUC: 0.628).

Conclusions: Increasing RAR levels are associated with a higher risk of MetS. Therefore, greater attention should be given to patients with high RAR levels for improved prevention and treatment of MetS.

Background: Subclinical hypothyroidism (SCH) is characterized by elevated levels of thyroid hormone (TSH) and normal levels of free thyroxine (FT4). The outcomes of SCH patients are crucial for determining treatment plans; therefore, our aim is to summarize the existing prospective studies to understand the changes in thyroid function over time in SCH patients and the factors influencing these changes, providing references for clinical diagnosis and treatment.

Methods: We searched PubMed, Embase, Cochrane Library, and Web of Science for prospective follow-up studies on natural outcomes of SCH published until September 2024. Results are presented as the overall risk ratio (RR) and 95% confidence intervals (CI).

Results: We reviewed 8 prospective follow-up studies involving 1,859 individuals and extracted data from them for a meta-analysis. We found that when TSH levels are ≥ 10 mU/L, patients with SCH are more likely to progress to overt hypothyroidism (OH) (RR11.38, 95%CI 4.98-26.03, P<0.001) and were less likely to return to normal TSH levels (RR 0.20, 95%CI 0.09-0.42, P<0.001) compared to patients with TSH between 4.5 and 9.9 mU/L. In addition, patients who test positive for thyroid peroxidase antibodies (TPOAb) are more likely to progress to OH (RR 2.53, 95% CI 1.86-3.44, P < 0.001) and less likely to return to euthyroidism (RR 0.68, 95% CI 0.60-0.76, P < 0.001) compared to TPOAb-negative patients.

Conclusion: The results indicated that a large proportion of patients diagnosed with SCH will return to normal TSH levels or maintain SCH. Additionally, patients with TSH levels ≥ 10 mU/L or positive for TPOAb are more likely to experience progression and should be closely monitored. However, we did not find any gender differences in the natural outcome of SCH.

Background: To explore the association of long non-coding RNA (lncRNA) SNHG1/ miR-195 axis with type 2 diabetes mellitus (T2DM) and islet function.

Methods: The expression of SNHG1 and miR-195 was measured in T2DM patients and in healthy subjects. Correlation between indciators was evaluated using Pearson correlation analysis. INS-1 cells were used to perform the cell function assays. Insulin secretion by INS-1 was detected using ELISA. Cell counting kit-8 (CCK-8) and flow cytometry was used to detect cell proliferation and apoptosis. Luciferase report assay was to used to verify the target of SNHG1.

Results: The expression of SNHG1 was increased and miR-195 level was decreased in the serum of T2DM patients. Both SNHG1 and miR-195 could be biomarkers for T2DM diagnosis. The fasting plasma glucose (FPG) and HbA1c were positively related to SNHG1 and negatively related to miR-195. SNHG1 inhibited insulin secretion, and cell proliferation and promoted apoptosis of INS-1 cells via binding to miR-195.

Conclusions: Detection of SNHG1 and miR-195 might predict T2DM. SNHG1 could suppress proliferation and insulin secretion, but promote apoptosis of INS-1 cells via sponging miR-195.

Background: Research on the relationship between dietary phytochemical intake and the anthropometric indices as well as the diabetic foot ulcers (DFUs) remains inconsistent. We aimed to investigate the associations of dietary phytochemical index (DPI) with the novel anthropometric indices and the severity of DFUs.

Methods: This cross-sectional study was conducted on 339 adults with type 2 diabetes. Dietary intake was assessed using three 24-hour dietary recalls. To quantify the phytochemical content of the diet, the DPI estimation was done through an equation proposed by McCarty. The International Working Group on the Diabetic Foot (IWGDF) criteria and Wagner classification system were applied to assess DFUs. New anthropometric indices including a body shape index (ABSI), body roundness index (BRI) and abdominal volume index (AVI) were calculated based on formulas. We employed analysis of variance (ANOVA), analysis of covariance (ANCOVA) and logistic regression to explore associations between DPI and the anthropometric indices and DFU variables.

Results: Our results revealed no significant association between DPI and the foot ulcer indices, following adjusting for the possible covariates. Furthermore, we also examined the relationship between diabetic neuropathy, as measured by monofilament score, and the DPI. Similarly, we did not find any substantial relationship between the DPI and monofilament score (OR: 1.18; 95% CI: 0.66-2.09; P_trend = 0.56), as well as biochemical indices. Our analysis did not reveal any significant associations between the DPI and ABSI (OR: 0.70; 95% CI: 0.35-1.38; P_trend = 0.30), BRI (OR: 0.80; 95% CI: 0.33-1.95; P_trend = 0.59), and AVI (OR: 1.99; 95% CI: 0.92-4.33; P_trend = 0.08).

Conclusions: Our study revealed no significant links between the DPI and foot ulcer indices, neuropathy measures, and anthropometric indices. These findings imply that factors beyond dietary phytochemical intake may exert greater influence on the development of foot ulcers in diabetes.

Background: This study aimed to compare the diagnostic accuracy of four indicators, including waist-to-height ratio (WHTR), vascular adiposity index (VAI), TG/HDL-C, and BMI/HDL-C for metabolic syndrome (MS) in Chinese adults aged 40 years and above. Additionally, the study aimed to develop an efficient diagnostic model displayed by a nomogram based on individual's BMI and circulating HDL-C level.

Methods: A cross-sectional study was conducted on 699 participants aged 40 years and above. Quartiles of BMI/HDL-C, TG/HDL-C, VAI, and WHTR were used as independent variables, and metabolic syndrome was used as the dependent variable. Logistic regression was conducted to explore the impact of each parameter on the risk of MS. The areas under the receiver operating characteristics were compared to determine the accuracy of the indicators in diagnosing MS in the participants. Logistic regression was run to construct the nomograms, and the performance of the nomogram was assessed by a calibration curve.

Results: MS subjects had higher levels of BMI, BFM, PBF, VFA, AMC, WC, SCR, TG, and insulin, but lower LDH and HDL-C levels than the subjects without MS. The BMI/HDL-C ratio was positively correlated with the prevalence of MS and its components. The final diagnostic model included five variables: gender, BFM, WC, TG, and BMI/HDL-C. The model showed good calibration and discrimination power with an AUC of 0.780. The cut-off value for the nomogram was 0.623 for diagnosing MS.

Conclusions: BMI/HDL-C ratio was an independent risk factor for MS in Chinese adults. BMI/HDL-C was significantly correlated with MS and its components. BMI/HDL-C was the most powerful diagnostic indicator compared to other indicators, including TG/HDL-C, VAI and WHTR for diagnosing MS. The nomogram drawn based on the diagnostic model provided a practical tool for diagnosing MS in Chinese adults.

Objective: This study aims to explore the association between the triglyceride-glucose (TyG) index and vitamin D status to enhance our understanding of how vitamin D status relates to metabolic health and to provide evidence for the early diagnosis of vitamin D deficiency (VDD) using the TyG index.

Methods: We conducted a comprehensive search in various databases, including PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), China Biology Medicine disc, China Science and Technology Journal Database, and Wanfang Data to gather articles published from the inception of these databases until February 19, 2024. We assessed the quality of included studies using the Newcastle-Ottawa Scale (NOS) for case-control studies and the Agency for Healthcare Research and Quality (AHRQ) methodology checklist for cross-sectional studies. Statistical analyses in this study were conducted using conversion methods for non-standard data formats and consolidation techniques for combining multiple groups. The Fisher transformation method was used for correlation coefficients. We used a random-effects model considering the inherent clinical heterogeneity among the studies, and assessed statistical heterogeneity with the Cochrane Q test and I² statistic, complemented by subgroup analyses and sensitivity analysis.

Results: Our meta-analysis selected a total of nine studies. The analysis revealed that patients with vitamin D deficiency (VDD group) exhibited a significantly higher TyG index than those without deficiency (no-VDD group), with a mean difference (MD) of 0.16 (95% CI: 0.10 to 0.23, I² = 93%). This association was particularly pronounced among patients with type 2 diabetes (T2DM), showing an MD of 0.15 (95% CI: 0.05 to 0.26, I² = 55%). Additionally, a negative correlation was observed between the TyG index and vitamin D levels, with a correlation coefficient (r) of -0.236 (95% CI: -0.310 to -0.159, I² = 91%). Excluding each study sequentially in the sensitivity analyses did not significantly alter the outcomes.

Conclusions: Our findings demonstrate a significant association between the TyG index and vitamin D status across diverse populations, including those with T2DM, subclinical hypothyroidism (SCH), and metabolic associated fatty liver disease (NAFLD). Our results reveal a notable disparity in the TyG index between vitamin D deficient and non-deficient groups, suggesting that vitamin D may play a critical role in metabolic health. These findings highlight the need for further research to explore the underlying mechanisms and clinical implications of vitamin D in the context of various metabolic disorders.

Background: Type 2 diabetes mellitus (T2DM) was one of the most prevalent comorbidities among patients with coronavirus disease 2019 (COVID-19). Interactions between different metabolic parameters contribute to the susceptibility to the virus; thereby, this study aimed to rank the importance of clinical and laboratory variables as risk factors for COVID-19 or as protective factors against it by applying machine learning methods.

Method: This study is a retrospective cohort conducted at a single center, focusing on a population with T2DM. The patients attended the Yazd Diabetes Research Center in Yazd, Iran, from February 20, 2020, to October 21, 2020. Clinical and laboratory data were collected within three months before the onset of the COVID-19 pandemic in Iran. 59 patients were infected with COVID-19, while 59 were not. The dataset was split into 70% training and 30% test sets. Principal Component Analysis (PCA) was applied to the data. The most important components were selected using a 'sequential feature selector' and scored by a Linear Discriminant Analysis model. PCA loadings were then multiplied by the PCs' scores to determine the importance of the original variables in contracting COVID-19.

Results: HDL-C, followed by eGFR, showed a strong negative correlation with the risk of contracting the virus. Higher levels of HDL-C and eGFR offer protection against COVID-19 in the T2DM population. But, the ratio of BUN to creatinine did not show any correlation. Conversely, the AIP, TyG index and TG showed the most positive correlation with susceptibility to COVID-19 in such a way that higher levels of these factors increase the risk of contracting the virus. The positive correlation of diastolic BP, TyG-BMI index, MAP, BMI, weight, TC, FPG, HbA1C, Cr, systolic BP, BUN, and LDL-C with the risk of COVID-19 decreased, respectively.

Conclusion: The atherogenic index of plasma, triglyceride glucose index, and triglyceride levels are the most significant risk factors for COVID-19 contracting in individuals with T2DM. Meanwhile, high-density lipoprotein cholesterol is the most protective factor.

Background: Hyperuricaemia is common among obese children and adolescents, and is closely related to insulin resistance. The aim of this study was to explore the relationships between youth insulin resistance and hyperuricaemia, as well as their relationships with lifestyle factors in youths, to provide early guidance on the risk factors for hyperuricaemia in adolescents.

Methods: This study included 233 adolescents aged 10 to 20 years. Insulin resistance was evaluated via the homeostasis model assessment-insulin resistance (HOMA-IR) method. Binary logistic regression analysis was used to assess the associations of HOMA-IR with hyperuricaemia status and serum uric acid (UA) levels. The participants were subsequently divided into two groups, the noninsulin resistant group (HOMA-IR ≤ 3.2) and the insulin resistant group (HOMA-IR > 3.2), to further explore the factors that may affect the serum UA level. Finally, the predictive ability of different indicators of hyperuricaemia was evaluated via the ROC curve.

Results: Binary logistic regression analysis revealed a significant increase in the risk of developing hyperuricaemia for individuals with elevated HOMA-IR (p < 0.001) and insulin resistance (p < 0.01). Spearman's correlation analysis revealed a significant positive linear correlation between HOMA-IR and serum UA levels (r = 0.4652, p < 0.001). Among insulin-resistant adolescents, UA levels were positively correlated with weight ratings, frequency of staying up late, and sugary beverages intake. Notably, individuals who engaged in 1-3 h of weekly exercise had the lowest UA levels. The area under the ROC curve for HOMA-IR was 0.847 (cut-off value = 2.165, p < 0.001), and the optimal prediction model included HOMA-IR, BMI z-score, and other lifestyle factors (AUC: 0.870, p < 0.001)).

Conclusion: HOMA-IR was identified as an independent risk factor for the development of hyperuricaemia and could be used as a sensitive indicator for the prediction its development in adolescents. In insulin-resistant adolescents with hyperuricaemia, maintaining normal weight, engaging in physical exercise for 1-3 h per week, avoiding staying up late and limiting sugary beverages intake are recommended to reduce the prevalence of hyperuricaemia among adolescents.