BMC Endocrine Disorders最新文献

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.

Objective: The current systematic review and meta-analysis assessed the prevalence of central precocious puberty (CPP) throughout the novel coronavirus disease 2019 (COVID-19) pandemic.

Design: A systematic review and meta-analysis were carried out following the principles outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA, 2020).

Data sources: PubMed, Embase, Web of Science, and WANFANG databases were searched from January 1, 2019, to March 30, 2023.

Eligibility criteria for selecting studies: (1) children and adolescents ≤ 15 years; (2) studies with the outcome of frequency of central precocious puberty, measured prior to and throughout the COVID-19 pandemic; (3) a novel CPP diagnosis was created depending on all of the following criteria: girls with a chronological age < 8 years and boys with a chronological age < 9 years at the onset of symptoms, basal luteinizing hormone (LH) levels > 0.3 UI/L, and/or GnRH-stimulated peak LH levels > 5 IU/L.

Data extraction and synthesis: The process of extracting data and evaluating the likelihood of bias was carried out by two independent reviewers. The data were pooled employing the generic inverse-variance method and presented as mean differences (MDs) with 95% CIs. The evaluation of heterogeneity was conducted employing the Cochran Q statistic, and the degree of heterogeneity was measured employing the I² statistic.

Results: This meta-analysis included 17 studies. In contrast to the same period prior to the COVID-19 pandemic, the occurrence of CPP elevated (OR = 2.57; 95% CI, 1.85-3.56). Moreover, body mass index standard deviation score (BMI SDS) differences between CPP patients prior to COVID-19 and throughout the pandemic follow-up was 0.12 (95% CI - 0.01 to 0.25 P = 0.06).

Conclusion: Overall, CPP frequency significantly elevated throughout the COVID-19 pandemic. Given the restricted number of cohort investigations in this meta-analysis, additional research may be conducted on larger groups of children in order to establish a correlation between the observed rise in precocious puberty and specific pathogenic factors.

Background: The triglyceride glucose (TyG) index, defined as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2], provides insights into overall metabolic status. However, the association between the TyG index and gout has not been investigated. Therefore, this study explored the correlation between the TyG index and gout.

Methods: Using data from the National Health and Nutrition Examination Survey, which was conducted from 2007 to 2018, this study investigated the relationship between the TyG index and gout. Demographic data and potential risk factors were analyzed and compared using t tests for continuous data and chi-square tests for categorical data. Logistic regression and subgroup analysis were performed to examine the association between the TyG index and gout.

Results: A total of 14,924 participants were enrolled, among whom 726 (4.86%) were diagnosed with gout. Without controlling for any covariates, a significant positive correlation was observed between an elevated TyG index and increased risk of gout, with an odds ratio (OR) of 2.07 and a 95% confidence interval (CI) ranging from 1.76 to 2.43. After full adjustment, this association remained statistically significant, with an adjusted OR of 1.43 and a 95% CI from 1.14 to 1.80. Subgroup analyses revealed significant interactions, particularly for females (OR = 2.55; 95% CI: 2.00-3.26), individuals with no military service history (OR = 2.15; 95% CI: 1.66-2.43), and those without diabetes (OR = 2.00; 95% CI: 1.64-2.43).

Conclusion: A positive correlation was observed between the TyG index and gout. Consequently, further large-scale prospective studies are warranted for a comprehensive analysis of the role of the TyG index in gout.

Background: The presence of hypertension significantly increases the risk of diabetes, particularly type 2 diabetes. Recently, Visceral Adiposity Index (VAI) has been introduced as a straightforward and robust alternative indicator for early detection of metabolic syndrome, cardiovascular disease, and T2DM. Visceral adiposity, more dangerous than subcutaneous fat, is associated with metabolic syndrome and cardiovascular diseases. The VAI and Lipid Accumulation Product (LAP) are indices that quantify visceral fat and lipid overaccumulation, respectively. This study aims to explore the association between VAI, LAP, and type 2 diabetes mellitus (T2DM) in US adults with hypertension using NHANES data from 2005 to 2018.

Methods: We analyzed data from 5,620 participants with hypertension in The National Health and Nutrition Examination Survey (NHANES). VAI and LAP were calculated using established formulas. The VAI is calculated based on a combination of waist circumference, body mass index (BMI), triglycerides, and high-density lipoprotein (HDL) cholesterol levels. Logistic regression models were applied to evaluate the association between these indices and T2DM, adjusting for potential confounders. Subgroup analyses by age and gender were also conducted to assess variations in risk.

Results: In all, 5,620 participants were enrolled in our analysis, with 2,754 (49%) being female, and a mean (standard deviation, SD) age of 57 (15) years. The mean (SD) cumulative average VAI and LAP among all participants was 241 (2.71) and 75 (67), respectively. Totally, higher VAI and LAP indices were significantly associated with an increased risk of T2DM in individuals with hypertension. For VAI, the odds ratios (OR) for T2DM were higher in older adults (≥ 60 years) [95% confidence interval (CI): 1.37, 1.22-1.53, per 1 SD increase] and females [95% confidence interval (CI): 1.39, 1.27-1.52, per 1 SD increase], indicating age and gender differences in risk. Non-linear relationships were observed, suggesting thresholds beyond which the risk of T2DM escalates dramatically.

Conclusions: Both VAI and LAP are reliable markers for assessing T2DM risk in individuals with hypertension. Incorporating these indices into clinical practice could enhance the identification of high-risk individuals and facilitate early intervention strategies. Future longitudinal studies are needed to confirm these associations and explore targeted interventions.

Background: The cardiometabolic index (CMI) is a novel metric for assessing cardiometabolic health and type 2 diabetes mellitus (DM), yet its relationship with insulin resistance (IR) and prediabetes (preDM) is not well-studied. There is also a gap in understanding the nonlinear associations between CMI and these conditions. Our study aimed to elucidate these associations.

Methods: We included 13,142 adults from the National Health and Nutrition Examination Survey (NHANES) 2007-2020. CMI was calculated by multiplying the triglyceride-to-high density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Using weighted multivariable linear and logistic regression explored the relationships of CMI with glucose metabolism markers, IR, preDM, and DM. Nonlinear associations were assessed using generalized additive models (GAM), smooth curve fittings, and two-piecewise logistic regression.

Results: Multivariate regression revealed positive correlations between CMI and glucose metabolic biomarkers, including FBG (β = 0.08, 95% CI: 0.06-0.10), HbA1c (β = 0.26, 95% CI: 0.22-0.31), FSI (β = 4.88, 95% CI: 4.23-5.54), and HOMA-IR (β = 1.85, 95% CI: 1.56-2.14). There were also significant correlations between CMI and increased risk of IR (OR = 3.51, 95% CI: 2.94-4.20), preDM (OR = 1.49, 95% CI: 1.29-1.71), and DM (OR = 2.22, 95% CI: 2.00-2.47). Inverse nonlinear L-shaped associations were found between CMI and IR, preDM, and DM, with saturation inflection points at 1.1, 1.45, and 1.6, respectively. Below these thresholds, increments in CMI significantly correlated with heightened risks of IR, preDM, and DM.

Conclusions: CMI exhibited inverse L-shaped nonlinear relationships with IR, preDM, and DM, suggesting that reducing CMI to a certain level might significantly prevent these conditions.

Background: 11β-hydroxylase deficiency (11β-OHD), caused by homozygosity or compound heterozygosity CYP11B1 variants, is the second most common cause of congenital adrenal hyperplasia (CAH). Due to the high degree of sequence identity between CYP11B1 and CYP11B2, chimeric genes, and complex structural variants (SVs), the conventional approach to gene testing for 11β-OHD is facing challenges. The study aimed to clarify the underlying genetic causes of two siblings of a Chinese family with 11β-OHD.

Methods: Peripheral blood samples and clinical information were collected from subjects and their family members. Sex steroid concentrations were measured using LC-MS/MS. Long-range PCR-based next-generation sequencing (NGS), PCR assay and target long-read sequencing were used to detect the pathogenic variants.

Results: Early onset hypertension, increased serum levels of adrenocorticotropin (ACTH), progesterone, testosterone, and decreased cortisol and potassium were detected in both affected siblings. Long-range PCR-based NGS identified a heterozygous missense variant (NM_000497.4:c.281 C > T, p.P94> L) in CYP11B1 gene in the two siblings. PCR detected no chimeric CYP11B2/CYP11B1 gene. We finally identified a second pathogenic variant in CYP11B1 gene via target long-read sequencing (T-LRS). This novel variant was a deletion-insertion variant and located chr8:143957269-143,957,579 (hg19) with the insertion of 'ACAG' (NM_000497.4:c.954 + 78_980delinsACAG), which was in trans with CYP11B1: c.281 C > T.

Conclusions: Our study suggests that the integrated long-range PCR-based NGS and T-LRS seem to be the most reliable and accurate method for 11β-OHD genetic diagnosis and carrier sequencing.

Background: Observational studies have demonstrated the alterations of gut microbiota composition in diabetic nephropathy (DN), however, the correlation between gut microbiota and DN remains unclear.

Methods: A two-sample Mendelian randomization (MR) analysis was designed to estimate the association between gut microbiota and DN. The summary statistics of gut microbiota from phylum level to genus level were obtained from a large-scale, genome-wide association study involving 18,340 individuals, and the data at the species level was derived from the study of TwinsUK Registry, including 1126 twin pairs. The summary statistics of DN were originated from the latest release data of FinnGen (R7, 299623 participants). The MR estimation was calculated using inverse variance weighted, weighted median, MR-Egger regression, and MR-PRESSO. Heterogeneity was assessed using Cochrane's Q test.

Results: Inverse variance weighted results indicated that the order Bacteroidetes and its corresponding class and phylum [odds ratio (OR), 1.58; 95% confidence interval (CI), 1.15-2.17], the family Verrucomicrobiaceae and its corresponding class and order (OR, 1.46; 95% CI, 1.14-1.87), the genera Akkermansia (OR, 1.46; 95% CI, 1.14-1.87) and Catenibacterium (OR, 1.33; 95% CI, 1.07-1.66) might be associated with a higher risk of DN; whereas the genera Coprococcus2 (OR, 0.68; 95% CI, 0.51-0.91) and Eubacterium_coprostanoligenes_group (OR, 0.69; 95% CI, 0.52-0.92) might play protective roles in DN.

Conclusions: This MR study suggested that several gut bacteria were potentially associated with DN, further studies are required to validate these findings.

Background: The use of non-invasive risk scores to detect undiagnosed type 2 diabetes (T2D) ensures the restriction of invasive and costly blood tests to those most likely to be diagnosed with the disease. This study assessed and compared the performance of the African Diabetes Risk Score (ADRS) with three other diabetes risk prediction models for identifying screen-detected diabetes based on fasting plasma glucose (FPG) or glycated haemoglobin (HBA1c).

Methods: Age, sex, waist circumference, body mass index, blood pressure, history of diabetes and physical activity levels from the SA-NW-PURE study were used to externally validate the ADRS and other established risk prediction models. Discrimination was assessed and compared using C-statistics and nonparametric methods. Calibration was assessed using calibration plots, before and after recalibration.

Results: Nine hundred and thirty-seven participants were included; 14% had prevalent undiagnosed T2D according to FPG and 26% according to HbA1c. Discrimination was acceptable and was mostly similar between models for both diagnostic measures. The C-statistics for diagnosis by FPG ranged from 0.69 for the Simplified FINDRISC model to 0.77 for the ADRS model and 0.77 for the Simplified FINDRISC model to 0.79 for the ADRS model for diagnosis by HbA1c. Calibration ranged from acceptable to good, though over- and underestimation were present. All models improved significantly following recalibration.

Conclusions: The models performed comparably, with the ADRS offering a non-invasive way to identify up to 79% of cases. Based on its ease of use and performance, the ADRS is recommended for screening for T2D in certain Black population groups in South Africa. HbA1c as a means of diagnosis also showed comparable performance with FPG. Therefore, further validation studies can potentially use HbA1c as the standard to compare to.