Frontiers in Endocrinology最新文献

Background: Diabetic foot ulcer (DFU) is one of the serious complications of type 2 diabetes (T2DM). Malnutrition is associated with amputation in DFU patients. We aimed to use the CONUT score to investigate the risk factors associated with DFU amputation.

Methods: A retrospective analysis was conducted on 387 DFU patients admitted to the First Affiliated Hospital of Guangxi Medical University from January 2024 to June 2025. The patients were divided into non-amputation group (n=231) and amputation group (n=156). Additionally, patients were divided into three groups based on the CONUT score. Demographic characteristics, blood biochemical indicators, amputation rate were measured. Multiple linear regression analysis, multivariate binary logistic regression analysis, subgroup analysis and receiver operating characteristic (ROC) curve analysis were conducted.

Results: The overall amputation rate of DFU patients was 40.3%. The amputation group had longer hospital stays, larger foot ulcer areas, higher incidence of osteomyelitis and peripheral arterial disease (PAD) than those without amputation. In addition, the amputees had lower levels of albumin, prealbumin, hemoglobin and prognostic nutritional index, but higher levels of WBC, ESR, and CONUT score. As the CONUT score increased, the amputation rate of patients also increased. Logistic regression found that CONUT score, osteomyelitis and PAD were independent risk factors of amputation in DFU patients. Subgroups analysis showed CONUT score remained significantly associated with DFU amputation in the subgroups except in patients with HbA1c < 7%. ROC analysis showed that the AUC was 0.705 (95% CI: 0.652-0.758, p<0.001), indicating that CONUT score had good predictive performance for DFU amputation.

Conclusion: CONUT score was associated with amputation in DFU patients. Early assessment of the patient's nutritional status and improvement of malnutrition can reduce the risk of amputation.

Background: Creatinine clearance (CCR) is an important marker of renal function. The association between CCR and lumbar bone mineral density (BMD) in patients with osteoporotic fractures (OPFs) remains unclear. With the increasing prevalence of OPFs in aging populations and the complications associated with renal impairment, understanding this relationship is essential for optimizing patient outcomes.

Methods: A total of 1,313 patients with OPFs (aged ≥50 years) were retrospectively analyzed. Patient CCR, lumbar BMD, and relevant clinical data were collected. Lumbar BMD (g/cm²) served as the outcome variable, while baseline CCR was the exposure variable. Multivariate analyses were performed with adjustments for multiple covariates.

Results: In fully adjusted multivariate regression analysis, baseline CCR showed a significant positive correlation with lumbar BMD (β = 0.16; 95% CI: 0.13-0.18; P < 0.01). A threshold effect was identified in the CCR-BMD relationship, characterized by an inflection point at approximately 130 mL/min CCR. Below this threshold, higher CCR was associated with increased lumbar BMD. Above 130 mL/min, further increases in CCR were not associated with further BMD gains (β = -0.05, P = 0.18). Mean lumbar BMD values increased across CCR tertiles (0.69 ± 0.13, 0.73 ± 0.14, and 0.81 ± 0.15 g/cm²; P < 0.01).

Conclusion: CCR was positively associated with lumbar BMD among elderly patients with OPFs, showing a non-linear relationship characterized by a CCR threshold (~130 mL/min). These findings highlight the potential importance of renal function assessment in osteoporosis risk evaluation and fracture management.

Background & aim: Leptin (LEP), a pro-inflammatory adipokine secreted by adipocytes acting through leptin receptor (LEPR), is critical in maintaining body weight, lipid and glucose metabolism. LEP and LEPR genetic variants are reportedly associated with type 2 diabetes (T2D). Among these, the LEPR rs1137101 A/G polymorphism has emerged as a potential determinant of metabolic risk. The current study investigates the association of LEP and LEPR genetic variants, along with their transcript and protein levels, and evaluates genotype-phenotype correlations with metabolic parameters and T2D susceptibility in the Gujarat population.

Methods: Genomic DNA isolated from PBMCs of 451 controls and 439 patients was used for genotyping LEP (rs7799039 G/A; rs2167270 G/A) and LEPR (rs1137101 A/G; rs1805094 G/C) polymorphisms by PCR-RFLP. RNA isolated from PBMCs was used to assess LEP and LEPR transcript levels by qPCR. Fasting Blood Glucose (FBG) levels, Body Mass Index (BMI) and plasma lipid profile were also assessed for the genotype-phenotype correlation analysis. ELISA was performed to estimate plasma protein levels of leptin and its soluble receptor (sOb-R).

Results: Our findings suggest a significant association of LEPR rs1137101 A/G with T2D, where the GG genotype and G allele conferred a 1.66- and 1.24-fold increased risk for the disease, respectively. The GG genotype also showed an association with increased FBG and TC levels. In addition, an increased GG haplotype frequency, increased LEP transcript and protein levels, and decreased LEPR transcript and protein levels were observed in T2D patients. Moreover, leptin protein levels showed a positive correlation with increased BMI and TG, while sOb-R protein levels showed a positive correlation with increased BMI, FBG, and TG levels.

Conclusion: The LEPR rs1137101 A/G polymorphism, together with elevated leptin, and decreased sOb-R protein levels, may increase susceptibility to T2D in the Gujarat population.

Background: Researchers have explored machine learning (ML) in diagnosing endometriosis. However, systematic evidence on its diagnostic accuracy for endometriosis remains scarce.

Objective: To systematically review the performance of machine learning for the diagnosis of endometriosis.

Search strategy: PubMed, Embase, Cochrane Library, and Web of Science were systematically searched up to October 11, 2024.

Selection criteria: Studies that constructed machine learning models to diagnose endometriosis.

Data collection and analysis: Two reviewers independently screened studies, extracted data, and assessed study quality. The risk of bias of the included studies was assessed using the Prediction Model Bias Risk Assessment Tool.

Main results: A total of 45 publications were included. Participant numbers ranged from 39 to 612,777. A meta-analysis showed that the area under the curve (AUC), sensitivity, and specificity of models based on clinical features were 0.810 (95% confidence interval [CI]: 0.786-0.835), 0.81 (95% CI: 0.77-0.84), and 0.76 (95% CI: 0.73-0.79) in the training sets, and 0.796 (95% CI: 0.770-0.822), 0.80 (95% CI: 0.75-0.84), and 0.76 (95% CI: 0.72-0.80) in the validation sets. The AUC, sensitivity, and specificity of models based on genetic information were 0.982 (95% CI: 0.975-0.990), 0.94 (95% CI: 0.90-0.97), and 0.99 (95% CI: 0.94-1.00) in the training sets. For the validation sets, these metrics were 0.865 (95% CI: 0.701-1.000), 0.83, and 0.59-0.96. Models based on imaging features exhibited an AUC of 0.979 (95% CI: 0.959-0.999) and 0.983 (0.971-0.995) in the training and validation sets, respectively.

Conclusions: ML models, particularly those based on genetic information and imaging, possess substantial accuracy for detecting endometriosis.

Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42024605113.

Background: Diabetic kidney disease is a major cause of end-stage renal disease. Herein, we aimed to assess the efficacy and safety of sodium-glucose cotransporter 2 (SGLT2) inhibitors in patients with diabetic kidney disease.

Methods: PubMed, Embase, and Web of Science databases were searched for eligible randomized clinical trials (RCTs) published up to July 2024. Effect sizes were summarized as risk ratios (RR) or weighted mean differences (WMD) with 95% confidence intervals (CI). Statistical analyses were performed using Stata.

Results: Fifteen studies (24463 patients) were included in the meta-analysis. The results of the meta-analysis showed that compared with the control group, SGLT2 inhibitor intervention could reduce the estimated glomerular filtration rate (WMD=-2.47; 95% CI: -3.18, -1.76), systolic blood pressure (WMD=-4.09; 95% CI: -4.97 to -3.21), diastolic blood pressure (WMD=-2.47; 95% CI: -3.06 to -1.88), and glycated hemoglobin (WMD=-0.27; 95% CI: -0.38, -0.17). Moreover, there was no significant difference between the SGLT2 inhibitor and control groups in terms of the incidence of overall adverse event, urinary tract infection, bone fracture and hypoglycemia. However, the incidence of genital infection and diabetic ketoacidosis in the SGLT2 inhibitor group was higher than that in the control group.

Conclusion: Our study confirms the beneficial effects in diabetic kidney disease patients, while also demonstrating a higher risk of genital infections and diabetic ketoacidosis in the SGLT2 inhibitor group compared to controls.

Background: Metabolic dysfunction-associated fatty liver disease (MAFLD) is highly prevalent in type 2 diabetes mellitus (T2DM) and may contribute to early myocardial dysfunction. The Fibrosis-4 (Fib-4) index is a reliable marker of hepatic fibrosis, and the triglyceride-glucose (TyG) index is an effective indicator of insulin resistance (IR). Both are linked to various cardiovascular diseases (CVDs).However, their combined impact on subclinical left ventricular dysfunction (SLVD) remains unclear. This study aimed to explore the associations between hepatic fibrosis, IR, and SLVD in T2DM patients.

Methods: We enrolled 270 T2DM patients between September 2024 and April 2025.MAFLD was diagnosed by ultrasonography, hepatic fibrosis was assessed using the Fib-4 index, and insulin resistance was estimated using the TyG index formula. Left Ventricular Global Longitudinal Strain (LV GLS) was measured by speckle-tracking echocardiography, with SLVD defined as absolute value of LV GLS <18%. T2DM patients were divided into three groups based on the presence of MAFLD and Fib-4 <1.3 or ≥1.3: non-MAFLD, MAFLD+Fib-4<1.3, and MAFLD+Fib-4≥1.3, and further stratified by the TyG median. Multivariable logistic regression models were used to evaluate the independent and interactive associations of Fib-4 index and TyG index with SLVD.

Results: Compared to non-MAFLD patients, T2DM patients with MAFLD were younger, had a shorter duration of diabetes, and exhibited worsened lipid profile, with higher TyG values and lower LV GLS. MAFLD independently predicted SLVD (adjusted OR = 3.21, 95% CI: 1.64-6.29). Even in patients with Fib-4 <1.3, MAFLD was associated with higher SLVD risk (OR = 2.94), while advanced fibrosis further increased SLVD risk (OR = 3.68). TyG independently predicted SLVD (adjusted OR = 2.73, 95% CI: 1.48-5.03). Importantly, patients with both high Fib-4 (≥1.3) and high TyG (≥9.03) had the greatest SLVD risk (OR = 7.58), whereas advanced fibrosis alone was not significant.

Conclusions: Fib-4 index and TyG index are independent predictors of SLVD in T2DM, and their coexistence exerts a synergistic effect. Combined assessment provides a simple, non-invasive tool for early risk stratification, highlighting the clinical importance of the liver-heart axis in identifying SLVD.

Objective: To analyze the pathogen distribution of older type 2 diabetes mellitus (T2DM) patients with urinary tract infection (UTI) and to develop and validate the feasibility of a nomogram risk prediction model for older T2DM patients with UTI.

Methods: We retrospectively analyzed clinical data from older patients with T2DM admitted to the Department of Endocrinology of The First People's Hospital of Lianyungang City from December 2023 to December 2024. Random number sequences were generated using R software, and all patients were assigned to the modeling cohort and validation cohorts in a ratio of 7:3 through simple random sampling. We compared baseline features between training and validation sets using appropriate statistical tests. We employed univariate and multivariate logistic regression models to identify factors independently associated with UTI in older patients with T2DM. Subsequently, we developed a nomogram prediction model, which was then validated using the validation group. Four methods of statistics, namely, the Hosmer-Lemeshow goodness-of-fit test (H-L test), subjects' work characteristic curve and area under the curve (AUC), calibration curve, and clinical decision curve (DCA), were comprehensively applied to evaluate the model's fit, discrimination, calibration, and clinical utility.

Results: Among 521 older patients with T2DM, 82 developed UTI, with an incidence of 15.74%. Logistic regression analyses identified female (odds ratio [OR] = 2.53, 95% confidence interval [CI]: 1.25-5.12, P = 0.011), HbA1c (OR = 1.35, 95% CI: 1.15-1.59, P = 0.001), SGLT-2i in previous year (OR = 2.29, 95% CI: 1.19-4.41, P = 0.013), indwelling urinary catheter (OR = 3.04, 95% CI: 1.02-9.23, P = 0.048), and UTI in previous year (OR = 5.22, 95% CI: 2.22-12.25, P = 0.001) as independent risk factors for older T2DM patients with UTI. The AUC was 0.764 (95% CI: 0.683-0.898) in the training group and 0.779 (95% CI: 0.703-0.855) in the validation group. The H-L test results for the training (χ² = 9.834, P = 0.277) and validation (χ² = 5.432, P = 0.711) groups indicated good goodness-of-fit for the prediction model, with the internal validation curve demonstrating satisfactory performance.

Conclusion: Establishing this simple, intuitive nomogram prediction model enables the early identification of risk factors in older T2DM patients with UTI. Based on readily accessible clinical variables, this model facilitates early risk stratification and promotes preventive interventions in a timely manner, ultimately reducing UTI incidence in clinical practice. Furthermore, the identified pathogen distribution and antimicrobial susceptibility profiles directly support targeted UTI treatment in this high-risk population, further enhancing the clinical utility of the predictive model and potentially improving patient outcomes.

Background: The accurate preoperative prediction of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) poses a significant clinical challenge. Although clinicopathological features are commonly utilized, their predictive accuracy remains limited, and the role of multi-gene co-mutations is not fully understood.

Objective: This study aimed to develop and validate an integrated risk model that combines next-generation sequencing (NGS) data with clinicopathologic features for the preoperative prediction of LNM in PTC.

Methods: We retrospectively analyzed 521 patients with PTC. Gene mutations were analyzed using NGS. Independent risk factors for central (CLNM) and lateral (LLNM) lymph node metastasis were identified through univariate and multivariate logistic regression analyses.

Results: The BRAF V600E mutation was the most prevalent (82.15%). Notably, high-risk multi-gene co-mutations -specifically, BRAF V600E co-occurring with TERT, PIK3CA, and/or TP53)-were identified as the strongest independent risk factor for CLNM (odds ratio [OR] = 6.319, 95% confidence interval [CI]: 1.738-22.976, P = 0.005). Other significant risk factors included male sex, age <45 years, bilateral lesions, tumor size >1 cm, lymphovascular invasion (LVI), and extrathyroidal extension,with gross ETE demonstrating the highest ORs (> 21).

Conclusion: Preoperative NGS profiling, particularly the detection of high-risk multi-gene co-mutations, provides a powerful tool for refined risk assessment. This molecularly guided strategy has the potential to inform personalized surgical planning directly, optimizing the extent of lymph node dissection to improve oncologic outcomes while minimizing unnecessary morbidity.

[This corrects the article DOI: 10.3389/fendo.2025.1592290.].