BMC Endocrine Disorders最新文献

Purpose: Netrin-1 is a urinary protein that may help in the diagnosis of diabetic nephropathy. The objectives of this study were to assess urinary netrin-1 levels in patients with type 2 diabetic nephropathy and to determine its correlation with renal function among them.

Methodology: This cross-sectional analytical study was conducted at a tertiary care teaching hospital in south India for 18 months. Study subjects were divided into four groups: non-diabetics, diabetics with normal to mildly increased albuminuria, moderately increased albuminuria, and severely increased albuminuria. Urinary albumin was quantified by nephelometry for all study subjects. The ELISA technique estimated urinary netrin-1 levels in all groups.

Results: Urinary netrin-1 levels were higher in diabetic subjects with normal to mildly increased and severely increased albuminuria than in the control group. Correlation analysis showed that there was a positive correlation of urinary netrin-1 with urinary albumin-creatinine ratio (UACR) and no correlation with estimated glomerular filtration rate (eGFR). Urinary netrin-1 showed a sensitivity of 88.3% and specificity of 75% at a cut-off value of 889.74 pg/mg creatinine for diagnosing diabetic nephropathy.

Conclusion: Urinary netrin-1 levels were elevated in diabetic subjects with moderately and severely increased albuminuria as compared to non-diabetic subjects. It showed a positive correlation with the urinary albumin-creatinine ratio and no correlation with eGFR in diabetic subjects.

Background: Glycated albumin (GA) is a useful marker for short-term glycemic control, but its levels may be influenced by body composition. Therefore, we aimed to investigate the impact of increasing body mass index (BMI) on GA levels in healthy individuals.

Methods: This cross-sectional study included healthy individuals with normal and elevated BMI. Individuals with diabetes mellitus, pregnancy, acute infection, a history of cardiovascular events, malignancy, chronic liver disease, nephrotic syndrome, thyroid dysfunction, anemia, morbid obesity (BMI ≥ 40 kg/m²), or any other condition known to affect GA levels were excluded. Anthropometric and biochemical measurements were obtained and compared between normal and elevated BMI groups. Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) version 22.0.

Results: A total of 52 individuals with elevated BMI and 49 with normal BMI were included in the analysis. Individuals with elevated BMI had significantly lower levels of GA (42.8 ± 7.2 vs. 51.3 ± 6.0, p < 0.001), while levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were markedly higher (0.6 ± 0.4 vs. 0.4 ± 0.2, p < 0.001 and 13.5 ± 12.3 vs. 8.3 ± 7.5, p = 0.002; respectively). BMI showed a moderate inverse association with GA (r=-0.583, p < 0.001). Moreover, BMI was positively associated with CRP (r = 0.366, p < 0.001) and ESR (r = 0.299, p = 0.002). In addition, GA levels exhibited negative correlations with CRP (r=-0.401, p < 0.001) and ESR (r=-0.384, p < 0.001). Multivariate regression analysis confirmed that BMI was independently associated with GA levels (B=-2.727, 95% CI:-5.077 to -0.377, p = 0.024).

Conclusion: Our results suggest a potential inverse association between BMI and GA levels.

Clinical trial number: Not applicable.

Background: The aim of this study was to investigate the association between estimated glucose disposal rate (eGDR) and the risk of future diabetes development in middle-aged and elderly adults, and to construct a diabetes prediction model.

Methods: The present study comprised a total of 8,072 participants, with 6,965 drawn from the China Health and Retirement Longitudinal Study (CHARLS) cohort and 1,107 from the English Longitudinal Study of Aging (ELSA) cohort. The correlation between eGDR and the onset of diabetes was analysed by means of a logistic regression model, and subgroup analyses and restricted cubic spline (RCS) curve analyses were performed to verify the non-linear relationship. A predictive model was constructed based on multivariable variables, and model efficacy was assessed by subject operating characteristic curves (AUC) and calibration curves.

Results: The prevalence of diabetes mellitus was 5.87% in the CHARLS cohort and 9.94% in the ELSA cohort. It was found that eGDR was significantly lower in both cohorts of diabetic patients (P < 0.001). Furthermore, an association was observed between eGDR reduction and an increased risk of developing diabetes. The multivariable-adjusted odds ratios (OR) for Q2-Q4 in the CHARLS cohort were 0.66 (0.51-0.84), 0.36 (0.25-0.51), and 0.31 (0.20-0.47), respectively, using the eGDR quartiles (Q1 as the reference); and for the ELSA cohort, the values were 0.40 (0.23-0.70), 0.30 (0.15-0.62), and 0.06 (0.01-0.28), respectively. RCS analyses revealed no evidence of nonlinear association between eGDR and diabetes, after adjusting for confounders. A column-line graphical model, incorporating variables of heart disease, stroke, BMI, lipids, glucose and eGDR, yielded AUCs of 0.75 (0.72-0.77) and 0.85 (0.82-0.89) in the CHARLS and ELSA cohorts, respectively. Calibration curves demonstrated adequate model fit, while decision curves indicated a substantial net benefit.

Conclusion: Reduced eGDR is an independent risk factor for the development of diabetes mellitus in middle-aged and elderly adults, and is linearly and negatively correlated with the risk of diabetes mellitus.

Background: Diabetes mellitus (DM) is a significant global public health concern, with prediabetes serving as a critical stage between normoglycemia and DM. Without intervention, individuals with prediabetes face an increased risk of developing DM, underscoring the need for effective preventive measures. The Hemoglobin Glycation Index (HGI)-which measures the discrepancy between actual and predicted glycated hemoglobin (HbA1c) levels-has shown promise in predicting the onset of both microvascular and macrovascular complications associated with DM. However, its potential role in assessing the risk of developing DM or prediabetes remains to be fully established. This study aims to investigate the predictive capacity of HGI for both DM and prediabetes.

Method: This retrospective cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS), involving participants aged 45 years and older who were assessed in 2011 and followed up in 2015. Univariate and multivariate logistic regression models were employed to analyze the relationship between HGI and the incidence of prediabetes and DM. Dose-response analyses were conducted using restricted cubic splines, and subgroup analyses were performed based on various demographic and health-related factors.

Results: Among 3,963 participants, 187 individuals (4.72%) developed prediabetes within four years, and 107 individuals (2.70%) developed DM. HGI was independently associated with an increased risk of developing both DM and prediabetes, with adjusted odds ratios of 1.61 (95% confidence interval [CI]: 1.19-2.16, p = 0.001) and 2.03 (95% CI: 1.40-2.94, p < 0.001), respectively. A linear relationship was observed between HGI and both DM and prediabetes. An interaction effect was identified between age and HGI; specifically, the association between higher HGI and incident DM was more pronounced in individuals aged 45 to 60 years. Among this age group, the OR was 3.93 (95% CI: 2.19-7.05, p < 0.001).

Conclusion: HGI is identified as an independent risk factor for both DM and prediabetes, demonstrating its utility in predicting the likelihood of their development, particularly within the population aged 45 to 60. These findings highlight the potential of HGI as a valuable biomarker for the early identification of DM risk, thereby facilitating the formulation of targeted intervention strategies.

Trial registration: Not applicable.

Background: The association between COVID-19 and newly diagnosed diabetes mellitus (DM) remains uncertain. This cross-sectional study examines the role of insulin resistance (IR) and selected inflammatory markers in COVID-19 associated newly diagnosed DM.

Research design and methods: A cross-sectional pilot study was conducted at an academic tertiary hospital and a primary healthcare facility, with COVID-19 patients additionally followed for three months post-discharge. Participants included patients hospitalised with moderate to severe COVID-19 during the third wave of predominantly the delta variant. Diagnostic markers predictive of newly diagnosed DM were assessed using logistic regression analysis. Four predictive diagnostic models were developed, incorporating combinations of triglyceride-glucose index (TyG index), homeostatic model assessment of insulin resistance (HOMA-IR), body mass index (BMI) and inflammatory cytokines. Model performance and optimal cutoff values were determined using Receiver Operating Characteristic (ROC) analysis and the Youden index.

Results: A total of 127 individuals were evaluated, consisting of 84 patients admitted with moderate to severe COVID-19 and 43 healthy controls. Among the 84 COVID-19 participants, 45 were newly diagnosed with DM, 20 had no DM, and 19 had pre-existing DM. Those with newly diagnosed DM exhibited significantly higher BMI and IR markers (HOMA-IR, and TyG index) compared to those without newly diagnosed DM (p < 0.001, p = 0.05 and p = 0.002, respectively). The predictive diagnostic model for newly diagnosed DM included the TyG index, BMI, IL-10 and IL-1β, achieving an area under the curve (AUC) of 0.91 (95% CI, 0.84-0.98). The TyG index was strongly associated with newly diagnosed DM (Crude Odds Ratio [COR] 11.25 (95% CI, 2.80-76.28; p-value = 0.01); Adjusted Odds Ratio (AOR) 6.83 (95% CI, 1.57, 42.96; p-value = 0.01) and showed improved predictive accuracy when used with BMI (AUC 0.86; 95% CI, 0.77-0.95), compared to the TyG index alone (AUC 0.73; 95% CI, 0.59-0.86). These findings support the potential role of the TyG index as a practical alternative to HOMA-IR in resource-limited settings where insulin measurement may not be feasible.

Conclusions: In our study population, IR rather than insulin deficiency was more strongly associated with newly diagnosed DM in patients with COVID-19. The TyG index may serve as a practical diagnostic marker for predicting newly diagnosed DM in resource-limited settings, with BMI and inflammatory markers further improving model accuracy. However, given our predominantly Black African study population, validation in larger and more diverse populations is needed.

Background: Diabetic nephropathy (DN) is a leading cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD), significantly impacting the global burden of diabetes mellitus (DM). Testosterone has been implicated in the pathophysiology of DN, yet its specific role remains unclear.

Methods: This retrospective cohort study analyzed 347 male patients with type 2 diabetes mellitus (T2DM), including 165 with DN and 182 without DN, to explore the relationship between plasma testosterone levels and DN progression. Clinical and biochemical data were collected, and univariate, multivariate regression, and logistic regression analyses were performed. A nomogram predictive model was developed incorporating DN risk factors.

Results: No significant difference in plasma testosterone levels was observed between DN and DM patients. However, testosterone levels varied significantly across DN stages, peaking in stage G5. Multivariate analysis identified testosterone (OR = 3.13, 95% CI [1.25-8.87]) as an independent risk predictor for poor DN prognosis. The predictive model combining Cyc, testosterone, Age, SBP, DBP, UACR, and demonstrated excellent prediction for the adverse outcomes in DN. (AUC = 0.923, 95% CI: 0.891-1).

Conclusion: Testosterone plays critical roles in DN progression and prognosis. The developed nomogram offers a practical tool for risk stratification and management of male DN patients. Further research is needed to validate these findings and elucidate the mechanistic pathways linking testosterone to DN progression.

Clinical trial number: No applicable.

Background: Remnant cholesterol (RC) is an independent predictor of cardiovascular events in type 2 diabetes mellitus (T2DM). Concurrently, vitamin D deficiency is a recognized risk factor for developing T2DM. However, the association between serum 25-hydroxyvitamin D (25(OH)D) levels and RC in patients with established T2DM remains incompletely elucidated. Specifically, potential non-linear relationships and modifications of this association by age and sex are unclear. This study investigates the relationship between 25(OH)D and RC in a cohort of 380 patients with T2DM.

Methods: A total of 380 T2DM patients (283 men and 97 women) were evaluated. Demographic data were analyzed descriptively. Statistical tests assessed the association between 25(OH)D levels and RC, and piecewise linear regression was utilized to explore potential threshold effects.

Results: Spearman correlation analysis revealed that female gender was significantly associated with higher RC levels (ρ = 0.163, p = 0.002). Piecewise linear regression identified a threshold effect at 18.8 ng/mL: below this threshold, each 1 ng/mL increase in 25(OH)D was associated with a decrease in RC of 0.01 mmol/L (β = -0.01, 95% CI: -0.02 to -0.00); above this threshold, it was associated with an increase of 0.02 mmol/L (β = 0.02, 95% CI: 0.00 to 0.03).Age significantly modified this association (interaction p < 0.05), suggesting an age-dependent inversion of the effect of vitamin D on RC.

Conclusion: This study demonstrates a complex, non-linear relationship between 25(OH)D levels and Remnant cholesterol in patients with type 2 diabetes. Age significantly modifies this relationship, suggesting that tailored interventions based on vitamin D status may be warranted to inform future interventional studies targeting RC modulation.