BMC Endocrine Disorders最新文献

Background: Carbohydrate intake, its type and characteristics including glycemic index (GI) and glycemic load (GL) may be associated with the risk of pancreatic steatosis (PS), but there is no conclusive evidence. The aim of the present study was to investigate whether the intake of carbohydrates, GI and GL were associated with an increased risk of PS.

Methods: To conduct this study, 278 patients with common bile duct stones (CBD) underwent endoscopic ultrasound, including 89 patients with PS (case group) and 189 healthy individuals (control group). In addition to demographic and anthropometric information, a 168-item questionnaire of food frequency was completed to calculate GL and GI.

Results: With the increase of GI and GL, the number of patients with PS increased significantly (P = 0.013, P < 0.001, respectively) and the risk of PS increased significantly. A similar increase in risk of PS was found with increased risk of carbohydrate, simple sugar and fructose intake. After adjusting all the confounders, the risk of PS with increasing simple sugar and fructose intake was 4.3 times (OR _{T3 vs. T1} = 4.3, 95% CI: 1.7-10.6, P trend < 0.001) and 5.3 times (OR _{T3 vs. T1} = 5.3, 95% CI: 2.2-12.9, P trend < 0.001), respectively, compared to the first tertile. Conversely, increased fiber intake showed a reverse association with the PS, so that those in the second and third tertiles of fiber intake were 84% (OR = 0.16, 95% CI: 0.05-0.45) and 87% (OR = 0.13, 95% CI: 0.04-0.39) less at risk of developing PS, respectively (P trend = 0.001).

Conclusions: These findings support the hypothesis of direct associations between GI and GL increased risk of PS.

Background: Innovations in diabetes technology have consistently improved outcomes of persons with type1 diabetes (PWDs). However, the volumes of data that these technologies yield require different workflows to alleviate healthcare professionals' (HCPs) workload and prevent losing relevant data in between visits for interpretation and treatment adaptations. CloudCare is a population health management tool that continuously oversees data from groups of individual PWDs, based on remote monitoring, screening and triaging of individual PWDs. This study assesses the effect of CloudCare on treatment satisfaction of PWDs, HCPs' workload and glycemic control of PWDs.

Methods: We evaluated the 6-month follow-up outcomes as part of an ongoing prospective cohort study analyzing the effect of CloudCare. Adult PWDs diagnosed > 6 months before inclusion were enrolled. The primary outcome was the change in PWD treatment satisfaction (DTSQc). Secondary outcomes included the number and type of contacts between HCPs and PWDs, diabetes-related distress (PAID-5), and glycemic control.

Results: In September 2024, 175 participants had baseline data available, with a median age of 29.9 years and a median diabetes duration of 17 years. Differences between baseline and 6 months could be calculated for 119 participants. After 6 months follow-up, the median increase in PWDs' treatment satisfaction (DTSQc) was + 6.0 (IQR 2-11; p < 0.001). The number of face-to-face contacts per PWD per 3 months decreased from 0.85 at baseline to 0.34 (p < 0.001) at 6 months. Diabetes-related distress was significantly decreased at 3 months (p < 0.001) and at 6 months (p = 0.034), compared with baseline. Glucometrics did not significantly change, with a TIR of 79% at baseline and 78% after 6 months (p = 0.39), and a mean glucose management indicator (GMI) of 50 mmol/mol (6.7%) at all timepoints.

Conclusions: In adult PWDs with good glycemic control, CloudCare decreases workload for HCPs, while increasing PWDs' treatment satisfaction and maintaining excellent glycemic control during 6 months, showing this concept can be applied in modern diabetes care with high density data availability.

Trial registration: Clinicaltrials.gov identifier: NCT05431140; registration date 21-6-2023.

Background: Numerous epidemiologic observational studies have demonstrated that smokers have an increased risk of developing cardiovascular-related diseases. However, less is known about the causal relationship between tobacco smoking and the metabolic syndrome. This study aimed to determine whether genetically predicted smoking is associated with metabolic syndrome using the Mendelian randomization (MR) approach.

Methods: This paper used individual-level genetic and personal data from the Taiwan Biobank dataset, including 80,072 Han Chinese individuals (15,773 cases of metabolic and 64,299 controls; 21,399 smokers and 58,673 nonsmokers). The literature was searched for smoking-associated single nucleotide polymorphisms (SNPs), and 14 SNPs satisfying MR assumptions were identified and used as instrumental variables. Weighted and unweighted genetic risk scores (GRSs) based on these significant SNPs were derived. MR analyses were performed using the two-stage approach of regression models.

Results: Genetically predicted smoking is associated with a higher risk of metabolic syndrome (odds ratio [OR]: 1.49, 95% CI: 1.47-1.52 per 1 standard deviation increase) for weighted and unweighted GRSs. When Q1 was used as the reference group, the adjusted ORs of metabolic syndrome for Q2, Q3, and Q4 were 1.15 (1.08, 1.22), 2.17 (2.05, 2.30), and 4.23 (3.98, 4.49), respectively, for the weighted GRS. The corresponding ORs for Q2, Q3, and Q4 were 1.16 (1.09, 1.24), 2.17 (2.05, 2.30), and 4.26 (4.02, 4.53), respectively, for the unweighted GRS.

Conclusions: Genetic predisposition toward tobacco smoking is strongly associated with a higher likelihood of metabolic syndrome. Further work is warranted to clarify the underlying mechanism of smoking in the development of metabolic syndrome.

Objective: This study aims to investigate the association between the Inflammatory Burden Index (IBI) and the prevalence of pre-diabetes (pre-DM) and diabetes mellitus (DM) in the U.S. population from 1999 to 2010. By analyzing relevant data collected during this period, the study seeks to understand IBI's role in the onset of pre-DM and DM and its potential implications for public health.

Methods: A cross-sectional analysis was conducted using data from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2010. A total of 29,554 participants were included, with diabetes status determined by self-reported diagnoses and clinical indicators (such as glycosylated hemoglobin and fasting blood glucose). The Inflammatory Burden Index (IBI) was calculated using C-reactive protein (CRP) multiplied by the neutrophil-to-lymphocyte ratio. The generalized additive model (GAM) was employed to examine the relationship between increasing IBI and the incidence of pre-DM and DM.

Result: The study included 29,554 participants, with 14,290 (48.4%) men and 15,264 (51.6%) women, and a mean age of 48.3 years (SD = 19.1). The findings revealed a significant association between IBI and the risk of pre-DM and DM. In the fully adjusted model, a stronger relationship was observed between pre-DM, DM, and IBI. The prevalence of pre-DM and DM was significantly higher in the fourth quartile (Q4) compared to the first quartile (Q1), with a 26% prevalence of pre-DM and an 18% prevalence of DM when IBI was greater than 1.04.

Conclusion: Our study demonstrates a significant correlation between IBI and the risk of pre-DM and DM in the U.S.

Population: Given these findings, we recommend that IBI be considered as a key indicator for the management and treatment of pre-DM and DM in clinical settings.

Objective: The most significant challenge faced by individuals with diabetes is poor blood sugar control. The objective of this review is to identify the most crucial predictors of poor glycemic control among patients with diabetes.

Materials: This review employed a comprehensive approach, utilizing all available analytical cross-sectional, case control and cohort studies to ascertain the pooled odds ratio/risk ratio of uncontrolled diabetes. The review encompassed articles from international databases, including Web of Science, PubMed, Scopus, and Google Scholar without restrictions on publication date or language. Data extraction was conducted until May 11, 2024, with statistical analyses performed using Stata 17 software, employing a random effects model at a 95% confidence level.

Results: Out of 157,841 records, a total of 59 cross-sectional studies, 4 case-control studies, and 3 cohort studies were included, comprising 284,558 participants with a mean age of 53.78 years (SD = 6.33). There was no statistically significant association between the seven factors analyzed-age, gender, smoking status, education level, systolic blood pressure, diastolic blood pressure, and BMI. However, we observed a significant decrease in the likelihood of poor glycemic control with each unit increase in physical activity. Specifically, as physical activity levels increased, the likelihood of poor glycemic control decreased (adjusted OR 0.41; 95% CI: 0.24, 0.72; p-value = 0.02).

Conclusion: Our systematic review and meta-analysis study showed that increased levels of physical activity in individuals with type 2 diabetes enhance the chances of achieving better glycemic control.

Purpose: Studies investigating hyperostosis frontalis interna (HFI) in acromegaly are limited. We aimed to investigate HFI and the association of disease control with frontal bone thickness (FBT) in acromegaly.

Methods: Adult patients with acromegaly were grouped according to the presence of HFI on the baseline MRI: Group 1 absent, Group 2 present. We measured FBT, parietal bone thickness (PBT) and occipital bone thickness (OBT) in the mid-sagittal plane on MRI. The changes between first and last measurements were analyzed. We grouped the patients as controlled vs. uncontrolled acromegaly, and as established disease control for at least 5-year vs. 1-5-years.

Results: Group 1/Group 2 comprised of 23/29 patients, female/male ratio was 34/18, and mean age 55.41(± 14.21) years. Median follow-up duration was 108 months (6-408). FBT^first (p = 0.001), FBT^last (p < 0.001), PBT^last (p = 0.025), and OBT^last (p = 0.028) were higher in Group 2 than in Group 1. FBT^change, PBT^change, and OBT^change were positive in Group 2 (p < 0.001, p = 0.008, and p = 0.008; respectively). The ratio of patients with FBT(increased) was higher in Group 2 than in Group 1 (p = 0.001). FBT^first, FBT^last, PBT^first, PBT^last, OBT^first, OBT^last, FBT^change, PBT^change and OBT^change were similar in controlled or uncontrolled acromegaly groups. FBT^change and OBT^change were positive in patients with disease control established for at least 5 years (n = 30) (p = 0.027 and p = 0.002, respectively).

Conclusion: HFI was common in patients with acromegaly. HFI is associated with a continuous increase in FBT, PBT and OBT. HFI, bone thickness, or increase in bone thickness seems independent of disease activity. Since headaches can be related to an increase in bone thickness, patients should be evaluated and graded during baseline imaging.

Background: Acute myocardial infarction (AMI) has a significant impact on global health, especially among individuals with diabetes, emphasizing the need for specialized glycemic management. This study examines the glycemic comparison index (GCI), a novel prognostic tool designed for patients with AMI and diabetes, aiming to enhance glucose management in critical care settings.

Methods: This retrospective cohort analysis used data from the Medical Information Mart for Intensive Care IV database (version 2.2). The GCI was calculated by comparing mean blood glucose levels in the intensive care unit (ICU) to baseline glucose levels. Patients were stratified into tertiles based on their GCI scores. The primary outcome measured was one-year all-cause mortality, while secondary outcomes included hospital mortality, ICU-free days, and hypoglycemic events. Statistical analyses included time-dependent receiver operating characteristic (ROC), cox proportional hazards models, generalized linear models (GLM), and restricted cubic spline analysis.

Results: The patient population comprised 622 individuals, with a mean age of 69.9 years and 64.6% male representation. The high GCI group exhibited the highest one-year mortality rate and fewer ICU-free days, while the low GCI group exhibited a higher incidence of hypoglycemia. Statistical analyses revealed that GCI was a significant predictor of one-year all-cause mortality (hazard ratio: 2.21, 95% confidence interval: 1.51-3.24). Analysis using time-dependent ROC confirmed the consistent predictive accuracy of GCI for survival at 1, 6, and 12 months (area under the curve: 0.671, 0.670, and 0.634, respectively). Furthermore, GLM analysis indicated that a higher GCI was associated with fewer ICU-free days.

Conclusions: Higher GCI values are associated with increased one-year mortality and fewer ICU-free days in patients with AMI and diabetes. In comparison, lower GCI values are correlated with a higher risk of hypoglycemia. The GCI demonstrates potential as a personalized prognostic tool, although further validation is needed.

Background: The issue of obesity is becoming more and more prominent. Understanding the metabolic profile of obese young adults and finding possible risk markers for early prediction and intervention is of great importance.

Methods: A total of 13,082 college students with an average age of 20 years were enrolled in this cross-sectional study. The lipid composition was measured and novel lipid profiles such as AIP, AI, LCI, Non-HDL-C, TC/HDL-C, LDL-C/HDL-C and TyG were calculated. Participants were then assessed as normal weight, overweight or obese based on their BMI. Pearson correlation analysis, multivariate logistic analysis, and predictive analysis were used to assess the association and discriminative power between lipid profile and obesity.

Results: The prevalence of obesity with dyslipidemia was 61.0% in males and 38.7% in females. Most obese patients were associated with only one dyslipidemia component, with the highest proportion having low HDL-C. We found a positive correlation between all lipid profiles except HDL-C and BMI. Multivariate logistics regression shows, AIP were strongly associated with obesity, which shows the largest OR = 12.86, 95%CI (9.46,17.48).

Conclusions: In the youth population, higher AIP levels were positively and strongly associated with obesity. AIP may be a novel and better risk biomarker for predicting obesity.

Objectives: This study aims to identify indicators of disease activity in patients with graves' orbitopathy (GO) by examining the microstructural characteristics of meibomian glands (MGs) and developed a diagnostic model.

Methods: We employed in vivo confocal microscopy (IVCM) to examine MGs in GO patients. Patients classified in the active phase were determined based on the clinical activity score (CAS). The research employed the least absolute shrinkage and selection operator (LASSO) method to select key indicators. Subsequently, a logistic regression model was constructed to predict GO disease activity.

Results: A total of 45 GO patients, corresponding to 90 eyes, were included in this study. A Lasso regression algorithm was utilized to select the predictor variables. Five predictor variables were included in our diagnostic model ultimately. The area under the curve (AUC) for the training set model reached 0.959, and for the validation set was 0.969. The training set and validation set models both demonstrated high accuracy in calibration. Finally, a Nomogram chart was constructed to visualize the diagnostic model.

Conclusion: We constructed a diagnostic model based on microstructural indicators of MGs obtained through IVCM and offered a clinical utility for assessing GO disease activity, aiding in the diagnosis and selection of treatment strategies for GO.