BMC Endocrine Disorders最新文献

{"title":"Efficacy of 12 months therapy with glucagon-like peptide-1 receptor agonists liraglutide and semaglutide on weight regain after bariatric surgery: a real-world retrospective observational study.","authors":"Anders Boisen Jensen, Ursina Machado, Frida Renström, Stefan Aczél, Patrick Folie, Magdalena Biraima-Steinemann, Stefan Bilz","doi":"10.1186/s12902-025-01913-4","DOIUrl":"10.1186/s12902-025-01913-4","url":null,"abstract":"<p><strong>Background: </strong>The role of glucagon-like peptide-1 receptor agonists (GLP1-RAs) in patients with weight regain after bariatric surgery remains unclear. The objective of this study was to determine the efficacy and safety of 12 months of GLP1-RA treatment in a real-world patient population with weight regain after bariatric surgery.</p><p><strong>Methods: </strong>A single-centre retrospective observational study. Patients with post-bariatric weight regain subsequently treated with GLP1-RA were identified, and the effect on weight after 12 months of treatment was determined. Data are presented as medians (interquartile ranges) or frequencies (%), and Wilcoxon signed-rank tests and Mann-Whitney U tests were used for paired and nonpaired group comparisons, respectively.</p><p><strong>Results: </strong>Forty patients (80% female) were included in the analysis. Liraglutide (3.0 mg, daily subcutaneous injection, n = 22) or semaglutide (1.0 mg, weekly subcutaneous injection, n = 18) was started 74.5 (51.0, 108.3) months after surgery following a weight regain of 14.7 (10.3, 19.6)%. After 12 months of GLP1-RA treatment, a total body weight, BMI, and percentage excess body weight reduction of 10.5 (6.1, 14.7) kg, 3.7 (2.5, 5.3) kg/m², and 41.7 (22.1, 70.5)% were observed, corresponding to a loss of 99.3 (61.0, 135.4)% of the weight regained (P-value < 0.0001). The observed reduction in BMI was significantly lower with liraglutide than with semaglutide, 3.1 (2.0, 4.7) vs. 4.7 (3.7, 6.0) kg/m² (P-value = 0.04). Adverse events were reported in 13 (32.5%) patients, all of which were mild and transient.</p><p><strong>Conclusion: </strong>GLP1-RA therapy with liraglutide or semaglutide for 12 months is efficacious and safe for the treatment of weight regain following bariatric surgery.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"93"},"PeriodicalIF":2.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of self-efficacy, quality of life, and well-being of patients with diabetes mellitus in Alqunfudah, Saudi Arabia.","authors":"Saleh Ahmed Alshaikhi, Fatimah Hassan Alfaqih, Atheer Khalid Alrashdi, Fatimah Ali Alamri, Amirah Saleh Alzubaidi, Amnah Ibrahim Alnashri, Bandar Mohammed Alsharidi, Salma Mohammed Alshreef, Amwaj Saaied Almantashri, Omar Ahmed Alshaikhi, Ayoub Ali Alshaikh, Mohammed Ahmed Alshaikhi, Ramy Mohamed Ghazy","doi":"10.1186/s12902-025-01894-4","DOIUrl":"10.1186/s12902-025-01894-4","url":null,"abstract":"<p><strong>Background: </strong>Patients with diabetes mellitus (DM) often experience psychological challenges, such as feelings of loss of control, self-care stress, and fear of complications. This study aimed to assess the prevalence of uncontrolled DM, self-efficacy, quality of life (QoL), and well-being among patients with DM in Alqunfudah, Saudi Arabia, and to investigate the associations between these factors and diabetes control.</p><p><strong>Methods: </strong>A cross-sectional study employing an online questionnaire was conducted among adults with DM. The questionnaire assessed demographic characteristics, diabetes-related history, and glycemic control based on glycated hemoglobin A1c (HbA1c) level. Self-efficacy was evaluated using the validated Arabic version of the Self-Efficacy for Managing Chronic Disease 6-Item Scale, and the Arabic version of the World Health Organization Quality of Life Brief Version was utilized to assess QoL. Well-being was measured using the Arabic version of the World Health Organization-Five Well-being Index (WHO-5).</p><p><strong>Results: </strong>Four hundred patients with diabetes were included with a mean age of 49.3 ± 14.6 years, 40.8% were males, and 49.25% had uncontrolled DM. Compared to the controlled group, the uncontrolled group had a lower percentage of patients living in urban areas (16.8% vs. 25.6%, p = 0.037), a larger proportion of participants having DM for > 10 years (42.6% vs. 26.6%, p < 0.001), lower median (interquartile [IQR]) self-efficacy score [39.0 (30.0-46.0) vs. 47.0 (34.0-54.0), p < 0.001], lower physical QoL [75.0(60.7-85.7) vs. 67.8 (50.0-82.1), p < 0.001], and lower environmental QoL [(78.1(62.5-87.5) vs. 68.7(59.3-84.3), p = 0.005]. Predictors of glycemic control included the physical domain of QoL [adjusted odd ratio (aOR) = 1.02 (95% CI: 1.01-1.03), p < 0.001] duration of DM for 1-2 years [aOR = 2.53 (95% CI: 1.08-5.91), p= 0.032], 3-5 years [aOR = 3.76 (95% CI: 1.90-7.43), p< 0.001 ], and 6-10 years [aOR = 1.85 (95% CI: 1.04-3.32), p = 0.036], and urban residence [aOR = 1.88 (95% CI: 1.11-3.18), p = 0.017].</p><p><strong>Conclusions: </strong>A large sector of patients with diabetes had uncontrolled blood sugar with greater affection of QoL and self-efficacy compared to the controlled group. Physical QoL, duration of DM, and residence were the key factors to be targeted for improved diabetes management.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"91"},"PeriodicalIF":2.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11974028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel obesity-prevention dietary score is associated with favorable metabolic status and lower blood pressure in obesity.","authors":"Amr Ali Mohamed Abdelgawwad El-Sehrawy, Mahmood Jawad, Hussein Salim Abed, Ashok Kumar Bishoyi, Salah Alghamdi, R Roopashree, Mayank Kundlas, Anima Nanda, Muthena Kariem, Yasser Fakri Mustafa, Mahfoudh Almusali Mohammed Abdulghani","doi":"10.1186/s12902-025-01912-5","DOIUrl":"10.1186/s12902-025-01912-5","url":null,"abstract":"<p><strong>Background: </strong>Obesity is a major worldwide health problem and is associated with numerous diseases including diabetes, cardiovascular diseases, and some types of cancers. In the current cross-sectional study, we aimed to evaluate the association between a novel dietary obesity prevention score (DOPS) with metabolic parameters including serum lipid profile, glycemic markers, electrolyte status and blood pressure in individuals with obesity.</p><p><strong>Methods: </strong>Three hundred and four individuals with obesity aged 18-65 years old were recruited through convenient sampling; anthropometric and dietary assessments were performed and blood pressure was measured. Biochemical parameters including serum lipids, glycemic markers, some of liver function tests and electrolyte status were measured by standard laboratory methods.</p><p><strong>Results: </strong>Lower adiposity including lower body mass index (BMI) and fat mass and low systolic and diastolic blood pressures were observed at higher tertiles of versus lower tertiles of DOPS (P < 0.05). Also, lower low density lipoprotein cholesterol (LDL-c) and higher serum albumin concentrations were observed at higher tertiles of DOPS. There was no significant difference between other parameters across DOPS tertiles.</p><p><strong>Conclusion: </strong>In our study, higher adherence to dietary obesity preventive score reduced obesity risk, blood pressure and serum LDL in individuals with obesity. Future longitudinal and interventional studies are needed to establish causal relationships.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"90"},"PeriodicalIF":2.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of dietary glycemic index and glycemic load with pancreatic steatosis: a case control study.","authors":"Mohammad Bahrizadeh, Danial Fotros, Maedeh Chegini, Amir Sadeghi, Azita Hekmatdoost, Zahra Yari","doi":"10.1186/s12902-025-01909-0","DOIUrl":"10.1186/s12902-025-01909-0","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>These findings support the hypothesis of direct associations between GI and GL increased risk of PS.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"89"},"PeriodicalIF":2.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating cloudcare, a population health management system, in persons with type 1 diabetes: an observational study.","authors":"Cornelis A J van Beers, Sander Last, Pim Dekker, Erwin Birnie, Nico Riegman, Francisca van der Pluijm, Christine Fransman, Henk J Veeze, Henk-Jan Aanstoot","doi":"10.1186/s12902-025-01905-4","DOIUrl":"10.1186/s12902-025-01905-4","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Trial registration: </strong>Clinicaltrials.gov identifier: NCT05431140; registration date 21-6-2023.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"88"},"PeriodicalIF":2.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relationship between tobacco smoking and metabolic syndrome: a Mendelian randomization analysis.","authors":"Cheng-Chieh Lin, Chia-Ing Li, Chiu-Shong Liu, Chih-Hsueh Lin, Shing-Yu Yang, Tsai-Chung Li","doi":"10.1186/s12902-025-01910-7","DOIUrl":"10.1186/s12902-025-01910-7","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"87"},"PeriodicalIF":2.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of inpatient mortality prediction models for patients with hyperglycemic crisis using machine learning approaches.","authors":"Rui He, Kebiao Zhang, Hong Li, Manping Gu","doi":"10.1186/s12902-025-01873-9","DOIUrl":"10.1186/s12902-025-01873-9","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Hyperglycemic crisis is one of the most common and severe complications of diabetes mellitus, associated with a high motarlity rate. Emergency admissions due to hyperglycemic crisis remain prevalent and challenging. This study aimed to develop and validate predictive models for in-hospital mortality risk among patients with hyperglycemic crisis admitted to the emergency department using various machine learning (ML) methods.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A multi-center retrospective study was conducted across six large general adult hospitals in Chongqing, western China. Patients diagnosed with hyperglycemic crisis were identified using an electronic medical record (EMR) database. Demographics, comorbidities, clinical characteristics, laboratory results, complications, and therapeutic interventions were extracted from the medical records to construct the prognostic prediction model. Seven machine learning algorithms, including support vector machines (SVM), random forest (RF), recursive partitioning and regression trees (RPART), extreme gradient boosting with dart booster (XGBoost), multivariate adaptive regression splines (MARS), neural network (NNET), and adaptive boost (AdaBoost) were compared with logistic regression (LR) for predicting the risk of in-hospital mortality in patients with hyperglycemic crisis. Stratified random sampling was used to split the data into training (80%) and validation (20%) sets. Ten-fold cross validation was performed on the training set to optimize model hyperparameters. The sensitivity, specificity, positive and negative predictive values, area under the curve (AUC) and accuracy of all models were computed for comparative analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 1668 patients were eligible for the present study. The in-hospital mortality rate was 7.3% (121/1668). In the training set, feature importance scores were calculated for each of the eight models, and the top 10 significant features were identified. In the validation set, all models demonstrated good predictive capability, with areas under the curve value exceeding 0.9 with a F1 score between 0.632 and 0.81, except the MARS model. Six machine learning algorithm models outperformed the referred logistic regression algorithm except the MARS model. Among the selected models, RPART, RF, and SVM achieved the best performance in the selected models (AUC values were 0.970, 0.968 and 0.968, F1 score were 0.652, 0.762, 0.762 respectively). Feature importance analysis identified novel predictors including mechanical ventilation, age, Charlson Comorbidity Index, blood gas index, first 24-hour insulin dosage, and first 24-hour fluid intake.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Most machine learning algorithms exhibited excellent performance predicting in-hospital mortality among patients with hyperglycemic crisis except the MARS model, and the best one was RPART model. These algorithms identified overlapping but different, ","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"86"},"PeriodicalIF":2.8,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11948940/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of the inflammatory burden index with the risk of pre-diabetes and diabetes mellitus: a cross-sectional study.","authors":"Shuo Yu, Jiaxin Li, He Chen, Fuyu Xue, Siyi Wang, Meihui Tian, Hongfeng Wang, Haipeng Huang, Mengyuan Li","doi":"10.1186/s12902-025-01911-6","DOIUrl":"10.1186/s12902-025-01911-6","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Result: </strong>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.</p><p><strong>Conclusion: </strong>Our study demonstrates a significant correlation between IBI and the risk of pre-DM and DM in the U.S.</p><p><strong>Population: </strong>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.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"82"},"PeriodicalIF":2.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperostosis frontalis interna and association of disease control with frontal bone thickness in acromegaly.","authors":"Ihsan Ayhan, Ömercan Topaloğlu, Taner Bayraktaroğlu","doi":"10.1186/s12902-025-01904-5","DOIUrl":"10.1186/s12902-025-01904-5","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"81"},"PeriodicalIF":2.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictor factors of uncontrolled diabetes.","authors":"Zahra Cheraghi, Amin Doosti-Irani, Parvin Cheraghi, Parham Mohammadi, Marzieh Otogara","doi":"10.1186/s12902-025-01906-3","DOIUrl":"10.1186/s12902-025-01906-3","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Materials: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":9152,"journal":{"name":"BMC Endocrine Disorders","volume":"25 1","pages":"84"},"PeriodicalIF":2.8,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}