Journal of Diabetes最新文献

{"title":"Trends in Pharmacological Treatment of Patients With New Onset Type 2 Diabetes: Usage Patterns in an Evolving Guideline Landscape","authors":"William Hou,&nbsp;Katherine R. Tuttle,&nbsp;Weining Shen,&nbsp;Andrew Reikes,&nbsp;Jonathan H. Watanabe","doi":"10.1111/1753-0407.70108","DOIUrl":"https://doi.org/10.1111/1753-0407.70108","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Aims</h3>\u0000 \u0000 <p>In patients with new onset type 2 diabetes, this study aimed to analyze glucose-lowering medication use patterns between 2014 and 2022.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and Methods</h3>\u0000 \u0000 <p>This retrospective study included adults with incident type 2 diabetes in the University of California Health System between 2014 and 2022. We determined medications used within 1 year of diagnosis and evaluated statistical evidence of use pattern changes via Mann–Kendall trend tests. Four categories of high-risk patients requiring cardio-kidney-metabolic protection were also evaluated in stratified analyses based on 2024 ADA guidelines.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of 40 150 patients with incident type 2 diabetes, 38.5% initiated glucose-lowering medication within 1 year. Metformin remained the most used medication from 2014 to 2022. From 2014 to 2022, usage of GLP-1 receptor agonists and SGLT-2 inhibitors increased exponentially. GLP-1 receptor agonist use increased from below 2.5%–21%. While SGLT-2 inhibitor use increased from less than 2.5%–14%. This growth coincided with a decline in sulfonylurea usage. Among high-risk, insulin was most prevalent in those with heart failure or chronic kidney disease. However, usage of insulin declined overall in all groups. Utilization of SGLT-2 inhibitors was particularly high in patients with prior heart failure.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In adults with new onset type 2 diabetes, GLP-1 receptor agonist and SGLT-2 inhibitor utilization has markedly increased, coordinating with evolving guidelines that emphasize cardiovascular and chronic kidney disease management. However, overall adoption rates of these medications remain low based on indicated populations. Sulfonylurea use declined while metformin remains the most frequently initiated treatment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time Trends in Cardiovascular Event Incidence in New-Onset Type 2 Diabetes: A Population-Based Cohort Study From Germany","authors":"Theresia Sarabhai,&nbsp;Karel Kostev","doi":"10.1111/1753-0407.70099","DOIUrl":"https://doi.org/10.1111/1753-0407.70099","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Events of cardiovascular disease (CVD) remain a critical concern in patients with Type 2 diabetes mellitus (T2D). Over 17 years, this study analyzed time changes in the 5-year incidence of myocardial infarction (MI), chronic coronary heart disease (CHD), transient ischemic attack (TIA), and ischemic stroke (IS).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This retrospective cohort study was conducted using the Disease Analyzer database, including patients aged ≥ 18 years with at least 12 months of no prior CVD with new-onset T2D in 2001–2006 (<i>n</i> = 10 162) and in 2013–2018 (<i>n</i> = 30 486), matched 1:3 by age and sex. Kaplan–Meier survival analysis estimated the 5-year cumulative incidence of the outcomes. Multivariable Cox regression models assessed temporal changes, adjusted for comorbidities.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The incidence of CHD and TIA significantly declined in 2013–2018 compared to 2001–2006, with HRs of 0.68 (95% CI: 0.63–0.73; <i>p</i> &lt; 0.001) and 0.63 (95% CI: 0.52–0.76; <i>p</i> &lt; 0.001), respectively. Reductions were more pronounced in women and older patients. Surprisingly, MI incidence showed only a trend of reduction (HR: 0.82; 95% CI: 0.68–0.99; <i>p</i> = 0.045) and IS incidence was not different (HR: 0.97; 95% CI: 0.85–1.12; <i>p</i> = 0.722) between time periods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study is the first to report time trends in CVD incidence in new-onset T2D in Germany. From 2001 to 2018, the 5-year incidence of CHD and TIA decreased in new-onset T2D, reflecting demographic-specific advancements in diabetes and cardiovascular care. However, the stable incidence of IS and MI underscores a persistent challenge in prevention strategies in patients with prediabetes and T2D.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential Detrimental Interactions Between Metformin and Supplemental Dietary Fiber in Type 2 Diabetes","authors":"Rosemary M. Hall,&nbsp;Amber Parry-Strong,&nbsp;David O'Sullivan,&nbsp;Jeremy D. Krebs,&nbsp;Olivier Gasser","doi":"10.1111/1753-0407.70101","DOIUrl":"https://doi.org/10.1111/1753-0407.70101","url":null,"abstract":"&lt;p&gt;Higher intakes of dietary fiber have been associated with a reduced risk of developing Type 2 Diabetes (T2DM) and cardiovascular disease [&lt;span&gt;1, 2&lt;/span&gt;]. Fiber supplementation improves overall glycaemia, with reductions in Hba1c and better insulin sensitivity [&lt;span&gt;3&lt;/span&gt;]. However, there is significant reported heterogeneity on the effects of supplemental fiber on glycaemic outcomes for people with T2DM, potentially due to variations in absorption and metabolism. These differences, we believe, are worth examining more closely, especially considering the complexities of the gut microbiome, medications used in T2DM, and the role of the background diet [&lt;span&gt;3&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;In our recent study, we investigated the impact of supplemental fiber on glucose metabolism and glycemic control in people with pre-diabetes and T2DM who had a low habitual fiber intake. We recruited 30 participants with HbA1c levels ranging from 45 to 70 mmol/mol, and provided them with psyllium fiber supplements for 12 weeks.&lt;/p&gt;&lt;p&gt;Although we observed reductions in body mass index (BMI) and improvements in lipid profiles, HbA1c levels did not significantly improve overall. Surprisingly, participants taking metformin alone experienced an increase in HbA1c, while those not taking metformin experienced a slight reduction (Figure 1).&lt;/p&gt;&lt;p&gt;This discrepancy points to a critical issue: the potential interaction between metformin and fiber supplementation. Metformin, the most common medication for T2DM works by reducing hepatic glucose production and improving insulin sensitivity [&lt;span&gt;4&lt;/span&gt;]. However, metformin primarily acts within the gastrointestinal tract, commonly producing gastrointestinal side-effects and may alter gut microbiome, with the potential to directly affect fiber absorption [&lt;span&gt;5&lt;/span&gt;]. Our findings suggest that when combined with fiber supplementation, metformin may impair the metabolic benefits typically associated with fiber, alongside a potential detrimental effect on the glycaemic benefits of metformin.&lt;/p&gt;&lt;p&gt;This phenomenon is consistent with previous research. For example, a study by Tramontana et al. found that a high-fiber diet did not improve HbA1c in 78 patients with T2DM on metformin monotherapy [&lt;span&gt;6&lt;/span&gt;], while other studies observed more promising results when fiber was combined with different medications [&lt;span&gt;7&lt;/span&gt;]. These findings highlight the need for a more nuanced understanding of how dietary fiber interacts with both the gut microbiome and medications like metformin.&lt;/p&gt;&lt;p&gt;Moreover, the gut microbiota's role in metabolism is integral to the overall metabolic benefits. Dietary fiber, especially in the form of psyllium, is fermented by gut bacteria into short-chain fatty acids (SCFAs), which have been shown to improve immune function and reduce inflammation—factors that are crucial in managing T2DM [&lt;span&gt;7, 8&lt;/span&gt;]. However, both metformin and fiber alter the gut microbiome in different ways, and this complex ","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sleep Phenotypes, Genetic Susceptibility, and Risk of Obesity in Patients With Type 2 Diabetes: A National Prospective Cohort Study","authors":"Lei Xi,&nbsp;Li Li,&nbsp;Songbo Fu,&nbsp;Yuancheng Dai,&nbsp;Juan Shi,&nbsp;Yanmei Yu,&nbsp;Ying Peng,&nbsp;Hongmei Qiu,&nbsp;Jinsong Kuang,&nbsp;Hongyun Lu,&nbsp;Huige Shao,&nbsp;Chunlei Yuan,&nbsp;Xiaohu Wang,&nbsp;Ping Zhang,&nbsp;Sheli Li,&nbsp;Yanhui Pan,&nbsp;Ling Hu,&nbsp;Zhigang Zhao,&nbsp;Yunxia Chen,&nbsp;Jian Kuang,&nbsp;Yi Shu,&nbsp;Jinhua Qian,&nbsp;Qibin Mao,&nbsp;Jieji Zhang,&nbsp;Yan Liu,&nbsp;Hong Yang,&nbsp;Zhaoli Yan,&nbsp;Weici Xie,&nbsp;Qian Zhang,&nbsp;Ping Zhang,&nbsp;Hongji Wu,&nbsp;Ling Gao,&nbsp;Yongjun Jin,&nbsp;Ning Xu,&nbsp;Chaoyang Xu,&nbsp;Xiaohui Sun,&nbsp;Zhimin Feng,&nbsp;Qing Zhang,&nbsp;Lin Li,&nbsp;Guang Ning,&nbsp;Yifei Zhang,&nbsp;Yanan Cao,&nbsp;Weiqing Wang","doi":"10.1111/1753-0407.70095","DOIUrl":"https://doi.org/10.1111/1753-0407.70095","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>To determine the associations between sleep phenotypes and the risks of specific obesity types and weight gain in patients with type 2 diabetes (T2D), especially in different genetic risk groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and Methods</h3>\u0000 \u0000 <p>We conducted a prospective study involving 58 890 participants. Sleep and napping were assessed according to the standardized questionnaire. General and abdominal obesity were defined by BMI or visceral fat area (VFA), respectively. Multivariable Cox regression, stratified, and joint analysis were performed to explore potential correlations. Furthermore, mediation models were constructed to figure out the mediating role of metabolic factors (blood pressure, UACR, and HbA1c).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>During a median 3.05-year follow-up period, short sleep increased the risk of obesity (HR 1.42, 95% CI 1.17–1.71; 1.33, 1.08–1.65) and weight gain (1.21, 1.09–1.34; 1.17, 1.06–1.29), while long sleep and napping were unrelated to abdominal obesity and weight gain. Mediation analysis showed that systolic blood pressure, UACR, and HbA1c mediated the statistical association between night sleep duration and general obesity with proportions (%) of 7.9, 1.8, and 8.8, respectively. Joint analysis showed both sleep and napping groups had no significance among the low genetic risk group, while long napping, short sleep, and long sleep increased the risk of general obesity in medium to high risk patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Short sleep, long sleep, and long napping increased the risk of general obesity and BMI-defined weight gain, and were more pronounced in the medium to high genetic risk group. Napping was unrelated to abdominal obesity. Metabolic factors partially explain the mechanism between sleep and obesity.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144100710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes and Alzheimer's Disease","authors":"Zachary Bloomgarden","doi":"10.1111/1753-0407.70103","DOIUrl":"https://doi.org/10.1111/1753-0407.70103","url":null,"abstract":"&lt;p&gt;The relationship between diabetes and Alzheimer's Disease (AD) has increasingly been recognized. Diabetes is associated with the doubling of vascular dementia and with a one-third increase in the risk of AD [&lt;span&gt;1&lt;/span&gt;]. AD prevalence and mortality have particularly increased over the past three decades in China, and among women in relation to increases in longevity, with higher levels of glycemia the major attributable risk factor, with further risk associated with cigarette use and obesity [&lt;span&gt;2&lt;/span&gt;], at least in part reflecting the association of all three of these factors with insulin resistance. During this time period, obesity has shown greater attributable risk while smoking has become a weaker risk factor, while other factors including environmental pollutants, nutritional deficiencies, alcohol use, and hypertension appear to be associated with considerably lower population attributable risk [&lt;span&gt;2&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Insulin plays a variety of roles in neuronal function and survival, with diabetes increasing AD risk indirectly as a function of underlying brain insulin resistance, leading to impaired cognitive processes and increasing AD susceptibility [&lt;span&gt;3&lt;/span&gt;]. In insulin-resistant states, brain insulin levels rise, leading to reduced insulin-degrading enzyme (IDE) activity. IDE is responsible for clearing Amyloid-beta (Aβ). Aβ monomers play roles in neuronal synaptic activity, but Aβ has a tendency to autoaggregate, with reduction in IDE activity resulting in greater levels of Aβ, promoting plaque formation and contributing to AD pathology from Aβ accumulation, aggregation, and fibril formation [&lt;span&gt;4&lt;/span&gt;]. Tau protein functions by stabilizing neuronal microtubules and plays a role in neuronal cell signaling. Insulin resistance downregulates an insulin signaling pathway, leading to decreased phosphoinositide 3-kinase (PI3K) activity, in turn altering the activity of the serine/threonine kinase Akt pathway, leading to activation of glycogen synthase kinase-3β (GSK-3β). In addition to its function in regulating glycogen synthesis, GSK-3β is an enzyme involved in phosphorylation of tau protein, with hyperphosphorylated tau aggregating to form neurofibrillary tangles [&lt;span&gt;4&lt;/span&gt;]. The typical pathologic findings of AD, then, are exacerbated by insulin resistance, underlying the association of diabetes with AD.&lt;/p&gt;&lt;p&gt;Both among individuals having and not having diabetes, higher average glucose levels are associated with an increased hazard ratio for dementia [&lt;span&gt;5&lt;/span&gt;]. Similarly, higher HbA1c levels are also associated with greater risk of dementia in patients with diabetes [&lt;span&gt;6&lt;/span&gt;]. There may be relationships between diabetes treatment approaches and dementia development. Of concern, sulfonylureas were associated with a higher risk of dementia development than dipeptidyl peptidase 4 inhibitors (DPP4i) [&lt;span&gt;7&lt;/span&gt;]. The glucagon-like protein-1 receptor agonists (GLP-1 RAs) may reduce brain Aβ lev","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plantar Tissue Characteristics in People With Diabetes With and Without Peripheral Neuropathy: A Novel Explanatory Model for DPN Risk Assessment","authors":"Yiming Li,&nbsp;Wei Wu,&nbsp;Liyun Xue,&nbsp;Tianyu Zhao,&nbsp;Yucheng Lu,&nbsp;Xiaohui Qiao,&nbsp;Hong Ding","doi":"10.1111/1753-0407.70094","DOIUrl":"https://doi.org/10.1111/1753-0407.70094","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>Diabetic peripheral neuropathy (DPN) may affect the biomechanical properties and morphology of the plantar tissue. This study aimed to compare plantar stiffness and thickness in individuals with diabetes with and without DPN and develop a novel explanatory model for DPN risk assessment by integrating these measures with clinical parameters.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials &amp; Methods</h3>\u0000 \u0000 <p>Thirty-two healthy controls and 84 people with diabetes (41 with DPN and 43 without DPN) were included. Shear wave elastography evaluated plantar thickness and stiffness at the heel, hallux, and first and fifth metatarsal heads (1st MTH, 5th MTH). An integrated thickness or stiffness index was generated at multiple locations by principal component analysis (PCA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>People with DPN showed a significant increase in plantar thickness (heel, 1st MTH) (<i>p &lt;</i> 0.001) and stiffness (all tested locations) compared to healthy controls (<i>p</i> &lt; 0.05). Moreover, plantar thickness at 1st MTH, plantar stiffness at 5th MTH, and integrated stiffness index generated by PCA were significantly higher in DPN than in the non-DPN group (<i>p</i> &lt; 0.05). A DPN explanatory model was developed using multivariate logistic regression, incorporating the integrated plantar stiffness index, diabetes duration, and gender. The model showed high discriminative ability (AUROC: 97.7%), with an optimal cutoff of 0.56 yielding 92.7% sensitivity and 95.3% specificity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The integrated plantar stiffness index, combined with gender and diabetes duration, offers a novel approach for DPN, providing a noninvasive tool for DPN risk assessment.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70094","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk Factors, Pathological Changes, and Potential Treatment of Diabetes-Associated Cognitive Dysfunction","authors":"Xiaoyu Meng,&nbsp;Haiyang Du,&nbsp;Danpei Li,&nbsp;Yaming Guo,&nbsp;Peiqiong Luo,&nbsp;Limeng Pan,&nbsp;Ranran Kan,&nbsp;Peng Yu,&nbsp;Yuxi Xiang,&nbsp;Beibei Mao,&nbsp;Yi He,&nbsp;Siyi Wang,&nbsp;Wenjun Li,&nbsp;Yan Yang,&nbsp;Xuefeng Yu","doi":"10.1111/1753-0407.70089","DOIUrl":"https://doi.org/10.1111/1753-0407.70089","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Diabetes is a prevalent public health issue worldwide, and the cognitive dysfunction and subsequent dementia caused by it seriously affect the quality of life of patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Recent studies were reviewed to provide a comprehensive summary of the risk factors, pathogenesis, pathological changes and potential drug treatments for diabetes-related cognitive dysfunction (DACD).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Several risk factors contribute to DACD, including hyperglycemia, hypoglycemia, blood sugar fluctuations, hyperinsulinemia, aging, and others. Among them, modifiable risk factors for DACD include blood glucose control, physical activity, diet, smoking, and hypertension management, while non-modifiable risk factors include age, genetic predisposition, sex, and duration of diabetes. At the present, the pathogenesis of DACD mainly includes insulin resistance, neuroinflammation, vascular disorders, oxidative stress, and neurotransmitter disorders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In this review, we provide a comprehensive summary of the risk factors, pathogenesis, pathological changes and potential drug treatments for DACD, providing information from multiple perspectives for its prevention and management.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to Commentary on “A Population-Based Correlation Analysis Between Hemoglobin A1c and Hemoglobin Levels”","authors":"Tingyu Zhang,&nbsp;Bin Cui","doi":"10.1111/1753-0407.70086","DOIUrl":"https://doi.org/10.1111/1753-0407.70086","url":null,"abstract":"<p>We appreciate the author's attention to our study [<span>1</span>] and their detailed comments. To address their questions and help the readers better understand this research, we would like to provide the following explanation of our study's process.</p><p>A study titled “Altitudes and Hemoglobin A1c Value” conducted by my colleagues was published in 2024 [<span>2</span>]. Their analysis, which included 95 052 individuals across 162 sites in China, revealed a positive correlation between altitude above 2500 m and HbA1c levels, while no such correlation was observed at altitudes below 2500 m. Due to the lack of data on hemoglobin concentrations and red blood cell counts, their study could not provide more in-depth results. Fortunately, our team had some data suitable for further investigation. We selected two cities with altitudes below 2500 m: Kunming (1891 m) and Chengdu (503 m). The hemoglobin concentration in Kunming (mean: 158.73 g/L, SD: 16.36) was higher than in Chengdu (mean: 145.44 g/L, SD: 16.82). There was no difference in HbA1c levels between two groups, with Kunming showing a mean of 5.40 (SD: 0.48) and Chengdu 5.41 (SD: 0.41).</p><p>Although hemoglobin levels in the two cities differ, they remain within the normal reference range. Moreover, residents of both cities shared similar lifestyles and socio-economic conditions; we combined the data to analyze the effects of gender and age. We employed the Generalized Additive Model (GAM) for our analysis, as it effectively captures nonlinear relationships and allows us to focus on trends and patterns. Notably, Figure D reveals a significant gender-based difference in the relationship between HbA1c and age. The HbA1c curve for women shows a distinctive turning point around age 45, which prompted our further investigation in Figures C and D. In Figure D, the disparity in HbA1c levels between women above and below the age of 45 is likely influenced by menopause and changes in estrogen levels. Unfortunately, our dataset does not include estrogen-related data, preventing further analyses in this direction. However, a previous study indicated that estrogen therapy in postmenopausal women with type 1 or type 2 diabetes can reduce HbA1c and fasting glucose levels, which supports our findings [<span>3</span>].</p><p>We acknowledge that our analysis has limitations regarding the types and scope of the real-world clinical data. Therefore, the findings presented in this article underscore the need for more epidemiological studies with rigorous system design to achieve more reliable conclusions.</p><p>T.Z. and B.C. drafted and revised the manuscript.</p><p>The authors declare no conflicts of interest.</p>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on “A Population-Based Correlation Analysis Between Hemoglobin A1c and Hemoglobin Levels”","authors":"Youyuan Hu,&nbsp;Tinghua Zhang","doi":"10.1111/1753-0407.70087","DOIUrl":"https://doi.org/10.1111/1753-0407.70087","url":null,"abstract":"&lt;p&gt;We read with interest the study by Zhang et al. [&lt;span&gt;1&lt;/span&gt;], which explored the gender- and age-specific associations between hemoglobin A1c (HbA1c) and hemoglobin levels in a large Chinese cohort. While the authors provide valuable insights into the potential role of estrogen in modulating HbA1c, several methodological and interpretative limitations warrant discussion to strengthen the validity and generalizability of their conclusions.&lt;/p&gt;&lt;p&gt;The study's reliance on health examination data from Southwest China raises concerns about external validity. Regional variations in genetic, dietary, and socioeconomic factors may influence hemoglobin and HbA1c dynamics, limiting extrapolation to global populations. Furthermore, while the authors adjusted for basic covariates (e.g., age, gender), critical confounders such as iron status, inflammation markers (e.g., C-reactive protein), and nutritional deficiencies—known to affect both hemoglobin and HbA1c—were omitted. For instance, iron deficiency anemia disproportionately impacts women and could confound the observed correlations [&lt;span&gt;2&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;The use of a binary age cutoff (≤ 45 vs. &gt; 45 years) to approximate menopausal status is problematic. Menopause timing varies widely across individuals and ethnicities, with a significant proportion of women experiencing it after 45. Without direct assessment of hormonal levels (e.g., estradiol, FSH) or menstrual history, the assumption that age alone accurately reflects estrogen status risks misclassification bias. This may obscure nuanced relationships, particularly in perimenopausal populations.&lt;/p&gt;&lt;p&gt;Although generalized additive models (GAMs) effectively capture non-linear trends, the absence of model diagnostics (e.g., residual plots, goodness-of-fit metrics) undermines confidence in their robustness. Additionally, the reported Pearson's correlation coefficients (figure 1) appear incongruent with the non-linear splines, suggesting potential overfitting. A sensitivity analysis comparing GAMs with simpler linear models adjusted for spline terms would clarify whether the observed associations are artifacts of modeling complexity.&lt;/p&gt;&lt;p&gt;The hypothesis linking estrogen decline to elevated HbA1c in postmenopausal women, while plausible, remains speculative. The study lacks direct measurements of estrogen or markers of insulin resistance (e.g., HOMA-IR), relying instead on indirect epidemiological inferences. Longitudinal data or subgroup analyses stratified by hormone replacement therapy (HRT) use could strengthen causal inference. Notably, HRT's glucose-lowering effects in diabetic women—a finding that aligns with the authors' hypothesis but was not leveraged in this cross-sectional design [&lt;span&gt;3&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;The gender-specific reference intervals (RIs) for hemoglobin, though aligned with WHO criteria, may not account for altitude-related variations in hemoglobin, prevalent in Southwest China's highland populations. This oversight could s","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adverse Liver and Renal Outcomes After Initiating SGLT-2i and GLP-1RA Therapy Among Patients With Diabetes and MASLD","authors":"Arunkumar Krishnan,&nbsp;Carolin V. Schneider,&nbsp;Diptasree Mukherjee,&nbsp;Tinsay A. Woreta,&nbsp;Saleh A. Alqahtani","doi":"10.1111/1753-0407.70069","DOIUrl":"https://doi.org/10.1111/1753-0407.70069","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Context&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The management of metabolic dysfunction-associated steatotic liver disease (MASLD) and type 2 diabetes mellitus (T2DM) presents a significant clinical challenge, with a focus on preventing progression to liver and renal complications.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Objective&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To evaluate the liver and renal outcomes among new users of sodium-glucose cotransporter 2 inhibitors (SGLT2i) versus glucagon-like peptide-1 receptor agonists (GLP-1RA), dipeptidyl peptidase-4 inhibitors (DPP4i) and other anti-diabetic medications in patients with MASLD and T2DM.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Retrospective cohort study.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Electronic health records.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Participants&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A total number of 88 306 patients with MASLD and T2DM were included in a propensity score-matched analysis comparing the effects of anti-diabetic drugs.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Intervention&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Patients were categorized into groups based on their initiation of anti-diabetic medications.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Main Outcome Measures&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The primary outcomes were the incidence of cirrhosis, hepatic decompensations, and hepatocellular carcinoma. Secondary outcomes were a progression of chronic kidney disease (CKD), severity of CKD stages, and the need for hemodialysis.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;In the SGLT2i versus DPP4i, a reduced risk of cirrhosis was observed in the SGLT2i (HR: 0.97), along with fewer hepatic decompensations (HR: 0.84) and a lower incidence of HCC (HR: 0.50). CKD progression, particularly to stages 4–5, was significantly lower in the SGLT2i (HR: 0.53), as was hemodialysis (HR: 0.38). However, SGLT2i exhibited a slightly lower risk of CKD progression (HR: 0.77) and a reduced need for hemodialysis (HR: 0.71) compared to the GLP-1RA, while there was no difference in hepatic outcomes between the GLP-1RA and SGLT2i.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;SGLT2 inhibitors in patients with MASLD and T2DM ","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}