Co-occurrence of depression and diabetes is a prototypical example of mental-physical comorbidity. This study aims to investigate the association between first-degree family history of diabetes (FHD) and the presence of depressive symptoms.
Methods
The present work was one part of the baseline survey from the REACTION study. First-degree FHD was defined as having one or more first-degree relatives with diabetes. The Patient Health Questionnaire-9 was administered to detect the presence of depressive symptoms with its score ≥ 5. Logistic regression analyses were performed to determine the association between first-degree FHD and the presence of depressive symptoms.
Results
A total of 4804 participants were enrolled in the present study. Individuals with first-degree FHD were more likely to suffer from depressive symptoms compared with those without first-degree FHD (7.2% versus 4.9%, p = 0.004). The odds ratio (OR) of depressive symptoms was increased by 49.8% with the presence of first-degree FHD after adjustment of gender, age, socioeconomic factors, lifestyle risk factors, and cardiometabolic risk factors (p = 0.007). There were no significant interactions of gender, age, each socioeconomic factor, lifestyle risk factor, and cardiometabolic risk factors on the association between first-degree FHD and the presence of depressive symptoms, respectively (all p > 0.05).
Conclusions
First-degree FHD was associated with depressive symptoms independent of socioeconomic factors, lifestyle risk factors, and cardiometabolic risk factors. Genetic background might mainly contribute to the familial aggregation of depressive symptoms in individuals with first-degree FHD, who should be paid early attention to their mental health.
{"title":"First-Degree Family History of Diabetes Is Associated With the Presence of Depressive Symptoms Independent of Lifestyle Risk Factors and Cardiometabolic Risk Factors","authors":"Mengying Chen, Huimin Xia, Yaohui Yu, Yuhua Wang, Wei Chen, Enyu Lou, Zhezhe Tang, Lijuan Yang, Shengjie Ge, Bo Yang, Xuejiang Gu, Xiang Hu","doi":"10.1111/1753-0407.70139","DOIUrl":"https://doi.org/10.1111/1753-0407.70139","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Co-occurrence of depression and diabetes is a prototypical example of mental-physical comorbidity. This study aims to investigate the association between first-degree family history of diabetes (FHD) and the presence of depressive symptoms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The present work was one part of the baseline survey from the REACTION study. First-degree FHD was defined as having one or more first-degree relatives with diabetes. The Patient Health Questionnaire-9 was administered to detect the presence of depressive symptoms with its score ≥ 5. Logistic regression analyses were performed to determine the association between first-degree FHD and the presence of depressive symptoms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 4804 participants were enrolled in the present study. Individuals with first-degree FHD were more likely to suffer from depressive symptoms compared with those without first-degree FHD (7.2% versus 4.9%, <i>p</i> = 0.004). The odds ratio (OR) of depressive symptoms was increased by 49.8% with the presence of first-degree FHD after adjustment of gender, age, socioeconomic factors, lifestyle risk factors, and cardiometabolic risk factors (<i>p</i> = 0.007). There were no significant interactions of gender, age, each socioeconomic factor, lifestyle risk factor, and cardiometabolic risk factors on the association between first-degree FHD and the presence of depressive symptoms, respectively (all <i>p</i> > 0.05).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>First-degree FHD was associated with depressive symptoms independent of socioeconomic factors, lifestyle risk factors, and cardiometabolic risk factors. Genetic background might mainly contribute to the familial aggregation of depressive symptoms in individuals with first-degree FHD, who should be paid early attention to their mental health.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861889","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}
This commentary discusses the study “Sleep Phenotypes, Genetic Susceptibility, and Risk of Obesity in Patients with Type 2 Diabetes: A National Prospective Cohort Study” by Xi et al. The study provides a perceptive investigation of how metabolic characteristics and genetic variables interact to affect body mass index (BMI) in people with T2D. It has some limitations, including reliance on self-reported sleep data and lack of in-depth sleep quality assessment. We highlight the importance of objective sleep data, detailed assessments of sleep quality, and advanced polygenic risk models in future research to better understand these relationships and inform precision diabetes care.
{"title":"Sleep Phenotypes, Genetic Susceptibility, and Risk of Obesity in Patients With Type 2 Diabetes: A National Prospective Cohort Study: A Commentary","authors":"Nimra Khan, Rida-e-Zehra, Nandni Dileep","doi":"10.1111/1753-0407.70146","DOIUrl":"https://doi.org/10.1111/1753-0407.70146","url":null,"abstract":"<p>This commentary discusses the study “Sleep Phenotypes, Genetic Susceptibility, and Risk of Obesity in Patients with Type 2 Diabetes: A National Prospective Cohort Study” by Xi et al. The study provides a perceptive investigation of how metabolic characteristics and genetic variables interact to affect body mass index (BMI) in people with T2D. It has some limitations, including reliance on self-reported sleep data and lack of in-depth sleep quality assessment. We highlight the importance of objective sleep data, detailed assessments of sleep quality, and advanced polygenic risk models in future research to better understand these relationships and inform precision diabetes care.</p>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853664","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}
The dual burden of diabetes and kidney disease poses a growing public health challenge in aging populations, yet its longitudinal trends and modifiable determinants in China remain understudied. This study aims to track the trends in the prevalence of the comorbidity of diabetes and kidney disease in Chinese middle-aged and older adults over a 10-year period (2011–2020) and identify social determinants and lifestyle factors associated with this comorbidity.
Methods
This study utilized data from five waves of the China Health and Retirement Longitudinal Study (CHARLS). Temporal trends and geographical distributions of the comorbidity of diabetes and kidney diseases were analyzed across survey years; disaggregated by sex, residence, and geographical region. Cox proportional hazards regression models were employed to explore the associations between SDOH burden, lifestyle factors, and the risk of comorbidity.
Results
The prevalence of diabetes and kidney disease comorbidity exhibited a clear upward trend among Chinese adults aged ≥ 45 years, increasing from 0.60% in 2011 to 3.10% in 2020. Geographical analysis revealed significant regional disparities, with Northeast China having the highest prevalence throughout the decade (4.76% in 2020). High SDOH burden was associated with an increased risk of comorbidity (HR = 1.24, 95% CI = 1.01–1.52). Conversely, maintaining a good (HR = 0.38, 95% CI = 0.27–0.54) or moderate (HR = 0.74, 95% CI = 0.59–0.91) lifestyle was associated with a reduced risk.
Conclusion
The rising prevalence and significant regional disparities of diabetes and kidney disease comorbidity underscore the urgent need for targeted public health interventions in China.
糖尿病和肾脏疾病的双重负担给老龄化人口带来了越来越大的公共卫生挑战，但其在中国的纵向趋势和可改变的决定因素仍未得到充分研究。本研究旨在追踪10年间（2011-2020年）中国中老年人糖尿病和肾脏疾病合并症的流行趋势，并确定与这种合并症相关的社会决定因素和生活方式因素。方法本研究利用中国健康与退休纵向研究（CHARLS）的五波数据。分析了糖尿病和肾脏疾病合并症的时间趋势和地理分布；按性别、居住地和地理区域分列。采用Cox比例风险回归模型探讨SDOH负担、生活方式因素与合并症风险之间的关系。结果中国≥45岁成人糖尿病和肾脏疾病合并症患病率呈明显上升趋势，从2011年的0.60%上升到2020年的3.10%。区域差异显著，东北地区10年患病率最高（2020年为4.76%）。高SDOH负担与合并症风险增加相关（HR = 1.24, 95% CI = 1.01-1.52）。相反，保持良好（HR = 0.38, 95% CI = 0.27-0.54）或适度（HR = 0.74, 95% CI = 0.59-0.91）的生活方式与降低风险相关。结论糖尿病和肾脏疾病合并症患病率的上升和显著的地区差异突出了中国迫切需要有针对性的公共卫生干预措施。
{"title":"Trends, Social Determinants, and Lifestyle Factors Associated With Comorbidity of Diabetes and Kidney Diseases Among Chinese Adults Aged ≥ 45 Years","authors":"Guanghui Cui, Shaojie Li, Yu Qin, Yuqin Zhang, Kunpeng Hui, Mengfan Feng, Weiwei Li, Xuezhi Zhang","doi":"10.1111/1753-0407.70142","DOIUrl":"https://doi.org/10.1111/1753-0407.70142","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The dual burden of diabetes and kidney disease poses a growing public health challenge in aging populations, yet its longitudinal trends and modifiable determinants in China remain understudied. This study aims to track the trends in the prevalence of the comorbidity of diabetes and kidney disease in Chinese middle-aged and older adults over a 10-year period (2011–2020) and identify social determinants and lifestyle factors associated with this comorbidity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study utilized data from five waves of the China Health and Retirement Longitudinal Study (CHARLS). Temporal trends and geographical distributions of the comorbidity of diabetes and kidney diseases were analyzed across survey years; disaggregated by sex, residence, and geographical region. Cox proportional hazards regression models were employed to explore the associations between SDOH burden, lifestyle factors, and the risk of comorbidity.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The prevalence of diabetes and kidney disease comorbidity exhibited a clear upward trend among Chinese adults aged ≥ 45 years, increasing from 0.60% in 2011 to 3.10% in 2020. Geographical analysis revealed significant regional disparities, with Northeast China having the highest prevalence throughout the decade (4.76% in 2020). High SDOH burden was associated with an increased risk of comorbidity (HR = 1.24, 95% CI = 1.01–1.52). Conversely, maintaining a good (HR = 0.38, 95% CI = 0.27–0.54) or moderate (HR = 0.74, 95% CI = 0.59–0.91) lifestyle was associated with a reduced risk.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The rising prevalence and significant regional disparities of diabetes and kidney disease comorbidity underscore the urgent need for targeted public health interventions in China.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843667","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}
Stress hyperglycemia ratio (SHR) and glycemic variability (GV) are established markers of glucose metabolism dysregulation. This study compared their predictive values for mortality and arrhythmic events in patients with reduced ejection fraction following acute myocardial infarction (AMI).
Materials and Methods
We analyzed 1933 AMI patients with reduced ejection fraction from the MIMIC-IV database (v3.1, 2008–2022). The primary endpoint was in-hospital mortality, with secondary endpoints including 1-year all-cause mortality, ventricular tachycardia/ventricular fibrillation (VT/VF), and cardiac arrest. Multivariate logistic regression models evaluated associations with in-hospital outcomes, while Cox proportional hazards models assessed 1-year mortality. Restricted cubic spline models examined non-linear relationships between SHR and outcomes, with discriminative ability compared using area under the receiver operating characteristic curve (AUC) analysis.
Results
Among patients (mean age 67.3 years), 401 (20.7%) died during hospitalization and 662 (34.2%) within one year. After adjustment, SHR showed the strongest association with in-hospital mortality (odds ratio [OR]: 1.51, 95% confidence interval [CI]: 1.24–1.82) compared to GV (OR: 1.00, 95% CI: 0.99–1.01). For 1-year mortality, SHR maintained superior performance (hazard ratio: 1.40, 95% CI: 1.19–1.65), with the highest tertile significantly associated with increased risk. ROC analysis confirmed SHR's superior predictive capacity for both mortality endpoints and VT/VF, while none of the indices significantly predicted cardiac arrest. SHR's predictive value was more pronounced in non-diabetic patients.
Conclusions
In post-AMI patients with reduced ejection fraction, SHR demonstrated superior predictive value for mortality compared to GV, supporting its incorporation into risk stratification models for individualized glucose management.
{"title":"Stress Hyperglycemia Ratio Outperforms Glycemic Variability in Predicting Mortality Among Acute Myocardial Infarction Patients With Reduced Ejection Fraction: A Retrospective Cohort Study","authors":"Weiyan Lai, Xiu Ying Peng, Hui Peng, Yang Chen","doi":"10.1111/1753-0407.70122","DOIUrl":"https://doi.org/10.1111/1753-0407.70122","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Aims</h3>\u0000 \u0000 <p>Stress hyperglycemia ratio (SHR) and glycemic variability (GV) are established markers of glucose metabolism dysregulation. This study compared their predictive values for mortality and arrhythmic events in patients with reduced ejection fraction following acute myocardial infarction (AMI).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and Methods</h3>\u0000 \u0000 <p>We analyzed 1933 AMI patients with reduced ejection fraction from the MIMIC-IV database (v3.1, 2008–2022). The primary endpoint was in-hospital mortality, with secondary endpoints including 1-year all-cause mortality, ventricular tachycardia/ventricular fibrillation (VT/VF), and cardiac arrest. Multivariate logistic regression models evaluated associations with in-hospital outcomes, while Cox proportional hazards models assessed 1-year mortality. Restricted cubic spline models examined non-linear relationships between SHR and outcomes, with discriminative ability compared using area under the receiver operating characteristic curve (AUC) analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among patients (mean age 67.3 years), 401 (20.7%) died during hospitalization and 662 (34.2%) within one year. After adjustment, SHR showed the strongest association with in-hospital mortality (odds ratio [OR]: 1.51, 95% confidence interval [CI]: 1.24–1.82) compared to GV (OR: 1.00, 95% CI: 0.99–1.01). For 1-year mortality, SHR maintained superior performance (hazard ratio: 1.40, 95% CI: 1.19–1.65), with the highest tertile significantly associated with increased risk. ROC analysis confirmed SHR's superior predictive capacity for both mortality endpoints and VT/VF, while none of the indices significantly predicted cardiac arrest. SHR's predictive value was more pronounced in non-diabetic patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In post-AMI patients with reduced ejection fraction, SHR demonstrated superior predictive value for mortality compared to GV, supporting its incorporation into risk stratification models for individualized glucose management.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832807","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}
Theocharis Koufakis, Djordje S. Popovic, Nikolaos Papanas
<p>For people living with diabetes, hypoglycemia has long been a silent threat—its approach often as unnoticed as a dog barking in the distance, unheard by those most at risk. Since the earliest clinical descriptions of diabetes, hypoglycemia has been recognized as a critical and sometimes life-threatening complication of glucose-lowering therapy [<span>1</span>]. In the early 20th century, with the advent of insulin therapy, hypoglycemia emerged as a new and immediate concern, often identified only after the onset of severe neuroglycopenic symptoms. Early detection relied primarily on clinical observation and patient self-reporting of warning signs, with little objective measurement available. The development of capillary blood glucose testing in the 1970s and 1980s marked a major advance, enabling both patients and clinicians to more accurately identify and document hypoglycemic episodes. Over the past two decades, the evaluation of hypoglycemia has been revolutionized by continuous glucose monitoring (CGM) technologies and the integration of predictive algorithms, which now allow for real-time detection, detailed glycemic profiling, and proactive management [<span>2</span>]. This historical progression from symptom-based recognition to advanced digital monitoring reflects the ongoing commitment to improving safety and outcomes in diabetes care.</p><p>However, hypoglycemia remains a principal barrier to optimal glycemic control in diabetes, particularly for individuals with type 1 diabetes mellitus (T1DM), who require intensive insulin regimens to prevent microvascular and macrovascular complications [<span>3</span>]. While intensified therapy reduces long-term risks, it simultaneously increases the incidence of hypoglycemia, necessitating careful therapeutic balancing. A significant subset of individuals with T1DM—estimated at up to 25%—develop hypoglycemia unawareness, where autonomic warning symptoms such as sweating and tremor become attenuated or absent [<span>4</span>]. This phenomenon greatly elevates the risk for severe events requiring external assistance, often forcing patients and clinicians to accept higher glucose targets than recommended [<span>5</span>]. Thus, the fear and reality of hypoglycemia continue to restrict the full benefits of modern diabetes therapies and remain a central concern in clinical care.</p><p>The implications of hypoglycemia extend well beyond transient physical symptoms. Acute episodes have been associated with a heightened risk of cardiac arrhythmias and myocardial ischemia, linked to autonomic surges and altered cardiac repolarization [<span>6</span>]. Hypoglycemia may also induce a prothrombotic state, increasing platelet activation and coagulation, thereby further raising cardiovascular risk—an especially pertinent issue in people already predisposed to vascular disease [<span>7</span>]. At a neurological level, repeated or severe events can result in cognitive dysfunction, seizures, and, rarely, irrev
{"title":"And the Dog Was Barking: Transforming Quality of Life in Diabetes Through Innovative Hypoglycemia Detection","authors":"Theocharis Koufakis, Djordje S. Popovic, Nikolaos Papanas","doi":"10.1111/1753-0407.70143","DOIUrl":"https://doi.org/10.1111/1753-0407.70143","url":null,"abstract":"<p>For people living with diabetes, hypoglycemia has long been a silent threat—its approach often as unnoticed as a dog barking in the distance, unheard by those most at risk. Since the earliest clinical descriptions of diabetes, hypoglycemia has been recognized as a critical and sometimes life-threatening complication of glucose-lowering therapy [<span>1</span>]. In the early 20th century, with the advent of insulin therapy, hypoglycemia emerged as a new and immediate concern, often identified only after the onset of severe neuroglycopenic symptoms. Early detection relied primarily on clinical observation and patient self-reporting of warning signs, with little objective measurement available. The development of capillary blood glucose testing in the 1970s and 1980s marked a major advance, enabling both patients and clinicians to more accurately identify and document hypoglycemic episodes. Over the past two decades, the evaluation of hypoglycemia has been revolutionized by continuous glucose monitoring (CGM) technologies and the integration of predictive algorithms, which now allow for real-time detection, detailed glycemic profiling, and proactive management [<span>2</span>]. This historical progression from symptom-based recognition to advanced digital monitoring reflects the ongoing commitment to improving safety and outcomes in diabetes care.</p><p>However, hypoglycemia remains a principal barrier to optimal glycemic control in diabetes, particularly for individuals with type 1 diabetes mellitus (T1DM), who require intensive insulin regimens to prevent microvascular and macrovascular complications [<span>3</span>]. While intensified therapy reduces long-term risks, it simultaneously increases the incidence of hypoglycemia, necessitating careful therapeutic balancing. A significant subset of individuals with T1DM—estimated at up to 25%—develop hypoglycemia unawareness, where autonomic warning symptoms such as sweating and tremor become attenuated or absent [<span>4</span>]. This phenomenon greatly elevates the risk for severe events requiring external assistance, often forcing patients and clinicians to accept higher glucose targets than recommended [<span>5</span>]. Thus, the fear and reality of hypoglycemia continue to restrict the full benefits of modern diabetes therapies and remain a central concern in clinical care.</p><p>The implications of hypoglycemia extend well beyond transient physical symptoms. Acute episodes have been associated with a heightened risk of cardiac arrhythmias and myocardial ischemia, linked to autonomic surges and altered cardiac repolarization [<span>6</span>]. Hypoglycemia may also induce a prothrombotic state, increasing platelet activation and coagulation, thereby further raising cardiovascular risk—an especially pertinent issue in people already predisposed to vascular disease [<span>7</span>]. At a neurological level, repeated or severe events can result in cognitive dysfunction, seizures, and, rarely, irrev","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814694","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}
Brian T. Steffen, Elizabeth R. Lusczek, David R. Jacobs Jr, Chi Chen, Venkatesh L. Murthy, Linda Van Horn, James G. Terry, John Jeffrey Carr, Lyn M. Steffen
Background
Artificial sweeteners have become ubiquitous additives in the food supply, and yet the safety of their regular consumption remains controversial. The present study examined whether intakes of aspartame or saccharin are related to aberrations in the plasma metabolome indicating disruptions in metabolism.
Methods
A cohort of 2160 male and female participants, mean age 32.1 years, was included in the analysis. Liquid chromatography and mass-spectrometry assessed 549 unique plasma metabolites. Diet was assessed using a validated questionnaire that allowed for estimation of aspartame and saccharin intakes. A generalized linear regression model evaluated associations of saccharin or aspartame intake with plasma metabolites with adjustment for potential confounders and multiple comparisons. Multiple sensitivity analyses and propensity score matching were conducted.
Results
Heavy aspartame intake (≥ 5 servings/day) was associated with plasma levels (per SD) of saccharin (β = 0.90; q = 9.0E-36), myo-inositol (β = 0.27; q = 3.7E-04), caffeine (β = 0.31; q = 4.1E-04), and five metabolites of caffeine including 1,7-dimethyluric acid (β = 0.37; q = 7.1E-06), 1-methylurate (β = 0.36; q = 7.1E-06), 5-acetylamino-6-amino-3-methyluracil (β = 0.38; q = 3.2E-6), theophylline (β = 0.36; q = 9.1E-06), and 1-methylxanthine (β = 0.32; q = 2.0E-03). Saccharin intake was associated with plasma levels of saccharin alone (β = 0.29; q = 1.8E-10). No associations with sugars, carbohydrates, lipids, amino acids, or other metabolites that would suggest metabolic perturbations were observed with either artificial sweetener; sensitivity analyses supported these findings.
Conclusions
In the largest metabolomics study to date, no link was found between metabolic disruptions and either aspartame or saccharin intake. We cannot exclude the possibility that more extreme intakes may be related to metabolic disruptions among consumers of artificial sweeteners.
{"title":"No Evidence of Metabolomic Disruptions From Real-World Intakes of Aspartame or Saccharin: The Coronary Artery Risk Development in Young Adults Study","authors":"Brian T. Steffen, Elizabeth R. Lusczek, David R. Jacobs Jr, Chi Chen, Venkatesh L. Murthy, Linda Van Horn, James G. Terry, John Jeffrey Carr, Lyn M. Steffen","doi":"10.1111/1753-0407.70138","DOIUrl":"https://doi.org/10.1111/1753-0407.70138","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Artificial sweeteners have become ubiquitous additives in the food supply, and yet the safety of their regular consumption remains controversial. The present study examined whether intakes of aspartame or saccharin are related to aberrations in the plasma metabolome indicating disruptions in metabolism.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A cohort of 2160 male and female participants, mean age 32.1 years, was included in the analysis. Liquid chromatography and mass-spectrometry assessed 549 unique plasma metabolites. Diet was assessed using a validated questionnaire that allowed for estimation of aspartame and saccharin intakes. A generalized linear regression model evaluated associations of saccharin or aspartame intake with plasma metabolites with adjustment for potential confounders and multiple comparisons. Multiple sensitivity analyses and propensity score matching were conducted.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Heavy aspartame intake (≥ 5 servings/day) was associated with plasma levels (per SD) of saccharin (<i>β</i> = 0.90; <i>q</i> = 9.0E-36), myo-inositol (<i>β</i> = 0.27; <i>q</i> = 3.7E-04), caffeine (<i>β</i> = 0.31; <i>q</i> = 4.1E-04), and five metabolites of caffeine including 1,7-dimethyluric acid (<i>β</i> = 0.37; <i>q</i> = 7.1E-06), 1-methylurate (<i>β</i> = 0.36; <i>q</i> = 7.1E-06), 5-acetylamino-6-amino-3-methyluracil (<i>β</i> = 0.38; <i>q</i> = 3.2E-6), theophylline (<i>β</i> = 0.36; <i>q</i> = 9.1E-06), and 1-methylxanthine (<i>β</i> = 0.32; <i>q</i> = 2.0E-03). Saccharin intake was associated with plasma levels of saccharin alone (<i>β</i> = 0.29; <i>q</i> = 1.8E-10). No associations with sugars, carbohydrates, lipids, amino acids, or other metabolites that would suggest metabolic perturbations were observed with either artificial sweetener; sensitivity analyses supported these findings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In the largest metabolomics study to date, no link was found between metabolic disruptions and either aspartame or saccharin intake. We cannot exclude the possibility that more extreme intakes may be related to metabolic disruptions among consumers of artificial sweeteners.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70138","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814692","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}
This study aims to explore the effect of the ketogenic diet (KD) on the occurrence of end-stage renal disease (ESRD) and the longitudinal relationship between circulating β-hydroxybutyrate (β-OHB) and kidney outcomes.
Methods
We used the dietary ketogenic ratio (DKR) to estimate the nutritional ketosis probability of KD and analyzed the association with ESRD using NHANES cross-sectional data by Spearman correlation coefficient and multivariate logistic regression model. We also used the Kaplan–Meier method, Cox regression analysis, and restricted cubic splines (RCS) to analyze the relationship between circulating β-OHB and renal outcomes in the T2DM-DKD longitudinal cohort of West China Hospital. Mendelian randomization (MR) was also employed to evaluate potential causal associations.
Results
The cross-sectional analysis revealed that non-ESRD patients had significantly higher baseline age, BMI, serum albumin, and DKR values, with a weak negative correlation between DKR and serum creatinine (ρ = −0.072, p = 0.011). Logistic regression consistently indicated a reduced ESRD prevalence in higher DKR quartiles. In the longitudinal study, elevated β-OHB levels were associated with improved renal survival and a lower risk of ESRD, with RCS analysis identifying the lowest risk at approximately 0.25 mmol/L. MR analyses supported these findings, showing inverse correlations between genetically predicted β-OHB and creatinine (p = 0.007) and cystatin c (p < 0.001).
Conclusion
These findings suggest that KD may be associated with a lower incidence of ESRD in DKD patients, with elevated β-OHB levels independently associated with a reduced risk of ESRD, warranting further research to confirm causality and elucidate underlying mechanisms.
{"title":"Ketogenic Diet, Serum Ketone Bodies and Risk of End-Stage Renal Disease in Patients With Diabetic Kidney Disease: A Multi-Cohort Study","authors":"Ke Liu, Qing Yang, Yanlin Lang, Yutong Zou, Jiamin Yuan, Jia Yang, Jing Ma, Linli Cai, Xianglin Kong, Fuhai Yang, Fang Liu","doi":"10.1111/1753-0407.70140","DOIUrl":"https://doi.org/10.1111/1753-0407.70140","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Aim</h3>\u0000 \u0000 <p>This study aims to explore the effect of the ketogenic diet (KD) on the occurrence of end-stage renal disease (ESRD) and the longitudinal relationship between circulating β-hydroxybutyrate (β-OHB) and kidney outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We used the dietary ketogenic ratio (DKR) to estimate the nutritional ketosis probability of KD and analyzed the association with ESRD using NHANES cross-sectional data by Spearman correlation coefficient and multivariate logistic regression model. We also used the Kaplan–Meier method, Cox regression analysis, and restricted cubic splines (RCS) to analyze the relationship between circulating β-OHB and renal outcomes in the T2DM-DKD longitudinal cohort of West China Hospital. Mendelian randomization (MR) was also employed to evaluate potential causal associations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The cross-sectional analysis revealed that non-ESRD patients had significantly higher baseline age, BMI, serum albumin, and DKR values, with a weak negative correlation between DKR and serum creatinine (<i>ρ</i> = −0.072, <i>p</i> = 0.011). Logistic regression consistently indicated a reduced ESRD prevalence in higher DKR quartiles. In the longitudinal study, elevated β-OHB levels were associated with improved renal survival and a lower risk of ESRD, with RCS analysis identifying the lowest risk at approximately 0.25 mmol/L. MR analyses supported these findings, showing inverse correlations between genetically predicted β-OHB and creatinine (<i>p</i> = 0.007) and cystatin c (<i>p</i> < 0.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>These findings suggest that KD may be associated with a lower incidence of ESRD in DKD patients, with elevated β-OHB levels independently associated with a reduced risk of ESRD, warranting further research to confirm causality and elucidate underlying mechanisms.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70140","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144814693","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}
Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disorder where the immune system targets and destroys insulin-producing β-cells in the pancreas. It generally emerges during childhood or adolescence, but individuals of any age can be affected. In contrast to Type 2 diabetes mellitus (T2DM) which is often associated with lifestyle factors, T1DM cannot be prevented and necessitates lifelong management. Currently, there is no definitive cure for T1DM, and patients rely on continuous insulin injections for their entire lives. Ongoing developments in insulin treatment, such as insulin pumps, continuous glucose monitoring, and hybrid closed-loop systems, offer promising alternatives. Despite advancements in intensive glycemic control that have reduced the occurrence of microvascular and macrovascular complications, a significant number of T1DM patients still these issues. Extensive research efforts are crucial to achieve early detection, prevent the loss of β-cells, and devise improved treatment strategies to enhance the quality of life and prognosis for those affected. This review explores the most noteworthy and recent advancements in the field of T1DM.
{"title":"A Comprehensive Review of Novel Advances in Type 1 Diabetes Mellitus","authors":"Yazdan Ebrahimpour, Sahbasadat Khatami, Mahsa Saffar, Alireza Fereidouni, Zahra Biniaz, Nafiseh Erfanian, Mohammad Fereidouni","doi":"10.1111/1753-0407.70120","DOIUrl":"https://doi.org/10.1111/1753-0407.70120","url":null,"abstract":"<p>Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disorder where the immune system targets and destroys insulin-producing β-cells in the pancreas. It generally emerges during childhood or adolescence, but individuals of any age can be affected. In contrast to Type 2 diabetes mellitus (T2DM) which is often associated with lifestyle factors, T1DM cannot be prevented and necessitates lifelong management. Currently, there is no definitive cure for T1DM, and patients rely on continuous insulin injections for their entire lives. Ongoing developments in insulin treatment, such as insulin pumps, continuous glucose monitoring, and hybrid closed-loop systems, offer promising alternatives. Despite advancements in intensive glycemic control that have reduced the occurrence of microvascular and macrovascular complications, a significant number of T1DM patients still these issues. Extensive research efforts are crucial to achieve early detection, prevent the loss of β-cells, and devise improved treatment strategies to enhance the quality of life and prognosis for those affected. This review explores the most noteworthy and recent advancements in the field of T1DM.</p>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144782283","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}
Periodontal disease is a prevalent and chronic inflammatory condition increasingly recognized for its systemic implications beyond oral health. While traditionally confined to dentistry, recent evidence reveals strong associations between periodontitis and chronic systemic conditions such as cardiovascular disease, diabetes mellitus, rheumatoid arthritis, inflammatory bowel disease, Alzheimer's disease, and various cancers. Mechanistic studies have identified plausible biological pathways, including systemic dissemination of periodontal pathogens and immune mediators, which can exacerbate distant organ inflammation and dysfunction. Experimental models highlight how oral bacteria influence immune responses, disrupt gut and vascular homeostasis, and contribute to oncogenesis and autoimmunity. Notably, bidirectional relationships, such as those between periodontitis and diabetes, underscore the need for integrated care approaches. Effective periodontal therapy has demonstrated systemic benefits, including improved glycemic control and reduced inflammation. Given the mounting evidence, periodontal disease should be approached as a critical component of systemic health, necessitating interdisciplinary collaboration among healthcare providers to optimize patient outcomes and public health.
{"title":"Periodontal Disease: A Contributing Factor to Adverse Outcome in Diabetes","authors":"Edgard El Chaar","doi":"10.1111/1753-0407.70136","DOIUrl":"https://doi.org/10.1111/1753-0407.70136","url":null,"abstract":"<p>Periodontal disease is a prevalent and chronic inflammatory condition increasingly recognized for its systemic implications beyond oral health. While traditionally confined to dentistry, recent evidence reveals strong associations between periodontitis and chronic systemic conditions such as cardiovascular disease, diabetes mellitus, rheumatoid arthritis, inflammatory bowel disease, Alzheimer's disease, and various cancers. Mechanistic studies have identified plausible biological pathways, including systemic dissemination of periodontal pathogens and immune mediators, which can exacerbate distant organ inflammation and dysfunction. Experimental models highlight how oral bacteria influence immune responses, disrupt gut and vascular homeostasis, and contribute to oncogenesis and autoimmunity. Notably, bidirectional relationships, such as those between periodontitis and diabetes, underscore the need for integrated care approaches. Effective periodontal therapy has demonstrated systemic benefits, including improved glycemic control and reduced inflammation. Given the mounting evidence, periodontal disease should be approached as a critical component of systemic health, necessitating interdisciplinary collaboration among healthcare providers to optimize patient outcomes and public health.</p>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70136","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767694","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}
Lijuan Xu, Liehua Liu, Zhiwei Xie, Zhimin Huang, Hai Li, Juan Liu, Xiaoying He, Wanping Deng, Yanbing Li
Background
Early short-term intensive insulin therapy may induce sustained glycemic remission in type 2 diabetes. However, patients' responses vary greatly. Exploring key factors to improve glycemic control and predict the probability of remission precisely is meaningful for future treatment.
Methods
Patients with newly diagnosed type 2 diabetes receiving 2–3 weeks of intensive insulin therapy were followed up for at least 1 year in three randomized clinical trials. Data of theirs were extracted with the same inclusion criteria and divided into training and validation sets. A nomogram was constructed in the training set and tested in the internal validation set.
Results
Among 302 patients with transient intensive insulin therapy, 162 (53.64%) patients achieved the 1-year glycemic remission. Severe hyperglycemia at baseline did not impede future remission, as the remission rate was 60.70% in those with HbA1c greater than 13%. Three parameters were identified as predictive factors in the nomogram: fasting glucose after short-term insulin treatment, mean glucose during insulin therapy, and postprandial glucose/fasting glucose ratio at baseline. The odds ratios were 0.06 (95% CI, 0.02–0.25), 0.41 (0.18–0.93) and 6.55 (1.39–30.89), respectively. Incorporating these factors, the nomogram achieved an accuracy of 81.50%.
Conclusion
Short-term intensive insulin therapy assists patients with newly diagnosed type 2 diabetes and significant hyperglycemia to achieve glycemic remission. A rigorous control of glucose during insulin treatment favors future remission. We construct a nomogram to predict sustained glycemic remission, which may help determine whether subsequent medication is needed and thus reduce both overtreatment and therapeutic inertia.
{"title":"Construction and Implications of Nomogram for Predicting Sustained Glycemic Remission After Short-Term Intensive Insulin Therapy in Newly Diagnosed Type 2 Diabetes","authors":"Lijuan Xu, Liehua Liu, Zhiwei Xie, Zhimin Huang, Hai Li, Juan Liu, Xiaoying He, Wanping Deng, Yanbing Li","doi":"10.1111/1753-0407.70135","DOIUrl":"https://doi.org/10.1111/1753-0407.70135","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Early short-term intensive insulin therapy may induce sustained glycemic remission in type 2 diabetes. However, patients' responses vary greatly. Exploring key factors to improve glycemic control and predict the probability of remission precisely is meaningful for future treatment.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Patients with newly diagnosed type 2 diabetes receiving 2–3 weeks of intensive insulin therapy were followed up for at least 1 year in three randomized clinical trials. Data of theirs were extracted with the same inclusion criteria and divided into training and validation sets. A nomogram was constructed in the training set and tested in the internal validation set.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Among 302 patients with transient intensive insulin therapy, 162 (53.64%) patients achieved the 1-year glycemic remission. Severe hyperglycemia at baseline did not impede future remission, as the remission rate was 60.70% in those with HbA1c greater than 13%. Three parameters were identified as predictive factors in the nomogram: fasting glucose after short-term insulin treatment, mean glucose during insulin therapy, and postprandial glucose/fasting glucose ratio at baseline. The odds ratios were 0.06 (95% CI, 0.02–0.25), 0.41 (0.18–0.93) and 6.55 (1.39–30.89), respectively. Incorporating these factors, the nomogram achieved an accuracy of 81.50%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Short-term intensive insulin therapy assists patients with newly diagnosed type 2 diabetes and significant hyperglycemia to achieve glycemic remission. A rigorous control of glucose during insulin treatment favors future remission. We construct a nomogram to predict sustained glycemic remission, which may help determine whether subsequent medication is needed and thus reduce both overtreatment and therapeutic inertia.</p>\u0000 </section>\u0000 </div>","PeriodicalId":189,"journal":{"name":"Journal of Diabetes","volume":"17 8","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1753-0407.70135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144751424","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}
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