Journal of Diabetes Investigation最新文献

Background: Whether hypothyroidism and cardiovascular disease (CVD) are causally related remains ambiguous. A bidirectional and multivariable two-sample Mendelian Randomization (MR) approach was employed to elucidate these associations.

Methods: All genome-wide association study (GWAS) data were obtained from the IEU Open GWAS project. For the MR estimates, inverse variance weighted (IVW), MR-Egger regression, weighted median, simple mode, and weighted mode were utilized to assess the causal relationships. Multivariate MR (MVMR) analyses were conducted to explore the direct effects. A series of sensitivity analyses was used to ensure the robustness of our findings. All analyses were replicated in the validation samples.

Results: Higher genetically predicted hypothyroidism was causally associated with increased the risk of coronary artery disease (CAD, OR = 3.220, 95% CI = 1.082-9.583, P = 0.036), myocardial infarction (MI, OR = 3.430, 95% CI = 1.088-10.819, P = 0.035) and peripheral atherosclerosis (PAS, OR = 31.852, 95% CI = 5.987-169.457, P < 0.001). Reversely, CAD (OR = 1.005, 95% CI = 1.001-1.009, P = 0.02) and MI (OR = 1.006, 95% CI = 1.002-1.011, P = 0.004) may be more likely to promote the onset of hypothyroidism. Moreover, no significant causal effects of hypothyroidism on heart failure (HF) and small vessel stroke (SVS) were observed in the discovery samples. However, in the validation samples, hypothyroidism was found to be causally associated with HF (OR = 1.429, 95% CI = 1.240-1.647, P < 0.001) and SVS (OR = 7.297, 95% CI = 1.440-36.984, P = 0.016). For the reverse MR from PAS, HF, and SVS to hypothyroidism, no significant causal associations were identified.

Conclusions: This study reveals suggestive evidence of the bidirectional causal associations between hypothyroidism and CAD and MI. Moreover, hypothyroidism may increase the risk of PAS.

Since the discovery of insulin over a century ago, how we conceptualize, describe and treat diabetes has evolved, paving the way for the identification of distinct diabetes subgroups and individualized treatment options. Historically, the term ketosis-prone diabetes (KPD) has comprised a group of diabetes syndromes characterized by severe pancreatic β-cell dysfunction, but lacking the autoimmunity and irreversibility that underlies type 1 diabetes (T1D). Similarly, the defining feature of KPD-diabetic ketoacidosis-is uncharacteristic of type 2 diabetes (T2D). Initially, it was thought to be unique to Black populations, which led to early etiologic investigations narrowly focused on monogenic causes. However, it is now recognized that KPD occurs in diverse racial and ethnic groups and may be provoked by infection, leading to an expanded view of its pathogenesis. Here, we review these updated mechanistic views, highlight novel research tools being deployed to advance our understanding of KPD, and discuss implications of these data to inform our views on β-cell biology.

Background: The impact of COVID-19 infection on diabetic ketoacidosis (DKA) outcomes remains incompletely characterized at a national level.

Methods: We analyzed adult DKA hospitalizations in the United States from the 2020-2021 National Inpatient Sample. The primary outcome was in-hospital mortality; secondary outcomes included acute kidney injury (AKI), mechanical ventilation, sepsis, vasopressor use, length of stay (LOS), and hospitalization cost. Multivariable regression and propensity score matching adjusted for patient- and hospital-level variables.

Results: Among 658,675 DKA hospitalizations, 69,005 (10.5%) involved COVID-19. Patients with COVID-19 were older and had a greater comorbidity burden. Crude in-hospital mortality was higher with COVID-19 (21.3% vs 3.4%, P < 0.001). After adjustment, COVID-19 was associated with over sixfold higher mortality (aOR 6.22; 95% CI: 5.85-6.62), increased risks of AKI (aOR 1.15), ventilation (aOR 3.91), sepsis (aOR 1.77), vasopressor use (aOR 2.33), longer LOS (+3.99 days), and higher costs (+$15,248; all P < 0.001). Propensity-matched analysis confirmed these findings. In subgroup analysis, type 1 diabetes was linked to higher mortality (aOR 2.12) vs type 2 diabetes. Predictors of mortality included age, comorbidity burden, hospital size, and Hispanic ethnicity; female sex was protective only in COVID-19 DKA.

Conclusions: In this national analysis, COVID-19 significantly worsened mortality, multiorgan failure, and healthcare utilization in DKA admissions. DKA with COVID-19 represents a high-acuity entity warranting early escalation and resource allocation.

Objective: To comprehensively analyze the characteristics of type 1 diabetes and compare the differences between childhood-onset and adult-onset groups.

Methods: Hospital records of patients with type 1 diabetes of all ages were collected from 18 hospitals across 14 prefectures in Northwest China between January 2016 and October 2023. Data included demographic characteristics, manifestation at onset, physical examination, laboratory tests, and management. Based on diagnosis status, participants were classified into newly diagnosed and previously diagnosed groups. Additionally, according to the age at onset, participants were further divided into childhood-onset and adult-onset groups.

Results: Among the 1,513 individuals with type 1 diabetes, the newly diagnosed group showed a younger median age of onset, lower mean body mass index, and better β-cell function compared with the previously diagnosed group. Diabetic ketoacidosis at diagnosis was common, and the prevalence of chronic complications increased with disease duration. Most patients in both groups used multiple daily injections. Additionally, patients with childhood-onset type 1 diabetes were more likely to have diabetic ketoacidosis at diagnosis and had higher HbA1c levels at disease onset, lower BMI levels, and lower prevalence of complications compared with those with adult-onset diabetes.

Conclusion: This multicenter, hospital-based study provided an overview of the characteristics of patients with type 1 diabetes. These findings may contribute to a better understanding of type 1 diabetes and underscore the importance of long-term diabetic education and management.

Aims/introduction: This study aimed to assess the impact of switching from FreeStyle Libre (FSL) to FreeStyle Libre 2 (FSL2) with real-time continuous glucose monitoring (rtCGM) functionality.

Materials and methods: We retrospectively assessed 58 patients who transitioned from FSL to FSL2 with rtCGM functionality.

Results: Switching from FSL to FSL2 significantly increased the valid data acquisition rate (76.8% ± 21.6% vs 90.4% ± 18.0%, P < 0.001). Time in range (TIR) improved from 57.7% ± 18.1% to 61.7% ± 17.1% (P = 0.023); time in tight range improved from 33.3% ± 15.4% to 38.6% ± 16.9% (P = 0.006), and glycated hemoglobin decreased from 7.6% ± 1.1% to 7.4% ± 1.0% (P = 0.009). TIR improvement was immediate in Type 1 diabetes but gradual after an initial decline in Type 2 diabetes. Logistic regression identified younger age as a predictor of TIR improvement (odds ratio: 0.95, 95% confidence interval: 0.90-0.99, P = 0.034).

Conclusions: Transitioning from FSL to FSL2 with rtCGM functionality improved valid data acquisition rate and glycemic control, particularly in patients with Type 1 diabetes. The duration required for improvement in continuous glucose monitoring-related parameters varied by diabetes type, with early improvements observed in Type 1 diabetes and gradual improvements in Type 2 diabetes.

Introduction: Although constipation is a major complication of type 2 diabetes mellitus and chronic kidney disease, evidence for its treatment in these patients remains limited. This study aimed to evaluate the therapeutic efficacy of Bifidobacterium bifidum for constipation in these patients.

Materials and methods: In this multicenter, open-label, single-arm trial, 30 patients with type 2 diabetes mellitus and chronic kidney disease complicated by constipation were administered Bifidobacterium bifidum G9-1 for 12 weeks. The primary endpoint was the proportion of patients with normal stool form (Bristol Scale score ≥3.5 to <4.5). Secondary endpoints included stool form, defecation frequency, and quality of life.

Results: The proportion of patients with normal stool form at week 12 (44.8%; 13 patients) was significantly higher than that at baseline (3.3%; 1 patient; P = 0.002). The median Bristol score significantly increased from baseline by 0.6 at week 6 (P < 0.001) and 0.5 at week 12 (P < 0.001). Defecation frequency significantly increased by 0.2 ± 0.4 from baseline to week 6 (P = 0.026). The total constipation, abdominal pain, and indigestion scores on the Gastrointestinal Symptom Rating Scale significantly decreased. Abdominal symptoms, including straining, feelings of incomplete evacuation, bloating, and discomfort, significantly decreased. Glycated hemoglobin and the estimated glomerular filtration rate did not change significantly, whereas urinary albumin levels significantly decreased at week 6.

Discussion: Bifidobacterium bifidum improved stool form, defecation frequency, and quality of life of patients with type 2 diabetes mellitus and chronic kidney disease complicated by constipation, without worsening glycemic control and renal function.

Background: Inflammation plays an essential role in the pathogenesis of diabetic nephropathy (DN). Linderalactone (LNL), as a natural sesquiterpene lactone, has been discovered to have anti-inflammatory activation. However, the effects of linderalactone on diabetes-associated renal damage remain unclear.

Methods: This study investigated the effects of LNL on renal function and inflammation in diabetic mice. Renal function, collagen deposition, and fibrosis were assessed. RNA-sequencing was performed to identify molecular pathways affected by LNL. The Dectin1-related pathway was analyzed in kidney tissues and RAW264.7 cells. Dectin1-deficient and Dectin1-overexpressing models were used to confirm the mechanism of LNL.

Results: LNL improved renal function, reduced collagen deposition and fibrosis in diabetic mice without affecting blood glucose levels. RNA-sequencing revealed that LNL primarily impacted the Dectin1 pathway. It inhibited the Dectin1/Syk/CARD9/IRF5/NF-κB pathway, reduced macrophage and neutrophil infiltration, and suppressed inflammatory cytokine expression. Dectin1 knockdown mimicked these effects, while Dectin1 overexpression reversed them.

Conclusions: LNL alleviates DN via suppression of macrophage inflammation by mediating the Dectin1/Syk/CARD9/IRF5/NF-κB signaling pathway, highlighting its potential as a therapeutic agent for diabetes-associated renal injury.

Objective: To explore the association between serum fibrinogen (FIB) and clinicopathological features and renal prognosis in type 2 diabetic nephropathy (T2DN), and develop a web-based dynamic model to predict renal progression.

Methods: This paper retrospectively enrolled 173 biopsy-proven T2DN patients and stratified them by the optimal FIB cutoff. Renal progression was defined as a >50% decline in eGFR, doubling of creatinine, or onset of end-stage renal disease (ESRD). Cox regression analysis was used to screen out the independent predictors of T2DN progression; a web-based dynamic prediction model was established and evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration curves, and decision curve analysis (DCA).

Results: Of the 173 patients, 81 (46.82%) experienced the renal endpoint event. Multifactorial Cox regression analysis showed that eGFR, hemoglobin, FIB, parathyroid hormone, and interstitial fibrosis and tubular atrophy (IFTA) scores were independent risk factors for T2DN progression (P < 0.05). Among the three models constructed based on these factors, model 3 had the highest areas under the curve (AUC) (90.42; 95% CI, 85.80-95.04). The AUC of the risk prediction model constructed by the nomogram was 0.846, 0.752, and 0.754 at 1, 3, and 5 years, showing good discrimination, and the Web-based dynamic nomogram demonstrated good calibration.

Conclusions: FIB is an independent predictor of T2DN progression. A web-based dynamic model was developed to predict renal progression risk in T2DN.