Diabetes, Obesity & Metabolism最新文献

Aims: To assess the level of adherence to glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment using real-world data and to investigate the sociodemographic and clinical factors associated with discontinuation of GLP-1RAs.

Methods: First-time users of GLP-1RAs with type 2 diabetes mellitus (T2DM), aged ≥18 years, in the period 2007 to 2020, were identified using Danish registries, allowing all participants a minimum of 18 months' follow-up. Adherence to GLP-1RA therapy (medication possession ratio >0.80) and discontinuation of GLP-1RA therapy was estimated at 6- and 12-month follow-ups. Multivariable cause-specific Cox regression was used to identify sociodemographic and clinical factors associated with risk of discontinuation.

Results: In total, 44 343 first-time users of GLP-1RAs with T2DM were identified (mean age 58.6 years, 42.7% female, median duration of T2DM 6.8 years, median glycated haemoglobin level 65 mmol/mol). The absolute risk of discontinuing GLP-1RA treatment within 6 months was 14.2% (95% confidence interval [CI] 13.9-14.6) and 21.2% (95% CI 20.8-21.5) within 12 months. At 6 months, 50.4% were adherent to GLP-1RA therapy and at 12 months, 48.6% remained adherent. In the multivariable model, younger (<40 years) and older age (>75 years), higher Charlson Comorbidity Index score, lower household income, high school and longer university degree as educational attainment level, and longer diabetes duration were associated with a higher risk of discontinuing GLP-1RA treatment.

Conclusion: Approximately one in five patients discontinued GLP-1RA therapy within the first 12 months and only half were adherent. Overall, lower socioeconomic status and higher comorbidity burden were associated with higher risk of discontinuing GLP-1RA treatment.

Aim: Poor cardiorespiratory fitness has been suggested to increase the risk of chronic diseases in obesity. We investigated the ability of key variables from cardiopulmonary exercise testing (CPET) to predict all-cause mortality in an obese cohort.

Methods: The sample included 469 participants of both sexes (mean age 40 ± 13 years) who underwent a CPET for clinical reasons between 1 March 2009 and 1 December 2023. All-cause mortality was the prognostic endpoint. A receiver operating characteristic analysis was performed to establish optimal cut-points for CPET variables. Kaplan-Meier and Cox regression analyses were used to determine the association between CPET variables and all-cause mortality.

Results: There were 46 deaths during a mean follow-up period of 69 ± 48 months, resulting in an annual mortality rate of 2%. Despite the sample being made up of mostly women (70%), there were more deaths in men (18 vs. 6%, p < 0.001).The optimal thresholds for discrimination of survival were as follows: (a) peak oxygen uptake (pVO₂) ≤16 mL/kg/min; (b) minute ventilation/carbon dioxide production (V_E/VCO₂) slope ≥31; (c) ventilatory power ≤5.8 mmHg; and (d) circulatory power ≤2980 mmHg/mL O₂/min. Kaplan-Meier survival plots revealed a significant positive association between lower pVO₂, circulatory power and ventilatory power values and survival (log-rank, p < 0.001) and higher mortality for men than women. Adjusted Cox regression models showed that a pVO₂ ≤16 mL/kg/min had a 20-fold higher risk of mortality when compared with >16 mL/kg/min.

Conclusion: Given the strong association of VO₂, ventilatory efficiency, circulatory and ventilatory power with all-cause mortality, our findings support the notion that poorer cardiorespiratory fitness is associated with a poor prognosis in patients with obesity.

Aim: To investigate whether the physician-encounter interval for patients with type 2 diabetes (T2D) can be optimized from 2-3 to 4-6 months among those with a calculated 10-year cardiovascular disease (CVD) risk score of less than 20% without compromising their long-term outcomes.

Materials and methods: Using territory-wide public electronic medical records in Hong Kong, we emulated a target trial to compare the effectiveness of the physician-encounter intervals of 4-6 versus 2-3 months for T2D patients without prior CVDs and with a predicted risk for CVDs of less than 20% (i.e. those patients not in the high-risk category). Propensity score matching was used to emulate the randomization of participants at baseline, where 42 154 matched individuals were included for analysis. The marginal structural model was applied to estimate the hazard ratio (HR) for CVD incidence and all-cause mortality, the incidence rate ratio of secondary and tertiary care utilization, as well as the between-group differences in HbA1c, blood pressure and cholesterol levels.

Results: During a follow-up period of up to 12 (average: 5.1) years, there was no significantly increased risk of CVD in patients with physician-encounter intervals of 4-6 months compared with those patients with physician-encounter intervals of 2-3 months (HR [95% confidence interval {CI}]: 1.01 [0.90, 1.14]; standardized 10-year risk difference [95% CI]: -0.1% [-0.7%, 0.6%]), nor for all-cause mortality (HR: 1.00 [0.84, 1.20]; standardized 10-year risk difference: -0.1% [-0.5%, 0.3%]). Additionally, there was no observable difference in the utilization of secondary and tertiary care or key clinical parameters between these two follow-up frequencies.

Conclusions: For T2D patients with a calculated 10-year CVD risk of less than 20%, the interval of regular physician encounters can be optimized from 2-3 to 4-6 months without compromising patients' long-term outcomes and saving substantial service resources in primary care.

Aim: To assess the likelihood of dementia in individuals with type 2 diabetes (T2D), distinguishing between those with and without microvascular diseases.

Methods: Leveraging the National Health Insurance Research Database in Taiwan, we identified individuals newly diagnosed with T2D from 1 January 2009 through 31 December 2014. Multivariable Cox proportional hazard models were used to compare the risk of outcomes.

Results: Individuals with microvascular disease had a significantly higher risk of all-cause dementia (adjusted hazard ratio [95% confidence interval] 1.13 [1.09, 1.17]) compared with matched individuals without microvascular disease. In addition, individuals with diabetic kidney disease and diabetic neuropathy were associated with a significantly increased risk of Alzheimer's disease (1.16 [1.02, 1.32] and 1.14 [1.03, 1.27]), vascular dementia (1.21 [1.06, 1.38] and 1.14 [1.02, 1.28]) and other dementia (1.11 [1.04, 1.19] and 1.10 [1.04, 1.16]), respectively, compared with those without microvascular disease.

Conclusions: This nationwide cohort study showed that patients with T2D and microvascular disease, particularly diabetic kidney disease and diabetic neuropathy, were associated with a significantly higher risk of Alzheimer's disease, vascular dementia, other dementia and all-cause dementia than those without microvascular disease.

Glucagon-like peptide-1 receptor agonist (GLP-1RA) medications have been shown to be effective in achieving optimal glucose control and reducing all-cause death, cardiovascular death, nonfatal myocardial infarction, hospitalization for heart failure, and end-stage kidney disease in individuals with type 1 (T1D) and type 2 diabetes (T2D). However, use of these medications has been associated with increased hypoglycaemia risk in patients treated with concomitant antihyperglycaemic medications. The risk is particularly high in patients with T1D due to their loss of glucagon counter-regulatory response. This article reviews the effect of GLP-1RA formulations on the development of hypoglycaemia in individuals with T1D and T2D treated with insulin therapy, discusses the benefits of continuous glucose monitoring with GLP-1RA treatment, and presents strategies for safely initiating GLP-1RA therapy in these individuals.

Aim: Efficient primary prevention of diabetic kidney disease (DKD) is currently lacking. The identification of people at high DKD risk and timely intervention are key to preventing DKD. Therefore, a model to classify people according to their risk for developing DKD was developed previously and used in the current analysis to assess the effect of semaglutide versus placebo on primary DKD prevention.

Methods: Participants with type 2 diabetes from the randomized, double-blind, placebo-controlled SUSTAIN 6 trial without DKD at baseline who received 0.5/1.0 mg semaglutide or placebo were grouped by baseline DKD risk, calculated using a validated model. The main post hoc outcome was the effect of semaglutide versus placebo on the proportion of participants who developed DKD [urinary albumin/creatinine ratio (UACR) ≥30 mg/g and/or estimated glomerular filtration rate <60 mL/min/1.73 m²]. Additional post hoc outcomes included changes in DKD risk score, UACR and estimated glomerular filtration rate over time.

Results: Of the total 1139 participants included in the analysis, 28.7% developed DKD; more participants with a high DKD risk (952/1139) developed DKD. Semaglutide significantly reduced the risk of developing DKD in both the total [odds ratio 0.56 (95% confidence interval: 0.42; 0.74; p < 0.0001)], and high DKD risk population [odds ratio 0.51 (95% confidence interval: 0.38; 0.69; p < 0.0001)] and significantly delayed DKD development versus placebo. The beneficial effects of semaglutide were largely driven by UACR changes. The number needed to treat for semaglutide in the high DKD risk population was 7.

Conclusions: This post hoc study indicates that semaglutide may have beneficial effects on primary DKD prevention in people with T2D.

Aims: To investigate the independent contributions of glycated haemoglobin (HbA1c) reduction and weight loss to clinical outcomes in patients with type 2 diabetes (T2D) treated with antidiabetic drugs, including glucagon-like peptide-1 receptor agonists (GLP-1RAs).

Materials and methods: This observational, retrospective cohort study used deidentified electronic health record-derived data from patients evaluated at the Cleveland Clinic (1 January 2000-31 December 2020). Cohort A included 8876 patients with newly diagnosed T2D treated with any of six antidiabetic drug classes. Cohort B included 4161 patients with T2D initiating GLP-1RA treatment. The effects of body mass index (BMI) and HbA1c reduction, variability, and durability on clinical outcomes were investigated.

Results: In Cohort A, each 1% BMI reduction was associated with 3%, 1%, and 4% reduced risk of heart failure (p = 0.017), hypertension (p = 0.006), and insulin initiation (p = 0.001), respectively. Each 1% (~11 mmol/mol) HbA1c reduction was associated with 4% and 29% reduced risk of hypertension (p = 0.041) and insulin initiation (p = 0.001), respectively. In Cohort B, each 1% BMI reduction was associated with 4% and 3% reduced risk of cardiovascular disease (p = 0.008) and insulin initiation (p = 0.002), respectively. Each 1% (~11 mmol/mol) HbA1c reduction was associated with 4% and 16% reduced risk of chronic kidney disease (p = 0.014) and insulin initiation (p = 1 × 10^-4), respectively. Lower BMI variability and greater BMI durability were associated with decreased risk of clinical outcomes in both cohorts.

Conclusions: Antidiabetic medication-associated, and specifically GLP-1RA-associated, weight loss and HbA1c reductions independently reduce real-world clinical outcome risk.

Aim: To evaluate the relationship between the stress-hyperglycaemia ratio (SHR) and the clinical prognosis of patients with moderate-to-severe coronary artery calcification (MSCAC).

Methods: We consecutively enrolled 3841 patients with angiography-detected MSCAC. The individuals were categorized into three groups based on SHR tertiles: T1 (SHR ≤ 0.77), T2 (0.77 < SHR ≤ 0.89) and T3 (SHR > 0.89). The SHR value was calculated using the formula SHR = [admission glucose (mmol/L)]/[1.59 × HbA1c (%) - 2.59]. The primary outcomes were major adverse cardiovascular and cerebrovascular events (MACCEs), including all-cause death, non-fatal myocardial infarction and non-fatal stroke.

Results: During a median follow-up of 3.11 years, 241 MACCEs were recorded. Kaplan-Meier survival analysis showed that the SHR T3 group had the highest incidence of MACCEs (P < .001). Moreover, findings from the restricted cubic spline analysis showed a significant and positive association between the SHR and MACCEs. This correlation remained consistent even after considering other variables that could potentially impact the results (P_non-linear = .794). When comparing SHR T1 with SHR T3, it was found that SHR T3 was significantly associated with an increased risk of the primary outcome (adjusted hazard ratio = 1.50; 95% confidence interval: 1.10-2.03).

Conclusions: Patients with MSCAC showed a positive correlation between the SHR and MACCE rate over a 3-year follow-up period. The study showed that an SHR value of 0.83 is the key threshold, indicating a poor prognosis. Future large-scale multicentre investigations should be conducted to determine the predictive value of the SHR in patients with MSCAC.

Aim: To characterize and stratify health-related quality of life in individuals with type 1 diabetes (T1D) using body mass index (BMI) and clustering analysis.

Material and methods: Baseline data on individuals with T1D were pooled from two studies. A post hoc analysis of health-related quality of life, measured using the 36-item Short-Form questionnaire, was performed, referenced to the 2010 US general population. Descriptive statistics were presented for the pooled cohort and per BMI category. K-means clustering was performed. One-way analysis of variance was conducted to examine differences in clinical characteristics between clusters.

Results: The pooled cohort consisted of 2256 individuals with T1D (age: 45.4 ± 15.0 years, BMI: 26.2 ± 4.6 kg/m², diabetes duration: 22.7 ± 13.5 years). All quality-of-life domains were slightly lower than 50(the general population's mean), except for vitality. Individuals with a BMI ≥30 kg/m² reported lower scores for bodily pain, physical functioning, general health, and vitality. A first cluster with a high and a second cluster with a low quality of life were identified, with significant differences in the mental (Cluster 1: 53.8 ± 6.8 vs. Cluster 2: 39.5 ± 10.7; p < 0.001) and physical component summary scores (Cluster 1: 49.6 ± 6.3 vs. Cluster 2: 35.2 ± 12.0; p < 0.001), which exceeded differences found between BMI categories.

Conclusions: In our population of people living with T1D, higher BMI may have adversely impacted physical domains of quality of life, but larger differences between the high- and low-quality-of-life cluster indicate that more factors play a role.