Dataset Information

Time in range and complications of diabetes: a cross-sectional analysis of patients with Type 1 diabetes.

ABSTRACT:

Background/ objective

To evaluate the association of CGM parameters and HbA1c with diabetes complications in patients with Type 1 Diabetes (T1D).

Methods

Patients with T1D using the CGM system Freestyle Libre were included in this analysis. The association of CGM-metrics and HbA1c with diabetes complications (any complication, microvascular complications, or macrovascular complications) was assessed using logistic regression unadjusted and adjusted for age, sex, and diabetes duration (model 1), and further adjusted for hypertension and dyslipidemia (model 2).

Results

One hundred and sixty-one patients with T1D were included. The mean (± SD) age was 37.4 ± 13.4 years old and the median T1D duration was 17.7 ± 10.6 years. Time in range (TIR) was associated with any complication and microvascular complications in the unadjusted model and in the adjusted models. TIR was associated with retinopathy in the unadjusted model as well as in model 1, and was associated with macrovascular complications only in the unadjusted model. HbA1c was associated with any complications, microvascular complications, and retinopathy in the unadjusted model but not in the adjusted models. HbA1c was associated with macrovascular complications in the unadjusted model and in the adjusted model 1.

Conclusions

In this cross-sectional analysis of patients with T1D using intermittent scanned CGM, TIR, and HbA1c were associated with complications of diabetes. TIR may be a better predictor than HbA1c of any complication and microvascular complications, while HbA1c may be a better predictor of macrovascular complications.

SUBMITTER: Bezerra MF

PROVIDER: S-EPMC10680248 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Time in range and complications of diabetes: a cross-sectional analysis of patients with Type 1 diabetes.

Bezerra Marta Fernandes MF Neves Celestino C Neves João Sérgio JS Carvalho Davide D

Diabetology & metabolic syndrome 20231127 1

<h4>Background/ objective</h4>To evaluate the association of CGM parameters and HbA1c with diabetes complications in patients with Type 1 Diabetes (T1D).<h4>Methods</h4>Patients with T1D using the CGM system Freestyle Libre were included in this analysis. The association of CGM-metrics and HbA1c with diabetes complications (any complication, microvascular complications, or macrovascular complications) was assessed using logistic regression unadjusted and adjusted for age, sex, and diabetes durat ...[more]

PMID: 38008747

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Project description:ObjectiveSleep and circadian disturbances emerge as novel factors influencing glycemic control in type 1 diabetes (T1D). We aimed to explore the associations among sleep, behavioral circadian parameters, self-care, and glycemic parameters in T1D.MethodsSeventy-six non-shift-working adult T1D patients participated. Blinded 7-day continuous glucose monitoring (CGM) and hemoglobin A1C (A1C) were collected. Percentages of time-in-range (glucose levels 70-180 mg/dL) and glycemic variability (measured by the coefficient of variation [%CV]) were calculated from CGM. Sleep (duration and efficiency) was recorded using 7-day actigraphy. Variability (standard deviation) of midsleep time was used to represent sleep variability. Nonparametric behavioral circadian variables were derived from actigraphy activity recordings. Self-care was measured by diabetes self-management questionnaire-revised. Multiple regression analyses were performed to identify independent predictors of glycemic parameters.ResultsMedian (interquartile range) age was 34.0 (27.2, 43.1) years, 48 (63.2%) were female, and median (interquartile range) A1C was 6.8% (6.2, 7.4). Sleep duration, efficiency, and nonparametric behavioral circadian variables were not associated with glycemic parameters. After adjusting for age, sex, insulin delivery mode/CGM use, and ethnicity, each hour increase in sleep variability was associated with 9.64% less time-in-range (B = -9.64, 95% confidence interval [-16.29, -2.99], p ≤ .001). A higher diabetes self-management questionnaire score was an independent predictor of lower A1C (B = -0.18, 95% confidence interval [-0.32, -0.04]).ConclusionGreater sleep timing variability is independently associated with less time spent in the desirable glucose range in this T1D cohort. Reducing sleep timing variability could potentially lead to improved metabolic control and should be explored in future research.Data availability statementData are available upon a reasonable request to the corresponding author.

Project description:ObjectiveEvidence shows that the conventional cardiometabolic risk factors do not fully explain the burden of microvascular complications in type 2 diabetes (T2D). One potential factor is the impact of pulmonary dysfunction on systemic microvascular injury. We assessed the associations between spirometric impairments and systemic microvascular complications in T2D.DesignCross-sectional study.SettingNational Diabetes Management and Research Centre in Ghana.ParticipantsThe study included 464 Ghanaians aged ≥35 years with established diagnosis of T2D without primary myocardial disease or previous/current heart failure. Participants were excluded if they had primary lung disease including asthma or chronic obstructive pulmonary disease.Primary and secondary outcome measuresThe associations of spirometric measures (forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC ratio) with microvascular complications (nephropathy (albumin-creatinine ratio ≥3 mg/g), neuropathy (vibration perception threshold ≥25 V and/or Diabetic Neuropathy Symptom score >1) and retinopathy (based on retinal photography)) were assessed using multivariable logistic regression models with adjustments for age, sex, diabetes duration, glycated haemoglobin concentration, suboptimal blood pressure control, smoking pack years and body mass index.ResultsIn age and sex-adjusted models, lower Z-score FEV1 was associated with higher odds of nephropathy (OR 1.55, 95% CI 1.19-2.02, p=0.001) and neuropathy (1.27 (1.01-1.65), 0.038) but not retinopathy (1.22 (0.87-1.70), 0.246). Similar observations were made for the associations of lower Z-score FVC with nephropathy (1.54 (1.19-2.01), 0.001), neuropathy (1.25 (1.01-1.54), 0.037) and retinopathy (1.19 (0.85-1.68), 0.318). In the fully adjusted model, the associations remained significant for only lower Z-score FEV1 with nephropathy (1.43 (1.09-1.87), 0.011) and neuropathy (1.34 (1.04-1.73), 0.024) and for lower Z-score FVC with nephropathy (1.45 (1.11-1.91), 0.007) and neuropathy (1.32 (1.03-1.69), 0.029). Lower Z-score FEV1/FVC ratio was not significantly associated with microvascular complications in age and sex and fully adjusted models.ConclusionOur study shows positive but varying strengths of associations between pulmonary dysfunction and microvascular complications in different circulations. Future studies could explore the mechanisms linking pulmonary dysfunction to microvascular complications in T2D.

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Project description:BackgroundThe relationship between serum uric acid (SUA) and several diabetic complications or co-morbidities remains a matter of debate. The study aims to explore the association between SUA levels and the prevalence of non-alcoholic fatty liver disease (NAFLD), diabetic retinopathy (DR), diabetic nephropathy (DN) and diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM).MethodsA total of 2,809 participants (1,784 males and 1,025 females) were included in this cross-sectional study. Clinical characteristics and the prevalence of each of the four diseases were analyzed based on gender-specific quartiles of SUA levels. The Pearson correlation analysis and linear-regression analysis were used to access the correlation between SUA levels and clinical characteristics. Furthermore, a binary logistic regression analysis was carried out to determine whether SUA was an independent risk factor for each of the four complications.ResultsSUA levels were positively correlated to BMI, BUN, Scr and TG, but negatively associated with eGFR, HDL, FBG, 2h-PG and HbA1c% for the patients with T2DM. The prevalence of NAFLD and DN, but not DR or DPN, were increased with SUA levels from the first to the fourth quartile. Binary logistic regression further disclosed that SUA was an independent risk factor for NAFLD (ORs Male = 1.002, ∗ P = 0.0013; ORs Female = 1.002, ∗ P = 0.015) and DN (ORs Male = 1.006, ∗ P < 0.001; ORs Female = 1.005, ∗ P < 0.001), but not for DR and DPN. After adjustment for the confounders, SUA levels were significantly associated with NAFLD within the 3rd (ORs = 1.829, P = 0.004) and 4th quartile (ORs = 2.064, P = 0.001) for women, but not independently associated with SUA for man. On the other hand, our results revealed increased prevalence of DN for SUA quartile 2 (ORs = 3.643, P = 0.039), quartile 3 (ORs = 3.967, P = 0.024) and quartile 4 (ORs = 9.133, P < 0.001) in men; however, SUA quartiles were significantly associated with DN only for quartile 4 (ORs = 4.083, P = 0.042) in women.ConclusionFor patients with T2DM, elevated SUA concentration is an independent risk factor for the prevalence of NAFLD and DN after adjustment for other indicators, but not DR or DPN.

Dataset Information

Time in range and complications of diabetes: a cross-sectional analysis of patients with Type 1 diabetes.

Background/ objective

Methods

Results

Conclusions

Publications

Time in range and complications of diabetes: a cross-sectional analysis of patients with Type 1 diabetes.

Similar Datasets

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