Diabetes epidemiology and management最新文献

Aims

To compare obstetric and neonatal outcomes in patients with type 2 diabetes mellitus (T2DM) who had scheduled delivery at full term (≥ 39 0/7 weeks) compared to early term (37 0/7 – 38 6/7 weeks) for T2DM indications.

Methods

This was a retrospective cohort study that included all singletons with T2DM with a scheduled delivery at a single tertiary care center between January 2008 and March 2022. Outcomes were compared using Fisher's exact test.

Results

107 singleton pregnancies were included. There was no significant difference in primary cesarean delivery between the two groups. The early term group had significantly higher rates of NICU admission compared to the term group (52% vs 32%, p = 0.05, OR 2.3, 95% CI 1.0–5.0), a finding that remained statistically significant on adjusted analysis (adjusted OR 2.81, 95% CI 1.04–7.58).

Conclusions

In singleton pregnancies undergoing scheduled delivery for T2DM-specific indications, early term deliveries were associated with significantly increased odds of NICU admission when compared to term deliveries, even after adjusting for surrogate markers of glycemic control. These findings suggest that early term delivery contributes to risk of NICU admission, rather than the indication for delivery itself. These findings should be replicated in a larger cohort.

Aims

This study reports the implementation of a diabetic retinopathy (DR) screening program in Aracaju, Brazil, emphasizing the challenges and premature termination.

Methods

The program, a collaboration between local health authorities, national DR screening experts, private clinics, trained retinal imaging technicians, a portable retinal camera with artificial intelligence (AI), telemedicine, and AI-assisted image analysis, was established. Screening occurred at primary care centers, with free specialized treatment for high-risk DR cases.

Results

After a public tender, two clinics were selected for screening, and four for secondary management. Initially, diabetic patient attendance at primary care clinics varied considerably, posing a challenge. Nonetheless, the screening continued, with 3,561 patients screened within six months. However, due to complaints and disputes between providers and authorities, the program was prematurely terminated, achieving less than a quarter of the initial screening target.

Conclusions

The DR screening program faced significant challenges, including low adherence and organizational issues at primary care centers. This experience highlights the need to standardize clinical workflows, enhance organization and integration among primary care units, and address cultural factors to improve adherence rates. These insights are valuable for implementing screening programs, particularly in low- and middle-income countries.

Aims

Machine learning (ML) approaches are beneficial when automatic identification of relevant features among numerous candidates is desired. We investigated the predictive ability of several ML models for new onset of diabetes mellitus.

Methods

In 10,248 subjects who received annual health examinations, 58 candidates including fatty liver index (FLI), which is calculated by using waist circumference, body mass index and levels of triglycerides and γ-glutamyl transferase, were used.

Results

During a 10-year follow-up period (mean period: 6.9 years), 322 subjects (6.5 %) in the training group (70 %, n=7,173) and 127 subjects (6.2 %) in the test group (30 %, n=3,075) had new onset of diabetes mellitus. Hemoglobin A1c, fasting glucose and FLI were identified as the top 3 predictors by random forest feature selection with 10-fold cross-validation. When hemoglobin A1c and FLI were used as the selected features, C-statistics analogous in receiver operating characteristic curve analysis in ML models including logistic regression, naïve Bayes, extreme gradient boosting and artificial neural network were 0.874, 0.869, 0.856 and 0.869, respectively. There was no significant difference in the discriminatory capacity among the ML models.

Conclusions

ML models incorporating hemoglobin A1c and FLI provide an accurate and straightforward approach for predicting the development of diabetes mellitus.

Aims

The objective was to compare anxiety, depression, and quality of life (QoL) in individuals living with type 1 (T1D) and type 2 (T2D) diabetes with matched controls during the second wave of the COVID-19 pandemic.

Methods

Via randomization, individuals living with diabetes T1D (n = 203) and T2D (n = 413), were identified during February-July 2021 through health-care registers. Population controls (n = 282) were matched for age, gender, and residential area. Questionnaires included self-assessment of anxiety, depression, QoL, and demographics in relation to SARS-CoV-2 exposure. Blood was collected through home-capillary sampling, and SARS-CoV-2 Nucleocapsid (NCP) and Spike antibodies (SC2_S1) were determined by multiplex Antibody Detection by Agglutination-PCR (ADAP) assays.

Results

Younger age and health issues were related to anxiety, depression, and QoL, with no differences between the study groups. Female gender was associated with anxiety, while obesity was associated with lower QoL. The SARS‑CoV‑2 NCP seroprevalence was higher in T1D (8.9 %) compared to T2D (3.9 %) and controls (4.0 %), while the SARS‑CoV‑2 SC2_S1 seroprevalence was higher for controls (25.5 %) compared to T1D (16.8 %) and T2D (14.0 %).

Conclusions

A higher SARS‑CoV‑2 infection rate in T1D may be explained by younger age and higher employment rate, and the associated increased risk for viral exposure.

Background

Diabetes-related lower extremity complications contribute significantly to the overall disability burden of type 2 diabetes (T2D). The screening of Diabetic Foot Ulcer (DFU) risk is essential for the early identification of its components, thereby preventing the progression to advanced complications. Unfortunately, identifying elements associated with foot ulcer development is often poorly executed, especially in primary care settings, particularly in low-middle-income countries. We assess the prevalence of DFU risk and its components while exploring sociodemographic, clinical, and behavioral characteristics linked to DFU risk in patients with T2D.

Methods

This cross-sectional study examined the characteristics of the feet of subjects involved in an initiative to enhance the quality of diabetes care in primary healthcare settings. The assessment included the presence of foot deformities, peripheral neuropathy through Loss of Protective Sensation (LOPS) evaluation, peripheral arterial disease (PAD), and a history of foot ulcer or lower extremity amputations. Participants were classified according to the IWGDF Risk Classification and subsequently divided into two groups for analysis: those at high risk for DFU (Groups 1, 2, or 3) and those at low risk for DFU (Group 0). Logistic regression was employed to identify the factors associated with DFU risk and its components.

Results

Among the 1361 patients evaluated, 63.8 % were found to be at an increased risk for DFU. The reported prevalence of foot deformities, LOPS, PAD, and a history of foot ulcer was 91.9 %, 44.3 %, 36.5 %, and 4.5 %, respectively. In the adjusted analysis, factors significantly associated with DFU risk included male gender (OR =2.01; 95 % IC 1.54–2.61), secondary education or less (OR =1.65, 95 % IC 1.27–2.14), diabetes duration exceeding 10 years (OR = 1.75, 95 % IC 1.34–2.30), chronic kidney disease (OR =1.44, 95 % IC 1.11 – 1.88), diabetic retinopathy (OR =1.93, 95 % IC 1.42–2.62), onychomycosis (OR =1.46, 95 % IC 1.14–1.88) properly cut toenails (OR =0.60, 95 % IC 0.45–0.79), and sedentary behavior (OR =1.56, 95 % IC 1.14–2.14).

Conclusions

Risk assessment of DFU within a comprehensive care program for adults with T2D receiving conventional primary care showed that a substantial proportion of these patients are at an increased risk and, thus, qualify as candidates for interventions aimed at mitigating foot-related risks. We recommend that diabetes healthcare schemes incorporate preventive organizational interventions to facilitate the timely identification of DFU risk, particularly in primary care scenarios, thereby alleviating the burden of diabetes-related lower limb complications.

Observational studies based on cohorts built from electronic health records (EHR) form the backbone of our current understanding of the risk of new-onset diabetes following COVID. EHR-based research is a powerful tool for medical research but is subject to multiple sources of bias. In this viewpoint, we define key sources of bias that threaten the validity of EHR-based research on this topic (namely misclassification, selection, surveillance, immortal time, and confounding biases), describe their implications, and suggest best practices to avoid them in the context of COVID-diabetes research.

Aims

To describe the incidence of type 1 diabetes (T1D), and incidence of comorbidities in addition to T1D, in adults over a 22-year period in Denmark and explore the associations between socioeconomic status (SES) and the development of comorbidities.

Methods

We conducted a retrospective cohort study on nationwide registry data to describe incidences of T1D and comorbidities in adults (≥21 years) from 1996 to 2018. People were followed from T1D diagnosis until a first event of interest, death, or end of follow-up, whichever came first. Cox proportional hazards models were used to quantify the associations between SES and development of comorbidities.

Results

We included 7877 adults with T1D. Overall, the incidence of T1D decreased (411 in 1996 to 191 in 2018) while it increased for comorbidities in addition to T1D (< 3 to 38 and 10 to 53). Low education and income, compared to higher counterparts, increased the risks of comorbidity by 55 % (HR 1.55, 95 % CI 1.36–1.77) and 82 % (HR 1.82, 95 % CI 1.60–2.06), respectively.

Conclusions

Low SES increases the risk of comorbidities in adults with T1D. This indicates that SES is an important factor to consider when aiming to prevent or predict development of comorbidities in adults with T1D.

Introduction

Successful diabetes management is associated with an effective partnership between People with Diabetes (PwD) and healthcare professionals. Though possible to measure using Patient-Reported Experience Measures (PREMs), none are specific to Type 1 or Type 2 Diabetes (T1D/T2D) and validated in French. Thus, we developed and validated the DREMS (Diabetes Reported Experience MeasureS) e-questionnaire.

Methodology

DREMS is comprised of 18 items evaluating 5 different factors. Validation for use by PwT1D and PwT2D (recruited online) was performed using: Exploratory Factor Analysis (EFA); Confirmatory Factor Analysis (CFA) and Cronbach's Alpha. Test-retest reliability was evaluated through Intraclass Correlation Coefficients (ICC) in a subsample.

Results

DREMS was tested by 2,513 respondents, including 942 PwT1D and 1,571 PwT2D. For both groups, EFA results indicated 18 items loaded substantially onto 5 clear factors. CFA showed all coefficients were significant in their respective factors. Goodness-of-fit, assessed using the Comparative Fit Index was >0.90 and by the RMSEA was <0.080. Cronbach's α for the entire DREMS e-questionnaire was ≥0.90. ICC was 0.87 for PwT1D (n = 136) and 0.74 for PwT2D (n = 169).

Innovation

DREMS is the first validated French-language diabetes-specific PREM for both PwT1D and PwT2D and can be useful to evaluate and improve health care management and patient health.

Atherosclerotic cardiovascular disease (ASCVD), heart failure (HF) and chronic kidney disease (CKD) are major complications of type 2 diabetes (T2DM). The objectives of preventing these complications are not fully reached in clinical practice. Sodium-glucose cotransporter 2 inhibitors (SGLT2is) have proven their efficacy in reducing major cardiovascular events, diminishing hospitalization for HF and limiting the progression of CKD to end-stage kidney disease in placebo-controlled randomised trials in high-risk patients with T2DM. These evidence-based benefits were confirmed in real-life cohort studies worldwide compared with other glucose-lowering agents. However, real-world data showed that only a minority of eligible patients with T2DM received an SGLT2i, yet encouraging increase was observed in recent years. Surprisingly, in several studies less patients with comorbidities (especially CKD) were treated with SGLT2is compared with T2DM patients without these complications. Bridging the gap between evidence-based cardiorenal protection with SGLT2is and their underuse in daily clinical practice in patients with T2DM at high risk is crucial from a public health viewpoint. Multifaceted and coordinated interventions involving all actors should be implemented to incite the adoption of SGLT2is as part of routine cardiovascular and renal care among patients with T2DM at high risk for these comorbidities.