Factors associated with withdrawal from insulin pump therapy: A large-population-based study

Effects of ADHD and ADHD treatment on glycemic management in type 1 diabetes: A systematic review and meta-analysis of observational studies

AIMS

Studies suggested a higher prevalence of Attention-deficit/hyperactivity disorder (ADHD) in individuals with Type 1 Diabetes Mellitus (T1D). However, it is unclear how ADHD impacts glycemia and diabetes-related complications. This systematic review and meta-analysis aimed to investigate the effect of ADHD and ADHD medications on HbA1c and acute complications in T1D.

METHODS

A literature search was conducted in PubMed, EMBASE, CINAHL, Scopus, PsycINFO, CENTRAL, and Web of Science collections up to November 22, 2023. Seventeen studies were selected for the systematic review by independent reviewers, with twelve included in the meta-analysis.

RESULTS

Mean HbA1c levels were significantly higher in T1D individuals with ADHD compared to those without ADHD (MD = 0.60; 95 % CI: 0.41, 0.79; I2 = 90.1 %; p-value < 0.001). The rates of suboptimal HbA1c levels, hospitalization, diabetic ketoacidosis, and hypoglycemia were all substantially higher in T1D individuals with ADHD than those without ADHD. No difference was found in mean HbA1c between those who received ADHD treatment and those who did not (mean difference = -0.52; 95 % confidence interval: -1.16, 0.13; I2 = 78.6 %; p-value = 0.12).

CONCLUSIONS

ADHD is associated with higher HbA1c and increased acute diabetes-related complications. More research is needed to assess the effects of ADHD treatments on T1D management.

Patient-level predictors of temporal regularity of primary care visits

BACKGROUND

Patients with chronic diseases should meet with their primary care doctor regularly to facilitate proactive care. Little is known about what factors are associated with more regular follow-up.

METHODS

We studied 70,095 patients age 40 + with one of three chronic conditions (diabetes mellitus, heart failure, chronic obstructive pulmonary disease), cared for by Leumit Health Services, an Israeli health maintenance organization. Patients were divided into the quintile with the least temporally regular care (i.e., the most irregular intervals between visits) vs. the other four quintiles. We examined patient-level predictors of being in the least-temporally-regular quintile. We calculated the risk-adjusted regularity of care at 239 LHS clinics with at least 30 patients. For each clinic, compared the number of patients with the least temporally regular care with the number predicted to be in this group based on patient characteristics.

RESULTS

Compared to older patients, younger patients (age 40-49), were more likely to be in the least-temporally-regular group. For example, age 70-79 had an adjusted odds ratio (AOR) of 0.82 compared to age 40-49 (p < 0.001 for all findings discussed here). Males were more likely to be in the least-regular group (AOR 1.18). Patients with previous myocardial infarction (AOR 1.07), atrial fibrillation (AOR 1.08), and current smokers (AOR 1.12) were more likely to have an irregular pattern of care. In contrast, patients with diabetes (AOR 0.79) or osteoporosis (AOR 0.86) were less likely to have an irregular pattern of care. Clinic-level number of patients with irregular care, compared with the predicted number, ranged from 0.36 (fewer patients with temporally irregular care) to 1.71 (more patients).

CONCLUSIONS

Some patient characteristics are associated with more or less temporally regular patterns of primary care visits. Clinics vary widely on the number of patients with a temporally irregular pattern of care, after adjusting for patient characteristics. Health systems can use the patient-level model to identify patients at high risk for temporally irregular patterns of primary care. The next step is to examine which strategies are employed by clinics that achieve the most temporally regular care, since these strategies may be possible to emulate elsewhere.

The Evolution of Diabetes Technology - Options Towards Personalized Care

Advances in diabetes technology, especially in the last few decades, have transformed our ability to deliver care to persons with diabetes (PWD). Developments in glucose monitoring, especially continuous glucose monitoring systems (CGM), have revolutionized diabetes care and empowered our patients to manage their disease. CGM has also played an integral role in advancing automated insulin delivery systems. Currently available and upcoming advanced hybrid-closed loop systems aim to decrease patient involvement and are approaching the functionality of a fully automated artificial pancreas. Other advances, such as smart insulin pens and daily patch pumps, offer more options for patients and require less complicated and costly technology. Evidence to support the role of diabetes technology is growing, and PWD and clinicians must choose the right type of technology with a personalized strategy to manage diabetes effectively. Here, we review currently available diabetes technologies, summarize their individual features and highlight key patient factors to consider when creating a personalized treatment plan. We also address current challenges and barriers to the adoption of diabetes technologies.

Insulin Pump Therapy Efficacy and Key Factors Influencing Adherence in Pediatric Population-A Narrative Review

Objective: we aimed to highlight the state of the art in terms of pediatric population adherence to insulin pumps. This study intends to underline the significance of identifying and minimizing, to the greatest extent feasible, the factors that adversely affect the juvenile population's adherence to insulin pump therapy. Materials and methods: articles from PubMed, Embase, and Science Direct databases were evaluated using the following search terms: adherence, pump insulin therapy, children, pediatric population, and type 1 diabetes, in combination with several synonyms such as compliance, treatment adherence, pump adherence, patient dropouts, and treatment refusal. Results: A better glycemic control is connected to a better adherence to diabetes management. We identify, enumerate, and discuss a number of variables which make it difficult to follow an insulin pump therapy regimen. Several key factors might improve adherence to insulin pump therapy: efficient communication between care provider and patients (including home-based video-visits), continuous diabetes education, family support and parental involvement, as well as informational, practical assistance, and emotional support from the society. Conclusions: every cause and obstacle that prevents young patients from adhering to insulin pumps optimally is an opportunity for intervention to improve glycemic control and, as a result, their quality of life.

Glycemic Control and Diabetes Related Complications in Adults with Type 1 Diabetes Mellitus and ADHD

OBJECTIVE

To assess the correlation of co-morbid ADHD and diabetes-related complications in patients with type-1-diabetes-mellitus (T1DM).

METHODS

A retrospective cross-sectional study was conducted during 2018 using the Leumit-Health-Services(LHS) database. Diabetes-related complications were assessed in patients with T1DM and ADHD (T1DM-ADHD+) and compared with patients with T1DM alone (T1DM-ADHD-).

RESULTS

Out of 789 adult-patients with T1DM, 75 (9.5%) were T1DM-ADHD+, matched to 225 T1DM-ADHD-. HbA₁C levels were higher in T1DM-ADHD+ patients (8.1% ± 1.6 vs. 7.4% ± 1.2, p < .01), as well as diabetes-related complications: neuropathy (22.7% vs. 5.8%, p < .01), ulcers (8% vs. 0.9%, p < .05), limb amputation (5.3% vs. 0.9%, p < .05), albuminuria (15.5% vs. 2.8%, p < .01), chronic renal failure (10.6% vs. 2.5%, p = .01), and emergency room admissions rate (26.7% vs. 15.1%, p < .05). In sub-analysis, lower average HbA₁C levels and diabetic ulcer rates were found among ADHD patients treated with stimulants, all p < .05.

CONCLUSION

Co-morbidity of ADHD and T1DM is associated with poor glycemic control and higher complication rates.

Non-mental diseases associated with ADHD across the lifespan: Fidgety Philipp and Pippi Longstocking at risk of multimorbidity?

Several non-mental diseases seem to be associated with an increased risk of ADHD and ADHD seems to be associated with increased risk for non-mental diseases. The underlying trajectories leading to such brain-body co-occurrences are often unclear - are there direct causal relationships from one disorder to the other, or does the sharing of genetic and/or environmental risk factors lead to their occurring together more frequently or both? Our goal with this narrative review was to provide a conceptual synthesis of the associations between ADHD and non-mental disease across the lifespan. We discuss potential shared pathologic mechanisms, genetic background and treatments in co-occurring diseases. For those co-occurrences for which published studies with sufficient sample sizes exist, meta-analyses have been published by others and we discuss those in detail. We conclude that non-mental diseases are common in ADHD and vice versa and add to the disease burden of the patient across the lifespan. Insufficient attention to such co-occurring conditions may result in missed diagnoses and suboptimal treatment in the affected individuals.