Socioeconomic deprivation, technology use, C-peptide, smoking and other predictors of glycaemic control in adults with type 1 diabetes

Influence of socioeconomic factors on glycemic control, therapeutic adherence and quality of life in children and adolescents with type 1 diabetes

OBJECTIVE

To stablish the relationship between socioeconomic status of a cohort of children and adolescents with type 1 diabetes (T1D) with glycemic control, therapeutic adherence and diabetes quality of life (DQoL).

PATIENTS Y METHODS

A cross-sectional, observational study with consecutive inclusion was carried out. Participants aged 8-18 years with T1D duration >1 year. Data on family structure, family income, parents' educational level and parental role on primary diabetes care supervision were registered. Adherence (DMQ-Sp) and DQoL (PedsQl) were analyzed. Linear and logistic regression models adjusted for demographics, family structure and parental role on primary diabetes care responsibility were applied.

RESULTS

A total of 323 patients (T1D duration 5,3 ± 3,3 years; HbA1c 7,7 ± 1,0%; age 13,3 ± 2,8 years; 49,8% females) were included. Patients living in a nuclear family and those whose main diabetes care supervision was shared by both parents showed lower HbA1c [adjusted for demographics and family structure (7,06; CI 95% 6,52-7,59); adjusted for demographics and role on primary diabetes care supervision (7,43; CI 95% 6,57-8,28)]. DMQ-Sp score (adjusted for demographics and role on main supervision) was higher in patients whose parents shared the diabetes care supervision (84,56; CI 95% 73,93-95,19). Parents sharing diabetes care supervision showed a significantly higher PedsQl score (both 74,63 ± 12,70 vs mother 68,53 ± 14,59; p = 0,001).

CONCLUSIONS

Children and adolescents with T1D had lower HbA1c, better therapeutic adherence and better DQoL when lived in a nuclear family, with higher socioeconomic status and the responsibility for supervising diabetes care was shared by both parents.

Heterogeneity of glycaemic phenotypes in type 1 diabetes

AIMS/HYPOTHESIS

Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes.

METHODS

In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA1c, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison.

RESULTS

We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA1c (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes.

CONCLUSIONS/INTERPRETATION

Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.

Effect of Christmas Holidays on Type 1 Diabetes Mellitus in Users of Glucose Flash Systems

OBJECTIVE

Christmas holidays can impact weight and glycemic control in type 2 diabetes, but their effect on type 1 diabetes (T1D) remains understudied. This study assessed how Christmas holidays affect individuals with T1D who use flash continuous glucose monitoring systems.

METHODS

This retrospective study involved 812 adults diagnosed with T1D recruited from 3 hospitals. Clinical, anthropometric, and socioeconomic data were collected. Glucose metrics from 14 days before January 1st, and before December 1st and February 1st as control periods, were recorded. Analyses adjusted for multiple variables were conducted to assess the holiday season's impact on glycemic control.

RESULTS

The average time in range during the holidays (60.0 ± 17.2%) was lower compared to December (61.9 ± 17.2%, P < .001) and February (61.7 ± 17.7%, P < .001). Time above range (TAR > 180 mg/dL) was higher during Christmas (35.8 ± 18.2%) compared to December (34.1 ± 18.3%, P < .001) and February (34.2 ± 18.4%, P < .001). Differences were also observed in TAR >250 mg/dL, coefficient of variation, and average glucose (P < .05). No differences were found in time below range or other metrics. Linear regression models showed that the holidays reduced time in range by 1.9% (β = -1.92, P = .005) and increased TAR >180 mg/dL by 1.8% (β = 1.75, P = .016).

CONCLUSION

Christmas holidays are associated with a mild and reversible deterioration in glucose metrics among individuals with T1D using flash continuous glucose monitoring, irrespective of additional influencing factors. These discoveries can be useful to advise individuals with diabetes during the festive season and to recognize potential biases within studies conducted during this timeframe.

Ongoing burden and recent trends in severe hospitalised hypoglycaemia events in people with type 1 and type 2 diabetes in Scotland: A nationwide cohort study 2016-2022

AIMS

We examined severe hospitalised hypoglycaemia (SHH) rates in people with type 1 and type 2 diabetes in Scotland during 2016-2022, stratifying by sociodemographics.

METHODS

Using the Scottish National diabetes register (SCI-Diabetes), we identified people with type 1 and type 2 diabetes alive anytime during 2016-2022. SHH events were determined through linkage to hospital admission and death registry data. We calculated annual SHH rates overall and by age, sex, and socioeconomic status. Summary estimates of time and stratum effects were obtained by fitting adjusted generalised additive models using R package mgcv.

RESULTS

Rates for those under 20 with type 1 diabetes reached their minimum at the 2020-2021 transition, 30% below the study period average. A gradual decline over time also occurred among 20-49-year-olds with type 1 diabetes. Overall, females had 15% higher rates than males with type 2 diabetes (rate ratio 1.15, 95% CI 1.08-1.22). People in the most versus least deprived quintile experienced 2.58 times higher rates (95% CI 2.27-2.93) in type 1 diabetes and 2.33 times higher (95% CI 2.08-2.62) in type 2 diabetes.

CONCLUSIONS

Despite advances in care, SHH remains a significant problem in diabetes. Future efforts must address the large socioeconomic disparities in SHH risks.

The impact of socioeconomic factors, social determinants, and ethnicity on the utilization of glucose sensor technology among persons with diabetes mellitus: a narrative review

Continuous glucose monitoring (CGM) usage has been shown to improve disease outcomes in people living with diabetes by facilitating better glycemic management. However, previous research has suggested that access to these devices can be influenced by nonmedical factors such as socioeconomic status and ethnicity. It is critical that equitable access to CGM devices is ensured as people from those groups experience poorer diabetes-related health outcomes. In this narrative review, we provide an overview of the various healthcare systems worldwide and how socioeconomic status, social context, and ethnicity shape device usage and the associated health outcomes. In general, we found that having a lower socioeconomic status and belonging to an ethnic minority group negatively impact CGM usage. While financial means proved to be an important mediator in this process, it was not the sole driver as disparities persisted even after adjustment for factors such as income and insurance status. Recommendations to increase CGM usage for people of a lower socioeconomic status and ethnic minorities include increasing the availability of financial, administrative, and educational support, for both patients and healthcare providers. However, recommendations will vary due to local country-specific circumstances, such as reimbursement criteria and healthcare ecosystems.

Scans per day as predictors of optimal glycemic control in people with type 1 diabetes mellitus using flash glucose monitoring: what number of scans per day should raise a red flag?

AIMS

This study aimed to determine the minimum frequency of flash glucose monitoring (FGM) scans necessary for optimal glycemic control in patients with type 1 diabetes (T1D).

METHODS

Data were collected from 692 patients (47.5% female, with a median age of 47.4 years) who used FGM systems daily and recorded their clinical variables and device data.

RESULTS

Logistic regression models showed that performing more than 12 scans per day was associated with improved T1D control (OR = 4.22, p < 0.001) and a reduction in HbA1c (7.6 vs 7.0%, 60-53 mmol/mol p < 0.001). However, those performing less than 6 scans showed no improvement in HbA1c (7.9 vs 7.8%, 63-61 mmol/mol p = 0.514). Thirteen daily scans were determined as the optimal cutoff point for predicting optimal glycemic control using a maximally selected rank algorithm. Significant reductions were observed in mean glucose (< 0.001), coefficient of variation (< 0.001), HbA1c (< 0.001), and an increase in TIR (< 0.001) in patients who performed more than 12 daily scans.

CONCLUSIONS

The results suggest that a higher frequency of daily scans by T1D patients using FGM systems leads to improved chronic glycemic control. The minimum recommended frequency for optimal control is 13 scans per day, and more than 6 daily scans are needed to improve HbA1c.

Study protocol: a randomised controlled proof-of-concept real-world study - does maximising time in range using hybrid closed loop insulin delivery and a low carbohydrate diet restore the glucagon response to hypoglycaemia in adults with type 1 diabetes?

INTRODUCTION

People with type 1 diabetes (T1D) develop an impaired glucagon response to hypoglycaemia within 5 years of diagnosis, increasing their risk of severe hypoglycaemia. It is not known whether eliminating hypoglycaemia and hyperglycaemia allows recovery of this glucagon response. Hybrid closed loop (HCL) technologies improve glycaemic time in range (TIR). However, post-prandial glycaemic excursions are still evident. Consuming a low carbohydrate diet (LCD) may minimise these excursions.

METHODS AND ANALYSIS

This feasibility study will assess if maximising TIR (glucose ≥3.9 mmol/L≤10 mmol/L) using HCL systems plus an LCD (defined here as <130 g carbohydrate/day) for >8 months, restores the glucagon response to insulin-induced hypoglycaemia. Adults (n=24) with T1D (C-peptide <200 pmol/L), naïve to continuous glucose monitoring (CGM) and HCL systems, will be recruited and randomised to: group 1 (non-HCL) to continue their standard diabetes care with intermittent blinded CGM; or group 2 (HCL-LCD) to use the HCL system and follow a LCD. Baseline data on diet and glycaemia will be collected from all participants. The HCL-LCD group will then enter a 2-week run-in to acclimatise to their devices. Throughout, the HCL-LCD group will have their glucose closely monitored and adjusted aiming for glycaemic TIR >70%. Participants will have their glucagon response to hypoglycaemia measured at the beginning and 8 months later at the study end using a stepped hyperinsulinaemic hypoglycaemic clamp, in combination with the stable isotopes 6,6-²H₂-glucose (D2-glucose) and 1,1,2,3,3-²H₅-glycerol (D5-glycerol) to assess glucose and glycerol kinetics. The impact of hypoglycaemia on symptoms and cognitive function will be assessed during each clamp study. The primary outcome is the difference in the glucagon response to hypoglycaemia between and within groups at baseline versus study end.

ETHICS AND DISSEMINATION

Ethical (20/SS/0117)/institutional review board (2021/0001) approval has been obtained. The study will be disseminated by peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

NCT04614168.

Glycemic Control and Bone in Diabetes

PURPOSE OF REVIEW

This review summarizes recent developments on the effects of glycemic control and diabetes on bone health. We discuss the foundational cellular mechanisms through which diabetes and impaired glucose control impact bone biology, and how these processes contribute to bone fragility in diabetes.

RECENT FINDINGS

Glucose is important for osteoblast differentiation and energy consumption of mature osteoblasts. The role of insulin is less clear, but insulin receptor deletion in mouse osteoblasts reduces bone formation. Epidemiologically, type 1 (T1D) and type 2 diabetes (T2D) associate with increased fracture risk, which is greater among people with T1D. Accumulation of cortical bone micro-pores, micro-vascular complications, and AGEs likely contribute to diabetes-related bone fragility. The effects of youth-onset T2D on peak bone mass attainment and subsequent skeletal fragility are of particular concern. Further research is needed to understand the effects of hyperglycemia on skeletal health through the lifecycle, including the related factors of inflammation and microvascular damage.

The impact of socio-economic deprivation on access to diabetes technology in adults with type 1 diabetes

BACKGROUND

With advances in technology, there is an emerging concern that inequalities exist in provision and diabetes outcomes in areas of greater deprivation. We assess the relationship between socio-economic status and deprivation with access to diabetes technology and their outcomes in adults with type 1 diabetes.

METHODS

Retrospective, observational analysis of adults attending a tertiary centre, comprising three urban hospitals in the UK. Socio-economic deprivation was assessed by the English Indices of Deprivation 2019. Data analysis was performed using one-way ANOVAs and chi-squared tests.

RESULTS

In total, 1631 adults aged 44 ± 15 years and 758 (47%) women were included, with 391 (24%) using continuous subcutaneous insulin infusion, 312 (19%) using real-time continuous glucose monitoring and 558 (34%) using intermittently scanned continuous glucose monitoring. The highest use of diabetes technology was in the least deprived quintile compared to the most deprived quintile (67% vs. 45%, respectively; p < 0.001). HbA_1c outcomes were available in 400 participants; no association with deprivation was observed (p = 0.872). Participation in structured education was almost twice as high from the most deprived to the least deprived groups (23% vs. 43%; p < 0.001). Adults with white or mixed ethnicity were more likely to use technology compared to black ethnicity (60% vs. 40%; p < 0.001).

CONCLUSIONS

Adults living in the most deprived quintile had less technology use. Irrespective of socio-economic status or ethnicity, glycaemia was positively affected in all groups. It is imperative that health disparities are further addressed.

Large socioeconomic gap in period life expectancy and life years spent with complications of diabetes in the Scottish population with type 1 diabetes, 2013–2018

Background

We report the first study to estimate the socioeconomic gap in period life expectancy (LE) and life years spent with and without complications in a national cohort of individuals with type 1 diabetes.

Methods

This retrospective cohort study used linked healthcare records from SCI-Diabetes, the population-based diabetes register of Scotland. We studied all individuals aged 50 and older with a diagnosis of type 1 diabetes who were alive and residing in Scotland on 1 January 2013 (N = 8591). We used the Scottish Index of Multiple Deprivation (SIMD) 2016 as an area-based measure of socioeconomic deprivation. For each individual, we constructed a history of transitions by capturing whether individuals developed retinopathy/maculopathy, cardiovascular disease, chronic kidney disease, and diabetic foot, or died throughout the study period, which lasted until 31 December 2018. Using parametric multistate survival models, we estimated total and state-specific LE at an attained age of 50.

Results

At age 50, remaining LE was 22.2 years (95% confidence interval (95% CI): 21.6 − 22.8) for males and 25.1 years (95% CI: 24.4 − 25.9) for females. Remaining LE at age 50 was around 8 years lower among the most deprived SIMD quintile when compared with the least deprived SIMD quintile: 18.7 years (95% CI: 17.5 − 19.9) vs. 26.3 years (95% CI: 24.5 − 28.1) among males, and 21.2 years (95% CI: 19.7 − 22.7) vs. 29.3 years (95% CI: 27.5 − 31.1) among females. The gap in life years spent without complications was around 5 years between the most and the least deprived SIMD quintile: 4.9 years (95% CI: 3.6 − 6.1) vs. 9.3 years (95% CI: 7.5 − 11.1) among males, and 5.3 years (95% CI: 3.7 − 6.9) vs. 10.3 years (95% CI: 8.3 − 12.3) among females. SIMD differences in transition rates decreased marginally when controlling for time-updated information on risk factors such as HbA1c, blood pressure, BMI, or smoking.

Conclusions

In addition to societal interventions, tailored support to reduce the impact of diabetes is needed for individuals from low socioeconomic backgrounds, including access to innovations in management of diabetes and the prevention of complications.

The Challenge of Sustainable Access to Telemonitoring Tools for People with Diabetes in Europe: Lessons from COVID-19 and Beyond

Telemedicine in diabetes care has been evolving over several years, particularly since the advent of cloud-connected technologies for diabetes management, such as glucose monitoring devices, including continuous glucose monitoring (CGM) systems, that facilitate sharing of glucose data between people with diabetes and their healthcare professionals in near-real time. Extreme social distancing and shielding in place for vulnerable patients during the COVID-19 pandemic has created both the challenge and the opportunity to provide care at a distance on a large scale. Available evidence suggests that glucose control has in fact improved during this period for people with diabetes who are able to use CGM devices for remote glucose monitoring. The development of telemedicine as part of the standard of care in diabetes faces significant challenges in the European context, particularly in terms of providing consistent and effective care at a distance to large populations of patients while using robust systems that can be supported by large regional and national healthcare services. These challenges include a fragmented approach to healthcare technology assessment and reimbursement, lack of eHealth education and literacy, particularly amongst healthcare professionals, lack of data integration, as well as concerns about electronic health records, patient consent and privacy. Here we review the benefits of and challenges to wider application of telemedicine and telemonitoring in the post-pandemic future, with the aim to ensure that the value of these eHealth services is provided to patients, healthcare providers and health systems.

Impact of COVID-19 lockdown on flash and real-time glucose sensor users with type 1 diabetes in England

AIMS

People with type 1 diabetes (T1D) face the daily task of implementing self-management strategies to achieve their glycaemic goals. The UK COVID-19 lockdown has had an impact on day-to-day behaviour, which may affect diabetes self-management and outcomes. We assessed whether sensor-based outcomes pre- and during lockdown periods were different in a cohort of glucose sensor users with T1D.

METHODS

Data were collected from Freestyle Libre (FSL) or Dexcom G6 sensor users who remotely shared their data with the diabetes clinic web platform. Sensor metrics according to international consensus were analysed and compared between pre-lockdown period and 2 and 3 weeks into lockdown (periods 1 and 2).

RESULTS

Two hundred and sixty-nine T1D patients (baseline HbA1c 57 ± 14 mmol/mol) were identified as FSL (n = 190) or Dexcom G6 (n = 79) users. In patients with sensor use > 70% (N = 223), compared to pre-lockdown period percentage TIR 3.9-10 mM (TIR) significantly increased during period 1 (59.6 ± 18.2 vs. 57.5 ± 17.2%, p = 0.002) and period 2 (59.3 ± 18.3 vs. 57.5 ± 17.2%, p = 0.035). The proportion of patients achieving TIR ≥ 70% increased from 23.3% pre-lockdown to 27.8% in period 1 and 30.5% in period 2. A higher proportion also achieved the recommended time below and above range, and coefficient of variation in periods 1 and 2. Dexcom G6 users had significantly lower % time below range (< 3.9 mM) compared to FSL users during both lockdown periods (period 1: Dexcom G6 vs. FSL: 1.8% vs. 4%; period 2: 1.4% vs. 4%, p < 0.005 for both periods).

CONCLUSION

Sensor-based glycaemic outcomes in people with T1D in the current cohort improved during COVID-19 lockdown, which may be associated with positive changes in self-management strategies. Further work is required to evaluate long-term sustainability and support.

COVID-19 and diabetes mellitus: from pathophysiology to clinical management

Initial studies found increased severity of coronavirus disease 2019 (COVID-19), caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in patients with diabetes mellitus. Furthermore, COVID-19 might also predispose infected individuals to hyperglycaemia. Interacting with other risk factors, hyperglycaemia might modulate immune and inflammatory responses, thus predisposing patients to severe COVID-19 and possible lethal outcomes. Angiotensin-converting enzyme 2 (ACE2), which is part of the renin-angiotensin-aldosterone system (RAAS), is the main entry receptor for SARS-CoV-2; although dipeptidyl peptidase 4 (DPP4) might also act as a binding target. Preliminary data, however, do not suggest a notable effect of glucose-lowering DPP4 inhibitors on SARS-CoV-2 susceptibility. Owing to their pharmacological characteristics, sodium-glucose cotransporter 2 (SGLT2) inhibitors might cause adverse effects in patients with COVID-19 and so cannot be recommended. Currently, insulin should be the main approach to the control of acute glycaemia. Most available evidence does not distinguish between the major types of diabetes mellitus and is related to type 2 diabetes mellitus owing to its high prevalence. However, some limited evidence is now available on type 1 diabetes mellitus and COVID-19. Most of these conclusions are preliminary, and further investigation of the optimal management in patients with diabetes mellitus is warranted.