The Risk Factors Potentially Influencing Hospital Admission in People with Diabetes, Following SARS-CoV-2 Infection: A Population-Level Analysis

The prevalence of long COVID in people with diabetes mellitus-evidence from a UK cohort

Background

It was apparent from the early phase of the SARS-CoV-2 virus (COVID-19) pandemic that a multi-system syndrome can develop in the weeks following a COVID-19 infection, now referred to as Long COVID. Given that people living with diabetes are at increased risk of hospital admission/poor outcomes following COVID-19 infection we hypothesised that they may also be more susceptible to developing Long COVID. We describe here the prevalence of Long COVID in people living with diabetes when compared to matched controls in a Northwest UK population.

Methods

This was a retrospective cohort study of people who had a recorded diagnosis of type 1 diabetes (T1D) or type 2 diabetes (T2D) who were alive on 1st January 2020 and who had a proven COVID-19 infection. We used electronic health record data from the Greater Manchester Care Record collected from 1st January 2020 to 16th September 2023, we determined the prevalence of Long COVID in people with T1D and T2D vs matched individuals without diabetes (non-DM).

Findings

There were 3087 T1D individuals with 14,077 non-diabetes controls and 3087 T2D individuals with 14,077 non-diabetes controls and 29,700 T2D individuals vs 119,951 controls. For T1D, there was a lower proportion of Long COVID diagnosis and/or referral to a Long COVID service at 0.33% vs 0.48% for matched controls. The prevalence of Long COVID In T2D individuals was 0.53% vs 1:3 matched controls 0.54%. For T2D, there were differences by sex in the prevalence of Long COVID in comparison with 1:3 matched controls. For Long COVID between males with T2D and their matched controls, the prevalence was lower in matched controls at 0.46%.vs 0.54% (0.008). When considering the prevalence of LC between females with T2D and their matched controls, the prevalence was higher in matched controls at 0.61% vs 0.53% (0.007). The prevalence of Long COVID in males with T2D vs females was not different. T2D patients at older vs younger age were at reduced risk of developing Long COVID (OR 0.994 [95% CI) [0.989, 0.999]). For females there was a minor increase of risk (OR 1.179, 95% CI [1.002, 1.387]). Presence of a higher body mass index (BMI) was also associated an increased risk of developing Long COVID (OR 1.013, 95% CI [1.001, 1.026]). The estimated general population prevalence of Long COVID based on general practice coding (not self-reported) of this diagnosis was 0.5% of people with a prior acute COVID-19 diagnosis.

Interpretation

Recorded Long COVID was more prevalent in men with T2D than in matched non-T2D controls with the opposite seen for T2D women, with recorded Long COVID rates being similar for T2D men and women. Younger age, female sex and higher BMI were all associated with a greater likelihood of developing Long COVID when taken as individual variables. There remains an imperative for continuing awareness of Long COVID as a differential diagnosis for multi-system symptomatic presentation in the context of a previous acute COVID-19 infection.

Funding

The time of co-author RW was supported by the NIHR Applied Research Collaboration Greater Manchester (NIHR200174) and the NIHR Manchester Biomedical Research Centre (NIHR203308).

An Overview of Real-World Data Infrastructure for Cancer Research

AIMS

There is increasing interest in the opportunities offered by Real World Data (RWD) to provide evidence where clinical trial data does not exist, but access to appropriate data sources is frequently cited as a barrier to RWD research. This paper discusses current RWD resources and how they can be accessed for cancer research.

MATERIALS AND METHODS

There has been significant progress on facilitating RWD access in the last few years across a range of scales, from local hospital research databases, through regional care records and national repositories, to the impact of federated learning approaches on internationally collaborative studies. We use a series of case studies, principally from the UK, to illustrate how RWD can be accessed for research and healthcare improvement at each of these scales.

RESULTS

For each example we discuss infrastructure and governance requirements with the aim of encouraging further work in this space that will help to fill evidence gaps in oncology.

CONCLUSION

There are challenges, but real-world data research across a range of scales is already a reality. Taking advantage of the current generation of data sources requires researchers to carefully define their research question and the scale at which it would be best addressed.

Sars-Cov-2 Infection in People with Type 1 Diabetes and Hospital Admission: An Analysis of Risk Factors for England

INTRODUCTION

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus (coronavirus disease 2019 [COVID-19]) pandemic revealed the vulnerability of specific population groups in relation to susceptibility to acute deterioration in their health, including hospital admission and mortality. There is less data on outcomes for people with type 1 diabetes (T1D) following SARS-CoV-2 infection than for those with type 2 diabetes (T2D). In this study we set out to determine the relative likelihood of hospital admission following SARS-CoV-2 infection in people with T1D when compared to those without T1D.

METHODS

This study was conducted as a retrospective cohort study and utilised an all-England dataset. Electronic health record data relating to people in a national England database (NHS England's Secure Data Environment, accessed via the BHF Data Science Centre's CVD-COVID-UK/COVID-IMPACT consortium) were analysed. The cohort consisted of patients with a confirmed SARS-CoV-2 infection, and the exposure was whether or not an individual had T1D prior to infection (77,392 patients with T1D). The patients without T1D were matched for sex, age and approximate date of the positive COVID-19 test, with three SARS-CoV-2-infected people living without diabetes (n = 223,995). Potential factors influencing the relative likelihood of the outcome of hospital admission within 28 days were ascertained using univariable and multivariable logistic regression.

RESULTS

Median age of the people living with T1D was 37 (interquartile range 25-52) years, 47.4% were female and 89.6% were of white ethnicity. Mean body mass index was 27 (standard error [SE] 0.022) kg/m2, and mean glycated haemoglobin (HbA1c) was 67.3 (SE 0.069) mmol/mol (8.3%). A significantly higher proportion of people with T1D (10.7%) versus matched non-diabetes individuals (3.9%) were admitted to hospital. In combined analysis including individuals with T1D and matched controls, multiple regression modelling indicated that the factors independently relating to a higher likelihood of hospital admission were: T1D (odds ratio [OR] 1.71, 95% confidence interval [CI] 1.62-1.80]), age (OR 1.02, 95% CI 1.02-1.03), social deprivation (higher Townsend deprivation score: OR 1.07, 95% CI 1.06-1.08), lower estimated glomerular filtration rate (eGFR) value (OR 0.975, 95% CI 0.974-0.976), non-white ethnicity (OR black 1.19, 95% CI 1.06-1.33/OR Asian 1.21, 95% CI 1.05-1.39) and having asthma (OR 1.27, 95% CI 1.19-1.35]), chronic obstructive pulmonary disease (OR 2.10, 95% CI 1.89-2.32), severe mental illness (OR 1.83, 95% CI 1.57-2.12) or hypertension (OR 1.44, 95% CI 1.37-1.52).

CONCLUSION

In this all-England study, we describe that, following confirmed infection with SARS-CoV-2, the risk factors for hospital admission for people living with T1D are similar to people without diabetes following confirmed SARS-CoV-2 infection, although the former were more likely to be admitted to hospital. The younger age of individuals with T1D in relation to risk stratification must be taken into account in any ongoing risk reduction strategies regarding COVID-19/future viral pandemics.

Risk phenotypes of diabetes and association with COVID-19 severity and death: an update of a living systematic review and meta-analysis

AIMS/HYPOTHESIS

To provide a systematic overview of the current body of evidence on high-risk phenotypes of diabetes associated with COVID-19 severity and death.

METHODS

This is the first update of our recently published living systematic review and meta-analysis. Observational studies investigating phenotypes in individuals with diabetes and confirmed SARS-CoV-2 infection with regard to COVID-19-related death and severity were included. The literature search was conducted from inception up to 14 February 2022 in PubMed, Epistemonikos, Web of Science and the COVID-19 Research Database and updated using PubMed alert to 1 December 2022. A random-effects meta-analysis was used to calculate summary relative risks (SRRs) with 95% CIs. The risk of bias was evaluated using the Quality in Prognosis Studies (QUIPS) tool and the certainty of evidence using the GRADE approach.

RESULTS

A total of 169 articles (147 new studies) based on approximately 900,000 individuals were included. We conducted 177 meta-analyses (83 on COVID-19-related death and 94 on COVID-19 severity). Certainty of evidence was strengthened for associations between male sex, older age, blood glucose level at admission, chronic insulin use, chronic metformin use (inversely) and pre-existing comorbidities (CVD, chronic kidney disease, chronic obstructive pulmonary disease) and COVID-19-related death. New evidence with moderate to high certainty emerged for the association between obesity (SRR [95% CI] 1.18 [1.04, 1.34], n=21 studies), HbA1c (53-75 mmol/mol [7-9%]: 1.18 [1.06, 1.32], n=8), chronic glucagon-like peptide-1 receptor agonist use (0.83 [0.71, 0.97], n=9), pre-existing heart failure (1.33 [1.21, 1.47], n=14), pre-existing liver disease (1.40 [1.17, 1.67], n=6), the Charlson index (per 1 unit increase: 1.33 [1.13, 1.57], n=2), high levels of C-reactive protein (per 5 mg/l increase: 1.07 [1.02, 1.12], n=10), aspartate aminotransferase level (per 5 U/l increase: 1.28 [1.06, 1.54], n=5), eGFR (per 10 ml/min per 1.73 m2 increase: 0.80 [0.71, 0.90], n=6), lactate dehydrogenase level (per 10 U/l increase: 1.03 [1.01, 1.04], n=7) and lymphocyte count (per 1×109/l increase: 0.59 [0.40, 0.86], n=6) and COVID-19-related death. Similar associations were observed between risk phenotypes of diabetes and severity of COVID-19, with some new evidence on existing COVID-19  vaccination status (0.32 [0.26, 0.38], n=3), pre-existing hypertension (1.23 [1.14, 1.33], n=49), neuropathy and cancer, and high IL-6 levels. A limitation of this study is that the included studies are observational in nature and residual or unmeasured confounding cannot be ruled out.

CONCLUSIONS/INTERPRETATION

Individuals with a more severe course of diabetes and pre-existing comorbidities had a poorer prognosis of COVID-19 than individuals with a milder course of the disease.

REGISTRATION

PROSPERO registration no. CRD42020193692.

PREVIOUS VERSION

This is a living systematic review and meta-analysis. The previous version can be found at https://link.springer.com/article/10.1007/s00125-021-05458-8 FUNDING: The German Diabetes Center (DDZ) is funded by the German Federal Ministry of Health and the Ministry of Culture and Science of the State North Rhine-Westphalia. This study was supported in part by a grant from the German Federal Ministry of Education and Research to the German Center for Diabetes Research (DZD).

Association of Elevated Glycated Hemoglobin (HbA1c) in COVID-19 Patients Admitted to the Intensive Care Unit and Their Clinical Outcomes

Aim The study aimed to collect retrospective data to investigate the association between elevated glycated hemoglobin (HbA1c) levels and clinical outcomes in COVID-19 patients admitted to the ICU, including in-hospital mortality and 90-day mortality.  Methods This is an observational retrospective study using electronic health records of patients with diabetes admitted to the ICU with COVID-19 across the University of Pittsburgh Medical Center (UPMC) in Central PA Hospitals. Our retrospective analysis was performed on patients admitted to the ICU between May 1^st, 2021, to May 1^st, 2022. The HbA1c level obtained within three months before their admission was evaluated and stratified to show their association with clinical outcomes, including in-hospital mortality and 90-day mortality. Additionally, the need for insulin drip and ICU and hospital length of stay were compared among these patients.  Results We analyzed 384 patients, which were distributed in three groups. The majority of the patients (183 patients or 47.66%) had an HbA1c below 7%, 113 patients (29.43%) had an HbA1c between 7-9%, and 88 patients (22.92%) had an HbA1c above 9%. The group with an HbA1c<7% had a mortality rate of 54.1% during the hospital stay, with a median stay of 13 days. The patients with an HbA1c between 7-9% had a higher mortality rate of 65.49% with a median stay of 12 days. The patients with HbA1c>9% had a mortality rate of 43.18% with a median stay of 11.5 days.  Conclusion This retrospective study found that there was no linear association between higher HbA1c levels and a higher risk of mortality during hospitalization. The 90-day mortality rate was not statistically different among the three HbA1c groups. The need for insulin drip was higher in patients with higher HbA1c levels. The majority of patients in all three groups were classified as low-risk based on their BMI, and there were no significant differences in the distribution of patients across BMI categories in the HbA1c groups.

The Effect of the COVID-19 Pandemic on HbA1c Testing: Prioritization of High-Risk Cases and Impact of Social Deprivation

INTRODUCTION

Studies show that the COVID-19 pandemic disproportionately affected people with diabetes and those from disadvantaged backgrounds. During the first 6 months of the UK lockdown, > 6.6 M glycated haemoglobin (HbA1c) tests were missed. We now report variability in the recovery of HbA1c testing, and its association with diabetes control and demographic characteristics.

METHODS

In a service evaluation, we examined HbA1c testing across ten UK sites (representing 9.9% of England's population) from January 2019 to December 2021. We compared monthly requests from April 2020 to those in the equivalent 2019 months. We examined effects of (i) HbA1c level, (ii) between-practice variability, and (iii) practice demographics.

RESULTS

In April 2020, monthly requests dropped to 7.9-18.1% of 2019 volumes. By July 2020, testing had recovered to 61.7-86.9% of 2019 levels. During April-June 2020, we observed a 5.1-fold variation in the reduction of HbA1c testing between general practices (12.4-63.8% of 2019 levels). There was evidence of limited prioritization of testing for patients with HbA1c > 86 mmol/mol during April-June 2020 (4.6% of total tests vs. 2.6% during 2019). Testing in areas with the highest social disadvantage was lower during the first lockdown (April-June 2020; trend test p < 0.001) and two subsequent periods (July-September and October-December 2020; both p < 0.001). By February 2021, testing in the highest deprivation group had a cumulative fall in testing of 34.9% of 2019 levels versus 24.6% in those in the lowest group.

CONCLUSION

Our findings highlight that the pandemic response had a major impact on diabetes monitoring and screening. Despite limited test prioritization in the > 86 mmol/mol group, this failed to acknowledge that those in the 59-86 mmol/mol group require consistent monitoring to achieve the best outcomes. Our findings provide additional evidence that those from poorer backgrounds were disproportionately disadvantaged. Healthcare services should redress this health inequality.

Diabetes in all hospitalized cases in Germany 2015-2019 and impact of the first COVID-19 year 2020

OBJECTIVE

To analyze the proportion of diabetes among all hospitalized cases in Germany between 2015 and 2020.

METHODS

Using the nationwide Diagnosis-Related-Groups statistics, we identified among all inpatient cases aged ≥ 20 years all types of diabetes in the main or secondary diagnoses based on ICD-10 codes, as well all COVID-19 diagnoses for 2020.

RESULTS

From 2015 to 2019, the proportion of cases with diabetes among all hospitalizations increased from 18.3% (3.01 of 16.45 million) to 18.5% (3.07 of 16.64 million). Although the total number of hospitalizations decreased in 2020, the proportion of cases with diabetes increased to 18.8% (2.73 of 14.50 million). The proportion of COVID-19 diagnosis was higher in cases with diabetes than in those without in all sex and age subgroups. The relative risk (RR) for a COVID-19 diagnosis in cases with vs without diabetes was highest in age group 40-49 years (RR in females: 1.51; in males: 1.41).

CONCLUSIONS

The prevalence of diabetes in the hospital is twice as high as the prevalence in the general population and has increased further with the COVID-19 pandemic, underscoring the increased morbidity in this high-risk patient group. This study provides essential information that should help to better estimate the need for diabetological expertise in inpatient care settings.

Causal relationship between type 1 diabetes mellitus and six high-frequency infectious diseases: A two-sample mendelian randomization study

Purpose

Type 1 diabetes mellitus (T1DM) is associated with different types of infections; however, studies on the causal relationship between T1DM and infectious diseases are lacking. Therefore, our study aimed to explore the causalities between T1DM and six high-frequency infections using a Mendelian randomization (MR) approach.

Methods

Two-sample MR studies were conducted to explore the causalities between T1DM and six high-frequency infections: sepsis, acute lower respiratory infections (ALRIs), intestinal infections (IIs), infections of the genitourinary tract (GUTIs) in pregnancy, infections of the skin and subcutaneous tissues (SSTIs), and urinary tract infections (UTIs). Data on summary statistics for T1DM and infections were obtained from the European Bioinformatics Institute database, the United Kingdom Biobank, FinnGen biobank, and Medical Research Council Integrative Epidemiology Unit. All data obtained for summary statistics were from European countries. The inverse-variance weighted (IVW) method was employed as the main analysis. Considering the multiple comparisons, statistical significance was set at p< 0.008. If univariate MR analyses found a significant causal association, multivariable MR (MVMR) analyses were performed to adjust body mass index (BMI) and glycated hemoglobin (HbA1c). MVMR-IVW was performed as the primary analysis, and the least absolute shrinkage and selection operator (LASSO) regression and MVMR-Robust were performed as complementary analyses.

Results

MR analysis showed that susceptibility to IIs increased in patients with T1DM by 6.09% using the IVW-fixed method [odds ratio (OR)=1.0609; 95% confidence interval (CI): 1.0281-1.0947, p=0.0002]. Results were still significant after multiple testing. Sensitivity analyses did not show any significant horizontal pleiotropy or heterogeneity. After adjusting for BMI and HbA1c, MVMR-IVW (OR=1.0942; 95% CI: 1.0666-1.1224, p<0.0001) showed significant outcomes that were consistent with those of LASSO regression and MVMR-Robust. However, no significant causal relationship was found between T1DM and sepsis susceptibility, ALRI susceptibility, GUTI susceptibility in pregnancy, SSTI susceptibility, and UTI susceptibility.

Conclusions

Our MR analysis genetically predicted increased susceptibility to IIs in T1DM. However, no causality between T1DM and sepsis, ALRIs, GUTIs in pregnancy, SSTIs, or UTIs was found. Larger epidemiological and metagenomic studies are required to further investigate the observed associations between the susceptibility of certain infectious diseases with T1DM.

COVID-19 Vaccination and Diabetes Mellitus: How Much Has It Made a Difference to Outcomes Following Confirmed COVID-19 Infection?

INTRODUCTION

Since early 2020 the whole world has been challenged by the SARS-CoV-2 virus (COVID-19), its successive variants and the associated pandemic caused. We have previously shown that for people living with type 2 diabetes (T2DM), the risk of being admitted to hospital or dying following a COVID-19 infection progressively decreased through the first months of 2021. In this subsequent analysis we have examined how the UK COVID-19 vaccination programme impacted differentially on COVID-19 outcomes in people with T1DM or T2DM compared to appropriate controls.

METHODS

T1DM and T2DM affected individuals were compared with their matched controls on 3:1 ratio basis. A 28-day hospital admission or mortality was used as the binary outcome variable with diabetes status and vaccination for COVID-19 as the main exposure variables.

RESULTS

A higher proportion of T1DM individuals vs their controls was found to be vaccinated at the point of their first recorded positive COVID-19 test when compared to T2DM individuals vs their controls. Regarding the 28-day hospital admission rate, there was a greater and increasing protective effect of subsequent vaccination dosage (one, two or three) in mitigating the effects of COVID-19 infection versus no vaccination in T1DM than in T2DM individuals when compared with matched controls. Similar effects were observed in T2DM for death. Across both diabetes and non-diabetes individuals, those at greater socio-economic disadvantage were more likely to test positive for COVID-19 in the early phase of the pandemic. For T2DM individuals socio-economic disadvantage was associated with a greater likelihood of hospital admission and death, independent of vaccination status. Age and male sex were also independently associated with 28-day hospital admission in T2DM and to 28-day mortality, independent of vaccination status. African ethnicity was also an additional factor for hospital admission in people with T2DM.

CONCLUSION

A beneficial effect of COVID-19 vaccination was seen in mitigating the harmful effects of COVID-19 infection; this was manifest in reduced hospital admission rate in T1DM individuals with a lesser effect in T2DM when compared with matched controls, regarding both hospital admission and mortality. Socio-economic disadvantage influenced likelihood of COVID-19 confirmed infection and the likelihood of hospital admission/death independent of the number of vaccinations given in T2DM.

Impact of diabetes status and related factors on COVID-19-associated hospitalization: A nationwide retrospective cohort study of 116,370 adults with SARS-CoV-2 infection

AIMS

We examined diabetes status (no diabetes; type 1 diabetes [T1D]; type 2 diabetes [T2D]) and other demographic and clinical factors as correlates of coronavirus disease 2019 (COVID-19)-related hospitalization. Further, we evaluated predictors of COVID-19-related hospitalization in T1D and T2D.

METHODS

We analyzed electronic health record data from the de-identified COVID-19 database (December 2019 through mid-September 2020; 87 US health systems). Logistic mixed models were used to examine predictors of hospitalization at index encounters associated with confirmed SARS-CoV-2 infection.

RESULTS

In 116,370 adults (>=18 years old) with COVID-19 (93,098 no diabetes; 802 T1D; 22,470 T2D), factors that independently increased risk for hospitalization included diabetes, male sex, public health insurance, decreased body mass index (BMI; <25.0-29.9 kg/m²), increased BMI (>25.0-29.9 kg/m²), vitamin D deficiency/insufficiency, and Elixhauser comorbidity score. After further adjustment for concurrent hyperglycemia and acidosis in those with diabetes, hospitalization risk was substantially higher in T1D than T2D and in those with low vitamin D and elevated hemoglobin A1c (HbA1c).

CONCLUSIONS

The higher hospitalization risk in T1D versus T2D warrants further investigation. Modifiable risk factors such as vitamin D deficiency/insufficiency, BMI, and elevated HbA1c may serve as prognostic indicators for COVID-19-related hospitalization in adults with diabetes.

Comorbidities increase COVID-19 hospitalization in young people with type 1 diabetes

OBJECTIVES

We evaluated COVID-19 outcomes in children and young adults with type 1 diabetes (T1D) to determine if those with comorbidities are more likely to experience severe COVID-19 compared to those without.

RESEARCH DESIGN AND METHODS

This cross-sectional study included questionnaire data on patients <25 years of age with established T1D and laboratory-confirmed COVID-19 from 52 sites across the US between April 2020 and October 2021. We examined patient factors and COVID-19 outcomes between those with and without comorbidities. Multivariate logistic regression analysis examined the odds of hospitalization among groups, adjusting for age, HbA1c, race and ethnicity, insurance type and duration of diabetes.

RESULTS

Six hundred fifty-one individuals with T1D and COVID-19 were analyzed with mean age 15.8 (SD 4.1) years. At least one comorbidity was present in 31%, and more than one in 10%. Obesity and asthma were the most frequently reported comorbidities, present in 19% and 17%, respectively. Hospitalization occurred in 17% of patients and 52% of hospitalized patients required ICU level care. Patients with at least one comorbidity were almost twice as likely to be hospitalized with COVID-19 than patients with no comorbidities (Odds ratio 2.0, 95% CI: 1.3-3.1). This relationship persisted after adjusting for age, HbA1c, race and ethnicity (minority vs nonminority), insurance type (public vs. private), and duration of diabetes.

CONCLUSIONS

Our findings show that comorbidities increase the risk for hospitalization with COVID-19 in children and young adults highlighting the need for tailored COVID-19 prevention and treatment strategies in T1D.

Distress and Living with Diabetes: Defining Characteristics Through an Online Survey

INTRODUCTION

There is considerable evidence for diabetes reducing quality of life. The impact of such a diagnosis on mental health is less well understood and was subsequently explored here.

METHODS

Online PHQ-9 scores (which calculate the severity of depression), Diabetes Distress Screening Scale (DDSS) and EQ-5D-5L (quality-of-life) questionnaires were completed by patients with diabetes, followed by the extraction of data where possible from responders' clinical records.

RESULTS

A total of 133 people submitted questionnaires. However, not all data items could be completed by each patient; 35% (45/130) had type I diabetes mellitus (T1DM); 55% (64/117) were women. The overall median age of 117 responders was 60 (IQR 50-68 years). The median aggregated response scores were: EQ-5D-5L 0.74 (IQR 0.64-0.85) (lower quality of life than UK population median of 0.83), DDSS 1.9 (IQR1.3-2.7) (≥ 2 indicates moderate distress) and PHQ-9 5 (IQR2-11) (≥ 5 indicates depression). Higher diabetes distress (DDSS)/lower quality of life EQ-5D-5L/higher depressive symptoms (PHQ-9) linked to female sex (DDSS 0.5/25% above median), younger age (< 50 years DDSS 0.7/35% above median), fewer years after diagnosis (< 10 years DDSS 0.8/40% above median), and obesity (BMI > 35 DDSS 0.6/30% above median). Additionally, a HbA1c reading of ≤ 48 mmol/mol was associated with higher DDSS scores, as did a reduction of more than 5 mmol/mol in HbA1c over the last three HbA1c measurements. The 30 individuals with a history of prescribed antidepressant medication also showed higher diabetes distress scores (DDSS 0.9, equating to 45% above the median). The DDSS score elevation came from an increase in emotional burden and regimen-related distress. DDSS scores were not significantly linked to diabetes type, insulin use, absolute level/change in blood glucose HbA1c. Physician-related distress showed a similar pattern.

CONCLUSIONS

A low level of stress in relation to diabetes management may be associated with lower HbA1c. The larger impact of diabetes on mental health in younger women/people with shorter diabetes duration should be noted when considering psychosocial intervention/behavior change messaging. Physician-related distress is a potentially remediable factor. However, this sample was self-selecting, limiting generalization to other samples.