Aims/hypothesis
Research using data-driven cluster analysis has proposed five novel subgroups of diabetes based on six measured variables in individuals with newly diagnosed diabetes. Our aim was (1) ...to validate the existence of differing clusters within type 2 diabetes, and (2) to compare the cluster method with an alternative strategy based on traditional methods to predict diabetes outcomes.
Methods
We used data from the Swedish National Diabetes Register and included 114,231 individuals with newly diagnosed type 2 diabetes.
k
-means clustering was used to identify clusters based on nine continuous variables (age at diagnosis, HbA
1c
, BMI, systolic and diastolic BP, LDL- and HDL-cholesterol, triacylglycerol and eGFR). The elbow method was used to determine the optimal number of clusters and Cox regression models were used to evaluate mortality risk and risk of CVD events. The prediction models were compared using concordance statistics.
Results
The elbow plot, with values of
k
ranging from 1 to 10, showed a smooth curve without any clear cut-off points, making the optimal value of
k
unclear. The appearance of the plot was very similar to the elbow plot made from a simulated dataset consisting only of one cluster. In prediction models for mortality, concordance was 0.63 (95% CI 0.63, 0.64) for two clusters, 0.66 (95% CI 0.65, 0.66) for four clusters, 0.77 (95% CI 0.76, 0.77) for the ordinary Cox model and 0.78 (95% CI 0.77, 0.78) for the Cox model with smoothing splines. In prediction models for CVD events, the concordance was 0.64 (95% CI 0.63, 0.65) for two clusters, 0.66 (95% CI 0.65, 0.67) for four clusters, 0.77 (95% CI 0.77, 0.78) for the ordinary Cox model and 0.78 (95% CI 0.77, 0.78) for the Cox model with splines for all variables.
Conclusions/interpretation
This nationwide observational study found no evidence supporting the existence of a specific number of distinct clusters within type 2 diabetes. The results from this study suggest that a prediction model approach using simple clinical features to predict risk of diabetes complications would be more useful than a cluster sub-stratification.
Graphical abstract
Aims/hypothesis
The aim of this work was to evaluate changes in glycaemic control (HbA
1c
) and rates of severe hypoglycaemia over a 2 year period after initiation of flash glucose monitoring (FM) in ...type 1 diabetes.
Methods
Using data from the Swedish National Diabetes Registry, 14,372 adults with type 1 diabetes with a new registration of FM during 2016–2017 and with continued FM for two consecutive years thereafter, and 7691 control individuals using conventional self-monitoring of blood glucose (SMBG) during the same observation period, were included in a cohort study. Propensity sores and inverse probability of treatment weighting (IPTW) were used to balance FM users with SMBG users. Changes in HbA
1c
and events of severe hypoglycaemia were compared.
Results
After the start of FM, the difference in IPTW change in HbA
1c
was slightly greater in FM users compared with the control group during the follow-up period, with an estimated mean absolute difference of −1.2 mmol/mol (−0.11%) (95% CI −1.64 −0.15, −0.75 −0.07;
p
< 0.0001) after 15–24 months. The change in HbA
1c
was greatest in those with baseline HbA
1c
≥70 mmol/mol (8.5%), with the estimated mean absolute difference being −2.5 mmol/mol (−0.23%) (95% CI −3.84 −0.35, −1.18 −0.11;
p
= 0.0002) 15–24 months post index. The change was also significant in the subgroups with initial HbA
1c
≤52 mmol/mol (6.9%) and 53–69 mmol/mol (7.0–8.5%). Risk of severe hypoglycaemic episodes was reduced by 21% for FM users compared with control individuals using SMBG (OR 0.79 95% CI 0.69, 0.91;
p
= 0.0014).
Conclusions/interpretation
In this large cohort, the use of FM was associated with a small and sustained improvement in HbA
1c
, most evident in those with higher baseline HbA
1c
levels. In addition, FM users experienced lower rates of severe hypoglycaemic events compared with control individuals using SMBG for self-management of glucose control.
Graphical abstract
Purpose
The primary aim of this study was to see whether perianal abscess rate differs between patients with type 1 and type 2 diabetes. A secondary aim was to determine whether poor glycemic control ...increases the risk for perianal abscess.
Methods
Data from the Swedish National Diabetes Registry and the Swedish National Patient Registry between January 2008 and June 2015 were matched. The risk for anal abscess was evaluated in univariate and multivariate analyses with type of diabetes, HbA1c level, BMI, and various diabetes complications as independent factors.
Results
Patients with type 1 diabetes had a lower rate of perianal abscess than patients with type 2 diabetes when adjusted for HbA1c, sex, and age (OR 0.65; 95% CI 0.57–0.73). The risk for perianal abscess increased with higher HbA1c. Incidence of perianal abscess was also elevated in diabetes patients with complications related to poor glycemic control such as ketoacidosis and coma (OR 2.63; 95% CI 2.06–3.35), gastroparesis, and polyneuropathy (OR 1.81; 95% CI 1.41–2.32).
Conclusions
The prevalence of perianal abscess was higher among patients with type 2 diabetes than those with type 1, suggesting that metabolic derangement may be more important than autoimmune factors. Poor glycemic control was associated with higher risk for perianal abscess.
ObjectivesTo study patient-reported outcome after open carpal tunnel release (OCTR) for carpal tunnel syndrome (CTS) in patients with or without diabetes using national healthcare quality ...registries.DesignRetrospective cohort study.SettingData from the Swedish National Quality Registry for Hand Surgery (HAKIR; www.hakir.se) were linked to data from the Swedish National Diabetes Register (NDR; www.ndr.nu).ParticipantsWe identified 9049 patients (10 770 hands) operated for CTS during the inclusion period (2010–2016).Primary outcome measuresPatient-reported outcome measures were analysed before surgery and at 3 and 12 months postoperatively using the QuickDASH as well as the HAKIR questionnaire with eight questions on hand symptoms and disability.ResultsPatients with diabetes (n=1508; 14%) scored higher in the QuickDASH both preoperatively and postoperatively than patients without diabetes, but the total score change between preoperative and postoperative QuickDASH was equal between patients with and without diabetes. The results did not differ between patients with type 1 or type 2 diabetes. Patients with diabetic retinopathy scored higher in QuickDASH at 3 months postoperatively than patients with diabetes without retinopathy. In the regression analysis, diabetes was associated with more residual symptoms at 3 and 12 months postoperatively.ConclusionsPatients with diabetes experience more symptoms both before and after OCTR, but can expect the same relative improvement from surgery as patients without diabetes . Patients with retinopathy, as a proxy for neuropathy, may need longer time for symptoms to resolve after OCTR. Smoking, older age, higher HbA1c levels and receiving a diabetes diagnosis after surgery were associated with more residual symptoms following OCTR.
Abstract
Although the increased risk of complications of type 2 diabetes (T2D) is well known, there is still little information about the long-term development of comorbidities in relation to risk ...factors. The purpose of the present study was to describe the risk trajectories of T2D complications over time in an observational cohort of newly diagnosed T2D patients, as well as to evaluate the effect of common risk factors on the development of comorbidities. This national cohort study investigated individuals with T2D in the Swedish National Diabetes Register regarding prevalence of comorbidities at the time of diagnosis, and the incidence of cardiovascular disease (CVD), chronic kidney disease (CKD) and heart failure in the entire patient cohort and stratified by HbA1c levels and age at baseline. Multivariable Cox regressions were used to evaluate risk factors predicting outcomes. We included 100,878 individuals newly diagnosed with T2D between 1998 and 2012 in the study, with mean 5.5 years follow-up (max 17 years). The mean age at diagnosis was 62.6 ± SD12.5 years and 42.7% of the patients were women. Prevalent CVD was reported for 17.5% at baseline. Although the prevalence of comorbidities was generally low for individuals 50 years or younger at diagnosis, the cumulative incidence of the investigated comorbidities increased over time. Newly diagnosed CVD was the most common comorbidity. Women were shown to have a lower risk of developing comorbid conditions than men. When following the risk trajectory of comorbidities over a period of up to 15 years in individuals with type 2 diabetes, we found that all comorbidities gradually increased over time. There was no distinct time point when onset suddenly increased.
IntroductionTo assess the prevalence of diabetic retinopathy (DR) in persons with newly diagnosed type 2 diabetes (T2D) to understand the potential need for intensified screening for early detection ...of T2D.Research design and methodsIndividuals from the Swedish National Diabetes Registry with a retinal photo <2 years after diagnosis of T2D were included. The proportion of patients with retinopathy (simplex or worse) was assessed. Patient characteristics and risk factors at diagnosis were analyzed in relation to DR with logistic regression.ResultsIn total, 77 681 individuals with newly diagnosed T2D, mean age 62.6 years, 41.1% females were included. Of these, 13 329 (17.2%) had DR.DR was more common in older persons (adjusted OR 1.03 per 10-year increase, 95% CI 1.01 to 1.05) and men compared with women, OR 1.10 (1.05 to 1.14). Other variables associated with DR were OR (95% CI): lower education 1.08 (1.02 to 1.14); previous stroke 1.18 (1.07 to 1.30); chronic kidney disease 1.29 (1.07 to 1.56); treatment with acetylsalicylic acid 1.14 (1.07 to 1.21); ACE inhibitors 1.12 (1.05 to 1.19); and alpha blockers 1.41 (1.15 to 1.73). DR was more common in individuals born in Asia (OR 1.16, 95% CI 1.08 to 1.25) and European countries other than those born in Sweden (OR 1.11, 95% CI 1.05 to 1.18).ConclusionsIntensified focus on screening of T2D may be needed in Sweden in clinical practice since nearly one-fifth of persons have retinopathy at diagnosis of T2D. The prevalence of DR was higher in men, birthplace outside of Sweden, and those with a history of stroke, kidney disease, and hypertension.
Health-related quality of life and glycaemic control are some of the central outcomes in clinical diabetes care and research. The purpose of this study was to describe the health-related quality of ...life and assess its association with glycaemic control in adults with type 1 and type 2 diabetes in a nationwide setting.
In this cross-sectional survey, people with type 1 (n = 2479) and type 2 diabetes (n = 2469) were selected at random without replacement from the Swedish National Diabetes Register. Eligibility criteria were being aged 18-80 years with at least one registered test of glycated haemoglobin (HbA
) the last 12 months. The generic 36-item Short Form version 2 (SF-36v2) was answered by 1373 (55.4%) people with type 1 diabetes and 1353 (54.8%) with type 2 diabetes.
Correlation analyses showed weak correlations between scores on the SF-36v2 and glycaemic control for both diabetes types. After the participants were divided into three groups based on their levels of HbA
, multivariate regression analyses adjusted for demographics, other risk factors and diabetes complications showed that among participants with type 1 diabetes, the high-risk group (≥70 mmol/mol/8.6%) had statistically significantly lower means in five out of eight domains of the SF-36v2 and the mental component summary measure, as compared with the well-controlled group (< 52 mmol/mol/6.9%). Among the participants with type 2 diabetes, the high-risk group had the lowest statistically significantly means in seven domains and both summary measures.
Among people with type 1 and type 2 diabetes, adults with high-risk HbA
levels have lower levels of health-related quality of life in most but not all domains of the SF-36v2. This finding was not explained by demographics, other risk factors, or diabetes complications. The weak individual-level correlations between HRQOL scores and levels of glycaemic control argues for the need to not focus exclusively on either HbA
levels or HRQOL scores but rather on both because both are important parts of a complex, life-long, challenging condition.
Whether infection with SARS-CoV-2 leads to excess risk of requiring hospitalization or intensive care in persons with diabetes has not been reported, nor have risk factors in diabetes associated with ...increased risk for these outcomes.
We included 44,639 and 411,976 adult patients with type 1 and type 2 diabetes alive on Jan 1, 2020, and compared them to controls matched for age, sex, and county of residence (n=204,919 and 1,948,900). Age- and sex-standardized rates for COVID-19 related hospitalizations, admissions to intensive care and death, were estimated and hazard ratios were calculated using Cox regression analyses.
There were 10,486 hospitalizations and 1,416 admissions into intensive care. A total of 1,175 patients with diabetes and 1,820 matched controls died from COVID-19, of these 53•2% had been hospitalized and 10•7% had been in intensive care. Patients with type 2 diabetes, compared to controls, displayed an age- and sex-adjusted hazard ratio (HR) of 2•22, 95%CI 2•13-2•32) of being hospitalized for COVID-19, which decreased to HR 1•40, 95%CI 1•34-1•47) after further adjustment for sociodemographic factors, pharmacological treatment and comorbidities, had higher risk for admission to ICU due to COVID-19 (age- and sex-adjusted HR 2•49, 95%CI 2•22-2•79, decreasing to 1•42, 95%CI 1•25-1•62 after adjustment, and increased risk for death due to COVID-19 (age- and sex-adjusted HR 2•19, 95%CI 2•03-2•36, complete adjustment 1•50, 95%CI 1•39-1•63). Age- and sex-adjusted HR for COVID-19 hospitalization for type 1 diabetes was 2•10, 95%CI 1•72-2•57), decreasing to 1•25, 95%CI 0•3097-1•62) after adjustment• Patients with diabetes type 1 were twice as likely to require intensive care for COVID-19, however, not after adjustment (HR 1•49, 95%CI 0•75-2•92), and more likely to die (HR 2•90, 95% CI 1•6554-5•47) from COVID-19, but not independently of other factors (HR 1•38, 95% CI 0•64-2•99). Among patients with diabetes, elevated glycated hemoglobin levels were associated with higher risk for most outcomes.
In this nationwide study, type 2 diabetes was independently associated with increased risk of hospitalization, admission to intensive care and death for COVID-19. There were few admissions into intensive care and deaths in type 1 diabetes, and although hazards were significantly raised for all three outcomes, there was no independent risk persisting after adjustment for confounding factors.
People with type 1 diabetes have a substantially increased risk of premature death. This nationwide, register-based cohort study evaluated the significance of risk factors and previous cardiovascular ...disease (CVD), heart failure and chronic kidney disease (CKD), for mortality in type 1 diabetes. Nationwide, longitudinal, register-based cohort study. Patients (n = 36,303) listed in the Swedish National Diabetes Register between January 1 2015 and December 31 2017 were included and followed until December 31, 2018. Data were retrieved from national health registries through each patient's unique identifier, to capture data on clinical characteristics, outcomes, or deaths, to describe mortality rates in risk groups. The mean follow-up time was 3.3 years, with 119,800 patient years of observation and 1127 deaths, corresponding to a crude overall mortality of 0.92% deaths/year. Statistically significant increased risk in multivariate analyzes was found in older age groups, in men, and in underweight or people with normal BMI, high HbA1c or blood pressure. A history of CVD, albuminuria and advanced stages of CKD was associated with an increased risk of mortality. Each combination of these conditions further increased the risk of mortality. These results emphasize the importance of risk factors and cardiovascular and renal diabetes complications. People with a combination of CKD, CVD, and heart failure, exhibit a markedly increased risk of dying prematurely. These findings provide strong arguments for optimized and individualized treatment of these groups of people with type 1 diabetes in clinical everyday life.
Aims
To update and extend a previous cross‐sectional international comparison of glycaemic control in people with type 1 diabetes.
Methods
Data were obtained for 520,392 children and adults with type ...1 diabetes from 17 population and five clinic‐based data sources in countries or regions between 2016 and 2020. Median HbA1c(IQR) and proportions of individuals with HbA1c < 58 mmol/mol (<7.5%), 58–74 mmol/mol (7.5–8.9%) and ≥75 mmol/mol (≥9.0%) were compared between populations for individuals aged <15, 15–24 and ≥25 years. Logistic regression was used to estimate the odds ratio (OR) of HbA1c < 58 mmol/mol (<7.5%) relative to ≥58 mmol/mol (≥7.5%), stratified and adjusted for sex, age and data source. Where possible, changes in the proportion of individuals in each HbA1c category compared to previous estimates were calculated.
Results
Median HbA1c varied from 55 to 79 mmol/mol (7.2 to 9.4%) across data sources and age groups so a pooled estimate was deemed inappropriate. OR (95% CI) for HbA1c< 58 mmol/mol (<7.5%) were 0.91 (0.90–0.92) for women compared to men, 1.68 (1.65–1.71) for people aged <15 years and 0.81 (0.79–0.82) aged15–24 years compared to those aged ≥25 years. Differences between populations persisted after adjusting for sex, age and data source. In general, compared to our previous analysis, the proportion of people with an HbA1c < 58 mmol/l (<7.5%) increased and proportions of people with HbA1c≥ 75 mmol/mol (≥9.0%) decreased.
Conclusions
Glycaemic control of type 1 diabetes continues to vary substantially between age groups and data sources. While some improvement over time has been observed, glycaemic control remains sub‐optimal for most people with Type 1 diabetes.
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