In an observational study, patients with type 2 diabetes who had glycated hemoglobin, LDL cholesterol, albuminuria, and blood pressure in target ranges and did not smoke had minimal excess risk of ...death, myocardial infarction, and stroke as compared with a general population.
Patients with type 1 or type 2 diabetes in Sweden were studied to examine trends in mortality and cardiovascular disease incidence between 1998 and 2014. Both outcomes declined substantially, ...although fatal outcomes declined less among patients with type 2 diabetes than among controls.
Diabetes mellitus is a complex and heterogeneous group of chronic metabolic diseases that are characterized by hyperglycemia. Type 1 diabetes occurs predominantly in young people (diagnosis at 30 years of age or younger) and is generally thought to be precipitated by an immune-associated destruction of insulin-producing pancreatic beta cells, leading to insulin deficiency and an absolute need for exogenous insulin replacement.
1
Type 2 diabetes is a progressive metabolic disease that is characterized by insulin resistance and eventual functional failure of pancreatic beta cells.
2
The prevalence of type 2 diabetes has been increasing dramatically over the past few decades,
3
with projections . . .
To assess the association between the use of sodium glucose cotransporter 2 (SGLT2) inhibitors and seven serious adverse events of current concern.
Register based cohort study.
Sweden and Denmark ...from July 2013 to December 2016.
A propensity score matched cohort of 17 213 new users of SGLT2 inhibitors (dapagliflozin, 61%; empagliflozin, 38%; canagliflozin, 1%) and 17 213 new users of the active comparator, glucagon-like peptide 1 (GLP1) receptor agonists.
The primary outcomes were lower limb amputation, bone fracture, diabetic ketoacidosis, acute kidney injury, serious urinary tract infection, venous thromboembolism, and acute pancreatitis, as identified from hospital records. Hazard ratios and 95% confidence intervals were estimated by using Cox proportional hazards models.
Use of SGLT2 inhibitors, as compared with GLP1 receptor agonists, was associated with an increased risk of lower limb amputation (incidence rate 2.7
1.1 events per 1000 person years, hazard ratio 2.32, 95% confidence interval 1.37 to 3.91) and diabetic ketoacidosis (1.3
0.6, 2.14, 1.01 to 4.52) but not with bone fracture (15.4
13.9, 1.11, 0.93 to 1.33), acute kidney injury (2.3
3.2, 0.69, 0.45 to 1.05), serious urinary tract infection (5.4
6.0, 0.89, 0.67 to 1.19), venous thromboembolism (4.2
4.1, 0.99, 0.71 to 1.38) or acute pancreatitis (1.3
1.2, 1.16, 0.64 to 2.12).
In this analysis of nationwide registers from two countries, use of SGLT2 inhibitors, as compared with GLP1 receptor agonists, was associated with an increased risk of lower limb amputation and diabetic ketoacidosis, but not with other serious adverse events of current concern.
AbstractObjectiveTo assess the association between use of sodium-glucose co-transporter 2 (SGLT2) inhibitors and risk of serious renal events in data from routine clinical practice.DesignCohort study ...using an active comparator, new user design and nationwide register data.SettingSweden, Denmark, and Norway, 2013-18.ParticipantsCohort of 29 887 new users of SGLT2 inhibitors (follow-up time: dapagliflozin 66.1%; empagliflozin 32.6%; canagliflozin 1.3%) and 29 887 new users of an active comparator, dipeptidyl peptidase-4 inhibitors, matched 1:1 on the basis of a propensity score with 57 variables. Mean follow-up time was 1.7 (SD 1.0) years.ExposuresSGLT2 inhibitors versus dipeptidyl peptidase-4 inhibitors, defined by filled prescriptions and analysed according to intention to treat.Main outcome measuresThe main outcome was serious renal events, a composite including renal replacement therapy, death from renal causes, and hospital admission for renal events. Secondary outcomes were the individual components of the main outcome.ResultsThe mean age of the study population was 61.3 (SD 10.5) years; 11 108 (19%) had cardiovascular disease, and 1974 (3%) had chronic kidney disease. Use of SGLT2 inhibitors, compared with dipeptidyl peptidase-4 inhibitors, was associated with a reduced risk of serious renal events (2.6 events per 1000 person years versus 6.2 events per 1000 person years; hazard ratio 0.42 (95% confidence interval 0.34 to 0.53); absolute difference −3.6 (–4.4 to −2.8) events per 1000 person years). In secondary outcome analyses, the hazard ratio for use of SGLT2 inhibitors versus dipeptidyl peptidase-4 inhibitors was 0.32 (0.22 to 0.47) for renal replacement therapy, 0.41 (0.32 to 0.52) for hospital admission for renal events, and 0.77 (0.26 to 2.23) for death from renal causes. In sensitivity analyses in each of the Swedish and Danish parts of the cohort, the model was further adjusted for glycated haemoglobin and estimated glomerular filtration rate (Sweden and Denmark) and for blood pressure, body mass index, and smoking (Sweden only); in these analyses, the hazard ratio moved from 0.41 (0.26 to 0.66) to 0.50 (0.31 to 0.81) in Sweden and from 0.42 (0.32 to 0.56) to 0.55 (0.41 to 0.74) in Denmark.ConclusionsIn this analysis using nationwide data from three countries, use of SGLT2 inhibitors, compared with dipeptidyl peptidase-4 inhibitors, was associated with a significantly reduced risk of serious renal events.
Abstract
The study of metabolomics has improved our knowledge of the biology behind type 2 diabetes and its related metabolic physiology. We aimed to investigate markers of adipose tissue morphology, ...as well as insulin and glucose metabolism in 53 non-obese male individuals. The participants underwent extensive clinical, biochemical and magnetic resonance imaging phenotyping, and we also investigated non-targeted serum metabolites. We used a multi-modal machine learning approach to evaluate which serum metabolomic compounds predicted markers of glucose and insulin metabolism, adipose tissue morphology and distribution. Fasting glucose was associated with metabolites of intracellular insulin action and beta-cell dysfunction, namely cysteine-s-sulphate and n-acetylgarginine, whereas fasting insulin was predicted by myristoleoylcarnitine, propionylcarnitine and other metabolites of beta-oxidation of fatty acids. OGTT-glucose levels at 30 min were predicted by 7-Hoca, a microbiota derived metabolite, as well as eugenol, a fatty acid. Both insulin clamp and HOMA-IR were predicted by metabolites involved in beta-oxidation of fatty acids and biodegradation of triacylglycerol, namely tartrate and 3-phosphoglycerate, as well as pyruvate, xanthine and liver fat. OGTT glucose area under curve (AUC) and OGTT insulin AUC, was associated with bile acid metabolites, subcutaneous adipocyte cell size, liver fat and fatty chain acids and derivates, such as isovalerylcarnitine. Finally, subcutaneous adipocyte size was associated with long chain fatty acids, markers of sphingolipid metabolism, increasing liver fat and dopamine-sulfate 1. Ectopic liver fat was predicted by methylmalonate, adipocyte cell size, glutathione derived metabolites and fatty chain acids. Ectopic heart fat was predicted visceral fat, gamma-glutamyl tyrosine and 2-acetamidophenol sulfate. Adipocyte cell size, age, alpha-tocopherol and blood pressure were associated with visceral fat. We identified several biomarkers associated with adipose tissue pathophysiology and insulin and glucose metabolism using a multi-modal machine learning approach. Our approach demonstrated the relative importance of serum metabolites and they outperformed traditional clinical and biochemical variables for most endpoints.
AbstractObjectiveTo investigate the cardiovascular effectiveness of sodium glucose cotransporter 2 (SGLT2) inhibitors in routine clinical practice.DesignCohort study using data from nationwide ...registers and an active-comparator new-user design.SettingDenmark, Norway, and Sweden, from April 2013 to December 2016.Participants20 983 new users of SGLT2 inhibitors and 20 983 new users of dipeptidyl peptidase 4 (DPP4) inhibitors, aged 35-84, matched by age, sex, history of major cardiovascular disease, and propensity score.Main outcome measuresPrimary outcomes were major cardiovascular events (composite of myocardial infarction, stroke, and cardiovascular death) and heart failure (hospital admission for heart failure or death due to heart failure). Secondary outcomes were the individual components of the cardiovascular composite and any cause death. In the primary analyses, patients were defined as exposed from treatment start throughout follow-up (analogous to intention to treat); additional analyses were conducted with an as-treated exposure definition. Cox regression was used to estimate hazard ratios.ResultsMean age of the study cohort was 61 years, 60% were men, and 19% had a history of major cardiovascular disease. Of the total 27 416 person years of follow-up in the SGLT2 inhibitor group, 22 627 (83%) was among patients who initiated dapagliflozin, 4521 (16%) among those who initiated empagliflozin, and 268 (1%) among those who initiated canagliflozin. During follow-up, 467 SGLT2 inhibitor users (incidence rate 17.0 events per 1000 person years) and 662 DPP4 inhibitor users (18.0) had a major cardiovascular event, whereas 130 (4.7) and 265 (7.1) had a heart failure event, respectively. Hazard ratios were 0.94 (95% confidence interval 0.84 to 1.06) for major cardiovascular events and 0.66 (0.53 to 0.81) for heart failure. Hazard ratios were consistent among subgroups of patients with and without history of major cardiovascular disease and with and without history of heart failure. Hazard ratios for secondary outcomes, comparing SGLT2 inhibitors with DPP4 inhibitors, were 0.99 (0.85 to 1.17) for myocardial infarction, 0.94 (0.77 to 1.15) for stroke, 0.84 (0.65 to 1.08) for cardiovascular death, and 0.80 (0.69 to 0.92) for any cause death. In the as-treated analyses, hazard ratios were 0.84 (0.72 to 0.98) for major cardiovascular events, 0.55 (0.42 to 0.73) for heart failure, 0.93 (0.76 to 1.14) for myocardial infarction, 0.83 (0.64 to 1.07) for stroke, 0.67 (0.49 to 0.93) for cardiovascular death, and 0.75 (0.61 to 0.91) for any cause death.ConclusionsIn this large Scandinavian cohort, SGLT2 inhibitor use compared with DPP4 inhibitor use was associated with reduced risk of heart failure and any cause death, but not with major cardiovascular events in the primary intention-to-treat analysis. In the additional as-treated analyses, the magnitude of the association with heart failure and any cause death became larger, and a reduced risk of major cardiovascular events that was largely driven by the cardiovascular death component was observed. These data help inform patients, practitioners, and authorities regarding the cardiovascular effectiveness of SGLT2 inhibitors in routine clinical practice.
BACKGROUND:Risk of cardiovascular disease (CVD) and mortality for patients with versus without type 2 diabetes mellitus (T2DM) appears to vary by the age at T2DM diagnosis, but few population studies ...have analyzed mortality and CVD outcomes associations across the full age range.
METHODS:With use of the Swedish National Diabetes Registry, everyone with T2DM registered in the Registry between 1998 and 2012 was included. Controls were randomly selected from the general population matched for age, sex, and county. The analysis cohort comprised 318 083 patients with T2DM matched with just <1.6 million controls. Participants were followed from 1998 to 2013 for CVD outcomes and to 2014 for mortality. Outcomes of interest were total mortality, cardiovascular mortality, noncardiovascular mortality, coronary heart disease, acute myocardial infarction, stroke, heart failure, and atrial fibrillation. We also examined life expectancy by age at diagnosis. We conducted the primary analyses using Cox proportional hazards models in those with no previous CVD and repeated the work in the entire cohort.
RESULTS:Over a median follow-up period of 5.63 years, patients with T2DM diagnosed at ≤40 years had the highest excess risk for most outcomes relative to controls with adjusted hazard ratio (95% CI) of 2.05 (1.81–2.33) for total mortality, 2.72 (2.13–3.48) for cardiovascular-related mortality, 1.95 (1.68–2.25) for noncardiovascular mortality, 4.77 (3.86–5.89) for heart failure, and 4.33 (3.82–4.91) for coronary heart disease. All risks attenuated progressively with each increasing decade at diagnostic age; by the time T2DM was diagnosed at >80 years, the adjusted hazard ratios for CVD and non-CVD mortality were <1, with excess risks for other CVD outcomes substantially attenuated. Moreover, survival in those diagnosed beyond 80 was the same as controls, whereas it was more than a decade less when T2DM was diagnosed in adolescence. Finally, hazard ratios for most outcomes were numerically greater in younger women with T2DM.
CONCLUSIONS:Age at diagnosis of T2DM is prognostically important for survival and cardiovascular risks, with implications for determining the timing and intensity of risk factor interventions for clinical decision making and for guideline-directed care. These observations amplify support for preventing/delaying T2DM onset in younger individuals.
Diabetes care – improvement through measurement Eliasson, Björn; Gudbjörnsdottir, Soffia
Diabetes research and clinical practice,
12/2014, Letnik:
106, Številka:
Suppl 2
Journal Article, Magazine Article
Recenzirano
Abstract The National Diabetes Register (NDR) of Sweden was initiated in response to The Saint Vincent Declaration (published 1990), to provide a tool for continuous quality assurance in diabetes ...care. The original purpose, to monitor the results of health centres from year to year and to compare these with national and regional means, is still the most important one, while continuous follow-up of guidelines, treatments and complications are as important on a national level. The data reported contain basal clinical characteristics of the patients, as well as measures of risk-factor control and the presence of diabetes complications. Many clinics use templates within the medical record systems to verify that all information relevant to good quality assurance in diabetes care is complete, and thereafter export data to NDR. In order to create extra value, the NDR web interface, online at ndr.nu, offers functions to use when interacting with the individual patient, such as reports of all information reported to NDR, including medications and risk-factor control. The clinical results are thus reported back to the health centres (printed and instantaneously online), but are also used for scientific analyses. Such are required by the funding bodies to develop the analytical methods by quality registers, and to widely spread information, including publishing in scientific journals. Ongoing studies address, e.g., the effects of different glucose-lowering therapies, the role of ethnicity and migration, patient-reported outcomes and risks of morbidity and mortality in diabetes mellitus.
The study aimed to identify the most predictive factors for the development of type 2 diabetes. Using an XGboost classification model, we projected type 2 diabetes incidence over a 10-year horizon. ...We deliberately minimized the selection of baseline factors to fully exploit the rich dataset from the UK Biobank. The predictive value of features was assessed using shap values, with model performance evaluated via Receiver Operating Characteristic Area Under the Curve, sensitivity, and specificity. Data from the UK Biobank, encompassing a vast population with comprehensive demographic and health data, was employed. The study enrolled 450,000 participants aged 40-69, excluding those with pre-existing diabetes. Among 448,277 participants, 12,148 developed type 2 diabetes within a decade. HbA1c emerged as the foremost predictor, followed by BMI, waist circumference, blood glucose, family history of diabetes, gamma-glutamyl transferase, waist-hip ratio, HDL cholesterol, age, and urate. Our XGboost model achieved a Receiver Operating Characteristic Area Under the Curve of 0.9 for 10-year type 2 diabetes prediction, with a reduced 10-feature model achieving 0.88. Easily measurable biological factors surpassed traditional risk factors like diet, physical activity, and socioeconomic status in predicting type 2 diabetes. Furthermore, high prediction accuracy could be maintained using just the top 10 biological factors, with additional ones offering marginal improvements. These findings underscore the significance of biological markers in type 2 diabetes prediction.
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
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