IntroductionThe aims of this study were to characterize insulin-treated individuals aged ≥75 years with type 2 diabetes using basal insulin analogs (BIA) or regular insulins (human insulin ...(HI)/neutral protamine Hagedorn (NPH)) and to compare the benefits and risks.Research design and methodsThe analysis was based on data from the DPV (Diabetes-Patienten-Verlaufsdokumentation) and DIVE (DIabetes Versorgungs-Evaluation) registries. To balance for confounders, propensity score matching for age, sex, diabetes duration, body mass index and hemoglobin A1c (HbA1c) as covariates was performed.ResultsAmong 167 300 patients aged ≥75 years with type 2 diabetes (mean age, 80.3 years), 9601 subjects used insulin regimens with basal insulin (HI/NPH or BIA). Of these 8022 propensity score-matched subjects were identified. The mean diabetes duration was ~12 years and half of the patients were male. At the time of switch, patients provided with BIA experienced more dyslipidemia (89.3% vs 85.9%; p=0.002) and took a greater number of medications (4.3 vs 3.7; p<0.001) and depression was more prevalent (8.4% vs 6.5%; p=0.01). Aggregated to the most actual treatment year, BIA was associated with a higher percentage of patients using basal-supported oral therapy (42.6% vs 14.4%) and intensified conventional insulin therapy (44.3% vs 29.4%) and lower total daily insulin doses (0.24 IU/kg/day vs 0.30 IU/kg/day; p<0.001). The study did not reveal significant differences in efficacy (HbA1c 7.4% vs 7.3%; p=0.06), hospitalizations (0.7 vs 0.8 per patient-year (PY); p=0.15), length of stay (16.3 vs 16.1 days per PY; p=0.53), or rates of severe hypoglycemia (4.07 vs 4.40 per 100 PY; p=0.88), hypoglycemia with coma (3.64 vs 3.26 per 100 PY; p=0.88) and diabetic ketoacidosis (0.01 vs 0.03 per 100 PY; p=0.36).ConclusionBIA were used in more individually and patient-centered therapy regimens compared with HI/NPH in patients with a mean age of 80 years. Both groups were slightly overtreated with mean HbA1c <7.5%. The risk of severe hypoglycemia was low and independent of insulin type. Further analyses of elderly patients with type 2 diabetes are needed to provide evidence for best practice approaches in this age group.
This study analyzed whether area deprivation is associated with disparities in health care of pediatric type 1 diabetes in Germany.
We selected patients <20 years of age with type 1 diabetes and ...German residence documented in the "diabetes patient follow-up" (Diabetes-Patienten-Verlaufsdokumentation DPV) registry for 2015/2016. Area deprivation was assessed by quintiles of the German Index of Multiple Deprivation (GIMD 2010) at the district level and was assigned to patients. To investigate associations between GIMD 2010 and indicators of diabetes care, we used multivariable regression models (linear, logistic, and Poisson) adjusting for sex, age, migration background, diabetes duration, and German federal state.
We analyzed data from 29,284 patients. From the least to the most deprived quintile, use of continuous glucose monitoring systems (CGMS) decreased from 6.3 to 3.4% and use of long-acting insulin analogs from 80.8 to 64.3%, whereas use of rapid-acting insulin analogs increased from 74.7 to 79.0%; average HbA
increased from 7.84 to 8.07% (62 to 65 mmol/mol), and the prevalence of overweight from 11.8 to 15.5%, but the rate of severe hypoglycemia decreased from 12.1 to 6.9 events/100 patient-years. Associations with other parameters showed a more complex pattern (use of continuous subcutaneous insulin infusion CSII) or were not significant.
Area deprivation was associated not only with key outcomes in pediatric type 1 diabetes but also with treatment modalities. Our results show, in particular, that the access to CGMS and CSII could be improved in the most deprived regions in Germany.
Aims
To describe clinical characteristics, treatment patterns and glucagon‐like peptide‐1 receptor agonist (GLP‐1 RA) persistence in individuals with type 2 diabetes (T2D) initiating their first ...GLP‐1 RA.
Materials and Methods
A real‐world analysis of adults with T2D initiating GLP‐1 RA therapy between 2007 and June 2020 from the multicentre Diabetes Prospective Follow‐Up (DPV) Registry, stratified by antidiabetes therapy at the time of GLP‐1 RA initiation: oral antidiabetic drugs (OAD), insulin ± OAD or lifestyle modification (LM). GLP‐1 RA treatment persistence in individuals with ≥12 months follow‐up was determined by Kaplan‐Meier analysis.
Results
Overall, 15 111 individuals with T2D initiating GLP‐1 RA therapy (55% men) were identified; median interquartile range (IQR) age 58.7 (50.6‐66.7) years, diabetes duration 8.5 (3.6‐14.7) years, glycated haemoglobin HbA1c; 8.2 (7.1‐9.8)%. Median (95% confidence interval) GLP‐1 RA persistence in eligible individuals (n = 5189) was 11 (10‐12) months; OAD 12 (11‐14) months (n = 2453); insulin ± OAD 11 (9‐12) months (n = 2204); and LM 7 (5‐9) months (n = 532). Median treatment persistence tended to increase from 2007‐2012 to 2017‐2020. Median (IQR) HbA1c decreased from baseline 8.2 (7.1‐9.8)% to discontinuation 7.5 (6.6‐8.7)%, with a greater decrease observed in individuals with persistence >12 months versus ≤12 months. Individuals who discontinued GLP‐1 RA therapy predominantly switched to insulin (if not already using) or dipeptidyl peptidase‐4 inhibitors.
Conclusion
Real‐world registry data revealed improved outcomes with longer median GLP‐1 RA persistence; ~50% of patients overall achieved HbA1c <7% at 12 months. Persistence was highest with baseline OAD and/or insulin, and tended to increase over the period 2007‐2020.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
To explore sex differences in diabetes therapy, outcome, and complications of adults with T2D in Germany. We included 77,587 individuals ≥18 years and ≥1 year of T2D duration that were treated ...between January 2019 and June 2022. Data was aggregated over the most recent treatment year and most recent diabetes therapy was evaluated. Age and diabetes duration-adjusted regression models are presented as estimated means with standard error mean. Median age was 71.1 61.3 - 80.0 years, diabetes duration 11.5 6.3 - 19.7 years, 55% male. Male patients were more often treated with insulin or OADs (51.3% vs. 50.5%, 28.1% vs. 26.5%, resp.). There were no sex differences in GLP1-RA treatment (male: 9.8%, female: 9.4%). SGLT2i treatment was significantly more frequent in males (18.4% vs. 12.2%). Females had a lower HbA1c compared with males, but a higher BMI. Dyslipidemia was more frequent in females compared with males, while hypertension was higher in males. We observed sex differences in treatment, especially newer OADs and GLP1-RA, metabolic control, and complications in all analyzed age groups. While women seem to be more affected by higher BMI and dyslipidemia, their metabolic control is better compared with males, although they have less medical treatment. Sex differences in compliance, adherence or disease perception could be the cause of these differences.
Disclosure
S.R.Tittel: None. G.Hess: Other Relationship; Novo Nordisk. S.Muehldorfer: None. A.Gillessen: None. R.Jung: Advisory Panel; Boehringer Ingelheim Inc., Speaker's Bureau; Novo Nordisk. M.D.Karl: None. S.Lanzinger: None.
Aims/hypothesis
Studies on the association between air pollution and metabolic control in children and adolescents with type 1 diabetes are rare and findings are inconsistent. We examined the ...relationship between air pollution variables (particulate matter with an aerodynamic diameter <10 μm PM
10
, NO
2
and accumulated ozone exposure O
3
-AOT) and metabolic variables (HbA
1c
and daily insulin dose U/kg body weight) in children and adolescents with type 1 diabetes.
Methods
We investigated 37,372 individuals with type 1 diabetes aged <21 years, documented between 2009 and 2014 in 344 German centres of the prospective diabetes follow-up registry (Diabetes-Patienten-Verlaufsdokumentation DPV). Long-term air pollution exposure (annual and quinquennial means) data were linked to participants via the five-digit postcode areas of residency. Cross-sectional multivariable regression analysis was used to examine the association between air pollution and metabolic control.
Results
After comprehensive adjustment, an interquartile range increase in O
3
-AOT was associated with a lower HbA
1c
(−3.7% 95% CI −4.4, −3.0). The inverse association between O
3
-AOT and HbA
1c
persisted after additional adjustment for degree of urbanisation or additional adjustment for PM
10
. Moreover, the inverse association remained stable in further sensitivity analyses. No significant associations between HbA
1c
and PM
10
or NO
2
were found. No association was observed between any of the three air pollutants and insulin dose.
Conclusions/interpretation
The inverse association between O
3
-AOT and HbA
1c
could not be explained by regional differences in diabetes treatment or by other differences between urban and rural areas. Furthermore, our results remained stable in sensitivity analyses. Further studies on the association between air pollution and HbA
1c
in children and adolescents with type 1 diabetes are needed to confirm our observed association and to elucidate underlying mechanisms.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Continuous glucose monitoring (CGM), insulin pump, and automated insulin delivery (AID) use improve glycemia and quality of life. Device use has increased in recent years. However, rates of device ...use in older adults are underreported. The aim of this study is to describe sensor, pump, and AID use and glycemic outcomes in adults with type 1 diabetes (T1D) age > 60 years from the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI) and the Diabetes Patients Follow-up (DPV) registry. We report cross-sectional data of adults > 60 years with T1D seen in 2022 in the T1DX-QI (n=1217) and DPV (n=2042) registries for device use and HbA1c. Median age was 67.5 years interquartile range {IQR} 63.4, 72.8 in T1DX-QI and 68.9 IQR 63.6, 75.7 in DPV. CGM use was similar (50.3%, 47.9%), insulin pump use was approximately 2x higher (40.7% vs 17%), and AID use approximately 3x higher (20.4% vs. 6.4%) in the T1DX-QI as compared to DPV registry (Fig. 1). HbA1c was lower in T1DX-QI (median 7.1% 6.5, 7.8) than DPV (median 7.4% 6.8, 8.19). Device use was lower with increasing age across both registries. Rates of insulin pump and AID use were higher, and median HbA1c was lower, across all age groups among older adults in the T1DX-QI as compared to the DPV registry. Disclosure K. Fantasia: None. S. Lanzinger: None. S. Rompicherla: None. J.J. Grammes: Advisory Panel; Novo Nordisk. Other Relationship; Novo Nordisk, Lilly Diabetes. G. O'Malley: Research Support; Dexcom, Inc., Insulet Corporation, Abbott, Tandem Diabetes Care, Inc., MannKind Corporation. J.K. Mader: Advisory Panel; Becton, Dickinson and Company. Speaker's Bureau; Becton, Dickinson and Company, A. Menarini Diagnostics, Boehringer-Ingelheim, diaTribe. Other Relationship; Diabetes UK. Stock/Shareholder; decide Clinical Software GmbH. Advisory Panel; embecta. Speaker's Bureau; embecta, Viatris Inc., Eli Lilly and Company. Advisory Panel; Eli Lilly and Company, Medtronic. Speaker's Bureau; Medtrust. Advisory Panel; Novo Nordisk A/S. Speaker's Bureau; Novo Nordisk A/S. Advisory Panel; PharmaSens, Roche Diabetes Care. Speaker's Bureau; Roche Diabetes Care. Board Member; Sanofi-Aventis Deutschland GmbH. Speaker's Bureau; Sanofi-Aventis Deutschland GmbH, Sanofi, Dexcom, Inc., Viatris Inc. Advisory Panel; Viatris Inc. Speaker's Bureau; Ypsomed AG. Research Support; European Union. Stock/Shareholder; elyte Diagnostics GmbH. Other Relationship; elyte Diagnostics GmbH. Board Member; European Association for the Study of Diabetes. Research Support; European Union Aviation Safety Agency. L. Golden: None. F. Kopp: Other Relationship; Lilly Diabetes, Lilly Diabetes. D.M. Maahs: Advisory Panel; Medtronic. Consultant; Abbott, LifeScan Diabetes Institute, Sanofi, Provention Bio, Inc., Bayer Inc., Kriya Therapeutics, BioSpex. P.M. Jehle: None. O. Ebekozien: Advisory Panel; Sanofi, Medtronic. Research Support; Vertex Pharmaceuticals Incorporated. Speaker's Bureau; Vertex Pharmaceuticals Incorporated. Research Support; Medtronic, Lilly Diabetes, Abbott, Dexcom, Inc. R.W. Holl: None. Funding Helmsley Charitable Trust
Sodium-glucose co-transporter-2 inhibitors (SGLT2is) represent a class of oral antidiabetic drugs that are used in the treatment of type 2 diabetes (T2D), and more recently have been approved for ...therapy of heart and kidney failure independent of T2D.1 Importantly, SGLT2is have shown the capacity to induce reverse cardiac remodelling,2 together with impressive reductions in cardiovascular, heart failure, and kidney endpoints in outcome trials.3 SGLT2is lower blood glucose levels independently of insulin action by inhibiting reabsorption of filtered glucose in the proximal tubule to increase urinary glucose excretion.4 Therefore, this mode of action is also effective in absolute insulin deficiency in type 1 diabetes (T1D). Consequently, similar to the clinical effects in T2D, clinical trials of SGLT2i use in patients with T1D as an oral adjunct to insulin therapy have shown reductions in HbA1c, body weight, and blood pressure. Moreover, reductions in insulin doses despite improvements in HbA1c, and most importantly, improvements of daily glucose excursions with reduced hypoglycaemia rates and increased time in physiological glucose range, have been observed.5 However, recognized as a rare side effect in T2D, SGLT2is significantly increased the risk for comparatively normoglycaemic diabetic ketoacidosis (DKA) in T1D trials.5, 6 In addition, no outcome trial results are available for the use of SGLT2is in T1D.To date, little is known about patient selection, and the risks and benefits of SGLT2is in T1D in the real world.6, 7 In Europe, the SGLT2i dapagliflozin, at a reduced dose of 5 mg, was licensed in 2019 for use in adults with T1D as an adjunct to insulin therapy for selected patients with a body mass index (BMI) of 27 kg/m2 or higher. The licensing of dapagliflozin in Europe for T1D was mandatorily associated with a risk mitigation strategy for the prevention of DKA, including patient and provider education programmes, provision of self-measurement meters for ketone bodies, and intense patient surveillance.6 Of note, off-label use of SGLT2is in T1D in clinical practice had been observed earlier, well before official licensing.7 The criteria upon which healthcare providers selected patients with T1D for treatment with SGLT2is in the real world are not well characterized. Further, therapeutic effects and adverse events of SGLT2is in T1D in daily clinical practice have not been substantially reported.Therefore, in this trial, we collected data on the characteristics of patients with T1D selected for SGLT2i treatment, and analysed efficacy and safety in clinical practice.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Evidence for the metabolic impact of long-term exposure to air pollution on diabetes is lacking. We investigated the association of particulate matter <10 μm (PM10) and <2.5 μm (PM2.5) with yearly ...averages of HbA1c, daily insulin dose (IU/kg) and rates of severe hypoglycaemia in type 1 diabetes (T1D).
We studied data of 44,383 individuals with T1D < 21 years which were documented in 377 German centres within the diabetes prospective follow-up registry (DPV) between 2009 and 2018. Outcomes were aggregated by year and by patient. PM10-and PM2.5-yearly averages prior to the respective treatment year were linked to individuals via the five-digit postcode areas of residency. Repeated measures linear and negative binomial regression were used to study the association between PM-quartiles (Q1 lowest, Q4 highest concentration) and yearly averages of HbA1c, daily insulin dose and rates of severe hypoglycaemia (confounders: sex, time-dependent age, age at diabetes onset, time-dependent type of treatment, migratory background, degree of urbanisation and socioeconomic index of deprivation).
Adjusted mean HbA1c increased with PM10 (Q1: 7.96% 95%-CI: 7.95–7.98, Q4: 8.03% 8.02–8.05, p-value<0.001) and with PM2.5 (Q1: 7.97% 7.95–7.99, Q4: 8.02% 8.01–8.04, p < 0.001). Changes in daily insulin dose were inversely related to PM (PM10 and PM2.5: Q1 0.85 IU/kg 0.84–0.85, Q4: 0.83 IU/kg 0.82–0.83, p < 0.001). Adjusted rates of severe hypoglycaemia increased with PM-quartile groups (PM10 Q1:11.2 events/100 PY 10.9–11.5, PM10 Q4: 15.3 14.9–15.7, p < 0.001; PM2.5 Q1: 9.9 events/100 PY 9.6–10.2, PM2.5 Q4: 14.2 13.9–14.6, p < 0.001).
Air pollution was associated with higher HbA1c levels and increased risk of severe hypoglycaemia in people with T1D, consequently indicating a higher risk of diabetes complications. Further studies are needed to explore causal pathways of the observed associations.
•Evidence for the metabolic impact of long-term exposure to air pollution on type 1 diabetes is lacking.•We investigated a large cohort of 44,383 children and adolescents with type 1 diabetes from the multicentre DPV registry.•PM10 and PM2.5 was associated with an increased risk of severe hypoglycaemia inindividuals with type 1 diabetes.•Our results indicate a higher risk of diabetes complications in association with air pollution.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Aim
To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup discovery algorithm.
Materials and Methods
Data from adults and ...children with type 1 diabetes and more than two diabetes‐related visits were analysed from the Diabetes Prospective Follow‐up Registry. Q‐Finder, a supervised non‐parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH less than 7.3 during a hospitalization event.
Results
Data for 108 223 adults and children, of whom 5609 (5.2%) had DKA, were studied. Q‐Finder analysis identified 11 profiles associated with an increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6‐10 years; age 11‐15 years; an HbA1c of 8.87% or higher (≥ 73 mmol/mol); no fast‐acting insulin intake; age younger than 15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycaemia; hypoglycaemic coma; and autoimmune thyroiditis. Risk of DKA increased with the number of risk profiles matching patients’ characteristics.
Conclusions
Q‐Finder confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Diabetic ketoacidosis (DKA) is a life-threatening complication of type 1 diabetes mellitus (T1DM) that results from absolute insulin deficiency and is marked by acidosis, ketosis, and hyperglycemia ...(1). Therefore, prevention of DKA is one goal in T1DM care, but recent data indicate increased incidence (2). For adult patients, only limited data are available on rates and risk factors for development of DKA, and this complication remains epidemiologically poorly characterized. The Diabetes Prospective Follow-up Registry (DPV) has followed patients with diabetes from 1995. Data for this study were collected from 2000 to 2016. Inclusion criteria were diagnosis of T1DM, age at diabetes onset ≥6 months, patient age at follow-up ≥18 years, and diabetes duration ≥1 year to exclude DKA at manifestation. DKA was defined as serum pH <7.3, and rates of DKA were analyzed based on a Poisson regression model accounting for overdisperson stratified by sex, age, diabetes duration, HbA1c, treatment regimen, size of diabetes center, and migration background, i.e., whether the patient or one or both parents were born outside the countries of the registry. A diabetes center that treated ≥50 adult patients with T1DM in the year 2016 was considered large.
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