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Basal insulin’s position in the type 2 diabetes (T2D) treatment paradigm has undergone significant revisions since the advent of diabetes medications such as glucagon-like peptide 1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT-2is), which offer cardiorenal protection for people with T2D (PwT2D). This study aimed to characterize the demographic, clinical, and diabetes medication utilization patterns of PwT2D initiating basal insulin between 2014 and 2020 over the time period when these revisions were occurring.
Methods
A retrospective study was conducted using the IBM® MarketScan® databases and included adults with T2D who initiated basal insulin therapy (basal insulin initiators) in 2015, 2017, or 2019. Patient characteristics, medication utilization patterns, and time to add an additional diabetes drug class were compared across years. Characteristics of users of basal insulin-GLP-1RA combination therapy (GLP-1RA-basal insulin dual users) were also compared across years.
Results
Between 2015 and 2019, initiation of basal insulin therapy remained steady, with 1.6–1.9% of PwT2D starting basal insulin in each year. GLP-1RA and SGLT-2i use increased pre- and post-basal insulin initiation (pre-basal: GLP-1RA, from 14.8% to 25.2%, p < 0.0001; SGLT-2i, from 11.4% to 20.5%, p < 0.0001; post-basal: GLP-1RA, from 16.7% to 30.5%, p < 0.0001; SGLT-2i, from 13.4% to 23.3%, p < 0.0001]). The proportion of PwT2D with underlying cardiovascular and renal diseases did not increase during this period. Among basal insulin initiators without prior GLP-1RA, SGLT-2i, or bolus insulin use, time to adding on these agents decreased, with 14.0–15.6% starting bolus insulin within the first year. Among GLP-1RA-basal insulin dual initiators, the proportion of those with underlying cardiovascular disease was not higher among GLP-1RA first users.
Conclusions
In this real-world study, insulin remained key in the T2D treatment paradigm. A growing proportion of PwT2D utilized GLP-1RAs and SGLT-2is before and after initiation of basal insulin therapy. At the same time, there was no increase in the proportion of those initiating basal insulin who had cardiorenal comorbidity profiles for which treatment guidelines have recommended the use of GLP-1RAs or SGLT-2is.
Basal insulin is an important component of the type 2 diabetes (T2D) treatment armamentarium. However, treatment guidelines for T2D have evolved from a glucose-centric approach to a more targeted approach and, consequently, basal insulin is positioned below novel therapies such as glucagon-like peptide 1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT-2is).
GLP-1RAs and SGLT-2is, which have demonstrated cardiovascular and renal benefits, are recommended and preferred over basal insulin for people with T2D who have established or are at high risk of atherosclerotic cardiovascular disease and/or renal disease.
Against the backdrop of changing treatment guidelines for T2D, this study aimed to describe real-world medication usage patterns among those with T2D who initiate and use basal insulin.
What was learned from this study?
Real-world initiation of basal insulin remained stable between 2014 and 2020 despite an increase in the use of newer non-insulin medications, such as GLP-1RAs and SGLT-2is, during this period.
A significant proportion of people who had initiated basal insulin therapy intensified treatment using bolus insulin early within the first year of basal insulin use.
GLP-1RAs and SGLT-2is were added earlier on in the treatment regimen among those who had initiated basal insulin therapy. However, among those who initiated therapy with the basal insulin-GLP-1RA combination, the proportion of PwT2D with a history of underlying cardiovascular disease was not higher among GLP-1RA first users.
It is likely that clinical practice and professional society guidelines that recommend GLP-1RAs and SGLT-2is over basal insulin for cardiorenal protection are not the sole drivers of their increasing use in PwT2D who initiate basal insulin therapy.
Introduction
Insulin’s Place in the Treatment Algorithm for Type 2 Diabetes
Type 2 diabetes (T2D) is a chronic, progressive disease characterized by insulin resistance that may result in the need for insulin supplementation when lifestyle interventions and other diabetes medications fail to achieve glycemic control [1, 2]. Approximately 36% of people with T2D (PwT2D) may require insulin within 8 years of diagnosis [3]. Insulin is among the most efficacious blood glucose-lowering medications, and basal insulin is often the initial insulin therapy recommended in T2D [4‐7]. The 2012 and 2015 American Diabetes Association (ADA) standards of care recommended considering basal insulin as a second-line of treatment after failure of metformin, or as an initial treatment in newly diagnosed PwT2D with symptomatic hyperglycemia [8]. However, basal insulin’s position in the T2D treatment paradigm has undergone significant revisions from the perspective of clinical practice and professional diabetes society guidelines since the publication of landmark cardiovascular outcomes trials, including EMPA-REG OUTCOME, CANVAS, LEADER, DECLARE-TIMI-58, CREDENCE, and REWIND [9‐16]. These trials have demonstrated that the use of glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT-2is) could reduce mortality and/or morbidity rates associated with congestive heart failure (CHF), stroke, and myocardial infarction (MI) [10‐16]. The CANVAS and CREDENCE trials [11, 15] also showed that SGLT-2is can reduce the progression of diabetic renal disease (Electronic Supplementary Material [ESM] Fig. S1). Insulin is generally added for T2D management if hemoglobin A1c (HbA1c) remains above target after the use of GLP-1RAs, SGLT-2is, and other non-insulin medications [7].
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Evolution of T2D Clinical and Professional Society Treatment Guidelines Over Time
Data from the above-mentioned trials have resulted in the evolution of the treatment paradigm for T2D from a glycemic-based approach to a patient-centered approach [6, 7]. In 2017, the ADA included a recommendation to consider the use of GLP-1RAs and SGLT-2is in PwT2D with established atherosclerotic cardiovascular disease (ASCVD) [17]. This recommendation was expanded in 2018 by the ADA/European Association for the Study of Diabetes (EASD) consensus report to include the use of GLP-1RAs and SGLT-2is in PwT2D with chronic kidney disease (CKD) or CHF [7]. Then in 2019, the ADA and the American Association of Clinical Endocrinologists (AACE)/American College of Endocrinology (ACE) further endorsed the use of GLP-1RAs and SGLT-2is with or without metformin as initial treatment for PwT2D with ASCVD at high risk of ASCVD, CHF, and/or CKD [9, 18]. These guidelines also recommended the use of insulin in combination with GLP-1RAs if HbA1c is very high (> 9%), or if PwT2D exhibit symptoms of catabolism suggesting insulin deficiency [9]. The most recent ADA 2022 standards of care have continued to endorse this therapeutic approach, recommending insulin use in combination with GLP-1RAs, or when HbA1c > 10% [2].
Given the high prevalence of ASCVD, CHF, and renal disease in PwT2D [19, 20], one would expect an increasing number of PwT2D to be prescribed GLP-1RAs and/or SGLT-2is. A recent real-world study reported an increasing trend in the use of GLP-1RAs (from 9% to 17%) and SGLT-2is (from 11% to 17%) between 2015 and 2019 [21]. Despite this, the overall use of GLP-1RAs and SGLT-2is is low (< 10%) among PwT2D with ASCVD or renal disease [22‐25]. Concurrently, a cross-sectional study reported that basal insulin use had also increased from 10% to 17% between 2005 and 2016 [26]. As guidelines for T2D treatment continue to evolve, with emphasis on a patient-centered approach that recommends the use of newer non-insulin medications such as GLP-1RAs and SGLT-2is over basal insulin, a better understanding of real-world diabetes medication use patterns among PwT2D who use basal insulin as part of their T2D management is warranted. This study aimed to characterize the demographic, clinical, and diabetes medication utilization patterns of PwT2D who initiated basal insulin (basal insulin initiators) between 2014 and 2020 over the time period when these guideline changes were occurring.
Objectives
The objectives of this study were:
1.
To describe the patterns of basal insulin use among PwT2D who initiated basal insulin therapy in 2015 (cohort 1), 2017 (cohort 2), and 2019 (cohort 3) (Fig. 1).
To describe and compare demographic and clinical characteristics in the year prior to basal insulin initiation (index date) between cohorts, and
To describe and compare diabetes medication utilization patterns in the year prior to and the year after initiation of basal therapy between cohorts.
2.
For each cohort year (2015, 2017, or 2019), to evaluate the time to first prescription fill for GLP-1RAs, SGLT-2is, and bolus or mixed insulin in the year after initiation of basal insulin therapy among those who added these agents to their therapeutic regimen after starting basal insulin in 2015, 2017, or 2019.
3.
In the subset of PwT2D from each cohort who used a GLP-1RA (basal insulin-GLP-1RA dual users), to describe and compare the demographic and clinical characteristics in the year prior to initiation of basal insulin therapy between those who initiated GLP-1RA first, basal insulin first, or concurrently initiated GLP-1RA and basal insulin (initiated within 1 month of each other) (ESM Fig. S2).
Fig. 1
Study cohorts and medications examined between 2014 and 2020. Other medications (marked with asterisk) included acarbose, miglitol, colesevelam, bromocriptine–IR, and pramlintide (post-index only). DPP-4i Dipeptidyl peptidase-4 inhibitor, IR immediate release, GLP-1RA glucagon-like peptide 1 receptor agonist, SGLT-2i sodium-glucose cotransporter-2 inhibitor
This retrospective, observational cohort study used data from the IBM® MarketScan® Commercial Claims and Encounters, Medicaid multi-state, and Medicare Supplemental databases. The IBM MarketScan databases contain fully de-identified patient-level healthcare claims data covered by commercially insured health plans, Medicare supplemental insurance, and Medicaid. The commercial and Medicare supplemental databases contain data for approximately 65.4 million covered lives over the 3 most recent years. The study period was 01 January 2014 to 31 December 2020, and the index date was defined as the earliest date of first prescription fill of basal insulin. Based on the year of initiation on basal insulin therapy, PwT2D were grouped into three mutually exclusive cohorts (2015: cohort 1; 2017: cohort 2; 2019: cohort 3; Fig. 1).
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This was an observational, non-interventional study based on secondary use of previously collected data; thus, patient consent and institutional review board approval were not required. De-identified data in this study complied with the US Health Insurance Portability and Accountability Act. This study was conducted in accordance with the principles outlined in the Declaration of Helsinki of 1964 and its subsequent amendments and was consistent with Good Pharmacoepidemiology Practices. The IBM® MarketScan® Research Databases is a commercial database, and the datasets were accessed through a license with IBM (IBM Corp., Armonk, NY, USA).
Study Population
In this study, PwT2D aged ≥ 19 years at index date were included if they met the following criteria: (1) at least two diagnoses of T2D (ICD-9-CM [International Classification of Diseases, Ninth Revision, Clinical Modification]: 250.x0 or 250.x2; ICD, Tenth Revision, Clinical Modification [ICD-10-CM]: E11.xx) or one diagnosis of T2D and one oral antidiabetic drug prescription fill within 1 year during the study period; (2) at least one prescription filled during the index period for a basal insulin product of either insulin neutral protamine Hagedorn, detemir, glargine, degludec, or Xultophy®, or Soliqua®; (3) at least 2 years of continuous insurance enrollment that spanned the year before and the year after the index date, inclusive of the index date; (4) no prescription fills for a basal insulin containing product in the 1-year pre-index date period; (5) and no prescription fills for bolus insulin or mixed insulin products on index date or in the 1-year pre-index date periods.
People were excluded from the study if they: (1) had a claim for insulin pump or insulin pump supplies within 1 year prior to or after the index date; (2) were pregnant (ICD-9-CM: V22.x, 630–679; ICD-10-CM: O00-O9A, Z34.xx) at any time during the pre-or post-index periods; or (3) were diagnosed with any other type of diabetes during the study period, including type 1 diabetes (ICD-9-CM: 250.x1 or 250.x3; ICD-10-CM: E10.xx), gestational diabetes (ICD-9-CM: 648.0x or 648.8x; ICD-10-CM: O24.xx), secondary diabetes (ICD-9-CM: 249.x; ICD-10-CM: E08.xx or E09.xx), or other abnormal glucose conditions (ICD-9-CM: 790.29; ICD-10-CM: R73.09) (ESM Fig. S3).
Study Variables
The following demographic variables were collected for PwT2D: age, gender, and payer type. The following clinical variables were also assessed: macrovascular conditions, including cerebrovascular disease (CVD), CHF, coronary arterial disease (CAD), MI, peripheral arterial disease (PAD), and unstable angina. Microvascular conditions assessed included end-stage renal disease (ESRD), CKD stages 3–4, peripheral neuropathy, and retinopathy. Other health conditions assessed included hyperlipidemia, hypertension, severe liver disease, and obesity; other scores and measures assessed included adapted Diabetes Complications Severity Index (aDCSI), which was developed specifically for use with claims data to quantify diabetes complications [27, 28], the Charlson Comorbidity Index (CCI) [29], HbA1c, and the presence of at least one diabetes complication. The use of the following medication classes was assessed in PwT2D: metformin, thiazolidinediones (TZD), sulfonylureas (SU), meglitinides, dipeptidyl peptidase-4 inhibitors (DPP-4i), GLP-1RAs, SGLT-2is, insulin, and other medications (acarbose, miglitol, colesevelam, bromocriptine immediate release, and pramlintide).
Statistical Analysis
Differences in study measures between cohorts were described using Chi-square tests for categorical variables and t-tests for continuous variables. Frequencies and percentages were reported for categorical variables. Means and standard deviations (SD) were reported for continuous variables. For the time to first fill analysis for GLP-1RAs, SGLT-2is, and bolus or mixed insulin, only the earliest drug added during the 1-year post-index period was evaluated. Specifically, if a patient added GLP-1RAs first and then switched to an SGLT-2i later, only the time from index date to adding GLP-1RAs to the therapeutic regimen was assessed. The significance level was set at a two-sided α level of 0.05.
Results
Characteristics of Basal Insulin Initiators Among PwT2D
Demographic Characteristics
Of the 6,396,441 PwT2D enrolled in the databases between 01 January 2014, and 31 December 2020, 17,801 (1.9%) initiated basal insulin therapy in 2015 (cohort 1), 13,487 (1.6%) in 2017 (cohort 2), and 10,870 (1.7%) in 2019 (cohort 3; Table 1). The mean (SD) age of basal insulin initiators decreased across cohorts, being 55.9 (SD 11.7) years in cohort 1, 54.0 (SD 10.7) years in cohort 2, and 52.1 (SD 9.6) years in cohort 3 (p < 0.05). However, the mean age of all PwT2D in the database was 60.6 (SD 12.2), 59.2 [12], and 56.8 (SD 10.8) years in 2015, 2017, and 2019, respectively. Between 2015 and 2019, there was an increase in the proportion of PwT2D on Medicaid (from 13.4% to 24.3%) while the proportion on Medicare decreased (from 18.0% to 3.0%). Demographic characteristics of basal insulin initiators in the three cohorts are presented in Table 1.
Table 1
Characteristics of people with type 2 diabetes in cohort 1 (2015), cohort 2 (2017), and cohort 3 (2019)
Variable
Cohort 1 (2015) (N = 17,801 PwT2D)
Cohort 2 (2017) (N = 13,487 PwT2D)
Cohort 3 (2019) (N = 10,870 PwT2D)
Total number of PwT2D in cohort year
948,805
834,320
622,455
New basal insulin initiators, n (%)
17,801 (1.9)
13,487 (1.6)
10,870 (1.8)
Age at index date, years
Mean [SD]
55.9 [11.7]
54.0 [10.7]a,b
52.1 [9.6]c
Sex, n (%)
Female
8403 (47.2)
6285 (46.6)
5132 (47.2)
Primary payer at index date, n (%)
Commercial
12,212 (68.6)
9506 (70.5)a,b
7905 (72.7)c
Medicaid
2389 (13.4)
2606 (19.3)
2637 (24.3)
Medicare
3200 (18.0)
1375 (10.2)
328 (3.0)
Macrovascular conditions, n (%)
Cerebrovascular disease
1232 (6.9)
830 (6.2)a
606 (5.6)c
Congestive heart failure
1130 (6.3)
827 (6.1)
648 (6.0)
Coronary arterial disease
2298 (12.9)
1560 (11.6)a,b
1062 (9.8)c
Myocardial infarction
560 (3.1)
475 (3.5)
381 (3.5)
Peripheral arterial disease
791 (4.4)
347 (2.6)a
287 (2.6)c
Unstable angina
356 (2.0)
121 (0.9)a
99 (0.9)c
Microvascular conditions, n (%)
End-stage renal disease
199 (1.1)
77 (0.6)a
60 (0.6)c
Chronic kidney disease stages 3–4
1043 (5.9)
671 (5.0)a,b
459 (4.2)c
Peripheral neuropathy
3231 (18.2)
2546 (18.9)
2108 (19.4)c
Retinopathy
431 (2.4)
988 (7.3)a
830 (7.6)c
Other conditions, n (%)
Hyperlipidemia
11,326 (63.6)
9173 (68.0)a
7280 (67.0)c
Hypertension
13,300 (74.7)
9937 (73.7)a
7935 (73.0)c
Severe liver disease
165 (0.9)
167 (1.2)a,b
178 (1.6)c
Obesity
3867 (21.7)
3765 (27.9)a,b
3373 (31.0)c
Adapted Diabetes Complications Severity Index
Mean [SD]
1.1 [1.6]
1.0 [1.4]a
1.0 [1.4]c
Charlson Comorbidity Index
Mean [SD]
2.3 [2.0]
2.5 [2.1]a
2.5 [2.1]c
Diabetes with complications, n (%)
6778 (38.1)
5277 (39.1)
4179 (38.4)
Differences between cohorts are evaluated using Chi-square (χ2-tests) for categorical variables and t-tests for continuous variables
Different lowercase letters following values indicate a statistically significant difference between groups at p < 0.05, with lowercase “a” indicating a significant difference between cohorts 1 and 2; lowercase “b” indicating a significant difference between cohorts 2 and 3; and lowercase “c” indicating a significant difference between cohorts 1 and 3
N Total number of PwT2D, n number of PwT2D in each subgroup, PwT2D people with type 2 diabetes, SD standard deviation, T2D type 2 diabetes
Clinical Characteristics
The proportion of PwT2D with a history of CHF or MI at basal insulin initiation in the three cohorts did not change significantly over time (2015 cohort vs. 2017 cohort vs. 2019 Cohort: CHF: 6.3% vs. 6.1% vs. 6.0%; MI: 3.1% vs. 3.5% vs. 3.5%). However, the proportion of PwT2D with a history of CAD at basal insulin initiation decreased over time (12.9% vs. 11.6% vs. 9.8%; p < 0.001). The proportion with a history of CVD, PAD, and unstable angina at basal insulin initiation decreased from 2015 to 2017 (CVD: 6.9% vs. 6.2%, p < 0.01; PAD: 4.4% vs. 2.6%, p < 0.001; unstable angina: 2.0% vs. 0.9%, p < 0.001), but not from 2017 to 2019 (Table 1). Conversely, the proportion of PwT2D who had obesity increased over time in the three cohorts (2015 cohort vs. 2017 cohort vs. 2019 cohort: 21.7% vs. 27.9% vs. 31.0%, p < 0.0001; Table 1).
With respect to microvascular conditions among PwT2D who initiated basal insulin in 2015, 2017, or 2019, the proportion with a history of CKD stages 3–4 at basal insulin initiation decreased over time (5.9% vs. 5.0% vs. 4.2%, p < 0.01). Similarly, the proportion with a history of ESRD decreased over time between 2015 and 2017 (from 1.1% to 0.6%, p < 0.0001), but not between 2017 and 2019. Assessment of HbA1c before and after basal insulin initiation showed a decrease of about 1.5% across all three cohorts (2015 cohort: 9.7% vs. 8.5%, 2017 cohort: 9.8% vs. 8.2%, 2019 cohort: 9.9% vs. 8.4%), although data on HbA1c were available for only 5–8% of PwT2D across cohorts.
Medication Use Patterns of Basal Insulin Initiators Among PwT2D
Across the study period (2014–2020), GLP-1RA and SGLT-2i use increased significantly before and after basal insulin initiation among PwT2D in this study (pre-basal insulin initiation: GLP-1RAs, from 14.8% to 25.2%, p < 0.0001; SGLT-2is, from 11.4% to 20.5%, p < 0.0001; post-basal insulin initiation: GLP-1RAs, from 16.7% to 30.5%, p < 0.0001; SGLT-2is, from 13.4% to 23.3%, p < 0.0001; Table 2). Conversely, the use of SU, though relatively high in all cohorts, and the use of DPP-4i decreased significantly before and after basal insulin initiation (pre-basal insulin initiation: SU, from 49.2% to 41.0%, p < 0.0001; DPP-4i, from 29.8% to 24.8%, p < 0.0001; post-basal insulin initiation: SU, from 41.1% to 33.6%, p < 0.0001; DPP-4i, from 26.4% to 20.1%, p < 0.0001; Table 2). The use of the insulin sensitizer metformin remained steady before basal insulin initiation (69.9–69.9%) but increased slightly after basal insulin initiation (from 71.8 to 74.8%, p < 0.0001). The use of TZD, another insulin sensitizer, remained steady before and after basal insulin initiation (Table 2). The addition of bolus or mixed insulin in the year after basal insulin initiation decreased slightly between 2014 and 2020 (bolus insulin: from 15.6% to 14.0%, p < 0.001; mixed insulin: from 1.7 to 1.1%; p < 0.0001), whereas use of the GLP-1RA and basal insulin fixed-dose combination products, though relatively low, increased significantly (from 0.0% to 6.1%, p < 0.0001; Table 2).
Table 2
Medication use during 1-year pre- and post-index among people with type 2 diabetes in the study cohorts
Pre-index diabetic medications, n (%)d,e
Cohort 1 (2015) (N = 17,801 PwT2D)
Cohort 2 (2017) (N = 13,487 PwT2D)
Cohort 3 (2019) (N = 10,870 PwT2D)
Metformin
12,443 (69.9)
9843 (73.0)a,b
7600 (69.9)
Thiazolidinedione
1543 (8.7)
1163 (8.6)
938 (8.6)
Sulfonylurea
8760 (49.2)
6466 (47.9)a,b
4453 (41.0)
Meglitinide
307 (1.7)
191 (1.4)a,b
101 (0.9)c
Dipeptidyl peptidase-4 inhibitor
5302 (29.8)
4025 (29.8)b
2701 (24.8)c
Sodium-glucose cotransporter-2 inhibitor
2034 (11.4)
2481 (18.4)a,b
2233 (20.5)c
Glucagon-like peptide-1 receptor agonist
2629 (14.8)
2722 (20.2)a,b
2743 (25.2)c
Post-index diabetic medications, n (%)e
Cohort 1 (N = 17,801 PwT2D)
Cohort 2 (N = 13,487 PwT2D)
Cohort 3 (N = 10,870 PwT2D)
Metformin
12,776 (71.8)
10,137 (75.2)a
8132 (74.8)c
Thiazolidinedione
1282 (7.2)
956 (7.1)
789 (7.3)
Sulfonylurea
7321 (41.1)
234 (38.8)a,b
3656 (33.6)c
Meglitinide
276 (1.6)
169 (1.3)a,b
95 (0.9)c
Dipeptidyl peptidase-4 inhibitor
4694 (26.4)
3380 (25.1)a,b
2187 (20.1)c
Sodium-glucose cotransporter-2 inhibitor
2384 (13.4)
2492 (18.5)a,b
2533 (23.3)c
Glucagon-like peptide-1 receptor agonist
2971 (16.7)
3031 (22.5)a,b
3311 (30.5)c
Bolus insulin
2785 (15.6)
1891 (14.0)a
1524 (14.0)c
Basal insulin
17,801 (100.0)
13,117 (97.3)a,b
10,403 (95.7)c
Mixed insulin
308 (1.7)
172 (1.3)a
120 (1.1)c
Glucagon-like peptide-1 receptor agonist and basal insulin combination
0 (0.0)
639 (4.7)a,b
665 (6.1)c
Differences between cohorts are evaluated using χ2-tests for categorical variables and t-tests for continuous variables
Different lowercase letters following values indicate a statistically significant difference between groups at p < 0.05, with lowercase “a” indicating a significant difference between cohorts 1 and 2; lowercase “b” indicating a significant difference between cohorts 2 and 3; and lowercase “c” indicating a significant difference between cohorts 1 and 3
N total number, n number in each subgroup
dPeople with T2D did not have any basal insulin fills during the pre-index period
eIn addition to those listed in the table, other medications were utilized by < 1% of the PwT2D between 2014 and 2020; these other medications included acarbose, miglitol, colesevelam, bromocriptine IR, and pramlintide (post-index only)
Analysis of Time to First Fill of Second Drug Class Among Basal Insulin Initiators
Of the PwT2D in each cohort who did not use any GLP-1RA, SGLT-2i, bolus, or mixed insulins prior to basal insulin initiation (2015 cohort: 13,831; 2017 cohort: 9263; and cohort 2019: 7000), we assessed the number of PwT2D who added on one of these agents to their therapeutic regimen in the first year after basal initiation. In the year after basal insulin initiation, the mean time to first fill of a GLP-1RA decreased significantly from the 2015 cohort to the 2019 cohort (132.4 [SD 114.3] vs. 120.5 [SD 109.6] days, p < 0.05; Table 3). Similarly, the time to first fill of an SGLT-2i decreased significantly from the 2015 cohort to the 2019 cohort (131.5 [SD 111.4] vs. 113.3 [SD 111.0] days, p < 0.01; Table 3). Following a similar pattern, the time to first fill of a bolus insulin decreased significantly from the 2015 cohort to the 2019 cohort (113.4 [SD 106.4] vs. 93.8 [SD 104.6] days, p < 0.0001; Table 3). However, the time to first fill of mixed insulin did not differ significantly between the 2015 cohort and the 2019 cohort (140.2 [SD 107.3] vs. 139.5 [115.9] days, respectively, p > 0.05; Table 3).
Table 3
Time to add glucagon-like peptide-1 receptor agonist, sodium-glucose cotransporter-2 inhibitor, bolus insulin, or mixed insulin in the 1-year post-index follow-up period among people with type 2 diabetes with no prior use of these medications
Time from index date to first fill of second drug class (days)
Cohort 1 (2015) (N = 13,831 PwT2D)
Cohort 2 (2017) (N = 9263 PwT2D)
Cohort 3 (2019) (N = 7000 PwT2D)
GLP-1RA added
n (%)
920 (6.7)
841 (9.1)
968 (13.8)
Mean [SD]
132.4 [114.3]
135.0 [111.9]a
120.5 [109.6]b
SGLT-2i added
n (%)
806 (5.8)
548 (5.9)
598 (8.5)
Mean [SD]
131.5 [111.4]
119.2 [109.7]c
113.3 [111.0]b
Bolus insulin added
n (%)
1952 (14.1)
1215 (13.1)
915 (13.1)
Mean [SD]
113.4 [106.4]
105.9 [106.9]a
93.8 [104.6]b
Mixed insulin added
n (%)
189 (1.4)
100 (1.1)
61 (0.9)
Mean [SD]
140.2 [107.3]
131.3 [102.8]
139.5 [115.9]
The difference between cohorts for time from index date to first fill of second drug class—but not proportion of patients adding a second drug—was analyzed using t-tests
Different lowercase letters following values indicate a statistically significant difference between groups at p < 0.05, with lowercase “a” indicating a significant difference between cohorts 2 and 3; lowercase “b” indicating a significant difference between cohorts 1 and 3; and lowercase “c” indicating a significant difference between cohorts 1 and 2
GLP-1RA glucagon-like peptide-1 receptor agonist, N total number, n number in each subgroup, SGLT-2i sodium-glucose cotransporter-2 inhibitor, SD standard deviation
Characteristics of Basal Insulin First Initiators, GLP-1RA First Initiators, and Concomitant Initiators
Of the PwT2D who initiated basal insulin in our study between 2015 and 2019, 3561 (20.0%), 3933 (29.2%), and 4105 (37.8%) were basal insulin-GLP-1RA dual users in 2015, 2017, and, 2019, respectively (Table 4). Demographic and clinical characteristics were compared in the three subgroups of basal insulin-GLP-1RA dual users by cohort year. These subgroups were: those who initiated GLP-1RA before basal insulin (GLP-1RA-first initiator), those who initiated GLP-1RA after basal insulin (basal insulin-first initiator), and those who started GLP-1RA and basal insulin concurrently (concomitant initiator; ESM Fig. S3). There was an increase in the proportion of PwT2D who initiated basal insulin-GLP-1RA concomitantly between 2015 (11.3%) and 2019 (19.0%) (Table 4).
Table 4
Characteristics of glucagon-like peptide-1 receptor agonist first initiators, basal insulin first initiators, and lucagon-like peptide-1 receptor-basal insulin concomitant initiators
Variable
Cohort 1 (2015) (N = 3561 PwT2D; 20.0%)
Cohort 2 (2017) (N = 3933 PwT2D; 29.2%)
Cohort 3 (2019) (N = 4105 PwT2D; 37.8%)
Concomitant initiators (n = 402; 11.3%)
Basal insulin–first initiators (n = 1033, 29.0%)
GLP-1RA-first initiators (n = 2126; 59.7%)
Concomitant initiators (n = 600; 15.3%)
Basal insulin–first initiators (n = 1069; 27.2%)
GLP-1RA-first initiators (n = 2264; 57.6%)
Concomitant initiators (n = 782; 19.0%)
Basal insulin–first initiators (n = 1068; 26.0)
GLP-1RA-first initiators (n = 2255; 54.9%)
Age at index date, years
Mean [SD]
53.3 [10.4]
52.3 [10.0]
55.2 [9.9]a,b
51.9 [10.2]
52.2 [9.7]
54.0 [9.1]a,b
50.7 [9.3]
50.7 [9.4]
52.6 [8.6]a,b
Sex, n (%)
Female
218 (54.2)
542 (52.5)
1109 (52.2)
294 (49.0)
548 (51.3)
1158 (51.1)
379 (48.5)c
568 (53.2)
1169 (51.8)
Primary payer at index date, n (%)
Cohort 1b
Cohort 2b,c
Cohort 3b,c
Commercial
307 (76.4)
827 (80.1)
1637 (77.0)
489 (81.5)
817 (76.4)
1785 (78.8)
629 (80.4)
803 (75.2)
1787 (79.2)
Medicaid
47 (11.7)
121 (11.7)
186 (8.7)
67 (11.2)
182 (17.0)
303 (13.4)
139 (17.8)
238 (22.3)
405 (18.0)
Medicare
48 (11.9)
85 (8.2)
303 (14.3)
44 (7.3)
70 (6.5)
176 (7.8)
14 (1.8)
27 (2.5)
63 (2.8)
Macrovascular conditions, n (%)
Cerebrovascular disease
29 (7.2)c
47 (4.5)
106 (5.0)
33 (5.5)
47 (4.4)
89 (3.9)
27 (3.5)
48 (4.5)
86 (3.8)
Congestive heart failure
16 (4.0)
48 (4.6)
65 (3.1)b
29 (4.8)
43 (4.0)
89 (3.9)
34 (4.3)
46 (4.3)
80 (3.5)
Coronary arterial disease
52 (12.9)
107 (10.4)
235 (11.1)
51 (8.5)
120 (11.2)
208 (9.2)
49 (6.3)c
94 (8.8)
224 (9.9)a
Myocardial infarction
13 (3.2)
21 (2.0)
33 (1.6)a
15 (2.5)
39 (3.6)
42 (1.9)b
16 (2.0)
37 (3.5)
54 (2.4)
Peripheral arterial disease
12 (3.0)
31 (3.0)
87 (4.1)
12 (2.0)
29 (2.7)
51 (2.3)
17 (2.2)
16 (1.5)
63 (2.8)b
Unstable angina
11 (2.7)
14 (1.4)
33 (1.6)
6 (1.0)
12 (1.1)
14 (0.6)
11 (1.4)
8 (0.7)
13 (0.6)a
Microvascular conditions, n (%)
End-stage renal disease
1 (0.2)
4 (0.4)
6 (0.3)
1 (0.2)
4 (0.4)
3 (0.1)
0 (0.0)c
6 (0.6)
5 (0.2)
CKD stages 3–4
10 (2.5)
41 (4.0)
125 (5.9)a,b
16 (2.7)
38 (3.6)
104 (4.6)a
16 (2.0)c
46 (4.3)
91 (4.0)a
Peripheral neuropathy
78 (19.4)
193 (18.7)
429 (20.2)
101 (16.8)
208 (19.5)
489 (21.6)a
135 (17.3)
215 (20.1)
513 (22.7)a
Retinopathy
7 (1.7)
20 (1.9)
51 (2.4)
37 (6.2)
73 (6.8)
193 (8.5)
55 (7.0)
86 (8.1)
200 (8.9)
Other conditions, n (%)
Hyperlipidemia
257 (63.9)
682 (66.0)
1580 (74.3)a,b
411 (68.5)
717 (67.1)
1810 (79.9)a,b
530 (67.8)
711 (66.6)
1796 (79.6)a,b
Hypertension
303 (75.4)
775 (75.0)
1678 (78.9)b
434 (72.3)
784 (73.3)
1811 (80.0)a,b
563 (72.0)
764 (71.5)
1769 (78.4)a,b
Severe liver disease
5 (1.2)
10 (1.0)
16 (0.8)
2 (0.3)
8 (0.7)
16 (0.7)
9 (1.2)
11 (1.0)
31 (1.4)
Obesity
105 (26.1)
290 (28.1)
590 (27.8)
196 (32.7)
352 (32.9)
763 (33.7)
265 (33.9)
364 (34.1)
832 (36.9)
Adapted Diabetes Complications Severity Index (mean [SD])
0.9 [1.3]
0.9 [1.4]
1.0 [1.4]b
0.8 [1.2]
0.8 [1.4]
0.8 [1.2]
0.8 [1.3]
0.9 [1.3]
0.9 [1.3]
Charlson Comorbidity Index (mean [SD])
2.0 [1.6]
2.0 [1.6]
2.3 [1.8]a,b
2.2 [1.7]
2.3 [1.8]
2.5 [1.9]a,b
2.3 [1.9]
2.4 [2.0]
2.6 [2.0]a,b
HbA1c (%) pre-index, most recent (mean [SD])
9.7 [2.0]
9.7 [1.9]
9.3 [1.7]
10.3 [2.2]
10.4 [1.9]
9.3 [1.6]a,b
10.2 [2.1]
10.5 [2.2]
9.3 [1.7]a,b
People with HbA1c available during pre-index, n (%)
28 (7.0)
62 (6.0)
127 (6.0)
29 (4.8)
51 (4.8)
126 (5.6)
80 (10.2)c
69 (6.5)
213 (9.4)b
HbA1c (%), post-index, most recent (mean [SD])
8.4 [1.8]
8.3 [1.9]
8.6 [1.7]
8.1 [1.9]
8.1 [1.7]
8.3 [1.7]
8.1 [1.9]
8.7 [2.4]
8.4 [1.9]
People with HbA1c available during post-index, n (%)
26 (6.5)
69 (6.7)
106 (5.0)
36 (6.0)
69 (6.5)
123 (5.4)
69 (8.8)
82 (7.7)
205 (9.1)
Diabetes with complications, n (%)
150 (37.3)
347 (33.6)
797 (37.5)b
208 (34.7)
399 (37.3)
883 (39.0)
255 (32.6)c
416 (39.0)
923 (40.9)c
Differences between cohorts are evaluated using χ2-tests for categorical variables and t-tests for continuous variables
Different lowercase letters following values indicate a statistically significant difference between groups at p < 0.05, with lowercase “a” indicating a significant difference between concomitant initiators and GLP-1RA–first initiators; lowercase “b” indicating a significant difference between GLP-1RA-first initiators and basal insulin–first initiators; and lowercase “c” indicating a significant difference between concomitant initiators and basal insulin-first initiators
CKD Chronic kidney disease, GLP-1RA glucagon-like peptide-1 receptor agonist, HbA1c hemoglobin A1c, N total number, n number in each subgroup, SD standard deviation
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With respect to the clinical characteristics of those who initiated GLP-1RA first versus those who initiated basal insulin first, there were no differences in the proportion of PwT2D with a history of CVD, CHF, CAD, MI, PAD, or unstable angina at the index date in 2015, 2017, and 2019, with some exceptions. A lower proportion of GLP-1RA-first initiators had a history of CHF in 2015 (3.1% vs. 4.6%, p < 0.05) and a history of MI in 2017 (1.9% vs. 3.6%, p < 0.01). A higher proportion of GLP-1RA-first initiators had a history of PAD in 2019 (2.8% vs. 1.5%, p < 0.05; Table 4). Similarly, with respect to microvascular complications, there were no differences between GLP-1RA-first initiators and basal insulin first-initiators in the proportion of PwT2D with a history of ESRD, CKD stages 3–4, peripheral neuropathy, or retinopathy except that a higher proportion of GLP-1RA first initiators in 2015 had CKD stages 3–4 (5.9% vs. 4.0%, p < 0.05; Table 4). While there was a trend of an increasing proportion of PwT2D who had obesity across 2015, 2017, and 2019, there were no differences between GLP-1RA-first initiators and basal insulin-first initiators in the proportion with obesity at the index date in any individual cohort year (Table 4). About ≤ 10% of PwT2D in all subgroups had data available on HbA1c, and no meaningful comparisons of HbA1c before and after the index date were made between the subgroups.
Discussion
Over the past 7 years, pharmacological treatment guidelines for diabetes have pivoted from a glucose-centric approach to an outcome-based approach that recommends considering the presence of comorbidities when determining the choice of medication [7, 9, 18]. Our real-world study reported longitudinal trends in patterns of diabetes medication use among basal insulin-initiating PwT2D in the background of evolving guidelines. In this study, a similar proportion of PwT2D initiated basal insulin between 2015 and 2019 despite basal insulin being placed behind GLP-1RAs and SGLT-2is in contemporary treatment guidelines [2, 7, 9]. About 14–16% of basal insulin initiators continued to add bolus insulin for treatment intensification within 1 year of basal insulin initiation. Additionally, the time to initiate bolus insulin showed a decreasing trend among basal insulin initiators, suggesting that perhaps the driver of this intensification was a desire to improve glycemic control. The paucity of data on HbA1c precluded making any definitive conclusions on the rationale for this increasing addition of bolus insulin. However, the insulin intensification by PwT2D in our study does highlight the continued importance given to insulin therapy in T2D management in the real world.
Between 2014 and 2020, there was a steady increase in the use of GLP-1RAs and SGLT-2is among basal insulin initiators in this study. This finding is consistent with prior research findings [21] and occurred in parallel with the evolution in guidelines [2, 7, 9, 18]. However, this increase was not accompanied by an increase in the proportion of PwT2D with a history of ASCVD, CHF, or renal disease in our study cohorts. In fact, we observed a decrease in the proportion of PwT2D with certain types of ASCVD. Furthermore, when we compared basal insulin-GLP-1RA dual users, we did not observe greater proportions of PwT2D with a history of CVD, MI, CHF, or CKD initiating GLP-1RA before basal insulin. These findings are consistent with prior evidence that have suggested suboptimal utilization of GLP-1RAs and SGLT-2is by PwT2D with underlying ASCVD or CKD [27, 30, 31]. This is despite new clinical and society guidelines supporting use of GLP-1RA before basal insulin in PwT2D with these comorbidities [2]. Similarly, we did not observe a greater proportion of PwT2D with obesity initiating GLP-1RA before basal insulin. This contrasts with recommendations from guidelines preferring GLP-1RAs for their effect on body weight gain [7, 32, 33]. Taken together, these findings from a real-world setting suggest that guidelines which recommend GLP-1RAs and SGLT-2is over basal insulin for their cardiorenal protection alone may not be driving the increasing utilization of GLP-1RAs and SGLT-2is among basal insulin users in our study. Guidelines acknowledge the cardiorenal protection offered by GLP-1RAs and SGLT-2is and recommend their prescription prior to basal insulin use for PwT2D with cardiorenal comorbidities. However, guidelines also recommend the use of GLP-1RAs and SGLT-2is when there is a need to minimize hypoglycemia or weight gain, or if treatment intensification with GLP-1RA is required, or when HbA1c is above target after metformin use [7]. It is also likely that clinicians have become more familiar with the benefits of GLP-1RAs/SGLT-2is and are prescribing them at higher rates, although there is evidence to suggest suboptimal utilization of these medications by PwT2D with cardiorenal comorbidities [22‐25, 34]. Furthermore, there may be other patient-related factors associated with GLP-1RA/SGLT-2i prescription that were not accounted for in our study. For example, Hanna et al. [30], reported GLP-1RA/SGLT-2i prescription was higher in US counties with a population of low median age, non-rural areas, northeastern USA, and in areas with a lower proportion of Hispanic individuals (GLP-1RA only). Thus, there is a need to further investigate patient-related factors associated with the increasing utilization of GLP-1RAs and SGLT-2is among basal insulin users in the real world.
In this study, we observed that GLP-1RAs or SGLT-2is were being added earlier to treatment during the sampling period. We also observed that bolus insulin or GLP-1RAs were added earlier to the treatment regimen by PwT2Ds who initiated basal insulin. In addition, in the subset of PwT2D who were basal insulin-GLP-1RA dual users, there was an increasing trend in the number who utilized basal insulin and GLP-1RA concomitantly. Collectively, these findings might indicate a waning of therapeutic inertia to intensify treatment in the real world. More research is needed to validate this finding given that therapeutic inertia is acknowledged as a significant barrier to achieving glycemic goals among PwT2D using non-insulins as well as basal insulin [31, 35, 36]. In our study, we did not have sufficient data on HbA1c to substantiate any possible correlations on this relationship. We also observed a significant downward trend between 2015 and 2019 in the proportion of PwT2D using SU and DPP-4i before and after basal insulin initiation. In particular, the decrease in the use of SU over time is consistent with guidelines due to its association with hypoglycemia and weight gain when combined with insulin [2, 37].
Anzeige
Despite the large sample size from a diverse, representative sample of PwT2D in the USA, our study has a few limitations. The use of administrative claims is limited by potential errors in coding due to its reliance on ICD-9-CM and ICD-10-CM codes for identification of health conditions. The number of overall PwT2D in the database decreased from 2015 to 2019 due to changes in the individual payer plans contributing data to the IBM MarketScan databases. Furthermore, this study used payer claims to identify filled prescriptions, and thus we cannot confirm if PwT2D took their medications exactly as prescribed. This study was also conducted using a sample of insured PwT2D in the USA, limiting its applicability to populations outside this country and to those who are uninsured in the USA.
Conclusions
Clinical practice and professional society guidelines for the treatment of T2D have evolved over time, emphasizing the use of pharmacological therapies with cardiorenal protection among PwT2D who are at high risk of CVD and renal complications. Our study captured data that spanned the duration in which treatment guidelines have changed. Our results demonstrated that both basal insulin and its intensification with bolus insulin continue to hold a prominent place in the real-world treatment of T2D in the USA. In addition, the use of GLP-1RAs and SGLT-2is both before and after basal insulin initiation increased significantly over the study period. However, the proportion of people with comorbidity profiles most suited to benefit from the cardiorenal benefits offered by GLP-1RAs and SGLT-2is did not mirror the change in the prescription of these medications. It is likely that changes to T2D guidelines recommending the use of GLP-1RAs/SGLT-2is for their cardiorenal protection as well as their ability to minimize hypoglycemia and weight gain may be driving the increased prescription of GLP-1RAs and SGLT-2is in this population. Further research may be needed to identify factors associated with this finding.
Acknowledgements
Funding
Sponsorship for this study was funded by Eli Lilly and Company. Funding for the Rapid Service Fee was sponsored by Eli Lilly and Company.
Medical Writing and Editorial Assistance
The manuscript was drafted by David Schapiro, Rattan Juneja, Magaly Perez-Nieves, and Felicia T. Gelsey. All authors have approved the final version of the manuscript. Editorial assistance in preparation of this manuscript was provided by Rajiv Ravishankar Ulpe, an employee of Eli Lilly Services India Private Limited. Support for this assistance was funded by Eli Lilly and Company.
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Authorship
All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of.the work as a whole and have given their approval for this version to be published.
Author Contributions
All named authors contributed to the study conception and design. Material preparation and data interpretation were performed by all named authors. Data analyses were performed by Dongju Liu and Ahong Huang. All named authors read and approved the final manuscript.
Disclosures
David Schapiro, Rattan Juneja, Alexandra Meeks, Dongju Liu, Felicia T. Gelsey, and Magaly Perez-Nieves are full-time employees and/or stockholders of Eli Lilly and Company. Ahong Huang is an employee of Tigermed-BDM (Somerset, NJ, USA) and a paid consultant to Eli Lilly and Company.
Compliance with Ethics Guidelines
This was an observational, non-interventional study based on secondary use of previously collected data; patient consent and institutional review board approval were not required. De-identified data in this study complied with the US Health Insurance Portability and Accountability Act. This study was conducted in accordance with the principles outlined in the Declaration of Helsinki of 1964 and its subsequent amendments and was consistent with Good Pharmacoepidemiology Practices. The IBM® MarketScan® Research Databases is a commercial database, and the datasets were accessed through a license with IBM (IBM Corp., Armonk, NY, USA)
Data Availability
The datasets generated during and/or analyzed during the current study are not publicly available due to their proprietary nature and the associated restrictions that apply to their availability to external sources. Data may be made available through the corresponding author upon reasonable request and with permission of IBM® MarketScan® Research Databases.
Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
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Ein zu niedriger pH-Wert von Intensivpatienten im akuten Nierenversagen lässt sich durch die Gabe von Bikarbonat zwar anheben. Doch welchen Nutzen hat diese Maßnahme? Die BICARICU-Forschungsgruppe hat nach Antworten gesucht.
Wer an Vorhofflimmern leidet, muss nicht aus Angst vor proarrhythmogenen Effekten auf Kaffee verzichten. Aktuelle Daten legen vielmehr nahe, dass ein moderater regelmäßiger Konsum das Rückfallrisiko nach Kardioversion verringert.
Hörverlust ist unter Patientinnen und Patienten mit Typ-2-Diabetes weit verbreitet, wie eine Analyse ergeben hat. Nachlassendes Gehör könnte ein frühes Warnzeichen dafür sein, dass Überwachung und Therapie angepasst werden müssen.
Serielle hs-TnT-Auswertungen könnten bei Personen, die mit Immuncheckpointhemmern behandelt werden, dabei helfen, kardiovaskuläre Therapierisiken besser einzuschätzen. Dafür sprechen zumindest retrospektive Daten aus den USA.
