Utilization Rates of SGLT2 Inhibitors and GLP-1 Receptor Agonists and Their Facility-Level Variation Among Patients With Atherosclerotic Cardiovascular Disease and Type 2 Diabetes: Insights From the Department of Veterans Affairs

Over the last decade, there has been accumulating evidence demonstrating the cardiovascular benefits of various classes of glucose-lowering agents (1). These drug classes not only differ in their glycemic profile but also have a varied impact on cardiovascular disease end points. Moreover, data have suggested differential cardiovascular benefit of such cardioprotective glucose-lowering drug classes among patients with a history of ischemic heart disease (IHD), heart failure (HF), and chronic kidney disease (CKD) (2,3). Across the different classes of glucose-lowering agents, the two predominant classes that have emerged to have shown benefit toward secondary prevention of atherosclerotic cardiovascular disease (ASCVD) are the sodium–glucose cotransporter 2 inhibitors (SGLT2i) and the glucagon-like peptide 1 receptor agonists (GLP-1 RA) (4). Although the exact mechanisms behind the observed cardiovascular benefits associated with these agents remain a topic of ongoing investigation, the hypothesized mechanisms are linked to pathways of blood pressure lowering, effects on vascular tone, anti-inflammatory pathways, cardiometabolic effects, and improvement in hemodynamics as well as overall fluid balance (57). Hence, the American Diabetes Association has recommended the use of these drug classes for patients with established ASCVD as part of the glucose-lowering regimen, independent of baseline glycemic control or use of concurrent glucose-lowering medications such as metformin (8).

With the increasing global burden of ASCVD and its associated cardiovascular morbidity and mortality (9), introduction of SGLT2i and GLP-1 RA has provided another tool within the armamentarium of secondary prevention therapies for such patients. Despite encouraging evidence regarding the use of these cardioprotective glucose-lowering drug classes funneling into the cardiovascular community, a degree of therapeutic inertia in adopting these agents into routine clinical practice may be observed. Even though investigators have evaluated practice patterns and use of these drug classes among patients with HF and diabetes (10), assessment of real-world use patterns among patients with ASCVD and diabetes remains scarce. Furthermore, there is a paucity of contemporary data regarding facility-level variation in the use of these cardioprotective glucose-lowering drug classes among patients with ASCVD and diabetes. Therefore, our primary aim was to conduct a cross-sectional nationwide analysis from the Veterans Affairs (VA) health care system to evaluate facility-level variation in use of SGLT2i and GLP-1 RA among patients with established ASCVD and type 2 diabetes mellitus (T2DM).

We used data from the nationwide VA administrative and clinical data set. Our database comprised veterans aged ≥18 and a diagnosis of ASCVD. Patients with at least one primary care visit between 1 January 2020 and 31 December 2020 at 130 VA facilities and their associated community-based outpatient clinics were included in our cohort. In cases where a patient had multiple primary care visits during the study duration, the last primary care visit in the year 2020 was defined as the index visit and used to anchor our analyses. Patients with ASCVD included patients with a prior history of IHD, peripheral arterial disease (PAD), or ischemic cerebrovascular disease (ICVD). ICD-10-CM diagnosis and procedure codes and Current Procedural Terminology (CPT) codes were used to identify patients with ASCVD (Supplementary Table 1).

The Institutional Review Boards at the Baylor College of Medicine and the Michael E. DeBakey VA Medical Center approved the study protocol. We also obtained a waiver for informed consent.

Among this selected cohort of patients with ASCVD, we further used ICD-10-CM diagnosis codes to identify patients with concomitant T2DM (Supplementary Table 2). At least one inpatient ICD-10-CM diagnosis code or two outpatient diagnosis codes pertaining to T2DM were required to classify patients as having T2DM. Additionally, we used the following clinical parameters to identify patients with a diagnosis of T2DM: hemoglobin (Hb) A1c of ≥6.5%, fasting plasma glucose of ≥126 mg/dL, or random plasma glucose of ≥200 mg/dL or higher, or use of diabetes medications within 2 years prior to the index visit. Patients with limited life expectancy were excluded from our cohort. Limited life expectancy was defined as patients receiving hospice care within the preceding 12 months or those with a history of metastatic cancer diagnosed in the last 5 years (n = 32,106) (11).

We conducted descriptive analyses to assess the distribution of various patient-level, facility-level, and provider-level variables across all patients with ASCVD and concomitant T2DM. We used the χ2 test to analyze categorical variables, and continuous variables were analyzed using an unpaired, two-tailed t test. We used a two-sided P < 0.05 to define statistical significance. We assessed the use of SGLT2i across patients with an eGFR ≥30 mL/min/1.73 m2, whereas the use of GLP-1 RA was assessed across all patients (irrespective of eGFR) with T2DM and ASCVD. Subsequently, we assessed facility-level rates of SGLT2i use and GLP-1 RA use across all eligible VA facilities. Facilities with <25 patients with ASCVD and concomitant T2DM during the study period were excluded from assessment of facility-level utilization rates. Similarly, we assessed Veterans Integrated Services Networks (VISNs)-level rates of SGLT2i use and GLP-1 RA use across all VISNs.

We first assessed median (interquartile range [IQR]) utilization rates of SGLT2i and GLP-1 RA across all eligible facilities. Thereafter, we constructed regression models to quantify facility-level variation among the use of these cardioprotective glucose-lowering drug classes. We first calculated median rate ratios (MRRs), which are well-established measures of facility-level variation (11,1317). The variation in utilization rates of SGLT2i and GLP-1 RA were computed as unadjusted MRRs. Adjusted MRRs were calculated after adjusting for patient age, sex, race, BMI, HbA1c, eGFR, presence of hypertension, IHD (vs. presence of PAD or ICVD), receipt of care via physician PCP (vs. advanced practice provider), receipt of care at a teaching facility (vs. nonteaching facility), number of PCP visits, and number of cardiology visits in 12 months preceding the index PCP visit (18). Using similar methodology, we assessed variation across the 18 VISNs and computed MRRs to depict VISN-level variation in use of SGLT2i and GLP-1 RA. The resultant adjusted MRRs represented the likelihood of two random facilities or two random VISNs differing in use of SGLT2i and GLP-1 RA among similar eligible patients with ASCVD and concomitant T2DM. Interpretation of the MRR value was such that a value of 1 corresponded to no facility-level or VISN-level variation in use of these cardioprotective glucose-lowering drug classes, whereas a MRR value of 1.6 signified 60% probability of differing in use of these drugs among two hypothetically similar patients with ASCVD and T2DM at two random facilities or across two random VISNs. Based on prior published literature (16), we determined an MRR of ≥1.2 is considered a threshold for significant facility-level variation given that some degree of variation in care is to be expected. All of our analyses were conducted using SAS 9.1.3 software (SAS Institute, Cary, NC) and Stata 14 software (StataCorp, College Station, TX).

After excluding patients with limited life expectancy (n = 32,106), we identified 1,203,461 patients with ASCVD. Among these patients, 537,980 patients had ASCVD and concomitant T2DM and constituted of our primary cohort. The overall use of glucose-lowering drug classes within our cohort was as follows: insulin, 35.9%; biguanides, 47.2%; sulfonylureas, 21.6%; thiazolidinediones, 2.6%; dipeptidyl peptidase 4 inhibitors, 9.6%; GLP-1 RA, 8.0%; and SGLT2i, 11.2%. A higher use of SGLT2i was noted among patients aged <65 years compared with those aged ≥65 (17.9% vs. 10.7%, P < 0.01). Similarly, patients aged <65 years had a higher use of GLP-1 RA compared with patients ≥65 years of age (11.3% vs. 7.4%, P < 0.01).

When compared with patients with ASCVD and T2DM not receiving SGLT2i (n = 477,488), those receiving SGLT2i (n = 60,492) (Table 1) were on average younger (mean age 69.2 vs. 73.0 years) and had a higher proportion of non-Hispanic Whites (72.8% vs. 70.6%) and a lower proportion of non-Hispanic Blacks (13.0% vs. 15.6%). Overall, patients receiving SGLT2i had a higher prevalence of hypertension (92.9% vs. 90.5%), worse glycemic control (mean HbA1c 8.0% vs. 7.2%), and better renal function (mean eGFR 68.4 vs. 63.0 mL/min/1.73 m2). Overall, prevalence of IHD was higher among patients receiving SGLT2i (85.6% vs. 78.8%), while prevalence of PAD (20.6% vs. 24.6%) and ICVD (23.9% vs. 27.4%) were lower among patients receiving SGLT2i compared with those not receiving SGLT2i. A higher proportion of patients receiving SGLT2i also received other cardioprotective agents, including aspirin (92.3% vs. 91.5%), any statin (93.1% vs. 86.1%), high-intensity statin (67.3% vs. 51.4%), and ACE inhibitor or ARB therapy (73.5% vs. 56.5%). A higher proportion of patients receiving SGLT2i received care at teaching facilities (35.2% vs. 33.9%) and had a higher mean number of PCP visits (9.6 vs. 7.4), cardiology visits (1.07 vs. 0.67), and endocrinology visits (0.42 vs. 0.17) in the 12 months preceding the index PCP visit. All comparisons were observed to be statistically significant with P < 0.01.

Table 1

Baseline characteristics of patients with ASCVD and concomitant T2DM receiving SGLT2i versus those not receiving SGLT2i

. SGLT2i users . SGLT2i nonusers .  .  . n = 60,492 (11.2%) . n = 477,488 (88.8%) . P value . Age, mean (SD), years 69.2 (8.0) 73.0 (9.0) <0.01 Male sex, n (%) 59,278 (98.0) 468,945 (97.6) <0.01 Race, n (%)   <0.01  Non-Hispanic White 44,081 (72.8) 336,925 (70.6)   Non-Hispanic Black 7,836 (13.0) 74,688 (15.6)   Others 8,575 (14.2) 65,875 (13.8)  Hypertension, n (%) 56,201 (92.9) 431,971 (90.5) <0.01 BMI, mean (SD), kg/m2 32.5 (6.1) 31.1 (6.3) <0.01 HbA1c, mean (SD), % 8.0 (1.4) 7.2 (1.4) <0.01 IHD, n (%) 51,751 (85.6) 376,058 (78.8) <0.01 PAD, n (%) 12,485 (20.6) 117,547 (24.6) <0.01 ICVD, n (%) 14,455 (23.9) 130,924 (27.4) <0.01 Aspirin, n (%) 47,234 (92.3) 344,310 (91.5) <0.01 Any statin, n (%) 56,333 (93.1) 411,301 (86.1) <0.01 High-intensity statin, n (%) 40,687 (67.3) 245,451 (51.4) <0.01 ACE inhibitor or ARB therapy, n (%) 44,441 (73.5) 269,618 (56.5) <0.01 Physician PCP, n (%) 46,463 (76.8) 359,817 (75.4) <0.01 eGFR, mean (SD), mL/min/1.73 m2 68.4 (19.4) 63.0 (22.8) <0.01 Receipt of care at a teaching facility, n (%) 21,290 (35.2) 161,969 (33.9) <0.01 Visits in the 12 months prior to the index PCP visit     PCP visits (primary care), mean (SD), n 9.6 (6.8) 7.4 (6.2) <0.01  Endocrinology visits, mean (SD), n 0.42 (1.28) 0.17 (0.85) <0.01  Cardiology visits, mean (SD), n 1.07 (2.33) 0.67 (1.75) <0.01  . SGLT2i users . SGLT2i nonusers .  .  . n = 60,492 (11.2%) . n = 477,488 (88.8%) . P value . Age, mean (SD), years 69.2 (8.0) 73.0 (9.0) <0.01 Male sex, n (%) 59,278 (98.0) 468,945 (97.6) <0.01 Race, n (%)   <0.01  Non-Hispanic White 44,081 (72.8) 336,925 (70.6)   Non-Hispanic Black 7,836 (13.0) 74,688 (15.6)   Others 8,575 (14.2) 65,875 (13.8)  Hypertension, n (%) 56,201 (92.9) 431,971 (90.5) <0.01 BMI, mean (SD), kg/m2 32.5 (6.1) 31.1 (6.3) <0.01 HbA1c, mean (SD), % 8.0 (1.4) 7.2 (1.4) <0.01 IHD, n (%) 51,751 (85.6) 376,058 (78.8) <0.01 PAD, n (%) 12,485 (20.6) 117,547 (24.6) <0.01 ICVD, n (%) 14,455 (23.9) 130,924 (27.4) <0.01 Aspirin, n (%) 47,234 (92.3) 344,310 (91.5) <0.01 Any statin, n (%) 56,333 (93.1) 411,301 (86.1) <0.01 High-intensity statin, n (%) 40,687 (67.3) 245,451 (51.4) <0.01 ACE inhibitor or ARB therapy, n (%) 44,441 (73.5) 269,618 (56.5) <0.01 Physician PCP, n (%) 46,463 (76.8) 359,817 (75.4) <0.01 eGFR, mean (SD), mL/min/1.73 m2 68.4 (19.4) 63.0 (22.8) <0.01 Receipt of care at a teaching facility, n (%) 21,290 (35.2) 161,969 (33.9) <0.01 Visits in the 12 months prior to the index PCP visit     PCP visits (primary care), mean (SD), n 9.6 (6.8) 7.4 (6.2) <0.01  Endocrinology visits, mean (SD), n 0.42 (1.28) 0.17 (0.85) <0.01  Cardiology visits, mean (SD), n 1.07 (2.33) 0.67 (1.75) <0.01 

Patients with ASCVD and T2DM receiving GLP-1 RA (n = 43,118) (Table 2) were on average younger (mean age 69.6 vs. 72.8 years) and had a greater proportion of non-Hispanic Whites (74.0% vs. 70.6%) and a lower proportion of non-Hispanic Blacks (12.5% vs. 15.6%) compared with those not receiving GLP-1 RA (n = 494,862). Patients receiving GLP-1 RA had a higher mean BMI (33.9 vs. 31.0 kg/m2) and worse glycemic control (mean HbA1c 8.0% vs. 7.2%). The prevalence of hypertension (94.0% vs. 90.5%) and IHD (83.4% vs. 79.2%) were higher, while prevalence of PAD (23.7% vs. 24.0%) and ICVD (25.6% vs. 27.2%) were lower among patients receiving GLP-1 RA. A higher proportion of patients receiving GLP-1 RA also received other cardioprotective agents, including any statin (92.7% vs. 86.4%), high-intensity statin (65.5% vs. 52.1%), and ACE inhibitor or ARB therapy (69.9% vs. 57.4%). Compared with patients not receiving GLP-1 RA, those receiving this medication had a higher mean number of PCP visits (11.0 vs. 7.4), cardiology visits (1.0 vs. 0.69), and endocrinology visits (0.63 vs. 0.16) in the 12 months leading up to the index PCP visit. P < 0.01 for all comparisons.

Table 2

Baseline characteristics of patients with ASCVD and concomitant T2DM receiving GLP-1 RA versus those not receiving GLP-1 RA

. GLP-1 RA users . GLP-1 RA nonusers .  .  . n = 43,118 (8.0%) . n = 494,862 (92.0%) . P value . Age, mean (SD), years 69.6 (8.0) 72.8 (9.0) <0.01 Male sex, n (%) 41,681 (96.7) 483,542 (97.7) <0.01 Race, n (%)   <0.01  Non-Hispanic White 31,902 (74.0) 349,104 (70.6)   Non-Hispanic Black 5,374 (12.5) 77,450 (15.6)   Others 5,842 (13.5) 68,608 (13.8)  Hypertension, n (%) 40,526 (94.0) 447,646 (90.5) <0.01 BMI, mean (SD), kg/m2 33.9 (6.5) 31.0 (6.2) <0.01 HbA1c, mean (SD), % 8.0 (1.5) 7.2 (1.4) <0.01 IHD, n (%) 35,960 (83.4) 391,849 (79.2) <0.01 PAD, n (%) 10,219 (23.7) 119,813 (24.2) 0.11 ICVD, n (%) 11,018 (25.6) 134,361 (27.2) <0.01 Aspirin, n (%) 32,878 (91.9) 358,666 (91.6) 0.06 Any statin, n (%) 39,950 (92.7) 427,684 (86.4) <0.01 High-intensity statin, n (%) 28,247 (65.5) 257,891 (52.1) <0.01 ACE inhibitor or ARB therapy, n (%) 30,142 (69.9) 283,917 (57.4) <0.01 Physician PCP, n (%) 32,461 (75.3) 373,819 (75.5) 0.24 eGFR, mean (SD), mL/min/1.73 m2 62.0 (22.5) 63.8 (22.5) <0.01 Receipt of care at a teaching facility, n (%) 14,950 (34.7) 168,309 (34.0) <0.01 Visits in the 12 months prior to index PCP visit     PCP visits, mean (SD), n 11.0 (7.6) 7.4 (6.1) <0.01  Endocrinology visits, mean (SD), n 0.63 (1.60) 0.16 (0.82) <0.01  Cardiology visits, mean (SD), n 1.00 (2.26) 0.69 (1.79) <0.01  . GLP-1 RA users . GLP-1 RA nonusers .  .  . n = 43,118 (8.0%) . n = 494,862 (92.0%) . P value . Age, mean (SD), years 69.6 (8.0) 72.8 (9.0) <0.01 Male sex, n (%) 41,681 (96.7) 483,542 (97.7) <0.01 Race, n (%)   <0.01  Non-Hispanic White 31,902 (74.0) 349,104 (70.6)   Non-Hispanic Black 5,374 (12.5) 77,450 (15.6)   Others 5,842 (13.5) 68,608 (13.8)  Hypertension, n (%) 40,526 (94.0) 447,646 (90.5) <0.01 BMI, mean (SD), kg/m2 33.9 (6.5) 31.0 (6.2) <0.01 HbA1c, mean (SD), % 8.0 (1.5) 7.2 (1.4) <0.01 IHD, n (%) 35,960 (83.4) 391,849 (79.2) <0.01 PAD, n (%) 10,219 (23.7) 119,813 (24.2) 0.11 ICVD, n (%) 11,018 (25.6) 134,361 (27.2) <0.01 Aspirin, n (%) 32,878 (91.9) 358,666 (91.6) 0.06 Any statin, n (%) 39,950 (92.7) 427,684 (86.4) <0.01 High-intensity statin, n (%) 28,247 (65.5) 257,891 (52.1) <0.01 ACE inhibitor or ARB therapy, n (%) 30,142 (69.9) 283,917 (57.4) <0.01 Physician PCP, n (%) 32,461 (75.3) 373,819 (75.5) 0.24 eGFR, mean (SD), mL/min/1.73 m2 62.0 (22.5) 63.8 (22.5) <0.01 Receipt of care at a teaching facility, n (%) 14,950 (34.7) 168,309 (34.0) <0.01 Visits in the 12 months prior to index PCP visit     PCP visits, mean (SD), n 11.0 (7.6) 7.4 (6.1) <0.01  Endocrinology visits, mean (SD), n 0.63 (1.60) 0.16 (0.82) <0.01  Cardiology visits, mean (SD), n 1.00 (2.26) 0.69 (1.79) <0.01 

In our nationwide cohort of patients with ASCVD and concomitant T2DM, we demonstrated an overall low use of SGLT2i and GLP-1 RA along with a significantly high facility-level and VISN-level variation in the use of these drug classes. The utilization rates were 14.9% for SGLT2i and 10.9% for GLP-1 RA. A greater use of these drug classes was observed among younger and White patients with worse glycemic control and more endocrinology, cardiology, and PCP visits. Across nationwide VA facilities, there exists a 55% facility-level and 22% VISN-level variation in SGLT2i use despite adjustment for patient-, provider-, and facility-level covariates. Similarly, we demonstrated a 78% adjusted facility-level variation and 23% adjusted VISN-level variation in use of GLP-1 RA among similar patients with ASCVD and concomitant T2DM receiving care at two random facilities or across two random VISNs in the VA health care system.

Therapeutic inertia within the realm of CVD prevention is a well-established barrier to widespread and consistent implementation of novel pharmacotherapeutic advances (19). Similar has been the case with cardioprotective glucose-lowering drug classes such as SGLT2i and GLP-1 RA. Although data supporting the use of these drugs in reducing cardiovascular death and major adverse cardiovascular events (MACE) have been established (2023), these drug classes remain underused and infrequently prescribed by primary care clinicians and specialists (24). Therapeutic inertia can sometimes be attributed to the recent introduction of novel drug classes and lack of real-world experience, but our results provide concerning evidence regarding persistently low use of these drug classes even several years since their approval by the U.S. Food and Drug Administration.

Another plausible reason for low utilization rates of SGLT2i and GLP-1 RA may be secondary to the phenomenon of attribution given the scope of drug prescribing ranging across various medical specialties, including nephrology, primary care, cardiology, or endocrinology (25). Thereby, the initiation of these drugs may be delayed due to deferment to other medical specialties such as endocrinology subspecialists. This was demonstrated in an observational analysis by Vaduganathan et al. (24), whereby most SGLT2i were prescribed by endocrinologists and PCPs while only ∼5% were prescribed by cardiologists. Furthermore, patients themselves may not be familiar with the concept or data behind the use of these glucose-lowering drug classes to prevent future adverse cardiovascular events. Therefore, patients with optimally controlled diabetes or those receiving insulin therapy may express hesitancy in starting these cardioprotective glucose-lowering drug classes, thereby contributing to their lower use in patients likely to benefit from them.

Our results highlight high levels of nationwide facility-level and VISN-level variation in the use of SGLT2i and GLP-1 RA among similar patients with ASCVD and T2DM. We demonstrated facility-level variation to be in the magnitude of 55%–70% while VISN-level variation was ∼20% for the use of these drug classes. Moreover, this level of variability persisted despite adjusting for patient-, facility-, and provider-level variables. There are several plausible reasons behind this observation. Providers’ familiarity and clinical comfort with indications, net clinical benefit, and associated adverse effects of these pharmacotherapies may create a level of variability in prescribing patterns at an individual clinician level. Given the relative recent introduction of these cardioprotective glucose-lowering drug classes, prescribing these agents may require having a thorough understanding of the Criteria for Use (CFU) and accordingly submitting a prior-authorization drug request (PADR), if needed. CFU are developed by the U.S. Department of Veterans Affairs and outline how and for which patient population certain drugs may be used. Similar to private health insurance entities, PADR refers to the process that allows the VA pharmacy staff to review the necessity and indication for the requested nonformulary medication prior to its approval. These additional steps in prescribing these drug classes may also deter initiation as well as introduce a greater degree of variation in prescribing patterns. The variability in prescribing patterns may be further amplified by institutional culture and practice cultures of the individual providers.

Our results demonstrate that patients receiving care at teaching hospitals had significantly higher rates of SGLT2i and GLP-1 RA use. Although our analysis were adjusted for this variable, residual confounding may be plausible given the observational nature of our study and thus impact the overall variation because teaching hospitals may be more inclined to widespread dissemination and earlier adoption of guideline-concordant glucose-lowering therapy. As with other primary and secondary cardiovascular prevention therapies (26,27), the responsibility entails cultivating a patient-provider relationship, environment of mutual trust, more inclusive formulary choices available to clinicians, and clinician motivation to inculcate these therapies and practices within the therapeutic regimen to prevent future MACE. Variability in such patient-provider relationships may have downstream effects on initiation or continuation of these cardioprotective glucose-lowering drug classes, thereby introducing the level of facility-level variation as observed in our analysis. Additionally, the residual level of facility-level variation, despite adjustment of various covariates, suggests a substantial proportion of variation residing at the level of individual providers, which may be difficult to mitigate. The baseline low rates of nationwide use may also statistically lend themselves to a greater degree of overall variation.

Finally, the significantly lower degree of variation across VISNs compared with across individual facilities may again suggest individual facility-level factors that contribute to the observed variation. Although facilities within the VA health care system may have similar management structure, at the individual facility level, factors such as processes for reviewing nonformulary requests, formulary versus nonformulary medications, level of pharmacy support, nuances within set CFU, and processing of PADR may all introduce variability, as observed in our analysis.

Our analysis provides contemporary insights from the nationwide VA health care system with regards to utilization rates and facility-level variation of SGLT2i and GLP-1 RA among patients with established ASCVD and T2DM. The VA health care system represents one of the largest health care systems in the U.S., providing health care services to >9 million veterans each year (28). Among the patient population within the VA health care system, the prevalence of T2DM has been estimated to be ∼13.3% (1.2 million veterans) (29), which is similar to the nationwide prevalence noted in the non-VA health care sectors (∼10.5%) (30). On the basis of prior analyses evaluating patients with T2DM, the prevalence of ASCVD has been observed to be 21–30% both within the VA and the non-VA health care systems (13,31,32), thereby suggesting a similar level of ASCVD burden within patients with T2DM, irrespective of the VA or non-VA health care system. Our findings of overall low utilization rates (∼10–15%) of SGLT2i and GLP-1 RA are similar to what has been reported by prior investigator groups. Using the Anthem pharmacy and medical claims database, Nelson et al. (33) reported the use of SGLT2i or GLP-1 RA to be 9.9%, while Arnold et al. (34) reported the use of SGLT2i and GLP-1 RA to be 9.0% and 7.9%, respectively, based on their U.S.-based registry. The subtle differences in utilization rates (SGLT2i: 14.9% in our analysis compared with ∼9% in prior analyses; GLP-1 RA: 10.9% in our analysis compared with ∼8% in the analysis by Arnold et al. [34]) may be secondary to the studied cohort years (the 2020 cohort year in our analysis compared with the 2018 cohort year in prior analyses). Although different patient samples, the slight increase between 2018 (prior analyses) and 2020 (our analysis) cohorts may represent an overall slow but steady dissemination as well as uptake of these therapies within patients with T2DM and ASCVD. Finally, it is noteworthy that other secondary prevention measures among this patient population have also experienced, albeit to a lesser extent, suboptimal utilization rates and facility-level variation. For example, among patients with T2DM and ASCVD, an overall utilization rate and facility-level variation for high-intensity statin therapy has been observed to be ∼ 60% and ∼30%, respectively (13), while optimal blood pressure control has been observed in ∼50% of patients with a facility-level variation of ∼25% (35).

Our study has several limitations. The inherent nature of the clinical data sets meant we were unable to reliably assess and adjust for additional variables such as doses of respective cardioprotective glucose-lowering drug classes. Optimal control of other cardiometabolic risk factors, such as hypertension, could not be ascertained from our data set, which may influence clinicians to focus their efforts in optimizing these risk factors prior to introducing SGLT2i or GLP-1 RA for cardiovascular prevention. Similarly, we were unable to assess for overall pill burden for patients included in our cohort, which may also influence the decision to initiate therapy. Intolerances and adverse effects to these therapies were not assessed in our cohort, which may have also contributed to the observed utilization rates and facility-level variation. Given the evolving nature of this field and changes in the American Diabetes Association guidance between 2020 and 2021 with regards to eligibility of patients with optimally controlled HbA1c on metformin therapy, it is plausible that patients within our 2020 cohort may not have met the standard of care use criteria, thereby decreasing utilization rates. Given the nature of our data set, we were unable to account for patient refusal resulting in lower use of these therapies. Finally, owing to our cohort of predominately elderly White men, the generalizability of our findings to other racial minorities and the nonveteran population may be limited.

Our findings serve as a benchmark for future research, guideline work, and implementation efforts in the area of cardioprotective glucose-lowering drug classes for secondary prevention of ASCVD. It is noteworthy that compared with recent outcomes trials whereby patients with high-risk features for ASCVD were included, our analysis consisted of all patients with T2DM and established ASCVD, thereby signifying a higher-risk population that may derive higher benefit compared with patients with diabetes and high-risk features. Research initiatives, especially qualitative investigations, are needed to clearly ascertain reasons behind such variation in practice patterns. As clinical data demonstrating cardiovascular benefit with the use of glucose-lowering drug classes (36) continue to accumulate, a paradigm shift is needed to redirect clinical focus on approaching these drugs not only as glucose-lowering agents but also as therapies for cardiovascular disease prevention. A paradigm shift in the hierarchy of glucose-lowering drug classes is needed. In addition to focusing on glucose-lowering properties, first-line therapies for patients with T2DM and established ASCVD should include drug classes such as GLP-1 RA and SGLT2i which supplement cardiovascular prevention in addition to their antiglycemic effects. Lastly, policy driven initiatives on a national level as well as focused efforts by guideline writing committees are necessary for further promoting these drug classes as therapies for cardiovascular prevention thereby increasing its utilization and narrowing the current level of facility-level variation.

Funding. C.M.B. has received grant/research support from National Institutes of Health, American Heart Association, and American Diabetes Association. This work was supported by a Department of Veterans Affairs (VA) Health Services Research & Development Service Investigator Initiated Grants (IIR 16-072, IIR 19-069), the Houston VA Health Services Research & Development Center for Innovations grant (CIN13-413), and the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (DK110341). Support for VA/Centers for Medicare & Medicaid Services data was provided by the Department of Veterans Affairs, VA Health Services Research and Development Service, VA Information Resource Center (Project Numbers SDR 02-237 and 98-004).

Duality of Interest. S.S.V. discloses honorarium from the American College of Cardiology (associate editor for Innovations, acc.org). C.J.L. has served as a consultant and promotional speaker for AstraZeneca of their SGLT2i. C.M.B. discloses grant/research support (all significant, paid to institution, not individual) from Akcea, Amgen, Esperion, Novartis, Regeneron, and Sanofi-Synthelabo, and as a consultant for Abbott Diagnostics, Akcea, Amarin, Amgen, Arrowhead, AstraZeneca, Corvidia, Denka Seiken, Esperion, Intercept, Matinas BioPharma, Merck, Novartis, Regeneron, and Sanofi-Synthelabo. Y.B. has received a research grant from AstraZeneca. S.N. has received honorarium from Bayer, Boehringer Ingelheim, Tricida, and Reata Pharmaceuticals. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. D.M. and S.S.V. contributed to conception, design, and interpretation of data, drafting and revising of the manuscript, and final approval of the manuscript submitted. D.J.R. and L.C. contributed to analysis and interpretation of data and final approval of the manuscript submitted. M.T.L., M.A.R., J.M.A., E.M.V., M.E.M., K.R.d.E.S., S.D.N., C.J.L., Y.B., C.M.B., and L.A.P. contributed to interpretation of data, revising of manuscript, and final approval of the manuscript. S.S.V. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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