Reviews
19 April 2024

Newer Pharmacologic Treatments in Adults With Type 2 Diabetes: A Systematic Review and Network Meta-analysis for the American College of PhysiciansFREE

Publication: Annals of Internal Medicine
Volume 177, Number 5

Abstract

Background:

Newer diabetes medications may have beneficial effects on mortality, cardiovascular outcomes, and renal outcomes.

Purpose:

To evaluate the effectiveness, comparative effectiveness, and harms of sodium–glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP1) agonists, dipeptidyl peptidase-4 (DPP4) inhibitors, and long-acting insulins as monotherapy or combination therapy in adults with type 2 diabetes mellitus (T2DM).

Data Sources:

MEDLINE and EMBASE for randomized controlled trials (RCTs) published from 2010 through January 2023.

Study Selection:

RCTs lasting at least 52 weeks that included at least 500 adults with T2DM receiving eligible medications and reported any outcomes of interest.

Data Extraction:

Data were abstracted by 1 reviewer and verified by a second. Independent, dual assessments of risk of bias and certainty of evidence (CoE) were done.

Data Synthesis:

A total of 130 publications from 84 RCTs were identified. CoE was appraised using GRADE (Grading of Recommendations Assessment, Development and Evaluation) criteria for direct, indirect, and network meta-analysis (NMA); the highest CoE was reported. Compared with usual care, SGLT2 inhibitors and GLP1 agonists reduce all-cause mortality (high CoE) and major adverse cardiovascular events (MACE) (moderate to high CoE), SGLT2 inhibitors reduce progression of chronic kidney disease (CKD) and heart failure hospitalizations and GLP1 agonists reduce stroke (high CoE), and SGLT2 inhibitors reduce serious adverse events and severe hypoglycemia (high CoE). The threshold for minimally important differences, which was predefined with the American College of Physicians Clinical Guidelines Committee, was not met for these outcomes. Compared with usual care, insulin, tirzepatide, and DPP4 inhibitors do not reduce all-cause mortality (low to high CoE). Compared with insulin, SGLT2 inhibitors and GLP1 agonists reduce all-cause mortality (low to moderate CoE). Compared with DPP4 inhibitors, GLP1 agonists reduce all-cause mortality (moderate CoE). Compared with DPP4 inhibitors and sulfonylurea (SU), SGLT2 inhibitors reduce MACE (moderate to high CoE). Compared with SU and insulin, SGLT2 inhibitors and GLP1 agonists reduce severe hypoglycemia (low to high CoE).

Limitations:

Infrequent direct comparisons between drugs of interest; sparse data for NMA on most outcomes; possible incoherence due to differences in baseline patient characteristics and usual care; insufficient data on predefined subgroups, including demographic subgroups, patients with prior cardiovascular disease, and treatment-naive persons.

Conclusion:

In adults with T2DM, SGLT2 inhibitors and GLP1 agonists (but not DPP4 inhibitors, insulin, or tirzepatide) reduce all-cause mortality and MACE compared with usual care. SGLT2 inhibitors reduce CKD progression and heart failure hospitalization and GLP1 agonists reduce stroke compared with usual care. Serious adverse events and severe hypoglycemia are less frequent with SGLT2 inhibitors and GLP1 agonists than with insulin or SU.

Primary Funding Source:

American College of Physicians. (PROSPERO: CRD42022322129)
Type 2 diabetes mellitus (T2DM) has historically been treated with injectable insulin and oral agents, including metformin, sulfonylurea (SU), and thiazolidinediones, with the goal of reaching a target hemoglobin A1c (HbA1c) level by reducing blood glucose (1). It was believed that achieving HbA1c targets would decrease risks for long-term outcomes, such as cardiovascular disease (CVD), the most common cause of death for persons with diabetes. Newer oral and injectable medications, such as dipeptidyl peptidase-4 (DPP4) inhibitors, sodium–glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP1) agonists, and dual GLP1 and glucose-dependent insulinotropic polypeptide (GIP) agonists, are now available. In addition to improving glycemic control, these newer diabetes medications may have beneficial effects on mortality, cardiovascular outcomes, and renal outcomes. Hence, pharmacologic management decisions may now explicitly include not only glycemic control but also considerations of prevention of CVD, congestive heart failure (CHF), and chronic kidney disease (CKD) (2).
We conducted a systematic review and network meta-analysis (NMA) of randomized controlled trials (RCTs) that included adults with T2DM to evaluate the effectiveness, comparative effectiveness, and harms of SGLT2 inhibitors, GLP1 agonists, DPP4 inhibitors, and long-acting insulins used either as monotherapy or in combination with other antidiabetic medications. We also conducted a contextual review to assess patient values and preferences for antidiabetic medications. This review was commissioned by the American College of Physicians (ACP) to inform its clinical guideline on treatment of T2DM by the ACP Clinical Guidelines Committee (CGC) (3). In a separate manuscript, we review the cost-effectiveness of these medications.

Methods

Data Sources and Searches

Our protocol was registered in PROSPERO on 30 March 2022 (CRD42022322129). Key question 1 (KQ1) addressed effectiveness, comparative effectiveness, and harms of SGLT2 inhibitors, GLP1 agonists, DPP4 inhibitors, and long-acting insulins used as monotherapy or combination therapy in adults with T2DM. Key question 2 (KQ2) addressed patients' values and preferences regarding antidiabetic medications for T2DM management. For KQ1, we searched MEDLINE and EMBASE for RCTs published from 2010 to 31 January 2023 (Appendix A of Supplement 1). We supplemented this search with reference lists of systematic reviews identified through the Cochrane Library and Agency for Healthcare Research and Quality databases. We searched U.S. Food and Drug Administration (FDA) websites on 13 January 2023 to identify drug- and class-specific adverse effects that were listed as occurring in at least 5% of participants and being more common than with placebo or were listed as contraindications or warnings (Appendixes A and B of Supplement 1). The threshold of 5% was chosen based on reporting threshold information for adverse reactions provided in FDA documents. For KQ2, we searched MEDLINE and EMBASE (Appendix A of Supplement 1) from 2010 to 31 January 2023 to identify non–industry-sponsored systematic reviews with a U.S. perspective related to values and preferences regarding antidiabetic medications among patients with T2DM.

Study Selection

Trials were eligible if they enrolled adult participants aged 18 years or older with T2DM; evaluated SGLT2 inhibitors, GLP1 agonists, DPP4 inhibitors, or long-acting insulins; had treatment duration of at least 52 weeks; enrolled at least 500 participants; and reported any outcome of interest (Tables 1 to 5 of Supplement 2). For medications approved after protocol development (for example, GLP1/GIP agonists), RCTs lasting 6 months or longer were eligible for inclusion. Thiazolidinediones and SU were originally listed as interventions in the protocol but are reported only as comparators. Titles and abstracts were screened by 2 reviewers for potential relevance to the KQs. Articles included by either reviewer underwent full-text screening. At the full-text screening stage, 2 independent reviewers agreed on the final decision about inclusion or exclusion. In cases where consensus between the 2 reviewers could not be reached, a third reviewer was included.

Data Abstraction and Quality Assessment

Study, population, and intervention characteristics and outcomes were abstracted into a customized DistillerSR database by one reviewer and verified by a second reviewer. Disagreements were resolved by consensus or by obtaining a third reviewer's opinion when consensus could not be reached. We categorized participants according to antidiabetic drug use before study enrollment (metformin, prior standard of care, or drug-naive). We abstracted baseline age, sex, ethnicity, body mass index (BMI), T2DM duration, HbA1c level, CVD status, and CKD status (Tables 1 and 2 of Supplement 2).
Two reviewers independently assessed risk of bias (RoB) for critical and important outcomes of included studies using the Cochrane RoB 2 tool (4). The 2 reviewers assessed each of 5 domains as having low RoB, some concerns, or high RoB, which were then summarized as an overall RoB. When consensus could not be reached, a third reviewer was included (Appendix C of Supplement 1).

Data Synthesis and Analysis

Eligible articles were summarized by drug class, critical outcomes (all-cause mortality, major adverse cardiovascular events [MACE], fatal and nonfatal myocardial infarction [MI], fatal and nonfatal stroke, and CKD), and important outcomes (hospitalization for CHF, severe hypoglycemia, weight loss of ≥10%, and serious adverse events) as determined by the ACP CGC (Appendix D of Supplement 1). MACE was defined by the trial authors and was not consistently defined across all studies. We accepted author definitions of MACE and serious adverse events. The specific adverse events classified as serious were variably and not always fully reported but typically included mortality and life-threatening events. Analysis of events was done at the participant level (that is, repeated events were ignored). We analyzed outcomes at the longest follow-up except for HbA1c level and changes in body weight, for which 1-year outcomes were prioritized. Predefined clinically important absolute effect thresholds, determined in agreement with the ACP CGC, were used to derive GRADE (Grading of Recommendations Assessment, Development and Evaluation) certainty of evidence (CoE). Because data on weight loss of at least 10% were sparse, we also assessed mean absolute weight loss as a continuous outcome.
To summarize direct comparisons between drug classes, we performed traditional pairwise meta-analyses for all intervention pathways with direct comparisons. If a trial included multiple groups assigned to the same class (including different doses of the same drug), the results were pooled based on the size of the 2 groups and the outcome data (number for event outcomes and mean and SD for continuous outcomes). The summary effect size was the risk ratio (RR) for event outcomes or mean differences (MDs) for continuous outcomes (change in HbA1c level or weight) with a 95% CI. Heterogeneity within each pairwise comparison was tested using the Cochran Q statistic and quantified with the I 2 statistic, which was used to assess heterogeneity (for example, possible individual drug effect within class or variation by baseline characteristics or comparator). To facilitate the NMA, we combined data regardless of the number of RCTs for a given comparison (some comparisons included <4 RCTs) and calculated both fixed and random effects.
We constructed a network graph for each outcome (MACE, overall mortality, total MI, total stroke, serious adverse events, CHF hospitalization, CKD stage 3+, severe hypoglycemia, HbA1c level, and change in weight) in which nodes were drugs grouped by class regardless of the dose and the method of administration. Most trials used 1 of 3 designs: comparison with placebo, an active comparator, or both. Some trials included monotherapy with a drug class of interest versus combination therapy with multiple drugs; however, these comparisons were not included in the network analysis (that is, all groups with multiclass combination therapy were dropped). Interventions that were not part of the connected graph were also dropped from the analysis. Only 1 placebo-controlled trial required all patients to be treatment-naive at entry; another 17 permitted treatment-naive participants to be included. Most trials allowed for various baseline medications (such as metformin or SU) but generally did not require their use and did not stratify participants by baseline medication. Participants were allowed add-on therapy during the trial at the discretion of the treating clinician. We therefore combined these concomitant diabetes treatment groups into the category of “usual care.” The trials were analyzed together, but heterogeneity due to this factor was assessed based on pairwise meta-analyses. We used the resulting network for the NMA (Table; Appendix E of Supplement 1).
Table. Summary of Findings
ComparisonTrials, k; Participants, n RR (95% CI)
Absolute Risk Difference per 1000 Persons (95% CI)*
Certainty of Evidence†
All-Cause MortalityMACEMIStrokeCHF HospitalizationCKD Stage 3+Serious Adverse EventSevere Hypoglycemia
Compared with placebo or usual care        
 DPP4 inhibitorsk = 10; n = 47 577
RR, 1.01 (0.94 to 1.08)‡
1 more (4 fewer to 5 more)
⊕⊕⊕
k = 5; n = 44 595
RR, 1.0 (0.94 to 1.06)‡
0 fewer (6 fewer to 6 more)
⊕⊕⊕
k = 2; n = 31 015
RR, 0.95 (0.85 to 1.06)‡
2 fewer (6 fewer to 2 more)
⊕⊕⊕
k = 1; n = 14 523
RR, 0.97 (0.79 to 1.19)‡
1 fewer (5 fewer to 5 more)
⊕⊕⊕
k = 3; n = 37 994
RR, 1.06 (0.96 to 1.17)‡
2 more (1 fewer to 6 more)
⊕⊕⊕
k = 2; n = 23 477
RR, 1.07 (0.95 to 1.21)‡
3 more (2 fewer to 9 more)
⊕⊕⊕
k = 9; n = 26 256
RR, 0.96 (0.92 to 1.01)‡
8 fewer (15 fewer to 2 more)
⊕⊕⊕
k = 9; n = 47 160
RR, 1.14 (1.00 to 1.30)‡
2 more (0 fewer to 5 more)
⊕⊕⊕
 GLP1 agonistsk = 8; n = 48 481
RR, 0.88 (0.83 to 0.94)‡
10 fewer (14 fewer to 5 fewer)
⊕⊕⊕
k = 6; n = 46 541
RR, 0.91 (0.87 to 0.96)‡
11 fewer (16 fewer to 5 fewer)
⊕⊕⊕
k = 5; n = 43 244
RR, 0.96 (0.89 to 1.04)‡
3 fewer (7 fewer to 3 more)
⊕⊕⊕
k = 5; n = 43 244
RR, 0.86 (0.77 to 0.95)‡
5 fewer (7 fewer to 2 fewer)
⊕⊕⊕
k = 4; n = 33 904
RR, 0.95 (0.85 to 1.06)‡
2 fewer (5 fewer to 2 more)
⊕⊕⊕
GLP1 agonists not in networkk = 8; n = 36 188
RR, 0.98 (0.95 to 1.01)‡
5 fewer (13 fewer to 3 more)
⊕⊕⊕
k = 8; n = 42 250
RR, 1.02 (0.92 to 1.15)‡
0 fewer (2 fewer to 3 more)
⊕⊕◯§
 SGLT2 inhibitorsk = 14; n = 47 478
RR, 0.86 (0.80 to 0.93)‡
9 fewer (13 fewer to 5 fewer)
⊕⊕⊕
k = 3; n = 19 659
RR, 0.90 (0.83 to 0.98)‡
12 fewer (21 fewer to 2 fewer)
⊕⊕◯§
k = 2; n = 15 266
RR, 0.97 (0.85 to 1.12)‡
2 fewer (8 fewer to 7 more)
⊕⊕⊕
k = 2; n = 15 266
RR, 1.12 (0.93 to 1.34)‡
4 more (2 fewer to 10 more)
⊕⊕⊕
k = 2; n = 11 421
RR, 0.64 (0.54 to 0.77)‡
19 fewer (24 fewer to 12 fewer)
⊕⊕⊕
k = 4; n = 32 713
RR, 0.66 (0.58 to 0.75)‡
12 fewer (14 fewer to 9 fewer)
⊕⊕⊕
k = 14; n = 46 096
RR, 0.93 (0.90 to 0.95)‡
23 fewer (33 fewer to 16 fewer)
⊕⊕⊕
k = 9; n = 39 902
RR, 0.85 (0.74 to 0.97)‡
3 fewer (5 fewer to 1 fewer)
⊕⊕⊕
 TirzepatideNMA
RR, 0.98 (0.56 to 1.73)
⊕◯◯‖¶**
◯◯◯††Tirzepatide not in networkTirzepatide not in networkTirzepatide not in networkTirzepatide not in networkk = 3; n = 1069
RR, 0.79 (0.51 to 1.22)‡
17 fewer (39 fewer to 17 more)
⊕⊕⊕
k = 3; n = 1373
RR, 1.32 (0.78 to 2.22)‡
15 more (10 fewer to 55 more)
⊕⊕◯¶
 Basal insulinNMA
RR, 1.23 (0.89 to 1.70)
⊕◯◯‖¶**
NMA
RR, 1.10 (0.83 to 1.46)
⊕◯◯‖¶**
Basal insulin not in networkBasal insulin not in networkNMA
RR, 1.01 (0.64 to 1.60)
⊕◯◯‖¶**
Basal insulin not in networkNMA
RR, 1.17 (0.99 to 1.39)
⊕⊕◯‖**
NMA
RR, 3.81 (2.70 to 5.38)
◯◯◯‖‡‡**
         
DPP4 inhibitors (head-to-head)        
 DPP4 inhibitors vs. GLP1 agonistsk = 4; n = 4612
RR, 1.64 (1.05 to 2.56)‡
7 more (1 more to 14 more)
⊕⊕◯§§
k = 1; n = 2515
RR, 1.42 (0.99 to 2.04)‡
16 more (0 fewer to 40 more)
⊕⊕⊕
NMA
RR, 0.98 (0.86 to 1.13)
⊕⊕◯‖**
NMA
RR, 1.14 (0.90 to 1.43)
⊕⊕◯‖**
k = 1; n = 2515
RR, 2.12 (1.13 to 3.98)‡
13 more (1 more to 33 more)
⊕⊕◯¶
GLP1 agonists not in networkk = 5; n = 5168
RR, 1.07 (0.89 to 1.29)‡
6 more (10 fewer to 26 more)
⊕⊕⊕
k = 4; n = 6724
RR, 1.25 (0.91 to 1.73)‡
7 more (2 fewer to 20 more)
⊕⊕⊕
 DPP4 inhibitors vs. basal insulink = 1; n = 2531
RR, 0.97 (0.64 to 1.48)‡
1 fewer (12 fewer to 16 more)
⊕⊕⊕
k = 1; n = 2521
RR, 1.06 (0.76 to 1.47)‡
3 more (12 fewer to 24 more)
⊕⊕⊕
Basal insulin not in networkBasal insulin not in networkk = 1; n = 2521
RR, 1.15 (0.68 to 1.93)‡
3 more (7 fewer to 19 more)
⊕⊕⊕
Basal insulin not in networkNMA
RR, 0.82 (0.68 to 0.97)
⊕◯◯§§‖**
k = 1; n = 2531
RR, 0.56 (0.25 to 1.26)‡
6 fewer (10 fewer to 3 more)
⊕⊕◯¶
 DPP4 inhibitors vs. SGLT2 inhibitorsk = 5; n = 3878
RR, 1.20 (0.32 to 4.48)‡
0 fewer (2 fewer to 8 more)
⊕◯◯‡‡
NMA
RR, 1.13 (1.03 to 1.25)
⊕⊕◯‖**
NMA
RR, 0.98 (0.82 to 1.17)
⊕⊕◯‖**
NMA
RR, 0.87 (0.66 to 1.15)
⊕⊕◯‖**
NMA
RR, 1.68 (1.36 to 2.07)
⊕◯◯‖¶**
NMA
RR, 1.62 (1.36 to 1.94)
⊕⊕◯‖**
k = 4; n = 3455
RR, 0.99 (0.75 to 1.31)‡
1 fewer (14 fewer to 17 more)
⊕⊕⊕
k = 4; n = 3105
RR, 0.78 (0.10 to 5.99)‡
0 fewer (2 fewer to 10 more)
⊕◯◯‡‡
 DPP4 inhibitors vs. sulfonylureak = 10; n = 22 352
RR, 0.90 (0.79 to 1.03)‡
4 fewer (8 fewer to 1 more)
⊕⊕⊕
k = 4; n = 12 715
RR, 0.96 (0.85 to 1.09)‡
3 fewer (12 fewer to 7 more)
⊕⊕⊕
k = 1; n = 6033
RR, 1.03 (0.83 to 1.28)‡
1 more (8 fewer to 14 more)
⊕⊕⊕
k = 1; n = 6033
RR, 0.86 (0.67 to 1.12)‡
6 fewer (13 fewer to 5 more)
⊕⊕⊕
k = 2; n = 8544
RR, 1.16 (0.91 to 1.47)‡
5 more (3 fewer to 13 more)
⊕⊕⊕
Sulfonylurea not in networkk = 10; n = 20 439
RR, 0.95 (0.91 to 0.99)‡
12 fewer (21 fewer to 2 fewer)
⊕⊕◯¶
k = 8; n = 18 081
RR, 0.14 (0.11 to 0.19)‡
44 fewer (46 fewer to 42 fewer)
⊕⊕⊕
 DPP4 inhibitors vs. tirzepatideNMA
RR, 1.04 (0.59 to 1.83)
⊕◯◯‖¶**
NMA
RR, 1.21 (0.76 to 1.92)
⊕◯◯‖¶**
Tirzepatide not in networkTirzepatide not in networkTirzepatide not in networkTirzepatide not in networkNMA
RR, 0.99 (0.80 to 1.22)
⊕⊕◯‖**
NMA
RR, 1.03 (0.68 to 1.57)
◯◯◯‖‡‡**
         
GLP1 agonists (head-to-head)        
 GLP1 agonists vs. DPP4 inhibitorsk = 4; n = 4612
RR, 0.61 (0.39 to 0.95)‡
9 fewer (14 fewer to 1 fewer)
⊕⊕◯§§
k = 1; n = 2515
RR, 0.70 (0.49 to 1.01)‡
16 fewer (28 fewer to 1 more)
⊕⊕⊕
NMA
RR, 1.02 (0.88 to 1.16)
⊕⊕◯‖**
NMA
RR, 0.88 (0.70 to 1.11)
⊕⊕◯‖**
k = 1; n = 2515
RR, 0.47 (0.25 to 0.88)‡
13 fewer (18 fewer to 3 fewer)
⊕⊕◯¶
GLP1 agonists not in networkk = 5; n = 5168
RR, 0.94 (0.78 to 1.13)‡
5 fewer (20 fewer to 12 more)
⊕⊕⊕
k = 4; n = 6724
RR, 0.81 (0.59 to 1.11)‡
4 fewer (9 fewer to 2 more)
⊕⊕⊕
 GLP1 agonists vs. basal insulink = 4; n = 4792
RR, 0.62 (0.41 to 0.93)‡
10 fewer (16 fewer to 2 fewer)
⊕⊕◯¶
k = 1; n = 2508
RR, 0.74 (0.52 to 1.07)‡
13 fewer (25 fewer to 4 more)
⊕⊕⊕
Basal insulin not in networkBasal insulin not in networkk = 1; n = 2508
RR, 0.54 (0.28 to 1.03)‡
10 fewer (15 fewer to 1 more)
⊕⊕◯¶
GLP1 agonists and basal insulin not in networkk = 5; n = 3579
RR, 0.86 (0.72 to 1.04)
16 fewer (33 fewer to 5 more)
⊕◯◯§¶
k = 6; n = 6104
RR, 0.23 (0.16 to 0.33)
38 fewer (42 fewer to 33 fewer)
⊕⊕◯¶‖‖
 GLP1 agonists vs. SGLT2 inhibitorsNMA
RR, 1.02 (0.93 to 1.12)
⊕⊕◯‖**
NMA
RR, 1.01 (0.92 to 1.11)
⊕⊕◯‖**
NMA
RR, 0.99 (0.85 to 1.16)
⊕⊕◯‖**
NMA
RR, 0.77 (0.62 to 0.95)
⊕⊕◯‖**
NMA
RR, 1.44 (1.16 to 1.78)
⊕⊕◯‖**
GLP1 agonists not in networkk = 2; n = 1249
RR, 0.93 (0.60 to 1.45)‡
4 fewer (25 fewer to 28 more)
⊕⊕◯¶
k = 3; n = 2068
RR, 1.00 (0.47 to 2.14)‡
0 fewer (7 fewer to 14 more)
⊕⊕◯¶
 GLP1 agonists vs. sulfonylureak = 3; n = 4281
RR, 0.67 (0.44 to 1.04)‡
8 fewer (13 fewer to 10 more)
⊕⊕⊕
k = 1; n = 2498
RR, 0.81 (0.56 to 1.18)‡
9 fewer (21 fewer to 9 more)
⊕⊕⊕
◯◯◯††◯◯◯††k = 1; n = 2498
RR, 0.47 (0.25 to 0.87)‡
13 fewer (18 fewer to 3 fewer)
⊕⊕◯¶
GLP1 agonists and sulfonylurea not in networkk = 2; n = 1765
RR, 1.08 (0.83 to 1.41)‡
9 more (20 fewer to 48 more)
⊕⊕◯¶
k = 3; n = 4281
RR, 0.49 (0.26 to 0.92)‡
7 fewer (10 fewer to 1 fewer)
⊕⊕◯¶
 GLP1 agonists vs. tirzepatidek = 1; n = 1878
RR, 0.25 (0.03 to 1.92)‡
6 fewer (8 fewer to 8 more)
⊕◯◯¶¶
NMA
RR, 1.08 (0.68 to 1.73)
⊕◯◯‖¶**
Tirzepatide not in networkTirzepatide not in networkTirzepatide not in networkGLP1 agonists and tirzepatide not in networkk = 2; n = 2143
RR, 0.57 (0.34 to 0.96)‡
24 fewer (37 fewer to 2 fewer)
⊕⊕◯§
k = 2; n = 2143
RR, 0.50 (0.11 to 2.23)‡
4 fewer (7 fewer to 9 more)
⊕◯◯‡‡
         
SGLT2 inhibitors (head-to-head)        
 SGLT2 inhibitors vs. DPP4 inhibitorsk = 5; n = 3878
RR, 0.91 (0.3 to 2.78)‡
0 fewer (1 fewer to 4 more)
⊕◯◯‡‡
NMA
RR, 0.88 (0.80 to 0.97)
⊕⊕◯‖**
NMA
RR, 1.02 (0.85 to 1.22)
⊕⊕◯‖**
NMA
RR, 1.15 (0.87 to 1.52)
⊕⊕◯‖**
NMA
RR, 0.60 (0.48 to 0.74)
⊕◯◯‖¶**
NMA
RR, 0.62 (0.52 to 0.74)
⊕⊕◯‖**
k = 5; n = 3878
RR, 1.01 (0.76 to 1.32)‡
1 more (13 fewer to 17 more)
⊕⊕⊕
k = 4; n = 3105
RR, 1.42 (0.26 to 7.59)‡
0 fewer (1 fewer to 6 more)
⊕◯◯‡‡
 SGLT2 inhibitors vs. GLP1 agonistsNMA
RR, 0.98 (0.89 to 1.08)
⊕⊕◯‖**
NMA
RR, 0.99 (0.90 to 1.09)
⊕⊕◯‖**
NMA
RR, 1.01 (0.86 to 1.18)
⊕⊕◯‖**
NMA
RR, 1.30 (1.05 to 1.61)
⊕⊕◯‖**
NMA
RR, 0.69 (0.56 to 0.86)
⊕⊕◯‖**
GLP1 agonists not in networkk = 2; n = 1249
RR, 1.08 (0.83 to 1.41)‡
5 more (10 fewer to 24 more)
⊕⊕◯¶
k = 3; n = 2068
RR, 1.00 (0.47 to 2.14)‡
0 fewer (7 fewer to 14 more)
⊕⊕◯¶
 SGLT2 inhibitors vs. basal insulinNMA
RR, 0.70 (0.51 to 0.98)
⊕◯◯‖¶**
NMA
RR, 0.81 (0.61 to 1.09)
⊕◯◯‖¶**
Basal insulin not in networkBasal insulin not in networkNMA
RR, 0.64 (0.39 to 1.04)
⊕◯◯‖¶**
Basal insulin not in networkNMA
RR, 0.79 (0.67 to 0.94)
⊕◯◯§§‖**
NMA
RR, 0.22 (0.15 to 0.32)
⊕◯◯§§‖**
 SGLT2 inhibitors vs. sulfonylureak = 4; n = 5134
RR, 1.09 (0.55 to 2.20)‡
1 more (3 fewer to 9 more)
⊕⊕◯¶
k = 2; n = 2995
RR, 0.57 (0.36 to 0.91)‡
14 fewer (21 fewer to 3 fewer)
⊕⊕⊕
◯◯◯††◯◯◯††k = 1; n = 625
RR, 0.33 (0.01 to 8.13)‡
2 fewer (3 fewer to 23 more)
⊕◯◯‡‡
Sulfonylurea not in networkk = 5; n = 5560
RR, 0.99 (0.87 to 1.14)‡
0 fewer (20 fewer to 21 more)
⊕⊕⊕
k = 5; n = 5744
RR, 0.10 (0.07 to 0.15)‡
83 fewer (86 fewer to 79 fewer)
⊕⊕⊕‖‖
         
Remaining comparisons        
 Sulfonylurea vs. tirzepatideNMA
RR, 1.14 (0.64 to 2.02)
◯◯◯‖‡‡**
NMA
RR, 1.30 (0.81 to 2.07)
◯◯◯‖‡‡**
Tirzepatide not in networkTirzepatide not in networkTirzepatide not in networkTirzepatide not in networkNMA
RR, 1.03 (0.83 to 1.28)
⊕◯◯§§‖**
NMA
RR, 6.72 (4.25 to 10.62)
◯◯◯‖‡‡**
 Tirzepatide vs. sulfonylureaNMA
RR, 0.88 (0.50 to 1.56)
◯◯◯‖‡‡**
NMA
RR, 0.77 (0.48 to 1.23)
◯◯◯‖‡‡**
Tirzepatide not in networkTirzepatide not in networkTirzepatide not in networkTirzepatide not in networkNMA
RR, 0.97 (0.78 to 1.20)
⊕◯◯§§‖**
NMA
RR, 0.15 (0.09 to 0.24)
◯◯◯‖‡‡**
 Basal insulin vs. tirzepatidek = 2; n = 3432
RR, 1.35 (0.82 to 2.21)‡
5 more (3 fewer to 17 more)
⊕⊕⊕
k = 1; n = 1995
RR, 1.31 (0.91 to 1.90)‡
15 more (4 fewer to 43 more)
⊕⊕⊕
Basal insulin and tirzepatide not in networkBasal insulin and tirzepatide not in networkTirzepatide not in networkBasal insulin and tirzepatide not in networkk = 2; n = 3432
RR, 1.26 (1.05 to 1.51)
27 more (5 more to 54 more)
⊕◯◯§¶
k = 1; n = 1437
RR, 4.86 (2.64 to 8.96)‡
57 more (24 more to 118 more)
⊕⊕◯¶
 Tirzepatide vs. basal insulink = 2; n = 3432
RR, 0.74 (0.45 to 1.22)‡
7 fewer (15 fewer to 6 more)
⊕⊕⊕
k = 1; n = 1995
RR, 0.76 (0.53 to 1.10)‡
15 fewer (29 fewer to 6 more)
⊕⊕⊕
Basal insulin and tirzepatide not in networkBasal insulin and tirzepatide not in networkTirzepatide not in networkBasal insulin and tirzepatide not in networkk = 2; n = 3432
RR, 0.80 (0.67 to 0.96)
32 fewer (52 fewer to 6 fewer)
⊕◯◯§¶
k = 1; n = 1437
RR, 0.21 (0.11 to 0.38)‡
57 fewer (64 fewer to 45 fewer)
⊕⊕◯¶
 Basal insulin vs. SGLT2 inhibitorsNMA
RR, 1.42 (1.02 to 1.98)
⊕◯◯‖¶**
NMA
RR, 1.23 (0.92 to 1.65)
⊕◯◯‖¶**
Basal insulin not in networkBasal insulin not in networkNMA
RR, 1.57 (0.96 to 2.58)
⊕◯◯‖¶**
Basal insulin not in networkNMA
RR, 1.26 (1.06 to 1.50)
⊕◯◯§§‖**
NMA
RR, 4.51 (3.13 to 6.49)
⊕◯◯§§‖**
 Basal insulin vs. sulfonylureak = 1; n = 2517
RR, 0.97 (0.64 to 1.14)‡
1 fewer (12 fewer to 16 more)
⊕⊕⊕
k = 1; n = 2504
RR, 1.09 (0.78 to 1.54)‡
4 more (10 fewer to 26 more)
⊕⊕⊕
Basal insulin not in networkBasal insulin not in networkk = 1; n = 2504
RR, 0.86 (0.51 to 1.45)‡
3 fewer (12 fewer to 11 more)
⊕⊕⊕
Basal insulin not in networkNMA
RR, 1.18 (0.99 to 1.41)
⊕◯◯§§‖**
k = 1; n = 2517
RR, 0.57 (0.31 to 1.04)‡
10 fewer (15 fewer to 1 more)
⊕⊕◯¶
 SGLT2 inhibitors vs. tirzepatide◯◯◯††◯◯◯††Tirzepatide not in networkTirzepatide not in networkTirzepatide not in networkTirzepatide not in networkNMA
RR, 0.96 (0.78 to 1.19)
⊕◯◯§§‖**
NMA
RR, 0.76 (0.50 to 1.17)
⊕◯◯‖¶**
CHF = congestive heart failure; CKD = chronic kidney disease; DPP4 = dipeptidyl peptidase-4; GLP1 = glucagon-like peptide-1; MACE = major adverse cardiovascular event; MI = myocardial infarction; NMA = network meta-analysis; RR = risk ratio; SGLT2 = sodium–glucose cotransporter-2.
* Based on RR; estimates from direct comparisons.
† GRADE (Grading of Recommendations Assessment, Development and Evaluation) certainty of evidence: ⊕⊕⊕ = high; ⊕⊕◯ = moderate; ⊕◯◯ = low; ◯◯◯ = insufficient.
‡ Estimate from direct comparison because it has a higher certainty of evidence than the network estimate.
§ Rated down for inconsistency.
‖ Rated down for indirectness.
¶ Rated down for imprecision.
** Comparison informed by indirect evidence only.
†† Class comparison informed only by indirect evidence; indirect network path length >2.
‡‡ Rated down twice for imprecision.
§§ Rated down for risk of bias.
‖‖ This outcome met the prespecified minimally important difference.
¶¶ Rated down twice for imprecision.
The network graphs were limited in that direct comparisons were present for a small subset of all possible class comparisons. Based on performance of these direct comparisons, feasibility of an NMA was assessed, specifically the assumption of transitivity. Where feasible, we performed an NMA using a frequentist graph-theoretical model and implemented in the R package netmeta (5, 6). Several of the networks included multigroup studies, and the necessary adjustments were made to the input pairwise comparisons before the NMA. A fixed- or random-effects model was used depending on the sparsity of the network, with a sparse network defined as one with pairwise associations present with fewer than 5 studies. The NMA included calculation of indirect and combined effects with league tables, forest plots, and assessment of model assumptions (that is, using generalized heterogeneity statistics for global incoherence and separate indirect from direct evidence [SIDE] for local incoherence).
We explored the feasibility of assessing whether treatment effects varied by baseline age, sex, ethnicity, HbA1c level, CVD, CHF, obesity (BMI >30 kg/m2), or CKD stage 3+ (Tables 1 and 4 of Supplement 2). We limited subgroup analyses to those that were prespecified by the individual study investigators and planned with stratified randomization. However, results were sparsely and variably reported, so we provide a brief narrative summary.
We used the GRADE approach to rate overall CoE for critical outcomes as high, moderate, low, or insufficient (7–9). As recommended by the ACP CGC, direct evidence was prioritized only when it provided a higher CoE than the NMA evidence. We downgraded by 1 level when no direct evidence was available. The NMA CoE was based on the component (direct or indirect) that had a larger contribution to the NMA as well as the consistency of direct and indirect estimates (10–12). The absolute risk differences were calculated using GRADEpro (McMaster University and Evidence Prime Inc.) for each of the direct comparisons using the common effect measures from the direct pairwise analysis (7). We determined whether absolute risk differences achieved thresholds indicating clinical minimally important differences (MIDs) for decision making about the effect of antidiabetic drugs on our critical outcomes. The MIDs were predetermined with the ACP CGC (Appendix F of Supplement 1).

Role of the Funding Source

This review was funded by ACP. The ACP CGC assisted in the development of KQs, study inclusion criteria, and outcome measures of interest. ACP and the CGC were not involved in data collection, analysis, or manuscript preparation, though they reviewed and provided comments on each.

Results

Overview

For KQ1, we identified 13 386 citations in MEDLINE and EMBASE. After removal of duplicates, conference abstracts, dissertations, and theses, 9437 citations were dual-reviewed at the abstract and title stage (Appendix Figure 1). In total, 701 citations underwent full-text review, and 130 citations summarizing findings from 84 trials were eligible for inclusion. On average, studies were large (mean n = 2600), were multinational, and enrolled mostly middle-aged adults (mean age, 58.7 years) with long-standing and previously treated T2DM (mean T2DM duration, 8.8 years). Mean baseline weight was 89.7 kg, and mean HbA1c level was 8.1%. Most participants had comorbidities, including hypertension, CVD, or a history of tobacco use. Ethnicity was infrequently reported. Mean follow-up was 90.2 weeks (median study duration, 68.5 weeks), with a range of 52 to 328.5 weeks. For KQ2, we identified a total of 2108 citations, of which 6 underwent full-text review. Three reviews were eligible for data extraction (Appendix Figure 2).
Appendix Figure 1. PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) study flow diagram: key question 1. Key question 1: In adults with type 2 diabetes mellitus, what are the effectiveness and comparative effectiveness and harms of sodium–glucose cotransporter-2 inhibitors, glucagon-like peptide-1 agonists, dipeptidyl peptidase-4 inhibitors, or long-acting insulins used either as monotherapy or in combination with other antidiabetic medications?
Appendix Figure 1. PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) study flow diagram: key question 1.
Key question 1: In adults with type 2 diabetes mellitus, what are the effectiveness and comparative effectiveness and harms of sodium–glucose cotransporter-2 inhibitors, glucagon-like peptide-1 agonists, dipeptidyl peptidase-4 inhibitors, or long-acting insulins used either as monotherapy or in combination with other antidiabetic medications?
Appendix Figure 2. PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) study flow diagram: key question 2. Key question 2: What are patients’ values and preferences regarding antidiabetic medications for type 2 diabetes mellitus management?
Appendix Figure 2. PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) study flow diagram: key question 2.
Key question 2: What are patients’ values and preferences regarding antidiabetic medications for type 2 diabetes mellitus management?
The lowest allowed HbA1c level for inclusion was 6.5%, although none had a mean baseline HbA1c level below 7%. Three trials required participants to have CKD, 4 required them to have existing CVD or acute coronary syndrome, 3 required them to have obesity or overweight, and 11 required them to be “at risk for cardiovascular disease,” with varying definitions. Of the 26 trials that allowed for monotherapy only, 21 allowed only metformin monotherapy. The remaining trials allowed for monotherapy or combinations. Fifty-seven trials allowed for a combination of therapies, often only specifying a drug that participants could not be taking. Review of inclusion criteria shows that trials were not completely jointly randomizable. However, the majority included a wide spectrum of prior treatment and thus the subnetwork of these trials was jointly randomizable, with exceptions having overlap with the broader trial population (Table 4 of Supplement 2). In addition, there was no statistical heterogeneity, so combining of trials did not lead to statistical incoherence.

Mortality and Vascular Outcomes

All-Cause Mortality

Sixty (13–72) trials reported all-cause mortality and were included in the NMA. SGLT2 inhibitors (RR, 0.86 [95% CI, 0.80 to 0.93]; high CoE) and GLP1 agonists (RR, 0.88 [CI, 0.83 to 0.94]; high CoE) reduce all-cause mortality compared with usual care. Absolute effects did not reach the MID of 2 percentage points. In contrast, DPP4 inhibitors (RR, 1.01 [CI, 0.94 to 1.08]; high CoE), tirzepatide (RR, 0.98 [CI, 0.56 to 1.73]; low CoE), and insulin (RR, 1.23 [CI, 0.89 to 1.70]; low CoE) do not differ from usual care.
SGLT2 inhibitors may reduce all-cause mortality compared with insulin (RR, 0.70 [CI, 0.51 to 0.98]; low CoE), and GLP1 agonists probably reduce all-cause mortality compared with insulin (RR, 0.62 [CI, 0.41 to 0.93]; moderate CoE) and DPP4 inhibitors (RR, 0.61 [CI, 0.39 to 0.95]; moderate CoE). All other comparisons were not statistically significant or had insufficient CoE (Tables 2 and 11 of Supplement 1; Figure 1 of Supplement 2).

Major Adverse Cardiovascular Events

Twenty-one trials (13, 14, 19–22, 24, 32–36, 38, 51, 54, 60, 63, 68, 73–75) reported MACE and were included in the NMA. Compared with usual care, SGLT2 inhibitors (RR, 0.90 [CI, 0.83 to 0.98]; moderate CoE) and GLP1 agonists (RR, 0.91 [CI, 0.87 to 0.96]; high CoE) reduce MACE. The absolute effect did not reach the MID of 5 percentage points. DPP4 inhibitors (RR, 1.0 [CI, 0.94 to 1.06]; high CoE) and insulin (RR, 1.10 [CI, 0.83 to 1.46]; low CoE) do not reduce MACE compared with usual care. Evidence is insufficient for tirzepatide.
SGLT2 inhibitors reduce MACE compared with SU (RR, 0.57 [CI, 0.36 to 0.91]; high CoE) and probably reduce MACE compared with DPP4 inhibitors (RR, 0.88 [CI, 0.80 to 0.97]; moderate CoE). All other comparisons were not statistically significant or had insufficient evidence (Tables 3 and 12 of Supplement 1; Figure 2 of Supplement 2).

Myocardial Infarction

Ten studies (20, 21, 24, 32–34, 36, 38, 54, 60) reported on fatal and nonfatal MI and were included in the NMA. SGLT2 inhibitors (RR, 0.97 [CI, 0.85 to 1.12]; high CoE), GLP1 agonists (RR, 0.96 [CI, 0.89 to 1.04]; high CoE), and DPP4 inhibitors (RR, 0.95 [CI, 0.85 to 1.06]; high CoE) do not reduce MI compared with usual care. Evidence is insufficient for tirzepatide and insulin.
All head-to-head comparisons were not statistically significant or had insufficient evidence (Tables 4 and 13 of Supplement 1; Figure 3 of Supplement 2).

Stroke

Nine studies (21, 24, 32–34, 36, 38, 54, 60) reported on fatal and nonfatal stroke and were included in the NMA. GLP1 agonists reduce stroke (RR, 0.86 [CI, 0.77 to 0.95]; high CoE) compared with usual care. The absolute effect did not reach the MID of 5 percentage points. SGLT2 inhibitors (RR, 1.12 [CI, 0.93 to 1.34]; high CoE) and DPP4 inhibitors (RR, 0.97 [CI, 0.79 to 1.19]; high CoE) do not differ from usual care for stroke. Evidence is insufficient for tirzepatide and insulin.
GLP1 agonists probably reduce stroke compared with SGLT2 inhibitors (RR, 0.77 [CI, 0.62 to 0.95]; moderate CoE). All other comparisons were not statistically significant or had insufficient evidence (Tables 5 and 14 of Supplement 1; Figure 4 of Supplement 2).

CHF Hospitalizations

Twelve trials (13, 20, 21, 24, 32, 34, 36, 38, 51, 54, 74, 76) reported hospitalization due to CHF and were included in the NMA. SGLT2 inhibitors reduce CHF hospitalizations compared with usual care (RR, 0.64 [CI, 0.54 to 0.77]; high CoE), although the absolute effect did not reach the MID of 5 percentage points. GLP1 agonists (RR, 0.95 [CI, 0.85 to 1.06]; high CoE), DPP4 inhibitors (RR, 1.06 [CI, 0.96 to 1.17]; high CoE), and insulin (RR, 1.01 [CI, 0.64 to 1.60]; low CoE) do not differ from usual care for CHF hospitalizations. Evidence is insufficient for tirzepatide.
GLP1 agonists probably reduce CHF hospitalizations compared with DPP4 inhibitors (RR, 0.47 [CI, 0.25 to 0.88]; moderate CoE) and SU (RR, 0.47 [CI, 0.25 to 0.87]; moderate CoE). SGLT2 inhibitors probably reduce CHF hospitalizations compared with GLP1 agonists (RR, 0.69 [CI, 0.56 to 0.86]; moderate CoE) and may reduce CHF hospitalizations compared with DPP4 inhibitors (RR, 0.60 [CI, 0.48 to 0.74]; low CoE). All other comparisons were not statistically significant or had insufficient evidence (Tables 6 and 15 of Supplement 1; Figure 5 of Supplement 2).

Chronic Kidney Disease

Six trials (13, 20, 51–53, 60) included change in CKD from baseline and were included in the NMA. SGLT2 inhibitors reduce progression to CKD stage 3+ compared with usual care (RR, 0.66 [CI, 0.58 to 0.75]; high CoE). The absolute effect did not reach the MID of 5 percentage points. DPP4 inhibitors do not differ from usual care (RR, 1.07 [CI, 0.95 to 1.21]; high CoE). Evidence is insufficient for GLP1 agonists, tirzepatide, and insulin.
SGLT2 inhibitors probably reduce progression to CKD stage 3+ compared with DPP4 inhibitors (RR, 0.62 [CI, 0.52 to 0.74]; moderate CoE). All other comparisons were not statistically significant or had insufficient evidence (Tables 7 and 16 of Supplement 1; Figure 6 of Supplement 2).

Serious Adverse Events

Sixty-seven trials (13, 15–19, 21–27, 30–34, 36, 37, 39–73, 77–89) reported on serious adverse events and were included in the NMA. Compared with usual care, SGLT2 inhibitors reduce serious adverse events (RR, 0.93 [CI, 0.90 to 0.95]; high CoE), although the effect did not reach the MID of 5 percentage points. GLP1 agonists (RR, 0.98 [CI, 0.95 to 1.01]; high CoE), DPP4 inhibitors (RR, 0.96 [CI, 0.92 to 1.01]; high CoE), tirzepatide (RR, 0.79 [CI, 0.51 to 1.22]; high CoE), and insulin (RR, 1.17 [CI, 0.99 to 1.39]; moderate CoE) do not differ from usual care.
DPP4 inhibitors may reduce serious adverse events compared with insulin (RR, 0.82 [CI, 0.68 to 0.97]; low CoE) and probably reduce serious adverse events compared with SU (RR, 0.94 [CI, 0.91 to 0.99]; moderate CoE). GLP1 agonists probably reduce serious adverse events compared with tirzepatide (RR, 0.57 [CI, 0.34 to 0.96]; moderate CoE). SGLT2 inhibitors (RR, 0.79 [CI, 0.67 to 0.94]; low CoE) and tirzepatide (RR, 0.80 [CI, 0.67 to 0.96]; low CoE) may reduce serious adverse events compared with insulin. All other comparisons were nonsignificant or had insufficient evidence (Tables 8 and 17 of Supplement 1; Figure 7 of Supplement 2).

Severe Hypoglycemia

Fifty trials (13, 14, 17–25, 27, 28, 30, 32–34, 36–44, 47, 48, 50, 52–55, 59, 60, 64, 65, 67, 69, 70, 72, 73, 79, 80, 85, 89–93) reported on severe hypoglycemia and were included in the NMA. Compared with usual care, SGLT2 inhibitors reduce severe hypoglycemia (RR, 0.85 [CI, 0.74 to 0.97]; high CoE), although the effect did not reach the MID of 5 percentage points. GLP1 agonists (RR, 1.02 [CI, 0.92 to 1.15]; moderate CoE), DPP4 inhibitors (RR, 1.14 [CI, 1.00 to 1.30]; high CoE), and tirzepatide (RR, 1.32 [CI, 0.78 to 2.22]; moderate CoE) do not differ from usual care for severe hypoglycemia. Evidence is insufficient for insulin.
DPP4 inhibitors reduce severe hypoglycemia compared with SU (RR, 0.14 [CI, 0.11 to 0.19]; high CoE). GLP1 agonists probably reduce severe hypoglycemia compared with insulin (RR, 0.23 [CI, 0.16 to 0.33]; moderate CoE) and SU (RR, 0.49 [CI, 0.26 to 0.92]; moderate CoE). SGLT2 inhibitors reduce severe hypoglycemia compared with SU (RR, 0.10 [CI, 0.07 to 0.15]; high CoE) and may reduce severe hypoglycemia compared with insulin (RR, 0.22 [CI, 0.15 to 0.32]; low CoE). Tirzepatide probably reduces severe hypoglycemia compared with insulin (RR, 0.21 [CI, 0.11 to 0.38]; moderate CoE). Only SGLT2 inhibitors compared with SU and tirzepatide compared with insulin had effects that reached the MID of 5 percentage points. All other comparisons were nonsignificant or had insufficient evidence (Tables 9 and 18 of Supplement 1; Figure 8 of Supplement 2).
See Appendix B of Supplement 1 for additional class- and drug-specific harms (>5% adverse events), warnings and precautions, and contraindications from the FDA website to summarize overall harms.

Weight Loss

Reporting of change in body weight varied. Weight change as the percentage of participants who achieved at least a 10% reduction from baseline was reported in only 6 trials (70, 81, 82, 89, 94, 95), so we were unable to perform an NMA on this outcome. Thirty-seven trials (17, 18, 23, 26, 27, 29–31, 37, 38, 43, 45, 47, 48, 56–59, 61, 67–71, 73, 76, 79, 84, 85, 89, 90, 96–101) included the mean change in body weight from baseline. All were assessed as having low RoB or some concerns.
The 6 trials that reported the percentage of participants who achieved a 10% weight reduction (our predefined outcome of interest) included the following treatments: tirzepatide (3% to 67% of participants achieved a 10% weight reduction), liraglutide (15.9% to 25.2% achieved a 10% reduction), semaglutide (21% achieved a 10% reduction), dulaglutide (1.7% to 10% achieved a 10% reduction), usual care (6.7% achieved a 10% reduction), exenatide extended-release (4% achieved a 10% reduction), glargine (0% achieved a 10% reduction), and placebo (0% achieved a 10% reduction) (70, 81, 82, 89, 94, 95). The ranges include combining of trials and different treatment doses used within a trial.
The NMA found that, compared with usual care, GLP1 agonists (MD, −2.22 kg [CI, −2.86 to −1.58 kg]), SGLT2 inhibitors (MD, −2.48 kg [CI, −3.03 to −1.92 kg]), and tirzepatide (MD, −8.47 kg [CI, −9.49 to −7.45 kg]) resulted in a mean reduction in weight over the study period. DPP4 inhibitors resulted in no weight change (MD, 0.00 kg [CI, –0.61 to 0.61 kg]) over the study period, and insulin (MD, 2.90 kg [CI, 1.76 to 4.04 kg]) resulted in a mean increase in weight (Table 10 of Supplement 1; Figure 9 of Supplement 2).

Glycemic Control

Forty-nine trials (16, 17, 19, 22, 23, 25–27, 29–31, 37–40, 44, 45, 47, 49, 56–59, 61, 67–70, 73, 76, 79, 81, 82, 84–87, 89, 90, 92, 94, 96–103) reported change in HbA1c level from baseline. Forty-eight trials were assessed as having either low RoB or some concerns, and 1 trial was assessed as having high RoB. The NMA for glycemic control had substantial heterogeneity and incoherence due to study design. Most trials allowed for postrandomization treatment with add-on medication in response to HbA1c level. Given these factors, we are unable to accurately report glycemic control by drug class.

Treatment Effects According to Participant Characteristics

We required subgroup analysis to be prespecified by the investigators and planned with stratified randomization. Outcomes were rarely reported by participant characteristics, and evidence was insufficient for analysis of the predefined subgroups of interest.

Patient Values and Preferences

All 3 of the eligible reviews (104–106) (Appendix Figure 2) identified glycemic control, weight loss, frequency of use, hypoglycemic episodes, and gastrointestinal events as attributes patients consider when choosing medications. The review by González-González and colleagues (104) was the only one to assess the overall CoE; they reported very low CoE for GLP1 agonists and no evidence for SGLT2 inhibitors, resulting in a finding of insufficient CoE. Weekly GLP1 agonists were preferred to once-daily or twice-daily GLP1 agonists. The other 2 reviews reported low RoB in the included studies and identified glycemic control, weight loss, frequency of use, hypoglycemic episodes, and gastrointestinal events as attributes patients consider when choosing medications (105, 106). However, the reviews by Purnell and González-González and their respective colleagues (104, 106) both expressed concern about the number of industry-funded trials (Appendix G of Supplement 1).

Discussion

SGLT2 inhibitors and GLP1 agonists reduce overall mortality compared with usual care and insulin in middle- aged, previously treated adults with T2DM. GLP1 agonists also reduce mortality compared with DPP4 inhibitors. SGLT2 inhibitors and GLP1 agonists reduce MACE compared with usual care, but neither drug reduces MI. Compared with usual care, GLP1 agonists but not SGLT2 inhibitors reduce stroke, and SGLT2 inhibitors reduce progression to CKD stage 3+, CHF hospitalizations, and severe hypoglycemia. Compared with each other, SGLT2 inhibitors reduce CHF hospitalizations and GLP1 agonists reduce stroke. DPP4 inhibitors and insulin did not reduce most outcomes of interest, and evidence was mostly insufficient for tirzepatide. SGLT2 inhibitors and GLP1 agonists tended to cause fewer serious adverse effects and severe hypoglycemic events than DPP4 inhibitors, SU, and insulin.
Our review focused on large longer-term studies of clinical outcomes rather than intermediate or surrogate measures. Nevertheless, no study lasted longer than 5 years, and most assessed relative reductions in composite outcomes over short follow-up (mean duration <2 years). In contrast to older drugs such as SU or metformin in “treat-to-target” studies, reductions in mortality and MACE with SGLT2 inhibitors and GLP1 agonists occurred in a fairly short time. Relative effects were between 10% and 15% (except for SGLT2 inhibitors on CHF hospitalizations), and absolute risk reductions were about 1 percentage point and did not reach predefined MIDs. Subgroup data are limited and did not permit specific conclusions (Tables 2 to 4 of Supplement 2).
Few studies enrolled treatment-naive patients or made a direct comparison with a nontreated control group. The majority involved metformin-based background therapy, but most did not require metformin and often allowed for various other baseline medications (Table 4 of Supplement 2). These medications were often not reported, which is why we chose to combine placebo and metformin-based background therapy into a single treatment category defined as “usual care.” Therefore, our review does not provide direct evidence on these newer drugs as first-line therapy in treatment-naive or newly diagnosed patients. Furthermore, although studies permitted enrollment of participants with an HbA1c level as low as 6.5%, most enrolled participants had moderate glycemic control, with a baseline HbA1c level of 8.1%. No study had a mean baseline HbA1c level below 7%. Studies were not designed to reach a prespecified HbA1c goal. All studies permitted enrollment of patients with existing CVD or CKD or those at high risk for CVD. However, as described in the Results section and in Table 4 of Supplement 2, only a minority required patients to have these comorbidities for enrollment. Although we did not identify statistical heterogeneity across study comparisons, differences in usual care and in baseline characteristics of patients may be limitations in our decision to conduct an NMA.
The effect on weight and glycemic control was limited by study design and data reporting. Reporting of weight change varied, with most studies reporting the mean weight change rather than our outcome of interest (10% reduction). The NMA found that GLP1 agonists, SGLT2 inhibitors, and tirzepatide resulted in a mean weight reduction compared with usual care. In contrast, DPP4 inhibitors resulted in no weight change, and insulin and SU resulted in a mean increase in weight.
Only 2 studies specifically precluded use of nonstudy antidiabetic medications based on HbA1c levels. Thus, the independent effect of these newer drugs on HbA1c levels cannot be adequately assessed. However, the relatively small differences in HbA1c level observed in these trials combined with multiple previous RCTs showing that intensive glycemic control with older drugs (target HbA1c level <7%) did not improve key clinical outcomes suggests that the clinical effects of newer drugs are not mediated solely through glycemic effects (107). Older reviews of these diabetes medication classes have evaluated glycemic control effects and thus serve as a better data source for independent short-term drug effects on glucose level.
Our review strengthens findings from prior reviews. An NMA by Tsapas and colleagues (108) found favorable cardiovascular outcome effects for GLP1 agonists and SGLT2 inhibitors among patients with high preexisting cardiovascular risk but not among those with low cardiovascular risk. Subgroup data in our review were inadequate to draw conclusions about effects of the medications in preexisting cardiovascular risk groups or by age, sex, ethnicity, or kidney disease but did show a reduction in MACE and mortality with GLP1 agonists and SGLT2 inhibitors regardless of baseline CVD status.
Similar to the current findings, an NMA by Palmer and colleagues (109) found benefits for all-cause mortality, cardiovascular outcomes, and renal outcomes with GLP1 agonists and SGLT2 inhibitors when added to usual care and reductions in stroke with GLP1 agonists and CHF outcomes with SGLT2 inhibitors. Duan and colleagues (110) also reported an NMA that found cardiovascular benefit from GLP1 agonists and SGLT2 inhibitors. These previous meta-analyses included variable drug add-on comparators, sometimes including insulin and DPP4 inhibitors but not the range of comparators in the current report. Studies and drugs not evaluated in prior reviews, such as the GLP1/GIP agonist tirzepatide and the SGLT2 inhibitor bexagliflozin, were evaluated in the present report. We included the GRADE (Glycemia Reduction Approaches in Type 2 Diabetes: A Comparative Effectiveness) trial, the only trial to directly assess clinical effectiveness and harms of 4 major, widely used classes of drugs: SU, insulin, GLP1 agonists, and DPP4 inhibitors (28, 74). Unfortunately, SGLT2 inhibitors were not evaluated in the GRADE trial.
SGLT2 inhibitors and GLP1 agonists consistently reduce severe hypoglycemia compared with either usual care or other medication classes, such as SU and insulin. This is consistent with previous reports and the mechanism of action of each medication class in that metformin, SGLT2 inhibitors, and GLP1 agonists do not independently cause hypoglycemia (108). For example, with the reduction in severe hypoglycemia with SGLT2 inhibitors compared with usual care, the hypoglycemia was likely caused by medications such as SU and insulin. We found that SGLT2 inhibitors reduce serious adverse events compared with usual care, whereas insulin increases serious adverse events compared with usual care. Serious adverse events were defined by authors, varied, and were not always fully reported. In general, they included events considered to be fatal or life-threatening. The reduction in serious adverse events seen with SGLT2 inhibitors compared with usual care is likely due to study authors' definitions of serious adverse events, which could include death or MACE.
We evaluated antidiabetic medications as a class and not by individual drug. Although most of the benefits of antidiabetic medications are believed to be a class effect, it is worth noting that GLP1 agonists are a highly heterogeneous class of medications. The class includes older and less potent medications, such as exenatide; more potent daily injection medications, such as liraglutide; even more potent weekly injections, such as semaglutide; and less potent oral GLP1 agonists. Each medication has a range of doses, and outcomes, including glycemic control and weight loss, are dose-dependent. Our study does not report on outcomes with individual GLP1 agonists, but these factors should be considered when choosing a medication within this class.
Only 1 eligible study with insufficient data evaluated combination use of GLP1 agonists and SGLT2 inhibitors (83). Therefore, we are unable to comment on the evidence behind the use of this combination therapy. We are also unable to comment on medication dosing, specific medications within a class, or the route of administration (for example, oral compared with injectable GLP1 agonist) given our study design. Finally, we are unable to provide evidence on the order of use of medications (first-line, second-line) due to the lack of placebo-controlled studies of treatment-naive patients and our definition of background usual care.
We performed a bridge search from 1 January 2023 through 13 January 2024, which identified a total of 1311 references in the databases. Only 3 were likely to be eligible (111–113) (Appendix H of Supplement 1). One of these trials compared oral semaglutide with varying doses of oral semaglutide and therefore would not have contributed to our analysis (113). In the remaining 2 trials, Cherney and colleagues (112) compared sotagliflozin with placebo and found no evidence of difference in renal outcomes at 52 weeks, and Wexler and colleagues (111) compared liraglutide, sitagliptin, insulin glargine, and glimepiride, all added to metformin, and found no evidence of difference in renal outcomes. Overall, these findings are consistent with and would not change our findings.
We found limited evidence from existing systematic reviews on patient values and preferences. Three reviews met our criteria and qualitatively summarized their findings. A common theme across these 3 reviews was the recommendation to provide materials summarizing both benefits and harms of the antidiabetic medications for physician use with patients. All 3 reviews identified glycemic control, weight loss, frequency of use, hypoglycemic episodes, and gastrointestinal events as attributes patients consider when deliberating about medications. Although these overlap with our outcomes, which are prioritized by ACP public panel members and ACP CGC members, there are key differences: Mortality, CVD prevention, and CKD prevention were not identified in these reviews as key patient preferences and values.
Limitations include infrequent comparisons between drugs of interest and sparse data for NMA for most outcomes. Incoherence may be present due to differences in usual care and baseline patient characteristics among trials. Trial design did not allow for valid assessment of differences in glycemic control. There were insufficient data on predefined subgroups of interest, including demographic subgroups, patients with prior CVD, or treatment-naive persons.
In conclusion, in adults with T2DM, SGLT2 inhibitors and GLP1 agonists (but not DPP4 inhibitors, insulin, or tirzepatide) reduce all-cause mortality and MACE compared with usual care. SGLT2 inhibitors reduce CKD progression, CHF hospitalization, and severe hypoglycemia, and GLP1 agonists reduce stroke. Serious adverse events and severe hypoglycemia seem to be less frequent with SGLT2 inhibitors and GLP1 agonists compared with insulin or SU.

Supplemental Material

Appendix Materials
Supplemental Materials

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Jonathon W. Senefeld, Jill A. Kanaley 7 May 2024
Lifestyle Changes among Adults with Type 2 Diabetes

We read with great interest the systematic review and network meta-analysis from Drake and colleagues (1) for the American College of Physicians which reported a significant reduction in all-cause mortality and major adverse cardiovascular events associated with sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP1) agonists (but not dipeptidyl peptidase-4 (DPP4) inhibitors, insulin, or tirzepatide) among adults with type 2 diabetes mellitus (T2DM). Importantly, SGLT2 inhibitors and GLP1 agonists were also associated with a reduction in weight and fewer serious adverse effects or hypoglycemic events than other pharmacological treatments (DPP4 inhibitors, sulfonylurea, and insulin). This review confirms previous findings (2) and informs clinical guidelines on treatment of T2DM. The authors should be lauded for conducting an excellent meta-analysis and for the scientific rigor with which they examined these important pharmacologic treatments for adults with T2DM. Unfortunately, however, this review does not discuss or contextualize the first-line therapy and cornerstone of management of type 2 diabetes— lifestyle changes (primarily diet and exercise) (3, 4). The health benefits associated with lifestyle changes for the prevention and management of T2DM are canonical (5). Indeed, there is no single intervention with greater efficacy than lifestyle changes to reduce the risk of virtually all chronic diseases (including T2DM) and major adverse cardiovascular events. In this context, readers should be reminded of the profound benefits associated with lifestyle changes for both all-cause mortality and major adverse cardiovascular events. Lifestyle changes and nondrug therapy represent first-line therapeutics in the armamentarium against T2DM. Additionally, emerging evidence suggests there may be important and synergistic interaction associated with these pharmacologic treatments (SGLT2 inhibitors and GLP1 agonists) and exercise. Better understanding these potential drug-exercise-interactions represents a key opportunity for future investigations.

References

1. Drake T, Landsteiner A, Langsetmo L, MacDonald R, Anthony M, Kalinowski C, et al. Newer Pharmacologic Treatments in Adults With Type 2 Diabetes: A Systematic Review and Network Meta-analysis for the American College of Physicians. Ann Intern Med. 2024.

2. Tsapas A, Avgerinos I, Karagiannis T, Malandris K, Manolopoulos A, Andreadis P, et al. Comparative Effectiveness of Glucose-Lowering Drugs for Type 2 Diabetes: A Systematic Review and Network Meta-analysis. Ann Intern Med. 2020;173(4):278-86.

3. Vijan S. Type 2 Diabetes. Ann Intern Med. 2019;171(9):ITC65-ITC80.

4. Kanaley JA, Colberg SR, Corcoran MH, Malin SK, Rodriguez NR, Crespo CJ, et al. Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine. Med Sci Sports Exerc. 2022;54(2):353-68.

5. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403.

Information & Authors

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Published In

cover image Annals of Internal Medicine
Annals of Internal Medicine
Volume 177Number 5May 2024
Pages: 618 - 632

History

Published online: 19 April 2024
Published in issue: May 2024

Keywords

Authors

Affiliations

Department of Medicine, VA Health Care System, and Department of Medicine, University of Minnesota, Minneapolis, Minnesota (T.D., C.J.B., A.K.)
Adrienne Landsteiner, PhD, MPH https://orcid.org/0000-0002-6557-6516
Center for Care Delivery & Outcomes Research, VA Health Care System, Minneapolis, Minnesota (A.L., R.M., M.A., C.K., K.U.)
Department of Medicine, University of Minnesota; Center for Care Delivery & Outcomes Research, VA Health Care System; and Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota (L.L.)
Roderick MacDonald, MS
Center for Care Delivery & Outcomes Research, VA Health Care System, Minneapolis, Minnesota (A.L., R.M., M.A., C.K., K.U.)
Maylen Anthony, MPH
Center for Care Delivery & Outcomes Research, VA Health Care System, Minneapolis, Minnesota (A.L., R.M., M.A., C.K., K.U.)
Caleb Kalinowski, MS
Center for Care Delivery & Outcomes Research, VA Health Care System, Minneapolis, Minnesota (A.L., R.M., M.A., C.K., K.U.)
Center for Care Delivery & Outcomes Research, VA Health Care System, Minneapolis, Minnesota (A.L., R.M., M.A., C.K., K.U.)
Charles J. Billington, MD
Department of Medicine, VA Health Care System, and Department of Medicine, University of Minnesota, Minneapolis, Minnesota (T.D., C.J.B., A.K.)
Department of Medicine, VA Health Care System, and Department of Medicine, University of Minnesota, Minneapolis, Minnesota (T.D., C.J.B., A.K.)
Department of Medicine, University of Minnesota, and Center for Care Delivery & Outcomes Research, VA Health Care System, Minneapolis, Minnesota (S.S.)
Timothy J. Wilt, MD, MPH
Department of Medicine, VA Health Care System; Department of Medicine, University of Minnesota; Center for Care Delivery & Outcomes Research, VA Health Care System; and Division of Health Policy & Management, School of Public Health, University of Minnesota, Minneapolis, Minnesota (T.J.W.).
Disclaimer: This article is the result of work supported by resources and use of facilities of the Minneapolis VA Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the U.S. Department of Veterans Affairs or the U.S. government.
Acknowledgment: The authors thank Mr. Kyle Moen for technical and administrative assistance and members of their Topic Expert Panel (Daniel Bessesen, MD; Chester B. Good, MD, MPH; Angela M. Leung, MD, MSc; and Sandeep Vijan, MD, MS) for their input on the study protocol, feedback on interim conference calls, and review of earlier drafts of this manuscript.
Financial Support: By the American College of Physicians.
Reproducible Research Statement: Study protocol: Available at www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=322129. Statistical code and data set: Interested individuals or organizations should contact the American College of Physicians (e-mail, [email protected]).
Corresponding Author: Timothy J. Wilt, MD, MPH, Department of Medicine, VA Health Care System, One Veterans Drive (111-0), Minneapolis, MN 55417; e-mail, [email protected].
Author Contributions: Conception and design: T. Drake, A. Landsteiner, L. Langsetmo, C.J. Billington, S. Sultan, T.J. Wilt.
Analysis and interpretation of the data: T. Drake, A. Landsteiner, L. Langsetmo, R. MacDonald, C. Kalinowski, C.J. Billington, A. Kaka, S. Sultan, T.J. Wilt.
Drafting of the article: T. Drake, A. Landsteiner, L. Langsetmo, K. Ullman, C.J. Billington, S. Sultan, T.J. Wilt.
Critical revision for important intellectual content: T. Drake, A. Landsteiner, L. Langsetmo, C.J. Billington, A. Kaka, S. Sultan, T.J. Wilt.
Final approval of the article: T. Drake, A. Landsteiner, L. Langsetmo, R. MacDonald, M. Anthony, C. Kalinowski, K. Ullman, C.J. Billington, A. Kaka, S. Sultan, T.J. Wilt.
Statistical expertise: L. Langsetmo, R. MacDonald, T.J. Wilt.
Obtaining of funding: T.J. Wilt.
Administrative, technical, or logistic support: A. Landsteiner, M. Anthony, C. Kalinowski, K. Ullman, T.J. Wilt.
Collection and assembly of data: A. Landsteiner, L. Langsetmo, R. MacDonald, M. Anthony, C. Kalinowski, K. Ullman, T.J. Wilt.
This article was published at Annals.org on 19 April 2024.

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Tyler Drake, Adrienne Landsteiner, Lisa Langsetmo, et al. Newer Pharmacologic Treatments in Adults With Type 2 Diabetes: A Systematic Review and Network Meta-analysis for the American College of Physicians. Ann Intern Med.2024;177:618-632. [Epub 19 April 2024]. doi:10.7326/M23-1490

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