Abstract
Objective
Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) such as semaglutide (SEM) and tirzepatide (TZP) were initially approved for type 2 diabetes management but are increasingly used for weight loss. Limited data exist on real‐world use among patients with rheumatic and musculoskeletal diseases (RMDs). This study aimed to describe characteristics and trends in SEM and TZP initiation among individuals with RMDs and to identify factors associated with weight loss.
Methods
We conducted a retrospective analysis using the American College of Rheumatology's Rheumatology Informatics System for Effectiveness registry. Adults with RMD prescribed SEM or TZP between 2018 and 2024 were included. Patients with an evaluation and management visit before first GLP‐1 RA prescription were classified as new users. The primary outcome was percent change in body weight from baseline to 12 months. Multivariable linear regression assessed factors associated with percent weight change, and logistic models identified predictors of ≥5%, ≥10%, and ≥15% weight loss.
Results
Among 60,198 patients with RMD treated with GLP‐1 RAs (72% SEM), 80.5% were female, and 54.9% had diabetes; the mean age was 57.0 years, and body mass index was 36.4. GLP‐1 RA use increased from 0.1% in 2018 to 6.8% in 2024. At 12 months, SEM and TZP users lost 5.8% and 8.2% of body weight, respectively. TZP users lost 2.2% (95% confidence interval [CI] 1.9–2.5) more weight than SEM users, and those without diabetes lost 1.8% (95% CI 1.5–2.1) more than those with diabetes.
Conclusion
GLP‐1 RA use is increasing among patients with RMD and is associated with clinically meaningful weight loss, particularly with TZP and in individuals without diabetes.
INTRODUCTION
Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) were first developed for type 2 diabetes mellitus (T2DM) but have since emerged as highly effective therapies for weight management. 1 Semaglutide (SEM), approved by the US Food and Drug Administration (FDA) in 2017 for T2DM and in 2021 for chronic weight management, and tirzepatide (TZP), a dual glucose‐dependent insulinotropic polypeptide (GIP)/GLP‐1 RA approved for these indications in 2022 and 2023, respectively, have shown remarkable efficacy in clinical trials. 2 , 3 The phase 3 Semaglutide Treatment Effect in People with Obesity (STEP) program demonstrated durable, clinically meaningful weight loss with SEM, 2 whereas the phase 3 SURMOUNT‐1 trials confirmed substantial and sustained reductions with TZP. 3 Real‐world studies corroborate these findings, though they noted challenges such as long‐term adherence with discontinuation rates ranging from 20% to 50% within the first year, variability tolerability, and heterogeneity in response, 4 , 5 , 6 with weight regain observed in some patients, approximately about 7% after six months discontinuation. 7
Although obesity remains a common and long‐term public health issue, the potential clinical relevance of GLP‐1 RAs extends beyond metabolic diseases, with important implications for rheumatic and musculoskeletal diseases (RMDs). 8 Obesity (body mass index [BMI] >30) is a well‐established risk factor and disease modifier across a range of RMDs, including rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, and osteoarthritis (OA). 9 , 10 For example, excess body weight increases mechanical loading on joints, exacerbating pain and functional limitation. Moreover, adiposity contributes to systemic inflammation through the secretion of proinflammatory cytokines which can amplify RMD disease activity. 10 , 11 , 12 Observations with RMD populations suggest that a weight reduction of ≥5% is associated with improvements in both disease activity and patient‐reported outcomes (PROs). 13
Despite this strong rationale for GLP‐1 RA uptake among patients with RMD, several knowledge gaps remain. There is limited information on the real‐world use of SEM and TZP among individuals with RMDs. 14 National‐level data describing temporal trends in uptake, patient demographics, and variation across RMD subtypes are scarce. Certain inflammatory RMDs may increase the impetus for clinicians to use GLP‐1 treatments given their beneficial effect on outcomes (eg, joint replacement), somewhat attenuated benefit of typical lifestyle factors such as exercise to contribute to weight loss due to mobility limitations, and use of therapies such as glucocorticoids that may inhibit patients maintaining a healthy weight. Also, predictors of weight loss specific to RMD populations remain poorly characterized. It is unclear which sociodemographic and clinical factors influence the magnitude of weight reduction achieved with GLP‐1 therapy. Understanding these contextual factors is critical for optimizing treatment strategies and identifying patients most likely to benefit from GLP‐1–based therapy. Using a large US rheumatology electronic health record (EHR)–based registry, the objectives of this study were to describe characteristics and temporal trends in initiation of SEM and TZP among individuals with RMDs and to identify factors associated with weight loss.
PATIENTS AND METHODS
Data source
Data were derived from the American College of Rheumatology's (ACR) Rheumatology Informatics System Effectiveness (RISE) registry. RISE is a national EHR‐enabled registry containing information for more than 3.5 million rheumatology patients from the practices of more than 1,000 US rheumatologists. 15 Available data included patient demographics, diagnoses, medications, disease activity scores, and encounter type and dates.
Study population
Using all available RISE registry data from January 2014 to September 2024, we identified all patients with at least one prescription for SEM or TZP during the study period. For the time trend analysis described subsequently, but not for other analyses, RISE patient status was assessed annually, with patients contributing data each calendar year if they had two or more provider evaluation and management visits within that year. Patients could transition in and out of active status across years.
Exposure definition
Patients were classified as new or prevalent GLP‐1 RA users. New users had at least one provider visit before their first SEM or TZP prescription, with the index date defined as the date of the provider visit immediately before the first prescription for SEM/TZP. Prevalent users lacked a prior visit, and their index date was defined as the date of the first recorded GLP‐1 RA prescription in the EHR data. Analyses evaluating weight trajectories and predictors of weight loss were restricted to new GLP‐1 RA users to ensure that baseline weight measurements preceded treatment initiation.
Outcomes
The main outcome was change in the body weight from index date to 12 months (±12 weeks) of postindex follow‐up. When multiple weights were available, the closest measure to the target month was used. To ensure data quality, implausible values were excluded, including outliers identified at both the population and individual levels (see Supplemental Document 1). Weight outcomes included the absolute and percent changes. All measurements were standardized to pounds (lb) before analysis (1 kg = 2.205 lb). Additional outcomes included attainment of weight loss of at least 5%, 10%, or 15%.
Covariates
Diabetes and RMD classification
Based on previous conventions using RISE data, 16 patients were classified as having diabetes if they met at least one of the following criteria: (1) hemoglobin A1C > 6.5% or random glucose level >200 mg/dL, (2) one or more diabetes diagnosis codes (International Classification of Diseases, Tenth Revision [ICD‐10]: E8‐E13), or (3) use of a diabetes medication other than SEM or TZP. For RMD classification, patients were assigned to 1 of 14 selected conditions if they received at least one ICD‐10 diagnosis for an RMD during the study period. When multiple RMD diagnoses were present, assignment followed a predefined hierarchy (see Supplemental Table 1). These 14 conditions were then collapsed into four broader RMD groups, with patients with insufficient diagnostic detail categorized as “other RMD” (Supplemental Table 1). The timing of diabetes and RMD assessment varied depending on the analysis; details are provided in the Statistical analysis section.
Other covariates
Characteristics such as age, sex, race (White, Black, or other, including Asian, American Indian or Alaska Native, multiracial, Native Hawaiian or other Pacific Islander, and no determinate), ethnicity (Hispanic or non‐Hispanic), smoking status (ever smoked), geographic region (Midwest, Northeast, South, or West), national Area Deprivation Index (ADI), and calendar year of the index date and first GLP‐1 RA prescription date (as well as the difference between the two dates, for new users only) were assessed on the index date. Weight and BMI were captured on the index date visit or the last measure before the index date. Clinical and laboratory characteristics were assessed within two years before or at the index date, including Clinical Disease Activity Index, blood tests (hemoglobin A1C [%], glucose [mg/dL], C‐reactive protein, and erythrocyte sedimentation rate), and PRO measures, including the Health Assessment Questionnaire, the Multidimensional Health Assessment Questionnaire, the pain scale, and the Routine Assessment of Patient Index Data 3. The RxRisk comorbidity index, a validated medication‐based measure of chronic diseases consisting of 46 categories of chronic conditions, 17 and use of other relevant medications (bimekizumab, secukinumab, ixekizumab, interleukin‐17, nonsteroidal antiinflammatory drugs [NSAIDs], opioids, and oral steroid) were assessed during the baseline period, defined as the time window including all available data before or at the index date.
Statistical analysis
Descriptive statistics were used to characterize the cohort of GLP‐1 RA users. Patients who received both medications were hierarchically classified in the TZP exposure group because TZP is commonly initiated after prior GLP‐1 therapy (eg, SEM) in clinical practice. 18 , 19
Time trend analysis
Trends in GLP‐1 RA use over time were assessed using cumulative prevalence, calculated annually from 2018 to 2024. For each year, the numerator included all patients who had initiated SEM or TZP up to and including that year, whereas the denominator comprised patients considered active in RISE, using the aforementioned definition. For the stratified analysis by GLP‐1 RA type, SEM and TZP were analyzed independently, allowing patients to contribute to both categories (ie, the two groups were not mutually exclusive). Cumulative prevalence was further stratified by diabetes, RMD classification, and patient's baseline region. For the diabetes‐stratified analysis, diabetes status was dynamically assessed each year; once a patient met the criteria for diabetes, they remained in that category for all subsequent years. Similarly, for the RMD‐stratified analysis, RMD classification was reassessed annually, allowing patients to contribute to different categories over time if their diagnoses changed.
Weight trajectories and comparative analyses
Weight trajectories were restricted to new GLP‐1 RA users with paired weight data, stratified by GLP‐1 RA (SEM or TZP) and by diabetes status. In these analyses, patients were classified as TZP or SEM users based on their first prescription; so as to be able to compare SEM and TZP users directly, those patients who were prescribed both medications were assigned to the TZP exposure group. Diabetes status was determined at baseline. Changes in body weight were evaluated both as absolute differences and as percentage change from baseline to 12 months after treatment initiation.
Multivariable linear regression models were used to assess factors associated with percentage weight loss at 12 months. The model included GLP‐1 RA type (SEM vs TZP), diabetes status, age, sex, baseline weight, race, region, ADI, RxRisk, RMD group, and use of opioid, NSAID, or oral steroid. For this analysis, both diabetes status and RMD classification were assessed at baseline. Adjusted mean differences and corresponding 95% confidence intervals (CIs) were estimated for each covariate. Potential treatment‐effect modification was evaluated by including an interaction term between baseline weight and GLP‐1 RA type. In addition, multivariable logistic regression models were fitted to identify predictors of achieving clinically meaningful weight loss thresholds of ≥5%, ≥10%, and ≥15% at 12 months. The same covariates as in the linear model were included, and results were reported as adjusted odds ratios (aORs) with 95% CIs.
All analyses were performed using R version 4.3.1 (Foundation for Statistical Computing) and SAS software, version 9.4 (TS1M6; SAS Institute Inc). The analyses were approved by the local Institutional Review Board (IRB‐300000748) at the University of Alabama at Birmingham.
RESULTS
A total of 60,198 patients with RMD treated with GLP‐1 RAs were identified (Supplemental Figure 1) within the RISE registry. This included 43,282 (71.9%) patients who received SEM exclusively and 16,916 patients who ever used TZP, including 4,026 patients who received both agents. Overall, 33,061 (54.9%) patients had diabetes; most patients were female (80.5%), with a mean age of 57.0 (SD 12.1) years and a mean baseline BMI of 36.4 (SD 8.0) (Table 1). Patient characteristics were similar across treatment groups (Supplemental Table 2). Among TZP users, 83% were female (vs 79% among SEM users), and the mean age was 55.3 years (vs 57.6 years in SEM users). Patients with diabetes were older and included a lower proportion of females than those without diabetes (Supplemental Table 2).
Table 1.
Baseline characteristics of patients using SEM and TZP using data from the American College of Rheumatology's Rheumatology Informatics System Effectiveness registry, 2018–2024*
| Characteristic | Overall (N = 60,198) a |
|---|---|
| GLP‐1 RA type, n (%) | |
| SEM | 43,282 (71.9) |
| TZP | 16,916 b (28.1) |
| Age, mean (SD), y | 57.0 (12.1) |
| Sex, n (%) | |
| Female | 48,436 (80.5) |
| Male | 11,762 (19.5) |
| Race, n (%) | |
| White | 39,032 (80.4) |
| Black | 5,822 (12.0) |
| Other c | 3,706 (7.6) |
| Ethnicity, n (%) | |
| Hispanic | 3,165 (10.5) |
| Non‐Hispanic | 27,115 (89.5) |
| Region, n (%) | |
| Midwest | 10,929 (18.4) |
| Northeast | 6,323 (10.6) |
| South | 36,913 (62.1) |
| West | 5,298 (8.9) |
| ADI, n (%) | |
| ADI < 80 (not high deprivation) | 44,496 (82.8) |
| ADI ≥ 80 (high deprivation) | 9,255 (17.2) |
| Calendar year of index date, d n (%) | |
| Pre‐2020 | 3,645 (6.1) |
| 2020 | 3,034 (5.0) |
| 2021 | 5,695 (9.5) |
| 2022 | 12,424 (20.6) |
| 2023 | 20,920 (34.8) |
| 2024 | 14,480 (24.1) |
| Calendar year of first GLP prescription, n (%) | |
| Pre‐2020 | 1,762 (2.9) |
| 2020 | 2,359 (3.9) |
| 2021 | 4,384 (7.3) |
| 2022 | 8,955 (14.9) |
| 2023 | 20,922 (34.8) |
| 2024 | 21,816 (36.2) |
| Days between first GLP record and the index date (new users only), e mean (SD) | 243.9 (412.1) |
| BMI, mean (SD) | 36.4 (8.0) |
| BMI category, n (%) | |
| 1. Underweight BMI < 18.5 | 44 (0.1) |
| 2. Healthy 18.5 ≤ BMI < 25 | 2,477 (4.5) |
| 3. Overweight 25 ≤ BMI < 30 | 9,160 (16.7) |
| 4. Obesity BMI ≥ 30 | 43,131 (78.7) |
| Weight, mean (SD), lb | 221.2 (50.7) |
| Ever smoker, n (%) | 17,916 (33.9%) |
| Baseline diabetes, f n (%) | 33,061 (54.9) |
| RMD, n (%) | |
| Inflammatory arthritis g | 22,448 (37.3) |
| SLE/myositis/Sjögren disease/vasculitis | 4,429 (7.4) |
| Gout/PsO | 3,081 (5.1) |
| Noninflammatory h | 11,076 (18.4) |
| Other RMD | 19,164 (31.8) |
| RxRisk (0–46), i mean (SD) | 9.7 (4.9) |
| A1C, mean (SD), % | 6.6 (1.5) |
| Glucose, mean (SD), mg/dL | 123.6 (47.1) |
| CRP, mean (SD), mg/L | 8.4 (12.9) |
| CRP category, n (%) | |
| CRP ≤ 10 mg/L | 20,443 (76.1) |
| CRP > 10 mg/L | 6,426 (23.9) |
| ESR, mean (SD), mm/hr | 20.6 (18.3) |
| ESR category, n (%) | |
| ESR ≤ 20 (male) or ESR ≤ 30 (female) | 13,370 (75.1) |
| ESR > 20 (male) or ESR > 30 (female) | 4,442 (24.9) |
| MDHAQ (0–3), mean (SD) | 0.6 (0.7) |
| HAQ (0–3), mean (SD) | 0.7 (0.6) |
| Pain (0–10), mean (SD) | 4.6 (2.9) |
| CDAI (0–76), mean (SD) | 11.1 (10.9) |
| RAPID3, mean (SD) | 11.3 (6.5) |
| RAPID3 category, n (%) | |
| Remission or near remission, ≤3 | 3,400 (13.1) |
| Low, >3 and ≤6 | 2,969 (11.4) |
| Moderate, >6 and ≤12 | 7,717 (29.7) |
| High, >12 | 11,927 (45.9) |
| Medication use, n (%) | |
| NSAID | 36,747 (61.0) |
| Opioid | 11,084 (18.4) |
| Oral steroid | 13,572 (22.5) |
A1C, hemoglobin A1C; ADI, Area Deprivation Index; BMI, body mass index; CDAI, Clinical Disease Activity Index; CRP, C‐reactive protein; ESR, erythrocyte sedimentation rate; FMS, Fibromyalgia syndrome; GLP‐1 RA, glucagon‐like peptide‐1 receptor agonist; HAQ, Health Assessment Questionnaire; JIA, Juvenile Idiopathic Arthritis; MDHAQ, Multidimensional Health Assessment Questionnaire; NSAID, nonsteroidal anti‐inflammatory drug; OA, Osteoarthritis; OP, Osteoporosis; PMR, Polymyalgia; PsO, psoriasis; RAPID3, Routine Assessment of Patient Index Data 3; RMD, rheumatic and musculoskeletal disease; RxRisk, prescription‐based comorbidity index; SEM, semaglutide; SLE, systemic lupus erythematosus; TZP, tirzepatide.
Values are based on the total study population (N = 60,198), unless otherwise specified. For the following variables, calculations were based on participants with available data: race (n = 48,560); ethnicity (n = 30,280); region (n = 59,463); ADI (n = 53,751); days between first GLP and the index date (n = 40,002); BMI (n = 54,812); weight (n = 48,523); ever smoker (n = 52,783); A1C (n = 4,132); glucose (n = 24,408); CRP (n = 26,869); ESR (n = 17,812); MDHAQ (n = 13,919); HAQ (n = 17,888); pain (n = 20,007); CDAI (n = 8,778); and RAPID3 (n = 26,013).
Including 4,026 patients who were prescribed both SEM and TZP.
Includes Asian, American Indian or Alaska Native, Multiracial, Native Hawaiian or other Pacific Islander, and no determinate.
Index date was the provider visit before the first SEM/TZP prescription for new users, or the date of the first recorded GLP‐1 RA prescription for prevalent users.
Corresponding to the time between the baseline rheumatology visit and the subsequent visit at which GLP‐1 RA use was first documented.
Baseline diabetes identified using the diabetes algorithm based on the following criteria: (1) International Classification of Diseases, Tenth Revision diagnosis codes, (2) diabetes drugs, (3) A1C > 6.5% at any time, or (4) glucose > 200 mg/dL at any time.
Inflammatory arthritis includes psoriatic arthritis, rheumatoid arthritis, JIA, axial spondyloarthritis, and PMR.
Noninflammatory group includes OP, FMS, and OA.
RxRisk is a medication‐based comorbidity index that classifies patients into 46 chronic condition categories based on dispensed medications. Scores reflect the number of condition categories present.
Trends in GLP‐1 RA use over time are shown in Figure 1. Among active RISE patients in each year, the proportion of GLP‐1 RA users increased steadily, from 0.1% in 2018 to 6.8% in 2024. SEM accounted for most of use, whereas TZP showed substantial uptake in recent years, representing about one‐third of all GLP‐1 RA use in 2024 (equivalent to 2.3% of all active RISE patients). Among patients with diabetes, use increased consistently but showed a sharper rise beginning in 2022, indicating accelerated uptake thereafter (Figure 2). In contrast, use among patients without diabetes remained relatively stable, with only a gradual upward trend in the most recent years. Increased use was observed across all RMD groups, with the highest uptake in the group of patients with gout or psoriasis, followed by those with inflammatory arthritis (Supplemental Figure 2). Trends in GLP‐1 RA use increased over time across all US regions (Supplementary Figure 3). Throughout the study period, prevalence of GLP‐1 use appeared higher in the South and Midwest compared with the Northeast and West.
Figure 1.
Trends in GLP‐1 RA use, overall and stratified by SEM or TZP, Rheumatology Informatics System Effectiveness registry, 2018 to 2024. GLP‐1 RA, glucagon‐like peptide‐1 receptor agonist; SEM, semaglutide; TZP, tirzepatide.
Figure 2.
Trends in glucagon‐like peptide‐1 receptor agonist use, stratified by patients with and without diabetes, Rheumatology Informatics System Effectiveness registry, 2018 to 2024.
Weight trajectories during follow‐up were evaluated among 16,481 GLP‐1 RA new users with paired weight data (Table 2). At 12 months, SEM users lost an average of 5.8% (SD 8.0) of their body weight, compared with 8.2% (SD 9.1) among TZP users. In both groups, patients without diabetes experienced greater weight loss than those with diabetes, with reductions being more pronounced among TZP users than SEM users.
Table 2.
Weight change during follow‐up among new users of SEM or TZP stratified by diabetes, Rheumatology Informatics System Effectiveness registry, 2018–2024*
| Characteristic | SEM, n = 12,132 | TZP, n = 4,349 a | ||||||
|---|---|---|---|---|---|---|---|---|
| Overall (n = 12,132) | With Diabetes, b (n = 7,304) | Without Diabetes, b (n = 4,828) | Difference c | Overall (n = 4,349) | With Diabetes, b (n = 2,620) | Without Diabetes, b (n = 1,729) | Difference c | |
| Weight at 12 mo ± 12 wk, lb | 0.05 | 0.17 | ||||||
| Mean (SD) | 212.1 (49.6) | 213.3 (49.3) | 210.4 (50.0) | 211.0 (51.5) | 214.0 (50.9) | 205.0 (52.3) | ||
| Median (IQR) | 206.0 (175.4 to 243.2) | 207.8 (177.0 to 244.8) | 204.0 (174.0 to 241.0) | 205.0 (172.6 to 242.2) | 208.0 (177.3 to 245.8) | 196.0 (165.0 to 236.0) | ||
| Weight change, lb | 0.24 | 0.31 | ||||||
| Mean (SD) | −13.3 (18.3) | −11.5 (17.2) | −16.0 (19.6) | −18.8 (21.4) | −16.5 (20.2) | −23.3 (22.9) | ||
| Median (IQR) | −10.8 (−23.0 to −1.0) | −9.0 (−20.0 to −1.0) | −14.0 (−27.8 to −2.2) | −15.6 (−31.4 to −4.0) | −13.5 (−28.0 to −2.9) | −21.0 (−37.0 to −6.6) | ||
| Weight change, % | 0.25 | 0.34 | ||||||
| Mean (SD) | −5.8 (8.0) | −5.1 (7.4) | −7.1 (8.6) | −8.2 (9.1) | −7.1 (8.6) | −10.2 (9.7) | ||
| Median (IQR) | −4.9(−10.5 to −0.6) | −4.1 (−9.0 to −0.4) | −6.3 (−12.5 to −1.0) | −6.9 (−14.2 to −1.7) | −5.9 (−12.3 to −1.2) | −9.5 (−16.9 to −2.9) | ||
| Weight loss, % | −0.25 | −0.34 | ||||||
| Mean (SD) | 5.8 (8.0) | 5.1 (7.4) | 7.1 (8.6) | 8.2 (9.1) | 7.1 (8.6) | 10.2 (9.7) | ||
| Median (IQR) | 4.9 (0.6 to 10.5) | 4.1 (0.4 to 9.0) | 6.3 (1.0 to 12.5) | 6.9 (1.7 to 14.2) | 5.9 (1.2 to 12.3) | 9.5 (2.9 to 16.9) | ||
| Weight loss ≥5%, n (%) | 6,006 (49.5) | 3,284 (45.0) | 2,722 (56.4) | −0.23 | 2,584 (59.4) | 1,594 (55.1) | 990 (68.0) | −0.27 |
| Weight loss ≥10%, n (%) | 3,233 (26.6) | 1,589 (21.8) | 1,644 (34.1) | −0.28 | 1,639 (37.7) | 942 (32.6) | 697 (47.9) | −0.32 |
| Weight loss ≥15%, n (%) | 1,556 (12.8) | 706 (9.7) | 850 (17.6) | −0.23 | 988 (22.7) | 528 (18.2) | 460 (31.6) | 0.31 |
IQR, interquartile range; SEM, semaglutide; TZP, tirzepatide.
Including 1,341 patients who were prescribed both SEM and TZP.
Baseline diabetes identified using the diabetes algorithm based on the following criteria: (1) International Classification of Diseases, Tenth Revision diagnosis codes, (2) diabetes drugs, (3) hemoglobin A1C > 6.5% at any time, or (4) glucose > 200 mg/dL at any time.
Standardized mean difference >0.10 is generally considered clinically meaningful.
In multivariable models adjusting for baseline characteristics (Table 3), TZP users lost approximately 2.2% more weight at 12 months than SEM users (coefficient = −2.20; 95% CI −2.4 to −1.9). In addition, patients without diabetes lost 1.8% (coefficient = −1.80; 95% CI −2.1 to −1.5) more weight than those with diabetes. Other characteristics associated with greater weight loss included sex (2.2% more weight loss among females than males), higher baseline weight, and having inflammatory arthritis or systemic lupus erythematosus/myositis/Sjögren disease/vasculitis (compared with other RMD groups). In contrast, Black individuals and those classified in other racial categories experienced less weight loss (1.1% and 1.3%, respectively) compared with White individuals. Similarly, having a lower socioeconomic status (as indicated by a higher social deprivation, ADI ≥80%) was associated with 0.9% less weight loss compared with a higher socioeconomic status. There was no evidence of effect modification by baseline weight on the relationship between GLP‐1 RA type and weight loss. Variables associated with achieving specific weight loss thresholds (5%, 10%, or 15% at 12 months) are shown in Figure 3, with additional model details provided in Supplemental Table 3. The odds of losing at least 5% of body weight were nearly 50% higher among TZP users compared with SEM users (aOR 1.46; 95% CI 1.36–1.57). For the ≥15% threshold, the odds were almost two‐fold higher (aOR 1.94; 95% CI 1.77–2.13). A similar pattern was observed when comparing patients without versus with diabetes, with aORs of 1.51 (95% CI 1.41–1.61) and 1.73 (95% CI 1.58–1.90) for achieving ≥5% and ≥15% weight loss, respectively. Other characteristics associated with weight loss thresholds included sex (higher odds for females vs males), race (lower odds for Black vs White individuals), and socioeconomic status, which was associated with lower odds of achieving weight loss among those in higher‐ADI (lower socioeconomic status) areas (only for the ≥5% weight loss threshold). The inflammatory arthritis group (compared with the other RMD group) was marginally associated with higher odds of weight loss, and only for the ≥5% threshold.
Table 3.
Factors associated with percent weight loss at 12 months among new users of GLP‐1 RA, Rheumatology Informatics System Effectiveness registry, 2018–2024*
| Characteristic | Coefficient a (95% CI) |
|---|---|
| GLP‐1 RA type | |
| SEM | Referent |
| TZP | −2.20 (−2.40 to −1.90) |
| Baseline weight (10 lb) | −0.06 (−0.09 to −0.03) |
| Age | 0.01 (0.00 to 0.02) |
| Sex | |
| Male | Referent |
| Female | −2.20 (−2.60 to −1.90) |
| Race | |
| White | Referent |
| Black | 1.10 (0.70 to 1.60) |
| Other b | 1.30 (0.77 to 1.90) |
| Missing | 0.46 (0.10 to 0.82) |
| Baseline diabetes c | |
| Without diabetes | Referent |
| With diabetes | −1.80 (−2.10 to −1.50) |
| Region | |
| South | Referent |
| Midwest | −0.60 (−0.94 to −0.27) |
| Northeast | −0.24 (−0.65 to 0.17) |
| West | 0.03 (−0.47 to 0.54) |
| Missing | 0.71 (−2.90 to 4.30) |
| ADI | |
| ADI < 80 (not high deprivation) | Referent |
| ADI ≥ 80 (high deprivation) | 0.56 (0.22 to 0.90) |
| Missing | −0.02 (−0.61 to 0.58) |
| RxRisk (0–46) d | 0.00 (−0.04 to 0.03) |
| Opioid | 0.17 (−0.15 to 0.49) |
| NSAID | −0.27 (−0.57 to 0.03) |
| Oral steroid | −0.22 (−0.50 to 0.07) |
| RMD group | |
| Other RMD | Referent |
| Inflammatory arthritis e | 0.19 (−0.51 to 0.89) |
| Gout or psoriasis | −0.61 (−1.00 to −0.18) |
| Noninflammatory f | −0.27 (−0.76 to 0.23) |
| SLE/myositis/Sjögren/vasculitis | −0.76 (−1.30 to −0.20) |
Bold values indicate p value < 0.05. ADI, Area Deprivation Index; CI, confidence interval; FMS, Fibromyalgia syndrome; GLP‐1 RA, glucagon‐like peptide‐1 receptor agonist; JIA, Juvenile Idiopathic Arthritis; NSAID, nonsteroidal anti‐inflammatory drug; OA, Osteoarthritis; OP, Osteoporosis; PMR, Polymyalgia; PsO, psoriasis; RMD, rheumatic and musculoskeletal disease; RxRisk, prescription‐based comorbidity index; SEM, semaglutide; SLE, systemic lupus erythematosus; TZP, tirzepatide.
Negative numbers for coefficients indicate greater weight loss.
Includes Asian, American Indian or Alaska Native, Multiracial, Native Hawaiian or other Pacific Islander, and no determinate.
Baseline diabetes identified using the diabetes algorithm based on the following criteria: (1) International Classification of Diseases, Tenth Revision diagnosis codes, (2) diabetes drugs, (3) hemoglobin A1C > 6.5% at any time, or (4) glucose > 200 mg/dL at any time.
RxRisk is a medication‐based comorbidity index that classifies patients into 46 chronic condition categories based on dispensed medications. Scores reflect the number of condition categories present.
Inflammatory arthritis includes psoriatic arthritis, rheumatoid arthritis, JIA, axial spondyloarthritis, and PMR.
Noninflammatory group includes OP, FMS, and OA.
Figure 3.
Adjusted odds ratio (log scale) and 95% CI for factors associated with achieving weight loss thresholds (≥5%, ≥10%, and ≥15%) at 12 months among new users of glucagon‐like peptide‐1 receptor agonist, Rheumatology Informatics System Effectiveness registry, 2018 to 2024. ADI, Area Deprivation Index; CI, confidence interval; RMD, rheumatic and musculoskeletal disease; SLE, systemic lupus erythematosus.
DISCUSSION
Using data from the ACR RISE registry, this study revealed a marked increase in the uptake of SEM and TZP among patients with RMDs, particularly since 2022. Prescribing was higher among the group of patients with gout, psoriasis, and inflammatory arthritis with increases year‐over‐year observed across all RMD groups, suggesting these therapies are being widely prescribed across most RMD conditions. A key finding of our study is that patients with RMD were able to achieve clinically meaningful weight loss in a 12‐month period using GLP‐1 RAs, with greater weight loss observed in nondiabetic individuals, and with TZP consistently outperforming SEM. To our knowledge, this is the first large‐scale study to characterize GLP‐1 use patterns and weight outcomes in a rheumatology population using national registry data.
The uptake of GLP‐1 RAs among patients with RMD observed in our study mirrors national prescribing trends in the United States. 20 Specifically, the use of GLP‐1 RAs appeared higher in the South and Midwest compared with the Northeast and West. These variations may reflect regional differences in obesity prevalence, prescribing practices, and access to care. More broadly, the use of these drugs has expanded beyond individuals with diabetes to include those who are overweight and living with obesity. 21 Furthermore, recent FDA approvals for the indications of cardiovascular risk 22 reduction (date), obstructive sleep apnea (date), and renal outcomes (date) 23 likely encouraged clinicians to consider GLP‐1 RAs for a wider range of patients. 24 , 25 , 26 Although data on GLP‐1 RA use in RMD populations remain limited, their growing adoption is somewhat expected given the high prevalence of obesity and related cardiovascular comorbidities in these patients. 12 , 27 , 28 Thus, the increased use we observed in the RMD population appears to reflect broader clinical applications of these drugs.
We observed important weight reductions among new GLP‐1 RAs users, particularly through 12 months of follow‐up, a finding consistent for both agents (SEM and TZP) and across subgroups of patients with and without diabetes. The extent of weight loss observed was broadly comparable between the two drugs (with TZP users achieving more weight loss than SEM users), though less pronounced than what has been reported in real‐world cohorts of overweight or obese individuals and in clinical trials. 29 , 30 , 31 This may in part be a consequence of limitations in mobility leading to reduced exercise tolerance, other lifestyle factors, or RMD treatment considerations (eg, long‐term glucocorticoid use) that may lessen the benefit of pharmacotherapy for weight loss in patients with RMD. Nevertheless, these results are relevant because obesity is associated with greater disease activity and poorer treatment outcomes for many of the RMD conditions we studied. 32 , 33 , 34 Although our analysis focused on characterizing weight loss, previous studies have highlighted the importance of such reductions in RMD populations. For example, weight loss of ≥5% has been associated with significant improvements in patient‐reported disease activity and quality‐of‐life measures. 13 Similarly, a recent randomized controlled trial of SEM in patients with obesity and knee OA demonstrated not only weight reduction but also improvements in pain, raising the possibility of weight‐independent effects on disease activity. 35 Given the well‐established links between obesity, disease activity, and treatment response in RMDs, our findings underscore the therapeutic relevance of weight management using GLP‐1 RAs in these populations, with potential implications for improving long‐term outcomes.
We also observed heterogeneity in weight responses, with greater reductions among patients without diabetes compared to those with diabetes, and larger effects with TZP relative to SEM. These patterns are consistent with findings from randomized controlled trials and other real‐world studies, where weight loss tends to be more pronounced in individuals without diabetes. 36 , 37 Possible mechanisms include altered metabolic set points, physiologic adaptations related to chronic hyperglycemia, and concomitant glucose‐lowering therapies that may promote weight gain and offset GLP‐1 RA effects. 36 Our results extend these observations to a large RMD cohort, reinforcing their relevance in routine practice.
The greater weight reductions observed with TZP compared to SEM align closely with evidence from head‐to‐head clinical trials, which have consistently demonstrated the superior efficacy of TZP. 31 , 38 This likely reflects its dual mechanism of action, which activates both GLP‐1 and GIP receptors, thereby enhancing metabolic effects beyond those achieved with GLP‐1 receptor stimulation alone. Observing these differences in a large, real‐world rheumatology cohort underscores the importance of recognizing that not all GLP‐1 RAs are equivalent in terms of weight loss efficacy.
This study has important strengths. The use of the ACR RISE registry enabled capture of data from a broad geographic distribution of US rheumatologists, with detailed clinical information on disease activity, comorbidities, and medication use, enhancing the relevance of our findings to real‐world practice. Our real‐world observational design reflects prescribing behaviors and patient outcomes outside the highly controlled conditions of clinical trials, thereby offering insights into how SEM and TZP are used across diverse RMD populations, including those who may be older, have comorbidities, or differ in disease severity. Finally, the availability of longitudinal data up to 18 months allowed us to assess weight trajectories over time, contributing to understanding of the durability of weight loss in this setting.
However, several limitations must be acknowledged. First, we could not determine whether or how long patients remained on GLP‐1 RA therapy after the initial prescription, nor how high of a dose that they were able to achieve, because RISE EHR data contain information about prescribing collected at rheumatology provider visits, not pharmacy fill information. In addition, medications initiated between visits prescribed by nonrheumatology providers will only appear in the EHR at a subsequent encounter, preventing precise determination of the exact treatment initiation date. As a related issue, our analyses conservatively reflect an “intent‐to‐treat” approach, which classifies exposure based on prescribing and does not require any minimum amount of exposure or dose escalation. For this reason, it is likely that the magnitude of weight loss we observed is numerically smaller compared to an as‐treated analysis that censors patients if they discontinue GLP‐1 treatment. Moreover, the benefit of GLP‐1 drugs is known to be associated with dose, with patients able to escalate to higher doses typically lose more weight, a factor not assessable in this data source. Similarly, because GLP‐1 RAs are frequently managed by primary care or endocrinology, they are often captured in the registry via EHR medication reconciliation rather than direct prescribing records. Consequently, specific details such as prescribing provider specialty may be missing or inconsistently recorded, which precluded a descriptive analysis of prescribing patterns by specialty. Second, we cannot ascertain whether patients were offered but declined GLP‐1 RA treatment, or whether comorbidities precluded their use; our data only capture what was prescribed, not what was actually filled. Third, because comorbidities managed outside rheumatology may be underascertained in rheumatology EHRs, baseline risks and confounders may be misclassified. Fourth, at present, RISE data primarily capture the experience of patients with RMD treated in community practice settings, with a low representation of academic health systems. In addition, our findings may not generalize to patients managed outside of rheumatology care settings, or to non‐US populations. Finally, there remains potential for unmeasured confounding: factors influencing the decision to initiate GLP‐1 RA (physician preference, patient motivation, insurance coverage, and lifestyle) may correlate with outcomes. Despite these limitations, our observational data provide timely, relevant evidence about prescribing patterns and weight loss trajectories in patients with RMD and can help guide hypotheses and design of future trials. Future studies with longitudinal designs are needed to determine if the observed weight loss translates into sustained improvements in clinical disease control or a reduction in systemic inflammatory burden.
AUTHOR CONTRIBUTIONS
All authors contributed to at least one of the following manuscript preparation roles: conceptualization AND/OR methodology, software, investigation, formal analysis, data curation, visualization, and validation AND drafting or reviewing/editing the final draft. As corresponding author, Dr Curtis confirms that all authors have provided the final approval of the version to be published and takes responsibility for the affirmations regarding article submission (eg, not under consideration by another journal), the integrity of the data presented, and the statements regarding compliance with institutional review board/Declaration of Helsinki requirements.
Supporting information
Disclosure form.
Supplementary Table 1: Rheumatic and musculoskeletal disease (RMD) categories and groups
Supplementary Table 2: Baseline characteristics stratified by GLP‐1 receptor agonist type, semaglutide (SEM) or tirzepatide (TZP), and by diabetes status, Rheumatology Informatics System Effectiveness (RISE), 2018‐2024.
Supplementary Table 3: Factors associated with achieving weight loss thresholds (≥5%, ≥10%, ≥15%) at 12 months among new users of GLP‐1 receptor agonists, Rheumatology Informatics System Effectiveness (RISE), ‐2024
Supplementary Figure 1: Flow diagram of cohort derivation and data availability. Counts represent unique patients in the registry.
Supplementary Figure 2: Trends in GLP‐1 receptor agonists use across RMD groups, RISE, 2018‐2024
Supplementary Figure 3: Trends in GLP‐1 receptor agonists use across the four U.S. Census regions, RISE, 2018‐2024
Supported in part by the National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH (grant P30‐AR‐072583).
1Department of Health Outcomes Research and Policy, Auburn University Harrison College of Pharmacy, Auburn, Alabama; 2Foundation for Advancing Science, Technology, Education, and Research, Birmingham, Alabama; 3Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham Heersink School of Medicine; 4Department of Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania; 5Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia; 6Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia.
Additional supplementary information cited in this article can be found online in the Supporting Information section (https://acrjournals.onlinelibrary.wiley.com/doi/10.1002/acr2.90068).
Author disclosures are available at https://onlinelibrary.wiley.com/doi/10.1002/acr2.90068.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Disclosure form.
Supplementary Table 1: Rheumatic and musculoskeletal disease (RMD) categories and groups
Supplementary Table 2: Baseline characteristics stratified by GLP‐1 receptor agonist type, semaglutide (SEM) or tirzepatide (TZP), and by diabetes status, Rheumatology Informatics System Effectiveness (RISE), 2018‐2024.
Supplementary Table 3: Factors associated with achieving weight loss thresholds (≥5%, ≥10%, ≥15%) at 12 months among new users of GLP‐1 receptor agonists, Rheumatology Informatics System Effectiveness (RISE), ‐2024
Supplementary Figure 1: Flow diagram of cohort derivation and data availability. Counts represent unique patients in the registry.
Supplementary Figure 2: Trends in GLP‐1 receptor agonists use across RMD groups, RISE, 2018‐2024
Supplementary Figure 3: Trends in GLP‐1 receptor agonists use across the four U.S. Census regions, RISE, 2018‐2024