Abstract
In the SELECT trial, once-weekly subcutaneous semaglutide reduced major adverse cardiovascular events (MACE) by 20% versus placebo in patients with atherosclerotic cardiovascular disease and obesity but without diabetes. We examined semaglutide in SELECT patients at high risk for substantial liver fibrosis in a prespecified secondary analysis. Liver biochemical tests and steatosis risk according to fatty liver index were assessed over 104 weeks. Subgroup analyses of the primary MACE (a composite endpoint including cardiovascular death, nonfatal myocardial infarction or nonfatal stroke) outcome used baseline Fibrosis-4 scores ≥ 1.3, age-specific (≥1.3 (<65 years) or ≥2.0 (≥65 years)) and any age with Fibrosis-4 > 2.67. MACE was reduced by 26% (hazard ratio (HR) 0.74; 95% confidence interval (CI) 0.63–0.88; P = 0.0004), 21% (HR 0.79; 95% CI 0.63–0.98; P = 0.035) and 34% (HR 0.66; 95% CI 0.39–1.10; P = 0.11), respectively. Semaglutide led to a 28% greater decrease in fatty liver index versus placebo (HR 0.72; 95% CI 0.71–0.73; P < 0.0001). In conclusion, semaglutide reduced MACE versus placebo in patients at risk for substantial liver fibrosis, as seen in the overall SELECT population. ClinicalTrials.gov registration no. NCT03574597
Subject terms: Cardiovascular diseases, Medical research
A prespecified analysis from the SELECT trial showed that semaglutide reduces major adverse cardiovascular events by 20% compared with placebo, particularly in patients at high risk of fibrosis, as indicated by the Fibrosis-4 index.
Main
The coexistence of overweight and obesity with metabolic dysfunction-associated steatotic liver disease (MASLD) underscores a substantial and intricate interplay between metabolic dysregulation and hepatic pathology. Overweight and obesity are established risk factors for the development and progression of MASLD, contributing to the accumulation of hepatic triglycerides and the subsequent transition from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH) and advanced fibrosis1,2. This overlap reflects the bidirectional relationship between metabolic syndrome and hepatic steatosis, with adipose tissue dysfunction, systemic insulin resistance and subclinical inflammation having pivotal roles in the pathogenesis of MASLD and progression to fibrotic MASH and cirrhosis3. The global prevalence of MASLD is estimated to be ~30% and has risen in parallel with the global surge in obesity, making it an increasingly recognized health concern4,5.
MASH contributes as an independent risk factor for the development and progression of atherosclerosis and atherosclerotic cardiovascular disease (ASCVD)1,6. The severity of hepatic steatosis and fibrosis in MASLD/MASH appears to significantly impact the cardiovascular (CV) risk profile, with a direct association between the degree of steatosis or fibrosis and the incidence of major adverse CV events (MACE)7,8. The interplay between MASLD/MASH, hepatic steatosis and fibrosis, and MACE underscores the need for a comprehensive risk assessment tailored to this high-risk population and is pivotal to refining therapeutic interventions aimed at mitigating the dual burden of hepatic and CV morbidity and mortality.
The diagnosis of MASH has historically relied on liver biopsy, allowing for the direct histological assessment of key MASH aspects such as steatosis, inflammation and staging of fibrosis. However, the invasiveness and potential sampling variability of liver biopsy have spurred the search for noninvasive diagnostic modalities9,10. The Fibrosis-4 (FIB-4) index, calculated from readily available measures (age, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and platelet count), has emerged as a validated tool for identifying individuals at risk of fibrosis10. This offers a pragmatic alternative to liver biopsy in a clinical setting for patients with metabolic risk factors. The FIB-4 index enables risk stratification of low (<1.3), intermediate (1.3–2.67) and high risk (>2.67) for advanced fibrosis and major adverse liver events1 and further correlates with MACE8. As age is a factor in the FIB-4 equation, an age-adjusted higher cutoff of 2.0 in individuals older than 65 years was applied to avoid overestimation of advanced liver fibrosis risk11. These cutoff values allow for increased accuracy in advanced MASH fibrosis risk stratification.
In the SELECT CV outcome trial, which included patients aged 45 years or older with a body mass index (BMI) ≥ 27.0 kg m−2 and established ASCVD but without diabetes, those treated with the glucagon-like peptide-1 receptor agonist (GLP-1RA), semaglutide 2.4 mg, had a 20% MACE reduction compared with those receiving placebo on top of standard of care12. The aim of this prespecified analysis was to evaluate the impact of semaglutide on liver enzymes and suspected MASLD in patients from the SELECT trial, and to explore the CV benefits of semaglutide in a subgroup at risk for substantial fibrosis as defined by FIB-4.
Results
FIB-4 score data were available for 16,585 patients (94.2% of the total SELECT population). All patients were stratified according to FIB-4 score for their predicted risk of substantial liver fibrosis: 6,567 patients (37.3% of all) with an FIB-4 score ≥ 1.3, 3,664 patients (20.8% of all) either aged <65 years with an FIB-4 score ≥ 1.3 or aged ≥65 years with an FIB-4 score ≥ 2.0 and 509 (2.9% of all) patients with predicted substantial fibrosis (an FIB-4 score > 2.67 irrespective of age) were included in this analysis. As summarized in Table 1, the baseline characteristics of the respective subgroups were generally similar between the semaglutide and placebo groups, respectively. Notably, the use of statins was slightly lower in the FIB-4 > 2.67 subgroup (85.5%) compared with the other FIB-4 subgroups (88.9% for FIB-4 ≥ 1.3 and 89.3% for the age-dependent FIB-4 subgroup), but similar between semaglutide and placebo groups.
Table 1.
Baseline characteristics according to FIB-4 stratification
| Characteristics | FIB-4 ≥ 1.3 | FIB-4 ≥ 1.3/≥ 2.0 according to age (<65 years/≥65 years) |
FIB-4 > 2.67 (any age) | ||||
|---|---|---|---|---|---|---|---|
| Semaglutide (n = 3,293) | Placebo (n = 3,274) | Semaglutide (n = 1,833) | Placebo (n = 1,831) | Semaglutide (n = 246) | Placebo (n = 263) | ||
| Age, years | 66.7 (7.9) | 66.6 (7.8) | 63.5 (8.4) | 63.4 (8.2) | 71.8 (8.1) | 70.5 (8.6) | |
| Female, n (%) | 765 (23.2) | 743 (22.7) | 354 (19.3) | 350 (19.1) | 37 (15.0) | 59 (22.7) | |
| Body weight, kg | 95.2 (16.9) | 95.5 (17.3) | 96.7 (17.6) | 96.8 (17.6) | 94.2 (17.8) | 94.1 (18.8) | |
| BMI, kg m−2 | 32.8 (4.7) | 32.8 (4.7) | 32.9 (4.9) | 33.0 (4.8) | 32.6 (5.3) | 32.4 (4.7) | |
| Waist circumference, cm | 111.0 (12.5) | 111.1 (12.6) | 111.2 (12.7) | 111.0 (12.6) | 111.6 (12.6) | 110.9 (12.9) | |
| Systolic blood pressure, mmHg | 131.5 (16.0) | 131.7 (15.5) | 131.3 (16.1) | 130.9 (15.4) | 131.4 (16.7) | 132.4 (16.1) | |
| Diastolic blood pressure, mmHg | 77.8 (10.1) | 77.9 (10.0) | 78.6 (10.1) | 78.6 (9.8) | 76.1 (10.4) | 76.2 (10.2) | |
| HbA1c, % | 5.8 (0.3) | 5.8 (0.3) | 5.7 (0.3) | 5.7 (0.3) | 5.7 (0.4) | 5.7 (0.4) | |
| Pre-diabetes, n (%) | 2,061 (62.6) | 2,033 (62.1) | 1,120 (61.1) | 1,108 (60.5) | 147 (59.8) | 145 (55.1) | |
| Triglycerides, mg dl−1, median (IQR) | 125 (93–174) | 127 (94–181) | 125 (93–176) | 129 (93–184) | 115 (87–164) | 118 (85–174) | |
| CVD history | |||||||
| ≥2 CVDs, n (%)a | 345 (10.5) | 346 (10.6) | 175 (9.5) | 159 (8.7) | 40 (16.3) | 33 (12.5) | |
| Only MI | 2,217 (67.3) | 2,203 (67.3) | 1,271 (69.3) | 1,293 (70.6) | 159 (64.6) | 173 (65.8) | |
| Only stroke | 531 (16.1) | 516 (15.8) | 284 (15.5) | 266 (14.5) | 34 (13.8) | 38 (14.4) | |
| Only PAD | 129 (3.9) | 128 (3.9) | 63 (3.4) | 64 (3.5) | 7 (2.8) | 10 (3.8) | |
| MI + stroke | 165 (5.0) | 163 (5.0) | 82 (4.5) | 84 (4.6) | 20 (8.1) | 18 (6.8) | |
| MI + PAD | 128 (4.0) | 121 (3.7) | 71 (3.9) | 56 (3.1) | 16 (6.5) | 8 (3.0) | |
| Stroke + PAD | 28 (0.9) | 37 (1.1) | 11 (0.6) | 11 (0.6) | 2 (0.8) | 6 (2.3) | |
| MI + stroke + PAD | 17 (0.5) | 20 (0.6) | 7 (0.4) | 6 (0.3) | 0 | 1 (0.4) | |
| Other | 77 (2.3) | 86 (2.6) | 44 (2.4) | 51 (2.8) | 8 (3.3) | 9 (3.4) | |
| FIB-4 score distribution | |||||||
| Mean (s.d.) | 1.84 (0.63) | 1.86 (0.80) | 2.01 (0.73) | 2.04 (0.98) | 3.46 (1.07) | 3.65 (1.80) | |
| Median (IQR) | 1.66 (1.46–2.01) | 1.65 (1.45–2.01) | 1.84 (1.47–2.30) | 1.80 (1.47–2.31) | 3.12 (2.86–3.67) | 3.16 (2.84–3.77) | |
| Geometric mean ALT level (CoV), U l−1 | 23.3 (60.4) | 23.2 (59.8) | 25.1 (67.3) | 25.1 (65.5) | 23.2 (92.0) | 23.9 (93.3) | |
| Geometric mean AST level (CoV), U l−1 | 25.1 (37.7) | 25.1 (38.6) | 27.6 (40.8) | 27.6 (41.5) | 32.8 (56.8) | 34.5 (61.3) | |
| Geometric mean GGT (CoV), U l−1 | 42.2 (61.9) | 43.5 (67.6) | 49.2 (77.9) | 50.1 (84.8) | 72.0 (144.5) | 82.8 (172.6) | |
| Thrombocytes, 109 per liter | 194.8 (22.4) | 194.5 (23.0) | 181.5 (23.2) | 180.6 (23.4) | 146.4 (32.3) | 143.3 (32.6) | |
| Medications, n (%) | |||||||
| Statins | 2,914 (88.5) | 2,925 (89.3) | 1,625 (88.7) | 1,646 (89.9) | 211 (85.8) | 224 (85.2) | |
Data are shown as the mean (±s.d.) unless otherwise stated.
aPatient numbers for MI + stroke, MI + PAD, stroke + PAD and MI + stroke + PAD do not add up to the patient number presented for ≥2 CVDs because of differences in how missing data were handled for the individual components and ≥2 CVDs. CoV, coefficient of variation in %; HbA1c, glycated hemoglobin; IQR, interquartile range; MI, myocardial infarction; PAD, peripheral artery disease.
Cardiovascular outcomes
In the cohort with an FIB-4 score ≥ 1.3, the primary MACE endpoint occurred in 244 of 3,233 patients (7.5%) in the semaglutide group and in 321 of 3,274 patients (9.8%) in the placebo group (HR 0.74; 95% CI 0.63–0.88; P = 0.0004; Fig. 1a).
Fig. 1. Cumulative incidence of MACE in patients at risk for significant fibrosis according to respective FIB-4 stratification.
Data from the in-trial period. a, FIB-4 ≥ 1.3 (intermediate risk). b, FIB-4 ≥ 1.3/≥ 2.0 (intermediate risk according to age <65 years/≥65 years). c, FIB-4 > 2.67 (high risk irrespective of age). Cumulative incidence estimates are based on time from randomization to first EAC-confirmed MACE with non-CV death modeled as competing risk using the Aalen–Johansen estimator. P value: two-sided P value for test of no difference. Patients without events of interest were censored at the end of their in-trial observation period. Values underneath the figures are the number of patients. EAC, event adjudication committee.
In the second subgroup with an FIB-4 score ≥ 1.3 or aged ≥65 years with an FIB-4 score ≥ 2.0, the primary MACE endpoint occurred in 140 of 1,833 patients (7.6%) in the semaglutide group and in 176 of 1,831 patients (9.6%) in the placebo group (HR 0.79; 95% CI 0.63–0.98; P = 0.035; Fig. 1b).
In the third subgroup of 475 patients with suspected substantial fibrosis (FIB-4 score > 2.67 irrespective of age), the primary MACE endpoint occurred in 24 of 246 patients (9.8%) treated with semaglutide compared with 38 of 263 patients treated with placebo (14.4%), yielding an HR of 0.66 (95% CI 0.39–1.10; P = 0.11; Fig. 1c). The HRs and incidence rates of each respective component of MACE taken individually and on death from any cause are depicted in Fig. 2.
Fig. 2. HRs for the components of MACE and on death from any cause.
Values shown in the forest plot are HRs and 95% CIs. P value for test of no interaction effect. IR, incidence rate; N, analyzed patients.
The primary MACE endpoint was also assessed using the fatty liver index (FLI) in patients with or without suspected MASLD (FLI ≥ 60 or <60, respectively) and without fibrosis (FIB-4 < 1.3), with no interaction effect observed between treatment groups in those with an FLI < 60 (HR 0.73; 95% CI 0.44–1.19) compared with those with an FLI ≥ 60 (HR 0.88; 95% CI 0.74–1.04; P = 0.48; Extended Data Fig. 1). Additionally, adjusting for changes in body weight and glycated hemoglobin in a time-dependent Cox regression suggested that these variables had a negligible influence on the effect of semaglutide on the primary MACE outcome (Extended Data Tables 1 and 2).
Extended Data Fig. 1. Time from randomization to first MACE by change in FLI.
Values shown in forest plot are HR and 95% CIs. P value for test of no interaction effect. CI, confidence interval; CV, cardiovascular; FIB-4, Fibrosis-4; FLI, fatty liver index; HR, hazard ratio; IR, incidence rate; MACE, major adverse cardiovascular events; MI, myocardial infarction; N, analyzed patients.
Extended Data Table 1.
Time to first MACE Cox regression analysis with change in body weight as a time-dependent covariate
Analysis is based on a Cox proportional hazards model with treatment (semaglutide, placebo) as a fixed factor, baseline body weight as a covariate and change in body weight after randomization as a time-dependent covariate. Missing measurements at planned visits were imputed with LOCF.
CI, confidence interval; LOCF, last observation carried forward; MACE, major adverse cardiovascular event.
Extended Data Table 2.
Time to first MACE Cox regression analysis with change in HbA1c as a time-dependent covariate
Analysis is based on a Cox proportional hazards model with treatment (semaglutide, placebo) as a fixed factor, baseline HbA1c as a covariate and change in HbA1c after randomization as a time-dependent covariate. Missing measurements at planned visits were imputed with LOCF.
CI, confidence interval; HbA1c, glycated hemoglobin; LOCF, last observation carried forward; MACE, major adverse cardiovascular event.
Liver measures and indices
Baseline characteristics for liver parameters and indices from the whole SELECT population are depicted in Table 2. From baseline values within the normal range, ALT, AST and gamma-glutamyl transferase (GGT) levels declined after initiation of semaglutide treatment, reaching a nadir at approximately 20 weeks, after which ALT and AST levels increased back toward the pretreatment baseline over time while GGT levels remained relatively stable (Fig. 3a–c). Mean ± standard deviation (s.d.) changes from baseline of ALT, AST and GGT levels upon treatment with semaglutide versus placebo were −1.83 ± 0.24 versus 0.07 ± 0.26 U l−1, −0.45 ± 0.15 versus 0.47 ± 0.16 U l−1 and −7.18 ± 0.47 versus 0.99 ± 0.50 U l−1, respectively. The corresponding estimated treatment difference versus placebo at week 104 was −1.9 U l−1 (95% CI −2.6 to −1.2; P < 0.0001), −0.9 U l−1 (95% CI −1.4 to −0.5; P < 0.0001) and −8.2 U l−1 (95% CI −9.5 to −6.8; P < 0.0001), respectively. The relative decrease from baseline at week 104 with semaglutide versus placebo treatment was 6% (95% CI 0.93–0.95; P < 0.0001) for ALT, 3% (95% CI 0.96–0.98; P < 0.0001) for AST and 17% (95% CI 0.82–0.84; P < 0.0001) for GGT. Mean ± s.d. FLI decreased by 18.63 ± 0.21 with semaglutide treatment versus 2.49 ± 0.19 with placebo (Fig. 3d), representing a treatment difference of 16.1 (95% CI −16.7 to −15.6; P < 0.0001), thereby showing a 28% greater decrease in FLI (95% CI 0.71–0.73; P < 0.0001) with semaglutide versus placebo. A higher proportion of patients treated with semaglutide showed improvement from baseline FLI compared with placebo (26.1% versus 11.6%, respectively), and a lower proportion showed worsening of FLI (3.0% versus 7.8%, respectively). As reported in the primary analysis of SELECT, semaglutide treatment also resulted in a −9.39% mean change in body weight over 104 weeks versus −0.88% with placebo (P < 0.0001)12.
Table 2.
Baseline characteristics of biochemical liver tests
| Characteristic | Semaglutide (n = 8,803) |
Placebo (n = 8,801) |
Overall (N = 17,604) |
|---|---|---|---|
| ALT, na | |||
| <24 U l−1 | 4,237 | 4,273 | 8,510 |
| ≥24 U l−1 | 4,320 | 4,290 | 8,610 |
| Geometric mean (CoV), U l−1 | 24.0 (53.6) | 24.0 (53.3) | 24.0 (53.4) |
| AST, na | |||
| <22 U l−1 | 4,265 | 4,290 | 8,555 |
| ≥22 U l−1 | 4,349 | 4,350 | 8,699 |
| Geometric mean (CoV), U l−1 | 22.3 (34.4) | 22.3 (34.8) | 22.3 (34.6) |
| GGT, na | |||
| <30 U l−1 | 4,694 | 4,689 | 9,383 |
| 30– < 60 U l−1 | 2,846 | 2,854 | 5,700 |
| ≥60 U l−1 | 1,185 | 1,199 | 2,384 |
| Geometric mean (CoV), U l−1 | 30.8 (69.4) | 30.8 (69.8) | 30.8 (69.6) |
| FLI, na | |||
| <85 | 4,247 | 4,291 | 8,538 |
| ≥85 | 4,423 | 4,395 | 8,818 |
| Geometric mean (CoV) | 77.7 (27.6) | 77.9 (27.2) | 77.8 (27.4) |
| FIB-4 index, na | |||
| ≥1.3 | 3,293 | 3,274 | 6,567 |
| ≥1.3/≥ 2.0 according to age (< 65/≥ 65 years) | 1,833 | 1,831 | 3,664 |
| >2.67 | 246 | 263 | 509 |
| Geometric mean (CoV) | 1.2 (44.2) | 1.2 (44.1) | 1.2 (44.1) |
Full analysis set. aData available for 17,120 (ALT), 17,254 (AST), 17,467 (GGT), 17,356 (FLI) and 16,585 (FIB-4) patients at baseline.
Fig. 3. Changes in liver parameters over time according to treatment group.
a–d, Ratio to baseline and standard errors of ALT (a), AST (b), GGT (c) and FLI (d) at baseline, and weeks 26, 52 and 104. Observed data from the in-trial period. Error bars are ±s.e. of the mean on the logarithmic scale back-transformed to natural scale with the exponential. P value: two-sided P value for test of no difference. Exact P values: P = 6.180993 × 10−21 (a); P = 3.497464 × 10−10 (b); P = 1.01892 × 10−170 (c); P < 0.0000000000000001 (d). The numbers shown below the graphs represent the number of patients contributing to the means. Measurements at weeks 4–16 are for a subpopulation of 6,011 patients from European countries in which extra laboratory measurements were mandated by safety guidance (Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Republic of Ireland, Italy, Latvia, the Netherlands, Norway, Poland, Portugal, Romania, Spain, Sweden and the UK). ETD, estimated treatment difference.
Discussion
This prespecified analysis of the SELECT trial yielded insights into the hepatic benefits of semaglutide treatment in individuals with overweight or obesity and established ASCVD but without diabetes, and established the cardioprotective benefits of semaglutide in the subgroup with high likelihood of substantial liver fibrosis based on the FIB-4 index. Within the SELECT population, there is a clinically notable proportion of approximately one of five patients at concomitant risk for substantial fibrosis as predicted by the FIB-4. As shown here, the primary results from the SELECT trial of a 20% MACE reduction in patients with established ASCVD and overweight or obesity12 is also seen in the subgroup of these patients with concurrent MASLD and at risk for substantial fibrosis. Further corroborating its metabolic benefits, parameters of liver health and steatosis improved significantly with semaglutide treatment in this population. Our comprehensive analysis, although hypothesis-generating, provides valuable clinical implications, offering a platform for understanding the potential of semaglutide as a multifaceted therapeutic agent.
Semaglutide 2.4 mg is currently under investigation for the specific treatment of MASH in patients with MASH and F2–F3 fibrosis in the phase III ESSENCE trial. Recently, results from the first 72-week interim analysis of 800 patients in ESSENCE showed that semaglutide 2.4 mg leads to significant resolution of MASH, improvement in liver fibrosis and improvements in cardiometabolic risk factors compared with placebo13. Moreover, improvements in AST and ALT levels with semaglutide versus placebo were apparent as early as week 12 (ref. 13). How this translates or contributes to the CV benefits of semaglutide in patients with MASH remains to be shown during the course of this ongoing phase III trial.
Recognizing the current knowledge on incretin-based therapies in metabolic disease, clinical practice guidelines for the treatment of people living with MASH already recommend optimizing the management of respective comorbidities such as type 2 diabetes and obesity by treatment with GLP-1RAs1. Therefore, the findings of our current analysis are relevant, shedding light on the potential of semaglutide in mitigating CV risk in individuals with overweight or obesity and established ASCVD and concurrent MASLD, at least to the same extent as in patients with overweight or obesity and ASCVD, regardless of MASH.
When interpreting these results from a mechanistic perspective, there are commonly shared pathways driving both ASCVD and MASH. Adipose tissue dysfunction, systemic insulin resistance and subclinical inflammation at the systemic and tissue levels are established pathophysiological key drivers for both ASCVD14–16 and MASH17–19. In turn, GLP-1RA treatment, directly and indirectly, addresses these pathways by reducing visceral fat mass and improving adipose tissue health and glucose homeostasis20. This substantially reduces the flux of free fatty acids and lipotoxic lipids, and improves glucose metabolism in the liver, along with decreasing inflammation and restoring normal adipokine levels21. Semaglutide has been shown to reduce markers of systemic inflammation, for example, high-sensitivity C-reactive protein22 and tissue-specific inflammation in the liver23 and heart24–26, and increases the secretion of adiponectin, an insulin-sensitizing adipokine27.
In addition to the CV outcomes, this analysis unveiled that semaglutide treatment significantly improved liver enzymes and FLI. The observed reductions in ALT, AST and GGT levels, along with the substantial decrease in FLI, underscore the potential of semaglutide not only in addressing CV risk but also its potential in concurrently ameliorating liver injury in people with overweight or obesity but without diabetes. Furthermore, the significant weight loss induced by semaglutide12 aligns with the known benefits of weight reduction in liver steatosis and inflammation28,29. Sustained weight loss ameliorates parameters of liver health in a dose-dependent manner; therefore, it is recommended by the EASL–EASD–EASO Clinical Practice Guidelines on the management of MASLD1. Whether achieved by dietary means, pharmacological intervention or bariatric surgery30–33, there is evidence that a body weight reduction of 5% or more is required to reduce liver lipid content, 7–10% to improve hepatic inflammation and 10% or more to improve fibrosis1. It is worth noting that in SELECT, the beneficial effects on ALT, AST and GGT levels occurred early after initiation of semaglutide, a similar pattern seen with glycemia34, but that maximal weight loss did not occur until beyond 52 weeks12, suggesting the impact on liver enzymes was mediated by more than just weight loss. The biology of weight loss after dietary intervention versus medical treatment versus bariatric surgery is different; therefore, the mode of weight loss may have implications for the clinical benefits.
Although the findings of this analysis present compelling evidence for the clinical relevance of semaglutide in addressing ASCVD and MASLD in individuals living with overweight or obesity, it is essential to acknowledge potential methodological limitations. Longitudinal studies assessing the sustained impact of semaglutide on liver health and CV outcomes are imperative (and ongoing) to elucidate its long-term benefits and inform optimal treatment durations. Although the observed MACE reduction with semaglutide treatment in this population at high risk for further CV events is acknowledged, the clinical relevance of improvement in biochemical liver tests within the normal range with GLP-1RAs is less evident35. While the subgroup with an FIB-4 ≥ 1.3 showed substantially lower incidence of CV events with semaglutide versus placebo, this pattern was observed as a trend across intermediate-risk and high-risk FIB-4 groups (FIB-4 < 1.3 versus ≥1.3 and FIB-4 > 2.67 versus FIB-4 ≤ 2.67). As this analysis was not powered to reach significance because of the subcategory population sizes in the intermediate-risk and high-risk FIB-4 groups, further investigations into these populations are needed. Additionally, noninvasive surrogate measures to determine liver health (such as FIB-4 and FLI) are limited in diagnostic accuracy and may need to be considered in complement to liver biopsy and histological techniques36.
In summary, the improvements in CV outcomes associated with semaglutide treatment highlight its potential as a therapeutic option for individuals with overweight or obesity and established ASCVD and concurrent MASH. Furthermore, hepatic parameters improving with semaglutide treatment in the whole SELECT population, irrespective of MASH, offers a rationale for the integration of comprehensive management strategies that address the interconnected burden of hepatic and CV complications in people with overweight and obesity.
Methods
Study design and patients
The protocol for SELECT was approved by the institutional review board and ethics committee at each participating center12. All patients provided written informed consent before commencement of any trial-specific activity.
The current work reports a prespecified analysis of SELECT, a randomized, double-blind, placebo-controlled, event-driven CV outcome trial (NCT03574597), details of which have been reported previously12,37,38. The trial evaluated once-weekly subcutaneous semaglutide 2.4 mg versus placebo in reducing the risk of MACE (a composite endpoint comprising CV death, nonfatal MI or nonfatal stroke) in individuals with established ASCVD and overweight or obesity but without diabetes.
Eligible patients were aged ≥45 years with a BMI of ≥27 kg m−2 and established ASCVD, defined as at least one of the following: prior MI, prior ischemic or hemorrhagic stroke, or symptomatic peripheral artery disease). Exclusion criteria included prior MI or stroke, hospitalization for unstable angina pectoris or a transient ischemic attack, all within 60 days of screening; glycated hemoglobin ≥6.5% (48 mmol mol−1); history of type 1 or 2 diabetes; New York Heart Association class IV heart failure; presence of end-stage kidney disease; or need for chronic or intermittent dialysis. A total of 17,604 patients were randomized (1:1) to escalating doses of once-weekly subcutaneous semaglutide over 16 weeks to a target dose of 2.4 mg (n = 8,803) or placebo (8,801)38. The mean (±s.d.) duration of exposure to semaglutide or placebo in the SELECT trial was 34.2 (13.7) months; the mean (±s.d.) duration of follow-up was 39.8 (9.4) months12.
This prespecified analysis explored the effect of semaglutide on the primary composite endpoint of MACE for patients at risk for substantial fibrosis as indicated by an FIB-4 score of 1.3 or greater. To adjust for patients’ ages, the analysis was also performed in a subgroup with an FIB-4 score of 1.3 or greater for patients aged less than 65 years and an FIB-4 score of 2.0 or greater for patients aged 65 years or older (FIB-4 age-specific) and for a further subgroup of patients of any age with an FIB-4 score greater than 2.67. After review of medical history and co-medication, patients with an etiology of liver disease other than MASH were not included in this analysis.
In addition, the aim of this analysis was to assess the impact of semaglutide on liver health in the overall population of patients in SELECT with overweight or obesity and established ASCVD. Liver enzymes, ALT, AST and GGT, as well as FLI, an algorithm based on BMI, waist circumference, triglycerides and GGT predictive of hepatic steatosis39, were assessed over 104 weeks.
Blood samples were collected at patient enrollment or randomization, then subsequently at weeks 20, 52, 104, 156, 208 and the end of the patient’s treatment phase. In certain European countries, additional blood tests were undertaken between weeks 4 and 16 given mandated safety requirements (Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Republic of Ireland, Italy, Latvia, the Netherlands, Norway, Poland, Portugal, Romania, Spain, Sweden and the UK).
Statistical analysis
The statistical analyses were based on the intention-to-treat principle using data from the in-trial period and included all randomized patients irrespective of adherence to semaglutide or placebo or changes to background medications. Time-to-event endpoints were analyzed using a Cox proportional hazards model with treatment group (semaglutide, placebo) as fixed factors together with the two-sided 95% CI and two-sided P values; subgroup analyses were based on the same model by adding an interaction between treatment group and the specific subgroup as a factor. Continuous endpoints were analyzed using an analysis of covariance model with treatment as a fixed factor and baseline value of the endpoint as a covariate, where the ratio to baseline and the corresponding baseline value were log-transformed before the analysis. Missing data at week 104 were imputed using a multiple imputation model, conducted separately for each treatment arm and including baseline value as a covariate, and fitted to patients having an observed datapoint (irrespective of adherence to randomized treatment) at week 104. The fit model was used to impute values for all patients with missing data at week 104 to create 500 complete datasets. Rubin’s rules were used to combine the results. CIs were not adjusted for multiplicity; therefore, they should not be used to infer definitive treatment effects. All statistical analyses were performed using the SAS software v.9.4 (SAS Institute).
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41591-026-04281-1.
Supplementary information
Full list of SELECT Consortia investigators.
Source data
Statistical source data.
Statistical source data.
Statistical source data.
Statistical source data.
Acknowledgements
Administrative and editorial support was provided by R. Munir of Apollo, OPEN Health Communications, and funded by Novo Nordisk A/S, in accordance with Good Publication Practice guidelines (www.ismpp.org/gpp-2022). This study was funded by Novo Nordisk A/S and is registered with ClinicalTrials.gov (registration no. NCT03574597). The funder was responsible for the study design and contributed to data collection, analysis and interpretation, and participated in the preparation and review of the manuscript in collaboration with the authors. All authors had full access to the data and all authors had final responsibility for the decision to submit for publication.
Extended data
Author contributions
S.M.M. and O.K.J. contributed to the data analysis. S.M.M. drafted the initial manuscript. All authors contributed to data interpretation, provided substantive contributions to the revisions and approved the final version of the submitted manuscript.
Peer review
Peer review information
Nature Medicine thanks Clemence Canivet and Paul Kwo for their contribution to the peer review of this work. Primary Handling Editor: Ashley Castellanos-Jankiewicz, in collaboration with the Nature Medicine team.
Data availability
Authorized researchers can request access to clinical trial data by submitting a research proposal for review and approval by Novo Nordisk and an internal independent review panel. Requests are usually considered after the research is finished and the main results have been published. If the research supports a regulatory application, requests will be considered after the product and its intended use are approved in both the European Union and the USA. Participant clinical data will be anonymized, after an approved internal process, before they are shared with external third parties. For details on how to request access to clinical data, visit novonordisk-trials.com or contact Maria Quiroga (mdzp@novonordisk.com). Source data are provided with this paper.
Competing interests
S.M.M. has received consulting honoraria from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Daichii-Sankyo, Eli Lilly, esanum, Gilead, Ipsen, Novartis, Novo Nordisk, Sandoz and Sanofi, and has received research grants from AstraZeneca, Eli Lilly and Novo Nordisk. B.C. has received consulting honoraria from Abbott, Amgen, AstraZeneca, Bristol Myers Squibb, Eli Lilly, Novartis, Novo Nordisk, Sanofi and Ultragenyx, and research funding to his institution from Air Liquide, Sanofi and Ypsomed. C.C. has received consulting honoraria from Brace Pharma, Eli Lilly, Eurofarma, Merck and Novo Nordisk. H.M.C. has served on advisory panels for Bayer and Novo Nordisk, has received research funding from IQVIA, Roche and Sanofi, has received grants from the Chief Scientist Office, Diabetes UK, the European Commission, the Juvenile Diabetes Research Foundation and the Medical Research Council, has served on a speaker’s bureau for Novo Nordisk and holds stock in Bayer and Roche Pharmaceuticals. J.D. has received consulting honoraria from Aegerion, Amgen, Bayer, Boehringer Ingelheim, Merck, Novartis, Novo Nordisk, Pfizer, Sanofi and Takeda, and research grants from Aegerion, the British Heart Foundation, Colgate, the Medical Research Council (UK), Merck Sharp & Dohme, the National Institute for Health and Care Research, Pfizer, Public Health England and Roche. M.T.L., O.K.J. and M.Q. are employees of Novo Nordisk. A.M.L. has received research grants from AbbVie, AstraZeneca, CSL Behring, Eli Lilly, Esperion Therapeutics and Novartis, paid to his institution, and has served as a consultant for Akebia Therapeutics, Alnylam Pharmaceuticals, Ardelyx, Canary Cure, Eli Lilly, FibroGen, GlaxoSmithKline, Intarcia, Medtronic Vascular, the Novartis Pharmaceuticals Corporation, Novo Nordisk, Provention Bio, and ReCor Medical. I.L. has received research grants from Boehringer Ingelheim, Merck, Mylan Pharmaceuticals, Novo Nordisk, Pfizer and Sanofi US Services, has served as a consultant for AstraZeneca, Bayer Healthcare Pharmaceuticals, Biomea, Boehringer Ingelheim, Carmot, Eli Lilly, Intarcia, Intercept Pharmaceuticals, Janssen Global Services, Johnson & Johnson Medical Devices & Diagnostics Group-Latin America, MannKind Corporation, Merck, Novo Nordisk, Pfizer, Sanofi US Services, Shionogi, StructureTherapeutics, Target Pharma, Valeritas and Zealand Pharma, and has received travel expenses from Boehringer Ingelheim, Eli Lilly, Johnson & Johnson Medical Devices & Diagnostics Group-Latin America, Novo Nordisk, Sanofi US Services and Zealand Pharma. J.P. has received consulting honoraria from Altimmune, Amgen, Esperion Therapeutics, Merck, MJH Life Sciences, Novartis and Novo Nordisk, has received a grant, paid to his institution, from Boehringer Ingelheim and holds the position of Director, Preventive Cardiology at Brigham and Women’s Hospital. P.N.N. has received consulting fees from Aligos, Boehringer Ingelheim, Madrigal, Novo Nordisk, Sagimet, Shionogi and 89bio. S.J.N. has received research support from Amgen, Anthera, AstraZeneca, Cerenis, CSL Behring, Cyclarity, Eli Lilly, Esperion, Infraredx, New Amsterdam Pharma, Novartis, Resverlogix and Sanofi-Regeneron, and is a consultant for Akcea, Amgen, AstraZeneca, Boehringer Ingelheim, CSL Behring, CSL Sequiris, Eli Lilly, Esperion, Kowa, Merck, Novo Nordisk, Pfizer, Sanofi-Regeneron, Takeda and Vaxxinity. F.S. has worked as a consultant for, participated in studies for or received travel funds from the following companies that are involved with obesity, lipodystrophy or diabetes: Aegerion/Amryt, BioItalia, Boehringer Ingelheim, Bruno Pharma, Lilly, Novo Nordisk and Pfizer. A.J.S. consults for and advises AstraZeneca and Avant Santé, consults for and has received grants from Akero, Bristol Myers Squibb, Eli Lilly, Intercept, Madrigal and Novo Nordisk, consults for and owns stock in Rivus, consults for 89bio, AGED Diagnostics, Albireo, Alnylam, Altimmune, Boehringer Ingelheim, Echosens, Genentech, Gilead, GlaxoSmithKline, HistoIndex, Mallinckrodt, Merck, NGM Bio, Novartis, PathAI, Pfizer, Poxel, Regeneron, Salix, Siemens, Surrozen, Takeda, Terns and Zydus, owns stock in Durect, Exalenz, Genfit, Indalo, Inversago and Tiziana, and has received royalties from Elsevier and Wolters Kluwer. S.E.K. has received consulting honoraria from Anii Pharmaceuticals, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk and Oramed, and stock options from Altpep.
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Extended data
is available for this paper at 10.1038/s41591-026-04281-1.
Supplementary information
The online version contains supplementary material available at 10.1038/s41591-026-04281-1.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Full list of SELECT Consortia investigators.
Statistical source data.
Statistical source data.
Statistical source data.
Statistical source data.
Data Availability Statement
Authorized researchers can request access to clinical trial data by submitting a research proposal for review and approval by Novo Nordisk and an internal independent review panel. Requests are usually considered after the research is finished and the main results have been published. If the research supports a regulatory application, requests will be considered after the product and its intended use are approved in both the European Union and the USA. Participant clinical data will be anonymized, after an approved internal process, before they are shared with external third parties. For details on how to request access to clinical data, visit novonordisk-trials.com or contact Maria Quiroga (mdzp@novonordisk.com). Source data are provided with this paper.