Contemporary treatment patterns of overweight and obesity: insights from the Mass General Brigham health care system.

Study Importance What is already known? The global prevalence of overweight and obesity continues to expand at a pandemic scale. Safe and highly effective pharmacotherapeutic strategies have emerged for the treatment of obesity and related conditions. Analyses describing the provision of contemporary obesity‐related care, including antiobesity medications (AOMs), have been limited. What does this study add? In this cross‐sectional analysis, 45% of all adults in a large, integrated US health care system were eligible for AOMs between 2018 and 2022. Despite broad eligibility, only 1% of eligible adults were prescribed AOMs. Female sex, younger age, higher BMI, commercial health insurance, and greater burden of obesity‐related conditions were associated with a higher prevalence of AOM prescriptions. How might these results change the direction of research or focus of clinical practice? These findings emphasize the need for novel health care delivery efforts to accelerate the closure of considerable gaps in the implementation of AOMs.

INTRODUCTION

The global prevalence of obesity has more than tripled over the past five decades and continues to expand at a pandemic scale [ 1 ]. According to contemporary estimates, over one‐half of the world's adult population, amounting to >3 billion individuals, is forecasted to have either overweight or obesity by 2035 [ 2 ], with implications for attendant increases in preventable premature death, disability, multimorbidity, and health care spending [ 3 , 4 , 5 , 6 ]. In the United States alone, obesity was associated with an estimated $261 billion in direct medical costs in 2016 [ 7 ].

Behavioral and lifestyle counseling are widely recommended and remain essential features of contemporary weight‐management approaches [ 8 , 9 ] but have failed to curtail progression of this public health priority. Even when comprehensively and intensively implemented, long‐term weight reduction tends to be limited to lifestyle‐oriented interventions alone, and cardiovascular outcomes are not consistently reduced even in high‐risk populations [ 10 ]. Furthermore, although metabolic surgery has traditionally been the most durable therapeutic option for well‐selected patients with obesity [ 8 ], there are important short‐ and long‐term risks, limitations to scalability, and up‐front cost and resource considerations. However, the emergence of safe and highly effective pharmacotherapeutic strategies has empowered patients, providers, and health care systems with new tools to achieve substantial weight reduction and improve health outcomes [ 11 , 12 ]. As such, when coupled with comprehensive lifestyle interventions and systematic efforts to address biopsychosocial and environmental root causes, implementation of antiobesity medications (AOMs) may help to address the rising prevalence and impact of overweight and obesity.

Characterization of treatment patterns for overweight and obesity in routine clinical care is critical to inform health care service design, public health policy, and research and training priorities, thereby improving implementation and patient outcomes. However, data describing the provision of contemporary obesity‐related care are limited. In this cross‐sectional analysis including individual‐level data from a large, integrated US tertiary care system, we systematically evaluated eligibility and allocation of major weight‐management interventions, with a focus on AOM prescription. These trends, as well as linkage to specialized obesity medicine care, were evaluated according to important sociodemographic factors and the extent of obesity‐related comorbidity burden.

METHODS

Data source

Data for this analysis were extracted from the Mass General Brigham (MGB) Enterprise Data Warehouse (EDW), a centralized and continuously updated data registry based on electronic health records (EHR) that consolidates clinical information from all sites within the MGB network. In brief, MGB (previously known as the Partners HealthCare system) is a large and integrated health care system that includes Brigham and Women's Hospital and Massachusetts General Hospital. The MGB health care system is located predominantly in the greater Boston, Massachusetts, area and serves a diverse population of more than 1.5 million patients annually. The EDW provides demographic and laboratory data, medical histories, diagnostic and procedural codes, medication records, vital signs and status, and relevant clinical documentation for patients meeting specified query criteria. This study was approved by the MGB Institutional Review Board.

Study design and patient population

All adults (aged ≥18 years) with body mass index (BMI) ≥ 18.5 kg/m 2 and ≥1 visit within the MGB health care system between January 1, 2018, and January 1, 2023, were identified. Overweight and obesity were defined according to World Health Organization thresholds, namely BMI (reported as weight in kilograms divided by height in meters squared throughout) between 25 and 29.9 and ≥30, respectively. The following criteria were then used to ascertain eligibility for receiving AOMs: BMI ≥ 30 or ≥27 with ≥1 obesity‐related condition (ORC) [ 9 ]. Individuals with active pregnancy, type 1 diabetes mellitus, end‐stage kidney disease, or cirrhosis or those receiving end‐of‐life care were excluded. Administrative codes and EHR‐based definitions employed for identification of each exclusion criterion are reviewed in Table S1 .

Individuals eligible for AOMs were categorized by most recent BMI (27–29.9, 30–34.9, 35–39.9, and ≥40) and described according to sociodemographic characteristics, insurance type, medication prescriptions, ORCs (number and type), and laboratory data. Standard units and reference ranges for reported laboratory values are presented in Table S2 . Eligibility for metabolic surgery was also evaluated in accordance with contemporary guidelines (BMI ≥ 35) [ 13 ]. Although these guidelines additionally support consideration for metabolic surgery among selected individuals with BMI between 30 and 34.9 if ≥1 concomitant metabolic condition was present, we did not include this as part of our eligibility criteria because we were unable to rigorously confirm individual‐level receipt of optimal nonsurgical approaches.

Identification of ORCs

Overweight and obesity are major upstream risk factors for the onset and progression of several chronic conditions [ 5 , 14 , 15 ], termed ORCs. In this analysis, we prespecified the inclusion of 18 diverse ORCs (Table S3 ), which were selected based on available evidence supporting a strong association with overweight and obesity [ 5 , 8 , 13 , 14 , 16 , 17 , 18 ]. Individual‐level ORCs were extracted from the MGB EDW using the International Classification of Diseases, Tenth Revision (ICD‐10) codes and EHR‐based problem lists.

AOM prescribing, metabolic surgery, and referrals to weight‐management clinics

In order to establish a contemporary perspective of multimodality obesity‐related care, we assessed AOM prescriptions (by type and as a proportion of all eligible individuals), metabolic surgery, and referrals to MGB weight‐management clinics. Specifically, prescriptions for AOMs approved by the US Food and Drug Administration (FDA) for long‐term weight management during the study period of January 1, 2018, to January 1, 2023 (naltrexone‐bupropion, orlistat, phentermine‐topiramate, liraglutide 3.0 mg, and semaglutide 2.4 mg) were extracted using medication records. Prescription patterns of other selected pharmacotherapies, including those indicated for short‐term obesity management (e.g., phentermine) and others known to reduce weight but that have not been indicated for long‐term obesity management (e.g., sodium‐glucose co‐transporter 2 inhibitors), were also evaluated. For a more comprehensive perspective, we additionally evaluated temporal trends in new AOM prescriptions over the study period by AOM type and prescriber specialty. The latter was determined through a combination of prescriber departmental affiliation and prescribing location, which were manually reviewed by one author (John W. Ostrominski) to ensure accuracy.

In order to assess whether patient profiles varied in relationship to AOM status (prescription vs. no prescription), sociodemographic, insurance, and comorbidity data were compared among groups. AOM prescriptions by number of ORCs (0, 1, 2, 3, 4, 5, 6, 7 and ≥8), by presence or absence of a prior major adverse cardiovascular event (MACE), and by type 2 diabetes (T2D) status were also compared. In this study, MACE was defined as cardiovascular death, nonfatal coronary artery disease events (acute coronary syndrome, myocardial infarction), coronary revascularization (percutaneous coronary intervention or coronary artery bypass grafting), nonfatal ischemic stroke, heart failure, or nonfatal adverse limb events (Table S4 ). Prior metabolic surgery was determined using surgical histories and relevant ICD/ Current Procedural Terminology (CPT) codes, as detailed in Table S4 . Referral for specialized obesity medicine care was also evaluated and expressed as the number of individuals referred to an MGB weight‐management clinic.

Statistical analysis

Categorical variables are reported as frequencies and proportions and were compared using χ 2 tests with Yates continuity correction. Continuous variables are reported as means with standard deviations (SD) or medians with interquartile ranges (IQR). In order to assess whether the prevalence of each ORC varied according to AOM prescription status, prevalence ratios (unadjusted) were calculated as the proportion of AOM‐eligible individuals with each ORC who were prescribed AOMs divided by the proportion of individuals with the same ORC who were not prescribed AOMs. Two‐sample t tests or one‐way ANOVA tests was used to compare groups depending on the number of subgroups examined. Statistical analyses were performed using Python SciPy (package version 1.10.1). Two‐sided p < 0.05 was considered significant.

RESULTS

Patient characteristics

Of 4,789,098 individuals with any encounter in the MGB system between January 1, 2018, and January 1, 2023, we identified 2,614,152 patients aged 18 to 80 years with a recorded BMI, of whom 144,678 (5.5%) were excluded due to having BMI < 18.5. As such, a total of 2,469,474 patients (mean [SD], age 53 [19] years; 57% female; BMI 28.1 [6.3]) comprised the final cohort (Figure S1 ). Of these, a total of 1,614,062 (65.4%) had either overweight or obesity. Among the 770,074 (31.2%) individuals with obesity, 455,249 (59.1%), 192,775 (25.0%), and 122,050 (15.8%) had BMI of 30 to 34.9, 35 to 39.9, and ≥40, respectively (Table 1 ). Prior metabolic surgery was observed in 26,377 individuals (1.1%). Obesity was explicitly listed as a diagnosis in the EHR in 21.0%, 39.3%, and 49.7% of individuals with class I, class II, and class III obesity, respectively.

In the contemporary MGB population, adults with overweight or obesity tended to be older and more often male compared with adults with BMI in the normal range (Table 1 ). However, among those with obesity (BMI ≥ 30), increasing BMI category was associated with a greater proportion of female sex and younger age. Conversely, older age (i.e., age >65 years) was less common among individuals with BMI ≥ 40 compared with BMI between 30 and 34.9. The proportion of individuals with self‐reported Black race (3.6% vs. 8.6% among those with BMI 18.5–24.9 vs. ≥40, respectively) and Hispanic ethnicity (5.8% vs. 10.4% among those with BMI 18.5–24.9 vs. ≥40, respectively) increased with increasing BMI category. Additionally, the proportion of patients with Medicaid insurance, a joint federal and state program that provides insurance to individuals with limited income, increased with increasing BMI category, whereas commercial insurance decreased. Generally, individuals with higher BMI were more likely to be active or former smokers and were more likely to have multiple cardiovascular risk factors (Table 1 ).

Heterogeneity was observed for most laboratory values among BMI categories. Individuals with a higher BMI category tended to have worse metabolic profiles, namely lower high‐density lipoprotein cholesterol levels (mean [SD], 48 [14] vs. 65 [20] mg/dL among those with BMI ≥ 40 vs. 18.5–24.9, respectively) and higher levels of triglycerides (149 [102] vs. 97 [63] mg/dL among those with BMI ≥ 40 vs. 18.5–24.9, respectively), serum glucose (141 [60] vs. 127 [54] mg/dL among those with BMI ≥ 40 vs. 18.5–24.9, respectively), and glycated hemoglobin (6.1% [2.5%] vs. 5.6% [4.1%] among those with BMI ≥ 40 vs. 18.5–24.9, respectively). Additionally, as shown in Table 1 , those with a higher BMI category were more likely to have higher alanine aminotransferase levels, higher alkaline phosphatase levels, and lower natriuretic peptide levels. Missing data for selected laboratory values were substantial, as noted in Table 1 . Notably, although glycated hemoglobin missingness declined with higher BMI values, it was unavailable in more than one in five individuals with BMI ≥ 40.

Eligibility for antiobesity medical therapy and metabolic surgery

Overall, a total of 1,110,251 patients within the MGB system were found to be eligible for AOMs (45.0% of the overall contemporary adult MGB population) on the basis of either BMI ≥ 30 or ≥27 with ORCs (Figure 1 ). Among those eligible for AOMs, 30.6%, 41.0%, 17.4%, and 11.0% had BMI of between 27 and 29.9, between 30 and 34.9, between 35 and 39.9, and ≥40, respectively (Table 1 ). A total of 314,825 (12.7%) patients were guideline‐eligible for metabolic surgery based on having BMI ≥ 35 alone.

Prevalence and type of ORCs

Within the contemporary MGB population, individuals with a higher BMI category were more likely to have each of the prespecified ORCs (Table 1 ). When comparing BMI ≥ 40 with BMI < 25, we observed a higher prevalence of obstructive sleep apnea (11‐fold increase), metabolic dysfunction‐associated steatotic liver disease (MASLD; 5‐fold increase), T2D (>4‐fold increase), heart failure with preserved ejection fraction (HFpEF; 3‐fold increase), osteoarthritis (>2‐fold increase), hypertension (>2‐fold increase), and asthma (>2‐fold increase).

In addition, obesity‐related multimorbidity was common in this population. Among those who were AOM‐eligible, 684,159 (61.6%) had ≥2 ORCs, 248,315 (22.4%) had ≥5 ORCs, and 50,499 (4.5%) had ≥8 ORCs (Table 2 ). Among those with ≥8 diagnosed ORCs, 96%, 93%, 93%, 71%, and 68% had musculoskeletal pain, dyslipidemia, hypertension, gastroesophageal reflux disease, and osteoarthritis, respectively. As ORC burden increased, older age, higher median BMI value, and the proportion of patients reporting White race and self‐reported non‐Hispanic ethnicity also increased. The proportion of English‐ or Spanish‐speaking individuals also generally increased with greater ORC burden, whereas the prevalence of those speaking other languages decreased. Individuals with greater obesity‐related morbidity were more likely to have Medicare (59.6% vs. 12.3% among those with ≥8 and 0 ORCs, respectively) or Medicaid (15.3% vs. 10.2% among those with ≥8 and 0 ORCs, respectively) insurance.

Contemporary patterns of AOM prescription and referrals to weight‐management clinics

Overall, 15,214 (1.4%) of those eligible for AOMs were observed to have ≥1 prescription for AOMs approved by the FDA for long‐term use (Table 3 ). Compared with those eligible but not prescribed AOMs, patients prescribed AOMs were younger (mean [SD], age 51 [13] vs. 56 [17] years) and were more likely to be women (77.0% vs. 52.9%) and to have higher mean BMI values (37.5 vs. 33.2) and a greater burden of ORCs (88.5% vs. 62.2% with ≥2 ORCs and 48.6% vs. 22.0% with ≥5 ORCs). Those prescribed AOMs were generally more likely to have self‐reported White or Black race. English speaking status was more common among those with AOM prescriptions. Commercial insurance was more prevalent among patients prescribed AOMs compared with those not prescribed AOMs (95.7% vs. 86.2%), whereas Medicare (12.3% vs. 25.8%) and Medicaid (5.9% vs. 10.3%) insurance was less common.

When stratified by ORC type, considerable heterogeneity in the prevalence of AOM prescriptions was observed (Figure S2 ). Among patients with ORCs, the highest observed prevalence of AOM prescriptions was observed among individuals with metabolic dysfunction‐associated steatohepatitis (6.3%), polycystic ovarian syndrome (5.5%), and MASLD (4.8%). Individuals with chronic kidney disease (CKD; 1.1%), obesity‐related cancer (1.2%), prior MACE (1.2%), and HFpEF (1.3%) were the least likely to be prescribed AOMs. In addition, there was wide variation in the relative prevalence of ORCs between AOM‐prescribed versus nonprescribed groups (Figure 2 ). Namely, individuals prescribed AOMs were more likely to have depression, dyslipidemia/hyperlipidemia, gastroesophageal reflux disease, hypertension, musculoskeletal pain, MASLD, metabolic dysfunction‐associated steatohepatitis, obstructive sleep apnea, osteoarthritis, polycystic ovarian syndrome, prediabetes, psoriasis, T2D, and urinary incontinence. However, a lower prevalence of history of obesity‐related cancer, CKD, HFpEF, or prior MACE was observed among AOM‐eligible patients prescribed AOMs compared with those not prescribed AOMs (Table 3 ). AOM prescriptions increased modestly with increasing BMI category, with 0.5% and 3.7% of those with BMI between 27 and 29.9 and ≥40 prescribed AOMs, respectively (Table 1 ; Figure 3 ). A similar trend was observed for extent of ORC burden, with 0.3% and 2.9% of those with zero and ≥5 ORCs prescribed AOMs.

Approximately 3000 patients (~20% of AOM‐treated individuals) had active prescriptions for ≥2 AOMs. Thus, 18,746 total AOM prescriptions were observed among the 15,214 AOM‐eligible individuals during this study period. Overall, liraglutide 3.0 mg was the most frequently prescribed (45.8%), followed by semaglutide 2.4 mg (34.2%), naltrexone‐bupropion (10.7%), phentermine‐topiramate (6.3%), and orlistat (3.1%) (Table 4 ). The proportion of naltrexone‐bupropion, phentermine‐topiramate, and orlistat prescriptions was generally similar across BMI categories. However, liraglutide 3.0 mg prescriptions comprised a higher proportion of all prescriptions with increasing BMI, whereas semaglutide 2.4 mg prescriptions comprised a lower proportion of all prescriptions with increasing BMI (Figure S3 ). Prescribing of AOMs approved for long‐term use increased >10‐fold over the study period, from 229 between January and March 2018 to 2335 between October and December 2022 (Figure 4A ). Moreover, of the 17,002 medications newly prescribed over the study interval, 13,481 (79.3%) were prescribed by internal medicine/primary care and weight‐management providers (Figure 4B ).

Among AOM‐eligible adults over the study period, we observed 14,873 prescriptions for phentermine monotherapy. We additionally observed 15,276 prescriptions for incretin‐based therapies approved for T2D management in the absence of an ICD diagnosis code for T2D, of which 6079 (39.8%) were for semaglutide 2.0 mg, 5233 (34.3%) were for dulaglutide, 1610 (10.5%) were for liraglutide 1.8 mg, 1222 (8.0%) were for tirzepatide, and 1132 (7.4%) were for oral semaglutide. Higher BMI category was generally associated with a greater prevalence of other pharmacotherapies of interest (Table S5 ).

Over the study interval, 34,683 referrals to an MGB weight‐management clinic were initiated, with the majority of these among individuals with higher BMI values (Table 1 ). Overall, a total of 32,292 (2.9%) AOM‐eligible individuals had received a referral to a weight‐management clinic. Of these, 9.3%, 28.0%, 28.0%, and 34.7% had an observed BMI between 27 and 29.9, between 30 and 34.9, between 35 and 39.9, and ≥40, respectively (Figure 3 ).

DISCUSSION

In this contemporary, cross‐sectional analysis of a large health care system in the United States, nearly two in three adults were impacted by overweight or obesity. Obesity was present in nearly one in three patients, with BMI ≥ 40 identified in one in twenty. Increasing BMI was associated with an adverse cardiometabolic profile and a greater burden of ORCs, including multimorbidity. Overall, nearly one‐half of adults in the MGB system were found to be eligible for AOMs; despite this, only ~1% of those eligible had received such prescriptions. Furthermore, fewer than 3% of AOM‐eligible individuals were referred to a weight‐management clinic. Taken together, AOM prescriptions in routine clinical care, even among high‐risk individuals with BMI ≥ 40 and established ORCs, were exceedingly low in this analysis. As such, intensive, equitable, multifaceted, and interdisciplinary efforts are critically needed to accelerate closure of these considerable implementation gaps.

Foremost, this analysis of nearly 2.5 million individuals supports and extends the global‐ and national‐level epidemiologic data that have firmly established the expanding pandemic of overweight and obesity [ 19 ]. According to the nationally representative pre‐pandemic National Health and Nutrition Examination Survey 2017–2018, the overall prevalence of obesity in the United States was 42% [ 20 ] and has been forecasted to approach 60% by 2035 [ 19 ]. Of note, the state‐level prevalence of obesity in Massachusetts, where the MGB health care system is based, is among the lowest in the United States. However, prevalence of obesity in the MGB population (31%) was slightly higher than Massachusetts‐level estimates previously reported by the 2022 Behavioral Risk Factor Surveillance System (25%–<30%) [ 21 ]. We also observed a twofold higher prevalence of BMI ≥ 40 among women and high prevalence of obesity‐related musculoskeletal pain, cardiovascular conditions, and depression. These findings, which may be unique to the MGB system, highlight the importance of health care system‐specific analyses to inform tailored antiobesity interventions. For instance, targeting these specific care pathways (e.g., cardiovascular, pain, mental health, women's health clinics) with especially high obesity‐related health burden may be especially effective to improve implementation in the MGB system. Further analyses are needed to ascertain whether these patterns are generalizable to other centers and health care systems, which may vary according to geographic location, policy ecosystem, and other population‐level factors.

This analysis identified important differences in the prevalence and complications of overweight and obesity by sex, race, ethnicity, and insurance status in alignment with prior findings [ 4 , 22 ]. Additionally, this analysis shows that differences in these domains extend to treatment patterns. Despite a higher burden of ORCs with increasing age, AOM prescriptions were less common among older individuals. Furthermore, fewer AOM prescriptions were observed among men and among those who were non‐English speakers. These observations emphasize the need for equitable approaches to risk stratification and treatment of obesity and related conditions.

Interestingly, the subpopulation of AOM‐eligible individuals without ORCs was younger and exhibited the highest BMI values when compared with those with a higher number of ORCs. Of note, AOM prescriptions were particularly low (~0.3%) in this cohort of eligible individuals without ORCs. Given the strong association between longitudinal exposure to an elevated BMI and loss of disease‐free and overall life‐years [ 3 , 23 ], these observations amplify the need for early diagnosis and intervention to prevent subsequent obesity‐related death, disability, and multimorbidity across the life‐span. Indeed, health care system innovations to improve identification of antiobesity treatment‐eligible individuals earlier in the disease course and streamline goal‐concordant care pathways would be expected to have substantial impact on public health and avert downstream health care expenditures.

Importantly, the prevalence of AOM prescriptions in this analysis was exceedingly low, even among those with BMI ≥ 40 and substantial obesity‐related health burden. This observation is similar to that of prior analyses [ 24 , 25 ], including a retrospective observational study of nationwide prescribing trends in the United States between 1999 and 2010 in which only 2% of eligible individuals reported treatment with AOMs [ 26 ]. Critically, our analysis, although limited to a single health care system, suggests that this has not improved more than a decade later. Persistently low AOM prescriptions are likely multifactorial but may relate to gaps in education, time constraints, limited access to obesity medicine specialists, patient‐level priorities, and concerns regarding long‐term safety, among other factors relating to insurance coverage and cost.

Among those eligible for AOMs, AOM prescriptions varied according to type of ORCs. Although most ORCs included in this analysis were associated with a higher probability of AOM prescription, HFpEF, prior MACE, and CKD were not. In the context of findings from the Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity (SELECT) trial and the Effect of Semaglutide 2.4 mg Once Weekly on Function and Symptoms in Subjects with Obesity‐related Heart Failure with Preserved Ejection Fraction (STEP‐HFpEF) program [ 27 , 28 ], these findings highlight important care delivery gaps in these high‐risk populations. These care delivery gaps may reflect historically limited engagement of cardiovascular medicine providers, responsible for <1% of all AOM prescriptions in this analysis, in multimodality weight management. The role of multimodality weight‐centered care in the prevention and management of kidney disease has also been increasingly recognized [ 27 , 29 , 30 , 31 , 32 , 33 , 34 ], and this analysis emphasizes key opportunities in this population.

Variation in insurance coverage for obesity‐related treatments remains a critical determinant of treatment. In this population, nearly all individuals who were prescribed AOMs had commercial insurance. As it has been well‐established that Medicare excluded coverage of pharmacotherapy to support weight reduction over the study interval (Medicare Part D coverage has since been expanded to include semaglutide 2.4 mg among adults with overweight and obesity and established cardiovascular disease) [ 35 , 36 ], these findings are unsurprising. Massachusetts was also among the states whose Medicaid program did not provide coverage for AOMs over the study interval. However, individuals with the highest BMI values and obesity‐related health burden in this analysis were more likely to have Medicaid and Medicare insurance, respectively. Therefore, as has been previously suggested [ 37 ], AOM coverage expansion through these payors represents a critical opportunity to do the following: 1) better reflect scientific realities of obesity as a deadly and debilitating chronic disease; 2) improve alignment between clinical necessity and access to important disease‐management resources; and 3) diminish prevailing inequities in the provision of obesity‐related care supported, in part, by wide variation in insurance coverage and cost‐sharing.

This analysis is among the first, to our knowledge, to evaluate pharmacotherapy‐level prescribing trends for overweight and obesity in a large health care system. We found that glucagon‐like peptide‐1 receptor agonists (liraglutide 3.0 mg and semaglutide 2.4 mg) predominated, with comparatively lower use of naltrexone‐bupropion, phentermine‐topiramate, and orlistat. Moreover, between 2018 and 2022, AOM prescribing increased more than 10‐fold in the MGB system, with sharp increments in glucagon‐like peptide‐1 receptor agonist prescribing after 2020, consistent with nationwide data [ 38 , 39 ]. Taken together, these observations emphasize the rapidly shifting landscape of AOM prescribing, with implications for patients, providers, health care systems, policymakers, and other diverse stakeholders.

Although the prevalence of overweight and obesity and its diverse complications necessitate engagement of the wide spectrum of clinicians caring for this population, specialized obesity medicine and weight‐management centers, as well as remote management programs [ 40 ], represent a promising and growing strategy by which to narrow implementation gaps and facilitate multidisciplinary obesity management [ 41 ]. However, referral patterns to specialty weight‐management clinics have not been widely reported. Herein, fewer than 3% of AOM‐eligible patients (fewer than 1 in 10 with BMI ≥ 40) were referred to a weight‐management clinic over the study interval. Expanding use of these emerging specialty care pathways, especially for individuals at highest risk, may be important to curtailing the substantial and rising impact of overweight and obesity.

Several limitations of this analysis should be discussed. First, we did not evaluate temporal changes in overweight, obesity, or ORCs. Second, because BMI is a proxy for excess or dysfunctional adiposity with well‐established limitations [ 42 ], use of this anthropometric may have led to misclassification of overweight or obesity status in some groups. Similarly, ethnicity‐specific BMI thresholds were not included in this analysis, which may have reclassified more individuals with Asian ethnicity as having overweight or obesity. Third, because ORCs were established by administrative codes and other EHR‐level data, misclassification of AOM eligibility status was possible. Although >200 chronic medical conditions have been shown to be related to overweight and obesity [ 5 , 22 , 43 ], a rigorous but relatively parsimonious list of ORCs was included in this analysis. As such, it is likely that estimates of AOM eligibility are sensitive to the number of ORCs considered. Fourth, the provision of lifestyle counseling and referrals to registered dietitians was not evaluated. Fifth, owing to the nature of the data available, it was not feasible to rigorously evaluate AOM adherence, dosing, or tolerance. As such, evaluation of prescriptions alone likely overestimates receipt, initiation, and optimal dose‐escalation of AOMs. Additionally, because we were unable to adjudicate precise indications for use of some pharmacotherapies (e.g., metformin) commonly prescribed off‐label for weight management, this analysis may have underestimated the totality of weight management‐oriented prescribing. Finally, these data from a single urban health care system in the United States may not be generalizable to all settings.

CONCLUSION

This analysis of a large tertiary health care system in the United States affirms the substantial prevalence and impact of overweight and obesity in the contemporary era. However, despite substantial and rising demand, prescription of AOMs was exceedingly low, even among adults with higher BMI categories, established cardiovascular‐kidney‐metabolic conditions, and obesity‐related multimorbidity. Novel and integrative efforts are needed to accelerate closure of prevailing implementation gaps in contemporary obesity care.

CONFLICT OF INTEREST STATEMENT

Joshua C. Toliver, Wojciech Michalak, Anthony Fabricatore, Bríain Ó Hartaigh, and Kelly Olsson are employees and shareholders of Novo Nordisk Inc. Heather J. Baer reports receiving consulting fees and/or research support from the Patient‐Centered Outcomes Research Institute (PCORI). Christopher P. Cannon reports receiving consulting fees and/or research support from Amgen Inc.; Better Therapeutics Inc; Daiichi Sankyo; Merck; Novo Nordisk A/S; Pfizer Inc.; Amryt Pharma/Chiesi Farmaceutici S.p.A.; Alnylam Pharmaceuticals, Inc.; Amarin Corporation; Applied Therapeutics; Ascendia Pharma; Biogen Inc.; Boehringer Ingelheim; Bristol‐Myers Squibb; CSL Behring; Eli Lilly and Company; Janssen Pharmaceuticals; Lexicon Pharmaceuticals, Inc.; Milestone Pharmaceuticals; Rhoshan Pharmaceuticals; and Sanofi S.A. Caroline M. Apovian reports having participated on advisory boards for Altimmune, Inc.; CinFina Pharma; Cowen and Company, LLC; Currax Pharmaceuticals LLC; EPG Communication Holdings Ltd.; Form Health; Gelesis SRL; L‐Nutra; NeuroBo Pharmaceuticals, Inc.; Novo Nordisk A/S; OptumRx, Inc.; PainScript Corporation; Palatin Technologies, Inc.; Pursuit By You; ReShape Lifesciences, Inc.; Riverview School; and Xeno Biosciences and receiving consulting fees and/or research support from PCORI and GI Dynamics, Inc. Naomi D. L. Fisher reports receiving research support and consulting fees from Recor Medical and consulting fees from Recor Medical, Medtronic plc, Aktiia, and AstraZeneca plc. Jorge Plutzky reports receiving consulting fees and/or research support from Boehringer Ingelheim, Janssen Pharmaceuticals, and Novo Nordisk A/S. Benjamin M. Scirica reports receiving consulting fees and/or research support from Better Therapeutics Inc.; Boehringer Ingelheim; Merck; Novo Nordisk A/S; Pfizer Inc.; AbbVie (Data and Safety Monitoring Board [DSMB]); AstraZeneca plc (DSMB); Bristol‐Myers Squibb; Elsevier PracticeUpdate Cardiology; Esperion Therapeutics, Inc.; Hanmi Pharmaceutical (DSMB); Lexeo Therapeutics (DSMB); and Lexicon Pharmaceuticals, Inc., and holding equity in Health at Scale. Alexander J. Blood reports receiving consulting fees and/or research support from Arsenal Capital Partners LP; Boehringer Ingelheim; Color Health, Inc.; Eli Lilly and Company; GE HealthCare; Medscape; Novo Nordisk A/S; and Walgreens Health and holding equity in Knownwell Health and Porter Health. The other authors declared no conflicts of interest.

Supporting information

Data S1. Supporting Information.

REFERENCES

1 Blüher M . Obesity: global epidemiology and pathogenesis . Nat Rev Endocrinol . 2019 ; 15 : 288 ‐ 298 . 30814686 10.1038/s41574-019-0176-8

2 World Obesity Federation. World Obesity Atlas 2024 . Published March 2024. Accessed May 23, 2024. https://data.worldobesity.org/publications/WOF-Obesity-Atlas-v7.pdf

3 Khan SS , Ning H , Wilkins JT , et al. Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity . JAMA Cardiol . 2018 ; 3 : 280 ‐ 287 . 29490333 10.1001/jamacardio.2018.0022 PMC5875319

4 Powell‐Wiley TM , Poirier P , Burke LE , et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association . Circulation . 2021 ; 143 : e984 ‐ e1010 . 33882682 10.1161/CIR.0000000000000973 PMC8493650

5 Kivimäki M , Strandberg T , Pentti J , et al. Body‐mass index and risk of obesity‐related complex multimorbidity: an observational multicohort study . Lancet Diabetes Endocrinol . 2022 ; 10 : 253 ‐ 263 . 35248171 10.1016/S2213-8587(22)00033-X PMC8938400

6 Okunogbe A , Nugent R , Spencer G , Powis J , Ralston J , Wilding J . Economic impacts of overweight and obesity: current and future estimates for 161 countries . BMJ Glob Health . 2022 ; 7 : e009773 . 10.1136/bmjgh-2022-009773 PMC9494015 36130777

7 Cawley J , Biener A , Meyerhoefer C , et al. Direct medical costs of obesity in the United States and the most populous states . J Manag Care Spec Pharm . 2021 ; 27 : 354 ‐ 366 . 33470881 10.18553/jmcp.2021.20410 PMC10394178

8 Jensen MD , Ryan DH , Apovian CM , et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association task force on practice guidelines and the Obesity Society . Circulation . 2014 ; 129 ( 25 Suppl 2 ): S102 ‐ S138 . 24222017 10.1161/01.cir.0000437739.71477.ee PMC5819889

9 Apovian CM , Aronne LJ , Bessesen DH , et al. Pharmacological management of obesity: an Endocrine Society clinical practice guideline . J Clin Endocrinol Metab . 2015 ; 100 : 342 ‐ 362 . 25590212 10.1210/jc.2014-3415

10 Wing RR , Bolin P , Brancati FL , et al. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes . N Engl J Med . 2013 ; 369 : 145 ‐ 154 . 23796131 10.1056/NEJMoa1212914 PMC3791615

11 Chakhtoura M , Haber R , Ghezzawi M , Rhayem C , Tcheroyan R , Mantzoros CS . Pharmacotherapy of obesity: an update on the available medications and drugs under investigation . EClinicalMedicine . 2023 ; 58 : 101882 . 36992862 10.1016/j.eclinm.2023.101882 PMC10041469

12 Jastreboff AM , Kushner RF . New frontiers in obesity treatment: GLP‐1 and nascent nutrient‐stimulated hormone‐based therapeutics . Annu Rev Med . 2023 ; 74 : 125 ‐ 139 . 36706749 10.1146/annurev-med-043021-014919

13 Eisenberg D , Shikora SA , Aarts E , et al. 2022 American Society for Metabolic and Bariatric Surgery (ASMBS) and International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO): indications for metabolic and bariatric surgery . Obes Surg . 2023 ; 33 : 3 ‐ 14 . 36336720 10.1007/s11695-022-06332-1 PMC9834364

14 Bhaskaran K , Douglas I , Forbes H , dos‐Santos‐Silva I , Leon DA , Smeeth L . Body‐mass index and risk of 22 specific cancers: a population‐based cohort study of 5.24 million UK adults . Lancet . 2014 ; 384 : 755 ‐ 765 . 25129328 10.1016/S0140-6736(14)60892-8 PMC4151483

15 Jastreboff AM , Kotz CM , Kahan S , Kelly AS , Heymsfield SB . Obesity as a disease: the Obesity Society 2018 position statement . Obesity (Silver Spring) . 2019 ; 27 : 7 ‐ 9 . 30569641 10.1002/oby.22378

16 GBD 2015 Obesity Collaborators ; Afshin A , Forouzanfar MH , Reitsma MB , et al. Health effects of overweight and obesity in 195 countries over 25 years . N Engl J Med . 2017 ; 377 : 13 ‐ 27 . 28604169 10.1056/NEJMoa1614362 PMC5477817

17 McClean KM , Kee F , Young IS , Elborn JS . Obesity and the lung: 1. Epidemiology . Thorax . 2008 ; 63 : 649 ‐ 654 . 18587034 10.1136/thx.2007.086801

18 Mobley D , Baum N . The obesity epidemic and its impact on urologic care . Rev Urol . 2015 ; 17 : 165 ‐ 170 . 26543431 PMC4633660

19 World Obesity Federation. World Obesity Atlas 2023 . Published March 2023. Accessed May 29, 2023. https://data.worldobesity.org/publications/?cat=19

20 National Center for Health Statistics . NCHS fact sheet . Published July 2020. Accessed December 28, 2023. https://www.cdc.gov/nchs/data/factsheets/factsheet_nhanes.pdf

21 Centers for Disease Control and Prevention . Adult obesity prevalence maps. Updated September 12, 2024. Accessed December 28. 2023 . https://www.cdc.gov/obesity/data‐and‐statistics/adult‐obesity‐prevalence‐maps.html

22 Bhaskaran K , dos‐Santos‐Silva I , Leon DA , Douglas IJ , Smeeth L . Association of BMI with overall and cause‐specific mortality: a population‐based cohort study of 3.6 million adults in the UK . Lancet Diabetes Endocrinol . 2018 ; 6 : 944 ‐ 953 . 30389323 10.1016/S2213-8587(18)30288-2 PMC6249991

23 Fontaine KR , Redden DT , Wang C , Westfall AO , Allison DB . Years of life lost due to obesity . JAMA . 2003 ; 289 : 187 ‐ 193 . 12517229 10.1001/jama.289.2.187

24 Thomas CE , Mauer EA , Shukla AP , Rathi S , Aronne LJ . Low adoption of weight loss medications: a comparison of prescribing patters of antiobesity pharmacotherapies and SGLT2s . Obesity (Silver Spring) . 2016 ; 24 : 1955 ‐ 1961 . 27569120 10.1002/oby.21533 PMC5669035

25 Samaranayake NR , Ong KL , Leung RY , Cheung BM . Management of obesity in the NHANES, 2007‐2008 . Ann Epidemiol . 2012 ; 22 : 349 ‐ 353 . 22305325 10.1016/j.annepidem.2012.01.001

26 Xia Y , Kelton CML , Guo JJ , Bian B , Heaton PC . Treatment of obesity: pharmacotherapy trends in the United States from 1999 to 2010 . Obesity (Silver Spring) . 2015 ; 23 : 1721 ‐ 1728 . 26193062 10.1002/oby.21136

27 Lincoff AM , Brown‐Frandsen K , Colhoun HM , et al. Semaglutide and cardiovascular outcomes in obesity without diabetes . N Engl J Med . 2023 ; 389 : 2221 ‐ 2232 . 37952131 10.1056/NEJMoa2307563

28 Kosiborod MN , Abildstrøm SZ , Borlaug BA , et al. Semaglutide in patients with heart failure with preserved ejection fraction and obesity . N Engl J Med . 2023 ; 389 : 1069 ‐ 1084 . 37622681 10.1056/NEJMoa2306963

29 Look AHEAD Research Group . Effect of a long‐term behavioral weight loss intervention on nephropathy in overweight or obese adults with type 2 diabetes: the Look AHEAD randomized clinical trial . Lancet Diabetes Endocrinol . 2014 ; 2 : 801 ‐ 809 . 25127483 10.1016/S2213-8587(14)70156-1 PMC4443484

30 Chang AR , Grams ME , Navaneethan SD . Bariatric surgery and kidney‐related outcomes . Kidney Int Rep . 2017 ; 2 : 261 ‐ 270 . 28439568 10.1016/j.ekir.2017.01.010 PMC5399773

31 Sheetz KH , Gerhardinger L , Dimick JB , Waits SA . Bariatric surgery and long‐term survival in patients with obesity and end‐stage kidney disease . JAMA Surg . 2020 ; 155 : 581 ‐ 588 . 32459318 10.1001/jamasurg.2020.0829 PMC7254435

32 Heerspink HJL , Apperloo E , Davies M , et al. Effects of semaglutide on albuminuria and kidney function in people with overweight or obesity with or without type 2 diabetes: exploratory analysis from the STEP 1, 2, and 3 trials . Diabetes Care . 2023 ; 46 : 801 ‐ 810 . 36801984 10.2337/dc22-1889 PMC10090901

33 Colhoun HM , Lingvay I , Brown PM , et al. Long‐term kidney outcomes of semaglutide in obesity and cardiovascular disease in the SELECT trial . Nat Med . 2024 ; 30 : 2058 ‐ 2066 . 38796653 10.1038/s41591-024-03015-5 PMC11271413

34 Perkovic V , Tuttle KR , Rossing P , et al. Effects of semaglutide on chronic kidney disease in patients with type 2 diabetes . N Engl J Med . 2024 ; 391 : 109 ‐ 121 . 38785209 10.1056/NEJMoa2403347

35 Baig K , Dusetzina SB , Kim DD , Leech AA . Medicare Part D coverage of antiobesity medications ‐ challenges and uncertainty ahead . N Engl J Med . 2023 ; 388 : 961 ‐ 963 . 36912541 10.1056/NEJMp2300516

36 FDA news release. FDA approves first treatment to reduce risk of serious heart problems specifically in adults with obesity or overweight. News release. US Food and Drug Administration; March 8, 2024 . https://www.fda.gov/news‐events/press‐announcements/fda‐approves‐first‐treatment‐reduce‐risk‐serious‐heart‐problems‐specifically‐adults‐obesity‐or . Accessed March 9th, 2024.

37 Stoops H , Dar M . Equity and obesity treatment ‐ expanding Medicaid‐covered interventions . N Engl J Med . 2023 ; 388 : 2309 ‐ 2311 . 37339118 10.1056/NEJMp2303268

38 Hegland TA , Fang Z , Bucher K . GLP‐1 medication use for type 2 diabetes has soared . JAMA . 2024 ; 332 : 952 ‐ 953 . 39212980 10.1001/jama.2024.18219

39 Yeo YH , Rezaie A , Hsieh TY‐J , et al. Shifting trends in the indication of glucagon‐like peptide‐1 receptor agonist prescriptions: a nationwide analysis . Ann Intern Med . 2024 ; 177 : 1289 ‐ 1291 . 39038291 10.7326/M24-0019

40 Blood AJ , Cannon CP , Gordon WJ , et al. Results of a remotely delivered hypertension and lipid program in more than 10 000 patients across a diverse health care network . JAMA Cardiol . 2023 ; 8 : 12 ‐ 21 . 36350612 10.1001/jamacardio.2022.4018 PMC9647559

41 Kahan S , Manson JE . Obesity treatment, beyond the guidelines: practical suggestions for clinical practice . JAMA . 2019 ; 321 : 1349 ‐ 1350 . 30896727 10.1001/jama.2019.2352

42 Ostrominski JW , Powell‐Wiley TM . Risk stratification and treatment of obesity for primary and secondary prevention of cardiovascular disease . Curr Atheroscler Rep . 2024 ; 26 : 11 ‐ 23 . 38159162 10.1007/s11883-023-01182-3

43 Wilding JPH , Jacob S . Cardiovascular outcome trials in obesity: a review . Obes Rev . 2021 ; 22 : e13112 . 32893459 10.1111/obr.13112