obesity research and clinical practice

Obesity Research and Clinical Practice

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Obesity Research & Clinical Practice

Volume 1 • Issue 6

• ISSN: 1871-403X
• 5 Year impact factor: 3
• Impact factor: 2.5
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Affiliated Societies: Asia Oceania Association for the Study of Obesity (AOASO), Australian and New Zealand Obesity Society (ANZOS), Taiwan Medical Association for the Study o… Read more

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Affiliated Societies: Asia Oceania Association for the Study of Obesity (AOASO) , Australian and New Zealand Obesity Society (ANZOS) , Taiwan Medical Association for the Study of Obesity (TMASO) , and Korean Society for the Study of Obesity (KSSO)

The aim of Obesity Research & Clinical Practice (ORCP) is to publish high quality clinical and basic research relating to the epidemiology, mechanism, complications and treatment of obesity and the complication of obesity. Studies relating to the Asia Oceania region are particularly welcome, given the increasing burden of obesity in Asia Pacific, compounded by specific regional population-based and genetic issues, and the devastating personal and economic consequences. The journal aims to expose health care practitioners, clinical researchers, basic scientists, epidemiologists, and public health officials in the region to all areas of obesity research and practice. In addition to original research the ORCP publishes reviews, patient reports, short communications, and letters to the editor (including comments on published papers). The proceedings and abstracts of the Annual Meeting of the Asia Oceania Association for the Study of Obesity is published as a supplement each year.

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• Effectiveness of...

Effectiveness of weight management interventions for adults delivered in primary care: systematic review and meta-analysis of randomised controlled trials

• Related content
• Peer review
• 1 Centre for Lifestyle Medicine and Behaviour (CLiMB), The School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
• 2 School of Primary and Allied Health Care, Monash University, Melbourne, Australia
• 3 Department of Psychology, Addiction and Mental Health Group, University of Bath, Bath, UK
• Correspondence to: C D Madigan c.madigan{at}lboro.ac.uk (or @claire_wm and @lboroclimb on Twitter)
• Accepted 26 April 2022

Objective To examine the effectiveness of behavioural weight management interventions for adults with obesity delivered in primary care.

Design Systematic review and meta-analysis of randomised controlled trials.

Eligibility criteria for selection of studies Randomised controlled trials of behavioural weight management interventions for adults with a body mass index ≥25 delivered in primary care compared with no treatment, attention control, or minimal intervention and weight change at ≥12 months follow-up.

Data sources Trials from a previous systematic review were extracted and the search completed using the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021.

Data extraction and synthesis Two reviewers independently identified eligible studies, extracted data, and assessed risk of bias using the Cochrane risk of bias tool. Meta-analyses were conducted with random effects models, and a pooled mean difference for both weight (kg) and waist circumference (cm) were calculated.

Main outcome measures Primary outcome was weight change from baseline to 12 months. Secondary outcome was weight change from baseline to ≥24 months. Change in waist circumference was assessed at 12 months.

Results 34 trials were included: 14 were additional, from a previous review. 27 trials (n=8000) were included in the primary outcome of weight change at 12 month follow-up. The mean difference between the intervention and comparator groups at 12 months was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, P<0.001), favouring the intervention group. At ≥24 months (13 trials, n=5011) the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, P<0.001) favouring the intervention. The mean difference in waist circumference (18 trials, n=5288) was −2.5 cm (−3.2 to −1.8 cm, I 2 =69%, P<0.001) in favour of the intervention at 12 months.

Conclusions Behavioural weight management interventions for adults with obesity delivered in primary care are effective for weight loss and could be offered to members of the public.

Systematic review registration PROSPERO CRD42021275529.

Introduction

Obesity is associated with an increased risk of diseases such as cancer, type 2 diabetes, and heart disease, leading to early mortality. 1 2 3 More recently, obesity is a risk factor for worse outcomes with covid-19. 4 5 Because of this increased risk, health agencies and governments worldwide are focused on finding effective ways to help people lose weight. 6

Primary care is an ideal setting for delivering weight management services, and international guidelines recommend that doctors should opportunistically screen and encourage patients to lose weight. 7 8 On average, most people consult a primary care doctor four times yearly, providing opportunities for weight management interventions. 9 10 A systematic review of randomised controlled trials by LeBlanc et al identified behavioural interventions that could potentially be delivered in primary care, or involved referral of patients by primary care professionals, were effective for weight loss at 12-18 months follow-up (−2.4 kg, 95% confidence interval −2.9 to−1.9 kg). 11 However, this review included trials with interventions that the review authors considered directly transferrable to primary care, but not all interventions involved primary care practitioners. The review included interventions that were entirely delivered by university research employees, meaning implementation of these interventions might differ if offered in primary care, as has been the case in other implementation research of weight management interventions, where effects were smaller. 12 As many similar trials have been published after this review, an updated review would be useful to guide health policy.

We examined the effectiveness of weight loss interventions delivered in primary care on measures of body composition (weight and waist circumference). We also identified characteristics of effective weight management programmes for policy makers to consider.

This systematic review was registered on PROSPERO and is reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. 13 14

Eligibility criteria

We considered studies to be eligible for inclusion if they were randomised controlled trials, comprised adult participants (≥18 years), and evaluated behavioural weight management interventions delivered in primary care that focused on weight loss. A primary care setting was broadly defined as the first point of contact with the healthcare system, providing accessible, continued, comprehensive, and coordinated care, focused on long term health. 15 Delivery in primary care was defined as the majority of the intervention being delivered by medical and non-medical clinicians within the primary care setting. Table 1 lists the inclusion and exclusion criteria.

Study inclusion and exclusion criteria

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We extracted studies from the systematic review by LeBlanc et al that met our inclusion criteria. 11 We also searched the exclusions in this review because the researchers excluded interventions specifically for diabetes management, low quality trials, and only included studies from an Organisation for Economic Co-operation and Development country, limiting the scope of the findings.

We searched for studies in the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021 (see supplementary file 1). Reference lists of previous reviews 16 17 18 19 20 21 and included trials were hand searched.

Data extraction

Results were uploaded to Covidence, 22 a software platform used for screening, and duplicates removed. Two independent reviewers screened study titles, abstracts, and full texts. Disagreements were discussed and resolved by a third reviewer. All decisions were recorded in Covidence, and reviewers were blinded to each other’s decisions. Covidence calculates proportionate agreement as a measure of inter-rater reliability, and data are reported separately by title or abstract screening and full text screening. One reviewer extracted data on study characteristics (see supplementary table 1) and two authors independently extracted data on weight outcomes. We contacted the authors of four included trials (from the updated search) for further information. 23 24 25 26

Outcomes, summary measures, and synthesis of results

The primary outcome was weight change from baseline to 12 months. Secondary outcomes were weight change from baseline to ≥24 months and from baseline to last follow-up (to include as many trials as possible), and waist circumference from baseline to 12 months. Supplementary file 2 details the prespecified subgroup analysis that we were unable to complete. The prespecified subgroup analyses that could be completed were type of healthcare professional who delivered the intervention, country, intensity of the intervention, and risk of bias rating.

Healthcare professional delivering intervention —From the data we were able to compare subgroups by type of healthcare professional: nurses, 24 26 27 28 general practitioners, 23 29 30 31 and non-medical practitioners (eg, health coaches). 32 33 34 35 36 37 38 39 Some of the interventions delivered by non-medical practitioners were supported, but not predominantly delivered, by GPs. Other interventions were delivered by a combination of several different practitioners—for example, it was not possible to determine whether a nurse or dietitian delivered the intervention. In the subgroup analysis of practitioner delivery, we refer to this group as “other.”

Country —We explored the effectiveness of interventions by country. Only countries with three or more trials were included in subgroup analyses (United Kingdom, United States, and Spain).

Intensity of interventions —As the median number of contacts was 12, we categorised intervention groups according to whether ≤11 or ≥12 contacts were required.

Risk of bias rating —Studies were classified as being at low, unclear, and high risk of bias. Risk of bias was explored as a potential influence on the results.

Meta-analyses

Meta-analyses were conducted using Review Manager 5.4. 40 As we expected the treatment effects to differ because of the diversity of intervention components and comparator conditions, we used random effects models. A pooled mean difference was calculated for each analysis, and variance in heterogeneity between studies was compared using the I 2 and τ 2 statistics. We generated funnel plots to evaluate small study effects. If more than two intervention groups existed, we divided the number of participants in the comparator group by the number of intervention groups and analysed each individually. Nine trials were cluster randomised controlled trials. The trials had adjusted their results for clustering, or adjustment had been made in the previous systematic review by LeBlanc et al. 11 One trial did not report change in weight by group. 26 We calculated the mean weight change and standard deviation using a standard formula, which imputes a correlation for the baseline and follow-up weights. 41 42 In a non-prespecified analysis, we conducted univariate and multivariable metaregression (in Stata) using a random effects model to examine the association between number of sessions and type of interventionalist on study effect estimates.

Risk of bias

Two authors independently assessed the risk of bias using the Cochrane risk of bias tool v2. 43 For incomplete outcome data we defined a high risk of bias as ≥20% attrition. Disagreements were resolved by discussion or consultation with a third author.

Patient and public involvement

The study idea was discussed with patients and members of the public. They were not, however, included in discussions about the design or conduct of the study.

The search identified 11 609 unique study titles or abstracts after duplicates were removed ( fig 1 ). After screening, 97 full text articles were assessed for eligibility. The proportionate agreement ranged from 0.94 to 1.0 for screening of titles or abstracts and was 0.84 for full text screening. Fourteen new trials met the inclusion criteria. Twenty one studies from the review by LeBlanc et al met our eligibility criteria and one study from another systematic review was considered eligible and included. 44 Some studies had follow-up studies (ie, two publications) that were found in both the second and the first search; hence the total number of trials was 34 and not 36. Of the 34 trials, 27 (n=8000 participants) were included in the primary outcome meta-analysis of weight change from baseline to 12 months, 13 (n=5011) in the secondary outcome from baseline to ≥24 months, and 30 (n=8938) in the secondary outcome for weight change from baseline to last follow-up. Baseline weight was accounted for in 18 of these trials, but in 14 24 26 29 30 31 32 44 45 46 47 48 49 50 51 it was unclear or the trials did not consider baseline weight. Eighteen trials (n=5288) were included in the analysis of change in waist circumference at 12 months.

Studies included in systematic review of effectiveness of behavioural weight management interventions in primary care. *Studies were merged in Covidence if they were from same trial

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Study characteristics

Included trials (see supplementary table 1) were individual randomised controlled trials (n=25) 24 25 26 27 28 29 32 33 34 35 38 39 41 44 45 46 47 50 51 52 53 54 55 56 59 or cluster randomised controlled trials (n=9). 23 30 31 36 37 48 49 57 58 Most were conducted in the US (n=14), 29 30 31 32 33 34 35 36 37 45 48 51 54 55 UK (n=7), 27 28 38 41 47 57 58 and Spain (n=4). 25 44 46 49 The median number of participants was 276 (range 50-864).

Four trials included only women (average 65.9% of women). 31 48 51 59 The mean BMI at baseline was 35.2 (SD 4.2) and mean age was 48 (SD 9.7) years. The interventions lasted between one session (with participants subsequently following the programme unassisted for three months) and several sessions over three years (median 12 months). The follow-up period ranged from 12 months to three years (median 12 months). Most trials excluded participants who had lost weight in the past six months and were taking drugs that affected weight.

Meta-analysis

Overall, 27 trials were included in the primary meta-analysis of weight change from baseline to 12 months. Three trials could not be included in the primary analysis as data on weight were only available at two and three years and not 12 months follow-up, but we included these trials in the secondary analyses of last follow-up and ≥24 months follow-up. 26 44 50 Four trials could not be included in the meta-analysis as they did not present data in a way that could be synthesised (ie, measures of dispersion). 25 52 53 58 The mean difference was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, τ 2 =3.38; P<0.001) in favour of the intervention group ( fig 2 ). We found no evidence of publication bias (see supplementary fig 1). Absolute weight change was −3.7 (SD 6.1) kg in the intervention group and −1.4 (SD 5.5) kg in the comparator group.

Mean difference in weight at 12 months by weight management programme in primary care (intervention) or no treatment, different content, or minimal intervention (control). SD=standard deviation

Supplementary file 2 provides a summary of the main subgroup analyses.

Weight change

The mean difference in weight change at the last follow-up was −1.9 kg (95% confidence interval −2.5 to −1.3 kg, I 2 =81%, τ 2 =2.15; P<0.001). Absolute weight change was −3.2 (SD 6.4) kg in the intervention group and −1.2 (SD 6.0) kg in the comparator group (see supplementary figs 2 and 3).

At the 24 month follow-up the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, τ 2 =3.13; P<0.001) (see supplementary fig 4). As the weight change data did not differ between the last follow-up and ≥24 months, we used the weight data from the last follow-up in subgroup analyses.

In subgroup analyses of type of interventionalist, differences were significant (P=0.005) between non-medical practitioners, GPs, nurses, and other people who delivered interventions (see supplementary fig 2).

Participants who had ≥12 contacts during interventions lost significantly more weight than those with fewer contacts (see supplementary fig 6). The association remained after adjustment for type of interventionalist.

Waist circumference

The mean difference in waist circumference was −2.5 cm (95% confidence interval −3.2 to −1.8 cm, I 2 =69%, τ 2 =1.73; P<0.001) in favour of the intervention at 12 months ( fig 3 ). Absolute changes were −3.7 cm (SD 7.8 cm) in the intervention group and −1.3 cm (SD 7.3) in the comparator group.

Mean difference in waist circumference at 12 months. SD=standard deviation

Risk of bias was considered to be low in nine trials, 24 33 34 35 39 41 47 55 56 unclear in 12 trials, 25 27 28 29 32 45 46 50 51 52 54 59 and high in 13 trials 23 26 30 31 36 37 38 44 48 49 53 57 58 ( fig 4 ). No significant (P=0.65) differences were found in subgroup analyses according to level of risk of bias from baseline to 12 months (see supplementary fig 7).

Risk of bias in included studies

Worldwide, governments are trying to find the most effective services to help people lose weight to improve the health of populations. We found weight management interventions delivered by primary care practitioners result in effective weight loss and reduction in waist circumference and these interventions should be considered part of the services offered to help people manage their weight. A greater number of contacts between patients and healthcare professionals led to more weight loss, and interventions should be designed to include at least 12 contacts (face-to-face or by telephone, or both). Evidence suggests that interventions delivered by non-medical practitioners were as effective as those delivered by GPs (both showed statistically significant weight loss). It is also possible that more contacts were made with non-medical interventionalists, which might partially explain this result, although the metaregression analysis suggested the effect remained after adjustment for type of interventionalist. Because most comparator groups had fewer contacts than intervention groups, it is not known whether the effects of the interventions are related to contact with interventionalists or to the content of the intervention itself.

Although we did not determine the costs of the programme, it is likely that interventions delivered by non-medical practitioners would be cheaper than GP and nurse led programmes. 41 Most of the interventions delivered by non-medical practitioners involved endorsement and supervision from GPs (ie, a recommendation or checking in to see how patients were progressing), and these should be considered when implementing these types of weight management interventions in primary care settings. Our findings suggest that a combination of practitioners would be most effective because GPs might not have the time for 12 consultations to support weight management.

Although the 2.3 kg greater weight loss in the intervention group may seem modest, just 2-5% in weight loss is associated with improvements in systolic blood pressure and glucose and triglyceride levels. 60 The confidence intervals suggest a potential range of weight loss and that these interventions might not provide as much benefit to those with a higher BMI. Patients might not find an average weight loss of 3.7 kg attractive, as many would prefer to lose more weight; explaining to patients the benefits of small weight losses to health would be important.

Strengths and limitations of this review

Our conclusions are based on a large sample of about 8000 participants, and 12 of these trials were published since 2018. It was occasionally difficult to distinguish who delivered the interventions and how they were implemented. We therefore made some assumptions at the screening stage about whether the interventionalists were primary care practitioners or if most of the interventions were delivered in primary care. These discussions were resolved by consensus. All included trials measured weight, and we excluded those that used self-reported data. Dropout rates are important in weight management interventions as those who do less well are less likely to be followed-up. We found that participants in trials with an attrition rate of 20% or more lost less weight and we are confident that those with high attrition rates have not inflated the results. Trials were mainly conducted in socially economic developed countries, so our findings might not be applicable to all countries. The meta-analyses showed statistically significant heterogeneity, and our prespecified subgroups analysis explained some, but not all, of the variance.

Comparison with other studies

The mean difference of −2.3 kg in favour of the intervention group at 12 months is similar to the findings in the review by LeBlanc et al, who reported a reduction of −2.4 kg in participants who received a weight management intervention in a range of settings, including primary care, universities, and the community. 11 61 This is important because the review by LeBlanc et al included interventions that were not exclusively conducted in primary care or by primary care practitioners. Trials conducted in university or hospital settings are not typically representative of primary care populations and are often more intensive than trials conducted in primary care as a result of less constraints on time. Thus, our review provides encouraging findings for the implementation of weight management interventions delivered in primary care. The findings are of a similar magnitude to those found in a trial by Ahern et al that tested primary care referral to a commercial programme, with a difference of −2.7 kg (95% confidence interval −3.9 to −1.5 kg) reported at 12 month follow-up. 62 The trial by Ahern et al also found a difference in waist circumference of −4.1 cm (95% confidence interval −5.5 to −2.3 cm) in favour of the intervention group at 12 months. Our finding was smaller at −2.5 cm (95% confidence interval −3.2 to −1.8 cm). Some evidence suggests clinical benefits from a reduction of 3 cm in waist circumference, particularly in decreased glucose levels, and the intervention groups showed a 3.7 cm absolute change in waist circumference. 63

Policy implications and conclusions

Weight management interventions delivered in primary care are effective and should be part of services offered to members of the public to help them manage weight. As about 39% of the world’s population is living with obesity, helping people to manage their weight is an enormous task. 64 Primary care offers good reach into the community as the first point of contact in the healthcare system and the remit to provide whole person care across the life course. 65 When developing weight management interventions, it is important to reflect on resource availability within primary care settings to ensure patients’ needs can be met within existing healthcare systems. 66

We did not examine the equity of interventions, but primary care interventions may offer an additional service and potentially help those who would not attend a programme delivered outside of primary care. Interventions should consist of 12 or more contacts, and these findings are based on a mixture of telephone and face-to-face sessions. Previous evidence suggests that GPs find it difficult to raise the issue of weight with patients and are pessimistic about the success of weight loss interventions. 67 Therefore, interventions should be implemented with appropriate training for primary care practitioners so that they feel confident about helping patients to manage their weight. 68

Unanswered questions and future research

A range of effective interventions are available in primary care settings to help people manage their weight, but we found substantial heterogeneity. It was beyond the scope of this systematic review to examine the specific components of the interventions that may be associated with greater weight loss, but this could be investigated by future research. We do not know whether these interventions are universally suitable and will decrease or increase health inequalities. As the data are most likely collected in trials, an individual patient meta-analysis is now needed to explore characteristics or factors that might explain the variance. Most of the interventions excluded people prescribed drugs that affect weight gain, such as antipsychotics, glucocorticoids, and some antidepressants. This population might benefit from help with managing their weight owing to the side effects of these drug classes on weight gain, although we do not know whether the weight management interventions we investigated would be effective in this population. 69

What is already known on this topic

Referral by primary care to behavioural weight management programmes is effective, but the effectiveness of weight management interventions delivered by primary care is not known

Systematic reviews have provided evidence for weight management interventions, but the latest review of primary care delivered interventions was published in 2014

Factors such as intensity and delivery mechanisms have not been investigated and could influence the effectiveness of weight management interventions delivered by primary care

What this study adds

Weight management interventions delivered by primary care are effective and can help patients to better manage their weight

At least 12 contacts (telephone or face to face) are needed to deliver weight management programmes in primary care

Some evidence suggests that weight loss after weight management interventions delivered by non-medical practitioners in primary care (often endorsed and supervised by doctors) is similar to that delivered by clinician led programmes

Ethics statements

Ethical approval.

Not required.

Data availability statement

Additional data are available in the supplementary files.

Contributors: CDM and AJD conceived the study, with support from ES. CDM conducted the search with support from HEG. CDM, AJD, ES, HEG, KG, GB, and VEK completed the screening and full text identification. CDM and VEK completed the risk of bias assessment. CDM extracted data for the primary outcome and study characteristics. HEJ, GB, and KG extracted primary outcome data. CDM completed the analysis in RevMan, and GMJT completed the metaregression analysis in Stata. CDM drafted the paper with AJD. All authors provided comments on the paper. CDM acts as guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: AJD is supported by a National Institute for Health and Care Research (NIHR) research professorship award. This research was supported by the NIHR Leicester Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. ES’s salary is supported by an investigator grant (National Health and Medical Research Council, Australia). GT is supported by a Cancer Research UK fellowship. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: This research was supported by the National Institute for Health and Care Research Leicester Biomedical Research Centre; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

The lead author (CDM) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported, and that no important aspects of the study have been omitted.

Dissemination to participants and related patient and public communities: We plan to disseminate these research findings to a wider community through press releases, featuring on the Centre for Lifestyle Medicine and Behaviour website ( www.lboro.ac.uk/research/climb/ ) via our policy networks, through social media platforms, and presentation at conferences.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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Adherence as a predictor of weight loss in a commonly used smartphone application

Affiliations.

• 1 Eating and Weight Disorders Program, Mount Sinai School of Medicine, United States. Electronic address: [email protected].
• 2 Fairleigh Dickinson University, 1000 River Road, Teaneck, NJ 07666, United States.
• 3 Icahn School of Medicine at Mount Sinai, 1 Gustave Levy Place, Box 1230, New York, NY 10029, United States.
• PMID: 27292942
• DOI: 10.1016/j.orcp.2016.05.001

As adherence to weight loss interventions has been shown in prior research to be crucial in achieving weight reduction, we were interested in examining whether this held true for individuals attempting to lose weight using smartphone applications. Archived data from an international community sample of 7633 overweight men and women using Noom, a smartphone-based behavioural weight loss program, were used to test the hypotheses that there would be significant weight loss after using the application for three months and that greater self-monitoring adherence would be positively associated with weight loss outcomes. An average 1.92 BMI points were lost after using Noom for three months, and for every 10% increase in adherence there was a decrease of 2.59 BMI points (β=-1.36kg, SE=.24, p<.001). Our results provide preliminary evidence suggesting that smartphone application use is linked to significant short-term weight loss and that this weight loss is associated with adherence.

Keywords: Behavioural modification; Obesity; Smartphone; Technology; Weight loss.

Copyright © 2016 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.

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Clinical practice guidelines for the prevention of childhood obesity: A systematic review of quality and content

Michelle gooey.

1 Health and Social Care Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne Victoria, Australia

Helen Skouteris

2 Warwick Business School, University of Warwick, Coventry UK

Juliana Betts

Kostas hatzikiriakidis, elizabeth sturgiss.

3 School of Primary and Allied Health Care, Monash University, Melbourne Victoria, Australia

Heidi Bergmeier

Peter bragge.

4 BehaviourWorks Australia, Monash Sustainable Development Institute, Monash University, Melbourne Victoria, Australia

Associated Data

Obesity in childhood is a significant global issue, and prevention is key to reducing prevalence. Healthcare providers can play an important role in the prevention of obesity. The aim of this systematic review was to identify and evaluate clinical practice guidelines (CPGs) for preventing childhood obesity with a focus on the role of medical doctors. Peer‐reviewed literature and gray literature sources were searched for CPGs published from 2010 to 2021. Eleven CPGs were identified. Quality was evaluated using the Appraisal of Guidelines for Research and Evaluation Collaboration (AGREE II) instrument; seven CPGs were higher quality and four lower quality. Recommendations within the CPGs covered three main areas: growth monitoring, maintaining a healthy weight, and managing overweight. The importance of involving the whole family and healthy lifestyle behaviors was emphasized. The majority of the CPGs rated poorly in guideline applicability highlighting the need for practical implementation tools. Although our review identified a number of CPGs relevant to the prevention of obesity for doctors working with children and their families, more research is needed to produce high‐quality meaningful and applicable CPGs to maximize uptake, implementation, and ultimately, benefit to children and their families.

Abbreviations

1. introduction.

Overweight and obesity in childhood are associated with increased risk of excess weight in adulthood, 1 and more specifically, childhood obesity is associated with increased co‐morbid health risks such as type 2 diabetes mellitus 2 as well as significant direct and indirect economic costs. 3 The prevention of childhood obesity is a critical part of the strategy to address its increasing global prevalence. 4

Preventive health care is the interaction between the clinician and patient to promote health and prevent illness. 5 And as part of a systems approach, healthcare providers can play an important role in the prevention of obesity 6 ; however, there are many cited barriers to obesity prevention in health services across adult and pediatric populations, 7 , 8 , 9 including lack of knowledge, time, and appropriate resources and discomfort of healthcare providers associated with talking about weight, and evidence supporting the role of clinicians in obesity prevention is reportedly scarce. 10 , 11

In this context, clinical practice guidelines (CPGs) can be an important resource for clinicians and health services. CPGs are formal statements containing “recommendations intended to optimize patient care that are informed by a systematic review of evidence and an assessment of the benefits and harms of alternative care options” (p. 15). 12 Potential benefits of CPGs include improvement in care quality, provision of guidance to clinicians unsure of appropriate care and increased consistency of care within the healthcare system. 13 However, diversity may be seen among CPGs and a systematic review of CPGs can be an effective way to explore their characteristics, quality and content relating to a specific topic. 14 The reasons for doing a systematic review of CPGs include assessing knowledge and gaps relating to available clinical guidance, 14 informing future guidelines, planning health services and policy formulation (Figure  1 ).

Schematic diagram illustrating the relationship between clinical practice guidelines (CPGs) and a CPG systematic review and their differing purposes

A number of reviews of CPGs relating to childhood overweight and/or obesity have been published. 15 , 16 , 17 , 18 , 19 , 20 , 21 Only one of these reviews, published within a doctoral thesis on nursing practice, was focused solely on prevention 21 ; three CPGs were identified and assessed for quality, but the review only included CPGs published between 2012 and 2017. Delgado‐Noguera et al.'s review 18 included CPGs published between January 1998 and August 2007 for the prevention and treatment of childhood overweight and obesity; 22 relevant documents were identified and assessed for quality. Despite the authors recommending six CPGs for use (and a further eight with provisos), the content of the recommendations in the CPGs was not described. 18 Polfuss et al. 16 conducted a more recent review of CPGs addressing the prevention and management of overweight and obesity; however, it focused on primary care and only included CPGs originating from the United States. Other previous CPG reviews had specific foci such as management in primary care, 17 the role of parents in the treatment of adolescent overweight and obesity, 15 and nutritional management. 19

However, to the best of the authors' knowledge, no peer‐reviewed CPG reviews have focused solely on childhood obesity prevention for doctors. As such, the aim of this systematic review was to identify and appraise the quality of national and international CPGs relating to the prevention of childhood obesity, specifically relevant to a doctor's clinical practice across all levels of healthcare settings (e.g., community‐based general practice, speciality clinicians and hospital‐based services). This review also aimed to provide an overview of the key recommendations within included CPGs however it is not intended to replace individual CPGs and provide specific clinical guidance.

This systematic review was registered on the PROSPERO database (registration number of CRD42021226153) and is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) Statement. 22 A preliminary version of the review was presented in poster form at the European Congress on Obesity in May 2021. 23

2.1. Key definitions

In clinical practice, weight status is described as a continuum of underweight, healthy weight, overweight and obesity, 24 with increasing health risks associated with high body fat levels. 24 For the purpose of this review, “prevention” was defined as actions to maintain a healthy weight or manage overweight, that is, to prevent the development of obesity. For the purposes of this review, the management of children who have already developed obesity was not considered to be consistent with obesity prevention and therefore not in scope.

2.2. Search strategy

Given previous coverage of older CPGs in reviews 18 and the geographical limitations of the more recent review, 16 we sought CPGs published globally from 2010 onwards. Both gray and peer‐reviewed literature were sought that related to the prevention of childhood obesity.

2.2.1. Peer‐reviewed literature

A search strategy was developed in consultation with specialist academic librarians using a combination of key words and Medical Subject Headings (MeSH) terms or subject headings (as relevant). In addition, search filters were added to focus on publications that were more likely to be CPGs using the Canadian Agency for Drugs and Technologies in Health (CADTH) database search filters 25 as a guide. Limits used were English language and dates of 2010 to current (or equivalent) (see supporting information for further details of the search strings). Databases searched were Medline, Embase and All EBM reviews via the OVID platform. All EBM reviews include eight databases: Cochrane Database of Systematic Reviews, ACP Journal Club, Database of Abstracts of Reviews of Effects, Cochrane Clinical Answers, Cochrane Central Register of Controlled Trials, Cochrane Methodology Register, Health Technology Assessment and the NHS Economic Evaluation Database. The date range was January 2010 to 15 February 2021 (“current”).

2.2.2. Gray literature

A gray literature search was included to identify CPGs that had not been published in conventional academic repositories (see supporting information for complete list of the 29 sources). Searches were carried out in January and February 2021 on the following:

• web‐based guideline repositories (e.g., TRIP database, Guidelines International Network and Guideline Central)
• websites of organizations who produce CPGs (e.g., National Institute for Health and Clinical Excellence and World Health Organization);
• websites from relevant obesity organizations (e.g., The Obesity Society, European Association for the Study of Obesity);
• websites of relevant Australian organizations (e.g., Australian and New Zealand Obesity Society, National Health and Medical Research Council, Royal Australian College of General Practitioners and National Aboriginal Community Controlled Health Organisation);
• General web search engines Google, Duck Duck Go and Google Scholar, using the term “obesity overweight child guideline” with a review of the results (up to the first 100 hits) on each of these search engines.

Screening of gray literature was carried out directly on the websites unless covered by database searches (e.g. Joanna Briggs Institute). If available on a given website, a search engine was used to search for relevant CPGs using key words such as “obesity”, “overweight” and “child”; the keyword of “prevention” was purposely not used with the intent of keeping the initial search broad. If an appropriate website search engine was not available, a more iterative approach was taken and the website was searched using the menu system initially, reviewing pages that appeared to have relevant information and then following additional links that may be available. The gray literature search details were documented on tailored Microsoft Excel spreadsheets and included website title and URL, search strategy including search terms (if applicable) and how many documents were screened.

Previously published reviews of CPGs identified by the formal search strategy 15 , 16 , 17 , 18 , 19 , 20 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 and references of CPGs included in the systematic review were also searched to identify additional CPGs.

2.3. Inclusion and exclusion criteria

In this review, the definition of children included those from birth to 17 years old.

Key criteria determining CPG eligibility included in this review were as follows:

• That overweight and/or obesity was a main focus, and the guideline contained content on prevention of obesity.
• That at least 20% of recommendations in the CPG related to children and/or the CPG had distinct section(s) relating to children.
• That key recommendations in the CPG were linked to underlying evidence.
• Based on a systematic search for evidence.
• For use by doctors in any setting (e.g., primary care or hospitals).

CPGs were excluded from this review if they were not published in English; had been formally retired or superseded; were accessible only to members of a particular group requiring payment, that is, a “paywall”—as these may not be available to all practising doctors; or whose geographical scope was narrower than national‐level (e.g., state or institution specific guidelines).

2.4. Study selection

Results from database and gray literature searching were imported into Covidence 39 for screening. All abstracts from the peer review search and full‐text papers from both the gray literature and the peer review were reviewed by one reviewer (MG or KH) with 10% independently coscreened by a second reviewer (MG, KH or JB). Additionally, three complete websites (Guidelines International Network, British Medical Journal Best Practice and World Obesity Federation) from the gray literature search were coscreened (KH). Conflicts were discussed by the two reviewers, and if resolution could not be reached, they were discussed with a third independent reviewer (PB) for adjudication.

2.5. Data extraction

This review focused on the “key recommendations” of each guideline. “Key recommendations” was defined as outlined by the Appraisal of Guidelines for Research and Evaluation Collaboration (AGREE II) instrument 40 —specifically, recommendations contained within a box, presented in bold type and/or found in the executive summary or a dedicated “recommendation” section. Individual recommendations were then assessed for relevancy to this review, that is, whether they pertained to the prevention of childhood obesity in clinical practice, so that only appropriate recommendations were extracted and analyzed. For example, recommendations that related to community advocacy or policy level interventions were not included as the focus of this review was the delivery of patient care with a focus on preventative measures.

Data extraction was conducted by one team member, with co‐extraction independently conducted on a selection of 10% of data points. Extracted data for each CPG included the sponsoring organization, country or region of origin, publication year, population, target audience, number of recommendations and the recommendations assessed relevant to this review. Conflicts pertaining to data extraction were discussed by the two reviewers, and if resolution could not be reached, a third independent reviewer was available for adjudication.

2.6. Analysis

A narrative analysis and summary of the content of the relevant recommendations was undertaken to generate themes and subthemes reflecting key recommendations, using NVivo 41 for coding recommendations to themes. Coding was undertaken by one researcher with 10% of recommendations recoded by a second researcher. Discrepancies were discussed by the two coders until an agreement was reached.

2.7. Quality assessment

Each guideline that met the inclusion and exclusion criteria was appraised for methodological quality using the AGREE II instrument 40 by two independent reviewers. AGREE II is a validated 23‐item instrument which assesses a range of methodological areas using a 7‐point scale across six domains: 1, Scope and Purpose; 2, Stakeholder Involvement; 3, Rigour of Development; 4, Clarity of Presentation; 5, Applicability and 6, Editorial Independence. Following completion of scoring, appraisers independently reviewed any items for a given guideline that scored “1” by one appraiser but not the other or with a difference in score of 5 or more to ensure critical information that may inform the AGREE II assessment had not been accidentally missed by one of the reviewers.

The AGREE II guideline does not specify quality thresholds; for the purposes of this review, CPGs with scores greater than 50% in the majority of domains (i.e., 4 or more) were categorized as a higher‐quality guideline. Fifty percent was chosen based on previously published approaches 42 and given this is usually the threshold for “pass or fail” assessment. An intraclass correlation coefficient (ICC) for consistency of the two raters' assessments of the 23 items included in the AGREE II appraisal was calculated for each of the CPGs, using a two‐way mixed model (IBM SPSS Statistics Version 27).

2.8. Strength of recommendations

To analyze the strength of recommendations in the higher‐quality CPGs, Semlitsch et al.'s 43 approach of using a single nomenclature to describe the relative strength of recommendation was adapted. The categories used were stronger recommendation, weaker recommendation, expert consensus or similar (e.g., best practice), insufficient evidence and not rated.

3.1. Results of literature search

As shown in Figure  2 , database searches yielded 7981 titles and abstracts; following deduplication and screening, 167 full‐text papers were reviewed. The gray literature search yielded 112 full‐text papers which were assessed for eligibility. Following screening of the 279 full‐text documents, 11 CPGs were eligible for inclusion in the review 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 which included two identified from the database search and nine from the gray literature search.

Flow diagram of included studies

Of the full‐text documents that were co‐screened, only two conflicts on inclusion and exclusion criteria could not be resolved between the reviewers and required additional input from a third independent reviewer; one CPG was included and the other excluded.

References pertaining to the individual CPG's acronyms are presented in Table  1A and ​ andB B and for ease of reading will not be repeated in the body of the results.

TABLE 1A and B

Guideline characteristics divided by higher (A) and lower (B) quality according to AGREE II assessment

Abbreviation: CPG, clinical practice guideline.

3.2. Characteristics of the CPGs (see Table  1A and ​ andB B )

Most CPGs were national in scope ( n =  8) comprising three from North America, 46 , 50 , 52 three from Europe 44 , 45 , 54 and one each from Asia 48 and the Middle East. 51 Three CPGs were published by international organizations or collaborations. 47 , 49 , 53 One CPG focused explicitly on low‐ and medium‐resource settings 53 and one on more affluent populations. 47 Similarly, the intended audiences of the CPGs varied, with some designed only for healthcare providers, 50 , 51 others for broader dissemination including to policy makers and service providers 44 , 46 , 53 and others intended for children and their families 46 and the community more broadly. 44 , 46 None of the guidelines were targeted exclusively to doctors. Several did not explicitly state the target audience within the body of the guidelines.

Most of the included CPGs were published in the latter 5 years of the search period (2016 onwards), with the most recent being the CPG from Qatar published in 2020. 51 At least two CPGs 44 , 45 were in the process of being updated at the time of this review with an expected publication date of 22 June 2023. 57

While some CPGs covered a broad age range of children, two specifically focused on a younger cohort of children 47 , 53 —the WHO CPG on children less than 5 years old and the EarlyNutrition CPG on children less than 4 years old. Three CPGs excluded children younger than 2 years old 45 , 46 or those who had not yet been weaned 44 from their scope. Three CPGs 44 , 45 , 52 additionally included guidance relating to people older than 17 years.

Two CPGs focused on specific interventions—behavioral 46 or dietary interventions 47 —and two CPGs focused on obesity prevention without obesity management recommendations. 44 , 47 The total number of recommendations in each guideline varied, ranging from five in the APA CPG to 113 in the NICE CG189 CPG.

3.3. Quality of the CPGs

Seven of the 11 CPGs met our criteria for higher quality 44 , 45 , 46 , 49 , 50 , 51 , 53 (Table  1A ). Complete domain and overall quality AGREE II scores for all CPGs are included in the supporting information .

In terms of domains, Domain 4 (Clarity of Presentation) was the domain in which all CPGs consistently scored above 50%, while Domain 5 (Applicability) had the least number of CPGs with scores above 50%.

With regard to specific individual AGREE II items, most guidelines performed well on “key recommendations are easily identifiable” (Domain 4: Clarity of Presentation) with scores of 6 or 7 out of a maximum of 7. Conversely, performance was poor (scores of 1 or 2) on “the potential resource implications of applying the recommendations have been considered” and “the guideline presents monitoring and/or auditing criteria”, both belonging to the “applicability” domain (Domain 5).

An analysis of the ICC for the AGREE II scores for the overall agreement across all 23 items for each guideline is included in the supporting information . Overall, agreement between the raters was acceptable, with the majority of values between 0.75 and 0.90 or greater than 0.90 indicating good and excellent reliability, respectively. 58 Agreement on AGREE II scoring for the two National Institute for Health and Care Excellence (NICE) CPGs was low with an ICC of less than 0.50 which is considered to be poor correlation. 58

3.4. Recommendation analysis

In total, there were 315 key recommendations contained within all 11 CPGS, and of these, 146 were related to the prevention of obesity and considered relevant to this review. Following 10% coextraction, there was good agreement on what was a key recommendation.

Following analysis and coding, four themes emerged: growth monitoring, maintaining a healthy weight, managing overweight and undernutrition. With regard to the theme of undernutrition, the recommendations were specifically pertaining to children with stunting and wasting, and in terms of the weight continuum, children with stunting and/or wasting are often (although not always) underweight, 59 so it was allocated a separate theme. Considering that undernutrition is associated with additional complex health implications 60 and that this theme included only three recommendations from a single guideline, 53 this theme will not be discussed further in this review.

3.4.1. Assessment of recommendations from higher‐quality CPGs ( n =  110)

One hundred and ten recommendations relevant to this systematic review were extracted from the seven higher‐quality CPGs (further details of individual recommendations are available from the authors on request). A varying level of detail was included in the individual recommendations across the different guidelines. The following is an overview of common recommendations relating to themes of growth monitoring; maintaining a healthy weight and managing overweight; focusing on the CPGs assessed as higher quality, that is, American Psychological Association (APA), World Health Organization (WHO), National Institute for Health and Care Excellence CPG 189 (NICE CPG189), National Institute for Health and Care Excellence NG7 (NICE NG7), Canadian Task Force on Preventive Health Care (CTFPHC), Endocrine Society (ES) and Ministry of Public Health, Qatar (QMOH); and excluding those assessed as lower‐quality guidelines.

3.4.2. Growth monitoring

Growth monitoring was addressed in five 45 , 49 , 50 , 51 , 53 CPGs, and 14 recommendations were coded to this theme. Recommendations for opportunistic growth monitoring during clinic visits were included in five CPGs. 45 , 49 , 50 , 51 , 53 Body mass index (BMI) measured against normative percentiles for age and sex was the recommended measurement for children, especially for those aged 2 years or older in the CTFPHC, ES and NICE189 CPGs. Measurement of weight for length was recommended for children younger than 2 years old in the ES guideline and weight for length/height for children less than 5 years old in WHO guideline.

Recommended standardized growth charts differed between CPGs and were often geographically specific. Differences in percentile cutoffs for defining obesity and overweight were also noted—for example, the ES CPG defines children 2 years of age or older with a BMI between the 85th and 95th percentile on the United States Centre for Disease Control growth chart as having overweight, whereas NICE CG189 refers to the UK 1990 BMI charts, which defined overweight as between 91st and 98th percentile 61 (and is consistent with updated charts 62 ).

3.4.3. Maintaining a healthy weight

Thirty recommendations from three CPGs 44 , 49 , 53 involved maintaining a healthy weight. Maintaining a healthy weight was the sole focus of only one CPG. 44

Promoting healthy lifestyle behaviors was the mainstay of this theme; CPGs recommended regular physical activity, 44 , 49 good sleep habits, 44 , 49 healthy eating 44 , 49 , 53 and consideration of television and other screen time 44 , 49 ; the NICE CG7 and ES CPGs included advice addressing multiple factors. As part of healthy feeding, both the ES and WHO CPGs included recommendations relating to breastfeeding—for infants in the ES CPG and for children aged up to at least 24 months in the WHO guideline. The ES and NICE CG7 CPGs recommended that clinicians involve family in promoting healthy behaviors for example, the NICE CG7 CPG recommended parents support an active lifestyle for their children and encourage eating meals as a family.

3.4.4. Managing overweight

Six CPGs included recommendations addressing management of overweight, 45 , 46 , 49 , 50 , 51 , 53 and 122 recommendations were coded to this theme. None of the CPGs solely focused on the management of overweight but instead included this as part of a broader discussion with obesity management. Furthermore, within some CPGs, 45 , 49 , 51 delineation between the management of overweight (within scope of this review) and management of obesity (outside scope of this review) was occasionally unclear; for example, despite a heading such as “management of obesity”, subsequent recommendations directly referred to the management of overweight as well as obesity.

Two CPGs made recommendations regarding the goals of weight management 45 , 51 and both recommended that the aim of weight management programs should be tailored to the child, considering factors such as age, 45 , 51 and that health benefits can be derived by maintaining modest weight loss 51 or an improvement in diet or physical activity levels even without weight loss. 45

Higher‐quality CPGs consistently recommended lifestyle changes as the initial focus of overweight management. Four CPGs 45 , 49 , 51 , 53 recommended physical activity counseling, and three CPGs 45 , 51 , 53 addressed dietary factors such as reducing energy intake 45 , 51 and improving nutrition. 45 , 51 , 53 Other recommendations included behavioral interventions 45 , 46 , 50 ; in particular, both APA and CTFPHC recommended formal, family‐inclusive behavior change programs of significant duration (weeks to months 50 or a minimum of 26 hours 46 ). The involvement of multidisciplinary team members was also recommended by several CPGs. 45 , 50 , 51 Three CPGs also recommended the referral to specialist care in certain circumstances, such as significant comorbidities or complex needs 45 , 51 or as part of the delivery of behavioral interventions by a specialized interdisciplinary team. 50 Assessment for comorbidities was recommended by the ES and NICE CG189 CPGs.

The role of the child's family in managing overweight was included in five of the CPGs' recommendations 45 , 46 , 49 , 50 , 51 —for example, educate the family about healthy food and physical activity, 45 , 49 involve the family as well as the child in formal behavior change interventions 46 , 50 and encourage whole family to make lifestyle changes. 45

Four CPGs included recommendations relating to pharmacological approaches in children with overweight 45 , 49 , 50 , 51 and generally recommended against drug treatment for most overweight children. Most CPGs did not discuss the role of surgery in overweight management; the CTFPHC guideline recommended against routine referral of patients for bariatric surgery by primary care practitioners.

3.5. Strength of recommendations of higher‐quality CPGs

Although nomenclature for describing strength of recommendations differed between CPGs, all but one higher quality CPG 51 had a two‐tier rating system base for recommendation strength, that is, each recommendation was determined by the guideline authors to be a higher or lower strength recommendation. The QMOH CPG had a three‐tiered strength rating and for the purposes of this analysis, a QMOH rating of RGA and RGC was both designated as “higher quality”. Three CPGs 49 , 51 , 53 additionally included an expert consensus/best practice statement (or similar) category.

Of the recommendations in the higher‐quality CPGs, 53 (47%) were considered by the guideline authors to be stronger recommendations, 20 (18%) were weaker recommendations and five (4%) were based on expert consensus or similar (see Table  2 ). Thirty‐one (27%) of the recommendations were not associated with a strength assessment. In the NICE CG189 guideline, strength was indicated by the wording of the recommendation; however, a number of the individual recommendations did not reliably incorporate current NICE writing standards 63 and therefore could not be analyzed. Additionally, some recommendations from QMOH CPG did not have a strength assessment. The APA CPG recommendations included four statements indicating insufficient evidence to make a formal recommendation.

Strength assessment of key recommendations from higher‐quality CPGs

3.6. Lower‐quality CPG recommendations ( n =  37)

The four lower‐quality CPGs included 115 key recommendations in total and 37 relating to the prevention of obesity in children (Table  1B ). Six of these recommendations were coded to growth monitoring, 29 recommendations coded to maintaining a healthy weight and 20 coded to managing of overweight.

Overall, recommendations within the lower‐quality guidelines were generally consistent with those in the higher‐quality guidelines. However, in comparison with the higher‐quality group, there were a number of subthemes that lower‐quality CPG recommendations did not address; for example, with respect to the management of overweight, there were no recommendations relating to formal behavioral interventions or the role of pharmacologic treatment. Recommendations relating to appropriate growth patterns in healthy weight children were included in two lower‐quality guidelines 47 , 54 but not seen in any higher‐quality guidelines.

4. DISCUSSION

This review identified 11 CPGs containing recommendations relating to childhood obesity prevention, of which seven were assessed to be higher quality. Recommendations covered three main themes of growth monitoring, maintaining a healthy weight and managing overweight. As far as we are aware, this is the first peer‐reviewed systematic review of CPGs that focused on childhood obesity prevention for doctors.

Of the 11 CPGs included, it is noteworthy that only two CPGs solely focused on obesity prevention without including the management of obesity. 44 , 47 In the context of healthcare systems which traditionally focus on disease treatment and a preventive lens is often lacking, 64 the relative lack of standalone prevention CPGs implicitly reinforces some views that prevention is not the “core business” (p 71) 7 of healthcare providers. 7 , 8 , 64

This review also found that consideration of practical implications for implementing recommendations is a gap in many of the included guidelines. Most CPGs included in this review scored less than 50% in the AGREE II assessment of Domain 5: applicability, which relates to the “likely barriers and facilitators to implementation, strategies to improve uptake, and resource implications of applying the guideline” (p. 7). 40 Applicability has similarly been found to be a frequently low scoring item in other guidelines outside of obesity prevention. 42 This is concerning given evidence indicating that the existence of a guideline does not automatically translate into clinical practice changes 65 and more specifically, findings of low uptake of other guidelines in the setting of childhood obesity prevention. 66 This issue has also been highlighted by a recently published systematic review by Ray et al., 9 which identified a range of challenges and facilitators at the level of the provider, parent and organization to implementing childhood obesity prevention practices. Although the Ray et al. review focused only on primary care and young children, many of the identified challenges are likely to be relevant to other healthcare sectors and a broader age range. For example, a frequently identified recommendation in this review was the need for growth monitoring. At face value, implementation should be relatively simple; however, there are underlying complexities to be considered. For example, the doctor needs access to accurate weight and height (and length for infants) measurement tools, tools to facilitate BMI calculation and the use of standardized growth charts appropriate for that jurisdiction, sufficient time during the consultation, knowledge regarding appropriate actions to follow up results, record keeping systems that provide reminders for repeat growth measurements and adequate reimbursement for the services provided. Such an example illustrates the potential complexity of guideline implementation relating to the prevention of obesity. Adequate dissemination, continuous education, direct interaction with educators such as local opinion leaders, decision support systems such as automated reminders and the use of standard orders and documentation are some considerations for improving guideline implementation. 65 The specific choice of implementation strategies should be informed by exploration of behavioral drivers of practice such as habit. 67 This enables targeted interventions and reduces resource waste.

This review focused on the doctor's role specifically, as it is informing a subsequent barriers and facilitators analysis which is best done at the level of individual health professions as behavioral drivers and perspectives vary between groups even for the same behavior. 68 However, it is acknowledged that other healthcare professionals can play an important role in childhood obesity prevention, and several CPGs in this review specifically highlighted the importance of multidisciplinary teams. 45 , 50 , 51 For example, nurses are already involved in the prevention of chronic disease 69 and are well placed to play an important part in childhood obesity prevention. 70 Thus, implementation strategies should also consider the role of broader health workforce and how they could contribute to effective execution.

As outlined earlier, weight is categorized along a continuum of underweight, healthy weight, overweight and obesity in a clinical setting. However, it is recognized that in the reality of clinical practice, the prevention and management of obesity is a continuous spectrum with many common management principles. Ideally, intervention to prevent obesity would occur as early in the continuum as possible, hence the focus of this review on maintenance of healthy weight and management of overweight to prevent obesity from developing. However, it was observed that the term “obesity” was sometimes, but not always, used as an umbrella term for “overweight and obesity” in some CPGs. For example, although several CPGs referred only to the management of obesity in their title, they explicitly also included the management of overweight within the guideline itself. This presented a challenge for our review as this required us to distinguish recommendations relating to the management of overweight from those relating to the management of obesity only. For cases in which the recommendation wording was not explicit, determination for inclusion and exclusion was made based on the context of the supporting text. A clearer and more consistent use of nomenclature should be considered in future CPGs as this blurring of terminology for two related but distinctly defined clinical entities may cause confusion, especially for doctors who are less familiar with this clinical area or those seeking specific knowledge about either obesity or overweight but not both.

This review also found that there were a small number of individual recommendations within the higher‐quality guidelines that were based on expert consensus (or similar) or specifically highlighted as having insufficient evidence. For example, although both the CTFPHC and APA CPGs included the value of formal behavioral interventions for children with overweight, the APA CPG found that there was insufficient evidence regarding the comparative effectiveness of the different components within a behavioral intervention. 46 Such recommendations indicate evidence gaps and potential areas for further research in the future.

Strengths of this study included a comprehensive search strategy incorporating both gray and peer‐reviewed literature, allowing a significant degree of confidence that relevant CPGs were included. The quality appraisal also facilitated focus on higher‐quality CPGs and enabled potential areas of improvement to be identified for future CPG development. For most CPGs, inter‐rater agreement of AGREE II scores between reviewers was either good or excellent. Standardization of the strength assessment of higher‐quality CPG recommendations was another strength of this review. In terms of benefit to individual clinicians, this review may help them distinguish between higher‐ and lower‐quality guidelines.

A limitation of this review is that the analysis focused on “key recommendations”, which have been specifically highlighted by the authors of the CPGs and are expected to be most easily recognized by busy clinicians looking for obesity prevention guidance. While some recommendations in the individual CPGs may not be considered “key” and therefore not included in this review, all reviewed guidelines are readily available for further reference. Furthermore, as stated previously, this systematic review is not intended to replace the individual guidelines or provide comprehensive clinical guidance but rather gives an overview of the main themes within the guidelines.

Other limitations included the exclusion of non‐English CPGs and the poor inter‐rater agreement for quality appraisal for both NICE sponsored guidelines. 44 , 45 With specific reference to the inter‐rater agreement scores for the NICE CPGs, the same degree of difference was not seen with the other CPGs, suggesting that there may be a particularity such as formatting within the NICE guidelines that impacted the AGREE II assessment. The inter‐rater agreement scores may have been improved with additional appraisers; however, the use of two assessors is consistent with the minimum number suggested by the AGREE II instrument's user manual. 40 Finally, although the majority of screening and extraction was done by one reviewer, 10% was checked by a second reviewer.

5. CONCLUSION

This systematic review is the first peer‐reviewed systematic review that focused on CPGs relating to childhood obesity prevention for doctors. The review identified 11 relevant CPGs; of these, the quality assessment identified seven CPGs of higher quality and four of lower quality. CPGs included recommendations for doctors working with children and their families for the prevention of obesity, such as growth monitoring and emphasizing the importance of healthy lifestyle behaviors. A key future challenge is improving implementation to optimize uptake of CPG recommendations into routine clinical practice.

Supporting information

Supporting Information S1

ACKNOWLEDGMENTS

We would like to thank Cassandra Freeman (Monash University Library), Emma Galvin (School of Public Health and Preventive Medicine, Monash University) and Tim Powers (Monash eResearch Centre, Monash University). Open access publishing facilitated by Monash University, as part of the Wiley ‐ Monash University agreement via the Council of Australian University Librarians.

Gooey M, Skouteris H, Betts J, et al. Clinical practice guidelines for the prevention of childhood obesity: A systematic review of quality and content . Obesity Reviews . 2022; 23 ( 10 ):e13492. doi: 10.1111/obr.13492 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Funding information This research is supported by an Australian Government Research Training Program (RTP) Scholarship (2020) and the National Health and Medical Research Council (NHMRC) postgraduate scholarship (GNT2005401; 2021–2023) for M. Gooey; NHMRC Investigator Grant, 2020–2024 for E. Sturgiss and the Victorian Public Health Medical Training Scheme (Victorian Department of Health) for J. Betts.

Funding information Australian Government Research Training Program; National Health and Medical Research Council, Grant/Award Numbers: Investigator Grant 2020‐2024 (ES), GNT2005401; 2021 ‐ 2023 (MG); Victorian Public Health Medical Training Scheme

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• Published: 09 September 2024

Cellular and Molecular Biology

Implications of obesity and insulin resistance for the treatment of oestrogen receptor-positive breast cancer

• Sohail Rooman Javed 1 ,
• Aglaia Skolariki 1 ,
• Mohammed Zeeshan Zameer 1 &
• Simon R. Lord   ORCID: orcid.org/0000-0001-7946-5609 1  

British Journal of Cancer ( 2024 ) Cite this article

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• Breast cancer

Breast cancer is the most common cancer in women, and incidence rates are rising, it is thought in part, due to increasing levels of obesity. Endocrine therapy (ET) remains the cornerstone of systemic therapy for early and advanced oestrogen receptor-positive (ER + ) breast cancer, but despite treatment advances, it is becoming more evident that obesity and insulin resistance are associated with worse outcomes. Here, we describe the current understanding of the relationship between both obesity and diabetes and the prevalence and outcomes for ER+ breast cancer. We also discuss the mechanisms associated with resistance to ET and the relationship to treatment toxicity.

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Introduction.

The obesity epidemic is contributing to the rising incidence rates of breast cancer, which remains the most common cancer for women worldwide [ 1 ]. Furthermore, the relationship between breast cancer and metabolic disorders, specifically obesity and insulin resistance, increases the complexity of breast cancer treatment, posing unique challenges in managing toxicities and treatment resistance.

As described by the World Health Organisation (WHO), a body mass index (BMI) of 30 kg/m 2 or greater is considered obese and is present in 13% of the world’s adult population, with greater prevalence in the Western world [ 2 ]. Obese patients with ER+ breast cancer are at a greater risk of cancer development, recurrence, and mortality [ 3 , 4 , 5 , 6 ], even after accounting for confounding variables such as concomitant diseases and chemotherapy underdosing [ 7 ]. Obesity also increases the risk of insulin resistance, characterised by cellular insensitivity to insulin, and is associated with a cluster of conditions, including hypertension, hyperglycaemia, central adiposity, and dyslipidaemia, known as metabolic syndrome [ 2 ]. These variables collectively raise the risk of developing Type 2 diabetes mellitus (T2DM), which in itself is associated with increased cancer risk [ 8 ].

In addition, there is growing evidence that the metabolic abnormalities associated with obesity and insulin resistance may have a detrimental impact on the efficacy of ET [ 4 , 9 , 10 ]. In this article, several hypotheses for this impaired efficacy have been explored, including the impaired regulation of aromatase in obesity as well as the role of PI3K, leptin, and FGFR1 signalling. Obesity and insulin resistance may also impact the altered toxicity profile of systemic cancer treatments.

In this paper, we consolidate the current understanding of the impact of obesity and diabetes on cancer risk, treatment outcomes, and toxicity in ER+ breast cancer.

Obesity, diabetes, breast cancer risk and outcome

In the last four decades, the rising incidence of breast cancer has been partly attributed to the introduction of national screening programmes worldwide, promoting the identification of small, early-stage tumours with favourable prognoses [ 11 ]. However, multiple epidemiological studies across diverse ethnic populations presented in Table  1 , have associated the simultaneous rise in obesity levels with the increasing breast cancer incidence.

As body size and fat mass increase, endogenous oestrogen production is heightened, while sex hormone-binding globulin levels decrease. This hormonal imbalance is hypothesised to account for the link between obesity and an elevated risk of breast cancer in postmenopausal women [ 12 , 13 ].

Although this link is well established in postmenopausal women, where increased androgen aromatisation in adipose tissue leads to higher oestrogen levels [ 14 , 15 , 16 ], the relationship between obesity and breast cancer risk in premenopausal women remains less clear. In this population, obesity is associated with a reduced incidence of breast cancer [ 14 , 17 ]. For example, a large prospective multicentre analysis of over 700,000 premenopausal women showed that higher BMI during early adulthood is associated with a reduced risk of developing future breast cancer. This inverse association was stronger at younger ages and persisted across all BMI distributions, suggesting that increased adiposity early in life might have a protective effect against premenopausal breast cancer [ 18 ]. To explain this paradoxical risk reduction, it has been proposed that there is decreased oestrogen exposure in premenopausal obese women due to increased anovulatory menstrual cycles, a later decline in progesterone levels during menstruation, and longer menstruation [ 14 , 15 , 16 , 19 ]. However, a longitudinal study did not find a clear relationship between BMI and ovulation-related variables like probable polycystic ovarian syndrome, oral contraceptives, and infertility secondary to an ovulatory disorder, which has cast doubt on this view [ 14 , 18 ]. There is added complexity when considering findings from a pooled analysis of seven prospective studies, which investigated how circulating oestrogen and androgens affect premenopausal breast cancer risk. This study indicated that although total oestradiol levels were inversely related to BMI and positively associated with cancer risk, suggesting that the lower risk in obese women might stem from its reduced levels, free oestradiol, oestrone, and androgens such as DHEAS, testosterone, and free testosterone were found to be positively associated both with BMI and premenopausal breast cancer risk [ 20 ].

Aside from decreased oestrogen exposure, a net reduction in progesterone production is also hypothesised to account for decreased breast cancer risk in pre-menopause [ 21 ]. Progesterone is considered a major mitogen in the adult mammary epithelium in both mice and humans and has been linked to mammary carcinogenesis [ 22 ]. In the obese, premenopausal population, increased total oestrogen levels from adipose tissue and ovarian oestrogen production lead to enhanced negative feedback on hypothalamic pituitary-controlled gonadotropin release, therefore reducing ovarian steroid synthesis and progesterone production. Unlike postmenopausal women who produce no ovarian oestrogen [ 21 ], the oestrogen-progesterone imbalance in premenopausal obese women has been put forward as an explanation for the reduced breast cancer risk observed in this group.

In postmenopausal women, obesity has also been shown to increase breast cancer-related disease recurrence and mortality [ 7 , 9 , 23 ]. A meta-analysis of 82 follow-up studies demonstrated that breast cancer survivors with a higher BMI have worse overall and breast cancer-specific survival [ 7 ]. Overweight or obese breast cancer patients often present with larger tumours, higher-grade malignancy, and more positive lymph nodes at diagnosis. However, even after adjusting for these known prognostic factors, obesity independently raises the risk of distant metastases and breast cancer-related death [ 24 ].

Similarly, several meta-analyses in the last 20 years, as detailed in Table  2 , have consistently shown that diabetes is associated with an increased incidence of breast cancer. It has been proposed that diabetes contributes to the onset of breast cancer via various mechanisms, such as mitogenic hyperinsulinaemia/insulin-like growth factor (IGF) pathway signalling, hyperglycaemia, inflammation caused by excess fat, and alterations in the levels of sex hormones [ 25 ]. These mechanisms are discussed further later in this article.

Furthermore, there is an approximate 40% increase in mortality following a breast cancer diagnosis among postmenopausal women with diabetes compared to women without diabetes. Nevertheless, this increase may, at least in part, be due to diabetes-related comorbidities [ 26 ]. Breast cancer-specific mortality also appears to be higher in diabetic women, although it is uncertain if mortality worsens with increasing severity of type 2 diabetes [ 27 ].

Genetic links and shared susceptibility in obesity, diabetes, and breast cancer

Various hypotheses have been proposed to explain the frequent co-occurrence of obesity, diabetes and breast cancer, with one of the most prominent being the shared genetic aetiology. Recent advancements in genetic research, particularly through large Genome-Wide Association Studies (GWAS), have revealed that specific genetic variants are associated with these complex diseases across different populations.

Several variants that are associated with T2DM have also been linked to breast cancer. Notable examples include polymorphisms mapping to loci at 10q25.2 and 9p21.3 at which transcription factor 7-like 2 (TCF7L2) and cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) have been proposed as the target genes and both of which are involved in signalling pathways that regulate cell-cycle progression and proliferation [ 28 , 29 , 30 , 31 ]. The first obesity susceptibility locus discovered by GWAS mapped to 16q12.2, proximal to the fat mass and obesity-associated (FTO) gene which has been recognised as a regulator in DNA repair mechanisms, DNA damage and inflammatory responses. Polymorphisms at this locus have also been associated with breast cancer risk [ 32 , 33 , 34 , 35 , 36 ]. Furthermore, the FTO-encoded protein, an RNA N6-methyladenosine (m6A) demethylase, has been implicated in breast tumourigenesis and progression [ 37 , 38 ].

Recently, interest in the FTO gene has been renewed following a systematic analysis exploring the potential overlap of known GWAS risk variants for obesity, T2DM and breast cancer. This study identified 91 candidate variants in linkage disequilibrium using datasets from the 1000-Genomes Project to analyse candidate haplotypic blocks. Surprisingly, all variants were located within the vicinity of the FTO gene, thus highlighting the significant association of this locus with these diseases and strengthening the hypothesis of a shared genetic basis [ 39 ].

However, conflicting evidence from previous case–control studies in women of various ethnicities has questioned the potential pleiotropic effects of these risk variants on breast cancer, obesity and diabetes traits [ 40 , 41 , 42 ]. In addition, a case–control study involving U.S. Caucasian women found non-significant correlations between intronic and intergenic single nucleotide polymorphisms (SNPs) located in or near 29 diabetes-related genes and breast cancer incidence and mortality, casting further uncertainty on the functional significance of these variants in relation to breast cancer risk [ 43 ]. Finally, Mendelian randomisation analyses have been implemented to infer causality between genetic instruments associated with obesity and diabetes and breast cancer risk [ 44 , 45 , 46 ]. This method provides clearer insights into causal associations by reducing bias and confounding, as well as mitigating reverse causation. Nevertheless, further mechanistic studies will be required to elucidate the underlying biological pathways and interactions that drive these associations.

Mechanisms of obesity-induced carcinogenesis and treatment resistance in breast cancer

Breast cancer development in the context of obesity has been linked to increased inflammation in adipose tissue, marked by macrophage infiltration and the formation of crown-like structures (CLS) around dead adipocytes. This increased inflammation within the adipose tissue microenvironment has been shown in obese mouse models and is associated with increased cell proliferation and higher levels of inflammatory cytokines, including TNF-alpha, IL-1β and Cox-2, as well as insulin resistance [ 47 , 48 , 49 ]. In a study of women undergoing mastectomy or breast cancer surgery, CLS was detected in 40% of cases, and was associated with higher levels of insulin, glucose, leptin, triglycerides, C-reactive protein and IL-6 [ 50 ]. The presence of CLS in breast tissue is linked to an increased risk of breast cancer and a poorer prognosis, with evidence showing associations with metastasis and decreased overall survival [ 49 , 51 , 52 ]. Several mechanisms may contribute to CLS formation and resistance to ET. Understanding and selectively targeting these mechanisms could affect both breast cancer development and associated insulin resistance. A summary of the mechanisms of breast cancer carcinogenesis in obesity is described in Fig.  1 .

In the presence of obesity, decreased SHBG production and heightened aromatase expression in the breast adipose stromal cells lead to increased oestrogen bioavailability and biosynthesis, a major contributor to the development and progression of ER+ breast cancer in postmenopausal women. The expanded adipose tissue is characterised by dysfunctional adipocytes and crown-like structures, and immune cell infiltration that accentuates a pro-inflammatory state. The state of hyperinsulinaemia and hyperglycaemia, concurrent with the release of adipokines, notably leptin and pro-inflammatory mediators by the macrophages contribute to the activation of pro-tumourigenic and metabolic pathways. Adipocyte hypoxia stabilises HIF-1α and VEGF upregulation, promoting angiogenesis. Together, these factors activate signalling cascades such as PI3K/AKT and MAPK/ERK, driving cell proliferation, survival, and breast cancer progression. SHBG sex hormone-binding globulin, WAT white adipose tissue, IL-1 interleukin 1, IL-6 interleukin 6, IL-8 interleukin 8, VEGF vascular endothelial growth factor, NEFAs non-esterified fatty acids, PGE2 prostaglandin E2, IGF-1 insulin-like growth factor 1, TGF-β transforming growth factor beta, TNF-α tumour necrosis factor alpha, IGFBPs insulin-like growth factor binding proteins, SFRP5 secreted frizzled-related protein 5, ER oestrogen receptor, ROS reactive oxygen species, HIF-1α hypoxia-inducible factor 1-alpha, NF-κB nuclear factor kappa B, PI3K/AKT phosphoinositide 3-kinase/protein kinase B, IGF-1R insulin-like growth factor 1 receptor, JAK/STAT Janus kinase/signal transducer and activator of transcription, MAPK/ERK mitogen-activated protein kinase/extracellular signal-regulated kinase. (Created with BioRender.com.).

RANKL/TNF-alpha/NF-κB activation

The accumulation of macrophages in CLS is thought to be caused by a decrease in macrophage apoptosis within obese adipose tissue through the activation of the transcription factor NF-κB [ 53 ]. NF-κB has been found to be activated in human breast cancer cell lines and is considered critical in the genesis of ET resistance in ER+ breast cancer, as it has been shown to promote tamoxifen resistance, early recurrence, metastasis, and worse overall survival [ 53 , 54 , 55 , 56 ]. There also appears to be cross-talk between ER and NF-κB, potentially working in tandem to support breast cancer cell survival and transition to a more aggressive phenotype [ 57 ]. The upregulation of NF-κB is independently associated with hyperinsulinemia, and reduced β-cell function [ 58 ].

Novel therapies inhibiting NF-κB gene activation could therefore potentially prevent ER+ tumour recurrence and restore endocrine responsiveness. Preclinical studies indicate that suppressing NF-κB significantly enhances the sensitivity of resistant breast cancer tumour cells to tamoxifen [ 59 , 60 ]. Riggins et al. demonstrated that pharmacologic inhibition of NF-κB by parthenolide, a small molecule inhibitor against NF-κB, could restore fulvestrant-mediated suppression of growth in breast cancer cell lines [ 61 ]. Despite this preclinical data, clinical trials exploring NF-κB inhibition have not been promising to date. Three Phase II studies investigating bortezomib, a proteosome inhibitor that blocks the NF-κB pathway, as a single agent or in combination with ET, did not elicit an objective tumour response in metastatic breast cancer patients [ 62 , 63 , 64 ].

Targeting upstream or downstream signals of NF-κB may provide more promising therapeutic prospects. RANK ligand (RANKL), a TNF-related molecule, has been shown to activate NF-κB in preclinical studies and thereby promote proliferative changes in the mammary epithelium as well as epithelial-mesenchymal transition, which induces tumour cell migration, invasion and metastasis [ 65 , 66 , 67 ]. Systemic and hepatic blockage of RANKL signalling can also improve hepatic insulin sensitivity and glucose tolerance [ 58 ]. Currently in clinical practice, the use of the RANKL inhibitor denosumab is not extended beyond the prevention or treatment of osteoporosis, primarily due to disappointment on its efficacy in improving disease-free survival (DFS) in patients [ 68 , 69 ]. However, the potential of RANKL inhibitors, particularly denosumab, to counteract NF-κB-mediated resistance in ER+ breast cancer may still merit further clinical investigation.

Similarly, TNF-alpha, a cytokine acting upstream of NF-κB, has been shown to induce proliferation in murine mammary tumour cells [ 70 ]. By upregulating PTEN and suppressing the AKT/eNOS/NO signalling pathway, TNF-alpha also contributes to vascular insulin resistance [ 71 ]. Infliximab, which binds to an neutralises TNF-alpha, was found to be tolerable in patients with advanced cancer with some evidence of on-target activity [ 72 ]. In metastatic breast cancer, a Phase II clinical trial demonstrated the safety of another anti-TNF-alpha agent, etanercept, in heavily pre-treated patients, although more research is required to understand efficacy and any treatment role [ 73 ]. TNF-alpha blockade may also have a role in overcoming resistance to anti-PD-1 therapy, and combination therapy should be assessed for feasibility [ 74 ].

Hypoxia and induction of hypoxia-inducible factor 1-alpha (HIF-1α)

The activation of NF-κB in obesity-related breast cancer may be driven by adipocyte hypoxia [ 75 ]. It is hypothesised that adipocyte hypertrophy without hyperplasia leads to accelerated tissue growth with insufficient supportive angiogenesis [ 76 ]. Hypoxia in turn triggers the activation of hypoxia-inducible factors (HIFs), which are associated with increased proliferation and expression of ER and VEGF, suggesting a possible relationship with more aggressive tumours [ 77 ]. HIF-1α expression is associated with poorly differentiated breast cancer, a higher pathological stage, and poor treatment response and outcome [ 77 , 78 ]. Obesity is also associated with elevated HIF-1α mRNA and protein in adipose tissue [ 79 ], while HIF-1α activation in macrophages is associated with the development of insulin resistance and glucose metabolism in addition to pro-tumour mechanisms [ 80 ]. This may be in part due to HIF-1α-induced upregulation of insulin receptor substrate 2 (IRS-2), which is an important mediator of insulin, glucose metabolism, and mitogenesis [ 81 ]. PI3K and downstream signalling effectors AKT and mTOR are activated through the recruitment of the IRS proteins.

Hypoxia is also a recognised driver of ET resistance, with elevated expression of HIF-2α observed in endocrine-resistant ERα-positive breast cancer cell lines [ 78 , 82 ]. Introducing HIF-2 into previously sensitive cells leads to their development of resistance to antioestrogens and inhibiting HIF-2α signalling can restore sensitivity in cells that have become resistant to ET [ 82 ]. Additionally, established HIF inhibitors such as digoxin and acriflavine appear to have activity against breast cancer metastatic niche formation [ 83 ], and have been shown to decelerate diet-induced obesity by various mechanisms in mouse studies, including decreasing lipogenesis [ 84 , 85 , 86 ]. Therefore, focusing on HIF inhibitors may not only help overcome resistance to ET in obesity but also provide valuable insights into preventing diet-induced obesity.

PI3K–AKT–mTOR pathway activation

PI3K–AKT–mTOR is a key signal transduction pathway that mediates cell growth, metabolism, and cell survival. PI3K–AKT–mTOR integrates upstream signals, including those from insulin and insulin growth factors (IGF-1 and IGF2) as well as cellular nutrients, energy and oxygen levels (Fig.  2 ). There is cross-talk between the PI3K–AKT–mTOR pathway and the oestrogen receptor (ER) pathway at multiple levels [ 87 , 88 ].

The cellular energy landscape in breast cancer is regulated by a complex network of metabolic and signalling pathways. The PI3K/AKT signalling cascade, triggered by insulin and IGF-1, leads to mTORC1 activation driving protein synthesis and cancer progression. There is cross-talk between the PI3K–AKT–mTOR pathway and oestrogen receptor (ER) signalling at multiple levels and several preclinical studies have shown that the PI3K–AKT–mTOR pathway plays a key role in mediating resistance to endocrine therapy in breast cancer. Dietary and drug interventions such as fasting-mimicking diets and SGLT2 inhibitors or metformin reduce circulating insulin levels, offering potential therapeutic value in combination with endocrine therapy. Inflammatory signalling via the NFκB and JAK/STAT pathways also contributes to the regulation of genes that are crucial for cell survival and growth. The MAPK/ERK pathway, activated by insulin and leptin, has cross-talk with the PI3K/AKT pathway mediating activity of several downstream targets that collectively promote cell proliferation and cell-cycle progression. AMPK, the key regulator of energy homoeostasis in the cell, is inactivated in the presence of high ATP levels, which are influenced by increased glucose uptake and reliance on aerobic glycolysis, especially under hypoxic conditions. The antidiabetic drug metformin can indirectly activate AMPK, the key regulator of energy homoeostasis in the cell, by inhibiting mitochondrial complex I, leading to a shift in cellular energy balance, which in turn may contribute to the inhibition of mTOR activity. GLUT1 glucose transporter 1, Ob-R leptin receptor, JAK Janus kinase, SHP2 src homology 2-containing protein tyrosine phosphatase-2, ER oestrogen receptor, IR insulin receptor, IGF-1R insulin-like growth factor 1 receptor, IRS insulin receptor substrate, PI3K phosphoinositide 3-kinase, PIP 2 phosphatidylinositol 4,5-bisphosphate, PIP 3 phosphatidylinositol 3,4,5-trisphosphate, PTEN phosphatase and tensin homologue, AKT protein kinase B, mTORC1 mTOR complex 1, Raptor regulatory-associated protein of mTOR, Deptor DEP domain-containing mTOR-interacting protein, PRAS40 proline-rich Akt substrate of 40 kDa, mTORC2 mTOR complex 2, Rictor rapamycin-insensitive companion of mTOR, mSIN1 mammalian stress activated MAP kinase-interacting protein 1, Protor protein observed with Rictor-1, PDK1 phosphoinositide-dependent kinase-1, FOXO forkhead box O, BAD Bcl-2-associated death promoter, CASP9 caspase-9, IKKα I kappa B kinase alpha, IKKβ I kappa B kinase beta, IKKγ I kappa B kinase gamma, Iκb inhibitor of kappa B, NFκB nuclear factor kappa B, TSC1/2 tuberous sclerosis complex 1/2, RHEB Ras homologue enriched in brain, ERK extracellular signal-regulated kinase, MEK mitogen-activated protein kinase kinase, RSK ribosomal S6 kinase, AP-1 activator protein 1, STAT3 signal transducer and activator of transcription 3, LBK1 liver kinase B1, AMP adenosine monophosphate, ATP adenosine triphosphate, AMPK AMP-activated protein kinase, S6K S6 kinase, 4E-BP1 eukaryotic translation initiation factor 4E-binding protein 1, HIF-1α hypoxia-inducible factor 1-alpha, CYP19A1 cytochrome p450 family 19 subfamily A member 1, GLUT4 glucose transporter 4, LDHA lactate dehydrogenase A, MMP9 matrix metallopeptidase 9, VEGF vascular endothelial growth factor. (Created with BioRender.com.).

Genetic alterations affecting different nodes of the PI3K–AKT–mTOR pathway are common in ER+ breast cancer [ 89 ]. The international data sharing consortium, AACR Project GENIE, showed that genetic alterations in PIK3CA , PTEN and AKT1 occur in ~36%, 7% and 5% of breast cancer, respectively [ 90 ]. It is not known whether these activating mutations are more likely to arise in breast cancer for patients that have insulin resistance or may influence the response to fasting in the context of ET.

Activation of the insulin receptor (IR) promotes downstream PI3K–AKT–mTOR signalling (see Fig.  2 ). Increased insulin levels are associated with higher breast cancer incidence and mortality [ 91 ]. Studies with both insulin analogues, blocking and stimulating anti-IR antibodies, and small molecule inhibitors, have shown a role for insulin signalling in breast cancer development and progression [ 92 ]. IR expression in breast cancer is well described, and high IR expression has been implicated in poor prognosis [ 93 ]. IR is more commonly expressed in endocrine-resistant breast cancer, and low expression correlates with improved survival [ 94 ].

Several preclinical studies have shown that the PI3K–AKT–mTOR pathway plays a key role in mediating resistance to ET in breast cancer, and the concept of targeting the PI3K–AKT–mTOR pathway to augment ET has now been proven in clinical settings. The BOLERO-2 Phase 3 trial demonstrated an improvement in median progression-free survival when the mTOR inhibitor everolimus was combined with the aromatase inhibitor exemestane in patients already refractory to single-agent aromatase inhibitor therapy [ 95 ]. The SOLAR-1 Phase 3 study has now also shown that the combination of the PI3K inhibitor, alpelisib, with fulvestrant led to an improvement in progression-free survival versus fulvestrant alone in PIK3CA-mutant, ER+ metastatic breast cancer resistant to first-line ET [ 96 ]. The Phase 3 placebo-controlled CAPltello-291 trial reported an improvement in progression-free survival with the addition of the AKT inhibitor, capivasertib to fulvestrant in patients with ER+ advanced breast cancer, irrespective of PIK3CA mutation status [ 97 ]. Lastly, the combination of inavolisib (a novel PI3K inhibitor) + palbociclib + fulvestrant in ER+ve metastatic breast cancer showed a significant improvement in investigator-assessed progression-free survival [ 98 ].

Drugs that lower circulating glucose and insulin levels, in particular metformin and SGLT2 inhibitors, have been proposed as treatments for breast cancer and could potentially synergise with ET by reducing PI3K–AKT–mTOR signalling (Fig.  2 ) [ 99 , 100 ]. In particular, metformin has been extensively studied as a potential anticancer therapy, and a number of window-of-opportunity clinical trials have suggested that metformin may reduce cancer cell proliferation, and this effect may be greater in insulin-resistant women [ 100 ]. One meta-analysis of 11 observational studies has reported improved overall and cancer-specific survival in patients with breast cancer and diabetes who received metformin when compared with patients receiving other antidiabetic treatments [ 101 ]. However, another pharmacodynamic clinical study showed no clear link between metformin-induced reductions in circulating insulin levels and changes in tumour biology [ 102 ]. A large Phase 3 trial of 5 years of adjuvant metformin therapy in breast cancer showed no evidence of clinical benefit, although this study excluded patients with diabetes [ 103 ].

Aside from targeted therapies, dietary interventions have the potential to modulate the PI3K–AKT-mTOR pathway. Caffa et al. found that combining a periodic or fasting-mimicking diet (FMD) with hormone therapy, specifically fulvestrant and tamoxifen, enhanced anticancer effects in ER+ breast cancer mouse models by reducing leptin, insulin, and IGF-1 levels. Besides promoting sustained tumour regression, this approach could also revert acquired drug resistance [ 104 ].

Adipokine dysregulation and breast cancer

Investigating adipocyte biology, which goes beyond passive fat storage, is essential for comprehending the microenvironmental alterations linked to obesity. Adipocytes modulate the adipose tissue microenvironment through adipokine-mediated paracrine and autocrine signalling pathways. Two key adipokines involved in breast carcinogenesis are leptin and adiponectin.

Excess body fat increases leptin release from adipocytes, and BMI correlates with elevated leptin levels. By stimulating its receptor and a number of downstream pathways, including Jak2/Stat3, MAPK and PI3K–AKT, leptin likely promotes cell invasion and proliferation [ 104 , 105 ]. A meta-analysis of 35 studies linked higher serum leptin levels with increased breast cancer risk, especially in postmenopausal women, suggesting its potential as a biomarker [ 106 ]. In addition, genetic variations in the leptin-coding genes LEP and ADIPOQ have been associated with elevated breast cancer risk [ 107 ]. Leptin has also been implicated in resistance mechanisms to tamoxifen and aromatase inhibitor treatment [ 108 , 109 ].

Visceral adipose tissue (VAT) is known to produce leptin [ 50 ], but the effect of locally generated leptin from breast fat tissue compared to circulating leptin on breast tumour progression is not well understood.

On the other hand, low levels of adiponectin are associated with obesity and Type 2 diabetes, and studies suggest adiponectin may suppress cancer growth by modulating a number of intracellular metabolism and proliferation pathways associated with mitogenesis, including TNF-alpha, AMPK and SREBP-1 signalling [ 110 , 111 ]. Unfavourable outcomes for breast cancer have been linked to both low adiponectin levels and increased leptin levels [ 112 , 113 ], and it is speculated that the adiponectin:leptin ratio may be more important for breast cancer growth than the absolute levels [ 114 ].

Because high leptin levels are associated with an increased risk of breast cancer and may increase resistance to ET, as demonstrated in preclinical breast cancer models [ 115 , 116 ], it has been speculated that lowering leptin levels through weight loss may improve outcomes for breast cancer survivors. Furthermore, a recent randomised study in this population demonstrated that both exercise and weight loss were associated with decreased leptin expression and improvements in the adipokine/leptin ratio [ 117 ], although whether this definitively translates to better clinical outcomes remains unanswered.

Obesity and FGF1, FGF2 and FGFR signalling

Another obesity-associated marker of elevated breast cancer risk, especially in the case of visceral fat, is fibroblast growth factor-2 (FGF2), which is released by adipose tissue. FGF2 binds to FGFR1 and FGFR2, and at least 10% of breast cancers harbour FGFR1 amplification, which is linked to early relapse and poor prognosis [ 118 ]. FGFR1 signalling directs healthy mammary duct development [ 119 ], and FGF2 levels are lowered in mice that have had a substantial fat pad removed, suggesting that FGF2 may have endocrine-mediated functions in addition to local ones [ 120 ]. Poor prognosis in breast cancer has been associated with elevated expression of the leptin receptor (LepR) and FGFR1 amplification, and co-expression of the FGFR1 gene and leptin protein copy number has been observed in primary breast tumours [ 121 ]. Antagonism of FGFR signalling in an obese mouse breast cancer model prevented outgrowth of pulmonary metastases [ 122 ] and FGFR inhibitors have already shown some promise in the clinic for the treatment of endocrine-resistant ER+ breast cancer [ 123 ].

In preclinical studies, elevated circulating levels of FGF2 have been linked to breast cancer development through the activation of oncogenic signalling pathways, including MAPK/ERK, cMYC and PI3K/AKT/mTOR. FGFR1 amplification is a key driver of ET resistance through MAPK signalling activation, and this therapeutic opportunity is currently being explored in clinical trials [ 124 , 125 ]. Direct targeting of FGF2 is also being considered as a potential clinical approach [ 126 ].

FGF1 promotes adipocyte glucose uptake through AKT cross-talk as well as transcriptional promotion of glucose transporter protein type 1 (GLUT1), the primary glucose transporter [ 127 , 128 , 129 ]. GLUT1 is associated with higher grade, proliferation, as well as poorer prognosis in breast cancer [ 130 , 131 ], although no link was observed between GLUT1 expression in breast cancer and background obesity or diabetes in one small study [ 132 ]. Notably, recent work has shown that FGF1 stimulates oestrogen receptor activation in obese mouse breast cancer models after oestrogen deprivation [ 128 ].

Aromatase overexpression in obesity

Aromatase inhibitors (AI) play a pivotal role in the treatment of ER+ breast cancer as a monotherapy in postmenopausal women. Postmenopausal status leads to a shift in the primary site of aromatase activity to the adipose tissue in the breast and gluteal areas. It is well described that AIs are less efficient at suppressing serum oestradiol levels in obese women [ 10 ]. A plausible explanation for this reduced efficiency is the observation that women with BMI >30, both with and without breast cancer, have elevated baseline oestrogen levels compared to those with BMI <22, and this may result in less effective suppression of oestrogen by an AI in postmenopausal women [ 133 ].

The formation of CLS in obesity is associated with heightened levels of gene transcription and increased activity of aromatase in mammary glands and visceral fat [ 51 ]. Aromatase expression is especially elevated in the adipose fibroblasts near breast tumours through the activation of proximal promoters, with immature fibroblasts primarily responsible for its production [ 134 ]. Furthermore, tumour cells in adipose tissue inhibit adipocyte differentiation by release of TNF-alpha and interleukin-11, thereby increasing the fibroblast:adipocyte ratio. This shift sustains elevated aromatase production, promoting local oestrogen synthesis and tumour progression [ 135 ].

A systematic review of three randomised controlled trials and five retrospective cohort studies suggested reduced efficacy of aromatase inhibitors in obesity, although the exact magnitude of this effect is not clearly established [ 136 ]. In a recent nationwide cohort study of 13,000 patients with hormone receptor-positive breast cancer, Harborg et al. showed that the risk of recurrence was higher among patients with obesity compared to those with a healthy weight (BMI 18.5–24.9) [ 4 ]. The ATAC study, which randomly assigned postmenopausal women with early-stage breast cancer to receive oral daily anastrozole alone, tamoxifen alone, or the combination, supported these findings. Specifically, women on anastrozole had a 27% lower recurrence rate compared to those taking tamoxifen, with women having a BMI <23 deriving an even greater benefit from treatment with an aromatase inhibitor [ 9 ].

In premenopausal women, AIs should be combined with ovarian suppression, typically with gonadotropin-releasing hormone (GnRh) analogues. Alternatively, selective oestrogen receptor modulators, such as tamoxifen, may be used alone or in combination with GnRh analogues. In premenopausal patients with HR-positive breast cancer who received adjuvant tamoxifen, a high BMI has been linked to a poorer prognosis [ 137 ]. However, to date, no similar association has been reported when ovarian suppression is used in conjunction with aromatase inhibitors.

AIs are usually provided at a standard dose that does not take specific inter-patient variation into consideration. Early studies investigating whether a larger dosage of AIs may improve outcomes for obese individuals with metastatic cancer suggested no additional benefits from an increased dose. However, these trials were conducted prior to the introduction of AIs as a standard-of-care option for postmenopausal ER+ breast cancer and therefore weight-dependent dosing may be revisited [ 138 , 139 ].

Another issue demanding attention is the heightened insulin resistance and increased adiposity associated with postmenopausal women with breast cancer undergoing treatment with AIs. Studies in aromatase knockout mice and rare cases of congenital aromatase deficiency indicate a correlation with elevated adiposity, hepatic steatosis and insulin resistance [ 140 , 141 , 142 ]. Consequently, the advantages of ET must be weighed against the potential risks of obesity, metabolic syndrome and diabetes. This emphasizes the need for further investigation as to whether these changes in metabolism are associated with a worse prognosis and whether early drug or dietary intervention might be beneficial.

Implications for toxicity

It has long been recognised that there is a challenge in correctly dosing obese patients with adjuvant chemotherapy, as the maximum tolerated dose for cytotoxic therapy will have typically been determined in a leaner population. Obese patients are thought to be often underdosed due to empirical dose reductions, contrary to guidelines recommending full weight-based dosing. Although there is limited clinical data, it is hypothesised that this may lead to worse outcomes [ 143 , 144 ]. There is also considerable uncertainty when using drugs with a high risk of cumulative toxicity, such as doxorubicin, fluoropyrimidines and cyclophosphamide. Studies are ongoing to determine whether better measures of body composition can more accurately predict toxicity in early breast cancer treatment [ 145 ].

Furthermore, studies have shown that obese patients with breast cancer receiving ET experience increased adverse effects, such as increased joint symptoms and cardiovascular events, which could potentially lead to treatment discontinuation [ 146 , 147 ]. Hence, lack of treatment compliance in this context may be a contributing factor to poorer outcomes. Notably, in the context of treatment with adjuvant CDK4/6 inhibitors, obese patients had lower rates of neutropaenia which translated into a reduced treatment discontinuation rate in the PALLAS trial. The investigators hypothesised that obese patients may have a lower distributional volume, although survival data are still immature [ 148 ]. This suggests a need for dosage adjustments based on body composition rather than standard weight-based protocols to maximise therapeutic effects in obese patients.

The use of mTOR and PI3K inhibitors in conjunction with ET for the treatment of breast cancer may be especially problematic in patients with obesity or insulin resistance. Using clinical trial data from two studies of PI3K inhibitors, Rodon et al. developed a risk prediction model for grade 3/4 hyperglycaemia, and identified five factors, including baseline fasting plasma glucose, HbA1c and BMI, as the strongest predictors for classifying patients as low or high risk [ 149 ]. Notably, preclinical research has shown that the insulin feedback causing hyperglycaemia can be prevented using dietary or pharmaceutical approaches, which greatly enhance the efficacy of treatment [ 150 ].

For some time, it has been understood that obesity and insulin resistance are associated with both an increased risk of developing ER+ breast cancer and poorer outcomes. Substantial preclinical evaluation has now provided greater insight into the mechanisms that drive these phenomena, and potential therapeutic strategies have been proposed. Clinical studies of interventions aimed at improving outcomes for breast cancer patients with metabolic disorders are warranted. More accurate measures of body composition beyond BMI and their association with patient outcome need to be assessed in the clinic and potential differences in treatment resistance between premenopausal and postmenopausal women in the context of obesity remain understudied. Lastly, the breast cancer community needs to evaluate strategies to effectively manage treatment toxicity in the context of obesity.

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SL holds project grants from the NIHR, Cancer Research UK, the World Cancer Research Fund and a fellowship from Against Breast Cancer supporting his work.

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Sohail Rooman Javed, Aglaia Skolariki, Mohammed Zeeshan Zameer & Simon R. Lord

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SL—Honoraria: Eisai, Prosigna, Roche, Pfizer, Novartis, Sanofi. Advisory: Shionogi, Sanofi, GLG Consulting: Rejuversen, Oxford Biodynamics. Research grant funding: CRUK, NIHR, Against Breast Cancer, Pathios Therapeutics. Travel/Accommodation/ Expenses: Pfizer, Roche, Synthon, Piqur Therapeutics. Stock holding: Mitox Therapeutics. Previous employment: Pfizer. My institution has received funding for clinical trials for which I am chief investigator or principal investigator from: Cancer Research UK, Boehringer Ingelheim, Piqur Therapeutics, AstraZeneca, Carrick Therapeutics, Sanofi, Merck KGaA, Synthon, Roche, Exscientia, BioInvent, RS Oncology. The remaining authors declare no competing interests.

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Javed, S.R., Skolariki, A., Zameer, M.Z. et al. Implications of obesity and insulin resistance for the treatment of oestrogen receptor-positive breast cancer. Br J Cancer (2024). https://doi.org/10.1038/s41416-024-02833-1

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Key factors identified that can impact long-term weight loss in patients with obesity prescribed GLP-1 RA medications

by Cleveland Clinic

A Cleveland Clinic study identified key factors that can impact the long-term weight loss of patients with obesity who were prescribed injectable semaglutide or liraglutide for the treatment of type 2 diabetes or obesity. The study was published in JAMA Network Open .

"In patients with obesity who were prescribed semaglutide or liraglutide , we found that long-term weight reduction varied significantly based on the medication 's active agent, treatment indication, dosage and persistence with the medication," said Hamlet Gasoyan, Ph.D., lead author of the study and a researcher with Cleveland Clinic's Center for Value-Based Care Research.

Semaglutide (sold under the brand names Wegovy and Ozempic) and liraglutide (sold under the brand names Saxenda and Victoza) are glucagon-like peptide-1 receptor agonists , or GLP-1 RA medications. Those FDA-approved medications help lower blood sugar levels and promote weight loss.

Obesity is a complex chronic disease that affects more than 41% of the U.S. adult population. Clinical trials have shown that anti-obesity medications are effective; however, there is limited data in real-world settings regarding the factors associated with long-term weight change and clinically significant weight loss.

In this study, the researchers identified key factors that were associated with long-term weight loss of patients with obesity. They also indicated the elements that were linked to the probability of achieving 10% or more weight loss.

This retrospective cohort study included 3,389 adult patients with obesity who initiated treatment with injectable semaglutide or liraglutide between July 1, 2015, and June 30, 2022. Follow-up ended in July 2023.

At the start of the study, the median baseline body mass index among study participants was 38.5; 82.2% had type 2 diabetes as treatment indication. Among the patients, 68.5% were white, 20.3% were Black, and 7.0% were Hispanic.

More than half of the participants were female (54.7%). Most of the patients received treatment for type 2 diabetes. Overall, 39.6% were prescribed semaglutide for type 2 diabetes, 42.6% liraglutide for type 2 diabetes, 11.1% semaglutide for obesity, and 6.7% liraglutide for obesity.

Results show that one year after the initial prescription's fill, weight change was associated with the following factors:

• The medication's active agent. On average, weight change was -5.1% with semaglutide versus -2.2% with liraglutide.
• The dosage. Patients experienced -3.5% mean weight change with low maintenance dose versus -6.6% with high dose.
• Treatment indication. Patients who received the medications for type 2 diabetes experienced -3.2% in mean weight change compared to -5.9% for obesity treatment.
• Persistence with medication. On average, patients who were persistent with the medication at one year experienced -5.5% weight change versus -2.8% among patients who had 90–275 medication coverage days within the first year and -1.8% among those with less than 90 covered days.

Researchers found that four in 10 patients (40.7%) were persistent with their medication one year after their initial prescription's fill. The proportion of patients who were persistent with semaglutide was 45.8% versus 35.6% in patients receiving liraglutide.

Among patients who persisted with their medication at 12 months, the average reduction in body weight was -12.9% with semaglutide for obesity, compared to -5.9% with semaglutide for type 2 diabetes. The reduction in body weight was -5.6% with liraglutide for obesity, compared to -3.1% with liraglutide for type 2 diabetes.

Studies have shown that achieving sustained weight loss of 10% or more provides clinically significant health benefits. With that in mind, Dr. Gasoyan and colleagues looked at the proportion of patients who achieved 10% or more weight reduction.

Overall, 37.4% of patients receiving semaglutide for obesity achieved 10% or more body weight reduction compared to 16.6% of patients receiving semaglutide for type 2 diabetes. In comparison, 14.5% of those receiving liraglutide for obesity achieved 10% or more body weight reduction versus 9.3% of those receiving liraglutide for type 2 diabetes.

Among patients who persisted with their medication one year after their initial prescriptions, the proportion who achieved 10% or more weight reduction was 61% with semaglutide for obesity, 23.1% with semaglutide for type 2 diabetes, 28.6% with liraglutide for obesity, and 12.3% with liraglutide for type 2 diabetes.

Based on the study's multivariable analysis that accounted for relevant socio-demographic and clinical variables, the following factors were associated with higher odds of achieving 10% or more weight reduction one year after the initial prescriptions:

• Patients who received semaglutide versus liraglutide
• A high maintenance dose of the medication versus low
• Obesity as a treatment indication versus type 2 diabetes
• Patients who persisted with the medication within the first year or had between 90–275 days of medication coverage versus less than 90 days of medication coverage
• Patients who had higher initial BMI
• Patients who were female versus male

"Our findings could help inform patients and providers regarding some of the key factors that are associated with the probability of achieving sustained weight loss of a magnitude large enough to provide clinically significant health benefits," said Dr. Gasoyan. "Having real-world data could help manage expectations regarding weight reduction with GLP-1 RA medications and reinforce that persistence is key to achieve meaningful results."

In a previous study , Dr. Gasoyan and colleagues looked at the factors influencing the long-term use of anti-obesity medications. Future research will continue to explore patients' persistence and health outcomes with GLP-1 RA medications.

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