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Record W4410893328 · doi:10.1002/oby.24336

Nutritional priorities to support <scp>GLP</scp>‐1 therapy for obesity: A joint Advisory from the American College of Lifestyle Medicine, the American Society for Nutrition, the Obesity Medicine Association, and The Obesity Society

2025· article· en· W4410893328 on OpenAlexaff
Dariush Mozaffarian, Monica Agarwal, Monica Aggarwal, Lydia Alexander, Caroline M. Apovian, Shagun Bindlish, Jonathan P. Bonnet, W. Scott Butsch, Sandra Christensen, Eugenia Gianos, Mahima Gulati, Alka Gupta, Ryan M. Kane, Deepa Sannidhi, Fatima Cody Stanford, Emily A. Callahan

Bibliographic record

VenueObesity · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsCanadian Asian Studies Association
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineWeight lossPsychological interventionObesityLifestyle medicineGerontologyPhysical therapyFamily medicinePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Glucagon-like peptide 1 receptor agonists and combination medications (hereafter collectively referred to as GLP-1s) are shifting the treatment landscape for obesity. However, real-world challenges and limited clinician and public knowledge on nutritional and lifestyle interventions can limit GLP-1 efficacy, equitable results, and cost-effectiveness. OBJECTIVES: We aimed to identify pragmatic priorities for nutrition and other lifestyle interventions relevant to GLP-1 treatment of obesity for the practicing clinician. METHODS: An expert group comprising multiple clinical and research disciplines appraised the scientific literature, informed by expert knowledge and clinical experience, to identify and summarize relevant topics, priorities, and emerging directions. RESULTS: GLP-1s reduce body weight by 5% to 18% in trials, with modestly lower effects in real-world analyses, and multiple demonstrated clinical benefits. Challenges include side effects, especially gastrointestinal; nutritional deficiencies due to calorie reduction; muscle and bone loss; low long-term adherence with subsequent weight regain; and high costs with resulting low cost-effectiveness. Numerous practice guidelines recommend multicomponent, evidence-based nutritional and behavioral therapy for adults with obesity, but use of such therapies with GLP-1s is not widespread. Priorities to address this include: (a) patient-centered initiation of GLP-1s, including goals for weight reduction and health; (b) baseline screening, including usual dietary habits, emotional triggers, disordered eating, and relevant medical conditions; (c) comprehensive exam including muscle strength, function, and body composition assessment; (d) social determinants of health screening; (e) and lifestyle assessment including aerobic activity, strength training, sleep, mental stress, substance use, and social connections. During GLP-1 use, nutritional and medical management of gastrointestinal side effects is critical, as is navigating altered dietary preferences and intakes, preventing nutrient deficiencies, preserving muscle and bone mass through resistance training and appropriate diet, and complementary lifestyle interventions. Supportive strategies include group-based visits, registered dietitian nutritionist counseling, telehealth and digital platforms, and Food is Medicine interventions. Drug access, food and nutrition insecurity, and nutrition and culinary knowledge influence equitable obesity management with GLP-1s. Emerging areas for more study include dietary modulation of endogenous GLP-1, strategies to improve compliance, nutritional priorities for weight maintenance post-cessation, combination or staged intensive lifestyle management, and diagnostic criteria for clinical obesity. CONCLUSIONS: Evidence-based nutritional and lifestyle strategies play a pivotal role to address key challenges around GLP-1 treatment of obesity, making clinicians more effective in advancing their patients' health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.270
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations83
Published2025
Admission routes1
Has abstractyes

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