Adiposity-based Chronic Diseases and the Therapeutic Role of Glucagon-like Peptide-1 Receptor Agonists
Bibliographic record
Abstract
Abstract Adiposity-Based Chronic Disease (ABCD) is a complex, multifactorial condition that recognizes obesity as a chronic, relapsing disease influenced by biological, behavioral, cultural, and environmental factors. The ABCD framework emphasizes a patient-centered, holistic approach to diagnosis and treatment, moving beyond weight-centric models to address the systemic consequences of adiposity. Among therapeutic options, GLP-1 receptor agonists (GLP-1RAs) have emerged as a cornerstone in the management of ABCD by improving metabolic parameters and targeting the underlying pathophysiology of multiple interrelated conditions. GLP-1RAs enhance glycemic control and insulin sensitivity in type 2 diabetes while promoting weight loss and reducing cardiovascular risk by lowering blood pressure, improving lipid profiles, and reducing inflammation. They have shown efficacy in treating heart failure with preserved ejection fraction (HFpEF), particularly in patients with obesity. In metabolic dysfunction-associated steatotic liver disease (MASLD), GLP-1RAs reduce hepatic steatosis and inflammation, while in polycystic ovary syndrome (PCOS), they improve ovulation and menstrual regularity. Additionally, GLP-1RAs support kidney function preservation in chronic kidney disease (CKD) through blood pressure and glucose regulation and anti-inflammatory effects. Hence, the pleiotropic benefits of GLP-1Ras extend across the eight ABCD pillars obesity, type 2 diabetes, cardiovascular disease/heart failure, MASLD, polycystic ovary syndrome/infertility, CKD, hypertension, and dyslipidaemia positioning GLP-1RAs as a versatile, disease-modifying therapy. Also, the availability of oral semaglutide has further enhanced accessibility and convenience in the long-term management of ABCD, especially for patients preferring non-injectable therapies. In this clinical review, we will discuss the therapeutic role of GLP-1RAs, with a focus on oral semaglutide, across the eight pillars of ABCD, highlighting their multisystem benefits beyond weight loss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".