Glucagon-Like Peptide-1 Receptor Agonists Combined With Personalized Digital Health Care for the Treatment of Metabolic Syndrome in Adults With Obesity: Retrospective Observational Study
Bibliographic record
Abstract
Background: Metabolic syndrome (MetS) represents a complex and multifaceted health condition characterized by a clustering of interconnected metabolic abnormalities, including central obesity, insulin resistance, dyslipidemia, and hypertension. Effective management of MetS is crucial for reducing the risk of cardiovascular diseases and type 2 diabetes. Objective: This study aimed to assess the effectiveness of combining glucagon-like peptide-1 (GLP-1) and dual gastric inhibitory polypeptide (GIP)/GLP-1 agonists with a continuous, digitally delivered behavioral change model by an integrated care team, in treating MetS among individuals with obesity. Methods: The 6-month Zone.Health (meta[bolic]) weight loss program involved 51 participants (mean age 45, SD 10 years; mean BMI 35, SD 5 kg/m²), categorized by gender, and treated with either tirzepatide or semaglutide. Participants received continuous support via a digital health platform, which facilitated real time monitoring and personalized feedback from an integrated care team. Engagement levels with the digital platform, measured by the frequency of inbound interactions, were tracked and analyzed in relation to health outcomes. Results: Tirzepatide reduced waist circumference (WC) by -18.08 cm, compared with -13.04 cm with semaglutide (P<.001). Triglycerides decreased significantly with both drugs, with tirzepatide showing a reduction of -64.42 mg/dL and semaglutide -70.70 mg/dL (P<.001). Tirzepatide generally showed more pronounced improvements in fasting glucose, blood pressure (BP), low-density lipoprotein, and total cholesterol compared with semaglutide. Higher engagement with the digital health platform showed significant difference among the 3 groups; the group with the highest level of app-based interactions (≥25 interactions) had the greatest WC reduction (mean -19.04, SD 7.40 cm) compared with those with ≤15 interactions (mean -9.60, SD 5.10 cm; P=.002). Similarly, triglycerides showed the greatest reduction in the group with ≥25 interactions (mean -108.56, SD 77.06 mg/dL) compared with those with ≤15 interactions (mean -44.49, SD 50.85 mg/dL; P=.02). This group also exhibited the largest reduction in diastolic BP (mean -10.33, SD 7.40 mm Hg) compared with those with ≤15 interactions (mean -0.83, SD 7.83 mm Hg; P=.004), and the most substantial decrease in fasting glucose levels (mean -18.60, SD 10.82 mg/dL) compared with those with ≤15 interactions (mean -2.49, SD 27.54 mg/dL; P=.02). Participants in the highest quartile of digital engagement had a 60% greater likelihood of MetS reversal compared with those in the lowest quartile. Conclusions: This study shows that combining GLP-1 and dual GIP/GLP-1 agonists with a digital behavioral change model significantly improves MetS markers in individuals with obesity. Tirzepatide proved more effective than semaglutide, leading to greater reductions in WC and triglyceride levels, along with better improvements in fasting glucose, BP, and lipid profiles. Higher app-based engagement was linked to better health outcomes, with participants in the highest engagement group having a 60% greater likelihood of treating MetS compared with those with the lowest engagement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".