Espinosa, Maria Antonia

Background/Objectives: A 12-week trial demonstrated that phenotype-tailored lifestyle intervention (PLI) was associated with greater weight loss than standard lifestyle intervention (SLI); however, differences in dietary patterns between groups were not examined. Methods: This secondary analysis evaluated caloric and macronutrient intake at 12 weeks in adults with obesity (BMI ≥ 30 kg/m2), classified into four energy-balance phenotypes (abnormal satiation, reduced postprandial satiety, low energy expenditure, emotional eating) (n = 77) who underwent PLI or SLI. Dietary intake was assessed by 24-h recalls and verified by a blinded registered dietitian. Results: Groups were compared at 12 weeks adjusting for baseline values. PLI was associated with lower total energy intake than SLI (1273 ± 182 vs. 1894 ± 176 kcal/day; p = 0.018), due to lower carbohydrate (464 ± 76 vs. 818 ± 73 kcal/day; p = 0.002) and fat intake (479 ± 76 vs. 697 ± 73 kcal/day; p = 0.036); absolute protein intake did not differ. However, protein comprised a greater proportion of energy intake in PLI (26.8 ± 1.6%) than SLI (19.8 ± 1.5%; p = 0.003), and dairy protein contributed more to total energy in PLI (6.8 ± 0.8% vs. 3.7 ± 0.7%; p = 0.004). Conclusions: PLI was associated with lower energy intake, mainly due to reduced carbohydrate and fat intake, and a higher relative contribution of protein, consistent with phenotype-based recommendations rather than macronutrient prescriptions. These findings suggest a potential role for personalized dietary approaches in obesity management. However, the non-randomized design does not allow causal inferences.

Background/Objectives: Individuals with type 2 diabetes (T2D) achieve less total body weight loss (TBWL) with obesity medications compared to those without T2D. The individualized metabolic surgery (IMS) score, originally developed to predict T2D remission after bariatric surgery, inversely correlates with TBWL response to semaglutide. IMS reflects T2D severity, incorporating HbA1c and T2D duration and medication use. This study aims to evaluate TBWL with tirzepatide across IMS severity categories and identify predictors of response in a real-world cohort. Methods: This retrospective analysis included 717 adults with T2D using tirzepatide for overweight or obesity within the Mayo Clinic Health System. Patients were stratified by IMS severity (mild, moderate, severe) and quartiles. Primary endpoint: TBWL% at 15 months. Secondary endpoints: categorical thresholds (≥5%, ≥10%, ≥15%, ≥20%) and predictors of TBWL%. Linear mixed-effects models and regression models were employed. Results: At 15 months, TBWL was greater in mild versus severe IMS groups (14.8% vs. 11.0%, p = 0.015), with similar trends across quartiles. The proportion achieving ≥ 20% TBWL was nearly two-fold higher in mild versus severe IMS (27% vs. 14%, p = 0.03). Female sex independently predicted greater TBWL, whereas insulin use, higher T2D medication burden (particularly weight-promoting agents), and HbA1c > 7% were associated with less TBWL. Conclusions: Tirzepatide produced clinically meaningful TBWL across all IMS categories, although TBWL declined with increasing IMS severity. Glycemic control and T2D medication use emerged as strong predictors of TBWL. The IMS score may serve as a practical tool to anticipate weight-loss trajectories, guide personalized treatment decisions, and inform patient counseling.

Abstract Obesity is a chronic, heterogeneous, and complex disease associated with increased morbidity, mortality, and health-care costs. By 2030, 1 in 2 people in the United States will have obesity. Lifestyle interventions are the cornerstone of obesity management. However, they often fail to achieve clinically significant weight loss, necessitating additional treatments involving pharmacotherapy or procedures. Advancements in obesity pharmacotherapy have improved weight-loss outcomes and reduced associated comorbidities. Despite these advances, variability in response to treatment between individuals is still considerable. This variability reflects limitations in the current “one-size-fits-all” approach to obesity management. A precision medicine approach aims to address this gap by incorporating genetic, physiological, and behavioral characteristics to guide treatment selection and improve outcomes. Advances in multi-omics technologies, such as genomics, proteomics, metabolomics, and microbiome profiling, offer new opportunities to refine patient stratification and identify novel therapeutic targets. These tools may help move obesity care toward a more individualized and mechanism-based approach.