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.

Lifestyle interventions (LIs) are the cornerstone for obesity management. The Mayo Clinic Diet (MCD) offers two approaches for LIs: the In-Person LI (IPLI) and the Digital Enhanced LI (DELI). The IPLI includes a 2-day in-person program with monthly follow-ups, whereas the DELI provides on-demand digital tools. The comparative efficacy of these approaches is currently unknown. This retrospective study included two cohorts of adults with a body mass index (BMI) of ≥25 kg/m² and weight metrics at least 3 months after starting either the IPLI or DELI program. The primary endpoint was the total body weight loss percentage (TBWL%) at 6 months. The study included 133 participants in the IPLI cohort (mean age 46.3 years, 65.4 % female, BMI 36.4) and 9603 in the DELI cohort (mean age 60.1 years, 85.0 % female, BMI 33.1). The DELI group achieved superior TBWL% at 1, 3, and 6 months compared to the IPLI group (3.4 % vs. 1.5 %, 4.7 % vs. 2.4 %, 5.3 % vs. 2.9 %, respectively; p < 0.001). After adjusting for age, gender, and starting weight, the DELI group maintained a higher TBWL% (difference 2.0 %; 95 % CI [1.0, 3.0], p < 0.001) and a greater proportion of participants achieved >5 % TBWL at 6 months (OR 1.66; 95 % CI [1.08, 2.55], p < 0.023). The DELI approach resulted in superior weight loss outcomes compared to the IPLI. Further research is needed to explore how digital tools can improve weight loss effectiveness.

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.