Multiomic profiling is useful in characterizing heterogeneity of both health and disease states. Obesity exerts profound metabolic perturbation in individuals and is a risk factor for multiple chronic diseases. Here, we report a global atlas of cross-sectional and longitudinal changes associated with Body Mass Index (BMI) across 1,100+ blood analytes, as well as their correspondence to host genome and fecal microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program. Machine learning-based models predicting BMI from blood multiomics captured heterogeneous states of both metabolic and gut microbiome health better than classically measured BMI, suggesting that multiomic data can provide deeper insight into host physiology. Moreover, longitudinal analyses identified variable trajectories of BMI in response to a lifestyle intervention, depending on the analyzed omics platform; metabolomics-based BMI decreased to a greater extent than actual BMI, while proteomics-based BMI exhibited greater resistance. Our analysis further elucidated blood analyte-analyte associations which were significantly modified by obesity and partially reversed in the metabolically obese population through the program. Altogether, our findings provide an atlas of the gradual blood perturbations accompanying obesity and serve as a valuable resource for robustly characterizing metabolic health and identifying actionable targets for obesity.