15. Cong Wang, PhD, MPH

Background: Obesity is a risk factor for ≥13 cancers. Accumulating evidence implicates viscerally located adiposity and chronic inflammation as plausible causal pathways, particularly for digestive cancers. However, prior research has lacked precise phenotyping of visceral adiposity and has relied on more general inflammatory biomarkers, such as C-reactive protein.
Objective: Leveraging dual-energy X-ray absorptiometry (DXA) body composition imaging and plasma proteomics measured by the Olink 384 inflammation proteomic panel, we aimed to (i) characterize phenotypes of metabolically glycemic unhealthy obesity and (ii) develop novel inflammatory proteomic signatures (i.e. “inflammotypes”).
Methods: We analyzed 639 participants (age mean±SD = 63.7±6.1) from the VITamin D and OmegA-3 TriaL (VITAL) with DXA and proteomics at baseline. We defined four metabolically unhealthy obesity phenotypes for each participant, as the sum of standardized values of visceral adipose tissue (VAT) mass and one of four glycemic metabolic traits, including fasting glucose, hemoglobin A1c (HbA1c), homeostasis model of insulin resistance (HOMA-IR), and lipoprotein insulin resistance (LPIR; a lipid-based score predictive of incident diabetes). We also derived categorical phenotypes by cross-classifying VAT (high vs. low) with each glycemic trait (unhealthy vs. healthy), using cut-points that yielded the highest area-under-the-curve (AUC). We then used elastic net regression models to identify the inflammotypes related to each of the four metabolic obesity glycemic-trait based phenotypes. Finally, we performed an external validation in the COcoa Supplement and Multivitamin Outcomes Study (COSMOS; N=371, age mean±SD = 69.0±5.2).
Results: The continuous metabolic obesity measures were related to unique inflammotypes consisting of 86 to 102 proteins, with 21 proteins (including HGF, IL1RN, ISM1, PON3, WFIKKN2, and HSD11B1) shared across all phenotypes. For the categorical phenotypes, we identified 19, 22, and 3 proteins differentially expressed among the metabolically unhealthy obesity subgroups, as defined for HbA1c, HOMA-IR, and LPIR metabolically unhealthy, respectively, but none were specific to fasting glucose. The inflammotypes were highly correlated with the corresponding phenotypes in COSMOS (Pearson correlation coefficients ranged from 0.63 to 0.82).
Conclusion: We derived and externally validated novel proteomic inflammotypes of metabolically unhealthy obesity. These inflammotypes will be evaluated for their associations with subsequent risk of colorectal and liver cancer in prospective longitudinal cohorts, to elucidate the potential mechanisms underlying obesity’s role in the development of these cancers.