Multi-dimensional characterization of prediabetes in the Project Baseline Health Study
Authors: Chatterjee Ranee , Kwee Lydia Coulter , Pagidipati Neha , Koweek Lynne H. , Mettu Priyatham S. , Haddad Francois , Maron David J. , Rodriguez Fatima , Mega Jessica L. , Hernandez Adrian , Mahaffey Kenneth , Palaniappan Latha , Shah Svati H. ,   the Project Baseline Health Study
Published: July 2022

Abstract
Background
We examined multi-dimensional clinical and laboratory data in participants with normoglycemia, prediabetes, and diabetes to identify characteristics of prediabetes and predictors of progression from prediabetes to diabetes or reversion to no diabetes.

Methods
The Project Baseline Health Study (PBHS) is a multi-site prospective cohort study of 2502 adults that conducted deep clinical phenotyping through imaging, laboratory tests, clinical assessments, medical history, personal devices, and surveys. Participants were classified by diabetes status (diabetes [DM], prediabetes [preDM], or no diabetes [noDM]) at each visit based on glucose, HbA1c, medications, and self-report. Principal component analysis (PCA) was performed to create factors that were compared across groups cross-sectionally using linear models. Logistic regression was used to identify factors associated with progression from preDM to DM and for reversion from preDM to noDM.

Results
At enrollment, 1605 participants had noDM; 544 had preDM; and 352 had DM. Over 4 years of follow-up, 52 participants with preDM developed DM and 153 participants reverted to noDM. PCA identified 33 factors composed of clusters of clinical variables; these were tested along with eight individual variables identified a priori as being of interest. Six PCA factors and six a priori variables significantly differed between noDM and both preDM and DM after false discovery rate adjustment for multiple comparisons (q < 0.05). Of these, two factors (one comprising glucose measures and one of anthropometry and physical function) demonstrated monotonic/graded relationships across the groups, as did three a priori variables: ASCVD risk, coronary artery calcium, and triglycerides (q < 10–21 for all). Four factors were significantly different between preDM and noDM, but concordant or similar between DM and preDM: red blood cell indices (q = 8 × 10-10), lung function (q = 2 × 10-6), risks of chronic diseases (q = 7 × 10-4), and cardiac function (q = 0.001), along with a priori variables of diastolic function (q = 1 × 10-10), sleep efficiency (q = 9 × 10-6) and sleep time (q = 6 × 10-5). Two factors were associated with progression from prediabetes to DM: anthropometry and physical function (OR [95% CI]: 0.6 [0.5, 0.9], q = 0.04), and heart failure and c-reactive protein (OR [95% CI]: 1.4 [1.1, 1.7], q = 0.02). The anthropometry and physical function factor was also associated with reversion from prediabetes to noDM: (OR [95% CI]: 1.9 [1.4, 2.7], q = 0.02) along with a factor of white blood cell indices (OR [95% CI]: 0.6 [0.4, 0.8], q = 0.02), and the a priori variables ASCVD risk score (OR [95% CI]: 0.7 [0.6, 0.9] for each 0.1 increase in ASCVD score, q = 0.02) and triglycerides (OR [95% CI]: 0.9 [0.8, 1.0] for each 25 mg/dl increase, q = 0.05).

Conclusions
PBHS participants with preDM demonstrated pathophysiologic changes in cardiac, pulmonary, and hematology measures and declines in physical function and sleep measures that precede DM; some changes predicted an increased risk of progression to DM. A factor with measures of anthropometry and physical function was the most important factor associated with progression to DM and reversion to noDM. Future studies may determine whether these changes elucidate pathways of progression to DM and related complications and whether they can be used to identify individuals at higher risk of progression to DM for targeted preventive interventions.
Trial registration ClinicalTrials.gov NCT03154346