The Project Baseline Health Study: a step towards a broader mission to map human health.
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SUBMITTER: Arges K
PROVIDER: S-EPMC7275087 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
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The Project Baseline Health Study: a step towards a broader mission to map human health.
Arges Kristine K Assimes Themistocles T Bajaj Vikram V Balu Suresh S Bashir Mustafa R MR Beskow Laura L Blanco Rosalia R Califf Robert R Campbell Paul P Carin Larry L Christian Victoria V Cousins Scott S Das Millie M Dockery Marie M Douglas Pamela S PS Dunham Ashley A Eckstrand Julie J Fleischmann Dominik D Ford Emily E Fraulo Elizabeth E French John J Gambhir Sanjiv S SS Ginsburg Geoffrey S GS Green Robert C RC Haddad Francois F Hernandez Adrian A Hernandez John J Huang Erich S ES Jaffe Glenn G King Daniel D Koweek Lynne H LH Langlotz Curtis C Liao Yaping J YJ Mahaffey Kenneth W KW Marcom Kelly K Marks William J WJ Maron David D McCabe Reid R McCall Shannon S McCue Rebecca R Mega Jessica J Miller David D Muhlbaier Lawrence H LH Munshi Rajan R Newby L Kristin LK Pak-Harvey Ezra E Patrick-Lake Bray B Pencina Michael M Peterson Eric D ED Rodriguez Fatima F Shore Scarlet S Shah Svati S Shipes Steven S Sledge George G Spielman Susie S Spitler Ryan R Schaack Terry T Swamy Geeta G Willemink Martin J MJ Wong Charlene A CA
NPJ digital medicine 20200605
The Project Baseline Health Study (PBHS) was launched to map human health through a comprehensive understanding of both the health of an individual and how it relates to the broader population. The study will contribute to the creation of a biomedical information system that accounts for the highly complex interplay of biological, behavioral, environmental, and social systems. The PBHS is a prospective, multicenter, longitudinal cohort study that aims to enroll thousands of participants with div ...[more]
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Project description:BackgroundWe 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.MethodsThe 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.ResultsAt 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).ConclusionsPBHS 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.
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Project description:ObjectiveObesity is associated with a higher risk of cardiovascular disease. Understanding the associations between comprehensive health parameters and body mass index (BMI) may lead to targeted prevention efforts.MethodsProject Baseline Health Study (PBHS) participants were divided into six BMI categories: underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5-24.9 kg/m2), overweight (BMI 25-29.9 kg/m2), class I obesity (30-34.9 kg/m2), class II obesity (35-39.9 kg/m2), and class III obesity (BMI ≥40 kg/m2). Demographic, cardiometabolic, mental health, and physical health parameters were compared across BMI categories, and multivariable logistic regression models were fit to evaluate associations.ResultsA total of 2,493 PBHS participants were evaluated. The mean age was 50±17.2 years; 55 % were female, 12 % Hispanic, 16 % Black, and 10 % Asian. The average BMI was 28.4 kg/m2±6.9. The distribution of BMI by age group was comparable to the 2017-2018 National Health and Nutrition Examination Survey (NHANES) dataset. The obesity categories had higher proportions of participants with CAC scores >0, hypertension, diabetes, lower HDL-C, lower vitamin D, higher triglycerides, higher hsCRP, lower mean step counts, higher mean PHQ-9 scores, and higher mean GAD-7 scores.ConclusionWe identified associations of cardiometabolic and mental health characteristics with BMI, thereby providing a deeper understanding of cardiovascular health across BMI.
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Project description:BackgroundCoronary artery calcium (CAC) and left ventricular diastolic dysfunction (LVDD) are strong predictors of cardiovascular events and share common risk factors. However, their independent association remains unclear.MethodsIn the Project Baseline Health Study (PBHS), 2082 participants underwent cardiac-gated, non-contrast chest computed tomography (CT) and echocardiography. The association between left ventricular (LV) diastolic function and CAC was assessed using multidimensional network and multivariable-adjusted regression analyses. Multivariable analysis was conducted on continuous LV diastolic parameters and categorical classification of LVDD and adjusted for traditional cardiometabolic risk factors. LVDD was defined using reference limits from a low-risk reference group without established cardiovascular disease, cardiovascular risk factors or evidence of CAC, (n = 560). We also classified LVDD using the American Society of Echocardiography recommendations.ResultsThe mean age of the participants was 51 ± 17 years with 56.6% female and 62.6% non-Hispanic White. Overall, 38.1% had hypertension; 13.7% had diabetes; and 39.9% had CAC >0. An intertwined network was observed between diastolic parameters, CAC score, age, LV mass index, and pulse pressure. In the multivariable-adjusted analysis, e', E/e', and LV mass index were independently associated with CAC after adjustment for traditional risk factors. For both e' and E/e', the effect size and statistical significance were higher across increasing CAC tertiles. Other independent correlates of e' and E/e' included age, female sex, Black race, height, weight, pulse pressure, hemoglobin A1C, and HDL cholesterol. The independent association with CAC was confirmed using categorical analysis of LVDD, which occurred in 554 participants (26.6%) using population-derived thresholds.ConclusionIn the PBHS study, the subclinical coronary atherosclerotic disease burden detected using CAC scoring was independently associated with diastolic function.ClinicaltrialsGov identifierNCT03154346.
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