The Project Baseline Health Study: a step towards a broader mission to map human health

Arges, Kristine; Assimes, Themistocles L.; Bajaj, Vikram S.; Balu, Suresh; Bashir, Mustafa R.; Beskow, Laura M.; Blanco, Rosalia; Califf, Robert M.; Campbell, Paul; Carin, Lawrence; Christian, Victoria; Cousins, Scott W.; Das, Millie; Dockery, M; Douglas, Pamela S.; Dunham, Ashley; Eckstrand, Julie; Fleischmann, Dominik; Ford, Emily; Fraulo, Elizabeth S.; French, John; Gambhir, Sanjiv S.; Ginsburg, Geoffrey S.; Green, Robert C.; Haddad, François; Hernandez, Adrian F.; Hernandez, John; Huang, Erich; Jaffe, Glenn J.; King, Daniel; Koweek, Lynne; Langlotz, Curtis P.; Liao, Yaping Joyce; Mahaffey, Kenneth W.; Marcom, Kelly; Marks, William J.; Maron, David J.; McCabe, Reid; McCall, Shannon J.; McCue, Rebecca; Mega, Jessica L.; Miller, David P.; Muhlbaier, Lawrence H.; Munshi, Rajan; Newby, L. Kristin; Pak-Harvey, Ezra; Patrick-Lake, Bray; Pencina, Michael J.; Peterson, Eric D.; Rodríguez, Fátima; Shore, Scarlet; Shah, Svati H.; Shipes, Steven; Sledge, George W.; Spielman, Susie; Spitler, Ryan; Schaack, Terry; Swamy, Geeta K.; Willemink, Martin J.; Wong, Charlene A.

doi:10.1038/s41746-020-0290-y

Cited by 56 publications

(54 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Baseline Health Study BHS methods have been previously described, 10 including entry and exclusion criteria, the institutional review board and participant consent procedures, the data collection scheme, and key components of study procedures. BHS is enrolling a large number of participants, beginning with intensive measurement of the first 2502 people (the deeply phenotyped cohort) in whom a large volume of multimodal data are collected.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Importance of Social Determinants in Screening for Depression

et al. 2021

View full text Add to dashboard Cite

Importance The most common screening tool for depression is the Patient Health Questionnaire-9 (PHQ-9). Despite extensive research on the clinical and behavioral implications of the PHQ-9, data are limited on the relationship between PHQ-9 scores and social determinants of health and disease. Objective To assess the relationship between the PHQ-9 at intake and other measurements intended to assess social determinants of health. Design, Setting, and Participants Cross-sectional analyses of 2502 participants from the Baseline Health Study (BHS), a prospective cohort of adults selected to represent major demographic groups in the US; participants underwent deep phenotyping on demographic, socioeconomic, clinical, laboratory, functional, and imaging findings. Interventions None. Main Outcomes and Measures Cross-sectional measures of clinical and socioeconomic status (SES). Results In addition to a host of clinical and biological factors, higher PHQ-9 scores were associated with female sex, younger participants, people of color, and Hispanic ethnicity. Multiple measures of low SES, including less education, being unmarried, not currently working, and lack of insurance, were also associated with higher PHQ-9 scores across the entire spectrum of PHQ-9 scores. A summative score of SES, which was the 6th most predictive factor, was associated with higher PHQ-9 score after adjusting for 150 clinical, lab testing, and symptomatic characteristics. Conclusions and Relevance Our findings underscore that depression should be considered a comorbidity when social determinants of health are addressed, and both elements should be considered when designing appropriate interventions.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The Baseline Health Study (BHS) 10 is a prospective cohort study of an adult population selected to represent major demographic groups in the US. In BHS, deep phenotyping of numerous demographic, clinical, laboratory, functional, and imaging findings is coupled with ongoing longitudinal followup.…”

mentioning

confidence: 99%

Importance of Social Determinants in Screening for Depression

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The Baseline Health Study BHS methods have been previously described, [11][12][13] including entry and exclusion criteria, the institutional review board and participant consent procedures, the data collection scheme and key components of study procedures.…”

Section: Methodsmentioning

confidence: 99%

Biological and clinical correlates of the patient health questionnaire-9: exploratory cross-sectional analyses of the baseline health study

et al. 2022

View full text Add to dashboard Cite

ObjectivesWe assessed the relationship between the Patient Health Questionnaire-9 (PHQ-9) at intake and other measurements intended to assess biological factors, markers of disease and health status.Design, setting and participantsWe performed a cross-sectional analysis of 2365 participants from the Baseline Health Study, a prospective cohort of adults selected to represent major demographic groups in the USA. Participants underwent deep phenotyping on demographic, clinical, laboratory, functional and imaging findings.ImportanceDespite extensive research on the clinical implications of the PHQ-9, data are limited on the relationship between PHQ-9 scores and other measures of health and disease; we sought to better understand this relationship.InterventionsNone.Main outcomes and measuresCross-sectional measures of medical illnesses, gait, balance strength, activities of daily living, imaging and laboratory tests.ResultsCompared with lower PHQ-9 scores, higher scores were associated with female sex (46.9%–66.7%), younger participants (53.6–42.4 years) and compromised physical status (higher resting heart rates (65 vs 75 bpm), larger body mass index (26.5–30 kg/m2), greater waist circumference (91–96.5 cm)) and chronic conditions, including gastro-oesophageal reflux disease (13.2%–24.7%) and asthma (9.5%–20.4%) (p<0.0001). Increasing PHQ-9 score was associated with a higher frequency of comorbidities (migraines (6%–20.4%)) and active symptoms (leg cramps (6.4%–24.7%), mood change (1.2%–47.3%), lack of energy (1.2%–57%)) (p<0.0001). After adjustment for relevant demographic, socioeconomic, behavioural and medical characteristics, we found that memory change, tension, shortness of breath and indicators of musculoskeletal symptoms (backache and neck pain) are related to higher PHQ-9 scores (p<0.0001).ConclusionsOur study highlights how: (1) even subthreshold depressive symptoms (measured by PHQ-9) may be indicative of several individual- and population-level concerns that demand more attention; and (2) depression should be considered a comorbidity in common disease.Trial registration numberNCT03154346.

show abstract

“…The PBHS study established a portal that incorporates and analyzes personalized, longitudinal, multidimensional data, with a greater focus on future than past. It further explore the biological heterogeneity of healthy individuals or individuals with chronic disease in detail for a longer period of time to create reference health status by integrating various aspects of health [ 137 ].…”

Section: Recent Developments In the Fieldmentioning

confidence: 99%

Precision medicine in the era of artificial intelligence: implications in chronic disease management

et al. 2020

View full text Add to dashboard Cite

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome—a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual’s biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases.

show abstract

The Project Baseline Health Study: a step towards a broader mission to map human health

Cited by 56 publications

References 37 publications

Importance of Social Determinants in Screening for Depression

Importance of Social Determinants in Screening for Depression

Biological and clinical correlates of the patient health questionnaire-9: exploratory cross-sectional analyses of the baseline health study

Precision medicine in the era of artificial intelligence: implications in chronic disease management

Contact Info

Product

Resources

About