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Using Big Data to Understand the Human Condition: The Kavli HUMAN Project.

ABSTRACT: Until now, most large-scale studies of humans have either focused on very specific domains of inquiry or have relied on between-subjects approaches. While these previous studies have been invaluable for revealing important biological factors in cardiac health or social factors in retirement choices, no single repository contains anything like a complete record of the health, education, genetics, environmental, and lifestyle profiles of a large group of individuals at the within-subject level. This seems critical today because emerging evidence about the dynamic interplay between biology, behavior, and the environment point to a pressing need for just the kind of large-scale, long-term synoptic dataset that does not yet exist at the within-subject level. At the same time that the need for such a dataset is becoming clear, there is also growing evidence that just such a synoptic dataset may now be obtainable-at least at moderate scale-using contemporary big data approaches. To this end, we introduce the Kavli HUMAN Project (KHP), an effort to aggregate data from 2,500 New York City households in all five boroughs (roughly 10,000 individuals) whose biology and behavior will be measured using an unprecedented array of modalities over 20 years. It will also richly measure environmental conditions and events that KHP members experience using a geographic information system database of unparalleled scale, currently under construction in New York. In this manner, KHP will offer both synoptic and granular views of how human health and behavior coevolve over the life cycle and why they evolve differently for different people. In turn, we argue that this will allow for new discovery-based scientific approaches, rooted in big data analytics, to improving the health and quality of human life, particularly in urban contexts.

SUBMITTER: Azmak O

PROVIDER: S-EPMC4605457 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Using Big Data to Understand the Human Condition: The Kavli HUMAN Project.

Azmak Okan O Bayer Hannah H Caplin Andrew A Chun Miyoung M Glimcher Paul P Koonin Steven S Patrinos Aristides A

Big data 20150901 3

Until now, most large-scale studies of humans have either focused on very specific domains of inquiry or have relied on between-subjects approaches. While these previous studies have been invaluable for revealing important biological factors in cardiac health or social factors in retirement choices, no single repository contains anything like a complete record of the health, education, genetics, environmental, and lifestyle profiles of a large group of individuals at the within-subject level. Th ...[more]

PMID: 26487987

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Project description:BackgroundOsteoporosis, obesity, and fatty liver are increasingly common chronic diseases that seriously threaten people's health. Low-dose chest computed tomography (LDCT) scan is frequently used for lung cancer screening in health screenings and checkups. Quantitative computed tomography (QCT) enables the accurate measurement of volumetric bone mineral density (vBMD), liver fat content, and abdominal fat area using the existing LDCT data without extra radiation. We initiated a new project, the China Health Big Data (China Biobank), which combines the LDCT scan images from lung cancer screening of participants in health checkup with QCT to investigate the added value of QCT to LDCT, in order to establish the normative reference database and diagnosis criteria for the three aforementioned conditions.MethodsThe China Biobank project is a prospective nationwide multicenter cohort study that will combine QCT technology with LDCT scans to measure bone mineral density (BMD), intra-abdominal fat distribution, and liver fat content of the generally healthy checkup participants. Mindways QCT calibration phantom (Mindways Software Inc., Austin, TX, USA) and analysis software QCT PRO v6.0 will be used for all centers. Before data collection begins, the European Spine Phantom (ESP) will be used for quality control analysis at each collaborating center. The inclusion criteria are a healthy checkup participant aged 30-90 years, with LDCT as a part of his/her health checkup protocol. Exclusion criteria are pregnant women or participants with a metal implant in the CT scan area. The LDCT images will be transferred to the Mindways workstation for analysis, and vBMD in the L1 and L2 vertebrae, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and liver fat content will be measured. As part of the health checkup, the demographic, anthropometric parameters, blood pressure, and a routine blood laboratory test will be collected. The estimated sample size will be about 30,000.ResultsThe combination of QCT with LDCT of the chest was validated in this project. The vBMD of spine, visceral fat and liver fat can be measured with a LDCT chest scan.ConclusionsThe China Biobank project will assess the added value of QCT to LDCT, and enable accurate evaluation of the prevalence of osteoporosis, obesity, and fatty liver disease in a very large Chinese cohort.

Dataset Information

Using Big Data to Understand the Human Condition: The Kavli HUMAN Project.

Publications

Using Big Data to Understand the Human Condition: The Kavli HUMAN Project.

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