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Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SUMMARY: The COVID-19 pandemic has fundamentally changed how auditors work and interact with team members and others in the financial reporting process. In particular, there has been a move away from face-to-face interactions to the use of virtual teams, with strong indications many of these changes will remain post-pandemic. We examine the impacts of the pandemic on group judgment and decision making (JDM) research in auditing by reviewing research on auditor interactions with respect to the review process (including coaching), fraud brainstorming, consultations within audit firms, and parties outside the audit firm such as client management and the audit committee. Through the pandemic lens and for each auditor interaction, we consider new research questions for audit JDM researchers to investigate and new ways of addressing existing research questions given these fundamental changes. We also identify potential impacts on research methods used to address these questions during the pandemic and beyond. [ABSTRACT FROM AUTHOR]