A recent article proposed a new way of analyzing patient serum as a means to broadly assess health status (see: Plasma protein patterns as comprehensive indicators of health). Below is an excerpt from the abstract:
Proteins are effector molecules that mediate the functions of genes and modulate comorbidities, behaviors and drug treatments. 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 scanning coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health.
To state that the approach described in the paper is a totally new way to monitor health status is an understatement. In the study, about 5,000 different serum proteins were measured from about the 17,000 subjects, accounting for about 85 million assays. The authors labeled this new lab testing approach as a liquid health check. The protein-phenotype models were then developed using AI techniques. My eye was immediately caught by one of the possible uses of this liquid health check -- to identify excess alcohol consumption in a patient. Here an excerpt from an article on the common alcohol use biomarkers from ARUP (see: Alcohol Use Biomarkers):
Approximately 20% of primary care patients in the United States drink alcohol (ethanol) at levels harmful to health....Diagnostic criteria for alcohol use disorder (AUD) vary, but the most widely used criteria are found in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Laboratory tests for acute alcohol ingestion include ethanol, ethyl glucuronide (EtG), and ethyl sulfate (EtS) tests. Carbohydrate-deficient transferrin (CDT) and phosphatidylethanol (PEth) are useful markers for monitoring abstinence following long-term use.
Given that about one primary care patients in five has a serious drinking problem, it seems appropriate (or even necessary) to provide some sort of routine screening test and the liquid health check may provide such a test. Here's a quote from an article about clinicians' acumen in diagnosing this problem (see: Clinician Suspicion of an Alcohol Problem: An Observational Study From the AAFP National Research Network):
Clinician suspicion of alcohol problems had poor sensitivity but high specificity for identifying patients who had a positive screening test for alcohol problems. These data support the routine use of a screening tool to supplement clinicians’ suspicions, which already provide reasonable positive predictive value.
In medical diagnosis, test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate) whereas test specificity is the ability of the test to correctly identify those without the disease (true negative rate). Clinicians thus have trouble identifying patients with an "alcohol problem" based on a positive screening test but little trouble identifying non-alcoholics with a positive test.
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