Lipemic Plasma Identified Blood Donors: Triglyceride Variability and Exploratory Machine Learning Analysis

TL;DR

This study explores metabolic variability in blood donors with lipemic plasma, finding high triglyceride levels and suggesting potential for targeted metabolic monitoring.

Contribution

The study introduces exploratory machine learning analysis of blood donor data to detect metabolic irregularities.

Findings

Males had higher and more dispersed triglyceride levels compared to females.

Triglycerides correlated with BMI and cardiovascular risk metrics, with age being the strongest contributor to cardiovascular risk.

A Random Forest classifier predicted elevated triglyceride levels with an AUC of 0.86.

Abstract

Background/Objectives: Early detection of cardiometabolic irregularities is crucial for averting cardiovascular illness; however, demographic cohorts that consistently engage with healthcare systems like habitual blood donors are inadequately leveraged for metabolic monitoring. Methods: This study performed lipid profiling and cardiovascular risk assessment among blood donors identified with visually lipemic plasma during routine screening, in order to explore metabolic variability within this selected donor subgroup. Of 13,818 screened donors, 160 with lipemic plasma were included, and multivariable and machine-learning analyses were restricted to 90 donors with complete clinical data. Results: We observed substantial variability in triglyceride levels, with males displaying higher and more dispersed values. Correlation analysis indicated that triglycerides were associated with BMI and composite cardiovascular risk metrics, while age was the strongest contributor to the calculated 10-year cardiovascular risk score. Using a Random Forest classifier, elevated triglyceride levels were predicted with an AUC of 0.86; however, given the limited sample size, this analysis should be interpreted as exploratory and proof-of-concept in nature. Conclusions: In this selected subgroup of donors with lipemic plasma, clinically relevant hypertriglyceridemia was frequently observed. These findings suggest that routine donor data may provide opportunities for targeted metabolic monitoring, although the results cannot be generalized to the broader blood donor population. Further studies in larger and more representative cohorts are warranted.

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