Lipemic Plasma Identified Blood Donors: Triglyceride Variability and Exploratory Machine Learning Analysis
Sirinya Sitthirak1,2, Sodsai Narkpetch3, Rujira Nonsa-Ard4
1Department of Medical Technology, School of Allied Health Sciences, Walailak University, Nakhon Si Thammarat 80160, Thailand.
Insights
Blood donors with lipemic plasma frequently show high triglyceride levels, indicating potential for metabolic monitoring. This exploratory study highlights hypertriglyceridemia in this group, suggesting future research opportunities.
Area of Science:
- Cardiovascular disease research
- Metabolic health monitoring
- Blood donor screening
Background:
- Early detection of cardiometabolic issues is key to preventing cardiovascular disease.
- Habitual blood donors are an underutilized resource for metabolic monitoring.
- Lipemic plasma in donors may indicate underlying metabolic irregularities.
Purpose of the Study:
- To investigate metabolic variability in blood donors with lipemic plasma.
- To assess lipid profiles and cardiovascular risk in this selected subgroup.
- To explore the potential of donor data for metabolic surveillance.
Main Methods:
- Lipid profiling and cardiovascular risk assessment were performed on 160 blood donors with lipemic plasma.
- Multivariable and machine-learning analyses were conducted on 90 donors with complete data.
- Random Forest classification was used to predict elevated triglyceride levels.
Main Results:
- Significant triglyceride variability was observed, with higher and more dispersed values in males.
- Triglycerides correlated with Body Mass Index (BMI) and composite cardiovascular risk metrics.
- Age was the primary determinant of the 10-year cardiovascular risk score.
- A Random Forest model achieved an Area Under the Curve (AUC) of 0.86 for predicting elevated triglycerides (exploratory).
Conclusions:
- Clinically relevant hypertriglyceridemia was common in blood donors with lipemic plasma.
- Routine donor data may offer avenues for targeted metabolic monitoring.
- Findings are specific to this subgroup and cannot be generalized; larger studies are needed.
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.
