Principal component analysis of routine blood test results with Parkinson's disease: A case-control study

This study aimed to explore the association of routine blood test values and blood cell ratios with the risk or severity of Parkinson's disease (PD). The medical records of 453 PD patients and 436 controls were retrospectively reviewed. The severity of PD was quantified by the modified Hoehn-Yahr (HY) scale. We performed principal component analysis (PCA) of significant values/ratios and used logistic regression analysis to explore the relationship between principal components (PCs) and the risk of PD. Spearman correlation and ordinal logistic regression analyses were performed to explore the relationship between indicators and the severity of PD. The PCA generated 9 PCs, which contributed to 90.86% of the total variance. Logistic regression analysis revealed positive associations of PC2 (a measure monocyte ratios) and PC6 (a measure of platelet ratios and volume) and negative associations of PC1 (a comprehensive measure of lymphocyte, eosinophil, neutrophil, and red blood cell values), PC4 (a measure of red blood cell values), and PC7 (a measure of red blood cell values and platelet volume) with the risk of PD. However, we observed no associations of variables with the severity of PD. In conclusion, PCA reduced the dimensionality of the data. Peripheral blood disorders may be associated with PD.

Automated summary

This study found that peripheral blood disorders may be associated with the risk of Parkinson's disease (PD), but not with the severity of PD, after analyzing blood data from PD patients and controls. Principal component analysis helped reduce data dimensionality and identified potential associations between certain blood values/ratios and PD risk.