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Background/Aims: Assessment of the levels of vital blood parameters in donors is essential to evaluate their health status, ensure their suitability for donation, preserve the integrity of the circulatory system, and facilitate comprehensive health monitoring. The aim of our study was to analyse the levels of haemoglobin, haematocrit, erythrocyte count, MCV, MCH, and MCHC in 12 groups of first-time donors and experienced donors of both sexes at the John Paul II Regional Blood Donation and Treatment Centre in Słupsk, northern Poland. The donors were divided into three age groups (18-30 years, 31-45 years, and 46-65 years). Methods: Using MANOVA multivariate significance tests, we examined the main effects of donor-related factors (age, sex, donor stage) on morphological blood parameters to evaluate different haematological parameters, such as Hb, Ht, RBC, MCV, MCH, and MCHC, and identified statistically significant relationships between all variables. Results: The multivariate analysis of these three main factors showed that the variation in haemoglobin (Hb) levels accounted for 46% of the explained dependence in this statistical model. In particular, approximately half of the variability in the multivariate statistical analysis was attributed to the role of Hb and haematocrit (Ht). In addition, the β-coefficient values for Hb and Ht were statistically higher in relation to donor sex and donor type (single versus repeat). These β-coefficient values from our data represent the strength and direction of the relationship between the haematological parameters (Hb and Ht) and the specific donor characteristics. A higher β-coefficient indicates a stronger influence of donor sex and donor type on these parameters, suggesting that these factors contribute significantly to the variation in the Hb and Ht levels. Based on our results, the comprehensive analysis of the entire statistical model of metabolic biomarkers revealed the following hierarchy: Hb > Ht > MCHC > MCV > RBC > MCH. The results obtained showed strong statistical relationships, as indicated by the high values of the key statistical indicators in our analysis. The coefficient of determination (R²) showed that the model explained a significant proportion of the variance in the data, while the F-test statistic confirmed the significance of the predictors. Conclusions: These strong statistical dependencies provided a clear justification for selecting this model over others, as it effectively represented the underlying relationships within the data. These statistics help to assess how well the model matches the actual data, thereby helping to reduce the risks associated with blood donation, optimise donor safety, and maintain the quality and efficiency of blood transfusion services. [ABSTRACT FROM AUTHOR]