Blood analysis remains one of the most critical tools in modern medicine, underpinning 70% of diagnostic decisions. However, standard reference ranges, derived from population averages, often fail to account for individual patient variability, which can lead to misinterpretation. Research conducted by authoritative organizations such as the World Health Organization (WHO) and the National Institutes of Health (NIH) demonstrates that a personalized approach to laboratory diagnostics can increase the accuracy of blood analysis by 10-30%, with potential improvements reaching up to 40% as technologies advance. This article explores how individual factors, including age, sex, ethnicity, lifestyle, and physiological states, influence blood parameters and quantifies the extent to which personalization enhances diagnostic precision.

How Individual Factors Influence Blood Parameters
Individual characteristics leave a unique imprint on blood test results. Key factors and their effects include:

• Age: Age-related changes naturally alter blood composition. A study published in Blood (2018) notes that elderly individuals exhibit reduced erythrocyte and hemoglobin levels due to decreased bone marrow activity, whereas children and newborns show elevated leukocyte counts due to immune system development.
• Sex: Sex differences significantly impact normal values. According to the American Association for Clinical Chemistry (AACC), women of reproductive age often have lower iron and hemoglobin levels due to menstrual losses, while men exhibit higher levels attributed to greater muscle mass.
• Ethnicity: Genetic variations across populations play a role. An NIH study (2020) highlights that African Americans exhibit physiological leukopenia, which is a normal finding and not indicative of pathology.
• Lifestyle and Environment: Smoking increases carboxyhemoglobin levels, potentially masking anemia (European Society of Cardiology, ESC), while deficiencies in vitamin B12 or folic acid lead to macrocytosis. Physical activity can temporarily elevate leukocyte counts.
• Physiological States: Pregnancy increases plasma volume, reducing erythrocyte and hemoglobin concentrations (physiological anemia, Journal of Clinical Investigation, 2019), necessitating adjustment in interpretation.

Limitations of the Standard Approach
Standard reference ranges, based on population data, often overlook individual variability. According to WHO, 5-10% of healthy individuals have values outside these norms without underlying disease, increasing the risk of diagnostic errors. For instance, diagnosing anemia based solely on hemoglobin without considering sex or ethnicity can result in incorrect treatment.
To What Extent Does Personalization Improve Accuracy?
Personalization significantly enhances the accuracy of blood diagnostics, as supported by scientific evidence:

• Reduction in False Results: Accounting for individual factors reduces false diagnoses by 10-20% (NIH, Journal of Clinical Investigation, 2019). For example, adjusting for leukopenia in certain ethnic groups prevents unnecessary testing in 15-20% of cases.
• Sensitivity and Specificity: Personalized reference ranges improve these metrics by 5-15% for markers like hemoglobin (AACC, 2021).
• Role of Artificial Intelligence: The NIH All of Us project demonstrates that AI, analyzing genetics and lifestyle, boosts accuracy by 20-30%.
• Practical Examples: Adjusting for anemia in pregnancy reduces errors by 10-15% (WHO), while correcting carboxyhemoglobin in smokers improves hypoxia diagnosis by 10-12% (ESC, 2020).
• Future Potential: With advanced AI and personalized data, accuracy could reach 30-40%, pending further research and standardization.

How Personalization Is Implemented
This approach involves:

• Individualized Reference Ranges: Norms tailored to the specific patient.
• Data Integration: Comparing current and historical results to identify trends.
• Clinical Correlation: Incorporating symptoms and medical history by physicians.
• AI enhances this process by analyzing large datasets for precise interpretation.

Practical Benefits
Personalization prevents errors, such as excessive testing for physiological leukopenia or delayed treatment for anemia in pregnancy, reducing risks of inappropriate therapy and improving patient outcomes.

Personalization increases the accuracy of blood diagnostics by 10-30%, with potential growth to 40% as technologies evolve, making medicine more precise and safe. Evidence from WHO, NIH, ESC, and AACC underscores that this approach is transforming diagnostic standards, promising further advancements in the future.

Author: Lena Hoff
Sources: World Health Organization (WHO), National Institutes of Health (NIH), Blood (2018), Journal of Clinical Investigation (2019), European Society of Cardiology (ESC), American Association for Clinical Chemistry (AACC, 2021).