Blood Biomarkers for Screening Active Women

Considerations for the Semi-elite Female Athlete

Not every marker in Figure 1 needs to be assessed in every screen, nor can every practitioner or athlete access the full panel. For the semi-elite female athlete, two practical realities shape every panel decision: financial cost and access. Comprehensive biomarker testing represents a meaningful investment, and a blanket reduction of the panel is not the right response to budget constraints. The smarter approach is to apply individualized risk stratification. Clinicians should determine the highest-yield markers for each individual athlete with some level of sophistication, as they will deliver the greatest clinical return and should be treated as non-negotiable regardless of budget. Then, clinicians can incorporate lower-priority additions when resources allow or when a specific clinical picture warrants them. Group testing through sports medicine clinics or pathology providers with athlete-specific service agreements can substantially reduce per-test cost. Access to venous blood collection also requires a qualified practitioner and a laboratory, and the blood draw volume and frequency should always be kept to a minimum, with a clearly justified purpose. 

It is also worth distinguishing between two approaches to biomarker measurement: screening (infrequent testing, several months apart, to identify deficiencies or excesses) and monitoring (high-frequency testing, days or weeks apart, to track ongoing adaptation and recovery). For most semi-elite female athletes, screening is the realistic and appropriate starting point.

Underpinning all of these decisions is the principle of individualization. No two semi-elite female athletes present with the same combination of training history, menstrual health, dietary pattern, life stressors, and clinical history. A panel appropriate for one woman may overlook the most relevant deficiency in another. Clinicians need to evaluate the conditions that determine whether a screening program will succeed in practice and the suitability of any individual marker before it is included (see Table 1 and 2)⁽³⁾. Frameworks are tools for building a defensible, individualized panel, not a universal checklist to be applied uniformly.

Table 1: Key factors for the success of a biomarker screening program

(adapted from Pedlar et al. 2019)

Table 2: Biomarker suitability checklist for selection decisions 

(adapted from Pedlar et al. 2019)

Blood collection

A screening program is only as reliable as the conditions under which samples are collected. Poor pre-analytic control, the conditions surrounding the blood draw itself, is one of the most consistent sources of error in applied biomarker testing, yet it is frequently overlooked outside formal laboratory settings^{(4, 5)}. For the clinical setting, the practical pre-draw protocol checklist captures the essentials:

1. Sample collected in the morning following an overnight fast,
2. No high-intensity exercise in the 24 hours prior to collection,
3. Seated posture for at least 10 minutes before venipuncture,
4. Menstrual cycle phase documented: early follicular phase preferred for hormonal markers,
5. OCP use recorded: suppresses sex steroids, shifting diagnostic weight to fT3 and IGF-1,
6. Recent illness or injury noted: both elevate CRP and can artificially raise ferritin,
7. Same sampling site used consistently if capillary collection (earlobe or fingertip).

Consistency matters more than perfection. The same pre-analytic conditions across screens are what make longitudinal comparison meaningful. When clinicians cannot achieve ideal conditions, they should document deviations so that anomalous results can be contextualized rather than misinterpreted.

Interpreting Results

Once results are returned, a fundamental interpretive challenge presents itself. A single biomarker result, read without context or longitudinal comparison, carries limited diagnostic value. Blood testing shares a limitation similar to other monitoring tools discussed earlier: it can confirm that something is already wrong, but only trends across time allow a problem to be caught before it becomes symptomatic and detrimental to performance. As researchers at the University of Connecticut observed, "a single measurement of a biomarker does not allow for precise determination of an individual’s health status”. A single result describes the present situation and nothing more; a series of results reveals the trajectory. Yet in practice, clinicians classify results against population-derived reference ranges into categories such as “normal, borderline, and action required". This approach is familiar and widely used, and for the female athlete, it is frequently inadequate. Population norms are derived from cross-sectional samples of the general population; they do not account for physiological variation introduced by the menstrual cycle, training status, or energy availability. And this is where the science currently fails the practitioner.

Research in this area has disproportionately focused on population-level thresholds (the arbitrary ‘lines in the sand’ that define ‘normal’ and ‘not-normal’) rather than on within-individual changes that are clinically meaningful. Clinicians shouldn’t interpret single markers in isolation. Researchers at St Mary’s University demonstrated that individualized reference ranges built from repeated testing substantially outperform population-based clinical thresholds for detecting meaningful change within an individual⁽⁴⁾. Two data points collected months apart under consistent pre-analytic conditions are already more informative than one. Three or four, spanning a training year, begin to reveal the individual’s personal ranges, seasonal patterns, and responses to training load that no population norm can provide.

A Contextual Example

Given the breadth of markers available and the considerations outlined above, the following example illustrates how clinicians may approach panel selection for a hypothetical composite athlete. She is 34 years old, runs four times per week, has followed a plant-based diet for 18 months, donates blood every three months, is a mother of two children under five, and reports heavy periods (see Figure 2). She has no current symptoms and is presenting for a baseline pre-season screen. 

Four independent iron-loss pathways converge in this single individual, making her a useful teaching case, as the panel’s decisions follow directly from her profile.