Blood Biomarkers with Charlie Pedlar, PhD | KoopCast Episode #186
Episode overview:
Charlie Pedlar PhD is a researcher at St. Mary’s University in Twickenham London. He started out as a research assistant for the British Olympic Association based at Northwick Park Hospital. He has since held positions as London Region Lead Physiologist at the English Institute of Sport (primary sport: British Athletics) and Chief Science Officer at Orreco. Charlie was the Director of the Centre for Health, Applied Sport and Exercise Science at St Mary’s between 2009 and 2015. Whilst embedded in high performance sport Charlie completed his PhD at Brunel University in 2007 entitled 'Sleep and Exercise during Acclimation and Acclimatisation to Moderate Altitude in Elite Athletes', which involved a combination of field data collected during moderate altitude training camps and laboratory data, investigating responses to altitude in the GB national squads for Speedskating, Biathlon, Rowing, Kayaking and Athletics.
Episode highlights:
(19:19) Necessary biomarkers to track: full blood count (FBC) aka complete blood count (CBC), ferritin, Vitamin D, nutrition-related markers
(25:30) Standardizing blood tests: timing tests around training and menstrual cycles (continued at 39:41), fasted and rested conditions, guidelines for getting good test data, practicality of standardized blood testing
(54:30) Blood testing and performance: companies don’t know if you are performing well, they can’t tell you what is optimal, retrospective data analysis, health and performance are complementary, athlete intake example
Our conversation:
(0:00) Introduction: conclusion of Western States, using blood testing to inform training, Charlie’s background in research, sport, and the commercial space, interpreting your blood test results
(3:01) Charlie’s background: setup, working in academia and with Orreco
(4:38) Changing landscape of blood biomarkers: traditional communication gaps, coaches tracking blood biomarkers, emergence of research, current prevalence of data
(6:16) Benefits of blood work: objective measures of health and training, data can confirm good training and explain low points
(8:13) Blood work and training optimization: objectives of blood work, know why you are getting tested, mechanisms of training and adaptation, individuality, there is no optimal state
(10:32) Reference ranges: based on healthy but not athletic populations, examples, build your own reference ranges
(12:53) Terrain trap avoidance: blood testing is a blunt tool, hematocrit and hemoglobin example, optimization is not the goal, the goal is to ensure the athlete is healthy
(14:45) Markers of endurance: anemia and endurance underperformance, high altitude example, ferritin threshold
(17:49) Altitude and ferritin: measuring changes in hemoglobin mass, ferritin threshold example, failures of generalized biomarker ranges
(19:19) Necessary biomarkers to track: full blood count (FBC) aka complete blood count (CBC), ferritin, Vitamin D, nutrition-related markers
(21:59) Complete metabolic panel: a nicety but not a necessity, homeostatic indicators, liver function tests, both are likely to be normal in healthy populations, endocrine markers, general markers of recovery, examples
(24:30) Inflammation example: athletes naturally have inflammation from exercise, blood testing during inflammation will yield out-of-range results
(25:30) Standardizing blood tests: timing tests around training and menstrual cycles, fasted and rested conditions, guidelines for getting good test data, practicality of standardized blood testing
(30:05) Frequency of blood tests: quarterly tests are standard, condition for monthly tests, application to the everyday athlete, practicality of quarterly testing
(32:35) Timing blood tests: scheduling around key events, make sure you have time to adjust based on the test results
(34:39) Setting the table: early blood testing to ensure the athlete is ready for critical training months, life obstacles, identifying sensible times to test
(37:15) Test results and confidence: example of testing before a hard training block, athletes think they are unoptimized, give yourself a runway to course correct
(39:41) Timing around menstrual cycles: lack of research on the subject, standardize the phase of your cycle, example of changing iron levels, don’t test on your period, follicular phase may be a good stable time to test
(43:18) Types of analysis: comparison to healthy non-athlete ranges, individualized reference ranges, professional analysis
(45:31) Guidelines for analysis: companies with individualized ranges make things easier, obstacles to DIYing blood test analysis, simple directional analysis, 5-7 tests give you enough data to employ higher statistics
(49:15) Koop on individualization versus optimization: companies present an “optimized” range, the “optimized” range is not necessarily the individual range, anemic athlete example
(51:28) Charlie on individualization versus optimization: optimization indicates “best”, company-provided ranges are broad and not well individualized
(54:30) Blood testing and performance: companies don’t know if you are performing well, they can’t tell you what is optimal, retrospective data analysis, health and performance are complementary, athlete intake example
(57:16) Applying biomarkers to performance: mapping blood markers and training, menstrual cycle biomarkers and life stress, resources in show notes
(59:42) Exotic biomarkers: complete blood count (CBC) is sufficient, utility of “exotic” biomarker ratios
(1:01:36) Testosterone-cortisol ratio, frequent testing and standardized testing is crucial, indications of recovery and performance, testing is expensive
(1:03:40) Challenges with analyzing biomarker ratios: finding the signal in the noise, additional context required, conflicting and confusing results, timing around training is crucial, Christian Cook’s research, outside the scope of the lay athlete
(1:07:29) Accessibility of biomarker testing: the future of biomarker testing, examples of accessibility and relevant biomarkers
(1:11:10) Caution for biomarker analysis: less direct measurement techniques, banter, the tsunami of data, obsession over specific biomarkers
(1:13:40) Takeaways: start with a general analysis of biomarkers and tailor your analysis as you gather more individual data, avoid obsessing over one data point, cortisol example
(1:15:20) Dietary biomarkers: fatigued athlete study, healthy fats, biomarkers assist with nutritional insights
(1:16:52) Common interventions: train correctly, eat more, sleep more, allostatic load
(1:18:38) Wrap-up: where to learn more, Orreco.com, Charlie’s research
(1:20:00) Outro: giving thanks, the complications of using blood biomarkers, terrain trap avoidance, find a reliable practitioner, share the KoopCast
Additional resources:
Papers discussed-
A case study of an iron-deficient female Olympic 1500m runner.
Buy Training Essentials for Ultrarunning on Amazon or Audible
Information on coaching-
Koop’s Social Media
Twitter/Instagram- @jasonkoop