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Original Research

Molecular Deficits Relevant to Concussion Are Prevalent in Top-Ranked Football Players Entering the National Football League Draft

Kunces, Laura J.1,2; Keenan, John3; Schmidt, Caleb M.4; Schmidt, Michael A.4

Author Information
Journal of Strength and Conditioning Research: November 2021 - Volume 35 - Issue 11 - p 3139-3144
doi: 10.1519/JSC.0000000000004131
  • Open

Abstract

Introduction

Among sports, American football (6) is associated with one of the highest incidences of concussion, also known as traumatic brain injury (TBI). Modest literature explores the potential role of nutrition in preventing and treating concussions, and obtaining consistent results has been elusive (27). Often, therapeutic or prophylactic intervention is not coupled to the nutritional status of individuals. This raises an interesting question of whether nutritional or metabolic deficits in an individual before entering a contact environment adversely influence the clinical trajectory when that individual's brain is exposed to acceleration forces.

Although there are data on the nutritional status of individuals or teams in collegiate and professional football, there are little data on the top athletes derived from the top 25-ranked collegiate teams in the transition period from college to the National Football League (NFL). We sought to understand the status of molecular markers by observing postcollegiate football players and asking 3 fundamental questions: What is the status of molecular markers central to metabolic networks, are these markers mechanistically relevant to brain metabolic networks in those who sustain impact injuries, and what is the prevalence of athletes with multiple concurrent suboptimal molecular values? This article is insightful for the multidisciplinary health care team of football programs that wish to optimize their current personalized data, training, and nutrition practices as these biomarkers may directly relate injury outcomes.

Previous research established the average American's Omega-3 (O3) Index reference range as 2.9–12.9%, with the optimum set at 8%. The average 20-year-old American is just less than 4% (10,26). Active-duty military, a cohort not unlike our collegiate athlete cohort, had a reported O3 Index of 3.5%, which is significantly below the U.S. average (4.5%) and below the ideal target of 8–10% (12). A low O3 Index is associated with lower cognitive flexibility, lower executive function, mood and personality issues, and altered brain functional assessments (18).

Collegiate athletes loading docosahexaenoic (DHA) at 2 g, 4 g, and 6 g per day was associated with a variable, but favorable impact on neurofilament light (21), a sensitive marker of head trauma in cerebrospinal fluid. Recent reviews suggest that optimizing O3 status through dietary loading may protect the brain against numerous sequelae of TBI (4,16).

Although there are little data on homocysteine (Hcy) and concussion, there is substantial research on Hcy and cerebrovascular function. A meta-analysis shows a strong correlation between elevated serum Hcy and the pathogenesis of cerebral infarction, suggesting Hcy may be an important biomarker in the early diagnosis and treatment assessment (8). Homocysteine has been shown significantly higher in individuals who died because of TBI compared with those still alive at the end of a study period (22). Previously, the prevalence of hyperhomocysteinemia in athletes has been upward of 47% (5).

The influence of vitamin D (vitD) on brain health has garnered interest because it modulates neuronal apoptosis, neuroinflammation, oxidative stress, excitotoxicity, and myelin and axon repair (3). VitD significantly enhances the proliferation of neural stem cells and enhances their differentiation into neurons and oligodendrocytes. Neural stem cells treated with 1,25(OH)2D3 showed increased expression of brain-derived neurotrophic factor (and other neurotrophins), important for neural cell survival and differentiation (25). Although different standards of adequacy have been defined, substantial research has shown vitD inadequacy is common among NCAA athletes (28), professional athletes, and the average Americans. Despite this, athletes should maintain a minimum of 40 ng·ml−1 throughout the year to maximize vitD storage in the muscle (20).

Magnesium (Mg) status may also be important in the brain's response to acceleration injury. Mg is the most widespread metal ion cofactor in humans, supporting over 600 enzymatic reactions (2), including maintaining heart rhythm, muscular function, blood pressure, immune system function, glucose levels, calcium absorption, and other crucial functions. The sole biologically active form of adenosine triphosphate (ATP) is bound to Mg, while Mg is also an element of ATP synthase, crucial to athletic performance and neural function. Low red blood cell (RBC) Mg (<4.0 mg·dl−1) is a common clinical issue among Americans, and it has been suggested athletes need a higher reference range compared with average adults (17).

Because these and other biomarkers have a mechanistic role in brain structure and function, we set out to access the degree to which suboptimal values were present in a cohort of the top athletes derived from top 25-ranked college football teams in advance of the NFL draft.

Methods

Experimental Approach to the Problem

This observational study took place within one morning, after 24 hours of no training or alcohol consumption.

Subjects

Elite collegiate American football athletes were recruited for this study within 4 weeks of completing their collegiate careers (ages 19–23). Athletes were from Division 1 university programs, of all positions on a team, selected to participate in the NFL scouting combine, and were hopeful NFL draft picks for 2016. Per this observational study, medical history or injuries were not considered an inclusion or exclusion factor.

Each athlete received written and oral descriptions of all procedures, evaluations, time constraints, benefits, and risks, before signing the institutional review board–approved written consent form. Athletes did not receive any financial compensation for their participation. The study was approved by the Compass IRB Ethics Committee for Human Use (00729) and conformed to the Declaration of Helsinki on the use of human subjects for research.

Procedure

Athletes reported for a 12-hour fasted morning antecubital vein blood draw performed by a trained phlebotomist. All blood draws were completed between 7:00 and 9:00 am Athletes were instructed to drink water liberally throughout the fast. Four milliliters of whole blood were collected in an ethylenediamine tetraacetic acid tube and shipped overnight for RBC Omega-3 fatty acid composition analysis to OmegaQuant Analytics, LLC (Sioux Falls, SD). The Omega-3 Index is calculated as the sum of EPA and DHA, expressed as a percent of total erythrocyte fatty acids. The Trans Fat Index is a measure of trans fat percentages, which is considered a direct reflection of dietary trans fats consumed (10).

For other blood variables, approximately 30 ml of blood was taken in respective tubes and immediately processed at the local Quest Diagnostics (Madison, NJ). Analytes included a lipid panel (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, and ratios), hormones (estradiol, free testosterone, total testosterone, cortisol, DHEA, and TSH), markers of inflammation (high-sensitivity c-reactive protein and Hcy), biomarkers of insulin resistance (serum glucose, serum insulin, and glycosylated hemoglobin [HbA1c]), vitamins (vitamin B12, 25-hydroxyvitamin D, and folate), and minerals (red blood cell magnesium, calcium, ferritin, and zinc) and analytes (data not shown) used to describe liver, kidney, and thyroid function. Raw data were returned to researchers by WellnessFX (San Francisco, CA) platform. Normal clinical ranges of serum analytes were based on Quest Diagnostics values with optimized ranges for athletes provided by previous research. Glucose and insulin were used to calculate an index of insulin resistance (HOMA-IR).

Observational Variables

Anthropometrics were taken to assess body mass index: height by a tape measure attached to the wall and body mass with a floor scale, accurate to ±100 g. A trained registered dietitian assessed body composition by 7-site ultrasound measurement using the BodyMetrix Pro (IntelaMetrix, Livermore, CA).

Statistical Analyses

Thirty male athletes completed blood and anthropometrics with descriptive statistics reporting mean ± SD in the tables. The 95% confidence intervals were calculated using R and were based on a t-distribution because of smaller sample size, and effect sizes (Cohen's d) for the 5 main variable outcomes were calculated comparing our cohort with nonathletic men, aged 20–25 in the WellnessFX database. Frequency and percentage data were presented comparing the number of athletes with 0–5 of the primary suboptimal variants. Significance for statistical variables was set at p ≤ 0.05 where necessary.

Results

Of the 30 athlete cohort, 21 (70%) recently attended a Division 1 collegiate program with a full-time performance staff including athletic trainers, registered dietitians, and strength and conditioning coaches dedicated to their football program. The characteristics of these 30 elite male American football players are found in Table 1.

Table 1 - Characteristics of elite American collegiate football athletes.*
n = 30
Age (y) 22 ± 0.9
Body mass (kg) 110.7 ± 24.8
Height (cm) 188.7 ± 6.4
BMI 30.8 ± 5.5
% body fat 16.4 ± 7.5
Position (B/S/BS) 13/7/10
Signed contract with the NFL 25
*B = “big” (defensive players, linemen); S = “skill” (wide receiver, cornerback, safety, return specialist, and others); BS = “big skill” (quarterback, running back, halfback, tight end, and full back); NFL = National Football League.
Values are mean ± SD.

Omega-3 Index

The average O3 Index was 4.66% (Figure 1A, Table 3), with only one athlete above the recommended 8% optimum performance threshold (optimal performance range is considered 8–10%) (23). The effect size of our cohort compared with the nonathletic cohort (n = 94; average 4.11% ± 2.1) was d = 0.292 and was considered small to medium.

Figure 1.
Figure 1.:
Blood values of suboptimal analytes in elite American collegiate football players. The scatter plots show the individual athlete values and the dashed line represents the standard reference value. The shaded area represents the optimized reference range for athletes.

Arachidonic Acid:Eicosapentaenoic Acid

Red blood cell AA content was 17%, the RBC EPA content was 0.67%, and the arachidonic acid:eicosapentaenoic acid (AA:EPA) ratio was found to be 29.13 (standard reference AA:EPA ratio <5 and optimal performance AA:EPA ratio <3) (19) (Table 3). The effect size of this group compared with the nonathletic cohort (n = 2; average 9.8 ± 0.57) was d = 1.43 and is considered large; however, we did not have an adequate sample size to establish a reliable effect size. All 30 athletes were significantly above both of these reference ranges.

Homocysteine

Using the standard reference limit of 11 µmol·L−1, Hcy was elevated in 40% of athletes on entering this study, despite normal vitamin B12 and folate levels (Table 2). The effect size compared with nonathletes (n = 142; average 9.81 ± 4.23 µmol·L−1) was d = 0.374 and considered medium. When applying an optimal performance reference limit of 9 µmol·L−1 (15), 90% of athletes had suboptimal Hcy values.

Table 2 - Selected nutritional blood variables of the elite American collegiate football athletes.*
n = 30 95% CI
Mean SD Range
Cholesterol panel
 Total cholesterol (mg·dl−1) 156 37.1 94–254 141.90–168.57
 LDL-C (mg·dl−1) 93 32.2 47–185 80.52–104.54
 HDL-C (mg·dl−1) 50 14.7 14–76 44.30–55.30
 Triglycerides (mg·dl−1) 67 26.5 35–159 57.03–76.83
Hormones
 Estradiol (pg·dl−1) 29 7.2 15–47 26.24–31.63
 Free testosterone (pg·dl−1) 82 28.3 16–149 71.02–93.40
 Total testosterone (ng·dl−1) 511 169.4 222–972 443.55–577.56
 Cortisol (µg·dl−1) 14.6 3.3 8.3–20.4 13.36–15.83
 DHEA-S (µg·dl−1) 278 95 147–488 242.09–313.04
Inflammation
 hs-CRP (mg·L−1) 1.2 1.6 0.19–8 0.66–1.82
 Homocysteine (µmol·L−1) 11.4 3.4 8.2–27.2 10.08–12.62
Insulin resistance
 Serum glucose (mg·dl−1) 73 7.2 59–88 70.08–75.58
 Serum insulin (mIU·L1) 6.8 4.9 1.8–22.9 4.98–8.63
 HbA1c (%) 5.7 0.7 5.1–9.3 5.43–5.99
 HOMA-IR 1.33 1.2 0.39–6.28 0.90–1.80
Vitamins
 Vitamin B12 (pg·ml−1) 738 213 432–1,164 658.5–817.6
 Serum (25[OH]D) (ng·ml−1) 30 11.4 17–76 25.7–34.3
 Folate (ng·ml−1) 18 4.7 4.8–25 16.16–19.70
Minerals
 RBC magnesium (mg·dl−1) 4.1 0.8 3.2–6 3.75–4.46
 Calcium (mg·dl−1) 9.5 0.3 9–10.3 9.33–9.58
 Serum ferritin (ng·ml−1) 127 69.4 11–369 100.92–152.75
 Zinc (mcg·dl−1) 75 10.5 55–102 70.91–79.09
*LDL-C = low-density lipoprotein cholesterol; HDL-C = high-density lipoprotein cholesterol; hs-CRP =high-sensitivity c-reactive protein.

Vitamin D

Vitamin D3 (serum 25[OH] D) was below the standard reference range (>30 ng·ml−1) in 63.3% of athletes and was below the optimal performance reference range (40–60 ng·ml−1) (20) in 83.3% of athletes. The effect size of our cohort compared with nonathletes (n = 531; average 36.58 ± 16.65 ng·ml−1) was d = 0.375 and considered medium. Our cohort had one outlier (76 ng·ml−1).

Magnesium (red blood cell magnesium)

Red blood cell Mg levels were below the standard reference range (4.5–6.5 mg·dl−1) in 86% of athletes and below the loosely defined narrower, optimal performance reference range (17) (5.5–6.5 mg·dl−1) in 90% of athletes at assessment (mean = 4.1 mg·dl−1). The effect size compared with nonathletes (n = 274; average 4.85 ± 0.67 mg·dl−1) was d = 1.098 and considered large.

Other Analytes

Other nutrition-related blood variables are presented in Tables 2 and 3. On average, athletes presented with a cholesterol panel, fatty acids, and androgen hormone levels within the clinically normal range. Fourteen percent (n = 4) presented with glycosylated hemoglobin (HbA1c) at or above 5.7%; however, they had normal fasting glucose and insulin on average. Trans-fatty acids characterized less than 1% of total fatty acids on average.

Table 3 - Red blood cell phospholipid fatty acid composition of elite American collegiate football athletes.
n = 30 95% CI
Mean SD Range
Omega-3 Index (%) 4.66 1.16 2.64–8.07 4.24–5.11
Omega-3 Fatty Acids (%) 7.18 1.31 4.86–11.29 6.70–7.68
 Alpha-linolenic (18:3n3) 0.16 0.03 0.09–0.24 0.11–0.20
 Eicosapentaenoic (EPA, 20:5n3) 0.67 0.29 0.29–1.85 0.59–0.81
 Docosahexaenoic (DHA, 22:6n3) 3.99 0.94 2.18–6.22 3.58–4.32
Omega-6 fatty acids (%) 37.15 1.92 31.45–41.03 36.42–37.87
 Linoleic (18:2n6) 12.96 1.51 10.61–16.18 12.4–13.5
 Arachidonic (AA, 20:4n6) 17.08 1.33 14.22–19.45 16.6–17.6
cis-monounsaturated fatty acids (%) 14.52 1.03 12.95–16.89 14.1–14.9
Saturated fatty acids (%) 40.13 1.07 37.57–41.74 39.75–40.54
Trans fat index (%) 0.92 0.15 0.55–1.35 0.86–0.98
AA:EPA 29.13 10.78 8.2–62.1 25.10–33.20
Omega-6:Omega-3 5.35 1.06 2.8–77 4.96–5.75

Frequency of Undesirable Values

A frequency analysis of the primary variants is presented in Tables 4 and 5. Using the standard reference range, 36.7% of athletes had 3 suboptimal values, 40% had 4 suboptimal values, and 16.7% had 5 suboptimal values. Using the sport optimized reference ranges, 10% of athletes had 3 suboptimal values, 40% had 4 suboptimal values, and 50% had 5 suboptimal values.

Table 4 - Percentage of athletes with baseline levels outside the standard reference and optimal performance reference range.
Omega-3 index (%) AA:EPA Homocysteine (µmol·L−1) Vitamin D (ng·ml−1) RBC magnesium (mg·dl−1)
Total subjects (n) 30 30 30 30 21
Standard reference value 8–10 5.0 <11 30–50 4.5–6.5
% of athletes with suboptimal values, according to standard reference ranges (n) 97% (29) 100% (30) 40% (12) 73% (22) 81% (17)
Optimal performance reference value 8–10 <3.0 <9 40–60 5.5–6.5
% of athletes with suboptimal values, according to optimal performance ranges (n) 97% (29) 100% (30) 90% (27) 90% (27) 90% (19)

Table 5 - Frequency of suboptimal molecular variants.*
0 variants 1 variants 2 variants 3 variants 4 variants 5 variants
Standard reference ranges (n) 0% (0) 0% (0) 6.7% (2) 36.7% (11) 40% (12) 16.7% (5)
Optimal performance ranges (n) 0% (0) 0% (0) 0% (0) 10% (3) 40% (12) 50% (15)
*The table shows the percentage (number) of athletes presenting with 0, 1, 2, 3, 4, or 5 suboptimal baseline levels of the 5 primary analytes found in this study. This is presented for the purpose of understanding the extent of single vs. multiple converging suboptimal biomarkers. The analytes include Omega-3 Index (RBC), AA:EPA ratio, homocysteine, serum vitamin D, and RBC magnesium.

Discussion

This pilot aimed to examine the status of selected molecular biomarkers involved in metabolic networks and how that may be relevant to an impact injury in the brain in elite American football athletes chosen to compete at the NFL combine while preparing for the NFL draft. This observational design in a novel cohort while transitioning from the collegiate to professional league helped us explore the likelihood of an athlete having suboptimal nutrition or metabolic biomarkers when entering the NFL contact environment. Similarly, this study helps lay the groundwork for a precision nutrition approach to athletes in training. Beyond nutrient effects on performance and recovery, the analytes in this study can be regarded in the context of anatomical structure, biological function, and with attention to how their status may affect the trajectory of an acceleration impact to the brain, potentially providing new insights into concussion prevention and management.

The most compelling results are the frequency distribution of suboptimal values found in this cohort. Using the optimal performance ranges, there were no athletes with fewer than 2 suboptimal levels in blood values. Significantly, 10% of athletes had 3 suboptimal values, 40% had 4 suboptimal values, and 50% had 5 suboptimal values. This suggests elite, young, and small cohorts of the best athletes from a given collegiate season may be playing with multiple suboptimal values important to metabolic networks. Considering the role of related molecular networks in brain metabolism and the potentially nonlinear converging impact of multiple suboptimal levels (24), this argues the case for prophylactic molecular profiling of individual athletes. It is time to consider the roles of these molecules in brain metabolism and clinical outcomes associated with brain acceleration forces.

These athletes presented with a suboptimal O3 Index after their collegiate season. A study of German elite athletes revealed an O3 Index comparable with our cohort (4.97 ± 1.19% vs. 4.66 ± 1.16%, respectively) (23). Lewis et al. compared DHA levels in 800 active military suicide deaths with 800 controls. Suicide risk was 14% higher per std of lower DHA percentage (OR = 1.14; 95% CI, 1.02–1.27; p < 0.03). Among men, the risk of suicide death was 62% greater with low serum DHA status (adjusted OR = 1.62; 95% CI, 1.12–2.34) (16).

In our cohort, the mean AA:EPA ratio of 29.13 was substantially higher than that of average American men (16.2) and the optimum level (<3) (19). Linoleic acid contributes to the AA:EPA ratio and Omega-6:Omega-3 ratio. An a priori paired sample T-test of our data found linoleic acid was significantly lower in athletes who were drafted versus those who were not drafted (p ≤ 0.05). In a study of 170 adults, linoleic acid and arachidonic acid were 2 of 6 metabolites that could be used to differentiate between patients with TBI with and without cognitive impairment (30).

Homocysteine is an intermediate formed during methionine to cysteine metabolism and is directly dependent on 1-carbon metabolism (B12, folate, betaine, and choline). Previous studies have shown excessive Hcy causes vascular endothelium injury, facilitates smooth muscle cell proliferation, accumulates in the blood, and increases the risk of venous thrombosis (9). Also, elevated Hcy is associated with brain atrophy, silent brain infarcts, and white matter hyperintensity. Such brain vascular changes may have particular relevance to athletes competing in concussion-prone sports.

In a study examining the changes in methionine/Hcy network, severe TBI decreased methionine, S-Adenosyl methionine, betaine, and 2-methylglycine, as compared with healthy volunteers. Mild TBI also decreased methionine, α-ketobutyrate, 2-hydroxybutyrate, and glycine, although to a lesser degree than the severe TBI group. Of particular interest was a decrease in betaine, a direct methyl donor to Hcy and crucial in removing excess Hcy. This further suggests that Hcy may be implicated in TBI outcomes (7).

In our cohort, 90% showed elevated Hcy (11.4 ± 3.4, range 8.2–27.2), which is in accordance with other studies (5). Elevated Hcy is frequently associated with low B12 or folate. In our cohort, vitamin B12 and folate were clinically normal, which suggests other potential Hcy influencers. VitD binds and activates the vitD receptor, regulating cystathionine-β-synthase transcription and facilitating the conversion of Hcy to cystathionine (14). Although this relationship is known, vitD and Hcy were not correlated (r = −0.167, p = 0.376) in our cohort. Larger studies are needed to understand the full extent to which vitD influences Hcy levels in athletes.

There are little published data on vitD and TBI. However, a study of 353 adults (26.6–48.3 years) seen 0.3–56.5 months after moderate-to-severe TBI is informative. Traumatic brain injury adults with vitD deficiency had significantly lower Addenbrooke's Cognitive Examination scores, compared with those who were vitD insufficient and replete (p = 0.003 and p = 0.034, respectively). VitD deficiency was also associated with more severe depressive symptoms (11).

VitD influences athletic performance through hundreds of processes, including exercise-induced inflammation, neurological function, cardiovascular health, glucose metabolism, bone health, and skeletal muscle performance, including strength and power. In our cohort, 66% of athletes just left a school above the 37th parallel in the United States where it was the winter season, and time spent outside was likely with full gear or clothing. 97% of this study's athletes had vitD levels lower than the suggested minimum for optimal athletic performance (50 ng·ml−1) and minimal risk for CVD (60 ng·ml−1) (20).

Neuronal Mg concentrations are of central importance in the regulation of N-methyl-d-aspartate receptor excitability. N-methyl-d-aspartate receptors are essential for neuronal plasticity, excitatory synaptic transmission, and excitotoxicity, playing an important role in developmental plasticity, learning, and memory (2). Mg in the brain is crucial to function, although its role in concussion is not definitive. Research has explored Mg status preinjury (13) and the effect of Mg postinjury (1) with equivocal results. However, Mg treatments have significantly delayed ischemic infarction (29). In our cohort, Mg depletion may result from inadequate intake or following intense exercise, stress, or sweating when low plasma volume causes a cellular shift.

Research has proposed that essential and conditionally essential micronutrient deficits (or excess) aggregate and spread across molecular networks. A suboptimal biomarker can have a broad impact on normal function and response to trauma. More than one converging suboptimal biomarker level can amplify across molecular networks in ways that may not be linear (24).

This study has several limitations, including a small sample size, the absence of a control group, and intervention data, but this study design was intentionally observational to examine biomarkers in top-ranked athletes and the relationship to the health risks of the sport. Therefore, concussion status was not targeted as a covariate. Also, we examined a smaller pool of (targeted) analytes in this pilot, which limited our ability to more broadly survey molecular pathways and networks for additional physiological health risks to these athletes. However, we suggest that these data provide insight into a novel timepoint during a professional athlete's career and explore the molecular status of seemingly the best athletes from many Division 1 programs rather than from one team. Further prospective research should also consider the monitoring of concussion history and clinical practices by the multidisciplinary medical team in relation to the molecular phenotype so that more precision applications can be deployed in the future.

Practical Applications

In conclusion, molecular deficits in this cohort entering the NFL draft were common, with a significant number of athletes presenting with multiple suboptimal biomarkers, all with relevant influence on brain health and function. These data warrant consideration of early metabolic phenotyping (early in career, preseason, and in-season) and prophylactic precision nutrition countermeasures for athletes, including but not limited to those in sports where head injuries are common. Coaches, trainers, and dietitians should use these data to justify frequent and comprehensive molecular testing, necessary budgets and personnel to do so, and dietary solutions for optimizing nutrition for the safety and longevity of their athletes in sports where head injuries occur.

Acknowledgments

The authors thank Zung Vu Tran, PhD, for his statistical review of the article and Billy McCamy and Julian C. Schmidt for technical and editorial support.

Blood work was supported by WellnessFX, San Francisco, CA. The authors declare no conflict or competing interest.

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Keywords:

blood metabolites; precision nutrition; human performance; TBI

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the National Strength and Conditioning Association.