Key words
overreaching - satellite cells - myostatin - recovery - resistance-training - performance
Introduction
Anaerobic exercise increases force and power output in a volume-, intensity-, and
frequency-dependent manner [1]. According to classic Adaptation Theory, exercise stress yields a general adaptation
response that is triggered by a disruption in homeostasis [2]. Following this disruption, an increase in biological function occurs which protects
against future bouts of exercise stress [1]. Imbalances between the exercise stimulus and recovery may disrupt these adaptations
and place an athlete or trainee in a maladapted state [3]. Extended periods of intensified exercise can result in an accumulation of fatigue,
leading to short-term (overreaching) and long-term (overtraining) decrements in the
ability to perform [3]. These outcomes have been confirmed in various athlete types including rugby [4], soccer [5]
[6], basketball [7], American football [8], and triathletes [9].
Excessive exercise may contribute to disturbances in biological function through physiological
deregulation that leads to impairments in function and recovery [10]. From a molecular standpoint, this deregulation of recovery is controlled by a complex
array of myogenic regulatory factors that drive satellite cell activation, differentiation,
and replenishment [11]
[12]. Indeed, research has indicated satellite cells as the key variable driving recovery
from exercise [13]
[14]. Impairment in the recruitment of these cells ultimately leads to a failing ability
to compensate for training load-induced stress [13]
[15]. Key positive regulators of satellite cell cycle progression include myogenic differentiation
factor (MyoD) and neural cell adhesion molecule (NCAM), a multifunctional cell-surface
protein that has been found to drive muscle recovery [16]. In contrast, myostatin impairs muscle recovery by inhibiting satellite cell progression
and increasing protein breakdown [17].
Chronic molecular changes are eventually reflected in performance outcomes [16]. One of the major areas impacted during overreaching and overtraining scenarios
is explosive strength [18]. Explosive strength is the speed or rate an athlete can develop force [19]. This motor ability predicts performances of both sport-specific and functional
daily tasks to a greater degree than maximal strength, which is seldom expressed in
the time constraints of sport or daily activity [19]. Moreover, this measure has been determined to be more sensitive to detect acute
(24–72 hrs) [18] and chronic (>4 wk) [16]
[20] changes in neuromuscular function as compared to non-time constrained maximal strength
measures. In addition, overreaching leads to impaired readiness to train, which is
also termed “perceived recovery.” Perceived recovery is strongly predictive of multiple
indices of performance and biochemical related measures of recovery [21]
[22]. As such, perceived recovery is a validated tool for determining whether an individual
is recovered.
While coaches find it challenging to change the demands of a season, they may alter
their athletes’ responses through the consumption of supplements that are rich in
ingredients capable of influencing recovery. Dietitians have made a push for natural,
sustainable superfoods, which contain an array of performance aids [23]. In response, scientists have isolated a unique source of marine phytoplankton (microalga
Tetraselmis chuii), which contains highly active superoxide dismutase (SOD). Our previous research
found that marine phytoplankton was able to robustly increase intramuscular antioxidant
enzymes, lower muscle damage, and sustain anaerobic performance in a short term (3
days) repeated high-intensity competition style challenge [24]. However, the effects on long-term recovery of this natural marine-derived ingredient
remain to be investigated. Therefore, the purpose of this study was to investigate
the effects of targeted marine phytoplankton supplementation on recovery as assessed
through perceived recovery, explosive strength, and ground reaction forces that are
pertinent to anaerobic performance. While we previously reported significant long-term
improvements in antioxidant status [24], we wanted to further explore the impact of this ingredient on regenerative pathways.
Therefore, a secondary purpose of this study was to determine the effects of this
ingredient on myogenic molecular markers known to promote muscle recovery [11]
[12] using a rat model. Finally, we assessed the long-term safety of marine phytoplankton
supplementation. We hypothesized that marine phytoplankton would enhance recovery
from 5 weeks of training meant to induce non-functional overreaching, and that it
would simultaneously increase positive and decrease negative regulators of satellite
cell progression, respectively. We also hypothesized that the ingredient would be
safe for human consumption.
Materials and Methods
Human Trial
Subjects
Male and female subjects were recruited by word of mouth, email contact, and direct
website inquiries from online advertisement. Subjects were excluded from the study
if they: had a body mass index (BMI) ≥ 30 kg/m2, have allergies to fish, shellfish, algae, or seaweed; have any cardiovascular, metabolic,
or endocrine disease; have undergone surgery that affects digestion and absorption,
smoke, drink heavily (>7 and>14 drinks per week for women and men, respectively),
were pregnant or planning to be pregnant, were on medication to regulate blood glucose,
lipids, and/or blood pressure; have used anabolic-androgenic steroids, were currently
using antioxidant supplements, non-steroidal anti-inflammatory drugs, or nutritional
supplements known to stimulate recovery or muscle mass accretion. For inclusion, subjects
were required to be between 18–45 years old and have continuously exercised for the
past year a minimum of 3 days week-1, achieving 30 min of vigorous activity (≥75% age-predicted maximum heart rate) per
session.
To determine sample size for the study, an a priori power analysis (G*Power, 3.0.10, Universität, Germany) was carried out with the given
α, power and effect size values. The test family was set as F-test and the statistical
test was set as a repeated-measures analysis of variance (ANOVA) inputting the following
parameters: α=0.05; 1-β=0.8; effect size=0.5; number of groups=2; repetitions=4. In
our previous investigation on the same marine phytoplankton ingredient (microalga
Tetraselmis chuii ) , we observed an effect size of 0.54 and 0.65 on performance metrics in the countermovement
and squat jump, respectively [24]. Thus, we believe the effect size estimation of 0.5 for the power analysis was practical.
The resulting outcome parameters indicated a total sample size of 22, equating to
11 subjects per group. In total, 26 subjects enrolled for participation in the study.
Three subjects were lost, due to unforeseen time constraints and one due to a family
bereavement prior to completion of the study. Therefore, descriptive statistics were
reported for the 22 subjects that completed the trial ([Table 1]). Prior to engaging in any study procedures, subjects signed a written informed
consent for participation that was approved by an Institutional Review Board (IntegReview,
Austin, TX) and in agreement with the Declaration of Helsinki. The study is in accordance
with the ethical standards of the International Journal of Sports Medicine [25].
Table 1 Baseline descriptive statistics.
|
Variable
|
MP (n=12; m=6, f=6)
|
PLA (n=10; m=5, f=5)
|
Total (N=22)
|
p-value
|
|
Age (yrs)
|
29.2±2.1
|
29.2±2.0
|
29.2±1.5
|
0.981
|
|
Height (cm)
|
173.7±3.4
|
173.6±2.5
|
173.6±2.1
|
0.989
|
|
Body Mass (kg)
|
74.0±5.4
|
78.9±4.9
|
76.1±3.7
|
0.522
|
|
Fat-Free Mass (kg)
|
55.4±4.5
|
59.4±4.2
|
57.2±3.1
|
0.536
|
|
Fat Mass (kg)
|
18.6±1.9
|
19.5±1.9
|
19.0±1.3
|
0.737
|
|
Body Fat (%)
|
25.4±2.1
|
24.8±2.2
|
25.1±1.5
|
0.850
|
|
BMI (kg/m2)
|
24.2±1.0
|
26.0±1.2
|
25.0±1.0
|
0.278
|
BMI=Body mass index, p-value=Probability value for unpaired, two-tailed t-test. Data are mean±standard error of the mean.
Study design
This study was carried out in a randomized, double-blind, placebo-controlled, parallel
manner. Prior to allocation into conditions, subjects were assessed for body composition
via whole-body, dual-energy x-ray absorptiometry (DXA) scan. Subjects were then classified
into quartiles according to fat-free mass, and subjects from each quartile were randomly
assigned to conditions. Following condition allocation, subjects underwent baseline
testing on day 0 (Pre) which included: blood sample donation, force plate countermovement
jump, and perceptual measures of the Perceived Recovery Status scale. Force plate
countermovement jump assessments were subsequently performed upon subject arrival
to the laboratory for the last training session of week 2 (Wk2-Post) and week 5 (Wk5-Post),
and the final day of week 6 (Post). Blood samples were donated again at Post. The
Perceived Recovery Status scale was assessed on each training day throughout the study
and averaged for the week.
Dual-Energy X-ray absorptiometry
Upon arriving to the laboratory following an overnight fast (~10 h), subjects were
instructed to void to eliminate any flatus in the gastrointestinal tract and urine
in the bladder. Thereafter, body mass to the nearest 0.1kg and height to the nearest
cm were measured with a digital scale and stadiometer, respectively (Seca, Chino,
CA). A whole-body scan on a dual-energy X-ray absorptiometry device (Horizon A DXA
System, Hologic Inc, Marlborough, MA) was performed with the subject lying in a supine
position with knees and elbows extended. Subjects were instructed not to move for
the entire duration of the scan (approximately 5 min). The DXA has a switching-pulse
system that rapidly alternates the voltage of the X-ray generator, producing two beams
of high and low energies. The attenuated X-rays that have passed through the subject
are measured sequentially with a detector situated on the scanning arm above the patient.
An internal wheel corrects for any small fluctuations caused by this method of beam
generation. Results from each scan were uploaded and accessed on a computer that was
directly linked to the DXA device. Calibration of the DXA device was done against
a phantom provided by the manufacturing company prior to testing.
Supplement protocol
Following random assignment, subjects were given either marine phytoplankton (MP)
(Tetraselmis chuii; Oceanix™, Lonza Consumer Health Inc; Morristown, NJ, USA) or microcrystalline cellulose-based
placebo (PLA). [Table 2] provides nutritional information for the Tetraselmis chuii product used in the study, which was also independently examined by Brunswick Laboratories
(Southborough, MA, USA) for Oxygen Radical Absorbance Capacity (ORAC) expressed in
micromole Trolox equivalency (µmole TE) per gram. The results indicated that values
were high for hydroxyl radicals at 178.71 µmole TE/gram and super oxide anions at
348.11 µmole TE/gram, moderate in peroxynitrite and peroxyl radicals at 8.65 and 29.65
µmole TE/gram, respectively, and not detectable in singlet oxygen and hypochlorite.
The ORAC values for the super oxide anion corresponded with high total values (38 000
IU per 100 grams) of SOD in raw powder measured. Conditions were stored in visually
identical capsules and containers. Subjects were required to consume one serving (25mg)
a day, either 30 min prior to exercise or with the first meal of the day on non-exercise
days. Supplement compliance was assessed by supplement logs and collection of supplement
containers. Subjects were instructed to refrain from consuming any nutritional supplements
for the duration of the study.
Table 2 Nutrient profile for microalga Tetraselmis chuii.
|
Variable
|
Value
|
|
Kcal·g-1
|
3.37
|
|
Fat (%)
|
8.0
|
|
Carbohydrate (%)
|
52.0
|
|
Protein (%)
|
40.0
|
|
Vitamin E (µg/g)
|
520
|
|
Vitamin C (µg/g)
|
3333
|
|
Potassium (mg/g)
|
10.4
|
|
Magnesium (mg/g)
|
5.06
|
|
Calcium (mg/g)
|
33.8
|
Nutritional data per gram of microalga Tetraselmis chuii.
Resistance training protocol
All subjects completed a 5-week resistance training program. Resistance training sessions
were completed 3d · wk-1 during weeks 1,3, and 4 while week 2 and week 5 increased to 5d · wk-1 . Week 1 was a baseline maintenance training week where subjects did not perform
at repetition maximum loads. Weeks 2 and 5 were programmed to induce non-functional
overreaching (124 sets·week-1). Weeks 3 and 4 consisted of moderate volume training. All sets performed during
weeks 2–5 were performed with repetition maximum loads such that sets were either
performed to or near muscular failure. Whenever subjects missed the targeted repetition
count by 2 or more repetitions, the load was decreased by 5%; similarly, whenever
subjects surpassed the targeted repetition count by 2 or more, the load was increased
by 5%. Immediately following the completion of all resistance training sessions, subjects
completed a cool down consisting of static stretching of the major muscle groups targeted
within the resistance training session. All stretches were repeated twice and held
for 20 s each. All warm-up, resistance training, and cool down sessions were supervised
by a certified strength and conditioning specialist (NSCA-CSCS) who also monitored
training loads for each exercise [26]. Whenever five or more subjects were on the training floor at once, two certified
strength and conditioning specialists were supervising the training floor. A maximum
of ten subjects were allowed on the training floor. A detailed description of the
resistance training program is provided in [Table 3]. All training sessions were completed with at least 24-h recovery from the previous
training session.
Table 3 Resistance training protocol.
|
Protocol for Weeks 1, 3, & 4
|
|
Monday
|
Tuesday
|
Wednesday
|
|
|
Thursday
|
Friday
|
|
Warm-Up
|
Sets
|
Reps
|
OFF
|
Warm-Up
|
Sets
|
Reps
|
OFF
|
Warm-Up
|
Sets
|
Reps
|
|
BW Glute Bridge
|
2
|
12E
|
Arm Swings
|
2
|
6E
|
Hip Circles
|
2
|
10E
|
|
Infant Squats
|
2
|
8
|
Wall Lateral Reach
|
2
|
8
|
TRX Sumo Squat
|
2
|
10
|
|
Hip Circles
|
2
|
10E
|
Scap Walk
|
2
|
5E
|
High Low KB Squat
|
2
|
5
|
|
Forward/Lateral Leg Swings
|
2
|
10E
|
Trigger Point Chest
|
2
|
N/A
|
KB Swings
|
2
|
5
|
|
Exercise
|
Sets
|
Reps
|
Exercise
|
Sets
|
Reps
|
Exercise
|
Sets
|
Reps
|
|
Trap Bar Deadlift
|
3
|
10/8/6
|
A-Incline DB Press
|
3
|
10
|
A-Squat Trushers
|
3
|
10
|
|
Goblet Squats
|
4
|
12
|
B-Close Grip Lat Pulldown
|
3
|
10
|
B-Battle Rope
|
3
|
30s
|
|
Walking Lunges
|
3
|
12E
|
DB OHP
|
3
|
10/8/6
|
C-Russian Twist
|
3
|
10E
|
|
TRX Ham Curl
|
3
|
12
|
A-DB Chest Fly
|
3
|
12
|
A-KB Swings
|
3
|
12
|
|
Sled Push
|
3
|
60m
|
B-Reverse DB Fly
|
3
|
12
|
B-Med Ball Slam
|
3
|
12
|
|
|
|
Cable Row
|
3
|
10
|
C-Walking Plank
|
3
|
30s
|
|
|
|
A-Bilateral DB Bicep Curl
|
3
|
12E
|
|
|
|
|
|
|
B-TRX Tricp Ext
|
3
|
12
|
|
|
|
|
Protocol for Weeks 2 & 5
|
|
Monday
|
Tuesday
|
Wednesday
|
Thursday
|
Friday
|
|
Warm-Up
|
Sets
|
Reps
|
Warm-Up
|
Sets
|
Reps
|
Warm-Up
|
Sets
|
Reps
|
Warm-Up
|
Sets
|
Reps
|
Warm-Up
|
Sets
|
Reps
|
|
Frog Stretch
|
2
|
8
|
Arm Swings
|
2
|
6E
|
Hip Circles
|
2
|
10E
|
T-Spine Rotation
|
2
|
6E
|
Hip Circles
|
2
|
10E
|
|
Infant Squats
|
2
|
8
|
Wall Lateral Reach
|
2
|
8
|
TRX Sumo Rock
|
2
|
10
|
Mini Band WallScap Walk
|
2
|
5E
|
Frog Stretch
|
2
|
8
|
|
Glute Activation
|
2
|
12E
|
Scap Walk
|
2
|
5E
|
High Low KB Squat
|
2
|
5
|
Yoga Push Up
|
2
|
5
|
Forward/Lateral Leg Swings
|
2
|
10E
|
|
Forward/Lateral Leg Swings
|
2
|
10E
|
Trigger Point Chest
|
2
|
N/A
|
KB Swings
|
2
|
5
|
Plate Complex
|
2
|
8E
|
KB Swings
|
2
|
5
|
|
Exercise
|
Sets
|
Reps
|
Exercise
|
Sets
|
Reps
|
Exercise
|
Sets
|
Reps
|
Exercise
|
Sets
|
Reps
|
Exercise
|
Sets
|
Reps
|
|
A-Back Squat
|
3
|
10
|
A-Bench Press
|
3
|
10
|
Watt Bike Sprints
|
3
|
30s
|
A-Sorinex Row
|
3
|
10
|
A-Banded Sprints
|
3
|
30m
|
|
B-Sled Pull
|
3
|
30s
|
B-Isometric Pullup
|
3
|
20s
|
Romanian Deadlift
|
3
|
10/8/6
|
B-Med Ball Chest Pass
|
3
|
25
|
B-Banded Back Pedal
|
3
|
30m
|
|
Bulgarian Split Squats
|
4
|
15E
|
Incline DB Press
|
3
|
12
|
Glute-Ham Raise
|
3
|
AMRAP
|
Barbell Jumping Jacks
|
3
|
20s
|
A-Reverse DB Lunge
|
3
|
12E
|
|
Runner's Jump
|
3
|
12E
|
A-Bent Over Row
|
3
|
12
|
BB Glute Bridge
|
3
|
10
|
A-Sled Push
|
3
|
90s
|
B-Box Jumps
|
3
|
10
|
|
Walking Lunges
|
3
|
15E
|
B-Pull ups
|
3
|
AMRAP
|
TRX Ham Curl
|
3
|
12
|
B-Sled Row
|
3
|
90s
|
A-Front Loaded Step ups
|
3
|
10E
|
|
Sled Push
|
3
|
90s
|
C-Push ups
|
3
|
AMRAP
|
Row Machine
|
3
|
250m
|
Alternating DB Press
|
3
|
12E
|
B-SL Lat Plate Push
|
3
|
12E
|
|
|
|
A-DB Lateral Raise
|
3
|
10/8/6
|
|
|
|
Banded DB Row
|
3
|
20s
|
A-Banded Sprints
|
3
|
30m
|
|
|
|
B-DB Front Raise
|
3
|
10/8/6
|
|
|
|
A-Pull ups
|
3
|
AMRAP
|
B-Banded Back Pedal
|
3
|
30m
|
|
|
|
C-DB Reverse Fly
|
3
|
10/8/6
|
|
|
|
B-Push ups
|
3
|
AMRAP
|
|
|
|
|
|
|
Farmer's Carry
|
3
|
90s
|
|
|
|
|
|
|
|
|
|
|
|
|
A-Fat Bar Curls
|
3
|
15
|
|
|
|
|
|
|
|
|
|
|
|
|
B-Band Tricep Ext
|
3
|
15
|
|
|
|
|
|
|
|
|
|
Abbreviations: AMRAP=as many reps as possible; BB=barbell; DB=dumbbell; E=each side; Ext=Extension;
KB=kettle bell; m=meters; OHP=overhead press; s=seconds; SL=single leg. Exercises
marked with 'A' and 'B' indicate that the exercises are performed in a superset fashion.
Exercises marked with 'A', 'B', and 'C' indicate that the exercises are performed
as a tri-set.
Perceived recovery
Subject responses for perceived recovery were collected using a Perceived Recovery
Status (PRS) scale in a manner like Laurent and colleagues [22]. The PRS consists of a scalar representation numbering from 0–10. One the scale,
responses of 0–2 are “very poorly recovered” with “expected decline in performance”,
4–6 are “low to moderately recovered” and “expected similar performance”, and 8–10
represent high perceived recovery with “expected increases in performance”. Subjects
PRS were recorded following the dynamic warm-up and prior to each training session.
Data are reported as weekly averages. Because week one of this study was considered
maintenance training, perceived recovery was expressed relative to this week for all
other weeks.
Force Plate Countermovement Jump (CMJ)
Prior to performing a CMJ, subjects completed a warm-up of 10 bodyweight squats and
two submaximal effort CMJs. Subjects were instructed to stand in a comfortable and
upright position with the feet about shoulder-width apart and parallel to each other.
Subjects then performed a countermovement by flexing the hips and knees. Once subjects
reached a preferred countermovement depth, they explosively extended their hip, knee,
and ankle joints to perform a maximal vertical jump. All CMJs were performed with
an arm swing. Subjects performed 2 maximal effort CMJs in this manner separated by
30s rest. The jump that resulted in the greatest jump height (determined as center
of mass displacement) was used for statistical analysis.
The force platform has a length of 66 cm, a width of 66 cm, and a height of 7 cm.
The platform was composed of two symmetrical force plates that separate the platform
into left and right halves. Each plate contained four strain gauge force sensors (the
whole platform thus had eight force sensors). The force sensors measured the magnitude
and rate of vertical ground reaction force development exerted on the platform. The
sensors were connected to a laptop computer via a USB 2.0 connection. The signals
from the force sensors were sampled at a frequency of 800 Hz and analyzed using the
Mechanography GRFP Research Edition® software (in this study version 4.4b01.62 was
used).
Blood sampling
Venous blood was extracted by venipuncture of the antecubital vein using a 21-gauge
draw needle and collected into a 10mL EDTA tube (BD Vacutainer®, Becton, Dickinson
and Company, Franklin Lakes, NJ) by a certified phlebotomist. Afterward, blood samples
were centrifuged at 2500 rpm for 10 min at 4°C. Resulting serum and plasma samples
were then aliquoted and stored at –80°C until analysis. From these samples, the following
markers were quantified: general chemistry panel, complete blood count, and automated
differential cell count.
Statistical analysis
Prior to performing statistical analysis, normality was assessed for all dependent
variables via Shapiro-Wilk testing and for outliers using visual inspection of box
blots. Data passed normality and no outliers were detected. Following outlier and
normality testing, dependent variables were scrutinized using a two-way mixed analysis
of variance (ANOVA) with condition as the “between-group” factor time as the “within-group”
factor and subjects as a random factor. Whenever a significant F-value was obtained,
post-hoc testing was performed with a Bonferroni correction for multiple comparisons.
For ANOVA procedures, homogeneity of variances and covariances were confirmed by Levene’s
test and Box’s M test, respectively. Additionally, Mauchly’s test of sphericity was
used to test the assumption of sphericity for two-way interactions. In addition, select
variables were expressed as a percentage (100* Time2 /Time1) and analyzed using two-tailed, unpaired t-test for between group comparisons. Baseline characteristics were compared between
groups with two-tailed, unpaired t-test ([Table 1]). Statistical significance was set a priori p≤0.05 and data are reported as mean±standard error. Data were analyzed using SPSS
software (IBM SPSS, Version 26.0; SPSS, Inc., Chicago, IL, USA) and GraphPad Prism
8 software (GraphPad Software, Inc., La Jolla, CA, USA).
Rat Trial
Rats and protocol
Male Wistar albino rats (n=28, 8 weeks old) were provided from the Laboratory Animal
Research Center, Firat University (Elazig, Turkey). The animals were kept at a room
with standard conditions (22±2°C temperature, 55±5% humidity, a 12-h light–12-h dark
cycle). The ethical permission of the experiment was obtained from the Animal Experimentation
Ethics Committee of Firat University (2019/139–206) according to the relevant laws,
guidelines, and restrictions.
Rats were randomly divided into four groups (n=7): (i) Control (no exercise and placebo),
(ii) Exercise (E), (iii) Exercise+marine phytoplankton 1 (2.55 mg·day−1, [E+MP1]), (iv) Exercise+marine phytoplankton 2 (5.1 mg·day−1 [E+MP2]). Marine phytoplankton and placebo (physiological saline) were administered
orally via gavage every day before exercise during the experiment period (6 weeks).
The rats were subjected to treadmill exercise on a motorized rodent treadmill (Commat
Limited, Ankara, Turkey). The treadmill contained a stimulus grid at the back end
of the treadmill giving an electric shock when the animal placed its paw on the grid.
The apparatus had a 5-lane animal exerciser utilizing a single belt unit divided with
walls suspended over the tread surface. In order to eliminate the diurnal variations,
all exercise tests were applied during the same time of the day. A week of adaptation
was provided as pre-training practice for the animals in order to get familiar with
the treadmill equipment and handling. In doing so, the rats in the exercise training
groups were accustomed by treadmill exercise over 5 days such that: (i) 1st day, 10
m·min−1, 10 min, (ii) 2nd day, 20 m·min−1, 10 min, (iii) 3rd day, 25 m·min−1, 10 min, (iv) 4th day, 25 m·min−1, 20 min and (v) 5th day, 25 m·min−1, 30 min. Upon an adaptation of a week to the treadmill system for the novel and stress
impacts, the rats in treadmill exercise groups ran on the treadmill at 25 m · min−1, 45 min · d−1 five days per week for 6 weeks according to the protocol described by Liu et al.
[27].
At the end of the study, animals in all groups were sacrificed by cervical dislocation
under xylazine (10 mg·kg−1, i.m.) and ketamine (50 mg·kg−1, i.m.) anesthesia on the same day; thereafter, blood and gastrocnemius muscle were
collected. Serum samples were obtained by taking blood samples to gel biochemical
tubes after centrifugation (5000 rpm at 4°C for 10 min). Serum creatine kinase concentrations
were assayed using a portable automated chemistry analyzer (Samsung LABGEO PT10, Samsung
Electronics Co., Suwon, Korea). Samples of the gastrocnemius muscle (taken from approximately
the same location each time) were quickly removed, placed on ice, and kept at –80°C
until further analysis.
Western blot analysis
Muscle samples were pooled and homogenized in 1 ml ice-cold hypotonic buffer A including
10 mM HEPES (pH 7.8), 10 mM KCl, 2 mM MgCl2, 1 mM DTT, 0.1 mM EDTA, and 0.1 mM phenylmethylsulfonyl-fluoride
(PMSF). Eighty µl of 10% Nonidet P-40 (NP-40) solution was added to the homogenates
and the mixture then was implemented with centrifugation for 2 h at 14 000 g. 500 µl of buffer A plus 40 µl of 10% NP-40 was used to wash the precipitates containing
nuclei. The precipitates were then centrifuged and resuspended in 200 µl of buffer
C [50 mM HEPES (pH 7.8), 50 mM KCl, 300 mM NaCl, 0.1 mM EDTA, 1 mM DTT, 0.1 mM PMSF,
20% glycerol], and centrifuged for 30 min at 14.800 g. The supernatant was removed to new tubes. Western blot analyses were run on the
tissue homogenates for MyoD and NCAM for positive regulators of satellite cell progression,
and myostatin as a negative regulator of satellite cells and its downstream targets,
muscle atrophy F-box (MAFbx) and muscle RING finger 1 (MuRF-1), which increase protein
breakdown. Protein concentration was measured using the Lowry method. A pool of tissue
samples was created with the same amounts of protein (50 µg) and the samples were
electrophoresed (12% SDS-PAGE gels) followed by transfer to nitrocellulose membrane
(Schleicher and Schuell Inc., Keene, NH, USA). The primary antibody for beta-actin
was delivered (Abcam Inc., UK) and reduced in strength (1:1000) in the same buffer
containing 0.05 % Tween-20. The antibody with the nitrocellulose membrane was incubated
overnight at 4 °C. After washing, the blots were incubated with goat anti-mouse IgG
(horseradish peroxidase-conjugated secondary antibody) with a dilution of 1:5000 (Abcam,
Cambridge, UK). Protein bands were quantified via scanning densitometry using an image
analysis system (Image J; National Institute of Health, Bethesda, MD, USA). The protein
bands were normalized by the corresponding β-actin band values and compared with the
control group.
Statistical analysis
Data were presented as the mean±SEM. All tests were performed with the SPSS software
program (IBM SPSS, Version 22.0; Chicago, IL, USA). Significance was determined by
one-way ANOVA. Whenever a significant F-value was obtained, the Tukey HSD post-hoc
test was used for multiple comparisons. Statistical significance for data was previously
defined as p<0.05.
Results
Human Trial
Perceived Recovery Status Scale (PRS)
Analysis of Perceived Recovery Status (PRS) scale changes relative to Wk1 levels indicated
significant within-group differences for MP whereby PRS at Wk6 (p<0.05, ∆+1.1, + 16%
was greater than Wk2). Additionally, relative change in PRS from Wk1 to Wk6 was significantly
greater for MP at compared to PLA (p<0.05; MP: + 1.1±0.4 a.u., + 16%; PLA: –0.5±0.9
a.u, –6%). These results are shown in [Fig. 1].
Fig. 1 PRS Change Relative to Wk1. Changes in Perceived Recovery Status scale relative to
Wk1 (Time2 – Wk1). Red line indicates mean value. *= Significantly greater than Week 2 (p<0.05).
^=Significantly different between groups (p<0.05).
Force Plate CMJ
Significant group by time interactions were detected for both peak and mean explosive
strength (RFD). Post-hoc analysis indicated that both groups experienced decreased
peak and mean RFD at Wk2-Post and Wk5-Post (p<0.05) compared to Pre. However, PLA
remained decreased at Post (p<0.05) while MP had returned to Pre levels (98.0% vs.
PLA: 67.7%, [Fig. 2]). Furthermore, peak and mean RFD at Post was significantly different between groups
(p<0.05). A significant main effect of time was demonstrated for peak force (p<0.05).
Post-hoc testing revealed that levels at Wk2-Post were significantly lower than Post
(p<0.05). The relative Pre to Post change for Hmax/Hmin was significantly different between groups (p<0.05, MP: +11.3, PLA: -16.1). No significant
between- or within-group differences were detected for jump displacement (p>0.05,
[Table 4]).
Fig. 2 Explosive Strength Measured as Mean RFD (kN·s-1). Percentage of Pre-Test value at subsequent timepoints determined as (100 * (Time2/Pre)). *,**=significantly lower than Pre (p<0.05, p<0.01). ^=significantly different
between groups (p<0.05).
Table 4 Ground reaction force parameters during the countermovement jump.
|
Pre
|
Wk2-Post
|
Wk5-Post
|
Post
|
|
Peak Force (N)
|
|
|
|
|
|
MP
|
1674±145
|
1578±150
|
1632±148
|
1725±172
|
|
PLA
|
1775±139
|
1691±145
|
1737±139
|
1736±142
|
|
Peak RFD (N·s
-1
)
|
|
|
|
|
|
MP
|
8718±935
|
6759±720*
|
6300±886*
|
8565±1282^
|
|
PLA
|
8973±1664
|
7123±1607*
|
5047±719**
|
6030±934*
|
|
Mean RFD (N·s
-1
)
|
|
|
|
|
|
MP
|
3970±412
|
3139±322*
|
2929±386*
|
3890±543^
|
|
PLA
|
4036±586
|
3161±644*
|
2318±294**
|
2731±384*
|
|
Jump Displacement (cm)
|
|
|
|
|
|
MP
|
44.3±3.3
|
44.1±3.0
|
43.4±3.5
|
44.5±3.6
|
|
PLA
|
45.5±4.7
|
45.4±4.1
|
45.3±4.0
|
45.7±4.4
|
|
H
max
/H
min
(%)
|
|
|
|
|
|
MP
|
168.6±11.6
|
151.5±7.9
|
149.6±8.9
|
179.9±17.7#
|
|
PLA
|
169.9±13.4
|
158.7±15.4
|
161.1±14.2
|
153.8±12.4
|
Peak RFD=Peak rate of force development during take-off phase. Mean RFD=average rate
of force development during take-off phase. Hmax/Hmin =Relation between jumping height and extent of countermovement. ANOVA with Bonferroni
post-hoc: *,**=Significantly different from Pre (p<0.05, p<0.01), ^=Significantly
different between groups (p<0.05). Unpaired t-test: #=Pre to Post change significantly different between groups (p<0.05). Data
are mean±standard error of the mean.
Blood safety parameters
Blood chemistry and hematology analyses were performed at Pre and at the conclusion
of the study. Results for comprehensive metabolic panel, complete blood counts, and
automated differential cell count are shown in [Tables 5]
[6], and [7], respectively. No statistically or clinically significant changes were detected
in any blood chemistry or hematological variable (p>0.05). Additionally, no adverse
effects due to supplementation were reported in this study.
Table 5 Comprehensive metabolic panel.
|
MP
|
PLA
|
|
|
PRE
|
POST
|
PRE
|
POST
|
p-value
|
|
Glucose (mg/dL)
|
90.3±1.9
|
94.3±3.6
|
89.3±2.7
|
91.3±3.4
|
0.672
|
|
BUN (mg/dL)
|
14.8±1.1
|
15.1±1.9
|
13.3±1.1
|
13.8±1.2
|
0.902
|
|
Creatinine (mg/dL)
|
0.9±0.1
|
1.0±0.1
|
0.9±0.0
|
0.9±0.1
|
0.189
|
|
Sodium (mmol/L)
|
141.0±0.5
|
141.4±0.4
|
141.8±0.6
|
141.5±0.9
|
0.316
|
|
Potassium (mmol/L)
|
4.2±0.1
|
4.3±0.1
|
4.3±0.0
|
4.6±0.1
|
0.405
|
|
Chloride (mmol/L)
|
104.9±0.6
|
105.1±0.5
|
105.2±0.7
|
105.2±0.4
|
0.851
|
|
CO
2
(mmol/L)
|
28.2±0.6
|
27.5±0.5
|
28.7±0.5
|
28.9±0.3
|
0.207
|
|
Calcium (mg/dL)
|
9.3±0.1
|
9.0±0.1
|
9.2±0.2
|
9.2±0.1
|
0.323
|
|
Total Protein (g/dL)
|
7.2±0.1
|
6.9±0.1
|
7.3±0.2
|
7.1±0.1
|
0.781
|
|
Albumin (g/dL)
|
4.3±0.1
|
4.4±0.1
|
4.4±0.1
|
4.4±0.1
|
0.990
|
|
Globulin (g/dL)
|
2.9±0.1
|
2.6±0.1
|
2.9±0.1
|
2.7±0.1
|
0.586
|
|
Bilirubin (mg/dL)
|
0.7±0.1
|
0.7±0.1
|
0.9±0.2
|
0.8±0.2
|
0.541
|
|
Alkaline Phosphate (IU/L)
|
59.3±4.3
|
61.4±3.5
|
64.4±6.8
|
68.9±8.4
|
0.494
|
|
ALT (IU/L)
|
22.6±3.9
|
26.2±4.4
|
25.0±2.6
|
26.7±4.0
|
0.569
|
|
AST (IU/L)
|
25±2.5
|
26.5±2
|
27.3±3.2
|
26.3±2.4
|
0.304
|
|
Albumin: Globulin
|
1.5±0.1
|
1.7±0.1
|
1.5±0.1
|
1.7±0.1
|
0.759
|
|
BUN: Creatinine
|
17.4±0.8
|
15.1±0.9
|
15.1±1.0
|
15.2±1.2
|
0.071
|
BUN=Blood urea nitrogen, CO2=Carbon dioxide, ALT=Alanine aminotransferase, AST=Aspartate aminotransferase, p-value=Group
by time probability value from two-way mixed model ANOVA. Data are mean±standard error
of the mean.
Table 6 Complete blood count.
|
MP
|
PLA
|
|
|
PRE
|
POST
|
PRE
|
POST
|
p-value
|
|
WBC (K/uL)
|
4.6±0.4
|
4.6±0.3
|
4.3±0.3
|
4.5±0.4
|
0.405
|
|
RBC (M/uL)
|
4.5±0.2
|
4.4±0.2
|
4.5±0.1
|
4.5±0.1
|
0.595
|
|
Hemoglobin (g/dL)
|
13.4±0.7
|
13.2±.07
|
14±0.3
|
13.9±0.4
|
0.833
|
|
Hematocrit (%)
|
43.3±1.8
|
40.7±1.8
|
45.0±0.9
|
43.1±1.0
|
0.561
|
|
MCV (fl)
|
95.8±2.8
|
91.8±2.5
|
99.2±1.7
|
95.4±1.7
|
0.793
|
|
MCH (pg)
|
29.6±1.1
|
29.7±1.1
|
30.9±0.7
|
30.8±0.7
|
0.328
|
|
MCHC (g/dL)
|
30.9±0.4
|
32.2±0.4
|
31.2±0.2
|
32.2±0.2
|
0.244
|
|
RDW (%)
|
16±0.6
|
15.1±0.6
|
15.1±0.2
|
14.2±0.2
|
0.823
|
|
Platelet Count (k/uL)
|
238.8±21.0
|
223.4±19.8
|
218.1±7.8
|
228.3±9.0
|
0.056
|
|
MPV (fl)
|
9.0±0.4
|
9.4±0.4
|
8.6±0.2
|
9.0±0.3
|
0.932
|
WBC=White blood cells, RBC=Red blood cells, MCV=Mean corpuscular volume , MCH=Mean
corpuscular hemoglobin , MCHC=Mean corpuscular hemoglobin concentration , RDW=Red
cell distribution width, MPV=Mean platelet volume, p-value=Group by time probability
value from two-way mixed model ANOVA. Data are mean±standard error of the mean.
Table 7 Automated differential cell count.
|
MP
|
PLA
|
|
|
PRE
|
POST
|
PRE
|
POST
|
p-value
|
|
Granulocyte (%)
|
56.4±2.7
|
55.0±2.4
|
52.9±2.0
|
51.6±2.1
|
0.733
|
|
Lymphocyte (%)
|
35.3±2.0
|
34.7±2.0
|
37.1±1.8
|
36.9±2.2
|
0.653
|
|
Monocyte (%)
|
5.7±0.7
|
7.6±0.5
|
5.9±0.3
|
7.1±0.4
|
0.479
|
|
Eosinophil (%)
|
2.2±0.5
|
2.2±0.8
|
3.6±0.7
|
4.0±0.7
|
0.389
|
|
Basophil (%)
|
0.5±0.1
|
0.5±0.1
|
0.5±0.1
|
0.5±0.1
|
0.867
|
|
Granulocyte # (K/uL)
|
2.7±0.3
|
2.5±0.2
|
2.3±0.2
|
2.4±0.3
|
0.429
|
|
Lymphocyte # (K/uL)
|
1.6±0.1
|
1.5±0.1
|
1.6±0.1
|
1.6±0.1
|
0.404
|
|
Monocyte # (K/uL)
|
0.2±0.0
|
0.3±0.0
|
0.3±0.0
|
0.3±0.0
|
0.389
|
|
Eosinophil # (K/uL)
|
0.1±0.0
|
0.1±0.0
|
0.2±0.0
|
0.2±0.0
|
0.478
|
|
Basophil # (K/uL)
|
0.0±0.0
|
0.0±0.0
|
0.0±0.0
|
0.0±0.0
|
n/a
|
p-value=group by time probability value from two-way mixed model ANOVA. Data are mean±standard
error of the mean.
Rat Trial
Expression of proteins driving skeletal muscle repair and breakdown
For the exercise arm and both supplement arms, MAFbx expression was significantly
lower than control ([Fig. 3a]). However, MAFbx expression in both supplement arms was lower than the exercise
arm, and E+MP2 was lower than E+MP1 (p<0.05). For the exercise arm and both supplement
arms, MURF-1 expression was significantly depressed compared to control ([Fig. 3b]). Additionally, MURF-1 expression in both supplement arms was lower than the exercise
arm (p<0.05). MyoD expression was significantly increased in the exercise arm and
both supplement arms compared to control (p<0.05, [Fig. 3c]). Moreover, E+MP1 had greater MyoD expression than the exercise and E+MP2 arms (p<0.05).
For the exercise and both supplement arms, myostatin expression was significantly
lower than control (p<0.05, [Fig. 3b]). Additionally, E+MP2 demonstrated lower myostatin expression than the exercise
arm (p<0.05, [Fig. 3c]). Expression of NCAM was greater in both supplement arms than the exercise and control
arms (p<0.05, [Fig. 3d]).
Fig. 3 Effects of Marine Phytoplankton on muscle MAFbx a, MuRF-1 b, MyoD c, Myostatin d, and NCAM e levels in treadmill running rats. The densitometric analysis of the relative intensity
according to the control group of the western blot bands was performed with β-actin
normalization to ensure equal protein loading. The error bars above the lines point
out the standard deviation of the mean. Different symbols (a-d) indicate statistical
differences among the groups (ANOVA and Turkey's post-hoc test; p<0.05). MAFbx=Muscle atrophy F-box; MuRF-1=Muscle RING-finger protein-1; MyoD=Myogenic
differentiation factor; NCAM=Neural cell adhesion molecules.
Serum Creatine Kinase (CK) concentration
All conditions demonstrated greater CK concentration compared to control (p<0.05,
[Fig. 4]) while both supplement arms demonstrated lower CK levels compared to the exercise
arm (p<0.05). Additionally, CK concentration was significantly lower in E+MP2 compared
to E+MP1 (p<0.05).
Fig. 4 Serum Creatine Kinase Concentration. Serum creatine kinase (CK) concentration across
all four arms. Data are expressed as mean and standard error of the mean. Different
symbols (a-d) indicate statistical differences among the groups (ANOVA and Turkey's post-hoc test; p<0.05).
Discussion
The results of this study indicate that 25 mg of daily marine phytoplankton (microalga
Tetraselmis chuii, [MP]) supplementation improved long-term recovery during a 5-week training program
designed to induce non-functional overreaching. We also found that MP supplementation
better preserved explosive strength following a non-functional overreaching cycle
compared to the PLA group. In addition, no undesirable changes in blood chemistry
or hematology were found following MP supplementation. Furthermore, MP supplementation
increased positive (NCAM and MyoD) and decreased negative (myostatin, MuRF-1, MAFbx)
myogenic factors regulating satellite cell proliferation in exercising rats. These
findings confirm our hypotheses about supplementation with marine phytoplankton.
Generally speaking, performance following muscle damaging exercise recovers within
72 to 96 h [28]. Explosive strength, as shown by rate of force development (RFD), was reduced at
Wk2-Post and Wk5-Post. Moreover, in the PLA condition, these changes remained depressed
for seven days following the final overreaching phase of the study. These findings
indicate that the resistance-training program induced mild non-functional overreaching
in the PLA. However, explosive strength had recovered in the MP group suggesting that
marine phytoplankton supplementation normalized recovery to that seen with typical
muscle damaging protocols. These findings indicate that the training program successfully
imposed a mild non-functional overreaching stimulus that was substantiated by levels
of fatigue that remained for at least a week following strenuous training, and that
MP supplementation was able to aid in the recovery of explosive strength.
Overreaching can be viewed on a continuum form mild to severe symptoms [28]. When observing jump displacement and peak force output, no significant between-
or within-group differences were indicated despite significant changes in explosive
strength. Due to the symptomology not including these changes we can suggest that
the level of overreaching induced by the training program was mild and not severe.
However, there are plausible explanations for the lack of changes in force metrics
and jump displacement in our study. The first possible explanation for these outcomes
may be due to storage and reutilization of elastic energy [29]
[30]. During the countermovement jump, active muscle is pre-stretched, and energy is
absorbed and temporarily stored in passive elastic components to be reused upon concentric
contraction. This series of events could allow subjects to overcome fatigue and sustain
force-generation capacity in a countermovement jump [31]
[32]. These findings are corroborated by previous literature reporting no significant
changes in force-generation capacity during the countermovement jump following fatiguing
physical activity [33]
[34]
[35].
An additional explanation for similar performance (jump displacement and peak force)
in the countermovement jump, despite differences in RFD, are changes in the efficiency
of the jump between conditions. In our study, efficiency was inferred by the ratio
between jumping height and the extent of the countermovement (Hmax/Hmin). It appears that the change in efficiency from Pre to Post in the MP group was greater
than PLA. This data suggest that the PLA group increased the magnitude of the countermovement
without demonstrating increases in jumping height. Thus, it is possible that the PLA
group exhibited this performance behavior as a means to overcome neuromuscular fatigue
(e. g., decrements in RFD). Specifically, increasing the extent of the countermovement
could promote neuromuscular compensatory mechanisms such as increasing motor unit
recruitment and modulating the motor unit firing rate [36]
[37]
[38]. Furthermore, increased countermovement could trigger stronger stretch reflexes
[39], promote more cross bridge formation [40] and induce longer latency responses to enhance muscle stimulation [41]; all of which can enhance force development and strengthen muscle contraction in
the lower-body extensors prior to shortening of the concentric phase [29]
[40].
Exercise-induced fatigue can occur both peripherally and centrally. Peripheral fatigue
occurs primarily in skeletal muscle and is exacerbated by depletion of energy stores,
accumulation of metabolic byproducts, and muscle damage from mechanical and chemical
disturbances [42]. On the other hand, central fatigue is simply considered a reduction in the ability
to maximally activate a given muscle [43]. This type of fatigue can be mediated by group III and IV afferent feedback loops
[44], increased BCAA metabolism [3], and increased motor cortex excitability [43].
The impairment in explosive strength performance combined with the reduction in perceived
recovery allows us to theorize that A) the training program was sufficient at inducing
neuromuscular (central) fatigue, and, B) the subjects completed the post-testing protocol
in a state of neuromuscular and/or central fatigue. This hypothesis follows the findings
that explosive strength is better able to detect both acute (24–72 hrs) [18] and chronic (> 4 weeks) [20] changes in neuromuscular function as compared to less-time constrained peak force
measures.
Since ameliorating the accumulation of fatigue and promoting recovery is of great
interest to athletes, it is worth exploring how supplementation with the marine phytoplankton-derived
microalga Tetraselmis chuii may influence these variables. From a molecular standpoint, recovery is controlled
by a complex array of myogenic regulatory factors that drive satellite cell activation,
differentiation, and replenishment [11]
[12]. Indeed, research has indicated satellite cells as the key variable driving recovery
from exercise [13]
[14]. Impairment in the recruitment of these cells ultimately leads to a failing ability
to compensate for training load-induced stress [13]
[15]. Key positive regulators of satellite cell cycle progression include MyoD and NCAM,
a multifunctional cell-surface protein that has been found to drive muscle recovery
[16]. In contrast, myostatin impairs muscle recovery by inhibiting satellite cell progression
and increasing protein breakdown [17]. Muscle damaging exercise can induce a robust myogenic regulatory response that
facilitates regeneration. This response occurs via the activation, proliferation,
and late stage differentiation of satellite cells to fuse to and repair skeletal muscle.
The importance of nutritional status in regulating these regenerative processes has
been demonstrated. Nutrients high in antioxidants may buffer elevated levels of reactive
oxygen species and reduce oxidative stress in muscles, thereby favoring satellite
cell differentiation and proliferation [45]. In addition, research has demonstrated that essential fatty acids may modulate
the myogenic program of the stem cell population within skeletal muscles and therefore
speed exercise recovery [46]
[47]. This unique marine phytoplankton ingredient is high in fatty acids and antioxidant
enzymes such as SOD. In fact, our previous research demonstrated that marine phytoplankton
supplementation directly increased an array of intramuscular antioxidant enzymes [24]. These findings led us to hypothesize that it may improve skeletal muscle regeneration.
In order to explore a mechanism of action for exercise recovery, we implemented a
rat model in which the exercise protocol significantly increased serum CK concentration,
indicating the occurrence of muscle damage. Marine phytoplankton supplementation demonstrated
the ability to reduce the elevation of CK concentration from exercise in a dose dependent
manner. Additionally, the rat model supports our hypotheses that marine phytoplankton
supplementation was able to increase the expression of early (MyoD) and late (NCAM)
cell cycle regulators. While exercise alone was able to increase early expression,
it did not elevate late cell cycle parameters. These findings indicate an incomplete
regenerative response that is characteristic of overreaching and slowed recovery.
Additionally, supplementation was also able to depress myostatin expression. It is
known that the interaction of myostatin with its receptor increases muscle degradation
through activating the ubiquitin pathway [48]. Skeletal muscle recovery is largely impaired by the ubiquitin-proteasome and autophagy-lysosomal
systems. The ubiquitin-proteasome ligases targeting proteins for degradation are MAFbx
and MuRF1 [49]. Therefore, as expected, the expression of these downstream ubiquitin rate limiting
E3 ligases were depressed in the present study. These findings suggest that marine
phytoplankton may improve recovery through a combination of optimized cell cycle regulation,
improved protein turnover, and mitigation of muscle damage indices.
There are some notable limitations to this study. Subjects were instructed to maintain
their typical eating habits (e. g. eat the same number of meals with the same meal
schedule) and avoid drastic changes in diet types. However, dietary intake was not
tracked, therefore we have no data to present here. Additionally, other habits potentially
impacting recovery, such as sleep and non-exercise related activity outside of the
laboratory, were not monitored. Next, in the human model, we did not evaluate common
inflammatory [50]
[51]
[52]
[53], immune [9]
[54]
[55]
[56]
[57], or hormonal biomarkers [58]
[59]
[60]
[61]
[62] used in previous research to evaluate training stress and recovery. While no single
biomarker exist to reliably detect overreaching [28], including such a measure would have permitted a more comprehensive evaluation of
the subject’s physiological state.
Conclusions
We can conclude that the training program was effective at inducing a state of neuromuscular
fatigue. However, marine phytoplankton supplementation was able to improve long-term
recovery perceptually and functionally via explosive strength following this fatiguing
period. Since recovery can be defined as, “returning what was lost due to exercise”
[63], we have shown that marine phytoplankton supplementation can improve exercise recovery
following non-functional overreaching in a human model. Mechanistically, these changes
appear to be driven through cell cycle regulation and a potential to improve protein
turnover. Finally, it was demonstrated that the ingredient had no negative impact
on any blood safety parameter examined. These findings have direct nutritional and
supplementation implications by providing practitioners with additional strategies
for improving recovery during periods of high physiological stress.
