External Workload Indicators of Muscle and Kidney Mechanical Injury in Endurance Trail Running

dney injury in endurance athletes is worrying for health, and its relationship with physical external workload (eWL) needs to be explored. This study aimed to analyze which eWL indexes have more in uence on muscle and kidney injury biomarkers. 20 well-trained trail runners (age=38.95 9.99 years) ran ~35.27 km (thermal-index=23.2 1.8 C, cumulative-ascend =1815 m) wearing inertial measurement units (IMU) in six di erent spots (malleolus peroneus [MPleft/MPright], vastus lateralis [VLleft/VLright], lumbar [L1–L3], thoracic [T2–T4]) for eWL measuring using a special suit. Muscle and kidney injury serum biomarkers (creatin-kinase [sCK], creatinine (sCr), ureic-nitrogen (sBUN), albumin [sALB]) were assessed pre-, -post0hand post24h. A principal component (PC) analysis was performed in each IMU spot to extract the main variables that could explain eWL variance. After extraction, PC factors were inputted in multiple regression analysis to explain biomarkers delta change percentage (D%). sCK, sCr, sBUN, sALB presented large di erences (p<0.05) between measurements (pre<post24h<post0h). PC's explained 77.5–86.5% of total eWL variance. sCKD% was

post24h. A principal component (PC) analysis was performed in each IMU spot to extract the main variables that could explain eWL variance. After extraction, PC factors were inputted in multiple regression analysis to explain biomarkers delta change percentage (D%). sCK, sCr, sBUN, sALB presented large di erences (p<0.05) between measurements (pre<post24h<post0h). PC's explained 77.5–86.5% of total eWL variance. sCKD% was predicted in 40 to 47% by L1–L3and MPleft; sCrD% in 27% to 45% by L1–L3 and MPleft; and sBUND% in 38%-40% by MPrightand MPleft. These ndings could lead to a better comprehension of how eWL (impacts, player load and approximated entropy) could predict acute kidney and muscle injury. These ndings support the new hypothesis of mechanical kidney injury during trail running based on L1–L3external workload data. Keywords: principal component analysis; mountain sport; acute kidney injury; acute renal failure; exertional rhabdomyolysis 1. Introduction Recently, inertial measurement units (IMU) composed by di erent microsensors (gyroscope, accelerometer and magnetometer) have been developed and used for the analysis of human movement [1]. In sports, these types of sensors have been used to quantify the external workload in di erent team and individual sports [2]. Although the external workload and gait biomechanics Int. J. Environ. Res. Public Health2019,16, 3909; doi:10.3390 /ijerph16203909 /journal/ijerph

Int. J. Environ. Res. Public Health2019,16, 3909 2 of 13 has traditionally analyzed under laboratory conditions using three-dimensional capture systems [3], this microsensors technology have been started to use for the analysis of external workload and biomechanical aspects for the improvement of optimal performance in laboratory conditions [4–7]. Nowadays, there has been a growing interest in quantifying workload in in eld settings. In this sense, di erent researches have assessed by microtechnology the external workload in individual and team sports. The most common used variables were peak acceleration [8], impacts at di erent ranges [9], accumulated accelerometer load indexes [10–13] and speci c events during competition and training sessions such as collisions, jumps or speci c events among others [14–17]. In running sports, IMU sensors have been used for the analysis of velocity [18], stride length [18], vertical ground reaction forces [19], body segment kinematics [19], postural stability [4] multi-joints external workload [5,6] and peak accelerations [20], among others. Currently, the usefulness of this type of measurements of external workload has been questioned without having data on the impact they cause at the physiological level, which is why research has been carried out that analyzes both variables to have a clearer understanding of the physical demands and physiological together [21]. The understanding of both internal and external workload variables could better explain the mechanisms of physical damage. Recently, studies have been carried out in individual and team sports on how this external workload a ects muscle function and damage, related to impacts [22,23], jumps [24], changes of direction and speed [25,26]. This muscle damage has been quanti ed by biochemical (creatine kinase, lactate dehydrogenase, magnesium ant others) [22,27], functional [28] and perceptual methods [29]. On the other hand, the impact that the body receives on each action can also a ect the renal function due to the constant mechanical trauma that the kidney can su er during long-term and moderate to high intensity events, although there is insu cient evidence to relate the external workload and the possible mechanical trauma at the renal level [30]. The most common methods to

that the body receives on each action can also a ect the renal function due to the constant mechanical trauma that the kidney can su er during long-term and moderate to high intensity events, although there is insu cient evidence to relate the external workload and the possible mechanical trauma at the renal level [30]. The most common methods to quantify renal function are cystatin C (sCyst-C), serum creatinine (sCr), estimation of glomerular ltration rate (sGFR), creatinine clearance and blood ureic nitrogen (sBUN) [31–33]; in addition to other novel markers that could suggest subclinical injury as serum albumin (sALB), neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule 1 (KIM-1) [34]. Muscle and kidney injury in endurance athletes has been widely reported [35] and is a point of concern in the health of these types of athletes because the combination of factors such as workload, dehydration and heat strain can trigger acute kidney injury and cause future complications. It has been found that sports that cause a lot of eccentric actions, are carried out for prolonged hours and are exposed to adverse environmental conditions are those that are most likely to cause muscle and kidney injury, this is the case of trail running [33]. Due to the lack of information regarding the external workload indicators that could a ect muscle and kidney injury, the purpose of the study was to explore which external workload factors have more in uence on the responses of muscle and kidney injury biomarkers in experienced endurance trail runners. 2. Methods 2.1. Design Participants were assessed -pre (serum test), during (physical external workload), -post0h(serum test) and -post24h(serum test) a trail running event. Participants were asked to run 3 11.76 km trail circuit (total distance: ~35.27 km, cumulative positive ascend: 1815 m [from 906 to 1178 m.a.s.l.]). The altimetry of the event and variables with its measurement time can be assessed at Figure. The thermal stress index (WetBulb-Globe Temperature [WBGT]) registered throughout the event was 23.2 1.8 C (temperature: 24.46 2.42 C and humidity 77.88 10.91%) according to the WBGT (QuestTemp 36, 3M, MN, USA). Final running time

positive ascend: 1815 m [from 906 to 1178 m.a.s.l.]). The altimetry of the event and variables with its measurement time can be assessed at Figure. The thermal stress index (WetBulb-Globe Temperature [WBGT]) registered throughout the event was 23.2 1.8 C (temperature: 24.46 2.42 C and humidity 77.88 10.91%) according to the WBGT (QuestTemp 36, 3M, MN, USA). Final running time was 290.3 54.2 min.

Int. J. Environ. Res. Public Health2019,16, 3909 3 of 13Int. J. Environ. Res. Public Health 2019, 16, x 3 of 13 Figure 1. Schematic design of study variables with time measurement and trail altimetry. 2.2. Participants A total of 20 male runners (age = 38.95 ± 9.99 years, weight = 71.94 ± 12.59 kg, height = 171.15 ± 9.52 cm) took part of the study. Participants were recruited among heat-acclimatized (life and train near event place), trained (running training = 533.1 ± 201.6 min/week) and experienced ultra- endurance runners (years of trail running experience = 6.3 ± 5.8 years). Participants who reported any muscular or metabolic diseases or recent (<6 months) physical injury of the lower limbs were excluded from the study. Experimental protocol was approved by the Institutional Review Board (Reg. Code UNA- CECUNA-2019-P005). All the participants were informed of the details of the experiment procedures and the associated risks and discomforts. Each subject gave written informed consent, according to the criteria of the Declaration of Helsinki, regarding biomedical research involving human subjects (18th Medical Assembly, 1964, revised in 2013 in Fortaleza). 2.3. Material and Procedures 2.3.1. Serum Markers A 5 mL of blood was drawn from an antecubital vein directly into a blood collection sterile tube (BD Vacutainer ® , New Jersey, NJ, USA) containing spray-coated silica particles activator and a gel polymer for serum separation. Samples were centrifuged at 2000 g relative centrifugal force (RCF) for 10 min using tube centrifuge (PLC-01, Gemmy Industrial Corp., Taipei, Taiwan). During sample collecting process, blood samples were stored on ice in a special cooler (45QW Elite, Pelican TM , California, CA, USA) until they were stored in a freezer (−20 °C) the same sample extraction day. Sample processing was performed a day after the event under controlled and isolated room using an automatic biochemical analyzer (BS-200E, Mindray, China) by photometry method. The variables extracted from analysis were serum creatinine (sCr, mg/dL), serum creatine kinase (sCK, IU/L), serum ureic nitrogen (sBUN, mg/dL), serum albumin (sALB, IU/L). Delta percentage of change was calculated for each variable between pre- and -post0h or -post24h.

a day after the event under controlled and isolated room using an automatic biochemical analyzer (BS-200E, Mindray, China) by photometry method. The variables extracted from analysis were serum creatinine (sCr, mg/dL), serum creatine kinase (sCK, IU/L), serum ureic nitrogen (sBUN, mg/dL), serum albumin (sALB, IU/L). Delta percentage of change was calculated for each variable between pre- and -post0h or -post24h. Kidney functional loss and Acute Kidney Injury (AKI) was considered and classified following Acute Kidney Injury Network (AKIN), the Risk, Injury, Failure, Loss of kidney Function, and End-stage kidney disease (RIFLE) [36] and the Kidney Disease Improving Global Outcomes (KDIGO) [37] criteria as follow: AKIrisk (sCr increase of 150% or acute increase or ≥0.3 mg/dL) and AKIinjury (sCr increase of 200%). Additionally, exertional rhabdomyolysis (ER) was considered if sCK level exceeded 1000 UI/L [38]. 2.3.2. Physical External Workload To assess locomotion and kinematic variables, inertial measurement units (IMU) (WIMU PRO™, RealTrack Systems, Almería, Spain) were used in order to register the external workload during running. Six different IMU were attached at six different anatomical spots using a special spandex dark-suit (pat. pending) developed for the research. The suit was made with pockets for each IMU’s in six different spots (one IMU at T2–T4, one IMU at L1–L3; two IMU at right [VLright] and left [VLleft] vastus lateralis muscle bellies and two IMU 3 cm cephalic to right [MPright] and left [MPleft] malleolus peroneus) (see Figure 2a). Suit incorporated dark elastic straps were used to avoid vibrations or non- wanted movements of the devices during running (see Figure 2b). Figure 1.Schematic design of study variables with time measurement and trail altimetry. 2.2. Participants A total of 20 male runners (age=38.95 9.99 years, weight=71.94 12.59 kg, height=171.15 9.52 cm) took part of the study. Participants were recruited among heat-acclimatized (life and train near event place), trained (running training=533.1 201.6 min/week) and experienced ultra-endurance runners (years of trail running experience=6.3 5.8 years). Participants who reported any muscular or metabolic diseases or recent (<6 months) physical injury of the lower limbs were excluded from the study. Experimental protocol was approved by the Institutional Review

the study. Participants were recruited among heat-acclimatized (life and train near event place), trained (running training=533.1 201.6 min/week) and experienced ultra-endurance runners (years of trail running experience=6.3 5.8 years). Participants who reported any muscular or metabolic diseases or recent (<6 months) physical injury of the lower limbs were excluded from the study. Experimental protocol was approved by the Institutional Review Board (Reg. Code UNA-CECUNA- 2019-P005). All the participants were informed of the details of the experiment procedures and the associated risks and discomforts.Each subject gave written informed consent, according to the criteria of the Declaration of Helsinki, regarding biomedical research involving human subjects (18th Medical Assembly, 1964, revised in 2013 in Fortaleza). 2.3. Material and Procedures 2.3.1. Serum Markers A 5 mL of blood was drawn from an antecubital vein directly into a blood collection sterile tube (BD Vacutainer ® , New Jersey, NJ, USA) containing spray-coated silica particles activator and a gel polymer for serum separation. Samples were centrifuged at 2000grelative centrifugal force (RCF) for 10 min using tube centrifuge (PLC-01, Gemmy Industrial Corp., Taipei, Taiwan). During sample collecting process, blood samples were stored on ice in a special cooler (45QW Elite, Pelican TM , California, CA, USA) until they were stored in a freezer ( 20 C) the same sample extraction day. Sample processing was performed a day after the event under controlled and isolated room using an automatic biochemical analyzer (BS-200E, Mindray, China) by photometry method. The variables extracted from analysis were serum creatinine (sCr, mg/dL), serum creatine kinase (sCK, IU/L), serum ureic nitrogen (sBUN, mg/dL), serum albumin (sALB, IU/L). Delta percentage of change was calculated for each variable between pre- and -post0hor -post24h. Kidney functional loss and Acute Kidney Injury (AKI) was considered and classi ed following Acute Kidney Injury Network (AKIN), the Risk, Injury, Failure, Loss of kidney Function, and End-stage kidney disease (RIFLE) [36] and the Kidney Disease Improving Global Outcomes (KDIGO) [37] criteria as follow: AKIrisk(sCr increase of 150% or acute increase or 0.3 mg/dL) and AKIinjury(sCr increase of 200%). Additionally, exertional rhabdomyolysis (ER) was considered if sCK level exceeded 1000 UI/L [38].

following Acute Kidney Injury Network (AKIN), the Risk, Injury, Failure, Loss of kidney Function, and End-stage kidney disease (RIFLE) [36] and the Kidney Disease Improving Global Outcomes (KDIGO) [37] criteria as follow: AKIrisk(sCr increase of 150% or acute increase or 0.3 mg/dL) and AKIinjury(sCr increase of 200%). Additionally, exertional rhabdomyolysis (ER) was considered if sCK level exceeded 1000 UI/L [38]. 2.3.2. Physical External Workload To assess locomotion and kinematic variables, inertial measurement units (IMU) (WIMU PRO—, RealTrack Systems, Almer½a, Spain) were used in order to register the external workload during running. Six di erent IMU were attached at six di erent anatomical spots using a special spandex dark-suit (pat. pending) developed for the research. The suit was made with pockets for each IMU's in six di erent spots (one IMU at T2–T4, one IMU at L1–L3; two IMU at right [VLright] and left [VLleft] vastus lateralis muscle bellies and two IMU 3 cm cephalic to right [MPright] and left [MPleft] malleolus peroneus) (see Figurea). Suit incorporated dark elastic straps were used to avoid vibrations or non-wanted movements of the devices during running (see Figureb).

Int. J. Environ. Res. Public Health2019,16, 3909 4 of 13Int. J. Environ. Res. Public Health 2019, 16, x 4 of 13 Figure 2. IMU device’s setting (a) 1: T2–T4, 2: L1–L3, 3: VLleft and 4: VLright vastus lateralis, 5: MPleft and 6: MPright malleolus peroneus; (b). body attachment anti-vibration straps system. The IMU’s were previously calibrated following protocols for this specific microsensor [39]. This IMU has been used for the assessment of neuromuscular running workload [6] and its reliability had been tested in it use, attached to different body places [39]. Total variables data extracted from IMU software were analyzed using a principal component analysis (PCA) in order to explain total variance [40]. Total variables analyzed were: Player Load per min (AU, PL/min), Player Load difference between segments (AU, PLDif), approximated entropy (ApEn, AU), maximum acceleration (m/s −1 , Accmax), total accelerations (Acc, n/min), total decelerations (Dec, n/min), average acceleration (Accavg, m/s 2 ), average deceleration (Decavg, m/s 2 ), maximum speed (Speedmax, m/s), average speed (Speedavg, m/s), metabolic power (MP, W/kg), high metabolic load distance (HMLD, m/min), explosive distance (>16 km/h) (D>16 km/h, m/min), maximum heart rate (HRmax, bpm), average heart rate (HRavg, bpm), total impacts (Impactstotal, n/min), and total impacts at 1 g ranges from 0 to >30 g (Impactstotal, n/min). 2.4. Statistical Analysis All the variables were reported using the mean, standard deviation and lower and upper limits. Mean significant differences of serum tests variables were explored using a one-way analysis of variance and main differences between time measures were confirmed using Bonferroni method. The magnitude of the differences (effect size) was qualitatively interpreted using partial omega squared (ωp²) as follows: >0.01 small; >0.06 moderate and >0.14 large [41]. Change delta’s percentage (Δ%) was reported between time measurement in each variable as follow: í µíº«%= &#3627408425;&#3627408424;&#3627408428;&#3627408429;−&#3627408425;&#3627408427;í µí°ž &#3627408425;&#3627408427;í µí°ž ∗&#3627409359;&#3627409358;&#3627409358; From a total of 458 variables extracted from external workload assessment, only 169 max, average and relative variables were selected for correlation matrix exploration (31 for T2–T4, 20 for L1–L3, 24 for VLright, 24 for VLleft, 35 for MPright and 35 for MPleft). A threshold of r

measurement in each variable as follow: í µíº«%= &#3627408425;&#3627408424;&#3627408428;&#3627408429;−&#3627408425;&#3627408427;í µí°ž &#3627408425;&#3627408427;í µí°ž ∗&#3627409359;&#3627409358;&#3627409358; From a total of 458 variables extracted from external workload assessment, only 169 max, average and relative variables were selected for correlation matrix exploration (31 for T2–T4, 20 for L1–L3, 24 for VLright, 24 for VLleft, 35 for MPright and 35 for MPleft). A threshold of r < 0.7 was used as a criteria selection for extract non correlated variables for running each Principal Component Analysis (PCA). Selected variables were prior scaled and centered (Z-Score) and PCA’s were suitable considering Kaiser-Meyer-Olkin values (KMO = 0.61–0.635) and Barleth Sphericity test was significant (p < 0.01). After PCA, eingvalues greater than 1 were included for extraction in respective principal component (PC). An orthogonal rotation using VariMax method was used for identification of respective loadings in each PC, then only loadings greater than 0.6 were retained for interpretation and the highest loading was reported when a cross loading was identified between PC’s. After PC’s were extracted, multiple lineal regressions (R 2 ) were performed in order to analyze how the principal components found from each body segment explain both muscle and kidney injury markers change after the event. Alpha was prior set as p < 0.05. Data analysis was performed using the Statistical Package for the Social Sciences (SPSS, IBM, SPSS Statistics, v.22.0, Illinois, USA). Figure 2. IMU device's setting (a) 1: T 2–T 4, 2: L 1–L 3, 3: VL leftand 4: VL rightvastus lateralis, 5: MP left and 6: MP rightmalleolus peroneus; (b). body attachment anti-vibration straps system. The IMU's were previously calibrated following protocols for this speci c microsensor [39]. This IMU has been used for the assessment of neuromuscular running workload [6] and its reliability had been tested in it use, attached to di erent body places [39]. Total variables data extracted from IMU software were analyzed using a principal component analysis (PCA) in order to explain total variance [40]. Total variables analyzed were: Player Load per min (AU, PL/min), Player Load di erence between segments (AU, PLDif), approximated entropy (ApEn, AU), maximum acceleration (m/s 1

been tested in it use, attached to di erent body places [39]. Total variables data extracted from IMU software were analyzed using a principal component analysis (PCA) in order to explain total variance [40]. Total variables analyzed were: Player Load per min (AU, PL/min), Player Load di erence between segments (AU, PLDif), approximated entropy (ApEn, AU), maximum acceleration (m/s 1 , Accmax), total accelerations (Acc, n/min), total decelerations (Dec, n/min), average acceleration (Accavg, m/s 2 ), average deceleration (Decavg, m/s 2 ), maximum speed (Speedmax, m/s), average speed (Speedavg, m/s), metabolic power (MP, W/kg), high metabolic load distance (HMLD, m/min), explosive distance (>16 km/h) (D>16 km/h , m/min), maximum heart rate (HRmax, bpm), average heart rate (HRavg, bpm), total impacts (Impactstotal, n/min), and total impacts at 1 g ranges from 0 to>30 g (Impactstotal, n/min). 2.4. Statistical Analysis All the variables were reported using the mean, standard deviation and lower and upper limits. Mean signi cant di erences of serum tests variables were explored using a one-way analysis of variance and main di erences between time measures were con rmed using Bonferroni method. The magnitude of the di erences (e ect size) was qualitatively interpreted using partial omega squared (!p 2) as follows:>0.01 small;>0.06 moderate and>0.14 large [41]. Change delta's percentage (D%) was reported between time measurement in each variable as follow: D%= post pre pre 100 From a total of 458 variables extracted from external workload assessment, only 169 max, average and relative variables were selected for correlation matrix exploration (31 for T2–T4, 20 for L1–L3, 24 for VLright, 24 for VLleft, 35 for MPrightand 35 for MPleft). A threshold of r<0.7 was used as a criteria selection for extract non correlated variables for running each Principal Component Analysis (PCA). Selected variables were prior scaled and centered (Z-Score) and PCA's were suitable considering Kaiser-Meyer-Olkin values (KMO=0.61–0.635) and Barleth Sphericity test was signi cant (p<0.01). After PCA, eingvalues greater than 1 were included for extraction in respective principal component (PC). An orthogonal rotation using VariMax method was used for identi cation of respective loadings in each PC, then only loadings

The study explores external workload indicators affecting muscle and kidney injuries in endurance trail runners.