*Article* **Associations of Blood and Performance Parameters with Signs of Periodontal Inflammation in Young Elite Athletes—An Explorative Study**

**Cordula Leonie Merle 1,2,\*, Lisa Richter 2, Nadia Challakh 2, Rainer Haak 2, Gerhard Schmalz 2, Ian Needleman 3,4, Peter Rüdrich 5, Bernd Wolfarth 5,6,7, Dirk Ziebolz 2,† and Jan Wüstenfeld 5,6,†**


**Abstract:** This retrospective cross-sectional study aimed to explore interactions between signs of periodontal inflammation and systemic parameters in athletes. Members of German squads with available data on sports medical and oral examination were included. Groups were divided by gingival inflammation (median of papillary bleeding index, PBI ≥ median) and signs of periodontitis (Periodontal Screening Index, PSI ≥ 3). Age, gender, anthropometry, blood parameters, echocardiography, sports performance on ergometer, and maximal aerobic capacity (VO2max) were evaluated. Eighty-five athletes (f = 51%, 20.6 ± 3.5 years) were included (PBI < 0.42: 45%; PSI ≥ 3: 38%). Most associations were not statistically significant. Significant group differences were found for body fat percentage and body mass index. All blood parameters were in reference ranges. Minor differences in hematocrit, hemoglobin, basophils, erythrocyte sedimentation rates, urea, and HDL cholesterol were found for PBI, in uric acid for PSI. Echocardiographic parameters (*n* = 40) did not show any associations. Athletes with PSI ≥ 3 had lower VO2max values (55.9 ± 6.7 mL/min/kg vs. 59.3 ± 7.0 mL/min/kg; *p* = 0.03). In exercise tests (*n* = 30), athletes with PBI < 0.42 achieved higher relative maximal load on the cycling ergometer (5.0 ± 0.5 W/kg vs. 4.4 ± 0.3 W/kg; *p* = 0.03). Despite the limitations of this study, potential associations between signs of periodontal inflammation and body composition, blood parameters, and performance were identified. Further studies on the systemic impact of oral inflammation in athletes, especially regarding performance, are necessary.

**Keywords:** performance; systemic inflammation; physical endurance; physical fitness; maximal aerobic capacity; gingivitis

#### **1. Introduction**

The high-performance standards of elite athletes are built on foundations of physical fitness, health, and wellbeing. It may be a surprise, therefore, that oral ill health is common in elite athletes and results in an increased oral inflammatory burden [1]. The prevalence of both gingivitis and periodontitis can be high [1] and differs significantly from non-elite controls [2–4]. For instance, among footballers, a periodontitis prevalence of 41% was reported [5].

Oral infections, including periodontal diseases, cause increased systemic inflammation [6], which can resolve following treatment [7], although there are inconsistencies between studies [8]. The relationship between oral health and physical activity could

**Citation:** Merle, C.L.; Richter, L.; Challakh, N.; Haak, R.; Schmalz, G.; Needleman, I.; Rüdrich, P.; Wolfarth, B.; Ziebolz, D.; Wüstenfeld, J. Associations of Blood and Performance Parameters with Signs of Periodontal Inflammation in Young Elite Athletes—An Explorative Study. *J. Clin. Med.* **2022**, *11*, 5161. https://doi.org/10.3390/ jcm11175161

Academic Editors: Jiwu Chen and Yaying Sun

Received: 27 July 2022 Accepted: 25 August 2022 Published: 31 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

be bidirectional. Some studies have reported an impairment from poor oral health on measures of physical activity and performance [9]. On the other hand, intensive physical activity leads to systemic changes: levels of (pro-)inflammatory cytokines [10,11] as well as stress hormones [12] increase. On the other side, immunoglobulin A levels decrease [13]. A transitional reduced cellular immune response [14,15] has been proposed to lead to an open window for infections [16]. However, the impact of these changes on oral inflammation is not clear.

The relationship between oral health and anaerobic capacity of athletes has received very little attention. A recent study in elite rowers did not find a relationship between dental caries and anaerobic capacity, although the study had few participants and differences in oral health status between comparison groups were small [17]. There has been no published research investigating the influence of oral inflammation on the performance of athletes or systemic biomarkers. Nevertheless, several studies have found negative impacts of poor oral health on self-reported measures of performance [18,19]. Consequently, this retrospective explorative study aimed to investigate associations between signs of periodontal inflammation and systemic parameters in elite athletes. Associations between gingival and periodontal inflammation to blood, echocardiographic, and performance parameters were investigated. It was hypothesized that these parameters would be affected in athletes with increased signs of periodontal inflammation.

#### **2. Materials and Methods**

#### *2.1. Study Design and Participants*

This pilot study was based on a retrospective data evaluation from a collaboration between the Department of Cariology, Endodontology and Periodontology and the Institute for Applied Scientific Training (IAT) Leipzig. Dental examinations were performed as a supplement to the annual sports medical and performance diagnostics.

Inclusion criteria were athletes of German national teams, perspective, or youth squads, aged between 18 and 30 years, male and female. The sports medical and standardized dental examination (performed on the same day) were conducted between May and December 2019. Participants with incomplete dental examination were excluded. A comprehensive description of the cohort and oral health status was already published elsewhere [4].

The study was reviewed and approved by the Ethics Committee of the medical faculty of Leipzig University, Germany (No. 091/20-ek). All participants were informed verbally and in writing about the scientific use of their clinical data and provided their informed consent for participation in research studies. The recommendations for strengthening the reporting of cross-sectional studies (STROBE) were considered [20].

#### *2.2. Data Collection*

Data on general characteristics, blood parameters, echocardiographic examination, and sports performance tests as part of the sports medical records were exported from the IAT database. Data on signs of periodontal inflammation were extracted from patients' dental records.

General characteristics. Recorded general characteristics were age, gender, training, and anthropometric data including body mass index (BMI), body fat percentage (BFP), lean body mass (LBM), and resting heart rate (RHR).

Blood parameters. The annual sports medical and performance diagnostics comprised extensive blood tests for all athletes. A complete blood count with the number of erythrocytes, leukocytes, thrombocytes, lymphocytes, neutrophils, basophils, eosinophils and monocytes, hematocrit, hemoglobin, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), immature reticulocyte fraction (IFR), high (HFR), medium (MFR), and low-fluorescence reticulocytes (LFR) was performed. Neutrophil–lymphocyte (NLR), monocyte–lymphocyte (MLR), and platelet–lymphocyte ratios (PLR) were calculated. Further determined blood parameters were erythrocyte sedimentation rates after 1 (ESR1h) and 2 h (ESR2h), iron, ferritin, natrium, calcium, potassium, magnesium, gamma-glutamyl transferase (GGT), glutamicpyruvate-transaminase (GPT), urea, uric acid, creatine kinase, total protein, total cholesterol, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, LDL/HDL ratio, glucose, and triglycerides.

Echocardiographic examination. Additionally, if available, sport-specific and performancerelated measurements of transthoracic echocardiographic examination were exported: absolute heart volume (HV\_abs), relative heart volume (HV\_rel) (calculated by the equation of Dickhuth) [21], left atrial size (LA), left ventricular end-diastolic dimension (LVEDd), and tricuspid annular plane systolic excursion (TAPSE).

Sports performance. Maximal aerobic capacity (VO2max) by spiroergometry was extracted if available. If not, it was estimated by the equation of Rexhepi and Brestoci [22]. Furthermore, data from sports performance tests with incremental exercise tests on running or cycle ergometer were considered: RHR, heart rates (HF), lactate, and power, respectively, and speed were extracted for analysis. Besides minimum, maximum, and differences, the speed/power output at individual anaerobic threshold (IAnT), lactate threshold 1 (LT1, initial rise after basal lactate), lactate threshold 2 (LT2, Dickhuth model: basal lactate + 1.5 mmol/L), without load (*p* = 0), and maximal load (Pmax) at the ergometer were tested.

Signs of periodontal inflammation. Data for both gingival and periodontal inflammation were extracted from patients' dental records. A comprehensive (standardized) orofacial examination was performed using a headlight on an examination couch at the IAT. A single skilled dentist that was trained in these periodontal parameters examined all athletes (kappa > 80%). Gingival inflammation was assessed by the papillary bleeding index (PBI) [23] which discriminates five scores after probing (0: no bleeding; 1: single bleeding point; 2: several bleeding points or fine line; 3: interdental triangle filled with blood, 4: profuse bleeding). The PBI index was calculated per patient by division of the total sum by the total number of interdental papillae. Periodontal conditions (= sign of periodontitis/periodontal treatment need) were examined using the Periodontal Screening Index [24]: score 0 to 2 has probing depths less than 3.5 mm. Score 0 shows no bleeding, no calculus, score 1 bleeding on probing, and score 2 calculus. A score of 3 or 4 indicates increased probing depths (3: pocket depth 3.5–5.5 mm; 4: pocket depth > 5.5 mm) as a sign of periodontitis. Third molars were not included in this evaluation despite they took a more anterior position.

#### *2.3. Statistical Analysis*

Statistical analysis was performed with SPSS Statistics for Windows (version 23.0, IBM Corp., Armonk, NY, USA). Possible associations from signs of periodontal inflammation to anthropometric data, blood, echocardiographic, and exercise test parameters were examined. For analyzing associations to gingival inflammation, the athletes were divided into two groups by median of the PBI (PBI < median vs. PBI ≥ median). Regarding signs of periodontitis, group division was based on having increased probing depths (≥3.5 mm) or not (PSI < 3 vs. PSI ≥ 3). Quantitative variables were presented by mean and standard deviation (SD). Independent, normal-distributed samples were analyzed with a *t*-test. For non-normal distributed samples, the Mann–Whitney U test was used. All tests were performed two sided, with a significance level at *p* < 0.05 and under exclusion of missing data. Normal distribution was verified by Kolmogorov–Smirnov test. For parameters with an association (*p* < 0.1) and plausible link to PBI or PSI, a multivariate analysis of variance (MANOVA) and, for significant models, linear regression were planned.

#### **3. Results**

#### *3.1. Athletes*

Records of 85 athletes from the German national elite, perspective, and youth squads (f = 51%, 20.6 ± 3.5 years) were included for retrospective evaluation. Table 1 shows their characteristics, training, and anthropometric data.

**Table 1.** Characteristics of the athletes (entire cohort).


Abbreviations: *n*: number of participants; VO2max: maximal aerobic capacity. <sup>a</sup> Missing data for eight participants (*n* = 77).

#### *3.2. Signs of Periodontal Inflammation*

Mean gingival inflammation (PBI) was 0.48 ± 0.29 and the median was 0.42 (IQR: 0.31; 0.69). The subgroup PBI < 0.42 contained 40 and the subgroup PBI ≥ 0.42 45 athletes. As such, 53 athletes had a PSI < 3 and 32 a PSI ≥ 3 with 11 having a PSI ≥ 3 in more than one sextant. No athlete showed a PSI score of 4. The associations between body composition and performance with periodontal health are shown in Table 2. Most associations were not statistically significant at *p* < 0.05. BFP was significantly lower in PBI ≥ 0.42 (PBI < 0.42: 14.4 ± 4.8 vs. PBI ≥ 0.42: 11.9 ± 4.3; *p* = 0.02) but significantly higher in PSI ≥ 3 (PSI < 3: 12.4 ± 4.9 vs. PSI ≥ 3: 14.3 ± 4.2; *p* = 0.047). Athletes with signs of periodontitis also had a higher BMI (PSI < 3: 20.3 ± 2.0 vs. PSI ≥ 3: 21.5 ± 2.0; *p* = 0.01).

#### *3.3. Blood Parameters*

Results of the complete blood count (Table 3) and further blood parameters (Table 4) are presented for the entire cohort and separately for the divided groups by PBI and PSI. Again, most associations were not statistically significant. However, statically significant differences between athletes with a lower and those with a higher PBI were found for hematocrit (PBI < 0.42: 41.5 ± 2.8% vs. PBI ≥ 0.42: 42.6 ± 2.4%; *p* = 0.04), hemoglobin (14.2 ± 1.2 g/dL vs. 14.7 ± 0.9 g/dL; *p* = 0.04), basophils (0.5 ± 0.2% vs. 0.4 ± 0.2%; *p* = 0.03), ESR1h (5.1 ± 3.3 mm vs. 3.8 ± 2.8 mm; *p* = 0.01), ESR2h (10.6 ± 7.2 mm vs. 8.0 ± 5.7 mm; *p* = 0.04), urea (6.3 ± 1.7 mmol/L vs. 5.5 ± 1.4 mmol/L; *p* = 0.04), and HDL

cholesterol (1.9 ± 0.3 mmol/l vs. 1.7 ± 0.2 mmol/L; *p* = 0.02). In relation to periodontitis based on PSI ≥ 3, statistically significant differences were found only for uric acid (PSI < 3: 251.3 ± 74.1 μmol/L vs. PSI ≥ 3: 283.1 ± 60.8 μmol/L; *p* = 0.04). Multivariate linear regression was performed for urea, uric acid, HDL cholesterol, thrombocytes, and iron, whereby ANOVA revealed significance for two different models, including urea, uric acid, and thrombocytes, however, showing a small effect size (Supplementary Materials Table S1).

#### *3.4. Echocardiographic Parameters*

An echocardiographic examination was performed on a subgroup of 40 athletes. The results of the quantitative measurements are presented in Supplementary Materials Table S2. HV\_rel was, on average, 12 mL/kg, LA 3.6 cm, and TAPSE 2.5 cm. There were no statistically significant associations with PBI or PSI.

#### *3.5. Performance Parameters*

Spiroergometric data were available for 41 athletes; 30 completed further performance diagnostics with incremental exercise tests (Table 5). Ergometer types were running (*n* = 20, biathletes) or cycling (*n* = 10, cross-country skiers). Overall, in athletes, those with signs of periodontitis had lower VO2max values (55.9 ± 6.7 mL/min/kg vs. 59.3 ± 7.0 mL/min/kg; *p* = 0.03). Detailed data on power on the ergometer are presented in Table 6; the group with less gingival inflammation achieved a higher relative maximal load on the cycling ergometer (PBI < 0.42: 5.0 ± 0.5 W/kg vs. PBI ≥ 0.42: 4.4 ± 0.3 W/kg; *p* = 0.03).


**Table 2.** Characteristics of the athletes (entire cohort) and their associations with periodontal health (PBI and PSI).

Abbreviations: BMI: body mass index, RHR: resting heart rate; BFP: body fat percentage; LBM: lean body mass; n: number of participants; PBI: Bleeding Index; PSI: Periodontal Screening Index with PSI ≥ 3 indicating increased probing depths as a sign of probable periodontitis; VO2max: maximal aerobiccapacity. Bold marks significant differences (*<sup>p</sup>* < 0.05).



PSI: Periodontal Screening Index with PSI ≥ 3 indicating increased probing depths as a sign of probable periodontitis.

0.05).

 Bold marks significant differences (*<sup>p</sup>* <


**Table 4.** Further blood parameters of the athletes (entire cohort) and their associations to with periodontal health (PBI and

 PSI).

Periodontal Screening Index with PSI ≥ 3 indicating increased probing depths as a sign of probable periodontitis.

Missing data for two participants (*<sup>n</sup>* = 83); b Missing data for 18 participants (*<sup>n</sup>* = 67).

 Bold marks significant differences (*<sup>p</sup>* < 0.05); a


**Table 5.**

Performance

 test parameters,

 heart frequencies,

 and lactate values during incremental

 exercise test and their association

 with

Pmax: maximum power on ergometer; Pmax\_rel: relative maximum power. Bold marks significant differences (*<sup>p</sup>* < 0.05).

#### **4. Discussion**

Overall, young athletes showed low mean gingival inflammation (PBI = 0.48 ± 0.29) but, importantly, signs of periodontitis (PSI ≥ 3) were present in 38% of the athletes. Group differences between athletes with lower or higher gingival inflammation were found for several blood parameters (hematocrit, hemoglobin, basophils, ESR1h, ESR2h, and urea), maximal aerobic capacity (VO2max), and maximum load on the cycling ergometer. Athletes with signs of periodontitis differed in body composition (BMI, BFP), uric acid, and VO2max.

One explanation for the differences between groups of different oral health status is that increased oral inflammation affects systemic parameters. Despite controversial discussion [8], various changes in blood values have been observed in periodontitis patients, including inflammation markers, cytokines, and changes in both white and red blood cell counts [25–29]. Furthermore, periodontal treatment that reduces local inflammation also reduces these systemic effects [7,30,31]. In the presented cohort of young athletes, the prevalence of signs of periodontitis was quite high (38%) in comparison to the overall population (1.7%) at this young age [32]. Moreover, this cohort of elite athletes showed a higher prevalence for signs of periodontitis than amateur athletes, despite similar oral health behavior [4]. Moderately elevated periodontal pockets (PSI score 3: none above 5.5 mm) were assessed. This low severity is in line with a previous study on periodontitis in footballers that reported overall mild periodontitis and a similar prevalence of periodontitis [5]. Even though the extent of systemic changes depends on the severity of periodontitis [28], increased CRP values have also been stated due to experimental gingivitis caused by cessation of oral hygiene [33]. Consequently, a systemic impact is possible, even for mild periodontitis and gingivitis. Regarding the gingival inflammation status in the present study, the PBI per papilla was below one (median: 0.42, IQR: 0.31;0.69), indicating mild or localized gingivitis.

Interestingly, the current study also revealed differences in the anthropometric data depending on periodontal status: individuals with probable signs of periodontitis showed higher BMI and BFP (Table 2). In contrast, another study could not reveal such differences between athletes, with and without periodontitis [5]. The values of BMI and BFP of the athletes were generally at a low level. For low BMI (18 to 22), a negative correlation between BMI and generalized aggressive periodontitis was already described [34] as well as in athletes, between BFP and periodontal probing depths [5]. In athletes with lower BMI and BFP, the phenomena of 'Relative Energy Deficiency in Sport' must be considered [35]. However, the results of the current study are inconclusive between the groups of gingival and periodontal inflammation: athletes with higher gingival inflammation showed lower BFP measured by skin folds (Table 2).

Some blood parameters showed significant differences: basophils, hematocrit, hemoglobin, ESR1, ESR2, urea, HDL cholesterol (by PBI), and uric acid (by PSI). The detected extensions were not of clinical relevance, as all investigated blood markers were within the reference ranges and the differences were small. As the direction of the group differences was inconsistent between the groups of gingival and periodontal inflammation and partly even in the same comparison (ESR1 and ESR 2), the significance of these differences is questionable in general. Nevertheless, the direction and extent of the revealed differences for uric acid, hemoglobin, and hematocrit would be in line with the results of a study in blood donors with increased probing depths compared to periodontally "healthy" ones [36]. In contrast to the stated difference in HDL cholesterol in the present study, experimental gingivitis did not lead to differences in cholesterol fractions [33].

Regarding the results of the performance tests, on the cycling ergometer, athletes with a lower level of signs of periodontal inflammation consistently reached higher power. Despite the small subgroup size, several trends for gingival inflammation became apparent and athletes with less gingival inflammation reached a significantly higher relative maximum power (Table 6). The revealed differences are relevant, especially as the subgroup is a homogeneous elite group from one sport discipline. Furthermore, in general, athletes with signs of periodontitis achieved lower VO2max values (Table 2). These results are in line with the stated negative influence of periodontitis on physical fitness in other population cohorts [9]. Athletes with higher oral inflammation could be compromised in their performance due to a systemic effect. In contrast, no impact of caries on the anaerobic capacity of athletes was found by another study [17]. However, this does not contradict a potential influence of oral inflammation as superficial caries generally have less systemic impact. The possibility of such systemic influence of oral health in athletes is underlined by potential associations between poor oral health and injuries [5,37,38].

Strengths and limitations: This explorative study was, to the best of the author's knowledge, the first published on possible associations between signs of periodontal inflammation and systemic parameters in competitive athletes. Including data from 85 athletes from the German national elite, perspective, or youth squads, allowed us to evaluate a considerable cohort. The limitation in athletes between 18 and 30 years indicates to include the typical age of elite athletes. With the resulting medium age of 21 years, this study presents the stage of young elite athletes. Moreover, a detailed description of the oral health status and oral health behavior of this cohort of elite athletes is available [4]. A major strength of the current study is the comprehensive number of available parameters, including blood parameters, echocardiographic parameters, as well as performance parameters. One limitation of the present study is the multiple statistical testing. Nevertheless, due to the explorative character, data were not adjusted [39]. Therefore, all statistical differences should be interpreted with caution. Overall, this applies to the performance and echocardiographic examinations, as only small subgroups could be analyzed. In addition, a potential selection bias must be considered, because it cannot be excluded that athletes with more severe signs of periodontal inflammation were more strongly affected and could not fulfill the squad levels for inclusion. In addition, the methods for the assessment of signs of periodontal inflammation must be discussed. The evaluated data originate from oral examinations that were part of the annual sports medical diagnostics and aimed to detect treatment need. Regarding the PSI, it must be considered that this screening index only indicates gingival inflammation and/or increased probing depths as a sign of probable periodontitis [23] and could also be caused by local swelling due to gingivitis. However, the stated prevalence of signs of periodontitis (38%) complies with the prevalence of a study with comprehensive periodontal examination, according to the current classification (41%, initial periodontitis, stage I, in all but two athletes) [5]. The current classification of periodontal disease (staging/grading matrix) [40] allows for the correct diagnosis with periodontitis. Nevertheless, these diagnoses are mainly based on attachment loss and may be in a stable status without inflammation [40]. The question of current periodontal inflammation and stability depends on periodontal probing depths and bleeding on probing (BOP) [40] but the BOP is not integrated in the basis diagnosis (stage/grade) of periodontitis. For the precise identification to periodontitis and/or periodontal inflammation, a complete periodontal chart (periodontal probing depths, clinical attachment loss for stage, and grade as well as BOP) would be necessary. The concept of the periodontal inflammation surface area (PISA) [41] could quantify the resulting inflammatory burden. These data were not available in the present study. This should be taken into account for interpretation of the presented data and for future studies. Nevertheless, despite not exactly identifying the diagnosis of periodontitis, the PSI identifies elevated periodontal probing depths in the case of full mouth and all-around-the-tooth examination [42]. Thus, it can detect current signs of periodontal inflammation (= inflammatory burden) and periodontal treatment need (PSI Score ≥ 3). Regarding the periodontal attachment loss, under- and, in young age groups, overestimation by the PSI have been discussed [43]. For gingival inflammation, such strict group definition (health vs. presence of inflammation) was not possible, as all athletes showed bleeding as a sign of gingivitis or periodontitis (no PSI score 0) [4]. The performed PBI is a gingivitis index that evaluates the gingival inflammation by the intensity of bleeding on probing at the interdental sites [23]. Generally, gingival inflammation as well as signs of periodontitis were only mild or localized. Due to the resulting small inflammation (PBI: median: 0.42, IQR: 0.31;0.69; PSI ≥ 3 in 38%, localized in 34% of them), the group size could still be too small for detecting these slight systemic effects. Further

limitations must be addressed regarding the compared subgroups. The group differences of gingival inflammation (PBI < 0.42 vs. PBI ≥ 0.42) were small and might have limited the ability to assess the differences in the systemic effects. As, in addition to PSI score 1 to 2, score 3 could indicate the status of gingivitis due to localized swelling, the group division by PSI might not distinguish clearly enough between those athletes with and those without periodontal inflammation. A larger sample size as well as comprehensive periodontal examination might improve the identification of the small, but potentially important, systemic effects for both initial periodontitis and gingival inflammation. In addition, cohorts with more severe periodontal inflammation or experimental gingivitis are further interesting research possibilities. The blood parameters investigated in this study were those from routine medical tests due to the retrospective nature of the project. Thus, the available blood parameters are an unspecific part of the routine diagnostics. Even though, for periodontitis patients, some studies could reveal such differences [26,28,29], these parameters are probably not sensitive enough for such localized, mild inflammatory group differences. Furthermore, VO2max was determined by spiroergometry in only less than half of the participants. The used formula for VO2max in the others is based on age, body mass, and RHR. Nevertheless, it can be considered an appropriate estimation in case of missing exercise tests [22].

#### **5. Conclusions**

The present study supports the hypothesis for an influence of oral inflammation in athletes; body composition, blood, and performance test parameters differed slightly between athletes with different levels of signs of periodontal inflammation. A potential systemic impact of oral inflammation on athletic performance should be investigated.

This explorative study identifies some aspects for future research; prospective studies during a uniform exercise test with spiroergometry of all participants should be carried out. Blood analysis should include more sensitive inflammatory parameters, such as CRP and interleukins. As a marker for the oral status, the PISA and salivary biomarkers would be recommendable. A cohort with a higher level of inflammation burden could simplify the discrimination. Similarly, a larger sample size, based on an appropriate power calculation with consideration for the variability in outcome measures, will be important. Furthermore, an intervention study could prove the connection by showing the systemic effect of periodontal treatment in athletes.

**Supplementary Materials:** The following supporting information can be downloaded at: https:// www.mdpi.com/article/10.3390/jcm11175161/s1, Table S1: Multivariate linear regression analysis of the influence of some blood parameters on gingival inflammation (PBI); Table S2: Echocardiographic parameters and their associations with periodontal health (PBI and PSI).

**Author Contributions:** Conceptualization, D.Z., J.W., G.S., R.H., B.W. and C.L.M.; methodology, G.S., D.Z., J.W. and C.L.M.; formal analysis, P.R. and C.L.M.; investigation, L.R. and J.W.; resources, R.H., B.W., D.Z. and J.W.; data curation, L.R. and C.L.M.; writing—original draft preparation, C.L.M.; writing—review and editing, G.S., D.Z., J.W., I.N., R.H. and N.C.; visualization, C.L.M.; supervision, D.Z. and J.W.; project administration, C.L.M. and D.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This publication was supported by Open Access Publishing Fund of the University of Regensburg.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the ethics committee of the medical faculty of Leipzig University, Germany (No. 091/20-ek, 26 May 2020).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data supporting this study's findings are available from the corresponding author upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players**

**Francisco Martins 1,2, Krzysztof Przednowek 3, Cíntia França 1,2, Helder Lopes 1, Marcelo de Maio Nascimento 4, Hugo Sarmento 5, Adilson Marques 6,7, Andreas Ihle 8,9,10, Ricardo Henriques <sup>11</sup> and Élvio Rúbio Gouveia 1,2,9,\***


**Abstract:** Injuries are one of the most significant issues for elite football players. Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players through a sports season. The sample comprised 36 male elite football players who competed in the First Portuguese Soccer League in the 2020/2021 season. The models were calculated based on 22 independent variables that included players' information, body composition, physical fitness, and one dependent variable, the number of injuries per season. In the net elastic analysis, the variables that best predicted injury risk were sectorial positions (defensive and forward), body height, sit-andreach performance, 1 min number of push-ups, handgrip strength, and 35 m linear speed. This study considered multiple-input single-output regression-type models. The analysis showed that the most accurate model presented in this work generates an error of RMSE = 0.591. Our approach opens a novel perspective for injury prevention and training monitorization. Nevertheless, more studies are needed to identify risk factors associated with injury prediction in elite soccer players, as this is a rising topic that requires several analyses performed in different contexts.

**Keywords:** sports injuries; machine learning; injury prediction; sports monitorization; elite football

#### **1. Introduction**

Injuries are one of the most significant hampering issues for elite football players [1]. Football is known for its fast-paced and powerful actions [2,3], which might contribute to players' increased risk of injuries [4]. Due to their effects on individuals' mental states and overall teams' performances, elite players' injuries significantly impact the sports business [5,6]. Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for coaches and their technical staff members [7].

**Citation:** Martins, F.; Przednowek, K.; França, C.; Lopes, H.; de Maio Nascimento, M.; Sarmento, H.; Marques, A.; Ihle, A.; Henriques, R.; Gouveia, É.R. Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players. *J. Clin. Med.* **2022**, *11*, 4923. https:// doi.org/10.3390/jcm11164923

Academic Editors: Jiwu Chen and Yaying Sun

Received: 21 July 2022 Accepted: 18 August 2022 Published: 22 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

From the clinical standpoint, the literature describes the lower limbs as the most affected body zone by sports injuries [4,8–14], particularly for muscle injuries in the thigh area, the quadriceps, and the groin [4,10,15,16]. Since injuries in professional soccer are an increasingly problem, it is crucial that the work done in training sessions reflects the demands of competition, aiming at the development of athletes' performance, which includes injury prevention [17–19].

Machine learning or statistical learning methods are currently tools that can significantly support decision-making in various aspects of the training process. For instance, it has been reported in the literature that some models can optimize training loads [20], which reinforces the applicability of machine learning in improving injury prediction [21,22].

Researchers, managers, and coaches are becoming increasingly involved in injury forecasting, using regular data collection that will allow them to act consciously and intervene on time on this global issue [23]. An investigation conducted over 18 years showed that the total injury rate in practice and competition has dropped during the past years [24]. Although the cause leading to this decrease is still unknown, one potential explanation for this decrease may be related to the effectiveness of injury prevention. If so, it is likely that the motivation of the medical staff at elite football teams is increasing, in terms of implementing and overseeing preventive injury programs [24].

Machine learning offers a modern statistical method that uses algorithms mainly created to deal with unbalanced data sets and enable the modeling of interactions between a large number of variables [25]. In the football context, machine learning has been used in injury prediction, physical performance prediction, training load and monitoring, players' career trajectories, clubs' performance, and match attendance [26].

There has been some research done on elite-football-injury prediction up to this point [23,25,27–31]. In 2019, 96 male elite football players participated in a study throughout a season, with hamstring-strain injuries being the primary anticipated consequence. In that study, the prediction model showed moderate to high accuracy for identifying players at risk of hamstring-strain injuries during pre-season testing [31]. Another example involved 26 elite football players participating in year-long research to forecast non-contact injuries. The authors reported that machine learning was far more accurate than baselines and modern injury-risk-estimating approaches, detecting roughly 80% of injuries with about 50% accuracy [23]. In another study conducted with 132 male elite football and handball players, the prediction model accurately identified elite players at risk of developing muscular injuries [25].

Two types of variables are highlighted in the previous research on predictive modeling of injury risk [30]. The first block of predictor variables is modifiable variables, i.e., training loads or physiological and physical fitness tests. The second type is non-modifiable variables, including demographic variables, anthropometric parameters, and injury histories. Indeed, body composition and physical fitness tests are the most commonly assessed by sports staff given their close relationship with game performance and players' health. Moreover, evaluating and monitoring players' characteristics during the season provides valuable information to understand better players' behavioral changes and support coaches' decision-making in the training and match process. In the sports injury literature, most of the investigation conducted aimed to assess one specific variable at a time to predict injury risk. However, this approach limits the correlation of injury risk and a global interpretation of players' performance in professional football [23]. Therefore, this study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players across a sports season.

#### **2. Materials and Methods**

#### *2.1. Participants*

Thirty-six players from a professional football team participated in this study. This team competed in the First Portuguese League during the 2020/2021 season.

A description of the variables together with the basic statistics (M—mean value, SD—standard deviation) is given in Table 1. The models were calculated based on 22 independent variables (x1–x22) and one dependent variable (y). Independent variables include players' information (sectorial position, age, experience, and number of previous injuries), anthropometric parameters with body composition, and components of physical fitness (flexibility, general strength, explosive strength, speed, agility, and aerobic endurance). The dependent variable is the number of injuries per season. The predictive analysis did not use the data of all athletes. Twenty-four players' data were used. This was due to the fact that some of the athletes were noted to have missing data related to not taking certain physical fitness tests.


**Table 1.** Description of the variables used to construct the predictive model (N = 24).

\*—qualitative variable, M (mean value), sd (standard deviation), Me (median), TBW (total body water), BFM (body fat mass), FFM (fat free mass), CMJ (countermovement jump), SJ (squat jump), LS (linear speed), y (years), kg (kilograms), cm (centimeters), L (liters), *n* (number), s (speed), min (minutes), m (meters).

All procedures applied were approved by the Ethics Committee of the Faculty of Human Kinetics, CEIFMH No. 34/2021. The investigation was conducted following the Declaration of Helsinki, and informed consent was obtained from all participants.

#### *2.2. Body-Composition Assessment*

Body-composition variables were assessed using hand-to-foot bioelectrical impedance analysis (InBody 770, Cerritos, CA, USA). Height was measured to the nearest 0.1 cm using a stadiometer (SECA 213, Hamburg, Germany). The measurements occurred in the early morning, with participants fasting and wearing only their underwear. During the assessment, participants were barefoot, standing with both arms 45◦ apart from the trunk, with both feet bare on the spots of the platform. A total of 26 evaluations of body composition were considered during the season. Body mass, total body water (TBW), body fat mass (BFM), and fat-free mass (FFM) were retained for analysis.

#### *2.3. Physical Fitness Assessment*

The sit-and-reach bilateral test was used to evaluate flexibility measurement. A box (32.4 cm high and 53.3 cm long) with a 23 cm heel line mark was used. The participants sat barefoot in front of the box, with both knees fully extended and heels against the box. The research team held one hand lightly against each participant's knees to ensure complete leg extension. Then, participants placed their hands on top of each other, palms down, and slowly bent forward along the measuring scale. The forward-hold position was repeated twice. The third and final forward stretch was held for three seconds, and the score was recorded to the nearest 0.1 cm.

The push-ups test protocol consisted in performing the highest number of push-ups in one minute, respecting the success criteria judged by the evaluator. The participants started the test in the down position to get correct hand placement and then assumed the up position, from which they did the maximum number of push-ups possible. No cadence was used, although participants were encouraged to execute push-ups with good form but fast enough to obtain the best possible score in a minute. The evaluator independently counted the number of push-ups correctly executed.

The handgrip protocol consisted of three alternated data collection trials for each arm, performed using a hand dynamometer (Jamar Plus+, Chicago, IL, USA). Participants were instructed to hold a dynamometer in one hand, laterally to the trunk with the elbow at a 90◦ position [32]. From this position, participants were instructed to squeeze as hard as possible, progressively and continuously squeezing the hand dynamometer for about two seconds. The dynamometer could not contact the participant's body; otherwise, the trial was repeated. The best score of the three trials was retained for analysis.

The countermovement jump (CMJ) and the squat jump (SJ) were used to assess lowerbody explosive strength [33]. Both protocols included four data collection trials and were performed using the Optojump Next (Microgate, Bolzano, Italy) system of analysis and measurement. In both tests, participants were encouraged to jump to their maximum height. Before data collection, three experimental trials were performed by each participant to ensure correct execution. For the CMJ, participants began in a tall standing position, with feet placed hip-width to shoulder-width apart. Then, participants dropped into the countermovement position to a self-selected depth, followed by a maximal-effort vertical jump. Hands remained on the hips for the entire movement to eliminate any influence of arm swing. If the hands were removed from the hips at any point, or excessive knee flexion was exhibited during the countermovement, the trial was repeated. The participants reset to the starting position after each jump. The SJ protocol testing began with the participant in a squat position at a self-selected depth of approximately 90◦ of knee flexion, holding this position for the researchers' count of three before jumping. If a dipping movement of the hips was evident, then the trial was repeated. The participants reset to the starting position after each jump.

Linear speed was assessed with maximal sprints at 5, 10, and 35 m, starting from a stationary position. Sprint time was recorded using Witty-Gate photocells (Microgate, Bolzano, Italy). Participants were allowed two trials for each sprinting distance, and the best time was used for analysis.

A yoyo intermittent recovery test was applied to evaluate the athlete's maximum oxygen uptake under repeated high-intensity aerobic exercise [34,35]. The test consists of a 2 × 20 m shuttle run at increasing speeds, interspersed with 10 s of active recovery, controlled by audio signals. The test terminated when the subject was no longer able to maintain the required speed. The total distance and VO2 maximum record were used as results [36]. The results used were based on the athletes' performance in the yoyo test, which is an indirect method of measuring such variables.

All tests were performed on the same day within a 4 h period in the morning (8 a.m.–12 p.m.). They were conducted by trained staff from the research team, who were familiar with each protocol. All protocols were followed with the utmost rigor, and the organization of the sequence of physical tests was designed to reduce the fatigue factor throughout all tests.

#### *2.4. Injury Report*

This study followed the Union of European Football Association (UEFA)'s recommendations for epidemiological investigations. An injury was defined as an event during a scheduled training session or match, resulting in an absence from the next training session or match [37]. Regarding the variables under analysis, the type, zone, and specific location of the injury are complementary variables that identify the part of the body that suffered structural and/or functional changes. The mechanism of injury is intended to understand if the injury was traumatic or if it was contracted by overload. The severity of the injury considers the period, in days, from the athlete's stoppage until resuming field work with the consent of the clinical department. Finally, an injury was marked as recurrent when a player was injured in the same place and type where they were previously affected by an injury. Injury records during the season, including in training and competitive moments, were made daily by the clinical department.

#### *2.5. Predictive Modeling*

In this analysis, multiple-input single-output models for prediction were used. The output of the model is a continuous variable and represents the number of occurrences of potential injuries. Therefore, we consider regression-type models, not classifiers. Classic regression models (OLS), shrinkage regression, and stepwise regression were used in the models' calculations. All predictive models were calculated using R Software version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria, 2022). The implemented methods included:


The presented methods were used to calculate models from all variables (Table 1). Additionally, OLS, Ridge, LASSO, and elastic net models have been reimplemented for the best subset of input variables computed from stepwise regression. All models calculated in the study were tested by leave-one-out cross validation (LOOCV). In this method, the data set is divided into two subsets: learning and testing (validation). In LOOCV, the test set is composed of a selected pair of data (*xi*, *yi*), and the number of tests is equal to the number of data *n*. During the cross-validation, *RMSECV* error was calculated:

$$RMSE\_{CV} = \sqrt{\frac{1}{n} \sum\_{i=1}^{n} \left( y\_i - \hat{y}\_{-i} \right)^2}$$

where *n*—number of patterns, *y*−*i*—the output value of the model built in the *i*-th step of cross-validation based on a data set containing no testing pair (*xi*, *yi*), *y*ˆ*i*—the output value of the model built in the *i*-th step based on the full data set, and *RMSECV*—root mean square error of prediction.

#### **3. Results**

Table 2 summarizes the data regarding the participants and injuries characterization of Club Sport Marítimo in the 2020/2021 season. Of the 36 players participating in the study, 23 contracted at least one injury over the 2020/2021 season. Injured players missed an average of 14.3 days per injury. There were 0.9 injuries contracted by the number of participants (34 injuries/36 players) over the study period. Most injuries were classified as traumatic (52.9%). About 50% of the injuries were, according to their severity, moderate, since the athletes missed between 8 and 28 days of training and/or competition. Finally, four of the injuries counted were classified as recurrent.

**Table 2.** Characterization of participants and injuries of CS Marítimo in the 2020/2021 season.


\* Number of days missed by a player due to a sports injury contracted in training or match.

Figures 1–3 summarize the type, area, and specific location of injuries. The lower limbs were the body area most affected by injuries (85.2%). Sprains (35.2%) and muscle injuries (35.2%) were the most recurrent type of injuries throughout the study period, particularly in the ankles (29.4%), quadriceps (11.7%), and hamstrings (11.7%).

Table 3 presents the errors for each model and the sets of predictors calculated by the variable selection methods. The classical OLS regression model has the worst predictive ability, for which the error of RMSE = 18.57. Such a large error shows that the injuryprediction problem is complex and needs to be regularized by, among other things, using shrinkage regression. The use of shrinkage models (Ridge, LASSO, and elastic net) resulted in a sharp decrease in error and, thus, an improvement in the predictive ability of the model. The best model performing injury-prediction tasks for all predictors is the Ridge model, in which the RMSE error was 0.698. The optimal Ridge model was calculated for λ = 82.2. Optimizations of all shrinkage models are presented in Figure 4. The LASSO model for all predictors was not calculated because the algorithm does not work properly for such a configuration of the number of variables and patterns. Therefore, the following model used was the elastic net regression model. For elastic net regression, a very small prediction

error was obtained (RMSE = 0.633), and the number of predictors was reduced due to the properties of this method. The result of the elastic net analysis was that the best set of input variables is the set of seven variables: x1—sectorial position 1, x3—sectorial position 3, x7—body height, x12—sit and reach, x13—*n* push-ups, x15—handgrip (l), and x20—V35 m.

**Figure 1.** Injury frequency by zone (*n*).

**Figure 2.** Injury frequency by type (*n*).

**Figure 3.** Injury frequency by specific location (*n*).


**Table 3.** Predictive errors for calculated models.

**Figure 4.** *Cont*.

**Figure 4.** Optimization of predictive models (the red line indicates the optimal model).

The forward regression showed that the significant predictors are x1 – sectorial position 1, x12—sit and reach, x13—*n* push-ups, and x15—handgrip l). All the predictors determined by forward regression are contained in the set determined by elastic net regression. The model determined by forward regression generates an error of RMSE = 0.618. The predictors obtained using elastic net (E) and forward regression (F) were used in further predictive analysis. Both sets were used to recalculate the Ridge and LASSO models. The Ridge model with the set calculated by elastic net generates an error of RMSE = 0.592, and a very similar error was obtained for the Ridge model, with the set calculated by forward regression, with RMSE = 0.591. Both Ridge models with new sets of predictors show the best ability. LASSO models for enumerated sets of predictors showed worse predictive abilities than Ridge models. In the case of the best model, the model predicts the number of injury occurrences with an error of 0.59. This means that if a player has three injuries, the model would predict a value from the range of 2.41 to 3.59. The equations for the best models are presented in Table 4.



#### **4. Discussion**

This study aimed to analyze predictive modeling of injury risk based on players' sectorial position, body composition variables (i.e., weight, height, TBW, FAT, and FFM), and selected physical fitness tests, which include sit-and-reach, push-ups, handgrip, CMJ, SJ, 5 m, 10 m, 35 m, and yoyo tests.

This study considered multiple-input single-output regression-type models. It allowed us to select the best model to perform injury prediction tasks, considering all predictors. Previous work on predictive injury risk models is mostly based on classification learning models [31,43,44]. These models' predictive accuracy ranged from 75% to 82.9% [30]. The present study did not use a categorical variable but rather a continuous variable. A similar solution was presented in another work, where a continuous variable was also placed in the output [45]. A direct comparison of the models' predictive ability with those presented by other authors is complex because different quality criteria were used.

The value of cross-validation error is important, but a more critical element of the analysis presented was the identification of significant predictors of injury risk. An important part of the analysis was the variable-selection methods, resulting in a very clear and simplified model structure. The simple structure of the model and the linear nature of the methods made it possible to interpret the impact of individual variables on injury

risk. Data-selection mechanisms were also used by other authors who have also used LASSO [44].

According to the data collected for this study, a professional football team can experience 0.9 injuries for every player on the field. This number is noticeably lower than that reported in a study following the analysis of three sports seasons, averaging 1.5 injuries per player [4]. In reality, training load and competitive load—both internal and external—are variables that are related to muscle injuries and that change depending on the situation and level of competition. In this study, sprains and muscular injuries were the most common types of injuries in the lower limbs. The quadriceps and hamstrings were the next most afflicted muscles, followed by the ankles. These results are consistent with the previous findings in the literature [10,12–14,16]. In reality, the lower limbs are under more pressure in this activity because of the tactical–technical maneuvers needed, which justifies their increased risk of damage. Overload injuries were more common than traumatic injuries. A recent investigation also established the existence of such prevalence [4]. In contrast, a different article discovered that overload was the cause of two out of every three injuries in their study [12]. Since there is a strong link between training load and the likelihood of injury, it is imperative to emphasize the significance of appropriately structuring the training cycles according to the players' attributes and physical condition. When individual training loads are measured using the right tools, this process happens more reliably and consistently. Coaches, players, and their technical-support personnel increasingly monitor and evaluate the sports load using a scientific method [46]. In reality, keeping an eye on the training process is essential for assessing the level of athlete weariness, which may help to lower the risk of injury. Soccer involves physical contact and high intensity. Therefore, injury-prevention procedures should take both overload and traumatic injuries into account. Each athlete missed 14.3 days of practice or competition after suffering an injury, on average. This finding differs from that seen in the literature, with players missing an average of seven to eight days owing to injury [4,8,12]. On the other hand, we draw the conclusion that more serious injuries result in a longer period of player absence. This demonstrates the necessity of strengthening all preventative and rehabilitation efforts, while taking into consideration the predictive variables of injury as well as more frequent medical checkups and physical testing. Some authors claim that muscle injuries in soccer are the most common [9,10], converging with our findings. The injury-recurrence rate in our study is consistent with the rates reported in the literature, which range from 8% to 22% [9,47,48]. According to earlier research, these percentage discrepancies may result from the resources available in the individual clinical departments as well as a particular club's infrastructure and material-resource capabilities to respond quickly, in order to maximize the injury prevention and healing process.

Regarding the impact of selected predictors included in the models, first of all, for sectorial position, the defensive and forward sectors were the ones that presented a higher risk of injury. A previous study conducted across three consecutive seasons with 123 Chilean elite male football players also reported that the defensive and forward sectors were the ones that contracted more injuries over the study period [4]. Among 71 Spanish elite male players, forwards were the ones who presented the highest rates in both incidence and severity of injury [14]. Indeed, the literature has described that certain positions, such as fullbacks and forwards, have more demanding tasks both in-game and during training sessions, such as covering greater distances and running with higher intensity than their peers. Overall, fullbacks and forwards perform a total of 29–35 sprints, which is higher than other positions (approx. 17–23 sprints) [49], which may justify their higher injury rates (i.e., hamstring injuries) [50,51]. Therefore, managing training loads appropriately following the physical demands of different sectors and playing positions might be a helpful method to lower the risk of injury in football [52]. Sports agents and coaches should consider load exposure according to players' position, particularly when designing training sessions [52]. Moreover, our results consolidate the need to consider the players' position as a variable to be included in the definition of injury-risk programs.

Another important predictor identified in our study was lower-limb flexibility. The sit-and-reach test is one of the physical fitness tests mostly used to predict the injury risk of elite football players across a sports season. In the literature, several studies have concluded that reduced flexibility in the lower limbs is related to the increased risk of injuries in elite football players [53–56]. Some studies report that it is essential to develop and introduce a standard battery assessment of flexibility in preseason tests, contributing to the awareness of the players' profile [56,57]. The newest Guidelines for Exercise Testing and Prescription from the American College of Sports Medicine reported that maintaining good flexibility in all joints depends on many specific variables, including distensibility of the joint capsule and muscle viscosity, which facilitates movement and may prevent injuries [58]. However, we must acknowledge some limitations on the topic. First, it is not entirely understood if pre-activity stretching unequivocally reduces injuries associated with training load. Secondly, the most recent guidelines recommend direct measures of range of motion (i.e., goniometer and inclinometer) rather than indirect methods, such as sit-and-reach tests assessing flexibility. This means that most of the indirect measures that we most often use in various sports context are coming into disuse. It is recommended that direct measures of range of motion should be used more regularly. In general, the important focus will be that future studies continue to investigate this topic, so we can draw more reliable and valid conclusions regarding the relationship between flexibility and sports injuries.

According to our analyses, the push-up, handgrip, and 35 m linear sprint tests may be reliable predictors of injury risk among elite football players. Besides, height was also one of the variables significantly integrated into injury-prediction models in elite football players. Those variables can be related to each other, since they all end up influencing the players' sports performance. In fact, the main value of this study is directed towards sports monitoring and injury prevention, as we analyzed the relationship between overall strength and height in elite soccer players as predictors of injury, and this is a topic on the rise. In the literature, we identified two studies conducted with youth footballers that have determined that injured players were significantly stronger, bigger, and more experienced than noninjured players [59,60]. This aspect becomes even more relevant when we talk about elite football players, since their demands are higher. The slightest physical differences can make all the difference in the outcome of individual action, dictating the outcome of crucial moments of games and seasons. We believe that these achievements can support future research on the topic to disentangle this complex net of variables that may affect the injury profile.

There are some limitations to this study that need to be acknowledged. The sample size and the fact that we only evaluated the elite players for 26 weeks across 42 weeks of the season are the main limitations of this study. The sample size is related to the number of patterns teaching predictive models. The greater the amount of recorded-injury information is, the better the material for calculating predictive models. Continued collection of learning patterns will improve the predictive ability of the models. Moreover, this is a cross-sectional study, which does not allow a cause–effect of the presented results. However, these results bring important and specific practical implications for those involved in the elite football context, mainly for the topics of injury prevention and training monitorization, since these are issues that are gaining significant attention in the sports business.

#### **5. Conclusions**

Addressing the need for further studies to identify risk factors for predicting injuries in elite football players, our approach opens a novel perspective on injury prevention and training monitorization, providing a methodology for evaluating and interpreting the complex relations between injury risk and players' performance in elite football. Players' sectorial position, body-composition variables, and physical fitness tests (sit-and-reach, push-up, handgrip, countermovement jump, squat jump, linear speed, and yoyo tests), were all important predictors that may be considered in the injury-risk prevention in elite

football players. It would be an added value if future studies analyzed the influence of bodycomposition factors and physical fitness tests in elite football teams across different seasons.

**Author Contributions:** Conceptualization, F.M., É.R.G., K.P. and C.F.; methodology, F.M., É.R.G., K.P., C.F. and R.H.; validation, K.P., H.L. and R.H.; formal analysis, F.M., É.R.G., K.P. and C.F.; investigation, F.M., C.F. and R.H.; resources, É.R.G. and H.L.; writing—original draft preparation, F.M., É.R.G., K.P. and C.F.; writing—review and editing, M.d.M.N., H.S., A.M. and A.I.; visualization, M.d.M.N., H.S., A.M. and A.I.; project administration, É.R.G. and H.L.; funding acquisition, É.R.G. and H.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** C.F., F.M. and E.R.G. acknowledge support from LARSyS—the Portuguese national funding agency for science, research, and technology (FCT)—pluriannual funding 2020–2023 (Reference: UIDB/50009/2020). This study is framed in the Marítimo Training Lab Project. The project received funding under application no. M1420-01-0247-FEDER-000033 in the System of Incentives for the Production of Scientific and Technological Knowledge in the Autonomous Region of Madeira—PROCiência 2020.

**Institutional Review Board Statement:** This study was conducted according to the guidelines of the Declaration of Helsinki, was approved by the Ethics Committee of the Faculty of Human Kinetics (CEIFMH Nº34/2021), and followed the ethical standards of the Declaration of Helsinki for Medical Research in Humans (2013) and the Oviedo Convention (1997).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from all players to publish this paper.

**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.

**Acknowledgments:** The authors would like to thank all players and their respective legal guardians for participating in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Article* **Exercise Hypertension in Athletes**

**Karsten Keller 1,2,3,\*, Katharina Hartung 1, Luis del Castillo Carillo 1, Julia Treiber 1, Florian Stock 1, Chantal Schröder 1, Florian Hugenschmidt <sup>1</sup> and Birgit Friedmann-Bette <sup>1</sup>**


**Abstract: Background:** An exaggerated blood pressure response (EBPR) during exercise testing is not well defined, and several blood pressure thresholds are used in different studies and recommended in different guidelines. **Methods:** Competitive athletes of any age without known arterial hypertension who presented for preparticipation screening were included in the present study and categorized for EBPR according to American Heart Association (AHA), European Society of Cardiology (ESC), and American College of Sports Medicine (ACSM) guidelines as well as the systolic blood pressure/MET slope method. **Results:** Overall, 1137 athletes (mean age 21 years; 34.7% females) without known arterial hypertension were included April 2020–October 2021. Among them, 19.6%, 15.0%, and 6.8% were diagnosed EBPR according to ESC, AHA, and ACSM guidelines, respectively. Left ventricular hypertrophy (LVH) was detected in 20.5% of the athletes and was approximately two-fold more frequent in athletes with EBPR than in those without. While EBPR according to AHA (OR 2.35 [95%CI 1.66–3.33], *p* < 0.001) and ACSM guidelines (OR 1.81 [95%CI 1.05–3.09], *p* = 0.031) was independently (of age and sex) associated with LVH, EBPR defined according to ESC guidelines (OR 1.49 [95%CI 1.00–2.23], *p* = 0.051) was not. In adult athletes, only AHA guidelines (OR 1.96 [95%CI 1.32–2.90], *p* = 0.001) and systolic blood pressure/MET slope method (OR 1.73 [95%CI 1.08–2.78], *p* = 0.023) were independently predictive for LVH. **Conclusions:** Diverging guidelines exist for the screening regarding EBPR. In competitive athletes, the prevalence of EBPR was highest when applying the ESC (19.6%) and lowest using the ACSM guidelines (6.8%). An association of EBPR with LVH in adult athletes, independently of age and sex, was only found when the AHA guideline or the systolic blood pressure/MET slope method was applied.

**Keywords:** arterial hypertension; exercise hypertension; blood pressure; exercise testing

#### **1. Introduction**

Arterial hypertension is the most important and most common cardiovascular risk factor (CVRF) for morbidity and mortality worldwide [1–4]. The prevalence of arterial hypertension is high [5], affecting approximately 78 million adults in the United States of America [6]. While the prevalence of arterial hypertension increases substantially with age [7–10], its prevalence in athletes is low, at approximately 3% [11].

Diagnosis of arterial hypertension by resting blood pressure is well defined. In Europe, a systolic blood pressure (BP) value of ≥140 mmHg and a diastolic BP value of ≥90 mmHg are the defined thresholds of arterial hypertension [12–15]. In contrast, an exaggerated blood pressure response (EBPR) during treadmill and bicycle exercise testing is not well defined and poorly recognized, and several blood pressure thresholds were used in the different studies and are recommended in different guidelines [9,14,16–22]. While the American Heart Association (AHA) guideline [23] (EBPR threshold: systolic peak BP >210 mmHg

**Citation:** Keller, K.; Hartung, K.; del Castillo Carillo, L.; Treiber, J.; Stock, F.; Schröder, C.; Hugenschmidt, F.; Friedmann-Bette, B. Exercise Hypertension in Athletes. *J. Clin. Med.* **2022**, *11*, 4870. https://doi.org/ 10.3390/jcm11164870

Academic Editors: Jiwu Chen and Yaying Sun

Received: 26 July 2022 Accepted: 17 August 2022 Published: 19 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

in men, >190 mmHg in women, and/or >90 mmHg diastolic peak BP in both sexes) and the European Society of Cardiology (ESC) guideline [22,24] (EBPR threshold: systolic peak BP >220 mmHg in men, >200 mmHg in women, and/or >85 mmHg in men and 80 mmHg in women for diastolic peak BP) used sex-specific EBPR thresholds, the American College of Sports Medicine (ACSM) guideline [20,21] (EBPR threshold: systolic peak BP >225 mmHg and/or >90 mmHg for diastolic peak BP in both sexes) recommends the same systolic and diastolic thresholds values for both sexes.

However, for arterial-hypertension-naïve individuals with EBPR during the exercise testing, it was shown that these individuals are at increased risk of developing both arterial hypertension as well as cardiovascular events in the future, underlining the importance of this phenomenon [1,4,17,25–37].

In the context of arterial hypertension, it is well known that an increase in left ventricular mass and left ventricular hypertrophy (LVH) are associated with cardiovascular disease (CVD) as well as an elevated number of cardiovascular events and mortality [37,38]. Despite the development of the heart in highly trained athletes, a septal thickness of ≥13 mm was observed in only a very small number of athletes and should be considered as LVH in athletes [22,39–41].

Thus, the objectives of the present study were to evaluate (I) how prevalent EBPR is in athletes and (II) which definition of an EBPR during exercise testing was best associated with LVH in athletes without known arterial hypertension.

#### **2. Materials and Methods**

We performed a retrospective analysis of athletes of any age without known arterial hypertension who presented at the Department of Sports Medicine (Medical Clinic VII) of the University Hospital Heidelberg (Germany) for their preparticipation screening examination between April 2020 and October 2021.

#### *2.1. Enrolled Subjects*

Athletes were eligible for this study if they performed regular training for competition, were able to perform an exercise test at our department, had no contraindications for exercise testing, and had no known diagnosis of arterial hypertension. Exclusion criteria were a known diagnosis of arterial hypertension and contraindications regarding performing exercise testing [22,23].

#### *2.2. Ethical Aspects*

The requirement for informed consent was waived as we used only anonymized retrospective data routinely collected during the health screening process. Studies in Germany involving a retrospective analysis of diagnostic standard data of anonymized patients do not require an ethics statement.

#### *2.3. Definitions*

Arterial hypertension at rest was defined according to the ESC guidelines [42]. In all athletes, a transthoracic echocardiography was performed. Investigated echocardiographic parameters were defined according to current guidelines [22,43].

LVH was defined as (I) septal or posterior left ventricular (LV) wall diameter ≥13 mm [22,40] or (II) LV mass >162 g in female or >224 g in male individuals [43]. LV mass was computed according the established 2D echocardiography arealength method: LV mass = 0.80 × (1.04 × [(septal LV wall thickness + LV end-diastolic diameter + posterior LV wall thickness)3 − (LV end-diastolic diameter)3]) + 0.6 g [43]. LVH was considered to be present if one or both of the definitions applied.

EBPR was defined on the basis of the peak BP values during exercise testing according to three different guidelines and the systolic BP/MET slope method:

• American Heart Association (AHA) guidelines [23]: systolic peak BP >210 mmHg in men, >190 mmHg in women, and/or >90 mmHg diastolic peak BP in both sexes.


Obesity was defined as body mass index (BMI) ≥30 kg/m2 according to the World Health Organization.

#### *2.4. Statistics*

Athletes categorized as athletes with EBPR according to the three aforementioned guidelines and the systolic BP/MET slope method were compared to those athletes not categorized as EBPR (normal BP response during the exercise test) with the help of the Wilcoxon–Mann–Whitney U test for continuous variables and Fisher's exact or chi<sup>2</sup> test for categorical variables, as appropriate. Data of continuous variables were presented as median and interquartile range and categorical variables as absolute numbers with related percentages.

We performed univariate and multivariate logistic regression models to investigate the association between EBPR (defined according to the three guidelines) as well as BP values at rest and maximum values during exercise on the one hand and LVH on the other hand. Multivariate regression models were adjusted for age and sex in order to prove the independence of the statistical results of athletes' age and sex. Results of the logistic regressions are presented as odds ratio (OR) and 95% Confidence interval (CI).

All statistical analyses were carried out with the use of SPSS software (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY, USA). Only the *p* values < 0.05 (two-sided) were considered to be statistically significant. No adjustment for multiple testing was applied to the present analysis.

#### **3. Results**

#### *3.1. Athletes' Characteristics*

Overall, 1137 athletes (mean age 21 years; median 18 years (IQR 15/25); 395 (34.7%) females) without known arterial hypertension were included in the present study between April 2020 and October 2021. Most included athletes were in the second or third decade of life (Figure 1A). Among them, CVRF were rare, with nicotine abuse reported in 34 (3.0%) and obesity detected in 14 (1.2%) athletes. LVH was diagnosed in 233 athletes (regardless of athletes' sex: 20.5%; 87 female athletes (22.0%); 146 male athletes (19.7%)). Median past training period was 8 (IQR 5/12) years.

**Figure 1.** Included numbers of athletes and proportion of blood pressure deviations stratified for age by decade. Panel (**A**) Total numbers of included athletes stratified for age by decade. Panel (**B**) Proportion of athletes with exaggerated blood pressure response according to American Heart Association (AHA) guideline stratified for age by decade. Panel (**C**) Proportion of athletes with exaggerated blood pressure response according to European Society of Cardiology (ESC) guideline stratified for age by decade. Panel (**D**) Proportion of athletes with exaggerated blood pressure response according to American College of Sports Medicine (ACSM) guideline stratified for age by decade.

#### *3.2. Prevalence of Exaggerated Blood Pressure Response (EBPR) during Exercise Testing*

Overall, 223 athletes (regardless of athletes' sex: 19.6%; 74 female athletes (18.7%); 149 male athletes (20.1%)) had a diagnosis of EBPR according to AHA guidelines (Table 1), 171 (regardless of athletes' sex: 15.0%; 66 female athletes (16.7%); 105 male athletes (14.2%)) according to ESC guidelines (Table 2), and 77 (regardless of athletes' sex: 6.8%; 11 female athletes (2.8%); 66 male athletes (8.9%)) according to ACSM guidelines (Table 3).

**Table 1.** Patient characteristics of the 1137 examined athletes without known arterial hypertension stratified for exaggerated blood pressure response according to AHA guideline.



#### **Table 1.** *Cont.*


**Table 2.** Patient characteristics of the 1137 examined athletes without known arterial hypertension stratified for exaggerated blood pressure response according to ESC guideline.


#### **Table 2.** *Cont.*

**Table 3.** Patient characteristics of the 1137 examined athletes without known arterial hypertension stratified for exaggerated blood pressure response according to ACSM guideline.



#### **Table 3.** *Cont.*

*3.3. Comparison of Athletes with and without Exaggerated Blood Pressure Response (EBPR) during Exercise Testing*

While the proportions of female athletes with and without EBPR according to ESC and AHA guidelines were widely balanced, comprising approximately 1/3 of the athletes with EBPR, the proportion of male athletes with EBPR according to ACSM was distinctly higher, with 85.7% of all individuals with EBPR (Table 3). CVRF nicotine abuse and obesity were both more prevalent in athletes with EBPR regardless of which definition of EBPR was chosen (Tables 1–3). The criteria regarding full effort during the exercise test did not differ between athletes with and without EBPR (Tables 1–3).

The proportion of athletes with EBPR increased with inclining age regardless of the chosen definition. Notably, EBPR was more often diagnosed due to maximum systolic in comparison to maximum diastolic blood pressure values during exercise (Figure 1B–D).

#### *3.4. Prevalence of Left Ventricular Hypertrophy (LVH) in Athletes*

LVH was approximately two-fold more frequent in athletes with EBPR than in those without (risk ratios (RR) 2.2, 1.8, and 2.0 when using the definitions of AHA guidelines, ESC guidelines, and ACSM guidelines, respectively).

Interestingly, aortic valve regurgitation and mitral valve regurgitation were both more prevalent in athletes with EBPR (Tables 1–3).

#### *3.5. Association of Exaggerated Blood Pressure Response (EBPR) during Exercise Testing and Left Ventricular Hypertrophy (LVH) in Athletes*

In addition, we computed logistic regression models in order to analyse associations between EBPR defined according to the different guidelines on the one hand and LVH on the other hand. While EBPR according to the definition of the AHA guidelines (OR 2.35 (95%CI 1.66–3.33), *p* < 0.001) and the ACSM guidelines (OR 1.81 (95%CI 1.05–3.09), *p* = 0.031) were independently (of age and sex) associated with LVH, EBPR defined according to the ESC guidelines (OR 1.49 (95%CI 1.00–2.23), *p* = 0.051) was not independently associated with LVH (Figure 2B, Table 4).

**Figure 2.** Exaggerated blood pressure response and left ventricular hypertrophy. Panel (**A**) Proportion of left ventricular hypertrophy stratified for age by decades. Panel (**B**) Association of exaggerated blood pressure response according to AHA, ESC, and ACSM guidelines with left ventricular hypertrophy.

**Table 4.** Association between of exaggerated blood pressure response, blood pressure values at rest, and maximum value during exercise on the one hand and left ventricular hypertrophy on the other hand (univariate and multivariate logistic regression model).


In addition, LVH was associated with systolic BP at rest and maximum systolic BP during exercise, but not with diastolic BP values (Table 4).

#### *3.6. Prevalence of Exaggerated Blood Pressure Response (EBPR) during Exercise Testing and Left Ventricular Hypertrophy (LVH) in Adult Athletes*

When focusing on the adult athletes only, 598 athletes (33.1% females; median age 23.0 (19.0–29.0) years) aged 18 years or older remained in the analysis. Among these, 180 (30.1%) had an LVH.

According to the guideline definitions, 170 (regardless of athletes' sex: 28.4%; 54 female athletes (27.3%); 116 male athletes (29.0%)) athletes were classified as EBPR according to AHA guidelines, 137 (regardless of athletes' sex: 22.9%; 54 female athletes (27.3%); 83 male athletes (20.8%)) according to ESC guidelines, and 65 (regardless of athletes' sex: 10.9%; 11 female athletes (5.6%); 54 male athletes (13.5%)) according to ACSM guidelines.

#### *3.7. Association of Exaggerated Blood Pressure Response (EBPR) during Exercise Testing and Left Ventricular Hypertrophy (LVH) in Adult Athletes*

In adult athletes, only the definition of EBPR according to AHA guidelines was independently predictive for LVH (univariate: OR 1.88 (95%CI 1.29–2.74), *p* = 0.001; multivariate: OR 1.96 (95% CI 1.32–2.90), *p* = 0.001). EBPR according to the ESC (univariate: OR 1.40 (95% CI 0.94–2.10), *p* = 0.100; multivariate: OR 1.44 (95%CI 0.93–2.22), *p* = 0.104) as well as ACSM guidelines (univariate: OR 1.64 (95% CI 0.97–2.79), *p* = 0.067; multivariate: OR 1.73 (95% CI 0.98–3.07), *p* = 0.060) were not associated with LVH independently of age and sex.

#### *3.8. Prevalence of Exaggerated Blood Pressure Response (EBPR) during Exercise Testing Identified by Systolic BP/MET Slope Method with a Cutoff Value > 6.2 mmHg/MET*

When using the systolic BP/MET slope method with a cutoff value > 6.2 mmHg/MET to define an EBPR in those 639 athletes, who underwent spiroergometric testing, we detected 386 athletes (60.4%) with normal BP response and 253 athletes with EBPR (regardless of athletes' sex: 39.6%; 80 female athletes (36.5%); 173 male athletes (41.2%)) (Table 5). LVH was more prevalent in athletes with than without EBPR (29.6% vs. 16.6%, *p* < 0.001).

**Table 5.** Patient characteristics of the 639 examined athletes with spiroergometry and without known arterial hypertension stratified for exaggerated blood pressure response according to systolic blood pressure/MET slope.



**Table 5.** *Cont.*

*3.9. Association of Exaggerated Blood Pressure Response (EBPR) during Exercise Testing Identified by Systolic BP/MET Slope Method with a Cutoff Value > 6.2 mmHg/MET and Left Ventricular Hypertrophy (LVH) in Athletes*

Systolic BP/MET slope > 6.2 mmHg/MET was associated with LVH in the univariate regression analysis (OR 2.12 (95% CI 1.45–3.10), *p* < 0.001), but this association remained not significant after adjustment for age and sex (OR 2.26 (95% CI 0.40–12.66), *p* = 0.355). Sex-specific analyses revealed a significant association of systolic BP/MET slope > 6.2 mmHg/MET with LVH in male (OR 2.348 (95%CI 1.472–3.746), *p* < 0.001) in contrast to female athletes (OR 1.706 (95%CI 0.878–3.315), *p* = 0.115).

In contrast, in the 398 adult athletes with spiroergometric evaluation, systolic BP/MET slope > 6.2 mmHg/MET was associated with LVH in both, the univariate (OR 1.67 (95% CI 1.07–2.60), *p* = 0.023) as well as multivariate logistic regression analysis adjusted for age and sex (OR 1.73 (95% CI 1.08–2.78), *p* = 0.023). However, sex-specific analyses also revealed sex-specific differences in adult athletes. While systolic BP/MET slope > 6.2 mmHg/MET was associated with LVH in male adult athletes (OR 1.848 (95% CI 1.079–3.166), *p* = 0.025), in females, no association was seen (OR 1.325 (95% CI 0.603–2.913), *p* = 0.484).

#### **4. Discussion**

Arterial hypertension is accompanied by substantially increased cardiovascular morbidity and mortality [2,4,7,9,17,49–51].

Among individuals who were not categorized as patients with arterial hypertension [12–15] a number of individuals revealed EBPR during exercise testing. The consequences of this phenomenon are not well elucidated, and study results are inconsistent. In previous investigations, a large number of different definitions of EBPR were used, hampering a clear interpretation of study results [1,4,17,25–37]. However, several studies have shown that individuals without known arterial hypertension who present with EBPR during the exercise testing are at increased risk to develop arterial hypertension in the future and might also be prone to develop cardiovascular events [1,4,17,25–37]. Three guideline definitions are currently available and valid: the AHA [23], the ESC [22,24], and the ACSM guidelines [20,21]. In this context, it is widely unclear from which study sample these definitions were derived and whether these definitions were able to predict cardiovascular morbidity, e.g., LVH, in athletes.

Thus, the objectives of our present study were to evaluate the prevalence of EBPR in athletes and which definition regarding EBPR during exercise testing was best/strongest associated with LVH in athletes without known arterial hypertension.

The main results of the study can be summarized as follows:


Our study results reveal a large variation regarding the prevalence of EBPR according to the different guideline definitions in athletes without known arterial hypertension (variation of 12.8% according to different guideline recommendations). The prevalence was highest when categorized according to the ESC guidelines [22,24] (19.6%) and lowest when classified according to the ACSM guidelines [20,42] (6.8%). In contrast to the study of Caselli at al. [24], who reported that only a rate of 7.5% of the 1876 investigated athletes had an EBPR defined according to the ESC guidelines, we identified a frequency of 19.6% in the athletes presenting with EBPR according the ESC guidelines' definition. However, the differences between our results and the aforementioned study might be based on

differences regarding the performance level of the examined athletes and athletes' ages in both studies.

As expected, CVRF, such as nicotine abuse and obesity, were in our study more prevalent in those athletes with EBPR. This finding is in line with the literature, reporting a close relation between obesity and elevated blood pressure [52,53]. Arterial hypertension is frequently observed in individuals who are obese [53]. In addition, smoking was strongly associated with arterial hypertension in several studies [54,55].

The proportion of athletes with EBPR increased significantly with inclining age regardless of the chosen definition. In this context, studies underlined a physiological increase in BP with age [4,56–58]. While at birth, the systolic and diastolic BP values are on average at levels of 70 mmHg and 50 mmHg, respectively [4,56,58], BP values rise progressively throughout childhood and adolescence [4,56–58]. As aforementioned, BP is substantially determined by body weight, and it is of key interest that BP in childhood has a strong impact on adult BP levels [4,57,58]. Individuals aged ≥70 years reach an average systolic BP of approximately 140 mmHg. Diastolic BP tends also to rise with the aging process but the intense of this increase is less steep and after the 50th life year, diastolic mean BP either inclines only slightly or even declines [4,56]. These changes in BP reflect normal age-dependent development, while BP deviations due to arterial hypertension could be detected in every period of life [4,56]. The association between a growing burden of arterial hypertension with increasing age is well known and described [4,6,56,59]. While in Germany, 10–35% of the citizens aged between 30 and 60 were diagnosed with arterial hypertension, the frequency increases to higher than 65% in people aged 60 years and older [8]. In light of the quoted literature, an age-dependent increase regarding the proportion of athletes with EBPR might be expected but could also be interpreted as an increasing number of athletes who might have undiagnosed or masked arterial hypertension.

In stress situations, the BP rises from resting to stress level depending on the exercise intensity and the affecting stressor [4,17,19,60]. The BP responses to exercise are a result of cardiac output and peripheral vascular resistance [61]. Cardiac output is elevated to provide oxygenated blood and nutrition for the active regions of the body according to increased demand [62]. During physical activity, BP values increase, whereby the rise in systolic BP values becomes more pronounced compared to diastolic BP. BP values generally increase to an exercise dependent and predetermined individual limit [1,4,17,61]. Normal systolic BP response in progressive exercise testing on a bicycle stress test comprise a systolic BP increase of approximately 7 to 10 mmHg per 25 watt workload incline [19]. Expected maximal BP values in bicycle testing are approximately 200/100 mmHg in healthy untrained adults in the general population and approximately 215/105 mmHg in those individuals who are older than 50 years [16]. Notably, only systolic BP values, not diastolic values, could be reliably measured with the standardly used non-invasive methods [1].

Thus, in our present study, it is of outstanding importance that EBPR was more often diagnosed due to maximum systolic in comparison to maximum diastolic BP values during exercise, although all of the guideline recommendations defined a diastolic threshold regarding EBPR [20–24].

Although three different guideline recommendations for the definition of EBPR are available, only the EBPR definition of the AHA guidelines [23] was able to predict LVH independently of age and sex in both the overall sample as well as in adult athletes only in our study. Nevertheless, despite this result, we do not think that the definition of EBPR as systolic BP > 210 mmHg in men, > 190 mmHg in women, and/or > 90 mmHg diastolic peak BP in both sexes [23] is well suited to identify individuals at risk and deduce further consequences as a singular diagnostic tool in athletes. From the experiences of daily routine in sports medicine, the defined systolic BP values regarding EBPR are too low for exercise testing in male and female athletes. In accordance with these experiences of daily practice, it has been reported in the literature that very fit and powerful athletes reach physiologically higher BP values during competition as well as exercise testing [4,16,19,63]. Although, systolic BP values ≥ 250 mmHg and diastolic BP values ≥ 120 mmHg were defined as stopping criteria for bicycle ergometry exercise testing [16,63,64]—especially in young athletes, who exceed these thresholds within their normal sports practice—a stop of the exercise testing even at this higher and rigid recommended thresholds (250/120 mmHg) seems limited in its usefulness and the decision to stop should be made individually [16,19,63].

In order to encounter these only-in-part useful definitions of EBPR for athletes, a workload-indexed EBPR definition was introduced by different authors with promising results [44–47]. Our study confirmed these results—that an EBPR defined according to the systolic BP/MET slope method with a cutoff value >6.2 mmHg/MET was able to predict LVH in adult athletes independently of age and sex. A threshold of 6.2 mmHg/MET was chosen since a systolic BP/MET slope >6.2 mmHg/MET was in the study of Hedman et al. associated with a 27% higher risk for mortality during a 20-year observational period in males compared to those with <4.3 mmHg/MET [44,46]. However, we detected sex-specific differences regarding this associations between EBPR defined according to the systolic BP/MET slope method with a cutoff value >6.2 mmHg/MET and LVH with significant associations in males and missing associations in females. In accordance, several studies revealed sex-specific differences regarding blood pressure response in males and females [65–67]. In studies, men had significantly higher systolic BP values at 50%, 75%, and 100% of maximum exercise efforts [67].

Nevertheless, although these recommended EBPR thresholds—defined by the three guidelines—seem only in part to be suitable for athletes (but more for the general untrained population), an identified EBPR and especially a prolonged and delayed decline in blood pressure after exercise testing could provide clues regarding a masked arterial hypertension or development of a manifest arterial hypertension in the future [4,63].

In athletes with EBPR and/or a prolonged and delayed decline in blood pressure after exercise testing, a 24 h blood pressure measurement could give important and valuable additional diagnostic information [15]. Where the threshold regarding EBPR in athletes from which further diagnostic procedures should be implemented is still controversial [16,19,63].

#### **5. Conclusions**

EBPR was diagnosed in between 6.8% and 19.6% of all athletes without known arterial hypertension. Prevalence was highest when athletes were categorized according to ESC guidelines (19.6%) and lowest when categorized according to ACSM guidelines (6.8%). The proportion of athletes with EBPR increased with inclining age regardless of the chosen definition. Only the EBPR definition of the AHA guidelines and the systolic blood pressure/MET slope method were associated with LVH independently of age and sex in adult athletes. However, the prognostic value of this association remains to be elucidated by sufficiently powered in-depth long-term studies. Such studies are also necessary to further evaluate the importance of the identification of EBPR in athletes and the significance of actual EBPR guidelines as diagnostic tools in young athletes.

**Author Contributions:** Conceptualization, K.K. and B.F.-B.; Data curation, K.K. and J.T.; Formal analysis, K.K.; Investigation, K.K.; Methodology, K.K.; Project administration, K.K.; Supervision, K.K.; Visualization, K.K.; Writing—original draft, K.K.; Writing—review & editing, K.K., K.H., L.d.C.C., J.T., F.S., C.S., F.H. and B.F.-B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The requirement for informed consent was waived as we used only anonymized retrospective data routinely collected during the health screening process. Studies in Germany involving a retrospective analysis of diagnostic standard data of anonymized patients do not require an ethics statement.

**Informed Consent Statement:** The requirement for informed consent was waived as we used only anonymized retrospective data routinely collected during the health screening process. Studies in Germany involving a retrospective analysis of diagnostic standard data of anonymized patients do not require an ethics statement.

**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

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