Abstract
round:Various creatinine-based equations are used to estimate the glomerular filtration rate (eGFR) in athletes, but each has limitations. The aim of our study was to identify the most suitable formula for use in athletes.Methods:We evaluated 490 Olympic athletes (27±5.3 yo) with normal values of serum creatinine and no his- tory of kidney diseases. Athletes were divided into those practicing skills and endurance disciplines. The EGFR was calculated with Cockcroft–Gault (CG), MDRD, MCQE and CKD-EPI, and classified as stages G1–G5 according to the Kidney Disease Improving Global Outcomes (KDIGO) GFR categories.Results:Endurance athletes showed higher serum cre- atinine (0.91±0.14 mg/dL vs. 0.88±0.13 mg/dL in skills,p= 0.014). The eGFR calculated with the CKD-EPI and MCQE formulas showed no differences between the groups. The CG formula produced a lower eGFR for endurance athletes(113.6±27 mL/min/1.73 m 2 )
CKD-EPI, and classified as stages G1–G5 according to the Kidney Disease Improving Global Outcomes (KDIGO) GFR categories.Results:Endurance athletes showed higher serum cre- atinine (0.91±0.14 mg/dL vs. 0.88±0.13 mg/dL in skills,p= 0.014). The eGFR calculated with the CKD-EPI and MCQE formulas showed no differences between the groups. The CG formula produced a lower eGFR for endurance athletes(113.6±27 mL/min/1.73 m 2 ) compared to skills athletes (122.6±30.8,p= 0.008), while MDRD produced higher values for endurance athletes (129.3±25.8 vs. 122.6±24 mL/min/1.73 m 2 ,p= 0.004). According to CKD-EPI, all athletes were in G1, while with MCQE, 0.5% of skills athletes and 1% of endurance athletes were in G2. With the CG formula, a significant percentage of athletes were in G2 (13.2% of skills athletes and 18.5% of endurance athletes,p= 0.125). With the MDRD formula, 29 athletes (5.9%) were in G2 (6% for skills athletes and 5.8% for endurance athletes,p= 0.927).Conclusions:CKD-EPI and MCQE showed better stability and reliability, making them the most suitable for kidney function evaluation in athletes. Keywords:kidney; athletes; endurance; creatinine; sports medicine 1. Introduction Quantitative assessment of kidney function is a fundamental element in daily clinical practice, as well as in sports medicine, where it is used to assess athletes’ overall health status. This is of special relevance for endurance athletes, particularly those participating in high-volume, high-intensity ultra-running events, as they are at increased risk of acute kidney injury (AKI) [1,2], which raises concerns about the chronic development of pro- gressive renal scarring [3]. In this regard, however, the prevalence of AKI, which appears to be a rapidly self-resolving phenomenon in the absence of medical intervention [4–6], varies widely due to differences and inconsistency among the methods used to assess renal J. Clin. Med.2025,14, 2955 https://doi.org/10.3390/jcm14092955
J. Clin. Med.2025,14, 2955 2 of 13 function in this setting, with the risk of underestimating the extent of the problem [1,2,7]. Glomerular filtration rate (GFR) is traditionally considered the key index of kidney function, but its precise measurement by inulin or radioisotope studies is invasive, time-consuming, expensive, associated with radiation exposure and technically difficult [8–10]. Therefore, in view of the existence of an inverse, nonlinear relationship between GFR and serum creatinine concentration [11], a skeletal muscle catabolite closely influenced by muscle mass and dietary protein intake, several equations have been developed to estimate GFR by using serum creatinine data combined with readily available indices of muscular mass, such as gender, age and weight [9,10,12,13]. Furthermore, it is important to note that eGFR equations are not suitable for the assessment of AKI in athletes, as they assume a steady state of serum creatinine. In athletes, particularly after endurance exercise, creatinine levels may fluctuate rapidly, and eGFR calculations may significantly misrepresent acute changes in kidney function. The most common equations used in adults are the Cockcroft–Gault equation (CG) [14] and the simplified equation from the Modification of Diet in Renal Disease Study (MDRD) [15]. These equations differ in various aspects, including the predicted index (with CG, creatinine clearance is predicted, which is an estimate of the GFR value plus the tubular secretion, while with MDRD, true GFR is predicted), prediction units (ml/min for CG, which is uncorrected for BSA, as opposed to mL/min×1.73 m 2 for MDRD) and variables involved (serum creatinine, gender, age and body weight for CG; serum creatinine, gender, age and race for MDRD). However, estimation is only an approxi- mate calculation and reasons why the measurements might be incorrect are numerous, thus resulting in many criticisms related to the pros and cons of each formula [16]. CG, in fact, has a tendency to underestimate in older-age or underweight individuals and overestimate in overweight/obese individuals, as it includes body weight as an inappropriate indicator of muscle mass, without taking into account the fat component [9,10,12,16,17]. Moreover, having been designed to calculate an estimate of creatinine clearance, which includes
to the pros and cons of each formula [16]. CG, in fact, has a tendency to underestimate in older-age or underweight individuals and overestimate in overweight/obese individuals, as it includes body weight as an inappropriate indicator of muscle mass, without taking into account the fat component [9,10,12,16,17]. Moreover, having been designed to calculate an estimate of creatinine clearance, which includes both glomerular filtration and tubular secretion, it tends toward overestimation in people with normal renal function [9,16]. Similarly, MDRD has limitations too: being derived from subjects with moderate-to-severe renal failure, it exhibits lower accuracy in subjects without renal disease, with a tendency to underestimate the GFR, especially in those with a normal or slightly increased range of serum creatinine concentration and in females, irrespective of BMI, and underestimates to a greater extent in young subjects [9,10,12,17,18]. Therefore, in light of the reduced performance of MDRD in the healthy population, the Mayo Clinic Quadratic Equation (MCQE), based on serum creatinine, gender and age, was subsequently devised, with the aim of obtaining a more accurate estimate of true GFR in subjects without renal disease [19]. However, the latter equation, despite performing moderately well in comparison with gold-standard radionuclide GFR measurements, has not subsequently demonstrated any performance advantage in clinical practice over MDRD, even in healthy populations, and has shown a tendency to overestimate in males and individuals with a non-low GFR and obesity [12,20]. More recently, in view of the limited precision of the previous formulas, an additional new equation, the Chronic Kidney Disease Epidemiol- ogy Collaboration (CKD-EPI), was developed and derived from a population consisting predominantly of young or middle-aged people, with and without kidney disease, using the same four variables as the MDRD equation [21,22]. The CKD-EPI shows improved precision and gives the best overall accuracy in estimating the GFR throughout its whole range, providing a less biased GFR assessment than the MDRD study equation, particularly in middle-aged and female patients and those with GFR levels >60 mL/min/1.73 m 2 , where it reduces the apparent prevalence of chronic kidney disease (CKD) [23–28]. Lastly, owing to exercise-induced metabolic adaptations, the
and gives the best overall accuracy in estimating the GFR throughout its whole range, providing a less biased GFR assessment than the MDRD study equation, particularly in middle-aged and female patients and those with GFR levels >60 mL/min/1.73 m 2 , where it reduces the apparent prevalence of chronic kidney disease (CKD) [23–28]. Lastly, owing to exercise-induced metabolic adaptations, the interpretation of several biochemical
J. Clin. Med.2025,14, 2955 3 of 13 data requires caution in elite athletes, as specific reference values for laboratory parameters have never been established in this population, where those retrieved from the general population, including serum creatinine, are routinely applied. Although creatinine-based GFR estimates overcome some shortcomings of serum creatinine, only a few studies, with limited numbers of participants, have previously evaluated their use and performance in endurance athletes, showing significant variations in the estimated GFR among athletes and the influence of multiple factors in this particular group of individuals. Hence, the aim of our study was to compare different formulas for eGFR in a large cohort of elite endurance and skill athletes, in order to determine which equation provides the most appropriate estimate of kidney function in this unique population. 2. Materials and Methods The Institute of Sports Medicine and Science in Rome is an establishment of the Italian National Olympic Committee, with the mission of medically evaluating athletes selected for participation in the Olympic Games, World Championships and Mediterranean Games. In the present analysis, we recruited 490 Caucasian elite athletes evaluated before participating in Rio 2016, PyeongChang 2018 and Tokyo 2020 Olympic Games. Power calculation was performed based on effect sizes and variability reported in previous studies investigating renal function and serum creatinine in athletic populations. Athletes underwent complete physical examination, anthropometric analysis and complete blood tests. None of the enrolled athletes were taking chronic pharmacological therapy or creatine supplementation. Twenty-three Afro-Caribbean athletes were excluded; of these, five athletes with high blood pressure taking chronic pharmacological therapy were excluded, and two athletes with type 1 diabetes were excluded. All athletes included in our study presented normal values of serum creatinine and no previous history of kidney disease. Athletes participated in different sports disciplines, and were classified into two groups, according to European Society of Cardiology (ESC) guidelines [29,30]: (1)Skills: archery, equestrian, golf, shooting, figure skating, sailing, curling, diving, surfing and equestrian sports. (2)Endurance: cycling, rowing, canoeing, triathlon, long-distance running, long-distance swimming (over 800 m), cross-country skiing, pentathlon, biathlon, Nordic combined and long-distance skating. Body composition and fat mass
participated in different sports disciplines, and were classified into two groups, according to European Society of Cardiology (ESC) guidelines [29,30]: (1)Skills: archery, equestrian, golf, shooting, figure skating, sailing, curling, diving, surfing and equestrian sports. (2)Endurance: cycling, rowing, canoeing, triathlon, long-distance running, long-distance swimming (over 800 m), cross-country skiing, pentathlon, biathlon, Nordic combined and long-distance skating. Body composition and fat mass percentage were measured using Bioelectric Impedance Analysis (BIA 101 Quantum, Akern, Pisa, Italy) with a constant sinusoidal current at an intensity of 50 kHz and 400µA. Body height and weight were obtained for each subject, and the body mass index (BMI) was calculated as weight (kg)\height (m) 2 . Body surface area (BSA) was derived with the Mosteller formula [31]. The CV risk factors evaluated in this study were defined as follows: cigarette smoking was defined as regular smoking of at least one cigarette per day; obesity was defined as BMI > 30. Blood samples were drawn after fasting with an aseptic technique from a vein in the cubital fossa, and were transported to an adjacent laboratory, where analysis was performed on the same day. The following biochemical parameters were assessed: full blood count, ferritin, transferrin, iron, potassium, calcium, magnesium, urate, creatinine, aspartate amino-transaminase (AST), alanine aminotransferase (ALT), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and vitamin D. The estimated glomerular filtration rate (eGFR), expressed with the unit mL/min/1.73 m 2 , was calculated by the following formulas: - Cockcroft–Gault (CG) formula (27,28): eGFR = (140−age)×weight (kg)/(72×serum creatinine)×(0.85 if female) [13,14]
J. Clin. Med.2025,14, 2955 4 of 13 - MDRD: eGFR = 175×(serum creatinine −1.154 )×(age −0.203 )×1.212 (if black) or\and×0.742 (if female) [15] - MCQE: eGFR = exp {1.911 + (5.249/serum creatinine)−(2.114/serum creatinine 2 )− 0.00686×age (−0.205 if female)} [19] - CKD-EPI (Chronic Kidney Disease Epidemiology): eGFR = 141×min (Scr/κ,1) α × max (Scr/κ, 1) −1.209 × 0.993 Age × 1.018 [if female]×1.159 [if black]; Scr is serum creatinine (mg/dL),κis 0.7 for females and 0.9 for males,αis−0.329 for females and−0.411 for males, min indicates the minimum of Scr/κor 1, and max indicates the maximum of Scr/κor 1 [ Participants were classified as stages G1-G5, according to Kidney Disease Improving Global Outcomes (KDIGO) glomerular filtration rate (GFR) categories (1): G1: eGFR≥ 90 mL/min/m 2 , normal; G2: eGFR 60–90 mL/min/m 2 , mildly decreased; G3a: eGFR 45–59.9 mL/min/m 2 , mildly to moderately decreased; G3b: eGFR 30–44.9 mL/min/m 2 , moderately to severely decreased; G4: eGFR 15–29.9 mL/min/m 2 ; severely decreased; G5: eGFR < 15 mL/min/m 2 , kidney failure [32]. The study design of the present investigation was evaluated and approved by the Review Board of the Institute of Medicine and Sports Science. All athletes included in this study were fully informed of the types and nature of evaluation and signed the consent form, according to Italian Law and Institute policy. The data that support the findings of this study are not openly available due to reasons of sensitivity, and are available from the corresponding author upon reasonable request. The data are located in controlled-access data storage at the Institute of Sports Medicine and Science. The work described has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki). 3. Statistical Analysis Categorical variables were expressed as frequencies and percentages in parentheses and compared using Fisher’s exact test or the Chi-square test, as appropriate. Normality criteria were checked for any continuous variables, which were presented as the mean and standard deviation (SD) and compared using Student’st-test for independent data if normally distributed. All tests were significant ifp< 0.05. Statistical analysis was
Analysis Categorical variables were expressed as frequencies and percentages in parentheses and compared using Fisher’s exact test or the Chi-square test, as appropriate. Normality criteria were checked for any continuous variables, which were presented as the mean and standard deviation (SD) and compared using Student’st-test for independent data if normally distributed. All tests were significant ifp< 0.05. Statistical analysis was performed with STATA Statistics for Windows (SE, version 17). 4. Results We enrolled 490 Olympic athletes, with a mean age of 27±5.3 years and a mean BMI of 22.7±3.2; 292 (59.5) were male, 5.3% (26) were active smokers and 20.8% (102) had a family history of cardiovascular diseases. Athletes practiced different sporting disciplines, divided into skills (182 athletes, 37.1%) and endurance (308 athletes, 62.9%). Table blood test results. Compared to athletes practicing skills disciplines, endurance athletes presented a lower BMI (21.9±3.1 kg/m 2 vs. 23.9±3.1 kg/m 2 ,p< 0.0001) and a lower fat mass (13.2±5.3% vs. 20.3±7.8%,p< 0.0001), with an accordingly lower prevalence of obesity (0% vs. 6%,p< 0.0001). Differential diet composition was noted between the athlete categories, with endurance athletes having a higher daily kcal intake (2807.4±738 vs. 2231.9±482.2,p< 0.0001), a higher carbohydrate intake (51.8±5% vs. 49.3±5.2%, p= 0.001) and a similar protein intake (p= 0.131).
J. Clin. Med.2025,14, 2955 5 of 13 Table 1.Demographic characteristics, anthropometric and clinical parameters and blood test result differences between athletes, according to sporting discipline. Skills Endurance p-Value n, (%) 182 (37.1) 308 (62.9) Male, n (%) 106 (58.2) 186 (60.4) 0.639 Age, mean 27.5 ±5.5 26.6 ±4.6 0.086 Weight, kg 72.5.4 ±11.5 69.1 ±14.6 0.013 BMI, kg\m 2 23.9±3.1 21.9 ±3.1 <0.0001 BSA 1.85 ±0.21 1.82 ±0.22 0.171 Fat mass, % 20.3 ±7.8 13.2 ±5.3 <0.0001 Smokers, n (%) 25 (13.7) 1 (0.3) <0.0001 Family history for CVD, n (%) 44 (24.2) 58 (18.8) 0.159 SPB, mmHg 109.0 ±24.2 107.7 ±16.9 0.433 DBP, mmHg 68.7 ±10.9 67.1 ±10.7 0.152 Obesity, n (%); BMI > 30 kg/m 2 11 (6) 0 (0) <0.0001 Kcal 2231.9 ±482.2 2807.4 ±738 <0.0001 Protein, % 19.6 ±4.0 18.8 ±3.5 0.131 Fat, % 30.2 ±5.4 29.2 ±3.3 0.087 Carbohydrate, % 49.3 ±5.2 51.8 ±5 0.001 CPK, U/L 178.3 ±211 260.9 ±325.3 0.002 AST, U/L 21.3 ±6.5 29.3 ±15.5 <0.0001 ALT, U/L 20.7 ±9.7 24 ±12.5 0.002 Creatinine, mg/dL 0.88 ±0.13 0.91 ±0.14 0.014 CKD-EPI, mL/min×1.73 m 2 121.7±7.9 121 ±7.1 0.321 G2: eGFR 60–89.9 mL/min×1.73 m 2 0 (0) 0 (0) G1: eGFR≥90 mL/min×1.73 m 2 182 (100) 308 (100) 1.000 CG, mL/min×1.73 m 2 122.6±30.8 113.6 ±27 0.0008 G2: eGFR 60–89.9 mL/min×1.73 m 2 24 (13.2) 57 (18.5) 0.125 G1: eGFR≥90 mL/min×1.73 m 2 158 (86.8) 251 (81.5) MCQE, mL/min×1.73 m 2 134.5±12.9 133.8 ±14.4 0.593 G2: eGFR 60–89.9 mL/min×1.73 m 2 1 (0.5) 3 (1) 0.614 G1: eGFR≥90 mL/min×1.73 m 2 181 (99.5) 305 (99) MDRD, mL/min×1.73 m 2 122.6±24 129.3 ±25.8 0.004 G2: eGFR 60–89.9 mL/min×1.73 m 2 11 (6) 18 (5.8) 0.927 G1: eGFR≥90 mL/min×1.73 m 2 171 (94) 290 (94.2) Abbreviations: ALT: alanine aminotransferase; AST: aspartate-transferase; BMI: body mass index; BSA: body surface area; CG: Cockcroft–Gault; CKD-EPI: Chronic Kidney Disease Epidemiology; CPK: creatine phosphoki- nase; CVD: cardiovascular diseases; DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate; MCQE: Mayo Clinic Quadratic Equation; MDRD: Modification of Diet in Renal Disease formula. 4.1. Kidney Function Endurance athletes showed higher serum creatinine values
Abbreviations: ALT: alanine aminotransferase; AST: aspartate-transferase; BMI: body mass index; BSA: body surface area; CG: Cockcroft–Gault; CKD-EPI: Chronic Kidney Disease Epidemiology; CPK: creatine phosphoki- nase; CVD: cardiovascular diseases; DBP: diastolic blood pressure; eGFR: estimated glomerular filtration rate; MCQE: Mayo Clinic Quadratic Equation; MDRD: Modification of Diet in Renal Disease formula. 4.1. Kidney Function Endurance athletes showed higher serum creatinine values (0.91±0.14 mg/dL vs. 0.88±0.13 mg/dL,p= 0.014) compared to skills athletes. According to the CKD-EPI and MCQE formulas, similar eGFRs were found between the two athlete categories: with CKD-EPI, 121.7±7.9 mL/min×1.73 m 2 in skills athletes vs. 121±7.1 mL/min×1.73 m 2 in endurance athletes, withp= 0.321; with MCQE, 134.5±12.9 mL/min×1.73 m 2 in skills athletes vs. 133.8±14.4 mL/min×1.73 m 2 in endurance athletes, withp= 0.593. Significant differences were found with the CG formula, with endurance athletes showing lower eGFR values: 122.6±30.8 mL/min×1.73 m 2 vs. 113.6±27 mL/min×1.73 m 2 , p= 0.008. On the contrary, significant differences were found with the MDRD for- mula, but endurance athletes showed higher values (129.3±25.8 mL/min×1.73 m 2 vs.122.6±24 mL/min× 1.73 m 2 ,p= 0.004. Globally, no cases of athletes in the G3b, G4 or G5 categories were observed. According to CKD-EPI, all athletes were in G1, while with
J. Clin. Med.2025,14, 2955 6 of 13 MCQE, only one (0.5%) skills athlete and three (1%) endurance athletes were in G2. On the contrary, with the CG formula, a substantial percentage of athletes (of both categories) were in G2 (n = 24, 13.2% for skills, and n = 57, 18.5% for endurance,p= 0.125). With the MDRD formula, a total of 29 athletes (5.9%) were in G2 (n = 11, 6% for skills, and n = 18, 5.8% for endurance,p= 0.927). 4.2. Gender Differences As shown in Table, several significant gender differences were found. Table 2.Gender differences in anthropometric and clinical parameters and blood test results, accord- ing to sporting discipline. Male, n = 292 Female, n = 198 Skills Endurance Skills Endurance p- Value Skills Endurance p- Value Male vs. Female Male vs. Female n, (%) 106 (36.3) 186 (63.7) 76 (38.4) 122 (61.6) Age, mean 28.2 ±6.3 26.8±4.5 0.032 26.6 ±5.8 26.4±4.8 0.871 0.089 0.542 Weight, kg 78.9 ±12.8 75.7±11.9 0.035 63.5 ±10.3 59.1±12.4 0.010 <0.0001 <0.0001 BMI, Kg\m 2 24.6±3.2 22.7±2.6 <0.0001 23±3 20.9 ±3.4 <0.00010.0006 <0.0001 BSA 1.96 ±0.18 1.94±0.19 0.360 1.69 ±0.15 1.64±0.13 0.012 <0.0001 <0.0001 Fat mass, % 16.7 ±7 10 ±3.3 <0.000125±6.1 18 ±4 <0.0001<0.0001 <0.0001 Creatinine, mg/dL 0.93 ±0.1 0.97±0.1 0.048 0.81 ±0.1 0.84±0.12 0.132 <0.0001 <0.0001 K + , mEqu/L 4.47 ±0.3 4.56±0.3 0.033 4.49 ±0.4 4.52±0.3 0.549 0.702 0.400 CKD-EPI, mL/min×1.73 m 2 118.4±6.2 118.3±6.2 0.891 126.2 ±7.8 125.1±6.4 0.266 <0.0001 <0.0001 G2: eGFR 60–89.9 mL/min×1.73 m 2 0 (0) 0 (0) 0 (0) 0 (0) G1: eGFR≥90 mL/min×1.73 m 2 106 (100) 186 (100) 1.000 76 (100) 122 (100) 1.000 CG, mL/min×1.73 m 2 133.2±27.4 125±20.7 0.047 107.8 ±29 96±26.1 0.003 <0.0001 <0.0001 G2: eGFR 60–89.9 mL/min×1.73 m 2 0 (0) 4 (2.1) 0.132 24 (31.6) 53 (43.3) 0.095 <0.0001 <0.0001 G1: eGFR≥90 mL/min×1.73 m 2 106 (100) 182 (97.9) 52 (68.4) 69 (54.7) MCQE, mL/min×1.73 m 2 133.3±12.4 130.5± 15.4 0.120 136.3± 13.3 138.8± 11.1 0.145 0.124 <0.0001 G2: eGFR 60–89.9 mL/min×1.73 m 2 0 (0) 3 (1.6) 0.187 1 (1.3) 0 (0) 0.204 0.263 0.158 G1: eGFR≥90 mL/min×1.73
Description
This study evaluates kidney function estimation formulas in elite endurance athletes.