Dataset Information

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

ABSTRACT: Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood draw at baseline and after a three-year follow-up period. In order to compile a statistical model containing baseline laboratory results allowing prediction of follow-up ergometry performance, the cohort was subgrouped into a model training (n = 25) and a test sample (n = 22). The model containing significant predictors in univariate analysis (alanine aminotransferase, urea, folic acid, myeloperoxidase and total cholesterol) presented with high statistical significance and excellent goodness of fit (R2 = 0.789, ROC-AUC = 0.951±0.050) in the model training sample and was validated in the test sample (ROC-AUC = 0.786±0.098). Our results suggest that standard laboratory parameters could be particularly useful for predicting future physical capacity in elderly marathoners. It hence merits further research whether these conclusions can be translated to other disciplines or age groups.

SUBMITTER: Haslacher H

PROVIDER: S-EPMC5419574 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

Haslacher Helmuth H Ratzinger Franz F Perkmann Thomas T Batmyagmar Delgerdalai D Nistler Sonja S Scherzer Thomas M TM Ponocny-Seliger Elisabeth E Pilger Alexander A Gerner Marlene M Scheichenberger Vanessa V Kundi Michael M Endler Georg G Wagner Oswald F OF Winker Robert R

PloS one 20170505 5

Endurance sports are enjoying greater popularity, particularly among new target groups such as the elderly. Predictors of future physical capacities providing a basis for training adaptations are in high demand. We therefore aimed to estimate the future physical performance of elderly marathoners (runners/bicyclists) using a set of easily accessible standard laboratory parameters. To this end, 47 elderly marathon athletes underwent physical examinations including bicycle ergometry and a blood dr ...[more]

PMID: 28475643

Similar Datasets

Project description:Background: Properly performed training is a matter of importance for endurance athletes (EA). It allows for achieving better results and safer participation. Recently, the development of machine learning methods has been observed in sports diagnostics. Velocity at anaerobic threshold (VAT), respiratory compensation point (VRCP), and maximal velocity (Vmax) are the variables closely corresponding to endurance performance. The primary aims of this study were to find the strongest predictors of VAT, VRCP, Vmax, to derive and internally validate prediction models for males (1) and females (2) under TRIPOD guidelines, and to assess their machine learning accuracy. Materials and Methods: A total of 4001 EA (nmales = 3300, nfemales = 671; age = 35.56 ± 8.12 years; BMI = 23.66 ± 2.58 kg·m−2; VO2max = 53.20 ± 7.17 mL·min−1·kg−1) underwent treadmill cardiopulmonary exercise testing (CPET) and bioimpedance body composition analysis. XGBoost was used to select running performance predictors. Multivariable linear regression was applied to build prediction models. Ten-fold cross-validation was incorporated for accuracy evaluation during internal validation. Results: Oxygen uptake, blood lactate, pulmonary ventilation, and somatic parameters (BMI, age, and body fat percentage) showed the highest impact on velocity. For VAT R2 = 0.57 (1) and 0.62 (2), derivation RMSE = 0.909 (1); 0.828 (2), validation RMSE = 0.913 (1); 0.838 (2), derivation MAE = 0.708 (1); 0.657 (2), and validation MAE = 0.710 (1); 0.665 (2). For VRCP R2 = 0.62 (1) and 0.67 (2), derivation RMSE = 1.066 (1) and 0.964 (2), validation RMSE = 1.070 (1) and 0.978 (2), derivation MAE = 0.832 (1) and 0.752 (2), validation MAE = 0.060 (1) and 0.763 (2). For Vmax R2 = 0.57 (1) and 0.65 (2), derivation RMSE = 1.202 (1) and 1.095 (2), validation RMSE = 1.205 (1) and 1.111 (2), derivation MAE = 0.943 (1) and 0.861 (2), and validation MAE = 0.944 (1) and 0.881 (2). Conclusions: The use of machine-learning methods allows for the precise determination of predictors of both submaximal and maximal running performance. Prediction models based on selected variables are characterized by high precision and high repeatability. The results can be used to personalize training and adjust the optimal therapeutic protocol in clinical settings, with a target population of EA.

Dataset Information

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

Publications

A combination of routine blood analytes predicts fitness decrement in elderly endurance athletes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets