Šoltys Katarína, Lendvorský Leonard, Hric Ivan, Baranovičová Eva, Penesová Adela, Mikula Ivan, Bohmer Miroslav, Budiš Jaroslav, Vávrová Silvia, Grones Jozef, Grendar Marian, Kolísek Martin, Bielik Viktor
Department of Microbiology and Virology, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovakia.
Comenius University Science Park, Comenius University in Bratislava, Bratislava, Slovakia.
Front Physiol. 2021 Jun 22;12:670989. doi: 10.3389/fphys.2021.670989. eCollection 2021.
Regular physical activity seems to have a positive effect on the microbiota composition of the elderly, but little is known about the added possible benefits of strenuous endurance training. To gain insight into the physiology of the elderly and to identify biomarkers associated with endurance training, we combined different omics approaches. We aimed to investigate the gut microbiome, plasma composition, body composition, cardiorespiratory fitness, and muscle strength of lifetime elderly endurance athletes (LA) age 63.5 (95% CI 61.4, 65.7), height 177.2 (95% CI 174.4, 180.1) cm, weight 77.8 (95% CI 75.1, 80.5) kg, VO2max 42.4 (95% CI 39.8, 45.0) ml.kg.min ( = 13) and healthy controls age 64.9 (95% CI 62.1, 67.7), height 174.9 (95% CI 171.2, 178.6) cm, weight 83.4 (95% CI 77.1, 89.7) kg, VO2max 28.9 (95% CI 23.9, 33.9), ml.kg.min ( = 9). Microbiome analysis was performed on collected stool samples further subjected to 16S rRNA gene analysis. NMR-spectroscopic analysis was applied to determine and compare selected blood plasma metabolites mostly linked to energy metabolism. The machine learning (ML) analysis discriminated subjects from the LA and CTRL groups using the joint predictors 1.8E + 00 (95% CI 1.1, 2.5)%, 3.8E + 00 (95% CI 2.7, 4.8)% ( = 0.002); 1.3 (95% CI 0.28, 2.4)%, 0.1 (95% CI 0.07, 0.3)% ( = 0.02); 1.3E-02 (95% CI 9.3E-03, 1.7E-02)%, 5.9E-03 (95% CI 3.9E-03, 7.9E-03)% ( = 0.002), 7.9E-02 (95% CI 2.5E-02, 1.3E-02)%, 3.2E-02 (95% CI 1.8E-02, 4.6E-02)% ( = 0.02); and the ratio of to 133 (95% CI -86.2, 352), 732 (95% CI 385, 1079.3) ( = 0.03), leading to an ROC curve with AUC of 0.94. Further, random forest ML analysis identified VO2max, BMI, and the to ratio as appropriate, joint predictors for discriminating between subjects from the LA and CTRL groups. Although lifelong endurance training does not bring any significant benefit regarding overall gut microbiota diversity, strenuous athletic training is associated with higher cardiorespiratory fitness, lower body fat, and some favorable gut microbiota composition, all factors associated with slowing the rate of biological aging.
规律的体育活动似乎对老年人的微生物群组成有积极影响，但关于高强度耐力训练可能带来的额外益处却知之甚少。为了深入了解老年人的生理机能并识别与耐力训练相关的生物标志物，我们结合了不同的组学方法。我们旨在调查63.5岁（95%置信区间61.4, 65.7）、身高177.2厘米（95%置信区间174.4, 180.1）、体重77.8千克（95%置信区间75.1, 80.5）、最大摄氧量42.4毫升·千克·分钟（95%置信区间39.8, 45.0）（n = 13）的终身老年耐力运动员（LA）以及64.9岁（95%置信区间62.1, 67.7）、身高174.9厘米（95%置信区间171.2, 178.6）、体重83.4千克（95%置信区间77.1, 89.7）、最大摄氧量28.9毫升·千克·分钟（95%置信区间23.9, 33.9）（n = 9）的健康对照者的肠道微生物群、血浆成分、身体组成、心肺适能和肌肉力量。对收集的粪便样本进行微生物群分析，并进一步进行16S rRNA基因分析。应用核磁共振光谱分析来确定和比较主要与能量代谢相关的选定血浆代谢物。机器学习（ML）分析使用联合预测因子1.8E + 00（95%置信区间1.1, 2.5）%、3.8E + 00（95%置信区间2.7, 4.8）%（p = 0.002）；1.3（95%置信区间0.28, 2.4）%、0.1（95%置信区间0.07, 0.3）%（p = 0.02）；1.3E - 02（95%置信区间9.3E - 03, 1.7E - 02）%、5.9E - 03（95%置信区间3.9E - 03, 7.9E - 03）%（p = 0.002），7.9E - 02（95%置信区间2.5E - 02, 1.3E - 02）%、3.2E - 02（95%置信区间1.8E - 02, 4.6E - 02）%（p = 0.02）以及与的比值133（95%置信区间 - 86.2, 352）、732（95%置信区间385, 1079.3）（p = 0.03）对LA组和CTRL组的受试者进行区分，从而得到曲线下面积（AUC）为0.94的ROC曲线。此外，随机森林ML分析确定最大摄氧量、体重指数（BMI）以及与的比值为区分LA组和CTRL组受试者的合适联合预测因子。尽管终身耐力训练在整体肠道微生物群多样性方面没有带来任何显著益处，但高强度体育训练与更高的心肺适能、更低的体脂以及一些有利的肠道微生物群组成相关，所有这些因素都与减缓生物衰老速度有关。
