Fatigue shifts and scatters heart rate variability in elite endurance athletes

PURPOSE: This longitudinal study aimed at comparing heart rate variability (HRV) in elite athletes identified either in 'fatigue' or in 'no-fatigue' state in 'real life' conditions. METHODS: 57 elite Nordic-skiers were surveyed over 4 years. R-R intervals were recorded supine (SU) and standing (ST). A fatigue state was quoted with a validated questionnaire. A multilevel linear regression model was used to analyze relationships between heart rate (HR) and HRV descriptors [total spectral power (TP), power in low (LF) and high frequency (HF) ranges expressed in ms(2) and normalized units (nu)] and the status without and with fatigue. The variables not distributed normally were transformed by taking their common logarithm (log10). RESULTS: 172 trials were identified as in a 'fatigue' and 891 as in 'no-fatigue' state. All supine HR and HRV parameters (Beta+/-SE) were significantly different (P<0.0001) between 'fatigue' and 'no-fatigue': HRSU (+6.27+/-0.61 bpm), logTPSU (-0.36+/-0.04), logLFSU (-0.27+/-0.04), logHFSU (-0.46+/-0.05), logLF/HFSU (+0.19+/-0.03), HFSU(nu) (-9.55+/-1.33). Differences were also significant (P<0.0001) in standing: HRST (+8.83+/-0.89), logTPST (-0.28+/-0.03), logLFST (-0.29+/-0.03), logHFST (-0.32+/-0.04). Also, intra-individual variance of HRV parameters was larger (P<0.05) in the 'fatigue' state (logTPSU: 0.26 vs. 0.07, logLFSU: 0.28 vs. 0.11, logHFSU: 0.32 vs. 0.08, logTPST: 0.13 vs. 0.07, logLFST: 0.16 vs. 0.07, logHFST: 0.25 vs. 0.14). CONCLUSION: HRV was significantly lower in 'fatigue' vs. 'no-fatigue' but accompanied with larger intra-individual variance of HRV parameters in 'fatigue'. The broader intra-individual variance of HRV parameters might encompass different changes from no-fatigue state, possibly reflecting different fatigue-induced alterations of HRV pattern.

Site of the action of a synthetic atrial natriuretic peptide evaluated in humans.

The renal site of the natriuretic effect of human, atrial natriuretic peptide (hANP) was studied using clearance techniques in eight salt-loaded normal volunteers undergoing maximal water diuresis. Lithium was used as a marker of proximal sodium reabsorption. According to a two-way, single blind, crossover design, hANP (Met12-(3-28)-eicosahexapeptide, (2 micrograms/min) or its vehicle (Ve) were infused for two hours, followed by a two-hour recovery period. Blood pressure, heart rate and insulin clearance remained unchanged. During hANP infusion, the filtration fraction increased slightly from 19.6 to 24.3% (P less than 0.001), fractional water excretion rose transiently at the beginning of the infusion. Fractional excretion of sodium increased markedly from 2.2% to 7.4% (P less than 0.001) but remained unchanged with Ve. ANP increased fractional excretion of lithium slightly from 46 to 58% (P less than 0.01), while it remained stable at 47% during Ve. The distal tubular rejection fraction of sodium calculated from sodium and lithium clearances rose markedly from 4.7 to 13% (P less than 0.001) and returned to 6.2% at the end of the recovery period. Thus, under salt loading and water diuresis conditions, hANP infusion did not alter GFR, but reduced proximal reabsorption of sodium, and markedly enhanced the fraction of sodium escaping distal tubular reabsorption, suggesting that hANP-induced natriuresis is due, for an important part, to inhibition of sodium reabsorption in the distal nephron.

Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.