Assessment of renal function relies on the estimation of the glomerular filtration rate (eGFR). Existing eGFR equations, usually based on serum levels of creatinine and/or cystatin C, are not uniformly accurate across patient populations. In the present study, we expanded a recent proof-of-concept approach to optimize an eGFR equation targeting the adult population with and without chronic kidney disease (CKD), based on a nuclear magnetic resonance spectroscopy (NMR) derived ‘metabolite constellation’ (GFR_NMR). A total of 1855 serum samples were partitioned into development, internal validation and external validation datasets. The new GFR_NMR equation used serum myo-inositol, valine, creatinine and cystatin C plus age and sex. GFR_NMR had a lower bias to tracer measured GFR (mGFR) than existing eGFR equations, with a median bias (95% confidence interval [CI]) of 0.0 (−1.0; 1.0) mL/min/1.73 m² for GFR_NMR vs. −6.0 (−7.0; −5.0) mL/min/1.73 m² for the Chronic Kidney Disease Epidemiology Collaboration equation that combines creatinine and cystatin C (CKD-EPI₂₀₁₂) (p < 0.0001). Accuracy (95% CI) within 15% of mGFR (1-P15) was 38.8% (34.3; 42.5) for GFR_NMR vs. 47.3% (43.2; 51.5) for CKD-EPI₂₀₁₂ (p < 0.010). Thus, GFR_NMR holds promise as an alternative way to assess eGFR with superior accuracy in adult patients with and without CKD.

The aim of this work was to explore whether real-world walking speed (RWS) would change as a consequence of 60-day bed-rest. The main hypothesis was that daily RWS would decrease after the bed-rest, with a subsequent recovery during the first days of re-ambulation. Moreover, an exploratory analysis was done in order to understand whether there is an agreement between the loss in RWS after bed-rest and the loss in the maximum oxygen uptake capacity (VO_2max), or the loss in maximal vertical jump power (JUMP) respectively. Twenty-four subjects were randomly assigned to one of three groups: a continuous artificial gravity group, an intermittent artificial gravity group, or a control group. The fitted linear mixed effects model showed a significant decrease (p < 0.001) of RWS after the 60-day bed-rest and a subsequent increase (p < 0.001) of RWS during the 14-day recovery period in the study facility. No or little agreement was found between the loss in RWS and the loss in VO_2max capacity or the loss in maximal vertical jumping power (RWS vs. VO_2max: p = 0.81, RWS vs. JUMP: p = 0.173). Decreased RWS after bed-rest, with a follow-up recovery was observed for all three groups, regardless of the training intervention. This suggests that RWS, also in these settings, was able to reflect a de-conditioning and follow-up recovery process.

An accurate estimate of glomerular filtration rate (eGFR) is essential for proper clinical management, especially in patients with kidney dysfunction. This prospective observational study evaluated the real-world performance of the nuclear magnetic resonance (NMR)-based GFR_NMR equation, which combines creatinine, cystatin C, valine, and myo-inositol with age and sex. We compared GFR_NMR performance to that of the 2021 CKD-EPI creatinine and creatinine-cystatin C equations (CKD-EPI_2021Cr and CKD-EPI_2021CrCys), using 115 fresh routine samples of patients scheduled for urinary iothalamate clearance measurement (mGFR). Median bias to mGFR of the three eGFR equations was comparably low, ranging from 0.4 to 2.0 mL/min/1.73 m². GFR_NMR outperformed the 2021 CKD-EPI equations in terms of precision (interquartile range to mGFR of 10.5 vs. 17.9 mL/min/1.73 m² for GFR_NMR vs. CKD-EPI_2021CrCys; p = 0.01) and accuracy (P15, P20, and P30 of 66.1% vs. 48.7% [p = 0.007], 80.0% vs. 60.0% [p < 0.001] and 95.7% vs. 86.1% [p = 0.006], respectively, for GFR_NMR vs. CKD-EPI_2021CrCys). Clinical parameters such as etiology, comorbidities, or medications did not significantly alter the performance of the three eGFR equations. Altogether, this study confirmed the utility of GFR_NMR for accurate GFR estimation, and its potential value in routine clinical practice for improved medical care.

Objectives: Understanding differences between real-world walking speed (RWS) and laboratory-measured walking speed (LWS) is crucial for comprehensive mobility assessments, especially in context of prolonged immobilization. This study aimed to investigate disparities in walking speed following a 60-day bed-rest period. Methods: In 11 male participants, RWS was continuously monitored using a tri-axial accelerometer worn on the waist, while LWS was assessed via a 10-m walk test at preferred speed, on three different study days after immobilization. Statistical analyses included Bland–Altman and Pearson’s correlation to evaluate agreement between RWS and LWS, alongside paired-sample t-tests and univariate linear regression models to assess significance of differences and temporal effects on gait speed. Results: Results of Bland-Altman analysis showed no agreement between RWS and LWS (mean difference 0.77 m/s) and nonsignificant correlation (r=0.19, p-value=0.3). Paired-sample t-tests indicated significantly lower RWS compared to LWS for all study days (p-value <0.001). Univariate linear regression models demonstrated a significant effect of test day on RWS (p-value <0.001) but not on LWS (p-value=0.23). Conclusions: These findings emphasize the importance of integrating both assessments to capture comprehensive mobility changes following prolonged periods of inactivity. Particularly significant is that RWS is constantly lower than LWS, with the former being more representative as it reflects what normally participants would do when not under observation. Lastly, understanding discrepancies between RWS and LWS would allow for more appropriate rehabilitation programs to speed up recovery while simultaneously keeping the rehabilitation safe and tailored.

Provides statistical methods for analytical method comparison and validation studies. Implements Bland-Altman analysis for assessing agreement between measurement methods (Bland & Altman (1986) ), Passing-Bablok regression for non-parametric method comparison (Passing & Bablok (1983) ), and Deming regression accounting for measurement error in both variables (Linnet (1993) ). Also includes tools for setting quality goals based on biological variation (Fraser & Petersen (1993) ) and calculating Six Sigma metrics. Commonly used in clinical laboratory method validation. Provides publication-ready plots and comprehensive statistical summaries.