Estimating glomerular filtration rate (eGFR) in kidney transplant recipients (KTR) typically relies on plasma creatinine, which is influenced by muscle mass. Reduced muscle mass is suspected to reduce eGFR performance in this population but this effect has not been rigorously evaluated. This study quantified the impact of muscle mass on eGFR accuracy and its confounding effect on the association between kidney function and mortality in KTR.
We studied a prospective and consecutive cohort of 1829 KTR (mean age 52 ± 14 years; 38.9% female) who underwent GFR measurement using iohexol clearance (ioGFR). Muscle mass was assessed by creatinine excretion rate (CER) from timed urine collections. We evaluated the impact of muscle mass on the performance of five eGFR equations (MDRD, CKDEPI2009, CKDEPI2021, EKFC and RFKTS) using multiple regression and subgroup analysis. The association between eGFRs, ioGFR and mortality was examined using Cox proportional hazards models.
All eGFR equations showed a significant negative correlation with CER. EKFC was the least sensitive to CER (β coefficient 95% confidence interval [CI]: −0.17 to −0.12). All eGFR equations demonstrated reduced accuracy in the lowest muscle mass tertile. In multivariable analyses, ioGFR was significantly associated with mortality (hazard ratio 95% CI: 0.972–0.995) but eGFRs were not. Including CER in the Cox models resulted in convergence of the mortality hazard ratios for ioGFR and eGFRs (hazard ratio 95% CI: ioGFR: 0.98–0.999; MDRD: 0.98–0.999; CKDEPI2021: 0.99–1; EKFC (0.98–1) RFKS: 0.98–0999).
The performance of all tested creatinine-based eGFR equations is strongly impacted by muscle mass. Muscle mass is also a key confounder in the mortality risk assessment using eGFR. Incorporating muscle mass into KTR's evaluations may improve kidney function assessments in KTR.
While the clinical implications are clear, translating these findings into public health and community-based interventions warrants further exploration. Here, we discuss three actionable dimensions to amplify the societal impact of this research.
First, Scalable Adherence Monitoring via Digital Health Technologies [1]: The study's categorization of adherence (BR, MR, AR) highlights the critical need for real-time adherence tracking. Wearable sensors (e.g., ActiGraph, Fitbit) and AI-driven platforms can objectively monitor PA frequency, intensity and duration in community settings. For instance, remote coaching apps with automated feedback loops, similar to the SPRINTT trial's technological support, could personalize adherence goals based on baseline SPPB scores. Integrating such tools into existing community health programmes (e.g., the WHO's Integrated Care for Older People, ICOPE) would democratize access, particularly in rural or resource-limited areas [1].
Second, Community Health Workers as Adherence Facilitators [2]: Participants with SPPB 3–7 benefitted most from > 3 sessions/week, yet sustaining high adherence remains challenging. Task-shifting to community health workers (CHWs) could bridge this gap. CHWs trained in geriatric exercise protocols can conduct home visits, deliver nutritional counselling and foster social accountability through peer-support groups. Embedding CHWs within primary care networks would operationalize the study's adherence framework while addressing socioeconomic barriers [2].
Third, Policy Integration Regarding Reimbursement and Infrastructure: The differential benefit of AR adherence (HR: 0.33 for SPPB 3–7) underscores the need for policy-level changes. Medicare's current reimbursement for ‘exercise as medicine’ is limited to cardiac rehabilitation. Expanding coverage to include multimodal PA programmes for PF&S—with incentives tied to adherence metrics—would incentivize healthcare systems to adopt SPRINTT-like interventions [3]. Concurrently, investing in public infrastructure (e.g., safe walking paths, subsidized community gyms) could reduce environmental barriers to adherence, aligning with the WHO's ‘Decade of Healthy Ageing’ goals.
In conclusion, Alvarez-Bustos et al.'s work provides a robust template for combating mobility disability. By leveraging digital tools, community networks and policy reform, we can transform adherence from a clinical variable into a public health priority.
The authors declare no conflicts of interest.
In elderly haemodialysis patients, poor functional status correlates with malnutrition, morbidity and mortality. This study aimed to assess 1-year survival in an elderly haemodialysis population and evaluate the predictive capacity of commonly used scales for comorbidity, malnutrition, dependence and frailty.
We conducted a 1-year observational study (2022) on prevalent haemodialysis patients aged > 75 years with > 3 months of treatment. Mortality was assessed during the follow-up. We analysed sociodemographic, dialysis-related, analytical and lifestyle variables. Additionally, we evaluated comorbidity (Charlson), dependence (Barthel), malnutrition-inflammation (MIS scale) and frailty (FRIED). Statistical analysis included comparisons between continuous variables using the Mann–Whitney U test or Student's t test, based on normality distribution. Categorical variables were compared using the chi-square test. Kaplan–Meier survival analysis with log-rank test was used to compare survival curves. Multivariate analysis was performed using Cox proportional hazards models, adjusting for confounders including dialysis adequacy and underlying kidney disease aetiology. Optimal cutoff points for predicting mortality were determined using receiver operating characteristic (ROC) curves.
A total of 107 haemodialysis patients (57% male, mean age 81.3 ± 4.53 years, mean time on haemodialysis 51.71 ± 51.04 months) were included. Sixteen patients (15%) died within 1 year. Deceased patients were older (83.94 ± 4.52 vs. 80.34 ± 4.39 years, p = 0.011) and had longer dialysis duration (76.46 ± 54.73 vs. 47.35 ± 49.4 months, p = 0.035), lower albumin (3.51 ± 0.54 vs. 3.86 ± 0.31 g/dL, p < 0.001) and lower creatinine levels (5.48 ± 1.12 vs. 6.50 ± 1.67 mg/dL, p = 0.021). They scored higher on all four scales analysed: Charlson (10.94 ± 1.81 vs. 8.95 ± 1.92, p < 0.001), MIS (11.31 ± 4.22 vs. 6.07 ± 3.27, p < 0.001), Barthel (52.50 ± 27.2 vs. 78.41 ± 23.11, p < 0.001), and FRIED (3.19 ± 1.05 vs. 2.18 ± 1.32, p = 0.005). Institutionalization (p = 0.004), inability to walk (p < 0.001) and stretcher transport (p < 0.001) were also significantly associated with mortality. The optimal cutoff points for predicting mortality were Charlson index ≥ 9.5 (AUC 0.788, 95% CI: 0.65–0.88), MIS ≥ 7.5 (AUC 0.844, 95% CI: 0.73–0.93), Barthel ≤ 67.5 (AUC 0.79, 95% CI: 0.68–0.79) and FRIED ≥ 2.5 (AUC 0.719, 95% CI: 0.56–0.83). In multivariable analysi
Bigossi, G, Marcozzi, S, Giuliani, ME, Lai, G, Bartozzi, B, Orlando, F, Gerosa, L, Malvandi, AM, Putavet, D, Bouma, E, Keizer, PL, Lombardi, G, Malavolta, M. “ A Comparative Analysis of Grip Strength Evaluation Methods in a Large Cohort of Aged Mice,” Journal of Cachexia, Sarcopenia and Muscle 16, no. 5 (2025): e70050, https://doi.org/10.1002/jcsm.70050.
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