Electrophysiology and frailty: Is there potential to move from clinical scales to biomarker?
Bibliographic record
Abstract
Frailty is a syndrome that can manifest in a variety of ways in different individuals, and it is often difficult to identify, because there is currently no biomarker available to diagnose frailty. Diagnosis and/or characterization of frailty is based upon an accumulation of health deficits derived from self-report, questionnaire and physical performance measures (frailty index) or from criteria scores established from population-based studies (Fried's frailty phenotype), wherein the agreement between scales on frailty status is known to be fickle (Jones, Neubauer, O'Connor, & Jakobi, 2016). To gain a better understanding of the progressive accumulation of health deficits and functional decline associated with frailty, physiological measures need to be considered alongside function, because changes in physiology should precede observable functional decline. In this issue of Experimental Physiology, Swiecicka et al. (2019) investigated the association between the electrophysiological characteristics of muscle and measures of frailty in ageing men. The loss of muscle mass and strength is a defining feature of sarcopenia, and reduced functional abilities in frail persons are demonstrably associated with reductions in contractile tissue and muscle weakness. This research group recently showed that reductions in amplitude of the compound muscle action potential (CMAP) and the motor unit potential (MUP) are evident in sarcopenic individuals (Piasecki et al., 2018), and thus the evaluation of these electrophysiological markers relative to clinical scales of frailty was a natural and progressive step towards ascertaining neuromuscular electrophysiological characteristics that underlie frailty. In their study, Swiecicka et al. (2019) compared CMAP and MUP amplitudes recorded from the vastus lateralis of 86 men >65 years of age with frailty classification of these individuals obtained from the frailty index and frailty phenotype, which are commonly used clinical scales. The CMAPs were elicited through stimulation of the femoral nerve, and potentials were recorded at the level of the vastus lateralis motor point with surface electromyography (sEMG). Using a concentric needle electrode inserted into the vastus lateralis at the motor point, MUPs were recorded while participants sustained isometric knee-extension contractions at 25% of maximum for 12–15 s. The association of CMAP and MUP amplitude was determined for both clinical scales of frailty status using regression analysis. From the frailty phenotype scores, 28 participants were classified as robust, 40 as pre-frail and 18 as frail, and the mean frailty index was 0.18. From the regression analysis, a higher CMAP amplitude was associated with a lower level of frailty assessed by both the frailty index and the frailty phenotype. Higher MUP amplitudes were associated with a lower frailty index. Using only the frailty phenotype, higher MUP and CMAP amplitudes were both associated with a lower risk of frailty, and the CMAP and MUP amplitudes both decreased with an increasing level of frailty. These results signify how electrophysiological changes underlie observable declines in function that are commonly identified through clinical scales of frailty. To gain a sense of changes in electrophysiological properties of muscle as a function of ageing and/or disease, a number of studies have typically used measures of motor unit number estimates (MUNE; Gooch et al., 2014). The MUNE is calculated from the ratio of a CMAP amplitude to a surface motor unit potential (SMUP) detected through sEMG; however, the SMUP and CMAP measures are often highly variable within and between populations. The high variability arises from the influence of surrounding structures on the sEMG signal. For recordings of small muscles in close proximity to other muscles (such as muscles of the hand), the sEMG signal used to obtain CMAPs and SMUPs could be contaminated through cross-talk from other surrounding muscles, whereas sEMG signals recorded from deeper muscles, such as the vastus lateralis, could be attenuated owing to underlying subcutaneous tissue. These elements, in addition to the surprisingly similar number of motor units estimated between small and large muscles and the differences observed between anatomical counts and surface estimates, highlight the need for consideration of an alternative means to evaluate neuromuscular electrophysiological properties that underpin functional change. By recording MUPs from needle electrodes embedded in the muscle belly, cross-talk contamination and signal attenuation are minimized, and thus the signal is less susceptible to the variability seen in sEMG. Future studies need to investigate the sensitivity, validity and reliability of this approach across populations inclusive of normal ageing in women, in addition to disease states that have well-established MUNE data. Nevertheless, it seems that the use of the MUP alongside the CMAP provides a complementary and, potentially, supplementary insight to a MUNE value, offering an accurate and representative outcome measure indicative of functional change. Swiecicka et al. (2019) provide a progressive step towards defining the electrophysiological changes that precede overt physical decline that is currently used in commonly applied clinical assessment tools to diagnose frailty. The use of CMAP and MUP in a clinical setting might offer a biomarker of frailty that is unaffected by the dynamic changes in function that individuals experience across the spectrum of the frailty syndrome (Roland, Theou, Jakobi, Swan, & Jones, 2014) and that currently limits early and accurate classification. The study by Swiecicka et al. (2019) reveals that changes in electrophysiological properties are associated with clinical scales of frailty. Electrophysiological measures might offer earlier, consistent and clearer identification of frailty and its phenotypes (non-frail, pre-frail or frail). Future studies of clinical tests examining frailty should consider the use of MUPs and CMAPs as additional measures in the diagnosis of frailty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".