Dataset Information

Sarcopenia during COVID-19 lockdown restrictions: long-term health effects of short-term muscle loss

ABSTRACT: The COVID-19 pandemic is an extraordinary global emergency that has led to the implementation of unprecedented measures in order to stem the spread of the infection. Internationally, governments are enforcing measures such as travel bans, quarantine, isolation, and social distancing leading to an extended period of time at home. This has resulted in reductions in physical activity and changes in dietary intakes that have the potential to accelerate sarcopenia, a deterioration of muscle mass and function (more likely in older populations), as well as increases in body fat. These changes in body composition are associated with a number of chronic, lifestyle diseases including cardiovascular disease (CVD), diabetes, osteoporosis, frailty, cognitive decline, and depression. Furthermore, CVD, diabetes, and elevated body fat are associated with greater risk of COVID-19 infection and more severe symptomology, underscoring the importance of avoiding the development of such morbidities. Here we review mechanisms of sarcopenia and their relation to the current data on the effects of COVID-19 confinement on physical activity, dietary habits, sleep, and stress as well as extended bed rest due to COVID-19 hospitalization. The potential of these factors to lead to an increased likelihood of muscle loss and chronic disease will be discussed. By offering a number of home-based strategies including resistance exercise, higher protein intakes and supplementation, we can potentially guide public health authorities to avoid a lifestyle disease and rehabilitation crisis post-COVID-19. Such strategies may also serve as useful preventative measures for reducing the likelihood of sarcopenia in general and in the event of future periods of isolation.

SUBMITTER: Kirwan R

PROVIDER: S-EPMC7528158 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundSarcopenia is a known risk factor for poor outcomes across many chronic diseases. The impact on outcomes of both pre-existing sarcopenia and acute muscle wasting (AMW) in acute critical illness caused by sepsis remain unclear.MethodsWe conducted a prospective longitudinal cohort study of critically ill patients with intra-abdominal sepsis utilizing abdominal computed tomography at sepsis onset to determine baseline skeletal muscle index (SMI). Biomarkers of inflammation and catabolism were measured through 28 days while hospitalized. We performed follow-up evaluations of strength and physical function at 3, 6, and 12 months, with interval CT analyses at 3 and 12 months to evaluate changes in muscle mass. Measured clinical outcomes included development of chronic critical illness (≥14 days in intensive care with persistent organ dysfunction), long-term functional status, and 1 year mortality.ResultsAmong 47 sepsis patients enrolled (mean age 53 ± 14 years), half (n = 23; 49%) were sarcopenic at baseline. Overall, sepsis patients exhibited acute and persistent muscle wasting with an average 8% decrease in SMI from baseline at 3 months (P = 0.0008). Sarcopenic (SAR) and non-sarcopenic (NSAR) groups were similar in regards to age and comorbidity burden. SAR patients had greater acute physiologic derangement (APACHE II, 18 vs. 12.5), higher incidence of multiple organ failure (57% vs. 17%), longer hospital (21 vs. 12 days) and intensive care unit length of stays (13 vs. 4 days), and higher inpatient mortality (17% vs. 0%; all P < 0.05). Pre-existing SAR was a strong independent predictor of early death or developing chronic critical illness (odds ratio 11.87, 95% confidence interval CI 1.88-74.9; P = 0.009, area under the curve 0.880) and was associated with significantly higher risk of 1-year mortality (34.9% vs. 4.2%, p = 0.007). Lower baseline SMI was also predictive of poor functional status at 12 months (OR 0.89, 95% confidence interval 0.80-0.99; p = 0.039, area under the curve 0.867). Additionally, SAR patients had AMW with persistent muscle mass loss at 3 months that was associated with decreased health-related quality of life and SF-36 physical function domains (P < 0.05). Persistent AMW at 3 months was not predictive of mortality or poor functional status, with return to near-baseline muscle mass among sepsis survivors by 6 months.ConclusionsCritically ill patients have an acute and persistent loss of muscle mass after intra-abdominal sepsis, which is associated with decreased health-related quality of life and physical function at 3 months. However, pre-existing sarcopenia, rather than persistent acute muscle mass loss at 3 months after sepsis, is independently associated with poor long-term functional status and increased 1 year mortality.

Project description:BackgroundThe accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.MethodsFirst, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager-Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.ResultsThe proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.ConclusionsThe presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.

Dataset Information

Sarcopenia during COVID-19 lockdown restrictions: long-term health effects of short-term muscle loss

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets