The Need for Standardized Assessment of Muscle Quality in Skeletal Muscle Function Deficit and Other Aging-Related Muscle Dysfunctions: A Symposium Report

A growing body of scientific literature suggests that not only changes in skeletal muscle mass, but also other factors underpinning muscle quality, play a role in the decline in skeletal muscle function and impaired mobility associated with aging. A symposium on muscle quality and the need for standardized assessment was held on April 28, 2016 at the International Conference on Frailty and Sarcopenia Research in Philadelphia, Pennsylvania. The purpose of this symposium was to provide a venue for basic science and clinical researchers and expert clinicians to discuss muscle quality in the context of skeletal muscle function deficit and other aging-related muscle dysfunctions. The present article provides an expanded introduction concerning the emerging definitions of muscle quality and a potential framework for scientific inquiry within the field. Changes in muscle tissue composition, based on excessive levels of inter- and intra-muscular adipose tissue and intramyocellular lipids, have been found to adversely impact metabolism and peak force generation. However, methods to easily and rapidly assess muscle tissue composition in multiple clinical settings and with minimal patient burden are needed. Diagnostic ultrasound and other assessment methods continue to be developed for characterizing muscle pathology, and enhanced sonography using sensors to provide user feedback and improve reliability is currently the subject of ongoing investigation and development. In addition, measures of relative muscle force such as specific force or grip strength adjusted for body size have been proposed as methods to assess changes in muscle quality. Furthermore, performance-based assessments of muscle power via timed tests of function and body size estimates, are associated with lower extremity muscle strength may be responsive to age-related changes in muscle quality. Future aims include reaching consensus on the definition and standardized assessments of muscle quality, and providing recommendations to address critical clinical and technology research gaps within the field

Analyse der Körperzusammensetzung: Messung der Skelettmuskulatur mit Computertomographie und Implikationen für die Patientenversorgung

Objective: This thesis aims to evaluate the relationship between the skeletal muscle index derived from computed tomography (CT) images and patient outcomes, as well as its implications for patient care. This goal was pursued in five individual studies: Studies A and B evaluated the relationship between the lumbar skeletal muscle index (L3SMI) and patient outcomes in the intensive care unit (ICU) and oncology setting, respectively. Studies C and D evaluated the effect of CT acquisition parameters on body composition measures. Study E proposed a novel technique to automate the segmentation of skeletal muscle using a fully automated deep learning system. Material and methods: In total, 1328 axial CT images were included in the five studies. Patients in studies A and B were part of the clinical trials NCT01967056 and NCT01401907 at Massachusetts General Hospital (MGH), respectively. Body composition indices were computed using semi-automated segmentation. Multivariable regression models with a priori defined covariates were used to analyze clinical outcomes. To evaluate whether CT acquisition parameters influence segmentation, the Bland-Altman approach was used. In study E, a fully convolutional neural network was implemented for deep learning-based automatic segmentation. Results: Study A found lower L3SMI to be a predictor of increased mortality within 30 days of extubation (p = 0.033), increased rate of pneumonia within 30 days of extubation (p = 0.002), increased adverse discharge disposition (p = 0.044), longer hospital stays post-extubation (p = 0.048), and higher total hospital costs (p = 0.043). In study B, low L3SMI was associated with worse quality of life (p = 0.048) and increased depression symptoms (p = 0.005). Threshold-based segmentation of skeletal muscle in study C and adipose tissue compartments in study D were significantly affected by CT acquisition parameters. The proposed deep learning system in study E provided automatic segmentation of skeletal muscle cross-sectional area and achieved a high congruence to segmentations performed by domain experts (average Dice coefficient of 0.93). Conclusion: L3SMI is a useful tool for the assessment of muscle mass in clinical practice. In critically ill patients, L3SMI can provide clinically useful information about patient outcomes at the time of extubation. Patients with advanced cancer who suffered from low muscle mass reported worse quality of life and increased depression symptoms. This highlights the clinical relevance of addressing muscle loss early on as part of a multimodal treatment plan. Importantly, indices utilized in body composition analysis are significantly affected by CT acquisition parameters. These effects should be considered when body composition analysis is used in clinical practice or research studies. Finally, our fully automated deep learning system enabled instantaneous segmentation of skeletal muscle.Zielsetzung: Das Ziel der vorliegenden Dissertation war es, den Einfluss des auf CT-Bildern berechneten Skelettmuskelindexes auf klinische Ergebnisse von Patienten und die daraus resultierenden Implikationen für die Patientenversorgung zu evaluieren. Dieses Ziel wurde in fünf Einzelstudien verfolgt: In den Studien A und B wurde der Einfluss des lumbalen Skelettmuskelindex (L3SMI) auf klinische Endpunkte von Patienten auf der Intensivstation sowie in der Onkologie untersucht. Die Studien C und D evaluierten die Auswirkungen von CT-Akquisitionsparametern auf Indizes der Körperzusammensetzung. Studie E stellte eine neuartige Technik der automatisierten Segmentierung von Skelettmuskulatur vor, die durch maschinelles Lernen ermöglicht wurde. Material und Methoden: Insgesamt wurden 1328 axiale CT-Bilder in die fünf Studien eingeschlossen. Die Patienten der Studien A und B waren Teilnehmer der klinischen Studien NCT01967056 und NCT01401907 am Massachusetts General Hospital. Die Indizes der Körperzusammensetzung wurden mithilfe halbautomatischer Segmentierung berechnet. Die klinischen Endpunkte wurden in multivariablen Regressionsmodellen mit a priori definierten Kovariaten analysiert. Um zu evaluieren, ob CT-Akquisitionsparameter die Segmentierung beeinflussen, wurde der Bland-Altman-Ansatz verwendet. In Studie E wurden ein künstliches neuronales Netzwerk sowie maschinelles Lernen für die automatische Segmentierung eingesetzt. Ergebnisse: In Studie A war ein niedriger L3SMI ein Prädiktor für eine höhere Mortalität (p = 0.033) und Pneumonierate (p = 0.002) innerhalb von 30 Tagen nach der Extubation sowie für mehr ungünstige Entlassungen (p = 0.044) und höhere Behandlungskosten für den gesamten Krankenhausaufenthalt (p = 0.043). Ein niedriger L3SMI war in Studie B mit einer schlechteren Lebensqualität (p = 0.048) und stärkeren depressiven Symptomen (p = 0.005) assoziiert. Die schwellenwertbasierte Segmentierung der Skelettmuskulatur in Studie C und der Fettgewebekompartimente in Studie D wurde durch CT-Akquisitionsparameter signifikant beeinflusst. Das in Studie E vorgestellte vollautomatische Segmentierungssystem erreichte eine hohe Übereinstimmung mit den durch Experten erstellten Segmentationen (durchschnittlicher Dice-Koeffizient von 0.93). Fazit: Der L3SMI ist ein Werkzeug zur Beurteilung von Muskelmasse. Bei Intensivpatienten kann L3SMI zum Zeitpunkt der Extubation nützliche klinische Informationen liefern. Patienten mit fortgeschrittener Krebserkrankung, die zudem eine geringere Muskelmasse hatten, berichteten über eine schlechtere Lebensqualität und stärkere depressive Symptome. Dies unterstreicht die Notwendigkeit, die Muskulatur frühzeitig als Teil eines multimodalen Behandlungskonzeptes zu adressieren. Die Indizes der Körperzusammensetzung werden signifikant von CT-Akquisitionsparametern beeinflusst. Darüber hinaus ermöglichte unser vollautomatisiertes System dank maschinellen Lernens die verzögerungsfreie Segmentierung von Skelettmuskulatur

Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.This work was sponsored by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF) across projects RTC-2017-6321-1 AEI/FEDER, UE, TEC2016-76021-C2-2-R AEI/FEDER, UE and PID2019-107270RB-C21/AEI/10.13039/501100011033, UE

Comprehensive Evaluation of the 5XFAD Mouse Model for Preclinical Testing Applications: A MODEL-AD Study.

The ability to investigate therapeutic interventions in animal models of neurodegenerative diseases depends on extensive characterization of the model(s) being used. There are numerous models that have been generated to study Alzheimer\u27s disease (AD) and the underlying pathogenesis of the disease. While transgenic models have been instrumental in understanding AD mechanisms and risk factors, they are limited in the degree of characteristics displayed in comparison with AD in humans, and the full spectrum of AD effects has yet to be recapitulated in a single mouse model. The Model Organism Development and Evaluation for Late-Onset Alzheimer\u27s Disease (MODEL-AD) consortium was assembled by the National Institute on Aging (NIA) to develop more robust animal models of AD with increased relevance to human disease, standardize the characterization of AD mouse models, improve preclinical testing in animals, and establish clinically relevant AD biomarkers, among other aims toward enhancing the translational value of AD models in clinical drug design and treatment development. Here we have conducted a detailed characterization of the 5XFAD mouse, including transcriptomics, electroencephalogram

Comprehensive Evaluation of the 5XFAD Mouse Model for Preclinical Testing Applications: A MODEL-AD Study.

The ability to investigate therapeutic interventions in animal models of neurodegenerative diseases depends on extensive characterization of the model(s) being used. There are numerous models that have been generated to study Alzheimer\u27s disease (AD) and the underlying pathogenesis of the disease. While transgenic models have been instrumental in understanding AD mechanisms and risk factors, they are limited in the degree of characteristics displayed in comparison with AD in humans, and the full spectrum of AD effects has yet to be recapitulated in a single mouse model. The Model Organism Development and Evaluation for Late-Onset Alzheimer\u27s Disease (MODEL-AD) consortium was assembled by the National Institute on Aging (NIA) to develop more robust animal models of AD with increased relevance to human disease, standardize the characterization of AD mouse models, improve preclinical testing in animals, and establish clinically relevant AD biomarkers, among other aims toward enhancing the translational value of AD models in clinical drug design and treatment development. Here we have conducted a detailed characterization of the 5XFAD mouse, including transcriptomics, electroencephalogram

Comprehensive Evaluation of the 5XFAD Mouse Model for Preclinical Testing Applications: A MODEL-AD Study.

The ability to investigate therapeutic interventions in animal models of neurodegenerative diseases depends on extensive characterization of the model(s) being used. There are numerous models that have been generated to study Alzheimer\u27s disease (AD) and the underlying pathogenesis of the disease. While transgenic models have been instrumental in understanding AD mechanisms and risk factors, they are limited in the degree of characteristics displayed in comparison with AD in humans, and the full spectrum of AD effects has yet to be recapitulated in a single mouse model. The Model Organism Development and Evaluation for Late-Onset Alzheimer\u27s Disease (MODEL-AD) consortium was assembled by the National Institute on Aging (NIA) to develop more robust animal models of AD with increased relevance to human disease, standardize the characterization of AD mouse models, improve preclinical testing in animals, and establish clinically relevant AD biomarkers, among other aims toward enhancing the translational value of AD models in clinical drug design and treatment development. Here we have conducted a detailed characterization of the 5XFAD mouse, including transcriptomics, electroencephalogram

An investigation into the measurement and management of frailty in surgical patients

AimsThis thesis intends to investigate a method of identification of frailty in the surgical population, CT defined sarcopenia, and a possible method to attenuate its effects in the preoperative period, prehabilitation.Methods845 patients that underwent emergency laparotomy in 4 acute hospitals were screened for sarcopenia by review of CT scans assessing sarcopenia by psoas density (PD) and area (PA). Primary outcomes were 30 day and 1 year mortality.A pilot RCT was undertaken to assess the acceptability and achievability of walking- based prehabilitation monitored by wearable technology. Participants were randomised to either normal activity or a walking based exercise programme.ResultsSarcopenia measured by PD was associated with increased mortality compared to non-sarcopenic patients at 30-days (23.2% vs. 9.6% p<0.0001 OR=2.84 (95% CI 1.88-4.30) and 1-year 37% vs. 19.2% p<0.0001 OR=2.46 (95% CI 1.75-3.47). Increased mortality was seen in sarcopenic patients measured by PA at 30-days (16.3% vs. 7.8% p=0.001 OR=2.31 (95% CI 1.38-3.88) and 1-year 32% vs. 18.7% p=<0.0001 OR=2.25 (95% CI 1.52-3.34)For the RCT 45 patients were approached to recruit 40 participants. The median time in study was 12.5days (IQR 6-18). Mean compliance to the exercise programme was 58%. Mean distance change between initial and pre-operative assessment for the exercise and normal-activity groups was +16.4m and -13.6m respectively, p=0.013. Mean distance change between initial and 3-month postoperative assessment was - 11.4m and -40m p=0.11.ConclusionSarcopenia assessed by PD and PA on CT is associated with increased mortality following emergency laparotomy. The use of sarcopenia as a predictive tool may be useful to direct geriatric input and guide expectations in emergency surgery.This pilot study confirms that acceptable compliance can be achieved using a user- friendly pedometer and that this is associated with measurable improvements in fitness. Further work is required to establish whether this translates into improved patient outcomes after surgery

Development of a real-time classifier for the identification of the Sit-To-Stand motion pattern

The Sit-to-Stand (STS) movement has significant importance in clinical practice, since it is an indicator of lower limb functionality. As an optimal trade-off between costs and accuracy, accelerometers have recently been used to synchronously recognise the STS transition in various Human Activity Recognition-based tasks. However, beyond the mere identification of the entire action, a major challenge remains the recognition of clinically relevant phases inside the STS motion pattern, due to the intrinsic variability of the movement. This work presents the development process of a deep-learning model aimed at recognising specific clinical valid phases in the STS, relying on a pool of 39 young and healthy participants performing the task under self-paced (SP) and controlled speed (CT). The movements were registered using a total of 6 inertial sensors, and the accelerometric data was labelised into four sequential STS phases according to the Ground Reaction Force profiles acquired through a force plate. The optimised architecture combined convolutional and recurrent neural networks into a hybrid approach and was able to correctly identify the four STS phases, both under SP and CT movements, relying on the single sensor placed on the chest. The overall accuracy estimate (median [95% confidence intervals]) for the hybrid architecture was 96.09 [95.37 - 96.56] in SP trials and 95.74 [95.39 \u2013 96.21] in CT trials. Moreover, the prediction delays ( 4533 ms) were compatible with the temporal characteristics of the dataset, sampled at 10 Hz (100 ms). These results support the implementation of the proposed model in the development of digital rehabilitation solutions able to synchronously recognise the STS movement pattern, with the aim of effectively evaluate and correct its execution

An investigation of CT derived body composition, host nutritional status, systemic inflammation and clinical outcomes in patients with common solid tumours

Colorectal and lung cancers are common solid tumours in Western populations. While colorectal cancer presents largely at an early, operable stage, lung cancer presents largely at an advanced inoperable stage. Although tumour related characteristics are important part of cancer staging, host factors are increasingly recognised to impact on oncological treatment and clinical outcomes. Recently CT-derived body composition treatment has become available to supplement other host factors such as malnutrition risk, frailty, performance status and systemic inflammation. Importantly, these host characteristics are potentially modifiable. The aim of the present thesis was to examine the relationships between CT-derived body composition, host nutritional status, systemic inflammation and clinical outcomes in patients with common solid tumours. Chapter 1, critically reviewed the importance of CT derived body composition and the barriers to universal application of this modality for improving the staging and treatment of common solid tumours. Specifically, computed tomography (CT) based body composition analysis methods were critically reviewed and further directions to achieve body composition in routine clinical practice were highlighted. Moreover, the relationship between imaging based body composition and systemic inflammation in patients with common solid tumours was systematically reviewed. The systemic inflammatory response was directly associated with low skeletal muscle index (SMI) and low skeletal muscle density (SMD). Chapter 2, examined the relationship between psoas and all other skeletal muscles at L3 level with regards to clinical outcomes in patients with operable CRC. Critical analysis of value of L3 skeletal muscle and psoas muscle area in 1002 patients with operable CRC was performed. Both psoas and whole skeletal muscles at L3 were moderately correlated and both had prognostic value in terms of clinical outcomes including length of hospital stay and overall survival. However, only SMI had independent prognostic value in patients with operable CRC. Chapter 3, examined the relationship between MUST, systemic inflammation, body composition and clinical outcomes in patients with operable colorectal cancer. In patients with mild and moderate / high nutrition risk, systemic inflammation was associated with low SMI, greater length of stay and poorer overall survival. The MUST and mGPS has complementary prognostic value and may form the basis of routine disease related malnutrition assessment in patients with primary operable CRC. It was also proposed that cachexia may be defined as disease related malnutrition with systemic inflammation. The management directions for these patients should include reducing catabolism and improving anabolic response by addressing malnutrition, SIR, muscle mass and function. Chapter 4, examined the relationship between MUST, systemic inflammation, body composition and survival in patients with advanced lung cancer. Similar relationships were seen as in patients with CRC. The patients who were malnourished, frail, inflamed and had low SMI had poor survival as compared to patients who were not. This study suggested that combination of MUST, ECOG and mGPS provides a framework to identify the groups of patients who will benefit from aggressive oncological treatment or referral to the palliative care team. Moreover, new GLIM criteria captures components of MUST and the mGPS, highlighting the fact that host characteristics including malnutrition, systemic inflammation are important characteristics in decision making process to decide targeted treatment. Chapter 5, examined the longitudinal relationship between MUST, SIR and body composition in patients with advanced lung cancer. Over approximately, 3 months longitudinal study period, there was increase in malnutrition, worsened performance status, increase in SIR (mGPS and NLR), decrease in subcutaneous, visceral adiposity, SMI and SMD. Longitudinal MUST, ECOG, mGPS and NLR were associated with overall survival. No measurement of body composition was associated with overall survival. The loss of muscle was associated with SIR. The loss of body mass should be considered in the context of malnutrition risk, performance status and systemic inflammation. Chapter 6, examined the comparative analysis of CT derived measures of body composition across two solid tumours (CRC and LC). The comparison was performed in view of significant differences in two cohorts. CRC cohort included patients with operable disease whereas LC included patients with advanced disease undergoing radiotherapy. CRC is less inflammatory cancer and patients maintain body composition over longitudinal study period, whereas LC is pro inflammatory and patients lose more fat and muscle mass. CRC involves gastrointestinal tract and LC did not. The percentage of obesity and low SMI were similar between two cohorts despite large differences in clinicopathological characteristics. It was also, highlighted in this comparison that CT derived body composition although prognostic, is a result of patient constitution rather than tumour itself. The systemic inflammatory response as evidenced by mGPS in this study can be considered as important therapeutic target and loss of muscle mass in patients with advanced cancer is related to systemic inflammatory response. Chapter 7, examined advanced lung cancer patients who had PET-CT pre-treatment and its relationship to MUST, systemic inflammation and metabolic uptake were examined. There was direct relationship between mGPS and FDG uptake. MUST, mGPS and FDG uptake were associated with overall survival. SIR was associated with loss of muscle and frailty. The combination of clinicopathological (MUST, ECOG, frailty) and radiological parameters (FDG uptake) provide comprehensive host assessment to guide targeted treatment. These observations are relevant in pre-treatment as well as when measured longitudinally at 3 months interval in advanced lung cancer cohort. The patients who continue to deteriorate despite radiotherapy with increased inflammation and loss of muscle mass, should be directed to the best palliative care. Chapter 8 Conclusions: Host and tumour characteristics are important for best possible outcome in treating a patient with cancer. Staging the host as well as staging the tumour is an important concept for decision making and to provide best targeted therapy. Important host characteristics include MUST, ECOG, SIR and CT derive body composition. These characteristics when applied to the patient treatment can provide comprehensive phenotype to decide the treatment or palliation course. This thesis examined these characteristics across two solid tumour types of diverse phenotypes. Inflammation and body composition were related to each other. The longitudinal studies as well as comparative analysis between two cancers provides a significant insight to determine future directions for targeted treatment and palliation. It was observed that patients with advanced lung cancer get more malnourished, more inflamed, more muscle loss and have worse overall survival when compared to operable CRC

Investigating the associations between oral colonisation with respiratory commensal pathogens, oral hygiene and hospital acquired pneumonia in older patients with lower limb fracture

PhD ThesisHospital acquired pneumonia (HAP) occurs in 1% of all hospital in-patients, and in around 10% of patients with lower limb fracture, with a mortality of 18- 43%. HAP arises from interactions between three main risk factor groups: resident oral microbiota, aspiration potential (dysphagia, reduced conscious level) and host factors (age, frailty, comorbidity). In this work novel multiplex real time PCR assays were used to study prospectively the oral colonisation dynamics of seven major commensal pathogens over the first fortnight after hospital admission in relation to oral health variables, medical variables and subsequent development of HAP. Of the 93 patients recruited, 10% developed HAP and 60% of in-hospital deaths after lower limb fracture were due to HAP. Persistent oral colonisation with E. coli or S. aureus was significantly associated with HAP or HAP/lower respiratory tract infection in older patients with lower limb fracture. In turn, S. aureus was associated with increased dental plaque at admission and with increased xerostomia indices at 14 days. Other factors such as witnessed aspiration and post-operative cough were also strongly associated with subsequent development of HAP. HAP was associated with increased risk of death and increased length of hospital admission. These findings suggest several potentially modifiable clinical risk factors, and a high risk population for HAP, to whom interventions could be targeted. Given the rise in the older population and the increased costs associated with HAP, early detection and prevention will become increasingly important. Further work is needed to understand the relationships between dental plaque, S. aureus and xerostomia, and also to identify microbial biomarkers which could be used at the start of hospital admission to stratify patients’ risk of HAP.MR