Healthy Aging Within an Image: Using Muscle Radiodensitometry and Lifestyle Factors to Predict Diabetes and Hypertension

Translational mobility medicine and ugo carraro: a life of significant scientific contributions reviewed in celebration

Prof. Ugo Carraro reached 80 years of age on 23 February 2023, and we wish to celebrate him and his work by reviewing his lifetime of scientific achievements in Translational Myology. Currently, he is a Senior Scholar with the University of Padova, Italy, where, as a tenured faculty member, he founded the Interdepartmental Research Center of Myology. Prof. Carraro, a pioneer in skeletal muscle research, is a world-class expert in structural and molecular investigations of skeletal muscle biology, physiology, pathology, and care. An authority in bidimensional gel electrophoresis for myosin light chains, he was the first to separate mammalian muscle myosin heavy chain isoforms by SDS-gel electrophoresis. He has demonstrated that long-term denervated muscle can survive denervation by myofiber regeneration, and shown that an athletic lifestyle has beneficial impacts on muscle reinnervation. He has utilized his expertise in translational myology to develop and validate rehabilitative treatments for denervated and ageing skeletal muscle. He has authored more than 160 PubMed listed papers and numerous scholarly books, including his recent autobiography. Prof. Carraro founded and serves as Editor-in-Chief of the European Journal of Translational Myology and Mobility Medicine. He has organized more than 40 Padua Muscle Days Meetings and continues this, encouraging students and young scientists to participate. As he dreams endlessly, he is currently validating non-invasive analyses on saliva, a promising approach that will allow increased frequency sampling to analyze systemic factors during the transient effects of training and rehabilitation by his proposed Full-Body in- Bed Gym for bed-ridden elderly.

Development of soft tissue asymmetry indicators to characterize aging and functional mobility

Introduction: The aging population poses significant challenges to healthcare systems globally, necessitating a comprehensive understanding of age-related changes affecting physical function. Age-related functional decline highlights the urgency of understanding how tissue composition changes impact mobility, independence, and quality of life in older adults. Previous research has emphasized the influence of muscle quality, but the role of tissue composition asymmetry across various tissue types remains understudied. This work develops asymmetry indicators based on muscle, connective and fat tissue extracted from cross-sectional CT scans, and shows their interplay with BMI and lower extremity function among community-dwelling older adults. Methods: We used data from 3157 older adults from 71 to 98 years of age (mean: 80.06). Tissue composition asymmetry was defined by the differences between the right and left sides using CT scans and the non-Linear Trimodal Regression Analysis (NTRA) parameters. Functional mobility was measured through a 6-meter gait (Normal-GAIT and Fast-GAIT) and the Timed Up and Go (TUG) performance test. Statistical analysis included paired t-tests, polynomial fitting curves, and regression analysis to uncover relationships between tissue asymmetry, age, and functional mobility. Results: Findings revealed an increase in tissue composition asymmetry with age. Notably, muscle and connective tissue width asymmetry showed significant variation across age groups. BMI classifications and gait tasks also influenced tissue asymmetry. The Fast-GAIT task demonstrated a substantial separation in tissue asymmetry between normal and slow groups, whereas the Normal-GAIT and the TUG task did not exhibit such distinction. Muscle quality, as reflected by asymmetry indicators, appears crucial in understanding age-related changes in muscle function, while fat and connective tissue play roles in body composition and mobility. Discussion: Our study emphasizes the importance of tissue asymmetry indicators in understanding how muscle function changes with age in older individuals, demonstrating their role as risk factor and their potential employment in clinical assessment. We also identified the influence of fat and connective tissue on body composition and functional mobility. Incorporating the NTRA technology into clinical evaluations could enable personalized interventions for older adults, promoting healthier aging and maintaining physical function.

Predicting stroke, neurological and movement disorders using single and dual-task gait in Korean older population

BACKGROUND

Single and motor or cognitive dual-gait analysis is often used in clinical settings to evaluate older adults affected by neurological and movement disorders or with a stroke history. Gait features are frequently investigated using Machine Learning (ML) with significant results that can help clinicians in diagnosis and rehabilitation. The present study aims to classify patients with stroke, neurological and movement disorders using ML to analyze gait characteristics and to understand the importance of the single and dual-task features among Korean older adults.

METHODS

A cohort of 122 non-hospitalized Korean older adult participated in a single and a cognitive dual-task gait performance analysis. The extracted temporal and spatial features, together with clinical data, were used as input for the binary classification using tree-based ML algorithms. A repeated-stratified 10-fold cross-validation was performed to better evaluate multiple classification metrics with a final feature importance analysis.

RESULTS AND SIGNIFICANCE

The best accuracy - maximum >90 % - for gait and neurological disorders classification was obtained with Random Forest. In the stroke classification a 91.7 % of maximum accuracy was reached, with a significant recall of 92 %. The feature importance analysis showed a substantial balance between single and dual-task, while clinical data did not show elevated importance. The current findings indicate that a cognitive dual-task gait performance is highly recommendable together with a single-task in the analysis of older population, particularly for patients with a history of stroke. The results could be useful to medical professionals in treating and diagnosing motor and neurological disorders, and to improve rehabilitation strategies for stroke patients. Furthermore, the results confirm the proficiency of the tree-based ML algorithms in biomedical data analysis. Finally, in the future, this research could be replicated with a non-Asian population dataset to deepen the understanding of gait differences between Asian-Korean population and other ethnicities.

RFFE - Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus

Diabetes is a category of metabolic disease commonly known as a chronic illness. It causes the body to generate less insulin and raises blood sugar levels, leading to various issues and disrupting the functioning of organs, including the retinal, kidney and nerves. To prevent this, people with chronic illnesses require lifetime access to treatment. As a result, early diabetes detection is essential and might save many lives. Diagnosis of people at high risk of developing diabetes is utilized for preventing the disease in various aspects. This article presents a chronic illness prediction prototype based on a person's risk feature data to provide an early prediction for diabetes with Fuzzy Entropy random vectors that regulate the development of each tree in the Random Forest. The proposed prototype consists of data imputation, data sampling, feature selection, and various techniques to predict the disease, such as Fuzzy Entropy, Synthetic Minority Oversampling Technique (SMOTE), Convolutional Neural Network (CNN) with Stochastic Gradient Descent with Momentum (SGDM), Support Vector Machines (SVM), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). This study uses the existing Pima Indian Diabetes (PID) dataset for diabetic disease prediction. The predictions' true/false positive/negative rate is investigated using the confusion matrix and the receiver operating characteristic area under the curve (ROCAUC). Findings on a PID dataset are compared with machine learning algorithms revealing that the proposed Random Forest Fuzzy Entropy (RFFE) is a valuable approach for diabetes prediction, with an accuracy of 98 percent.

Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea)

Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior-posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.

CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review

Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

Soft tissue radiodensity parameters mediate the relationship between self-reported physical activity and lower extremity function in AGES-Reykjavík participants

Although previous studies have highlighted the association between physical activity and lower extremity function (LEF) in elderly individuals, the mechanisms underlying this relationship remain debated. Our recent work has recognized the utility of nonlinear trimodal regression analysis (NTRA) parameters in characterizing changes in soft tissue radiodensity as a quantitative construct for sarcopenia in the longitudinal, population-based cohort of the AGES-Reykjavík study. For the present work, we assembled a series of prospective multivariate regression models to interrogate whether NTRA parameters mediate the 5-year longitudinal relationship between physical activity and LEF in AGES-Reykjavík participants. Healthy elderly volunteers from the AGES-Reykjavík cohort underwent mid-thigh X-ray CT scans along with a four-part battery of LEF tasks: normal gait speed, fastest-comfortable gait speed, isometric leg strength, and timed up-and-go. These data were recorded at two study timepoints which were separated by approximately 5 years: AGES-I (n = 3157) and AGES-II (n = 3098). Participants in AGES-I were likewise administered a survey to approximate their weekly frequency of engaging in moderate-to-vigorous physical activity (PAAGES-I). Using a multivariate mediation analysis framework, linear regression models were assembled to test whether NTRA parameters mediated the longitudinal relationship between PAAGES-I and LEFAGES-II; all models were covariate-adjusted for age, sex, BMI, and baseline LEF, and results were corrected for multiple statistical comparisons. Our first series of models confirmed that all four LEF tasks were significantly related to PAAGES-I; next, modelling the relationship between PAAGES-I and NTRAAGES-II identified muscle amplitude (Nm) and location (μm) as potential mediators of LEF to test. Finally, adding these two parameters into our PAAGES-I → LEFAGES-II models attenuated the prior effect of PAAGES-I; bootstrapping confirmed Nm and μm as significant partial mediators of the PAAGES-I → LEFAGES-II relationship, with the strongest effect found in isometric leg strength. This work describes a novel approach toward clarifying the mechanisms that underly the relationship between physical activity and LEF in aging individuals. Identifying Nm and μm as significant partial mediators of this relationship provides strong evidence that physical activity protects aging mobility through the preservation of both lean tissue quantity and quality.

Testing soft tissue radiodensity parameters interplay with age and self-reported physical activity

Aging well is directly associated to a healthy lifestyle. The focus of this paper is to relate individual wellness with medical image features. Non-linear trimodal regression analysis (NTRA) is a novel method that models the radiodensitometric distributions of x-ray computed tomography (CT) cross-sections. It generates 11 patient-specific parameters that describe the quality and quantity of muscle, fat, and connective tissues. In this research, the relationship of these 11 NTRA parameters with age, physical activity, and lifestyle is investigated in the 3,157 elderly volunteers AGES-I dataset. First, univariate statistical analyses were performed, and subjects were grouped by age and self-reported past (youth–midlife) and present (within 12 months of the survey) physical activity to ascertain which parameters were the most influential. Then, machine learning (ML) analyses were conducted to classify patients using NTRA parameters as input features for three ML algorithms. ML is also used to classify a Lifestyle index using the age groups. This classification analysis yielded robust results with the lifestyle index underlying the relevant differences of the soft tissues between age groups, especially in fat and connective tissue. Univariate statistical models suggested that NTRA parameters may be susceptible to age and differences between past and present physical activity levels. Moreover, for both age and physical activity, lean muscle parameters expressed more significant variation than fat and connective tissues.

Psoas Muscle Index Defined by Computer Tomography Predicts the Presence of Postoperative Complications in Colorectal Cancer Surgery

Background and Objectives: Sarcopenia is a recognized prognostic factor for both complications and survival in cancer patients. This study aims to analyze the relationship between sarcopenia measured by psoas muscle index on computer tomography scans and the presence of postoperative complications in colorectal cancer surgery. Materials and Methods: In a prospective study we recorded data from 51 patients who underwent colorectal cancer surgery in the Mures County Clinical Hospital, Romania. Total psoas muscle area and psoas density were measured at the level of the third lumbal vertebra (L3) for further index calculation. We also evaluated the general characteristics and laboratory analyses to obtain more information about status of the patients. Short-term postoperative complications were scored according to the Clavien-Dindo classification. Results: The majority of the 51 patients were male (61%) and the median age was 65 years. More than half of the cancer was located in the rectum (56.9%), a quarter in the right colon (25.5%), the rest in the sigmoid (11.8%), and the left colon (5.9%). Twenty-one patients (41.2%) developed a complication, five (9.8%) of these were Clavien-Dindo grade 3, 4 or 5 (high grade) and sixteen (31.3%) grade 1 or 2 (low grade). The low- and high-grade groups showed a significantly lower right psoas muscle area, left psoas muscle area, total psoas muscle area, and psoas muscle index (p < 0.001 in all cases). Among laboratory analyses, a significantly lower perioperative hematocrit, hemoglobin, and albumin level were found in patients who developed complications. Furthermore we observed that an elevated serum C-reactive protein level was associated with a higher grade of complication (p < 0.043). Conclusions: The psoas muscle index (PMI) influence on the postoperative outcome is an important factor in our single center prospective study and it appears to be a good overall predictor in colorectal surgery. A lower PMI is directly associated with a low or high grade complication by Clavien-Dindo classification. Perioperative inflammatory and nutritional status evidenced by serum C-reactive protein (CRP) and albumin level influences the presence of postoperative complications.