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

Submitted: 21 June 2021
Accepted: 5 July 2021
Published: 12 July 2021
Abstract Views: 1477
PDF: 364
HTML: 3
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

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.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Barberi L, Scicchitano BM, Musaro A. Molecular and cellular mechanisms of muscle aging and sarcopenia and effects of electrical stimulation in seniors. Eur J Transl Myol. 2015 Aug 25;25(4):231-6. DOI: https://doi.org/10.4081/ejtm.2015.5227
Kalyani RR, Corriere M, Ferrucci L. Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. Lancet Diabetes Endocrinol. 2014 Oct;2(10):819-29. DOI: https://doi.org/10.1016/S2213-8587(14)70034-8
Goodpaster BH, Carlson CL, Visser M, Kelley DE, Scherzinger A, Harris TB, Stamm E, Newman AB. Attenuation of skeletal muscle and strength in the elderly: The Health ABC Study. J Appl Physiol (1985). 2001 Jun;90(6):2157-65. DOI: https://doi.org/10.1152/jappl.2001.90.6.2157
Marzetti E, Calvani R, Tosato M, Cesari M, Di Bari M, Cherubini A, Collamati A, D'Angelo E, Pahor M, Bernabei R, Landi F; SPRINTT Consortium. Sarcopenia: an overview. Aging Clin Exp Res. 2017 Feb;29(1):11-17. DOI: https://doi.org/10.1007/s40520-016-0704-5
Newman AB, Kupelian V, Visser M, Simonsick E, Goodpaster B, Nevitt M, Kritchevsky SB, Tylavsky FA, Rubin SM, Harris TB; Health ABC Study Investigators. Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriatr Soc. 2003 Nov;51(11):1602-9. DOI: https://doi.org/10.1046/j.1532-5415.2003.51534.x
Brooks SV, Faulkner JA. Skeletal muscle weakness in old age: underlying mechanisms. Med Sci Sports Exerc. 1994 Apr;26(4):432-9. DOI: https://doi.org/10.1249/00005768-199404000-00006
Han P, Yu H, Ma Y, Kang L, Fu L, Jia L, Chen X, Yu X, Hou L, Wang L, Zhang W, Yin H, Niu K, Guo Q. The increased risk of sarcopenia in patients with cardiovascular risk factors in Suburb-Dwelling older Chinese using the AWGS definition. Sci Rep. 2017 Aug 29;7(1):9592. DOI: https://doi.org/10.1038/s41598-017-08488-8
Cruz-Jentoft AJ, Landi F, Schneider SM, Zúñiga C, Arai H, Boirie Y, Chen LK, Fielding RA, Martin FC, Michel JP, Sieber C, Stout JR, Studenski SA, Vellas B, Woo J, Zamboni M, Cederholm T. Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. Report of the International Sarcopenia Initiative (EWGSOP and IWGS). Age Ageing. 2014 Nov;43(6):748-59. DOI: https://doi.org/10.1093/ageing/afu115
Landi F, Marzetti E, Martone AM, Bernabei R, Onder G. Exercise as a remedy for sarcopenia. Curr Opin Clin Nutr Metab Care. 2014 Jan;17(1):25-31. DOI: https://doi.org/10.1097/MCO.0000000000000018
Šarabon N, Smajla D, Kozinc Ž, Kern H. Speed-power based training in the elderly and its potential for daily movement function enhancement. Eur J Transl Myol. 2020 Apr 1;30(1):8898. DOI: https://doi.org/10.4081/ejtm.2019.8898
Cvecka J, Tirpakova V, Sedliak M, Kern H, Mayr W, Hamar D. Physical Activity in Elderly. Eur J Transl Myol. 2015 Aug 25;25(4):249-52. DOI: https://doi.org/10.4081/ejtm.2015.5280
Visser M, Goodpaster BH, Kritchevsky SB, Newman AB, Nevitt M, Rubin SM, Simonsick EM, Harris TB. Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci. 2005 Mar;60(3):324-33. DOI: https://doi.org/10.1093/gerona/60.3.324
Glenmark B, Hedberg G, Jansson E. Prediction of physical activity level in adulthood by physical characteristics, physical performance and physical activity in adolescence: an 11-year follow-up study. Eur J Appl Physiol Occup Physiol. 1994;69(6):530-8. DOI: https://doi.org/10.1007/BF00239871
Srikanthan P, Karlamangla AS. Muscle mass index as a predictor of longevity in older adults. Am J Med. 2014 Jun;127(6):547-53. DOI: https://doi.org/10.1016/j.amjmed.2014.02.007
Mwangi B, Hasan KM, Soares JC. Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: a machine learning approach. Neuroimage. 2013 Jul 15;75:58-67. DOI: https://doi.org/10.1016/j.neuroimage.2013.02.055
Jonsson BA, Bjornsdottir G, Thorgeirsson TE, Ellingsen LM, Walters GB, Gudbjartsson DF, Stefansson H, Stefansson K, Ulfarsson MO. Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun. 2019 Nov 27;10(1):5409. DOI: https://doi.org/10.1038/s41467-019-13163-9
Edmunds KJ, Árnadóttir Í, Gíslason MK, Carraro U, Gargiulo P. Nonlinear Trimodal Regression Analysis of Radiodensitometric Distributions to Quantify Sarcopenic and Sequelae Muscle Degeneration. Comput Math Methods Med. 2016;2016:8932950. DOI: https://doi.org/10.1155/2016/8932950
Edmunds K, Gíslason M, Sigurðsson S, Guðnason V, Harris T, Carraro U, Gargiulo P. Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities. PLoS One. 2018 Mar 7;13(3):e0193241. DOI: https://doi.org/10.1371/journal.pone.0193241
Ricciardi C, Edmunds KJ, Recenti M, Sigurdsson S, Gudnason V, Carraro U, Gargiulo P. Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions. Sci Rep. 2020 Feb 18;10(1):2863. DOI: https://doi.org/10.1038/s41598-020-59873-9
Recenti M, Ricciardi C, Monet A, Jacob D, Ramos J, Gislason MK, Edmunds KJ, Carraro U, Gargiulo P. Predicting body mass index and isometric leg strength using soft tissue distributions from computed tomography scans. Health Technol. 2020 Nov 4; 1-11. DOI: https://doi.org/10.1007/s12553-020-00498-3
Recenti M, Ricciardi C, Edmunds KJ, Gislason MK, Sigurdsson S, Carraro U, Gargiulo P. Healthy Aging Within an Image: Using Muscle Radiodensitometry and Lifestyle Factors to Predict Diabetes and Hypertension. IEEE J Biomed Health Inform. 2021 Jun;25(6):2103-2112. DOI: https://doi.org/10.1109/JBHI.2020.3044158
Recenti M, Ricciardi C, Edmunds K, Gislason MK, Gargiulo P. Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images. Eur J Transl Myol. 2020 Apr 1;30(1):8892. DOI: https://doi.org/10.4081/ejtm.2019.8892
Recenti M, Ricciardi C, Gìslason MJ, Edmunds KJ, Carraro U, Gargiulo P. Machine learning algorithms predict body mass index using nonlinear trimodal regression analysis from computed tomography scans. Mediterranean Conference on Medical and Biological Engineering and Computing. 2019 Sept 25; 839-846. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-31635-8_100
Recenti M, Gìslason MK, Edmunds KJ, Gargiulo P. Aging Health Behind an Image: Quantifying Sarcopenia and Associated Risk Factors from Advanced CT Analysis and Machine Learning Technologies. In International Symposium on Computer Methods in Biomechanics and Biomedical Engineering. 2019 Aug;188-197. Springer, Cham. DOI: https://doi.org/10.1007/978-3-030-43195-2_15
Harris TB, Launer LJ, Eiriksdottir G, Kjartansson O, Jonsson PV, Sigurdsson G, Thorgeirsson G, Aspelund T, Garcia ME, Cotch MF, Hoffman HJ, Gudnason V. Age, Gene/Environment Susceptibility-Reykjavik Study: multidisciplinary applied phenomics. Am J Epidemiol. 2007 May 1;165(9):1076-87. DOI: https://doi.org/10.1093/aje/kwk115
Stanzione A, Ricciardi C, Cuocolo R, Romeo V, Petrone J, Sarnataro M, Mainenti PP, Improta G, De Rosa F, Insabato L, Brunetti A, Maurea S. MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study. J Digit Imaging. 2020 Aug;33(4):879-887. DOI: https://doi.org/10.1007/s10278-020-00336-y
Ricciardi C, Cuocolo R, Cesarelli G, Ugga L, Improta G, Solari D, Romeo V, Guadagno E, Cavallo LM, Cesarelli, M. Distinguishing functional from non-functional pituitary macroadenomas with a machine learning analysis. Paper presented at the IFMBE Proceedings. 2019 Sep 25; 76 1822-1829.
Ricciardi C, Improta G, Amato F, Cesarelli G, Romano M. Classifying the type of delivery from cardiotocographic signals: A machine learning approach. Comput Methods Programs Biomed. 2020 Nov;196:105712. DOI: https://doi.org/10.1016/j.cmpb.2020.105712
Ricciardi C, Valente AS, Edmund K, Cantoni V, Green R, Fiorillo A, Picone I, Santini S, Cesarelli M. Linear discriminant analysis and principal component analysis to predict coronary artery disease. Health Informatics J. 2020 Sep;26(3):2181-2192. DOI: https://doi.org/10.1177/1460458219899210
Recenti M, Ricciardi C, Aubonnet R, Picone I, Jacob D, Svansson HÁR, Agnarsdóttir S, Karlsson GH, Baeringsdóttir V, Petersen H, Gargiulo P. Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals. Front Bioeng Biotechnol. 2021 Apr 1;9:635661. DOI: https://doi.org/10.3389/fbioe.2021.635661
Ricciardi C, Jónsson H Jr, Jacob D, Improta G, Recenti M, Gíslason MK, Cesarelli G, Esposito L, Minutolo V, Bifulco P, Gargiulo P. Improving Prosthetic Selection and Predicting BMD from Biometric Measurements in Patients Receiving Total Hip Arthroplasty. Diagnostics (Basel). 2020 Oct 14;10(10):815. DOI: https://doi.org/10.3390/diagnostics10100815
Gargiulo P, Edmunds KJ, Gíslason MK, Latour C, Hermannsson Þ, Esposito L, Bifulco P, Cesarelli M, Fraldi M, Cristofolini L, Jónsson H Jr. Patient-specific mobility assessment to monitor recovery after total hip arthroplasty. Proc Inst Mech Eng H. 2018 Oct;232(10):1048-1059. DOI: https://doi.org/10.1177/0954411918797971
Fraldi M, Esposito L, Perrella G, Cutolo A, Cowin SC. Topological optimization in hip prosthesis design. Biomech Model Mechanobiol. 2010 Aug;9(4):389-402. doi: 10.1007/s10237-009-0183-0. DOI: https://doi.org/10.1007/s10237-009-0183-0
Esposito L, Bifulco P, Gargiulo P, Fraldi M. Singularity-free finite element model of bone through automated voxel-based reconstruction. Comput Methods Biomech Biomed Engin. 2016 Feb;19(3):257-262. DOI: https://doi.org/10.1080/10255842.2015.1014347
Esposito L, Bifulco P, Gargiulo P, Gíslason MK, Cesarelli M, Iuppariello L, Jónsson H, Cutolo A, Fraldi M. Towards a patient-specific estimation of intra-operative femoral fracture risk. Comput Methods Biomech Biomed Engin. 2018 Sep;21(12):663-672. DOI: https://doi.org/10.1080/10255842.2018.1508570
Esposito, L, Minutolo, V, Gargiulo, P, Jonsson, H Jr, Gislason, MK, Fraldi, M. Towards an App to Estimate Patient-Specific Perioperative Femur Fracture Risk. Appl Sci. 2020 Sep; 10(18) 6409. DOI: https://doi.org/10.3390/app10186409

How to Cite

Recenti, M., Ricciardi, C., Edmunds, K., Jacob, D., Gambacorta, M., & Gargiulo, P. (2021). Testing soft tissue radiodensity parameters interplay with age and self-reported physical activity. European Journal of Translational Myology, 31(3). https://doi.org/10.4081/ejtm.2021.9929