role of autoimmune co-morbidity on MS diagnosis delay, disease activity and disability progression, brain atrophy, and treatment choice.

**Author Contributions:** Writing—original draft preparation, V.N. and M.R.; writing—review and editing, V.N. and M.R.; visualization, V.N.; supervision, V.N.; project administration, V.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest related to this manuscript.

## References

1. Dobson, R.; Giovannoni, G. Multiple sclerosis—A review. *Eur. J. Neurol.* **2019**, *26*, 27–40. [CrossRef]

2. Kalincik, T.; Diouf, I.; Sharmin, S.; Malpas, C.; Spelman, T.; Horakova, D.; Havrdova, E.K.; Trojano, M.; Izquierdo, G.; Lugaresi, A.; et al. Effect of Disease-Modifying Therapy on Disability in Relapsing-Remitting Multiple Sclerosis over 15 Years. *Neurology* **2021**, *96*, e783–e797. [CrossRef] [PubMed]

3. Naegele, M.; Martin, R. The good and the bad of neuroinflammation in multiple sclerosis. *Handb. Clin. Neurol.* **2014**, *122*, 59–87. [CrossRef] [PubMed]

4. Magyari, M.; Sorensen, P.S. Comorbidity in Multiple Sclerosis. *Front. Neurol.* **2020**, *11*, 851. [CrossRef]

5. Valderas, J.M.; Starfield, B.; Sibbald, B.; Salisbury, C.; Roland, M. Defining comorbidity: Implications for understanding health and health services. *Ann. Fam. Med.* **2009**, *7*, 357–363. [CrossRef] [PubMed]

6. Hauer, L.; Perneczky, J.; Sellner, J. A global view of comorbidity in multiple sclerosis: A systematic review with a focus on regional differences, methodology, and clinical implications. *J. Neurol.* **2021**, *268*, 4066–4077. [CrossRef]

7. Marrie, R.A.; Reingold, S.; Cohen, J.; Stuve, O.; Trojano, M.; Sorensen, P.S.; Cutter, G.; Reider, N. The incidence and prevalence of psychiatric disorders in multiple sclerosis: A systematic review. *Mult. Scler. J.* **2015**, *21*, 305–317. [CrossRef] [PubMed]

8. Jadidi, E.; Mohammadi, M.; Moradi, T. High risk of cardiovascular diseases after diagnosis of multiple sclerosis. *Mult. Scler.* **2013**, *19*, 1336–1340. [CrossRef]

9. Kaplan, T.B.; Berkowitz, A.L.; Samuels, M.A. Cardiovascular Dysfunction in Multiple Sclerosis. *Neurologist* **2015**, *20*, 108–114. [CrossRef]

10. Lo, L.M.P.; Taylor, B.V.; Winzenberg, T.; Palmer, A.J.; Blizzard, L.; van der Mei, I. Change and onset-type differences in the prevalence of comorbidities in people with multiple sclerosis. *J. Neurol.* **2021**, *268*, 602–612. [CrossRef] [PubMed]

11. Koch-Henriksen, N.; Laursen, B.; Stenager, E.; Magyari, M. Excess mortality among patients with multiple sclerosis in Denmark has dropped significantly over the past six decades: A population based study. *J. Neurol. Neurosurg. Psychiatry* **2017**, *88*, 626–631. [CrossRef] [PubMed]

12. Marrie, R.A. Comorbidity in multiple sclerosis: Implications for patient care. *Nat. Rev. Neurol.* **2017**, *13*, 375–382. [CrossRef] [PubMed]

13. Marrie, R.A.; Horwitz, R.; Cutter, G.; Tyry, T.; Campagnolo, D.; Vollmer, T. Comorbidity delays diagnosis and increases disability at diagnosis in MS. *Neurology* **2009**, *72*, 117–124. [CrossRef] [PubMed]

14. Marrie, R.A.; Reider, N.; Cohen, J.; Trojano, M.; Sorensen, P.S.; Cutter, G.; Reingold, S.; Stuve, O. A systematic review of the incidence and prevalence of sleep disorders and seizure disorders in multiple sclerosis. *Mult. Scler.* **2015**, *21*, 342–349. [CrossRef] [PubMed]

15. Koch, M.; Uyttenboogaart, M.; Polman, S.; De Keyser, J. Seizures in multiple sclerosis. *Epilepsia* **2008**, *49*, 948–953. [CrossRef]

16. Gambardella, A.; Valentino, P.; Labate, A.; Sibilia, G.; Ruscica, F.; Colosimo, E.; Nisticò, R.; Messina, D.; Zappia, M.; Quattrone, A. Temporal lobe epilepsy as a unique manifestation of multiple sclerosis. *Can. J. Neurol. Sci.* **2003**, *30*, 228–232. [CrossRef]

17. Sokic, D.V.; Stojsavljevic, N.; Drulovic, J.; Dujmovic, I.; Mesaros, S.; Ercegovac, M.; Peric, V.; Dragutinovic, G.; Levic, Z. Seizures in multiple sclerosis. *Epilepsia* **2001**, *42*, 72–79. [CrossRef]

18. Gasparini, S.; Ferlazzo, E.; Ascoli, M.; Sueri, C.; Cianci, V.; Russo, C.; Pisani, L.R.; Striano, P.; Elia, M.; Beghi, E.; et al. Risk factors for unprovoked epileptic seizures in multiple sclerosis: A systematic review and meta-analysis. *Neurol. Sci.* **2017**, *38*, 399–406. [CrossRef]

19. Kinnunen, E.; Wikström, J. Prevalence and prognosis of epilepsy in patients with multiple sclerosis. *Epilepsia* **1986**, *27*, 729–733. [CrossRef]

20. Nicoletti, A.; Sofia, V.; Biondi, R.; Lo Fermo, S.; Reggio, E.; Patti, F.; Reggio, A. Epilepsy and multiple sclerosis in Sicily: A population-based study. *Epilepsia* **2003**, *44*, 1445–1448. [CrossRef]
21. Calabrese, M.; Grossi, P.; Favaretto, A.; Romualdi, C.; Atzori, M.; Rinaldi, F.; Perini, P.; Saladini, M.; Gallo, P. Cortical pathology in multiple sclerosis patients with epilepsy: A 3 year longitudinal study. *J. Neurol. Neurosurg. Psychiatry* **2012**, *83*, 49–54. [CrossRef] [PubMed]

22. Martínez-Lapiscina, E.H.; Ayuso, T.; Lacruz, F.; Gurtubay, I.G.; Soriano, G.; Otano, M.; Bujanda, M.; Bacaicoa, M.C. Cortic-o-juxta cortical involvement increases risk of epileptic seizures in multiple sclerosis. *Acta Neurol. Scand.* **2013**, *128*, 24–31. [CrossRef]

23. Pack, A. Is There a Relationship Between Multiple Sclerosis and Epilepsy? If So What Does It Tell Us About Epileptogenesis? *Epilepsy. Curr.* **2018**, *18*, 95–96. [CrossRef] [PubMed]

24. Wang, L.; Zhang, J.; Deng, Z.R.; Zu, M.D.; Wang, Y. The epidemiology of primary headaches in patients with multiple sclerosis. *Brain Behav.* **2021**, *11*, e01830. [CrossRef] [PubMed]

25. Applebee, A. The clinical overlap of multiple sclerosis and headache. *Headache* **2012**, *52* (Suppl. S2), 111–116. [CrossRef]

26. Mrabet, S.; Wafa, M.; Giovannoni, G. Multiple sclerosis and migraine: Links, management and implications. *Mult. Scler. Relat. Disord.* **2022**, *68*, 104152. [CrossRef]

27. Graziano, E.; Hagemeier, J.; Weinstock-Guttman, B.; Ramasamy, D.P.; Zivadinov, R. Increased contrast enhancing lesion activity in relapsing–remitting multiple sclerosis migraine patients. *NeuroImage Clin.* **2015**, *9*, 110–116. [CrossRef]

28. D'Amico, D.; La Mantia, L.; Rigamonti, A.; Usai, S.; Mascoli, N.; Milanese, C.; Bussone, G. Prevalence of primary headaches in people with multiple sclerosis. *Cephalalgia* **2004**, *24*, 980–984. [CrossRef]

29. Gee, J.R.; Chang, J.; Dublin, A.B.; Vijayan, N. The association of brainstem lesions with migraine-like headache: An imaging study of multiple sclerosis. *Headache* **2005**, *45*, 670–677. [CrossRef]

30. Tortorella, P.; Rocca, M.A.; Colombo, B.; Annovazzi, P.; Comi, G.; Filippi, M. Assessment of MRI abnormalities of the brainstem from patients with migraine and multiple sclerosis. *J. Neurol. Sci.* **2006**, *244*, 137–141. [CrossRef]

31. Eikermann-Haerter, K.; Ayata, C. Cortical Spreading Depression and Migraine. *Curr. Neurol. Neurosci. Rep.* **2010**, *10*, 167–173. [CrossRef] [PubMed]

32. Merkler, D.; Klinker, F.; Jürgens, T.; Glaser, R.; Paulus, W.; Brinkmann, B.G.; Sereda, M.W.; Stadelmann-Nessler, C.; Guedes, R.C.; Brück, W.; et al. Propagation of spreading depression inversely correlates with cortical myelin content. *Ann. Neurol.* **2009**, *66*, 355–365. [CrossRef] [PubMed]

33. Gursoy-Ozdemir, Y.; Qiu, J.; Matsuoka, N.; Bolay, H.; Bermpohl, D.; Jin, H.; Wang, X.; Rosenberg, G.A.; Lo, E.H.; Moskowitz, M.A. Cortical spreading depression activates and upregulates MMP-9. *J. Clin. Investig.* **2004**, *113*, 1447–1455. [CrossRef] [PubMed]

34. Villani, V.; Prosperini, L.; Pozzilli, C.; Salvetti, M.; Sette, G. Quality of life of multiple sclerosis patients with comorbid migraine. *Neurol. Sci.* **2011**, *32*, 149. [CrossRef] [PubMed]

35. Marrie, R.A.; Horwitz, R.I.; Cutter, G.; Tyry, T.; Vollmer, T. Smokers with multiple sclerosis are more likely to report comorbid autoimmune diseases. *Neuroepidemiology* **2011**, *36*, 85–90. [CrossRef] [PubMed]

36. Langer-Gould, A.; Albers, K.B.; Van Den Eeden, S.K.; Nelson, L.M. Autoimmune diseases prior to the diagnosis of multiple sclerosis: A population-based case-control study. *Mult. Scler.* **2010**, *16*, 855–861. [CrossRef]

37. Nielsen, N.M.; Frisch, M.; Rostgaard, K.; Wohlfahrt, J.; Hjalgrim, H.; Koch-Henriksen, N.; Melbye, M.; Westergaard, T. Autoim-mune diseases in patients with multiple sclerosis and their first-degree relatives: A nationwide cohort study in Denmark. *Mult. Scler.* **2008**, *14*, 823–829. [CrossRef]

38. Ramagopalan, S.V.; Dyment, D.A.; Valdar, W.; Herrera, B.M.; Criscuoli, M.; Yee, I.M.; Sadovnick, A.D.; Ebers, G.C. Autoimmune disease in families with multiple sclerosis: A population-based study. *Lancet Neurol.* **2007**, *6*, 604–610. [CrossRef]

39. Marrie, R.A.; Reider, N.; Cohen, J.; Stuve, O.; Sorensen, P.S.; Cutter, G.; Reingold, S.C.; Trojano, M. A systematic review of the incidence and prevalence of autoimmune disease in multiple sclerosis. *Mult. Scler.* **2015**, *21*, 282–293. [CrossRef]

40. Dobson, R.; Giovannoni, G. Autoimmune disease in people with multiple sclerosis and their relatives: A systematic review and meta-analysis. *J. Neurol.* **2013**, *260*, 1272–1285. [CrossRef]

41. Nielsen, N.M.; Westergaard, T.; Frisch, M.; Rostgaard, K.; Wohlfahrt, J.; Koch-Henriksen, N.; Melbye, M.; Hjalgrim, H. Type 1 diabetes and multiple sclerosis: A Danish population-based cohort study. *Arch. Neurol.* **2006**, *63*, 1001–1004. [CrossRef] [PubMed]

42. Marrosu, M.G.; Cocco, E.; Lai, M.; Spinicci, G.; Pischedda, M.P.; Contu, P. Patients with multiple sclerosis and risk of type 1 diabetes mellitus in Sardinia, Italy: A cohort study. *Lancet* **2002**, *359*, 1461–1465. [CrossRef]

43. Bechtold, S.; Blaschek, A.; Raile, K.; Dost, A.; Freiberg, C.; Askenas, M.; Fröhlich-Reiterer, E.; Molz, E.; Holl, R.W. Higher relative risk for multiple sclerosis in a pediatric and adolescent diabetic population: Analysis from DPV database. *Diabetes Care* **2014**, *37*, 96–101. [CrossRef] [PubMed]

44. Karni, A.; Abramsky, O. Association of MS with thyroid disorders. *Neurology* **1999**, *53*, 883–885. [CrossRef] [PubMed]

45. Niederwieser, G.; Buchinger, W.; Bonelli, R.M.; Berghold, A.; Reisecker, F.; Költringer, P.; Archelos, J.J. Prevalence of autoimmune thyroiditis and non-immune thyroid disease in multiple sclerosis. *J. Neurol.* **2003**, *250*, 672–675. [CrossRef] [PubMed]

46. Munteis, E.; Cano, J.F.; Flores, J.A.; Martinez-Rodriguez, J.E.; Miret, M.; Roquer, J. Prevalence of autoimmune thyroid disorders in a Spanish multiple sclerosis cohort. *Eur. J. Neurol.* **2007**, *14*, 1048–1052. [CrossRef]

47. Roshanisefat, H.; Bahmanyar, S.; Hillert, J.; Olsson, T.; Montgomery, S. Shared genetic factors may not explain the raised risk of comorbid inflammatory diseases in multiple sclerosis. *Mult. Scler.* **2012**, *18*, 1430–1436. [CrossRef]

48. Edwards, L.J.; Constantinescu, C.S. A prospective study of conditions associated with multiple sclerosis in a cohort of 658 consecutive outpatients attending a multiple sclerosis clinic. *Mult. Scler.* *J.* **2004**, *10*, 575–581. [CrossRef]
49. Kosmidou, M.; Katsanos, A.H.; Katsanos, K.H.; Kyritsis, A.P.; Tsivgoulis, G.; Christodoulou, D.; Giannopoulos, S. Multiple sclerosis and inflammatory bowel diseases: A systematic review and meta-analysis. *J. Neurol. 2017*, *264*, 254–259. [CrossRef]

50. Marrie, R.A.; Patten, S.B.; Tremlett, H.; Wolfson, C.; Leung, S.; Fisk, J.D. Increased incidence and prevalence of psoriasis in multiple sclerosis. *Mult. Scler. Relat. Disord. 2017*, *13*, 81–86. [CrossRef]

51. Fellner, A.; Dano, M.; Regev, K.; Mosek, A.; Karni, A. Multiple sclerosis is associated with psoriasis. A case-control study. *J. Neurol. Sci. 2014*, *338*, 226–228. [CrossRef] [PubMed]

52. Farez, M.F.; Balbuena Aguirre, M.E.; Varela, F.; Köhler, A.A.; Correale, J. Autoimmune Disease Prevalence in a Multiple Sclerosis Cohort in Argentina. *Mult. Scler. Int. 2014*, *2014*, 828162. [CrossRef] [PubMed]

53. Silfvast-Kaiser, A.S.; Homan, K.B.; Mansouri, B. A narrative review of psoriasis and multiple sclerosis: Links and risks. *Psoriasis 2019*, *9*, 81–90. [CrossRef]

54. Costelloe, L.; Jones, J.; Coles, A. Secondary autoimmune diseases following alemtuzumab therapy for multiple sclerosis. *Expert Rev. Neurother. 2012*, *12*, 335–341. [CrossRef]

55. Caraccio, N.; Dardano, A.; Manfredonia, F.; Manca, L.; Pasquali, L.; Iudice, A.; Murri, L.; Ferrannini, E.; Monzani, F. Long-term follow-up of 106 multiple sclerosis patients undergoing interferon-beta 1a or 1b therapy: Predictive factors of thyroid disease development and duration. *J. Clin. Endocrinol. Metab. 2005*, *90*, 4133–4137. [CrossRef] [PubMed]

56. Sahraian, M.A.; Owji, M.; Naser Moghadasi, A. Concomitant multiple sclerosis and another autoimmune disease: Does the clinical course change? *Clin. Neurol. Neurosurg. 2016*, *150*, 92–95. [CrossRef]

57. Fanouriakis, A.; Mastorodemos, V.; Pamfil, C.; Papadaki, E.; Sidiropoulos, P.; Plaitakis, A.; Amoiridis, G.; Bertsias, G.; Boumpas, D.T. Coexistence of systemic lupus erythematosus and multiple sclerosis: Prevalence, clinical characteristics, and natural history. *Semin. Arthritis Rheum. 2014*, *43*, 751–758. [CrossRef]

58. Zéphir, H.; Gower-Rousseau, C.; Salleron, J.; Simon, O.; Debouverie, M.; Le Page, E.; Bouhnik, Y.; Lebrun-Frenay, C.; Papeix, C.; Vigneron, B.; et al. Milder multiple sclerosis course in patients with concomitant inflammatory bowel disease. *Mult. Scler. 2014*, *20*, 1135–1139. [CrossRef]

59. Zivadinov, R.; Raj, B.; Ramanathan, M.; Teter, B.; Durfee, J.; Dwyer, M.G.; Bergsland, N.; Kolb, C.; Hojnacki, D.; Benedict, R.H.; et al. Autoimmune Comorbidities Are Associated with Brain Injury in Multiple Sclerosis. *AJNR Am. J. Neuroradiol. 2016*, *37*, 1010–1016. [CrossRef]

60. Lorefice, L.; Fenu, G.; Pitzalis, R.; Scalas, G.; Frau, J.; Coghe, G.; Musu, L.; Sechi, V.; Barracciu, M.A.; Marrosu, M.G.; et al. Autoimmune comorbidities in multiple sclerosis: What is the influence on brain volumes? A case-control MRI study. *J. Neurol. 2018*, *265*, 1096–1101. [CrossRef]

61. Barnett, M.; Bergsland, N.; Weinstock-Guttman, B.; Butzkueven, H.; Kalincik, T.; Desmond, P.; Gaillard, F.; van Pesch, V.; Ozakbas, S.; Rojas, J.I.; et al. Brain atrophy and lesion burden are associated with disability progression in a multiple sclerosis real-world dataset using only T2-FLAIR: The NeuroSTREAM MSBase study. *Neuroimage Clin. 2021*, *32*, 102802. [CrossRef] [PubMed]

62. Losseff, N.A.; Webb, S.L.; O'Riordan, J.I.; Page, R.; Wang, L.; Barker, G.J.; Tofts, P.S.; McDonald, W.I.; Miller, D.H.; Thompson, A.J. Spinal cord atrophy and disability in multiple sclerosis. A new reproducible and sensitive MRI method with potential to monitor disease progression. *Brain 1996*, *119*, 701–708. [CrossRef] [PubMed]

63. Kunchok, A.; Aksamit, A.J., Jr.; Davis, J.M., 3rd; Kantarci, O.H.; Keegan, B.M.; Pittock, S.J.; Weinshenker, B.G.; McKeon, A. Association Between Tumor Necrosis Factor Inhibitor Exposure and Inflammatory Central Nervous System Events. *JAMA Neurol. 2020*, *77*, 937–946. [CrossRef] [PubMed]

64. La Mantia, L.; Capsoni, F. Psoriasis during interferon beta treatment for multiple sclerosis. *Neurol. Sci. 2010*, *31*, 337–339. [CrossRef] [PubMed]

65. Hojjati, S.M.; Heidari, B.; Babaei, M. Development of rheumatoid arthritis during treatment of multiple sclerosis with interferon beta 1-a. Coincidence of two conditions or a complication of treatment: A case report. *J. Adv. Res. 2016*, *7*, 611–613. [CrossRef] [PubMed]

66. Schott, E.; Paul, F.; Wuerfel, J.T.; Zipp, F.; Rudolph, B.; Wiedenmann, B.; Baumgart, D.C. Development of ulcerative colitis in a patient with multiple sclerosis following treatment with interferon beta 1a. *World J. Gastroenterol. 2007*, *13*, 3638–3640. [CrossRef] [PubMed]

67. Killestein, J.; van Oosten, B. Emerging safety issues in alemtuzumab-treated MS patients. *Mult. Scler. 2019*, *25*, 1206–1208. [CrossRef]

68. Lambrianides, S.; Kinnis, E.; Leonidou, E.; Pantzaris, M. Does Natalizumab Induce or Aggravate Psoriasis? A Case Study and Review of the Literature. *Case Rep. Neurol. 2018*, *10*, 286–291. [CrossRef]

69. Su, E.; Novic, J.; Han, M.H. Emergence of rheumatoid arthritis following exposure to natalizumab. *Mult. Scler. Relat. Disord. 2020*, *40*, 101936. [CrossRef]

70. Khoy, K.; Mariotte, D.; Defer, G.; Petit, G.; Toutirais, O.; Le Mauff, B. Natalizumab in Multiple Sclerosis Treatment: From Biological Effects to Immune Monitoring. *Front. Immunol. 2020*, *11*, 549842. [CrossRef]

71. MacDonald, J.K.; McDonald, J.W. Natalizumab for induction of remission in Crohn's disease. *Cochrane Database Syst. Rev. 2007*, CD006097. [CrossRef] [PubMed]
72. Targan, S.R.; Feagan, B.G.; Fedorak, R.N.; Lashner, B.A.; Panaccione, R.; Present, D.H.; Spehlmann, M.E.; Rutgeerts, P.J.; Tulassay, Z.; Volfova, M.; et al. Natalizumab for the treatment of active Crohn's disease: Results of the ENCORE Trial. *Gastroenterology* **2007**, *132*, 1672-1683. [CrossRef] [PubMed]

73. Abushouk, A.I.; Ahmed, H.; Ismail, A.; Elmaraezy, A.; Badr, A.S.; Gadelkarim, M.; Elnenny, M. Safety and efficacy of ocrelizumab in rheumatoid arthritis patients with an inadequate response to methotrexate or tumor necrosis factor inhibitors: A systematic review and meta-analysis. *Rheumatol. Int.* **2017**, *37*, 1053-1064. [CrossRef] [PubMed]

74. Kristjánsson, V.B.; Lund, S.H.; Gröndal, G.; Sveinsdóttir, S.V.; Agnarsson, H.R.; Jónasson, J.G.; Björnsson, E.S. Increased risk of inflammatory bowel disease among patients treated with rituximab in Iceland from 2001 to 2018. *Scand. J. Gastroenterol.* **2021**, *56*, 46-52. [CrossRef] [PubMed]

75. Darwin, E.; Romanelli, P.; Lev-Tov, H. Ocrelizumab-induced psoriasiform dermatitis in a patient with multiple sclerosis. *Dermatol. Online J.* **2018**, *24*. [CrossRef]

76. Sunjaya, D.B.; Taborda, C.; Obeng, R.; Dhere, T. First Case of Refractory Colitis Caused by Ocrelizumab. *Inflamm. Bowel. Dis.* **2020**, *26*, e49. [CrossRef]

77. Schaper, K.; Dickhaut, J.; Japtok, L.; Kietzmann, M.; Mischke, R.; Kleuser, B.; Bäumer, W. Sphingosine-1-phosphate exhibits anti-proliferative and anti-inflammatory effects in mouse models of psoriasis. *J. Dermatol. Sci.* **2013**, *71*, 29-36. [CrossRef]

78. Deguchi, Y.; Andoh, A.; Yagi, Y.; Bamba, S.; Inatomi, O.; Tsujikawa, T.; Fujiyama, Y. The S1P receptor modulator FTY720 prevents the development of experimental colitis in mice. *Oncol. Rep.* **2006**, *16*, 699-703. [CrossRef]

79. Ladrón Abia, P.; Alcalá Vicente, C.; Martínez Delgado, S.; Bastida Paz, G. Fingolimod-induced remission in a patient with ulcerative colitis and multiple sclerosis. *Gastroenterol. Hepatol.* **2021**, *44*, 156-157. [CrossRef]

80. Sandborn, W.J.; Feagan, B.G. Ozanimod Treatment for Ulcerative Colitis. *N. Engl. J. Med.* **2016**, *375*, e17. [CrossRef]

81. Altmeyer, P.J.; Matthes, U.; Pawlak, F.; Hoffmann, K.; Frosch, P.J.; Ruppert, P.; Wassilew, S.W.; Horn, T.; Kreysel, H.W.; Lutz, G.; et al. Antipsoriatic effect of fumaric acid derivatives. Results of a multicenter double-blind study in 100 patients. *J. Am. Acad. Dermatol.* **1994**, *30*, 977-981. [CrossRef]

82. Ghoreschi, K.; Brück, J.; Kellerer, C.; Deng, C.; Peng, H.; Rothfuss, O.; Hussain, R.Z.; Gocke, A.R.; Respa, A.; Glocova, I.; et al. Fumarates improve psoriasis and multiple sclerosis by inducing type II dendritic cells. *J. Exp. Med.* **2011**, *208*, 2291-2303. [CrossRef] [PubMed]

83. Brummer, T.; Ruck, T.; Meuth, S.G.; Zipp, F.; Bittner, S. Treatment approaches to patients with multiple sclerosis and coexisting autoimmune disorders. *Ther. Adv. Neurol. Disord.* **2021**, *14*, 17562864211035542. [CrossRef] [PubMed]

84. Berrigan, L.I.; Fisk, J.D.; Patten, S.B.; Tremlett, H.; Wolfson, C.; Warren, S.; Fiest, K.M.; McKay, K.A.; Marrie, R.A. Health-related quality of life in multiple sclerosis: Direct and indirect effects of comorbidity. *Neurology* **2016**, *86*, 1417-1424. [CrossRef]

85. Salter, A.; Tyry, T.; Wang, G.; Fox, R.J.; Cutter, G.; Marrie, R.A. Examining the joint effect of disability, health behaviors, and comorbidity on mortality in MS. *Neurol. Clin. Pract.* **2016**, *6*, 397-408. [CrossRef]

86. Newland, P.K.; Lorenz, R.; Budhathoki, C.; Jensen, M.P. The Presence of Symptoms With Comorbid Conditions in Individuals With Multiple Sclerosis (MS). *Clin. Nurs. Res.* **2016**, *25*, 532-548. [CrossRef]

87. Marrie, R.A.; Miller, A.; Sormani, M.P.; Thompson, A.; Waubant, E.; Trojano, M.; O'Connor, P.; Reingold, S.; Cohen, J.A. The challenge of comorbidity in clinical trials for multiple sclerosis. *Neurology* **2016**, *86*, 1437-1445. [CrossRef]

88. Handel, A.E.; Handunnetthi, L.; Ebers, G.C.; Ramagopalan, S.V. Type 1 diabetes mellitus and multiple sclerosis: Common etiological features. *Nat. Rev. Endocrinol.* **2009**, *5*, 655-664. [CrossRef]

89. Winer, S.; Astsatsurov, I.; Cheung, R.; Gunaratnam, L.; Kubiak, V.; Cortez, M.A.; Moscarello, M.; O'Connor, P.W.; McKerlie, C.; Becker, D.J.; et al. Type I diabetes and multiple sclerosis patients target islet plus central nervous system autoantigens; nonimmunized nonobese diabetic mice can develop autoimmune encephalitis. *J. Immunol.* **2001**, *166*, 2831-2841. [CrossRef]

90. Banwell, B.; Bar-Or, A.; Cheung, R.; Kennedy, J.; Krupp, L.B.; Becker, D.J.; Dosch, H.M. Abnormal T-cell reactivities in childhood inflammatory demyelinating disease and type 1 diabetes. *Ann. Neurol.* **2008**, *63*, 98-111. [CrossRef]

91. Baranzini, S.E. The genetics of autoimmune diseases: A networked perspective. *Curr. Opin. Immunol.* **2009**, *21*, 596-605. [CrossRef] [PubMed]

92. Richard-Miceli, C.; Criswell, L.A. Emerging patterns of genetic overlap across autoimmune disorders. *Genome Med.* **2012**, *4*, 6. [CrossRef] [PubMed]
<figure><img src="image_21.png" /></figure>

Article

# DNA Methylation Analysis Reveals Distinct Patterns in Satellite Cell-Derived Myogenic Progenitor Cells of Subjects with Spastic Cerebral Palsy

Karyn G. Robinson<fnref n="1" />, Adam G. Marsh<fnref n="2" />, Stephanie K. Lee<fnref n="1" />, Jonathan Hicks<fnref n="2" />, Brigette Romero<fnref n="3" />, Mona Batish<fnref n="3" /> ID,
Erin L. Crowgey<fnref n="1" />, M. Wade Shrader<fnref n="4" /> and Robert E. Akins<fnref n="1" />,* ID

**Abstract:** Spastic type cerebral palsy (CP) is a complex neuromuscular disorder that involves altered skeletal muscle microanatomy and growth, but little is known about the mechanisms contributing to muscle pathophysiology and dysfunction. Traditional genomic approaches have provided limited insight regarding disease onset and severity, but recent epigenomic studies indicate that DNA methylation patterns can be altered in CP. Here, we examined whether a diagnosis of spastic CP is associated with intrinsic DNA methylation differences in myoblasts and myotubes derived from muscle resident stem cell populations (satellite cells; SCs). Twelve subjects were enrolled (6 CP; 6 control) with informed consent/assent. Skeletal muscle biopsies were obtained during orthopedic surgeries, and SCs were isolated and cultured to establish patient-specific myoblast cell lines capable of proliferation and differentiation in culture. DNA methylation analyses indicated significant differences at 525 individual CpG sites in proliferating SC-derived myoblasts (MB) and 1774 CpG sites in differentiating SC-derived myotubes (MT). Of these, 79 CpG sites were common in both culture types. The distribution of differentially methylated 1 Mbp chromosomal segments indicated distinct regional hypo- and hyper-methylation patterns, and significant enrichment of differentially methylated sites on chromosomes 12, 13, 14, 15, 18, and 20. Average methylation load across 2000 bp regions flanking transcriptional start sites was significantly different in 3 genes in MBs, and 10 genes in MTs. SC derived MBs isolated from study participants with spastic CP exhibited fundamental differences in DNA methylation compared to controls at multiple levels of organization that may reveal new targets for studies of mechanisms contributing to muscle dysregulation in spastic CP.

**Keywords:** cerebral palsy; muscle spasticity; primary cell culture; satellite cells; skeletal muscle; muscle; skeletal; humans; epigenomics; DNA methylation; regulatory non-coding RNAs

## 1. Introduction

Cerebral palsy (CP) is the most common cause of physical disability in childhood, with a prevalence of 2–4 per 1000 live births [1–3]. It is a heterogeneous set of movement disorders associated with a static encephalopathy that occurs during fetal development or early postnatal life [4]. There are three fundamental types of CP: ataxic, dyskinetic, and spastic [5], with spastic CP accounting for about 80% of cases [6]. Spastic CP is characterized by hypertonia, exaggerated reflexes, and poor muscle growth associated with progressive musculoskeletal deformities that often require surgical correction [5–13].

Individuals with spastic CP have difficulties with movement, movement control, and muscle function [2,3]. Ultrasound and MRI studies have demonstrated that subjects with spastic CP have decreased muscle length [14], cross-sectional area [15], and volume [16,17], leading to diminished force generation, reduced range of motion, and weakness [18–21].

**Citation:** Robinson, K.G.; Marsh, A.G.; Lee, S.K.; Hicks, J.; Romero, B.; Batish, M.; Crowgey, E.L.; Shrader, M.W.; Akins, R.E. DNA Methylation Analysis Reveals Distinct Patterns in Satellite Cell-Derived Myogenic Progenitor Cells of Subjects with Spastic Cerebral Palsy. *J. Pers. Med.* **2022**, *12*, 1978. https://doi.org/10.3390/jpm12121978

Academic Editor: Anne-Marie Caminade

Received: 17 November 2022

Accepted: 25 November 2022

Published: 30 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_22.png" /></figure>

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Nemours Children's Research, Nemours Children's Health System, Wilmington, DE 19803, USA</footnote>

<footnote n="2">² Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19716, USA</footnote>

<footnote n="3">³ Medical and Molecular Sciences, University of Delaware, Newark, DE 19716, USA</footnote>

<footnote n="4">⁴ Department of Orthopedics, Nemours Children's Hospital Delaware, Wilmington, DE 19803, USA</footnote>

<footnote>* Correspondence: robert.akins@nemours.org; Tel.: +1-302-651-6779</footnote>
Additionally, studies of muscle indicate that patients with CP exhibit increased sarcomere length [22], disorganized neuromuscular junctions [23–25], extracellular matrix abnormalities [22], tissue-level differences in gene expression profiles [26,27], and limited myogenic potential [28].

While all individuals with spastic CP have some movement dysfunction, there is a high degree of variability in phenotype between individuals. A more thorough understanding of the mechanisms associated with dysfunction in the peripheral neuromotor system is needed in order to develop more targeted and enhanced therapeutics addressing the major challenges facing individuals with spastic CP. Importantly, spastic CP is associated with alterations in the muscle-resident stem cell populations (satellite cells; SC) responsible for skeletal muscle growth and repair. Surgical patients with CP have a reduced SC population, which may account for aspects of their impaired muscle growth and decreased ability to strengthen muscle [28–30]. Isolated SC-derived myoblasts (MB) from CP donors appear to have altered phenotypes in culture compared to control MBs [28,31,32], and an RNAseq study from our group showed differential expression of mRNAs and miRNAs in spastic CP muscle [26]. These data suggest that MBs from CP patients may retain intrinsic differences through the cell isolation and culture process.

Genetic alterations may account for intrinsic differences in isolated MB populations. A number of potentially causative genetic variants have been identified in CP [33], but not all types of CP are easily detected or characterized by genomic data. Several rare copy number variants and mutations have been identified, but there is considerable genetic heterogeneity in patients with CP [33,34]. While some CP cases may be associated with certain types of genetic abnormality [35], the conventional view of CP remains that environmental factors affecting neuromotor maturation are responsible for most cases, especially among individuals with spastic CP [36,37].

Epigenetic modification may also account for some retained phenotypic differences in isolated MB behavior. In recent work, DNA methylation pattern differences were identified in peripheral blood cells from subjects with CP [38–41], and some early studies indicate that muscle in CP may be similarly altered, with differential DNA methylation in CP resulting in a decreased capacity for MBs to fuse and differentiate into MTs [32]. It has been demonstrated in several disease states that patterns of altered DNA methylation may uncover molecular etiologies and reveal potential therapeutic targets [42–51], and such may be the case in spastic CP as well [39]. DNA methylation changes may serve as markers for diagnosis, prognosis, tailoring the best treatment for a subclass of disease, monitoring treatment efficacy, and identifying genes to be examined for the development of genetically or epigenetically targeted therapies [52]. In the current study, DNA methylomes were analyzed to provide insight into individual CpG site differences and altered DNA methylation patterns in chromosomal segments and near transcription start site (TSS) in spastic CP SCs compared to controls.

## 2. Materials and Methods

### 2.1. *Subject Enrollment*

Six subjects with a diagnosis of spastic CP and 6 control subjects were enrolled in an IRB-approved study at Nemours Children's Hospital, Delaware, after informed consent/assent. The control cohort comprised children with an idiopathic condition or an injury. Subjects with a chromosomal disorder, degenerative neurological disease, or muscular dystrophy were excluded.

### 2.2. *Satellite Cell Isolation*

Skeletal muscle biopsies collected during orthopedic surgeries were enzymatically digested and a double-immunomagnetic isolation approach was used to collect a population of mononuclear Cells positive for the surface markers neural cell adhesion molecule 1 and C-X-C motif chemokine receptor 4 (anti-NCAM1 and anti-CXCR4, both at 2.5 ng/mL, Miltenyi, San Diego, CA, USA) as previously described [26]. Previous studies have demon-
strated that CXCR4 marks human SCs and that selection using the combination of NCAM1 (CD56) and CXCR4 more effectively removes non-satellite cells than using either marker alone [53,54]. This method resulted in a nearly pure SC population as verified by positive PAX7 immunofluorescence signal obtained after 24–48 h in culture (Anti-PAX7 from hybridoma cells deposited to the DSHB by Kawakami, A., Developmental Studies Hybridoma Bank, Iowa City, IA, USA). Cell populations that were at least 90% positive for PAX7 expression were utilized for experiments (Supplemental Table S1).

Cells were seeded at passage 3–5 (Supplemental Table S1) and proliferated in medium consisting of Zenbio Skeletal Muscle Growth Medium (Zenbio, Triangle Park, NC, USA) supplemented to a final concentration of 20% Qualified FBS (Thermo Fisher Scientific, Philadelphia, PA, USA), 4 g/L of D Glucose (Thermo Fisher Scientific, Philadelphia, PA, USA), 1 ng/mL of human bFGF (PeproTech, Rocky Hill, NJ, USA), and 1% penicillin-streptomycin, (Thermo Fisher Scientific, Philadelphia, PA, USA) was exchanged every other day until cells reached confluence. Proliferating MBs were collected at 50% confluence (2–5 days after seeding; Supplemental Table S1). For differentiation, cultures were switched to low-serum medium consisting of high glucose DMEM (Thermo Fisher Scientific, Philadelphia, PA, USA) supplemented with 2% horse serum (Thermo Fisher Scientific, Philadelphia, PA, USA), 2% human insulin (Sigma, St. Louis, MO, USA) and 1% penicillin-streptomycin upon reaching 90–100% confluence. Cells were differentiated for 24 h to initiate MB fusion into MTs [28] and collected.

## 2.3. DNA Extraction, Library Preparation, and Sequencing

Genomic DNA was isolated using Gentra Puregene kits (Qiagen, Germantown, MD, USA). A previously published DNA methylation assay [39,55,56] was utilized. Briefly, DNA libraries for next generation sequencing (NGS) were prepared by digesting genomic DNA with methyl-sensitive restriction endonuclease HpaII, which recognizes CCGG sites. A standard sequencing protocol was then performed including randomized shearing (Covaris, Woburn, MA, USA) and synthesis of a gDNA fragment library using Illumina TruSeq Nano library synthesis kits (San Diego, CA, USA). NGS was performed on an Illumina ×10 platform by Psomagen (Rockville, MD, USA). The protocol generated single end reads (150 bp) with >20× coverage of the regions captured. FASTQ data files were processed to calculate the probability of methylation at individual CpG sites through a commercial bioinformatics pipeline and software platform (Genome Profiling LLC, Newark, DE, USA). For convenience, the term “CpG” in this paper refers to “C(CpG)G” HpaII restriction sites.

## 2.4. Methylation Analysis

FASTQ files were aligned to human reference genome hg19 using BWA (Burrows-Wheeler Aligner algorithm [57]). To reduce false discovery associated with the inclusion of both male and female participants in the study, sites on the X and Y chromosomes were excluded from analyses. For each CpG per sample, a methylation score was calculated proportional to the probability that a specific CpG site was methylated. These scores were then compared between cohorts using a set of analytic modules from the R-packages edgeR [58,59] and limma [60] to compare pairwise for each CpG site. The response scale of the methylation data sets used here is within the operational boundaries of log-scaled gene expression data for which edgeR and limma were designed, and the well-developed false-discovery rate calculations in these R packages are ideal for methylation score data distributions [61,62].

Statistical significance at the level of individual CpG sites and was evaluated using a Likelihood Ratio Test with a one-way ANOVA contrast (LRT-ANOVA). Potentially informative CpG sites were selected for the PCA plots by filtering the LRT-ANOVA *p*-values with an appropriate cutoff (<0.01). Gene annotations were derived from the Ensembl GRCh37 database based on chromosomal locus (www.enssembl.org; last accessed on 15 November 2022 for validation). Enhancer regions were derived from Ensembl's GRCh37 BioMart tool. The ontology terms Muscle Organ Development, Muscle System Processes, and Skeletal
Muscle Cell Differentiation (GO Enrichment Analysis; amigo.geneontology.org) were used to determine 575 unique genes annotated to be involved in muscle physiology. Fisher's exact test was utilized to determine enrichment of significant CpGs within chromosomes.

Because methylation of the region around the TSS of a gene is thought to be highly informative of gene expression [63], the individual CpG methylation scores were averaged per TSS using 1000 bp upstream of the TSS and 1000 bp downstream [64]. TSS were identified from Ensembl's GRCh37 BioMart data mining tool (release 106) [65]. If one or fewer CpGs were found within this range, the TSS was excluded from further analysis. For each sample, the mean methylation load in this 2000 bp range was calculated and a likelihood ratio test performed on the methylation loads. No more than one transcript for each gene was included in the statistical analysis.

### 3. Results

Samples from 12 unique study participants were included in the study; demographic data are summarized in Table 1. Briefly, the control group consisted of *n* = 6 subjects (males = 3, and females = 3) with an average age of 13.9 ± 1.7 years; the CP group consisted of *n* = 6 subjects (males = 3, and females = 3) with an average age of 15.5 ± 3.0 years. Biopsies from different muscles were included based on the availability of viable muscle tissue suitable for cell isolation and to help identify methylation signals generally associated with spastic CP rather than a specific muscle: SCs were isolated from spinalis or semitendinosus muscle for controls and from spinalis, rectus femoris, adductor longus, or vastus lateralis for the CP group. Although differences likely exist between muscle types, previous studies of MTs derived from different muscles found low inter-muscle variability in RNA-sequencing data [26].

<table><caption>**Table 1.** Demographic information for subjects in the study *.</caption><thead><tr><td>Sample</td><td>Diagnosis</td><td>Age</td><td>Sex</td><td>GMFCS</td><td>Tissue Source</td></tr></thead><tbody><tr><td>CN1</td><td>Spondylolysis</td><td>16.6</td><td>M</td><td>N/A</td><td>Spinalis</td></tr><tr><td>CN2</td><td>Torn ACL</td><td>12.6</td><td>M</td><td>N/A</td><td>Semitendinosus</td></tr><tr><td>CN3</td><td>Idiopathic scoliosis</td><td>12.1</td><td>F</td><td>N/A</td><td>Spinalis</td></tr><tr><td>CN4</td><td>Torn ACL</td><td>12.7</td><td>F</td><td>N/A</td><td>Semitendinosus</td></tr><tr><td>CN5</td><td>Idiopathic scoliosis</td><td>15.1</td><td>M</td><td>N/A</td><td>Spinalis</td></tr><tr><td>CN6</td><td>Idiopathic scoliosis</td><td>14.3</td><td>F</td><td>N/A</td><td>Spinalis</td></tr><tr><td>CP1</td><td>Spastic CP</td><td>15.6</td><td>M</td><td>5</td><td>Vastus lateralis</td></tr><tr><td>CP2</td><td>Spastic CP</td><td>19.1</td><td>M</td><td>5</td><td>Adductor longus</td></tr><tr><td>CP3</td><td>Spastic CP</td><td>12.6</td><td>M</td><td>4</td><td>Rectus femoris</td></tr><tr><td>CP4</td><td>Spastic CP</td><td>13.8</td><td>F</td><td>2</td><td>Rectus femoris</td></tr><tr><td>CP5</td><td>Spastic CP</td><td>19.0</td><td>F</td><td>5</td><td>Spinalis</td></tr><tr><td>CP6</td><td>Spastic CP</td><td>12.8</td><td>F</td><td>5</td><td>Spinalis</td></tr></tbody></table>

To evaluate differences in DNA methylation patterns associated with affected muscle of subjects with spastic CP, whole genome methylation patterns were determined from proliferating MBs and differentiating MTs. Cells derived from subjects with CP appeared morphologically similar to those derived from controls (Supplemental Figure S1) and exhibited no significant differences in proliferation rates (Supplemental Table S1). NGS was performed after methylation sensitive restriction endonuclease (HpaII) digestion. The hg19 reference genome assembly from the University of California Santa Cruz [66] includes 2.29 × 10⁶ HpaII target CCGG motifs, which represent ~15% of the 14 × 10⁶ CpG sites in the haploid hg19 genome [39]. Alignment of the HpaII restricted sites in our 12 samples yielded

<footnote>* CN = control; CP = cerebral palsy; ACL = anterior cruciate ligament; M = male; F = female; GMFCS = Gross Motor Function Classification System.</footnote>
1,483,038 sites were in common across all subjects for MBs and MTs. DNA methylation patterns were analyzed at the individual CpG site level using dimensionality reduction by principal component analysis (PCA) to assess the degree of discrimination between CP and non-CP cohorts. All potentially informative CpG sites (n = 20,254 for MBs and 27,834 for MTs), were integrated as one pattern and demonstrated strong discrimination based on diagnosis (Figure 1, Supplemental Figure S2).

<figure><img src="image_23.png" /><figcaption>**Figure 1.** Principal component analysis to identify discriminating methylation patterns between CP and non-CP cohorts. For MBs (a) and MTs (b), the first two component axes (PC1, PC2) were plotted with % variance explained in parenthesis. Each point represents the similarity position of a subject based on all potentially informative CpG sites (*p* < 0.01). CP subjects are represented in gray and control subjects in black. Ellipses represent 90% confidence intervals. The complete segregation of the two cohorts indicates that DNA methylation patterns fundamentally differ between cohorts.</figcaption></figure>

In MBs, 525 CpG sites were found to have differential methylation load scores (FDR < 0.05; heatmap in Figure 2a, volcano plot in Supplemental Figure S3A, list of significant CpGs in Supplemental Table S2). Of these, 11 were within genes known to be involved in muscle physiology and 21 were within known gene enhancer regions. 1774 CpG sites were found to have differential methylation load scores in MTs between the CP and control cohorts (heatmap in Figure 2b, volcano plot in Supplemental Figure S3B, list of significant CpGs in Supplemental Table S3), of which 43 CpG sites were within genes known to be involved in muscle physiology and 97 were within gene enhancer regions. The differentially methylated CpGs included 79 CpG sites that were significantly different under both cell conditions. Of these, 36 were significantly hypermethylated and 43 were significantly hypomethylated in the CP cohort compared to the control cohort (Table 2).

To determine the methylation differences over larger regions of the genome, the chromosomal distribution of significant CpGs was visualized (Supplemental Figure S4) and Fisher's exact test was employed to analyze enrichment of differentially methylated CpGs on individual chromosomes. For both MB and MT cell populations, significant enrichment was found on chromosomes 12, 13, 14, 15, 18, and 20. Interestingly, the same chromosomes were identified as having significant CpG site enrichment in previous studies of both muscle tissue and peripheral blood cells from subjects with CP (Table 3). To further assess regional differences, methylation load scores were calculated across 1 Mbp chromosomal segments. There was a strong correlation between the 1 Mbp methylation loads of MBs and MTs (Figure 3a), indicating stability of the methylome. When these 1 Mpb regions were mapped to the chromosomes, regions of accentuated differential methylation load were noted on all chromosomes except 1 and 17 (Figure 3b,c). These large-scale changes in DNA methylation could affect higher-order chromatin structure and regulation of gene expression [52].
<figure><img src="image_24.png" /><figcaption>**Figure 2.** Heatmap clustering of the top 200 CpG sites. Of the common CpG sites, there was significant differential methylation in 525 distinct CpGs for MBs (a) and 1774 for MTs (b) (FDR-corrected *p*-value < 0.05). Heatmaps based on the 200 CpG sites with the lowest FDR-corrected *p*-values were generated using Euclidean distances and complete linkage clustering. Each row represents the score for a single CpG site across all subjects with blue indicating hypomethylation and red indicating hypermethylation. Quantitative differences in CpG site methylation by diagnosis were apparent.</figcaption></figure>

<table><caption>**Table 2.** CpG sites that were differentially methylated in both MBs and MTs *.</caption><thead><tr><th>Position</th><th>MB LogFC</th><th>MB FDR<br>Corrected p-Value</th><th>MT LogFC</th><th>MT FDR<br>Corrected p-Value</th><th>Gene</th></tr></thead><tbody><tr><td>chr2.0003882321</td><td>0.88</td><td>2.29 × 10<sup>-2</sup></td><td>0.90</td><td>2.40 × 10<sup>-2</sup></td><td></td></tr><tr><td>chr2.0029850455</td><td>1.24</td><td>6.74 × 10<sup>-4</sup></td><td>1.32</td><td>1.11 × 10<sup>-5</sup></td><td>ALK</td></tr><tr><td>chr2.0033057636</td><td>-0.84</td><td>3.40 × 10<sup>-3</sup></td><td>-0.85</td><td>1.94 × 10<sup>-2</sup></td><td>LINC00486</td></tr><tr><td>chr2.0035092870</td><td>-1.09</td><td>1.00 × 10<sup>-2</sup></td><td>-1.19</td><td>8.26 × 10<sup>-5</sup></td><td>AC012593.1</td></tr><tr><td>chr2.0056193463</td><td>1.32</td><td>1.79 × 10<sup>-3</sup></td><td>1.55</td><td>1.22 × 10<sup>-6</sup></td><td>RP11-481J13.1,<br>AC011306.2</td></tr><tr><td>chr2.0223166989</td><td>0.87</td><td>4.07 × 10<sup>-2</sup></td><td>0.90</td><td>4.80 × 10<sup>-2</sup></td><td>CCDC140</td></tr><tr><td>chr2.0235215325</td><td>-1.05</td><td>6.08 × 10<sup>-4</sup></td><td>-1.08</td><td>6.49 × 10<sup>-4</sup></td><td></td></tr><tr><td>chr3.0053784559</td><td>0.91</td><td>1.58 × 10<sup>-2</sup></td><td>1.14</td><td>8.96 × 10<sup>-4</sup></td><td>CACNA1D</td></tr><tr><td>chr3.0060919598</td><td>-0.85</td><td>1.89 × 10<sup>-3</sup></td><td>-0.86</td><td>3.09 × 10<sup>-2</sup></td><td>FHIT</td></tr><tr><td>chr3.0119863345</td><td>1.28</td><td>2.04 × 10<sup>-3</sup></td><td>1.48</td><td>1.19 × 10<sup>-5</sup></td><td>GPR156</td></tr><tr><td>chr3.0119990864</td><td>-1.14</td><td>2.05 × 10<sup>-3</sup></td><td>-1.01</td><td>4.01 × 10<sup>-2</sup></td><td>GPR156</td></tr><tr><td>chr3.0127606140</td><td>-1.40</td><td>1.26 × 10<sup>-4</sup></td><td>-1.12</td><td>6.36 × 10<sup>-3</sup></td><td></td></tr><tr><td>chr3.0182124231</td><td>-1.14</td><td>3.82 × 10<sup>-2</sup></td><td>-1.15</td><td>5.10 × 10<sup>-3</sup></td><td></td></tr><tr><td>chr3.0189791239</td><td>-1.34</td><td>1.56 × 10<sup>-2</sup></td><td>-1.13</td><td>5.00 × 10<sup>-2</sup></td><td>LEPREL1</td></tr><tr><td>chr3.0196595774</td><td>-1.37</td><td>3.54 × 10<sup>-2</sup></td><td>-1.33</td><td>1.44 × 10<sup>-2</sup></td><td>SENP5</td></tr><tr><td>chr4.0101719592</td><td>-1.04</td><td>4.74 × 10<sup>-5</sup></td><td>-1.15</td><td>1.77 × 10<sup>-7</sup></td><td>EMCN</td></tr><tr><td>chr5.0011534641</td><td>0.90</td><td>2.48 × 10<sup>-2</sup></td><td>1.05</td><td>7.82 × 10<sup>-3</sup></td><td>CTNND2</td></tr><tr><td>chr5.0039219698</td><td>1.22</td><td>3.91 × 10<sup>-2</sup></td><td>1.37</td><td>2.24 × 10<sup>-2</sup></td><td>FYB</td></tr><tr><td>chr5.0164483805</td><td>-0.92</td><td>2.51 × 10<sup>-2</sup></td><td>-1.11</td><td>1.05 × 10<sup>-3</sup></td><td>CTC-340A15.2</td></tr></tbody></table>
<table><caption>Table 2. *Cont.*</caption><thead><tr><th>Position</th><th>MB LogFC</th><th>MB FDR Corrected p-Value</th><th>MT LogFC</th><th>MT FDR Corrected p-Value</th><th>Gene</th></tr></thead><tbody><tr><td>chr5.0166472226</td><td>-1.03</td><td>1.03 × 10<sup>-2</sup></td><td>-1.16</td><td>4.86 × 10<sup>-4</sup></td><td></td></tr><tr><td>chr6.0008948266</td><td>1.16</td><td>2.27 × 10<sup>-2</sup></td><td>1.24</td><td>2.52 × 10<sup>-3</sup></td><td></td></tr><tr><td>chr6.0016145414</td><td>-1.17</td><td>2.94 × 10<sup>-4</sup></td><td>-1.24</td><td>9.54 × 10<sup>-5</sup></td><td>MYLIP</td></tr><tr><td>chr6.0019413218</td><td>0.81</td><td>2.95 × 10<sup>-3</sup></td><td>0.86</td><td>1.70 × 10<sup>-2</sup></td><td></td></tr><tr><td>chr6.0031008851</td><td>0.96</td><td>2.29 × 10<sup>-2</sup></td><td>1.01</td><td>1.56 × 10<sup>-2</sup></td><td>RASSF3</td></tr><tr><td>chr6.0154640863</td><td>1.37</td><td>9.68 × 10<sup>-3</sup></td><td>1.34</td><td>3.64 × 10<sup>-3</sup></td><td>IPCEF1</td></tr><tr><td>chr6.0161063597</td><td>-2.29</td><td>3.71 × 10<sup>-3</sup></td><td>-1.62</td><td>2.31 × 10<sup>-2</sup></td><td>LPA</td></tr><tr><td>chr7.0016768868</td><td>-0.75</td><td>1.18 × 10<sup>-2</sup></td><td>-1.03</td><td>1.03 × 10<sup>-5</sup></td><td></td></tr><tr><td>chr7.0044621160</td><td>0.91</td><td>1.71 × 10<sup>-2</sup></td><td>0.97</td><td>4.26 × 10<sup>-2</sup></td><td>TMED4</td></tr><tr><td>chr7.0147581299</td><td>-0.68</td><td>3.30 × 10<sup>-2</sup></td><td>-0.83</td><td>3.24 × 10<sup>-2</sup></td><td>CNTNAP2</td></tr><tr><td>chr11.0123045794</td><td>-1.36</td><td>1.13 × 10<sup>-3</sup></td><td>-1.54</td><td>2.08 × 10<sup>-6</sup></td><td>CLMP</td></tr><tr><td>chr11.0129565594</td><td>1.28</td><td>1.59 × 10<sup>-2</sup></td><td>1.68</td><td>1.23 × 10<sup>-4</sup></td><td></td></tr><tr><td>chr12.0003241735</td><td>1.19</td><td>4.26 × 10<sup>-3</sup></td><td>1.04</td><td>3.25 × 10<sup>-2</sup></td><td>TSPAN9</td></tr><tr><td>chr12.0026672531</td><td>1.00</td><td>4.67 × 10<sup>-2</sup></td><td>1.28</td><td>1.68 × 10<sup>-2</sup></td><td>ITPR2</td></tr><tr><td>chr12.0048360477</td><td>-1.69</td><td>3.13 × 10<sup>-4</sup></td><td>-1.10</td><td>4.25 × 10<sup>-2</sup></td><td>TMEM106C</td></tr><tr><td>chr12.0054366343</td><td>0.87</td><td>4.94 × 10<sup>-2</sup></td><td>1.07</td><td>3.53 × 10<sup>-2</sup></td><td>HOTAIR</td></tr><tr><td>chr12.0055783991</td><td>1.19</td><td>4.36 × 10<sup>-2</sup></td><td>1.29</td><td>2.11 × 10<sup>-2</sup></td><td></td></tr><tr><td>chr12.0083436417</td><td>1.62</td><td>4.94 × 10<sup>-3</sup></td><td>2.14</td><td>6.72 × 10<sup>-5</sup></td><td>TMTC2</td></tr><tr><td>chr12.0114887843</td><td>1.42</td><td>1.84 × 10<sup>-4</sup></td><td>0.62</td><td>4.91 × 10<sup>-2</sup></td><td></td></tr><tr><td>chr12.0116068191</td><td>-1.32</td><td>1.42 × 10<sup>-2</sup></td><td>-1.58</td><td>5.07 × 10<sup>-6</sup></td><td>RP11—1028N23.4</td></tr><tr><td>chr12.0128167651</td><td>1.13</td><td>3.28 × 10<sup>-2</sup></td><td>1.57</td><td>8.87 × 10<sup>-4</sup></td><td></td></tr><tr><td>chr12.0131689822</td><td>1.29</td><td>2.19 × 10<sup>-2</sup></td><td>1.74</td><td>5.09 × 10<sup>-5</sup></td><td>RP11—638F5.1</td></tr><tr><td>chr13.0021286449</td><td>-1.10</td><td>4.87 × 10<sup>-2</sup></td><td>-1.33</td><td>2.22 × 10<sup>-2</sup></td><td>IL17D</td></tr><tr><td>chr13.0027424109</td><td>1.51</td><td>1.23 × 10<sup>-2</sup></td><td>1.56</td><td>5.47 × 10<sup>-4</sup></td><td></td></tr><tr><td>chr13.0033220266</td><td>-1.24</td><td>5.87 × 10<sup>-3</sup></td><td>-1.31</td><td>2.18 × 10<sup>-3</sup></td><td>PDS5B</td></tr><tr><td>chr13.0047191668</td><td>-1.30</td><td>6.51 × 10<sup>-3</sup></td><td>-1.34</td><td>6.36 × 10<sup>-3</sup></td><td>LRCH1</td></tr><tr><td>chr13.0093896533</td><td>1.50</td><td>2.96 × 10<sup>-2</sup></td><td>2.41</td><td>2.86 × 10<sup>-5</sup></td><td>GPC6</td></tr><tr><td>chr13.0099687193</td><td>1.01</td><td>3.54 × 10<sup>-2</sup></td><td>1.06</td><td>4.85 × 10<sup>-2</sup></td><td>DOCK9</td></tr><tr><td>chr13.0107176083</td><td>-1.69</td><td>7.74 × 10<sup>-3</sup></td><td>-1.68</td><td>1.29 × 10<sup>-2</sup></td><td>EFNB2</td></tr><tr><td>chr13.0109856377</td><td>-1.48</td><td>4.00 × 10<sup>-2</sup></td><td>-1.52</td><td>6.19 × 10<sup>-3</sup></td><td>MYO16</td></tr><tr><td>chr14.0021177142</td><td>-1.22</td><td>3.13 × 10<sup>-4</sup></td><td>-1.24</td><td>7.38 × 10<sup>-5</sup></td><td></td></tr><tr><td>chr14.0021316565</td><td>-1.29</td><td>2.23 × 10<sup>-2</sup></td><td>-1.56</td><td>2.05 × 10<sup>-4</sup></td><td></td></tr><tr><td>chr14.0025947530</td><td>0.91</td><td>1.48 × 10<sup>-2</sup></td><td>1.05</td><td>7.13 × 10<sup>-3</sup></td><td></td></tr><tr><td>chr14.0080449863</td><td>-1.84</td><td>5.11 × 10<sup>-5</sup></td><td>-1.74</td><td>2.05 × 10<sup>-4</sup></td><td></td></tr><tr><td>chr14.0085404000</td><td>-1.38</td><td>4.50 × 10<sup>-3</sup></td><td>-1.38</td><td>3.07 × 10<sup>-3</sup></td><td></td></tr><tr><td>chr14.0104190006</td><td>-1.48</td><td>3.71 × 10<sup>-3</sup></td><td>-1.75</td><td>2.66 × 10<sup>-4</sup></td><td>ZFYVE21</td></tr><tr><td>chr15.0046178808</td><td>-0.97</td><td>1.71 × 10<sup>-2</sup></td><td>-0.70</td><td>4.78 × 10<sup>-2</sup></td><td>RP11—718O11.1</td></tr><tr><td>chr15.0069824154</td><td>1.42</td><td>1.25 × 10<sup>-4</sup></td><td>1.55</td><td>4.82 × 10<sup>-6</sup></td><td>RP11—279F6.1</td></tr></tbody></table>
<table><caption>**Table 2. Cont.**</caption><thead><tr><th>Position</th><th>MB LogFC</th><th>MB FDR<br>Corrected p-Value</th><th>MT LogFC</th><th>MT FDR<br>Corrected p-Value</th><th>Gene</th></tr></thead><tbody><tr><td>chr15.0092982723</td><td>1.45</td><td>3.91 × 10<sup>-5</sup></td><td>1.73</td><td>9.39 × 10<sup>-9</sup></td><td>ST8SIA2</td></tr><tr><td>chr16.0004815786</td><td>-0.89</td><td>8.94 × 10<sup>-4</sup></td><td>-0.92</td><td>9.24 × 10<sup>-3</sup></td><td>ZNF500</td></tr><tr><td>chr16.0077912976</td><td>-1.23</td><td>8.49 × 10<sup>-3</sup></td><td>-1.28</td><td>2.22 × 10<sup>-3</sup></td><td>VAT1L</td></tr><tr><td>chr16.0079468883</td><td>1.11</td><td>4.82 × 10<sup>-3</sup></td><td>1.28</td><td>3.78 × 10<sup>-5</sup></td><td></td></tr><tr><td>chr17.0018941025</td><td>-1.85</td><td>1.91 × 10<sup>-3</sup></td><td>-1.96</td><td>2.09 × 10<sup>-4</sup></td><td>GRAP</td></tr><tr><td>chr17.0019045779</td><td>-1.55</td><td>7.74 × 10<sup>-3</sup></td><td>-1.56</td><td>8.05 × 10<sup>-4</sup></td><td>GRAPL,<br>CTC—457L16.2</td></tr><tr><td>chr17.0028803808</td><td>-1.20</td><td>1.47 × 10<sup>-2</sup></td><td>-1.24</td><td>4.72 × 10<sup>-3</sup></td><td></td></tr><tr><td>chr17.0070499160</td><td>1.04</td><td>1.42 × 10<sup>-4</sup></td><td>1.06</td><td>1.48 × 10<sup>-3</sup></td><td>LINC00511</td></tr><tr><td>chr17.0074566299</td><td>0.87</td><td>3.28 × 10<sup>-2</sup></td><td>1.14</td><td>3.76 × 10<sup>-4</sup></td><td>ST6GALNAC2,<br>RP11—666A8.9</td></tr><tr><td>chr18.0043923940</td><td>-1.66</td><td>5.63 × 10<sup>-6</sup></td><td>-1.73</td><td>7.53 × 10<sup>-7</sup></td><td>RNF165</td></tr><tr><td>chr18.0045011716</td><td>1.10</td><td>2.73 × 10<sup>-2</sup></td><td>1.41</td><td>4.57 × 10<sup>-5</sup></td><td>CTD—2130O13.1</td></tr><tr><td>chr18.0047177650</td><td>-1.15</td><td>3.29 × 10<sup>-4</sup></td><td>-0.64</td><td>8.52 × 10<sup>-3</sup></td><td></td></tr><tr><td>chr18.0047230566</td><td>-1.39</td><td>1.98 × 10<sup>-2</sup></td><td>-1.30</td><td>3.48 × 10<sup>-3</sup></td><td></td></tr><tr><td>chr18.0072250823</td><td>1.12</td><td>7.11 × 10<sup>-3</sup></td><td>1.05</td><td>2.58 × 10<sup>-2</sup></td><td>CNDP1</td></tr><tr><td>chr19.0002867898</td><td>1.36</td><td>5.19 × 10<sup>-9</sup></td><td>1.12</td><td>1.72 × 10<sup>-2</sup></td><td>ZNF556</td></tr><tr><td>chr19.0041126191</td><td>-0.80</td><td>1.99 × 10<sup>-2</sup></td><td>-0.92</td><td>4.84 × 10<sup>-3</sup></td><td>LTBP4</td></tr><tr><td>chr20.0031210733</td><td>1.20</td><td>2.29 × 10<sup>-2</sup></td><td>1.36</td><td>2.68 × 10<sup>-2</sup></td><td></td></tr><tr><td>chr20.0052825772</td><td>-1.35</td><td>1.91 × 10<sup>-3</sup></td><td>-1.31</td><td>3.11 × 10<sup>-3</sup></td><td>PFDN4</td></tr><tr><td>chr20.0055369320</td><td>-1.24</td><td>9.46 × 10<sup>-4</sup></td><td>-1.68</td><td>1.37 × 10<sup>-4</sup></td><td></td></tr><tr><td>chr20.0060501154</td><td>2.02</td><td>6.08 × 10<sup>-4</sup></td><td>1.87</td><td>9.34 × 10<sup>-4</sup></td><td>CDH4</td></tr><tr><td>chr21.0030689317</td><td>-0.78</td><td>4.49 × 10<sup>-3</sup></td><td>-0.86</td><td>1.16 × 10<sup>-3</sup></td><td>BACH1</td></tr><tr><td>chr22.0050332646</td><td>-1.23</td><td>5.83 × 10<sup>-3</sup></td><td>-1.35</td><td>1.32 × 10<sup>-3</sup></td><td></td></tr></tbody></table>

Since DNA methylation in promoter regions of genes has been associated with regulation of expression, the individual CpG methylation scores were averaged across TSS flanking regions using 1000 bp upstream of the TSS and 1000 bp downstream. The promoter methylation data revealed distinct patterns between the control and CP cohorts for both MBs (Table 4) and MTs (Table 5). Of 31,844 unique promoters identified, there were 3 promoters with statistically different methylation loads between CP and control subjects in the MBs and 10 promoters in MTs (FDR < 0.05). The majority of the differentially methylated promoters were in non-coding genes, with only one of the MB promoters and two of the MT promoters associated with protein coding transcripts. To explore the relationship between methylation loads and RNA expression, the correlation of methylation load in protein coding genes was analyzed against previously published RNA-seq count data per gene, but no correlation was found (data not shown).

<footnote>* LogFC = log₂ fold change; positive logFC = hypermethylated in CP; negative logFC = hypomethylated in CP.</footnote>
<table><caption>Table 3. Chromosome enrichment analysis *.</caption><thead><tr><th rowspan="2">Chromosome</th><th colspan="2">MB</th><th colspan="2">MT</th><th colspan="2">Muscle</th><th colspan="2">Blood</th></tr><tr><th>Significant CpGs</th><th>Enrichment p-Value</th><th>Significant CpGs</th><th>Enrichment p-Value</th><th>Significant CpGs</th><th>Enrichment p-Value</th><th>Significant CpGs</th><th>Enrichment p-Value</th></tr></thead><tbody><tr><td>1</td><td>0</td><td>1.000</td><td>1</td><td>1.000</td><td>1</td><td>1.000</td><td>10</td><td>1.000</td></tr><tr><td>2</td><td>26</td><td>0.997</td><td>103</td><td>1.000</td><td>84</td><td>0.361</td><td>312</td><td>1.000</td></tr><tr><td>3</td><td>45</td><td>4.81 × 10<sup>-3</sup></td><td>112</td><td>0.146</td><td>77</td><td>9.94 × 10<sup>-3</sup></td><td>650</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>4</td><td>3</td><td>1.000</td><td>9</td><td>1.000</td><td>7</td><td>1.000</td><td>21</td><td>1.000</td></tr><tr><td>5</td><td>14</td><td>0.998</td><td>82</td><td>0.863</td><td>64</td><td>0.082</td><td>222</td><td>1.000</td></tr><tr><td>6</td><td>29</td><td>0.348</td><td>98</td><td>0.209</td><td>109</td><td>1.22 × 10<sup>-12</sup></td><td>544</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>7</td><td>8</td><td>1.000</td><td>23</td><td>1.000</td><td>23</td><td>1.000</td><td>136</td><td>1.000</td></tr><tr><td>8</td><td>9</td><td>1.000</td><td>26</td><td>1.000</td><td>28</td><td>0.999</td><td>169</td><td>1.000</td></tr><tr><td>9</td><td>18</td><td>0.921</td><td>54</td><td>1.000</td><td>22</td><td>1.000</td><td>208</td><td>1.000</td></tr><tr><td>10</td><td>13</td><td>0.999</td><td>35</td><td>1.000</td><td>4</td><td>1.000</td><td>76</td><td>1.000</td></tr><tr><td>11</td><td>33</td><td>0.103</td><td>101</td><td>0.092</td><td>58</td><td>0.196</td><td>618</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>12</td><td>76</td><td>2.20 × 10<sup>-16</sup></td><td>204</td><td>2.20 × 10<sup>-16</sup></td><td>67</td><td>7.43 × 10<sup>-3</sup></td><td>519</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>13</td><td>21</td><td>3.73 × 10<sup>-2</sup></td><td>104</td><td>7.37 × 10<sup>-14</sup></td><td>61</td><td>9.60 × 10<sup>-9</sup></td><td>344</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>14</td><td>41</td><td>3.31 × 10<sup>-7</sup></td><td>137</td><td>2.20 × 10<sup>-16</sup></td><td>68</td><td>6.01 × 10<sup>-8</sup></td><td>448</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>15</td><td>31</td><td>1.63 × 10<sup>-3</sup></td><td>130</td><td>2.20 × 10<sup>-16</sup></td><td>72</td><td>6.96 × 10<sup>-9</sup></td><td>298</td><td>3.44 × 10<sup>-9</sup></td></tr><tr><td>16</td><td>20</td><td>0.835</td><td>93</td><td>0.111</td><td>68</td><td>3.04 × 10<sup>-3</sup></td><td>369</td><td>1.37 × 10<sup>-5</sup></td></tr><tr><td>17</td><td>26</td><td>0.564</td><td>86</td><td>0.659</td><td>56</td><td>0.329</td><td>300</td><td>0.912</td></tr><tr><td>18</td><td>35</td><td>4.65 × 10<sup>-8</sup></td><td>107</td><td>2.20 × 10<sup>-16</sup></td><td>47</td><td>1.42 × 10<sup>-5</sup></td><td>341</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>19</td><td>9</td><td>1.000</td><td>47</td><td>1.000</td><td>25</td><td>1.000</td><td>170</td><td>1.000</td></tr><tr><td>20</td><td>47</td><td>4.59 × 10<sup>-11</sup></td><td>153</td><td>2.20 × 10<sup>-16</sup></td><td>64</td><td>4.93 × 10<sup>-8</sup></td><td>484</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>21</td><td>2</td><td>0.995</td><td>6</td><td>1.000</td><td>2</td><td>1.000</td><td>9</td><td>1.000</td></tr><tr><td>22</td><td>19</td><td>0.101</td><td>63</td><td>1.09 × 10<sup>-2</sup></td><td>31</td><td>0.257</td><td>293</td><td>2.20 × 10<sup>-16</sup></td></tr><tr><td>Total</td><td>525</td><td></td><td>1774</td><td></td><td>1038</td><td></td><td>6541</td><td></td></tr></tbody></table>

<figure><img src="image_25.png" /><figcaption>**Figure 3.** Chromosome-based circos plots. Mean logFC values for CpG methylation load (CP over control) were calculated for sequential 1 Mbp intervals. There was a strong correlation between differential methylation in MBs and MTs (a). Scatterplots of values for each chromosome are shown for MBs (b) and MTs (c); values outside the 95% confidence interval of the average logFCs across the whole genome are red indicating significant hypermethylation in CP or blue for significant hypomethylation in CP. The gray ring shows points within the 95% CI for the overall data.</figcaption></figure>

<footnote>* Fisher's exact test was used to determine chromosomes that contained an enrichment of differentially methylated CpGs. Significant *p*-values (*p* < 0.05) are indicated in **green**. Both cell conditions contained an enrichment of differentially methylated CpGs on the chromosomes indicated in **orange**. Significant CpG sites were enriched on the same chromosomes when analyzing muscle tissue and blood cells from subjects with CP.</footnote>
<table><caption>**Table 4.** Significant promoter regions (+/– 1000 bp from TSS) in MBs *.</caption><thead><tr><th>TSS</th><th>LogFC</th><th>FDR Corrected<br><i>p</i>-Value</th><th>Gene</th><th>Class</th></tr></thead><tbody><tr><td>chr16:51277965</td><td>-0.85</td><td>3.82 × 10<sup>-4</sup></td><td>AC137527.2</td><td>Pseudogene</td></tr><tr><td>chr13:115039303</td><td>0.20</td><td>1.97 × 10<sup>-3</sup></td><td>MIR4502</td><td>miRNA</td></tr><tr><td>chr17:34397734</td><td>0.39</td><td>4.41 × 10<sup>-2</sup></td><td>CCL18</td><td>Protein coding</td></tr></tbody></table>

<table><caption>**Table 5.** Significant promoter regions (+/– 1000 bp from TSS) in MTs *.</caption><thead><tr><th>TSS</th><th>LogFC</th><th>FDR Corrected<br><i>p</i>-Value</th><th>Gene</th><th>Class</th></tr></thead><tbody><tr><td>chr17:73070401</td><td>0.59</td><td>9.50 × 10<sup>-6</sup></td><td>AC111186.1</td><td>Pseudogene</td></tr><tr><td>chr17:75148756</td><td>0.36</td><td>4.01 × 10<sup>-4</sup></td><td>RNU4-47P</td><td>snRNA</td></tr><tr><td>chr19:48673949</td><td>0.60</td><td>8.84 × 10<sup>-4</sup></td><td>ZSWIM9</td><td>Protein coding</td></tr><tr><td>chr11:46134769</td><td>0.55</td><td>1.51 × 10<sup>-3</sup></td><td>AC024475.1</td><td>miRNA</td></tr><tr><td>chr4:111866955</td><td>0.30</td><td>1.81 × 10<sup>-3</sup></td><td>LYPLA1P2</td><td>Pseudogene</td></tr><tr><td>chr12:7072409</td><td>0.25</td><td>6.45 × 10<sup>-3</sup></td><td>U47924.27</td><td>lincRNA</td></tr><tr><td>chr1:242187356</td><td>-0.14</td><td>6.93 × 10<sup>-3</sup></td><td>RNU6-1139P</td><td>snRNA</td></tr><tr><td>chr12:7072408</td><td>0.25</td><td>7.37 × 10<sup>-3</sup></td><td>EMG1</td><td>Protein coding</td></tr><tr><td>chr11:93971316</td><td>1.04</td><td>1.67 × 10<sup>-2</sup></td><td>RP11-680H20.2</td><td>lincRNA</td></tr><tr><td>chr2:47454056</td><td>-0.67</td><td>4.96 × 10<sup>-2</sup></td><td>AC106869.2</td><td>lincRNA</td></tr></tbody></table>

## 4. Discussion

This study found that DNA methylation patterns in skeletal muscle SCs grown in culture differed significantly between a cohort of study participants with spastic CP and non-CP controls. DNA methylation is a common and widespread chemical modification involving the addition of a methyl group to the 5-carbon position of cytosine, predominantly within CpG dinucleotides [67]. DNA methylation patterns can change during normal developmental processes, and it has been shown that altered DNA methylation can be passed to daughter cells and sustained later in life [68–75]. Specific DNA methylation changes can modify gene expression, and DNA methylation is well known to be involved in X-chromosome inactivation, gene imprinting, and the silencing of transposable elements [76]. Changes in DNA methylation patterns can also occur as a result of pathophysiologic processes or acute exposures to environmental or physiologic stress [77–79]. Altered DNA methylation has been linked to a number of risk factors and potential causes for CP including prematurity, hypoxia-ischemia, and infection [80–83]. In a prior study, we found that DNA methylation patterns in peripheral blood cells of spastic CP patients varied significantly from controls [39], raising the possibility of methylome alterations in both hematopoietic stem cell and myogenic stem/SC lineages in spastic CP. Furthermore, epigenetic patterns from adolescents were able to be used to predict diagnosis of much younger patients [39], suggesting that at least some methylation pattern differences are associated with the onset of CP and are preserved over time.

The present study examined methylation pattern differences but did not look directly at differences in cell phenotype or behavior. Studies show that individuals with CP have significantly reduced numbers of SCs and that MBs derived from these SCs have a decreased capacity to fuse and differentiate into MTs in culture [29,84]. A recent report indicated that SC-derived MB progenitors from contractured muscle in CP have globally hyperme-

<footnote>* TSS = transcription start site, logFC = log₂ fold change; positive logFC = hypermethylated in CP; negative logFC = hypomethylated in CP.</footnote>

<footnote>* TSS = transcription start site, logFC = log₂ fold change; positive logFC = hypermethylated in CP; negative logFC = hypomethylated in CP.</footnote>
thylated DNA and gene expression patterns that favor proliferation over quiescence and differentiation; in that study, a 24 h treatment with a hypomethylating agent reduced DNA methylation to control levels and promoted an exit from mitosis [32]. While previous studies of DNA methylation in CP SC-derived MBs have either examined DNA methylation of a specific CpG island [28] or used Infinium Human MethylationEPIC Beadchip arrays of 850,000 targeted CpGs to identify hypermethylated regions [32], the current study used a different sequencing technology and computational pipeline to examine over 1.4 million CpGs distributed throughout the genome in both MBs and MTs. In addition, the current study was able to take advantage of more closely age-matched samples than had been possible previously. This combination of more closely matched controls and a broader technology platform allowed for identification of both hypermethylated and hypomethylated regions as well as individual CpGs, which provide higher likelihood candidates for biomarker platforms.

The identification of differentially methylated CpG sites and regions in common across cells from the CP cohort suggests that fundamental molecular alterations associated with diagnosis were sustained after the cells were removed from the structural, biomechanical, and humoral environments of the muscle tissue. Such an effect has been described as muscle epigenetic memory wherein DNA methylation is stably altered by prior events like biomechanical loading or acute early life exposure to inflammatory cytokines [85]. Here, we identified 525 CpGs in MBs and 1774 in MTs that were differentially methylated in spastic CP versus controls. Interestingly, MBs demonstrated similar proliferation rates and RNA-seq profiles between cohorts in a previous study [26], indicating that while these 525 CpGs may be biomarkers for CP, they may not be associated with functional changes within the cells. Further work is needed to elucidate fully specific linkages between DNA methylation and the regulation of protein levels and cell activities. The larger number of significant sites in MTs was consistent with a larger number of differentially expressed genes in RNA-seq and may indicate that a mixture of cells at different stages of myogenic differentiation was present at the time of DNA isolation. In addition, reports indicate that native SC populations actually comprise multiple different subpopulations that may differentially contribute to variability; studies focused on clonal cells rather than heterogenous populations may be needed. Interestingly, in both MBs and MTs, several significant CpG sites were within genes known to be involved in muscle physiology, including skeletal muscle differentiation (HLF, NOTCH1), muscle organ development (BMP2, COL6A3, DCN, FZD2, HEG1, HLF, ITGA11, ITGA7, LAMA5, LARGE1, MAPK14, MYBPC1, MYH6, MYLK, NOTCH1, NRG1, PKP2, SGCD, SMAD7, TBX1, TCF12, TEAD4, WNT5A, ZFHX3, ZNF609), and muscle system process (ACTN3, ATP8A2, CACNA1C, CACNA1D, DTNA, DYSF, EDN3, GNAO1, HCN4, ITGB5, KCNQ1, LTB4R, MYBPC1, MYH6, MYLK, NEDD4L, PDLIM5, PKP2, PLA2G6, ROCK1, SGCD, TRDN). Of the differentially methylated CpGs identified in MBs and MTs, 79 were in common, with 36 being significantly hypermethylated and 43 significantly hypomethylated in the CP cohort under both conditions (Table 2). Alterations in DNA methylation can be sustained long-term and previous studies indicated that the majority of the DNA methylome remained relatively preserved through myogenesis, from SC to MT formation [76,86]. These 79 sites may therefore represent stable methylation signals indicative of CP; however, more studies are needed to determine the implication(s) of these differentially methylated sites and their roles in muscle impairment in CP.

Chromosome enrichment analysis determined that there was an enrichment of significant CpGs on chromosomes 12, 13, 14, 15, 18, and 20 for both MBs and MTs (Table 3). Interestingly, significant CpGs were also enriched on the same chromosomes in the CP cohort in skeletal muscle tissue and blood cells. An analysis of differential methylation over 1 Mpb regions in MBs demonstrated that chromosomes 6, 9, 11, and 21 contained regions of hypermethylation in the CP cohort, while chromosomes 5, 7, 8, 16, 19, and 20 contained regions of hypomethylation, and chromosomes 2, 3, 4, 10, 12, 13, 14, 15, and 18 contained regions of both hypermethylation and hypomethylation. MTs contained more hypermethylated regions and fewer hypomethylated regions in the CP cohort than MBs.
Chromosomes 4, 8, 9, 11, 12, 14, 16, 21, and 22 contained regions of hypermethylation, while chromosome 7 contained regions of hypomethylation, and chromosomes 2, 3, 5, 6, 10, 13, 15, 18, and 20 contained regions of both hypermethylation and hypomethylation (Figure 3). Overall, the differential methylation levels over 1 Mbp regions were well correlated between MBs and MTs, again suggesting stability of the methylome during the course of myogenesis. The large regions of differential methylation between the CP and control cohorts throughout the genome suggest differences in chromatin structure within the CP cohort as various chromatin states based on histone modifications and nucleosome positioning can determine DNA methylation patterning [67]. There are complex mechanisms underlying the molecular crosstalk between DNA and histone methylation [87], and additional studies are needed to investigate specific histone modifications in CP to understand this complex relationship. While we are in the early stages of unraveling how these alterations are relevant to CP, several key chromosomes were identified as potential targets for future investigation in spastic CP.

We also identified statistically significant differences in methylation of the promoter regions of genes and assessed the relationship between these differences and an RNA-seq study of the same samples [26] to investigate the effect of differential DNA methylation on gene expression. Of particular note, specific associations between protein coding DNA methylation patterns and RNA expression were not readily resolved in our study. However, methylation/expression relationships are difficult to resolve in general and several studies have demonstrated that relationships between methylation status and gene expression can be complex [88,89]. Additionally, there is no current gold standard method for rolling up methylation scores across groups of individual CpG sites into a relevant burden for individual genes. Furthermore, approximately 95% of CpG island promoter regions are unmethylated independent of gene activity and recent studies suggest that methylation of promoter CpG islands is not the primary determinant of gene activity [52,90]. Many CpG islands occur in gene bodies, intergenic regions, or enhancers and may be relevant to gene expression [52]. In fact, recent studies have suggested that altered methylation in enhancer regions rather than promoter regions may be more indicative of changes in gene expression [52,91–93]. Enhancers can regulate the transcription of one or more genes, regardless of orientation or relative distance to the target promoter [94]. While 21 differentially methylated CpGs were identified within annotated enhancer regions in MBs and 97 in MTs, enhancers are difficult to map experimentally [52], enhancer activity is context and stimulus-dependent, and there are no genome-wide enhancer sets linked to specific promoters [94]. It was not possible to investigate the effects on gene expression due to these limitations in the annotation of enhancer elements. Therefore, it will be essential to continue investigating genome-wide analysis approaches that can accurately associate high-throughput expression data with methylation signatures.

Of note, the majority of the differentially methylated promoters identified in MBs and MTs were for regulatory RNAs; a result that may be indicative of differences in RNA processing within cells isolated from CP tissue (Tables 4 and 5). These regulatory RNAs comprise the majority of the transcriptome and play critical roles in maintaining gene expression regulation. The most well-known regulatory RNAs include micro RNAs and long non-coding RNAs. MicroRNAs (miRNAs) are small noncoding RNAs (~22 nucleotides in length) that play important roles in developmental processes such as myogenesis and neurogenesis [95]. Long non-coding RNAs (lncRNAs) may mediate chromatin remodeling and modification, interact with transcription factors for gene regulation, interact with mRNAs to regulate post-transcriptional processes [96], and interact with miRNAs to facilitate myogenesis [97]. Additional studies are warranted to investigate the complex interplay between DNA methylation, histone modifiers, and non-coding RNAs in order to provide a comprehensive understanding of these epigenetic modulations on SC physiology and myogenesis in spastic CP.

The findings of our study compellingly support the idea that spastic CP is associated with altered epigenetic pathways, but our studies are limited by our reliance on the ability
to obtain biopsies from individuals presenting for surgery. Because of this, our number of samples is small, SCs must be derived from different muscles in order to age-match samples, and our CP population presenting for surgery consists of mostly severely affected individuals with a high level of motor impairment and inactivity (GMFCS V); therefore, extrapolation of our results to a larger CP community may require additional research. Additionally, the technology used interrogates $2.29 \times 10^6$ CCGG motifs, which represent ~15% of the $14 \times 10^6$ CpG sites in the genome. The majority of the CpG sites in our study do not overlap with those selected for inclusion in the Infinium MethylationEPIC technology making comparisons to other studies using Infinium data challenging.

**5. Conclusions**

In this report, an innovative DNA methylation analysis was employed on SC-derived MBs and MTs collected from individuals with and without CP. We identified differential methylation in the CP cohort at the levels of individual CpGs, 1 Mpb regions, and promoters. The work presented here leverages our novel methylation approach with ex vivo cell studies to elucidate aberrant methylation signatures.

**Supplementary Materials:** The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/jpm12121978/s1, Figure S1: Cell morphology, Figure S2: Scree plots for explained variance, Figure S3: Volcano plots, Figure S4: Manhattan plots, Table S1: Cell culture information, Table S2: Significant CpGs in MBs, Table S3: Significant CpGs in MTs.

**Author Contributions:** Conceptualization, K.G.R., A.G.M., E.L.C., M.W.S., R.E.A.; methodology, A.G.M., S.K.L., K.G.R., R.E.A.; software, K.G.R., A.G.M., J.H.; validation, J.H.; formal analysis, K.G.R., A.G.M., J.H.; investigation, K.G.R., S.K.L.; resources, M.W.S.; data curation, K.G.R., A.G.M., S.K.L., J.H.; writing—original draft preparation, K.G.R.; writing—review and editing, K.G.R., A.G.M., S.K.L., J.H., B.R., M.B., E.L.C., M.W.S., R.E.A.; visualization, K.G.R., A.G.M., S.K.L., J.H., B.R.; supervision, E.L.C., M.B., R.E.A.; project administration, R.E.A.; funding acquisition, R.E.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Delaware Bioscience Center for Advanced Technology; an American Academy for Cerebral Palsy and Developmental Medicine Pedal with Pete Foundation award to R.E.A.; the Delaware CTR ACCEL Program [U54-GM104941] to E.L.C.; and US National Science Foundation awards [0944557, 1316055] to A.G.M. The authors would like to thank the Nemours Foundation, Nemours Biomedical Research, and the Department of Pediatrics for institutional support of E.L.C., K.G.R., S.K.L. and R.E.A., as well as The Swank Foundation for their support to R.E.A. allowing the establishment of the neuro-orthopedic tissue repository at Nemours that provided samples for the work.

**Institutional Review Board Statement:** The study was conduction according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Nemours (protocol number 687629).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Methylomic data will be made publicly accessible via the controlled access system provided by the NIH database of Genotypes and Phenotypes (dbGaP) or can be made available upon a direct request to the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

1. Christensen, D.; Braun, K.V.N.; Doernberg, N.S.; Maenner, M.J.; Arneson, C.L.; Durkin, M.S.; Benedict, R.E.; Kirby, R.S.; Wingate, M.S.; Fitzgerald, R.; et al. Prevalence of cerebral palsy, co-occurring autism spectrum disorders, and motor functioning—Autism and Developmental Disabilities Monitoring Network, USA, 2008. *Dev. Med. Child Neurol.* **2013**, *56*, 59–65. [CrossRef] [PubMed]

2. Graham, H.K.; Rosenbaum, P.; Paneth, N.; Dan, B.; Lin, J.P.; Damiano, D.L.; Becher, J.G.; Gaebler-Spira, D.; Colver, A.; Lieber, R.L.; et al. Cerebral palsy. *Nat. Rev. Dis. Prim.* **2016**, *2*, 15082. [CrossRef] [PubMed]

3. Mandaleson, A.; Lee, Y.; Kerr, C.; Graham, H.K. Classifying cerebral palsy: Are we nearly there? *J. Pediatr. Orthop.* **2015**, *35*, 162–166. [CrossRef] [PubMed]
4. Oskoui, M.; Coutinho, F.; Dykeman, J.; Jetté, N.; Pringsheim, T. An update on the prevalence of cerebral palsy: A systematic review and meta-analysis. *Dev. Med. Child Neurol.* **2013**, *55*, 509–519. [CrossRef]

5. NICHD. What Are the Types of Cerebral Palsy? Available online: https://www.nichd.nih.gov/health/topics/cerebral--palsy/conditioninfo/types (accessed on 14 April 2022).

6. CDC. Data and Statistics for Cerebral Palsy. Available online: https://www.cdc.gov/ncbddd/cp/data.html (accessed on 14 April 2022).

7. Accardo, P. *Neurodevelopmental Disabilities in Infancy and Childhood*, 3rd ed.; Volume I: Neurodevelopmental Diagnosis and Treatment; Paul H. Brookes Publishing Co.: Baltimore, MD, USA, 2007.

8. Lieber, R.L.; Friden, J. Spasticity causes a fundamental rearrangement of muscle-joint interaction. *Muscle Nerve* **2002**, *25*, 265–270. [CrossRef]

9. Maenner, M.J.; Blumberg, S.J.; Kogan, M.D.; Christensen, D.; Yeargin-Allsopp, M.; Schieve, L.A. Prevalence of cerebral palsy and intellectual disability among children identified in two U.S. National Surveys, 2011–2013. *Ann. Epidemiol.* **2016**, *26*, 222–226. [CrossRef]

10. Mockford, M.; Caulton, J.M. The Pathophysiological Basis of Weakness in Children With Cerebral Palsy. *Pediatr. Phys. Ther.* **2010**, *22*, 222–233. [CrossRef]

11. Sankar, C.; Mundkur, N. Cerebral palsy-definition, classification, etiology and early diagnosis. *Indian J. Pediatr.* **2005**, *72*, 865–868. [CrossRef]

12. Sharan, D. *Cerebral Palsy—Challenges for the Future*; InTech: Vienna, Austria, 2014.

13. Von Walden, F.; Gantelius, S.; Liu, C.; Borgström, H.; Björk, L.; Gremark, O.; Stål, P.; Nader, G.A.; Pontén, E. Muscle contractures in patients with cerebral palsy and acquired brain injury are associated with extracellular matrix expansion, pro-inflammatory gene expression, and reduced rRNA synthesis. *Muscle Nerve* **2018**, *58*, 277–285. [CrossRef]

14. Oberhofer, K.; Stott, N.; Mithraratne, K.; Anderson, I. Subject-specific modelling of lower limb muscles in children with cerebral palsy. *Clin. Biomech.* **2010**, *25*, 88–94. [CrossRef]

15. Bandholm, T.; Magnusson, P.; Jensen, B.R.; Sonne-Holm, S. Dorsiflexor muscle-group thickness in children with cerebral palsy: Relation to cross-sectional area. *NeuroRehabilitation* **2009**, *24*, 299–306. [CrossRef] [PubMed]

16. Fry, N.R.; Gough, M.; McNee, A.E.; Shortland, A.P. Changes in the Volume and Length of the Medial Gastrocnemius After Surgical Recession in Children With Spastic Diplegic Cerebral Palsy. *J. Pediatr. Orthop.* **2007**, *27*, 769–774. [CrossRef] [PubMed]

17. Lampe, R.; Grassl, S.; Mitternacht, J.; Gerdesmeyer, L.; Gradinger, R. MRT-measurements of muscle volumes of the lower extremities of youths with spastic hemiplegia caused by cerebral palsy. *Brain Dev.* **2006**, *28*, 500–506. [CrossRef] [PubMed]

18. Barber, L.; Hastings-Ison, T.; Baker, R.; Barrett, R.; Lichtwark, G. Medial gastrocnemius muscle volume and fascicle length in children aged 2 to 5 years with cerebral palsy. *Dev. Med. Child Neurol.* **2011**, *53*, 543–548. [CrossRef]

19. de Bruin, M.; Smeulders, M.J.; Kreulen, M. Why is joint range of motion limited in patients with cerebral palsy? *J. Hand Surg. (Eur. Vol.)* **2013**, *38*, 8–13. [CrossRef] [PubMed]

20. Elder, G.C.B.; Kirk, J.; Stewart, G.; Cook, K.; Weir, D.; Marshall, A.; Leahey, L. Contributing factors to muscle weakness in children with cerebral palsy. *Dev. Med. Child Neurol.* **2003**, *45*, 542–550. [CrossRef]

21. Hussain, A.W.; Onambélé, G.L.; Williams, A.G.; Morse, C.I. Muscle size, activation, and coactivation in adults with cerebral palsy. *Muscle Nerve* **2013**, *49*, 76–83. [CrossRef]

22. Smith, L.R.; Lee, K.S.; Ward, S.R.; Chambers, H.G.; Lieber, R.L. Hamstring contractures in children with spastic cerebral palsy result from a stiffer extracellular matrix and increased in vivo sarcomere length. *J. Physiol.* **2011**, *589 Pt 10*, 2625–2639. [CrossRef]

23. Robinson, K.G.; Mendonca, J.L.; Militar, J.L.; Theroux, M.C.; Dabney, K.W.; Shah, S.A.; Miller, F.; Akins, R.E. Disruption of Basal Lamina Components in Neuromotor Synapses of Children with Spastic Quadriplegic Cerebral Palsy. *PLoS ONE* **2013**, *8*, e70288. [CrossRef]

24. Theroux, M.C.; Akins, R.E.; Barone, C.; Boyce, B.; Miller, F.; Dabney, K.W. Neuromuscular junctions in cerebral palsy: Presence of extrajunctional acetylcholine receptors. *Anesthesiology* **2002**, *96*, 330–335. [CrossRef]

25. Theroux, M.C.; Oberman, K.G.; Lahaye, J.; Boyce, B.A.; DuHadaway, D.; Miller, F.; Akins, R.E. Dysmorphic neuromuscular junctions associated with motor ability in cerebral palsy. *Muscle Nerve* **2005**, *32*, 626–632. [CrossRef] [PubMed]

26. Robinson, K.G.; Crowgey, E.L.; Lee, S.K.; Akins, R.E. Transcriptional analysis of muscle tissue and isolated satellite cells in spastic cerebral palsy. *Dev. Med. Child Neurol.* **2021**, *63*, 1213–1220. [CrossRef] [PubMed]

27. Smith, L.R.; Pontén, E.; Hedström, Y.; Ward, S.R.; Chambers, H.G.; Subramaniam, S.; Lieber, R.L. Novel transcriptional profile in wrist muscles from cerebral palsy patients. *BMC Med. Genom.* **2009**, *2*, 44. [CrossRef] [PubMed]

28. Domenighetti, A.A.; Mathewson, M.A.; Pichika, R.; Sibley, L.A.; Zhao, L.; Chambers, H.G.; Lieber, R.L. Loss of myogenic potential and fusion capacity of muscle stem cells isolated from contractured muscle in children with cerebral palsy. *Am. J. Physiol. Physiol.* **2018**, *315*, C247–C257. [CrossRef]

29. Dayanidhi, S.; Dykstra, P.B.; Lyubasyuk, V.; McKay, B.R.; Chambers, H.G.; Lieber, R.L. Reduced satellite cell number in situ in muscular contractures from children with cerebral palsy. *J. Orthop. Res.* **2015**, *33*, 1039–1045. [CrossRef]

30. Smith, L.R.; Chambers, H.G.; Subramaniam, S.; Lieber, R.L. Transcriptional Abnormalities of Hamstring Muscle Contractures in Children with Cerebral Palsy. *PLoS ONE* **2012**, *7*, e40686. [CrossRef]
31. Corvelyn, M.; De Beukelaer, N.; Duelen, R.; Deschrevel, J.; Van Campenhout, A.; Prinsen, S.; Gayan-Ramirez, G.; Maes, K.; Weide, G.; Desloovere, K.; et al. Muscle Microbiopsy to Delineate Stem Cell Involvement in Young Patients: A Novel Approach for Children With Cerebral Palsy. *Front. Physiol.* **2020**, *11*, 945. [CrossRef]

32. Sibley, L.A.; Broda, N.; Gross, W.R.; Menezes, A.F.; Embry, R.B.; Swaroop, V.T.; Chambers, H.G.; Schipma, M.J.; Lieber, R.L.; Domenighetti, A.A. Differential DNA methylation and transcriptional signatures characterize impairment of muscle stem cells in pediatric human muscle contractures after brain injury. *FASEB J.* **2021**, *35*, e21928. [CrossRef]

33. Fahey, M.C.; Maclennan, A.H.; Kretzschmar, D.; Gecz, J.; Kruer, M.C. The genetic basis of cerebral palsy. *Dev. Med. Child Neurol.* **2017**, *59*, 462–469. [CrossRef]

34. McMichael, G.L.; Bainbridge, M.N.; Haan, E.; Corbett, M.; Gardner, A.; Thompson, S.E.; Van Bon, B.W.M.; van Eyk, C.; Broadbent, J.; Reynolds, C.A.; et al. Whole-exome sequencing points to considerable genetic heterogeneity of cerebral palsy. *Mol. Psychiatry* **2015**, *20*, 176–182. [CrossRef]

35. Kubota, N.; Yokoyama, T.; Hoshi, N.; Suyama, M. Identification of a candidate enhancer for DMRT3 involved in spastic cerebral palsy pathogenesis. *Biochem. Biophys. Res. Commun.* **2018**, *496*, 133–139. [CrossRef] [PubMed]

36. Gulati, S.; Sondhi, V. Cerebral Palsy: An Overview. *Indian J. Pediatr.* **2017**, *85*, 1006–1016. [CrossRef] [PubMed]

37. Sewell, M.D.; Eastwood, D.M.; Wimalasundera, N. Managing common symptoms of cerebral palsy in children. *BMJ* **2014**, *349*, g5474. [CrossRef] [PubMed]

38. Bahado-Singh, R.O.; Vishweswaraiah, S.; Aydas, B.; Mishra, N.K.; Guda, C.; Radhakrishna, U. Deep Learning/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy. *Int. J. Mol. Sci.* **2019**, *20*, 2075. [CrossRef] [PubMed]

39. Crowgey, E.L.; Marsh, A.G.; Robinson, K.G.; Yeager, S.K.; Akins, R.E. Epigenetic machine learning: Utilizing DNA methylation patterns to predict spastic cerebral palsy. *BMC Bioinform.* **2018**, *19*, 225. [CrossRef]

40. Mohandas, N.; Bass-Stringer, S.; Maksimovic, J.; Crompton, K.; Loke, Y.J.; Walstab, J.; Reid, S.M.; Amor, D.J.; Reddihough, D.; Craig, J.M.; et al. Epigenome-wide analysis in newborn blood spots from monozygotic twins discordant for cerebral palsy reveals consistent regional differences in DNA methylation. *Clin. Epigenetics* **2018**, *10*, 25. [CrossRef]

41. Yuan, Y. Study of global DNA methylation in monozygotic twins with cerebral palsy. *Pak. J. Pharm. Sci.* **2017**, *30* (Suppl. S4), 1467–1473.

42. Alag, A. Machine learning approach yields epigenetic biomarkers of food allergy: A novel 13-gene signature to diagnose clinical reactivity. *PLoS ONE* **2019**, *14*, e0218253. [CrossRef]

43. Aref-Eshghi, E.; Rodenhiser, D.I.; Schenkel, L.C.; Lin, H.; Skinner, C.; Ainsworth, P.; Paré, G.; Hood, R.L.; Bulman, D.E.; Kernohan, K.D.; et al. Genomic DNA Methylation Signatures Enable Concurrent Diagnosis and Clinical Genetic Variant Classification in Neurodevelopmental Syndromes. *Am. J. Hum. Genet.* **2018**, *102*, 156–174. [CrossRef]

44. Capper, D.; Jones, D.T.W.; Sill, M.; Hovestadt, V.; Schrimpf, D.; Sturm, D.; Koelsche, C.; Sahm, F.; Chavez, L.; Reuss, D.E.; et al. DNA methylation-based classification of central nervous system tumours. *Nature* **2018**, *555*, 469–474. [CrossRef]

45. De Bellis, M.; Camerino, D.C.; Desaphy, J.-F. Toward precision medicine in myotonic syndromes. *Oncotarget* **2017**, *8*, 14279–14280. [CrossRef] [PubMed]

46. Kulis, M.; Heath, S.; Bibikova, M.; Queirós, A.C.; Navarro, A.; Clot, G.; Martínez-Trillos, A.; Castellano, G.; Brun-Heath, I.; Pinyol, M.; et al. Epigenomic analysis detects widespread gene-body DNA hypomethylation in chronic lymphocytic leukemia. *Nat. Genet.* **2012**, *44*, 1236–1242. [CrossRef] [PubMed]

47. Liu, B.; Liu, Y.; Pan, X.; Li, M.; Yang, S.; Li, S.C. DNA methylation markers for Pan-Cancer prediction by deep learning. *Genes* **2019**, *10*, 778. [CrossRef]

48. Orozco, J.I.J.; Knijnenburg, T.A.; Manughian-Peter, A.O.; Salomon, M.P.; Barkhoudarian, G.; Jalas, J.R.; Wilmott, J.S.; Hothi, P.; Wang, X.; Takasumi, Y.; et al. Epigenetic profiling for the molecular classification of metastatic brain tumors. *Nat. Commun.* **2018**, *9*, 4627. [CrossRef]

49. Queiros, A.; Villamor, N.; Clot, G.; Martineztrillos, A.; Kulis, M.; Navarro, A.; Penas, E.M.M.; Jayne, S.; Majid, A.M.S.A.; Richter, J.A.; et al. A B-cell epigenetic signature defines three biologic subgroups of chronic lymphocytic leukemia with clinical impact. *Leukemia* **2014**, *29*, 598–605. [CrossRef] [PubMed]

50. Robinson, D.R.; Wu, Y.-M.; Lonigro, R.J.; Vats, P.; Cobain, E.; Everett, J.; Cao, X.; Rabban, E.; Kumar-Sinha, C.; Raymond, V.; et al. Integrative clinical genomics of metastatic cancer. *Nature* **2017**, *548*, 297–303. [CrossRef] [PubMed]

51. Zhang, X.; Hu, Y.; Aouizerat, B.E.; Peng, G.; Marconi, V.C.; Corley, M.J.; Hulgan, T.; Bryant, K.J.; Zhao, H.; Krystal, J.H.; et al. Machine learning selected smoking-associated DNA methylation signatures that predict HIV prognosis and mortality. *Clin. Epigenetics* **2018**, *10*, 155. [CrossRef]

52. Ehrlich, M. DNA hypermethylation in disease: Mechanisms and clinical relevance. *Epigenetics* **2019**, *14*, 1141–1163. [CrossRef]

53. Bareja, A.; Holt, J.A.; Luo, G.; Chang, C.; Lin, J.; Hinken, A.C.; Freudenberg, J.; Kraus, W.E.; Evans, W.J.; Billin, A.N. Human and Mouse Skeletal Muscle Stem Cells: Convergent and Divergent Mechanisms of Myogenesis. *PLoS ONE* **2014**, *9*, e90398. [CrossRef]

54. Garcia, S.M.; Tamaki, S.; Lee, S.; Wong, A.; Jose, A.; Dreux, J.; Kouklis, G.; Sbitany, H.; Seth, R.; Knott, P.D.; et al. High-Yield Purification, Preservation, and Serial Transplantation of Human Satellite Cells. *Stem Cell Rep.* **2018**, *10*, 1160–1174. [CrossRef]

55. Marsh, A.G.; Pasqualone, A.A. DNA methylation and temperature stress in an Antarctic polychaete, Spiophanes tcherniai. *Front. Physiol.* **2014**, *5*, 173. [CrossRef] [PubMed]

56. Rambo, I.M.; Marsh, A.; Biddle, J.F. Cytosine Methylation within Marine Sediment Microbial Communities: Potential Epigenetic Adaptation to the Environment. *Front. Microbiol.* **2019**, *10*, 1291. [CrossRef] [PubMed]
57. Li, H. Toward better understanding of artifacts in variant calling from high-coverage samples. *Bioinformatics* **2014**, *30*, 2843–2851.
[CrossRef] [PubMed]

58. McCarthy, D.J.; Chen, Y.; Smyth, G.K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. *Nucleic Acids Res.* **2012**, *40*, 4288–4297. [CrossRef] [PubMed]

59. Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. EdgeR: A Bioconductor package for differential expression analysis of digital gene expression data. *Bioinformatics* **2010**, *26*, 139–140. [CrossRef]

60. Ritchie, M.E.; Belinda, P.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. *Nucleic Acids Res.* **2015**, *43*, e47. [CrossRef]

61. Li, R.; Yang, Y.-E.; Yin, Y.-H.; Zhang, M.-Y.; Li, H.; Qu, Y.-Q. Methylation and transcriptome analysis reveal lung adenocarcinoma-specific diagnostic biomarkers. *J. Transl. Med.* **2019**, *17*, 324. [CrossRef]

62. Maksimovic, J.; Phipson, B.; Oshlack, A. A cross-package Bioconductor workflow for analysing methylation array data. *F1000Research* **2016**, *5*, 1281. [CrossRef]

63. Jones, P.A.; Baylin, S.B. The Epigenomics of Cancer. *Cell* **2007**, *128*, 683–692. [CrossRef]

64. Contreras-Romero, C.; Pérez-Yépez, E.-A.; Martinez-Gutierrez, A.D.; Campos-Parra, A.; Zentella-Dehesa, A.; Jacobo-Herrera, N.; López-Camarillo, C.; Corredor-Alonso, G.; Martínez-Coronel, J.; Rodríguez-Dorantes, M.; et al. Gene Promoter-Methylation Signature as Biomarker to Predict Cisplatin-Radiotherapy Sensitivity in Locally Advanced Cervical Cancer. *Front. Oncol.* **2022**, *12*, 773438. [CrossRef]

65. Cunningham, F.; Allen, J.E.; Allen, J.; Alvarez-Jarreta, J.; Amode, M.R.; Armean, I.M.; Austine-Orimoloye, O.; Azov, A.G.; Barnes, I.; Flicek, P.; et al. Ensembl 2022. *Nucleic Acids Res.* **2022**, *50*, D988–D995. [CrossRef] [PubMed]

66. University of California Santa Cruz (UCSC) Genome Browser Gateway. Available online: https://genome.ucsc.edu/ (accessed on 15 November 2022). (accessed multiple times in 2021 and 2022 with final access date for validation).

67. Robertson, K.D. DNA methylation and chromatin—Unraveling the tangled web. *Oncogene* **2002**, *21*, 5361–5379. [CrossRef] [PubMed]

68. Blaze, J.; Roth, T.L. Evidence from clinical and animal model studies of the long-term and transgenerational impact of stress on DNA methylation. *Semin. Cell Dev. Biol.* **2015**, *43*, 76–84. [CrossRef] [PubMed]

69. Hartley, I.; Elkhoury, F.F.; Shin, J.H.; Xie, B.; Gu, X.; Gao, Y.; Zhou, D.; Haddad, G.G. Long-Lasting Changes in DNA Methylation Following Short-Term Hypoxic Exposure in Primary Hippocampal Neuronal Cultures. *PLoS ONE* **2013**, *8*, e77859. [CrossRef] [PubMed]

70. Houtepen, L.C.; Vinkers, C.H.; Carrillo-Roa, T.; Hiemstra, M.; Van Lier, P.A.; Meeus, W.; Branje, S.; Heim, C.; Nemeroff, C.B.; Mill, J.; et al. Genome-wide DNA methylation levels and altered cortisol stress reactivity following childhood trauma in humans. *Nat. Commun.* **2016**, *7*, 10967. [CrossRef]

71. Labonté, B.; Suderman, M.; Maussion, G.; Navaro, L.; Yerko, V.; Mahar, I.; Bureau, A.; Mechawar, N.; Szyf, M.; Meaney, M.J.; et al. Genome-wide Epigenetic Regulation by Early-Life Trauma. *Arch. Gen. Psychiatry* **2012**, *69*, 722–731. [CrossRef]

72. Pacis, A.; Tailleux, L.; Morin, A.M.; Lambourne, J.; MacIsaac, J.L.; Yotova, V.; Dumaine, A.; Danckaert, A.; Luca, F.; Grenier, J.-C.; et al. Bacterial infection remodels the DNA methylation landscape of human dendritic cells. *Genome Res.* **2015**, *25*, 1801–1811. [CrossRef]

73. Rask-Andersen, M.; Martinsson, D.; Ahsan, M.; Enroth, S.; Ek, W.E.; Gyllensten, U.; Johansson, Å. Epigenome-wide association study reveals differential DNA methylation in individuals with a history of myocardial infarction. *Hum. Mol. Genet.* **2016**, *25*, 4739–4748. [CrossRef]

74. Stirzaker, C.; Zotenko, E.; Song, J.Z.; Qu, W.; Nair, S.S.; Locke, W.; Stone, A.; Armstong, N.J.; Robinson, M.D.; Dobrovic, A.; et al. Methylome sequencing in triple-negative breast cancer reveals distinct methylation clusters with prognostic value. *Nat. Commun.* **2015**, *6*, 5899. [CrossRef]

75. Xiao, X.; Zhao, Y.; Jin, R.; Chen, J.; Wang, X.; Baccarelli, A.; Zhang, Y. Fetal growth restriction and methylation of growth-related genes in the placenta. *Epigenomics* **2016**, *8*, 33–42. [CrossRef]

76. Carrió, E.; Lois, S.; Mallona, I.; Cases, I.; Forn, M.; Peinado, M.A.; Suelves, M.; Díez-Villanueva, A. Deconstruction of DNA Methylation Patterns During Myogenesis Reveals Specific Epigenetic Events in the Establishment of the Skeletal Muscle Lineage. *Stem Cells* **2015**, *33*, 2025–2036. [CrossRef] [PubMed]

77. Martin, E.M.; Fry, R.C. Environmental Influences on the Epigenome: Exposure– Associated DNA Methylation in Human Populations. *Annu. Rev. Public Health* **2018**, *39*, 309–333. [CrossRef] [PubMed]

78. Sosnowski, D.W.; Booth, C.; York, T.P.; Amstadter, A.B.; Kliewer, W. Maternal prenatal stress and infant DNA methylation: A systematic review. *Dev. Psychobiol.* **2018**, *60*, 127–139. [CrossRef] [PubMed]

79. Watamura, S.E.; Roth, T.L. Looking back and moving forward: Evaluating and advancing translation from animal models to human studies of early life stress and DNA methylation. *Dev. Psychobiol.* **2018**, *61*, 323–340. [CrossRef] [PubMed]

80. Bustelo, M.; Barkhuizen, M.; Hove, D.L.A.V.D.; Steinbusch, H.W.M.; Bruno, M.A.; Loidl, C.F.; Gavilanes, A.W.D. Clinical Implications of Epigenetic Dysregulation in Perinatal Hypoxic-Ischemic Brain Damage. *Front. Neurol.* **2020**, *11*, 483. [CrossRef]

81. Menon, R.; Conneely, K.N.; Smith, A.K. DNA Methylation: An Epigenetic Risk Factor in Preterm Birth. *Reprod. Sci.* **2012**, *19*, 6–13. [CrossRef]

82. Richetto, J.; Massart, R.; Weber-Stadlbauer, U.; Szyf, M.; Riva, M.A.; Meyer, U. Genome-wide DNA Methylation Changes in a Mouse Model of Infection-Mediated Neurodevelopmental Disorders. *Biol. Psychiatry* **2016**, *81*, 265–276. [CrossRef]
83. Vaiserman, A.M. Epigenetic programming by early-life stress: Evidence from human populations. *Dev. Dyn.* **2014**, *244*, 254–265.
[CrossRef]

84. Smith, L.; Chambers, H.G.; Lieber, R.L. Reduced satellite cell population may lead to contractures in children with cerebral palsy.
*Dev. Med. Child Neurol.* **2012**, *55*, 264–270. [CrossRef]

85. Sharples, A.P.; Polydorou, I.; Hughes, D.; Owens, D.J.; Hughes, T.M.; Stewart, C. Skeletal muscle cells possess a 'memory' of acute early life TNF-α exposure: Role of epigenetic adaptation. *Biogerontology* **2015**, *17*, 603–617. [CrossRef]

86. Tsumagari, K.; Baribault, C.; Terragni, J.; Varley, K.; Gertz, J.; Pradhan, S.; Badoo, M.; Crain, C.M.; Song, L.; Crawford, G.E.; et al.
Early de novo DNA methylation and prolonged demethylation in the muscle lineage. *Epigenetics* **2013**, *8*, 317–332. [CrossRef]
[PubMed]

87. Li, Y.; Chen, X.; Lu, C. The interplay between DNA and histone methylation: Molecular mechanisms and disease implications.
*EMBO Rep.* **2021**, *22*, e51803. [CrossRef] [PubMed]

88. Lea, A.J.; Vockley, C.M.; Johnston, R.A.; Del Carpio, C.A.; Barreiro, L.; Reddy, T.E.; Tung, J. Genome-wide quantification of the effects of DNA methylation on human gene regulation. *eLife* **2018**, *7*, e37513. [CrossRef] [PubMed]

89. Wagner, J.R.; Busche, S.; Ge, B.; Kwan, T.; Pastinen, T.; Blanchette, M. The relationship between DNA methylation, genetic and expression inter-individual variation in untransformed human fibroblasts. *Genome Biol.* **2014**, *15*, R37. [CrossRef] [PubMed]

90. Angeloni, A.; Bogdanovic, O. Enhancer DNA methylation: Implications for gene regulation. *Essays Biochem.* **2019**, *63*, 707–715.
[CrossRef]

91. Bae, M.G.; Kim, J.Y.; Choi, J.K. Frequent hypermethylation of orphan CpG islands with enhancer activity in cancer. *BMC Med. Genom.* **2016**, *9* (Suppl. S1), 38. [CrossRef]

92. Glass, J.L.; Hassane, D.; Wouters, B.J.; Kunimoto, H.; Avellino, R.; Garrett-Bakelman, F.E.; Guryanova, O.A.; Bowman, R.; Redlich, S.; Intlekofer, A.M.; et al. Epigenetic Identity in AML Depends on Disruption of Nonpromoter Regulatory Elements and Is Affected by Antagonistic Effects of Mutations in Epigenetic Modifiers. *Cancer Discov.* **2017**, *7*, 868–883. [CrossRef]

93. Wang, Y.; Hao, D.-P.; Li, J.-J.; Wang, L.; Di, L.-J. Genome-wide methylome and chromatin interactome identify abnormal enhancer to be risk factor of breast cancer. *Oncotarget* **2017**, *8*, 44705–44719. [CrossRef]

94. Benton, M.L.; Talipineni, S.C.; Kostka, D.; Capra, J.A. Genome-wide enhancer annotations differ significantly in genomic distribution, evolution, and function. *BMC Genom.* **2019**, *20*, 511. [CrossRef]

95. Romero, B.; Robinson, K.G.; Batish, M.; Akins, R.E. An Emerging Role for Epigenetics in Cerebral Palsy. *J. Pers. Med.* **2021**, *11*, 1187. [CrossRef]

96. Mbadhi, M.N.; Tang, J.-M.; Zhang, J.-X. Histone Lysine Methylation and Long Non-coding RNA: The New Target Players in Skeletal Muscle Cell Regeneration. *Front. Cell Dev. Biol.* **2021**, *9*, 759237. [CrossRef] [PubMed]

97. Luo, H.; Lv, W.; Tong, Q.; Jin, J.; Xu, Z.; Zuo, B. Functional Non-coding RNA During Embryonic Myogenesis and Postnatal Muscle Development and Disease. *Front. Cell Dev. Biol.* **2021**, *9*, 628339. [CrossRef] [PubMed]
<figure><img src="image_26.png" /></figure>
<figure><img src="image_27.png" /></figure>

Article

# The Effect of Lumbar Belts with Different Extensibilities on Kinematic, Kinetic, and Muscle Activity of Sit-to-Stand Motions in Patients with Nonspecific Low Back Pain

Sang-Cheol Im ID, Seong-Wook Seo, Na-Yeon Kang, Hoon Jo ID and Kyoung Kim *

Department of Physical Therapy, College of Rehabilitation Science, Daegu University, Gyeongsan 38453, Korea

* Correspondence: kykim257@hanmail.net

**Abstract:** Although lumbar belts can be used for the treatment and prevention of low back pain, the role of the lumbar belt remains unclear without clear guidelines. This study aimed to investigate the effect of lumbar belts with different extensibilities on the kinematics, kinetics, and muscle activity of sit-to-stand motions in terms of motor control in patients with nonspecific low back pain. A total of 30 subjects participated in the study: 15 patients with nonspecific low back pain and 15 healthy adults. Participants performed the sit-to-stand motion in random order of three conditions: no lumbar belt, wearing an extensible lumbar belt, and wearing a non-extensible lumbar belt. The sit-to-stand motion's kinematic, kinetic, and muscle activity variables in each condition were measured using a three-dimensional motion analysis device, force plate, and surface electromyography. An interaction effect was found for the time taken, anterior pelvic tilt angle, and muscle activity of the vastus lateralis and biceps femoris. The two lumbar belts with different extensibilities had a positive effect on motor control in patients with nonspecific low back pain. Therefore, both types of extensible lumbar belts can be useful in the sit-to-stand motion, which is an important functional activity for patients with nonspecific low back pain.

**Keywords:** electromyography; kinematic; kinetic; low back pain; lumbar belt; sit-to-stand

## 1. Introduction

Low back pain is one of the most prevalent disorders today [1], and people with low back pain move differently than healthy people do [2]. In particular, changes in motion variability may indicate an altered adaptability of motor control systems [3,4]. As reported in previous studies, a decrease in motion variability during running [5] and bending [6] activities in patients with low back pain is a protective behavior when pain is present or when pain is expected [7], and repeated tissue stress can occur over time [8]. In addition, when the motion variability increases, as observed in reaching [9] and walking [10], in patients with low back pain, errors or noises in proprioceptive sensory feedback and motor commands may occur, thereby reducing the robustness of the system [11]. Thus, low back pain can cause significant functional loss and affect the performance of dynamic tasks and static postural control [12]. The sit-to-stand motion is a common and functionally important task in daily life activities [13]. Because it is associated with worsening low back pain symptoms, studies on the sit-to-stand motion are essential in back pain-related research [14].

Although there is hope that individualized treatment will be effective for people suffering from low back pain [15], most studies claiming to be effective have been shown to be flawed [16,17]. In addition, claims of treatment efficacy in subgroups of individuals with low back pain are reliable when there is a biological basis, but this is difficult in nonspecific low back pain [18]. Because nonspecific low back pain has no known pathological cause, treatment focuses on reducing pain and consists of education, analgesics, and psychotherapy, but many patients do not require medical treatment [19]. Strategies for preventing

**Citation:** Im, S.-C.; Seo, S.-W.; Kang, N.-Y.; Jo, H.; Kim, K. The Effect of Lumbar Belts with Different Extensibilities on Kinematic, Kinetic, and Muscle Activity of Sit-to-Stand Motions in Patients with Nonspecific Low Back Pain. *J. Pers. Med.* **2022**, *12*, 1678. https://doi.org/10.3390/jpm12101678

Academic Editor: Anne-Marie Caminade

Received: 19 September 2022

Accepted: 6 October 2022

Published: 9 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_28.png" /></figure>

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
low back pain include ergonomic control at the workplace where repetitive movements and tasks are performed [20], exercises, such as training in waist mechanics and lifting techniques [21], and the use of lumbar belts, which can also be used to treat low back pain [22].

To explain the clinical benefits of lumbar belts, psychological, neuromuscular, and biomechanical mechanisms have been proposed, but these remain unproven [23]. Psychological benefits may be related to the perception of mechanical support generated by the lumbar belt, and neuromuscular benefits may include mechanisms that affect lumbar stability, such as lumbar proprioception, trunk muscle feedforward, and reflex activity [24,25]. Biomechanical benefits are related to the mechanical stiffness of the lumbar belt, such as a reduction in lumbar range of motion, a reduction in the stress of the posterior lumbar passive tissue, and a potential reduction in the compressive load of the lumbar spine [26,27]. However, the potential role of lumbar belts in treating low back pain remains unclear [28], and low-quality studies and insufficient evidence imply that there are no clear guidelines for using lumbar belts in patients with low back pain [29]. For this reason, it is necessary to study the effect of lumbar belts on the biomechanics and motor control mechanism of patients with low back pain.

Most previous studies on lumbar belt have analyzed proprioception or balance, and there is an insufficient number of studies on the effect of wearing a lumbar belt while the patient with low back pain performs a functional task. In particular, the effect of sit-to-stand motion, an important functional activity for modern people who spend a lot of time sitting and for patients with low back pain, has not been studied so far. Therefore, this study sought to investigate the effects of wearing two lumbar belts with different extensibilities on the kinematics, kinetics, and muscle activity in terms of motor control of the sit-to-stand motion in patients with nonspecific low back pain and healthy adults. From this research, we intend to present the evidence for the effective use of lumbar belts for patients with nonspecific low back pain and provide basic data necessary for the prevention and treatment of low back pain. For the purpose of this study, we hypothesized that the effects of the sit-to-stand motion on kinematics, kinetics, and muscle activity would differ depending on the presence or absence of back pain and the extensibility of the lumbar belt.

## 2. Methods

### 2.1. Participants

Participants in this study were recruited from outpatients and caregivers between the ages of 20 and 50 years who visited a spine specialty hospital located in Daegu, Korea, from April 2020 to May 2020. Of the 30 participants, 15 patients with nonspecific low back pain were assigned to the experimental group, and 15 normal adults were assigned to the control group. Participants in the control group were matched to those in the experimental group according to age, sex, and body mass index (BMI). We used G\*Power software program (version 3.1.9.4, Franz Faul, Düsseldorf, Germany) to determine the appropriate number of subjects, and we referred to a previous study with the same design (two groups × three conditions) as this study [30]. We used the effect size of the lumbar total range of motion variable of the manual material-handling task in the previous study. Based on an effect size of 0.31, a significance level of 0.05, and a power of 80%, we calculated that a total of 30 subjects were required [31].

The selection criterion for the experimental group was lumbar or lumbar spine pain for at least 4 weeks without radiating pain below the knee (nonacute phase), and the exclusion criteria were spinal surgery, specific lumbar pathology, scoliosis, systemic or degenerative disease, BMI > 30 kg/m², hypertension, neurological history unrelated to back pain, and medications that could affect neuronal excitability [30]. The exclusion criterion for the control group was a history of low back pain lasting more than 1 week during the past year [32]. Additional exclusion criteria for both groups were a neurological or respiratory disease, trauma, or pregnancy that could affect the sit-to-stand motion [33]. The orthopedic
surgeon verified the qualifications of these participants through medical records and image
evaluations and interviews.

After explaining the purpose of the study, the contents of the experiment, and the
degree of exposure during the experiment to all participants in advance, written consent
for voluntary participation was obtained. All participants' sex, age, and anthropometric
data were collected, and pain (visual analog scale) and disability (Oswestry Disability
Index) data were additionally investigated for patients with nonspecific low back pain
in the experimental group. This study was approved by the Bioethics Committee of
Daegu University (1040621-201911-HR-025-02) and was conducted in accordance with
the 1964 Declaration of Helsinki and its later amendments. In addition, this study was
registered with the Clinical Research Information Service for information sharing, ethics,
and transparency of clinical research (KCT0004970).

## 2.2. Lumbar Belts

After consulting with an orthopedic surgeon, two models of lumbar belt were selected, taking into consideration flexibility, comfort, and economy for use in daily life and work. These two types of lumbar belts consisted of two layers of straps secured with Velcro. The initial adjustment and placement of the lumbar belt were made with the inner layer, and the final tension was adjusted with the outer layer. The extensible lumbar belt (REDIX-K210, Acetech, Seoul, Korea) was made up of elastic materials, but the nonextensible lumbar belt (REDIX-L350, Acetech) was made up of nonstretchable nylon materials only. Ready-made products in three sizes (large, medium, and small) were used based on the body type of the participants. The position of the lumbar belt to be worn was such that the lower edge did not touch the thigh when sitting but covered the anterior superior iliac spine (ASIS) of the pelvis [32]. Then, while the participant was standing still, the wearing pressure was standardized to 60 mmHg as measured from a thin force-sensing resister sensor inserted between the waist belt and the participant's right iliac crest [34].

## 2.3. *Experimental Procedure*

A comfortable environment was created so that participants did not feel uncomfortable with the temperature and surrounding environment of the laboratory. In order not to interfere with the measurement, all participants wore a sleeveless T-shirt made of lightweight material and short shorts. Before obtaining other measurements, body measurements were obtained by the same examiner to determine the participants' general characteristics.

When the electromyography (EMG) electrode placement and motion analysis reflex marker were prepared, the participants placed their bare feet at a comfortable width on two force plates installed on the floor. They sat down in height-adjustable chairs without armrests or backrests (Figure 1). The chair's height was adjusted to be 90% of the length between the knee and the floor, which is the distance between the fibular head and the floor. Participants looked forward and sat in their preferred comfortable position, with their arms folded in front of their chest to avoid interference from shaking and occlusion of markers [35].

Following verbal cues from a research assistant operating a computer connected to the data acquisition system, the participants stood up at a rate of their choosing, held a comfortable upright position for 3 s, and then sat back down on their chairs without further instructions. To help the participants understand the experimental procedure, measurements were taken after two to three practice sessions following the researcher's demonstration. Participants performed the sit-to-stand motion in the most comfortable way of their choosing, with the only limitation under which they did not change their feet positions. Paper tape was used to mark the initial position of the foot, and in the subsequent conditions, the sit-to-stand motion was performed with the same foot position.

Three experimental conditions were set: no lumbar belt-wearing condition (condition 1), extensible lumbar belt-wearing condition (condition 2), and nonextensible lumbar belt-wearing condition (condition 3). To exclude a learning effect, the experimental conditions
were conducted in a random order. A card with the experimental conditions was placed in a sealed envelope, and the experiment was conducted in the order in which each participant drew the card. The participants performed the sit-to-stand motion under each condition, and the biomechanical variables were then measured (Figure 2). To increase reliability, three successful measurements were taken for each condition, and the average value was used for analysis. All participants performed the sit-to-stand motion a total of nine times. The test was considered successful if all markers were visible and the EMG signals of all muscles were recorded correctly. There was a one-minute rest period between each test and a five-minute rest period between the lumbar belt conditions.

<figure><img src="image_29.png" /><figcaption>Figure 1. Experimental preparation posture.</figcaption></figure>

<figure><img src="image_30.png" /><figcaption>Figure 2. Study flowchart.</figcaption></figure>

## 2.4. Measurement Method

### 2.4.1. Measurement of Kinematic and Kinetic Parameters

In this study, we used a three-dimensional (3D) motion analysis system (Qualisys Medical AB, Gothenburg, Sweden) that included 6 infrared cameras and a force plate (Kistler, Winterthur, Switzerland) to collect the kinematic and kinetic data of the participants' body movements when performing the sit-to-stand motion. The resolution of the Qualisys
Motion Capture System was 1280 × 1024 pixels, and the sampling frequency was set to 200 Hz. The 3D spatial coordinates were made using a nonlinear transformation method. The sampling frequency of the force plate was set to 2000 Hz. All kinematic and kinetic data were measured using Qualisys Track Manager (Qualisys Medical AB) software and were low-pass filtered using a fourth-order Butterworth filter with cutoff frequencies of 25 Hz and 10 Hz. Data were then analyzed using Visual 3D software (C-Motion, Germantown, MD, USA) [36]. Moments were normalized by the system to weight and height [37].

Prior to conducting this test, we performed calibration to correct errors that may occur in the infrared camera. One examiner modified the methods of the previous study according to the purpose of this study and attached 36 reflective markers on the landmark skin of the body [38]. A static test was performed by attaching a reflective marker and checking the position of each joint on a computer screen in the still state of standing barefoot. During the static test, which was performed before measuring each experimental condition, the participant stood still for 2 s on the force plate. All markers were fixed by banding using Kinesio tape, which does not reflect light, to prevent shaking or falling. After the static examination, the length from both lateral ankles to the markers (ASIS, PSIS, top of iliac crest) that were covered when the lumbar belt was worn while standing upright and the length between each marker were recorded with a tape measure. In the lumbar belt-wearing condition, the marker was reattached by maintaining the same position between the experimental conditions using this length.

### 2.4.2. Measurement of Muscle Activity

We measured the muscle activity of the vastus lateralis (VL), biceps femoris (BF), tibialis anterior (TA), and gastrocnemius (GM) muscles during the sit-to-stand motion using the Wireless Bipolar Cometa Mini Wave Plus EMG system (Cometa, Bareggio, Italy) synchronized with Visual 3D software (C-Motion). The sampling rate of the surface EMG signal was set to 2000 Hz. The raw surface EMG signal was rectified after band-pass filtering at 10–1000 Hz and low-pass filtered with a Butterworth filter [39]. As the ground electrode, a disposable anode surface electrode made of silver-silver chloride was used and attached parallel to the muscle fiber. The distance between electrodes in the surface EMG was set to 20 mm. To minimize the skin resistance and reduce the noise of muscle activity measurement, the hair on the skin was removed using a disposable razor, and after gently polishing with sandpaper, the skin was cleaned with alcohol. After the electrode attachment site was dried, two active electrodes and a reference electrode were attached. The electrodes of the four muscles of the dominant lower extremity (VL, BF, TA, GM) were placed according to the SENIAM guidelines [40] (Figure 3).

<figure><img src="image_31.png" /><figcaption>**Figure 3.** Attachment of EMG electrodes.</figcaption></figure>
All of the measured raw EMG data were converted into root mean square, which provides a value close to the actual output value of the EMG signal. To analyze the activity of each muscle, the maximal voluntary isometric contraction (MVIC) of VL, BF, TA, and GM was measured, and the EMG signal collected during the sit-to-stand motion under each experimental condition was calculated as the percentage of MVIC [41]. Surface EMG data were synchronized using the pulses generated at the beginning of the recording of kinematics and kinetics, and the average muscle activity measurements during the sit-to-stand motion were compared.

## 2.5. Data Processing

The sit-to-stand motion was divided into three points and two phases to conduct the analysis of the kinematics, kinetics, and muscle activity variables (Figure 4). The three points were determined using the ground reaction force (GRF) measured on the force plate. The initiation point [42] and the seat-off point [43] were determined based on the vertical GRF, and the termination point was determined based on the anterior-posterior GRF (A-P GRF) [44]. To detect potential indicator changes in the sit-to-stand motion, the minimum and maximum values of each GRF data were used for calculation. The initiation point was detected when a 5% threshold change occurred from the reference value of the vertical GRF in the sitting position, and the termination point was detected when a 7.5% threshold change occurred from the final value of A-P GRF in the standing position, going backward [45]. The seat-off point was set as the maximum value point of the vertical GRF [44]. The phase from the initiation point to the seat-off point was set as the flexion phase, and the phase between the seat-off point to the termination point was set as the extension phase [46]. Each sit-to-stand motion was normalized to 101 data points from the initiation point to the termination point [33]. The time from the initiation point to the termination point was measured, and the value of the seat-off point of the sit-to-stand motion was used for the angle of joints in the kinematic variable. The maximum value during the sit-to-stand motion was used for the moment in the kinetic variable. The average muscle activities of the extension and flexion phases were used for the muscle activity variable.

<figure><img src="image_32.png" /><figcaption>**Figure 4.** Indicators and classification of the sit-to-stand motion.</figcaption></figure>

## 2.6. Statistical Analysis

For statistical processing of the data collected in this study, we used SPSS 22.0 for Windows (IBM Corp., Armonk, NY, USA), and all data are reported as mean ± standard deviation. The normality test was performed using the Shapiro-Wilk test. The participants' general characteristics were analyzed using descriptive statistics, and an independent t test and a chi-square test were used to test the homogeneity of the two groups. Two-way repeated analysis of variance (ANOVA) was performed to confirm the interaction between the presence or absence of back pain and the change in lumbar belt-wearing conditions. If
the interaction was significant, the Bonferroni method was used for post hoc analysis. The least square difference was used for the main effect comparison. When both the interaction and the main effect were significant, we analyzed the interaction only. The statistical significance level (α) was set to 0.05.

Cohen's d formula was used for the effect size corresponding to the detected effect for the interaction between the experimental group and the control group, the main effect, and the comparison between groups [47]. The effect size was calculated using the G-power 3.1.9.4 program. In ANOVA, 0.10 was interpreted as "small," 0.25 as "medium," and 0.40 as "large" for effect size *f* [48].

### 3. Results

#### 3.1. *Analysis of The General Characteristics of Experimental and Control Groups*

Table 1 shows the general characteristics of the experimental group and the control group. There was no significant difference between the two groups in terms of participants' general characteristics (*p* > 0.05).

<table><caption>**Table 1.** General characteristics of the experimental and control groups.</caption><thead><tr><th>Variable</th><th>CG (n = 15)</th><th>EG (n = 15)</th><th>p</th></tr></thead><tbody><tr><td>Sex (Male, %)</td><td>9 (60.0)</td><td>8 (53.3)</td><td>0.500<sup>†</sup></td></tr><tr><td>Age (year)</td><td>34.53 (4.40)</td><td>35.60 (5.19)</td><td>0.549<sup>‡</sup></td></tr><tr><td>Height (cm)</td><td>171.40 (9.83)</td><td>170.20 (8.08)</td><td>0.718<sup>‡</sup></td></tr><tr><td>Weight (kg)</td><td>70.60 (12.50)</td><td>67.40 (14.60)</td><td>0.525<sup>‡</sup></td></tr><tr><td>BMI (kg/m<sup>2</sup>)</td><td>23.85 (2.26)</td><td>23.02 (3.38)</td><td>0.437<sup>‡</sup></td></tr><tr><td>Visual analog scale (score)</td><td></td><td>3.80 (0.86)</td><td></td></tr><tr><td>Oswestry Disability Index (%)</td><td></td><td>19.60 (4.42)</td><td></td></tr><tr><td>Onset (month)</td><td></td><td>43.06 (33.35)</td><td></td></tr></tbody></table>

#### 3.2. *Analysis of the Time Taken, Kinematics, and Kinetics Variables According to Lumbar Belt Wearing Conditions of the Experimental Group and Control Group*

There was an interaction effect in the required time (*p* < 0.05, *f* = 0.36; Table 2). As a result of the post hoc analysis, there was a significant difference between the condition in which the experimental group did not wear a lumbar belt (condition 1) and the conditions in which the lumbar belt was worn (conditions 2 and 3). There was an interaction effect in the pelvic anterior tilt angle (*p* < 0.05, *f* = 0.50; Table 2). Post hoc analysis showed a significant difference between the control group not wearing the lumbar belt (condition 1) and the lumbar belt-wearing conditions (conditions 2 and 3), and there was a significant difference between the control group and experimental group in the condition of not wearing the lumbar belt (condition 1).

There were main effects among lumbar belt-wearing conditions in trunk flexion angle, hip flexion angle, knee joint flexion angle, hip flexion-extension moment, and knee joint flexion-extension moment (*p* < 0.05, *f* = 0.36–0.75; Table 2). Post hoc analysis showed a significant difference between the condition in which the lumbar belt was not worn (condition 1) and the conditions in which the lumbar belt was worn (conditions 2 and 3). There was a main effect between groups in hip flexion-extension moment (*p* < 0.05, *f* = 0.65; Table 2). Post hoc analysis showed a significant difference between the control group and experimental group.

<footnote>† Chi-squared test, ‡ Independent *t*-test, CG: Control group, EG: Experimental group.</footnote>
<table><caption>**Table 2.** Analysis of the time taken, kinematics, and kinetics variables for the sit-to-stand motion according to the experimental conditions.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Condition 1<br>(No LB)</th><th colspan="2">Condition 2<br>(Extensible LB)</th><th colspan="2">Condition 3<br>(Nonextensible LB)</th><th rowspan="2">p<br>(f)</th><th rowspan="2">G × C</th><th rowspan="2">Post Hoc</th></tr><tr><th>CG</th><th>EG</th><th>CG</th><th>EG</th><th>CG</th><th>EG</th></tr></thead><tbody><tr><td>Time taken<br>(s)</td><td>1.76<br>(0.19)</td><td>1.90<br>(0.28)</td><td>1.76<br>(0.22)</td><td>1.78<br>(0.20)</td><td>1.75<br>(0.22)</td><td>1.77<br>(0.20)</td><td>0.401<br>(0.39)</td><td>0.033 *<br>(0.36)</td><td>EG: C1 > C2, C1 > C3</td></tr><tr><td>Trunk flexion angle<br>(°)</td><td>45.70<br>(10.42)</td><td>40.79<br>(6.41)</td><td>45.96<br>(10.07)</td><td>45.17<br>(6.12)</td><td>46.89<br>(10.28)</td><td>44.09<br>(5.65)</td><td>0.336<br>(0.38)</td><td>0.102</td><td>C1 < C2, C1 < C3</td></tr><tr><td>Pelvic anterior tilt angle<br>(°)</td><td>34.14<br>(5.94)</td><td>42.05<br>(6.16)</td><td>45.08<br>(8.30)</td><td>45.46<br>(8.89)</td><td>45.55<br>(8.57)</td><td>44.74<br>(6.31)</td><td>0.290<br>(0.87)</td><td>&lt;0.001 *<br>(0.87)</td><td>CG: C1 < C2, C1 < C3<br>C1: CG < EG</td></tr><tr><td>Hip flexion angle<br>(°)</td><td>100.16<br>(8.82)</td><td>104.30<br>(11.45)</td><td>108.07<br>(13.42)</td><td>110.73<br>(10.79)</td><td>111.97<br>(11.86)</td><td>110.24<br>(9.72)</td><td>0.640<br>(0.75)</td><td>&lt;0.001 *<br>(0.75)</td><td>C1 < C2, C1 < C3</td></tr><tr><td>Knee flexion angle<br>(°)</td><td>73.13<br>(9.09)</td><td>77.05<br>(9.34)</td><td>72.13<br>(7.82)</td><td>74.77<br>(9.29)</td><td>71.77<br>(8.94)</td><td>75.08<br>(9.13)</td><td>0.308<br>(0.37)</td><td>0.028 *<br>(0.37)</td><td>C1 > C2, C1 > C3</td></tr><tr><td>Ankle flexion angle<br>(°)</td><td>-6.74<br>(4.28)</td><td>-5.73<br>(3.71)</td><td>-6.14<br>(4.91)</td><td>-6.61<br>(3.85)</td><td>-6.41<br>(4.44)</td><td>-6.57<br>(5.92)</td><td>0.933<br>(0.908)</td><td>0.908</td><td>0.422</td></tr><tr><td>Hip flexion-extension moment<br>(nm/kg)</td><td>-1082.71<br>(272.10)</td><td>-875.20<br>(171.62)</td><td>-1163.58<br>(213.83)</td><td>-968.21<br>(112.71)</td><td>-1193.42<br>(222.59)</td><td>-948.38<br>(151.12)</td><td>0.002 *<br>(0.65)</td><td>0.005 *<br>(0.45)</td><td>0.699</td><td>CG > EG<br>C1 < C2, C1 < C3</td></tr><tr><td>Hip adduction-abduction moment<br>(nm/kg)</td><td>202.97<br>(164.75)</td><td>178.82<br>(139.13)</td><td>215.26<br>(117.89)</td><td>181.27<br>(103.57)</td><td>203.81<br>(94.11)</td><td>186.31<br>(88.26)</td><td>0.532<br>(0.848)</td><td>0.821</td><td></td><td></td></tr><tr><td>Hip Int. Rot-Ext. Rot moment<br>(nm/kg)</td><td>176.89<br>(117.37)</td><td>170.56<br>(103.88)</td><td>177.02<br>(163.95)</td><td>159.37<br>(112.66)</td><td>185.39<br>(186.78)</td><td>188.46<br>(109.36)</td><td>0.883<br>(0.365)</td><td>0.675</td><td></td><td></td></tr><tr><td>Knee flexion-extension moment<br>(nm/kg)</td><td>-593.82<br>(145.11)</td><td>-702.46<br>(168.38)</td><td>-563.46<br>(153.38)</td><td>-655.45<br>(161.79)</td><td>-549.03<br>(134.16)</td><td>-661.88<br>(155.61)</td><td>0.055<br>(0.36)</td><td>0.820<br>(0.36)</td><td>C1 > C2, C1 > C3</td><td></td></tr><tr><td>Knee varus-valgus moment<br>(nm/kg)</td><td>-70.03<br>(32.95)</td><td>-70.54<br>(57.46)</td><td>-70.41<br>(38.22)</td><td>-73.85<br>(56.54)</td><td>-60.15<br>(43.77)</td><td>-61.66<br>(57.35)</td><td>0.912<br>(0.158)</td><td>0.971</td><td></td><td></td></tr><tr><td>Knee Int. Rot-Ext. Rot moment<br>(nm/kg)</td><td>-108.82<br>(62.90)</td><td>-108.31<br>(67.98)</td><td>-110.77<br>(64.01)</td><td>-107.18<br>(76.34)</td><td>-102.12<br>(55.16)</td><td>-111.63<br>(68.13)</td><td>0.934<br>(0.972)</td><td>0.763</td><td></td><td></td></tr><tr><td>Ankle flexion-extension moment<br>(nm/kg)</td><td>107.93<br>(43.72)</td><td>104.49<br>(34.09)</td><td>113.31<br>(34.75)</td><td>96.59<br>(37.56)</td><td>108.47<br>(52.49)</td><td>93.34<br>(30.89)</td><td>0.322<br>(0.756)</td><td>0.621</td><td></td><td></td></tr><tr><td>Ankle inversion-eversion moment<br>(nm/kg)</td><td>29.02<br>(25.26)</td><td>30.87<br>(21.77)</td><td>32.93<br>(26.19)</td><td>34.26<br>(25.48)</td><td>30.96<br>(24.01)</td><td>36.06<br>(22.46)</td><td>0.750<br>(0.091)</td><td>0.551</td><td></td><td></td></tr><tr><td>Ankle adduction-abduction moment<br>(nm/kg)</td><td>-1.54<br>(6.61)</td><td>-4.37<br>(7.25)</td><td>-2.36<br>(9.06)</td><td>-3.12<br>(4.34)</td><td>-3.76<br>(7.41)</td><td>-2.69<br>(6.91)</td><td>0.708<br>(0.912)</td><td>0.243</td><td></td><td></td></tr></tbody></table>

### 3.3. Analysis of Muscle Activity According to Lumbar Belt Wearing Conditions of the Experimental Group and Control Group

An interaction effect was observed in the VL flexion phase (*p* < 0.05, *f* = 0.52; Table 3). Post hoc analysis showed a significant difference between the control group and experimental group in the condition in which the lumbar belt was not worn (condition 1). There was an interaction effect observed in the BF flexion phase (*p* < 0.05, *f* = 0.46; Table 3). Post hoc analysis showed a significant difference between the condition in which the lumbar belt was not worn (condition 1) and the conditions in which the lumbar belt was worn (conditions 2 and 3) in the experimental group.

<table><caption>**Table 3.** Analysis of muscle activity variables of the sit-to-stand motion according to the experimental conditions (%MVIC).</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Condition 1<br>(No LB)</th><th colspan="2">Condition 2<br>(Extensible LB)</th><th colspan="2">Condition 3<br>(Nonextensible LB)</th><th rowspan="2">p<br>(f)</th><th rowspan="2">G × C</th><th rowspan="2">Post Hoc</th></tr><tr><th>CG</th><th>EG</th><th>CG</th><th>EG</th><th>CG</th><th>EG</th></tr></thead><tbody><tr><td>Vastus lateralis flexion phase</td><td>18.16<br>(6.48)</td><td>31.71<br>(10.52)</td><td>21.00<br>(7.14)</td><td>28.28<br>(9.04)</td><td>20.39<br>(7.97)</td><td>28.35<br>(9.86)</td><td>0.003 *<br>(0.61)</td><td>0.762<br>(0.52)</td><td>C1: CG < EG</td></tr><tr><td>Vastus lateralis extension phase</td><td>28.50<br>(12.09)</td><td>40.89<br>(13.92)</td><td>27.41<br>(12.94)</td><td>40.39<br>(14.48)</td><td>29.59<br>(11.53)</td><td>41.04<br>(13.94)</td><td>0.014 *<br>(0.49)</td><td>0.728<br>(0.46)</td><td>CG < EG</td></tr><tr><td>Biceps femoris flexion phase</td><td>3.59<br>(1.44)</td><td>5.05<br>(2.87)</td><td>3.96<br>(1.41)</td><td>4.47<br>(2.38)</td><td>3.85<br>(1.37)</td><td>4.23<br>(2.87)</td><td>0.312<br>(0.277)</td><td>0.004 *<br>(0.46)</td><td>EG: C1 > C2, C1 > C3</td></tr><tr><td>Biceps femoris extension phase</td><td>9.37<br>(4.39)</td><td>8.09<br>(4.63)</td><td>9.94<br>(4.40)</td><td>10.11<br>(4.92)</td><td>10.28<br>(5.25)</td><td>10.68<br>(6.69)</td><td>0.893<br>(0.47)</td><td>0.219<br>(0.44)</td><td>C1 < C2, C1 < C3</td></tr><tr><td>Tibialis anterior flexion phase</td><td>19.91<br>(13.06)</td><td>24.47<br>(14.29)</td><td>17.97<br>(8.95)</td><td>21.58<br>(11.68)</td><td>15.49<br>(8.77)</td><td>22.19<br>(13.59)</td><td>0.246<br>(0.007 *<br>(0.44))</td><td>0.321<br>(0.44)</td><td>C1 > C2, C1 > C3</td></tr><tr><td>Tibialis anterior extension phase</td><td>5.31<br>(3.17)</td><td>9.19<br>(4.92)</td><td>5.06<br>(3.37)</td><td>8.14<br>(6.86)</td><td>5.91<br>(3.44)</td><td>7.78<br>(6.22)</td><td>0.092<br>(0.423)</td><td>0.136</td><td></td></tr><tr><td>Gastrocnemius flexion phase</td><td>3.08<br>(1.42)</td><td>4.26<br>(1.75)</td><td>2.75<br>(1.39)</td><td>3.83<br>(1.48)</td><td>2.24<br>(1.03)</td><td>4.06<br>(1.56)</td><td>0.010 *<br>(0.52)</td><td>0.076<br>(0.42)</td><td>CG < EG<br>C1 > C2, C1 > C3</td></tr><tr><td>Gastrocnemius extension phase</td><td>5.25<br>(2.32)</td><td>6.65<br>(2.67)</td><td>4.76<br>(1.84)</td><td>5.68<br>(1.76)</td><td>3.91<br>(1.80)</td><td>5.59<br>(2.55)</td><td>0.063<br>(0.005 *<br>(0.45))</td><td>0.565<br>(0.45)</td><td>C1 > C2, C1 > C3</td></tr></tbody></table>

There was a main effect between the lumbar belt-wearing conditions in the BF extension phase, TA flexion phase, GM flexion phase, and GM extension phase (*p* < 0.05, *f* = 0.42–0.47; Table 3). Post hoc analysis showed a significant difference between the condi-

<footnote>* *p* < 0.05, *f*: effect size "f", CG: Control group, EG: Experimental group, C1: Condition 1, C2: Condition 2, C3: Condition 3.</footnote>

<footnote>* *p* < 0.05, *f*: effect size "f", CG: Control group, EG: Experimental group, C1: Condition 1, C2: Condition 2, C3: Condition 3.</footnote>
tion in which the lumbar belt was not worn (condition 1) and the conditions in which the lumbar belt was worn (conditions 2 and 3). There was a main effect between groups in VL extension phase and GM flexion phase (*p* < 0.05, *f* = 0.49–0.52; Table 3). The results of the post hoc analysis suggested there was a significant difference between the control group and experimental group.

**4. Discussion**

The purpose of this study was to investigate the effect of the presence or absence of back pain and the difference in the extensibility of the lumbar belt on kinematics, kinetics, and muscle activity in terms of motion control in the sit-to-stand motion. The results of this study indicated that there was an interaction effect according to the presence or absence of back pain and the difference in the extensibility of the lumbar belt, and the two types of lumbar belts with different extensibility had a similar effect overall.

**4.1. Analysis of The Interaction between the Presence or Absence of Low Back Pain and the Extensibility of the Lumbar Belt**

The interaction effect was found in the time taken to perform the sit-to-stand motion. The post hoc analysis indicated no difference between the lumbar belt-wearing conditions in the healthy adult group, but there was a decrease in the time taken in the patients with nonspecific low back pain in the conditions in which the lumbar belt was worn (conditions 2 and 3) compared with the condition in which the lumbar belt was not worn (condition 1). The lumbar belt had effects such as improving proprioception [49], increasing mechanical stiffness [23], and relieving pain [30]. Although the mechanical stiffness increased in both the nonspecific low back pain group and the healthy adult group, wearing a lumbar belt in this study reduced the required time only in the group with nonspecific low back pain. It has been reported that proprioceptive sensation is impaired in patients with low back pain [50,51], and impaired proprioceptive sensation is associated with motor control disorders [52]. Therefore, we believe that the required time was reduced because the lumbar belt improved the proprioceptive sense of the patients with nonspecific low back pain and had a positive effect on motor control. Considering the normal effect size, we believe that the lumbar belt was partially helpful in improving the sit-to-stand motion by reducing the time taken for the patients with nonspecific low back pain.

In the kinematics of the sit-to-stand motion, an interactive effect was found on the angle of pelvic anterior tilt at the seat-off point. Results of the post hoc analysis showed that the experimental group had a greater pelvic anterior tilt angle than the control group in the condition in which the lumbar belt was not worn (condition 1). In conjunction with the angle of trunk flexion in this experiment, this difference in the angle of anterior pelvic tilt is thought to be a protection strategy for patients with low back pain to reduce the angle of trunk flexion and maintain the lumbar region in a rigid state. In the control group, the angle of the anterior pelvic tilt was greater in the conditions in which the lumbar belt was worn (conditions 2 and 3) than in the condition in which the lumbar belt was not worn (condition 1). In a previous study analyzing the effect of the lumbar belt on the range of motion of the lumbar spine and the lumbar–pelvic rhythm, it was reported that wearing a lumbar belt restricts the range of motion of the lumbar spine and also changes the lumbar–pelvic rhythm [23]. In this study, the lumbar belt was worn so that the lower edge of the lumbar belt covered the ASIS and the iliac crest to include the pelvis, which may have had a stiffening effect on the lumbar belt, as in the previous study on the spine–pelvic connection. The forward-driving force that accelerates the body’s center of mass (COM) in the sit-to-stand motion is generated by trunk flexion [53]. In the experimental group, it is thought that the front driving force required to perform the sit-to-stand motion was generated as the trunk flexion angle increased by wearing the lumbar belt. In the control group, there was no change in the trunk flexion angle because the lumbar belt was worn, and the anterior pelvic tilt angle may have increased as a result of the stiffening effect of the lumbar belt. The interaction of the pelvic anterior tilt angle at the seat-off point
showed a large effect size. Therefore, the difference in the angle of the anterior pelvic tilt is a characteristic that shows that the nonspecific low back pain patient group and the normal adult group performed the sit-to-stand motion differently, and the stiffening effect of the lumbar belt acting on the lumbar–pelvic connection is considered to be sufficient.

An interaction effect was found on the average muscle activity of VL and BF during the sit-to-stand motion. There was an interaction effect on the average muscle activity of the VL flexion phase. As a result of the post hoc analysis, the mean VL muscle activity of the nonspecific low back pain patient group was significantly higher than that of the healthy adult group under the condition in which the lumbar belt was not worn (condition 1). In this study, the nonspecific low back pain patient group showed a tendency to perform the sit-to-stand motion using the knee joint, increasing the angle and extension moment of the knee joint while keeping the lumbar–pelvic region in a rigid state. Thus, we believe that VL was involved in knee joint control in the flexion phase, and muscle activity was high in the patients with nonspecific low back pain. A previous study also reported that the increase in the knee joint extension moment was due to the high activity of the knee joint extensors, which supports the results of this study [54]. There was an interaction effect on the average muscle activity of the BF flexion phase. As a result of the post hoc analysis, there was no difference between the lumbar belt-wearing conditions in the healthy adult group; however, as compared with the condition without the lumbar belt (condition 1), the average muscle activity was significantly decreased in the nonspecific low back pain patient group in the condition with the lumbar belt (conditions 2 and 3). According to a study using EMG, the compensatory mechanism of muscle activation in the painful state could occur in antagonist muscles and induce changes in the pattern of muscle recruitment [55]. Because the sit-to-stand motion is an action that causes the body to stand up against gravity, the knee joint is regulated by the contraction of VL in the flexion phase. Therefore, the high muscle activity of the BF, the antagonist of the VL, in the condition that the lumbar belt is not worn (condition 1) is a change in the muscle recruitment pattern and is considered to be a compensatory mechanism in patients with nonspecific low back pain. According to the results of this study, the muscle activity of the VL and BF was reduced as the compensatory mechanism was reduced because the trunk flexion angle of the nonspecific low back pain patient group was increased by wearing a lumbar belt, which secured the trunk flexion angle necessary for the sit-to-stand motion. We found a large effect size of the interaction in the muscle activity of the VL and BF. From this large effect size, the lumbar belt is considered to have an advantage in terms of motor control.

## 4.2. *Analysis of Main Effects between Patients with Nonspecific Low Back Pain and Healthy Adults*

During the sit-to-stand motion, there was a difference between the nonspecific low back pain patient group and the healthy adult group. The extension moment of the healthy adult group was significantly greater in the flexion–extension moment of the hip joint. Although the extension moment of the nonspecific low back pain patient group was not statistically significant in the flexion–extension moment of the knee joint, it tended to be larger. Healthy individuals complete the process of standing upright by simultaneously flexing the spine and hip and then simultaneously extending the spine and hip when performing a sit-to-stand motion [56]. In this study, the healthy adult group also correctly performed the sit-to-stand motion using the hip extension moment. In contrast, in the nonspecific low back pain patient group, the sit-to-stand motion was performed using the knee joint rather than the hip joint. The surface EMG results of the lower extremity muscles showed a tendency for the muscle activity to be high in the nonspecific low back pain patient group. In the flexion phase, the VL and GM muscle activity of the nonspecific low back pain patient group was high, and in the extension phase, the VL muscle activity of the nonspecific low back pain patient group was high. In a previous study, the authors claimed there was a causal relationship between motor outcome and neuromuscular activity to create a protective mechanism [2]. In the flexion phase, the VL
and GM play a role in regulating the knee and ankle joints, and in the extension phase, the VL more greatly extends the flexed knee joint; thus, it is considered that the muscle activity of the nonspecific low back pain patient group was high.

Previous studies have discussed the various mechanisms for nonspecific low back pain [57], and motor control disorders have been reported to possibly be one of the causes [58]. Because of the link between neuromuscular activity and biomechanical con- sequences, changes in the neuromuscular system resulting from motor control disorders lead to slower movement and affect the movement of higher muscle activity [59]. In this study, the nonspecific low back pain patient group performed the sit-to-stand motion with the lumbar spine rigid by increasing the angle of pelvic anterior tilt and decreasing the angle of trunk flexion. This could be due to a short-term protective strategy to reduce the load on the posterior structure of the lumbar spine and avoid further stress on the painful area. In addition, to compensate for reduced body flexion angle, the knee joint flexion angle was increased when performing the sit-to-stand motion. This is consistent with the results of the nonspecific low back pain patient group, which had a small hip extension moment and a large knee joint extension moment, as compared with the healthy adult group. Previous studies also reported that in patients with low back pain, the lower extremities were involved as a possible compensatory exercise for pain [60]. As such, we confirmed that movement control disorders in patients with nonspecific low back pain had an effect on daily activities, such as performing the sit-to-stand motion.

### 4.3. *Analysis of The Main Effects between Lumbar Belt Wearing Conditions*

The trunk flexion angle, hip flexion angle, and hip joint extension moment at the seat-off point of the sit-to-stand motion significantly increased in the conditions with the lumbar belt (conditions 2 and 3) compared with the condition without the lumbar belt (condition 1). In the knee joint, flexion angle and extension moment were significantly decreased. The lumbar belt can reduce the stress on the posterior viscoelastic structure of the lumbar vertebrae or the compression load of the lumbar vertebrae [26,27]. This is an effect of the limited lumbar movement due to the increased mechanical rigidity when the lumbar belt is worn, and it can prevent the load of a specific vertebral structure [61]. In this study, we found that the protection mechanism for reducing the angle of trunk flexion was reduced by wearing a lumbar belt to avoid stress on the painful area and thus the angle of trunk flexion was increased. Trunk flexion in the sit-to-stand motion creates a forward-driving force that accelerates the body's COM [53]. We believe that the compensatory action occurring at the knee joint was reduced because the angle of trunk flexion and hip flexion increased by wearing the lumbar belt.

The EMG results showed that by wearing the lumbar belt, the muscle activity in the flexion phase significantly decreased in the TA, and the muscle activity in the flexion phase and extension phase in the GM significantly decreased. The TA and GM are involved in the control of knee and ankle joints in the sit-to-stand motion. Previous studies also reported that the TA prepares for anterior displacement of the body's COM by moving the tibia forward [62]. It is thought that the muscle activity of the TA and GM decreased as the knee joint flexion angle decreased as a result of lumbar belt wearing. There was a significant increase in the muscle activity in the extension phase of the BF. Compensation is a motor strategy used to restore postural balance by sensory feedback [63]. In a previous study, a relatively small activity of the hip extensor was reported because small trunk flexion required less forward speed of the body's COM [54]. In this study, we consider that the muscle activity of the BF acting as a hip extensor increased because the body flexion angle increased by wearing a lumbar belt, which required greater forward speed of the body's COM.

### 4.4. *Limitations*

Because of the relatively small number of subjects in this study, it is difficult to generalize the results. We investigated only the immediate effect of the lumbar belt as
opposed to the long-term effect. Because the spine was investigated as a single body, the movement of each vertebral segment and the compensatory action of the thoracic vertebrae were not considered. To focus on the effect of the lumbar belt on the sit-to-stand motion, there were restrictions on the height of the chair and the position of the feet. Therefore, different results may be obtained if the chair height and foot position are freely set. In addition, because the types and designs of the lumbar belts are diverse, different results could be obtained if there is a difference in the extensibility and design of the lumbar belt used. Finally, the muscle activity of the trunk muscles could not be measured because it could be disturbed by wearing a lumbar belt. Therefore, further studies using more diverse lumbar belt designs, including study designs using more subjects and a placebo effect, and lumbar belts equipped with biofeedback devices are needed in the future.

**5. Conclusions**

Patients with nonspecific low back pain performed the sit-to-stand motion in a pattern different from that of healthy adults, resulting in impaired motor control and decreased ability. The two lumbar belts with different extensibilities had a positive effect on motor control and improved performance in patients with nonspecific low back pain. Therefore, both types of lumbar belts with different extensibilities can help with the sit-to-stand motion, which is an important functional activity for patients with low back pain.

**Author Contributions:** Conceptualization, S.-C.I., S.-W.S. and K.K.; data curation, S.-C.I. and K.K.; formal analysis, S.-W.S., H.J. and K.K.; investigation, S.-C.I. and N.-Y.K.; methodology, S.-C.I., H.J. and K.K.; supervision, K.K.; validation, S.-C.I. and K.K.; visualization, S.-W.S., N.-Y.K. and H.J.; writing—original draft preparation, S.-C.I. and S.-W.S.; writing—review and editing, N.-Y.K. and K.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by a Daegu University research grant in 2019 (2019-0415).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of the Daegu University (IRB no. 1040621-201911-HR-025-02).

**Informed Consent Statement:** Informed consent was obtained from all participants involved in this study. Written informed consent was obtained from the participants to publish this paper.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical restrictions.

**Acknowledgments:** The authors are grateful to all volunteer participants for their co-operation and participation in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

1. Hoy, D.; Bain, C.; Williams, G.; March, L.; Brooks, P.; Blyth, F.; Woolf, A.; Vos, T.; Buchbinder, R. A systematic review of the global prevalence of low back pain. *Arthritis Rheum*. **2012**, *64*, 2028–2037. [CrossRef] [PubMed]

2. Hodges, P.W.; Tucker, K. Moving differently in pain: A new theory to explain the adaptation to pain. *Pain* **2011**, *152*, S90–S98. [CrossRef] [PubMed]

3. Bartlett, R.; Wheat, J.; Robins, M. Is movement variability important for sports biomechanists? *Sports Biomech*. **2007**, *6*, 224–243. [CrossRef]

4. Stergiou, N.; Decker, L.M. Human movement variability, nonlinear dynamics, and pathology: Is there a connection? *Hum. Mov. Sci*. **2011**, *30*, 869–888. [CrossRef] [PubMed]

5. Seay, J.F.; Van Emmerik, R.E.; Hamill, J. Low back pain status affects pelvis-trunk coordination and variability during walking and running. *Clin. Biomech*. **2011**, *26*, 572–578. [CrossRef] [PubMed]

6. Mokhtarinia, H.R.; Sanjari, M.A.; Chehrehrazi, M.; Kahrizi, S.; Parnianpour, M. Trunk coordination in healthy and chronic nonspecific low back pain subjects during repetitive flexion-extension tasks: Effects of movement asymmetry, velocity and load. *Hum. Mov. Sci*. **2016**, *45*, 182–192. [CrossRef]

7. Moseley, G.L.; Hodges, P.W. Reduced variability of postural strategy prevents normalization of motor changes induced by back pain: A risk factor for chronic trouble? *Behav. Neurosci*. **2006**, *120*, 474–476. [CrossRef] [PubMed]
8. Hamill, J.; van Emmerik, R.E.; Heiderscheit, B.C.; Li, L. A dynamical systems approach to lower extremity running injuries. *Clin. Biomech.* **1999**, *14*, 297-308. [CrossRef]

9. Silfies, S.P.; Bhattacharya, A.; Biely, S.; Smith, S.S.; Giszter, S. Trunk control during standing reach: A dynamical system analysis of movement strategies in patients with mechanical low back pain. *Gait Posture* **2009**, *29*, 370-376. [CrossRef] [PubMed]

10. Lamoth, C.J.; Meijer, O.G.; Daffertshofer, A.; Wuisman, P.I.; Beek, P.J. Effects of chronic low back pain on trunk coordination and back muscle activity during walking: Changes in motor control. *Eur. Spine. J.* **2006**, *15*, 23-40. [CrossRef] [PubMed]

11. Reeves, N.P.; Narendra, K.S.; Cholewicki, J. Spine stability: Lessons from balancing a stick. *Clin. Biomech.* **2011**, *26*, 325-330. [CrossRef] [PubMed]

12. Claeys, K.; Dankaerts, W.; Janssens, L.; Brumagne, S. Altered preparatory pelvic control during the sit-to-stance-to-sit movement in people with non-specific low back pain. *J. Electromyogr. Kinesiol.* **2012**, *22*, 821-828. [CrossRef] [PubMed]

13. Shum, G.L.; Crosbie, J.; Lee, R.Y. Effect of low back pain on the kinematics and joint coordination of the lumbar spine and hip during sit-to-stand and stand-to-sit. *Spine* **2005**, *30*, 1998-2004. [CrossRef] [PubMed]

14. Sedrez, J.A.; Mesquita, P.V.; Gelain, G.M.; Candotti, C.T. Kinematic Characteristics of Sit-to-Stand Movements in Patients with Low Back Pain: A Systematic Review. *J. Manip. Physiol. Ther.* **2019**, *42*, 532-540. [CrossRef]

15. Bishop, F.L.; Dima, A.L.; Ngui, J.; Little, P.; Moss-Morris, R.; Foster, N.E.; Lewith, G.T. "Lovely Pie in the Sky Plans": A Qualitative Study of Clinicians' Perspectives on Guidelines for Managing Low Back Pain in Primary Care in England. *Spine* **2015**, *40*, 1842-1850. [CrossRef]

16. Hancock, M.J.; Kjaer, P.; Korsholm, L.; Kent, P. Interpretation of subgroup effects in published trials. *Phys. Ther.* **2013**, *93*, 852-859. [CrossRef]

17. Mistry, D.; Patel, S. Hee, S.W.; Stallard, N.; Underwood, M. Evaluating the quality of subgroup analyses in randomized controlled trials of therapist-delivered interventions for nonspecific low back pain: A systematic review. *Spine* **2014**, *39*, 618-629. [CrossRef]

18. Sun, X.; Briel, M.; Walter, S.D.; Guyatt, G.H. Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses. *BMJ* **2010**, *340*, c117. [CrossRef]

19. Maher, C.; Underwood, M.; Buchbinder, R. Non-specific low back pain. *Lancet* **2017**, *389*, 736-747. [CrossRef]

20. Gallagher, K.M.; Callaghan, J.P. Standing on a declining surface reduces transient prolonged standing induced low back pain development. *Appl. Ergon.* **2016**, *56*, 76-83. [CrossRef]

21. Lahad, A.; Malter, A.D.; Berg, A.O.; Deyo, R.A. The effectiveness of four interventions for the prevention of low back pain. *JAMA* **1994**, *272*, 1286-1291. [CrossRef] [PubMed]

22. Ammendolia, C.; Kerr, M.S.; Bombardier, C. Back belt use for prevention of occupational low back pain: A systematic review. *J. Manip. Physiol. Ther.* **2005**, *28*, 128-134. [CrossRef] [PubMed]

23. Larivière, C.; Caron, J.M.; Preuss, R.; Mecheri, H. The effect of different lumbar belt designs on the lumbopelvic rhythm in healthy subjects. *BMC Musculoskelet. Disord.* **2014**, *15*, 307. [CrossRef]

24. Preuss, R.; Fung, J. Can acute low back pain result from segmental spinal buckling during sub-maximal activities? A review of the current literature. *Man. Ther.* **2005**, *10*, 14-20. [CrossRef] [PubMed]

25. Panjabi, M.M. Clinical spinal instability and low back pain. *J. Electromyogr. Kinesiol.* **2003**, *13*, 371-379. [CrossRef]

26. McGill, S.M.; Kippers, V. Transfer of loads between lumbar tissues during the flexion-relaxation phenomenon. *Spine* **1994**, *19*, 2190-2196. [CrossRef]

27. Katsuhira, J.; Sasaki, H.; Asahara, S.; Ikegami, T.; Ishihara, H.; Kikuchi, T.; Hirai, Y.; Yamasaki, Y.; Wada, T.; Maruyama, H. Comparison of low back joint moment using a dynamic 3D biomechanical model in different transferring tasks wearing low back belt. *Gait Posture* **2008**, *28*, 258-264. [CrossRef]

28. Foster, N.E.; Anema, J.R.; Cherkin, D.; Chou, R.; Cohen, S.P.; Gross, D.P.; Ferreira, P.H.; Fritz, J.M.; Koes, B.W.; Peul, W.; et al. Prevention and treatment of low back pain: Evidence, challenges, and promising directions. *Lancet* **2018**, *391*, 2368-2383. [CrossRef]

29. Van Duijvenbode, I.C.; Jellema, P.; van Poppel, M.N.; van Tulder, M.W. Lumbar supports for prevention and treatment of low back pain. *Cochrane Database Syst. Rev.* **2008**, *2008*, CD001823. [CrossRef]

30. Shahvarpour, A.; Preuss, R.; Sullivan, M.J.L.; Negrini, A.; Larivière, C. The effect of wearing a lumbar belt on biomechanical and psychological outcomes related to maximal flexion-extension motion and manual material handling. *Appl. Ergon.* **2018**, *69*, 17-24. [CrossRef]

31. Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.G. Statistical power analyses using G\*Power 3.1: Tests for correlation and regression analyses. *Behav. Res. Methods* **2009**, *41*, 1149-1160. [CrossRef]

32. Boucher, J.A.; Roy, N.; Preuss, R.; Larivière, C. The effect of two lumbar belt designs on trunk repositioning sense in people with and without low back pain. *Ann. Phys. Rehabil. Med.* **2017**, *60*, 306-311. [CrossRef] [PubMed]

33. Ippersiel, P.; Robbins, S.; Preuss, R. Movement variability in adults with low back pain during sit-to-stand-to-sit. *Clin. Biomech.* **2018**, *58*, 90-95. [CrossRef] [PubMed]

34. Ludvig, D.; Preuss, R.; Larivière, C. The effect of extensible and non-extensible lumbar belts on trunk muscle activity and lumbar stiffness in subjects with and without low-back pain. *Clin. Biomech.* **2019**, *67*, 45-51. [CrossRef] [PubMed]

35. Orakifar, N.; Shaterzadeh-Yazdi, M.J.; Salehi, R.; Mehravar, M.; Namnik, N. Muscle Activity Pattern Dysfunction During Sit to Stand and Stand to Sit in the Movement System Impairment Subgroups of Low Back Pain. *Arch. Phys. Med. Rehabil.* **2019**, *100*, 851-858. [CrossRef] [PubMed]
36. Sinclair, J.; Atkins, S.; Richards, J.; Vincent, H. Modelling of Muscle Force Distributions During Barefoot and Shod Running. *J. Hum. Kinet.* **2015**, *47*, 9–17. [CrossRef] [PubMed]

37. Costigan, P.A.; Wyss, U.P.; Deluzio, K.J.; Li, J. Semiautomatic three-dimensional knee motion assessment system. *Med. Biol. Eng. Comput.* **1992**, *30*, 343–350. [CrossRef] [PubMed]

38. Cappozzo, A.; Catani, F.; Croce, U.D.; Leardini, A. Position and orientation in space of bones during movement: Anatomical frame definition and determination. *Clin. Biomech.* **1995**, *10*, 171–178. [CrossRef]

39. Koopman, A.S.; Kingma, I.; Faber, G.S.; de Looze, M.P.; van Dieën, J.H. Effects of a passive exoskeleton on the mechanical loading of the low back in static holding tasks. *J. Biomech.* **2019**, *83*, 97–103. [CrossRef] [PubMed]

40. Hermens, H.J.; Freriks, B.; Disselhorst-Klug, C.; Rau, G. Development of recommendations for SEMG sensors and sensor placement procedures. *J. Electromyogr. Kinesiol.* **2000**, *10*, 361–374. [CrossRef]

41. Farahpour, N.; Jafarnezhadgero, A.; Allard, P.; Majlesi, M. Muscle activity and kinetics of lower limbs during walking in pronated feet individuals with and without low back pain. *J. Electromyogr. Kinesiol.* **2018**, *39*, 35–41. [CrossRef] [PubMed]

42. Etnyre, B.; Thomas, D.Q. Event standardization of sit-to-stand movements. *Phys. Ther.* **2007**, *87*, 1651–1666. [CrossRef] [PubMed]

43. Schenkman, M.; Riley, P.O.; Pieper, C. Sit to stand from progressively lower seat heights—alterations in angular velocity. *Clin. Biomech.* **1996**, *11*, 153–158. [CrossRef]

44. Stevermer, C.A.; Gillette, J.C. Kinematic and Kinetic Indicators of Sit-to-Stand. *J. Appl. Biomech.* **2016**, *32*, 7–15. [CrossRef] [PubMed]

45. Pai, Y.C.; Rogers, M.W. Control of body mass transfer as a function of speed of ascent in sit-to-stand. *Med. Sci. Sports Exerc.* **1990**, *22*, 378–384. [CrossRef]

46. Nuzik, S.; Lamb, R.; VanSant, A.; Hirt, S. Sit-to-stand movement pattern. A kinematic study. *Phys. Ther.* **1986**, *66*, 1708–1713. [CrossRef]

47. Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for *t*-tests and ANOVAs. *Front. Psychol.* **2013**, *4*, 863. [CrossRef] [PubMed]

48. Fritz, C.O.; Morris, P.E.; Richler, J.J. Effect size estimates: Current use, calculations, and interpretation. *J. Exp. Psychol. Gen.* **2012**, *141*, 2–18. [CrossRef] [PubMed]

49. Calmels, P.; Fayolle-Minon, I. An update on orthotic devices for the lumbar spine based on a review of the literature. *Rev. Rhum. Engl. Ed* **1996**, *63*, 285–291. [PubMed]

50. Boucher, J.A.; Abboud, J.; Nougarou, F.; Normand, M.C.; Descarreaux, M. The Effects of Vibration and Muscle Fatigue on Trunk Sensorimotor Control in Low Back Pain Patients. *PLoS ONE* **2015**, *10*, e0135838. [CrossRef] [PubMed]

51. Willigenburg, N.W.; Kingma, I.; Hoozemans, M.J.; van Dieën, J.H. Precision control of trunk movement in low back pain patients. *Hum. Mov. Sci.* **2013**, *32*, 228–239. [CrossRef] [PubMed]

52. Koch, C.; Hänsel, F. Chronic Non-specific Low Back Pain and Motor Control During Gait. *Front. Psychol.* **2018**, *9*, 2236. [CrossRef] [PubMed]

53. Roebroeck, M.E.; Doorenbosch, C.A.; Harlaar, J.; Jacobs, R.; Lankhorst, G.J. Biomechanics and muscular activity during sit-to-stand transfer. *Clin. Biomech.* **1994**, *9*, 235–244. [CrossRef]

54. Jeon, W.; Jensen, J.L.; Griffin, L. Muscle activity and balance control during sit-to-stand across symmetric and asymmetric initial foot positions in healthy adults. *Gait Posture* **2019**, *71*, 138–144. [CrossRef] [PubMed]

55. Svendsen, J.H.; Svarrer, H.; Laessoe, U.; Vollenbroek-Hutten, M.; Madeleine, P. Standardized activities of daily living in presence of sub-acute low-back pain: A pilot study. *J. Electromyogr. Kinesiol.* **2013**, *23*, 159–165. [CrossRef] [PubMed]

56. Tully, E.A.; Fotoohabadi, M.R.; Galea, M.P. Sagittal spine and lower limb movement during sit-to-stand in healthy young subjects. *Gait Posture* **2005**, *22*, 338–345. [CrossRef]

57. Saragiotto, B.T.; Maher, G.C.; Yamato, T.P.; Costa, L.O.; Menezes Costa, L.C.; Ostelo, R.W.; Macedo, L.G. Motor control exercise for chronic non-specific low-back pain. *Cochrane Database Syst. Rev.* **2016**, *2016*, CD012004. [CrossRef] [PubMed]

58. Götze, M.; Ernst, M.; Koch, M.; Blickhan, R. Influence of chronic back pain on kinematic reactions to unpredictable arm pulls. *Clin. Biomech.* **2015**, *30*, 290–295. [CrossRef]

59. Van Dieën, J.H.; Selen, L.P.; Cholewicki, J. Trunk muscle activation in low-back pain patients, an analysis of the literature. *J. Electromyogr. Kinesiol.* **2003**, *13*, 333–351. [CrossRef]

60. Sung, P.S.; Danial, P. Analysis of relative kinematic index with normalized standing time between subjects with and without recurrent low back pain. *Eur. Spine J.* **2017**, *26*, 518–527. [CrossRef]

61. Van Poppel, M.N.; de Looze, M.P.; Koes, B.W.; Smid, T.; Bouter, L.M. Mechanisms of action of lumbar supports: A systematic review. *Spine* **2000**, *25*, 2103–2113. [CrossRef]

62. Khemlani, M.M.; Carr, J.H.; Crosbie, W.J. Muscle synergies and joint linkages in sit-to-stand under two initial foot positions. *Clin. Biomech.* **1999**, *14*, 236–246. [CrossRef]

63. Haddas, R.; Satin, A.; Lieberman, I. What is actually happening inside the “cone of economy”: Compensatory mechanisms during a dynamic balance test. *Eur. Spine J.* **2020**, *29*, 2319–2328. [CrossRef] [PubMed]
<figure><img src="image_33.png" /></figure>

Article

# Postoperative Inpatient Rehabilitation Does Not Increase Knee Function after Primary Total Knee Arthroplasty

Dominik Rak <fnref n="1" />,*, Alexander J. Nedopil <fnref n="1" /> ID, Eric C. Sayre <fnref n="2" />, Bassam A. Masri <fnref n="3" /> and Maximilian Rudert <fnref n="1" /> ID

**Abstract:** Inpatient rehabilitation (IR) is a common postoperative protocol after total knee replacement (TKA). Because IR is expensive and should therefore be justified, this study determined the difference in knee function one year after TKA in patients treated with IR or outpatient rehabilitation, fast-track rehabilitation (FTR) in particular, which also entails a reduced hospital length of stay. A total of 205 patients were included in this multi-center prospective cohort study. Of the patients, 104 had primary TKA at a German university hospital and received IR, while 101 had primary TKA at a Canadian university hospital and received FTR. Patients receiving IR or FTR were matched by pre-operative demographics and knee function. Oxford Knee Score (OKS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), and EuroQol visual analogue scale (EQ-VAS) determined knee function one year after surgery. Patients receiving IR had a 2.8-point lower improvement in OKS (p = 0.001), a 6.7-point lower improvement in WOMAC (p = 0.063), and a 12.3-point higher improvement in EQ-VAS (p = 0.281) than patients receiving FTR. IR does not provide long-term benefits to patient recovery after primary uncomplicated TKA under the current rehabilitation regime.

**Keywords:** total knee arthroplasty; fast track rehabilitation; inpatient rehabilitation; postoperative rehabilitation; patient reported outcome measures

## 1. Introduction

Over the last two decades, the number of total joint replacement surgeries has consistently increased due to ageing in Western societies and growing numbers of people with overweight or obesity [1]. Due to the expected increase in patients needing a total knee arthroplasty (TKA), alternative rehabilitation pathways besides inpatient rehabilitation (IR) have been explored with the aim to provide comparable functional outcomes while reducing the time a patient is required to stay away from home.

IR after TKA is common practice in Western European countries. IR usually includes 3 weeks of medical rehabilitation at a rehabilitation center to reintroduce patients to independent living and social integration. Patients receive daily physical therapy, occupational therapy, and functional training. In addition, patients are provided accommodation and daily meals. After discharge from IR, outpatient physical therapy is continued for four more weeks.

Post-TKA fast-track rehabilitation (FTR) was developed with the goal of reducing costs and providing patients with a faster return to home while maintaining comparable functional outcomes. FTR entails a reduced length of stay in the hospital with discharge to home and not to IR. Outpatient physical therapy or inhouse visits by a physical therapist help patients to regain their knee function and return to an independent lifestyle, while forgoing IR can save a patient $3450 [2].

Because of the potential cost reduction, multiple countries have steered away from utilizing IR after uncomplicated TKAs [3]. One of these countries is Canada. In Germany,

**Citation:** Rak, D.; Nedopil, A.J.; Sayre, E.C.; Masri, B.A.; Rudert, M. Postoperative Inpatient Rehabilitation Does Not Increase Knee Function after Primary Total Knee Arthroplasty. *J. Pers. Med.* **2022**, *12*, 1934. https://doi.org/10.3390/jpm12111934

Academic Editor: Anne-Marie Caminade

Received: 14 October 2022

Accepted: 17 November 2022

Published: 21 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_34.png" /></figure>

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

<footnote n="1">¹ Orthopädische Klinik König-Ludwig-Haus, Lehrstuhl für Orthopädie der Universität Würzburg, 97074 Würzburg, Germany</footnote>

<footnote n="2">² Arthritis Research Canada, Vancouver, BC V5Y 3P2, Canada</footnote>

<footnote n="3">³ Department of Orthopedic Surgery, University of British Columbia, Vancouver, BC V5Z 1M9, Canada</footnote>

<footnote>* Correspondence: d-rak.klh@uni-wuerzburg.de</footnote>
IR is still the standard postoperative rehabilitation regime after uncomplicated TKA. As insured patients in Germany would be reluctant to be randomized to a therapy other than the one to which they feel entitled, it is difficult to conduct a randomized controlled trial that compares a resource-intensive pathway (=IR) with a less resource-intensive alternative (=FTR). Consequently, the similarities between the Canadian and German healthcare systems offer an opportunity to compare functional improvements after these two different rehabilitation regimes [4].

Multiple studies—mainly performed outside Germany—have demonstrated equal or even improved patient joint function when FTR after TKA or total hip arthroplasty was compared to IR [2,5–7]. While German patients remain unwilling to forfeit their right to IR, a plausible method to compare the influence of IR and FTR on the functional improvement after TKA is to analyze PROMs between German patients receiving IR and patients from another country receiving FTR. To minimize the inherent bias when comparing patients from two different countries, the patients’ general health and pre-operative knee function and the surgical technique and implant design should be comparable.

Accordingly, the purpose of the study was to compare the functional improvement after TKA among patients receiving IR in Germany with patients receiving FTR in Canada. The hypothesis was that FTR results in comparable functional improvement one year after TKA. A follow-up period of one year seemed reasonable, since knee function reaches a plateau within the first postoperative year, which remains stable in the following years [8–11]. A cost analysis, however, was not performed because of the countries’ different reimbursement policies.

## 2. Materials and Methods

The study was planned and conducted as a transnational evaluation of prospectively collected data, in which the one-year clinical improvement after the Canadian FTR and the German IR following primary TKA was compared. The study was a mutual project of the orthopedic departments of University of British Columbia, Vancouver, Canada, and Julius Maximilian Universität, Würzburg, Germany. IR for the German patients took place at a preselected rehabilitation center. Canadian patients instead conducted outpatient physical therapy with corresponding home exercises or through home visits by a physical therapist.

Upon receipt of approval from both the Canadian (The University of British Columbia, Office of Research Ethics, Study ID: H18-02307, 05.09.2018) and German (Ethik-Kommission der Universität Würzburg, Number: 20210925 01, 26.20.2021) institutional ethics boards, we identified all patients between December 2019 and February 2020 who fulfilled the criteria for medical need for TKA treatment. Included were all patients with primary osteoarthritis and radiographic evidence of Kellgren–Lawrence grades II-IV osteoarthritic changes who underwent an uncomplicated unilateral TKA using the Stryker Triathlon TKA System. Patients receiving simultaneous bilateral TKAs, patients with previous fracture of the affected lower extremity, patients with metabolic or inflammatory joint disease (e.g., rheumatoid arthritis or osteonecrosis), and patients not following the standardized postoperative rehabilitation regime were not eligible to participate in the study. Patients lost to follow-up were excluded from the final data analysis.

During the above-mentioned time-period, a total of 117 TKAs were performed at the German hospital, of which the Stryker Triathlon System was used in 113. Nine patients were lost to follow-up, leaving 104 patients for final analysis. In Canada, 115 TKAs were performed in the above-mentioned time-period, all utilizing the Stryker Triathlon System; 14 patients were lost to follow-up, leaving 101 patients for final analysis (Table 1).

All TKAs were performed using a standard medial parapatellar approach, mechanical alignment principles, and a cruciate retaining femoral component with a cruciate retaining (CR) or cruciate-substituting polyethylene (CR-CS) Triathlon TKA system (Stryker, Kalamazoo, MI, USA).

The target hospital length-of-stay for the IR patients was 4–5 days before they were transferred to IR. IR was scheduled for three weeks and included daily physical ther-
apy, occupational therapy, and functional training. In the FTR group, the target hospital length-of-stay was 1–2 days before home discharge and onset of outpatient rehabilitation. Outpatient rehabilitation included physical therapy twice per week for 6 weeks with instruction to perform independent exercises at home.

<table><caption>**Table 1.** Patient pre-operative demographics.</caption><thead><tr><th>Rehabilitation Protocol</th><th></th><th>FTR-Canada (n = 101)</th><th>IR-Germany (n = 104)</th><th>p-Value</th></tr></thead><tbody><tr><td>Age (years) mean ± SD</td><td></td><td>66 ± 8</td><td>67 ± 10</td><td>0.703</td></tr><tr><td rowspan="2">Sex</td><td>Female (n) (%)</td><td>65 (64%)</td><td>57 (55%)</td><td rowspan="2">0.205</td></tr><tr><td>Male (n) (%)</td><td>36 (36%)</td><td>47 (45%)</td></tr><tr><td>BMI mean ± SD</td><td></td><td>30.1 ± 6.4</td><td>31.6 ± 6.0</td><td>0.029</td></tr><tr><td rowspan="3">ASA</td><td>I (n) (%)</td><td>9 (9%)</td><td>4 (4%)</td><td rowspan="3">0.189</td></tr><tr><td>II (n) (%)</td><td>71 (70%)</td><td>70 (67%)</td></tr><tr><td>III (n) (%)</td><td>21 (21%)</td><td>30 (29%)</td></tr></tbody></table>

BMI, body mass index; ASA, American Society of Anesthesiologists Physical Status; SD, standard deviation; FTR, fast-track rehabilitation; IR, inhouse rehabilitation.

To compare the baseline health status between the two groups the pre-operative age, sex, body mass index (BMI), and American Society of Anesthesiologists (ASA) Physical Status score were obtained.

To evaluate and assess knee function and the functional improvement after TKA the following patient-reported outcome measures (PROMs) were conducted. The Oxford Knee Score (OKS) is scored from 0 (worst) to 48 (best) and assesses knee function before and after TKA. It is self-conducted by the patient and comprises 12 questions that are divided into two subscales: pain and physical function. The Western Ontario and McMaster Universities Arthritis Index (WOMAC) is a self-administered questionnaire to assess pain, function, and stiffness of the knee. The descriptors range from no difficulty (0 point) to extreme difficulties (4 points). The WOMAC Score is a commonly used standardized questionnaire to evaluate the condition of patients with osteoarthritis of the knee and hip. The WOMAC is scored from 0 (best) to 96 (worst). Furthermore, the EuroQol visual analogue scale (EQ-VAS), which measures health-related quality of life, was completed by patients. It is scored from 0 (worst to 100 (best) and quantifies the patient's perception of their health state. It is self-conducted by the patient and has 5 different dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression.

The difference between the OKS, WOMAC, and EQ-VAS obtained pre-operatively and at the one-year follow-up visit defined clinical improvement. The minimum clinically important difference (MCID) for OKS was 5.0, for WOMAC, 10, and for EQ-VAS, 8.0 [12–14].

## Statistical Analysis

In unadjusted analyses, IR and FTR were compared with pre-operative age, sex, BMI, ASA, OKS, WOMAC, and EQ-5D VAS. Binary or categorical comparisons were made via exact chi-squared tests, while continuous comparisons were made via the Wilcoxon rank-sum test. Adjusted multivariable linear regression models were fitted to predict improvements from pre-operative to follow-up in OKS, WOMAC, and EQ-5D VAS vs. rehabilitation protocol, adjusting for age, sex, BMI, ASA, and the pre-operative value of the PROM being analyzed. Fit of linear regression models were assessed via normal quantile-quantile plots of the standardized residuals. Fit of logistic regression models were assessed with the Hosmer and Lemeshow goodness-of-fit test at alpha = 0.05 [15]. SAS software (version 9.4, SAS Institute Inc., Cary, NC, USA) was used for statistical analyses.
### 3. Results

A total of 205 patients participated in the study with available PROMs at 1-year follow-up. Patient pre-operative demographics and knee function were comparable between the FTR and IR groups (Tables 1 and 2).

<table><caption>**Table 2.** Clinical scores in both groups.</caption><thead><tr><th></th><th>Rehabilitation Protocol</th><th>Pre-Operative Mean ± SD</th><th>p-Value</th><th>1-Year Follow-Up Mean ± SD</th><th>p-Value</th><th>Clinical Improvement Δ Mean ± SD</th><th>p-Value</th></tr></thead><tbody><tr><td rowspan="2">OKS</td><td>FTR</td><td>21.9 ± 8.4</td><td rowspan="2">0.111</td><td>38.7 ± 8.8</td><td rowspan="2">&lt;0.001</td><td>16.8 ± 10.6</td><td rowspan="2">0.001</td></tr><tr><td>IR</td><td>19.9 ± 6.6</td><td>33.9 ± 9.9</td><td>14.0 ± 10.7</td></tr><tr><td rowspan="2">WOMAC</td><td>FTR</td><td>47.7 ± 20.1</td><td rowspan="2">0.177</td><td>14.7 ± 15.2</td><td rowspan="2">0.571</td><td>33.2 ± 20.6</td><td rowspan="2">0.063</td></tr><tr><td>IR</td><td>44.8 ± 19.8</td><td>18.3 ± 18.7</td><td>26.5 ± 25.5</td></tr><tr><td rowspan="2">EQ-VAS</td><td>FTR</td><td>66.4 ± 19.6</td><td rowspan="2">&lt;0.001</td><td>78.6 ± 17.6</td><td rowspan="2">0.103</td><td>11.9 ± 24.2</td><td rowspan="2">0.281</td></tr><tr><td>IR</td><td>48.4 ± 17.3</td><td>72.6 ± 22.1</td><td>24.2 ± 26.9</td></tr></tbody></table>

OKS, Oxford Knee Score; WOMAC, Western Ontario and McMaster Universities Arthritis Index; EQ-VAS, EuroQol visual analogue scale; FTR, fast-track rehabilitation; IR, inhouse rehabilitation; *p*-values for clinical improvements are from multivariable models; improvements in WOMAC are shown in the positive direction for comparison.

Patients receiving IR had a significantly lower mean improvement in OKS (14.0 ± 10.7) and a trend of lower improvement in WOMAC (−26.5 ± 25.5) than patients receiving FTR (OKS 16.8 ± 10.6; WOMAC −33.2 ± 20.6), resulting in adjusted models for *p*-values of 0.001 and 0.063, respectively. The EQ-VAS tended to improve more after IR (24.2 ± 26.9) than after FTR (11.9 ± 24.2) (*p* = 0.281) (Table 2 and Figure 1). All differences in follow-up OKS, WOMAC, and EQ-VAS between the FTR and IR groups were below the MCID.

<figure><img src="image_35.png" /><figcaption>**Figure 1.** Improvement in clinical outcome. OKS, Oxford Knee Score; WOMAC, Western Ontario and McMaster Universities Arthritis Index; EQ-VAS, EuroQol visual analogue scale; FTR, fast-track rehabilitation; IR, inhouse rehabilitation; *p*-values for clinical improvements are from multivariable models; improvements in WOMAC are shown in the positive direction for comparison.</figcaption></figure>

### 4. Discussion

The most important finding from the present study was that FTR after TKA did not result in inferior functional improvement when compared to IR. Indeed, patients receiving FTR showed higher improvement in PROMs than patients receiving IR. While outcomes scores at one year were higher in the FTR group regardless of the degree of improvement, this difference was not clinically significant. These results may encourage insurance companies to recommend FTR to their patients, as FTR does not compromise patient outcome.
These findings agree with multiple international studies comparing outpatient and inpatient rehabilitation [4,7]. Prospective and retrospective studies, mainly from outside Europe, demonstrated comparable or even improved function upon utilizing outpatient rehabilitation after TKA, with reduced treatment costs [2,4,16–19]. As the risk of complications seemed not to increase, the conclusion of these studies was predominantly to not recommend IR to patients undergoing uncomplicated TKA. A dissertation conducted at the University of Rostock/Germany comparing outpatient and inpatient rehabilitation was in agreement with these findings [5]. Unfortunately, the dissertation has not yet been published in the medical literature. Because the improvements in PROMs in the present study were adjusted for age, sex, ASA, and the pre-operative value of the corresponding PROM, the observed differences in improvements being influenced by these variables can be ruled out. I.e., irrespective of the patients’ age, sex, ASA, and pre-operatively assessed PROM, the improvement in PROMs is comparable or slightly higher after FTR than IR. While comparison of clinical improvement after FTR or IR can be confounded by a selection bias, i.e., patients receiving FTR could be fitter, healthier, and more motivated to improve than patients receiving IR, the results from this study are not biased by a selection process because both patient groups followed the standard postoperative protocol of the treating hospital [17].

In addition to providing comparable functional recovery, FTR has also been shown to reduce costs [2]. These cost reductions include costs associated with the necessary clinical wound check and staple removal follow-up of patients not receiving IR. Obviously, not providing patients with accommodation, food, and sanitary needs reduces costs. Outpatient rehabilitation also reduces readmission rate after TKA and the risk of peri-prosthetic complications [7,18]. The number of diagnostic tests can be reduced with no negative effect on patient outcome [19]. Because FTR includes a reduced length of hospital stay, it can be expected that costs can be further reduced using this rehabilitation protocol [20].

Because this study favors FTR due to comparable or slightly higher functional improvement one year after TKA, it could motivate carriers of IR facilities to reorient the focus of their rehabilitation regime. One potential reason that patients after FTR achieve higher improvement than after IR is the self-guidance and patient responsibility to regain their independence and function. The self-guidance potentially improves function in two ways. (1) It fosters salutogenesis by focusing the patient towards solving problems after TKA, which include regaining range of motion, mobility, and strength [21]. (2) It prevents externalization of the rehabilitation process, i.e., success or failure to improve is not the physical therapist’s responsibility, it is the patient’s responsibility.

There are some limitations to this study. First, the difference in clinical improvement could be influenced by country-specific pain awareness of patients and their interpretation of functional limitations. Because the pre-operative OKS and WOMAC were not different between the two countries and because the comparison of clinical improvement was normalized to the pre-operative PROM, the country-specific influence on clinical improvement should be minimal. Second, because patients were treated by two different orthopedic surgeon teams in different countries, multiple variables can influence patient clinical improvement besides the rehabilitation protocol. To minimize the influence of other variables, we ensured that surgical technique, component alignment target, and component design were identical between patient groups. Consequently, only a single hospital participated in Germany and in Canada, which could limit the generalizability of the results. Still, these were large teaching hospitals with high volumes, and as such were representative for the comparison of the two different rehabilitation protocols. Third, a cost analysis and comparison between both rehabilitation protocols could not be made, since profoundly different reimbursement policies were used. However, the gained knowledge from this study that functional gains after TKA are not compromised by omitting IR could motivate German insurance carriers, hospital systems, and healthcare authorities to conduct prospective studies analyzing the financial impact of omitting IR after uncomplicated TKA. Fourth, shorter follow-up intervals within the first year after TKA could have provided a more
detailed comparison between FTR and IR [10]. Ideally, this comparison can be performed
in a prospective study within Germany, which will also allow an accurate cost analysis that
includes the financial impact of patient sick leave. Finally, the results from this study only
apply to patients undergoing primary TKA with an ASA of I to III.

**5. Conclusions**

Among adults undergoing primary TKA, the use of IR compared with FTR does not
yield a higher improvement in knee function. These findings do not support IR for this
group of patients with the current rehabilitation regime. This study can provide a thought-
provoking impulse to IR carriers to reorient their focus on rehabilitation after TKA by
fostering patient salutogenesis.

**Author Contributions:** Conceptualization, D.R., A.J.N., B.A.M. and M.R.; methodology, D.R., A.J.N., B.A.M. and M.R.; software, A.J.N. and E.C.S.; validation, D.R., A.J.N., B.A.M., E.C.S. and M.R.; formal analysis, A.J.N. and E.C.S.; investigation, D.R., A.J.N. and E.C.S.; data curation, D.R., A.J.N., E.C.S., B.A.M. and M.R.; writing—original draft preparation, D.R. and A.J.N.; writing—review and editing, D.R., A.J.N., E.C.S., B.A.M. and M.R.; visualization, D.R. and A.J.N.; supervision, B.A.M. and M.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by both the Canadian (The University of British Columbia, Office of Research Ethics, Study ID: H18-02307, 05.09.2018) and German (Ethik-Kommission der Universität Würzburg, Number: 20210925 01, 26.20.2021) Institutional Review Board.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data sets to support the findings of this study are included within the article, including figures and tables. Any other data used to support the findings of this study are available from the corresponding authors upon request.

**Acknowledgments:** The authors thank BaCaTeC for supporting open access publication and Raman Johal at UBC for her help in research coordination.

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

1. Jiang, L.; Rong, J.; Wang, Y.; Hu, F.; Bao, C.; Li, X.; Zhao, Y. The relationship between body mass index and hip osteoarthritis: A systematic review and meta-analysis. *Jt. Bone Spine Rev. Du Rhum.* **2011**, *78*, 150. [CrossRef] [PubMed]

2. Mahomed, N.N.; Davis, A.M.; Hawker, G.; Badley, E.; Davey, J.R.; Syed, K.A.; Coyte, P.C.; Gandhi, R.; Wright, J.G. Inpatient compared with home-based rehabilitation following primary unilateral total hip or knee replacement: A randomized controlled trial. *J. Bone Jt. Surg. Am.* **2008**, *90*, 1673. [CrossRef] [PubMed]

3. Burnett, R.A., III; Serino, J.; Yang, J.; Della Valle, C.J.; Courtney, P.M. National trends in post-acute care costs following total knee arthroplasty from 2007 to 2016. *J. Arthroplast.* **2021**, *36*, 2268. [CrossRef]

4. Naylor, J.M.; Hart, A.; Mittal, R.; Harris, I.; Xuan, W. The value of inpatient rehabilitation after uncomplicated knee arthroplasty: A propensity score analysis. *Med. J. Aust.* **2017**, *207*, 250. [CrossRef] [PubMed]

5. El-Aarid, N. *Effektivität von Stationärer und Ambulanter Rehabilitation bei Patienten Nach Knieendoprothesen-Implantation*; Universität Rostock: Rostock, Germany, 2019.

6. Fussenich, W.; Gerhardt, D.M.; Pauly, T.; Lorenz, F.; Olieslagers, M.; Braun, C.; van Susante, J.L. A comparative health care inventory for primary hip arthroplasty between Germany versus the Netherlands. Is there a downside effect to fast-track surgery with regard to patient satisfaction and functional outcome? *Hip. Int.* **2020**, *30*, 423. [CrossRef] [PubMed]

7. Onggo, J.R.; Onggo, J.D.; De Steiger, R.; Hau, R. The Efficacy and Safety of Inpatient Rehabilitation Compared with Home Discharge After Hip or Knee Arthroplasty: A Meta-Analysis and Systematic Review. *J. Arthroplast.* **2019**, *34*, 1823. [CrossRef]

8. Scott, C.E.H.; Bell, K.R.; Ng, R.T.; MacDonald, D.J.; Patton, J.T.; Burnett, R. Excellent 10-year patient-reported outcomes and survival in a single-radius, cruciate-retaining total knee arthroplasty. *Knee Surg. Sports Traumatol. Arthrosc.* **2019**, *27*, 1106. [CrossRef]

9. Cosendey, K.; Eudier, A.; Fleury, N.; Pereira, L.C.; Favre, J.; Jolles, B.M. Ten-year follow-up of a total knee prosthesis combining multi-radius, ultra-congruency, posterior-stabilization and mobile-bearing insert shows long-lasting clinically relevant improvements in pain, stiffness, function and stability. *Knee Surg. Sports Traumatol. Arthrosc.* **2022**; online ahead of print.
10. Fransen, B.L.; Hoozemans, M.J.; Argelo, K.D.; Keijser, L.; Burger, B.J. Fast-track total knee arthroplasty improved clinical and functional outcome in the first 7 days after surgery: A randomized controlled pilot study with 5-year follow-up. *Arch. Orthop. Trauma Surg.* **2018**, *138*, 1305. [CrossRef] [PubMed]

11. Seetharam, A.; Deckard, E.R.; Ziemba-Davis, M.; Meneghini, R.M. The AAHKS clinical research award: Are minimum two-year patient-reported outcome measures necessary for accurate assessment of patient outcomes after primary total knee arthroplasty? *J. Arthroplast.* **2022**, *37*, S716-S720. [CrossRef] [PubMed]

12. Clement, N.; MacDonald, D.; Simpson, A. The minimal clinically important difference in the Oxford knee score and Short Form 12 score after total knee arthroplasty. *Knee Surg. Sports Traumatol. Arthrosc.* **2014**, *22*, 1933. [CrossRef] [PubMed]

13. Clement, N.D.; Bardgett, M.; Weir, D.; Holland, J.; Gerrand, C.; Deehan, D.J. What is the Minimum Clinically Important Difference for the WOMAC Index After TKA? *Clin. Orthop. Relat. Res.* **2018**, *476*, 2005. [CrossRef] [PubMed]

14. Tsai, A.P.Y.; Hur, S.A.; Wong, A.; Safavi, M.; Assayag, D.; Johannson, K.A.; Morisset, J.; Fell, C.; Fisher, J.H.; Manganas, H.; et al. Minimum important difference of the EQ-5D-5L and EQ-VAS in fibrotic interstitial lung disease. *Thorax* **2021**, *76*, 37. [CrossRef] [PubMed]

15. Hosmer, D.W., Jr.; Lemeshow, S.; Sturdivant, R.X. *Applied Logistic Regression*; John Wiley & Sons: Hoboken, NJ, USA, 2013.

16. Buhagiar, M.A.; Naylor, J.M.; Harris, I.A.; Xuan, W.; Kohler, F.; Wright, R.; Fortunato, R. Effect of Inpatient Rehabilitation vs a Monitored Home-Based Program on Mobility in Patients With Total Knee Arthroplasty: The HIHO Randomized Clinical Trial. *JAMA* **2017**, *317*, 1037. [CrossRef] [PubMed]

17. Chan, H.Y.; Sultana, R.; Yeo, S.J.; Chia, S.L.; Pang, H.N.; Lo, N.N. Comparison of outcome measures from different pathways following total knee arthroplasty. *Singap. Med. J.* **2018**, *59*, 476.

18. Jorgenson, E.S.; Richardson, D.M.; Thomasson, A.M.; Nelson, C.L.; Ibrahim, S.A. Race, Rehabilitation, and 30-Day Readmission After Elective Total Knee Arthroplasty. *Geriatr. Orthop. Surg. Rehabil.* **2015**, *6*, 303. [CrossRef] [PubMed]

19. White, P.B.; Carli, A.V.; Meftah, M.; Ghazi, N.; Alexiades, M.M.; Windsor, R.E.; Ranawat, A.S. Patients Discharged to Inpatient Rehabilitation Facilities Undergo More Diagnostic Interventions with No Improvement in Outcomes. *Orthopedics* **2018**, *41*, E841. [CrossRef] [PubMed]

20. Nöth, U.; Rackwitz, L.; Clarius, M. Herausforderungen der Fast-Track-Endoprothetik in Deutschland. *Der Orthopäde* **2020**, *49*, 334. [CrossRef] [PubMed]

21. Lindström, B.; Eriksson, M. Salutogenesis. *J. Epidemiol. Community Health* **2005**, *59*, 440. [CrossRef] [PubMed]
<figure><img src="image_36.png" /></figure>
<figure><img src="image_37.png" /></figure>

Article

# Individualized 3D-Printed Bone-Anchored Maxillary Protraction Device for Growth Modification in Skeletal Class III Malocclusion

Minji Kim<sup>1,2 ID</sup>, Jingwen Li<sup>1 ID</sup>, Sehyang Kim<sup>1</sup>, Wonho Kim<sup>2</sup>, Sun-Hyun Kim<sup>1 ID</sup>, Sung-Min Lee<sup>3</sup>, Young Long Park<sup>3</sup>, Sook Yang<sup>4 ID</sup> and Jin-Woo Kim<sup>1,3,* ID</sup>

**Citation:** Kim, M.; Li, J.; Kim, S.; Kim, W.; Kim, S.-H.; Lee, S.-M.; Park, Y.L.; Yang, S.; Kim, J.-W. Individualized 3D-Printed Bone-Anchored Maxillary Protraction Device for Growth Modification in Skeletal Class III Malocclusion. *J. Pers. Med. 2021*, *11*, 1087. https://doi.org/10.3390/jpm1111087

Academic Editor: Anne-Marie Caminade

Received: 27 September 2021

Accepted: 23 October 2021

Published: 26 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

<figure><img src="image_38.png" /></figure>

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

**Abstract:** Bone-anchored maxillary protraction (BAMP) is effective for skeletal Class III malocclusion. However, infection, screw and plate loosening, and device failures occur with conventional plates. This pilot prospective study analyzed the feasibility of individualized BAMP using preoperative simulation and 3D titanium printing in patients referred by the orthodontic department for four BAMP miniplates. Preoperative cone beam computed tomography data were analyzed using CAD/CAM software to fabricate the individualized 3D-printed BAMP device. The customized plates were printed using selective laser sintering and inserted onto the bone through an adjunct transfer jig. The accuracy of preoperative simulation and actual placement of the BAMP device were tested by superimposing simulated positioned digital images and postoperative computed tomography data. The growth modification effect depended on superimposition of lateral cephalograms and comparative changes in SNA, SNB, ANB, and Wits. Two male patients were finally included in the study. BAMP decreased the ANB difference (−4.56 to −1.09) and Wits appraisal (−7.52 to −3.26) after 2 years. Normal measurement indices for sagittal and vertical growth indicated successful growth modification. The mean accuracy between preoperative simulation and actual surgery was 0.1081 ± 0.5074 mm. This treatment modality involving preoperative simulation and 3D titanium printing for fabricating and placing customized BAMP devices precisely at planned locations is effective for treating skeletal Class III malocclusion.

**Keywords:** bone-anchored maxillary protraction; skeletal class III malocclusion; 3D printing; growth modification

## 1. Introduction

Skeletal Class III malocclusion is a common orthodontic deformity involving a single or a combination of protrusive mandibles, deficient or retrusive maxilla, protrusive mandibular dentition, and retrusive maxillary dentition, which not only affect a patient's masticatory function but also their esthetic appearance and mental health [1,2]. Prevalence of Skeletal Class III was reported as 7.8% [3], and Class III presents the highest prevalence among orthognathic cases [4].

In growing patients, early intervention for correcting the craniofacial relationship has been used for several decades with significant outcomes [5–7].

It is better to use extraoral anchorage than intraoral anchorage for skeletal growth control in the early intervention for Class III malocclusion to prevent undesirable dental

<footnote n="1">1. Graduate School of Clinical Dentistry, Ewha Womans University, Seoul 07985, Korea; minjikim@ewha.ac.kr (M.K.); jingwen7883@gmail.com (J.L.); shkim0363@gmail.com (S.K.); dhsh0828@gmail.com (S.-H.K.)</footnote>

<footnote n="2">2. Department of Orthodontics, School of Medicine, Ewha Womans University, Seoul 07985, Korea; kimwonho311@gmail.com</footnote>

<footnote n="3">3. Department of Oral and Maxillofacial Surgery, School of Medicine, Ewha Womans University, Seoul 07985, Korea; sm930513@dent.dku.edu (S.-M.L.); 21628@eumc.ac.kr (Y.L.P.)</footnote>

<footnote n="4">4. Cusmedi Co., Ltd., Suwon-si 400815, Korea; syang@cusmedi.com</footnote>

<footnote>* Correspondence: jwkim84@ewha.ac.kr; Tel.: +82-2-2650-2720; Fax: +82-2-2650-2754</footnote>