Structural remodeling of the lumbar multifidus, thoracolumbar fascia and lateral abdominal wall perimuscular connective tissues: A search for its potential determinants
Notice bibliographique
Résumé
Recently remodeling of lumbar soft tissues has received increased research attention. However, the major determinants that influence remodeling need to be elucidated in order to understand the impact of different rehabilitation modalities on tissue remodeling. The main aim of this study was to explore the between-subject variance of different measures of lumbar soft tissues quantified with rehabilitative ultrasound imaging (RUSI). RUSI measures (n = 8) were collected from 30 subjects without and 34 patients with LBP: (1) lumbar multifidus (LM) echogenicity (fatty infiltration/fibrosis) at three vertebral levels (L3/L4, L4/L5 and L5/S1) (n = 3); (2) posterior layer thickness of the thoracolumbar fascia (n = 1); and (3) thickness of the fasciae surrounding the external oblique (EO), internal oblique (IO), and transversus abdominis (TrA) (n = 4). Forward stepwise multivariate regression modeling was conducted with these RUSI measures as dependent variables, using the following independent variables as potential determinants: age, sex, the presence of LBP, body size/composition characteristics (height, weight, trunk length, subcutaneous tissue thickness over the abdominal, and LM muscles), trunk muscle function (or activation) as determined with the percent thickness change of LM, EO, IO, and TrA muscles during a standardized effort (RUSI measures), and physical activity level during sport and leisure activities as estimated with a self-report questionnaire. Two or three statistically significant predictors (or determinants) were selected in the regression model of each RUSI measure (n = 8 models), accounting for 26-64% of their total variance. The subcutaneous tissue thickness on the back accounted for 15-30% variance of LM echogenicity measures and thoracolumbar fascia thickness while the subcutaneous tissue thickness over the abdominals accounted for up to 42% variance of the fascia separating the subcutaneous adipose tissues and the EO muscle. The thickness of IO at rest accounted for 13-21% variance of all investigated abdominal fasciae except the fascia separating the subcutaneous adipose tissue and EO. Pain status accounted for 13-18% variance of the anterior and posterior fasciae of the TrA. Age accounted for 11-14% variance of LM echogenicity at all investigated vertebral levels while sex accounted for 15-21% variance of LM echogenicity at L3/L4 and fascia separating subcutaneous adipose tissue and EO muscle. The function (or activation) of EO and LM at L3/L4 accounted for 8-11% variance of the thoracolumbar fascia and fascia separating TrA and intra-abdominal content (TrA posterior fascia), respectively. Finally, the physical activity level during sport activities accounted for 7% variance of the fascia separating the subcutaneous adipose tissues and the EO muscle. These findings suggest that determinants other than body size characteristics may impact the remodeling of lumbar soft tissues, more importantly the subcutaneous adipose tissue deposits (thickness RUSI measures), which are associated with ectopic fat deposition in the LM and in the fasciae that are more closely positioned to the surface. While age, sex, and pain status explain some variability, modifiable factors such as physical activity level as well as trunk muscle thickness and function were involved. Overall, these results suggest that rehabilitation can potentially impact tissue remodeling, particularly in terms of intramuscular and perimuscular adipose tissues.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».