Age- and Level-Dependence of Fatty Infiltration in Lumbar Paravertebral Muscles of Healthy Volunteers

Crawford, Rebecca; Filli, Lukas; Elliott, James M.; Nanz, Daniel; Fischer, Michael A.; Marcon, Magda; Ulbrich, Erika J.

doi:10.3174/ajnr.a4596

Cited by 194 publications

(220 citation statements)

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“…Although none of the effect moderators affected our main outcome, our study agreed with the existing evidence about the correlation between fat infiltration in LMM and age [35, 36], gender [17, 36], duration of pain [8] and BMI [15, 19]. …”

Section: Discussionsupporting

confidence: 90%

Correlation between lumbar dysfunction and fat infiltration in lumbar multifidus muscles in patients with low back pain

Hildebrandt¹,

Fankhauser²,

Meichtry

et al. 2017

BMC Musculoskelet Disord

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BackgroundLumbar multifidus muscles (LMM) are important for spinal motion and stability. Low back pain (LBP) is often associated with fat infiltration in LMM. An increasing fat infiltration of LMM may lead to lumbar dysfunction. The purpose of this study was to investigate whether there is a correlation between the severity of lumbar dysfunction and the severity of fat infiltration of LMM.MethodsIn a cross-sectional study, 42 patients with acute or chronic LBP were recruited. Their MRI findings were visually rated and graded using three criteria for fat accumulation in LMM: Grade 0 (0–10%), Grade 1 (10–50%) and Grade 2 (>50%). Lumbar sagittal range of motion, dynamic upright and seated posture control, sagittal movement control, body awareness and self-assessed functional disability were measured to determine the patients’ low back dysfunction.ResultsThe main result of this study was that increased severity of fat infiltration in the lumbar multifidus muscles correlated significantly with decreased range of motion of lumbar flexion (p = 0.032). No significant correlation was found between the severity of fat infiltration in LMM and impaired movement control, posture control, body awareness or self-assessed functional disability.ConclusionThis is the first study investigating the relationship between the severity of fat infiltration in LMM and the severity of lumbar dysfunction. The results of this study will contribute to the understanding of the mechanisms leading to fat infiltration of LMM and its relation to spinal function. Further studies should investigate whether specific treatment strategies are effective in reducing or preventing fat infiltration of LMM.

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Section: Discussionsupporting

confidence: 90%

Correlation between lumbar dysfunction and fat infiltration in lumbar multifidus muscles in patients with low back pain

Hildebrandt¹,

Fankhauser²,

Meichtry

et al. 2017

BMC Musculoskelet Disord

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“…It is acknowledged that acquiring such data is time-consuming, even using semi-automated or automated programmes. By way of potential compromise toward a time-efficient capture of lumbar paravertebral MFI on the basis of volume, Crawford et al [5] have shown that the fat content at L4 best represents that of the entire lumbar region in healthy participants. Measuring multiple slices at this level alone may present an effective option in busy clinical radiology environments, although further data are required to support the validity and reliability of the technique.…”

Section: Discussionmentioning

confidence: 99%

“…cross sectional area and/or volume alone) in the assessment of muscle degeneration [14–17]. However, inconsistent associations are also reported [18] and confounded by normative age-related changes [5, 7, 19], degenerative features of the vertebrae or discs [9, 19, 20], and spinal curvature [21, 22]. …”

Section: Introductionmentioning

confidence: 99%

“…Gray-scale signal threshold semi-automatically determined for each region based on user selected small region of muscleMuscle volume =total volume – fat volumeBhadresha et al, 2016 [19]2 raters ( n = 20); inter-rater ICC = 0.33–0.76, Cronbach’s alpha = 0.59–0.91T2 FSEI slice per level at IVD L3-4, L4-5, L5-S1MF, ES, PSVisual inspection18×27 (1 cm apart) grid applied to image. By visual inspection: # of grid points touching fat vs. muscle countedMuscle to Fat Ratio=Muscle/#fat pixelsCrawford et al, 2016 [5]No2-point DIXON (3D fast-field echo T1) whole bodyEvery 3 rd slice L1-L5 (with interpolation to full volume)MF, ESSemiautomatic segmentation with linear interpolation (Myrian Intrasense, Paris, France)Semi-automated ROI from water to fat imageFat signal fraction (FSF) =(Signalfat/[Signalwater + Signalfat])*100D’Hooge et al, 2012 [10]NoT1 FSE1 slice per level at L3 superior endplate, L4 superior endplate, L4 inferior endplateMF, ES, PSImageJ manual traceAutomated based on pixel intensity gray-scale thresholdMFI =(signal intensity of “lean muscle”)/(signal intensity of user defined fat ROI)Fortin et al, 2014 [13]1 rater; intra-rater: ICC = 0.90 –0.96)T21 slice per level at IVD L3-4 and L5-S1MF, ES, MF + ESImageJ manual trace following fascial bordersUser selected pixel intensity gray-scale threshold selected from 4–6 sample ROIs within visible ‘lean muscle’FCSA =(CSAFat)/(CSATotal);Proportion estimate of muscle fat content = Average signal intensity of Total ROIHebert et al, 2014 [18]1 rater; intra-rater ( n = 30) ICC (3,1) = 0.93, Bias = −0.70, 95% LOA = −8.11–6.72T1 SE (0.2 Tesla)1 slice per level at L4 and L5MFManual traceCustom MatLab script; threshold set from midpoint between histogram peaks for ‘fat and muscle’% IMAT =CSAFat/CSATotal(IMAT = intramuscular adipose tissue)Hu et al, 2011 [54]3 raters ( n = 29); intra-rater ICC (3,1) for FCSA = 0.832–0.847, for T2 SI = 0.926–0.957; inter-rater CC for FCSA = 0.858–0.894, for T2 SI = 0.891–0.923T2 FSE1 slice per level at IVD L3-4, L4-5, L5-S1MF, ESPACS workstation manual trace: lean muscle CSADrawn avoiding visible fat;Fat% - following outer perimeterPACS embedded ROI and gray-scale histogram software calculated from mean T2 signal intensityLean muscle FCSA =Manually traced Muscle CSAFat Infiltration =T2 signal intensityKjaer et al, 2007 [8]2 raters ( n = 50); intra-rater k = 0.86;inter-rater k = 0.58T1 SE (0.2 Tesla)1 slice per level at IVD L3-4, L4-5, L5-S1...…”

Section: Introductionmentioning

confidence: 99%

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Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging: a proposed method for the lumbar spine with anatomical cross-reference

Crawford

Cornwall

Abbott

et al. 2017

BMC Musculoskelet Disord

100

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BackgroundThere is increasing interest in paravertebral muscle composition as a potential prognostic and diagnostic element in lumbar spine health. As a consequence, it is becoming popular to use magnetic resonance imaging (MRI) to examine muscle volume and fatty infiltration in lumbar paravertebral muscles to assess both age-related change and their clinical relevance in low back pain (LBP). A variety of imaging methods exist for both measuring key variables (fat, muscle) and for defining regions of interest, making pooled comparisons between studies difficult and rendering post-production analysis of MRIs confusing. We therefore propose and define a method as an option for use as a standardized MRI procedure for measuring lumbar paravertebral muscle composition, and to stimulate discussion towards establishing consensus for the analysis of skeletal muscle composition amongst clinician researchers.MethodIn this descriptive methodological study we explain our method by providing an examination of regional lumbar morphology, followed by a detailed description of the proposed technique. Identification of paravertebral muscles and vertebral anatomy includes axial E12 sheet-plastinates from cadaveric material, combined with a series of axial MRIs that encompass sequencing commonly used for investigations of muscle quality (fat-water DIXON, T1-, and T2-weighted) to illustrate regional morphology; these images are shown for L1 and L4 levels to highlight differences in regional morphology. The method for defining regions of interest (ROI) for multifidus (MF), and erector spinae (ES) is then described.ResultsOur method for defining ROIs for lumbar paravertebral muscles on axial MRIs is outlined and discussed in relation to existing literature. The method provides a foundation for standardising the quantification of muscle quality that particularly centres on examining fatty infiltration and composition. We provide recommendations relating to imaging parameters that should additionally inform a priori decisions when planning studies examining lumbar muscle tissues with MRI.ConclusionsWe intend this method to provide a platform towards developing and delivering meaningful comparisons between MRI data on lumbar paravertebral muscle quality.

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“…16,17 With respect to physical function, evidence suggests that muscle quality, i.e. intramuscular fat, may be more important than size.…”

mentioning

confidence: 99%

Multifidi Muscle Characteristics and Physical Function Among Older Adults With and Without Chronic Low Back Pain

Sions

Coyle

Velasco

et al. 2017

Archives of Physical Medicine and Rehabilitation

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Objective The purpose of this study was to determine if multifidi size and/or intramuscular fat were associated with self-reported and performance-based physical function in older adults with and without chronic LBP. Design Case-control study Setting Individuals participated in a standardized evaluation in a clinical laboratory and underwent magnetic resonance imaging (MRI) of the lumbar spine at a nearby facility. Patient Sample A volunteer sample of 106 community-dwelling older adults, aged 60-85 years, with (n=57) and without (n=49) chronic LBP were included in this secondary data analysis. Intervention Average right-left, L5 multifidi relative, i.e. total, cross-sectional area (rCSA); muscle-fat infiltration index (MFI), i.e. a measure of intramuscular fat; and relative muscle cross-sectional area (rmCSA), i.e. total CSA minus intramuscular fat CSA, were determined from MRIs. Linear regression modeling was performed with physical function measures as the dependent variables. Age, sex, and body mass index were entered as covariates. The main effects of L5 multifidi MFI and rmCSA, as well as their interaction with group assignment, were compared as independent variables. Main Outcome Measures Short Form-36 Health Survey Physical Functioning subscale (SF-36 PFS), Timed Up and Go (TUG), gait speed, and fast stair descent performance. Results Interaction terms between L5 multifidi MFI and group assignment were found to be significant contributors to the variance explained in all physical function measures (p≤.012). Neither the main effect, nor the interaction with group assignment for L5 multifidi rmCSA, significantly contributed to the variance explained in any of the physical function measures (p>.012). Conclusions Among older adults with chronic LBP of at least moderate intensity, L5 multifidi muscle composition, but not size, may help to explain physical function.

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Age- and Level-Dependence of Fatty Infiltration in Lumbar Paravertebral Muscles of Healthy Volunteers

Cited by 194 publications

References 40 publications

Correlation between lumbar dysfunction and fat infiltration in lumbar multifidus muscles in patients with low back pain

Correlation between lumbar dysfunction and fat infiltration in lumbar multifidus muscles in patients with low back pain

Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging: a proposed method for the lumbar spine with anatomical cross-reference

Multifidi Muscle Characteristics and Physical Function Among Older Adults With and Without Chronic Low Back Pain

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