Subcutaneous Fat

Recently, Chu et al. (21 (link)) performed a GWAS of subcutaneous and ectopic fat depots, as measured by CT and MRI, in a multi-ancestry sample. Since the meta-analysis results are publicly available (https://grasp.nhlbi.nih.gov/FullResults.aspx and Supplementary Material, Information for further details), we took the index SNPs from our WHRadjBMI meta-analyses (combined sample as well as sex-specific), checked for allele consistency, aligned effects to the reference allele and tested for associations with the imaging-based measures of subcutaneous and ectopic fat. We repeated these analyses in men and women separately. The depots investigated in the imaging-based GWAS were pericardial tissue (PAT), PAT adjusted for height and weight, subcutaneous adipose tissue (SAT), SAT Hounsfield units as measured by MRI, visceral adipose tissue (VAT), VAT Hounsfield units, ratio of VAT to SAT and VAT adjusted for BMI.
We calculated Pearson’s r correlations between z-scores in WHRadjBMI (calculated by dividing the SNP beta by the standard error) and SNP z-scores reported in Chu et al. (21 (link)). We evaluated significance of the correlation by performing a t-test (implemented as cor.test() in R). Correlations were considered significant if P-value < 0.05/3 sample groups/9 phenotypes = 1.9 × 10⁻³.

Following the institutional review board approval for the study (number: 119/2019; Muğla Sıtkı Koçman University Ethical Committee), a retrospective cohort analysis was performed using the medical records of patients. For the current study, patient consent is not required. All procedures executed involving human participants were in accordance with the ethical standards of the institutional ethical committee and with the 1964 Helsinki declaration.
A total of 146 patients who applied to the neurosurgery outpatient clinic with a recent abdominal CT (max three months) because of a lower back pain complaint were included in the study. Patients with a previous history of surgery or a vertebral fracture were excluded. After excluded patients, a total of 146 patients were included in the study, of whom 90 were female (61.6%) and 56 were male (38.4%). The mean age of the patients was 51.42±13.91 (20-82) years.
Lumbar vertebra CT scans of all patients were reviewed retrospectively. CT images at the level from L3-L4 intervertebral disc were analyzed for body composition of fat tissue and muscle mass volume through the dedicated CT software (Syngo.via, SOMATOM Definition Flash: Siemens Healthcare, Forchheim, Germany). The L3-L4 level was selected in sagittal reformat CT images with the software (Figure 1).
The density range of -200, -40 HU was selected for the fat density measurement in the cross-section with the "region grooving" application in the angled axial images obtained parallel to the disc plane at this level. First, the fat volume in the whole section was measured (visceral and subcutaneous). Then, only the visceral adipose tissue volume was calculated by drawing borders to exclude subcutaneous adipose tissue (Figure 2). The subcutaneous fat tissue volume was obtained by subtracting the visceral fat tissue volume from the total fat volume (Figure 3).
With the same application, muscle density was selected and paravertebral muscle tissue volume was calculated (bilateral musculus psoas major, musculus quadratus lumborum, musculus iliocostalis, musculus longissimus, musculus multifidus volumes). A Spearman correlation model was used to analyze visceral adiposity, subcutaneous fat, and muscle mass.
In CT images, each intervertebral disc space was evaluated in terms of the presence of osteophytes, loss of disc height, sclerosis in the end plates, and spinal stenosis (spinal canal narrowing under 15 mm AP diameter) to investigate the presence of degeneration. Each level was scored according to the presence of findings, with 1 point for the presence of osteophytes, loss of disc height, sclerosis in the end plates, and spinal stenosis. The total score at all levels (L1-S1) was calculated for each patient.
Statistical analyses were performed using IBM SPSS version 20.0 software (IBM Corp., Armonk, NY). The conformity of the data to normal distribution was assessed using the Shapiro-Wilk test. Normally distributed variables were presented as mean±standard deviation and those not showing normal distribution as median (minimum-maximum) values. Categorical variables were presented as numbers (n) and percentages (%). The Spearman's rank correlation coefficient test was used to determine the correlation between the measured parameters in various vertebral pathologies. Continuous variables were compared using the Mann-Whitney U test. The receiver operating characteristic (ROC) analysis was used to detect the area under the curve (AUC) and define the cutoff values with their sensitivities and specificities of the measurements. An alpha value of p<0.05 was accepted as statistically significant.

We defined two STIR-based radiomic features to be used as an alternative to the conventional textural features of WF1 and WF2. We use these new features as the only covariates in the implementation of ML algorithms to test whether the prediction performance of ML models could be improved over those obtained by the previously described workflows. Firstly, we applied the same segmentation method of FSHD patients on the pre-processed STIR images of each healthy control (HC). In particular, six contiguous HCs slices of mid-calf region were segmented in order to ensure a robust pixel statistics of the grayscale intensity distributions. Then, two reference limits, Upper Limit (UL) and Lower Limit (LL), were defined as follows. Inspired by Dahlqvist et al. (41 (link)), UL was defined for each calf muscle through the extraction of a pixel-wise histogram of signal intensity distribution from all slices. The six muscle-wise UL were set at the mean μ of the associated pixels-intensity distribution added to 2 standard deviation (S.D.) σ:
with i indexing the six calf muscles.
Due to non-uniform fat suppression of STIR sequence, LL was calculated as a representative value of fat signal intensity. Therefore, subcutaneous fat (average thickness at medial level of HCs was about 10.5 mm) was manually drawn in HCs slices to ensure the extraction of LL feature. In particular, from subcutaneous fat ROI of all slices the pixel-wise histogram of signal intensity distribution was extracted. Subsequently, the LL was set as the mode of the distribution. In this way, we could calculate a more realistic fat intensity representative value, limiting the contribution of blood vessels present in the subcutaneous fat, which tend to shift the mean value of the associated distribution toward greater value due to the hyperintesity STIR signal of the blood.
Moreover, the obtained LL and muscle-wise UL coefficients were set as the reference limits to quantify, for every FSHD patient, fat infiltration grade (FFG) and muscle edema grade (MEG) by expressing the number of pixels below LL and above UL as a percentage of the total pixels in each calf muscle. FFG and MEG were then used as covariates in ML models to predict FF and wT2, respectively. Particularly, muscle-wise FFG and MEG values were separately collected into datasets according to calf muscles and neuromuscular biomarker and used as input for machine learning algorithms.
As described in WF1, we implemented both parametric and non-parametric models using the k-folds cross validation as a resampling approach. WF3 brought the advantage of testing the prediction accuracy of neuromuscular biomarkers with two features that were easy to compute by means of a stand-alone Python routine, without going through commercial texture software and any dimensionality reduction techniques.