Using a strategy to quantify subcutaneous fat in the lower extremity that was based on connectivity analysis, we found significant differences between subcutaneous adipose tissue in the mid-calf and mid-thigh sections of FPLD patients compared to normal controls. We found significantly reduced lower extremity subcutaneous adipose tissue in a subject with FPLD2 than in a subject with FPLD3. Specifically, no subcutaneous adipose tissue could be quantified in the calf of the FPLD2 patient compared to 19.2 ± 1.7% subcutaneous adipose tissue in FPLD3 (P < 0.0001). Similarly, the percent subcutaneous adipose tissue in the thigh was 24.3 ± 3.7% and 34.4 ± 2.5% (P < 0.0001), for the FPLD2 and FPLD3 patients respectively.
Current clinical assessment of adipose tissue distribution in common obesity and metabolic syndrome and subjects with FPLD2 and FPLD3 is still in its infancy. Also, BIA failed to capture differences in percent fat in lower extremities in FPLD2 vs
FPLD3 perhaps because so much fat was infiltrated into muscle in FPLD2. In contrast, MRI adipose connectedness maps and semi-automated subcutaneous adipose tissue quantification with very high resolution and reproducibility, captured traits that could be compared statistically, confirming the subtle clinical differences [3
This semi-automated method involved a Connected Threshold Grower tool which specified inclusion of only adipose tissue connected to the initial subcutaneous seed point. Based on this pilot study of FPLD patients, we observed very high intra- and inter-observer correlation values: r > 0.99 and >0.98, respectively. In addition to its reproducibility, the described method yields results quickly and accurately, with minimal user intervention. The method was limited by including only connected infiltrated adipose tissue. However, given the imprecise definition of subcutaneous adipose tissue in extremities, we elected to include the connected infiltrated adipose tissue in our calculations, again since this would require no user judgment and/or intervention, thus reducing another potential source of analytic variation. An additional limitation inherent in the ImageJ software, which does not affect reproducibility but affects image dynamic, is that of the 16-bit to 8-bit change to the image stacks prior to analysis. This reduction in image dynamic, which reduces resolution, is a common setback in medical image processing where similar general-purpose software libraries are used. Future development of the software to utilize original raw images would be advantageous in maintaining image integrity and reflecting more accurate analysis data acquired from quantification.
Evaluating FPLD patients theoretically allowed for assessment of the lower limits of resolution of the method; however, the method appeared insensitive for calf adipose measurements in FPLD2, since there was no subcutaneous fat according to the definition specified in the quantification methodology. Future application of this quantification method may include quantification of both thigh and calf depots for "garden variety" obesity, metabolic syndrome or diabetes. This approach might also be applicable to quantify metabolically important substrata of fat [11
We recognize that this study was limited due to the small subject numbers from whom subcutaneous adipose tissue values were extracted. Acquisition of such values from a larger number of patients with both FPLD subtypes would verify the likely results observed here. Furthermore, controls were not ideally matched for age and BMI: while the FPLD2 patient had a similar BMI as the young control individual, unmeasured and uncontrolled factors related to age might have further contributed to variation in subcutaneous adipose tissue. Expanding the sample size in future studies would clearly be helpful in this regard.
The whole body scans suggested that this method can be adapted for other fat depots or bodily organs. However, widespread application would depend on development of standards with respect to regions surveyed, anatomical landmarks, number of measurements, etc – similar to the consensus standards agreed upon for carotid intima-media thickness measurements using ultrasound. Also, intramuscular fat is distributed either in intra- or inter-myocellular depots; which could be more specifically evaluated using proton magnetic resonance spectroscopy (MRS) and/or fat selective MRI [12
]. Such regional distribution could be an additional MRI analyte that could be considered together with other intermediate traits in subjects with FPLD or even common metabolic syndrome. Furthermore, it is possible to obtain carbon-13 nuclear magnetic resonance (NMR) spectra of human muscle glycogen in vivo
in diabetic patients [15
], which has helped understand the pathogenesis of insulin resistance, metabolic syndrome and type 2 diabetes. Quantification of fat depots using MRI and appropriate image analysis software could provide complementary analytes for research and perhaps eventually for the diagnosis and monitoring of interventions.