Introduction

Risk stratification strategies in patients with metabolic diseases are urgently needed for the selection of optimal lifestyle interventions. Pancreatic fat content has been shown to be associated with metabolic syndrome and type 2 diabetes mellitus [1]. Therefore, MRI-based metabolic phenotyping quantifying and characterizing not only different fat depots but also the pancreatic fat content has been suggested as a methodology to improve risk stratification [2]. Proton density fat fraction (PDFF) mapping based on a multi-echo gradient echo acquisition is the method of choice for non-invasive pancreatic fat quantification [3]. However, little is known about the relations between pancreas PDFF and PDFF in adipose tissue and their change during weight loss in people with obesity. The purpose of this study was to evaluate how the magnetic resonance imaging (MRI)-based PDFF of the pancreas correlates to adipose tissue PDFF and evolves after an 8-week weight loss intervention.

Methods

We recruited 64 persons with obesity (35 female, mean age 42.8 years) from the lifestyle intervention study (LION study [4]) to undergo an MRI scan of the abdomen/pelvis on a 3T scanner (Elition, Philips Healthcare). Of those, 22 participants underwent a follow-up MRI scan after an 8-week calorie-restriction (low caloric formula-based diet). For PDFF and volume measurements of adipose tissue, a 6-echo multi-echo gradient echo sequence with bipolar gradients was used in four stacks, covering the abdomen/pelvis from the liver dome to the femoral heads: TR = 7 ms, TE1 = 1.14 ms, ΔTE = 0.8, flip angle = 3°, bandwidth = 2367 Hz/pixel, 132x180x19 acquisition matrix size, FOV = 400x543x144 mm³, 3x3x6 mm³ acquisition voxel size, acceleration factor R = 3.5 and reconstructed using Compressed SENSE. For the pancreas an additional 6-echo multi-echo gradient echo sequence was performed: TR = 8.6 ms, TE1 = 1.19 ms, ΔTE = 1.2, flip angle = 3°, bandwidth = 1034 Hz/pixel, 152x199x40 acquisition matrix size, FOV = 300x400x120 mm³, 2x2x3 mm³ acquisition voxel size, acceleration factor R = 6 and reconstructed using Compressed SENSE. PDFF maps were generated using the online complex-based fat quantification algorithm, accounting for known confounding factors including the presence of multiple fat peaks, a single T2* correction and phase errors. The whole pancreas was segmented manually in the resulting water map avoiding larger vessels using ITKsnap (version 3.8.0). The visceral and subcutaneous adipose tissue (VAT and SAT) were segmented automatically using a custom-built post-processing algorithm (https://github.com/BMRRgroup/vatsatseg) and segmentations were corrected manually if necessary. VAT and SAT volumes and VAT, SAT and pancreas PDFF were extracted. Body weight was measured in light clothing before each MRI scan using MPD 250K100M (Kern and Sohn, Germany). Wilcoxon test and paired samples t-test were used to test for changes between baseline and follow-up (for non-normally and normally distributed data, respectively). Spearman Rank correlation coefficient and partial correlation with body weight as covariate were used to evaluate associations between the parameters.

Results

At baseline, median body weight was 99.85 kg, median PDFF values were as follows: pancreas PDFF 12.0 %, SAT PDFF 90.8 %, VAT PDFF 86.5 %. Mean SAT volume was 15.33 L and mean VAT volume 4.28 L. In those participants with follow-ups, body weight decreased from 100.74 kg to 88.97 kg, pancreas PDFF decreased from 12.0 to 9.6 %, SAT PDFF from 91.1 to 89.1 %, VAT PDFF from 86.7 to 83.3 %, SAT volume from 15.48 to 11.98 L and VAT volume from 4.11 to 2.94 L (all p<0.01) (Figure 1). Pancreas PDFF significantly correlated with body weight, VAT PDFF and volume at baseline (ρ=0.3, p=0.02; ρ=0.64, p<0.01 and ρ=0.68, p<0.01) (Figure 2). At follow-up, PDFF of pancreas and VAT still showed a tendency to correlate (ρ=0.44, p=0.05) and pancreas PDFF remained significantly correlated with body weight and VAT volume (ρ=0.53, p=0.02 and ρ=0.66, p<0.01). With body weight as covariate, the correlation between pancreas PDFF and VAT PDFF and volume remained significant at baseline (r=0.42 and 0.51, both p<0.01) and pancreas PDFF and VAT volume correlated at follow-up (r=0.49, p=0.03). No significant correlations could be found between pancreas PDFF and SAT PDFF or volumes.

Discussion

The present study demonstrates that after an 8-week formula-based weight loss intervention, not only body weight and PDFF and volume of SAT and VAT decrease but also pancreas PDFF. Furthermore, pancreatic PDFF correlates with VAT measurements both at baseline and after weight loss. VAT is considered as crucial fat depot regarding metabolic risk [5]. Therefore, the observed correlations of VAT measurements with pancreatic PDFF further corroborate the potential of pancreas PDFF as imaging marker for metabolic phenotyping to improve risk stratification in persons living with obesity.

Conclusion

Pancreas PDFF should be taken into account in metabolic phenotyping for metabolic risk stratification.

Acknowledgements

This study is funded by the German Federal Ministry of Education and Research (BMBF, grant number: 01EA1709) within the framework of the Junior Research Group for Personalized Nutrition & eHealth (PeNut) of the enable Nutrition Cluster. Further, the present work was supported by the German Research Foundation (project number 450799851). The authors also acknowledge research support from Philips Healthcare.

References

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