Constraints in estimating the proton density fat fraction

The genetically predicted causal associations between circulating 3-hydroxybutyrate levels and malignant neoplasms: A pan-cancer Mendelian randomization study

OBJECTIVE

The ketogenic diet or exogenous supplementation with 3-hydroxybutyrate (3HB) is progressively gaining recognition as a valuable therapeutic or health intervention strategy. However, the effects of 3HB on cancers have been inconsistent in previous studies. This study aimed to comprehensively investigate the causal effects of circulating 3HB levels on 120 cancer phenotypes, and explore the 3HB mediation effect between liver fat accumulation and cancers.

METHODS

Univariate Mendelian randomization (UVMR) was used in this study to investigate the causal impact of circulating 3HB levels on cancers. We conducted meta-analyses for 3HB-cancer associations sourced from different exposure data. In multivariate MR(MVMR), the body mass index, alcohol frequency and diabetes were included as covariates to investigate the independent effect of 3HB on cancer risk. Additionally, utilizing mediation MR analysis, we checked the potential mediating role of 3HB in the association between liver fat and cancer.

RESULTS

Integrating findings from UVMR and MVMR, we observed that elevated circulating 3HB levels were associated with reduced risk of developing diffuse large B-cell lymphoma(DLBCL) (OR[95%CI] = 0.28[0.14-0.57] p = 3.92e-04), biliary malignancies (OR[95%CI] = 0.30[0.15-0.60], p = 7.67e-04), hepatocellular carcinoma(HCC) (OR[95%CI] = 0.25[0.09-0.71], p = 9.33e-03), primary lymphoid and hematopoietic malignancies (OR[95%CI] = 0.76[0.58-0.99], p = 0.045). Further UVMR analysis revealed that an increase in the percent liver fat was associated with reduced 3HB levels (Beta[95%CI] = -0.073[-0.122∼-0.024], p = 0.0034) and enhanced susceptibility to HCC (OR[95%CI] = 13.9[9.76-19.79], p = 3.14e-48), biliary malignancies (OR[95%CI] = 4.04[3.22-5.07], p = 1.64e-33), nasopharyngeal cancer (OR[95%CI] = 3.26[1.10-9.67], p = 0.03), and primary lymphoid and hematopoietic malignancies (OR[95%CI] = 1.27[1.13-1.44], p = 1.04e-4). Furthermore, 3HB fully mediated the effect of liver fat on susceptibility to DLBCL (OR[95%CI] = 1.076[1.01-1.15], p = 0.034).

CONCLUSIONS

Circulating 3HB is associated with a reduced susceptibility to developing DLBCL, HCC, biliary malignancies, and primary lymphoid and hematopoietic malignancies. The impaired ketogenesis induced by metabolic-dysfunction associated fatty liver disease (MAFLD) contributes to risk of DLBCL.

Trajectory correction enables free-running chemical shift encoded imaging for accurate cardiac proton-density fat fraction quantification at 3T

BACKGROUND

Metabolic diseases can negatively alter epicardial fat accumulation and composition, which can be probed using quantitative cardiac chemical shift encoded (CSE) cardiovascular magnetic resonance (CMR) by mapping proton-density fat fraction (PDFF). To obtain motion-resolved high-resolution PDFF maps, we proposed a free-running cardiac CSE-CMR framework at 3T. To employ faster bipolar readout gradients, a correction for gradient imperfections was added using the gradient impulse response function (GIRF) and evaluated on intermediate images and PDFF quantification.

METHODS

Ten minutes free-running cardiac 3D radial CSE-CMR acquisitions were compared in vitro and in vivo at 3T. Monopolar and bipolar readout gradient schemes provided 8 echoes (TE1/ΔTE = 1.16/1.96 ms) and 13 echoes (TE1/ΔTE = 1.12/1.07 ms), respectively. Bipolar-gradient free-running cardiac fat and water images and PDFF maps were reconstructed with or without GIRF correction. PDFF values were evaluated in silico, in vitro on a fat/water phantom, and in vivo in 10 healthy volunteers and 3 diabetic patients.

RESULTS

In monopolar mode, fat-water swaps were demonstrated in silico and confirmed in vitro. Using bipolar readout gradients, PDFF quantification was reliable and accurate with GIRF correction with a mean bias of 0.03% in silico and 0.36% in vitro while it suffered from artifacts without correction, leading to a PDFF bias of 4.9% in vitro and swaps in vivo. Using bipolar readout gradients, in vivo PDFF of epicardial adipose tissue was significantly lower compared to subcutaneous fat (80.4 ± 7.1% vs 92.5 ± 4.3%, P < 0.0001).

CONCLUSIONS

Aiming for an accurate PDFF quantification, high-resolution free-running cardiac CSE-MRI imaging proved to benefit from bipolar echoes with k-space trajectory correction at 3T. This free-breathing acquisition framework enables to investigate epicardial adipose tissue PDFF in metabolic diseases.

Genetic Evidence for Distinct Biological Mechanisms That Link Adiposity to Type 2 Diabetes: Toward Precision Medicine

We aimed to unravel the mechanisms connecting adiposity to type 2 diabetes. We used MR-Clust to cluster independent genetic variants associated with body fat percentage (388 variants) and BMI (540 variants) based on their impact on type 2 diabetes. We identified five clusters of adiposity-increasing alleles associated with higher type 2 diabetes risk (unfavorable adiposity) and three clusters associated with lower risk (favorable adiposity). We then characterized each cluster based on various biomarkers, metabolites, and MRI-based measures of fat distribution and muscle quality. Analyzing the metabolic signatures of these clusters revealed two primary mechanisms connecting higher adiposity to reduced type 2 diabetes risk. The first involves higher adiposity in subcutaneous tissues (abdomen and thigh), lower liver fat, improved insulin sensitivity, and decreased risk of cardiometabolic diseases and diabetes complications. The second mechanism is characterized by increased body size and enhanced muscle quality, with no impact on cardiometabolic outcomes. Furthermore, our findings unveil diverse mechanisms linking higher adiposity to higher disease risk, such as cholesterol pathways or inflammation. These results reinforce the existence of adiposity-related mechanisms that may act as protective factors against type 2 diabetes and its complications, especially when accompanied by reduced ectopic liver fat.

ARTICLE HIGHLIGHTS

Comparison of Vendor-Independent Software Tools for Liver Proton Density Fat Fraction Estimation at 1.5 T

(1) Background: Open-source software tools are available to estimate proton density fat fraction (PDFF). (2) Methods: We compared four algorithms: complex-based with graph cut (GC), magnitude-based (MAG), magnitude-only estimation with Rician noise modeling (MAG-R), and multi-scale quadratic pseudo-Boolean optimization with graph cut (QPBO). The accuracy and reliability of the methods were evaluated in phantoms with known fat/water ratios and a patient cohort with various grades (S0-S3) of steatosis. Image acquisitions were performed at 1.5 Tesla (T). (3) Results: The PDFF estimates showed a nearly perfect correlation (Pearson r = 0.999, p < 0.001) and inter-rater agreement (ICC = from 0.995 to 0.999, p < 0.001) with true fat fractions. The absolute bias was low with all methods (0.001-1%), and an ANCOVA detected no significant difference between the algorithms in vitro. The agreement across the methods was very good in the patient cohort (ICC = 0.891, p < 0.001). However, MAG estimates (-2.30% ± 6.11%, p = 0.005) were lower than MAG-R. The field inhomogeneity artifacts were most frequent in MAG-R (70%) and GC (39%) and absent in QPBO images. (4) Conclusions: The tested algorithms all accurately estimate PDFF in vitro. Meanwhile, QPBO is the least affected by field inhomogeneity artifacts in vivo.

Comparative review of algorithms and methods for chemical-shift-encoded quantitative fat-water imaging

PURPOSE

To propose a standardized comparison between state-of-the-art open-source fat-water separation algorithms for proton density fat fraction (PDFF) andR 2 * $$ {R}_2^{\ast } $$ quantification using an open-source multi-language toolbox.

METHODS

Eight recent open-source fat-water separation algorithms were compared in silico, in vitro, and in vivo. Multi-echo data were synthesized with varying fat-fractions, B0 off-resonance, SNR and TEs. Experimental evaluation was conducted using calibrated fat-water phantoms acquired at 3T and multi-site open-source phantoms data. Algorithms' performances were observed on challenging in vivo datasets at 3T. Finally, reconstruction algorithms were investigated with different fat spectra to evaluate the importance of the fat model.

RESULTS

In silico and in vitro results proved most algorithms to be not sensitive to fat-water swaps andB 0 $$ {\mathrm{B}}_0 $$ offsets with five or more echoes. However, two methods remained inaccurate even with seven echoes and SNR = 50, and two other algorithms' precision depended on the echo spacing scheme (p < 0.05). The remaining four algorithms provided reliable performances with limits of agreement under 2% for PDFF and 6 s-1 forR 2 * $$ {R}_2^{\ast } $$ . The choice of fat spectrum model influenced quantification of PDFF mildly (<2% bias) and ofR 2 * $$ {R}_2^{\ast } $$ more severely, with errors up to 20 s-1 .

CONCLUSION

In promoting standardized comparisons of MRI-based fat and iron quantification using chemical-shift encoded multi-echo methods, this benchmark work has revealed some discrepancies between recent approaches for PDFF andR 2 * $$ {R}_2^{\ast } $$ mapping. Explicit choices and parameterization of the fat-water algorithm appear necessary for reproducibility. This open-source toolbox further enables the user to optimize acquisition parameters by predicting algorithms' margins of errors.

Improving the accuracy of fatty liver index to reflect liver fat content with predictive regression modelling

Background

The fatty liver index (FLI) is frequently used as a non-invasive clinical marker for research, prognostic and diagnostic purposes. It is also used to stratify individuals with hepatic steatosis such as non-alcoholic fatty liver disease (NAFLD), and to detect the presence of type 2 diabetes or cardiovascular disease. The FLI is calculated using a combination of anthropometric and blood biochemical variables; however, it reportedly excludes 8.5-16.7% of individuals with NAFLD. Moreover, the FLI cannot quantitatively predict liver fat, which might otherwise render an improved diagnosis and assessment of fatty liver, particularly in longitudinal studies. We propose FLI+ using predictive regression modelling, an improved index reflecting liver fat content that integrates 12 routinely-measured variables, including the original FLI.

Methods and findings

We evaluated FLI+ on a dataset from the UK Biobank containing 28,796 individual estimates of proton density fat fraction derived from magnetic resonance imaging across normal to severe levels and interpolated to align with the original FLI range. The results obtained for FLI+ outperform the original FLI by delivering a lower mean absolute error by approximately 47%, a lower standard deviation by approximately 20%, and an increased adjusted R² statistic by approximately 49%, reflecting a more accurate representation of liver fat content.

Conclusions

Our proposed model predicting FLI+ has the potential to improve diagnosis and provide a more accurate stratification than FLI between absent, mild, moderate and severe levels of hepatic steatosis.

Tissue attenuation imaging and tissue scatter imaging for quantitative ultrasound evaluation of hepatic steatosis

We aimed to assess the feasibility of ultrasound-based tissue attenuation imaging (TAI) and tissue scatter distribution imaging (TSI) for quantification of liver steatosis in patients with nonalcoholic fatty liver disease (NAFLD). We prospectively enrolled 101 participants with suspected NAFLD. The TAI and TSI measurements of the liver were performed with a Samsung RS85 Prestige ultrasound system. Based on the magnetic resonance imaging proton density fat fraction (MRI-PDFF), patients were divided into ≤5%, 5-10%, and ≥10% of MRI-PDFF groups. We determined the correlation between TAI, TSI, and MRI-PDFF and used multiple linear regression analysis to identify any association with clinical variables. The diagnostic performance of TAI, TSI was determined based on the area under the receiver operating characteristic curve (AUC). The intraclass correlation coefficient (ICC) was calculated to assess interobserver reliability. Both TAI (rs = 0.78, P < .001) and TSI (rs = 0.68, P < .001) showed significant correlation with MRI-PDFF. TAI overperformed TSI in the detection of both ≥5% MRI-PDFF (AUC = 0.89 vs 0.87) and ≥10% (AUC = 0.93 vs 0.86). MRI-PDFF proved to be an independent predictor of TAI (β = 1.03; P < .001), while both MRI-PDFF (β = 50.9; P < .001) and liver stiffness (β = -0.86; P < .001) were independent predictors of TSI. Interobserver analysis showed excellent reproducibility of TAI (ICC = 0.95) and moderate reproducibility of TSI (ICC = 0.73). TAI and TSI could be used successfully to diagnose and estimate the severity of hepatic steatosis in routine clinical practice.

Analysis of MRI-derived spleen iron in the UK Biobank identifies genetic variation linked to iron homeostasis and hemolysis

The spleen plays a key role in iron homeostasis. It is the largest filter of the blood and performs iron reuptake from old or damaged erythrocytes. Despite this role, spleen iron concentration has not been measured in a large, population-based cohort. In this study, we quantify spleen iron in 41,764 participants of the UK Biobank by using magnetic resonance imaging and provide a reference range for spleen iron in an unselected population. Through genome-wide association study, we identify associations between spleen iron and regulatory variation at two hereditary spherocytosis genes, ANK1 and SPTA1. Spherocytosis-causing coding mutations in these genes are associated with lower reticulocyte volume and increased reticulocyte percentage, while these common alleles are associated with increased expression of ANK1 and SPTA1 in blood and with larger reticulocyte volume and reduced reticulocyte percentage. As genetic modifiers, these common alleles may explain mild spherocytosis phenotypes that have been observed clinically. Our genetic study also identifies a signal that co-localizes with a splicing quantitative trait locus for MS4A7, and we show this gene is abundantly expressed in the spleen and in macrophages. The combination of deep learning and efficient image processing enables non-invasive measurement of spleen iron and, in turn, characterization of genetic factors related to the lytic phase of the erythrocyte life cycle and iron reuptake in the spleen.

Precision MRI phenotyping enables detection of small changes in body composition for longitudinal cohorts

Longitudinal studies provide unique insights into the impact of environmental factors and lifespan issues on health and disease. Here we investigate changes in body composition in 3088 free-living participants, part of the UK Biobank in-depth imaging study. All participants underwent neck-to-knee MRI scans at the first imaging visit and after approximately two years (second imaging visit). Image-derived phenotypes for each participant were extracted using a fully-automated image processing pipeline, including volumes of several tissues and organs: liver, pancreas, spleen, kidneys, total skeletal muscle, iliopsoas muscle, visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue, as well as fat and iron content in liver, pancreas and spleen. Overall, no significant changes were observed in BMI, body weight, or waist circumference over the scanning interval, despite some large individual changes. A significant decrease in grip strength was observed, coupled to small, but statistically significant, decrease in all skeletal muscle measurements. Significant increases in VAT and intermuscular fat in the thighs were also detected in the absence of changes in BMI, waist circumference and ectopic-fat deposition. Adjusting for disease status at the first imaging visit did not have an additional impact on the changes observed. In summary, we show that even after a relatively short period of time significant changes in body composition can take place, probably reflecting the obesogenic environment currently inhabited by most of the general population in the United Kingdom.

Addressing concomitant gradient phase errors in time-interleaved chemical shift-encoded MRI fat fraction and R2 * mapping with a pass-specific phase fitting method

PURPOSE

Concomitant gradients induce phase errors that increase quadratically with distance from isocenter. This work proposes a complex-based fitting method that addresses concomitant gradient phase errors in chemical shift encoded (CSE) MRI estimation of proton density fat fraction (PDFF) and R₂ ^* through joint estimation of pass-specific phase terms. This method is applicable to time-interleaved multi-echo gradient-echo acquisitions (i.e., multi-pass acquisitions) and does not require prior knowledge of gradient waveforms typically needed to address concomitant gradient phase errors.

THEORY AND METHODS

A CSE-MRI spoiled gradient echo signal model, with pass-specific phase terms, is introduced for non-linear least squares estimation of PDFF and R₂ ^* in the presence of concomitant gradient phase errors. Cramér-Rao lower bound analysis was used to determine noise performance tradeoffs of the proposed fitting method, which was then validated in both phantom and in vivo experiments.

RESULTS

The proposed fitting method removed PDFF and R₂ ^* estimation errors up to 12% and 10 s^-1 , respectively, at ±12 cm off isocenter (S/I) in a water phantom. In healthy volunteers, PDFF and R₂ ^* bias was reduced by ~10% (12 cm off-isocenter) and ~30 s^-1 (16 cm off-isocenter), respectively. An evaluation in 29 clinical liver datasets demonstrated reduced PDFF bias and variability (8.4% improvement in the coefficient of variation), even with the imaging volume centered at isocenter.

CONCLUSION

Concomitant gradient induced phase errors in multi-pass CSE-MRI acquisitions can result in PDFF and R₂ ^* estimation biases away from isocenter. The proposed fitting method enables accurate PDFF and R₂ ^* quantification in the presence of concomitant gradient phase errors without knowledge of imaging gradient waveforms.

Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease

To understand the causal role of adiposity and ectopic fat in type 2 diabetes and cardiometabolic diseases, we aimed to identify two clusters of adiposity genetic variants: one with "adverse" metabolic effects (UFA) and the other with, paradoxically, "favorable" metabolic effects (FA). We performed a multivariate genome-wide association study using body fat percentage and metabolic biomarkers from UK Biobank and identified 38 UFA and 36 FA variants. Adiposity-increasing alleles were associated with an adverse metabolic profile, higher risk of disease, higher CRP, and higher fat in subcutaneous and visceral adipose tissue, liver, and pancreas for UFA and a favorable metabolic profile, lower risk of disease, higher CRP and higher subcutaneous adipose tissue but lower liver fat for FA. We detected no sexual dimorphism. The Mendelian randomization studies provided evidence for a risk-increasing effect of UFA and protective effect of FA for type 2 diabetes, heart disease, hypertension, stroke, nonalcoholic fatty liver disease, and polycystic ovary syndrome. FA is distinct from UFA by its association with lower liver fat and protection from cardiometabolic diseases; it was not associated with visceral or pancreatic fat. Understanding the difference in FA and UFA may lead to new insights in preventing, predicting, and treating cardiometabolic diseases.

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8-44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.

Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy

MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.