Comparison of the accuracy of commercial two-point and multi-echo Dixon MRI for quantification of fat in liver, paravertebral muscles, and vertebral bone marrow.

PURPOSE: Excess fat accumulation contributes significantly to metabolic dysfunction and diseases. This study aims to systematically compare the accuracy of commercially available Dixon techniques for quantification of fat fraction in liver, skeletal musculature, and vertebral bone marrow (BM) of healthy individuals, investigating biases and sex-specific influences. METHOD: 100 healthy White individuals (50 women) underwent abdominal MRI using two-point and multi-echo Dixon sequences. Fat fraction (FF), proton density fat fraction (PDFF) and T2* values were calculated for liver, paravertebral muscles (PVM) and vertebral BM (Th8-L5). Agreement and systematic deviations were assessed using linear correlation and Bland-Altman plots. RESULTS: High correlations between FF and PDFF were observed in liver (r = 0.98 for women; r = 0.96 for men), PVM (r = 0.92 for women; r = 0.93 for men) and BM (r = 0.97 for women; r = 0.95 for men). Relative deviations between FF and PDFF in liver (18.92 % for women; 13.32 % for men) and PVM (1.96 % for women; 11.62 % for men) were not significant. Relative deviations in BM were significant (38.13 % for women; 27.62 % for men). Bias correction using linear models reduced discrepancies. T2* times were significantly shorter in BM (8.72 ms for women; 7.26 ms for men) compared to PVM (13.45 ms for women; 13.62 ms for men) and liver (29.47 ms for women; 26.35 ms for men). CONCLUSION: While no significant differences were observed for liver and PVM, systematic errors in BM FF estimation using two-point Dixon imaging were observed. These discrepancies - mainly resulting from organ-specific T2* times - have to be considered when applying two-point Dixon approaches for assessment of fat content. As suitable correction tools, linear models could provide added value in large-scale epidemiological cohort studies. Sex-specific differences in T2* should be considered.

Analysis of type 2 diabetes heterogeneity with a tree-like representation: insights from the prospective German Diabetes Study and the LURIC cohort.

BACKGROUND: Heterogeneity in type 2 diabetes can be represented by a tree-like graph structure by use of reversed graph-embedded dimensionality reduction. We aimed to examine whether this approach can be used to stratify key pathophysiological components and diabetes-related complications during longitudinal follow-up of individuals with recent-onset type 2 diabetes. METHODS: For this cohort analysis, 927 participants aged 18-69 years from the German Diabetes Study (GDS) with recent-onset type 2 diabetes were mapped onto a previously developed two-dimensional tree based on nine simple clinical and laboratory variables, residualised for age and sex. Insulin sensitivity was assessed by a hyperinsulinaemic-euglycaemic clamp, insulin secretion was assessed by intravenous glucose tolerance test, hepatic lipid content was assessed by 1 H magnetic resonance spectroscopy, serum interleukin (IL)-6 and IL-18 were assessed by ELISA, and peripheral and autonomic neuropathy were assessed by functional and clinical measures. Participants were followed up for up to 16 years. We also investigated heart failure and all-cause mortality in 794 individuals with type 2 diabetes undergoing invasive coronary diagnostics from the Ludwigshafen Risk and Cardiovascular Health (LURIC) cohort. FINDINGS: There were gradients of clamp-measured insulin sensitivity (both dimensions: p<0·0001) and insulin secretion (pdim1<0·0001, pdim2=0·00097) across the tree. Individuals in the region with the lowest insulin sensitivity had the highest hepatic lipid content (n=205, pdim1<0·0001, pdim2=0·037), pro-inflammatory biomarkers (IL-6: n=348, pdim1<0·0001, pdim2=0·013; IL-18: n=350, pdim1<0·0001, pdim2=0·38), and elevated cardiovascular risk (nevents=143, pdim1=0·14, pdim2<0·00081), whereas individuals positioned in the branch with the lowest insulin secretion were more prone to require insulin therapy (nevents=85, pdim1=0·032, pdim2=0·12) and had the highest risk of diabetic sensorimotor polyneuropathy (nevents=184, pdim1=0·012, pdim2=0·044) and cardiac autonomic neuropathy (nevents=118, pdim1=0·0094, pdim2=0·06). In the LURIC cohort, all-cause mortality was highest in the tree branch showing insulin resistance (nevents=488, pdim1=0·12, pdim2=0·0032). Significant gradients differentiated individuals having heart failure with preserved ejection fraction from those who had heart failure with reduced ejection fraction. INTERPRETATION: These data define the pathophysiological underpinnings of the tree structure, which has the potential to stratify diabetes-related complications on the basis of routinely available variables and thereby expand the toolbox of precision diabetes diagnosis. FUNDING: German Diabetes Center, German Federal Ministry of Health, Ministry of Culture and Science of the state of North Rhine-Westphalia, German Federal Ministry of Education and Research, German Diabetes Association, German Center for Diabetes Research, European Community, German Research Foundation, and Schmutzler Stiftung.

Precision subclassification of type 2 diabetes: a systematic review.

BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study.

Mechanisms of weight loss-induced remission in people with prediabetes: a post-hoc analysis of the randomised, controlled, multicentre Prediabetes Lifestyle Intervention Study (PLIS).

BACKGROUND: Remission of type 2 diabetes can occur as a result of weight loss and is characterised by liver fat and pancreas fat reduction and recovered insulin secretion. In this analysis, we aimed to investigate the mechanisms of weight loss- induced remission in people with prediabetes. METHODS: In this prespecified post-hoc analysis, weight loss-induced resolution of prediabetes in the randomised, controlled, multicentre Prediabetes Lifestyle Intervention Study (PLIS) was assessed, and the results were validated against participants from the Diabetes Prevention Program (DPP) study. For PLIS, between March 1, 2012, and Aug 31, 2016, participants were recruited from eight clinical study centres (including seven university hospitals) in Germany and randomly assigned to receive either a control intervention, a standard lifestyle intervention (ie, DPP-based intervention), or an intensified lifestyle intervention for 12 months. For DPP, participants were recruited from 23 clinical study centres in the USA between July 31, 1996, and May 18, 1999, and randomly assigned to receive either a standard lifestyle intervention, metformin, or placebo. In both PLIS and DPP, only participants who were randomly assigned to receive lifestyle intervention or placebo and who lost at least 5% of their bodyweight were included in this analysis. Responders were defined as people who returned to normal fasting plasma glucose (FPG; <5·6 mmol/L), normal glucose tolerance (<7·8 mmol/L), and HbA1c less than 39 mmol/mol after 12 months of lifestyle intervention or placebo or control intervention. Non-responders were defined as people who had FPG, 2 h glucose, or HbA1c more than these thresholds. The main outcomes for this analysis were insulin sensitivity, insulin secretion, visceral adipose tissue (VAT), and intrahepatic lipid content (IHL) and were evaluated via linear mixed models. FINDINGS: Of 1160 participants recruited to PLIS, 298 (25·7%) had weight loss of 5% or more of their bodyweight at baseline. 128 (43%) of 298 participants were responders and 170 (57%) were non-responders. Responders were younger than non-responders (mean age 55·6 years [SD 9·9] vs 60·4 years [8·6]; p<0·0001). The DPP validation cohort included 683 participants who lost at least 5% of their bodyweight at baseline. Of these, 132 (19%) were responders and 551 (81%) were non-responders. In PLIS, BMI reduction was similar between responders and non-responders (responders mean at baseline 32·4 kg/m2 [SD 5·6] to mean at 12 months 29·0 kg/m2 [4·9] vs non-responders 32·1 kg/m2 [5·9] to 29·2 kg/m2 [5·4]; p=0·86). However, whole-body insulin sensitivity increased more in responders than in non-responders (mean at baseline 291 mL/[min × m2], SD 60 to mean at 12 months 378 mL/[min × m2], 56 vs 278 mL/[min × m2], 62, to 323 mL/[min × m2], 66; p<0·0001), whereas insulin secretion did not differ within groups over time or between groups (responders mean at baseline 175 pmol/mmol [SD 64] to mean at 12 months 163·7 pmol/mmol [60·6] vs non-responders 158·0 pmol/mmol [55·6] to 154·1 pmol/mmol [56·2]; p=0·46). IHL decreased in both groups, without a difference between groups (responders mean at baseline 10·1% [SD 8·7] to mean at 12 months 3·5% [3·9] vs non-responders 10·3% [8·1] to 4·2% [4·2]; p=0·34); however, VAT decreased more in responders than in non-responders (mean at baseline 6·2 L [SD 2·9] to mean at 12 months 4·1 L [2·3] vs 5·7 L [2·3] to 4·5 L [2·2]; p=0·0003). Responders had a 73% lower risk of developing type 2 diabetes than non-responders in the 2 years after the intervention ended. INTERPRETATION: By contrast to remission of type 2 diabetes, resolution of prediabetes was characterised by an improvement in insulin sensitivity and reduced VAT. Because return to normal glucose regulation (NGR) prevents development of type 2 diabetes, we propose the concept of remission of prediabetes in analogy to type 2 diabetes. We suggest that remission of prediabetes should be the primary therapeutic aim in individuals with prediabetes. FUNDING: German Federal Ministry for Education and Research via the German Center for Diabetes Research; the Ministry of Science, Research and the Arts Baden-Württemberg; the Helmholtz Association and Helmholtz Munich; the Cluster of Excellence Controlling Microbes to Fight Infections; and the German Research Foundation.

Extracellular Matrix Expression in Human Pancreatic Fat Cells of Patients with Normal Glucose Regulation, Prediabetes and Type 2 Diabetes.

Previously, we found that human pancreatic preadipocytes (PPAs) and islets influence each other and that the crosstalk with the fatty liver via the hepatokine fetuin-A/palmitate induces inflammatory responses. Here, we examined whether the mRNA-expression of pancreatic extracellular matrix (ECM)-forming and -degrading components differ in PPAs from individuals with normal glucose regulation (PPAs-NGR), prediabetes (PPAs-PD), and type 2 diabetes (PPAs-T2D), and whether fetuin-A/palmitate impacts ECM-formation/degradation and associated monocyte invasion. Human pancreatic resections were analyzed (immuno)histologically. PPAs were studied for mRNA expression by real-time PCR and protein secretion by Luminex analysis. Furthermore, co-cultures with human islets and monocyte migration assays in Transwell plates were conducted. We found that in comparison with NGR-PPAs, TIMP-2 mRNA levels were lower in PPAs-PD, and TGF-ß1 mRNA levels were higher in PPAs-T2D. Fetuin-A/palmitate reduced fibronectin, decorin, TIMP-1/-2 and TGF-ß1 mRNA levels. Only fibronectin was strongly downregulated by fetuin-A/palmitate independently of the glycemic status. Co-culturing of PPAs with islets increased TIMP-1 mRNA expression in islets. Fetuin-A/palmitate increased MMP-1, usherin and dermatopontin mRNA-levels in co-cultured islets. A transmigration assay showed increased monocyte migration towards PPAs, which was enhanced by fetuin-A/palmitate. This was more pronounced in PPAs-T2D. The expression of distinct ECM components differs in PPAs-PD and PPAs-T2D compared to PPAs-NGR, suggesting that ECM alterations can occur even in mild hyperglycemia. Fetuin-A/palmitate impacts on ECM formation/degradation in PPAs and co-cultured islets. Fetuin-A/palmitate also enhances monocyte migration, a process which might impact on matrix turnover.

Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment.

Among 20 leading global risk factors for years of life lost in 2040, reference forecasts point to three metabolic risks-high blood pressure, high BMI, and high fasting plasma glucose-as being the top risk variables. Building upon these and other risk factors, the concept of metabolic health is attracting much attention in the scientific community. It focuses on the aggregation of important risk factors, which allows the identification of subphenotypes, such as people with metabolically unhealthy normal weight or metabolically healthy obesity, who strongly differ in their risk of cardiometabolic diseases. Since 2018, studies that used anthropometrics, metabolic characteristics, and genetics in the setting of cluster analyses proposed novel metabolic subphenotypes among patients at high risk (eg, those with diabetes). The crucial point now is whether these subphenotyping strategies are superior to established cardiometabolic risk stratification methods regarding the prediction, prevention, and treatment of cardiometabolic diseases. In this Review, we carefully address this point and conclude, firstly, regarding cardiometabolic risk stratification, in the general population both the concept of metabolic health and the cluster approaches are not superior to established risk prediction models. However, both subphenotyping approaches might be informative to improve the prediction of cardiometabolic risk in subgroups of individuals, such as those in different BMI categories or people with diabetes. Secondly, the applicability of the concepts by treating physicians and communication of the cardiometabolic risk with patients is easiest using the concept of metabolic health. Finally, the approaches to identify cardiometabolic risk clusters in particular have provided some evidence that they could be used to allocate individuals to specific pathophysiological risk groups, but whether this allocation is helpful for prevention and treatment still needs to be determined.

Systematic review of precision subclassification of type 2 diabetes.

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort.

This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)-based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of >0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index-related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.

Automated shape-independent assessment of the spatial distribution of proton density fat fraction in vertebral bone marrow.

This work proposes a method for automatic standardized assessment of bone marrow volume and spatial distribution of the proton density fat fraction (PDFF) in vertebral bodies. Intra- and interindividual variability in size and shape of vertebral bodies is a challenge for comparable interindividual evaluation and monitoring of changes in the composition and distribution of bone marrow due to aging and/or intervention. Based on deep learning image segmentation, bone marrow PDFF of single vertebral bodies is mapped to a cylindrical template and corrected for the inclination with respect to the horizontal plane. The proposed technique was applied and tested in a cohort of 60 healthy (30 males, 30 females) individuals. Obtained bone marrow volumes and mean PDFF values are comparable to former manual and (semi-)automatic approaches. Moreover, the proposed method allows shape-independent characterization of the spatial PDFF distribution inside vertebral bodies.

The role of hepatokines in NAFLD.

Non-alcoholic fatty liver disease (NAFLD) is not only a consequence of insulin resistance, but it is also an important cause of insulin resistance and major non-communicable diseases (NCDs). The close relationship of NAFLD with visceral obesity obscures the role of fatty liver from visceral adiposity as the main pathomechanism of insulin resistance and NCDs. To overcome this limitation, in analogy to the concept of adipokines, in 2008 we introduced the term hepatokines to describe the role of fetuin-A in metabolism. Since then, several other hepatokines were tested for their effects on metabolism. Here we address the dysregulation of hepatokines in people with NAFLD. Then, we discuss pathophysiological mechanisms of cardiometabolic diseases specifically related to NAFLD by focusing on hepatokine-related organ crosstalk. Finally, we propose how the determination of major hepatokines and adipokines can be used for pathomechanism-based clustering of insulin resistance in NAFLD and visceral obesity to better implement precision medicine in clinical practice.

Lower Hepatic Fat Is Associated With Improved Insulin Secretion in a High-Risk Prediabetes Subphenotype During Lifestyle Intervention.

The objective of this work was to investigate whether impaired insulin secretion can be restored by lifestyle intervention in specific subphenotypes of prediabetes. We assigned 1,045 participants from the Prediabetes Lifestyle Intervention Study (PLIS) to six recently established prediabetes clusters. Insulin secretion was assessed by a C-peptide-based index derived from oral glucose tolerance tests and modeled from three time points during a 1-year intervention. We also analyzed the change of glycemia, insulin sensitivity, and liver fat. All prediabetes high-risk clusters (cluster 3, 5, and 6) had improved glycemic traits during the lifestyle intervention, whereas insulin secretion only increased in clusters 3 and 5 (P < 0.001); however, high liver fat in cluster 5 was associated with a failure to improve insulin secretion (Pinteraction < 0.001). Thus, interventions to reduce liver fat have the potential to improve insulin secretion in a defined subgroup of prediabetes.

Diabetes and the Prostate: Elevated Fasting Glucose, Insulin Resistance and Higher Levels of Adrenal Steroids in Prostate Cancer.

Although epidemiological studies suggest a lower prostate cancer incidence rate in patients with type 2 diabetes, cancer survival is markedly reduced. Underlying mechanisms that connect the two diseases are still unclear. Potential links between type 2 diabetes and prostate cancer are hallmarks of the metabolic syndrome, such as hyperglycemia and dyslipidemia. Therefore, we explored the systemic metabolism of 103 prostate cancer patients with newly diagnosed and yet untreated prostate cancer compared to 107 healthy controls, who were carefully matched for age and BMI. Here, we report that patients with prostate cancer display higher fasting blood glucose levels and insulin resistance, without changes in insulin secretion. With respect to lipid metabolism, serum triglyceride levels were lower in patients with prostate cancer. In addition, we report increased adrenal steroid biosynthesis in these patients. Our results indicate that higher fasting glucose levels in patients with prostate cancer may be explained at least in part by insulin resistance, due to the enhanced synthesis of adrenal steroids.

Interscapular fat is associated with impaired glucose tolerance and insulin resistance independent of visceral fat mass.

OBJECTIVE: Dysregulated body fat distribution is a major determinant of various diseases. In particular, increased visceral fat mass and ectopic lipids in the liver are linked to metabolic disorders such as insulin resistance and type 2 diabetes. Furthermore, interscapular fat is considered to be a metabolically active fat compartment. METHODS: This study measured interscapular fat mass and investigated its relationship with glucose metabolism in 822 individuals with a wide range of BMI values and different glucose tolerance statuses. Magnetic resonance imaging was used to quantify body fat depots, and an oral glucose tolerance test was performed to determine glucose metabolism. RESULTS: Elevated interscapular fat mass was positively associated with age, BMI, and total body, visceral, and subcutaneous adipose tissue mass. High interscapular fat mass associated with elevated fasting glucose levels, glucose levels at 2 hours during the oral glucose tolerance test, glycated hemoglobin, and insulin resistance, independent of sex, age, and total body and visceral fat mass. CONCLUSIONS: In conclusion, interscapular fat might be a highly specific fat compartment with a potential impact on glucose metabolism and the pathogenesis of diabetes mellitus.

Fat Distribution Patterns and Future Type 2 Diabetes.

Fat accumulation in the liver, pancreas, skeletal muscle, and visceral bed relates to type 2 diabetes (T2D). However, the distribution of fat among these compartments is heterogenous and whether specific distribution patterns indicate high T2D risk is unclear. We therefore investigated fat distribution patterns and their link to future T2D. From 2,168 individuals without diabetes who underwent computed tomography in Japan, this case-cohort study included 658 randomly selected individuals and 146 incident cases of T2D over 6 years of follow-up. Using data-driven analysis (k-means) based on fat content in the liver, pancreas, muscle, and visceral bed, we identified four fat distribution clusters: hepatic steatosis, pancreatic steatosis, trunk myosteatosis, and steatopenia. In comparisons with the steatopenia cluster, the adjusted hazard ratios for incident T2D were 4.02 (95% CI 2.27-7.12) for the hepatic steatosis cluster, 3.38 (1.65-6.91) for the pancreatic steatosis cluster, and 1.95 (1.07-3.54) for the trunk myosteatosis cluster. The clusters were replicated in 319 German individuals without diabetes who underwent MRI and metabolic phenotyping. The distribution of the glucose area under the curve across the four clusters found in Germany was similar to the distribution of T2D risk across the four clusters in Japan. Insulin sensitivity and insulin secretion differed across the four clusters. Thus, we identified patterns of fat distribution with different T2D risks presumably due to differences in insulin sensitivity and insulin secretion.