The relationship between genetically-driven liver fat and coronary heart disease (CHD) remains unclear. ApoB-containing lipoproteins are known causal factors for CHD and may explain this ...relationship.
We conducted a genome-wide association study (GWAS) in the UK Biobank to identify genetic variants associated with liver fat. We then investigated the effects that these genetic variants had on both apoB-containing lipoproteins and CHD. Using Mendelian Randomization (MR) analyses, we examined if the relationship between genetically-driven liver fat and CHD could be attributed to its effect on apoB-containing lipoproteins. We found 25 independent liver-fat associated single-nucleotide polymorphisms (SNPs) with differing effects on lipoprotein metabolism. The SNPs were classified into three groups/clusters. The first cluster (N = 3 SNPs) displayed lipoprotein-raising effects. The second cluster (N = 12 SNPs) displayed neutral effects on lipoproteins and the third cluster (N = 10 SNPs) displayed lipoprotein-lowering effects. For every 1% higher liver fat, the first cluster showed an increased risk of CHD (OR = 1.157 95% CI: 1.108–1.208). The second cluster showed a non-significant effect on CHD (OR = 0.988 95% CI: 0.965–1.012, whereas the third cluster showed a protective effect of increased liver fat on CHD (OR = 0.942 95% CI: 0.897–0.989). When adjusting for apoB, the risk for CHD became null.
Here, we identify 25 liver-fat associated SNPs. We find that SNPs that increase, decrease or have neutral effects on apoB-containing lipoproteins show increased, decreased or neutral effects on CHD, respectively. Therefore, the relationship between genetically-driven liver fat and CHD is mediated by the causal effect of apoB.
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•Liver fat-related genetic variants may or may not raise coronary heart disease risk.•Previous studies have shown different results using different genetic variants.•The effect on apoB-containing lipoproteins may explain previous results.•Here, we were able to directly account for the possible effect on plasma apoB.•We found that the effect on coronary heart disease risk is fully explained by apoB.
Human muscles are tailored towards ATP synthesis. When exercising at high work rates muscles convert glucose to lactate, which is less nutrient efficient than respiration. There is hence a trade-off ...between endurance and power. Metabolic models have been developed to study how limited catalytic capacity of enzymes affects ATP synthesis. Here we integrate an enzyme-constrained metabolic model with proteomics data from muscle fibers. We find that ATP synthesis is constrained by several enzymes. A metabolic bypass of mitochondrial complex I is found to increase the ATP synthesis rate per gram of protein compared to full respiration. To test if this metabolic mode occurs in vivo, we conduct a high resolved incremental exercise tests for five subjects. Their gas exchange at different work rates is accurately reproduced by a whole-body metabolic model incorporating complex I bypass. The study therefore shows how proteome allocation influences metabolism during high intensity exercise.
Abdominal obesity is associated with a number of important metabolic abnormalities including liver steatosis, insulin resistance and an atherogenic lipoprotein profile (termed dyslipidemia). The ...purpose of this review is to highlight recent progress in understanding the pathogenesis of this dyslipidemia.
Recent results from kinetic studies using stable isotopes indicate that the hypertriglyceridemia associated with abdominal obesity stems from dual mechanisms: (1) enhanced secretion of triglyceride-rich lipoproteins and (2) impaired clearance of these lipoproteins. The over-secretion of large triglyceride-rich VLDLs from the liver is linked to hepatic steatosis and increased visceral adiposity. The impaired clearance of triglyceride-rich lipoproteins is linked to increased levels of apolipoprotein C-III, a key regulator of triglyceride metabolism.
Elucidation of the pathogenesis of the atherogenic dyslipidemia in abdominal obesity combined with the development of novel treatments based on apolipoprotein C-III may in the future lead to better prevention, diagnosis and treatment of the atherogenic dyslipidemia in abdominal obesity.
Non-alcoholic fatty liver disease (NAFLD) is associated with increased secretion of apoB-containing lipoproteins and increased risk of coronary heart disease (CHD). ApoB-containing lipoproteins ...include low-density lipoproteins (LDLs) and triglyceride-rich lipoproteins (TRLs); and since both LDLs and TRLs are causally related to CHD, they may mediate a portion of the increased risk of atherosclerosis seen in people with NAFLD. In a cohort of 4161 middle aged men and women, we performed mediation analysis in order to quantify the mediating effect of apoB-containing lipoproteins in the relationship between liver fat and atherosclerosis-as measured by coronary artery calcium score (CACS). We found plasma apoB to mediate 17.6% (95% CI 11-24) of the association between liver fat and CACS. Plasma triglycerides and TRL-cholesterol (both proximate measures of TRL particles) mediated 22.3% (95% CI 11-34) and 21.6% (95% CI 10-33) of the association respectively; whereas LDL-cholesterol mediated 5.4% (95% CI 2.0-9.4). In multivariable models, the mediating effect of TRL-cholesterol and plasma triglycerides showed, again, a higher degree of mediation than LDL-cholesterol, corroborating the results seen in the univariable models. In summary, we find around 20% of the association between liver fat and CACS to be mediated by apoB-containing lipoproteins. In addition, we find that TRLs mediate the majority of this effect whereas LDLs mediate a smaller effect. These results explain part of the observed CAD-risk burden for people with NAFLD and further suggest that TRL-lowering may be particularly beneficial to mitigate NAFLD-associated coronary artery disease risk.
Abstract
Aims
The strength of the relationship of triglyceride-rich lipoproteins (TRL) with risk of coronary heart disease (CHD) compared with low-density lipoprotein (LDL) is yet to be resolved.
...Methods and results
Single-nucleotide polymorphisms (SNPs) associated with TRL/remnant cholesterol (TRL/remnant-C) and LDL cholesterol (LDL-C) were identified in the UK Biobank population. In a multivariable Mendelian randomization analysis, TRL/remnant-C was strongly and independently associated with CHD in a model adjusted for apolipoprotein B (apoB). Likewise, in a multivariable model, TRL/remnant-C and LDL-C also exhibited independent associations with CHD with odds ratios per 1 mmol/L higher cholesterol of 2.59 95% confidence interval (CI): 1.99–3.36 and 1.37 95% CI: 1.27–1.48, respectively. To examine the per-particle atherogenicity of TRL/remnants and LDL, SNPs were categorized into two clusters with differing effects on TRL/remnant-C and LDL-C. Cluster 1 contained SNPs in genes related to receptor-mediated lipoprotein removal that affected LDL-C more than TRL/remnant-C, whereas cluster 2 contained SNPs in genes related to lipolysis that had a much greater effect on TRL/remnant-C. The CHD odds ratio per standard deviation (Sd) higher apoB for cluster 2 (with the higher TRL/remnant to LDL ratio) was 1.76 (95% CI: 1.58–1.96), which was significantly greater than the CHD odds ratio per Sd higher apoB in cluster 1 1.33 (95% CI: 1.26–1.40). A concordant result was obtained by using polygenic scores for each cluster to relate apoB to CHD risk.
Conclusion
Distinct SNP clusters appear to impact differentially on remnant particles and LDL. Our findings are consistent with TRL/remnants having a substantially greater atherogenicity per particle than LDL.
Structured Graphical Abstract
Structured Graphical Abstract
Cluster-based SNP analysis showing higher per particle atherogenicity for triglyceride-rich lipoproteins/remnants than for low density lipoproteins.
Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, ...the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.
Lipoprotein(a) (Lp(a)) is recognized as a causal factor for coronary heart disease (CHD) but its atherogenicity relative to that of low-density lipoprotein (LDL) on a per-particle basis is ...indeterminate.
The authors addressed this issue in a genetic analysis based on the fact that Lp(a) and LDL both contain 1 apolipoprotein B (apoB) per particle.
Genome-wide association studies using the UK Biobank population identified 2 clusters of single nucleotide polymorphisms: one comprising 107 variants linked to Lp(a) mass concentration, the other with 143 variants linked to LDL concentration. In these Lp(a) and LDL clusters, the relationship of genetically predicted variation in apoB with CHD risk was assessed.
The Mendelian randomization-derived OR for CHD for a 50 nmol/L higher Lp(a)-apoB was 1.28 (95% CI: 1.24-1.33) compared with 1.04 (95% CI: 1.03-1.05) for the same increment in LDL-apoB. Likewise, use of polygenic scores to rank subjects according to difference in Lp(a)-apoB vs difference in LDL-apoB revealed a greater HR for CHD per 50 nmol/L apoB for the Lp(a) cluster (1.47; 95% CI: 1.36-1.58) compared with the LDL cluster (1.04; 95% CI: 1.02-1.05). From these data, we estimate that the atherogenicity of Lp(a) is approximately 6-fold (point estimate of 6.6; 95% CI: 5.1-8.8) greater than that of LDL on a per-particle basis.
We conclude that the atherogenicity of Lp(a) (CHD risk quotient per unit increase in particle number) is substantially greater than that of LDL. Therefore, Lp(a) represents a key target for drug-based intervention in a significant proportion of the at-risk population.
The volume of epicardial adipose tissue (EATV) is increased in type-2 diabetes (T2D), while its attenuation (EATA) appears to be decreased. Similar patterns have been suggested in pre-diabetes, but ...data is scarce. In both pre-diabetes and T2D, any independent role of EATV and EATA in disease development remains to be proven, a task complicated by their substantial co-variation with other anthropometrics, e.g. BMI, waist circumference, and abdominal visceral adipose tissue (VAT). EATV and EATA was quantified in computed tomography (CT) images in a population study (n = 1948) using an automatic technique. Data was available on BMI, waist circumference, abdominal visceral adipose tissue (VAT) area, insulin resistance (IR) and glucose tolerance, the latter ranging from normal (NGT), over pre-diabetes (impaired fasting glucose IFG, n = 414 impaired glucose tolerance IGT, n = 321 and their combination CGI, n = 128), to T2D. EATV was increased in pre-diabetes, T2D and IR in univariable analyses and when adjusting for BMI, however not when adjusting for waist or VAT. EATA was reduced in pre-diabetes, T2D and IR in univariable analyses and when adjusting for BMI and waist, however not when adjusting for VAT. Adjustment for other co-variates had little influence on the results. In conclusion, EATV is increased and EATA reduced in pre-diabetes, T2D and IR, however, significant co-variation with other anthropometrics, especially VAT, obscures their function in disease development. The current results do not exclude a pathophysiological role of epicardial fat, but future studies need to adjust for anthropometrics, or focus on the microenvironment within the pericardial sac.
Aims/hypothesis
This study explored the hypothesis that significant abnormalities in the metabolism of intestinally derived lipoproteins are present in individuals with type 2 diabetes on statin ...therapy. These abnormalities may contribute to residual CVD risk.
Methods
To investigate the kinetics of ApoB-48- and ApoB-100-containing lipoproteins, we performed a secondary analysis of 11 overweight/obese individuals with type 2 diabetes who were treated with lifestyle counselling and on a stable dose of metformin who were from an earlier clinical study, and compared these with 11 control participants frequency-matched for age, BMI and sex. Participants in both groups were on a similar statin regimen during the study. Stable isotope tracers were used to determine the kinetics of the following in response to a standard fat-rich meal: (1) apolipoprotein (Apo)B-48 in chylomicrons and VLDL; (2) ApoB-100 in VLDL, intermediate-density lipoprotein (IDL) and LDL; and (3) triglyceride (TG) in VLDL.
Results
The fasting lipid profile did not differ significantly between the two groups. Compared with control participants, in individuals with type 2 diabetes, chylomicron TG and ApoB-48 levels exhibited an approximately twofold higher response to the fat-rich meal, and a twofold higher increment was observed in ApoB-48 particles in the VLDL
1
and VLDL
2
density ranges (all
p
< 0.05). Again comparing control participants with individuals with type 2 diabetes, in the latter, total ApoB-48 production was 25% higher (556 ± 57 vs 446 ± 57 mg/day;
p
< 0.001), conversion (fractional transfer rate) of chylomicrons to VLDL was around 40% lower (35 ± 25 vs 82 ± 58 pools/day;
p
=0.034) and direct clearance of chylomicrons was 5.6-fold higher (5.6 ± 2.2 vs 1.0 ± 1.8 pools/day;
p
< 0.001). During the postprandial period, ApoB-48 particles accounted for a higher proportion of total VLDL in individuals with type 2 diabetes (44%) compared with control participants (25%), and these ApoB-48 VLDL particles exhibited a fivefold longer residence time in the circulation (
p
< 0.01). No between-group differences were seen in the kinetics of ApoB-100 and TG in VLDL, or in LDL ApoB-100 production, pool size and clearance rate. As compared with control participants, the IDL ApoB-100 pool in individuals with type 2 diabetes was higher due to increased conversion from VLDL
2
.
Conclusions/interpretation
Abnormalities in the metabolism of intestinally derived ApoB-48-containing lipoproteins in individuals with type 2 diabetes on statins may help to explain the residual risk of CVD and may be suitable targets for interventions.
Trial registration
ClinicalTrials.gov NCT02948777.
Graphical Abstract
Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. ...Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.
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