| 1. |
- Björnson, Elias, 1988, et al.
(författare)
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Lipoprotein(a) Is Markedly More Atherogenic Than LDL: An Apolipoprotein B-Based Genetic Analysis
- 2024
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Ingår i: Journal of the American College of Cardiology. - 0735-1097 .- 1558-3597. ; 83:3, s. 385-395
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Tidskriftsartikel (refereegranskat)abstract
- Background: 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. Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.
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| 2. |
- Björnson, Elias, 1988, et al.
(författare)
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The relationship between genetic liver fat and coronary heart disease is explained by apoB-containing lipoproteins
- 2024
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Ingår i: ATHEROSCLEROSIS. - 0021-9150 .- 1879-1484. ; 388
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Tidskriftsartikel (refereegranskat)abstract
- Background: 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. Methods and Results: 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. Conclusions: 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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| 3. |
- Royer, Patrick, et al.
(författare)
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Large-scale plasma proteomics in the UK Biobank modestly improves prediction of major cardiovascular events in a population without previous cardiovascular disease
- 2024
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Ingår i: EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY. - 2047-4873 .- 2047-4881.
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Tidskriftsartikel (refereegranskat)abstract
- [ ' A i m s I m p r o v e d i d e n t i f i c a t i o n o f i n d i v i d u a l s a t h i g h r i s k o f d e v e l o p i n g c a r d i o v a s c u l a r d i s e a s e w o u l d e n a b l e t a r g e t e d i n t e r v e n t i o n s a n d p o t e n t i a l l y l e a d t o r e d u c t i o n s i n m o r t a l i t y a n d m o r b i d i t y . O u r a i m w a s t o d e t e r m i n e w h e t h e r u s e o f l a r g e - s c a l e p r o t e o m i c s i m p r o v e s p r e d i c t i o n o f c a r d i o v a s c u l a r e v e n t s b e y o n d t r a d i t i o n a l r i s k f a c t o r s ( T R F s ) . M e t h o d s a n d r e s u l t s U s i n g p r o x i m i t y e x t e n s i o n a s s a y s , 2 9 1 9 p l a s m a p r o t e i n s w e r e m e a s u r e d i n 3 8 3 8 0 p a r t i c i p a n t s o f t h e U K B i o b a n k . B o t h d a t a - a n d l i t e r a t u r e - b a s e d f e a t u r e s e l e c t i o n a n d t r a i n e d m o d e l s u s i n g e x t r e m e g r a d i e n t b o o s t i n g m a c h i n e l e a r n i n g w e r e u s e d t o p r e d i c t r i s k o f m a j o r c a r d i o v a s c u l a r e v e n t s ( M A C E s : f a t a l a n d n o n - f a t a l m y o c a r d i a l i n f a r c t i o n , s t r o k e , a n d c o r o n a r y a r t e r y r e v a s c u l a r i z a t i o n ) d u r i n g a 1 0 - y e a r f o l l o w - u p . A r e a u n d e r t h e c u r v e ( A U C ) a n d n e t r e c l a s s i f i c a t i o n i n d e x ( N R I ) w e r e u s e d t o e v a l u a t e t h e a d d i t i v e v a l u e o f s e l e c t e d p r o t e i n p a n e l s t o M A C E p r e d i c t i o n b y S y s t e m a t i c C O r o n a r y R i s k E v a l u a t i o n 2 ( S C O R E 2 ) o r t h e 1 0 T R F s u s e d i n S C O R E 2 . S C O R E 2 a n d S C O R E 2 r e f i t t e d t o U K B i o b a n k d a t a p r e d i c t e d M A C E w i t h A U C s o f 0 . 7 4 0 a n d 0 . 7 4 9 , r e s p e c t i v e l y . D a t a - d r i v e n s e l e c t i o n i d e n t i f i e d 1 1 4 p r o t e i n s o f g r e a t e s t r e l e v a n c e f o r p r e d i c t i o n . P r e d i c t i o n o f M A C E w a s n o t i m p r o v e d b y u s i n g t h e s e p r o t e i n s a l o n e ( A U C o f 0 . 7 5 8 ) b u t w a s s i g n i f i c a n t l y i m p r o v e d b y c o m b i n i n g t h e s e p r o t e i n s w i t h S C O R E 2 o r t h e 1 0 T R F s ( A U C = 0 . 7 7 1 , P < 0 0 1 , N R I = 0 . 1 4 0 , a n d A U C = 0 . 7 6 7 , P = 0 . 0 3 , N R I 0 . 0 5 3 , r e s p e c t i v e l y ) . L i t e r a t u r e - b a s e d p r o t e i n s e l e c t i o n ( 1 1 3 p r o t e i n s f r o m f i v e p r e v i o u s s t u d i e s ) a l s o i m p r o v e d r i s k p r e d i c t i o n b e y o n d T R F s w h i l e a r a n d o m s e l e c t i o n o f 1 1 4 p r o t e i n s d i d n o t . C o n c l u s i o n L a r g e - s c a l e p l a s m a p r o t e o m i c s w i t h d a t a - d r i v e n a n d l i t e r a t u r e - b a s e d p r o t e i n s e l e c t i o n m o d e s t l y i m p r o v e s p r e d i c t i o n o f f u t u r e M A C E b e y o n d T R F s . L a y s u m m a r y T h e r i s k o f h a v i n g a m y o c a r d i a l i n f a r c t i o n o r s t r o k e i s u s u a l l y a s s e s s e d b y c l i n i c a l s c o r e s i n c l u d i n g t r a d i t i o n a l r i s k f a c t o r s f o r c a r d i o v a s c u l a r d i s e a s e . T h e d e v e l o p m e n t o f n e w t e c h n o l o g i e s e n a b l e s t h e r a p i d m e a s u r e m e n t o f a n i n c r e a s i n g n u m b e r o f b l o o d p r o t e i n s . I n t h i s s t u d y , w e a p p l i e d m a c h i n e l e a r n i n g t e c h n i q u e s i n a U K - b a s e d c o h o r t o f 3 8 3 8 0 p a r t i c i p a n t s w i t h 2 9 1 9 b l o o d p r o t e i n s m e a s u r e d . W e o b t a i n e d a s e t o f 1 1 4 p r o t e i n s t h a t i m p r o v e d t h e p r e d i c t i o n o f t h e 1 0 - y e a r r i s k o f m a j o r c a r d i o v a s c u l a r e v e n t w h e n a d d e d t o t r a d i t i o n a l r i s k f a c t o r s . I m p r o v e m e n t s w e r e a l s o a c h i e v e d u s i n g a s e t o f 1 1 3 p r o t e i n s f o u n d i n p r e v i o u s s t u d i e s . H o w e v e r , t h e m a g n i t u d e o f t h e s e i m p r o v e m e n t s w a s r e l a t i v e l y l o w a n d t h e c l i n i c a l u t i l i t y o f c o m b i n i n g t h e s e p r o t e i n s w i t h t r a d i t i o n a l r i s k f a c t o r s i n p r i m a r y p r e v e n t i o n w i l l h a v e t o b e f u r t h e r i n v e s t i g a t e d . ' , ' [ G R A P H I C S ] ' , ' . ' ]
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| 4. |
- Royer, Patrick, et al.
(författare)
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Plasma proteomics for prediction of subclinical coronary artery calcifications in primary prevention
- 2024
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Ingår i: American Heart Journal. - : Elsevier BV. - 0002-8703 .- 1097-6744. ; 271, s. 55-67
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Tidskriftsartikel (refereegranskat)abstract
- Background and aims: Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors. Methods: Using proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS. Results: The best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors. Conclusions: A plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.
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