Differential DNA methylation in familial hypercholesterolemia

Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment

Familial hypercholesterolemia (FH) poses a global health challenge due to high incidence rates and underdiagnosis, leading to increased risks of early-onset atherosclerosis and cardiovascular diseases. Early detection and treatment of FH is critical in reducing the risk of cardiovascular events and improving the long-term outcomes and quality of life for affected individuals and their families. Traditional therapeutic approaches revolve around lipid-lowering interventions, yet challenges persist, particularly in accurate and timely diagnosis. The current diagnostic landscape heavily relies on genetic testing of specific LDL-C metabolism genes, often limited to specialized centers. This constraint has led to the adoption of alternative clinical scores for FH diagnosis. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) present promising solutions to these diagnostic challenges. This review explores the intricacies of FH, highlighting the challenges that are encountered in the diagnosis and management of the disorder. The revolutionary potential of ML, particularly in large-scale population screening, is highlighted. Applications of ML in FH screening, diagnosis, and risk stratification are discussed, showcasing its ability to outperform traditional criteria. However, challenges and ethical considerations, including algorithmic stability, data quality, privacy, and consent issues, are crucial areas that require attention. The review concludes by emphasizing the significant promise of AI and ML in FH management while underscoring the need for ethical and practical vigilance to ensure responsible and effective integration into healthcare practices.

Methylation status of LDLR, PCSK9 and LDLRAP1 is associated with cardiovascular events in familial hypercholesterolemia

Aim: Methylation of LDLR, PCSK9 and LDLRAP1 CpG sites was assessed in patients with familial hypercholesterolemia (FH). Methods: DNA methylation of was analyzed by pyrosequencing in 131 FH patients and 23 normolipidemic (NL) subjects. Results: LDLR, PCSK9 and LDLRP1 methylation was similar between FH patients positive (MD) and negative (non-MD) for pathogenic variants in FH-related genes. LDLR and PCSK9 methylation was higher in MD and non-MD groups than NL subjects (p < 0.05). LDLR, PCSK9 and LDLRAP1 methylation profiles were associated with clinical manifestations and cardiovascular events in FH patients (p < 0.05). Conclusion: Differential methylation of LDLR, PCSK9 and LDLRAP1 is associated with hypercholesterolemia and cardiovascular events. This methylation profile maybe useful as a biomarker and contribute to the management of FH.

CRISPR-Cas9-guided amplification-free genomic diagnosis for familial hypercholesterolemia using nanopore sequencing

Familial hypercholesterolemia is an inherited disorder that remains underdiagnosed. Conventional genetic testing methods such as next-generation sequencing (NGS) or target PCR are based on the amplification process. Due to the efficiency limits of polymerase and ligase enzymes, these methods usually target short regions and do not detect large mutations straightforwardly. This study combined the long-read nanopore sequencing and CRISPR-Cas9 system to sequence the target DNA molecules without amplification. We originally designed and optimized the CRISPR-RNA panel to target the low-density lipoprotein receptor gene (LDLR) and proprotein convertase subtilisin/kexin type 9 gene (PCSK9) from human genomic DNA followed by nanopore sequencing. The average coverages for LDLR and PCSK9 were 106× and 420×, versus 1.2× for the background genome. Among them, continuous reads were 52x and 307x, respectively, and spanned the entire length of LDLR and PCSK9. We identified pathogenic mutations in both coding and splicing donor regions in LDLR. We also detected an 11,029 bp large deletion in another case. Furthermore, using continuous long reads generated from the benchmark experiment, we demonstrated how a false-positive 670 bp deletion caused by PCR amplification errors was easily eliminated.

Atherosclerosis as the Damocles' sword of human evolution: insights from nonhuman ape-like primates, ancient human remains, and isolated modern human populations

Atherosclerosis is the leading cause of death worldwide, and the predominant risk factors are advanced age and high-circulating low-density lipoprotein cholesterol (LDL-C). However, the findings of atherosclerosis in relatively young mummified remains and a lack of atherosclerosis in chimpanzees despite high LDL-C call into question the role of traditional cardiovascular risk factors. The inflammatory theory of atherosclerosis may explain the discrepancies between traditional risk factors and observed phenomena in current literature. Following the divergence from chimpanzees several millennia ago, loss of function mutations in immune regulatory genes and changes in gene expression have resulted in an overactive human immune system. The ubiquity of atherosclerosis in the modern era may reflect a selective pressure that enhanced the innate immune response at the cost of atherogenesis and other chronic disease states. Evidence provided from the fields of genetics, evolutionary biology, and paleoanthropology demonstrates a sort of circular dependency between inflammation, immune system functioning, and evolution at both a species and cellular level. More recently, the role of proinflammatory stimuli, somatic mutations, and the gene-environment effect appear to be underappreciated elements in the development and progression of atherosclerosis. Neurobiological stress, metabolic syndrome, and traditional cardiovascular risk factors may instead function as intermediary links between inflammation and atherosclerosis. Therefore, considering evolution as a mechanistic process and atherosclerosis as part of the inertia of evolution, greater insight into future preventative and therapeutic interventions for atherosclerosis can be gained by examining the past.

Novel DNA methylome biomarkers associated with adalimumab response in rheumatoid arthritis patients

Background and aims

Rheumatoid arthritis (RA) patients are currently treated with biological agents mostly aimed at cytokine blockade, such as tumor necrosis factor-alpha (TNFα). Currently, there are no biomarkers to predict therapy response to these agents. Here, we aimed to predict response to adalimumab (ADA) treatment in RA patients using DNA methylation in peripheral blood (PBL).

Methods

DNA methylation profiling on whole peripheral blood from 92 RA patients before the start of ADA treatment was determined using Illumina HumanMethylationEPIC BeadChip array. After 6 months, treatment response was assessed according to the European Alliance of Associations for Rheumatology (EULAR) criteria for disease activity. Patients were classified as responders (Disease Activity Score in 28 Joints (DAS28) < 3.2 or decrease of 1.2 points) or as non-responders (DAS28 > 5.1 or decrease of less than 0.6 points). Machine learning models were built through stability-selected gradient boosting to predict response prior to ADA treatment with predictor DNA methylation markers.

Results

Of the 94 RA patients, we classified 49 and 43 patients as responders and non-responders, respectively. We were capable of differentiating responders from non-responders with a high performance (area under the curve (AUC) 0.76) using a panel of 27 CpGs. These classifier CpGs are annotated to genes involved in immunological and pathophysiological pathways related to RA such as T-cell signaling, B-cell pathology, and angiogenesis.

Conclusion

Our findings indicate that the DNA methylome of PBL provides discriminative capabilities in discerning responders and non-responders to ADA treatment and may therefore serve as a tool for therapy prediction.

Applications of machine learning in familial hypercholesterolemia

Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.

LDLR gene's promoter region hypermethylation in patients with familial hypercholesterolemia

Familial hypercholesterolemia (FH) is characterized by high low-density lipoprotein cholesterol (LDL-C) levels and a high risk of early coronary heart disease. Structural alterations in the LDLR, APOB, and PCSK9 genes were not found in 20-40% of patients diagnosed using the Dutch Lipid Clinic Network (DCLN) criteria. We hypothesized that methylation in canonical genes could explain the origin of the phenotype in these patients. This study included 62 DNA samples from patients with a clinical diagnosis of FH according to the DCLN criteria, who previously tested negative for structural alterations in the canonical genes, and 47 DNA samples from patients with normal blood lipids (control group). All DNA samples were tested for methylation in the CpG islands of the three genes. The prevalence of FH relative to each gene was determined in both groups and the respective prevalence ratios (PRs) were calculated. The methylation analysis of APOB and PCSK9 was negative in both groups, showing no relationship between methylation in these genes and the FH phenotype. As the LDLR gene has two CpG islands, we analyzed each island separately. The analysis of LDLR-island1 showed PR = 0.982 (CI 0.33-2.95; χ² = 0.001; p = 0.973), also suggesting no relationship between methylation and the FH phenotype. Analysis of LDLR-island2 showed a PR of 4.12 (CI 1.43-11.88; χ² = 13,921; p = 0.00019), indicating a possible association between methylation on this island and the FH phenotype.

Epigenetic Signatures Discriminate Patients With Primary Sclerosing Cholangitis and Ulcerative Colitis From Patients With Ulcerative Colitis

Background

Primary sclerosing cholangitis (PSC) is a chronic inflammatory liver disease affecting the intra- and extrahepatic bile ducts, and is strongly associated with ulcerative colitis (UC). In this study, we explored the peripheral blood DNA methylome and its immune cell composition in patients with PSC-UC, UC, and healthy controls (HC) with the aim to develop a predictive assay in distinguishing patients with PSC-UC from those with UC alone.

Methods

The peripheral blood DNA methylome of male patients with PSC and concomitant UC, UC and HCs was profiled using the Illumina HumanMethylation Infinium EPIC BeadChip (850K) array. Differentially methylated CpG position (DMP) and region (DMR) analyses were performed alongside gradient boosting classification analyses to discern PSC-UC from UC patients. As observed differences in the DNA methylome could be the result of differences in cellular populations, we additionally employed mass cytometry (CyTOF) to characterize the immune cell compositions.

Results

Genome wide methylation analysis did not reveal large differences between PSC-UC and UC patients nor HCs. Nonetheless, using gradient boosting we were capable of discerning PSC-UC from UC with an area under the receiver operator curve (AUROC) of 0.80. Four CpG sites annotated to the NINJ2 gene were found to strongly contribute to the predictive performance. While CyTOF analyses corroborated the largely similar blood cell composition among patients with PSC-UC, UC and HC, a higher abundance of myeloid cells was observed in UC compared to PSC-UC patients.

Conclusion

DNA methylation enables discerning PSC-UC from UC patients, with a potential for biomarker development.

Genetic Determinants of Plasma Low-Density Lipoprotein Cholesterol Levels: Monogenicity, Polygenicity, and "Missing" Heritability

Changes in plasma low-density lipoprotein cholesterol (LDL-c) levels relate to a high risk of developing some common and complex diseases. LDL-c, as a quantitative trait, is multifactorial and depends on both genetic and environmental factors. In the pregenomic age, targeted genes were used to detect genetic factors in both hyper- and hypolipidemias, but this approach only explained extreme cases in the population distribution. Subsequently, the genetic basis of the less severe and most common dyslipidemias remained unknown. In the genomic age, performing whole-exome sequencing in families with extreme plasma LDL-c values identified some new candidate genes, but it is unlikely that such genes can explain the majority of inexplicable cases. Genome-wide association studies (GWASs) have identified several single-nucleotide variants (SNVs) associated with plasma LDL-c, introducing the idea of a polygenic origin. Polygenic risk scores (PRSs), including LDL-c-raising alleles, were developed to measure the contribution of the accumulation of small-effect variants to plasma LDL-c. This paper discusses other possibilities for unexplained dyslipidemias associated with LDL-c, such as mosaicism, maternal effect, and induced epigenetic changes. Future studies should consider gene-gene and gene-environment interactions and the development of integrated information about disease-driving networks, including phenotypes, genotypes, transcription, proteins, metabolites, and epigenetics.

Lipid Phenotypes and DNA Methylation: a Review of the Literature

PURPOSE OF REVIEW

Epigenetic modifications via DNA methylation have previously been linked to blood lipid levels, dyslipidemias, and atherosclerosis. The purpose of this review is to discuss current literature on the role of DNA methylation on lipid traits and their associated pathologies.

RECENT FINDINGS

Candidate gene and epigenome-wide approaches have identified differential methylation of genes associated with lipid traits (particularly CPT1A, ABCG1, SREBF1), and novel approaches are being implemented to further characterize these relationships. Moreover, studies on environmental factors have shown that methylation variations at lipid-related genes are associated with diet and pollution exposure. Further investigation is needed to elucidate the directionality of the associations between the environment, lipid traits, and epigenome. Future studies should also seek to increase the diversity of cohorts, as European and Asian ancestry populations are the predominant study populations in the current literature.