Current opinion in lipidology最新文献

Purpose of review: Familial hypercholesterolemia is a monogenic Mendelian disorder characterized by elevated LDL cholesterol and premature atherosclerotic cardiovascular disease. It is caused by pathogenic variants in LDLR , APOB , and PCSK9 , with rarer involvement of LDLRAP1 and APOE . Despite advances in molecular diagnostics, no causative variant is identified in approximately 25-75% of clinically diagnosed cases.

Recent findings: Familial hypercholesterolemia is currently defined as an autosomal semi-dominant disorder with a gene-dosage effect, whereby biallelic pathogenic variants result in markedly more severe phenotypes than heterozygous variants. Terminology for homozygous familial hypercholesterolemia has been refined. Former terms such as 'true homozygote', 'compound heterozygote', and 'double heterozygotes' have been replaced by monogenic biallelic forms, with identical or different variants, and digenic biallelic forms involving two familial hypercholesterolemia-associated genes. Polygenic risk score (PRS) and lipoprotein(a) measurement help explain familial hypercholesterolemia-like phenotypes in patients without a monogenic cause and enable determination of polygenic severe hypercholesterolemia and/or hyperlipoproteinemia(a). Although advances in molecular genetics have improved variant detection, interpretation remains challenging. Integration of case-level data and functional studies, including high-throughput LDLR assays and APOB structural analyses, has enhanced variant pathogenicity classification.

Summary: Combining monogenic variant detection, PRS determination and lipoprotein(a) assessment enables comprehensive diagnosis, tailored risk stratification, and personalized familial hypercholesterolemia management.

Purpose of review: Our understanding of the genetic regulation of lipoprotein(a) [Lp(a)] is hindered by the complex structure of the LPA gene, limited non-European datasets and its elusive cellular receptor(s). This review summarizes recent efforts and advances providing new insights on its genetic architecture, variability across ancestries and regulators beyond the LPA gene.

Recent findings: Impressive advances in DNA sequencing and bioinformatics now resolve LPA variants and kringle IV-type 2 copy number at scale. This provides new reference datasets and enables tools that unlock hidden variation also from already available sequencing datasets. In parallel, genetic studies broaden our understanding of the regulation of Lp(a) across ancestries and improve genetic risk scores. Finally, while recent studies implicate new mechanisms for Lp(a) uptake, upcoming genome-wide gene knockout screens allow comprehensive, agnostic scans for regulators and receptors. Puzzlingly, this still converges on the LDL receptor, whose exact role in Lp(a) uptake remains enigmatic.

Summary: Technological advances establish a foundation for more accurate genetic risk assessment across ancestries. These advances are enhancing our understanding of Lp(a) regulation and build a framework for future integrative genetic studies, which may shed new light on the evolution of the Lp(a) trait, adding important context for its physiological and clinical relevance.

Purpose of review: Greenlanders differ from other populations in terms of traditional lifestyle and genetic architecture. This might have a great impact on lipid levels in the population and the spectrum of genetic variants associated with lipid traits. Here, we review recent advances in lipid genetics in Greenlanders and highlight the potential of moving from single lipid trait analyses to more comprehensive lipidomic profiling.

Recent findings: Genetic association studies in the Greenlandic population have identified variants, including PCSK9 (rs12117661), LDLR (rs730882082), and SI (rs781470490), associated with large effects on triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and total cholesterol (TC) levels, as well as altered risk of cardiovascular disease (CVD). These variants are common in the Greenlandic population and explain more lipid variation than variants observed in Europeans. Accordingly, European-derived polygenic scores (PGSs) underperform in Greenlanders, but including the Greenlandic variants increases the performance of lipid PGSs. Lipidomic profiling has the potential to reveal strong cardiometabolic-risk signatures.

Summary: The Greenlandic population harbors high-impact variants in PCSK9 , LDLR , and SI, particularly affecting the levels of TG, LDL-C, and TC. Obtaining information on these variants could facilitate earlier detection and potentially prevention of CVD, and advance precision medicine for Greenlanders.

Purpose of review: Lipoprotein(a), Lp(a), is a genetically determined, lifelong risk factor for atherosclerotic cardiovascular disease (ASCVD). Despite broad guideline support for universal one-time testing, Lp(a) measurement remains rare in clinical practice. This review summarizes recent advances in machine learning-based strategies that can enhance the efficiency, yield, and equity of Lp(a) screening.

Recent findings: To date, three studies have developed and validated machine learning models to identify individuals with elevated Lp(a) using routinely available clinical variables. The ARISE framework, derived from the UK Biobank and validated across multiple US cohorts, reduced the number needed to test by more than 50% while maintaining consistent discrimination across demographic subgroups. Additional studies have confirmed the feasibility of decision-tree and neural network models to improve case finding for elevated Lp(a) in both clinical and population-based settings.

Summary: Machine learning-based strategies provide a scalable means of operationalizing universal Lp(a) testing recommendations within health systems. When developed using unbiased data, externally validated, and assessed for fairness and interpretability, these models can support systematic identification of individuals with elevated Lp(a) and integration of Lp(a) measurement into routine cardiovascular risk assessment.

Purpose of review: The purpose of this report is to summarize evidence supporting the use of nonfasting lipid testing for cardiovascular risk assessment, the potential reasons nonfasting lipid testing predicts cardiovascular risk better than fasting measurement, and to provide a preliminary survey of the status of adoption of nonfasting lipid testing by individual physicians and patients.

Recent findings: There is increased awareness of the importance of remnant lipoprotein cholesterol, which is increased after eating, as a key factor predicting risk for ischemic vascular disease. Nonfasting lipid measurement is now recommended in guidelines and consensus statements worldwide, but has not yet been adopted in many countries. Preliminary evidence suggests physician's practice of requesting a fasting glucose along with a lipid profile is decreasing over time, but still limits implementation of nonfasting lipid testing. Patient's perception of the optimal conditions for lipid testing as well as their preferred time of day to perform the test may also be limiting adoption of nonfasting measurements.

Summary: Nonfasting testing is now accepted as the preferred method of lipid measurement for cardiovascular risk prediction and lipid target achievement. Further acceptance of nonfasting lipid testing requires increased awareness by physicians and patients of the rationale for this recommendation.

Purpose of review: Growth differentiation factor-15 (GDF15) is widely described as a hormone that conveys somatic distress to the brain, yet this framework does not explain why GDF15 is elevated in many common metabolic states. Recent work shows that GDF15 rises most consistently when fatty acid availability exceeds mitochondrial and endoplasmic reticulum capacity. This review synthesizes emerging evidence that positions GDF15 as an endocrine sensor of lipid load rather than a general stress signal.

Recent findings: Across acute dietary lipid exposure, endogenous lipolysis during fasting, chronic overnutrition, ketogenic feeding, and mitochondrial dysfunction, free fatty acids activate lipid-sensitive transcriptional pathways that induce GDF15 expression in kidney, liver, intestine, and adipose tissue macrophages. Once elevated, GDF15 engages hindbrain glial-cell-derived neurotrophic factor family receptor α-like (GFRAL) signaling to increase sympathetic outflow, promote whole-body fatty acid oxidation, redistribute lipid burden, and improve metabolic flexibility. These effects occur independently of reduced food intake and reflect coordinated actions across liver, adipose tissue, and skeletal muscle.

Summary: Viewing GDF15 as a lipid-responsive hormonal signal reshapes our understanding of its physiological role and provides new insight into metabolic adaptations to lipid overload. This pattern suggests that GDF15 is part of a feedback system that attempts to match fatty acid oxidation with supply, analogous to how carbohydrate ingestion stimulates insulin to promote glucose oxidation and suppress hepatic glucose production to restore euglycemia. Within this framework, individual tissues respond in complementary ways to reduce lipid burden and maintain metabolic balance. Understanding this coordinated lipid-responsive network highlights opportunities to target the GDF15 pathway in disorders characterized by impaired fatty acid handling including obesity, type 2 diabetes, cardiovascular disease, cancer cachexia and metabolic dysfunction-associated steatotic liver disease (MASLD).

Purpose of review: More than 95% of human genes undergo alternative pre-mRNA processing based on cell type, developmental stages, and environmental stimuli, among other factors. Not all alternatively spliced mRNAs are translated to proteins, and some of the noncoding mRNA isoforms play vital roles in cellular homeostasis. This review summarizes protein coding and noncoding RNA isoforms reported for key genes involved in lipoprotein metabolism, and emerging technologies that can be exploited to specifically induce a desired isoform.

Recent findings: As sequencing technologies become more accessible, more variations in gene transcripts are being detected. Publicly available databases collate these as they arise, but not all of them are captured. Additionally, the function, if any, of many of these alternatively spliced transcripts is currently unknown. Novel strategies to investigate specific transcripts are also continuously evolving.

Summary: Most human genes are alternatively spliced, generating various mRNAs and protein isoforms. Any cis or trans factors that alter the balance of these isoforms can have deleterious effects. The fundamental knowledge on the role of each isoform in maintaining cellular health is currently lacking. Emerging technologies which allow modulation of natural mRNA splicing can be used to further our understanding of natural isoform expression and function.

Purpose of review: Risk assessment in patients with familial hypercholesterolemia (FH) remains an important clinical challenge. The polygenic susceptibility for plasma lipoprotein traits or coronary artery disease (CAD) can be assessed by polygenic risk scores (PRS). The purpose of this review is to discuss the potential roles of PRS in the context of FH management.

Recent findings: Recent studies suggested that a high PRS for lipoprotein(a) falsely explains the phenotype in a fifth of variant-negative FH patients, whereas a larger proportion can be explained by high low-density lipoprotein cholesterol (LDL-C) PRS. The cardiovascular risk, but also the risk of type 2 diabetes, is different in patients with polygenic hypercholesterolemia compared to monogenic FH. Lastly, it has been shown that a PRS for CAD, but not for LDL-C or lipoprotein(a), was associated with increased lifelong incidence of cardiovascular disease in patients with monogenic FH, independently of clinical variables.

Summary: Several studies have explored the potential clinical relevance of PRS in FH, including for diagnostic purpose and in cardiovascular risk stratification. Prior to implementation in clinical practice for cardiovascular risk stratification, future studies in FH should determine whether the polygenic information offers incremental predictive value over conventional clinical variables.