Alzheimer’s disease is the most common form of dementia, and the prodromal phases of Alzheimer’s disease can last for decades. Vascular dementia is the second most common form of dementia and is distinguished from Alzheimer’s disease by evidence of previous stroke or hemorrhage and current cerebrovascular disease. A compiled group of vascular-related dementias (vascular dementia and unspecified dementia) is often referred to as non-Alzheimer dementia. Recent evidence indicates that preventing dementia by lifestyle interventions early in life with a focus on reducing cardiovascular risk factors is a promising strategy for reducing future risk. Approximately 40% of dementia cases is estimated to be preventable by targeting modifiable, primarily cardiovascular risk factors. The aim of this review is to describe the association between risk factors for atherosclerotic cardiovascular disease and the risk of Alzheimer’s disease and non-Alzheimer dementia by providing an overview of the current evidence and to shed light on possible shared pathogenic pathways between dementia and cardiovascular disease. The included risk factors are body mass index (BMI); plasma triglyceride-, high-density lipoprotein (HDL) cholesterol-, low-density lipoprotein (LDL) cholesterol-, and total cholesterol concentrations; hypertension; diabetes; non-alcoholic fatty liver disease (NAFLD); physical inactivity; smoking; diet; the gut microbiome; and genetics. Furthermore, we aim to disentangle the difference between associations of risk factors in midlife as compared with in late life.

High density lipoprotein (HDL) cholesterol has traditionally been considered the “good cholesterol”, and most of the research regarding HDL cholesterol has for decades revolved around the possible role of HDL in atherosclerosis and its therapeutic potential within atherosclerotic cardiovascular disease. Randomized trials aiming at increasing HDL cholesterol have, however, failed and left questions to what role HDL cholesterol plays in human health and disease. Recent observational studies involving non-cardiovascular diseases have shown that high levels of HDL cholesterol are not necessarily associated with beneficial outcomes as observed for age-related macular degeneration, type II diabetes, dementia, infection, and mortality. In this narrative review, we discuss these interesting associations between HDL cholesterol and non-cardiovascular diseases, covering observational studies, human genetics, and plausible mechanisms.

Atherosclerotic cardiovascular disease (ASCVD) is among the leading causes of death worldwide, and technological advances have made it possible to expand the repertoire of biomarkers used in diagnostics and treatment of ASCVD. These include different omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics). We introduce the various layers of omic data and how they can be used in diagnostics and treatment of ASCVD. Further, we discuss future possibilities of combining multiomic data with machine learning (ML) and artificial intelligence (AI) to develop algorithms for facilitating precision medicine. we reviewed the current literature on omic data in ASCVD and its integration with ML/AI. Genomics has been used to generate polygenic risk scores (PRS), which have shown promising results in risk prediction of ASCVD. Key epigenetic changes implicated in atherosclerosis include deoxyribonucleic acid (DNA) methylation. Transcriptomics has been used to identify transcripts, including micro ribonucleic acid (miRNAs), implicated in atherosclerosis progression. Proteomic risk scores have shown independent predictive information and outperformed clinical risk models, and within the metabolomics field, lipidomics has emerged as a promising predictive tool. The combination of multiomic data analysis with ML and AI methods has already demonstrated potential in the development of clinical models. A major effort is necessary to bring omic data and technologies to the clinical field. Further support will be offered by the generation of clinically applicable/approved AI/ML algorithms able to translate large datasets into valuable information for accurate precision medicine approaches.

Clusterin, also known as apolipoprotein J (apoJ), is one of the most abundantly expressed apolipoproteins in the brain after apolipoprotein E (apoE). Like the ε4 allele of the apolipoprotein E gene (APOE), the clusterin gene (CLU) is a risk locus for Alzheimer’s disease, and may play additional roles in atherosclerosis pathogenesis. We tested whether genetic variation in CLU was associated with either Alzheimer’s disease or atherosclerosis-related diseases. We studied individual data on 103,987 participants from the Copenhagen General Population Study (CGPS) and the Copenhagen City Heart Study (CCHS). We genotyped a common CLU variant (rs9331896) and two common APOE variants (rs7412 and rs429358), defining the ε2, ε3, and ε4, alleles in CGPS and CCHS. All individuals in the CGPS and CCHS cohorts were followed from study inclusion to occurrence of event, death, emigration, or until 10 November 2014, whichever came first. Summary consortia data on 258,351 individuals from the International Genomics of Alzheimer’s Project (IGAP) and the Coronary Artery Disease Genome-wide Replication and Meta-analysis plus the Coronary Artery Disease (C4D) Genetics and 1000-Genomes-based genome-wide association studies (CARDIoGRAMplusC4D) were used in meta-analyses. In CGPS and CCHS, multifactorially adjusted hazard ratios for Alzheimer’s disease, all dementia, vascular dementia, ischemic cerebrovascular disease, and ischemic heart disease were 1.18 (1.07–1.30), 1.09 (1.02–1.17), 0.96 (0.80–1.17), 1.02 (0.97–1.07), and 0.97 (0.93–1.01) per T allele, respectively. Multifactorially adjusted hazard ratios for Alzheimer’s disease and all dementia were 2.72 (2.45–3.01) and 2.21 (2.05–2.38) for the APOE ɛ4 allele. There was no interaction between rs9331896 in CLU and rs429358 (defining the ɛ4 allele) in APOE in predicting Alzheimer’s disease or all dementia (P = 0.39 and P = 0.21). In a meta-analysis including consortium data, the overall fixed- and random-effects odds ratios for Alzheimer’s disease per T allele were 1.16 (1.13–1.18) (I 2 = 0.0%; P for heterogeneity = 0.89). A common variant in CLU was associated with a high risk of Alzheimer’s disease and all dementia in the general population but not with vascular dementia or ischemic vascular disease. Important novel aspects compared to previous studies are the incorporation of individual risk factor data, the exact causative ε4 allele, and several subtypes of dementia and atherosclerosis-related endpoints.

INTRODUCTION: We tested whether genetically proxied non‐high‐density lipoprotein cholesterol (non‐HDL‐C)–lowering drug targets reduce risk of all‐cause dementia. METHODS: We included 1,091,775 individuals from three prospective general population cohorts with individual‐level data and two consortia with summary‐level data. We selected genetic variants within HMGCR, NPC1L1, PCSK9, ANGPTL4, LPL , and CETP associated with non‐HDL‐C. These variants were used as exposures in Cox regression and one‐ and two‐sample Mendelian randomization. Results were meta‐analyzed. RESULTS: Meta‐analysis of one‐sample Mendelian randomization odds ratios per 1 mmol/L (39 mg/dL) lower non‐HDL‐C was 0.24 (0.18–0.31) for HMGCR , 0.18 (0.12–0.25) for NPC1L1 , 0.97 (0.70–1.35) for PCSK9 , 1.66 (0.52–5.36) for ANGPTL4 , 1.41 (0.63–3.16) for LPL , and 0.30 (0.26–0.34) for CETP . Cox regression and two‐sample Mendelian randomization results were mostly directionally consistent. DISCUSSION: Genetic lowering of non‐HDL cholesterol via HMGCR, NPC1L1 , and CETP reduces the risk of dementia. This reflects the effect of lifelong differences in non‐HDL cholesterol on risk of dementia. Highlights: Variants in HMGCR, NPC1L1 , and CETP reduce the risk of dementia via non‐high‐density lipoprotein cholesterol (non‐HDL‐C). An effect of PCSK9, ANGPTL4 , and LPL variants on dementia risk cannot be excluded. This reflects the effect of lifelong lower non‐HDL‐C on risk of dementia.

Plasma triglyceride levels are a strong cardiovascular risk marker. However, triglyceride–lowering therapies have not demonstrated a reduction in cardiovascular events, suggesting that triglycerides may serve as surrogate markers for other atherogenic mechanisms. The aim is to investigate the role of triglycerides in derangements of the global lipoprotein profile, as assessed by ¹H–NMR spectroscopy. Serum lipoprotein profiles were analyzed in patients with metabolic alterations attending the Lipid Unit of a University hospital (n = 822). Lipoprotein particle number, size, and composition were evaluated and visualized through a graphic representation of a lipoprotein network analysis referred to lipid silhouette in patients sorted by triglyceride quartiles. Profound alterations in lipoprotein quantity and composition were associated with incremental triglyceride concentrations independently on the presence of diabetes or obesity, including a significantly increased number of VLDL and smaller LDL particles, and higher remnant cholesterol, representing up to 30% of all cholesterol in quartile 4. It was also a significant triglyceride enrichment of LDL and HDL particles. Triglycerides are not merely components of atherogenic dyslipidemia; they are a key driver of the overall changes and are strongly associated with a proatherogenic plasma lipoprotein profile. Visualization of these alterations provides supplementary insights to support cardiovascular prevention strategies.