Plasma High Density Lipoprotein Small Subclass is Reduced in Alzheimer's Disease Patients and Correlates with Cognitive Performance

Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer's dementia

High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). However, obtaining an accurate HDL size measurement is challenging. We demonstrated the utility of measuring the diameter of more than 1,800,000 HDL particles with the deep learning model YOLOv7 (you only look once) from micrographs of 183 HDL samples, including patients with dementia or normal cognition (controls). This method was shown to be more efficient and accurate than conventional image analysis software. Using this method, we found a higher abundance of small HDLs in participants with dementia compared to controls in patients with the apolipoprotein E (APOE) ε3ε4 genotype, whereas patients with the APOE ε3ε3 genotype had higher variability in the abundance of different HDL subclasses. Our results show an example of accurate individual HDL particle diameter measurement for large-scale clinical samples, which can be expanded to characterize the relationship between disease risk and other nanoparticles in the sub-20-nm diameter size range.

The association of high-density lipoprotein cargo proteins with brain volume in older adults in the Atherosclerosis Risk in Communities (ARIC)

BACKGROUND

High-density lipoprotein (HDL) modulates the blood-brain barrier and cerebrovascular integrity, likely influencing the risk of Alzheimer's disease (AD), neurodegeneration, and cognitive decline.

OBJECTIVE

This study aims to identify HDL protein cargo associated with brain amyloid deposition and brain volume in regions vulnerable to AD pathology in older adults.

METHODS

HDL was separated from the plasma of 65 non-demented participants of the Atherosclerosis Risk in Communities (ARIC) study using a fast protein liquid chromatography method. HDL cargo proteins were measured using a label-free, untargeted proteomic method based on mass spectrometry and data-independent acquisition. Linear regression with multiple imputations assessed the associations between each HDL cargo protein (log2-transformed) and brain amyloid deposition or temporal-parietal meta-ROI volume, adjusting for covariates.

RESULTS

The mean (SD) age of the participants was 76.3 (5.4) years old, 53.8% (35/65) female, 30.8% (20/65) black, and 28.1% (18/64, 1 missing) APOE4 carriers. We found few HDL cargo proteins associated with brain amyloid deposition and considerably more HDL cargo proteins associated with temporal-parietal meta-ROI volume. Two HDL cargo proteins mostly associated with temporoparietal meta-ROI volume were fibrinogen B (FGB) and plasminogen (PLG). A doubling of FGB in HDL was associated with a greater temporoparietal meta-ROI volume of 1638 mm3 (95% CI [688, 2589]). In comparison, a doubling of PLG in HDL was associated with a lower temporoparietal meta-ROI of 2025 mm3 (95% CI [-3669, -1034]).

CONCLUSIONS

This study suggests that HDL cargo proteins associated with temporal-parietal meta-ROI volume are involved in complement and coagulation pathways.

Associations Between High-Density Lipoprotein Cholesterol Efflux and Brain Grey Matter Volume

Objective: High-density lipoprotein cholesterol efflux function may prevent brain amyloid beta deposition and neurodegeneration. However, the relevance of this finding has not been established in the diverse middle-aged population. Methods: We examined 1826 adults (47% Black adults) who participated in the Dallas Heart Study to determine associations between high-density lipoprotein (HDL) measures and brain structure and function. White matter hyperintensities (WMH) and whole-brain grey matter volume (GMV) were measured using brain MRI, and the Montreal Cognitive Assessment (MoCA) was used to measure neurocognitive function. HDL cholesterol efflux capacity (HDL-CEC) was assessed using fluorescence-labeled cholesterol efflux from J774 macrophages, and HDL particle size measures were assessed using nuclear magnetic resonance (NMR) spectroscopy (LipoScience). Multivariable linear regressions were performed to elucidate associations between HDL-CEC and brain and cognitive phenotypes after adjustment for traditional risk factors such as age, smoking status, time spent in daily physical activity, and education level. Results: Higher HDL-CEC and small HDL particle (HDL-P) concentration were positively associated with higher GMV normalized to total cranial volume (TCV) (GMV/TCV) after adjustment for relevant risk factors (β = 0.078 [95% CI: 0.029, 0.126], p = 0.002, and β = 0.063 [95% CI: 0.014, 0.111], p = 0.012, respectively). Conversely, there were no associations between HDL measures and WMH or MoCA (all p > 0.05). Associations of HDL-CEC and small HDL-P with GMV/TCV were not modified by ApoE-ε4 status or race/ethnicity. Interpretation: Higher HDL cholesterol efflux and higher plasma concentration of small HDL-P were associated with higher GMV/TCV. Additional studies are needed to explore the potential neuroprotective functions of HDL.

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

Circulating metabolites and risk of incident dementia: A prospective cohort study

Identifying circulating metabolites associated with dementia, cognition, and brain volume may improve the understanding of dementia pathogenesis and provide novel insights for preventive and therapeutic interventions. This cohort study included a total of 87 885 participants (median follow-up of 9.1 years, 54% female) without dementia at baseline from the UK Biobank. A total of 249 plasma metabolites were measured using nuclear magnetic resonance spectroscopy at baseline. Cox proportional regression was used to examine the associations of each metabolite with incident dementia (cases = 1134), Alzheimer's disease (AD; cases = 488), and vascular dementia (VD; cases = 257) during follow-up. Dementia-associated metabolites were further analyzed for association with cognitive deficits (N = 87 885) and brain volume (N = 7756) using logistic regression and linear regression. We identified 26 metabolites associated with incident dementia, of which 6 were associated with incident AD and 5 were associated with incident VD. These 26 dementia-related metabolites were subfractions of intermediate-density lipoprotein, large low-density lipoprotein (L-LDL), small high-density lipoprotein (S-HDL), very-low-density lipoprotein, fatty acids, ketone bodies, citrate, glucose, and valine. Among them, the cholesterol percentage in L-LDL (L-LDL-C%) was associated with lower risk of AD (HR [95% CI] = 0.92 [0.87-0.97], p = 0.002), higher brain cortical (β = 0.047, p = 3.91 × 10-6 ), and hippocampal (β = 0.043, p = 1.93 × 10-4 ) volume. Cholesteryl ester-to-total lipid ratio in L-LDL (L-LDL-CE%) was associated with lower risk of AD (HR [95% CI] = 0.93 [0.90-0.96], p = 1.48 × 10-4 ), cognitive deficits (odds ratio = 0.98, p = 0.009), and higher hippocampal volume (β = 0.027, p = 0.009). Cholesteryl esters in S-HDL (S-HDL-CE) were associated with lower risk of VD (HR [95% CI] = 0.81 [0.71-0.93], p = 0.002), but not AD. Taken together, circulating levels of L-LDL-CE% and L-LDL-C% were robustly associated with risk of AD and AD phenotypes, but not with VD. S-HDL-CE was associated with lower risk of VD, but not with AD or AD phenotypes. These metabolites may play a role in the advancement of future intervention trials. Additional research is necessary to gain a complete comprehension of the molecular mechanisms behind these associations.

A Comprehensive Investigation of the Potential Role of Lipoproteins and Metabolite Profile as Biomarkers of Alzheimer's Disease Compared to the Known CSF Biomarkers

Introduction

While cerebrospinal fluid (CSF) core biomarkers have been considered diagnostic biomarkers for a long time, special attention has been recently dedicated to lipoproteins and metabolites that could be potentially associated with Alzheimer's disease (AD) neurodegeneration. Herein, we aimed to investigate the relationship between the levels of CSF core biomarkers including Aβ-42, TAU, and P-TAU and plasma lipoproteins and metabolites of patients with AD from the baseline cohort of the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Method

Using the ADNI database, fourteen subclasses of lipoproteins as well as a number of lipids and fatty acids and low-molecular metabolites including amino acids, ketone bodies, and glycolysis-related metabolites in blood samples were measured as potential noninvasive markers, and their association with the CSF core biomarkers was statistically investigated controlling for age and gender.

Results

A total number of 251 AD subjects were included, among whom 71 subjects were negative for the Apo-E ε4 allele and 150 were positive. There was no significant difference between the two groups regarding cognitive assessments, CSF core biomarkers, and lipoproteins and metabolites except the level of Aβ-42 (p < 0.001) and phenylalanine (p = 0.049), which were higher in the negative group. CSF TAU and P-TAU were significantly correlated with medium and small HDL in the negative group, and with extremely large VLDL in the positive group. Our results also indicated significant correlations of metabolites including unsaturated fatty acids, glycerol, and leucine with CSF core biomarkers.

Conclusion

Based on our findings, a number of lipoproteins and metabolites were associated with CSF core biomarkers of AD. These correlations showed some differences in Apo-E ε4 positive and negative groups, which reminds the role of Apo-E gene status in the pathophysiology of AD development. However, further research is warranted to explore the exact association of lipoproteins and other metabolites with AD core biomarkers and pathology.

Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank

BACKGROUND

Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions.

METHODS

Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model.

RESULTS

Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups.

CONCLUSIONS

Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

Emerging role of HDL in brain cholesterol metabolism and neurodegenerative disorders

High-density lipoproteins (HDLs play a key role in cholesterol homeostasis maintenance in the central nervous system (CNS), by carrying newly synthesized cholesterol from astrocytes to neurons, to support their lipid-related physiological functions. As occurs for plasma HDLs, brain lipoproteins are assembled through the activity of membrane cholesterol transporters, undergo remodeling mediated by specific enzymes and transport proteins, and finally deliver cholesterol to neurons by a receptor-mediated internalization process. A growing number of evidences indicates a strong association between alterations of CNS cholesterol homeostasis and neurodegenerative disorders, in particular Alzheimer's disease (AD), and a possible role in this relationship may be played by defects in brain HDL metabolism. In the present review, we summarize and critically examine the current state of knowledge on major modifications of HDL and HDL-mediated brain cholesterol transport in AD, by taking into consideration the individual steps of this process. We also describe potential and encouraging HDL-based therapies that could represent new therapeutic strategies for AD treatment. Finally, we revise the main plasma and brain HDL modifications in other neurodegenerative disorders including Parkinson's disease (PD), Huntington's disease (HD), and frontotemporal dementia (FTD).

High-density lipoprotein in Alzheimer's disease: From potential biomarkers to therapeutics

The inverse correlation between high-density lipoprotein (HDL) levels in vivo and the risk of Alzheimer's disease (AD) has become an inspiration for HDL-inspired AD therapy, including plain HDL and various intelligent HDL-based drug delivery systems. In this review, we will focus on the two endogenous HDL subtypes in the central nervous system (CNS), apolipoprotein E-based HDL (apoE-HDL) and apolipoprotein A-I-based HDL (apoA-I-HDL), especially their influence on AD pathophysiology to reveal HDL's potential as biomarkers for risk prediction, and summarize the relevant therapeutic mechanisms to propose possible treatment strategies. We will emphasize the latest advances of HDL as therapeutics (plain HDL and HDL-based drug delivery systems) to discuss the potential for AD therapy and review innovative techniques in the preparation of HDL-based nanoplatforms to provide a basis for the rational design and future development of anti-AD drugs.

High-Density Lipoprotein Subfractions: Much Ado about Nothing or Clinically Important?

High-density lipoproteins (HDL) are a heterogenous group of plasma molecules with a large variety in composition. There is a wide specter in lipid content and the number of different proteins that has been associated with HDL is approaching 100. Given this heterogeneity and the fact that the total amount of HDL is inversely related to the risk of coronary heart disease (CHD), there has been increasing interest in the function of specific HDL subgroups and in what way measuring and quantifying these subgroups could be of clinical importance in determining individual CHD risk. If certain subgroups appear to be more protective than others, it may also in the future be possible to pharmacologically increase beneficial and decrease harmful subgroups in order to reduce CHD risk. In this review we give a short historical perspective, summarize some of the recent clinical findings regarding HDL subclassifications and discuss why such classification may or may not be of clinical relevance.