Analysis of pleiotropic genetic effects on cognitive impairment, systemic inflammation, and plasma lipids in the Health and Retirement Study

Exploring the relationship between lipid metabolism and cognition in individuals living with stable-phase Schizophrenia: a small cross-sectional study using Olink proteomics analysis

BACKGROUND

Cognitive impairment is a core symptom of schizophrenia. Metabolic abnormalities impact cognition, and although the influence of blood lipids on cognition has been documented, it remains unclear. We conducted a small cross-sectional study to investigate the relationship between blood lipids and cognition in patients with stable-phase schizophrenia. Using Olink proteomics, we explored the potential mechanisms through which blood lipids might affect cognition from an inflammatory perspective.

METHODS

A total of 107 patients with stable-phase schizophrenia and cognitive impairment were strictly included. Comprehensive data collection included basic patient information, blood glucose, blood lipids, and body mass index. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) and the MATRICS Consensus Cognitive Battery (MCCB). After controlling for confounding factors, we identified differential metabolic indicators between patients with mild and severe cognitive impairment and conducted correlation and regression analyses. Furthermore, we matched two small sample groups of patients with lipid metabolism abnormalities and used Olink proteomics to analyze inflammation-related differential proteins, aiming to further explore the association between lipid metabolism abnormalities and cognition.

RESULTS

The proportion of patients with severe cognitive impairment (SCI) was 34.58%. Compared to patients with mild cognitive impairment (MCI), those with SCI performed worse in the Attention/Alertness (t = 2.668, p = 0.009) and Working Memory (t = 2.496, p = 0.014) cognitive dimensions. Blood lipid metabolism indicators were correlated with cognitive function, specifically showing that higher levels of TG (r = -0.447, p < 0.001), TC (r = -0.307, p = 0.002), and LDL-C (r = -0.607, p < 0.001) were associated with poorer overall cognitive function. Further regression analysis indicated that TG (OR = 5.578, P = 0.003) and LDL-C (OR = 5.425, P = 0.001) may be risk factors for exacerbating cognitive impairment in individuals with stable-phase schizophrenia. Proteomics analysis revealed that, compared to individuals with stable-phase schizophrenia and normal lipid metabolism, those with hyperlipidemia had elevated levels of 10 inflammatory proteins and decreased levels of 2 inflammatory proteins in plasma, with these changes correlating with cognitive function. The differential proteins were primarily involved in pathways such as cytokine-cytokine receptor interaction, chemokine signaling pathway, and IL-17 signaling pathway.

CONCLUSION

Blood lipids are associated with cognitive function in individuals with stable-phase schizophrenia, with higher levels of TG, TC, and LDL-C correlating with poorer overall cognitive performance. TG and LDL-C may be risk factors for exacerbating cognitive impairment in these patients. From an inflammatory perspective, lipid metabolism abnormalities might influence cognition by activating or downregulating related proteins, or through pathways such as cytokine-cytokine receptor interaction, chemokine signaling pathway, and IL-17 signaling pathway.

Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.

Potential Causal Association between C-Reactive Protein Levels in Age-Related Macular Degeneration: A Two-Sample Mendelian Randomization Study

Researchers have proposed a possible correlation between age-related macular degeneration (AMD) and inflammation or C-reactive protein (CRP) levels. We investigated the potential causal relationship between CRP levels and AMD. Single-nucleotide polymorphisms (SNPs) associated with CRP exposure were selected as the instrumental variables (IVs) with significance (p < 5 × 10-8) from the genome-wide association study (GWAS) meta-analysis data of Biobank Japan and the UK Biobank. GWAS data for AMD were obtained from 11 International AMD Genomics Consortium studies. An evaluation of causal estimates, utilizing the inverse-variance-weighted (IVW), weighted-median, MR-Egger, MR-Pleiotropy-Residual-Sum, and Outlier tests, was conducted in a two-sample Mendelian randomization (MR) study. We observed significant causal associations between CRP levels and AMD (odds ratio [OR] = 1.13, 95% CI = [1.02-1.24], and p = 0.014 in IVW; OR = 1.18, 95% CI = [1.00-1.38], and p = 0.044 in weight median; OR = 1.31, 95% CI = [1.13-1.52], and p < 0.001 in MR-Egger). The causal relationship between CRP and AMD warrants further research to address the significance of inflammation as a risk factor for AMD.

A narrative review about cognitive impairment in metabolic Dysfunction-Associated liver disease (MASLD): Another matter to face through a holistic approach

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic hepatic disorder worldwide in both adults and children. It is well established that MASLD represents the hepatic manifestation of the metabolic syndrome whose definition includes the presence of obesity, type 2 diabetes (T2D), dyslipidemia, hypertension and hypercoagulability. All these conditions contribute to a chronic inflammatory status which may impact on blood brain barrier (BBB) integrity leading to an impaired function of central nervous system (CNS).

AIM OF REVIEW

Since the mechanisms underlying the brain-liver-gut axis derangement are still inconclusive, the present narrative review aims to make a roundup of the most recent studies regarding the cognitive decline in MASLD also highlighting possible therapeutic strategies to reach a holistic advantage for the patients.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Due to its ever-growing prevalence, the MASLD-related mental dysfunction represents an enormous socio-economic burden since it largely impacts on the quality of life of patients as well as on their working productivity. Indeed, cognitive decline in MASLD translates in low concentration and processing speed, reduced memory, sleepiness but also anxiety and depression. Chronic systemic inflammation, hyperammonemia, genetic background and intestinal dysbiosis possibly contribute to the cognitive decline in MASLD patients. However, its diagnosis is still underestimated since the leading mechanisms are multi-faceted and unexplained and do not exist standardized diagnostic tools or cognitive test strategies. In this scenario, nutritional and lifestyle interventions as well as intestinal microbiota manipulation (probiotics, fecal transplantation) may represent new approaches to counteract mental impairment in these subjects. In sum, to face the "mental aspect" of this multifactorial disease which is almost unexplored, cognitive tools should be introduced in the management of MASLD patients.

Identification of an APOE ε4-specific blood-based molecular pathway for Alzheimer's disease risk

INTRODUCTION

The precise apolipoprotein E (APOE) ε4-specific molecular pathway(s) for Alzheimer's disease (AD) risk are unclear.

METHODS

Plasma protein modules/cascades were analyzed using weighted gene co-expression network analysis (WGCNA) in the Alzheimer's Disease Neuroimaging Initiative study. Multivariable regression analyses were used to examine the associations among protein modules, AD diagnoses, cerebrospinal fluid (CSF) phosphorylated tau (p-tau), and brain glucose metabolism, stratified by APOE genotype.

RESULTS

The Green Module was associated with AD diagnosis in APOE ε4 homozygotes. Three proteins from this module, C-reactive protein (CRP), complement C3, and complement factor H (CFH), had dose-dependent associations with CSF p-tau and cognitive impairment only in APOE ε4 homozygotes. The link among these three proteins and glucose hypometabolism was observed in brain regions of the default mode network (DMN) in APOE ε4 homozygotes. A Framingham Heart Study validation study supported the findings for AD.

DISCUSSION

The study identifies the APOE ε4-specific CRP-C3-CFH inflammation pathway for AD, suggesting potential drug targets for the disease.Highlights: Identification of an APOE ε4 specific molecular pathway involving blood CRP, C3, and CFH for the risk of AD.CRP, C3, and CFH had dose-dependent associations with CSF p-Tau and brain glucose hypometabolism as well as with cognitive impairment only in APOE ε4 homozygotes.Targeting CRP, C3, and CFH may be protective and therapeutic for AD onset in APOE ε4 carriers.

Metabolic Overlap between Alzheimer's Disease and Metabolic Syndrome Identifies the PVRL2 Gene as a New Modulator of Diabetic Dyslipidemia

BACKGROUND

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) share metabolic alterations such as abnormal insulin and lipid metabolism and have some common genetic factors such as APOE genotype. Taking this into account, we hypothesized that we could identify common genetic factors involved in the development of diabetes and cardiovascular diseases.

METHODOLOGY

We first genotyped 48 single nucleotide polymorphisms (SNPs) previously associated with AD in a cohort composed of 330 patients with cognitive impairment (CI) to assess their association with plasma lipids. Second, we conducted pleiotropy-informed conjunctional false discovery rate (FDR) analysis designed to identify shared variants between AD and plasma lipid levels. Finally, we used the SNPs to be found associated with lipid parameters and AD to search for associations with lipoprotein parameters in 281 patients with cardiometabolic risk.

RESULTS

Five SNPs were significantly associated with lower levels of cholesterol transported in remnant lipoprotein particles (RLPc) in subjects with CI; among these SNPs was the rs73572039 variant in PVRL2. Stratified QQ-plots were conducted on GWAS designed for AD and triglycerides (TG). The cross-trait analysis resulted in a total of 22 independent genomic loci associated with both AD and TG levels with a conjFDR < 0.05. Among these loci, two pleiotropic variants were located in PVRL2 (rs12978931 and rs11667640). The three SNPs in PVRL2 were significantly associated with RLPc, TG, and number of circulating VLDL and HDL particles in subjects with cardiometabolic risk.

CONCLUSIONS

We have identified three variants in PVRL2 that predispose individuals to AD that also influence the lipid profile that confers cardiovascular risk in T2DM subjects. PVRL2 is a potential new modulating factor of atherogenic dyslipidemia.

Cognitive resilience and severe Alzheimer's disease neuropathology

Cognitive resilience in Alzheimer's disease (AD) can be defined as retention of high cognition despite presence of considerable cerebral AD lesions. We sought to identify factors associated with this phenomenon. Data were obtained from National Alzheimer's Coordinating Centre (NACC) dataset. Subjects with severe AD neuropathology, based on National Institute on Aging-Reagan (NIA-Reagan) criteria, no other primary neuropathology, and a ≤ 2-year interval between last follow-up and death were included. Mini-mental status examination score ≥ 24 was used as a proxy for normal cognition. In total, 654 cases were included; 59 (9%) were cognitively resilient. Multivariable logistic regression model showed that resilient participants were more educated, had a lower body mass index (BMI), were more likely to be lifetime/recent smoker or use an anticoagulant/antiplatelet agent, compared with cognitively impaired subjects. In addition to expected protective factors such as higher education and lower BMI, our results showed that smoking (especially recent smoking) and anticoagulant/antiplatelet consumption are associated with resilience to clinical cognitive expression of severe AD pathology. Pharmacological approaches using this information might be explored for clinical AD amelioration.

Crosstalk Between Gut Microflora and Vitamin D Receptor SNPs Are Associated with the Risk of Amnestic Mild Cognitive Impairment in a Chinese Elderly Population

Background: The interactions between environmental factors and genetic variants have been implicated in the pathogenesis of Alzheimer’s disease (AD). The altered gut microbiota (GM) and vitamin D deficiency are closely associated with the higher risk of AD. Objective: This study was performed to evaluate whether the crosstalk between GM and single nucleotide polymorphisms (SNPs) of vitamin D receptor (VDR) or vitamin D binding protein (VDBP) have a link with the risk of amnestic mild cognitive impairment (aMCI) in the Chinese elderly population. Methods: A total of 171 aMCI patients and 261 cognitive normal controls (NC) were enrolled in this study. Six tag SNPs of VDR and VDBP were genotyped by PCR-RFLP. The serum levels of vitamin D, Aβ1-42, and p-tau (181P) were determined by using of ELISA kits. The alterations in the GM were analyzed by full-length 16S ribosomal RNA (rRNA) gene sequencing. Results: The frequencies of AG genotype and A allele of VDR rs1544410 in aMCI group were significantly higher than that in NC group (genotype: p = 0.002, allele: p = 0.003). Patients with aMCI showed an abnormal GM composition compared with NC group. Interestingly, significant differences in GM composition were found between aMCI and NC group among individuals with AG genotype, as well as between individuals with AG and GG genotype of VDR rs1544410 among patients with aMCI. Conclusion: These results implicated that the crosstalk between gut microflora and vitamin D receptor variants are associated with the risk of aMCI in Chinese elderly population.

Likelihood ratio statistics for gene set enrichment in Alzheimer's disease pathways

INTRODUCTION

The study of Alzheimer's disease (AD) has revealed biological pathways with implications for disease neuropathology and pathophysiology. These pathway-level effects may also be mediated by individual characteristics or covariates such as age or sex. Evaluation of AD biological pathways in the context of interactions with these covariates is critical to the understanding of AD as well as the development of model systems used to study the disease.

METHODS

Gene set enrichment methods are powerful tools used to interpret gene-level statistics at the level of biological pathways. We introduce a method for quantifying gene set enrichment using likelihood ratio-derived test statistics (gsLRT), which accounts for sample covariates like age and sex. We then use our method to test for age and sex interactions with protein expression levels in AD and to compare the pathway results between human and mouse species.

RESULTS

Our method, based on nested logistic regressions is competitive with the existing standard for gene set testing in the context of linear models and complex experimental design. The gene sets we identify as having a significant association with AD-both with and without additional covariate interactions-are validated by previous studies. Differences between gsLRT results on mouse and human datasets are observed.

DISCUSSION

Characterizing biological pathways involved in AD builds on the important work involving single gene drivers. Our gene set enrichment method finds pathways that are significantly related to AD while accounting for covariates that may be relevant to disease development. The method highlights commonalities and differences between human AD and mouse models, which may inform the development of higher fidelity models for the study of AD.

A multivariate model of time to conversion from mild cognitive impairment to Alzheimer's disease

The present study was aimed at determining which combination of demographic, genetic, cognitive, neurophysiological, and neuroanatomical factors may predict differences in time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). To this end, a sample of 121 MCIs was followed up during a 5-year period. According to their clinical outcome, MCIs were divided into two subgroups: (i) the "progressive" MCI group (n = 46; mean time to progression 17 ± 9.73 months) and (ii) the "stable" MCI group (n = 75; mean time of follow-up 31.37 ± 14.58 months). Kaplan-Meier survival analyses were applied to explore each variable's relationship with the progression to AD. Once potential predictors were detected, Cox regression analyses were utilized to calculate a parsimonious model to estimate differences in time to progression. The final model included three variables (in order of relevance): left parahippocampal volume (corrected by intracranial volume, LP_ ICV), delayed recall (DR), and left inferior occipital lobe individual alpha peak frequency (LIOL_IAPF). Those MCIs with LP_ICV volume, DR score, and LIOL_IAPF value lower than the defined cutoff had 6 times, 5.5 times, and 3 times higher risk of progression to AD, respectively. Besides, when the categories of the three variables were "unfavorable" (i.e., values below the cutoff), 100% of cases progressed to AD at the end of follow-up. Our results highlighted the relevance of neurophysiological markers as predictors of conversion (LIOL_IAPF) and the importance of multivariate models that combine markers of different nature to predict time to progression from MCI to dementia.

Genetic and non-genetic factors associated with the phenotype of exceptional longevity & normal cognition

In this study, we split 2156 individuals from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) data into two groups, establishing a phenotype of exceptional longevity & normal cognition versus cognitive impairment. We conducted a genome-wide association study (GWAS) to identify significant genetic variants and biological pathways that are associated with cognitive impairment and used these results to construct polygenic risk scores. We elucidated the important and robust factors, both genetic and non-genetic, in predicting the phenotype, using several machine learning models. The GWAS identified 28 significant SNPs at p-value [Formula: see text] significance level and we pinpointed four genes, ESR1, PHB, RYR3, GRIK2, that are associated with the phenotype though immunological systems, brain function, metabolic pathways, inflammation and diet in the CLHLS cohort. Using both genetic and non-genetic factors, four machine learning models have close prediction results for the phenotype measured in Area Under the Curve: random forest (0.782), XGBoost (0.781), support vector machine with linear kernel (0.780), and [Formula: see text] penalized logistic regression (0.780). The top four important and congruent features in predicting the phenotype identified by these four models are: polygenic risk score, sex, age, and education.

A minor allele of the haplotype located in the 19q13 loci is associated with a decreased risk of hyper-LDL-cholesterolemia, and a balanced diet and high protein intake can reduce the risk

BACKGROUND

Although the human chromosome 19q13 loci are reported to be associated with hyper-LDL-cholesterolemia, the haplotype of single nucleotide polymorphism (SNP) has not been studied. Therefore, the association of the haplotype in 19q13 loci with hyper-LDL-cholesterolemia was determined and their interactions with lifestyles and nutrient intakes were evaluated in 28,445 Koreans aged > 40 years.

METHODS

SNPs were selected from 19q13 loci that had an association with hyper-LDL-cholesterolemia with the adjustment of confounders (age, gender, area of residence, and body mass index). Haplotype was constructed from the selected SNPs. An adjusted odds ratio of the haplotype for hyper-LDL-cholesterolemia and the interaction between haplotype and lifestyles was analyzed after adjusting for covariates.

RESULTS

Hyper-LDL-cholesterolemia had an association with apolipoprotein E (APOE)_ rs7259620, translocase of outer mitochondrial membrane 40(TOMM40)_rs157581, poliovirus receptor-related 2(PVRL2)_rs403155, exocyst complex component 3-like 2(EXOC3L2)_ rs10406604 and CD3e molecule-associated protein (CD3EAP)_rs3212986 in 19q13. The haplotype of these SNPs had a negative association with hyper-total-cholesterolemia and hyper-LDL-cholesterolemia by 0.669 and 0.684 times, respectively, after adjusting for covariates. The incidence of cardiovascular diseases, especially myocardial infarction, had a negative association with the minor alleles. The balanced diet pattern (BD) and protein intake had a significant interaction with the haplotype: the major-allele of the haplotype exhibited a positive association with hyper-LDL-cholesterolemia, compared to the minor allele, only when combined with a high intake of BD. The participants with the minor allele exhibited a lower hyper-LDL-cholesterolemia risk compared to those with the major allele only with high protein intake.

CONCLUSION

The minor allele of haplotype located in 19q13 loci protected against hyper-LDL-cholesterolemia, especially with BD and high protein intake. The minor allele also had a negative association with myocardial infarction events.

Shared genetic etiology underlying late-onset Alzheimer's disease and posttraumatic stress syndrome

INTRODUCTION

Late-onset Alzheimer's disease (LOAD) manifests comorbid neuropsychiatric symptoms and posttraumatic stress disorder (PTSD) is associated with an increased risk for dementia in late life, suggesting the two disorders may share genetic etiologies.

METHODS

We performed genetic pleiotropy analysis using LOAD and PTSD genome-wide association study (GWAS) datasets from white and African-American populations, followed by functional-genomic analyses.

RESULTS

We found an enrichment for LOAD across increasingly stringent levels of significance with the PTSD GWAS association (LOAD|PTSD) in the discovery and replication cohorts and a modest enrichment for the reverse conditional association (PTSD|LOAD). LOAD|PTSD association analysis identified and replicated the MS4A genes region. These genes showed similar expression pattern in brain regions affected in LOAD, and across-brain-tissue analysis identified a significant association for MS4A6A. The African-American samples showed moderate enrichment; however, no false discovery rate-significant associations.

DISCUSSION

We demonstrated common genetic signatures for LOAD and PTSD and suggested immune response as a common pathway for these diseases.

Shared genetic etiology underlying Alzheimer's disease and major depressive disorder

Patients with late-onset Alzheimer's disease (LOAD) frequently manifest comorbid neuropsychiatric symptoms with depression and anxiety being most frequent, and individuals with major depressive disorder (MDD) have an increased prevalence of LOAD. This suggests shared etiologies and intersecting pathways between LOAD and MDD. We performed pleiotropy analyses using LOAD and MDD GWAS data sets from the International Genomics of Alzheimer's Project (IGAP) and the Psychiatric Genomics Consortium (PGC), respectively. We found a moderate enrichment for SNPs associated with LOAD across increasingly stringent levels of significance with the MDD GWAS association (LOAD|MDD), of maximum four and eightfolds, including and excluding the APOE-region, respectively. Association analysis excluding the APOE-region identified numerous SNPs corresponding to 40 genes, 9 of which are known LOAD-risk loci primarily in chromosome 11 regions that contain the SPI1 gene and MS4A genes cluster, and others were novel pleiotropic risk-loci for LOAD conditional with MDD. The most significant associated SNPs on chromosome 11 overlapped with eQTLs found in whole-blood and monocytes, suggesting functional roles in gene regulation. The reverse conditional association analysis (MDD|LOAD) showed a moderate level, ~sevenfold, of polygenic overlap, however, no SNP showed significant association. Pathway analyses replicated previously reported LOAD biological pathways related to immune response and regulation of endocytosis. In conclusion, we provide insights into the overlapping genetic signatures underpinning the common phenotypic manifestations and inter-relationship between LOAD and MDD. This knowledge is crucial to the development of actionable targets for novel therapies to treat depression preceding dementia, in an effort to delay or ultimately prevent the onset of LOAD.