The SNP rs6859 in NECTIN2 gene is associated with underlying heterogeneous trajectories of cognitive changes in older adults.

BACKGROUND: Functional decline associated with dementia, including in Alzheimer's disease (AD), is not uniform across individuals, and respective heterogeneity is not yet fully explained. Such heterogeneity may in part be related to genetic variability among individuals. In this study, we investigated whether the SNP rs6859 in nectin cell adhesion molecule 2 (NECTIN2) gene (a major risk factor for AD) influences trajectories of cognitive decline in older participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). METHODS: We retrospectively analyzed records on 1310 participants from the ADNI database for the multivariate analysis. We used longitudinal measures of Mini-Mental State Examination (MMSE) scores in participants, who were cognitively normal, or having AD, or other cognitive deficits to investigate the trajectories of cognitive changes. Multiple linear regression, linear mixed models and latent class analyses were conducted to investigate the association of the SNP rs6859 with MMSE. RESULTS: The regression coefficient per one allele dose of the SNP rs6859 was independently associated with MMSE in both cross-sectional (-2.23, p < 0.01) and linear mixed models (-2.26, p < 0.01) analyses. The latent class model with three distinct subgroups (class 1: stable and gradual decline, class 2: intermediate and late decline, and class 3: lowest and irregular) performed best in the posterior classification, 42.67% (n = 559), 21.45% (n = 281), 35.88% (n = 470) were classified as class 1, class 2, and class 3. In the heterogeneous linear mixed model, the regression coefficient per one allele dose of rs6859 - A risk allele was significantly associated with MMSE class 1 and class 2 memberships and related decline; Class 1 (-2.28, 95% CI: -4.05, -0.50, p < 0.05), Class 2 (-5.56, 95% CI: -9.61, -1.51, p < 0.01) and Class 3 (-0.37, 95% CI: -1.62, 0.87, p = 0.55). CONCLUSIONS: This study found statistical evidence supporting the classification of three latent subclass groups representing complex MMSE trajectories in the ADNI cohort. The SNP rs6859 can be suggested as a candidate genetic predictor of variation in modeling MMSE trajectory, as well as for identifying latent classes with higher baseline MMSE. Functional studies may help further elucidate this relationship.

Genome-Wide Epistatic Network Analyses of Semantic Fluency in Older Adults.

Semantic fluency impairment has been attributed to a wide range of neurocognitive and psychiatric conditions, especially in the older population. Moderate heritability estimates on semantic fluency were obtained from both twin and family-based studies suggesting genetic contributions to the observed variation across individuals. Currently, effort in identifying the genetic variants underlying the heritability estimates for this complex trait remains scarce. Using the semantic fluency scale and genome-wide SNP genotype data from the Long Life Family Study (LLFS), we performed a genome-wide association study (GWAS) and epistasis network analysis on semantic fluency in 2289 individuals aged over 60 years from the American LLFS cohorts and replicated the findings in 1129 individuals aged over 50 years from the Danish LLFS cohort. In the GWAS, two SNPs with genome-wide significance (rs3749683, p = 2.52 × 10-8; rs880179, p = 4.83 × 10-8) mapped to the CMYAS gene on chromosome 5 were detected. The epistasis network analysis identified five modules as significant (4.16 × 10-5 < p < 7.35 × 10-3), of which two were replicated (p < 3.10 × 10-3). These two modules revealed significant enrichment of tissue-specific gene expression in brain tissues and high enrichment of GWAS catalog traits, e.g., obesity-related traits, blood pressure, chronotype, sleep duration, and brain structure, that have been reported to associate with verbal performance in epidemiological studies. Our results suggest high tissue specificity of genetic regulation of gene expression in brain tissues with epistatic SNP networks functioning jointly in modifying individual verbal ability and cognitive performance.

Differences in Risk of Alzheimer's Disease Following Later-Life Traumatic Brain Injury in Veteran and Civilian Populations.

OBJECTIVE: To directly compare the effect of incident age 68+ traumatic brain injury (TBI) on the risk of diagnosis of clinical Alzheimer's disease (AD) in the general population of older adults, and between male veterans and nonveterans; to assess how this effect changes with time since TBI. SETTING AND PARTICIPANTS: Community-dwelling traditional Medicare beneficiaries 68 years or older from the Health and Retirement Study (HRS). DESIGN: Fine-Gray models combined with inverse-probability weighting were used to identify associations between incident TBI, post-TBI duration, and TBI treatment intensity, with a diagnosis of clinical AD dementia. The study included 16 829 older adults followed over the 1991-2015 period. For analyses of veteran-specific risks, 4281 veteran males and 3093 nonveteran males were identified. Analysis of veteran females was unfeasible due to the age structure of the population. Information on occurrence(s) of TBI, and onset of AD and risk-related comorbidities was constructed from individual-level HRS-linked Medicare claim records while demographic and socioeconomic risk factors were based on the survey data. RESULTS: Later-life TBI was strongly associated with increased clinical AD risk in the full sample (pseudo-hazard ratio [HR]: 3.22; 95% confidence interval [CI]: 2.57-4.05) and in veteran/nonveteran males (HR: 5.31; CI: 3.42-7.94), especially those requiring high-intensity/duration care (HR: 1.58; CI: 1.29-1.91). Effect magnitude decreased with time following TBI (HR: 0.72: CI: 0.68-0.80). CONCLUSION: Later-life TBI was strongly associated with increased AD risk, especially in those requiring high-intensity/duration care. Effect magnitude decreased with time following TBI. Univariate analysis showed no differences in AD risk between veterans and nonveterans, while the protective effect associated with veteran status in Fine-Gray models was largely due to differences in demographics, socioeconomics, and morbidity. Future longitudinal studies incorporating diagnostic procedures and documentation quantifying lifetime TBI events are necessary to uncover pathophysiological mediating and/or moderating mechanisms between TBI and AD.

Epidemiology of geographic disparities in heart failure among US older adults: a Medicare-based analysis.

BACKGROUND: There are prominent geographic disparities in the life expectancy (LE) of older US adults between the states with the highest (leading states) and lowest (lagging states) LE and their causes remain poorly understood. Heart failure (HF) has been proposed as a major contributor to these disparities. This study aims to investigate geographic disparities in HF outcomes between the leading and lagging states. METHODS: The study was a secondary data analysis of HF outcomes in older US adults aged 65+, using Center for Disease Control and Prevention sponsored Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) database and a nationally representative 5% sample of Medicare beneficiaries over 2000-2017. Empiric estimates of death certificate-based mortality from HF as underlying cause of death (CBM-UCD)/multiple cause of death (CBM-MCD); HF incidence-based mortality (IBM); HF incidence, prevalence, and survival were compared between the leading and lagging states. Cox regression was used to investigate the effect of residence in the lagging states on HF incidence and survival. RESULTS: Between 2000 and 2017, HF mortality rates (per 100,000) were higher in the lagging states (CBM-UCD: 188.5-248.6; CBM-MCD: 749.4-965.9; IBM: 2656.0-2978.4) than that in the leading states (CBM-UCD: 79.4-95.6; CBM-MCD: 441.4-574.1; IBM: 1839.5-2138.1). Compared to their leading counterparts, lagging states had higher HF incidence (2.9-3.9% vs. 2.2-2.9%), prevalence (15.6-17.2% vs. 11.3-13.0%), and pre-existing prevalence at age 65 (5.3-7.3% vs. 2.8-4.1%). The most recent rates of one- (77.1% vs. 80.4%), three- (59.0% vs. 60.7%) and five-year (45.8% vs. 49.8%) survival were lower in the lagging states. A greater risk of HF incidence (Adjusted Hazards Ratio, AHR [95%CI]: 1.29 [1.29-1.30]) and death after HF diagnosis (AHR: 1.12 [1.11-1.13]) was observed for populations in the lagging states. The study also observed recent increases in CBMs and HF incidence, and declines in HF prevalence, prevalence at age 65 and survival with a decade-long plateau stage in IBM in both leading and lagging states. CONCLUSION: There are substantial geographic disparities in HF mortality, incidence, prevalence, and survival across the U.S.: HF incidence, prevalence at age 65 (age of Medicare enrollment), and survival of patients with HF contributed most to these disparities. The geographic disparities and the recent increase in incidence and decline in survival underscore the importance of HF prevention strategies.

A Genome-Wide Association Study of 2304 Extreme Longevity Cases Identifies Novel Longevity Variants.

We performed a genome-wide association study (GWAS) of human extreme longevity (EL), defined as surviving past the 99th survival percentile, by aggregating data from four centenarian studies. The combined data included 2304 EL cases and 5879 controls. The analysis identified a locus in CDKN2B-AS1 (rs6475609, p = 7.13 × 10-8) that almost reached genome-wide significance and four additional loci that were suggestively significant. Among these, a novel rare variant (rs145265196) on chromosome 11 had much higher longevity allele frequencies in cases of Ashkenazi Jewish and Southern Italian ancestry compared to cases of other European ancestries. We also correlated EL-associated SNPs with serum proteins to link our findings to potential biological mechanisms that may be related to EL and are under genetic regulation. The findings from the proteomic analyses suggested that longevity-promoting alleles of significant genetic variants either provided EL cases with more youthful molecular profiles compared to controls or provided some form of protection from other illnesses, such as Alzheimer's disease, and disease progressions.

Partitioning of time trends in prevalence and mortality of lung cancer.

BACKGROUND: Time trends of lung cancer prevalence and mortality are the result of three competing processes: changes in the incidence rate, stage-specific survival, and ascertainment at early stages. Improvements in these measures act concordantly to improve disease-related mortality, but push the prevalence rate in opposite directions making a qualitative interpretation difficult. The goal of this paper is to evaluate the relative contributions of these components to changes in lung cancer prevalence and mortality. METHODS: Partitioning of prevalence and mortality trends into their components using SEER data for 1973-2013. RESULTS: The prevalence of lung cancer increases for females and decreases for males. In 1998, the former was due to increased incidence (45%-50% of total trend), improved survival (40%-45%), and increased ascertainment at early stages (10%-15%). In males, a rapidly declining incidence rate overpowered the effects of survival and ascertainment resulting in an overall decrease in prevalence over time. Trends in lung cancer mortality are determined by incidence during 1993-2002 with noticeable contribution of survival after 2002. CONCLUSION: Lung cancer incidence was the main driving force behind trends in prevalence and mortality. Improved survival played essential role from 2000 onwards. Trends in stage ascertainment played a small but adverse role. Our results suggest that further improvement in lung cancer mortality can be achieved through advances in early stage ascertainment, especially for males, and that in spite of success in treatment, adenocarcinoma continues to exhibit adverse trends (especially in female incidence) and its role among other histology-specific lung cancers will increase in the near future.

Pleiotropic Associations of Allelic Variants in a 2q22 Region with Risks of Major Human Diseases and Mortality.

Gaining insights into genetic predisposition to age-related diseases and lifespan is a challenging task complicated by the elusive role of evolution in these phenotypes. To gain more insights, we combined methods of genome-wide and candidate-gene studies. Genome-wide scan in the Atherosclerosis Risk in Communities (ARIC) Study (N = 9,573) was used to pre-select promising loci. Candidate-gene methods were used to comprehensively analyze associations of novel uncommon variants in Caucasians (minor allele frequency~2.5%) located in band 2q22.3 with risks of coronary heart disease (CHD), heart failure (HF), stroke, diabetes, cancer, neurodegenerative diseases (ND), and mortality in the ARIC study, the Framingham Heart Study (N = 4,434), and the Health and Retirement Study (N = 9,676). We leveraged the analyses of pleiotropy, age-related heterogeneity, and causal inferences. Meta-analysis of the results from these comprehensive analyses shows that the minor allele increases risks of death by about 50% (p = 4.6×10-9), CHD by 35% (p = 8.9×10-6), HF by 55% (p = 9.7×10-5), stroke by 25% (p = 4.0×10-2), and ND by 100% (p = 1.3×10-3). This allele also significantly influences each of two diseases, diabetes and cancer, in antagonistic fashion in different populations. Combined significance of the pleiotropic effects was p = 6.6×10-21. Causal mediation analyses show that endophenotypes explained only small fractions of these effects. This locus harbors an evolutionary conserved gene-desert region with non-coding intergenic sequences likely involved in regulation of protein-coding flanking genes ZEB2 and ACVR2A. This region is intensively studied for mutations causing severe developmental/genetic disorders. Our analyses indicate a promising target region for interventions aimed to reduce risks of many major human diseases and mortality.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data.

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10-7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10-7 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

rqt: an R package for gene-level meta-analysis.

MOTIVATION: Despite recent advances of modern GWAS methods, it is still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. RESULTS: We developed an R package rqt, which offers gene-level GWAS meta-analysis. The package can be easily included into bioinformatics pipeline or used stand-alone. We applied this tool to the analysis of Alzheimer's disease data from three datasets CHS, FHS and LOADFS. Test results from meta-analysis of three Alzheimer studies show its applicability for association testing. AVAILABILITY AND IMPLEMENTATION: The package rqt is freely available under the following link: https://github.com/izhbannikov/rqt. CONTACT: ilya.zhbannikov@duke.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

stpm: an R package for stochastic process model.

BACKGROUND: The Stochastic Process Model (SPM) represents a general framework for modeling the joint evolution of repeatedly measured variables and time-to-event outcomes observed in longitudinal studies, i.e., SPM relates the stochastic dynamics of variables (e.g., physiological or biological measures) with the probabilities of end points (e.g., death or system failure). SPM is applicable for analyses of longitudinal data in many research areas; however, there are no publicly available software tools that implement this methodology. RESULTS: We developed an R package stpm for the SPM-methodology. The package estimates several versions of SPM currently available in the literature including discrete- and continuous-time multidimensional models and a one-dimensional model with time-dependent parameters. Also, the package provides tools for simulation and projection of individual trajectories and hazard functions. CONCLUSION: In this paper, we present the first software implementation of the SPM-methodology by providing an R package stpm, which was verified through extensive simulation and validation studies. Future work includes further improvements of the model. Clinical and academic researchers will benefit from using the presented model and software. The R package stpm is available as open source software from the following links: https://cran.r-project.org/package=stpm (stable version) or https://github.com/izhbannikov/spm (developer version).

Puzzling role of genetic risk factors in human longevity: "risk alleles" as pro-longevity variants.

Complex diseases are major contributors to human mortality in old age. Paradoxically, many genetic variants that have been associated with increased risks of such diseases are found in genomes of long-lived people, and do not seem to compromise longevity. Here we argue that trade-off-like and conditional effects of genes can play central role in this phenomenon and in determining longevity. Such effects may occur as result of: (i) antagonistic influence of gene on the development of different health disorders; (ii) change in the effect of gene on vulnerability to death with age (especially, from "bad" to "good"); (iii) gene-gene interaction; and (iv) gene-environment interaction, among other factors. A review of current knowledge provides many examples of genetic factors that may increase the risk of one disease but reduce chances of developing another serious health condition, or improve survival from it. Factors that may increase risk of a major disease but attenuate manifestation of physical senescence are also discussed. Overall, available evidence suggests that the influence of a genetic variant on longevity may be negative, neutral or positive, depending on a delicate balance of the detrimental and beneficial effects of such variant on multiple health and aging related traits. This balance may change with age, internal and external environments, and depend on genetic surrounding. We conclude that trade-off-like and conditional genetic effects are very common and may result in situations when a disease "risk allele" can also be a pro-longevity variant, depending on context. We emphasize importance of considering such effects in both aging research and disease prevention.

Protective role of the apolipoprotein E2 allele in age-related disease traits and survival: evidence from the Long Life Family Study.

The apolipoprotein E (apoE) is a classic example of a gene exhibiting pleiotropism. We examine potential pleiotropic associations of the apoE2 allele in three biodemographic cohorts of long-living individuals, offspring, and spouses from the Long Life Family Study, and intermediate mechanisms, which can link this allele with age-related phenotypes. We focused on age-related macular degeneration, bronchitis, asthma, pneumonia, stroke, creatinine, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, diseases of heart (HD), cancer, and survival. Our analysis detected favorable associations of the ε2 allele with lower LDL-C levels, lower risks of HD, and better survival. The ε2 allele was associated with LDL-C in each gender and biodemographic cohort, including long-living individuals, offspring, and spouses, resulting in highly significant association in the entire sample (ß = -7.1, p = 6.6 × 10-44). This allele was significantly associated with HD in long-living individuals and offspring (relative risk [RR] = 0.60, p = 3.1 × 10-6) but this association was not mediated by LDL-C. The protective effect on survival was specific for long-living women but it was not explained by LDL-C and HD in the adjusted model (RR = 0.70, p = 2.1 × 10-2). These results show that ε2 allele may favorably influence LDL-C, HD, and survival through three mechanisms. Two of them (HD- and survival-related) are pronounced in the long-living parents and their offspring; the survival-related mechanism is also sensitive to gender. The LDL-C-related mechanism appears to be independent of these factors. Insights into mechanisms linking ε2 allele with age-related phenotypes given biodemographic structure of the population studied may benefit translation of genetic discoveries to health care and personalized medicine.

How the effects of aging and stresses of life are integrated in mortality rates: insights for genetic studies of human health and longevity.

Increasing proportions of elderly individuals in developed countries combined with substantial increases in related medical expenditures make the improvement of the health of the elderly a high priority today. If the process of aging by individuals is a major cause of age related health declines then postponing aging could be an efficient strategy for improving the health of the elderly. Implementing this strategy requires a better understanding of genetic and non-genetic connections among aging, health, and longevity. We review progress and problems in research areas whose development may contribute to analyses of such connections. These include genetic studies of human aging and longevity, the heterogeneity of populations with respect to their susceptibility to disease and death, forces that shape age patterns of human mortality, secular trends in mortality decline, and integrative mortality modeling using longitudinal data. The dynamic involvement of genetic factors in (i) morbidity/mortality risks, (ii) responses to stresses of life, (iii) multi-morbidities of many elderly individuals, (iv) trade-offs for diseases, (v) genetic heterogeneity, and (vi) other relevant aging-related health declines, underscores the need for a comprehensive, integrated approach to analyze the genetic connections for all of the above aspects of aging-related changes. The dynamic relationships among aging, health, and longevity traits would be better understood if one linked several research fields within one conceptual framework that allowed for efficient analyses of available longitudinal data using the wealth of available knowledge about aging, health, and longevity already accumulated in the research field.

ON IDENTIFIABILITY OF MIXTURES OF INDEPENDENT DISTRIBUTION LAWS, .

We consider representations of a joint distribution law of a family of categorical random variables (i.e., a multivariate categorical variable) as a mixture of independent distribution laws (i.e. distribution laws according to which random variables are mutually independent). For infinite families of random variables, we describe a class of mixtures with identifiable mixing measure. This class is interesting from a practical point of view as well, as its structure clarifies principles of selecting a "good" finite family of random variables to be used in applied research. For finite families of random variables, the mixing measure is never identifiable; however, it always possesses a number of identifiable invariants, which provide substantial information regarding the distribution under consideration.

Genome-Related Mechanisms Contributing to Differences in Alzheimer's Disease Incidence Between White and Black Older US Adults.

In this manuscript, we leverage a modified GWAS algorithm adapted for use with multidimensional Cox models and data from the Health and Retirement Study to explore how genetic variation influences the size of the disparity in Alzheimer's disease (AD) incidence between older Black and White US adults. We identified four loci that were associated with higher AD incidence levels in older Black adults: (1) rs11077034 (hazard ratio (HR), 4.98) from the RBFOX1 gene; (2) rs7144494 (HR, 1.68) from the HISLA gene; (3) rs7660552 (HR, 3.07) from the SLC25A4 gene; and (4) rs12599485 (HR, 3.181) from the NIP30 gene. The RBFOX1, HISLA, SLC25A4, and NIP30 genes are known to be associated with AD (RBFOX1, NIP30) directly, and also influence the risk of AD risk-related morbidities such as hypertension (RBFOX1, SLC25A4), depression (SLC25A4), and certain cancers (HISLA, SLC25A4). A likely disparity-generating mechanism may lie in endocytosis and abnormal tissue growing mechanisms, depending on inherited gene mutations and the structure of proxies as well as gene-environment interactions with other risk factors such as lifestyle, education level, and access to adequate medical services.

Graves disease is associated with increased risk of clinical Alzheimer's disease: evidence from the Medicare system.

BACKGROUND: Identification of modifiable risk factors for Alzheimer's Disease (AD) onset is an important aspect of controlling the burden imposed by this disease on an increasing number of older U.S. adults. Graves disease (GD), the most common cause of hyperthyroidism in the U.S., has been hypothesized to be associated with increased AD risk, but there is no consensus. In this study, we explore the link between GD and risk of clinical AD. METHODS: Cox and Fine-Grey models were applied to a retrospective propensity-score-matched cohort of 19,798 individuals with GD drawn from a nationally representative 5% sample of U.S. Medicare beneficiaries age 65 + over the 1991-2020 period. RESULTS: Results showed that the presence of GD was associated with a higher risk of AD (Hazard Ratio [HR]:1.19; 95% Confidence Interval [CI]:1.13-1.26). Competing risk estimates were consistent with these findings (HR:1.14; CI:1.08-1.20) with the magnitude of associated risk varying across subgroups: Male (HR:1.25; CI:1.07-1.47), Female (HR:1.09; CI:1.02-1.16), White (HR:1.11; CI:1.03-1.19), and Black (HR:1.23; CI:1.02-1.49). CONCLUSIONS: Our results indicate a robust and consistent association between a diagnosis of GD and a subsequent diagnosis of AD in later stages of life. The precise biological pathways that could potentially connect these two conditions remain unclear as is the role of treatment in this relationship. Replications of these findings on datasets with both biomarkers and laboratory test results, especially in underrepresented groups is vital.

APOE4 and Infectious Diseases Jointly Contribute to Brain Glucose Hypometabolism, a Biomarker of Alzheimer's Pathology: New findings from the ADNI.

Introduction: We investigated the interplay between infections and APOE4 on brain glucose hypometabolism, an early preclinical feature of Alzheimer's Disease (AD) pathology. Methods: Multivariate linear regression analysis was performed on 1,509 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The outcomes were the rank-normalized hypometabolic convergence index (HCI) and statistical regions of interest (SROI) for AD and mild cognitive impairment (MCI). Further, the HCI and its change in the presence and absence of APOE4 were evaluated. Results: Infections were associated with greater hypometabolism [0.15, 95% CI: 0.03, 0.27, p=0.01], with a more pronounced effect among APOE4 carriers, indicating an interaction effect. A higher HCI (0.44, p=0.01) was observed in APOE4 carriers with multiple infections, compared to (0.11, p=0.08) for those with a single infection, revealing a dose-response relationship. The corresponding estimates for the association of infections with SROI AD and SROI MCI were -0.01 (p=0.02) and -0.01 (p=0.04) respectively. Conclusion: Our findings suggest that infections and APOE4 jointly contribute to brain glucose hypometabolism and AD pathology, supporting a "multi-hit" mechanism in AD development.

Traffic-related air pollution and APOE4 can synergistically affect hippocampal volume in older women: new findings from UK Biobank.

A growing research body supports the connection between neurodegenerative disorders, including Alzheimer's disease (AD), and traffic-related air pollution (TRAP). However, the underlying mechanisms are not well understood. A deeper investigation of TRAP effects on hippocampal volume (HV), a major biomarker of neurodegeneration, may help clarify these mechanisms. Here, we explored TRAP associations with the HV in older participants of the UK Biobank (UKB), taking into account the presence of APOE e4 allele (APOE4), the strongest genetic risk factor for AD. Exposure to TRAP was approximated by the distance of the participant's main residence to the nearest major road (DNMR). The left/right HV was measured by magnetic resonance imaging (MRI) in cubic millimeters (mm3). Analysis of variance (ANOVA), Welch test, and regression were used to examine statistical significance. We found significant interactions between DNMR and APOE4 that influenced HV. Specifically, DNMR <50m (equivalent of a chronically high exposure to TRAP), and carrying APOE4 were synergistically associated with a significant (P = 0.01) reduction in the right HV by about 2.5% in women aged 60-75 years (results for men didn't reach a statistical significance). Results of our study suggest that TRAP and APOE4 jointly promote neurodegeneration in women. Living farther from major roads may help reduce the risks of neurodegenerative disorders, including AD, in female APOE4 carriers.

The association between rs6859 in NECTIN2 gene and Alzheimer's disease is partly mediated by pTau.

Introduction: Emerging evidence suggests a connection between vulnerability to infections and Alzheimer's disease (AD). The nectin cell adhesion molecule 2 (NECTIN2) gene coding for a membrane component of adherens junctions is involved in response to infections, and its single nucleotide polymorphism (SNP) rs6859 was significantly associated with AD risk in several human cohorts. It is unclear, however, how exactly rs6859 influences the development of AD pathology. The aggregation of hyperphosphorylated tau protein (pTau) is a key pathological feature of neurodegeneration in AD, which may be induced by infections, among other factors, and potentially influenced by genes involved in both AD and vulnerability to infections, such as NECTIN2. Materials and methods: We conducted a causal mediation analysis (CMA) on a sample of 708 participants in the Alzheimer's disease Neuroimaging Initiative (ADNI). The relationship between rs6859 and Alzheimer's disease (AD), with AD (yes/no) as the outcome and pTau-181 levels in the cerebrospinal fluid (CSF) acting as a mediator in this association, was assessed. Adjusted estimates from the probit and linear regression models were used in the CMA model, where an additive model considered an increase in dosage of the rs6859 A allele (AD risk factor). Results: The increase in dose of allele A of the SNP rs6859 resulted in about 0.144 increase per standard deviation (SD) of pTau-181 (95% CI: 0.041, 0.248, p < 0.01). When included together in the probit model, the change in A allele dose and each standard deviation change in pTau-181 predicted 6.84% and 9.79% higher probabilities for AD, respectively. In the CMA, the proportion of the average mediated effect was 17.05% and was higher for the risk allele homozygotes (AA), at 19.40% (95% CI: 6.20%, 43.00%, p < 0.01). The sensitivity analysis confirmed the evidence of a robust mediation effect. Conclusion: This study reported a new potential causal relationship between pTau-181 and AD. We found that the association between rs6859 in the NECTIN2 gene and AD is partly mediated by pTau-181 levels in CSF. The rest of this association may be mediated by other factors. Our finding sheds light on the complex interplay between genetic susceptibility, protein aggregation, and neurodegeneration in AD. Further research, using other biomarkers, is needed to uncover the remaining mechanisms of the association between the NECTIN2 gene and AD.

The association between rs6859 in NECTIN2 gene and Alzheimer's disease is partly mediated by pTau.

Introduction: Emerging evidence suggests a connection between vulnerability to infections and Alzheimer's disease (AD). The nectin cell adhesion molecule 2 (NECTIN2) gene coding for a membrane component of adherens junctions is involved in response to infection, and its single nucleotide polymorphism (SNP) rs6859 was significantly associated with AD risk in several human cohorts. It is unclear, however, how exactly rs6859 influences the development of AD pathology. The aggregation of hyperphosphorylated tau protein (pTau) is a key pathological feature of neurodegeneration in AD, which may be induced by infections, among other factors, and potentially influenced by genes involved in both AD and vulnerability to infections, such as NECTIN2. Materials and methods: We conducted a causal mediation analysis (CMA) on a sample of 708 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI). The relationship between rs6859 and Alzheimer's disease (AD), with AD (yes/no) as the outcome and pTau-181 levels in the cerebrospinal fluid (CSF) acting as a mediator in this association, was assessed. Adjusted estimates from the probit and linear regression models were used in the CMA model, where an additive model considered an increase in dosage of the rs6859 A allele (AD risk factor). Results: The increase in dose of allele A of the SNP rs6859 resulted in about 0.144 increase per standard deviation (SD) of pTau-181 (95% CI: 0.041, 0.248, p<0.01). When included together in the probit model, the change in A allele dose and each standard deviation change in pTau-181 predicted 6.84% and 9.79% higher probabilities for AD, respectively. In the CMA, the proportion of the average mediated effect was 17.05% and was higher for the risk allele homozygotes (AA), at 19.40% (95% CI: 6.20%, 43.00%, p<0.01). The sensitivity analysis confirmed the evidence of a robust mediation effect. Conclusion: This study reported a new causal relationship between pTau-181 and AD. We found that the association between rs6859 in the NECTIN2 gene and AD is partly mediated by pTau-181 levels in CSF. The rest of this association may be mediated by other factors. Further research, using other biomarkers, is needed to uncover the remaining mechanisms of the association between the NECTIN2 gene and AD.