Mitochondrial DNA abundance in blood is associated with Alzheimer's disease- and dementia-risk.

The mitochondrial cascade hypothesis of Alzheimer's disease (AD) has been portrayed through molecular, cellular, and animal studies; however large epidemiological studies are lacking. This study aimed to explore the association of mitochondrial DNA copy number (mtDNAcn), a marker representative of mtDNA abundance per cell, with risk of incident all-cause dementia, AD, and vascular dementia diagnosis within 17 years and dementia-related blood biomarkers (P-tau181, GFAP, and NfL). Additionally, sex-stratified analyses were completed. In this German population-based cohort study (ESTHER), 9940 participants aged 50-75 years were enrolled by general practitioners and followed for 17 years. Participants were included in this study if information on dementia status and blood-based mtDNAcn measured via real-time polymerase chain reaction were available. In a nested case-control approach, a subsample of participants additionally had measurements of P-tau181, GFAP, and NfL in blood samples taken at baseline. Of 4913 participants eligible for analyses, 386 were diagnosed with incident all-cause dementia, including 130 AD and 143 vascular dementia cases, while 4527 participants remained without dementia diagnosis within 17 years. Participants with low mtDNAcn (lowest 10%) experienced 45% and 65% percent increased risk of incident all-cause dementia and AD after adjusting for age and sex (all-cause dementia: HRadj, 95%CI:1.45, 1.08-1.94; AD: HRadj, 95%CI: 1.65, 1.01-2.68). MtDNAcn was not associated to vascular dementia diagnosis and was more strongly associated with all-cause dementia among women. In the nested case-control study (n = 790), mtDNAcn was not significantly associated with the dementia-related blood biomarkers (P-tau181, GFAP, and NfL) levels in blood from baseline before dementia diagnosis. This study provides novel epidemiological evidence connecting mtDNA abundance, measured via mtDNAcn, to incident dementia and AD at the population-based level. Reduced mitochondrial abundance may play a role in pathogenesis, especially among women.

Plasma biomarkers and genetics in the diagnosis and prediction of Alzheimer's disease.

Plasma biomarkers for Alzheimer's disease-related pathologies have undergone rapid developments during the past few years, and there are now well-validated blood tests for amyloid and tau pathology, as well as neurodegeneration and astrocytic activation. To define Alzheimer's disease with biomarkers rather than clinical assessment, we assessed prediction of research-diagnosed disease status using these biomarkers and tested genetic variants associated with the biomarkers that may reflect more accurately the risk of biochemically defined Alzheimer's disease instead of the risk of dementia. In a cohort of Alzheimer's disease cases [n = 1439, mean age 68 years (standard deviation = 8.2)] and screened controls [n = 508, mean age 82 years (standard deviation = 6.8)], we measured plasma concentrations of the 40 and 42 amino acid-long amyloid-ß (Aß) fragments (Aß40 and Aß42, respectively), tau phosphorylated at amino acid 181 (P-tau181), neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) using state-of-the-art Single molecule array (Simoa) technology. We tested the relationships between the biomarkers and Alzheimer's disease genetic risk, age at onset and disease duration. We also conducted a genome-wide association study for association of disease risk genes with these biomarkers. The prediction accuracy of Alzheimer's disease clinical diagnosis by the combination of all biomarkers, APOE and polygenic risk score reached area under receiver operating characteristic curve (AUC) = 0.81, with the most significant contributors being ε4, Aß40 or Aß42, GFAP and NfL. All biomarkers were significantly associated with age in cases and controls (P < 4.3 × 10-5). Concentrations of the Aß-related biomarkers in plasma were significantly lower in cases compared with controls, whereas other biomarker levels were significantly higher in cases. In the case-control genome-wide analyses, APOE-ε4 was associated with all biomarkers (P = 0.011-4.78 × 10-8), except NfL. No novel genome-wide significant single nucleotide polymorphisms were found in the case-control design; however, in a case-only analysis, we found two independent genome-wide significant associations between the Aß42/Aß40 ratio and WWOX and COPG2 genes. Disease prediction modelling by the combination of all biomarkers indicates that the variance attributed to P-tau181 is mostly captured by APOE-ε4, whereas Aß40, Aß42, GFAP and NfL biomarkers explain additional variation over and above APOE. We identified novel plausible genome wide-significant genes associated with Aß42/Aß40 ratio in a sample which is 50 times smaller than current genome-wide association studies in Alzheimer's disease.

Dementias Platform UK: Bringing genetics into life.

INTRODUCTION: The Dementias Platform UK (DPUK) Data Portal is a data repository bringing together a wide range of cohorts. Neurodegenerative dementias are a group of diseases with highly heterogeneous pathology and an overlapping genetic component that is poorly understood. The DPUK collection of independent cohorts can facilitate research in neurodegeneration by combining their genetic and phenotypic data. METHODS: For genetic data processing, pipelines were generated to perform quality control analysis, genetic imputation, and polygenic risk score (PRS) derivation with six genome-wide association studies of neurodegenerative diseases. Pipelines were applied to five cohorts. DISCUSSION: The data processing pipelines, research-ready imputed genetic data, and PRS scores are now available on the DPUK platform and can be accessed upon request though the DPUK application process. Harmonizing genome-wide data for multiple datasets increases scientific opportunity and allows the wider research community to access and process data at scale and pace.

Alzheimer's disease-associated complement gene variants influence plasma complement protein levels.

BACKGROUND: Alzheimer's disease (AD) has been associated with immune dysregulation in biomarker and genome-wide association studies (GWAS). GWAS hits include the genes encoding complement regulators clusterin (CLU) and complement receptor 1 (CR1), recognised as key players in AD pathology, and complement proteins have been proposed as biomarkers. MAIN BODY: To address whether changes in plasma complement protein levels in AD relate to AD-associated complement gene variants we first measured relevant plasma complement proteins (clusterin, C1q, C1s, CR1, factor H) in a large cohort comprising early onset AD (EOAD; n = 912), late onset AD (LOAD; n = 492) and control (n = 504) donors. Clusterin and C1q were significantly increased (p < 0.001) and sCR1 and factor H reduced (p < 0.01) in AD plasma versus controls. ROC analyses were performed to assess utility of the measured complement biomarkers, alone or in combination with amyloid beta, in predicting AD. C1q was the most predictive single complement biomarker (AUC 0.655 LOAD, 0.601 EOAD); combining C1q with other complement or neurodegeneration makers through stepAIC-informed models improved predictive values slightly. Effects of GWS SNPs (rs6656401, rs6691117 in CR1; rs11136000, rs9331888 in CLU; rs3919533 in C1S) on protein concentrations were assessed by comparing protein levels in carriers of the minor vs major allele. To identify new associations between SNPs and changes in plasma protein levels, we performed a GWAS combining genotyping data in the cohort with complement protein levels as endophenotype. SNPs in CR1 (rs6656401), C1S (rs3919533) and CFH (rs6664877) reached significance and influenced plasma levels of the corresponding protein, whereas SNPs in CLU did not influence clusterin levels. CONCLUSION: Complement dysregulation is evident in AD and may contribute to pathology. AD-associated SNPs in CR1, C1S and CFH impact plasma levels of the encoded proteins, suggesting a mechanism for impact on disease risk.

Comparison of long-term effects of metformin on longevity between people with type 2 diabetes and matched non-diabetic controls.

BACKGROUND: Metformin, a medication for type 2 diabetes, has been linked to many non-diabetes health benefits including increasing healthy lifespan. Previous work has only examined the benefits of metformin over periods of less than ten years, which may not be long enough to capture the true effect of this medication on longevity. METHODS: We searched medical records for Wales, UK, using the Secure Anonymised Information Linkage dataset for type 2 diabetes patients treated with metformin (N = 129,140) and sulphonylurea (N = 68,563). Non-diabetic controls were matched on sex, age, smoking, and history of cancer and cardiovascular disease. Survival analysis was performed to examine survival time after first treatment, using a range of simulated study periods. FINDINGS: Using the full twenty-year period, we found that type 2 diabetes patients treated with metformin had shorter survival time than matched controls, as did sulphonylurea patients. Metformin patients had better survival than sulphonylurea patients, controlling for age. Within the first three years, metformin therapy showed a benefit over matched controls, but this reversed after five years of treatment. INTERPRETATION: While metformin does appear to confer benefits to longevity in the short term, these initial benefits are outweighed by the effects of type 2 diabetes when patients are observed over a period of up to twenty years. Longer study periods are therefore recommended for studying longevity and healthy lifespan. EVIDENCE BEFORE THIS STUDY: Work examining the non-diabetes outcomes of metformin therapy has suggested that there metformin has a beneficial effect on longevity and healthy lifespan. Both clinical trials and observational studies broadly support this hypothesis, but tend to be limited in the length of time over which they can study patients or participants. ADDED VALUE OF THIS STUDY: By using medical records we are able to study individuals with Type 2 diabetes over a period of two decades. We are also able to account for the effects of cancer, cardiovascular disease, hypertension, deprivation, and smoking on longevity and survival time following treatment. IMPLICATIONS OF ALL THE AVAILABLE EVIDENCE: We confirm that there is an initial benefit to longevity of metformin therapy, but this benefit does not outweigh the negative effect on longevity of diabetes. Therefore, we suggest that longer study periods are required for inference to be made about longevity in future research.

The pinna enhances angular discrimination in the frontal hemifield.

Human sound localization in the horizontal dimension is thought to be dominated by binaural cues, particularly interaural time delays, because monaural localization in this dimension is relatively poor. Remaining ambiguities of front versus back and up versus down are distinguished by high-frequency spectral cues generated by the pinna. The experiments in this study show that this account is incomplete. Using binaural listening throughout, the pinna substantially enhanced horizontal discrimination in the frontal hemifield, making discrimination in front better than discrimination at the rear, particularly for directions away from the median plane. Eliminating acoustic effects of the pinna by acoustically bypassing them or low-pass filtering abolished the advantage at the front without affecting the rear. Acoustic measurements revealed a pinna-induced spectral prominence that shifts smoothly in frequency as sounds move from 0° to 90° azimuth. The improved performance is discussed in terms of the monaural and binaural changes induced by the pinna.

Polygenic risk and pleiotropy in neurodegenerative diseases.

In this paper we explore the phenomenon of pleiotropy in neurodegenerative diseases, focusing on Alzheimer's disease (AD). We summarize the various techniques developed to investigate pleiotropy among traits, elaborating in the polygenic risk scores (PRS) analysis. PRS was designed to assess a cumulative effect of a large number of SNPs for association with a disease and, later for disease risk prediction. Since genetic predictions rely on heritability, we discuss SNP-based heritability from genome-wide association studies and its contribution to the prediction accuracy of PRS. We review work examining pleiotropy in neurodegenerative diseases and related phenotypes and biomarkers. We conclude that the exploitation of pleiotropy may aid in the identification of novel genes and provide further insights in the disease mechanisms, and along with PRS analysis, may be advantageous for precision medicine.

Subjective hearing and memory problems are associated with dementia and cognition in later life.

INTRODUCTION: Subjective hearing and memory problems are detectable earlier than objective measures of sensory loss and cognitive decline, which are known to be related to an increased risk of dementia in later life. METHODS: Using a population-representative cohort of 6006 individuals (aged 50-75) we examined whether participants who self-reported hearing and short-term memory issues showed greater rates of dementia within 17 years of follow-up. A sub-cohort was tested for audiometric threshold and cognition after 14 years. RESULTS: Hearing and memory problems were associated with a greater risk of dementia (hazard ratios [HRs] = 1.42 [95% confidence interval: 1.11-1.81], 1.57 [1.30-1.90]), and poorer cognition 14 years later. The risk was greatest in those reporting both problems (HR = 1.99 [1.42-2.80]). At follow-up, the level of hearing loss was associated with lower cognitive scores. DISCUSSION: Self-reports of hearing and short-term memory problems are associated with poorer cognitive performance and a greater risk of dementia. Subjective assessments may have predictive power over more than a decade. Highlights: In a sample of older adults subjective hearing and memory problems were associated with dementia risk.Cross-sectionally, the audiometric screening threshold was associated with cognitive test scores.Subjective sensory and memory loss questions are easy to implement and show good predictive power.

Comparison of subjective cognitive decline and polygenic risk score in the prediction of all-cause dementia, Alzheimer's disease and vascular dementia.

BACKGROUND: Polygenic risk scores (PRS) and subjective cognitive decline (SCD) are associated with the risk of developing dementia. It remains to examine whether they can improve the established cardiovascular risk factors aging and dementia (CAIDE) model and how their predictive abilities compare. METHODS: The CAIDE model was applied to a sub-sample of a large, population-based cohort study (n = 5,360; aged 50-75) and evaluated for the outcomes of all-cause dementia, Alzheimer's disease (AD) and vascular dementia (VD) by calculating Akaike's information criterion (AIC) and the area under the curve (AUC). The improvement of the CAIDE model by PRS and SCD was further examined using the net reclassification improvement (NRI) method and integrated discrimination improvement (IDI). RESULTS: During 17 years of follow-up, 410 participants were diagnosed with dementia, including 139 AD and 152 VD diagnoses. Overall, the CAIDE model showed high discriminative ability for all outcomes, reaching AUCs of 0.785, 0.793, and 0.789 for all-cause dementia, AD, and VD, respectively. Adding information on SCD significantly increased NRI for all-cause dementia (4.4%, p = 0.04) and VD (7.7%, p = 0.01). In contrast, prediction models for AD further improved when PRS was added to the model (NRI, 8.4%, p = 0.03). When APOE ε4 carrier status was included (CAIDE Model 2), AUCs increased, but PRS and SCD did not further improve the prediction. CONCLUSIONS: Unlike PRS, information on SCD can be assessed more efficiently, and thus, the model including SCD can be more easily transferred to the clinical setting. Nevertheless, the two variables seem negligible if APOE ε4 carrier status is available.

Severe psychiatric disorders are associated with increased risk of dementia.

BACKGROUND: Individuals with psychiatric disorders have an increased risk of developing dementia. Most cross-sectional studies suffer from selection bias, underdiagnosis and poor population representation, while there is only limited evidence from longitudinal studies on the role of anxiety, bipolar and psychotic disorders. Electronic health records (EHRs) permit large cohorts to be followed across the lifespan and include a wide range of diagnostic information. OBJECTIVE: To assess the association between four groups of psychiatric disorders (schizophrenia, bipolar disorder/mania, depression and anxiety) with dementia in two large population-based samples with EHR. METHODS: Using EHR on nearly 1 million adult individuals in Wales, and from 228 937 UK Biobank participants, we studied the relationships between schizophrenia, mania/bipolar disorder, depression, anxiety and subsequent risk of dementia. FINDINGS: In Secure Anonymised Information Linkage, there was a steep increase in the incidence of a first diagnosis of psychiatric disorder in the years prior to the diagnosis of dementia, reaching a peak in the year prior to dementia diagnosis for all psychiatric diagnoses. Psychiatric disorders, except anxiety, were highly significantly associated with a subsequent diagnosis of dementia: HRs=2.87, 2.80, 1.63 for schizophrenia, mania/bipolar disorder and depression, respectively. A similar pattern was found in the UK Biobank (HRs=4.46, 3.65, 2.39, respectively) and anxiety was also associated with dementia (HR=1.34). Increased risk of dementia was observed for all ages at onset of psychiatric diagnoses when these were divided into 10-year bins. CONCLUSIONS: Psychiatric disorders are associated with an increased risk of subsequent dementia, with a greater risk of more severe disorders. CLINICAL IMPLICATIONS: A late onset of psychiatric disorders should alert clinicians of possible incipient dementia.

New cases of dementia are rising in elderly populations in Wales, UK.

Dementia is one of the most common diseases in elderly populations, and older populations are one of the fastest growing groups globally. Consequently, the number of people developing and living with dementia is likely to grow. Using longitudinal medical records from Wales, UK between 1999 and 2018, diagnoses of overall dementia and common subtypes were combined with demographic data to assess numbers of new and existing cases per year. Data extraction resulted in 161,186 diagnoses from 116,645 individuals. Mean age at diagnosis of dementia increased over this period, resulting in fewer younger people with the disease. New cases of dementia have risen, as has the number of people living with dementia. Individuals with dementia are also living longer, even accounting for their older age. This may present a challenge for healthcare systems as the number of elderly people living with dementia is expected to continue to grow.

Identifying individuals with high risk of Alzheimer's disease using polygenic risk scores.

Polygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals' scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals' scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review.

Alzheimer's disease is a neurodegenerative disorder and the most common form of dementia. Early diagnosis may assist interventions to delay onset and reduce the progression rate of the disease. We systematically reviewed the use of machine learning algorithms for predicting Alzheimer's disease using single nucleotide polymorphisms and instances where these were combined with other types of data. We evaluated the ability of machine learning models to distinguish between controls and cases, while also assessing their implementation and potential biases. Articles published between December 2009 and June 2020 were collected using Scopus, PubMed and Google Scholar. These were systematically screened for inclusion leading to a final set of 12 publications. Eighty-five per cent of the included studies used the Alzheimer's Disease Neuroimaging Initiative dataset. In studies which reported area under the curve, discrimination varied (0.49-0.97). However, more than half of the included manuscripts used other forms of measurement, such as accuracy, sensitivity and specificity. Model calibration statistics were also found to be reported inconsistently across all studies. The most frequent limitation in the assessed studies was sample size, with the total number of participants often numbering less than a thousand, whilst the number of predictors usually ran into the many thousands. In addition, key steps in model implementation and validation were often not performed or unreported, making it difficult to assess the capability of machine learning models.