The value of hippocampal and temporal horn volumes and rates of change in predicting future conversion to AD.

Hippocampal pathology occurs early in Alzheimer disease (AD), and atrophy, measured by volumes and volume changes, may predict which subjects will develop AD. Measures of the temporal horn (TH), which is situated adjacent to the hippocampus, may also indicate early changes in AD. Previous studies suggest that these metrics can predict conversion from amnestic mild cognitive impairment (MCI) to AD with conversion and volume change measured concurrently. However, the ability of these metrics to predict future conversion has not been investigated. We compared the abilities of hippocampal, TH, and global measures to predict future conversion from MCI to AD. TH, hippocampi, whole brain, and ventricles were measured using baseline and 12-month scans. Boundary shift integral was used to measure the rate of change. We investigated the prediction of conversion between 12 and 24 months in subjects classified as MCI from baseline to 12 months. All measures were predictive of future conversion. Local and global rates of change were similarly predictive of conversion. There was evidence that the TH expansion rate is more predictive than the hippocampal atrophy rate (P=0.023) and that the TH expansion rate is more predictive than the TH volume (P=0.036). Prodromal atrophy rates may be useful predictors of future conversion to sporadic AD from amnestic MCI

The age-dependent associations of white matter hyperintensities and neurofilament light in early- and late-stage Alzheimer's disease

Neurofilament light (NFL) is an emerging marker of axonal degeneration. This study investigated the relationship between white matter hyperintensities (WMHs) and plasma NFL in a large elderly cohort with, and without, cognitive impairment. We used the Alzheimer's Disease Neuroimaging Initiative and included 163 controls, 103 participants with a significant memory concern, 279 with early mild cognitive impairment (EMCI), 152 with late mild cognitive impairment (LMCI), and 130 with Alzheimer's disease, with 3T MRI and plasma NFL data. Multiple linear regression models examined the relationship between WMHs and NFL, with and without age adjustment. We used smoking status, history of hypertension, history of diabetes, and BMI as additional covariates to examine the effect of vascular risk. We found increases of between 20% and 41% in WMH volume per 1SD increase in NFL in significant memory concern, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease groups (p < 0.02). Marked attenuation of the positive associations between WMHs and NFL were seen after age adjustment, suggesting that a significant proportion of the association between NFL and WMHs is age-related. No effect of vascular risk was observed. These results are supportive of a link between WMH and axonal degeneration in early to late disease stages, in an age-dependent, but vascular risk-independent manner

The value of hippocampal and temporal horn volumes and rates of change in predicting future conversion to AD.

Hippocampal pathology occurs early in Alzheimer disease (AD), and atrophy, measured by volumes and volume changes, may predict which subjects will develop AD. Measures of the temporal horn (TH), which is situated adjacent to the hippocampus, may also indicate early changes in AD. Previous studies suggest that these metrics can predict conversion from amnestic mild cognitive impairment (MCI) to AD with conversion and volume change measured concurrently. However, the ability of these metrics to predict future conversion has not been investigated. We compared the abilities of hippocampal, TH, and global measures to predict future conversion from MCI to AD. TH, hippocampi, whole brain, and ventricles were measured using baseline and 12-month scans. Boundary shift integral was used to measure the rate of change. We investigated the prediction of conversion between 12 and 24 months in subjects classified as MCI from baseline to 12 months. All measures were predictive of future conversion. Local and global rates of change were similarly predictive of conversion. There was evidence that the TH expansion rate is more predictive than the hippocampal atrophy rate (P=0.023) and that the TH expansion rate is more predictive than the TH volume (P=0.036). Prodromal atrophy rates may be useful predictors of future conversion to sporadic AD from amnestic MCI

CSF amyloid is a consistent predictor of white matter hyperintensities across the disease course from aging to Alzheimer's disease.

This study investigated the relationship between white matter hyperintensities (WMH) and cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers. Subjects included 180 controls, 107 individuals with a significant memory concern, 320 individuals with early mild cognitive impairment, 171 individuals with late mild cognitive impairment, and 151 individuals with AD, with 3T MRI and CSF Aβ1-42, total tau (t-tau), and phosphorylated tau (p-tau) data. Multiple linear regression models assessed the relationship between WMH and CSF Aβ1-42, t-tau, and p-tau. Directionally, a higher WMH burden was associated with lower CSF Aβ1-42 within each diagnostic group, with no evidence for a difference in the slope of the association across diagnostic groups (p = 0.4). Pooling all participants, this association was statistically significant after adjustment for t-tau, p-tau, age, diagnostic group, and APOE-ε4 status (p < 0.001). Age was the strongest predictor of WMH (partial R2~16%) compared with CSF Aβ1-42 (partial R2~5%). There was no evidence for an association with WMH and either t-tau or p-tau. These data are supportive of a link between amyloid burden and presumed vascular pathology

Using CSF biomarkers to replicate genetic associations in Alzheimer's disease.

Defining cases and controls on the basis of biomarkers rather than clinical diagnosis may reduce sample sizes required for genetic studies. The aim of this study was to assess whether characterizing case/control status on the basis of cerebrospinal fluid (CSF) profile would increase power to replicate known genetic associations for Alzheimer's disease (AD). Independent of clinical diagnosis, Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects with 2 CSF biomarkers for AD (Aβ1–42 23 pg/mL, “CSF-positive”) were compared with those without CSF evidence for AD (Aβ1–42 > 192 pg/mL and 181-phosphorylated tau < 23 pg/mL, “CSF-negative”). Minor allele frequency (MAF) and odds ratios (ORs) between these 2 groups were calculated for 7 single-nucleotide polymorphisms (SNPs) of interest. Two hundred thirty-two individuals were CSF-positive and 94 CSF-negative. There were no differences in age (74.7 ± 7.2 vs. 75.0 ± 6.5 years, p = 0.7), but significant differences in Mini Mental State Examination (MMSE) (25.9 ± 2.6 vs. 28.2 ± 1.7, p < 0.001) between the CSF-positive and CSF-negative groups. Significant differences in MAF (p < 0.05, uncorrected) were seen for CR1 (rs1408077; OR, 1.59; 95% confidence interval [CI], 1.01–2.49), PICALM (rs541458; OR, 0.68, 95% CI, 0.47–0.98), TOMM40 (rs2075650; OR, 4.30; 95% CI, 2.61–7.06); and possession of 1 or more APOE ε4 alleles (OR, 9.84; 95% CI, 5.48–17.67). These results suggest that using biomarkers of AD pathology to define case and control status may increase power in genetic association studies

Transferability of Alzheimer's disease progression subtypes to an independent population cohort

In the past, methods to subtype or biotype patients using brain imaging data have been developed. However, it is unclear whether and how these trained machine learning models can be successfully applied to population cohorts to study the genetic and lifestyle factors underpinning these subtypes. This work, using the Subtype and Stage Inference (SuStaIn) algorithm, examines the generalisability of data-driven Alzheimer's disease (AD) progression models.We first compared SuStaIn models trained separately on Alzheimer's disease neuroimaging initiative (ADNI) data and an AD-at-risk population constructed from the UK Biobank dataset. We further applied data harmonization techniques to remove cohort effects. Next, we built SuStaIn models on the harmonized datasets, which were then used to subtype and stage subjects in the other harmonized dataset.The first key finding is that three consistent atrophy subtypes were found in both datasets, which match the previously identified subtype progression patterns in AD: ‘typical’, ‘cortical’ and ‘subcortical’. Next, the subtype agreement was further supported by high consistency in individuals’ subtypes and stage assignment based on the different models: more than 92% of the subjects, with reliable subtype assignment in both ADNI and UK Biobank dataset, were assigned to an identical subtype under the model built on the different datasets. The successful transferability of AD atrophy progression subtypes across cohorts capturing different phases of disease development enabled further investigations of associations between AD atrophy subtypes and risk factors. Our study showed that (1) the average age is highest in the typical subtype and lowest in the subcortical subtype; (2) the typical subtype is associated with statistically more-AD-like cerebrospinal fluid biomarkers values in comparison to the other two subtypes; and (3) in comparison to the subcortical subtype, the cortical subtype subjects are more likely to associate with prescription of cholesterol and high blood pressure medications.In summary, we presented cross-cohort consistent recovery of AD atrophy subtypes, showing how the same subtypes arise even in cohorts capturing substantially different disease phases. Our study opened opportunities for future detailed investigations of atrophy subtypes with a broad range of early risk factors, which will potentially lead to a better understanding of the disease aetiology and the role of lifestyle and behaviour on AD

Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups

Categorizing people with late-onset Alzheimer's disease into biologically coherent subgroups is important for personalized medicine. We evaluated data from five studies (total n = 4050, of whom 2431 had genome-wide single-nucleotide polymorphism (SNP) data). We assigned people to cognitively defined subgroups on the basis of relative performance in memory, executive functioning, visuospatial functioning, and language at the time of Alzheimer's disease diagnosis. We compared genotype frequencies for each subgroup to those from cognitively normal elderly controls. We focused on APOE and on SNPs with p < 10-5 and odds ratios more extreme than those previously reported for Alzheimer's disease (<0.77 or >1.30). There was substantial variation across studies in the proportions of people in each subgroup. In each study, higher proportions of people with isolated substantial relative memory impairment had ≥1 APOE ε4 allele than any other subgroup (overall p = 1.5 × 10-27). Across subgroups, there were 33 novel suggestive loci across the genome with p < 10-5 and an extreme OR compared to controls, of which none had statistical evidence of heterogeneity and 30 had ORs in the same direction across all datasets. These data support the biological coherence of cognitively defined subgroups and nominate novel genetic loci.R01 AG042437/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG029672/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ K23 AG046377/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ U01 AG042904/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ P30 AG010133/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG019771/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ P50 AG005136/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ K01 AG050699/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ P50 AG005133/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG030653/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG041718/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG017917/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ U01 AG032984/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ R01 AG030146/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ U01 AG006781/U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)/ U01 HG006375/U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)/ P50 AG005136/AG/NIA NIH HHS/United States P50 AG005133/AG/NIA NIH HHS/United States K23 AG046377/AG/NIA NIH HHS/United State

Plasma tau in Alzheimer disease

Objective: To test whether plasma tau is altered in Alzheimer disease (AD) and whether it is related to changes in cognition, CSF biomarkers of AD pathology (including β-amyloid [Aβ] and tau), brain atrophy, and brain metabolism. Methods: This was a study of plasma tau in prospectively followed patients with AD (n 179), patients with mild cognitive impairment (n 195), and cognitive healthy controls (n 189) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and cross-sectionally studied patients with AD (n 61), mild cognitive impairment (n 212), and subjective cognitive decline (n 174) and controls (n 274) from the Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably (BioFINDER) study at Lund University, Sweden. A total of 1284 participants were studied. Associations were tested between plasma tau and diagnosis, CSF biomarkers, MRI measures, 18 fluorodeoxyglucose-PET, and cognition. Results: Higher plasma tau was associated with AD dementia, higher CSF tau, and lower CSF Aβ 42, but the correlations were weak and differed between ADNI and BioFINDER. Longitudinal analysis in ADNI showed significant associations between plasma tau and worse cognition, more atrophy, and more hypometabolism during follow-up. Conclusions: Plasma tau partly reflects AD pathology, but the overlap between normal aging and AD is large, especially in patients without dementia. Despite group-level differences, these results do not support plasma tau as an AD biomarker in individual people. Future studies may test longitudinal plasma tau measurements in AD

Alzheimer's polygenic risk scores are associated with cognitive phenotypes in Down syndrome

Funder: NeuroGenomics and Informatics CenterFunder: Washington University School of MedicineFunder: Chan Zuckerberg Initiative; doi: http://dx.doi.org/10.13039/100014989Funder: Washington UniversityFunder: National Institute on Aging; doi: http://dx.doi.org/10.13039/100000049Funder: German Center for Neurodegenerative Diseases; doi: http://dx.doi.org/10.13039/501100005224Funder: FLENIFunder: Japan Agency for Medical Research and Development; doi: http://dx.doi.org/10.13039/100009619Funder: KHIDI; doi: http://dx.doi.org/10.13039/501100003710Funder: National Institute of Biomedical Imaging and Bioengineering; doi: http://dx.doi.org/10.13039/100000070Funder: NICHD; doi: http://dx.doi.org/10.13039/100009633INTRODUCTION: This study aimed to investigate the influence of the overall Alzheimer's disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) biomarkers. METHODS: AD polygenic risk scores (PRS) were tested for association with DS‐related traits. RESULTS: The AD risk PRS was associated with disease status in several cohorts of sporadic late‐ and early‐onset and familial late‐onset AD, but not in familial early‐onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRS APOE , p = 2.84 × 10−4; PRS excluding APOE, PRS nonAPOE , p = 1.60 × 10−2). PRS APOE exhibited significant associations with Aβ42, tTau, pTau, and Aβ42/40 ratio in DS. DISCUSSION: These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits. Highlights: Examination of the polygenic risk of AD in DS presented here is the first of its kind. AD PRS influences memory aspects in DS individuals, independently of APOE genotype. These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population. APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults

Genetic overlap between Alzheimer's Disease and Parkinson's Disease at the MAPT locus

We investigated the genetic overlap between Alzheimer’s disease (AD) and Parkinson’s disease (PD). Using summary statistics (P-values) from large recent genome-wide association studies (GWAS) (total n=89 904 individuals), we sought to identify single nucleotide polymorphisms (SNPs) associating with both AD and PD. We found and replicated association of both AD and PD with the A allele of rs393152 within the extended MAPT region on chromosome 17 (meta analysis P-value across five independent AD cohorts=1.65 × 10−7). In independent datasets, we found a dose-dependent effect of the A allele of rs393152 on intra-cerebral MAPT transcript levels and volume loss within the entorhinal cortex and hippocampus. Our findings identify the tau-associated MAPT locus as a site of genetic overlap between AD and PD, and extending prior work, we show that the MAPT region increases risk of Alzheimer’s neurodegeneration