Sex differences in brain atrophy in dementia with Lewy bodies.

INTRODUCTION: Sex influences neurodegeneration, but it has been poorly investigated in dementia with Lewy bodies (DLB). We investigated sex differences in brain atrophy in DLB using magnetic resonance imaging (MRI). METHODS: We included 436 patients from the European-DLB consortium and the Mayo Clinic. Sex differences and sex-by-age interactions were assessed through visual atrophy rating scales (n = 327; 73 ± 8 years, 62% males) and automated estimations of regional gray matter volume and cortical thickness (n = 165; 69 ± 9 years, 72% males). RESULTS: We found a higher likelihood of frontal atrophy and smaller volumes in six cortical regions in males and thinner olfactory cortices in females. There were significant sex-by-age interactions in volume (six regions) and cortical thickness (seven regions) across the entire cortex. DISCUSSION: We demonstrate that males have more widespread cortical atrophy at younger ages, but differences tend to disappear with increasing age, with males and females converging around the age of 75. HIGHLIGHTS: Male DLB patients had higher odds for frontal atrophy on radiological visual rating scales. Male DLB patients displayed a widespread pattern of cortical gray matter alterations on automated methods. Sex differences in gray matter measures in DLB tended to disappear with increasing age.

Sex differences in brain atrophy in dementia with Lewy bodies.

Background and objectives: Sex is an important contributing factor to neuroimaging phenotypes in brain disorders. However, little is known about the contribution of sex differences to the neurodegeneration in dementia with Lewy bodies (DLB). We investigated sex differences in probable DLB patients by using both visual rating scales of lobar atrophy and automated estimations of regional atrophy. Methods: We included 442 probable DLB patients from the European-DLB consortium and the Mayo Clinic who have magnetic resonance imaging (MRI) data available. We assessed sex differences and the sex-by-age interaction in two largely independent samples through visual rating scales of lobar atrophy (n = 333; mean age 73 ± 8 years, 62% males) and automated regional estimations of gray matter (GM) volume and mean cortical thickness (CTh) (n = 165; mean age 69 ± 9 years, 72% males). We used binary logistic regression and ANOVA for statistical analysis. Results: We found a statistically significantly higher likelihood of frontal atrophy measured by the global cortical atrophy-frontal subscale (GCA-F) in males (40% of males had an abnormal GCA-F score versus 29% of females, P-value = 0.006). Using automated estimations, we found smaller GM volumes in 6 cortical regions in males compared with females, as well as smaller GM volume in the entorhinal cortex and thinner olfactory cortices in females, compared with males. The sex-by-age interaction showed statistically significant results in 6 cortical volumes and 7 mean CTh estimations (P-value ≤ 0.05), accentuated in the right middle frontal gyrus (FDR-adjusted P-value = 0.047). These cross-sectional interactions indicated that while females have statistically significantly less atrophy than males at younger ages, differences become non-significant at older ages, with females showing the same level of atrophy than males around the age of 75. Conclusions: This study demonstrates sex differences on brain atrophy in probable DLB. While male DLB patients have a more widespread pattern of cortical atrophy at younger ages, these sex differences tend to disappear with increasing age. Longitudinal studies will help establish these cross-sectional findings and inform on sex and age considerations to the use of MRI in clinical routine, as the field moves towards precision medicine.

Description of the Method for Evaluating Digital Endpoints in Alzheimer Disease Study: Protocol for an Exploratory, Cross-sectional Study.

BACKGROUND: More sensitive and less burdensome efficacy end points are urgently needed to improve the effectiveness of clinical drug development for Alzheimer disease (AD). Although conventional end points lack sensitivity, digital technologies hold promise for amplifying the detection of treatment signals and capturing cognitive anomalies at earlier disease stages. Using digital technologies and combining several test modalities allow for the collection of richer information about cognitive and functional status, which is not ascertainable via conventional paper-and-pencil tests. OBJECTIVE: This study aimed to assess the psychometric properties, operational feasibility, and patient acceptance of 10 promising technologies that are to be used as efficacy end points to measure cognition in future clinical drug trials. METHODS: The Method for Evaluating Digital Endpoints in Alzheimer Disease study is an exploratory, cross-sectional, noninterventional study that will evaluate 10 digital technologies' ability to accurately classify participants into 4 cohorts according to the severity of cognitive impairment and dementia. Moreover, this study will assess the psychometric properties of each of the tested digital technologies, including the acceptable range to assess ceiling and floor effects, concurrent validity to correlate digital outcome measures to traditional paper-and-pencil tests in AD, reliability to compare test and retest, and responsiveness to evaluate the sensitivity to change in a mild cognitive challenge model. This study included 50 eligible male and female participants (aged between 60 and 80 years), of whom 13 (26%) were amyloid-negative, cognitively healthy participants (controls); 12 (24%) were amyloid-positive, cognitively healthy participants (presymptomatic); 13 (26%) had mild cognitive impairment (predementia); and 12 (24%) had mild AD (mild dementia). This study involved 4 in-clinic visits. During the initial visit, all participants completed all conventional paper-and-pencil assessments. During the following 3 visits, the participants underwent a series of novel digital assessments. RESULTS: Participant recruitment and data collection began in June 2020 and continued until June 2021. Hence, the data collection occurred during the COVID-19 pandemic (SARS-CoV-2 virus pandemic). Data were successfully collected from all digital technologies to evaluate statistical and operational performance and patient acceptance. This paper reports the baseline demographics and characteristics of the population studied as well as the study's progress during the pandemic. CONCLUSIONS: This study was designed to generate feasibility insights and validation data to help advance novel digital technologies in clinical drug development. The learnings from this study will help guide future methods for assessing novel digital technologies and inform clinical drug trials in early AD, aiming to enhance clinical end point strategies with digital technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35442.

Phenotypic Displays of Cholinergic Enzymes Associate With Markers of Inflammation, Neurofibrillary Tangles, and Neurodegeneration in Pre- and Early Symptomatic Dementia Subjects.

Background: Cholinergic drugs are the most commonly used drugs for the treatment of Alzheimer's disease (AD). Therefore, a better understanding of the cholinergic system and its relation to both AD-related biomarkers and cognitive functions is of high importance. Objectives: To evaluate the relationships of cerebrospinal fluid (CSF) cholinergic enzymes with markers of amyloidosis, neurodegeneration, neurofibrillary tangles, inflammation and performance on verbal episodic memory in a memory clinic cohort. Methods: In this cross-sectional study, 46 cholinergic drug-free subjects (median age = 71, 54% female, median MMSE = 28) were recruited from an Icelandic memory clinic cohort targeting early stages of cognitive impairment. Enzyme activity of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) was measured in CSF as well as levels of amyloid-ß1-42 (Aß42), phosphorylated tau (P-tau), total-tau (T-tau), neurofilament light (NFL), YKL-40, S100 calcium-binding protein B (S100B), and glial fibrillary acidic protein (GFAP). Verbal episodic memory was assessed with the Rey Auditory Verbal Learning (RAVLT) and Story tests. Results: No significant relationships were found between CSF Aß42 levels and AChE or BuChE activity (p > 0.05). In contrast, T-tau (r = 0.46, p = 0.001) and P-tau (r = 0.45, p = 0.002) levels correlated significantly with AChE activity. Although neurodegeneration markers T-tau and NFL did correlate with each other (r = 0.59, p < 0.001), NFL did not correlate with AChE (r = 0.25, p = 0.09) or BuChE (r = 0.27, p = 0.06). Inflammation markers S100B and YKL-40 both correlated significantly with AChE (S100B: r = 0.43, p = 0.003; YKL-40: r = 0.32, p = 0.03) and BuChE (S100B: r = 0.47, p < 0.001; YKL-40: r = 0.38, p = 0.009) activity. A weak correlation was detected between AChE activity and the composite score reflecting verbal episodic memory (r = -0.34, p = 0.02). LASSO regression analyses with a stability approach were performed for the selection of a set of measures best predicting cholinergic activity and verbal episodic memory score. S100B was the predictor with the highest model selection frequency for both AChE (68%) and BuChE (73%) activity. Age (91%) was the most reliable predictor for verbal episodic memory, with selection frequency of both cholinergic enzymes below 10%. Conclusions: Results indicate a relationship between higher activity of the ACh-degrading cholinergic enzymes with increased neurodegeneration, neurofibrillary tangles and inflammation in the stages of pre- and early symptomatic dementia, independent of CSF Aß42 levels.

A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.

Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.

Cerebrospinal Fluid C18 Ceramide Associates with Markers of Alzheimer's Disease and Inflammation at the Pre- and Early Stages of Dementia.

BACKGROUND: Understanding how dysregulation in lipid metabolism relates to the severity of Alzheimer's disease (AD) pathology might be critical in developing effective treatments. OBJECTIVE: To identify lipid species in cerebrospinal fluid (CSF) associated with signature AD pathology and to explore their relationships with measures reflecting AD-related processes (neurodegeneration, inflammation, deficits in verbal episodic memory) among subjects at the pre- and early symptomatic stages of dementia. METHODS: A total of 60 subjects that had been referred to an Icelandic memory clinic cohort were classified as having CSF AD (n = 34) or non-AD (n = 26) pathology profiles. Untargeted CSF lipidomic analysis was performed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) for the detection of mass-to-charge ratio (m/z) features. CSF proteins reflecting neurodegeneration (neurofilament light [NFL]) and inflammation (chitinase-3-like protein 1 [YKL-40], S100 calcium-binding protein B [S100B], glial fibrillary acidic protein [GFAP]) were also measured. Rey Auditory Verbal Learning (RAVLT) and Story tests were used for the assessment of verbal episodic memory. RESULTS: Eight out of 1008 features were identified as best distinguishing between the CSF profile groups. Of those, only the annotation of the m/z feature assigned to lipid species C18 ceramide was confirmed with a high confidence. Multiple regression analyses, adjusted for age, gender, and education, demonstrated significant associations of CSF core AD markers (Aß42: st.ß= -0.36, p = 0.007; T-tau: st.ß= 0.41, p = 0.005) and inflammatory marker S100B (st.ß= 0.51, p = 0.001) with C18 ceramide levels. CONCLUSION: Higher levels of C18 ceramide associated with increased AD pathology and inflammation, suggesting its potential value as a therapeutic target.

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets-collected with different scanners, protocols and disease populations-and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.

Association of glial and neuronal degeneration markers with Alzheimer's disease cerebrospinal fluid profile and cognitive functions.

BACKGROUND: Neuroinflammation has gained increasing attention as a potential contributing factor in the onset and progression of Alzheimer's disease (AD). The objective of this study was to examine the association of selected cerebrospinal fluid (CSF) inflammatory and neuronal degeneration markers with signature CSF AD profile and cognitive functions among subjects at the symptomatic pre- and early dementia stages. METHODS: In this cross-sectional study, 52 subjects were selected from an Icelandic memory clinic cohort. Subjects were classified as having AD (n = 28, age = 70, 39% female, Mini-Mental State Examination [MMSE] = 27) or non-AD (n = 24, age = 67, 33% female, MMSE = 28) profile based on the ratio between CSF total-tau (T-tau) and amyloid-ß1-42 (Aß42) values (cut-off point chosen as 0.52). Novel CSF biomarkers included neurofilament light (NFL), YKL-40, S100 calcium-binding protein B (S100B) and glial fibrillary acidic protein (GFAP), measured with enzyme-linked immunosorbent assays (ELISAs). Subjects underwent neuropsychological assessment for evaluation of different cognitive domains, including verbal episodic memory, non-verbal episodic memory, language, processing speed, and executive functions. RESULTS: Accuracy coefficient for distinguishing between the two CSF profiles was calculated for each CSF marker and test. Novel CSF markers performed poorly (area under curve [AUC] coefficients ranging from 0.61 to 0.64) compared to tests reflecting verbal episodic memory, which all performed fair (AUC > 70). LASSO regression with a stability approach was applied for the selection of CSF markers and demographic variables predicting performance on each cognitive domain, both among all subjects and only those with a CSF AD profile. Relationships between CSF markers and cognitive domains, where the CSF marker reached stability selection criteria of > 75%, were visualized with scatter plots. Before calculations of corresponding Pearson's correlations coefficients, composite scores for cognitive domains were adjusted for age and education. GFAP correlated with executive functions (r = - 0.37, p = 0.01) overall, while GFAP correlated with processing speed (r = - 0.68, p < 0.001) and NFL with verbal episodic memory (r = - 0.43, p = 0.02) among subjects with a CSF AD profile. CONCLUSIONS: The novel CSF markers NFL and GFAP show potential as markers for cognitive decline among individuals with core AD pathology at the symptomatic pre- and early stages of dementia.

The Power of EEG to Predict Conversion from Mild Cognitive Impairment and Subjective Cognitive Decline to Dementia.

INTRODUCTION: The aim of this study was to examine if quantitative electroencephalography (qEEG) using the statistical pattern recognition (SPR) method could predict conversion to dementia in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHODS: From 5 Nordic memory clinics, we included 47 SCD patients, 99 MCI patients, and 67 healthy controls. EEGs analyzed with the SPR method together with clinical data recorded at baseline were evaluated. The patients were followed up for a mean of 62.5 (SD 17.6) months and reexamined. RESULTS: Of 200 participants with valid clinical information, 70 had converted to dementia, and 52 had developed Alzheimer's disease. Receiver-operating characteristic analysis of the EEG results as defined by a dementia index (DI) ranging from 0 to 100 revealed that the area under the curve was 0.78 (95% CI 0.70-0.85), corresponding to a sensitivity of 71%, specificity of 69%, and accuracy of 69%. A logistic regression analysis showed that by adding results of a cognitive test at baseline to the EEG DI, accuracy could improve. CONCLUSION: We conclude that applying qEEG using the automated SPR method can be helpful in identifying patients with SCD and MCI that have a high risk of converting to dementia over a 5-year period. As the discriminant power of the method is of moderate degree, it should be used in addition to routine diagnostic methods.

Cholinergic dysfunction, neurodegeneration, and amyloid-beta pathology in neurodegenerative diseases.

Cholinergic dysfunction is central in Alzheimer's disease (AD) and dementia with Lewy bodies (DLB). The electroencephalography-based acetylcholine index (EEG-Ach index) has been proposed as a biomarker of cholinergic dysfunction. However, it is unclear how the EEG-Ach index relates to amyloid-beta pathology and neurodegeneration. We investigated the association between the EEG-Ach index and cerebrospinal fluid (CSF) amyloid-beta, CSF total tau, cortical thickness, and hippocampal volume from magnetic resonance imaging (MRI), and cognition. A total of 127 patients with different neurodegenerative diseases were studied. The EEG-Ach index was calculated from quantitative EEG using statistical pattern recognition. The EEG-Ach index was associated with hippocampal volume and cortical thickness in frontal, temporal, and occipital cortices. Cross-sectional sub-analyses based on a small sample suggests that the EEG-Ach index increases the closest to AD dementia, downstream to amyloid-beta pathology, CSF total tau, and hippocampal volume. We conclude that cholinergic dysfunction correlates with atrophy in brain areas important for AD pathogenesis, and this association is more prominent in the dementia stage. These results together with previous studies from this project suggest that the EEG-Ach index may be a useful biomarker for cholinergic dysfunction, with value for differential diagnosis of dementia and monitoring patients at the dementia stage.

Changes in the left temporal microstate are a sign of cognitive decline in patients with Alzheimer's disease.

INTRODUCTION: Large-scale brain networks are disrupted in the early stages of Alzheimer's disease (AD). Electroencephalography microstate analysis, a promising method for studying brain networks, parses EEG signals into topographies representing discrete, sequential network activations. Prior studies indicate that patients with AD show a pattern of global microstate disorganization. We investigated whether any specific microstate changes could be found in patients with AD and mild cognitive impairment (MCI) compared to healthy controls (HC). MATERIALS AND METHODS: Standard EEGs were obtained from 135 HC, 117 patients with MCI, and 117 patients with AD from six Nordic memory clinics. We parsed the data into four archetypal microstates. RESULTS: There was significantly increased duration, occurrence, and coverage of microstate A in patients with AD and MCI compared to HC. When looking at microstates in specific frequency bands, we found that microstate A was affected in delta (1-4 Hz), theta (4-8 Hz), and beta (13-30 Hz), while microstate D was affected only in the delta and theta bands. Microstate features were able to separate HC from AD with an accuracy of 69.8% and HC from MCI with an accuracy of 58.7%. CONCLUSIONS: Further studies are needed to evaluate whether microstates represent a valuable disease classifier. Overall, patients with AD and MCI, as compared to HC, show specific microstate alterations, which are limited to specific frequency bands. These alterations suggest disruption of large-scale cortical networks in AD and MCI, which may be limited to specific frequency bands.

Author Correction: Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Author Correction: GBA and APOE ε4 associate with sporadic dementia with Lewy bodies in European genome wide association study.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer's disease.

OBJECTIVE: Quantitative EEG power has not been as effective in discriminating between healthy aging and Alzheimer's disease as conventional biomarkers. But EEG coherence has shown promising results in small samples. The overall aim was to evaluate if EEG connectivity markers can discriminate between Alzheimer's disease, mild cognitive impairment, and healthy aging and to explore the early underlying changes in coherence. METHODS: EEGs were included in the analysis from 135 healthy controls, 117 patients with mild cognitive impairment, and 117 patients with Alzheimer's disease from six Nordic memory clinics. Principal component analysis was performed before multinomial regression. RESULTS: We found classification accuracies of above 95% based on coherence, imaginary part of coherence, and the weighted phase-lag index. The most prominent changes in coherence were decreased alpha coherence in Alzheimer's disease, which was correlated to the scores of the 10-word test in the Consortium to Establish a Registry for Alzheimer's Disease battery. CONCLUSIONS: The diagnostic accuracies for EEG connectivity measures are higher than findings from studies investigating EEG power and conventional Alzheimer's disease biomarkers. Furthermore, decreased alpha coherence is one of the earliest changes in Alzheimer's disease and associated with memory function. SIGNIFICANCE: EEG connectivity measures may be useful supplementary diagnostic classifiers.

GBA and APOE ε4 associate with sporadic dementia with Lewy bodies in European genome wide association study.

Dementia with Lewy Bodies (DLB) is a common neurodegenerative disorder with poor prognosis and mainly unknown pathophysiology. Heritability estimates exceed 30% but few genetic risk variants have been identified. Here we investigated common genetic variants associated with DLB in a large European multisite sample. We performed a genome wide association study in Norwegian and European cohorts of 720 DLB cases and 6490 controls and included 19 top-associated single-nucleotide polymorphisms in an additional cohort of 108 DLB cases and 75545 controls from Iceland. Overall the study included 828 DLB cases and 82035 controls. Variants in the ASH1L/GBA (Chr1q22) and APOE ε4 (Chr19) loci were associated with DLB surpassing the genome-wide significance threshold (p < 5 × 10-8). One additional genetic locus previously linked to psychosis in Alzheimer's disease, ZFPM1 (Chr16q24.2), showed suggestive association with DLB at p-value < 1 × 10-6. We report two susceptibility loci for DLB at genome-wide significance, providing insight into etiological factors. These findings highlight the complex relationship between the genetic architecture of DLB and other neurodegenerative disorders.

Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk.

Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.

A signature pattern of cortical atrophy in dementia with Lewy bodies: A study on 333 patients from the European DLB consortium.

INTRODUCTION: We explored regional brain atrophy patterns and their clinical correlates in dementia with Lewy bodies (DLB). METHODS: In this multicentre study, we included a total of 333 patients with DLB, 352 patients with Alzheimer's disease (AD), and 233 normal controls and used medial temporal lobe atrophy, posterior atrophy, and frontal atrophy (GCA-F) visual rating scales. Patients were classified according to four atrophy patterns. RESULTS: Patients with DLB had higher scores on all the three atrophy scales than normal controls but had less medial temporal lobe atrophy than those with AD (all P values < .001). A signature hippocampal-sparing pattern of regional atrophy was observed in DLB. The magnetic resonance imaging measures showed 65% ability to discriminate between DLB and AD and marginally contributed to the discrimination over and above the core clinical features. DISCUSSION: The most common pattern of atrophy of DLB was hippocampal-sparing. Future studies should explore whether comorbid AD pathology underlies the atrophy patterns seen in DLB.

Meta-analysis of Alzheimer's disease on 9,751 samples from Norway and IGAP study identifies four risk loci.

A large fraction of genetic risk factors for Alzheimer's Disease (AD) is still not identified, limiting the understanding of AD pathology and study of therapeutic targets. We conducted a genome-wide association study (GWAS) of AD cases and controls of European descent from the multi-center DemGene network across Norway and two independent European cohorts. In a two-stage process, we first performed a meta-analysis using GWAS results from 2,893 AD cases and 6,858 cognitively normal controls from Norway and 25,580 cases and 48,466 controls from the International Genomics of Alzheimer's Project (IGAP), denoted the discovery sample. Second, we selected the top hits (p < 1 × 10-6) from the discovery analysis for replication in an Icelandic cohort consisting of 5,341 cases and 110,008 controls. We identified a novel genomic region with genome-wide significant association with AD on chromosome 4 (combined analysis OR = 1.07, p = 2.48 x 10-8). This finding implicated HS3ST1, a gene expressed throughout the brain particularly in the cerebellar cortex. In addition, we identified IGHV1-68 in the discovery sample, previously not associated with AD. We also associated USP6NL/ECHDC3 and BZRAP1-AS1 to AD, confirming findings from a follow-up transethnic study. These new gene loci provide further evidence for AD as a polygenic disorder, and suggest new mechanistic pathways that warrant further investigation.