Mendelian randomisation confirms the role of Y-chromosome loss in Alzheimer’s disease aetiopathogenesis in men

Citation: García-González, P.; de Rojas, I.; Moreno-Grau, S.; Montrreal, L.; Puerta, R.; Alarcón-Martín, E.; Quintela, I.; Orellana, A.; Andrade, V.; Adami, P.V.M.; et al. Mendelian Randomisation Confirms the Role of Y-Chromosome Loss in Alzheimer’s Disease Aetiopathogenesis in Men. Int. J. Mol. Sci. 2023,24, 898. https://doi.org/10.3390/ ijms24020898 Academic Editor: Bruno Imbimbo Received: 4 November 2022 Revised: 21 December 2022 Accepted: 31 December 2022 Published: 4 January 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Molecular Sciences Article Mendelian Randomisation Confirms the Role of Y-Chromosome Loss in Alzheimer’s Disease Aetiopathogenesis in Men Pablo García-González 1,2 , Itziar de Rojas 1,2 , Sonia Moreno-Grau 1,3, Laura Montrreal 1, Raquel Puerta 1, Emilio Alarcón-Martín1, Inés Quintela 4, Adela Orellana 1, Victor Andrade 5,6, Pamela V. Martino Adami 5, Stefanie Heilmann-Heimbach 7, Pilar Gomez-Garre 2,8, María Teresa Periñán2,8, Ignacio Alvarez 9,10 , Monica Diez-Fairen 9,10, Raul Nuñez Llaves 1, Claudia OlivéRoig 1, Guillermo Garcia-Ribas 11, Manuel Menéndez-González 12,13,14 , Carmen Martínez 13,15, Miquel Aguilar 9,10, Mariateresa Buongiorno 9,10, Emilio Franco-Macías 16, Maria Eugenia Saez 17 , Amanda Cano 1,2, Maria J. Bullido 2,18,19 , Luis Miguel Real 20,21 , Eloy Rodríguez-Rodríguez 2,22, Jose Luís Royo 21, Victoria Álvarez 13,23 , Pau Pastor 9,10, Gerard Piñol-Ripoll 24,25 , Pablo Mir 2,8,26, Miguel Calero Lara 2,27 , Miguel Medina Padilla 2,28 , Pascual Sánchez-Juan 2,22,28, Angel Carracedo 4,29 , Sergi Valero 1,2, Isabel Hernandez 1,2, Lluis Tàrraga 1,2, Alfredo Ramirez 5,6,30,31,32 , MercéBoada 1,2 and Agustín Ruiz 1,2,* 1Research Center and Memory Clinic, Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, 08017 Barcelona, Spain 2CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, 28220 Madrid, Spain 3Galatea Bio Inc., Hialeah, FL 33010, USA 4Grupo de Medicina Xenómica, Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII), Universidade de Santiago de Compostela, 15705 Santiago de Compostela, Spain 5Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, 50937 Cologne, Germany 6Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Clinic Bonn, 53127 Bonn, Germany 7Institute of Human Genetics, School of Medicine & University Hospital Bonn, University of Bonn, 53127 Bonn, Germany 8 Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain 9FundacióDocència i Recerca MútuaTerrassa, 08221 Terrassa, Spain 10 Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, 08221 Terrassa, Spain 11 Hospital Universitario Ramon y Cajal, IRYCIS, 28034 Madrid, Spain 12 Servicio de Neurología, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain 13 Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 33011 Asturias, Spain 14 Departamento de Medicina, Universidad de Oviedo, 33011 Oviedo, Spain 15 Servicio de Neurología, Hospital Universitario de Cabueñes, 33394 Gijón, Spain 16 Unidad de Demencias, Servicio de Neurología y Neurofisiología, Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío, CSIC, Universidad de Sevilla, 41013 Sevilla, Spain 17 CAEBI, Centro Andaluz de Estudios Bioinformáticos, 41013 Sevilla, Spain 18 Centro de Biología Molecular Severo Ochoa (UAM-CSIC), 28049 Madrid, Spain 19 Instituto de Investigacion Sanitaria ‘Hospital la Paz’ (IdIPaz), 28029 Madrid, Spain 20 Unidad Clínica de Enfermedades Infecciosas y Microbiología, Hospital Universitario de Valme, 41013 Sevilla, Spain 21 Depatamento de Especialidades Quirúrgicas, Bioquímica e Inmunología, Facultad de Medicina, Universidad de Málaga, 29016 Málaga, Spain 22 Neurology Service, Marqués de Valdecilla University Hospital, University of Cantabria and IDIVAL, 39008 Santander, Spain 23 Laboratorio de Genética, Hospital Universitario Central de Asturias, 33011 Oviedo, Spain 24 Unitat Trastorns Cognitius, Hospital Universitari Santa Maria de Lleida, 25198 Lleida, Spain 25 Institut de Recerca Biomedica de Lleida (IRBLLeida), 25198 Lleida, Spain 26 Departamento de Medicina, Facultad de Medicina, Universidad de Sevilla, 41009 Seville, Spain 27 Instituto de Salud Carlos III, 28222 Majadahonda, Spain 28 CIEN Foundation/Queen Sofia Foundation Alzheimer Center, 28220 Madrid, Spain 29 Fundación Pública Galega de Medicina Xenómica-CIBERER-IDIS, 15705 Santiago de Compostela, Spain 30 German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany Int. J. Mol. Sci. 2023,24, 898. https://doi.org/10.3390/ijms24020898 https://www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2023,24, 898 2 of 17 31 Department of Psychiatry and Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, San Antonio, TX 78229, USA 32 Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, 50931 Cologne, Germany *Correspondence: ar[email protected]; Tel.: +34-93-430-4720 Abstract: Mosaic loss of chromosome Y (mLOY) is a common ageing-related somatic event and has been previously associated with Alzheimer’s disease (AD). However, mLOY estimation from genotype microarray data only reflects the mLOY degree of subjects at the moment of DNA sampling. Therefore, mLOY phenotype associations with AD can be severely age-confounded in the context of genome-wide association studies. Here, we applied Mendelian randomisation to construct an age-independent mLOY polygenic risk score (mloy-PRS) using 114 autosomal variants. The mloy-PRS instrument was associated with an 80% increase in mLOY risk per standard deviation unit (p= 4.22 ×10−20) and was orthogonal with age. We found that a higher genetic risk for mLOY was associated with faster progression to AD in men with mild cognitive impairment (hazard ratio (HR) = 1.23, p= 0.01) . Importantly, mloy-PRS had no effect on AD conversion or risk in the female group, suggesting that these associations are caused by the inherent loss of the Y chromosome. Additionally, the blood mLOY phenotype in men was associated with increased cerebrospinal fluid levels of total tau and phosphorylated tau181 in subjects with mild cognitive impairment and dementia. Our results strongly suggest that mLOY is involved in AD pathogenesis. Keywords: Alzheimer’s disease; mosaic loss of chromosome Y; disease progression; GWAS; Mendelian randomization; GR@ACE/DEGESCO; EADB; mild cognitive impairment; polygenic risk score; CSF biomarkers 1. Introduction Alzheimer’s disease (AD) is the leading cause of dementia worldwide, accounting for 60–80% of total cases [ 1 ]. While Mendelian inheritance is suspected to cause early onset AD (<65 years) [ 2 ], late-onset AD (LOAD, >65 years) is a complex, multifactorial disease influenced by both genetic factors and life exposures. The genetic contribution to LOAD is estimated to be 60–80% [ 3 ], with APOE being the most prominent locus discovered to date [ 4 ]. However, demographic features also play a predominant role in AD. Notably, age is considered the most important risk factor for LOAD [ 5 ], and women represent nearly two-thirds of the global population with AD [ 1 ], showing higher rates of cognitive decline [ 6 , 7 ] than men. However, whether sex should be considered a risk factor for AD or rather a source of disease heterogeneity is a matter of intense debate [ 8 ]. Recent reviews have highlighted the importance of reporting results for sex interactions and sex-stratified AD data instead of the more widely used approach of adjusting data by sex [ 9 ]. These approaches may help elucidate differences in sex-specific AD risk profiles, which will be of great value in the incoming age of precision medicine. The male-specific region of chromosome Y is one of the most unexplored regions of the human genome—and it has long been considered a genetic wasteland. Mosaic loss of chromosome Y (mLOY) in blood cells is the most common known form of somatic mosaicism in humans [ 10 – 12 ]. Genetic factors together with age, smoking, and other environmental stressors are well-known risk factors for mLOY [ 13 ]. Genetic variants associated with mLOY risk are mainly related to mitotic processes, cell cycle regulation, DNA damage sensing and response, and apoptotic processes [ 14 ]. mLOY was initially considered a phenotypically innocuous, age-related trait [ 15 – 18 ]. However, there is increasing evidence that mLOY in blood cells has a direct effect in the aetiopathogenesis of several diseases affecting different tissues. Specifically, blood cell mLOY has been associated with susceptibility to multiple ageing-related diseases, including AD [ 19 ], non-haematological cancer [ 10 , 20 ], cardiovascular diseases [ 21 , 22 ], and all-cause mortality risk [ 10 ]. The main proposed mechanism to explain blood mLOY pathogenesis is impairment of immune functions caused by the loss Int. J. Mol. Sci. 2023,24, 898 3 of 17 of the Y chromosome in leucocytes [ 23 – 25 ]. However, it has been described that autosomal genetic predisposition for mLOY is associated with breast cancer in women, indicating that the underlying genomic instability can also explain the associations between mLOY and disease risk [14]. Here, we aimed to study the impact of mLOY on AD risk in the GR@ACE and Dementia Genetics Spanish Consortium (DEGESCO) cohorts [ 26 , 27 ]. First, we checked for blood mLOY associations with AD in a case-control setting and in the phenoconversion process from mild cognitive impairment (MCI) to all-cause dementia and AD. Subsequently, to remove age-confounding effects, we generated an autosomal, age-independent mLOY polygenic risk score (mloy-PRS) and analysed its effect on AD status and progression in both sexes. Finally, we analysed the impact of mLOY in different AD-related biomarkers in cerebrospinal fluid (CSF). 2. Results We calculated the mean log R ratios of probes in the X (LRR-X) and Y (LRR-Y) chromosomes to check the sex chromosome dosages of 7954 clinically reported male samples in the GR@ACE-DEGESCO cohort (Figure S1). We detected one individual with a gain of chromosome Y (GOY, XYY), compatible with a supermale syndrome, and three individuals with Klinefelter syndrome (XXY). We removed women (XX), GOY, Klinefelter individuals, and outliers prior to mLOY computation. For the 7843 remaining XY individuals, we removed second-degree or lower relatives as well as samples with a low genotype call rate ( ≤ 0.97) or excess heterozygosity (>3 standard deviations [SD] over the mean heterozygosity of the cohort). We ran principal component analysis to identify the population structure and removed 72 individuals from non-European population (>6 SD from the 1000 Genomes European population mean). We also excluded subjects with detectable autosomal chromosomopathies, (i.e., Down’s syndrome). After applying these exclusion criteria, we split the remaining 6955 male samples into two randomised batches and calculated the mean LRR of probes found at the male-specific region of chromosome Y (mLRR-Y). We used the mLRR-Y thres method of the MADloy R package [ 28 ] to call the mLOY status. We did not detect batch effect due to cohort splitting (Figure S2). We excluded 12 additional samples with LRR SD > 0.46. Finally, we plotted mLRR-Y and pseudoautosomal region 1 B-deviation (PAR1-Bdev) values to identify and remove individuals with detectable anomalies in chromosome Y (i.e., partial loss of chromosome Y) or loss of heterozygosity in the PAR1 region (Figure S1). Quality control (QC) and filtering steps for analysis of mLOY phenotypes and mloy-PRS are summarised in Figure S3. For an initial glimpse at our data, we plotted mLRR-Y values of all AD cases and controls with respect to age (Figure 1). The first thing that became apparent was that our control population is significantly younger than the AD population. Additionally, mLOY occurrence before 65 years was a very rare event in our cohort, indicating that our control population below this age threshold may not be representative for assessing the effect of mLOY on AD. Moreover, our control population mostly lacked individuals older than 85 years . Consequently, we decided to establish a 65–85-year age window to analyse the effect of mLOY on AD. This matches the usual age at onset range for preclinical, prodromal, and mild dementia stages for LOAD in our population [ 29 ] and helped reduce the age gap between our case and control groups (Table S1). Concordant with previous reports, we observed a clear age-related increase in mLOY events in the older individuals (Figure 1). Age was associated with mLOY occurrence in men aged 65–85 years, with an estimated 1% increase in the chance of developing LOY every year (p= 3.50 ×10−11). Int. J. Mol. Sci. 2023,24, 898 4 of 17 Int. J. Mol. Sci. 2023, 23, x FOR PEER REVIEW 4 of 17 population below this age threshold may not be representative for assessing the effect of mLOY on AD. Moreover, our control population mostly lacked individuals older than 85 years. Consequently, we decided to establish a 65–85-year age window to analyse the effect of mLOY on AD. This matches the usual age at onset range for preclinical, prodromal, and mild dementia stages for LOAD in our population [29] and helped reduce the age gap between our case and control groups (Table S1). Concordant with previous reports, we observed a clear age-related increase in mLOY events in the older individuals (Figure 1). Age was associated with mLOY occurrence in men aged 65–85 years, with an estimated 1% increase in the chance of developing LOY every year (p = 3.50 × 10−11). Figure 1. mLRR-y variation with age in the GR@ACE-DEGESCO cohort. (a) Age and mLRR-Y distribution in the case and control groups. The dots represent mLRR-Y values for individual samples, and the histogram represents the age distribution across the case and control groups. (b) Proportion of individuals with mLOY in the different age groups based on age at blood sampling. (c,d) mLRR- Y distribution for the different age groups based on age at blood sampling in control and AD individuals, respectively. Then, we assessed if continuous mLRR-Y values were differentially distributed among the cases and controls. Both the unadjusted Kolmogorov–Smirnoff test (D = 0.18124; p < 2.2 × 10−16) and analysis of covariance (ANCOVA) adjusted by age at DNA sampling and APOE genotype (F = 68.0, p = 2.54 × 10−16, Table S2) yielded highly significant results in the models including all available men in our cohort. Next, we fitted logistic regressions for AD status using mLOY calls and mLRR-Y, defining three experimental setups: (a) all men with available age at DNA sampling, (b) 65–85-year-old men, and (c) dividing the data in age groups (65–70, 70–75, 75–80, and 80– 85 years). We adjusted logistic regressions by age at blood sampling, APOE genotype and relevant principal components (PCs) (Table S3). We found that the continuous mLRR-Y variable was associated with AD in the group including all men (N = 2697, odds ratio (OR) = 2.74, p = 0.01), indicating that AD cases had an increased degree of LOY mosaicism compared with controls. We also observed increased mLOY levels in AD men in the 65–85 (N = 1944, OR = 2.19, p = 0.09) and agestratified groups with respect to controls, but these differences were not statistically Figure 1. mLRR-y variation with age in the GR@ACE-DEGESCO cohort. ( a ) Age and mLRR-Y distribution in the case and control groups. The dots represent mLRR-Y values for individual samples, and the histogram represents the age distribution across the case and control groups. ( b ) Proportion of individuals with mLOY in the different age groups based on age at blood sampling. ( c , d ) mLRR-Y distribution for the different age groups based on age at blood sampling in control and AD individuals, respectively. Then, we assessed if continuous mLRR-Y values were differentially distributed among the cases and controls. Both the unadjusted Kolmogorov-Smirnoff test (D = 0.18124; p< 2.2 ×10−16 ) and analysis of covariance (ANCOVA) adjusted by age at DNA sampling and APOE genotype (F = 68.0, p= 2.54 × 10 −16 , Table S2) yielded highly significant results in the models including all available men in our cohort. Next, we fitted logistic regressions for AD status using mLOY calls and mLRR-Y, defining three experimental setups: (a) all men with available age at DNA sampling, (b) 65–85-year-old men, and (c) dividing the data in age groups (65–70, 70–75, 75–80, and 80–85 years). We adjusted logistic regressions by age at blood sampling, APOE genotype and relevant principal components (PCs) (Table S3). We found that the continuous mLRR-Y variable was associated with AD in the group including all men (N= 2697, odds ratio (OR) = 2.74, p= 0.01), indicating that AD cases had an increased degree of LOY mosaicism compared with controls. We also observed increased mLOY levels in AD men in the 65–85 (N= 1944, OR = 2.19, p= 0.09) and age-stratified groups with respect to controls, but these differences were not statistically significant (Table 1). Importantly, we noticed that the significant effect observed in the model including all available men could, at least partially, be driven by the dramatic age differences between AD cases and controls in our cohort (Figure 1and Table S1) even after adjusting by age. mLOY calls were not significantly associated with AD in the group including all men (N= 2697, OR = 1.14, p= 0.35), the 65–85-year-old group (N= 1944, OR = 1.04, p= 0.81), or in the age-stratified groups (Table 1). Int. J. Mol. Sci. 2023,24, 898 5 of 17 Table 1. Logistic regression results using mLRR-Y and mLOY calls as predictors for AD status. We defined three experimental setups: (a) all men with available age at DNA sampling, (b) 65–85-year-old men, and (c) a stratification of 65–85-year-old men into age groups (65–70, 70–75, 75–80, and 80–85 years old). We adjusted models by age at blood sampling, APOE genotype, and PCs. In the age-stratified models, only the effect of mLRR-Y or mLOY are displayed. The 95% confidence interval is presented as the 2.5% quantile (CI2.5) and the 97.5% quantile (CI97.5). OR, odds ratio; SE, standard error. All Available Men 65–85-Year-Old Men Stratified 65–85-Year-Old Men OR SE pCI2.5 CI97.5 OR SE pCI2.5 CI97.5 Age Group OR SE pCI2.5 CI97.5 mLRR-Y mLRR-Y 2.74 0.4 1.13 ×10−21.29 6.16 2.19 0.47 9.28 ×10−20.18 1.1 65–70 1.47 1.43 0.79 0.07 21.96 Age 1.16 0.01 2.74 ×10−104 1.15 1.18 1.27 0.01 6.61 ×10−70 1.24 1.31 70–75 1.74 0.77 0.48 0.39 8.18 APOE 2.52 0.08 1.61 ×10−32 2.17 2.94 2.86 0.10 5.40 ×10−28 2.38 3.46 75–80 2.37 0.77 0.27 0.57 12.16 PC1 2.01 ×1032.6 3.39 ×10−312.48 3.29 ×1050.01 3.33 0.11 0.00 3.48 80–85 44.44 2.51 0.13 0.99 2.63 ×104 PC2 0.02 2.58 0.15 0.00 3.8 0.00 3.45 0.12 0.00 4.00 META 2.20 0.50 0.12 0.83 5.88 mLOY calls mLOY 1.14 0.14 0.35 0.87 1.49 1.04 0.16 0.81 0.76 1.43 65–70 0.55 0.45 0.19 0.22 1.30 Age 1.16 0.01 1.11 ×10−106 1.15 1.18 1.28 0.01 1.73 ×10−71 1.24 1.31 70–75 1.17 0.3 0.6 0.65 2.11 APOE 2.52 0.08 2.17 ×10−32 2.17 2.94 2.86 0.10 6.93 ×10−28 2.37 3.46 75–80 1.00 0.25 0.99 0.62 1.66 PC1 2.29 ×1032.59 2.78 ×10−314.5 3.69 ×10−50.00 3.32 0.10 0.00 2.96 80–85 3.70 0.77 0.09 1.00 24.08 PC2 0.02 2.58 0.14 0.00 3.44 0.00 3.44 0.11 0.00 3.34 META 1.03 0.17 0.86 0.74 1.45 Int. J. Mol. Sci. 2023,24, 898 6 of 17 To check for an effect of mLOY on risk of conversion to all-cause dementia and AD, we fitted Cox proportional-hazards models adjusted by age at sampling and APOE genotype in our prospective cohort of men with MCI (N= 400). The continuous mLRR-Y variable had a non-significant risk effect in MCI conversion to all-cause dementia (hazard ratio (HR) = 1.93; p= 0.10). The effect size increased when we calculated the model exclusively using conversion to AD but did not reach statistical significance (HR = 2.05, p= 0.19). mLOY calls also showed similar but smaller non-significant positive effects for conversion to dementia (HR = 1.17, p= 0.40) and AD (HR = 1.38, p= 0.20, Figure 2). The Cox model results are summarised in Table 2. Int. J. Mol. Sci. 2023, 23, x FOR PEER REVIEW 6 of 17 Figure 2. Association of mLOY phenotypes with risk of conversion to dementia and AD dementia over time for men with MCI in the GR@ACE-DEGESCO cohort. Kaplan–Meier plots showing survival time in years for conversion to (a) dementia or (b) AD for prospective MCI men with LOY (blue) or without LOY (red) in the GR@ACE-DEGESCO cohort. Table 2. Cox proportional-hazards model results for conversion of men with MCI to all-cause dementia or AD dementia. We adjusted the models by age at sampling and APOE genotype. CI, confidence interval. Conversion to All-Cause Dementia Conversion to AD Dementia HR SE p 95% CI HR SE p 95% CI Association results for mLRR-Y (continuous mLOY variable) mLRR-Y 1.93 0.40 0.10 0.87–4.27 2.05 0.55 0.19 0.70–5.97 Age 1.10 0.01 3.62 × 10−15 1.07–1.12 1.13 0.02 2.38 × 10−12 1.09–1.16 APOE 1.29 0.13 4.10 × 10−02 1.01–1.66 1.56 0.17 8.89 × 10−3 1.12–2.17 Association results for mLOY calls mLOY 1.17 0.19 0.40 0.81–1.70 1.38 0.25 0.20 0.85–2.24 Age 1.10 0.01 1.33 × 10−15 1.07–1.13 1.13 0.02 1.62 × 10−12 1.09–1.17 APOE 1.28 0.13 5.09 × 10−2 1.00–1.63 1.54 0.17 1.05E × 10−2 1.11–2.13 Because the impact of age on mLOY and AD might obscure genuine associations between both phenotypes (mLOY and AD), we decided to construct an mLOY polygenic risk score (mloy-PRS) to evaluate the impact of the genetic variance associated with the mLOY phenotype in AD risk. Our rationale was to implement a Mendelian randomisation strategy reasoning that, if blood cell mLOY is genuinely associated with AD, the genetic factors linked to blood mLOY risk should also be associated with AD and its related endophenotypes. To this end, we generated the mloy-PRS instrument based on a list of autosomal genome-wide significant single-nucleotide polymorphisms (SNPs) associated with the mLOY phenotype identified in a recent genome-wide association study (Table S4) [14]. For benchmarking purposes of the constructed PRS, we initially validated the effect of mloy-PRS in the mLOY cell phenotype in our cohort (65–85 years old). We fitted a logistic regression for mLOY calls with PRS, age at DNA sampling, and APOE genotype as predictors. mloy-PRS (OR = 1.80, p = 4.22 × 10−20) and age at sampling (OR = 1.08, p = 5.07 Figure 2. Association of mLOY phenotypes with risk of conversion to dementia and AD dementia over time for men with MCI in the GR@ACE-DEGESCO cohort. Kaplan–Meier plots showing survival time in years for conversion to ( a ) dementia or ( b ) AD for prospective MCI men with LOY (blue) or without LOY (red) in the GR@ACE-DEGESCO cohort. Table 2. Cox proportional-hazards model results for conversion of men with MCI to all-cause dementia or AD dementia. We adjusted the models by age at sampling and APOE genotype. CI, confidence interval. Conversion to All-Cause Dementia Conversion to AD Dementia HR SE p95% CI HR SE p95% CI Association results for mLRR-Y (continuous mLOY variable) mLRR-Y 1.93 0.40 0.10 0.87–4.27 2.05 0.55 0.19 0.70–5.97 Age 1.10 0.01 3.62 ×10−15 1.07–1.12 1.13 0.02 2.38 ×10−12 1.09–1.16 APOE 1.29 0.13 4.10 ×10−21.01–1.66 1.56 0.17 8.89 ×10−31.12–2.17 Association results for mLOY calls mLOY 1.17 0.19 0.40 0.81–1.70 1.38 0.25 0.20 0.85–2.24 Age 1.10 0.01 1.33 ×10−15 1.07–1.13 1.13 0.02 1.62 ×10−12 1.09–1.17 APOE 1.28 0.13 5.09 ×10−21.00–1.63 1.54 0.17 1.05E ×10−21.11–2.13 Because the impact of age on mLOY and AD might obscure genuine associations between both phenotypes (mLOY and AD), we decided to construct an mLOY polygenic risk score (mloy-PRS) to evaluate the impact of the genetic variance associated with the mLOY phenotype in AD risk. Our rationale was to implement a Mendelian randomisation strategy reasoning that, if blood cell mLOY is genuinely associated with AD, the genetic factors linked to blood mLOY risk should also be associated with AD and its re- Int. J. Mol. Sci. 2023,24, 898 7 of 17 lated endophenotypes. To this end, we generated the mloy-PRS instrument based on a list of autosomal genome-wide significant single-nucleotide polymorphisms (SNPs) associated with the mLOY phenotype identified in a recent genome-wide association study (Table S4) [14]. For benchmarking purposes of the constructed PRS, we initially validated the effect of mloy-PRS in the mLOY cell phenotype in our cohort (65–85 years old). We fitted a logistic regression for mLOY calls with PRS, age at DNA sampling, and APOE genotype as predictors. mloy-PRS (OR = 1.80, p= 4.22 × 10 −20 ) and age at sampling (OR = 1.08, p= 5.07 ×10−11 ) but not APOE (OR = 0.88, p= 0.15) were significantly associated with mLOY in our population (Table S5). Importantly, mloy-PRS was orthogonal with age and evenly distributed across the age spectrum (Figure S4). Our results corroborate the validity of mloy-PRS as a Mendelian randomisation instrument for investigating the causal role of mLOY in AD and its endophenotypes and independently confirm the combined risk effect of previously reported loci in the mLOY phenotype [14]. In the case-control setup, we checked the effect of mloy-PRS on AD risk by fitting logistic regressions adjusted by APOE genotype, age, and relevant PCs (Table S6). Interestingly, the effect of mloy-PRS on AD could also be measured in the female samples. Therefore, we established three analysis groups: all (men + women), men only, and women only. We found no association between mloy-PRS and AD in the group including both sexes (Table 3). However, after sex stratification, we found a weak, non-significant, positive effect of mloy-PRS with respect to AD in the male subgroup (N= 2471, OR = 1.07, p= 0.12 ), while the effect was mostly neutral in the female subset (N= 4 978, OR = 1.00, p= 0.93). Next, we assessed the effect of mloy-PRS in disease progression. We adjusted Cox models by age, APOE genotype, and cohort ascertainment. We found a male-specific positive effect of mloy-PRS in the disease progression models (N = 682) (Table 3and Figure S5), with a suggestive signal for MCI-to-dementia progression (HR = 1.11, p= 0.08) and a significant risk effect for MCI-to-AD progression (HR = 1.23, p= 0.01). Of note, we found no association between mloy-PRS and conversion to all-cause dementia (HR = 0.99, p= 0.81) or AD (HR = 0.99, p= 0.85) in the female group (N= 1082). Table 3. Association results for mloy-PRS. ( a ) Results of logistic regressions for case-control AD. We only included individuals aged 65–85 years in the models. We adjusted the models by APOE genotype, age, and principal components. ( b ) Results of joint analysis of prospective MCIs in the GR@ACE-DEGESCO and EADB-DEGESCO cohorts using Cox proportional-hazards models for progression from MCI to all-cause dementia or AD. We adjusted the models by APOE genotype, age, and cohort ascertainment. The 95% confidence interval is presented as the 2.5% quantile (CI2.5) and the 97.5% quantile (CI97.5). OR, odds ratio; SE, standard error. (a) Logistic regression results: OR SE Z pCI2.5 CI97.5 Case-control, all 1.03 0.03 1.09 0.28 0.98 1.09 Case-control, men 1.07 0.05 1.55 0.12 0.98 1.18 Case-control, women 1.00 0.03 0.10 0.93 0.90 1.10 (b) Cox model results: HR SE Z pCI2.5 CI97.5 All, MCI to dementia 1.04 0.04 0.97 0.33 0.96 1.12 All, MCI to AD 1.07 0.05 1.45 0.15 0.98 1.16 Men, MCI to dementia 1.11 0.06 1.77 7.68 ×10−20.99 1.26 Men, MCI to AD 1.23 0.08 2.53 1.14 ×10−21.05 1.43 Women, MCI to dementia 0.99 0.05 −0.23 0.81 0.90 1.08 Women, MCI to AD 0.99 0.06 −0.19 0.85 0.89 1.11 Following these results, we proceeded to examine the existence of associations between mLRR-Y and the levels of core AD biomarkers in CSF: Abeta-42, phosphorylated tau 181 Int. J. Mol. Sci. 2023,24, 898 8 of 17 (p-tau), and total tau. We only kept individuals aged 65–85 years at the moment of the lumbar puncture (LP), and we excluded those with a gap of >5 years between DNA sampling and the LP (N= 214). We adjusted linear regressions by APOE genotype, age at LP, and the time window between blood sampling and LP. To account for the effect of syndromic status on the levels of Abeta-42, p-tau, and total tau, we calculated the effect of mLRR-Y in two groups (MCI N= 148; dementia N= 66) and then performed an inversevariance weighted fixed-effect meta-analysis. We found that both p-tau ( β = 41.92, p= 0.01 ) and total tau ( β = 396.69; p= 0.004 ) levels were increased in individuals with a higher degree of mLOY (Figure 3). Int. J. Mol. Sci. 2023, 23, x FOR PEER REVIEW 8 of 17 and total tau (β = 396.69; p = 0.004) levels were increased in individuals with a higher degree of mLOY (Figure 3). Figure 3. mLRR-Y associations with CSF protein levels. (a–c) Forest plots showing the effect size obtained in linear regression models for mLRR-Y on (a) Abeta-42, (b) phospho-tau, and (c) total tau in men with MCI or dementia, along with the meta-analysis results. (d–g) Volcano plots showing association of CSF proteins in the Olink inflammation and neurology panels with (d) mLRR-Y, (e) mLRR-Y adjusted by total tau, (f) APOE genotype, and (g) total tau. (h–k) QQ plots obtained in the models for (h) mLRR-Y, (i) mLRR-Y adjusted by total tau, (j) APOE genotype, and (k) total tau. We adjusted the models by age, the time window between CSF and DNA sampling, and APOE genotype. Next, we checked for mLRR-Y associations with proteomics data obtained with the Olink ProSeek® multiplex immunoassay for paired plasma and CSF samples in 135 men with MCI. Because mLOY is known to affect the immune system [10,24,30], and inflammation is involved in many processes related to AD pathogenesis [31], we analysed Olink neurology and inflammation panels. We detected inflation in our models (λ = 1.86), with most proteins showing increased levels in the CSF of individuals with a higher degree of blood mLOY (Figure 3). We observed a similar pattern when we analysed the effect of APOE genotype and total tau levels, with a large fraction of the proteins showing increased CSF levels in individuals carrying APOE risk alleles or displaying higher tau levels, respectively (Figure 3). Moreover, after adjusting our models by total tau, we lost most CSF associations, and the inflation factor was drastically reduced to λ = 0.86 (Figure 3). After covariation with total tau, we found seven nominally significant markers in plasma and one nominally significant marker in CSF. Nevertheless, no proteins passed false- Figure 3. mLRR-Y associations with CSF protein levels. ( a – c ) Forest plots showing the effect size obtained in linear regression models for mLRR-Y on (a) Abeta-42, (b) phospho-tau, and (c) total tau in men with MCI or dementia, along with the meta-analysis results. ( d – g ) Volcano plots showing association of CSF proteins in the Olink inflammation and neurology panels with ( d ) mLRR-Y, ( e ) mLRR-Y adjusted by total tau, ( f )APOE genotype, and ( g ) total tau. ( h – k ) QQ plots obtained in the models for ( h ) mLRR-Y, ( i ) mLRR-Y adjusted by total tau, ( j )APOE genotype, and ( k ) total tau. We adjusted the models by age, the time window between CSF and DNA sampling, and APOE genotype. Next, we checked for mLRR-Y associations with proteomics data obtained with the Olink ProSeek ® multiplex immunoassay for paired plasma and CSF samples in 135 men with MCI. Because mLOY is known to affect the immune system [ 10 , 24 , 30 ], and inflammation is involved in many processes related to AD pathogenesis [ 31 ], we analysed Olink neurology and inflammation panels. We detected inflation in our models ( λ = 1.86), with most proteins showing increased levels in the CSF of individuals with a higher degree of blood mLOY (Figure 3). We observed a similar pattern when we analysed the effect of APOE genotype and total tau levels, with a large fraction of the proteins showing increased CSF levels in individuals carrying APOE risk alleles or displaying higher tau levels, respectively (Figure 3). Moreover, after adjusting our models by total tau, we lost most CSF associations, and the inflation factor was drastically reduced to λ = 0.86 (Figure 3). After Int. J. Mol. Sci. 2023,24, 898 9 of 17 covariation with total tau, we found seven nominally significant markers in plasma and one nominally significant marker in CSF. Nevertheless, no proteins passed false-discovery rate (FDR) correction, suggesting that most mLOY associations can be explained by the previously observed correlation between mLOY and tau levels. Summary statistics for association of mLRR-Y to the CSF and plasma proteins are available (Tables S7–S10). 3. Discussion In the present study, we found that MCI men with high genetic risk of developing mLOY have increased chances of progressing to AD over time. The autosomal loci used to construct mloy-PRS had no effect on AD progression in the female subset of our cohort, strongly suggesting that the observed effect is produced via loss of the Y chromosome among men. Importantly, modelling mLOY through its associated genetic variance allowed us to observe mLOY-induced alterations in AD pathogenesis in an age-independent manner, an approach that is unparalleled in previous studies. These results add to previous evidence reporting mLOY as a male-specific AD pathogenic factor. mLOY is the most common known form of somatic mosaicism among men [ 10 ]. Concordantly, we detected mLOY in 18.9% men aged 65–85 years in our cohort. Although classically considered to be a harmless age-related trait, recent studies have revealed that mLOY increases risk of all-cause mortality and several diseases [ 10 , 20 – 22 ]. With such a high prevalence in the older population, interest in determining the effect of mLOY in age-related diseases has increased over the past decade. Previous studies have reported that mLOY is associated to an increased risk and progression rate for AD [ 19 ]. A more recent publication claimed that extreme transcriptomic downregulation of chromosome Y decreases AD resilience in men [ 32 ]. However, whether mLOY acts as an AD-promoting factor or is just a by-product of ageing needs to be clearly established. Consistent with previous studies [ 19 ], we found a higher degree of mLOY mosaicism (mLRR-Y) in our AD versus control population in unadjusted Kolmogorov-Smirnoff models (D = 0.18124, p< 2.2 × 10 −16 ) and age-adjusted ANCOVA (F = 68.0, p= 2.54 × 10 −16 ). We then performed a case-control logistic regression in all available men in our cohort, obtaining significant results (OR = 2.74; p= 0.01). Even though we adjusted for age, these results should be interpreted cautiously due to the dramatic age differences between the AD and control groups (Figure 1), as age is an important risk factor for both phenotypes. Thus, aiming to reduce age confounding in our models, we restricted analysis to men aged 65–85 years. However, despite not completely correcting the age gap between the groups, this also reduced our sample size (Table S1). We found an increased degree of LOY mosaicism (mLRR-Y) in 65–85-year-old cases versus controls (OR = 2.19, p= 0.09), but statistical significance was not reached in the models. Researchers have also found that mLOY increases the rate of AD conversion in MCI men [ 19 ]. We selected individuals recruited at the ACE Alzheimer Center Barcelona with an MCI diagnosis at the moment of sampling and available clinical follow-ups and fitted Cox proportional-hazards models. Even though we found risk, i.e., positive, effect directions for mLOY phenotypes towards AD progression, the models were not significant (Figure 2and Table 2). However, given our small sample size (N= 400), we may have lacked sufficient statistical power in this analysis. Remarkably, we noticed that the quantitative mLRR-Y variable performed superiorly in the case-control and disease progression models compared with mLOY calls, implying that if the effects are genuine, the mLOY-induced increase in AD risk and progression may be proportional to the mosaic fraction of LOY cells in blood. Due to the age-dependent nature of both mLOY and AD, controlling age confounding was very challenging in our cohort. 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