Cited by 169Alzheimer's disease research and treatmentsDementia and Cognitive Impairment ResearchNeuroinflammation and Neurodegeneration Mechanisms

The DOI matches its Crossref record; 59 of the 60 references it cites were found in a registry or an index; 23 of the 28 sources its claims cite were checked for support.

Clonal hematopoiesis is associated with protection from Alzheimer’s disease

Open access
Bouzid, Hind, Belk, Julia A., Jan, Max, Qi, Yanyan, Sarnowski, Chloé, Wirth, Sara, Ma, Lisa, Chrostek, Matthew R., Ahmad, Herra, Nachun, Daniel, Yao, Winnie, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Bis, Joshua, Psaty, Bruce, Beiser, Alexa, Bick, Alexander G., Bis, Joshua C., Fornage, Myriam, Longstreth, William T., Lopez, Oscar L., Natarajan, Pradeep, Psaty, Bruce M., Satizabal, Claudia L., Weinstock, Joshua, Larson, Eric B., Crane, Paul K., Keene, C. Dirk, Seshadri, Sudha, Satpathy, Ansuman T., Montine, Thomas J., Jaiswal, Siddhartha·2023·Nature Medicine

Published version of a preprint

Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) is a premalignant expansion of mutated hematopoietic stem cells. As CHIP-associated mutations are known to alter the development and function of myeloid cells, we hypothesized that CHIP may also be associated with the risk of Alzheimer’s disease (AD), a disease in which brain-resident myeloid cells are thought to have a major role. To perform association tests between CHIP and AD dementia, we analyzed blood DNA sequencing data from 1,362 individuals with AD and 4,368 individuals without AD. Individuals with CHIP had a lower risk of AD dementia (meta-analysis odds ratio (OR) = 0.64, P = 3.8 × 10 −5 ), and Mendelian randomization analyses supported a potential causal association. We observed that the same mutations found in blood were also detected in microglia-enriched fraction of the brain in seven of eight CHIP carriers. Single-nucleus chromatin accessibility profiling of brain-derived nuclei in six CHIP carriers revealed that the mutated cells comprised a large proportion of the microglial pool in the samples examined. While additional studies are required to validate the mechanistic findings, these results suggest that CHIP may have a role in attenuating the risk of AD.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

Clonal hematopoiesis of indeterminate potential (CHIP) was associated with lower risk of Alzheimer’s disease (AD) dementia across cohorts, with Mendelian randomization supporting a potential causal protective effect. CHIP-mutant cells were also detected in brain microglia-enriched fractions, suggesting marrow-derived myeloid cells may infiltrate the brain and influence AD risk.

Key findings

  • In a meta-analysis of ADSP, CHS, and FHS, CHIP carriers had lower risk of AD dementia (OR = 0.64, P = 3.8 × 10−5); in ADSP APOE ε3ε3 participants, CHIP with VAF > 0.08 was associated with lower AD risk (OR = 0.66, P = 5.5 × 10−4).
  • Mendelian randomization using 24 CHIP-associated polymorphisms found higher genetic risk of CHIP associated with reduced odds of AD (OR = 0.90 per 1 log-odds increase in CHIP risk, P = 3.3 × 10−4), while reverse-direction MR showed no evidence (OR = 0.97, P = 0.26).
  • CHIP was associated with lower AD neuropathologic change in participants without dementia: lower CERAD neuritic plaque score (OR = 0.50, P = 3.2 × 10−3) and lower Braak stage (OR = 0.56, P = 0.015).
  • CHIP variants were detected in the microglia-enriched NeuN− c-MAF+ brain fraction in seven of eight CHIP carriers; snATAC-seq estimated 30% to 95% of microglia were mutant in six CHIP samples, and microglia proportion among glia was higher in CHIP carriers (8.2%) than controls (5.1%; P = 0.042).

Methods

  • Association analyses used blood DNA sequencing from TOPMed cohorts FHS (n = 2,437; 92 incident AD) and CHS (n = 743; 166 incident AD), plus ADSP case-control WES limited to APOE ε3ε3 individuals (1,104 AD cases, 1,446 controls), with CHIP defined from curated gene variants and VAF cutoffs.
  • Time-to-event competing risks regression, logistic regression, fixed-effects meta-analysis, and Mendelian randomization were used; ADSP autopsy subsets were analyzed for CERAD score (n = 427) and Braak stage (n = 454) by ordinal logistic regression.
  • Brain validation used postmortem ACT samples from eight CHIP carriers and four noncarriers, with nuclei isolation, NeuN/c-MAF sorting, amplicon sequencing, and snATAC-seq of 94,367 high-quality nuclei plus reference datasets.

Limitations

  • The paper states that no differences in chromatin accessibility were found between microglia from CHIP carriers and noncarriers that could explain AD protection, possibly due to limited sample size, mutational heterogeneity, or assessment only in people without AD or significant ADNC.
  • The paper states it cannot conclude that protection from AD is due to infiltrating mutant cells; identifying biologically meaningful differences between mutant and wild-type microglia would strengthen the causal hypothesis.
  • Evident from the text: brain mechanistic analyses relied on small numbers of donors (eight CHIP carriers, four noncarriers; six with snATAC-seq), and the ADSP association replication was restricted to APOE ε3ε3 individuals because of age/sex imbalance in other genotype groups.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1038/s41591-023-02397-2

Cites

59 of 59 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Fig. 1 Association of CHIP and AD in longitudinal cohorts. a, Effect of CHIP on the risk of incident AD in CHS and FHS. SHR, CI95 and two-sided Wald P values were calculated for each covariate from CRR models, which included age at blood draw, sex and APOE genotype as covariates. The measure of center is the SHR and the lengths of the lines represent the CI95 for the SHR. Results from CHS and FHS were then meta-analyzed using a fixed-effects model for the two cohorts, the center of the diamonds represents the SHR and the lengths represent the CI95 for the SHR. People in FHS, n = 2,437 and people in CHS, n = 743. b, Kaplan–Meier curve showing AD-free probability in CHIP noncarriers (left) and carriers (right), stratified by APOE genotype. Analysis was restricted to those in FHS and CHS older than 60 years at the time of blood draw, and the results of a two-sided log-rank test are shown. People for non-CHIP carriers, n = 1,800 and people for CHIP carriers, n = 246. CI95, 95% confidence interval.
Fig. 1 Association of CHIP and AD in longitudinal cohorts. a, Effect of CHIP on the risk of incident AD in CHS and FHS. SHR, CI95 and two-sided Wald P values were calculated for each covariate from CRR models, which included age at blood draw, sex and APOE genotype as covariates. The measure of center is the SHR and the lengths of the lines represent the CI95 for the SHR. Results from CHS and FHS were then meta-analyzed using a fixed-effects model for the two cohorts, the center of the diamonds represents the SHR and the lengths represent the CI95 for the SHR. People in FHS, n = 2,437 and people in CHS, n = 743. b, Kaplan–Meier curve showing AD-free probability in CHIP noncarriers (left) and carriers (right), stratified by APOE genotype. Analysis was restricted to those in FHS and CHS older than 60 years at the time of blood draw, and the results of a two-sided log-rank test are shown. People for non-CHIP carriers, n = 1,800 and people for CHIP carriers, n = 246. CI95, 95% confidence interval.CC BY · PubMed Central
Fig. 2 CHIP is associated with protection from AD dementia and AD neuropathologic change (ADNC). a. Effect of CHIP on the risk of AD in those with APOE ε3ε3 genotype from the ADSP. OR, CI95 and two-sided Wald P value were calculated from a logistic regression model that also included age at the time of blood draw and sex as covariates (Supplementary Table 5 for full regression results). People, n = 2,550. b, Fixed-effect meta-analysis for risk of AD in CHIP carriers using logistic regression in ADSP, FHS and CHS with two-sided Wald P value shown. c, MR for risk of AD based on genetic risk of CHIP using the weighted median estimator on summary statistics from Schwartzentruber et al.18 AD GWAS and GWAX, Finngen AD GWAS and Gr@ACE AD GWAS, which were meta-analyzed using a fixed-effects model. OR, CI95 and two-sided Wald P values for risk of AD per 1 log-odds increase in genetic risk of CHIP are shown. The GWAX analysis was one-sample MR (both CHIP and AD effect estimates were derived from UK Biobank data), while the other studies were two-sample MR. People, n = 692,045. d, Effect of CHIP on the risk of increased CERAD neuritic plaque score in ADSP participants without a dementia diagnosis. OR and CI95 were calculated from an ordinal logistic regression model, which included age at autopsy, APOE genotype, sex and CHIP status as covariates. Two-sided P values were calculated by comparing the t-statistic for each covariate against a standard normal distribution. Full regression results are in Supplementary Table 9. People, n = 427. e, Effect of CHIP on the risk of increased Braak stage in ADSP participants without a dementia diagnosis. OR and CI95 were calculated from an ordinal logistic regression model, which included age at autopsy, APOE genotype, sex and CHIP status as covariates. Two-sided P values were calculated by comparing the t-statistic for each covariate against a standard normal distribution. Full regression results are in Supplementary Table 9. People, n = 454. For all forest plots, the measure of center is the OR and the lines represent the CI95 for the OR.
Fig. 2 CHIP is associated with protection from AD dementia and AD neuropathologic change (ADNC). a. Effect of CHIP on the risk of AD in those with APOE ε3ε3 genotype from the ADSP. OR, CI95 and two-sided Wald P value were calculated from a logistic regression model that also included age at the time of blood draw and sex as covariates (Supplementary Table 5 for full regression results). People, n = 2,550. b, Fixed-effect meta-analysis for risk of AD in CHIP carriers using logistic regression in ADSP, FHS and CHS with two-sided Wald P value shown. c, MR for risk of AD based on genetic risk of CHIP using the weighted median estimator on summary statistics from Schwartzentruber et al.18 AD GWAS and GWAX, Finngen AD GWAS and Gr@ACE AD GWAS, which were meta-analyzed using a fixed-effects model. OR, CI95 and two-sided Wald P values for risk of AD per 1 log-odds increase in genetic risk of CHIP are shown. The GWAX analysis was one-sample MR (both CHIP and AD effect estimates were derived from UK Biobank data), while the other studies were two-sample MR. People, n = 692,045. d, Effect of CHIP on the risk of increased CERAD neuritic plaque score in ADSP participants without a dementia diagnosis. OR and CI95 were calculated from an ordinal logistic regression model, which included age at autopsy, APOE genotype, sex and CHIP status as covariates. Two-sided P values were calculated by comparing the t-statistic for each covariate against a standard normal distribution. Full regression results are in Supplementary Table 9. People, n = 427. e, Effect of CHIP on the risk of increased Braak stage in ADSP participants without a dementia diagnosis. OR and CI95 were calculated from an ordinal logistic regression model, which included age at autopsy, APOE genotype, sex and CHIP status as covariates. Two-sided P values were calculated by comparing the t-statistic for each covariate against a standard normal distribution. Full regression results are in Supplementary Table 9. People, n = 454. For all forest plots, the measure of center is the OR and the lines represent the CI95 for the OR.CC BY · PubMed Central
Fig. 3 Associations of CHIP to AD by APOE genotype and mutated driver gene. a, Effect of CHIP on AD dementia risk in participants from CHS and FHS stratified by APOE genotype. Participants were binned into those with neutral (APOE ε3ε3), low-risk (APOE ε2ε2 and ε2ε3) and high-risk (any APOE ε4 allele) groups. SHR, 95% confidence intervals (CI95) and two-sided Wald P values were calculated for each covariate (age at the time of blood draw for sequencing, sex and CHIP carrier status) from CRR models, and results from FHS and CHS were then meta-analyzed using a fixed-effects model (Supplementary Table 10 for full regression results). Adjustments for multiple comparisons were not performed. People, n = 3,180. b, Effect of CHIP on ADNC score in cognitively intact participants from ADSP stratified by APOE genotype. Participants were binned into those with neutral (APOE ε3ε3), low-risk (APOE ε2ε2 and ε2ε3) and high-risk (any APOE ε4 allele) groups. ORs, CI95 and two-sided Wald P values were calculated for a six-point composite score of CERAD and Braak stages and analyzed using an ordinal logistic regression model with age at death, sex and CHIP carrier status as covariates (Supplementary Table 10 for full regression results). Adjustments for multiple comparisons were not performed. People, n = 422. c, Effect of mutated CHIP gene on AD in participants from CHS, FHS and ADSP. OR, CI95 and two-sided Wald P values were calculated for each covariate (age at the time of blood draw for sequencing, sex, cohort and APOE genotype) from logistic regression models, and results from the TOPMed cohorts and ADSP were then meta-analyzed using a fixed-effects model (Supplementary Table 12 for full regression results). Adjustments for multiple comparisons were not performed. People, n = 5,730. For all forest plots, the measure of center is the SHR or OR and the lines represent the CI95 for the SHR or OR.
Fig. 3 Associations of CHIP to AD by APOE genotype and mutated driver gene. a, Effect of CHIP on AD dementia risk in participants from CHS and FHS stratified by APOE genotype. Participants were binned into those with neutral (APOE ε3ε3), low-risk (APOE ε2ε2 and ε2ε3) and high-risk (any APOE ε4 allele) groups. SHR, 95% confidence intervals (CI95) and two-sided Wald P values were calculated for each covariate (age at the time of blood draw for sequencing, sex and CHIP carrier status) from CRR models, and results from FHS and CHS were then meta-analyzed using a fixed-effects model (Supplementary Table 10 for full regression results). Adjustments for multiple comparisons were not performed. People, n = 3,180. b, Effect of CHIP on ADNC score in cognitively intact participants from ADSP stratified by APOE genotype. Participants were binned into those with neutral (APOE ε3ε3), low-risk (APOE ε2ε2 and ε2ε3) and high-risk (any APOE ε4 allele) groups. ORs, CI95 and two-sided Wald P values were calculated for a six-point composite score of CERAD and Braak stages and analyzed using an ordinal logistic regression model with age at death, sex and CHIP carrier status as covariates (Supplementary Table 10 for full regression results). Adjustments for multiple comparisons were not performed. People, n = 422. c, Effect of mutated CHIP gene on AD in participants from CHS, FHS and ADSP. OR, CI95 and two-sided Wald P values were calculated for each covariate (age at the time of blood draw for sequencing, sex, cohort and APOE genotype) from logistic regression models, and results from the TOPMed cohorts and ADSP were then meta-analyzed using a fixed-effects model (Supplementary Table 12 for full regression results). Adjustments for multiple comparisons were not performed. People, n = 5,730. For all forest plots, the measure of center is the SHR or OR and the lines represent the CI95 for the SHR or OR.CC BY · PubMed Central
Fig. 4 CHIP variants can be found in the MG-enriched fraction of the brain. a, Barplot of putative CHIP mutations identified from whole exome sequencing of brain DNA from 1,775 persons in ADSP. Details about the variants identified are in Supplementary Table 13. b, Schematic of experimental workflow. Autopsy samples from the occipital cortex, cerebellum and putamen were digested to prepare single nuclei suspensions. Nuclei were then stained and sorted using antibodies to c-MAF+ (a marker of myeloid cells) and NeuN+ (a marker of neuronal cells), followed by amplicon sequencing for CHIP variants. c, Barplot of the VAF of the CHIP variants from eight donors (ACT1 to ACT8). For each sample, the VAF in the blood and in the brain c-MAF+ NeuN− population is shown. The occipital cortex was available for all eight donors. A bar for cerebellum or putamen is shown if available, otherwise, NA in the corresponding color designates lack of an available sample (purple for cerebellum and red for putamen). The CHIP mutations present in each participant are reported in the box on the right of the barplot. Details about the samples and the VAF from sorted populations are shown in Supplementary Table 14.
Fig. 4 CHIP variants can be found in the MG-enriched fraction of the brain. a, Barplot of putative CHIP mutations identified from whole exome sequencing of brain DNA from 1,775 persons in ADSP. Details about the variants identified are in Supplementary Table 13. b, Schematic of experimental workflow. Autopsy samples from the occipital cortex, cerebellum and putamen were digested to prepare single nuclei suspensions. Nuclei were then stained and sorted using antibodies to c-MAF+ (a marker of myeloid cells) and NeuN+ (a marker of neuronal cells), followed by amplicon sequencing for CHIP variants. c, Barplot of the VAF of the CHIP variants from eight donors (ACT1 to ACT8). For each sample, the VAF in the blood and in the brain c-MAF+ NeuN− population is shown. The occipital cortex was available for all eight donors. A bar for cerebellum or putamen is shown if available, otherwise, NA in the corresponding color designates lack of an available sample (purple for cerebellum and red for putamen). The CHIP mutations present in each participant are reported in the box on the right of the barplot. Details about the samples and the VAF from sorted populations are shown in Supplementary Table 14.CC BY · PubMed Central
Fig. 5 snATAC-seq of brain samples from CHIP carriers reveals that the mutated cells are similar to MG and comprise a large proportion of the microglial pool. a, snATAC-seq profiles of 187,754 cells from our dataset, the Corces 2020 adult human brain dataset and the Satpathy 2019 human blood dataset. Each dot represents the snATAC-seq profile of one cell and is colored by its assigned cluster. b, snATAC-seq profiles of all cells colored by which donor it originated from. Samples from Corces 2020 and Satpathy 2019 are aggregated and shown in gray and dark gray, respectively. c, Cluster composition of hematopoietic cells in each sample in this study. d, Pseudo-bulk tracks for selected gene loci. The top eight tracks show snATAC-seq coverage of cells from the indicated sample (or aggregated Corces 2020 samples) within cluster C6–MG. Clusters C7–monocyte and C8–DC are also included for visual reference. e, Calculation of the proportion of MG bearing a CHIP mutation in each sample. Error bars depict the simulated 95% confidence interval for percent mutant MG in each sample using n = 106 random samples (see Methods for details). f, Correspondence between the fraction of mutant blood cells and mutant MG for each donor (n = 6). Pearson product-moment correlation coefficient and two-sided P value calculated from a t-statistic based on the correlation coefficient are shown. g, Fraction of MG relative to the total glial pool in occipital cortex samples from CHIP and non-CHIP donors, computed from frequencies of the indicated clusters. Two-sided Wald P value = 0.042 by quasibinomial regression of the cell counts of MG, oligodendrocytes and astrocytes. n = 10 donors. Box plot shows median, first quartile and third quartile. Box plot whiskers show minimum and maximum values, which are capped at a distance of 1.5× (interquartile range) away from the box. The colors in d–g represent the sample name as depicted in the box under Fig. 4b. Detailed information for each donor is available in Supplementary Table 14. Samples are unsorted and are taken from the occipital cortex unless otherwise indicated. Ce, cerebellum; P, putamen.
Fig. 5 snATAC-seq of brain samples from CHIP carriers reveals that the mutated cells are similar to MG and comprise a large proportion of the microglial pool. a, snATAC-seq profiles of 187,754 cells from our dataset, the Corces 2020 adult human brain dataset and the Satpathy 2019 human blood dataset. Each dot represents the snATAC-seq profile of one cell and is colored by its assigned cluster. b, snATAC-seq profiles of all cells colored by which donor it originated from. Samples from Corces 2020 and Satpathy 2019 are aggregated and shown in gray and dark gray, respectively. c, Cluster composition of hematopoietic cells in each sample in this study. d, Pseudo-bulk tracks for selected gene loci. The top eight tracks show snATAC-seq coverage of cells from the indicated sample (or aggregated Corces 2020 samples) within cluster C6–MG. Clusters C7–monocyte and C8–DC are also included for visual reference. e, Calculation of the proportion of MG bearing a CHIP mutation in each sample. Error bars depict the simulated 95% confidence interval for percent mutant MG in each sample using n = 106 random samples (see Methods for details). f, Correspondence between the fraction of mutant blood cells and mutant MG for each donor (n = 6). Pearson product-moment correlation coefficient and two-sided P value calculated from a t-statistic based on the correlation coefficient are shown. g, Fraction of MG relative to the total glial pool in occipital cortex samples from CHIP and non-CHIP donors, computed from frequencies of the indicated clusters. Two-sided Wald P value = 0.042 by quasibinomial regression of the cell counts of MG, oligodendrocytes and astrocytes. n = 10 donors. Box plot shows median, first quartile and third quartile. Box plot whiskers show minimum and maximum values, which are capped at a distance of 1.5× (interquartile range) away from the box. The colors in d–g represent the sample name as depicted in the box under Fig. 4b. Detailed information for each donor is available in Supplementary Table 14. Samples are unsorted and are taken from the occipital cortex unless otherwise indicated. Ce, cerebellum; P, putamen.CC BY · PubMed Central
Extended Data Fig. 1 The VAF distribution in TOPMed and ADSP. Density plots of VAF from all CHIP carriers in ADSP (left) or in CHIP carriers with VAF greater than 0.08 (right) compared to VAF distribution from TOPMed CHIP carriers (red) in each plot.
Extended Data Fig. 1 The VAF distribution in TOPMed and ADSP. Density plots of VAF from all CHIP carriers in ADSP (left) or in CHIP carriers with VAF greater than 0.08 (right) compared to VAF distribution from TOPMed CHIP carriers (red) in each plot.CC BY · PubMed Central
Extended Data Fig. 2 Scatterplot of effect sizes from the Mendelian randomization study. a) Regression line for the IVW model is shown. The variants shown are from previously published genome-wide association studies (GWAS) of CHIP as described in Methods and listed in Supplementary Table 7. The measure of center is the beta estimate for each variant and the lines represent the 95 percent confidence interval for the beta estimate. All values were taken from published summary statistics data from CHIP or AD GWAS. b) Regression lines for each model are shown using the same data as in A).
Extended Data Fig. 2 Scatterplot of effect sizes from the Mendelian randomization study. a) Regression line for the IVW model is shown. The variants shown are from previously published genome-wide association studies (GWAS) of CHIP as described in Methods and listed in Supplementary Table 7. The measure of center is the beta estimate for each variant and the lines represent the 95 percent confidence interval for the beta estimate. All values were taken from published summary statistics data from CHIP or AD GWAS. b) Regression lines for each model are shown using the same data as in A).CC BY · PubMed Central
Extended Data Fig. 3 Sorting of nuclei from brains of CHIP carriers. Flow cytometry gating strategy for nuclei sorting for the 8 brain samples using c-MAF and NeuN markers. The arrow points to the c-MAF+ NeuN− sorted population and the VAF is indicated for each sample. The brain regions are abbreviated as Ce for Cerebellum, OC for Occipital cortex, and P for putamen.
Extended Data Fig. 3 Sorting of nuclei from brains of CHIP carriers. Flow cytometry gating strategy for nuclei sorting for the 8 brain samples using c-MAF and NeuN markers. The arrow points to the c-MAF+ NeuN− sorted population and the VAF is indicated for each sample. The brain regions are abbreviated as Ce for Cerebellum, OC for Occipital cortex, and P for putamen.CC BY · PubMed Central
Extended Data Fig. 4 Correlation of repeated VAF measurements from brain. a) c-MAF+ NeuN− nuclei were sorted from the samples shown and VAF for the CHIP variants was assessed. A repeat sample was sorted to determine the variability in the VAF of sorted populations. b) DNA from unsorted occipital cortex nuclei was obtained for the samples shown. VAF of the CHIP variants was determined from two independent sequencing libraries generated for each sample to assess the variability in the VAF across sequencing runs. The y = x line is drawn. R2, coefficient, and y-intercept values calculated from linear regression are shown.
Extended Data Fig. 4 Correlation of repeated VAF measurements from brain. a) c-MAF+ NeuN− nuclei were sorted from the samples shown and VAF for the CHIP variants was assessed. A repeat sample was sorted to determine the variability in the VAF of sorted populations. b) DNA from unsorted occipital cortex nuclei was obtained for the samples shown. VAF of the CHIP variants was determined from two independent sequencing libraries generated for each sample to assess the variability in the VAF across sequencing runs. The y = x line is drawn. R2, coefficient, and y-intercept values calculated from linear regression are shown.CC BY · PubMed Central
Extended Data Fig. 5 High-quality snATAC-seq libraries from ACT brain samples. Quality control metrics for each nucleus in the indicated scATAC-seq sample. Aggregated Corces 2020 samples and Satpathy 2019 samples are also included. Top: Enrichment of fragments in transcription start sites (TSS). Center: number of fragments. Bottom: fraction of reads in peaks (FRIP) for each nucleus. Unless otherwise indicated, the samples are derived from occipital cortex (OC) and are unsorted. The brain regions are abbreviated as Ce for Cerebellum, OC for Occipital cortex, and P for putamen. The n for each sample is the number of nuclei analyzed in a single experiment for each sample, which is listed in Supplementary Table 15 under column ‘TOTAL’.
Extended Data Fig. 5 High-quality snATAC-seq libraries from ACT brain samples. Quality control metrics for each nucleus in the indicated scATAC-seq sample. Aggregated Corces 2020 samples and Satpathy 2019 samples are also included. Top: Enrichment of fragments in transcription start sites (TSS). Center: number of fragments. Bottom: fraction of reads in peaks (FRIP) for each nucleus. Unless otherwise indicated, the samples are derived from occipital cortex (OC) and are unsorted. The brain regions are abbreviated as Ce for Cerebellum, OC for Occipital cortex, and P for putamen. The n for each sample is the number of nuclei analyzed in a single experiment for each sample, which is listed in Supplementary Table 15 under column ‘TOTAL’.CC BY · PubMed Central
Extended Data Fig. 6 Marker genes and additional data for scATAC-seq clusters. a) snATAC-seq profiles of all nuclei colored by accessibility of indicated marker genes (quantified using GeneScore). b) snATAC-seq profiles of all nuclei colored by which study they originated from (left), sort status (center), or brain region (right). Brain regions are abbreviated as Ce for Cerebellum, OC for Occipital cortex, and P for putamen.
Extended Data Fig. 6 Marker genes and additional data for scATAC-seq clusters. a) snATAC-seq profiles of all nuclei colored by accessibility of indicated marker genes (quantified using GeneScore). b) snATAC-seq profiles of all nuclei colored by which study they originated from (left), sort status (center), or brain region (right). Brain regions are abbreviated as Ce for Cerebellum, OC for Occipital cortex, and P for putamen.CC BY · PubMed Central
Extended Data Fig. 7 Quantification of gene accessibility in microglia subsets. a) For each gene, accessibility in each single nucleus is plotted (quantified via ArchR GeneScore). The n for each brain sample is the number of nuclei in the microglia cluster (C6) analyzed in a single experiment for each sample, which is listed in Supplementary Table 15. n = 7,982 nuclei for C7-monocyte and n = 2,369 nuclei for C8-DC. b) Fold change and statistical significance when comparing all pairs of groups for SALL1 (left) and ANPEP (right). (A-B) Within the microglia cluster (C6), cells are split by donor and brain region. Aggregated Corces 2020 microglia, as well as C7-Monocyte and C8-DC clusters, are included for reference. Statistical significance and fold change was computed for all pairs of groups for all GeneScores using the Wilcoxon test. * denotes FDR < 0.05.
Extended Data Fig. 7 Quantification of gene accessibility in microglia subsets. a) For each gene, accessibility in each single nucleus is plotted (quantified via ArchR GeneScore). The n for each brain sample is the number of nuclei in the microglia cluster (C6) analyzed in a single experiment for each sample, which is listed in Supplementary Table 15. n = 7,982 nuclei for C7-monocyte and n = 2,369 nuclei for C8-DC. b) Fold change and statistical significance when comparing all pairs of groups for SALL1 (left) and ANPEP (right). (A-B) Within the microglia cluster (C6), cells are split by donor and brain region. Aggregated Corces 2020 microglia, as well as C7-Monocyte and C8-DC clusters, are included for reference. Statistical significance and fold change was computed for all pairs of groups for all GeneScores using the Wilcoxon test. * denotes FDR < 0.05.CC BY · PubMed Central
Extended Data Fig. 8 Sub-clustering of cells in our samples within cluster C6-MG. (a) snATAC-seq profiles of each nucleus colored by which donor it originated from. (b) snATAC-seq profiles colored by sub-cluster (left) and sub-cluster composition of each sample (right). (c) snATAC-seq profiles of nuclei colored by brain region (top) or sort status (bottom). (d) Quantification of accessibility in each single nucleus for selected marker genes (ArchR GeneScore). Nuclei from our samples are grouped by sub-cluster, and microglia from the Corces 2020 reference dataset, as well as C7 and C8 from Fig. 4, are included for reference.
Extended Data Fig. 8 Sub-clustering of cells in our samples within cluster C6-MG. (a) snATAC-seq profiles of each nucleus colored by which donor it originated from. (b) snATAC-seq profiles colored by sub-cluster (left) and sub-cluster composition of each sample (right). (c) snATAC-seq profiles of nuclei colored by brain region (top) or sort status (bottom). (d) Quantification of accessibility in each single nucleus for selected marker genes (ArchR GeneScore). Nuclei from our samples are grouped by sub-cluster, and microglia from the Corces 2020 reference dataset, as well as C7 and C8 from Fig. 4, are included for reference.CC BY · PubMed Central

Verification

Claims the paper makes

43 Own result · 27 Own method · 15 Own interpretation · 9 Supported · 6 Partly supported · 5 No basis given · 3 No full text of the sources · 1 Supported so far · 1 Couldn’t confirm · accuracy not yet measured

Show all 63 claims

  • Probably not a substantial claim. 33
  • It may state two separate claims as one. 22
47 of 47
  • The authors state this as an assumption.

    All nonmicroglial hematopoietic cells in the brain samples were mutant.

    “We conservatively assumed that all nonmicroglial hematopoietic cells were mutant and subtracted the number of these cells from the total mutant cell count”
  • The authors state this as an assumption.

    The instrumental variables for CHIP are relevant, i.e., associated with CHIP.

    “The assumptions for the validity of MR analyses are relevance, independence and exclusion restriction of the independent variables (IVs)48.”
  • The authors state this as an assumption.

    The instrumental variables for CHIP are independent of unmeasured confounders.

    “Independence: This assumption presupposes that the CHIP IVs are not associated with unmeasured confounders.”
  • The authors state this as an assumption.

    The instrumental variables affect AD only through CHIP (exclusion restriction).

    “The assumptions for the validity of MR analyses are relevance, independence and exclusion restriction of the independent variables (IVs)48.”
  • The authors state this as an assumption.

    All nonmicroglial hematopoietic cells were mutant.

    “We assumed that all nonmicroglial hematopoietic cells were mutant”
  • The authors state this as an assumption.

    Only hematopoietic cells can carry the CHIP variant.

    “We assume that only hematopoietic cells can carry the CHIP variant.”
  • The paper relies on this without support.

    Higher blood pressure is a risk factor for Alzheimer's disease.

    “Given that higher blood pressure is a risk factor for AD”
7 of 7

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

4 of 4

Citations

Cites

Cited by

Sorted by citations

See all 169

Metadata

Authors
Bouzid, H., Belk, J. A., Jan, M., Qi, Y., Sarnowski, C., Wirth, S., Ma, L., Chrostek, M. R., Ahmad, H., Nachun, D., Yao, W., NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Bis, J., Psaty, B., Beiser, A., Bick, A. G., Bis, J. C., Fornage, M., Longstreth, W. T., Lopez, O. L., Natarajan, P., Psaty, B. M., Satizabal, C. L., Weinstock, J., Larson, E. B., Crane, P. K., Keene, C. D., Seshadri, S., Satpathy, A. T., Montine, T. J., & Jaiswal, S.
Affiliations
  • Stanford Medicine (US)
  • Stanford University (US)
  • Massachusetts General Hospital (US)
  • The University of Texas Health Science Center at Houston (US)
  • Boston University (US)
  • Vanderbilt University Medical Center (US)
  • Brown Foundation Institute of Molecular Medicine for the Prevention of Human Diseases (US)
  • Broad Institute (US)
  • University of Michigan (US)
  • Kaiser Permanente Washington Health Research Institute (US)
  • University of Washington (US)
  • The University of Texas at San Antonio Health Science Center (US)
Corresponding author
Jaiswal, Siddhartha
Venue
Nature Medicine
Published
2023-06-15
Volume
29
Issue
7
Pages
1662-1670
ISSN
  • 1078-8956 (ISSN-L)
  • 1546-170X
OpenAlex
W4380785142
Cited by
169
References
60
Citations by year
2026: 42 · 2025: 58 · 2024: 44 · 2023: 19
FWCI
23.48
Type
journal article
Language
en
Open access
Locations
  • Nature Medicine (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
  • Digital Commons-TMC (Texas Medical Center) (submittedVersion)
  • Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
Funders
  • U.S. Department of Health & Human Services | National Institutes of Health (DP2-HL157540, R01-HL148565, K08CA230188)
  • Burroughs Wellcome Fund (DP5 OD029586)
  • Fondation Leducq (TNE-18CVD04)
  • Ludwig Center for Cancer Stem Cell Research at Stanford University Knight Initiative for Brain Resilience
  • U.S. Department of Health & Human Services | NIH | National Institute on Aging (R00 AG066849)
  • Fireman Endowed Chair in Vascular Medicine
  • Nancy and Buster Alvord Endowment
  • U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (AG053959)
Topics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
Keywords
clonal hematopoiesis of indeterminate potential, Alzheimer's disease, dementia risk, microglia, myeloid cells, brain microglia, Mendelian randomization, causal relationship
MeSH terms
Clonal Hematopoiesis, Alzheimer Disease, Hematopoiesis, Hematopoietic Stem Cells, Humans, Mutation, Precancerous Conditions

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-19, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-19 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Cited sources are checked with a language model’s help (gpt-6-sol).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review