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December 2022; 8 (6) Research ArticleOpen Access

Identification of Sex-Specific Genetic Variants Associated With Tau PET

Xin Wang, Iris Broce, Kacie D. Deters, Chun Chieh Fan, Sarah Jane Banks; for the Alzheimer's Disease Neuroimaging Initiative*
First published December 8, 2022, DOI: https://doi.org/10.1212/NXG.0000000000200043
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Abstract

Background and Objectives Important sex differences exist in tau pathology along the Alzheimer disease (AD) continuum, with women showing enhanced tau deposition compared with men, especially during the mild cognitive impairment (MCI) phase. This study aims to identify specific genetic variants associated with sex differences in regional tau aggregation, as measured with PET.

Methods Four hundred ninety-three participants (women, n = 246; men, n = 247) who self-identified as White from the AD Neuroimaging Initiative study, with genotyping data and 18F-Flortaucipir tau PET data, were included irrespective of clinical diagnosis (cognitively normal [CN], MCI, and AD). We focused on the genetic variants within 10 genes previously shown to have sex-dependent effects on AD to reduce the burden of multiple comparisons: BIN1, MS4A6A, DNAJA2, FERMT2, APOC1, APOC1P1, FAM193B, C2orf47, TYW5, and CR1. Multivariate analysis of variance was applied to identify genetic variants associated with tau PET data in 3 regions of interests (composite regions of Braak I, Braak III/IV, and Braak V/VI stages) in women and men separately. We controlled for age, scanner manufacture, amyloid status, APOE ε4 carriership, diagnosis (CN vs MCI vs AD), and the first 10 genetic principal components to adjust for population stratification.

Results We identified 3 genetic loci within 3 different genes associated with tau deposits specifically in women: rs79711283 within DNAJA2, rs113357081 within FERMT2, and rs74614106 within TYW5. In men, we also identified 3 loci within CR1 associated with tau deposits: rs115096248, rs113698814, and rs78150633.

Discussion Our findings revealed sex-specific genetic variants associated with tau deposition independent of APOE ε4, amyloid status, and clinical diagnosis. These results provide potential molecular targets for understanding the mechanism of sex-specific tau aggregation and developing sex-specific gene-guided precision prevention or therapeutic interventions for AD.

Glossary

AD=
Alzheimer disease;
ADNI=
AD Neuroimaging Initiative;
CN=
cognitively normal;
FBB=
F-florbetaben;
FBP=
F-florbetapir;
FTP=
F-Flortaucipir;
Hsp40=
Heat Shock Protein Family;
MAF=
minor allele frequency;
MANOVA=
multivariate analysis of variance;
MCI=
mild cognitive impairment;
PVC=
partial volume corrected;
ROIs=
regions of interest;
SNPs=
single-nucleotide polymorphisms;
SUVRs=
standard uptake value ratios

Alzheimer disease (AD) affects men and women differently.1 Women with mild cognitive impairment (MCI) or AD show faster rates of cognitive decline than men with MCI or AD.2 Sex differences in AD neuropathology have also been reported with women showing faster rates of brain atrophy and higher numbers of neurofibrillary tangles than men.3,4 APOE ε4 is the strongest known genetic risk factor of AD and confers a substantially greater risk for developing AD in women than it does for men, especially between the ages of 65 and 75 years.5,6 Growing evidence has shown the sex-specific associations between APOE ε4 and cognitive decline or AD neuropathology.7 For example, women demonstrated a greater correlation between APOE ε4 and CSF tau than men, particularly in amyloid-positive individuals.8 Beyond APOE ε4, other genetic risk factors contributing to sex differences in AD have not been widely studied. Recent sex-stratified genetic association analyses conducted by our group revealed that polygenic risk of AD is strongly modulated by biological sex, such that several non-APOE genes, including BIN1, MS4A6A, FAM193B, and CR1, differentially contribute to the progression of AD between women and men.9

Abnormal tau deposition is a hallmark pathologic feature of AD. Autopsy studies exposed the stereotypic topographical spread pattern of tau pathology through what are known as Braak stages (I–VI).10 Pathologic tau deposits are found early in the entorhinal cortex (Braak I) and hippocampus (Braak II), then progress into the limbic region (Braak III/IV) and finally throughout the neocortex (Braak V/VI).10,11 PET imaging has enabled in vivo detection of this spatiotemporal tau pathology in the human brain. A growing number of studies have shown that tau PET is closely related to cognitive symptoms and disease severity.12,-,14 Compared with CSF and plasma tau measures that behave as “disease state” indicating the presence of the AD process, tau PET appears to reflect the continual spread of tau with AD progression and thus behaves as a biomarker of “disease stage.”15 Sex differences in tau PET were also reported recently, with women exhibiting higher regional tau PET levels than men,16 especially in those with an APOE ε4 allele.17,-,19 Whether specific genetic variants beyond APOE are associated with sex differences in tau PET remains unknown.

The aim of this study was to identify whether previously identified sex-specific genes associated with AD risk were associated with the spatiotemporal tau burden assessed with PET imaging in the human brain. Previous findings of our group have identified several genetic variants with sex-dependent effects on AD, such as age at AD onset, amyloid-β, and neurofibrillary tangles at autopsy: the effect sizes of BIN1, MS4A6A, DNAJA2, and FERMT2 in AD were higher in women than in men, while the effect size of FAM193B, C2ord47, TYW5, and CR1 were higher in men. At the same time, APOC1 and APOC1P1 did not show these sex differences in AD.9 In this study, we focused our analysis on these 10 genes to reduce the burden of multiple comparisons. To capture tau aggregation and progression temporally and spatially, the regions of interest (ROIs) were the stereotypical pattern of Braak stages: Braak I, Braak III/IV, and Braak V/VI.

Methods

Data Source

We obtained data from the AD Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was established in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD, with the goal of testing whether serial MRI, PET, other biological markers, and clinical evaluation and neuropsychological testing can be used to measure the progression of MCI and early AD. STrengthening the REporting of Genetic Association Studies reporting guidelines were used in this study.20

Standard Protocol Approvals, Registrations, and Patient Consents

As per ADNI protocols, all procedures performed in studies involving human participants following the ethical standards of the institutional research committees and the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed written consent was obtained from all participants at each site.

Participants

Individuals (n = 521) who had 18F-Flortaucipir (FTP) PET scans, genetic data, and amyloid PET scans were included irrespective of clinical diagnosis (cognitively normal [CN], MCI, and AD). Few participants diagnosed with AD but amyloid-negative were excluded (n = 7). Given the potential important effects of non-European ancestral genetics,21 we removed 21 participants who self-identified as non-White. Finally, 493 participants (women: n = 246, men: n = 247) were eventually assessed in this study (Table 1).

View this table:
Table 1

Characteristics of Participants

NeuroImaging

FTP regional summary data: nonpartial volume corrected (PVC) were downloaded from the ADNI. In brief, PET images were acquired at 75–105 minutes (6 × 5 minutes frames) after injection of 370 MBq (10.0 mCi) ± 10% of FTP. Preprocessed FTP PET images were coregistered to the corresponding bias-corrected T1 scans created by Freesurfer (version 7.1.1), and the mean FTP uptake was computed within each Freesurfer-defined region. The regional standard uptake value ratios (SUVRs) in the data set were also intensity normalized by the inferior cerebellum gray matter as the reference region. Detailed acquisition, processing, and calculation methods can be found elsewhere.22,23 Volume-weighted average tau SUVRs in 3 composite ROIs (Braak I, Braak III/IV, and Braak V/VI)22 were our phenotypes of interest. eFigure 1, links.lww.com/NXG/A565, displays the detailed regions of 3 ROIs. Braak II (hippocampus) was excluded because of contamination by known off-target binding with this ligand in the choroid plexus.23,24 PVC-tau PET data from ADNI were also used to corroborate the sex-specific effects of significant SNPs. We extracted the closest amyloid PET (18F-florbetapir [FBP] or 18F-florbetaben [FBB]) data to the baseline FTP PET scan for each participant. The mean interval between the baseline FTP scan and the closest amyloid PET scan was 0.15 ± 0.48 years. Standard thresholds (FBP: 1.11 and FBB: 1.08) were applied to the summary cortical SUVR normalized by the whole cerebellum to determine the amyloid positivity.25,26

Genetic Data

Genetic data for subsets (ADNI-1/GO/2/ADNI3) in PLINK data formats were all obtained from ADNI. Detailed information about sample acquisition, DNA extraction, processing, and genotyping could be found in the relevant ADNI publications.27,28 In brief, genomic DNAs from peripheral blood samples of participants were generated by using the Illumina Human 610-Quad BeadChip (for ADNI-1), OmniExpress BeadChip (for ADNI-GO/2), and Illumina Infinium Global Screening Array v2 (for ADNI-3). The intensity data were processed with GenomeStudio. Imputation was performed at the Michigan Imputation Server with 1000 Genomes phase 3 as the reference panel.29 Subsets (ADNI-1/GO/2/ADNI3)–imputed data were merged and underwent standard quality check to exclude individuals with more than 5% missing information, with single-nucleotide polymorphisms (SNPs) with more than 5% missing information, failing the Hardy-Weinberg equilibrium test at p = 1 × 10−6, and minor allele frequency (MAF) less than 1%. Approximately 6.5 million SNPs were shared. To increase the power of detecting associations and reduce the burden of multiple comparisons, we focused our analysis on SNPs within 10 genes previously shown to have sex-dependent effects on AD9: BIN1, MS4A6A, DNAJA2, FERMT2, APOC1, APOC1P1, FAM193B, C2orf47, TYW5, and CR1. We extracted SNPs of each gene from genetic data based on their chromosome and position.

Statistical Analysis

Group differences by sex were assessed using independent t tests for continuous variables (age, Braak I_SUVR, Braak III/IV_SUVR, and Braak V/VI_SUVR) and the Pearson χ2 test for categorical variables (amyloid positivity, diagnosis, and APOE ε4 carriership) (Table 1). Controlling for covariates, including sex, age, education, scanner manufacturers, APOE ε4 carriership (APOE ε4 carriers vs APOE ε4 noncarriers), amyloid status, clinical diagnosis (CN vs MCI vs AD), and 10 principal components, was performed via preresidualization of tau SUVR phenotypes.30 For sex-stratified analysis, sex was removed from the covariates. Because tau SUVR in 3 ROIs are correlated multivariate phenotypic traits, association tests for each SNP and residualized tau SUVR in 3 ROIs were conducted using 1-way multivariate analysis of variance (MANOVA) under the dominant genetic model. We first applied MANOVA across all individuals and then to women and men separately. Because MANOVA could only tell whether there are group differences but cannot tell which phenotype(s) the differences are from, for the secondary analysis, we applied linear regression model with residualized tau SUVR in each ROI as the dependent variable and each significant SNP from MANOVA as the independent variable to assess the specific regions that showed the associations. Individuals with missing genotype were excluded for each SNP analysis. In MANOVA analysis, we adjusted for multiple comparisons. Bonferroni correction was applied within each gene of interest. All analyses were performed using R software (version 4.0.4).

Data Availability

Data used in preparation of this manuscript were obtained from the Laboratory of Neuro Imaging database (ida.loni.usc.edu).

Results

Participants in ADNI

We included 493 participants (mean age [SD]: 74.7 [7.8] years) with baseline FTP PET scans of whom 246 were women (50%). Other characteristics of participants are summarized in Table 1. In this cohort, women were on average younger than men. The proportion of CN women was higher than that of men (72.4% vs 51.4%). Even so, women showed higher tau SUVR in the composite regions of Braak V/VI than men (p = 0.042). We also detected significant sex-by-diagnosis interaction effects on tau SUVR showing in AD, with women having higher tau SUVR in the Braak I, Braak III/IV, and V/VI regions than men, while among CN or MCI, the sex differences existing only in the Braak V/VI regions (eFigure 2, links.lww.com/NXG/A565).

MANOVA Analyses

First, we conducted MANOVA to identify SNPs associated with tau across all individuals. The top 5 SNPs within each gene (based on the descending p values) are summarized in eTable 1, links.lww.com/NXG/A565. In this analysis, only 1 SNP passed Bonferroni correction: rs79711283 in DNAJA2 (corrected p = 1.73 × 10−4, Table 2). We then conducted MANOVA, separately in men and women, to detect sex-specific SNPs associated with tau. For sex-stratified analysis, rs79711283 in DNAJA2 remained significantly associated with tau SUVR only in women (corrected p = 0.001) but not in men (Table 3), suggesting the significant association in the combined group might be driven by women. In addition, rs113357081 in FERMT2 and rs74614106 in TYW5 were significantly associated with tau SUVR in women (FERMT2, corrected p = 2.75 × 10−4; TYW5, corrected p = 5.04 × 10−6), but not in men (Table 3 and eTable 2, links.lww.com/NXG/A565). Three significant SNPs in CR1 were associated with tau SUVR specifically in men (rs113698814, corrected p = 1.99 × 10−4; rs115096248, corrected p = 2.41 × 10−4; rs78150633, corrected p = 2.41 × 10−4) (Table 3 and eTable 2, links.lww.com/NXG/A565). Of these, rs115096248 and rs78150633 showed strong linkage disequilibrium, suggesting that they reflected the same signal (D′ = 1, R2 = 1). Besides, these significant SNPs above also showed similar sex-specific effects on PVC-tau PET (eTable 3, links.lww.com/NXG/A565).

View this table:
Table 2

Significant SNPs Associated With Tau PET

View this table:
Table 3

Sex-Specific Significant SNPs Associated With Tau PET

Secondary Analysis

We assessed whether the sex-specific significant SNPs were associated with different Braak stages of tau using linear regression. eTable 4, links.lww.com/NXG/A565 summarizes the number of participants with each genotype for the significant SNPs and their MAF. Only 2 of these 6 significant SNPs have homozygous participants for the risk allele: rs79711283 in DNAJA2 and rs113698814 in CR1. One man with missing data for rs113698814 in CR1 was excluded in the secondary analysis of that SNP. We found that in women, rs79711283 was associated with Braak III/IV stage (DNAJA2, p = 0.047), rs113357081 was associated with Braak III/IV and Braak V/VI stages (FERMT2, Braak III/IV: p = 0.023, Braak V/VI: p = 2.76 × 10−4), and rs74614106 was associated with Braak I and Braak III/IV stages (TYW5, Braak I: p = 0.001; Braak III/IV: p = 2.78 × 10−4) (Table 4, Figure 1). In men, rs113698814 was associated with composite regions of Braak V/VI stage (CR1, p = 0.002) and rs115096248 and rs78150633 with Braak I and Braak III/IV stages (CR1, Braak I: p = 1.11 × 10−4, Braak V/VI: p = 0.005) (Table 5, Figure 2). These findings suggested that different loci may affect tau accumulation in different stages. Specifically, in women, rs74614106 in TYW5 might affect tau accumulation in the early stage of tau pathology, rs79711283 in DNAJA2, rs113357081 in FERMT2, and rs74614106 in TYW5 might affect the tau accumulation in the middle stage, and rs113357081 in FERMT2 might influence the tau accumulation in the late stage. In men, rs115096248 and rs78150633 in CR1 might affect tau accumulation in the early and middle stages of tau pathology, and rs113698814 in CR1 might affect the tau accumulation in the late stage.

View this table:
Table 4

Associations of the Women-Specific Genetic Variants With Tau SUVR in Each ROI

Figure 1
Figure 1 Comparison of Tau PET in Each ROI by Women-Specific SNPs
View this table:
Table 5

Associations of the Men-Specific Genetic Variants With Tau SUVR in Each ROI

Figure 2
Figure 2 Comparison of Tau PET in Each ROI by Men-Specific SNPs

Discussion

This study assessed the sex-specific genetic variants associated with in vivo tau in the human brain across the AD spectrum beyond APOE ε4. Focusing on genes previously shown to have sex-dependent effects on AD, we identified 3 SNPs within DNAJA2, FERMT2, and TYW5 associated with tau specifically in women as well as 3 SNPs within CR1 associated with tau specifically in men. These sex-specific significant SNPs were also associated with different stages of tau spread.

Earlier work by our group demonstrated the sex-dependent effects of autosomal SNPs on the clinical progression of AD.9 BIN1, MS4A6A, DNAJA2, and FERMT2 showed larger effect sizes on AD in women compared with men, while FAM193B, C2orf47, TYW5, and CR1 had larger effect sizes among men. Consistently, in this study, the effects of DNAJA2 and FERMT2 on tau PET were also women-specific, and the effect of CR1 was men-specific. Nevertheless, the effects of TYW5 variants on tau PET existed only in women, instead of men. We did not find significant associations with tau PET and MS4A6A, FAM193B, or C2orf47 either in participants as a group or sex-stratified groups, suggesting that these genes' variants might affect other AD-associated pathologies such as amyloid-β or inflammation.

DNAJA2, FERMT2, and TYW5 were found to be associated with tau PET in women. DNAJA2 (DnaJ heat shock protein family [Hsp40] member A2) encodes heat shock protein DNAJA2, the principal J domain partners of human Hsp70/Hsc70 chaperones, which could stimulate ATPase activity to regulate molecular chaperone activity.31 In the affected neurons of patients with MCI and those with AD, DNAJA2 protein levels are highly upregulated and have been found to suppress tau aggregation.32 Further studies have shown that DNAJA2 protein binds to both tau monomers and aggregation-prone forms of tau, thus reducing the speed of tau aggregation.33 The scaffolding protein FERMT2 encoded by gene FERMT2 (Fermitin family homolog 2) is critically involved in integrin-mediated cell-extracellular matrix interaction and controlling for axonal growth and synaptic plasticity.34,35 A study using the Drosophila model of AD has shown that FERMT2 plays a role in the modulation of tau toxicity.36 In addition, knockdown or knockout of FERMT2 in familiar AD human neurons has been shown to reduce the levels of phosphorylated tau.37 These previous studies above support the associations in this study. However, none of them were conducted in live human brains or incorporated sex-stratified analysis. Disaggregating by sex in this study, we identified the associations between these genetic variants and in vivo tau were sex specific. The mechanisms underlying these sex-specific associations are not known. Notably, DNAJA1, the functional homolog of protein DNAJA2 in mice, has been reported to regulate androgen receptor signaling.38 This may be relevant because testosterone (levels of which fluctuate in postmenopausal women) appears protective against p-tau,39 suggesting a potential mechanism for the sex-specific association between DNAJA2 and tau pathology. The relationship between TYW5 and tauopathy has not been reported directly. TYW5 (tRNA-YW synthesizing protein 5) encodes protein TYW5, a tRNA hydroxylase that is involved in epigenetic modification in the brain,40 and has been reported highly expressed in the dorsolateral prefrontal cortex in patients with schizophrenia as a risk gene of schizophrenia, which is related to a higher risk of AD.41 In addition, a genome-wide association study study reported the associations between an SNP in TYW5 and sleep apnea.42 Because sleep apnea has been reported to be related to tau aggregation in preclinical and prodromal stages of AD,43 we hypothesize that the significant associations between TYW5 and tau pathology in women in this study might be related to sleep apnea, which suggests a potential avenue for future research.

CR1 is found to be associated with tau PET in men. CR1 (Complement receptor1) encodes a transmembrane glycoprotein CR1, which regulates part of the complement system and hence is an important factor in innate and acquired immunity.44 The relationship between tau and CR1 has been reported before. Studies in mice models pointed to the role of CR1 in tau phosphorylation, showing deletion of Crry, the murine ortholog of CR1, decreased the phosphorylation of tau at serine 235, which is one of the key phosphoresidues controlling the microtubule binding.45 In addition, CR1 is found to be highly expressed in activated microglia, which is involved in the progression of tau pathology.46 Emerging data have demonstrated sex differences in microglia exist.47 It is reasonable to hypothesize that the sex-specific associations between CR1 and tau PET might be related to the sex differences in the microglia activation. Further research will be needed to clarify this point.

By leveraging topographical characteristics of tau and Braak stages, we further showed how different genetic variants were associated with different Braak stages, which implicates these genes might be relevant at different stages of the tau pathology. As a progressive neurodegenerative disease, AD pathology is viewed on a trajectory beginning at least a decade prior to the apparent cognitive impairment. Men and women appear to show different trajectories with a turning point around the MCI stage when women show elevated tau levels48 compared with men, and the long-held cognitive reserve in women, especially in verbal memory, begins to fail, promoting a steeper decline.49 It is thus interesting that all 3 significant SNPs in women in this study exerted their effects on tau during the Braak III/IV stage, which is the pathologic equivalent of MCI. CR1 in men is more distributed and depending on the exact SNPs involved affects 3 ROIs.

APOE ε4 is the strongest genetic risk factor for AD. Several studies have explored how sex could modify the associations between APOE ε4 and tau PET. A study in MCI found the interaction effects between sex and APOE ε4 carriership on regional tau PET. The associations between APOE ε4 carriership and regional tau PET were stronger in women.17 Furthermore, a recent study reported sex could modify the APOE ε4 dose effects on regional tau PET in CN individuals.19 Consistently, in the cohort of this study, we also detected that the double-dose effects of APOE ε4 only exist in men, not in women. We included APOE ε4 binary carriership as the covariates in this study, suggesting these sex-specific associations might be independent of APOE ε4. Besides, we also controlled for APOE ε4 dose (0 vs 1 vs 2), and the results remained similar.

Our study has some limitations. The first limitation is the small sample size. Studies less constrained by small sample sizes might incorporate a powerful and efficient new genome-wide association approach Multivariate Omnibus Statistical Test, which can discover more genetic loci associated with brain imaging modalities.30 Another limitation is that the participants of this study were self-identified as White, which may not generalize to non-White individuals. Future studies should test whether these findings are applicable to other populations. In addition, the genetic data after quality control did not include any SNPs within MAPT (Microtubule Associated Protein Tau) which is a widely known genetic factor associated with tau uptake.50

Collectively, this study discovered sex-specific genetic variants associated with tau assessed with PET independent of APOE ε4. These results might provide potential gene targets for understanding the mechanism of sex-specific tau aggregation and developing sex-specific gene-guided precision prevention or therapeutic interventions for AD.

Study Funding

NIA R01AG066088.

Disclosure

X. Wang, I. Broce, K.D. Deters, C.C. Fan, and S.J. Banks report no disclosures relevant to the manuscript. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/NG.

Acknowledgment

Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Appendix 1 Authors

Table

Appendix 2 Coinvestigators

Table

Footnotes

  • ↵* Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found in Appendix 2 at links.lww.com/NXG/A564.

  • Funding information and disclosures are provided at the end of the article. Full disclosure form information provided by the authors is available with the full text of this article at Neurology.org/NG.

  • The Article Processing Charge was funded by the NIH.

  • Submitted and externally peer reviewed. The handling editor was Stefan M. Pulst, MD, Dr med, FAAN.

  • Received June 29, 2022.
  • Accepted in final form September 22, 2022.

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

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