Inter- and intra-chromosomal modulators of the APOE ɛ2 and ɛ4 effects on the Alzheimer’s disease risk

The mechanisms of incomplete penetrance of risk-modifying impacts of apolipoprotein E (APOE) ε2 and ε4 alleles on Alzheimer’s disease (AD) have not been fully understood. We performed genome-wide analysis of differences in linkage disequilibrium (LD) patterns between 6,136 AD-affected and 10,555 AD-unaffected subjects from five independent studies to explore whether the association of the APOE ε2 allele (encoded by rs7412 polymorphism) and ε4 allele (encoded by rs429358 polymorphism) with AD was modulated by autosomal polymorphisms. The LD analysis identified 24 (mostly inter-chromosomal) and 57 (primarily intra-chromosomal) autosomal polymorphisms with significant differences in LD with either rs7412 or rs429358, respectively, between AD-affected and AD-unaffected subjects, indicating their potential modulatory roles. Our Cox regression analysis showed that minor alleles of four inter-chromosomal and ten intra-chromosomal polymorphisms exerted significant modulating effects on the ε2- and ε4-associated AD risks, respectively, and identified ε2-independent (rs2884183 polymorphism, 11q22.3) and ε4-independent (rs483082 polymorphism, 19q13.32) associations with AD. Our functional analysis highlighted ε2- and/or ε4-linked processes affecting the lipid and lipoprotein metabolism and cell junction organization which may contribute to AD pathogenesis. These findings provide insights into the ε2- and ε4-associated mechanisms of AD pathogenesis, underlying their incomplete penetrance. Supplementary information The online version contains supplementary material available at 10.1007/s11357-022-00617-0.


Introduction
The apolipoprotein E (APOE) gene is the strongest Alzheimer's disease (AD)-associated genetic factor [1][2][3], which can explain 13.4% of phenotypic variance and 25.2% of genetic variance of AD [4]. Minor alleles of the exonic single-nucleotide polymorphisms (SNPs) rs429358 and rs7412 in the APOE gene encode the ε4 and ε2 alleles, respectively. The ε2 allele is considered as a protective factor against AD, whereas the ε4 allele is advocated to be a major variant predisposing to AD [3,5].
Abstract The mechanisms of incomplete penetrance of risk-modifying impacts of apolipoprotein E (APOE) ε2 and ε4 alleles on Alzheimer's disease (AD) have not been fully understood. We performed genome-wide analysis of differences in linkage disequilibrium (LD) patterns between 6,136 AD-affected and 10,555 AD-unaffected subjects from five independent studies to explore whether the association of the APOE ε2 allele (encoded by rs7412 polymorphism) and ε4 allele (encoded by rs429358 polymorphism) with AD was modulated by autosomal polymorphisms. The LD analysis identified 24 (mostly inter-chromosomal) and 57 (primarily intra-chromosomal) autosomal polymorphisms with significant differences in LD with either rs7412 or rs429358, respectively, between AD-affected and AD-unaffected subjects, indicating their potential modulatory roles. Our Cox regression analysis showed that minor alleles of four inter-chromosomal and ten intra-chromosomal 1 3 Vol:. (1234567890) The APOE gene encodes a lipoprotein mainly involved in lipid transfer and metabolism. Nevertheless, its functional impacts are not limited to lipid profile alterations and related vasculopathies [6]. The APOE involvement in AD pathogenesis has been widely studied, revealing various molecular and biological processes differentially impacted by different APOE alleles. For instance, the ε4 allele has been linked to increased production and decreased clearance of β-amyloid, stress-mediated increased tau hyperphosphorylation, accelerated cortical atrophy (e.g., in the medial temporal lobe), baseline neuronal hyperactivity (e.g., in the hippocampus), reduced cerebral glucose metabolism, damaged synaptic structure and function, increased cytoskeletal and mitochondrial dysfunction, and abnormal hippocampal neurogenesis [7].
Despite strong associations between APOE and AD, neither the ε2 nor ε4 allele is considered as a causal factor for AD development [5,[8][9][10]. Addressing the mechanisms of actions of the ε2 and ε4 alleles is essential for understanding AD pathogenesis and AD risk assessment. The complex regional interactions and haplotype structures in the APOE locus (19q13.3) have been emphasized by a growing body of studies [11][12][13][14][15][16][17][18][19]. These studies indicate the potential roles of nearby polymorphisms in modulating the impacts of the APOE alleles on AD risks in the form of haplotypes and combinations of genotypes (called compound genotypes). The analyses of haplotypes leverage the idea that AD can be affected by haplotypes driven by genetic linkage between nearby SNPs [20]. The functional linkage may drive, however, compound genotypes consisting of not only local but also distant variants [21].
In this study, we used a comprehensive approach to examine intra-(cis-acting) and inter-(trans-acting) chromosomal modulators of the impacts of the APOE rs7412 or rs429358 SNPs on the AD risk in the ε4or ε2-negative sample. We leveraged samples of the AD-affected (N = 6,136) and unaffected (N = 10,555) subjects from five studies: (i) to perform a comparative analysis of LD between rs7412 or rs429358 and other autosomal SNPs in the human genome in the AD-affected and unaffected subjects, (ii) to examine AD risks for carriers of compound genotypes comprised of rs7412 or rs429358 and the identified intra-and inter-chromosomal SNPs in LD with them, and (iii) to identify biological functions and diseases enriched by genes harboring these SNPs.

Study participants
We used data on subjects of European ancestry from (Table S1): three National Institute on Aging (NIA) Alzheimer's Disease Centers data (ADCs) from the Alzheimer's Disease Genetics Consortium (ADGC) initiative [22], whole-genome sequencing (WGS) data from the Alzheimer's Disease Sequencing Project (ADSP-WGS) [23,24], Cardiovascular Health Study (CHS) [25], Framingham Heart Study (FHS) [26,27], and NIA Late-Onset Alzheimer's Disease Family Based Study (LOAD FBS) [28]. The ADSP-WGS's subjects who were also present in other datasets were excluded to make datasets independent. The APOE genotypes were either directly reported by original studies (ADGC, ADSP-WGS, FHS) or were determined based on the rs429358 and rs7412 genotypes (CHS and LOAD FBS). The diagnoses of AD cases in the five analyzed datasets were mainly based on the neurologic exams [29,30], and the AD status was reported either directly (ADGC, ADSP-WGS, FHS, LOAD FBS) or in the form of ICD-9 (International Classification of Disease codes, ninth revision) codes (CHS).

Design
Our analyses were performed separately in stratified samples obtained by dividing each dataset into four groups based on the APOE genotypes and AD status. First, we determined ε4-negative (ε2ε2, ε2ε3, and ε3ε3 genotypes) and ε2-negative (ε4ε4, ε3ε4, and ε3ε3 genotypes) subsamples. Then, each subsample was divided into AD-affected and unaffected groups (herein referred to as AD and NAD groups, respectively). We evaluated LD between the APOE rs7412 or rs429358 SNP and each SNP in the genome in two stages.

Stage 1: LD analysis in individual and pooled datasets
We examined LD (i.e., r statistics) using the haplotype-based method [33][34][35] in each of the four selected subsamples in each dataset individually and combined. The statistically significant LD estimates were determined using a conservative chi-square test χ 2 = r 2 n [35], where n is the number of subjects rather than gametes to address the uncertainty in inferring haplotypes from unphased genetic data [16,18,36,37]. The variances of the r statistics were calculated using the asymptotic variance method detailed in [37]. The LD analysis was performed using haplo.stats r package [38].
Stage 1 provided two sets of SNPs in LD with the APOE SNPs in each subsample. The first set was generated following the discovery-replication strategy (herein referred to as replication set). In this case, SNPs were selected if their LD with the APOE SNP attained: (1) genome-wide (P < 5E-08) or suggestive-effect (5E-08 ≤ P < 5E-06) significance in any of the five datasets, which was considered as a discovery set, and (2) Bonferroni-adjusted P < 0.0125 (= 0.05/4, where 4 is the number of potential replication sets) in at least one of the other four datasets [31]. The second set included SNPs in significant LD with the APOE SNPs at genomewide or suggestive significance in the pooled samples of all five datasets that were not in the replication set.

Stage 2: group-specific LD
We examined whether SNPs identified in stage 1 had group-specific LD by contrasting r between pooled AD and NAD groups, Δr = r AD -r NAD , using a permutation test [39,40]. Significant Δr indicated SNPs in group-specific LD with rs7412 or rs429358. Bonferroni-adjusted thresholds, accounting for the number of tested SNPs, were used to identify significant findings.

Analysis of the AD risk
For each group-specific SNP, survival-type analysis was performed to examine the impact of a compound genotype variable (CompG) on the AD risk. The CompG included four compound genotypes comprised of rs7412 or rs429358 genotypes and genotypes of a group-specific SNP ( Table 1).
We fitted the Cox regression model (coxme and survival R packages [41,42]), considering the age at onset of AD as a time variable. We used sex, the top five principal components of genetic data and ADC cohorts (in ADGC) as fixed-effects covariates, and family IDs (LOAD FBS, FHS, ADSP-WGS) as a random-effects covariate. The results from five datasets were combined through inverse-variance metaanalysis using GWAMA package [43]. The CompG1 compound genotype was the reference factor level. Table 1 Compound genotype constructed based on the genotypes at rs7412 or rs429358 and the identified group-specific SNPs Abbreviations: SNP, single-nucleotide polymorphism; CompG, compound genotype; 0, major allele homozygote; 1, heterozygote; 2, minor allele homozygote; CompG1, ε3ε3 subjects carrying major allele homozygotes of the SNP; CompG2, ε3ε3 subjects carrying at least one minor allele of the SNP; CompG3, ε2 carriers (i.e., ε2ε2 or ε2ε3 subjects in the rs7412 analysis) or ε4 carriers (i.e., ε4ε4 or ε3ε4 subjects in the rs429358 analysis) having major allele homozygotes of the SNP; CompG4, ε2 (rs7412 analysis) or ε4 (rs429358 analysis) carriers having at least one minor allele of the SNP We used a chi-square test with one degree of freedom [44] to estimate the significance of the difference between the effect sizes for CompG3 and CompG4: where b CompG3 (se CompG3 ) and b CompG4 (se compG4 ) are the beta coefficients (standard errors) corresponding to the CompG3 and CompG4 genotypes in the Cox model, respectively. Significant findings were identified at the Bonferroni-adjusted levels correcting for the numbers of ε2-and ε4-associated group-specific SNPs.

Functional enrichment analysis
The Database for Annotation, Visualization and Integrated Discovery (DAVID) [45] and Metascape [46] web tools were used to identify gene-enriched REACTOME pathways [47] and DisGeNET diseases [48]. The analysis was performed for genes harboring SNPs in group-specific LD with rs7412 or rs429358 separately. We used false discovery rate (FDR) adjusted significance cut off at P FDR < 0.05 [49] to identify significantly enriched terms by two or more genes.

SNPs in LD with rs7412 (APOE ε2 allele)
In stage 1, we found that 306 SNPs mapped to 27 loci were in LD with rs7412 at P < 5E-06 in the AD group (21 SNPs in 9 loci, . We also observed that the r signs were the same in these two groups for 272 of 306 SNPs. In stage 2, we found 24 SNPs (Table S5) having group-specific LD with rs7412 at a Bonferroniadjusted significance P < 1.63E-04 (= 0.05/306). Of them, 16 SNPs were mapped to 6 non-APOE loci. All of them were identified in the pooled sample of either the AD (14 SNPs) or NAD (2 SNPs) group. LD estimates for 14 of these 16 SNPs were characterized by opposite signs of r in these groups (Fig. 1). Also, 15 of them had larger magnitudes of r in the AD group than NAD group. The remaining 8 SNPs were in the APOE locus, of which rs11669338 (NECTIN2) attained significance only in NAD group, whereas all the others were significant in both groups. All 8 SNPs had the same signs of r in the AD and NAD groups, whose magnitudes were smaller in the AD than NAD group (Fig. 1).

SNPs in LD with rs429358 (APOE ε4 allele)
In stage 1, we found that rs429358 was in LD with 801 SNPs (143 loci) at P < 5E-06 in the AD group (301 SNP in 73 loci, Table S6), the NAD group (351 SNP in 81 loci, Table S7), or both AD and NAD groups (149 SNP; all in the APOE locus, except 2 SNPs, Table S8). In the AD and NAD groups, we identified LD of rs429358 with 159 (72 loci) and 344 (80 loci) SNPs not in the APOE region, respectively, totaling 503 SNPs. Of all 505 SNPs (154 loci) not in the APOE locus in AD, NAD, and AD&NAD groups, one locus harboring FXYD5 and FAM187B genes (11 SNPs, NAD group) was on chromosome 19, and the other 494 SNPs (153 loci) were not on chromosome 19. The LD magnitudes were smaller in the pooled AD than NAD group for 370 of 801 SNPs (26 of 296 SNPs in the APOE locus and 344 of 505 SNPs in the non-APOE loci). The r signs were the same in these two groups for 711 of 801 SNPs.
In stage 2, we identified 57 SNPs with groupspecific LD at a Bonferroni-adjusted significance P < 6.24E-05 (= 0.05/801). As seen in Table S9, 17 of 57 SNPs are mapped to 11 non-APOE loci. All of them were identified in the pooled sample of either the AD (10 SNPs) or NAD (7 SNPs) group. The magnitudes of r were larger in the pooled AD than NAD sample for SNPs whose significant LD was identified in the AD group and vice versa. The r signs for 13 of these 17 SNPs were opposite in these AD and NAD samples. The other 40 SNPs were located in the APOE locus. Magnitudes of r for all SNPs, except rs769449 (APOE), were larger in the pooled AD than NAD sample. For all SNPs, except rs11083767 (EXOC3L2), the r signs were the same in these AD and NAD samples (Fig. 2).

AD risk for carriers of compound genotypes
We performed Cox regression analysis to examine the impact of compound genotypes comprised of a groupspecific SNP and either rs7412 (Tables 2 and S10, Fig. 3A) or rs429358 (Tables 2 and S11, Fig. 3B) on the AD risk. An advantage of using compound genotypes is that we can explicitly examine the effect of a minor allele of a group-specific SNP independently of the effect of the ε2 or ε4 allele (CompG2), the impact of the ε2 or ε4 allele independently of the minor allele of that SNP (CompG3), and the combined effects of these minor alleles (CompG4) in the same model with the same reference genotype (CompG1) ( Table 1).
AD risk for carriers of 24 rs7412-bearing compound genotypes (Tables 2 and S10, Fig. 3A) Our analysis showed that none of eight CompG2 genotypes bearing SNPs from the APOE locus attained Bonferroni-adjusted significance P Bε2 = 2.08E-03 (= 0.05/24), although rs405509 minor allele was beneficially associated with AD, independently of ε2, at nominal significance P = 0.0238. In contrast, six of 16 CompG2 genotypes comprised of rs7412 and non-APOE locus SNPs were beneficially associated with AD at the nominal significance (P Bε2 ≤ P < 0.05). For one CompG2, we observed beneficial association of rs2884183 minor allele (11q22.3, DDX10) with AD at P < P Bε2 independently of the ε2 allele.
All CompG3 genotypes were beneficially associated with AD (although non-significantly for rs11668861) because of the leading role of the ε2 allele and the lack of minor alleles of the group-specific SNPs. Also, regardless of the significance, all CompG4 genotypes were beneficially associated with AD risk, with 10 of them (seven in the APOE locus) reaching P < P Bε2 . For all 16 group-specific interchromosomal SNPs, the effects for CompG4 were smaller in magnitude than those for CompG3 either at the nominal (12 SNPs) or P < P Bε2 (four SNPs) significance (Fig. 3A).
Each of 57 CompG3 and CompG4 genotypes was adversely associated with the AD risk. None of the differences in the effects between them attained P < P Bε4 for inter-chromosomal SNPs. In contrast, we identified seven (P Bε4 ≤ P < 0.05) and 10 (P < P Bε4 ) differences in the effects between CompG3 and CompG4 for SNPs within the APOE locus (Fig. 3B).

Biological functions and diseases
Our analysis was performed for 11 and 19 genes harboring SNPs in group-specific LD with ε2-encoding rs7412 and ε4-encoding rs429358, respectively. We found that 7 and 4 REACTOME pathways were enriched at P < 0.05 using genes from the ε2 (Fig. S1) and ε4 (Fig. S2) sets, respectively. Four of them, i.e., "plasma lipoprotein assembly," "plasma lipoprotein clearance," "NR1H3 and NR1H2 regulate gene expression linked to cholesterol transport and efflux," and "NR1H2 and NR1H3-mediated signaling," were enriched in both ε2 and ε4 sets. Three pathways, however, were ε2-specific, including "cell-cell junction organization," "plasma lipoprotein assembly, remodeling, and clearance," and "cell junction organization." There were no enriched ε4-specific pathways.
Disease annotations (Tables S12 and S13) included 14 terms that were enriched at P FDR < 0.05 by both the ε2 and ε4 gene sets. They were mainly related to neurological diseases (e.g., AD and other dementia phenotypes, memory performance, mild cognitive disorder, and primary progressive aphasia), serum lipid traits (e.g., dyslipoproteinemias, serum low-density lipoprotein (LDL) cholesterol measurement, and serum total cholesterol measurement), serum albumin measurement, and C-reactive protein measurement.
Seven terms were only enriched in the ε4 set at P FDR < 0.05 (Table S13) which included mental deterioration, atherogenesis, triglycerides measurement, and high-density lipoprotein measurement as well as multiple hematological and immune system-related terms (i.e., autoantibody measurement, acute monocytic leukemia, and peripheral T-cell lymphoma).

Discussion
Our comprehensive approach examining intra-and inter-chromosomal modulators of the impacts of the APOE rs7412 or rs429358 SNP encoding the ε2 or ε4 allele on the AD risk provided four insights.
Second, among these SNPs, we found significant differences in LD between AD and NAD groups for 24 (16 inter-chromosomal SNPs in 6 loci) and 57 (17 inter-chromosomal SNPs in 11 loci) SNPs with rs7412 and rs429358, respectively, at the Bonferroni-adjusted significance level (Figs. 1 and 2, and  Tables S5 and S9). This finding strongly supports modulating roles of the intra-and inter-chromosomal SNPs on the impacts of the ε2 or ε4 allele on the AD risk, predominantly tailored to either AD-affected or unaffected subjects.
Third, Cox regression analysis identified Bonferroni-adjusted associations of minor alleles    of rs2884183 (11q22.3, DDX10) and rs483082 (19q13.32, APOC1) with decreased and increased AD risk independently of the ε2 and ε4 alleles, respectively (Table 2). Fourth, Cox regression analysis revealed that the beneficial and adverse effects of the ε2 and ε4 alleles, respectively, on the AD risks were significantly modulated by other SNPs, and that this modulation was fundamentally different for these alleles. Specifically, the beneficial effect of the ε2 allele was decreased by minor alleles of all 16 group-specific inter-chromosomal SNPs (with a significant decrease at Bonferroni-adjusted level for variants mapped to JADE2 and SDK2 genes) (Fig. 3A). In contrast, the adverse effect of the ε4 allele was significantly modulated by ten APOE locus (intra-chromosomal) SNPs; the ε4 impact was weakened by minor alleles of four SNPs mapped to TOMM40 and APOE genes and major alleles of six SNPs mapped to NECTIN2, TOMM40, APOE, and APOC1 genes (Fig. 3B).
Next, we discuss JADE2 and SDK2 genes harboring inter-chromosomal SNPs, which significantly modulate the effects of the ε2 allele on AD risk ( Table 2). JADE2 is involved in ubiquitination of histone demethylase LSD1 [60] and may play roles in the LSD1-mediated regulation of neurogenesis and myogenesis [61,62]. LSD1 is required for neuronal survival and was implicated in tau-induced neurodegeneration in AD and frontotemporal dementia [63,64]. Additionally, JADE2 (alias PHF15) may regulate the microglial inflammatory response [65].
SDK2 is involved in lamina-specific synaptic connections which are essential to form neuronal circuits in retina that detect motion [66]. Visual impairments including motion detection abnormalities have been reported in AD [67] and Huntington's disease [68]. Also, visual working memory (i.e., object identification and location recall) was previously associated with the ε4 allele and β-amyloid accumulation [69].
We also highlight DDX10 gene harboring rs2884183, which is associated with AD risk independently of ε2 ( Table 2). The RNA helicase DDX10 affects ribosome assembly and modulates α-synuclein toxicity [70]. α-Synuclein may synergistically interact with β-amyloid and tau protein to promote their accumulation [71] and may be involved in the pathogenesis of AD in addition to synucleinopathie (e.g., Parkinson's disease) [72,73]. DDX10 may also affect ovarian senescence [74].
Our enrichment analysis of biological functions (Figs. S1 and S2) suggested that group-specific LD with rs7412 or rs429358 entails SNPs in genes, which are involved in lipid and lipoprotein metabolism. Additionally, LD with rs7412 entails SNPs in genes, which may contribute to cell junction organization. These biological processes have been implicated in AD pathogenesis [31,[75][76][77][78][79]. The disease enrichment analysis (Tables S12 and S13) mostly highlighted the enrichment of AD, dementia phenotypes, and other neurological diseases as well serum lipid traits in both the ε2 and ε4 gene sets. In addition, multiple lipid traits and neurological and immune system-related disorders were enriched in the ε4 gene set.
Despite the rigor, this study has limitations. The first is that GWAS datasets do not provide phased genetic data, and therefore, probabilistic estimates of haplotypes may adversely impact the power of LD analyses. Second, due to the small frequency of the ε2 allele in the general population, the LD analysis of rs7412 may not have optimal statistical power, particularly in the AD-affected group because of the protective role of the ε2 allele against AD. Third, because genotypes were available from WGS in ADSP and genome-wide arrays in the other datasets, we imputed SNPs to harmonize them across all five datasets. Imputation generally results in less accurate genotype calls compared with WGS, particularly in genomic regions with low coverage on the arrays. Low imputation quality may adversely impact the results of the analyses. Although we excluded SNPs with imputation quality of r 2 < 0.7 to offset the impacts of potential inaccuracies, replication of the results using directly genotyped SNPs could add robustness to our findings. Fourth, while the Cox regression analysis of genetic associations using AAO of a complex trait provides higher statistical power than the logistic regression analysis of the case-control status [83], we acknowledge limited abilities in determining exact AAO due to slow progression of AD. For instance, AD is not usually diagnosed when the brain pathologies start to develop years before clinical manifestations. Fifth, the small number of genes may affect the accuracy of the functional enrichment analysis. Finally, further stratifying of the AD group based on the pathological information on AD sub-phenotypes would provide valuable insights into the genetic heterogeneity of AD. Also, including subjects with mild cognitive impairment (MCI) in LD analyses as a separate stratum may help to identify APOE alleledependent genetic factors contributing to MCI progression to AD. Such additional stratifications would require large datasets with more comprehensive clinical and pathological data.

Conclusion
Our comprehensive analysis provides compelling evidence that intra-and inter-chromosomal variants can modulate the impacts of the ε2 and ε4 alleles on the AD risk. The survival-type analysis robustly shows predominant modulating roles of the interchromosomal SNPs for the ε2 allele and the APOEregion SNPs for the ε4 allele. We identified two variants in DDX10 (11q22.3) and APOC1 (19q13.32) genes with beneficial and adverse associations with AD risk independently of the ε2 and ε4 alleles, respectively. Functional enrichment analysis highlighted ε2-and/or ε4-linked processes involved in lipid and lipoprotein metabolism and cell junction organization which have been implicated in AD pathogenesis. Our results advance the understanding of the mechanisms of AD pathogenesis and help improve the accuracy of AD risk assessment. Fig. 3 The results of the meta-analysis of the associations of compound genotypes comprised of SNPs (shown on the x-axis) in group-specific linkage disequilibrium with (A) rs7412 in the ε4-negative sample or (B) rs429358 in the ε2-negative sample with the Alzheimer's disease risk. CompG2 (green) indicates ε3ε3 subjects carrying at least one minor allele of the SNP; CompG3 (red) denotes (A) ε2 or (B) ε4 carriers having major allele homozygotes of the SNP; CompG4 (blue) indicates (A) ε2 or (B) ε4 carriers having at least one minor allele of the SNP. CompG1 indicating the ε3ε3 subjects carrying major allele homozygotes of the SNP was the reference. Black vertical lines show 95% confidence intervals (negative direction for rs769449 was truncated for better resolution). The x-axis shows SNP identifiers, genes harboring these SNPs, and chromosomes. One asterisk (*) indicates nominally significant differences in the effects between CompG3 and CompG4 at (A) 2.08E-03 ≤ P < 0.05 and (B) 8.77E-04 ≤ P < 0.05. Two asterisks (**) indicate Bonferroni-adjusted significance in those differences at (A) P < 2.08E-03 and (B) P < 8.77E-04. No asterisk indicates non-significant differences in (A). B shows only 17 group-specific SNPs for which the differences in the effects between CompG3 and CompG4 attained P < 0.05 ◂ GeroScience (2023) 45:233-247 243