{"id":15023,"date":"2026-05-11T11:41:52","date_gmt":"2026-05-11T11:41:52","guid":{"rendered":"https:\/\/www.proefschriftmaken.nl\/portfolio\/yunfeng-liu\/"},"modified":"2026-05-11T11:42:10","modified_gmt":"2026-05-11T11:42:10","slug":"yunfeng-liu","status":"publish","type":"us_portfolio","link":"https:\/\/www.proefschriftmaken.nl\/en\/portfolio\/yunfeng-liu\/","title":{"rendered":"Yunfeng Liu"},"content":{"rendered":"","protected":true},"excerpt":{"rendered":"","protected":true},"author":7,"featured_media":15024,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"us_portfolio_category":[45],"class_list":["post-15023","us_portfolio","type-us_portfolio","status-publish","post-password-required","hentry","us_portfolio_category-new-template"],"acf":{"naam_van_het_proefschift":"Deciphering the link of DNA methylation with aging through population multi-omics analysis","samenvatting":"Nederlandse samenvatting \n\nDe visie van verouderingsonderzoek is om mensen in staat te stellen langer gezond te leven. Een veelbelovende weg naar dit doel is het identificeren van epigenetische veranderingen die zich in de loop van het leven opstapelen, aangezien deze worden beschouwd als een van de vijf primaire kenmerken die het verouderingsproces aansturen. Door DNA-methylering als uitgangspunt te nemen, beoogt dit proefschrift te onderzoeken hoe veroudering de methylering be\u00efnvloedt op functioneel relevante genomische loci en hoe deze methyleringspatronen worden gereguleerd. Op deze manier wordt ons mechanistisch begrip van het verouderingsproces en leeftijdsgerelateerde ziekten verder verdiept. Om dit te bereiken, werd een reeks omics-data geanalyseerd in een grote groep individuen uit een populatie-gebaseerde cohortstudie.\n\nDe clustered protocadherin (cPCDH)-genen vormen een bijzonder interessant genoomgebied op chromosoom 5q31, waar verschillen in DNA-methylering in bloed en hersenen sterk in verband zijn gebracht met uiteenlopende menselijke kenmerken en ziekten, waaronder BMI, leeftijd, kanker en verschillende hersenaandoeningen. Hoewel muisonderzoeken bepaalde regelmechanismen van cPCDH-methylering hebben ge\u00efdentificeerd, is het volledige beeld van de regulatie bij mensen nog onvolledig. In hoofdstuk 2 onderzochten we daarom de epigenetische regulatie van cPCDH-genen bij mensen met behulp van een systematische, genoomwijde methylation quantitative trait locus (meQTL)-analyse. We vergeleken de methyleringspatronen van cPCDH-genen tussen bloed en hersenweefsel en zagen dat cPCDH-methylering in deze twee weefsels sterk gecorreleerd was in vergelijking met veel andere genomische regio\u2019s, wat hun unieke methyleringsprofiel benadrukt. Vervolgens bevestigden we de brede effecten van het SMCHD1-gen \u2014 eerder in verband gebracht met cPCDH-regulatie bij muizen \u2014 op cPCDH-methylering in menselijk bloed, en valideerden we dit effect ook in de menselijke prefrontale cortex. Daarnaast ontdekten we twee nieuwe loci, SENP7 en VENTX, die eveneens brede effecten vertoonden op cPCDH-methylering in zowel bloed als hersenen. Interessant genoeg bleken alle drie deze loci betrokken te zijn bij dezelfde of verwante psychiatrische eigenschappen, zoals depressie en angst. Gezamenlijk onthullen deze bevindingen niet alleen de genetische basis achter de regulatie van cPCDH-methylering en de mogelijke relevantie ervan voor neurologische aandoeningen bij mensen, maar laten ze ook zien hoe ontdekkingen in een perifere weefselsoort (bloed) kunnen leiden tot nieuwe inzichten in epigenetische regulatie in de hersenen, een functioneel belangrijk weefsel.\n\nOm de invloed van leeftijd op de methylering van het X-chromosoom bij vrouwen te bepalen, hebben we in hoofdstuk 3 geslachtsgestratificeerde analyses uitgevoerd om leeftijdsafhankelijke DNA-methyleringsverschillen op het X-chromosoom te detecteren, zowel op het niveau van verschillen in gemiddelde methylering (leeftijdsgerelateerde differentieel gemethyleerde CpGs, aDMC\u2019s) als verschillen in variantie (leeftijdsgerelateerde variabel gemethyleerde CpGs, aVMC\u2019s) in monsters van 3,334 vrouwen en 2,612 mannen. We zagen dat aDMC\u2019s zeldzaam waren bij vrouwen maar juist vaak voorkwamen bij mannen, terwijl aVMC\u2019s frequent voorkwamen bij vrouwen maar zeldzaam bij mannen. Functionele annotatie liet verder zien dat aVMC\u2019s vooral voorkwamen in regio\u2019s die onderhevig zijn aan X-chromosoominactivering (XCI) op het inactieve X-chromosoom, aangezien ze verrijkt waren in CpG-eilanden (CGIs) en XCI-regio\u2019s. Verschillende aVMC\u2019s werden bovendien gevonden in genen die betrokken zijn bij leeftijdsgebonden, vrouw-specifieke ziekten, waaronder PGRMC1 en BTK. PGRMC1 is eerder in verband gebracht met kankertypes die vaker bij vrouwen voorkomen, zoals borst- en eierstokkanker, terwijl BTK, een belangrijke mediator van B-celreceptorsignalering, betrokken is bij leeftijdsgebonden auto-immuunziekten die vaker bij vrouwen voorkomen, zoals reumato\u00efde artritis (RA) en systemische lupus erythematosus (SLE). Samenvattend laat deze studie zien dat leeftijdsgerelateerde veranderingen in DNA-methylering bij vrouwen zich vooral voordoen als een toename in variabiliteit, en niet als veranderingen in gemiddelde methylering zoals vaak wordt bestudeerd. Dit wijst erop dat de epigenetische controle van het inactieve X-chromosoom geleidelijk kan verzwakken met de leeftijd, wat een belangrijk inzicht kan bieden in hoe \u2018epigenetische veranderingen\u2019\u2014een van de vijf primaire kenmerken van veroudering\u2014het verouderingsproces aansturen.\n\nEpigenetische klokken zijn naar voren gekomen als krachtige machine learning-instrumenten die niet alleen de chronologische en biologische leeftijd kunnen schatten, maar ook de effectiviteit van anti-verouderingsinterventies kunnen beoordelen. De meeste bestaande epigenetische klokken missen echter een duidelijke mechanistische interpretatie, waardoor het moeilijk te begrijpen is waarom ze voorspellend zijn voor leeftijdsgerelateerde uitkomsten. In dit kader bouwen we in Hoofdstuk 4 voort op de kennis die is opgedaan in Hoofdstuk 3, met als doel een nieuw, interpreteerbaar verouderingsbiomarker te ontwikkelen op basis van leeftijdsgerelateerde variabel gemethyleerde CpG\u2019s (aVMC\u2019s). Specifiek stelden we aVMC-gebaseerde entropie-indices voor die afzonderlijk zijn afgeleid van gerepresseerde polycomb (ReprPC)-regio\u2019s en transposabele elementen (TE\u2019s), respectievelijk aangeduid als ReprPC-aVMCs-entropie en TEs-aVMCs-entropie. Hoewel TEs-aVMCs-entropie geen duidelijke functionele consequenties vertoonde, was ReprPC-aVMCs-entropie geassocieerd met een genoomwijde derepressie van genen nabij ReprPC-regio\u2019s en met de opregulatie van genen die betrokken zijn bij immuunrespons en levensduur. Deze bevindingen suggereren dat ReprPC-aVMCs-entropie kan dienen als een veelbelovende index om verouderingsmechanismen en gerelateerde ziekte-uitkomsten beter te begrijpen.\n\nCollectief gezien biedt de focus op veranderingen in DNA-methylering en hun regulatie\u2014met name in functioneel belangrijke regio\u2019s of loci die betrokken zijn bij veroudering\u2014een veelbelovende benadering om de mechanismen achter het verouderingsproces en leeftijdsgerelateerde ziekten beter te begrijpen. Dit proefschrifts laat zien dat de op DNA-methylering gebaseerde epigenetische entropie-index potentie heeft als biomarker voor veroudering, en kan bijdragen aan het monitoren van interventies en het inschatten van ziekterisico. Dit werk biedt aanknopingspunten voor het testen van interventies die biologische veroudering kunnen vertragen en kan, op termijn, bijdragen aan het het doel om de gezonde levensduur te verlengen.","summary":"Summary \n\nThe vision of aging research is to allow people to live healthier for longer. A promising path toward this goal is identifying epigenetic changes that accumulate across the lifespan, as they are considered one of the five primary hallmarks driving the aging process. By taking DNA methylation as an entry point, this thesis aims to investigate how aging affects methylation at functionally relevant genomic loci and how these methylation patterns are regulated, thereby advancing our mechanistic understanding of the aging process and age-related diseases (Figure 1). To achieve this, a range of omics data in a large collection of individuals from the population-based cohort was analyzed.\n\nClustered protocadherin (cPCDH) genes constitute a particularly compelling genomic locus on chromosome 5q31, where differential DNA methylation in blood and brain has been strongly implicated in diverse human traits and diseases ranging from BMI, age, cancer, and various brain disorders. Although mouse studies have identified certain regulators of cPCDH methylation, the whole picture of its regulation in humans remains incomplete. In Chapter 2, we therefore investigated the epigenetic regulation of cPCDH genes in humans using a systematic genome-wide methylation quantitative trait locus (meQTL) analysis. We first compared the methylation patterns of cPCDH genes between blood and brain. Notably, cPCDH methylation in these two tissues exhibited pronounced correlations compared to many other genomic regions, highlighting their unique methylation profile. We subsequently confirmed the broad effects of the SMCHD1 gene\u2014previously implicated in cPCDH regulation in mice\u2014on cPCDH methylation in human blood and then validated this effect in human prefrontal cortex. Finally, we identified two novel loci (SENP7 and VENTX) with widespread effects on cPCDH methylation in both blood and brain. More interestingly, all three loci have been implicated in the same or related psychiatric traits, such as depression and anxiety. Collectively, these findings not only uncover the genetic basis underlying cPCDH methylation regulation and its possible relevance to neurological diseases in humans, but also provide an exciting showcase of a discovery analysis in a peripheral tissue (blood) that can be translated into novel insights into epigenetic regulation in the brain, a functionally important tissue. \n\nTo determine the influence of age on the methylation of chromosome X in females, in Chapter 3, we conducted sex-stratified analyses to detect age-related DNA methylation differences in X-chromosome methylation at the level of both differences in mean (age-related differentially methylated CpGs, aDMCs) and differences in variance (age-related variably methylated CpGs, aVMCs) in samples of 3,334 women and 2,612 men. We observed that aDMCs were rare in females but common in males, whereas aVMCs were common in females but rare in males. Functional annotation further revealed that aVMCs preferentially occur in regions subject to X-chromosome inactivation (XCI) on the inactive X, as they were enriched in CpG islands (CGIs) and regions subject to XCI. In fact, several aVMCs mapped to genes involved in age-related, female-specific diseases, including PGRMC1 and BTK. PGRMC1 has been associated with female-predominant cancer types such as breast cancer and ovarian cancer, whereas BTK, a key mediator of B cell receptor signaling, is linked to age-related autoimmune diseases more prevalent in females, such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). In summary, this study shows that age-related DNA methylation changes in females predominate as the accumulation of variability rather than commonly studied differences in mean. This implies that the epigenetic control at the inactive X may gradually wane with age, which may be a key insight in uncovering how \u2018Epigenetic alterations\u2019, one of the 5 primary Hallmarks of Aging, drive the aging process. \n\nEpigenetic clocks have emerged as powerful machine learning tools that not only estimate chronological and biological age but also assess the efficacy of anti-ageing interventions, offering a promising avenue for understanding the aging process and age-related health outcomes. However, most existing clocks lack a clear mechanistic interpretation, making it difficult to understand why they are predictive of age-related outcomes. To this end, in Chapter 4, we build on the knowledge obtained in Chapter 3 in an effort to construct a novel, interpretable aging biomarker using age-related variably methylated CpGs (aVMCs). Specifically, we proposed aVMCs-based entropy indices derived separately from repressed polycomb (ReprPC) regions and transposable elements (TEs), termed ReprPC-aVMCs entropy and TEs-aVMCs entropy. While TEs-aVMCs entropy did not display functional consequences, ReprPC-aVMCs entropy was associated with the genome-wide de-repression of genes near ReprPC regions and with the upregulation of genes linked to immune response and longevity. These findings suggest that ReprPC-aVMCs entropy may serve as a promising index for understanding aging mechanisms and related disease outcomes.\n\nThe power of combining epigenetics with genetics\nOver the past decades, genome-wide association studies (GWAS) have identified thousands of genetic loci associated with human traits or diseases in large-scale population studies, demonstrating the crucial role of genetic variants in shaping complex traits and disease susceptibility. However, as most GWAS significant hits commonly reside in non-coding regions, it remains challenging to pinpoint candidate causal variants and causal genes among these loci and understand their functional consequences. Due to advances in high-throughput technologies, multiple layers of omics data\u2014including gene expression, DNA methylation, histone modification, proteomic, and metabolomic data\u2014are now being generated. This makes it possible to integrate genetic variation with one or multiple molecular phenotypes to discover various types of quantitative trait loci (QTLs), such as expression QTLs (eQTLs), methylation QTLs (meQTLs), histone quantitative trait loci (hQTLs), proteomic QTLs (pQTLs), and metabolic QTLs (mQTLs), thereby enhancing the explanatory power of the functional consequences of genetic variants underlying complex traits in multiple perspectives. \n\nInvestigating genetic effects on DNA methylation is particularly relevant in the context of methylome changes observed in diseases, as it can reveal novel loci regulating DNA methylation as strong candidates for further follow-up and provide fresh insights into the regulatory genomic potential underlying GWAS signals. Chapter 2 showcases how we used a systematic genome-wide meQTL analysis to identify two novel loci (SENP7 and VENTX) influencing the methylation of a specific genomic locus\u2014cPCDH. In Chapter 2, we specifically investigated meQTL effects in two tissues, with a genome-wide discovery in blood and validation in the brain. This study design is based on the following considerations. First, differential cPCDH methylation associated with a broad range of human diseases has been observed in both blood and brain, although many of these findings were from blood-based studies. Second, cPCDH is predominantly expressed in brain, indicating that it is a functionally relevant tissue. Third, discoveries from blood analyses can yield valuable insights regarding brain-related complex traits and disorders, despite substantial differences in DNA methylation between these tissues. Specifically, Mendon\u00e7a et al. identified 18, 293 CpGs showing correlated blood\u2013brain methylation (r>0.7), 64 of which mapped to key genes implicated in neurological diseases, including Alzheimer\u2019s disease and Parkinson\u2019s disease. These findings support the rationale for using blood as a surrogate tissue to analyze methylation changes in the brain of patients, thereby facilitating the identification of novel disease biomarkers. Moreover, Qi et al. reported that the top cis-eQTL effects (r = 0.70) and cis-meQTL effects (r = 0.78) were highly correlated between brain and blood samples. Intriguingly, a large proportion of genes and CpG sites associated with brain-related phenotypes in meta-analyzed brain cis-eQTL\/meQTL data overlapped with those identified in blood, highlighting the utility of blood-derived signals for pinpointing putative functional genes involved in brain-related diseases. Collectively, this evidence is consistent with the results presented in Chapter 2, including the high correlation of cPCDH methylation observed between blood and brain (r ranging from 0.88 to 0.94), as well as successful validation of SENP7 and VENTX loci derived from blood in brain. \n\nIdentifying causal genes at the identified GWAS loci remains a major challenge. Integrating meQTLs and eQTLs is a promising strategy to infer potential candidate genes, as demonstrated by the SENP7 locus in Chapter 2. According to the eQTLGen and MetaBrain consortium, the lead trans-cPCDH meQTL rs13062095 was associated with seven genes in blood, which narrowed down to three genes in the brain cortex\u2014SENP7, IMPG2, and CEP97. Among these, SENP7 is the strongest candidate to regulate cPCDH methylation (See discussion in Chapter 2). Co-localization analysis further confirmed a shared genetic architecture between SENP7 expression and cPCDH methylation, but this does not necessarily imply a causal relationship between the two. Therefore, additional computational approaches, such as Mendelian randomization (MR), are needed to infer the causal role of the SENP7 gene in cPCDH methylation. Ultimately, to demonstrate the regulatory function of the SENP7 gene in cPCDH methylation with complete certainty, experimental validation using methods such as CRISPR-Cas9 will be required. Moreover, integrating meQTLs and GWAS can provide valuable insights into disease mechanisms. In Chapter 2, comparing trans-cPCDH meQTLs with the neurological disease GWAS outcomes implicated the SENP7, VENTX, and SMCHD1 loci in depression and anxiety. This suggests SENP7, VENTX, and SMCHD1 may contribute to depression via cPCDH methylation effects, which will be of interest to further test in the future. \n\nDifferentially versus variably methylated CpGs with age \n\nAge-related differentially methylated CpGs (aDMCs) and age-related variably methylated CpGs (aVMCs) represent two distinct types of age-related DNA methylation changes that vary in their characteristics. In particular, aDMCs display age-related shifts in mean DNA methylation that are widely studied, while aVMCs show increasing methylation variance with age that is drawing increasing attention. In Chapter 3, we report on a systematic analysis of age-related DNA methylation differences at the X chromosome and find that females harbor many more aVMCs (n = 987) than aDMCs (n = 33). Moreover, we observed striking differences between aDMCs and aVMCs in females with respect to XCI-related genomic features. First, aDMCs were depleted at CGIs in females and were more frequent in non-CGI regions, while aVMCs preferentially occurred at CGIs in females. This observation suggests that female aVMCs are linked to XCI, as CGI methylation is known to participate in XCI process. Second, female aDMCs all occurred outside regions subject to XCI according to consensus XCI status calls. In contrast, the large majority (85%) of annotated female aVMCs occurred in regions subject to XCI. Together, these findings imply that DNA methylation marks associated with XCI commonly accumulate variability with age instead of differences in mean. \n\nIn Chapter 4, we focus on aVMCs within two biologically important genomic regions (ReprPC regions and TEs) and investigate if they have potential in constructing a novel epigenetic clock that captures mechanistic aspects of aging. This assumption is motivated by three main considerations. First, current epigenetic clocks primarily rely on aDMCs, clocks based on aVMCs have not yet been developed. Second, aVMCs commonly display a striking variability in DNA methylation at higher ages, which might be useful to indicate the different ageing rates between individuals with the same chronological age in the elderly population. Hence, aVMCs fulfill a primary prerequisite for a good predictor of biological age. Finally, aVMCs have been associated with the expression of genes in trans that link to well-known ageing pathways such as DNA repair, apoptosis, immune activation and metabolism changes. Chapter 4 generated a robust catalogue of aVMCs for ReprPC regions (n=1182) and TEs (n=195) through stringent discovery and replication stages. Notably, the number of aVMCs identified in ReprPC regions was substantially higher than in TEs, indicating that greater variability over time accumulated in ReprPC regions. \n\nEpigenetic entropy index based on aVMCs \n\nShannon entropy serves as a useful metric for quantifying methylome-wide DNA methylation changes, providing insights into epigenetic aging by capturing the degree of \u2018chaos\u2019 or methylation disorder. Specifically, it can summarize all the age-related DNA methylation changes across genomic regions, yielding a single score for each sample at a particular age. In Chapter 4, we develop two entropy indices based on aVMCs\u2014one within ReprPC regions and another within TEs\u2014that differ from traditional epigenetic clocks in several ways. First, first-generation clocks (Hannum, Horvath, and Zhang) are designed to predict chronological age, whereas second-generation clocks (PhenoAge and GrimAge) estimate composite phenotypes comprised of age and clinical measurements. In contrast, aVMCs-based entropy measures the degree of methylome disorder that occurs with age. Second, unlike traditional epigenetic clocks that rely on \u201cblack-box\u201d predictive models trained via penalized regression to select CpG combinations randomly for outcome prediction, the aVMCs-based entropy approach does not train any models to predict age or age-related clinical biomarkers. Third, the CpGs used to construct aVMCs-based entropy are specifically located in genomic regions implicated in aging, and each displays increased methylation variance with age, while those in traditional clocks are distributed genome-wide and not necessarily age-associated. \n\nOwing to these design differences, it becomes possible to capture aging mechanisms through our epigenetic indices (aVMCs-based entropy), which in turn contribute to the development of biologically interpretable aging biomarkers. Indeed, we subsequently observed positive associations between ReprPC-aVMCs entropy and the fraction of derepressed genes in ReprPC regions. Additionally, ReprPC-aVMCs entropy was positively associated with the expression of longevity-related genes, including PARP1, SMAD3, and ACE. Notably, FDA-approved inhibitors targeting PARP1 or ACE have been shown to extend lifespan in model organisms such as Drosophila and C. elegans. These findings suggest that ReprPC-aVMCs entropy, as a candidate aging biomarker, may have promising clinical applications. For example, it can provide early indication of intervention effects on healthspan and\/or lifespan. It may also help prioritize candidate interventions for longer-term assessment and identify individuals most likely to benefit from treatment. In Chapter 3, we highlight that the female X chromosome accumulates substantial epigenetic variability with age, yet it remains unclear whether this variability is beneficial, detrimental or neutral for female aging or female-specific health outcomes. Building on the insights from Chapter 4, calculating an entropy index based on aVMCs in regions subject to XCI and testing its associations with female-specific disease outcomes may prove valuable in future studies. \n\nBeyond DNA methylation towards other epigenetic regulation layers \n\nWhile the entire epigenome is thought of as a complex interplay among different layers of epigenetic factors, human studies on the epigenetic mechanisms underlying aging have largely focused on DNA methylation. There are two key reasons behind this focus. First, DNA methylation involves a covalent bond that ensures chemical stability compared to other epigenetic mechanisms with noncovalent interactions. Second, advances in high-throughput array-based methods (450k and EPIC arrays) make it possible to measure DNA methylation efficiently in large-scale human populations. Indeed, extensive EWAS studies have well-characterized the relationship between age and DNA methylation changes in various human tissues and cell types, providing initial insights into the mechanisms underlying the aging process and age-related diseases. \n\nWhile DNA methylation is easily quantifiable, the complexity of epigenetic regulation goes far beyond this single mark, particularly considering the extensive interconnection among different layers of epigenetic modification, as exemplified by part of findings in Chapter 2 and Chapter 3. In Chapter 2, we observed high similarity in cPCDH methylation patterns between blood and brain, yet striking differences in the expression levels of cPCDH genes observed in public GTEx data (Figure 2). Specifically, 53 cPCDH genes showed extremely low expression in blood\u2014concordant with our observations in BIOS blood\u2014but markedly higher expression in brain cortex. This raises the question: how can DNA methylation patterns be so similar while gene expression levels differ so dramatically for cPCDH genes? This discrepancy likely arises because transcriptional regulation of genes involves far more than the DNA methylation we investigated, including contributions other regulatory layers. Moreover, despite identifying numerous females aVMCs (n=987) in Chapter 3, associations between methylation level at these aVMCs and expression were limited to just two X-linked genes, ALG13 and CDK16. This implies minimal effects of aVMC methylation on gene expression, contrasting with the assumed key role of DNA methylation in establishing and maintaining XCI. One explanation could be that XCI involves multiple levels of epigenetic repression beyond DNA methylation (e.g., histone modifications). Collectively, gene expression changes observed in these examples may arise from regulatory mechanisms other than DNA methylation, highlighting the importance of investigating other epigenetic factors that influence transcription. Evidence from previous studies indicates that alterations in histone modification can induce transcriptional changes without or with minimal effects on DNA methylation. Therefore, a comprehensive understanding of regulatory processes should consider the entire epigenome including histone modifications, chromatin remodeling, and non-coding RNAs.\n\nFuture perspectives \n\nAs a critical layer of epigenetic regulation, elucidating DNA methylation changes at loci implicated in aging provides a valuable entry point for understand the aging process and age-related diseases, which is the central focus of this thesis. However, a comprehensive understanding of the biological aging process still faces substantial challenges. \n\nPopulation studies of the aging methylome based on bulk tissues and cross-sectional study designs alone are insufficient. Most epigenetic clocks, for instance, are trained on bulk tissue samples such as blood containing many different cell types. As aging is inherently heterogeneous, with divergent rates across tissues and cell types, it underscores the need to develop tissue- and cell type\u2013specific clocks to quantify biological age more accurately. Moreover, the large majority of aging epigenetic studies in humans including those described in this thesis employ a cross-sectional design, where DNA methylation is measured at only one time point. Such a design cannot track dynamics of methylation changes over time within an individual, making it challenging to evaluate intraindividual variability. Notably, although the aVMCs identified in this thesis were validated through stringent discovery and replication steps, it is unknown whether their methylation variance also varies with age across individuals measured at different time points. Beyond this, important challenges remain in establishing causality and advancing the clinical implementation of aging biomarkers. First, researchers have begun to use Mendelian randomization (MR) to elucidate putative causal roles of epigenetic clocks in aging. For example, Ying et al. recently developed causality-enriched epigenetic clocks based on MR, including DamAge (CpGs methylation with detrimental health effects) and AdaptAge (CpGs methylation with protective health effects). Although validated in independent cohorts, they still lack experimental evidence for the causal role of the underlying CpGs. In addition, the limitations of MR\u2014such as low statistical power and pleiotropy\u2014can bias true causal effects. Consequently, no consensus exists on the causal role of DNA methylation in aging. Second, despite significant advances toward establishing reliable aging biomarkers in recent years, none of them have been validated for clinical use to date. \n\nAddressing these challenges above is far from easy. Therefore, future studies should consider study design with longitudinal measurements, incorporate single-cell technologies in refining epigenetic clocks, and combine computation approaches (e.g., Mendelian randomization) with experimental validation (e.g., CRISPR-Cas9) where appropriate. Such efforts would improve the accuracy and translational value of future findings.","auteur":"Yunfeng Liu","auteur_slug":"yunfeng-liu","publicatiedatum":"11 juni 2026","taal":"EN","url_flipbook":"https:\/\/ebook.proefschriftmaken.nl\/ebook\/yunfengliu?iframe=true","url_download_pdf":"https:\/\/ebook.proefschriftmaken.nl\/download\/cedba82e-4685-49da-90e9-13e14ca8118d\/optimized","url_epub":"","ordernummer":"18951","isbn":"978-94-6534-426-3","doi_nummer":"","naam_universiteit":"Universiteit Leiden","afbeeldingen":15025,"naam_student:":"","binnenwerk":"","universiteit":"Universiteit Leiden","cover":"","afwerking":"","cover_afwerking":"","design":""},"_links":{"self":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/15023","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio"}],"about":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/types\/us_portfolio"}],"author":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/comments?post=15023"}],"version-history":[{"count":1,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/15023\/revisions"}],"predecessor-version":[{"id":15026,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio\/15023\/revisions\/15026"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/media\/15024"}],"wp:attachment":[{"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/media?parent=15023"}],"wp:term":[{"taxonomy":"us_portfolio_category","embeddable":true,"href":"https:\/\/www.proefschriftmaken.nl\/en\/wp-json\/wp\/v2\/us_portfolio_category?post=15023"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}