Biology subjects

Kuo, P.-L.

Publications and source records attributed to Kuo, P.-L..

5 recordsLinked to original sources

bioRxiv2024

Compatibility and comparative analysis of chronological and biological aging between the legacy 450K and the EPIC v2.0 arrays

Several different epigenetic clocks built from DNA methylation beadchip arrays, including the Illumina Infinium Human Methylation 450K and EPICv1 BeadChips, have been developed and are used to evaluate biological aging. However, there is still limited information on effectively utilizing the novel EPICv2 arrays and addressing biases in epigenetic clock calculations resulting from missing probes initially present on legacy arrays. We address how the absence of probes on the EPICv2 array, originally present on the 450K array, affects many available epigenetic clock estimates. Using data from the InCHIANTI study of aging, our findings show that while clocks like Hannum and GrimAgeV2 exhibit significant over- or underestimation due to missing probes, the correlations between estimates from all 450K probes (unreduced) and those from shared probes between 450K and EPICv2 (reduced) remain strong. In this cohort, we consider AgeAcceleration from traditional epigenetic clocks to be the most reliable approach. Their values do not significantly change upon probe reduction, with unreduced and reduced values being nearly identical and with stable mean differences across chronological age.

genetics↗

bioRxiv2023

Epigenetic signature of human immune aging: the GESTALT study.

Age-associated DNA methylation in blood cells convey information on health status. However, the mechanisms that drive these changes in circulating cells and their relationships to gene regulation are unknown. We identified age-associated DNA methylation sites in six purified blood borne immune cell types (naive B, naive CD4+and CD8+ T cells, granulocytes, monocytes and NK cells) collected from healthy individuals interspersed over a wide age range. Of the thousand of age-associated sites, only 350 sites were differentially methylated in the same direction in all cell types and validated in an independent longitudinal cohort. Genes close to age-associated hypomethylated sites were enriched for collagen biosynthesis and complement cascade pathways, while genes close to hypermethylated sites mapped to neuronal pathways. In-silico analyses showed that in most cell types, the age-associated hypo- and hypermethylated sites were enriched for ARNT (HIF1{beta}) and REST transcription factor motifs respectively, which are both master regulators of hypoxia response. To conclude, despite spatial heterogeneity, there is a commonality in the putative regulatory role with respect to transcription factor motifs and histone modifications at and around these sites. These features suggest that DNA methylation changes in healthy aging may be adaptive responses to fluctuations of oxygen availability.

molecular biology↗

bioRxiv2021

A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data. Unfortunately, measurements for individual CpGs can be surprisingly unreliable due to technical noise, and this may limit the utility of epigenetic clocks. We report that noise produces deviations up to 3 to 9 years between technical replicates for six major epigenetic clocks. The elimination of low-reliability CpGs does not ameliorate this issue. Here, we present a novel computational multi-step solution to address this noise, involving performing principal component analysis on the CpG-level data followed by biological age prediction using principal components as input. This method extracts shared systematic variation in DNAm while minimizing random noise from individual CpGs. Our novel principal-component versions of six clocks show agreement between most technical replicates within 0 to 1.5 years, equivalent or improved prediction of outcomes, and more stable trajectories in longitudinal studies and cell culture. This method entails only one additional step compared to traditional clocks, does not require prior knowledge of CpG reliabilities, and can improve the reliability of any existing or future epigenetic biomarker. The high reliability of principal component-based epigenetic clocks will make them particularly useful for applications in personalized medicine and clinical trials evaluating novel aging interventions.

bioinformatics↗

bioRxiv2020

Genome-wide association studies identify 137 loci for DNA methylation biomarkers of ageing

Biological ageing estimators derived from DNA methylation (DNAm) data are heritable and correlate with morbidity and mortality. Leveraging DNAm and SNP data from >41,000 individuals, we identify 137 genome-wide significant loci (113 novel) from meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We report strong genetic correlations with longevity and lifestyle factors such as smoking, education, and obesity. Significant associations are observed in polygenic risk score analysis and to a lesser extent in Mendelian randomization analyses. This study illuminates the genetic architecture underlying epigenetic ageing and its shared genetic contributions with lifestyle factors and longevity.

genetics↗

bioRxiv2018

Phenotypic Age: a novel signature of mortality and morbidity risk

BackgroundA persons rate of aging has important implications for his/her risk of death and disease, thus, quantifying aging using observable characteristics has important applications for clinical, basic, and observational research. We aimed to validate a novel aging measure, \"Phenotypic Age\", constructed based on routine clinical chemistry measures, by assessing its applicability for differentiating risk for morbidity and mortality in both healthy and unhealthy populations of various ages.\n\nMethodsA nationally representative US sample, NHANES III, was used to derive \"Phenotypic Age\" based on a linear combination of chronological age and nine multi-system clinical chemistry measures, selected via cox proportional elastic net. Mortality predictions were validated using an independent sample (NHANES IV), consisting of 11,432 participants, for whom we observed a total of 871 deaths, ascertained over 12.6 year of follow-up. Proportional hazard models and ROC curves were used to evaluate predictions.\n\nResultsPhenotypic Age was significantly associated with all-cause mortality and cause-specific mortality. These results were robust to age and sex stratification, and remained even when excluding short-term mortality. Similarly, Phenotypic Age was associated with mortality among seemingly \"healthy\" participants--defined as those who were disease-free and had normal BMI at baseline--as well as the oldest-old (aged 85+)--a group with high disease burden.\n\nConclusionsPhenotypic Age is a reliable predictor of all-cause and cause-specific mortality in multiple subgroups of the population. Risk stratification by this composite measure is far superior to that of the individual measures that go into it, as well as traditional measures of health. It is able to differentiate individuals who appear healthy, who may have otherwise been missed using traditional health assessments. Further, it can differentiate risk among persons with shared disease burden. Overall, this easily measured metric may be useful in the clinical setting and facilitate secondary and tertiary prevention strategies.

epidemiology↗