Epigenetic Clocks Stay Ticking in Brain Fluid After Bleeding Stroke

When a brain aneurysm ruptures, blood floods into the cerebrospinal fluid that bathes the brain and spinal cord, triggering a cascade of inflammation, oxidative stress, and cellular damage that can reshape a patient’s entire recovery. For decades, clinicians have known that chronological age is one of the strongest predictors of how well patients fare after this devastating form of stroke, known as aneurysmal subarachnoid hemorrhage. But chronological age is a blunt instrument. Two people born on the same day can age at dramatically different rates depending on genetics, environment, and life history. A new study has now taken a close look at whether molecular measures of biological aging, read directly from the fluid surrounding an injured brain, hold up under the extreme conditions that follow aneurysm rupture, and the answer carries important implications for how researchers should design the next generation of studies on stroke recovery.

The research, published in the open-access journal Epigenetics Communications, represents the first systematic characterization of DNA methylation age in cerebrospinal fluid during the acute recovery period after aneurysmal subarachnoid hemorrhage. DNA methylation age, often called epigenetic age, is estimated from chemical tags called methyl groups that attach to specific positions in the genome, known as CpG sites. Over the past decade, scientists have developed several so-called epigenetic clocks, mathematical models that convert methylation patterns into an age estimate. When a person’s epigenetic age exceeds their chronological age, a phenomenon called age acceleration, it has been linked to cancer, Parkinson’s disease, cardiovascular disease, and all-cause mortality. What remained unknown was whether these clocks, most of which were trained on blood or a mixture of tissues, could produce meaningful readings in cerebrospinal fluid, a tissue that normally contains fewer than five cells per milliliter.

Lead author Lacey W. Heinsberg of the University of Pittsburgh and colleagues tackled this question using an extraordinary dataset: serial cerebrospinal fluid samples from 273 patients recovering from aneurysmal subarachnoid hemorrhage, yielding 850 individual measurements collected over the first 14 days after the hemorrhage. The samples came from external ventricular drains placed as part of standard clinical care to relieve pressure inside the skull, meaning the researchers could track the molecular environment of the injured central nervous system without any additional invasive procedures. For a subset of 72 participants, the team also had blood samples collected during the same early window, allowing a direct comparison between the epigenetic age signatures of the brain’s local environment and the peripheral circulation.

The researchers applied four different epigenetic clocks to their data: the Horvath clock, built from 353 CpG sites and designed as a pan-tissue estimator; the Hannum clock, built from 71 sites and trained on blood; the Levine clock, built from 513 sites and designed to estimate a broader biological metric called phenotypic age; and the Zhang clock, also known as the Improved Precision clock, built from 514 sites and trained on 13,661 blood and saliva samples, by far the largest training dataset of the four. Across all cerebrospinal fluid samples, DNA methylation age correlated moderately to strongly with chronological age, with Pearson correlation coefficients ranging from 0.66 for the Levine clock to a striking 0.97 for the Zhang clock. In blood, the correlations were similarly strong, ranging from 0.83 to 0.97, confirming that even in the chaotic aftermath of a brain hemorrhage, the fundamental relationship between methylation patterns and age remains intact.

The comparison between tissues proved especially revealing. For the 72 participants with both cerebrospinal fluid and blood data, the two tissues produced correlated but clearly distinct age estimates in the Horvath, Hannum, and Levine clocks, with correlations ranging from 0.69 to 0.87. The Zhang clock, however, produced a near-perfect correlation of 0.98 between the two tissues, suggesting that its estimates are essentially interchangeable regardless of whether the DNA comes from the fluid surrounding the brain or from the bloodstream. This robustness extended to the thorniest technical challenge in the study: cell-type heterogeneity. After an aneurysm ruptures, cerebrospinal fluid becomes heavily contaminated with blood cells that gradually clear during recovery, while cells originating in the brain and from the ruptured vessel appear in the mix. Because methylation patterns differ across cell types, these shifting proportions can distort age estimates. No reference-based deconvolution method exists for cerebrospinal fluid, so the team used a reference-free approach to estimate cell-type proportions and repeated their analyses with and without this adjustment.

The results of that sensitivity analysis were decisive. The Zhang clock’s data distributions looked essentially the same whether the estimates came from cerebrospinal fluid or blood, and whether or not cell-type heterogeneity was accounted for. The other clocks showed greater sensitivity to tissue source and cell composition. The authors conclude that, of the four clocks examined, the Zhang clock is the most robust to cell-type heterogeneity and is the recommended choice for complex tissues such as cerebrospinal fluid. This finding is particularly notable because the Zhang clock, like the others, was never trained on cerebrospinal fluid data at all. Its apparent resilience likely stems from the sheer scale of its training dataset, which taught the model to distinguish age-related methylation signals from tissue-specific noise more effectively than its predecessors.

To assess whether epigenetic age shifts during recovery, the team used group-based trajectory analysis, a statistical method that infers distinct subgroups of patients following different temporal patterns. For the Horvath clock, both adjusted and unadjusted for cell-type heterogeneity, the analysis identified four distinct but flat trajectory groups, meaning age acceleration did not change over the 14-day window. The Zhang clock, unadjusted, showed the same pattern of four stable groups. The Hannum clock showed modest temporal variation in its unadjusted form, but once cell-type heterogeneity was controlled, that variation washed out into flat trajectories as well. The Levine clock’s trajectory models failed the study’s posterior quality-control checks due to uncertainty in classifying middle groups, underscoring that this clock is the least well suited to this tissue. Taken together, the trajectories suggest that epigenetic age, as measured by these clocks, is not materially perturbed by the acute pathological environment after aneurysmal subarachnoid hemorrhage.

The study also surfaced intriguing associations between epigenetic aging and patient characteristics. Sex was associated with trajectory group membership in the Hannum clock, with the proportion of women declining as age acceleration rose: the lowest-acceleration group was 93.3 percent female, while the highest-acceleration group was only 16.7 percent female. This aligns with a broader literature showing that men tend to have higher DNA methylation age than women. Independent of trajectory groups, sex was also associated with age acceleration in the Horvath and Hannum clocks, race was associated with acceleration in the Hannum and Levine clocks, and smoking was associated with Levine clock acceleration after cell-type adjustment. Notably, none of the trajectory groups differed meaningfully in injury severity, body mass index, or other clinical characteristics, a somewhat surprising result the authors highlight as deserving further study.

The practical implications cut in two directions. On one hand, the stability of cerebrospinal fluid epigenetic age during recovery suggests that researchers designing future studies could gain statistical power by collecting a single measurement from more participants rather than generating expensive longitudinal methylation data for each patient. On the other hand, the very stability that makes the measurement reliable also suggests that age acceleration from these particular clocks is unlikely to add predictive value for recovery outcomes beyond what chronological age already provides. The authors argue that a clock specifically trained on cerebrospinal fluid methylation data from the acute period after hemorrhage would have the greatest clinical potential, joining a growing movement toward tissue- and disease-specific epigenetic clocks, such as those developed for placental aging and for hippocampal and cortical tissue in Alzheimer’s models.

The study is not without limitations. Blood samples were processed on separate plates from cerebrospinal fluid samples, preventing adjustment for potential plate batch effects, though the authors note that correlation coefficients are invariant to changes in origin and scale, and the near-perfect Zhang clock concordance between tissues supports the validity of their comparisons. The blood subset was small, at 72 participants with a single time point, precluding trajectory comparisons in blood. And because no other cohort with serial cerebrospinal fluid methylation data exists, the findings could not be replicated in an independent sample. Still, as the first window into how the epigenetic clocks behave in the fluid that cushions an injured brain, the work establishes a methodological foundation: the Zhang clock delivers precise, stable, tissue-agnostic age estimates even amid the cellular turbulence of a bleeding stroke, while the field moves toward clocks tailored to the tissues and diseases that matter most.

Subject of Research: DNA methylation age in cerebrospinal fluid during recovery from aneurysmal subarachnoid hemorrhage

Article Title: Characterization of cerebrospinal fluid DNA methylation age during the acute recovery period following aneurysmal subarachnoid hemorrhage

Article References: Characterization of cerebrospinal fluid DNA methylation age during the acute recovery period following aneurysmal subarachnoid hemorrhage. (n.d.). https://doi.org/10.1186/s43682-021-00002-6

Image Credits: AI Generated

DOI: 10.1186/s43682-021-00002-6

Keywords: epigenetic clocks, DNA methylation age, cerebrospinal fluid, aneurysmal subarachnoid hemorrhage, biological aging, age acceleration, cell-type heterogeneity, Zhang clock, Horvath clock, stroke recovery, epigenetics, biomarkers