EDC for epigenetic clock and biological age trials

Epigenetic clocks estimate a biological age from patterns of DNA methylation in a sample, usually blood. Longevity and healthy-ageing studies use them as an exploratory or secondary biomarker, often alongside other measures such as blood chemistry, grip strength, sleep and questionnaires. Whether a particular clock is valid for a given purpose is a scientific question for the investigators. The data question is simpler and just as important: can every result be traced to a participant, a visit, a sample and a laboratory run, and can anyone later see whether a value was changed?

A biomarker trial usually adds two layers to a normal visit schedule. The first is sample logistics: collection, processing, storage, shipment and assay. The second is analysis data returned by a laboratory, often as a file with one row per sample. Both need structure in the EDC. The wider glossary entry on biomarkers and the page on clinical trial endpoints are useful when you decide which measures are primary, secondary or exploratory. For the broader context of healthy-ageing designs see EDC for longevity clinical trials.

Epigenetic assays are batch-processed, and batch effects are a recognised issue in methylation work. That makes batch ID, plate position and assay date essential fields, not afterthoughts. Record them at the point the laboratory returns the data and keep them in the same dataset as the participant and visit, so an analyst can model them. If one participant's samples ended up in different batches, that must be visible, and ideally the protocol places a participant's repeated samples in the same batch so the within-person change is not confounded.

Capture does not replace a laboratory information system. It holds the study-facing record: what was collected, when, which sample ID, and what came back. Where a results file arrives as CSV or Excel, the study team reconciles it against the visit record, and discrepancies become queries in the normal way. You can export the whole dataset as CSV or Excel with an automatic data dictionary, filtered by date range or by site. See lab data and reference range management for how units and ranges are stored.

Biological age is rarely studied alone. Sleep, activity, heart-rate variability and diet are all candidates for secondary analyses, and a wearable gives a continuous record where a diary gives a snapshot. Capture connects wearables through a native sign-in: the study team enables the device for the study and may mark it required, the participant connects their own account, and data sync with a last-sync time and a disconnect control. Oura is the device named on the site; confirm others with us before planning around them. The wearable data sharing consent template helps with consent wording.

Pre-specify how sensor data become analysis variables, such as a seven-day mean before each visit, and what counts as a valid day. That decision belongs in the analysis plan, and the data export gives you the raw synced values to implement it. The EDC for wearable device studies page covers the pattern in more detail.

A biological age estimate can shift when an assay pipeline is updated or a sample is re-run. If a value in the database changes, the study needs to know why. Capture writes a field-level audit trail with timestamp, user, old value, new value and reason for change, and the trail is append-only, so a corrected result never erases the original. That gives the statistician and any reviewer a defensible history. The audit trail software page describes the controls, and Capture offers Part 11-aligned controls with the sponsor responsible for validated use.