Xella Clock: a female-specific epigenetic aging clock for menstrual fluid and endometrium
In an abstract, Xella Health reports a deep learning-based epigenetic aging clock trained on menstrual fluid from 66 people that predicted chronological age better than other widely used clocks in menstrual fluid and endometrial tissue. [1]
Automatic summary · not editorially reviewed
The key findings
The abstract says the female reproductive system is one of the first major organ systems to show age-related decline, and that menopause is associated with increased risk of diseases such as osteoporosis and cardiovascular disease. [1]
Xella Health collected menstrual fluid from a diverse cohort of 66 people and quantified genome-wide 5mC methylation levels. [1]
The Xella Clock outperformed other widely used epigenetic aging clocks at predicting chronological age from menstrual fluid data and on endometrial tissue, according to the abstract. [1]
The source at a glance
- Study type / format
- Abstract of a model-development study using methylation data
- Population / context
- Menstrual fluid samples from a diverse patient cohort of 66 people, plus endometrial tissue in the comparison.
- Summary based on
- Study abstract
- Publication status
- Preprint · peer-review status not confirmed
- Original publication
- August 10, 2026
Why a clock for menstrual fluid and endometrium
The abstract states that the female reproductive system is one of the first major organ systems to show age-related decline, and that menopause is associated with increased risk of diseases such as osteoporosis and cardiovascular disease. Menstrual fluid, a mixture of blood and endometrial tissue, is described as a noninvasive sample with potential for diagnostics related to female reproductive aging. Existing epigenetic aging clocks, however, show limited performance in hormone-dependent tissues such as the endometrium.
Supporting source [1]
Samples and methylation measurement in 66 people
At Xella Health, the team collected menstrual fluid samples from a diverse patient cohort of 66 people and quantified genome-wide 5mC methylation levels. On this basis they developed a deep learning-based epigenetic aging clock optimized for performance in menstrual fluid and endometrial tissue. The abstract does not report participant ages, the age range, how many samples came from each person, or how the cohort was recruited.
Supporting source [1]
Better age prediction than other clocks in the abstract
According to the abstract, the Xella Clock outperforms other widely used epigenetic aging clocks at predicting chronological age from menstrual fluid data and on endometrial tissue. The authors describe the model as a useful tool for advancing the study of female reproductive aging and say it can be used to examine associations between endometrial age acceleration and clinical factors. These are stated aims and associations, not demonstrated causal effects on disease or lifespan; the abstract gives no effect sizes, comparison details or validation in independent groups.
Supporting source [1]
How to interpret this
This is an abstract only; the full text was not reviewed. The abstract reports no effect sizes, no details of the comparison clocks, no participant ages or age range, no follow-up and no independent validation. It describes a model for research use, not a validated clinical test, and the stated uses concern associations, not causal effects.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Pavuluri A, Gould B, Indap A, Salakh N, Lacob K, Dantas A, Sazonova O, Ching J. · europepmc.org · August 10, 2026