The impact of the menstrual cycle on skin volatile profiles (Marion Risse, MDCW 2025)

🎤 Presenter: Marion Risse (ETH Zurich, Zürich, Switzerland)

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💡 Book in your calendar: 17th Multidimensional Chromatography Workshop (MDCW) 13 - 15. January 2026

Abstract

Historically, women have been underrepresented in biological and medical research, leading to significant gaps in knowledge with adverse implications for women’s health. This exclusion often aims to avoid potential temporal variation linked to the female reproductive cycle. In studies on human odors and disease vector attraction, such gaps can critically impact human health. Yet, sex-specific differences in odor cues influencing mosquito attraction remain largely unexplored. To address this gap, we conducted a longitudinal study investigating variations in female body odors throughout the menstrual cycle.

We collected skin volatile samples from female subjects during the menstrual, fertile, and luteal phases of their cycle, as well as from male controls. These samples were processed via two-dimensional gas chromatography-mass spectrometry (GC×GC-MS) and further analyzed using GCImage software. Our findings provide crucial insights into sex differences in body odor and the potential effects of the menstrual cycle. Identifying volatile markers specific to sex and menstrual phases could significantly enhance our understanding of vector attraction.

This knowledge may inform targeted strategies for improved health interventions and disease prevention and control, ultimately contributing to better public health outcomes.

Video transcription

Women are historically underrepresented in biological and medical research. Many studies excluded women to avoid temporal variation caused by the menstrual cycle. This exclusion leads to partial representation of populations and reduces the utility of results. Human odor studies and disease vector attraction are no exception.

The research questions were: Is there a sex-specific signature in skin volatiles? Do body odors vary consistently throughout the menstrual cycle? How do these variations impact mosquito attraction?

Study Design

Two longitudinal studies with repeated volatile collections were conducted. Female participants were sampled at three phases: menstrual phase, fertile phase, and luteal phase. Male controls were sampled at corresponding intervals. Samples were analyzed with GC×GC-MS and processed using GCImage software. Both arm and foot volatiles were studied, with results here focusing on arms.

Results: Sex Differences

PLS-DA analysis showed strong overlap between males and females, but machine learning (random forest) could predict sex with approximately 75% accuracy. Some compounds were identified as sex markers, though identification is still under verification.

Results: Menstrual Cycle Effects

PLS-DA revealed overlap between menstrual and other phases, but clearer separation between fertile and luteal phases. Random forest analysis indicated limited accuracy for menstrual vs. fertile, but better prediction for menstrual vs. luteal comparisons. Volcano plots highlighted significant compounds distinguishing phases: few compounds for menstrual vs. fertile, more for menstrual vs. luteal and fertile vs. luteal comparisons. Some overlapping compounds were identified between LIMMA and random forest analyses.

Results: Mosquito Attraction

Mosquito attraction tests showed no differences between menstrual cycle phases and no differences between males and females. However, there was strong individual variation. Some participants were consistently highly attractive to mosquitoes, while others were consistently unattractive. These preferences remained stable over time and were independent of the menstrual phase.

Conclusion

GC×GC-MS analysis revealed detectable sex differences in skin volatiles and some menstrual cycle-related differences, though with significant overlap. Mosquito assays did not show any preference based on sex or menstrual phase but demonstrated stable individual attractiveness patterns. Additional data are still being processed and verified.

This text has been automatically transcribed from a video presentation using AI technology. It may contain inaccuracies and is not guaranteed to be 100% correct.