Folkmann Hansen, T. | bioRxiv Science

bioRxiv2026

Menstrual cycle phase length variation is associated with daily symptom burden

Menstrual symptoms vary across the cycle, yet most research assumes a normative 28-day cycle with fixed phase durations, obscuring the physiological relevance of natural cycle variation. Using the mcPHASES dataset, we characterised cycle and phase length variation across 96 menstrual cycles from 37 participants, with ovulation timing estimated from daily urinary luteinizing hormone measurements using a Bayesian hierarchical model, and examined associations with daily symptoms in a subset of 64 cycles from 35 participants with complete symptom data. Twelve physical, mental, and behavioural symptom domains were modelled using Bayesian ordinal regression, with posterior uncertainty in phase-length predictors propagated via a measurement error framework. Total cycle length was not associated with daily symptom burden, except sleep disturbances. By contrast, phase length decomposition revealed systematic associations across multiple domains: longer menstrual phase length was broadly associated with greater symptom intensity spanning physical, gastrointestinal, affective, and sleep domains; longer luteal phase duration was associated with greater fatigue and more frequent headaches, but lower sore breast intensity and lower stress; and longer follicular phase duration and later ovulation were each associated with greater sore breast intensity and more frequent mood swings. These associations require knowledge of actual ovulation timing and cannot be recovered from cycle length alone, indicating that the common assumption of a fixed 14-day luteal phase introduces systematic misclassification of hormonal exposure. Daily symptom intensity was also predominantly person-specific, with cycle phase explaining little of the between-person variance across most symptoms. These findings indicate that calendar-based phase assignment is insufficient for research and clinical assessment of hormone-sensitive conditions, and that person-specific baselines, rather than population-level phase averages, are needed for clinically meaningful symptom monitoring.

physiology↗

bioRxiv2019

Genetic Identification of Cell Types Underlying Brain Complex Traits Yields Novel Insights Into the Etiology of Parkinson's Disease

Genome-wide association studies (GWAS) have discovered hundreds of loci associated with complex brain disorders, and provide the best current insights into the etiology of these idiopathic traits. However, it remains unclear in which cell types these variants may be active, which is essential for understanding disease etiology and for disease modelling. Here we integrate GWAS results with single-cell transcriptomic data from the entire nervous system to systematically identify cell types underlying psychiatric disorders, neurological conditions, and other brain complex traits. We show that psychiatric disorders are predominantly associated with excitatory neurons from the cortex/hippocampus, medium spiny neurons from the striatum, diverse sets of midbrain neurons, and inhibitory neurons from the cortex/hippocampus. Cognitive traits were generally associated with similar cell types but their associations were driven by different genes. Neurological disorders were associated with different cell types, consistent with other lines of evidence. Notably, we found that Parkinsons disease is not only genetically associated with dopaminergic neurons but also with serotonergic neurons and cells from the oligodendrocyte lineage. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Altogether, our study provides a solid framework for understanding the cellular basis of complex brain disorders and reveals a new unexpected role of oligodendrocytes in Parkinsons disease.

genomics↗