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Research summary ·
AI summary · not yet reviewedSubjects not reportedNot reportedPeer review unconfirmed

Winter Births Linked to Schizophrenia Risk and Spring Births to Multiple Sclerosis in Animal Studies

A review of 614 unique birth month/season associations found winter births associated with a slight increase in schizophrenia risk (OR = 1.05) and spring births related to multiple sclerosis. Most connections to autoimmunity and cancer were weak.

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This plain-English summary was written by AI from a published abstract and may contain errors. It is not medical advice. Read the original study and talk to your MS team before making decisions about treatment.

Why it matters

These findings suggest temporal patterns in birth months could be relevant for the study of certain neurological conditions, possibly guiding future research into environmental or seasonal factors influencing disease.

What this does not prove

No associations met strong criteria, indicating overall weak evidence for most findings, particularly in other health conditions like cancer and autoimmune diseases.

Next milestone

No next milestone was established from the available source.

Study facts
Study design
Not reported
Participants / samples
614 · basis not reported
Randomised
Not reported
Controlled
Not reported
Primary endpoint met
Not reported
Relevant MS type
Not reported
Publication date
2026-10-09
Evidence reviewed
Abstract only
Regulatory approval
Not reported
Research areas
Not classified

Original sources

Supporting passages (4)
publication datePublication date: 2026-10-09
sample size614 unique associations (defined as individual pairwise analyses between a specific birth month/season exposure and a distinct health outcome).
findingsThe most consistent findings involved neurodevelopmental and psychiatric outcomes, with winter births linked to a modest increase in schizophrenia risk (odds ratio (OR) = 1.05) and spring births to multiple sclerosis.
limitationsMost autoimmune, oncological, and perinatal associations were graded as weak (class III-IV) due to small effect sizes or heterogeneity.

AI assessment, not yet reviewed by a person · version 1 · Community votes are separate from evidence review.

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