The Evolutionary Biology of Female‐Biased Pain and Its Disorders

ABSTRACT This article describes and evaluates a general evolutionary theory for explaining variation among individuals in sensitivity to pain, the Vulnerability‐Value‐Opportunity model. The model posits that pain sensitivity is mediated by vulnerability to fitness‐reducing injury (probabilities of damage, including from infection), Fisherian reproductive value (fitness costs of injury), and trade‐offs of pain with other components of fitness (opportunity costs). Predictions of the model are evaluated through analyses of the life‐historical and hormonal correlates of pain, with special reference to females and female‐biased pain conditions in humans. Liability to pain and pain conditions are associated with (1) early age of menarche, (2) low testosterone, and (3) high leptin. Pain sensitivity in women thus appears to involve hormonally mediated trade‐offs between reproduction and survivorship (defense). Mechanistically, liability to pain in women is mediated by trade‐offs involving inflammation, an integral, adaptive component of reproduction, immune defenses and pain system regulation. Female‐biased pain is also increased by evolutionary mismatches, including earlier age of menarche and more frequent menstruation. This work has specific implications for the reduction and management of pain and pain conditions in women, and it outlines a program of evolutionary‐medical research designed to move this field forward.

Authors

Institutions

Publication Details

Journal
Evolutionary Applications
Published
2026-03-28
DOI
https://doi.org/10.1111/eva.70228
Primary Topic
Evolutionary Psychology and Human Behavior
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Evolutionary Biology of Female‐Biased Pain and Its Disorders

Bernard Crespi
Evolutionary Applications
Evolutionary Psychology and Human Behavior
article

The Evolutionary Biology of Female‐Biased Pain and Its Disorders

Bernard Crespi
article en

Abstract

ABSTRACT This article describes and evaluates a general evolutionary theory for explaining variation among individuals in sensitivity to pain, the Vulnerability‐Value‐Opportunity model. The model posits that pain sensitivity is mediated by vulnerability to fitness‐reducing injury (probabilities of damage, including from infection), Fisherian reproductive value (fitness costs of injury), and trade‐offs of pain with other components of fitness (opportunity costs). Predictions of the model are evaluated through analyses of the life‐historical and hormonal correlates of pain, with special reference to females and female‐biased pain conditions in humans. Liability to pain and pain conditions are associated with (1) early age of menarche, (2) low testosterone, and (3) high leptin. Pain sensitivity in women thus appears to involve hormonally mediated trade‐offs between reproduction and survivorship (defense). Mechanistically, liability to pain in women is mediated by trade‐offs involving inflammation, an integral, adaptive component of reproduction, immune defenses and pain system regulation. Female‐biased pain is also increased by evolutionary mismatches, including earlier age of menarche and more frequent menstruation. This work has specific implications for the reduction and management of pain and pain conditions in women, and it outlines a program of evolutionary‐medical research designed to move this field forward.

Evolutionary ApplicationsVol. 19(4)
Simon Fraser University (CA)
Gender equality
Openalex Percentile: Top 7%
Evolutionary Psychology and Human Behavior
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.