Relationship between sex hormones and anterior knee laxity across the menstrual cycle

Abstract

Purpose: To comprehensively quantify through daily, serial measures changes in knee laxity as a function of changing sex-hormone levels across one complete menstrual cycle.

Methods: Twenty-five females, 18 - 30 yr, body mass index < or = 30, who reported normal menstrual cycles (28-32 d) over the past 6 months participated. Participants were tested daily across one complete menstrual cycle; 5-7 cc of venous blood were withdrawn to assay serum levels of estradiol, progesterone, and testosterone. Knee laxity was measured as the amount of anterior tibial displacement at 133 N, using a standard knee arthrometer. To evaluate the relationship of knee laxity to changes in sex hormone concentrations, a multiple linear regression model with the possibility of a time delay was performed on each individual subject and the group as a whole.

Results: Individual regression equations revealed an average of 63% of the variance in knee laxity was explained by the three hormones and their interactions. All three hormones significantly contributed to the prediction equation, and the amount of variance explained was substantially greater when a time delay was considered. On average, knee laxity changed approximately 3, 4, and 4.5 d after changes in estradiol, progesterone, and testosterone, respectively. When females were analyzed as a group, only 8% of the variance in knee laxity was explained by sex-hormones levels.

Conclusion: Changes in sex hormones mediate changes in knee laxity across the menstrual cycle. However, the strength of this relationship, the relative contribution of each hormone, and the associated time delay are highly variable between women. This individual variability is consistent with the variability in menstrual cycle characteristics among women.

FIGURE 1

Graphic representation of subject 26, showing a characteristic time delay between change in hormone levels and change in knee joint laxity.

FIGURE 2

Graphic representation comparing fit of prediction equation to actual raw data collected for subject 15. Figure A compares raw data to prediction equation with constant. Figure B compares raw data to prediction equation with constant = 0.