Giving science the finger-is the second-to-fourth digit ratio (2D:4D) a biomarker of good luck? A cross sectional study.

Abstract

To explore whether random chance, weak research methodology, or inappropriate reporting can lead to claims of statistically significant (yet, biologically meaningless) biomarker associations, using as a model the relation between a common surrogate of prenatal testosterone exposure, second-to-fourth digit ratio (2D:4D), and a random indicator of good luck. Cross sectional study. University sports performance laboratory in the United States. Data were collected from May 2015 to February 2017. 176 adults (74 women, 102 men), including university students, faculty, and staff with no history of injuries, disease, or medical conditions that would affect digit length. 2D:4D, body composition parameters potentially influenced by androgens (bone mineral content, bone mineral density, body fat percentage), and good luck (using poker hands from randomly selected playing cards as a surrogate). 2D:4D significantly correlated with select body composition parameters (Spearman's <i>r</i> _s range -0.26 to 0.23; P<0.05), but the correlations varied by sex, participant hand measured, and the method of measuring 2D:4D (by photocopy or radiography). However, the strongest correlation observed was between right hand 2D:4D in men measured by radiograph and poker hand rank (<i>r</i> _s=0.28, P=0.004). Greater prenatal exposure to testosterone, as estimated by a lower 2D:4D, significantly increases good luck in adulthood, and also modulates body composition (albeit to a lesser degree). While these findings are consistent with a wealth of research reporting that 2D:4D is related to many seemingly disparate outcomes, they are not meant to provide confirmatory evidence that 2D:4D is a universal biomarker of nearly everything. Instead, the associations between 2D:4D and good luck are simply due to chance, and provide a "handy" example of the reproducibility crisis within medical and scientific research. Biologically sound hypotheses, pre-registration of trials, strong methodological and statistical analyses, transparent reporting of negative results, and unbiased interpretation of data are all necessary for biomarker studies and other areas of clinical research.