Why “sex as a biological variable” conflicts with precision medicine initiatives

DiMarco, Marina; Zhao, Helen; Boulicault, Marion; Richardson, Sarah S.

doi:10.1016/j.xcrm.2022.100550

Cited by 17 publications

(16 citation statements)

References 13 publications

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“…We chose to analyze traits with SNP heritabilities over 7.5% in the combined sample, to have higher statistical power. While there is not a strict one-to-one relationship between sex chromosome karyotype and biological sex, we label XX individuals as females and XY individuals as males, and view these labels as capturing group differences in distributions of contexts for autosomal effects, rather than as a dichotomy 14,17,32 . Throughout, we analyze GWAS on the raw measurement units as provided by UKB.…”

Section: Resultsmentioning

confidence: 99%

Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits

Zhu

Ming

Cole

et al. 2022

Preprint

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Sexual dimorphism in complex traits is suspected to be in part due to widespread gene-by-sex interactions (GxSex), but empirical evidence has been elusive. Here, we infer the mixture of ways polygenic effects on physiological traits covary between males and females. We find that GxSex is pervasive but acts primarily through systematic sex differences in the magnitude of many genetic effects (″amplification″), rather than in the identity of causal variants. Amplification patterns account for sex differences in trait variance. In some cases, testosterone may mediate amplification. Finally, we develop a population-genetic test linking GxSex to contemporary natural selection and find evidence for sexually antagonistic selection on variants affecting testosterone levels. Taken together, our results suggest that the amplification of polygenic effects is a common mode of GxSex that may contribute to sex differences and fuel their evolution.

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Section: Resultsmentioning

confidence: 99%

Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits

Zhu

Ming

Cole

et al. 2022

Preprint

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“…Some researchers still focus on males; others use mixed male and female groups that are typically underpowered to detect sex differences; a smaller percentage of researchers actually run studies that are designed to test the effect of sex [29,30]. This varied response and its outcomes also raised concerns that sex differences are now misreported and misinterpreted [32], and that this practice can hurt the precision medicine initiative in the long-run [33].…”

Section: Discussionmentioning

confidence: 99%

“…Multiple researchers have indicated that sex is a complex, multi-layered variable that should be used as a proxy rather than a variable that explains sex-based variation [35,44]. We were warned that male and female populations are largely overlapping in both animal [35] and clinical [45] research and that over-interpreting sex differences can bring us further from truth both in experimental research and in medicine [33,35], and it hurts transgender and gender-nonconforming individuals [31]. With our population graphs, we show that incorporating the estrous cycle stage, an exemplary sex-specific variable, increases our resolution so we can separate different populations and better interpret our data.…”

Section: The Estrous Cycle Tracking Is a Stressor And May Represent A...mentioning

confidence: 99%

“…Fast-forward to the past year or so, another acute problem in sex difference research has been called out. Voices have been raised that sex is a non-binary, multilayered, and context-dependent biological variable [31]; that sex differences may have been misreported due to improper design and data analyses [32]; and that, as such, SABV may hurt rather than help precision medicine initiatives [33]. In fact, rather than thinking about sex as a single variable, we may want to look at sex as a composite variable whose components, such as hormonal status or sex chromosome complement, can explain the sex-based variation better than sex, while at the same time allowing for more gender-inclusive research practices.…”

Section: Introductionmentioning

confidence: 99%

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Why the estrous cycle matters for neuroscience

2022

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Background Ovarian hormone fluctuations over the rodent estrous cycle and the human menstrual cycle are known to significantly impact brain physiology and disease risk, yet this variable is largely ignored in preclinical neuroscience research, clinical studies, and psychiatric practice. Methods To assess the importance of the estrous cycle information for the analysis of sex differences in neuroscience research, we re-analyzed our previously published data with or without the estrous cycle information, giving a side-by-side comparison of the analyses of behavior, brain structure, gene expression, and 3D genome organization in female and male mice. We also examined and compared the variance of female and male groups across all neurobehavioral measures. Results We show that accounting for the estrous cycle significantly increases the resolution of the neuroscience studies and allows for: (a) identification of masked sex differences; (b) mechanistic insight(s) into the identified sex differences, across different neurobehavioral outcomes, from behavior to molecular phenotypes. We confirm previous findings that female data from either mixed- or staged-female groups are, on average, not more variable than that of males. However, we show that female variability is not, at all, predictive of whether the estrous cycle plays an important role in regulating the outcome of interest. Conclusions We argue that “bringing back” the estrous cycle variable to the main stage is important in order to enhance the resolution and quality of the data, to advance the health of women and other menstruators, and to make research more gender-inclusive. We strongly encourage the neuroscience community to incorporate the estrous cycle information in their study design and data analysis, whenever possible, and we debunk some myths that tend to de-emphasize the importance and discourage the inclusion of this critically important biological variable. Highlights Ovarian hormone fluctuation impacts brain physiology and is a major psychiatric risk factor, yet this variable has been overlooked in neuroscience research and psychiatric practice. From rodent behavior to gene regulation, accounting for the estrous cycle increases the resolution of the neuroscience data, allowing identification and mechanistic insight(s) into sex differences. Female variability does not equal (and is not predictive of) the estrous cycle effect and should not be used as a proxy for the effects of ovarian hormones on the outcome of interest. Neuroscience researchers are advised to incorporate the estrous cycle information in their studies to foster more equitable, female- and gender-inclusive research. Studies of the ovarian cycle are especially important for improving women’s mental health.

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“…The NIH established an expectation for NIH-supported research to include investigations of sex as a biological variable in 2016 (Clayton & Collins, 2014; Legato et al, 2016; Rich-Edwards et al, 2018; Woitowich et al, 2020). While there has been some debate about the optimal approach to implement this requirement, there appears to be agreement that sex-related variables may emerge as relevant within the context of a specific research program (DiMarco et al, 2022). Although the majority of individuals may be assigned to one of two categories for some traits (e.g., karyotype sex), there are many biological traits (e.g., height) that represent continuous variables with considerable overlap between sexes (DiMarco et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Pharmacogenetics of SGLT2 Inhibitors: Validation of a sex-agnostic pharmacodynamic biomarker

Taylor

Cherng

Yazdi

et al. 2023

Preprint

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Background: SGLT2 inhibitors provide multiple benefits to patients with type 2 diabetes, including improved glycemic control and decreased risks of cardiorenal disease. Because drug responses vary among individuals, we initiated investigations to identify genetic variants associated with the magnitude of drug responses. Methods: Canagliflozin (300 mg) was administered to 30 healthy volunteers. Several endpoints were measured to assess clinically relevant responses, including drug-induced increases in urinary excretion of glucose, sodium, and uric acid. Results: This pilot study confirmed that canagliflozin (300 mg) triggered acute changes in mean levels of several biomarkers: fasting plasma glucose (-4.1 mg/dL; p=6E-05), serum creatinine (+0.05 mg/dL; p=8E-04), and serum uric acid (-0.90 mg/dL; p=5E-10). The effects of sex on glucosuria depended upon how data were normalized. Whereas responses of males were ~60% greater when data were normalized to body surface area, males and females exhibited similar responses when glucosuria was expressed as grams of urinary glucose per gram-creatinine. The magnitude of glucosuria was not significantly correlated with fasting plasma glucose, estimated GFR, or age in these healthy non-diabetic individuals with estimated GFR>60 mL/min/1.73 sq.m. Conclusions: Normalizing data relative to creatinine excretion will facilitate including data from males and females in a single analysis. Furthermore, because our ongoing pharmacogenomic study (NCT02891954) is conducted in healthy individuals, this will facilitate detection of genetic associations with limited confounding by other factors such as age and renal function. Registration:NCT02462421(clinicaltrials.gov) Funding: Research grants from the National Institute of Diabetes and Digestive and Kidney Diseases: R21DK105401, R01DK108942, T32DK098107, and P30DK072488.

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Why “sex as a biological variable” conflicts with precision medicine initiatives

Cited by 17 publications

References 13 publications

Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits

Amplification is the Primary Mode of Gene-by-Sex Interaction in Complex Human Traits

Why the estrous cycle matters for neuroscience

Pharmacogenetics of SGLT2 Inhibitors: Validation of a sex-agnostic pharmacodynamic biomarker

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