Why "sex as a biological variable" conflicts with precision medicine initiatives

From means to meaning in the study of sex/gender differences and similarities

The incorporation of sex and gender (S/G) related factors is commonly acknowledged as a necessary step to advance towards more personalized diagnoses and treatments for somatic, psychiatric, and neurological diseases. Until now, most attempts to integrate S/G-related factors have been reduced to identifying average differences between females and males in behavioral/ biological variables. The present commentary questions this traditional approach by highlighting three main sets of limitations: 1) Issues stemming from the use of classic parametric methods to compare means; 2) challenges related to the ability of means to accurately represent the data within groups and differences between groups; 3) mean comparisons impose a results' binarization and a binary theoretical framework that precludes advancing towards precision medicine. Alternative methods free of these limitations are also discussed. We hope these arguments will contribute to reflecting on how research on S/G factors is conducted and could be improved.

Mechanisms and consequences of sex differences in immune responses

Biological sex differences refer to differences between males and females caused by the sex chromosome complement (that is, XY or XX), reproductive tissues (that is, the presence of testes or ovaries), and concentrations of sex steroids (that is, testosterone or oestrogens and progesterone). Although these sex differences are binary for most human individuals and mice, transgender individuals receiving hormone therapy, individuals with genetic syndromes (for example, Klinefelter and Turner syndromes) and people with disorders of sexual development reflect the diversity in sex-based biology. The broad distribution of sex steroid hormone receptors across diverse cell types and the differential expression of X-linked and autosomal genes means that sex is a biological variable that can affect the function of all physiological systems, including the immune system. Sex differences in immune cell function and immune responses to foreign and self antigens affect the development and outcome of diverse diseases and immune responses.

Sex diversity in the 21st century: Concepts, frameworks, and approaches for the future of neuroendocrinology

Sex is ubiquitous and variable throughout the animal kingdom. Historically, scientists have used reductionist methodologies that rely on a priori sex categorizations, in which two discrete sexes are inextricably linked with gamete type. However, this binarized operationalization does not adequately reflect the diversity of sex observed in nature. This is due, in part, to the fact that sex exists across many levels of biological analysis, including genetic, molecular, cellular, morphological, behavioral, and population levels. Furthermore, the biological mechanisms governing sex are embedded in complex networks that dynamically interact with other systems. To produce the most accurate and scientifically rigorous work examining sex in neuroendocrinology and to capture the full range of sex variability and diversity present in animal systems, we must critically assess the frameworks, experimental designs, and analytical methods used in our research. In this perspective piece, we first propose a new conceptual framework to guide the integrative study of sex. Then, we provide practical guidance on research approaches for studying sex-associated variables, including factors to consider in study design, selection of model organisms, experimental methodologies, and statistical analyses. We invite fellow scientists to conscientiously apply these modernized approaches to advance our biological understanding of sex and to encourage academically and socially responsible outcomes of our work. By expanding our conceptual frameworks and methodological approaches to the study of sex, we will gain insight into the unique ways that sex exists across levels of biological organization to produce the vast array of variability and diversity observed in nature.

Pharmacogenetics of sodium-glucose co-transporter-2 inhibitors: Validation of a sex-agnostic pharmacodynamic biomarker

AIM

To validate pharmacodynamic responses to sodium-glucose co-transporter-2 (SGLT2) inhibitors and test for association with genetic variants in SLC5A4, SLC5A9, and SLC2A9.

METHODS

Canagliflozin (300 mg), a SGLT2 inhibitor, 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 = 6 × 10-5 ), serum creatinine (+0.05 mg/dL; P = 8 × 10-4 ) and serum uric acid (-0.90 mg/dL; P = 5 × 10-10 ). The effects of sex on glucosuria depended upon how data were normalized. Whereas males' responses 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 glomerular filtration rate or age in those healthy individuals without diabetes with an estimated glomerular filtration rate of more than 60 mL/min/1.73m2 .

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 HbA1c and renal function.

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

Aim

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=6x10), serum creatinine (+0.05 mg/dL; p=8×10 -4 ), and serum uric acid (-0.90 mg/dL; p=5×10 -10 ). The effects of sex on glucosuria depended upon how data were normalized. Whereas males' responses 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/1.73m 2 .

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.

Quality control and analytic best practices for testing genetic models of sex differences in large populations

Phenotypic sex-based differences exist for many complex traits. In other cases, phenotypes may be similar, but underlying biology may vary. Thus, sex-aware genetic analyses are becoming increasingly important for understanding the mechanisms driving these differences. To this end, we provide a guide outlining the current best practices for testing various models of sex-dependent genetic effects in complex traits and disease conditions, noting that this is an evolving field. Insights from sex-aware analyses will not only teach us about the biology of complex traits but also aid in achieving the goals of precision medicine and health equity for all.

Amplification is the primary mode of gene-by-sex interaction in complex human traits

Sex differences in complex traits are 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 in which 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 of sexually antagonistic selection on variants affecting testosterone levels. Our results suggest that amplification of polygenic effects is a common mode of GxSex that may contribute to sex differences and fuel their evolution.

Why the estrous cycle matters for neuroscience

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.