The role of sex and gender differences in precision medicine: the work of the Women’s Brain Project

Dimech, Annemarie Schumacher; Ferretti, Maria Teresa; Sandset, Else Charlotte; Chadha, Antonella Santuccione

doi:10.1093/eurheartj/ehab297

Cited by 8 publications

(8 citation statements)

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“…13 From the evidence mentioned above, plenty of research shows that gender differences are one of the significant factors that affect various diseases. In the healthcare service system, a study of gender differences may inform a viewpoint on improving precision medicine, 14,15 encouraging interventions, and broadening the relevance of scientific research. Consequently, we need some evidence to prove that there are gender differences since they relate to mental issues separately.…”

Section: Introductionmentioning

confidence: 99%

Trends and Gender Differences in Mental Disorders in Hospitalized Patients in Thailand

Suanrueang

Peltzer

Suen

et al. 2022

INQUIRY

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Previous studies have shown that gender differences play a substantial role in the influence of mental disorders. This study was designed to investigate gender differences in mental disorders by presenting odd ratio (OR) trends and cumulative effects over a period of 13 years in Thailand. This observational study used hospital data from the Ministry of Public Health on selected patients admitted to inpatient departments in public hospitals with mental disorders, according to ICD-10 cause groups from 2007 to 2019, counting from more than 1,90,000 cases in 2007 to more than 4,00,000 cases in 2019. Data were collected from the Thailand Ministry of Public Health website. The results indicate that compared to women, men were positively related to five mental disorders revealed by the OR and the ratio per 100,000 population (mean and SD): psychoactive alcohol use (OR = 7.31-9.07, 271.19 (59.26)), substance abuse (OR = 5.06-7.82, 59.25 (33.71)), schizophrenia (OR = 1.64-1.93, 108.32 (19.62)), mental retardation (OR = 1.15-1.58, 10.64 (1.88)), and other mental and behavioral disorders (OR = 1.10-1.55, 70.67 (22.75)). Three mental disorders in men were found to be negatively related: neurotic and related disorders (OR=.34-.46, 27.98 (3.26)), mood (affective) disorders (OR = .44-.56, 31.91 (9.59)), and dementia (OR = .78-10.82, 13.75 (2.73)). Gender can become a key biological element that contributes to the dissimilarity of mental illness. Preventive care for men and women should, therefore, be prioritized for health conditions separately. More specifically, screening and detection, and providing appropriate intervention.

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Section: Introductionmentioning

confidence: 99%

Trends and Gender Differences in Mental Disorders in Hospitalized Patients in Thailand

Suanrueang

Peltzer

Suen

et al. 2022

INQUIRY

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“…Historically, women have been underrepresented in clinical trials leading to medical solutions focusing on men at the risk to women’s health [42]. Many diseases differ between female and male patients with regards to the prevalence, progression or response to treatment [43]. For example, minor stroke is more often missed in female than male [44] patients, possibly due to definitions in clinical diagnosis reflecting typical manifestations in males [43].…”

Section: Discussionmentioning

confidence: 99%

“…Many diseases differ between female and male patients with regards to the prevalence, progression or response to treatment [43]. For example, minor stroke is more often missed in female than male [44] patients, possibly due to definitions in clinical diagnosis reflecting typical manifestations in males [43]. More recently, a sex bias has been recognized in modern ML solutions that are often developed and trained on male data and thus result in better performance in men [45].…”

Section: Discussionmentioning

confidence: 99%

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Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study)

Grossmann,

Risch,

Markovic

et al. 2023

Preprint

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Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD]=5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches. This work has received support from the Princely House of the Principality of Liechtenstein, the government of the Principality of Liechtenstein, the Hanela Foundation in Switzerland, and the Innovative Medicines Initiative (IMI) 2 Joint Undertaking under grant agreement No 101005177. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.

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“…Sex differences in brain and behaviour have long been of central interest in psychology and neurosciences and are still being hotly discussed amid several controversies. Recent availability of big brain imaging data sets combined with machine-learning (ML) methods has opened new ways to address this question together with the promise for individual level predictions that can help in precision diagnosis and care as sex differences in the brain are closely related to neuropsychiatric health risks [1][2][3][4]. Recently, ML-based sex prediction models have also been employed to investigate the brain representation of sex and gender interaction [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Accurate sex prediction of cisgender and transgender individuals without brain size bias

Lisa

Hamdan

Hoffstaedter

et al. 2022

Preprint

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Brain size differs substantially between human males and females. This difference in total intracranial volume (TIV) can cause bias when employing machine-learning approaches for the investigation of sex differences in brain morphology. TIV-biased models will likely not capture actual qualitative sex differences in brain organization but rather learn to classify an individual's sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. Here, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for brain size either through featurewise confound removal or by matching training samples for TIV. Our results provide evidence that non-TIV-biased models can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modelling to avoid bias in automated decision making.

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The role of sex and gender differences in precision medicine: the work of the Women’s Brain Project

Cited by 8 publications

References 0 publications

Trends and Gender Differences in Mental Disorders in Hospitalized Patients in Thailand

Trends and Gender Differences in Mental Disorders in Hospitalized Patients in Thailand

Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study)

Accurate sex prediction of cisgender and transgender individuals without brain size bias

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