Associations between reproductive history, hormone use, APOE ε4 genotype and cognition in middle- to older-aged women from the UK Biobank

Abstract

Introduction: Relative to men, women are at a higher risk of developing age-related neurocognitive disorders including Alzheimer's disease. While women's health has historically been understudied, emerging evidence suggests that reproductive life events such as pregnancy and hormone use may influence women's cognition later in life.

Methods: We investigated the associations between reproductive history, exogenous hormone use, apolipoprotein (APOE) ε4 genotype and cognition in 221,124 middle- to older-aged (mean age 56.2 ± 8.0 years) women from the UK Biobank. Performance on six cognitive tasks was assessed, covering four cognitive domains: episodic visual memory, numeric working memory, processing speed, and executive function.

Results: A longer reproductive span, older age at menopause, older age at first and last birth, and use of hormonal contraceptives were positively associated with cognitive performance later in life. Number of live births, hysterectomy without oophorectomy and use of hormone therapy showed mixed findings, with task-specific positive and negative associations. Effect sizes were generally small (Cohen's d < 0.1). While APOE ε4 genotype was associated with reduced processing speed and executive functioning, in a dose-dependent manner, it did not influence the observed associations between female-specific factors and cognition.

Discussion: Our findings support previous evidence of associations between a broad range of female-specific factors and cognition. The positive association between a history of hormonal contraceptive use and cognition later in life showed the largest effect sizes (max. d = 0.1). More research targeting the long-term effects of female-specific factors on cognition and age-related neurocognitive disorders including Alzheimer's disease is crucial for a better understanding of women's brain health and to support women's health care.

Keywords: big data; cognition; hormonal contraceptives; hormone therapy; population-based; pregnancy; women’s health.

Figure 1

Correlations (Pearson’s r) between demographics, female-specific factors and cognitive test scores. Correlation between each pair of variables is computed using all complete pairs of observations on those variables. Empty fields indicate no complete pairs for that pair of variables. BMI, body mass index; HT, hormone therapy; HC, hormonal contraceptive; N, number; First Birth, age at first live birth (years); Last Birth, age at last live birth (years); First HC, age at first HC use (years); Last HC, age at last HC use (years); First HT, age at first HT use (years); Last HT, age at last HT use (years).

Figure 2

Significant associations between female-specific factors and cognitive performance. Point plot of beta-values with standard error from separate multiple regression analysis with cognitive task as dependent variable and female-specific variables as independent variable. All models are adjusted for age, education, body mass index, Townsend deprivation score, and lifestyle score. In addition, the analyses for reproductive span and age at menopause were corrected for use of HT, use of HC, history of hysterectomy and bilateral oophorectomy, and number of live births. The HT models were additionally adjusted for history of hysterectomy and bilateral oophorectomy, and the hysterectomy/oophorectomy model was co-varied for use of HT. All variables were standardized prior to performing the multiple linear regression analysis (subtracting the mean and dividing by the standard deviation). For visualization purposes, cognitive tests marked with * are inverted (multiplied by −1) so that positive beta-values always indicate associations between higher values on the female-specific variables and better performance on cognitive tests.

Figure 3

Significant non-linear association between number of childbirths and cognitive performance. Point plot of beta values with standard error from multiple regression analyses with cognitive task as dependent variable and number of childbirths as categorical independent variable. All models are adjusted for age, education, body mass index, Townsend deprivation score, and lifestyle score. All variables were standardized prior to performing the multiple linear regression analysis (subtracting the mean and dividing by the standard deviation). For visualization purposes, cognitive tests marked with * are inverted (multiplied by −1) so that positive beta-values always indicate associations between higher values on the female-specific variables and better performance on cognitive tests. Sample size for Pair Matching (0 = 34,881, 1 = 23,387, 2 = 77,687, 3 = 30,478, 4 = 7,596, 5 = 1,569, 6 = 446, 7+ = 234). Sample size for Reaction Time (0 = 34,699, 1 = 23,232, 2 = 77,308, 3 = 30,262, 4 = 7,520, 5 = 1,545, 6 = 430, 7+ = 226).

Figure 4

Significant associations between APOE ε4 genotype and cognitive performance. Point plot of beta-values with standard error from multiple regression analyses with cognitive task as dependent variable and APOE ε4 genotype as categorical independent variable. All models are adjusted for age, education, body mass index, Townsend deprivation score, and lifestyle score. All variables were standardized prior to performing the multiple linear regression analysis (subtracting the mean and dividing by the standard deviation). For visualization purposes, cognitive tests marked with * are inverted (multiplied by −1). Sample size for Symbol Substitution (non-carrier = 34,266, carrier one e4 allele = 11,023, carrier two e4 allele = 1,028) and Trail Making B (non-carrier = 29,865, carrier one e4 allele = 9,559, carrier two e4 allele = 893).