Menopausal hormone therapy and the female brain: Leveraging neuroimaging and prescription registry data from the UK Biobank cohort

Ovarian hormones, including oestrogens and progesterone, fluctuate throughout each menstrual cycle, during and after pregnancy, and in the years leading up to menopause when ovarian function begins to decline. During these transitional years, up to 80% of women will experience symptoms such as hot flashes and night sweats, which are believed to stem from the brain.

Menopausal hormone therapy (MHT) often contains low doses of estrogens with or without progesterone and is commonly prescribed to minimize menopausal symptoms. MHT is believed to protect the brain and reduce the risk of Alzheimer’s disease; however, the evidence supporting this claim is conflicting.

Furthermore, additional research is necessary to evaluate the risks and benefits of MHT on brain health. It remains unclear whether other factors, including a genetic predisposition to Alzheimer’s disease, the age at which MHT begins, the formulation, and the route of administration (e.g., pills or a transdermal patch), affect the impact of MHT on the brain. Addressing these questions is crucial to inform clinical decision-making regarding the prescription of MHT and to subsequently enhance women’s brain health during and beyond the menopause transition.

Barth et al. analyzed brain imaging data as well as data about lifestyle and demographic factors and histories of gynecological surgeries, of over 20,000 current, past, and never users of MHT from the UK Biobank cohort. As a proxy for brain health, they calculated the so-called ‘brain age gap’, which refers to the difference between a person’s actual chronological age and their estimated brain age calculated from brain imaging data using machine learning; e.g., if someone’s chronological age is 50 years and their predicted brain age is 45, the brain age gap is –5 years. A negative brain age gap indicates a 'younger-looking' brain, while a positive brain age gap suggests a brain that appears 'older'. Barth et al. further looked at the volume of the hippocampus, a brain region important for memory, learning and emotion regulation.

They found that women who were current MHT users had an ‘older looking brain’ than women who had never taken MHT. The volume of the hippocampus was also smaller. In past users, the age at which MHT was last taken also made a difference. Those who were older at the time of their last use after menopause had an “older looking” brain and lower hippocampal volumes. Similar results were found for women who took MHT for longer.

However, women on MHT who had undergone surgery to remove their womb and/or both ovaries had a ‘younger looking brain’ than women on MHT without similar surgical histories. Interestingly, factors such as a genetic risk for Alzheimer’s disease, differences in formulations, or methods of administration did not appear to affect brain health in this study.

The findings of Barth et al. suggest that MHT does not have a general neuroprotective effect, nor does it lead to severe adverse effects on brain health. Instead, the impact of MHT on the brain may be influenced by various factors, including age, duration of use, and past surgical history. However, since the study is cross-sectional, meaning it was conducted at a single point in time, direct causality cannot be established.

Future research examining the long-term impact of MHT on brain health is crucial for understanding individual risk profiles and benefits. Women around the world are faced with critical decisions regarding MHT use, yet the current lack of comprehensive research leaves them without the necessary evidence to make informed choices.

Ovarian hormones such as estrogens and progesterone fluctuate across the female lifespan with natural declines occurring at menopause, typically between the ages of 45 and 55. The cessation of ovarian function during the menopausal transition has been linked to an array of neural changes (Brinton et al., 2015), including a decline in brain glucose metabolism (Ding et al., 2013), reductions in gray matter (GM) and white matter (WM) volume (Fjell et al., 2009; Goto et al., 2011; Mosconi et al., 2021; Mosconi et al., 2017), and increased amyloid-beta deposition (Mosconi et al., 2018) as well as WM lesions (Wen et al., 2009). In combination with other risk factors, these neural changes might foster the emergence of neurodegenerative diseases such as late-onset Alzheimer’s disease (AD), which is more often diagnosed in females relative to similarly aged males, with greater cognitive decline and neuropathological burden (Laws et al., 2018; Laws et al., 2016).

MHT is commonly prescribed to minimize vasomotor symptoms occurring during the menopausal transition and is generally thought to be neuroprotective, with a propensity to reduce the risk for AD (Simpkins et al., 2009; Hogervorst et al., 2000; Zandi et al., 2002; Kim et al., 2021) and improve cognition later in life (Maki et al., 2011). However, study results are equivocal (Mills et al., 2023), reporting both positive (Schelbaum et al., 2021; Erickson et al., 2005; Ha et al., 2007) and negative outcomes (Kantarci et al., 2016; Lange et al., 2020; Pourhadi et al., 2023). A 2021 study reported that reproductive history events signaling more estrogens exposure, including MHT use, were associated with greater gray matter volume in middle-aged females (Schelbaum et al., 2021), in line with other neuroimaging studies suggesting a protective effect of MHT on GM, WM, and ventricle size (Erickson et al., 2005; Ha et al., 2007). Conversely, MHT use has also been associated with greater atrophy (Resnick et al., 2009) and higher rates of ventricular expansion (Kantarci et al., 2016) in menopausal females. Similarly, in our previous UK Biobank study of ~16,000 females (Lange et al., 2020), we found positive associations between MHT use and older GM brain age, albeit with small effect sizes.

Besides mixed results in observational studies, randomized controlled trials (RCTs) such as the Women’s Health Initiative Memory Study (WHIMS) suggest an increased risk of dementia and cognitive decline with MHT use. In detail, WHIMS found negative effects of prolonged oral administration of both conjugated equine estrogen (CEE) alone (Shumaker et al., 2004) or in combination with medroxyprogesterone acetate (MPA, synthetic progestin) (Rapp et al., 2003; Shumaker et al., 2003) among females aged 65 y or older. Similarly, the Heart and Estrogen/Progestin Replacement Study (HERS) showed an association between 4 y of CEE +MPA treatment and lower cognitive performance in older postmenopausal females (71±6 y) (Grady et al., 2002). In contrast, administering oral CEE or transdermal estradiol plus micronized progesterone in recently postmenopausal females did not alter cognition in the Kronos Early Estrogen Prevention Study (KEEPS) (Gleason et al., 2015). These mixed findings raise the question of whether a combination of timing, formulation, and route of administration might play a crucial role in the effectiveness of MHT.

According to the ‘critical window hypothesis,’ MHT might be neuroprotective if it is initiated close to menopause (Maki, 2013). For example, MHT initiation during perimenopause has been associated with improved memory and hippocampal function later in life (Maki et al., 2011). Although emerging evidence supports this hypothesis (MacLennan et al., 2006; Gibbs and Gabor, 2003), oral CEE use in combination with MPA has been found to increase the risk for memory decline regardless of timing (Shumaker et al., 2003; Maki, 2013; Maki et al., 2007). Similarly, systemic MHT (i.e. oral and transdermal use) has been associated with a 9–17% increased risk of AD in a Finish nationwide case-control study, independent of MHT formulation and timing (Savolainen-Peltonen et al., 2019). However, vaginal estradiol use did not show such risk, indicating differential effects of route of administration (Savolainen-Peltonen et al., 2019). Vaginal as well as transdermal MHT formulations are mainly composed of estradiol, and CEE is a blend of compounds, mostly estrone (Wharton et al., 2013). Both estrogens have different affinities to bind to estrogen receptors (ER): relative to estradiol, estrone is approximately 2/3 the affinity to ER-alpha, and about 1/3 to ER-beta (Kuiper et al., 1997), and estrone is present at higher levels post-menopause than estradiol. Furthermore, contrary to oral use, vaginal and transdermal MHT formulations bypass hepatic metabolism, resulting in a steady-state concentration of estradiol (estrone:estradiol ratio of 1:1) (Wharton et al., 2013). Hence, vaginal, or transdermal estradiol-based MHT formulations might be more effective than oral estrone-based types. For instance, Gleason and colleagues found that females exposed to oral CEE exhibited poorer memory performance than either MHT-naïve or estradiol-exposed individuals (Gleason et al., 2006). In addition to estrogens, progestins are commonly added in non-hysterectomized females, and, like estrogens-based MHT, progestins can be administered in different forms, e.g., norethisterone acetate (synthetic progestin), micronized progesterone (bioidentical), or MPA (synthetic progestin). These progestin forms have been linked to different side-effect profiles (de Lignières, 1999) and can antagonize the effects of estrogens in MHT (Nilsen and Brinton, 2002).

In addition to the impact of MHT timing, formulation, and route of administration, effects of MHT on the female brain might be modulated by apolipoprotein ε type 4 (APOE ε4) genotype. Carried by 14% of the world’s population, the APOE ε4 allele is a known dose-dependent risk factor for late-onset AD. Yaffe and colleagues found that among non-carriers, current MHT use lowered the risk of cognitive impairment by almost half compared to never-users, while there was no such effect among carriers (Yaffe et al., 2000). Results from the Nurses’ Health Study found that MHT use was associated with worse rates of decline in general cognition, especially among females with an APOE ε4 allele (Kim et al., 2019). Conversely, we found no significant interactions between APOE ε4 genotype and MHT use on cognition in a 2023 UK Biobank study (Lindseth et al., 2023). We did, however, observe that earlier MHT initiation was linked to younger GM brain age, albeit weakly, only in APOE ε4 carriers (Lange et al., 2020). Moreover, a 2024 UK Biobank study showed smaller brain volumes in MHT users compared to never-users, specifically among females with the APOE ε4/ε4 genotype (Ambikairajah et al., 2024). Yet, the potential of APOE ε4 to modulate effects of MHT dosage, administration, and formulation on the female brain is understudied.

In summary, emerging evidence suggests differential effects of MHT formulation, age at initiation, route of administration, and genotype on the female brain and cognition. However, studies aiming to disentangle the effects of different MHT regimes are largely missing (Kim and Brinton, 2021). In this observational study, we investigated associations between MHT variables, different MHT regimes, APOE ε4 status, and brain measures in middle to older-aged females from the UK Biobank cohort. MHT variables included user status (i.e. current users, past users, never users), age at initiation, dosage and duration, formulation, route of administration, and type (i.e. bioidentical vs synthetic) as well as active ingredient (e.g. estradiol hemihydrate). MHT regimes based on prescription data were extracted from primary care records (general practice). Brain measures included GM and WM brain age gap based on brain age prediction, hippocampal volumes, and total WM hyperintensity volume as a proxy of vascular disease. These measures were chosen as they have been linked to both chronological and endocrine aging as well as MHT use in our studies (Lange et al., 2020; Ambikairajah et al., 2024; Schindler et al., 2023; Subramaniapillai et al., 2022).

This study assessed detailed MHT data, APOE ε4 genotype, and brain characteristics in a large, population-based sample of females in the UK. The results showed significantly higher GM and WM BAG (older brain age relative to chronological age) as well as smaller left and right hippocampus volumes in current MHT users, but not in past users, compared to never-users. Among MHT users, we found no significant associations between age at MHT initiation, alone and in relation to age at menopause, and brain measures. However, older age at last use after age at menopause was associated with higher GM and WM BAG, higher WMH volume, and lower left and right hippocampal volumes. Similar associations were found for longer duration of MHT use. MHT users with a history of hysterectomy ± bilateral oophorectomy showed lower GM BAG relative to MHT users without such history. In the sub-sample with prescription data, we found no significant associations between detailed MHT variables, such as MHT formulation, route of administration, type, active ingredients, and dosage, and brain measures, after FDR correction. Lastly, although we found lower right hippocampus volumes in carriers of two APOE ε4 alleles relative to non-carriers, we found no interactions with MHT variables after FDR correction.

Current MHT users showed higher GM and WM BAG as well as lower left and right hippocampus volumes relative to never-users. The effects were robust but relatively modest in magnitude, with the largest effect size indicating a group difference of 0.77 y (~9 mo) for GM BAG (standardized β = 0.218, Supplementary file 4). However, we found no significant differences in brain measures in past MHT users relative to never-users. Current MHT users were significantly younger than past- and never-users, and around 67% were menopausal relative to over 80% in the past- and never-user groups. The unequal distribution of age and menopausal status across groups may have influenced the observed findings. For instance, a larger proportion of the current users might be in the perimenopausal phase, which is often associated with debilitating neurological and vasomotor symptoms (Brinton et al., 2015). MHT is commonly prescribed to minimize such symptoms. Although MHT initiation during perimenopause has been associated with improved memory and hippocampal function, as well as lower AD risk later in life (Maki et al., 2011), the need for MHT might in itself be an indicator of neurological changes (Kantarci and Manson, 2023) here potentially reflected in higher BAG and lower hippocampal volumes. After the transition to menopause, symptoms might subside and some perimenopausal brain changes might revert or stabilize in the postmenopausal phase (Mosconi et al., 2021). Although the UK Biobank lacks detailed information on menopausal symptoms and perimenopausal staging, our results might be capturing subtle disturbances during perimenopause that later stabilize. This could explain why the largely postmenopausal groups of past MHT users and never-users present with lower GM and WM BAG than the current user group. Considering the critical window hypothesis emphasizing perimenopause as a key phase for MHT action (Maki, 2013; Kim and Brinton, 2021), future longitudinal studies are crucial to clarify the interplay between neurological changes and MHT use across the menopause transition.

Besides age-related differences, the current user group also showed a significantly unhealthier lifestyle, and higher rates of bilateral oophorectomy compared to the never-user group and the past-user group, respectively. These findings are contrary to the healthy user bias hypothesis (i.e. equating MHT use with healthy user status, Gleason et al., 2012), and might indicate that the current user group could be exposed to neurological changes prior to MHT use. According to the healthy cell bias hypothesis of estrogen action (Brinton, 2008), MHT use might be detrimental for brain health when initiated after cells are exposed to neurological degeneration. Although MHT use might have exacerbated adverse brain changes in the unhealthier group of current users, higher BAG was also linked to longer duration of MHT use and older age at last use post-menopause. Although the effect sizes were modest (Supplementary file 4), these findings might reflect subtle yet unfavorable effects of MHT on brain health in our sample, particularly when used continuously after natural menopause with uterus and ovaries still intact (i.e. all females with hysterectomy ± bilateral oophorectomy were excluded from this analysis).

On the contrary, MHT users with a history of hysterectomy ± bilateral oophorectomy showed lower GM BAG relative to MHT users without such history, and we found larger hippocampal volumes in MHT users with a hysterectomy without a bilateral oophorectomy. Previous work associates these surgical interventions with accelerated cognitive decline (Rocca et al., 2007), increased risk of dementia (Phung et al., 2010), morphological changes in regions of the medial temporal lobe (Zeydan et al., 2019), and accumulation of Alzheimer’s disease pathology (Georgakis et al., 2019). However, some of these studies relied on comparing surgery to no surgery (Phung et al., 2010; Rocca et al., 2007) without taking MHT use into account (Georgakis et al., 2016), or investigated the effect of surgery with MHT relative to surgery without MHT. For instance, a 2023 study highlighted that estrogen-only use in females with hysterectomy was associated with increased dementia rates relative to females with hysterectomy who never used MHT (Pourhadi et al., 2024). In the current study, we specifically compared the effect of MHT use on brain measures among females with and without surgical history and found lower GM BAG in the surgical MHT user group. These findings might be explained by differences in indication of MHT use for females with and without surgery, MHT formulation, and age at surgery. Without surgery, MHT use (i.e. often combined MHT formulation) is prescribed in females with intact uterus and ovaries to minimize menopausal symptoms that are largely neurological in nature, such as vasomotor symptoms as well as mood, cognitive, and sleep disturbances (Brinton et al., 2015). After hysterectomy, estrogen-only MHT might be prescribed but is not strictly needed as ovaries are still intact. Whereas after bilateral oophorectomy (=surgical menopause), combined or estrogen- only MHT is indicated to compensate for the acute and chronic deficiency of hormones normally produced by the ovaries. These distinctions might entail important differences in risk and side-effect profiles of estrogen-only or combined MHT in females with and without endogenous sex hormone production. It is also possible that the timing between MHT use and surgery is more tightly controlled and, therefore, more beneficial for brain aging (Kim and Brinton, 2021). For instance, studies suggest that MHT may mitigate the potential long-term adverse effects of bilateral oophorectomy before natural menopause on bone mineral density as well as cardiovascular, cognitive, and mental health (Blümel et al., 2022; Kaunitz et al., 2021; Stuursma et al., 2022). In addition, a 2024 UK Biobank study found that ever used MHT was associated with decreased odds of Alzheimer’s disease in women with bilateral oophorectomy (Calvo et al., 2024).

However, our study also showed group differences in several demographic and MHT-related variables which might influence the results. For instance, the hysterectomy MHT user group was significantly older than the non-surgery and bilateral oophorectomy group, started MHT at a younger age than the non-surgery group, and had surgery at a younger age than the bilateral oophorectomy group. More research is needed to disentangle this complex interplay of MHT effects in females with and without intact uteri and ovaries.

No significant associations were observed between brain measures and MHT regimes based on prescription data. However, given the relatively small sample size and the large number of comparisons, it is possible that we were unable to detect subtle effects of factors such as MHT formulation, route of administration, type, active ingredients, and dosage. Before FDR correction, we found higher WM BAG in estrogens +progestin users, in CEE + MPA users, and in mixed active ingredients users relative to never-users. In addition, WMH volume was higher among oral MHT users and lower with longer duration of estrogens use in estrogens-only users relative to never-users. These uncorrected results are partly in line with previous findings. For instance, the 2003 Women’s Health Initiative Memory Study reported that prolonged oral use of both CEE alone (Shumaker et al., 2004), or combined with MPA (Shumaker et al., 2003), increased the risk of dementia and cognitive decline among females aged 65 y and older. Contrarily, prolonged use of estrogen-only MHT has been linked to reduced white matter loss in aging (n=10) (Ha et al., 2007). Similar to our findings, Ha and colleagues did not find an association between gray matter volumes and estrogen use (Ha et al., 2007). Although uncorrected results must be interpreted with caution, our findings might indicate an unfavorable effect of mixed active ingredient use, including CEE ± MPA and oral administration, on white matter brain aging. This effect might be due to the higher estrone concentrations associated with such oral estrone-based MHT types (Longcope et al., 1985; Slater et al., 2001; Van Erpecum et al., 1991). In addition, over 50% of combined MHT users had mixed MHT use which suggests that it might be beneficial to examine the reasons for switching medication. In sum, these findings highlight the need for personalized MHT regimes, and longitudinal RCTs are needed to establish how different MHT regimes, and their respective hormonal profiles are causally linked to brain aging.

In the current study, we found significantly smaller right hippocampus volumes in carriers of two APOE ε4 alleles relative to non-carriers, which is in line with previous work linking the APOE ε4 genotype to greater rates of hippocampal atrophy in non-demented and Alzheimer’s disease samples (Saeed et al., 2021; Gorbach et al., 2020). However, after FDR correction, we did not find any interactions between APOE ε4 carrier status and MHT variables in relation to the brain measures. This finding was unexpected, as previous work has highlighted the APOE ε4 genotype as a crucial determinant of MHT effects on the female brain (Barth et al., 2023). For instance, one study associated MHT use with improved memory performance and larger entorhinal and amygdala volumes in female ε4 carriers versus non-carriers (Saleh et al., 2023). A 2023 prospective longitudinal study showed that MHT was associated with smaller changes towards Alzheimer’s disease pathophysiology than non-therapy and that APOE ε4 carrier status was linked to an amplified treatment outcome (Depypere et al., 2023). However, contrary results have also been reported. For instance, Yaffe and colleagues found lower risk of cognitive decline with estrogen-only MHT use among female APOE ε4 non-carriers, but there was no such effect among carriers (Yaffe et al., 2000). To understand these discrepancies, more research on MHT by genotype interactions is needed.

The current work represents the most comprehensive study of detailed MHT data, APOE ε4 genotype, and several brain measures in a large population-based cohort to date. Overall, our findings do not unequivocally support general neuroprotective effects of MHT, nor do they indicate severe adverse effects of MHT use on the female brain. The results suggest subtle yet complex relationships between MHT’s and brain health, highlighting the necessity for a personalized approach to MHT use. Importantly, our analyses provide a broad view of population-based associations and are not designed to guide individual-level decisions regarding the benefits versus risks of MHT use. Furthermore, several study limitations should be acknowledged. The presented data does not enable causal inference, and observational studies are subject to different sources of heterogeneity such as switching between MHT regimes (e.g. due to side effects or availability) and variable MHT formulation and dose. In addition, utilizing prescription registry data comes with its own set of challenges. In the UK, a national system for collecting or sharing primary care data is currently missing, and the availability, completeness, and level of detail in the data might vary between systems, suppliers, and over time. The primary care data used in the current study was drawn from an interim release, including data on approximately 231,000 participants. Hence, our analyses conducted on these participants’ data and the observed results might not generalize to the entire cohort. Furthermore, although we extracted prescription MHT data, medications that were prescribed were not necessarily dispensed or used. Moving forward, research utilizing unified prescription registry data is needed to overcome some of these hurdles. In addition, previous studies highlight that UK Biobank participants are considered healthier than the general population based on several lifestyle and health-related factors (Golomb et al., 2012; Bradley and Nichols, 2022). This healthy volunteer bias increases with age, likely resulting in a disproportionate number of healthier older adults. Together with the imbalance in age distributions across groups, this might explain the less apparent brain aging in the older MHT user groups. We have previously highlighted that age is negatively associated with the number of APOE ε4 carriers in the UK Biobank (Lange et al., 2020), which is indicative of survivor bias. In addition to these inherent biases in aging cohorts, the ethnic background of the sample is homogeneous (>96% white), further reducing the generalizability of the results. Lastly, although the UK Biobank has a wealth of female-specific variables, the acquired data relies on self-reports, which might not be reliable and data recording does not always align with best practice standards. For example, menopausal status in the UK Biobank is recorded based on whether the menstrual period has generally stopped, not whether it has been absent for at least 12 mo, in line with the STRAW criteria (Harlow et al., 2012).

In conclusion, our findings suggest that associations between MHT use and female brain health might vary depending on duration of use and past surgical history. Although the effect sizes were generally modest, future longitudinal studies and RCTs, particularly focused on the perimenopausal transition window, are warranted to fully understand how MHT use influences female brain health. Importantly, considering risks and benefits, decisions regarding MHT use should be made within the clinical context unique to each individual.

This research has been conducted using the UKB under Application 27412. The data that support the findings of this study are available through the UK Biobank application procedure (https://www.ukbiobank.ac.uk/enable-your-research/register). Code to extract prescription MHT data from primary care records (https://github.com/MelisAnaturk/dementia_risk_score/tree/main/scripts; Patel and Anaturk, 2023) and to run brain age prediction (https://github.com/dmlc/xgboost; XGBoost, 2025) is available on Github.

1. 1. Alfaro-Almagro F
   2. Jenkinson M
   3. Bangerter NK
   4. Andersson JLR
   5. Griffanti L
   6. Douaud G
   7. Sotiropoulos SN
   8. Jbabdi S
   9. Hernandez-Fernandez M
   10. Vallee E
   11. Vidaurre D
   12. Webster M
   13. McCarthy P
   14. Rorden C
   15. Daducci A
   16. Alexander DC
   17. Zhang H
   18. Dragonu I
   19. Matthews PM
   20. Miller KL
   21. Smith SM

   (2018) Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank

   NeuroImage 166:400–424.

   https://doi.org/10.1016/j.neuroimage.2017.10.034

   • PubMed
   • Google Scholar
2. 1. Ambikairajah A
   2. Khondoker M
   3. Morris E
   4. de Lange A-MG
   5. Saleh RNM
   6. Minihane AM
   7. Hornberger M

   (2024) Investigating the synergistic effects of hormone replacement therapy, apolipoprotein E and age on brain health in the UK Biobank

   Human Brain Mapping 45:e26612.

   https://doi.org/10.1002/hbm.26612

   • PubMed
   • Google Scholar
3. 1. Anatürk M
   2. Patel R
   3. Ebmeier KP
   4. Georgiopoulos G
   5. Newby D
   6. Topiwala A
   7. de Lange A-MG
   8. Cole JH
   9. Jansen MG
   10. Singh-Manoux A
   11. Kivimäki M
   12. Suri S

   (2023) Development and validation of a dementia risk score in the UK Biobank and Whitehall II cohorts

   BMJ Mental Health 26:e300719.

   https://doi.org/10.1136/bmjment-2023-300719

   • PubMed
   • Google Scholar
4. 1. Barth C
   2. Crestol A
   3. de Lange A-MG
   4. Galea LAM

   (2023) Sex steroids and the female brain across the lifespan: insights into risk of depression and Alzheimer’s disease

   The Lancet Diabetes & Endocrinology 11:926–941.

   https://doi.org/10.1016/S2213-8587(23)00224-3

   • Google Scholar
5. 1. Basser PJ
   2. Mattiello J
   3. LeBihan D

   (1994) MR diffusion tensor spectroscopy and imaging

   Biophysical Journal 66:259–267.

   https://doi.org/10.1016/S0006-3495(94)80775-1

   • PubMed
   • Google Scholar
6. 1. Beck D
   2. de Lange A-MG
   3. Pedersen ML
   4. Alnaes D
   5. Maximov II
   6. Voldsbekk I
   7. Richard G
   8. Sanders A-M
   9. Ulrichsen KM
   10. Dørum ES
   11. Kolskår KK
   12. Høgestøl EA
   13. Steen NE
   14. Djurovic S
   15. Andreassen OA
   16. Nordvik JE
   17. Kaufmann T
   18. Westlye LT

   (2022) Cardiometabolic risk factors associated with brain age and accelerate brain ageing

   Human Brain Mapping 43:700–720.

   https://doi.org/10.1002/hbm.25680

   • PubMed
   • Google Scholar
7. 1. Benjamini Y
   2. Hochberg Y

   (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing

   Journal of the Royal Statistical Society Series B 57:289–300.

   https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

   • Google Scholar
8. 1. Blümel JE
   2. Arteaga E
   3. Vallejo MS
   4. Ojeda E
   5. Meza P
   6. Martino M
   7. Rodríguez-Vidal D
   8. Ñañez M
   9. Tserotas K
   10. Rojas J
   11. Rodrígues MA
   12. Espinoza MT
   13. Salinas C
   14. Párraga-Párraga J
   15. Chedraui P

   (2022) Association of bilateral oophorectomy and menopause hormone therapy with mild cognitive impairment: the REDLINC X study

   Climacteric 25:195–202.

   https://doi.org/10.1080/13697137.2021.1951203

   • PubMed
   • Google Scholar
9. Preprint

   1. Bradley V
   2. Nichols TE

   (2022) Addressing Selection Bias in the UK Biobank Neurological Imaging Cohort

   medRxiv.

   https://www.medrxiv.org/content/10.1101/2022.01.13.22269266v2

   • Google Scholar
10. 1. Brinton RD

    (2008) The healthy cell bias of estrogen action: mitochondrial bioenergetics and neurological implications

    Trends in Neurosciences 31:529–537.

    https://doi.org/10.1016/j.tins.2008.07.003

    • PubMed
    • Google Scholar
11. 1. Brinton RD
    2. Yao J
    3. Yin F
    4. Mack WJ
    5. Cadenas E

    (2015) Perimenopause as a neurological transition state

    Nature Reviews. Endocrinology 11:393–405.

    https://doi.org/10.1038/nrendo.2015.82

    • PubMed
    • Google Scholar
12. 1. Bycroft C
    2. Freeman C
    3. Petkova D
    4. Band G
    5. Elliott LT
    6. Sharp K
    7. Motyer A
    8. Vukcevic D
    9. Delaneau O
    10. O’Connell J
    11. Cortes A
    12. Welsh S
    13. Young A
    14. Effingham M
    15. McVean G
    16. Leslie S
    17. Allen N
    18. Donnelly P
    19. Marchini J

    (2018) The UK Biobank resource with deep phenotyping and genomic data

    Nature 562:203–209.

    https://doi.org/10.1038/s41586-018-0579-z

    • PubMed
    • Google Scholar
13. 1. Calvo N
    2. McFall GP
    3. Ramana S
    4. Galper M
    5. Fuller-Thomson E
    6. Dixon RA
    7. Einstein G

    (2024) Associated risk and resilience factors of Alzheimer’s disease in women with early bilateral oophorectomy: Data from the UK Biobank

    Journal of Alzheimer’s Disease 102:119–128.

    https://doi.org/10.3233/JAD-240646

    • PubMed
    • Google Scholar
14. 1. Cox SR
    2. Bastin ME
    3. Ritchie SJ
    4. Dickie DA
    5. Liewald DC
    6. Muñoz Maniega S
    7. Redmond P
    8. Royle NA
    9. Pattie A
    10. Valdés Hernández M
    11. Corley J
    12. Aribisala BS
    13. McIntosh AM
    14. Wardlaw JM
    15. Deary IJ

    (2018) Brain cortical characteristics of lifetime cognitive ageing

    Brain Structure and Function 223:509–518.

    https://doi.org/10.1007/s00429-017-1505-0

    • Google Scholar
15. 1. de Lange A-MG
    2. Cole JH

    (2020) Commentary: correction procedures in brain-age prediction

    NeuroImage. Clinical 26:102229.

    https://doi.org/10.1016/j.nicl.2020.102229

    • PubMed
    • Google Scholar
16. 1. de Lignières B

    (1999) Oral micronized progesterone

    Clinical Therapeutics 21:41–60.

    https://doi.org/10.1016/S0149-2918(00)88267-3

    • PubMed
    • Google Scholar
17. 1. Depypere H
    2. Vergallo A
    3. Lemercier P
    4. Lista S
    5. Benedet A
    6. Ashton N
    7. Cavedo E
    8. Zetterberg H
    9. Blennow K
    10. Vanmechelen E
    11. Hampel H
    12. the Neurodegeneration Precision Medicine Initiative NPMI

    (2023) Menopause hormone therapy significantly alters pathophysiological biomarkers of Alzheimer’s disease

    Alzheimer’s & Dementia 19:1320–1330.

    https://doi.org/10.1002/alz.12759

    • Google Scholar
18. 1. Ding F
    2. Yao J
    3. Rettberg JR
    4. Chen S
    5. Brinton RD

    (2013) Early decline in glucose transport and metabolism precedes shift to ketogenic system in female aging and alzheimer’s mouse brain: implication for bioenergetic intervention

    PLOS ONE 8:e79977.

    https://doi.org/10.1371/journal.pone.0079977

    • Google Scholar
19. 1. Duarte-Guterman P
    2. Yagi S
    3. Chow C
    4. Galea LAM

    (2015) Hippocampal learning, memory, and neurogenesis: Effects of sex and estrogens across the lifespan in adults

    Hormones and Behavior 74:37–52.

    https://doi.org/10.1016/j.yhbeh.2015.05.024

    • PubMed
    • Google Scholar
20. 1. Erickson KI
    2. Colcombe SJ
    3. Raz N
    4. Korol DL
    5. Scalf P
    6. Webb A
    7. Cohen NJ
    8. McAuley E
    9. Kramer AF

    (2005) Selective sparing of brain tissue in postmenopausal women receiving hormone replacement therapy

    Neurobiology of Aging 26:1205–1213.

    https://doi.org/10.1016/j.neurobiolaging.2004.11.009

    • PubMed
    • Google Scholar
21. 1. Fieremans E
    2. Jensen JH
    3. Helpern JA

    (2011) White matter characterization with diffusional kurtosis imaging

    NeuroImage 58:177–188.

    https://doi.org/10.1016/j.neuroimage.2011.06.006

    • PubMed
    • Google Scholar
22. 1. Fischl B
    2. Salat DH
    3. Busa E
    4. Albert M
    5. Dieterich M
    6. Haselgrove C
    7. van der Kouwe A
    8. Killiany R
    9. Kennedy D
    10. Klaveness S
    11. Montillo A
    12. Makris N
    13. Rosen B
    14. Dale AM

    (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain

    Neuron 33:341–355.

    https://doi.org/10.1016/s0896-6273(02)00569-x

    • PubMed
    • Google Scholar
23. 1. Fjell AM
    2. Westlye LT
    3. Amlien I
    4. Espeseth T
    5. Reinvang I
    6. Raz N
    7. Agartz I
    8. Salat DH
    9. Greve DN
    10. Fischl B
    11. Dale AM
    12. Walhovd KB

    (2009) High consistency of regional cortical thinning in aging across multiple samples

    Cerebral Cortex 19:2001–2012.

    https://doi.org/10.1093/cercor/bhn232

    • PubMed
    • Google Scholar
24. 1. Foster HME
    2. Celis-Morales CA
    3. Nicholl BI
    4. Petermann-Rocha F
    5. Pell JP
    6. Gill JMR
    7. O’Donnell CA
    8. Mair FS

    (2018) The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort

    The Lancet. Public Health 3:e576–e585.

    https://doi.org/10.1016/S2468-2667(18)30200-7

    • PubMed
    • Google Scholar
25. 1. Georgakis MK
    2. Thomopoulos TP
    3. Diamantaras A-A
    4. Kalogirou EI
    5. Skalkidou A
    6. Daskalopoulou SS
    7. Petridou ET

    (2016) Association of age at menopause and duration of reproductive period with depression after menopause: a systematic review and meta-analysis

    JAMA Psychiatry 73:139–149.

    https://doi.org/10.1001/jamapsychiatry.2015.2653

    • PubMed
    • Google Scholar
26. 1. Georgakis MK
    2. Beskou-Kontou T
    3. Theodoridis I
    4. Skalkidou A
    5. Petridou ET

    (2019) Surgical menopause in association with cognitive function and risk of dementia: A systematic review and meta-analysis

    Psychoneuroendocrinology 106:9–19.

    https://doi.org/10.1016/j.psyneuen.2019.03.013

    • Google Scholar
27. 1. Gibbs RB
    2. Gabor R

    (2003) Estrogen and cognition: applying preclinical findings to clinical perspectives

    Journal of Neuroscience Research 74:637–643.

    https://doi.org/10.1002/jnr.10811

    • PubMed
    • Google Scholar
28. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
29. 1. Gleason CE
    2. Schmitz TW
    3. Hess T
    4. Koscik RL
    5. Trivedi MA
    6. Ries ML
    7. Carlsson CM
    8. Sager MA
    9. Asthana S
    10. Johnson SC

    (2006) Hormone effects on fMRI and cognitive measures of encoding: importance of hormone preparation

    Neurology 67:2039–2041.

    https://doi.org/10.1212/01.wnl.0000247277.81400.43

    • PubMed
    • Google Scholar
30. 1. Gleason CE
    2. Dowling NM
    3. Friedman E
    4. Wharton W
    5. Asthana S

    (2012) Using predictors of hormone therapy use to model the healthy user bias: how does healthy user status influence cognitive effects of hormone therapy?

    Menopause 19:524–533.

    https://doi.org/10.1097/gme.0b013e318238ff2c

    • PubMed
    • Google Scholar
31. 1. Gleason CE
    2. Dowling NM
    3. Wharton W
    4. Manson JE
    5. Miller VM
    6. Atwood CS
    7. Brinton EA
    8. Cedars MI
    9. Lobo RA
    10. Merriam GR
    11. Neal-Perry G
    12. Santoro NF
    13. Taylor HS
    14. Black DM
    15. Budoff MJ
    16. Hodis HN
    17. Naftolin F
    18. Harman SM
    19. Asthana S

    (2015) Effects of hormone therapy on cognition and mood in recently postmenopausal women: findings from the randomized, controlled KEEPS-cognitive and affective study

    PLOS Medicine 12:e1001833.

    https://doi.org/10.1371/journal.pmed.1001833

    • PubMed
    • Google Scholar
32. 1. Golomb BA
    2. Chan VT
    3. Evans MA
    4. Koperski S
    5. White HL
    6. Criqui MH

    (2012) The older the better: are elderly study participants more non-representative? A cross-sectional analysis of clinical trial and observational study samples

    BMJ Open 2:e000833.

    https://doi.org/10.1136/bmjopen-2012-000833

    • PubMed
    • Google Scholar
33. 1. Gorbach T
    2. Pudas S
    3. Bartrés-Faz D
    4. Brandmaier AM
    5. Düzel S
    6. Henson RN
    7. Idland A-V
    8. Lindenberger U
    9. Macià Bros D
    10. Mowinckel AM
    11. Solé-Padullés C
    12. Sørensen Ø
    13. Walhovd KB
    14. Watne LO
    15. Westerhausen R
    16. Fjell AM
    17. Nyberg L

    (2020) Longitudinal association between hippocampus atrophy and episodic-memory decline in non-demented APOE ε4 carriers

    Alzheimer’s & Dementia 12:e12110.

    https://doi.org/10.1002/dad2.12110

    • PubMed
    • Google Scholar
34. 1. Goto M
    2. Abe O
    3. Miyati T
    4. Inano S
    5. Hayashi N
    6. Aoki S
    7. Mori H
    8. Kabasawa H
    9. Ino K
    10. Yano K
    11. Iida K
    12. Mima K
    13. Ohtomo K

    (2011) 3 Tesla MRI detects accelerated hippocampal volume reduction in postmenopausal women

    Journal of Magnetic Resonance Imaging 33:48–53.

    https://doi.org/10.1002/jmri.22328

    • PubMed
    • Google Scholar
35. 1. Grady D
    2. Yaffe K
    3. Kristof M
    4. Lin F
    5. Richards C
    6. Barrett-Connor E

    (2002) Effect of postmenopausal hormone therapy on cognitive function: the heart and estrogen/progestin replacement study

    The American Journal of Medicine 113:543–548.

    https://doi.org/10.1016/s0002-9343(02)01270-6

    • PubMed
    • Google Scholar
36. 1. Griffanti L
    2. Zamboni G
    3. Khan A
    4. Li L
    5. Bonifacio G
    6. Sundaresan V
    7. Schulz UG
    8. Kuker W
    9. Battaglini M
    10. Rothwell PM
    11. Jenkinson M

    (2016) BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities

    NeuroImage 141:191–205.

    https://doi.org/10.1016/j.neuroimage.2016.07.018

    • PubMed
    • Google Scholar
37. 1. Ha DM
    2. Xu J
    3. Janowsky JS

    (2007) Preliminary evidence that long-term estrogen use reduces white matter loss in aging

    Neurobiology of Aging 28:1936–1940.

    https://doi.org/10.1016/j.neurobiolaging.2006.08.007

    • PubMed
    • Google Scholar
38. 1. Harlow SD
    2. Gass M
    3. Hall JE
    4. Lobo R
    5. Maki P
    6. Rebar RW
    7. Sherman S
    8. Sluss PM
    9. de Villiers TJ
    10. STRAW + 10 Collaborative Group

    (2012) Executive summary of the Stages of Reproductive Aging Workshop + 10: addressing the unfinished agenda of staging reproductive aging

    The Journal of Clinical Endocrinology and Metabolism 97:1159–1168.

    https://doi.org/10.1210/jc.2011-3362

    • PubMed
    • Google Scholar
39. 1. Ho AJ
    2. Raji CA
    3. Becker JT
    4. Lopez OL
    5. Kuller LH
    6. Hua X
    7. Dinov ID
    8. Stein JL
    9. Rosano C
    10. Toga AW
    11. Thompson PM

    (2011) The effects of physical activity, education, and body mass index on the aging brain

    Human Brain Mapping 32:1371–1382.

    https://doi.org/10.1002/hbm.21113

    • PubMed
    • Google Scholar
40. 1. Hogervorst E
    2. Williams J
    3. Budge M
    4. Riedel W
    5. Jolles J

    (2000) The nature of the effect of female gonadal hormone replacement therapy on cognitive function in post-menopausal women: a meta-analysis

    Neuroscience 101:485–512.

    https://doi.org/10.1016/s0306-4522(00)00410-3

    • PubMed
    • Google Scholar
41. 1. Holtorf K

    (2009) The bioidentical hormone debate: are bioidentical hormones (estradiol, estriol, and progesterone) safer or more efficacious than commonly used synthetic versions in hormone replacement therapy?

    Postgraduate Medicine 121:73–85.

    https://doi.org/10.3810/pgm.2009.01.1949

    • PubMed
    • Google Scholar
42. 1. Jensen JH
    2. Helpern JA
    3. Ramani A
    4. Lu H
    5. Kaczynski K

    (2005) Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging

    Magnetic Resonance in Medicine 53:1432–1440.

    https://doi.org/10.1002/mrm.20508

    • PubMed
    • Google Scholar
43. 1. Kaden E
    2. Kelm ND
    3. Carson RP
    4. Does MD
    5. Alexander DC

    (2016a) Multi-compartment microscopic diffusion imaging

    NeuroImage 139:346–359.

    https://doi.org/10.1016/j.neuroimage.2016.06.002

    • PubMed
    • Google Scholar
44. 1. Kaden E
    2. Kruggel F
    3. Alexander DC

    (2016b) Quantitative mapping of the per-axon diffusion coefficients in brain white matter

    Magnetic Resonance in Medicine 75:1752–1763.

    https://doi.org/10.1002/mrm.25734

    • PubMed
    • Google Scholar
45. 1. Kantarci K
    2. Tosakulwong N
    3. Lesnick TG
    4. Zuk SM
    5. Gunter JL
    6. Gleason CE
    7. Wharton W
    8. Dowling NM
    9. Vemuri P
    10. Senjem ML
    11. Shuster LT
    12. Bailey KR
    13. Rocca WA
    14. Asthana S
    15. Miller VM

    (2016) Effects of hormone therapy on brain structure: a randomized controlled trial

    Neurology 87:887–896.

    https://doi.org/10.1212/WNL.0000000000002970

    • PubMed
    • Google Scholar
46. 1. Kantarci K
    2. Manson JE

    (2023) Menopausal hormone therapy and dementia

    BMJ 381:1404.

    https://doi.org/10.1136/bmj.p1404

    • PubMed
    • Google Scholar
47. 1. Kaunitz AM
    2. Kapoor E
    3. Faubion S

    (2021) Treatment of women after bilateral salpingo-oophorectomy performed prior to natural menopause

    JAMA 326:1429–1430.

    https://doi.org/10.1001/jama.2021.3305

    • PubMed
    • Google Scholar
48. 1. Kim IY
    2. Grodstein F
    3. Kraft P
    4. Curhan GC
    5. Hughes KC
    6. Huang H
    7. Kang JH
    8. Hunter DJ

    (2019) Interaction between apolipoprotein E genotype and hypertension on cognitive function in older women in the Nurses’ Health Study

    PLOS ONE 14:e0224975.

    https://doi.org/10.1371/journal.pone.0224975

    • PubMed
    • Google Scholar
49. 1. Kim YJ
    2. Brinton RD

    (2021) Precision hormone therapy: identification of positive responders

    Climacteric 24:350–358.

    https://doi.org/10.1080/13697137.2021.1882418

    • PubMed
    • Google Scholar
50. 1. Kim YJ
    2. Soto M
    3. Branigan GL
    4. Rodgers K
    5. Brinton RD

    (2021) Association between menopausal hormone therapy and risk of neurodegenerative diseases: Implications for precision hormone therapy

    Alzheimer’s & Dementia 7:e12174.

    https://doi.org/10.1002/trc2.12174

    • Google Scholar
51. 1. Krogsrud SK
    2. Fjell AM
    3. Tamnes CK
    4. Grydeland H
    5. Mork L
    6. Due-Tønnessen P
    7. Bjørnerud A
    8. Sampaio-Baptista C
    9. Andersson J
    10. Johansen-Berg H
    11. Walhovd KB

    (2016) Changes in white matter microstructure in the developing brain--A longitudinal diffusion tensor imaging study of children from 4 to 11years of age

    NeuroImage 124:473–486.

    https://doi.org/10.1016/j.neuroimage.2015.09.017

    • PubMed
    • Google Scholar
52. 1. Kuiper GG
    2. Carlsson B
    3. Grandien K
    4. Enmark E
    5. Häggblad J
    6. Nilsson S
    7. Gustafsson JA

    (1997) Comparison of the ligand binding specificity and transcript tissue distribution of estrogen receptors alpha and beta

    Endocrinology 138:863–870.

    https://doi.org/10.1210/endo.138.3.4979

    • PubMed
    • Google Scholar
53. Book

    1. Lange AG
    2. Barth C
    3. Kaufmann T

    (2020) Women’s Brain Aging: Effects of Sex-Hormone Exposure, Pregnancies, and Genetic Risk for Alzheimer’s Disease

    Hum Brain Mapp.

    https://doi.org/10.1101/826123

    • Google Scholar
54. 1. Laws KR
    2. Irvine K
    3. Gale TM

    (2016) Sex differences in cognitive impairment in Alzheimer’s disease

    World Journal of Psychiatry 6:54–65.

    https://doi.org/10.5498/wjp.v6.i1.54

    • PubMed
    • Google Scholar
55. 1. Laws KR
    2. Irvine K
    3. Gale TM

    (2018) Sex differences in Alzheimer’s disease

    Current Opinion in Psychiatry 31:133–139.

    https://doi.org/10.1097/YCO.0000000000000401

    • PubMed
    • Google Scholar
56. 1. Lindseth LRS
    2. de Lange AMG
    3. van der Meer D
    4. Agartz I
    5. Westlye LT
    6. Tamnes CK
    7. Barth C

    (2023) Associations between reproductive history, hormone use, APOE ε4 genotype and cognition in middle- to older-aged women from the UK Biobank

    Frontiers in Aging Neuroscience 14:1014605.

    https://doi.org/10.3389/fnagi.2022.1014605

    • PubMed
    • Google Scholar
57. 1. Longcope C
    2. Gorbach S
    3. Goldin B
    4. Woods M
    5. Dwyer J
    6. Warram J

    (1985) The metabolism of estradiol; oral compared to intravenous administration

    Journal of Steroid Biochemistry 23:1065–1070.

    https://doi.org/10.1016/0022-4731(85)90068-8

    • PubMed
    • Google Scholar
58. 1. Lyall DM
    2. Ward J
    3. Ritchie SJ
    4. Davies G
    5. Cullen B
    6. Celis C
    7. Bailey MES
    8. Anderson J
    9. Evans J
    10. Mckay DF
    11. Mcintosh AM
    12. Sattar N
    13. Smith DJ
    14. Deary IJ
    15. Pell JP

    (2016) Alzheimer disease genetic risk factor APOE e4 and cognitive abilities in 111,739 UK Biobank participants

    Age and Ageing 45:511–517.

    https://doi.org/10.1093/ageing/afw068

    • PubMed
    • Google Scholar
59. 1. MacLennan AH
    2. Henderson VW
    3. Paine BJ
    4. Mathias J
    5. Ramsay EN
    6. Ryan P
    7. Stocks NP
    8. Taylor AW

    (2006) Hormone therapy, timing of initiation, and cognition in women aged older than 60 years: the REMEMBER pilot study

    Menopause 13:28–36.

    https://doi.org/10.1097/01.gme.0000191204.38664.61

    • PubMed
    • Google Scholar
60. 1. Maki PM
    2. Gast MJ
    3. Vieweg AJ
    4. Burriss SW
    5. Yaffe K

    (2007) Hormone therapy in menopausal women with cognitive complaints: a randomized, double-blind trial

    Neurology 69:1322–1330.

    https://doi.org/10.1212/01.wnl.0000277275.42504.93

    • PubMed
    • Google Scholar
61. 1. Maki PM
    2. Dennerstein L
    3. Clark M
    4. Guthrie J
    5. LaMontagne P
    6. Fornelli D
    7. Little D
    8. Henderson VW
    9. Resnick SM

    (2011) Perimenopausal use of hormone therapy is associated with enhanced memory and hippocampal function later in life

    Brain Research 1379:232–243.

    https://doi.org/10.1016/j.brainres.2010.11.030

    • PubMed
    • Google Scholar
62. 1. Maki PM

    (2013) Critical window hypothesis of hormone therapy and cognition: a scientific update on clinical studies

    Menopause 20:695–709.

    https://doi.org/10.1097/GME.0b013e3182960cf8

    • PubMed
    • Google Scholar
63. 1. Maximov II
    2. Alnaes D
    3. Westlye LT

    (2019) Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

    Human Brain Mapping 40:4146–4162.

    https://doi.org/10.1002/hbm.24691

    • PubMed
    • Google Scholar
64. 1. Maximov II
    2. van der Meer D
    3. de Lange A-MG
    4. Kaufmann T
    5. Shadrin A
    6. Frei O
    7. Wolfers T
    8. Westlye LT

    (2021) Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example

    Human Brain Mapping 42:3141–3155.

    https://doi.org/10.1002/hbm.25424

    • PubMed
    • Google Scholar
65. 1. Miller KL
    2. Alfaro-Almagro F
    3. Bangerter NK
    4. Thomas DL
    5. Yacoub E
    6. Xu J
    7. Bartsch AJ
    8. Jbabdi S
    9. Sotiropoulos SN
    10. Andersson JLR
    11. Griffanti L
    12. Douaud G
    13. Okell TW
    14. Weale P
    15. Dragonu I
    16. Garratt S
    17. Hudson S
    18. Collins R
    19. Jenkinson M
    20. Matthews PM
    21. Smith SM

    (2016) Multimodal population brain imaging in the UK Biobank prospective epidemiological study

    Nature Neuroscience 19:1523–1536.

    https://doi.org/10.1038/nn.4393

    • PubMed
    • Google Scholar
66. 1. Mills ZB
    2. Faull RLM
    3. Kwakowsky A

    (2023) Is hormone replacement therapy a risk factor or a therapeutic option for alzheimer’s disease?

    International Journal of Molecular Sciences 24:3205.

    https://doi.org/10.3390/ijms24043205

    • PubMed
    • Google Scholar
67. Book

    1. Mori S
    2. Wakana S
    3. Zijl PC
    4. Nagae-Poetscher L

    (2005)

    MRI Atlas of Human White

    matter: Elsevier.

    • Google Scholar
68. 1. Mosconi L
    2. Berti V
    3. Quinn C
    4. McHugh P
    5. Petrongolo G
    6. Varsavsky I
    7. Osorio RS
    8. Pupi A
    9. Vallabhajosula S
    10. Isaacson RS
    11. de Leon MJ
    12. Brinton RD

    (2017) Sex differences in Alzheimer risk: Brain imaging of endocrine vs chronologic aging

    Neurology 89:1382–1390.

    https://doi.org/10.1212/WNL.0000000000004425

    • PubMed
    • Google Scholar
69. 1. Mosconi L
    2. Rahman A
    3. Diaz I
    4. Wu X
    5. Scheyer O
    6. Hristov HW
    7. Vallabhajosula S
    8. Isaacson RS
    9. de Leon MJ
    10. Brinton RD

    (2018) Increased Alzheimer’s risk during the menopause transition: A 3-year longitudinal brain imaging study

    PLOS ONE 13:e0207885.

    https://doi.org/10.1371/journal.pone.0207885

    • PubMed
    • Google Scholar
70. 1. Mosconi L
    2. Berti V
    3. Dyke J
    4. Schelbaum E
    5. Jett S
    6. Loughlin L
    7. Jang G
    8. Rahman A
    9. Hristov H
    10. Pahlajani S
    11. Andrews R
    12. Matthews D
    13. Etingin O
    14. Ganzer C
    15. de Leon M
    16. Isaacson R
    17. Brinton RD

    (2021) Menopause impacts human brain structure, connectivity, energy metabolism, and amyloid-beta deposition

    Scientific Reports 11:10867.

    https://doi.org/10.1038/s41598-021-90084-y

    • PubMed
    • Google Scholar
71. 1. Nilsen J
    2. Brinton RD

    (2002) Impact of progestins on estrogen-induced neuroprotection: synergy by progesterone and 19-norprogesterone and antagonism by medroxyprogesterone acetate

    Endocrinology 143:205–212.

    https://doi.org/10.1210/endo.143.1.8582

    • PubMed
    • Google Scholar
72. Software

    1. Patel R
    2. Anaturk M

    (2023) Dementia_risk_score, version 69da2d3

    GitHub.

    https://github.com/MelisAnaturk/dementia_risk_score/tree/main/scripts
73. 1. Phung TKT
    2. Waltoft BL
    3. Laursen TM
    4. Settnes A
    5. Kessing LV
    6. Mortensen PB
    7. Waldemar G

    (2010) Hysterectomy, oophorectomy and risk of dementia: a nationwide historical cohort study

    Dementia and Geriatric Cognitive Disorders 30:43–50.

    https://doi.org/10.1159/000314681

    • PubMed
    • Google Scholar
74. 1. Pourhadi N
    2. Mørch LS
    3. Holm EA
    4. Torp-Pedersen C
    5. Meaidi A

    (2023) Menopausal hormone therapy and dementia: nationwide, nested case-control study

    BMJ 381:e072770.

    https://doi.org/10.1136/bmj-2022-072770

    • PubMed
    • Google Scholar
75. 1. Pourhadi N
    2. Mørch LS
    3. Holm EA
    4. Torp-Pedersen C
    5. Meaidi A

    (2024) Dementia in women using estrogen-only therapy

    JAMA 331:160–162.

    https://doi.org/10.1001/jama.2023.23784

    • PubMed
    • Google Scholar
76. 1. Rapp SR
    2. Espeland MA
    3. Shumaker SA
    4. Henderson VW
    5. Brunner RL
    6. Manson JE
    7. Gass MLS
    8. Stefanick ML
    9. Lane DS
    10. Hays J
    11. Johnson KC
    12. Coker LH
    13. Dailey M
    14. Bowen D
    15. WHIMS Investigators

    (2003) Effect of estrogen plus progestin on global cognitive function in postmenopausal women: the Women’s Health Initiative Memory Study: a randomized controlled trial

    JAMA 289:2663–2672.

    https://doi.org/10.1001/jama.289.20.2663

    • PubMed
    • Google Scholar
77. 1. Resnick SM
    2. Espeland MA
    3. Jaramillo SA
    4. Hirsch C
    5. Stefanick ML
    6. Murray AM
    7. Ockene J
    8. Davatzikos C

    (2009) Postmenopausal hormone therapy and regional brain volumes: the WHIMS-MRI Study

    Neurology 72:135–142.

    https://doi.org/10.1212/01.wnl.0000339037.76336.cf

    • PubMed
    • Google Scholar
78. 1. Rocca WA
    2. Bower JH
    3. Maraganore DM
    4. Ahlskog JE
    5. Grossardt BR
    6. de Andrade M

    (2007) Increased risk of cognitive impairment or dementia in women who underwent oophorectomy before menopause

    Neurology 69:1074–1083.

    https://doi.org/10.1212/01.wnl.0000276984.19542.e6

    • PubMed
    • Google Scholar
79. 1. Rosen AFG
    2. Roalf DR
    3. Ruparel K
    4. Blake J
    5. Seelaus K
    6. Villa LP
    7. Ciric R
    8. Cook PA
    9. Davatzikos C
    10. Elliott MA
    11. Garcia de La Garza A
    12. Gennatas ED
    13. Quarmley M
    14. Schmitt JE
    15. Shinohara RT
    16. Tisdall MD
    17. Craddock RC
    18. Gur RE
    19. Gur RC
    20. Satterthwaite TD

    (2018) Quantitative assessment of structural image quality

    NeuroImage 169:407–418.

    https://doi.org/10.1016/j.neuroimage.2017.12.059

    • PubMed
    • Google Scholar
80. 1. Saeed U
    2. Desmarais P
    3. Masellis M

    (2021) The APOE ε4 variant and hippocampal atrophy in Alzheimer’s disease and Lewy body dementia: a systematic review of magnetic resonance imaging studies and therapeutic relevance

    Expert Review of Neurotherapeutics 21:851–870.

    https://doi.org/10.1080/14737175.2021.1956904

    • PubMed
    • Google Scholar
81. 1. Saleh RNM
    2. Hornberger M
    3. Ritchie CW
    4. Minihane AM

    (2023) Hormone replacement therapy is associated with improved cognition and larger brain volumes in at-risk APOE4 women: results from the European Prevention of Alzheimer’s Disease (EPAD) cohort

    Alzheimer’s Research & Therapy 15:10.

    https://doi.org/10.1186/s13195-022-01121-5

    • PubMed
    • Google Scholar
82. 1. Savolainen-Peltonen H
    2. Rahkola-Soisalo P
    3. Hoti F
    4. Vattulainen P
    5. Gissler M
    6. Ylikorkala O
    7. Mikkola TS

    (2019) Use of postmenopausal hormone therapy and risk of alzheimer’s disease in finland: nationwide case-control study

    BMJ 364:l665.

    https://doi.org/10.1136/bmj.l665

    • PubMed
    • Google Scholar
83. 1. Schelbaum E
    2. Loughlin L
    3. Jett S
    4. Zhang C
    5. Jang G
    6. Malviya N
    7. Hristov H
    8. Pahlajani S
    9. Isaacson R
    10. Dyke JP
    11. Kamel H
    12. Brinton RD
    13. Mosconi L

    (2021) Association of reproductive history with brain mri biomarkers of dementia risk in midlife

    Neurology 97:e2328–e2339.

    https://doi.org/10.1212/WNL.0000000000012941

    • PubMed
    • Google Scholar
84. 1. Schindler LS
    2. Subramaniapillai S
    3. Barth C
    4. van der Meer D
    5. Pedersen ML
    6. Kaufmann T
    7. Maximov II
    8. Linge J
    9. Leinhard OD
    10. Beck D
    11. Gurholt TP
    12. Voldsbekk I
    13. Suri S
    14. Ebmeier KP
    15. Draganski B
    16. Andreassen OA
    17. Westlye LT
    18. de Lange A-MG

    (2022) Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women

    NeuroImage. Clinical 36:103239.

    https://doi.org/10.1016/j.nicl.2022.103239

    • PubMed
    • Google Scholar
85. 1. Schindler LS
    2. Subramaniapillai S
    3. Ambikairajah A
    4. Barth C
    5. Crestol A
    6. Voldsbekk I
    7. Beck D
    8. Gurholt TP
    9. Topiwala A
    10. Suri S
    11. Ebmeier KP
    12. Andreassen OA
    13. Draganski B
    14. Westlye LT
    15. de Lange A-MG

    (2023) Cardiometabolic health across menopausal years is linked to white matter hyperintensities up to a decade later

    Frontiers in Global Women’s Health 4:1320640.

    https://doi.org/10.3389/fgwh.2023.1320640

    • PubMed
    • Google Scholar
86. 1. Seripa D
    2. Matera MG
    3. Daniele A
    4. Bizzarro A
    5. Rinaldi M
    6. Gravina C
    7. Bisceglia L
    8. Corbo RM
    9. Panza F
    10. Solfrizzi V
    11. Fazio VM
    12. Forno GD
    13. Masullo C
    14. Dallapiccola B
    15. Pilotto A

    (2007) The missing ApoE allele

    Annals of Human Genetics 71:496–500.

    https://doi.org/10.1111/j.1469-1809.2006.00344.x

    • PubMed
    • Google Scholar
87. 1. Shumaker SA
    2. Legault C
    3. Rapp SR
    4. Thal L
    5. Wallace RB
    6. Ockene JK
    7. Hendrix SL
    8. Assaf AR
    9. Jackson RD
    10. Kotchen JM
    11. Wassertheil-Smoller S
    12. Wactawski-Wende J
    13. WHIMS Investigators

    (2003) Estrogen plus progestin and the incidence of dementia and mild cognitive impairment in postmenopausal women: the Women’s Health Initiative Memory Study: a randomized controlled trial

    JAMA 289:2651–2662.

    https://doi.org/10.1001/jama.289.20.2651

    • PubMed
    • Google Scholar
88. 1. Shumaker SA
    2. Legault C
    3. Kuller L
    4. Rapp SR
    5. Thal L
    6. Lane DS
    7. Fillit H
    8. Stefanick ML
    9. Hendrix SL
    10. Lewis CE
    11. Masaki K
    12. Coker LH
    13. Women’s Health Initiative Memory Study

    (2004) Conjugated equine estrogens and incidence of probable dementia and mild cognitive impairment in postmenopausal women: Women’s Health Initiative Memory Study

    JAMA 291:2947–2958.

    https://doi.org/10.1001/jama.291.24.2947

    • PubMed
    • Google Scholar
89. 1. Simpkins JW
    2. Perez E
    3. Wang X
    4. Yang S
    5. Wen Y
    6. Singh M

    (2009) The potential for estrogens in preventing Alzheimer’s disease and vascular dementia

    Therapeutic Advances in Neurological Disorders 2:31–49.

    https://doi.org/10.1177/1756285608100427

    • PubMed
    • Google Scholar
90. 1. Sitruk-Ware R

    (2006) New progestagens for contraceptive use

    Human Reproduction Update 12:169–178.

    https://doi.org/10.1093/humupd/dmi046

    • PubMed
    • Google Scholar
91. 1. Slater CC
    2. Hodis HN
    3. Mack WJ
    4. Shoupe D
    5. Paulson RJ
    6. Stanczyk FZ

    (2001) Markedly elevated levels of estrone sulfate after long-term oral, but not transdermal, administration of estradiol in postmenopausal women

    Menopause 8:200–203.

    https://doi.org/10.1097/00042192-200105000-00009

    • PubMed
    • Google Scholar
92. 1. Stuursma A
    2. Lanjouw L
    3. Idema DL
    4. de Bock GH
    5. Mourits MJE

    (2022) Surgical menopause and bilateral oophorectomy: effect of estrogen-progesterone and testosterone replacement therapy on psychological well-being and sexual functioning; a systematic literature review

    The Journal of Sexual Medicine 19:1778–1789.

    https://doi.org/10.1016/j.jsxm.2022.08.191

    • PubMed
    • Google Scholar
93. 1. Subramaniapillai S
    2. Suri S
    3. Barth C
    4. Maximov II
    5. Voldsbekk I
    6. van der Meer D
    7. Gurholt TP
    8. Beck D
    9. Draganski B
    10. Andreassen OA
    11. Ebmeier KP
    12. Westlye LT
    13. de Lange A-MG

    (2022) Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

    Human Brain Mapping 43:3759–3774.

    https://doi.org/10.1002/hbm.25882

    • PubMed
    • Google Scholar
94. 1. Van Erpecum KJ
    2. Van Berge Henegouwen GP
    3. Verschoor L
    4. Stoelwinder B
    5. Willekens FL

    (1991) Different hepatobiliary effects of oral and transdermal estradiol in postmenopausal women

    Gastroenterology 100:482–488.

    https://doi.org/10.1016/0016-5085(91)90220-f

    • PubMed
    • Google Scholar
95. 1. Voevodskaya O
    2. Simmons A
    3. Nordenskjöld R
    4. Kullberg J
    5. Ahlström H
    6. Lind L
    7. Wahlund LO
    8. Larsson EM
    9. Westman E

    (2014) The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer’s disease

    Frontiers in Aging Neuroscience 6:264.

    https://doi.org/10.3389/fnagi.2014.00264

    • PubMed
    • Google Scholar
96. 1. Voldsbekk I
    2. Barth C
    3. Maximov II
    4. Kaufmann T
    5. Beck D
    6. Richard G
    7. Moberget T
    8. Westlye LT
    9. de Lange A-MG

    (2021) A history of previous childbirths is linked to women’s white matter brain age in midlife and older age

    Human Brain Mapping 42:4372–4386.

    https://doi.org/10.1002/hbm.25553

    • PubMed
    • Google Scholar
97. 1. Wen W
    2. Sachdev PS
    3. Li JJ
    4. Chen X
    5. Anstey KJ

    (2009) White matter hyperintensities in the forties: their prevalence and topography in an epidemiological sample aged 44-48

    Human Brain Mapping 30:1155–1167.

    https://doi.org/10.1002/hbm.20586

    • PubMed
    • Google Scholar
98. 1. Westlye LT
    2. Walhovd KB
    3. Dale AM
    4. Bjørnerud A
    5. Due-Tønnessen P
    6. Engvig A
    7. Grydeland H
    8. Tamnes CK
    9. Ostby Y
    10. Fjell AM

    (2010) Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry

    Cerebral Cortex 20:2055–2068.

    https://doi.org/10.1093/cercor/bhp280

    • PubMed
    • Google Scholar
99. 1. Wharton W
    2. Gleason CE
    3. Miller VM
    4. Asthana S

    (2013) Rationale and design of the kronos early estrogen prevention study (KEEPS) and the KEEPS cognitive and affective sub study (KEEPS Cog)

    Brain Research 1514:12–17.

    https://doi.org/10.1016/j.brainres.2013.04.011

    • PubMed
    • Google Scholar
100. Software

     1. XGBoost

     (2025) EXtreme gradient boosting

     GitHub.

     https://github.com/dmlc/xgboost
101. 1. Yaffe K
     2. Haan M
     3. Byers A
     4. Tangen C
     5. Kuller L

     (2000) Estrogen use, APOE, and cognitive decline: evidence of gene-environment interaction

     Neurology 54:1949–1954.

     https://doi.org/10.1212/wnl.54.10.1949

     • PubMed
     • Google Scholar
102. 1. Zandi PP
     2. Carlson MC
     3. Plassman BL
     4. Welsh-Bohmer KA
     5. Mayer LS
     6. Steffens DC
     7. Breitner JCS
     8. Cache County Memory Study Investigators

     (2002) Hormone replacement therapy and incidence of Alzheimer disease in older women: the cache county study

     JAMA 288:2123–2129.

     https://doi.org/10.1001/jama.288.17.2123

     • PubMed
     • Google Scholar
103. 1. Zeydan B
     2. Tosakulwong N
     3. Schwarz CG
     4. Senjem ML
     5. Gunter JL
     6. Reid RI
     7. Gazzuola Rocca L
     8. Lesnick TG
     9. Smith CY
     10. Bailey KR
     11. Lowe VJ
     12. Roberts RO
     13. Petersen RC
     14. Miller VM
     15. Mielke MM
     16. Rocca WA
     17. Kantarci K

     (2019) Association of bilateral salpingo-oophorectomy before menopause onset with medial temporal lobe neurodegeneration

     JAMA Neurology 76:95–100.

     https://doi.org/10.1001/jamaneurol.2018.3057

     • PubMed
     • Google Scholar

Author details

1. Claudia Barth

   Division for Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   claudia.barth@medisin.uio.no

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6544-0945

2. Liisa AM Galea

   1. Centre for Addiction and Mental Health, Toronto, Canada
   2. Department of Psychiatry, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

3. Emily G Jacobs

   Psychological and Brain Sciences, University of California Santa Barbara, Santa Barbara, United States

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0001-5096

4. Bonnie H Lee

   Centre for Addiction and Mental Health, Toronto, Canada

   Contribution

   Conceptualization, Writing – review and editing

   Competing interests

   No competing interests declared

5. Lars T Westlye

   1. Department of Psychology, University of Oslo, Oslo, Norway
   2. Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8644-956X

6. Ann-Marie G de Lange

   1. Department of Psychology, University of Oslo, Oslo, Norway
   2. Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
   3. Department of Psychiatry, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Resources, Software, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

• 927

  views

• 52

  downloads

• 2

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Citations by DOI

• 2

  citations for Reviewed Preprint v1 https://doi.org/10.7554/eLife.99538.1