Elevated gonadotropin levels are associated with increased biomarker risk of Alzheimer’s disease in midlife women

Elevated gonadotropin levels are associated with increased biomarker risk of Alzheimer’s disease in midlife women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Elevated gonadotropin levels are associated with increased biomarker risk of Alzheimer’s disease in midlife women Matilde Nerattini, Federica Rubino, Steven Jett, Caroline Andy, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2351642/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Menopause has been implicated in women’s greater life-time risk for Alzheimer’s disease (AD) due to its disruptive action on multiple neurobiological mechanisms resulting in amyloid-β deposition and synaptic dysfunction.While these effects are typically attributed to declines in estradiol, mechanistic analyses implicate pituitary gonadotropins, follicle-stimulating hormone (FSH) and luteinizing hormone (LH), in AD pathology. In transgenic mouse models of AD, increasing FSH and LH accelerate amyloid-β deposition, while inhibiting these hormones prevents emergence of AD lesions and neurodegeneration. Herein, we take a translational approach to show that, among midlife women at risk for AD, FSH elevations over the menopause transition are associated with higher amyloid-β burden, and both FSH and LH increases are associated with lower gray matter volume in AD-vulnerable brain regions. Results were independent of age, hormone therapy usage, and plasma estradiol levels. These findings provide novel therapeutic targets for sex-based precision medicine strategies for AD prevention. Biological sciences/Neuroscience/Neural ageing Health sciences/Diseases/Neurological disorders/Neurodegenerative diseases/Alzheimer's disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Alzheimer’s disease (AD), the most common cause of dementia in the aging population, shows a greater prevalence in women than in men, with post-menopausal women accounting for nearly two thirds of all those affected 1 . This disparity is only partially explained by differences in survival rates 2 or genetic risk factors such as Apolipoprotein E epsilon 4 (APOE-4) allele 3 . Mounting evidence from preclinical and translational studies identifies loss of neuroprotective effects of sex steroid hormones following menopause as key biological mechanisms underlying women’s greater lifetime risk of AD 4,5 . Menopause exerts its actions on AD risk via alterations of multiple neurobiological mechanisms that can span decades 6 , forming the basis of the ~20 year prodromal phase of the disease with onset in midlife 7 , thus proximate to the menopause transition. In preclinical studies, menopause profoundly impacts cerebral bioenergetic aging processes, triggering emergence of aggregated amyloid-β (Aβ, a hallmark of AD pathology), mitochondrial compromise, and synaptic dysfunction 7 . Translational neuroimaging work provide consistent evidence that peri-menopausal and post-menopausal midlife women at risk for AD exhibit higher Aβ deposition and increased neurodegenerative biomarker load, chiefly glucose hypometabolism and gray matter volume (GMV) loss, as compared to pre-menopausal women and to age-controlled men 8-13 . Declines in 17β-estradiol (E2) have long been considered the main, and possibly only trigger for menopause-related neurodegenerative changes 7,14 . However, clinical research has provided contrasting evidence for associations between E2 and AD risk in women 15,16 . Additionally, while observational studies of menopause estrogen therapy report generally positive outcomes, randomized clinical trials have not shown consistent AD risk reduction effects 14,15,17 . While estrogen continues to be investigated, these disparities prompted examination of other hypothalamic-pituitary-gonadal axis (HPG) hormones, chiefly follicle-stimulating hormone (FSH) and luteinizing hormone (LH). As opposed to E2, which exhibits wide fluctuations in response to reduced steroidogenic synthesis of the aging ovary before reaching persistently low levels post-menopause, gonadotropin levels increase steadily starting in peri-menopause 18,19 , thus concomitant to emergence of Aβ pathology in women 8-13 . The idea that the brain is a target for pituitary glycoproteins is in stark contrast with the long-held view that these hormones act solely on endocrine targets 20 . Novel research has established broad ubiquity for pituitary hormone action in brain, highlighting a connection between gonadotropin levels and AD risk. In cell cultures, both FSH and LH increase amyloidogenic processing of amyloid precursor protein (APP), while pharmacological suppression of the gonadotropins reduces Aβ plaque formation 16,21-23 . In recent mechanistic analyses, the raise in FSH accelerated both Aβ and tau deposition in transgenic mouse models of AD, including female mice in an estrogen-replete state, whereas FSH blockade prevented emergence of AD pathology 22 . It is unknown whether gonadotropin elevations are linked to AD pathology in women. Herein, we take a translational approach to examine whether increasing FSH and LH levels from pre-menopausal to post-menopausal stages are associated with multi-modality biomarker evidence of AD risk, as reflected in higher Aβ deposition and lower GMV, in midlife women carrying established risk factors for AD such as a family history and/or APOE-4 genotype. Results Participants We enrolled 205 participants for this study. Of these, 14 were excluded due to incidental findings on Magnetic Resonance Imaging (MRI) (n = 5 small vessel disease or lacunar infarctions, n = 2 meningiomas, n = 1 mild hydrocephalus, n = 1 demyelination), image artifacts (n = 2), or incomplete hormonal test results (n = 3). The remaining 191 participants were examined in this study, including 45 pre-menopausal, 67 peri-menopausal, and 79 post-menopausal women. Participant characteristics are shown in Table 1 . Twenty percent of participants reported taking menopause hormone therapy (MHT), and 5% reported using oral contraceptives (OCP). Ten percent of participants had a history of hysterectomy and/or oophorectomy. Hormone therapy usage and menopause type (spontaneous vs. induced) were included as covariates. Table 1 Participants’ characteristics Pre-menopause Peri-menopause Post-menopause N 45 67 79 Age, year 44(4) 49(4) 55(4)* Education, years 17(1) 17(2) 17(2) Race, % white 75 76 82 APOE-4 carrier, % positive 44 39 50 MoCA score, unitless 28(1) 29(2) 29(1) Menopause hormone therapy, % current users n.a. 15 34 Oral contraceptives, % current users 4 12 n.a. Hysterectomy / oophorectomy status, % positive n.a. 1 22^ Hormone levels FSH (mIU/mL) 9(10) 31(35)* 78(31)*^ LH (mIU/mL) 8(6) 20(19)* 40(14)*^ Estradiol (pg/mL) 111(103) 83(91) 21(32)*^ Values are mean (SD) unless otherwise indicated. *Different from pre-menopausal group, p < 0.05 ^Different from peri-menopausal group, p < 0.05 Associations between FSH, LH and AD biomarkers We used multiple linear regressions to test for voxel-based associations of FSH and LH levels with AD biomarkers, including Aβ load on 11 C-Pittsburgh compound B (PiB) Positron Emission Tomography (PET) and GMV on volumetric MRI, adjusting by age, menopause status and modality-specific confounders, and after cluster-level family-wise error (FWE) multiple comparisons correction. Secondly, we tested for differential associations between FSH, LH and E2 with AD biomarker outcomes. Associations between FSH and AD biomarkers Aβ load Across all participants, FSH levels were positively associated with Aβ load in frontal cortex of the left hemisphere (P FWE ≤ 0.05; Fig. 1 A and Table 2 ). On post-hoc analysis, these associations were driven by the post-menopausal group, which exhibited significant associations between FSH and Aβ load in middle and superior frontal gyri, whereas no associations were found in the peri-menopausal and pre-menopausal groups (Fig. 1 A and Table 2 ) . Table 2 Associations between FSH levels and 11 C-PiB PET amyloid-beta load in AD-regions Cluster extent Coordinates x, y, z Z P FWE cluster* P voxel Anatomical Region Brodmann Area Overall 144 -43 37 29 4.49 0.010 < 0.001 Left Cerebrum, Frontal Lobe, Superior Frontal Gyrus 9 -47 28 33 3.98 < 0.001 Left Cerebrum, Frontal Lobe, Middle Frontal Gyrus 9 -41 44 23 3.81 < 0.001 Left Cerebrum, Frontal Lobe, Middle Frontal Gyrus 46 19 -29 26 − 19 3.62 0.050 < 0.001 Left Cerebrum Frontal Lobe Inferior Frontal Gyrus 47 19 -31 29 46 3.49 0.010 < 0.001 Left Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 -24 40 42 3.33 0.010 < 0.001 Left Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 Post-menopausal group 72 -47 2835 3.66 0.008 < 0.001 Left Cerebrum Frontal Lobe Middle Frontal Gyrus 9 -43 37 29 3.63 < 0.001 Left Cerebrum Frontal Lobe Superior Frontal Gyrus 9 Peri-menopausal group n.s. Pre-menopausal group n.s. *p < 0.05 cluster-level corrected for Family-Type Wise Error (FWE) within the AD MASK . Overall results are adjusted by age, menopause status and cerebellar PiB uptake. Results within each menopausal group are adjusted by age and cerebellar PiB uptake. Associations between FSH and Aβ load were significant, albeit attenuated, after further adjustment for APOE-4 status, menopause type and hormone therapy status (Fig. 1 B). Gray matter volume Across all participants, FSH levels were negatively associated with GMV in bilateral superior frontal cortex; medial, middle, and inferior frontal cortex of the right hemisphere; and precuneus and fusiform gyrus of the left hemisphere (P FWE ≤ 0.025; Fig. 2 A and Table 3 ). On post-hoc analysis, all menopausal groups exhibited negative associations between FSH and GMV in frontal cortices, which were stronger and more widespread in the post-menopausal group (P FWE ≤ 0.013; Fig. 2 A and Table 3 ). Additionally, negative associations between FSH and GMV were observed in left parahippocampal gyrus of the post-menopausal group, and in left inferior parietal lobule and fusiform gyrus of the peri-menopausal group (P FWE ≤ 0.006; Fig. 2 A and Table 3 ). Table 3 Associations between FSH levels and MRI gray matter volume in AD-regions Cluster extent Coordinates x, y, z Z P FWE cluster* P voxel Anatomical Region Brodmann Area Overall 29 -10 52 27 4.21 0.013 0.001 Left Cerebrum, Frontal Lobe, Superior Frontal Gyrus 9 44 33 35 26 3.96 0.007 0.001 Right Cerebrum, Frontal Lobe, Middle Frontal Gyrus 9 56 6 59 20 3.89 0.005 0.001 Right Cerebrum, Frontal Lobe, Medial Frontal Gyrus 10 6 52 26 3.44 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 9 46 14 28 − 20 3.89 0.007 0.001 Right Cerebrum, Frontal Lobe, Inferior Frontal Gyrus 11 37 10 53 − 21 3.64 0.010 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 11 34 8 35 44 3.54 0.011 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 27 -8 -69 49 3.54 0.015 0.001 Left Cerebrum, Parietal Lobe, Precuneus 7 31 -42 -48 -16 3.25 0.012 0.001 Left Cerebrum, Temporal Lobe, Fusiform Gyrus 37 16 47 25 − 11 3.30 0.025 0.001 Right Cerebrum, Frontal Lobe, Inferior Frontal Gyrus 47 53 31 − 10 3.14 0.001 Right Cerebrum, Frontal Lobe, Inferior Frontal Gyrus 47 Post-menopausal group 17 16 37 47 4.56 0.007 < 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 45 -39 -23 -16 3.88 0.002 < 0.001 Left Cerebrum, Limbic Lobe, Parahippocampal Gyrus 36 40 49 33 − 5 3.81 0.003 < 0.001 Right Cerebrum, Frontal Lobe, Inferior Frontal Gyrus 47 Peri-menopausal group 48 -45 -43 40 4.41 0.003 < 0.001 Left Cerebrum, Parietal Lobe, Inferior Parietal Lobule 40 32 -41 -48 -7 3.87 0.006 < 0.001 Left Cerebrum, Temporal Lobe, Fusiform Gyrus 37 26 -37 20 32 3.83 0.008 < 0.001 Left Cerebrum, Frontal Lobe, Middle Frontal Gyrus 9 25 8 57 − 19 3.68 0.008 < 0.001 Right Cerebrum, Frontal Lobe, Medial Frontal Gyrus 11 16 35 33 24 3.67 0.013 < 0.001 Right Cerebrum, Frontal Lobe, Middle Frontal Gyrus 9 17 -37 41 15 3.62 0.012 < 0.001 Left Cerebrum, Frontal Lobe, Middle Frontal Gyrus 10 Pre-menopausal group 73 6 54 25 4.51 0.001 < 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 9 31 -29 27 42 3.92 0.005 < 0.001 Left Cerebrum, Frontal Lobe, Middle Frontal Gyrus 8 17 -18 50 26 3.84 0.011 < 0.001 Left Cerebrum, Frontal Lobe, Superior Frontal Gyrus 9 16 29 36 37 3.82 0.012 < 0.001 Right Cerebrum, Frontal Lobe, Middle Frontal Gyrus 9 39 8 39 44 3.56 0.004 < 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 2 31 48 3.21 < 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 *p < 0.05 cluster-level corrected for Family-Type Wise Error (FWE) within the AD MASK . Overall results are adjusted by age, menopause status and total intracranial volume. Results within each menopausal group are adjusted by age and total intracranial volume. Associations between FSH and GMV remained significant after further adjustment for APOE-4-status, menopause type and hormone therapy status, although the strength of the association was reduced in the post-menopausal group (Fig. 2 B). Associations between LH and AD biomarkers Aβ load Adjusting for the same confounders as above, there were no significant associations between plasma LH levels and Aβ load in any region. Gray matter volume Across all participants, LH levels were negatively associated with GMV in bilateral precuneus, right superior frontal gyrus and left middle frontal gyrus (Fig. 3 A and Table 4 ). On post-hoc analysis, both post-menopausal and pre-menopausal groups exhibited LH-GMV associations in right superior frontal gyrus, while no associations were detected in the peri-menopausal group (Table 4 ). Table 4 Associations between LH levels and MRI gray matter volume in AD-regions Cluster extent Coordinates x, y, z Z P FWE cluster* P voxel Anatomical Region Brodmann Area Overall 132 -6 -69 48 4.01 < 0.001 < 0.001 Left Cerebrum, Parietal Lobe, Precuneus 7 -4 -55 55 3.59 < 0.001 Left Cerebrum, Parietal Lobe, Precuneus 7 -16 -70 51 3.55 < 0.001 Left Cerebrum, Parietal Lobe, Precuneus 7 78 8 35 44 3.90 0.002 < 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 23 10–53 58 3.66 0.012 < 0.001 Right Cerebrum, Parietal Lobe, Precuneus 7 21 -25 39 32 3.52 0.013 < 0.001 Left Cerebrum, Frontal Lobe, Middle Frontal Gyrus 9 Post-menopausal group 72 10 35 44 4.76 < 0.001 < 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 Peri-menopausal group n.s. Pre-menopausal group 23 4 31 48 3.60 0.003 < 0.001 Right Cerebrum, Frontal Lobe, Superior Frontal Gyrus 8 *p < 0.05 cluster-level corrected for Family-Type Wise Error (FWE) within the AD MASK . Overall results are adjusted by age, menopause status and total intracranial volume. Results within each menopausal group are adjusted by age and total intracranial volume. Associations between LH and GMV remained significant after further adjustment for APOE-4-status, menopause type and hormone therapy status, although the strength of the association was reduced in precuneus and middle frontal gyrus of the post-menopausal group (Fig. 3 B). Differential Associations of FSH and LH with AD biomarkers We conducted a stringent subtraction analysis 24 11 to determine whether FSH had fully independent associations with GMV independent of LH, and vice-versa. FSH . On subtraction analysis (e.g. after excluding all clusters with significant associations between LH and GMV) overall associations between FSH and GMV remained significant in all regions except precuneus and part of the right superior frontal gyrus (P FWE ≤ 0.020; Supplementary Table 1 ). Within each menopausal group, negative associations between FSH and GMV remained unchanged accounting for LH levels (P FWE <0.013; Supplementary Table 1 ). As shown in Fig. 4 A, remaining clusters showed no anatomical overlap with those associated with LH, indicating regionally independent effects of FSH on GMV. LH . On subtraction analysis (e.g. after excluding all clusters with significant associations between FSH and GMV), overall associations between LH and GMV remained significant in middle frontal gyrus and right precuneus, and were reduced in right superior frontal gyrus and left precuneus (P FWE ≤ 0.014; Supplementary Table 2 ). In the post-menopausal and pre-menopausal groups, adjusting by FSH, negative associations between LH and GMV in right superior frontal gyrus remained significant (P FWE ≤ 0.007; Supplementary Table 2) . As shown in Fig. 4 B, these clusters showed no anatomical overlap with those associated with FSH, indicating regionally independent effects of LH on GMV. Associations between E2 and AD biomarkers Aβ load Adjusting for the same confounders as above, there were no significant associations between plasma E2 levels and Aβ load in any region. Gray matter volume Across all participants, E2 levels were positively associated with GMV in orbitofrontal gyrus of the left hemisphere (cluster extent 101 voxels, x=-2, y=-51, z=-22, Z = 3.91, P FWE =0.003, Brodmann Area 11). On post-hoc analysis, there were no associations between E2 levels and GMV in any menopausal group. Differential Associations of FSH, LH and E2 with AD biomarkers We conducted a subtraction analysis to determine whether FSH and LH had fully independent associations with GMV from E2 by excluding all regions with significant E2-GMV associations. Associations between FSH and GMV remained unchanged after excluding clusters with E2-GMV associations (P FWE ≤ 0.024; Fig. 4 C and Supplementary Table 3 ), indicating regionally independent effects of FSH on GMV. Associations between LH and GMV also remained unchanged after excluding clusters with E2-GMV associations (P FWE ≤ 0.013; Fig. 4 D and Supplementary Table 4 ), indicating regionally independent effects of LH on GMV. Associations between FSH, LH and cognition Adjusting by age and education, there were no significant associations between FSH, LH and cognitive scores ( Supplementary Table 5 ). Discussion In midlife women at risk for AD, increasing serum FSH levels across the menopause transition were associated with progressively higher Aβ load and lower GMV in brain regions vulnerable to AD, especially in frontal cortex. FSH effects on Aβ load were driven by the post-menopausal group, whereas negative effects of FSH on GMV were evident already before menopause. Increasing LH levels were also associated with lower GMV in frontal regions, but not with Aβ load. Results were independent of age, APOE-4 status, menopause type (spontaneous vs. induced), hormone therapy status, and E2 levels. Female sex is inextricably linked to the midlife neuroendocrine aging transition of menopause 7 . With the aging of the global population in the coming decades, it is estimated that 1.2 billion women worldwide will be transitioning through menopause by the year 2030 25 . All women undergo menopause either through natural midlife aging processes or via surgical or pharmacological intervention. Neuroimaging studies have identified the menopause transition as a female-specific risk factor for AD 5 , 15 , 26 based on observations that midlife women exhibit AD endophenotypes including Aβ accumulation, glucose hypometabolism, and GMV loss in brain regions vulnerable to AD with onset in perimenopause 8 – 13 . These changes are generally attributed to the loss of neuroprotective effects of ovarian sex steroid hormones, E2 in particular 7 , 14 . However, recent preclinical work indicates a role for increasing gonadotropin FSH and LH levels in AD pathology in addition to, or independent of the better-characterized effects of estrogenic depletion 2 1, 2 2 . The menopause transition is characterized not only by a decline in E2 levels but also a rise in the pituitary gonadotropins FSH and LH 18 . During a woman’s reproductive years, FSH and LH stimulate ovulation and estrogen production in the ovary, and are inhibited by negative feedback from estrogen levels. In perimenopause, as E2 fluctuates, FSH and LH levels exhibit over 10-fold and 3-fold increases, respectively 27 , in response to the loss of ovarian feedback 18 . FSH also rises earlier than LH 28 , suggesting a possibly earlier role for this hormone in neuronal aging. In mechanistic analyses, high serum FSH and LH are associated with cerebral Aβ deposition both in vitro and in vivo , with reversible effects after pharmacological suppression 22 , 29 . Recent work demonstrates that daily injections of recombinant human FSH cause a marked acceleration of Aβ as well as tau protein accumulation in triple AD transgenic female mice, leading to subsequent neuroinflammation, synaptic degeneration, and cognitive impairment 22 . Oophorectomy exacerbated the development of AD-phenotype, whereas FSH blockade prevented accumulation of AD pathology and improved cognition 22 . Similar findings were obtained via experiments of female mice in an estrogen-replete state, indicating independent effects of FSH from those of E2 22 . In further analyses, FSH and LH were shown to induce neurodegenerative changes through their action on their respective receptors (FSHR and LHR), which in turn promote APP cleavage in a pro-amyloidogenic direction 22 , 29 as well as pro-inflammatory IL-1β and IL-6 cytokines expression 22 . Since FSHR and LHR also regulate structure and a diverse range of functions in the brain, dysregulation of the HPG axis at menopause leading to gonadotropin elevations constitutes a largely underexplored, physiologically relevant risk factor for neurodegeneration. Additionally, FSH increases upregulated low-density lipoprotein receptor-related protein (LRP) in neurons, astrocytes and activated microglia, and LRP binding to ApoE and APP unbalances Aβ metabolism in turn 30 , further implicating FSH elevations in AD pathogenesis 21 . Whether similar mechanisms are active in women remains unknown. Herein, we took a translational approach to characterize the effects of increasing FSH and LH from pre-menopausal to post-menopausal levels on AD pathology and neurodegenerative biomarkers among women at risk for AD. Our results are consistent with preclinical findings implicating FSH elevations in Aβ deposition, an effect that survived multiple comparison adjustment and masking in frontal areas of post-menopausal women. Additionally, both FSH and LH levels were associated with progressively lower GMV in frontal cortex with onset at the pre-menopausal stage. These results point to the frontal cortex, a brain region subserving higher order cognitive processes such as working memory and executive functions 31 , as a main site for gonadotropic action on AD risk. In vitro and ex vivo studies show that the frontal cortex is rich in FSHR and LHR in both rodent and human brain 22 , 32 , and both receptors are highly expressed in limbic cortex, an AD vulnerable region in turn. At post-mortem, FSHR expression is higher in frontal regions of AD patients as compared to controls 32 , further linking FSH to neurological harm. In vivo neuroimaging work also identifies the frontal cortex as a site of early vulnerability to both Aβ deposition and GMV loss during the prodromal phase of AD 11 , 33 . For women in particular, longitudinal studies reported increasing Aβ load in frontal regions of post-menopausal and peri-menopausal women, with no or limited changes in other regions 10 , identifying this area as an early site of AD-related vulnerability in women undergoing menopause. While Aβ effects were limited to frontal cortex, negative associations between FSH and GMV extended to parahippocampal gyrus, precuneus, inferior parietal and fusiform gyrus of post- and peri-menopausal participants. LH levels also correlated with GMV in precuneus across menopause stages. These results may reflect downstream effects of Aβ on neuronal integrity 34 , possibly induced by soluble Aβ oligomers, which promote neurotoxicity prior to fibrilization into plaques 35 but are undetectable by means of 11 C-PiB PET. To this point, previous imaging studies have shown distant associations of frontal Aβ uptake and temporo-parietal GMV reductions in AD patients 36 . Alternatively, gonadotropin-related GMV declines may be due to intracellular neurofibrillary tangles, which have been linked to neuronal loss in limbic and temporo-parietal regions prior to Aβ deposition 37 . In a recent tau-PET study, post-menopausal women exhibited higher tau deposition in parieto-occipital regions and in middle frontal gyrus as compared to age-controlled men 38 . It is unknown whether these differences were related to specific hormone levels. In the present study, plasma E2 levels collected at the same time as FSH and LH were not associated with Aβ load across participants or in any menopausal group, but were associated with GMV in orbitofrontal gyrus. This is consistent with the known trophic effects of E2 on synaptic density in frontal regions 15 . Nonetheless, there was no overlap between frontal areas impacted by E2, FSH and LH, suggesting that these hormones influence neuronal density in frontal cortex with sub-regional specificity. For the purpose of this discussion, the fact that FSH associations with Aβ biomarkers were statistically and anatomically independent of E2 and LH effects lends support to preclinical evidence of an early pathological contribution of this hormone to AD 22 . Additionally, negative effects of both gonadotropins on GMV in AD-regions beyond frontal cortex, and with onset at the peri-menopause stage, are consistent with the menopause transition being a tipping point for AD risk later in life 15 , indicating an optimal window of opportunity for therapeutic intervention in advance of irreversible neurological damage 7 , 10 . More work is needed to clarify the biological pathways by which FSH exerts negative effects on Aβ-biomarker risk in women, and to unravel the differential action of HPG hormones on neuronal aging and AD. Nonetheless, together with preclinical work, present results identify FSH as a potential female-specific Aβ-lowering therapeutic target, and support testing of therapies that regulate serum gonadotropin levels for AD prevention. Currently, estrogen MHT with or without a progestin is the treatment of choice for menopausal symptoms 19 and holds promise for AD prevention 39 . However, while observational studies generally report favorable effects of MHT on AD risk, clinical trials indicate null effects in early post-menopausal patients and null or negative effects in older post-menopausal women 14 , 15 , 17 . Protective effects are generally more consistent among younger women, especially following oophorectomy, with benefits pertaining to early initiation relative to surgery or age of menopause onset 40 . Besides increasing estrogen levels, MHT induces a dose-related decrease in serum FSH in post-menopausal women 41 . The neurological effects of decreasing FSH following MHT are not well understood. However, in the Kronos Early Estrogen Prevention Study (KEEPS), after 48 months of randomization to either oral conjugated equine estrogen (o-CEE), transdermal 17β estradiol (tE2) or placebo treatment, decreases in FSH were associated with smaller increases in white matter hyperintensities (WMH) in the tE2 group 42 . Given the potential impact of FSH on cardiovascular risk 30 , more work is warranted to examine whether lowering FSH by means of hormone therapy is a viable strategy to reduce WMH, which are a risk factor for AD in turn 43 . Estrogen- and progesterone-containing OCP inhibit the release of gonadotropin-releasing hormone (GnRH), suppressing levels of FSH and LH, and preventing follicular development and ovulation in turn 44 . OCP can be used to alleviate symptoms of menopause at the perimenopausal stage 45 , representing another therapeutic option for controlling gonadotropin effects on AD risk. Generally, MRI studies show greater GMV in OCP users compared to never-users, although results are not always consistent 15 . In an MRI study of middle-aged women at risk for AD, those who took OCP before menopause and MHT for menopause exhibited larger GMV in AD-vulnerable regions, including frontal and parietal cortex, medial temporal lobe, precuneus, and fusiform gyrus, as compared to never-users of both 13 . Notably, these are some of the same regions displaying negative effects of FSH on GMV in the present study, warranting further investigation. Further epidemiological support for a role of gonadotropins in AD is evidenced by a reduction in neurodegenerative disease among prostate cancer patients treated with GnRH agonists 21 . GnRH-agonists and antagonists commonly used to treat hormone-sensitive conditions, such as prostate cancer and endometriosis, disrupt physiological GnRH pulses, producing a drop in serum FSH and LH levels 46 . Preliminary evidence indicates that GnHR agonists reduced Aβ pathology and improved cognition in rodents 23 . Clinical trials of humanized monoclonal FSH antibodies, which proved effective in reversing AD pathology in mice 22 , are also warranted. Additionally, emerging research is investigating whether healthy ovarian tissue cryopreservation may restore pre-menopausal steroid hormone levels in post-menopausal women 47 , 48 . Initial studies show that serum FSH dramatically decreases after ovarian tissue transplantation, inducing the resumption of folliculogenesis in grafts 47 , 48 , thus possibly delaying the onset of menopause and associated neuropathological changes. Finally, the administration of platelet-rich plasma with recombinant FSH to the ovaries was shown to restore ovarian function, at least temporarily, in early post-menopausal women 49 . While this technique is aimed at prolonging a woman's fertility period 49 , it may also hold promise for prevention of menopause-associated AD risk. Strengths and Limitations To our knowledge, this is the first in vivo brain imaging study to investigate associations of FSH and LH levels on AD risk in women. We focused on carefully screened clinically healthy midlife women, ages 40–65 years, with no comorbidities or incidental findings. All participants received thorough clinical and cognitive exams, laboratory tests, menopause assessments, and brain imaging. From a methodological perspective, we capitalized on the menopause transition as a natural experiment of increasing FSH and LH levels by examining statistically powered groups of women at different menopausal stages. FSH and LH levels are progressively higher from pre-menopausal through post-menopausal stages, and show uncorrelated patterns and greater differentiation starting at perimenopause, which enabled us to test for differential associations with AD biomarker outcomes using a cross-sectional design. We used state-of-the-art voxel-based analysis paired with age correction procedures and stringent statistical reporting criteria, while using a translational approach to ensure that our results were both statistically and biologically valid. Results were significant after multi-variable correction for age, APOE-4 status, modality-specific confounders, plasma E2 levels, menopause type, and hormone therapy use. We chose this study design because the timing of menopause is highly variable, with a median age at menopause of 51 years, and a range of 40–58 years 18 . Longitudinal studies may require 10 or more years of follow-ups to capture simultaneous changes in hormones and AD biomarkers. While studies of oophorectomy ideally reduce follow-up times, the procedure is often performed as a result of medical conditions affecting the ovaries, and is associated with more severe neuropathological outcomes than spontaneous menopause 50 , 51 . However, given the cross-sectional nature of this study, a causal link between gonadotropin levels and AD biomarkers cannot be unequivocally established. Longitudinal studies are warranted to clarify whether FSH and LH effects on AD biomarkers are predictive of future dementia, and to test for differential effects of surgical and spontaneous menopause. Very little work has been done to compare FSH, LH and E2 effects on AD risk, which may have led to underestimating the role of the gonadotropins. In this study, associations between FSH, LH and AD biomarkers were independent of E2 levels, as determined via a stringent subtraction analysis. While all hormones were associated with GMV in frontal cortex, associated clusters exhibited no anatomical overlap, indicating synergistic but regionally independent effects on neuronal density in this region. As previously discussed, FSH was associated with Aβ load whereas LH and E2 were not, suggesting an earlier contribution of FSH to emerging AD pathology in midlife. While both FSH and LH levels raise in perimenopause, differences in control mechanisms may account for the earlier effects of FSH. First, FSH has greater sensitivity to the inhibition of HPG axis hormones such as inhibin B, whose serum levels fall during the early perimenopausal phase independent of changes in E2 28 . Secondly, while GnRH pulse frequencies regulate both FSH and LH secretion, secondary regulatory factors specific to FSH release exist 28 . However, since FSH and LH measures are intercorrelated and this is a cross-sectional analysis, we cannot fully parse out their effects. We offer that present results provide preliminary data for future hypothesis-driven studies aimed at assessing differential timeline and effects of sex hormones on AD biomarker risk before and after menopause. Finally, gonadotropin levels were not significantly associated with global cognition or memory scores. This may be due to the fact that the post-menopausal group did not exhibit impaired or diminished cognitive performance as compared to the other groups, consistent with observations that, while self-reports of poor memory and concentration are common in women of menopausal age 18 , menopause itself is not associated with clinically significant deficits 52 . However, all our participants had at least 12 years of education, and cognitive test results within norms by age and education, which may have hindered detection of associations between hormonal levels and cognition. It is also possible that our testing battery may not be sufficiently demanding to detect early effects of gonadotropin-related AD pathology on cognitive function. Future studies with larger cohorts of midlife women with different educational levels and socioeconomical backgrounds are needed to address these questions. Conclusions Our results provide novel evidence for emergence of gonadotropin-related AD endophenotypes in midlife women at risk for AD, with FSH levels being the main predictor of Aβ load. Outcomes are consistent with preclinical work implicating rising gonadotropin levels in women’s greater lifetime risk of AD, and provide novel hormonal therapeutic targets for precision medicine strategies for AD prevention. Declarations Acknowledgments This study was supported by grants from NIH/NIA (P01AG026572, R01AG05793, R01AG0755122), NIH/NCATS UL1TR002384, the Cure Alzheimer’s Fund, the Women’s Alzheimer’s Movement; and philanthropic support to the WCM Alzheimer’s Prevention Program. Online Methods Participants and Data This is a natural history, non-interventional study of cognitively normal women ages 40-65 years carrying risk factors for late-onset Alzheimer’s Disease (AD) such as a family history and/or APOE-4 genotype. We focused on women at different menopausal stages (pre-menopause, peri-menopause, and post-menopause) as a natural experiment of increasing follicle stimulating hormone (FSH) levels across the menopause transition. Participants were recruited at the Weill Cornell Medicine (WCM) Alzheimer’s Prevention Program between 2018-2022 by self-referral, flyers, and word of mouth. Our inclusion and exclusion criteria have been previously described 8-13 . Briefly, all participants had Montreal Cognitive Assessment (MoCA) score > 26 and normal cognitive test performance by age and education. Exclusion criteria included medical conditions that may affect brain structure or function (e.g., stroke, any neurodegenerative diseases, major psychiatric disorders, hydrocephalus, demyelinating disorders such as Multiple Sclerosis, intracranial mass, and infarcts on Magnetic Resonance (MRI)), use of psychoactive medications, and contraindications to MRI or Positron Emission Tomography (PET) imaging. All participants received medical, neurological, laboratory, cognitive and volumetric MRI exams within 6 months of each other. Half of the participants also received 11 C-Pittsburgh Compound B (PiB) PET to measure Aβ load. The patients’ sex was determined by self-report. APOE-4 genotype was determined using standard qPCR procedures 8-13 . Participants carrying one or two copies of the APOE-4 allele were grouped together as ApoE-4 carriers, and compared to non-carriers. A family history of late-onset AD was elicited using standardized questionnaires. Standard protocol approvals, registrations, and patient consents All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the WMC Institutional Review Board. Written informed consent was obtained from all participants. Cognitive measures Participants underwent a cognitive testing battery assessing memory [Rey Auditory Verbal Learning Test (RAVLT), Wechsler Memory Scale logical memory delayed recall], executive function [Trail Making Test, FAS], and language [object naming, animal naming] 8-13 . Cognitive measures were scaled to standard deviations and centered at 0. A composite memory score was obtained by z-scoring each memory test and averaging across measures. A global cognition score was obtained by z-scoring the remaining tests and averaging within and across the three domains. Hormonal panel Each participant received a blood draw by venipuncture after an overnight fast. Samples were shipped overnight to CLIA-certified Boston Heart Diagnostics [Framingham, MA] and analyzed on a Roche Cobas e801 analytical unit for immunoassay tests using Electrochemiluminescence technology (ECL) [Roche Diagnostics; Basel, Switzerland]. FSH and LH were assessed through electrochemiluminescence sandwich immunoassay (ELISA) with a measuring range of 0.3‑200 mIU/mL for both hormones. Estradiol (E2) was assessed through competitive immunoassay with a measuring range of 18.4‑11010 pmol/L (5‑3000 pg/mL). Menopause assessments Determination of menopausal status was based on the Stages of Reproductive Aging Workshop (STRAW) criteria 53 with hormone laboratory assessments as supportive criteria 13 . Participants were classified as pre-menopausal (regular cycler), peri-menopausal (irregular cyclers with an interval of amenorrhea > 60 days or > 2 skipped cycles) and post-menopausal (absence of menstrual cycle for > 12 months) 13 . A history of hysterectomy and/or oophorectomy before menopause was assessed through review of surgical history. Hormone therapy Semi-standardized questionnaires were used to obtain information on history of menopause hormone therapy (MHT) and oral contraceptive (OCP) usage. This information was used to classify participants as current vs. past or never users of MHT or OCP 13 . Image Acquisition and Analysis All brain scans were acquired following standardized procedures 8-13 . Participants received a 3D volumetric T 1 -weighted MRI scan on a 3.0 T GE MR 750 Discovery scanner (General Electric, Waukesha, WI) [Brain Volume Imaging (BRAVO); 1x1x1 mm resolution, 8.2 ms repetition time (TR), 3.2 ms echo time (TE), 12° flip angle, 25.6 cm field of view (FOV), 256x256 matrix with ARC acceleration] using a 32-channel head coil. The 11 C-PIB PET scan was acquired using a Siemens BioGraph mCT 64-slice PET/CT operating in 3D mode [70 cm transverse FOV, 16.2 cm axial FOV]. Summed PET images were obtained 60-90 min post-injection of 15 mCi of 11 C-PiB and corrected for attenuation, scatter and decay. Image analysis was performed using a fully automated image processing pipeline 8-13 . MRI and PET scans were realigned using the Normalized Mutual Information routine of Statistical Parametric Mapping (SPM12) 54 implemented in Matlab R2018a (MathWorks; Natick,MA). Each PET scan was co-registered to the corresponding T1-weighted MRI scan. MRI scans were spatially normalized to normalized to the template -normalized tissue probabilistic map (TPM) image included in SPM12, conforming to the Montreal Neurological Institute (MNI) space, using voxel-based morphometry (VBM). VBM processing included image segmentation, Jacobian modulation, high-dimensional warping (DARTEL) of the segments, and application of an 8mm full-width at half maximum smoothing kernel 55 . Gray matter (GM) segments were retained for statistical analysis. The MRI-coregistered PET scans were then spatially normalized to the TPM image using subject-specific transformation matrices using the MRI as the anchor, and smoothed using an 8-mm FWHM filter 54 . SPM12 was also used to obtain total intracranial volume (TIV) as the sum of gray, white and cerebrospinal fluid volume for each subject 55 . Cerebellar gray matter PiB uptake was extracted using the automated anatomical labeling (AAL3) atlas 56 and WFU PickAtlas 2.4 57 . Covariates Brain imaging analyses were adjusted by age, menopause status, and modality-specific confounders (MRI TIV; cerebellar PiB uptake). Cognitive analyses were adjusted by age and education (years). For exposures showing significant associations with outcome measures, we further examined as confounders: APOE-4 status (carrier vs. non-carrier); OCP (user vs. non-user) status for the pre- and peri-menopausal groups; MHT status (user vs. non-user) for peri- and post-menopausal groups; and menopause type (surgical vs. spontaneous) for the post-menopausal group. Statistical Analysis Analyses were performed in SPSS v.25, R v.4.2.0 and SPM12. Clinical measures were examined using general linear models or chi-squared tests as appropriate. Cohort characteristics are described using mean (standard deviation) and n, percentage (%), stratified by exposure group. We conducted several analyses to address the independent effects of FSH and LH levels from age and menopause status according to published methods 12,58 : (a) we used box plots and frequency diagrams to confirm that we had sufficient overlap among women of different menopause statuses (Table 1) which enabled us to examine the effects of FSH and LH separately from additional effects of menopause status; (b) included age and menopause status as covariates in analyses of the entire sample; (c) tested for associations between FSH, LH and AD biomarkers within each menopausal group, also adjusting by age; and (d) conducted a stringent subtraction analysis 24 to test for unique contributions of the gonadotropins to AD biomarkers independent of each other and additional confounders, as described below. FSH and LH associations with AD biomarkers We used voxel-based multivariate linear regressions with post-hoc t -contrasts to test for associations between exposures (FSH and LH) and AD biomarker outcomes (Aβ load and GMV) across all participants, adjusting by confounders. In presence of significant main outcomes, we tested for associations between exposures and outcomes within each menopausal group. Statistical maps were obtained at p<0.05, cluster-level corrected for Family-Wise Type Error (FWE) 59 within a binary masking image consisting of a priori defined regions with known vulnerability to AD (AD MASK ). These included bilateral cortical (inferior and superior parietal lobule; inferior, middle and superior temporal gyrus; inferior, orbital, middle, medial and superior frontal gyrus; fusiform gyrus; anterior, middle and posterior cingulate gyrus; precuneus) and subcortical areas (thalamus and medial temporal regions, including hippocampus, amygdala, parahippocampal gyrus, entorhinal and perirhinal cortex) 60 . In analysis of PiB data, medial temporal regions were excluded from the mask. The AD MASK was set as an explicit (inclusive) mask to conservatively restrict analysis to voxels within the mask to increase interpretability and reduce the number of voxel-wise comparisons, thus increasing sensitivity to detecting true effects and limiting the potential for false positives 24 . Cluster extent was set at > 16 voxels (e.g. twice the FWHM). Anatomical location of regions reaching significance was described using Talairach coordinates after conversion from MNI space. Biomarker measures were extracted from peak clusters using a volume-of-interest (VOI) approach using MarsBar 0.45 [https://marsbar-toolbox.github.io/download.html] for further analysis. To characterize the correlation between FSH and LH levels in selected brain regions and outcomes of interest, correlation graphs are presented both for the overall study sample as well as by menopause status. The stratified analysis was performed to investigate hypothesized differences in the strength of the correlations throughout menopause. Stratification by menopause status also mitigates the effects of its confounding on the overall correlations, since menopause status is known to be associated both with FSH and LH levels, and the outcomes of interest. As age, APOE-4 status, menopause type and hormone therapy status may also impact these relationships, all correlations also adjust for these variables, as appropriate. E2 associations with AD biomarkers The above procedures were used to test for associations between plasma E2 levels and AD biomarkers at p<0.05, cluster-level FWE corrected within the AD MASK . Subtraction analysis: testing for independent effects of each hormone on AD biomarkers In the interest of being maximally conservative, we conducted a stringent subtraction analysis 24 in the framework of SPM12 to determine whether FSH and LH had entirely independent associations with Aβ load and GMV from each other and from E2. This was accomplished by testing for associations between each exposure and AD biomarkers excluding all regional contributions of the other exposures. To this aim, we first identified brain regions with statistically significant associations between each exposure and each outcome at p<0.05, cluster-level FWE corrected in the AD MASK , as described above. Brain regions with significant associations between FSH, LH, E2 and the outcomes were then saved as binary masks (FSH MASK , LH MASK , E2 MASK ). These binary masks were set as explicit masks at the t-map generation step 24 and SPM12 was employed to evaluate the significant independent associations between each exposure and the outcomes excluding all voxels in the explicit masks of the other exposures, adjusting by clinical and modality-specific confounders. For example, in testing of associations between FSH and GMV, LH MASK and E2 MASK were set as explicit masks, and analysis of associations between FSH and GMV was restricted to the remaining regions (e.g. after masking out all regions showing LH-GMV and E2-GMV associations). For each exposure, significant associations are reported in brain regions that survive the masking and the multiple comparisons adjustment. 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Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Silky","middleName":"","lastName":"Pahlajani","suffix":""},{"id":159631652,"identity":"6fd2cde2-e226-43f3-b095-be682feca51b","order_by":11,"name":"Schantel Williams","email":"","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Schantel","middleName":"","lastName":"Williams","suffix":""},{"id":159631653,"identity":"e4a8a10e-1361-4456-8055-72506a3cd182","order_by":12,"name":"Valentina Berti","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Berti","suffix":""},{"id":159631654,"identity":"35753dcb-e1e0-4a9e-9dd4-db08d0102804","order_by":13,"name":"Jonathan Dyke","email":"","orcid":"https://orcid.org/0000-0001-7170-488X","institution":"Weill Cornell Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Dyke","suffix":""},{"id":159631655,"identity":"db7ca1f1-c647-4ccc-9fea-e3f6fe2be1ff","order_by":14,"name":"Roberta Diaz Brinton","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Roberta","middleName":"Diaz","lastName":"Brinton","suffix":""},{"id":159631656,"identity":"66d5ce06-2af5-4a38-ae4d-0e779eeadabe","order_by":15,"name":"Lisa Mosconi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACPmYGxgcJP/7JAdkGECF25gYGBjbcWtiYGZgNPvYcMEZoYWYkoAWIJGewHUhsIF4LO3eCNA/PnfS17c0bGH5U3MvjB2n5UHYYj8N4NxjzWDzL3XbmWAFjz5niYslmxgbGGefwa0nm4WHO3XYjx4CZsS0hccNhxgZm3jb8Wg7zsDGnm91/A9GyH6TlL34tGxtnsB1OMLvBA7UF6BcgA6+WzQwfe9IMt51JKzjYcyYhcQbQloM959JxauHnP7v9R8IPG3mz44c3PvhRkZDY39588MGPMmucWlDAAQzGKBgFo2AUjALyAADbOVZE4DN3ewAAAABJRU5ErkJggg==","orcid":"","institution":"Weill Cornell Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Mosconi","suffix":""}],"badges":[],"createdAt":"2022-12-06 23:50:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2351642/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2351642/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":30338769,"identity":"56231b8e-4f85-44fe-8cce-80d1b7c488fd","added_by":"auto","created_at":"2022-12-14 20:09:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":561773,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of FSH with Aβ load\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; Surface maps of positive voxel-wise associations between FSH levels and \u003csup\u003e11\u003c/sup\u003eC-PiB uptake, reflecting Aβ load, across all participants and within each menopausal group.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; Associations between FSH and PiB uptake extracted from peak clusters are further adjusted for APOE-4 status for all participants; oral contraceptive use for pre- and peri-menopausal groups; menopause hormone therapy status for the post- and peri-menopausal groups; and menopause type for the post-menopausal group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults are represented on color-coded scales with corresponding P values or partial correlation coefficients, as appropriate.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ALL, all clusters combined; FWE, correction for family wise type error; IFG, inferior frontal gyrus; MiFG, middle frontal gyrus; n.s., not significant; Peri, peri-menopause; Post, post-menopause; Pre, pre-menopause; SFG, superior frontal gyrus.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2351642/v1/b4254d29f471b2b2ef253dc7.png"},{"id":30338770,"identity":"d7f7a037-6335-4010-bfce-8f460b891d40","added_by":"auto","created_at":"2022-12-14 20:09:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":603902,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of FSH with gray matter volume\u003c/p\u003e\n\u003cp\u003e(A) Surface maps of negative voxel-wise associations between FSH levels and gray matter volume (GMV) across all participants and within each menopausal group.\u003c/p\u003e\n\u003cp\u003e(B) Associations between FSH and GMV extracted from peak clusters are further adjusted for APOE-4 status for all participants; oral contraceptive use for pre- and peri-menopausal groups; menopause hormone therapy status for the post- and peri-menopausal groups; and menopause type for the post-menopausal group.\u003c/p\u003e\n\u003cp\u003eResults are represented on color-coded scales with corresponding P values or partial correlation coefficients, as appropriate.\u003c/p\u003e\n\u003cp\u003eAbbreviations: FUS, fusiform; FWE, correction for family wise type error; IFG, inferior frontal gyrus; MiFG, medial and middle frontal gyrus; Peri, peri-menopause; PHG, parahippocampal gyrus; Post, post-menopause; Pre, pre-menopause; PCU, precuneus; SFG, superior frontal gyrus.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2351642/v1/361f0cd3f0a89c833282ed05.png"},{"id":30339091,"identity":"fc06ef7e-8766-4459-8042-906a8d71813c","added_by":"auto","created_at":"2022-12-14 20:17:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":514740,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of LH with gray matter volume\u003c/p\u003e\n\u003cp\u003e(A) Surface maps of negative voxel-wise associations between LH levels and gray matter volume (GMV) across all participants and within each menopausal group.\u003c/p\u003e\n\u003cp\u003e(B) Associations between LH and GMV extracted from peak clusters are further adjusted for APOE-4 status for all participants; oral contraceptive use for pre- and peri-menopausal groups; menopause hormone therapy status for the post- and peri-menopausal groups; and menopause type for the post-menopausal group.\u003c/p\u003e\n\u003cp\u003eResults are represented on color-coded scales with corresponding P values or partial correlation coefficients, as appropriate.\u003c/p\u003e\n\u003cp\u003eAbbreviations: FWE, correction for family wise type error; L, left; MiFG, middle frontal gyrus; Peri, peri-menopause; Post, post-menopause; Pre, pre-menopause; PCU, precuneus; R, right; SFG, superior frontal gyrus.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2351642/v1/60d6ac3c279a087d1af87ed3.png"},{"id":30338771,"identity":"d527ff38-a398-4c8c-b1b0-3e282900aa6f","added_by":"auto","created_at":"2022-12-14 20:09:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":708450,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of FSH and LH with gray matter volume independent of each other and of estradiol levels\u003c/p\u003e\n\u003cp\u003e(A) Statistical parametric maps displaying negative voxel-wise associations between (left) FSH and gray matter volume (GMV); and (middle) LH and GMV, are compared to (right) associations between FSH and GMVon subtraction analysis (e.g. excluding all regions that have significant associations with LH).\u003c/p\u003e\n\u003cp\u003e(B) Statistical parametric maps displaying negative voxel-wise associations between (left) LH and GMV; and (middle) FSH and GMV, are compared to (right) associations between LH and GMV on subtraction analysis (e.g. excluding all regions that have significant associations with FSH).\u003c/p\u003e\n\u003cp\u003e(C) Statistical parametric maps displaying negative voxel-wise associations between (left) FSH and GMV; and (middle) estradiol (E2) and GMV, are compared to (right) associations between FSH and GMV on subtraction analysis (e.g. excluding all regions that have significant associations with E2).\u003c/p\u003e\n\u003cp\u003e(D) Statistical parametric maps displaying negative voxel-wise associations between (left) LH and GMV; and (middle) E2 and GMV, are compared to (right) associations between LH and GMV on subtraction analysis (e.g. excluding all regions that have significant associations with E2).\u003c/p\u003e\n\u003cp\u003eResults are represented on a color-coded scale with corresponding P values.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2351642/v1/bc31dadf18e75b07fdce88eb.png"},{"id":39868982,"identity":"027ca26d-2008-4469-8f1b-93eac791daef","added_by":"auto","created_at":"2023-07-11 16:17:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2574879,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2351642/v1/9c6ecab5-95e2-449d-b1de-908c95039a4a.pdf"},{"id":30339169,"identity":"461e7d30-19af-4f28-afad-b62ac942a99e","added_by":"auto","created_at":"2022-12-14 20:25:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":36086,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2351642/v1/25cb4bfd7a24c058ad9b8e7f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Elevated gonadotropin levels are associated with increased biomarker risk of Alzheimer’s disease in midlife women","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD), the most common cause of dementia in the aging population, shows a greater prevalence in women than in men, with post-menopausal women accounting for nearly two thirds of all those affected\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;This disparity is only partially explained by differences in survival rates\u003csup\u003e2\u003c/sup\u003e or genetic risk factors such as Apolipoprotein E epsilon 4 (APOE-4) allele\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMounting evidence from preclinical and translational studies\u0026nbsp;identifies\u0026nbsp;loss of\u0026nbsp;neuroprotective effects of\u0026nbsp;sex steroid hormones\u0026nbsp;following menopause as key biological mechanisms underlying\u0026nbsp;women\u0026rsquo;s greater lifetime risk of AD\u003csup\u003e4,5\u003c/sup\u003e.\u0026nbsp;Menopause exerts its actions on AD risk via alterations of\u0026nbsp;multiple neurobiological mechanisms\u0026nbsp;that can span decades\u003csup\u003e6\u003c/sup\u003e, forming the basis of the\u0026nbsp;~20 year\u0026nbsp;prodromal phase of the disease with onset in midlife\u003csup\u003e7\u003c/sup\u003e,\u0026nbsp;thus proximate to the menopause transition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn preclinical studies, menopause profoundly impacts cerebral bioenergetic aging processes, triggering emergence of aggregated amyloid-\u0026beta; (A\u0026beta;,\u0026nbsp;a hallmark of AD pathology), mitochondrial compromise, and synaptic dysfunction\u003csup\u003e7\u003c/sup\u003e.\u0026nbsp;Translational neuroimaging work provide consistent evidence that peri-menopausal and post-menopausal midlife women at risk for AD exhibit higher A\u0026beta;\u0026nbsp;deposition and\u0026nbsp;increased neurodegenerative biomarker load, chiefly glucose hypometabolism and gray matter volume (GMV) loss, as compared to pre-menopausal women and to age-controlled\u0026nbsp;men\u003csup\u003e8-13\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDeclines in 17\u0026beta;-estradiol (E2) have long been considered the main, and possibly only trigger for menopause-related neurodegenerative changes\u003csup\u003e7,14\u003c/sup\u003e. However, clinical research has provided contrasting evidence for associations between E2 and AD risk in women\u003csup\u003e15,16\u003c/sup\u003e. Additionally, while observational studies of menopause estrogen therapy report generally positive outcomes, randomized clinical trials have not shown consistent AD risk reduction effects\u003csup\u003e14,15,17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWhile estrogen continues to be investigated, these disparities prompted examination of other hypothalamic-pituitary-gonadal axis (HPG) hormones, chiefly follicle-stimulating hormone (FSH) and luteinizing hormone (LH).\u0026nbsp;As opposed to E2, which\u0026nbsp;exhibits wide fluctuations in response to\u0026nbsp;reduced steroidogenic synthesis of the aging ovary\u0026nbsp;before reaching\u0026nbsp;persistently low levels post-menopause,\u0026nbsp;gonadotropin levels\u0026nbsp;increase\u0026nbsp;steadily\u0026nbsp;starting in\u0026nbsp;peri-menopause\u003csup\u003e18,19\u003c/sup\u003e, thus concomitant to emergence of\u0026nbsp;A\u0026beta;\u0026nbsp;pathology in women\u003csup\u003e8-13\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe idea that the brain is a target for pituitary glycoproteins is in stark contrast with the long-held view that these hormones act solely on endocrine targets\u003csup\u003e20\u003c/sup\u003e. Novel research has established broad ubiquity for pituitary hormone action in brain, highlighting a connection between gonadotropin levels and AD risk.\u0026nbsp;In cell cultures, both FSH and LH increase amyloidogenic processing of\u0026nbsp;amyloid precursor protein (APP), while pharmacological suppression of the gonadotropins reduces\u0026nbsp;A\u0026beta;\u0026nbsp;plaque formation\u003csup\u003e16,21-23\u003c/sup\u003e. In recent mechanistic analyses, the raise in FSH accelerated both\u0026nbsp;A\u0026beta;\u0026nbsp;and tau deposition in transgenic mouse models of AD, including female\u0026nbsp;mice in an estrogen-replete state, whereas FSH blockade prevented emergence of AD pathology\u003csup\u003e22\u003c/sup\u003e. It is unknown whether gonadotropin elevations are linked to AD pathology in women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHerein, we take a translational approach to examine whether increasing FSH and LH levels from pre-menopausal to post-menopausal stages are associated with multi-modality biomarker evidence of AD risk, as reflected in higher A\u0026beta; deposition and lower GMV, in midlife women carrying established risk factors for AD such as a family history and/or APOE-4 genotype.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eWe enrolled 205 participants for this study. Of these, 14 were excluded due to incidental findings on Magnetic Resonance Imaging (MRI) (n\u0026thinsp;=\u0026thinsp;5 small vessel disease or lacunar infarctions, n\u0026thinsp;=\u0026thinsp;2 meningiomas, n\u0026thinsp;=\u0026thinsp;1 mild hydrocephalus, n\u0026thinsp;=\u0026thinsp;1 demyelination), image artifacts (n\u0026thinsp;=\u0026thinsp;2), or incomplete hormonal test results (n\u0026thinsp;=\u0026thinsp;3). The remaining 191 participants were examined in this study, including 45 pre-menopausal, 67 peri-menopausal, and 79 post-menopausal women.\u003c/p\u003e \u003cp\u003eParticipant characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Twenty percent of participants reported taking menopause hormone therapy (MHT), and 5% reported using oral contraceptives (OCP). Ten percent of participants had a history of hysterectomy and/or oophorectomy. Hormone therapy usage and menopause type (spontaneous vs. induced) were included as covariates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants\u0026rsquo; characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-menopause\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeri-menopause\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePost-menopause\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55(4)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, % white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOE-4 carrier, % positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA score, unitless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenopause hormone therapy, % current users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral contraceptives, % current users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHysterectomy / oophorectomy status, % positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22^\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHormone levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSH (mIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(35)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(31)*^\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLH (mIU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(19)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(14)*^\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstradiol (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111(103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(32)*^\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are mean (SD) unless otherwise indicated.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Different from pre-menopausal group, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e^Different from peri-menopausal group, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociations between FSH, LH and AD biomarkers\u003c/p\u003e \u003cp\u003eWe used multiple linear regressions to test for voxel-based associations of FSH and LH levels with AD biomarkers, including Aβ load on \u003csup\u003e11\u003c/sup\u003eC-Pittsburgh compound B (PiB) Positron Emission Tomography (PET) and GMV on volumetric MRI, adjusting by age, menopause status and modality-specific confounders, and after cluster-level family-wise error (FWE) multiple comparisons correction. Secondly, we tested for differential associations between FSH, LH and E2 with AD biomarker outcomes.\u003c/p\u003e \u003cp\u003eAssociations between FSH and AD biomarkers\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eAβ load\u003c/h2\u003e \u003cp\u003eAcross all participants, FSH levels were positively associated with Aβ load in frontal cortex of the left hemisphere (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On post-hoc analysis, these associations were driven by the post-menopausal group, which exhibited significant associations between FSH and Aβ load in middle and superior frontal gyri, whereas no associations were found in the peri-menopausal and pre-menopausal groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between FSH levels and \u003csup\u003e11\u003c/sup\u003eC-PiB PET amyloid-beta load in AD-regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster extent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003cp\u003ex, y, z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003eFWE\u003c/sub\u003e cluster*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP voxel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnatomical Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBrodmann Area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-43 37 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-47 28 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-41 44 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-29 26\u0026thinsp;\u0026minus;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum Frontal Lobe Inferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-31 29 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24 40 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-47 2835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum Frontal Lobe Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-43 37 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum Frontal Lobe Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeri-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cluster-level corrected for Family-Type Wise Error (FWE) within the AD\u003csub\u003eMASK\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOverall results are adjusted by age, menopause status and cerebellar PiB uptake. Results within each menopausal group are adjusted by age and cerebellar PiB uptake.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociations between FSH and Aβ load were significant, albeit attenuated, after further adjustment for APOE-4 status, menopause type and hormone therapy status (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGray matter volume\u003c/h2\u003e \u003cp\u003eAcross all participants, FSH levels were negatively associated with GMV in bilateral superior frontal cortex; medial, middle, and inferior frontal cortex of the right hemisphere; and precuneus and fusiform gyrus of the left hemisphere (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.025; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On post-hoc analysis, all menopausal groups exhibited negative associations between FSH and GMV in frontal cortices, which were stronger and more widespread in the post-menopausal group (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.013; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, negative associations between FSH and GMV were observed in left parahippocampal gyrus of the post-menopausal group, and in left inferior parietal lobule and fusiform gyrus of the peri-menopausal group (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.006; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between FSH levels and MRI gray matter volume in AD-regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster extent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinates x, y, z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003eFWE\u003c/sub\u003e cluster*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP voxel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnatomical Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBrodmann Area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10 52 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 35 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 59 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Medial Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 52 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 28\u0026thinsp;\u0026minus;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Inferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 53\u0026thinsp;\u0026minus;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 35 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8 -69 49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Parietal Lobe, Precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-42 -48 -16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Temporal Lobe, Fusiform Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 25\u0026thinsp;\u0026minus;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Inferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 31\u0026thinsp;\u0026minus;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Inferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 37 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-39 -23 -16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Limbic Lobe, Parahippocampal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 33\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Inferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeri-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-45 -43 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Parietal Lobe, Inferior Parietal Lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-41 -48 -7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Temporal Lobe, Fusiform Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-37 20 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 57\u0026thinsp;\u0026minus;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Medial Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 33 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-37 41 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 54 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-29 27 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-18 50 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 36 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 39 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 31 48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cluster-level corrected for Family-Type Wise Error (FWE) within the AD\u003csub\u003eMASK\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOverall results are adjusted by age, menopause status and total intracranial volume. Results within each menopausal group are adjusted by age and total intracranial volume.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociations between FSH and GMV remained significant after further adjustment for APOE-4-status, menopause type and hormone therapy status, although the strength of the association was reduced in the post-menopausal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eAssociations between LH and AD biomarkers\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAβ load\u003c/h2\u003e \u003cp\u003eAdjusting for the same confounders as above, there were no significant associations between plasma LH levels and Aβ load in any region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGray matter volume\u003c/h2\u003e \u003cp\u003eAcross all participants, LH levels were negatively associated with GMV in bilateral precuneus, right superior frontal gyrus and left middle frontal gyrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). On post-hoc analysis, both post-menopausal and pre-menopausal groups exhibited LH-GMV associations in right superior frontal gyrus, while no associations were detected in the peri-menopausal group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between LH levels and MRI gray matter volume in AD-regions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster extent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinates x, y, z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003eFWE\u003c/sub\u003e cluster*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP voxel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnatomical Region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBrodmann Area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6 -69 48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Parietal Lobe, Precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4 -55 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Parietal Lobe, Precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16 -70 51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Parietal Lobe, Precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 35 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;53 58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Parietal Lobe, Precuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-25 39 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLeft Cerebrum, Frontal Lobe, Middle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePost-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 35 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeri-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-menopausal group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 31 48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRight Cerebrum, Frontal Lobe, Superior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 cluster-level corrected for Family-Type Wise Error (FWE) within the AD\u003csub\u003eMASK\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOverall results are adjusted by age, menopause status and total intracranial volume. Results within each menopausal group are adjusted by age and total intracranial volume.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociations between LH and GMV remained significant after further adjustment for APOE-4-status, menopause type and hormone therapy status, although the strength of the association was reduced in precuneus and middle frontal gyrus of the post-menopausal group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eDifferential Associations of FSH and LH with AD biomarkers\u003c/p\u003e \u003cp\u003eWe conducted a stringent subtraction analysis\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e 11\u003c/sup\u003e to determine whether FSH had fully independent associations with GMV independent of LH, and vice-versa.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFSH\u003c/span\u003e. On subtraction analysis (e.g. after excluding all clusters with significant associations between LH and GMV) overall associations between FSH and GMV remained significant in all regions except precuneus and part of the right superior frontal gyrus (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.020; \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Within each menopausal group, negative associations between FSH and GMV remained unchanged accounting for LH levels (P\u003csub\u003eFWE\u003c/sub\u003e\u0026lt;0.013; \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, remaining clusters showed no anatomical overlap with those associated with LH, indicating regionally independent effects of FSH on GMV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLH\u003c/span\u003e. On subtraction analysis (e.g. after excluding all clusters with significant associations between FSH and GMV), overall associations between LH and GMV remained significant in middle frontal gyrus and right precuneus, and were reduced in right superior frontal gyrus and left precuneus (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.014;\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). In the post-menopausal and pre-menopausal groups, adjusting by FSH, negative associations between LH and GMV in right superior frontal gyrus remained significant (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.007; \u003cb\u003eSupplementary Table\u0026nbsp;2)\u003c/b\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, these clusters showed no anatomical overlap with those associated with FSH, indicating regionally independent effects of LH on GMV.\u003c/p\u003e \u003cp\u003eAssociations between E2 and AD biomarkers\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAβ load\u003c/h2\u003e \u003cp\u003eAdjusting for the same confounders as above, there were no significant associations between plasma E2 levels and Aβ load in any region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGray matter volume\u003c/h2\u003e \u003cp\u003eAcross all participants, E2 levels were positively associated with GMV in orbitofrontal gyrus of the left hemisphere (cluster extent 101 voxels, x=-2, y=-51, z=-22, Z\u0026thinsp;=\u0026thinsp;3.91, P\u003csub\u003eFWE\u003c/sub\u003e=0.003, Brodmann Area 11). On post-hoc analysis, there were no associations between E2 levels and GMV in any menopausal group.\u003c/p\u003e \u003cp\u003eDifferential Associations of FSH, LH and E2 with AD biomarkers\u003c/p\u003e \u003cp\u003eWe conducted a subtraction analysis to determine whether FSH and LH had fully independent associations with GMV from E2 by excluding all regions with significant E2-GMV associations. Associations between FSH and GMV remained unchanged after excluding clusters with E2-GMV associations (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.024; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e), indicating regionally independent effects of FSH on GMV.\u003c/p\u003e \u003cp\u003eAssociations between LH and GMV also remained unchanged after excluding clusters with E2-GMV associations (P\u003csub\u003eFWE\u003c/sub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e0.013; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD and \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e), indicating regionally independent effects of LH on GMV.\u003c/p\u003e \u003cp\u003eAssociations between FSH, LH and cognition\u003c/p\u003e \u003cp\u003eAdjusting by age and education, there were no significant associations between FSH, LH and cognitive scores (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn midlife women at risk for AD, increasing serum FSH levels across the menopause transition were associated with progressively higher Aβ load and lower GMV in brain regions vulnerable to AD, especially in frontal cortex. FSH effects on Aβ load were driven by the post-menopausal group, whereas negative effects of FSH on GMV were evident already before menopause. Increasing LH levels were also associated with lower GMV in frontal regions, but not with Aβ load. Results were independent of age, APOE-4 status, menopause type (spontaneous vs. induced), hormone therapy status, and E2 levels.\u003c/p\u003e \u003cp\u003eFemale sex is inextricably linked to the midlife neuroendocrine aging transition of menopause\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. With the aging of the global population in the coming decades, it is estimated that 1.2\u0026nbsp;billion women worldwide will be transitioning through menopause by the year 2030\u003csup\u003e25\u003c/sup\u003e. All women undergo menopause either through natural midlife aging processes or via surgical or pharmacological intervention. Neuroimaging studies have identified the menopause transition as a female-specific risk factor for AD\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e based on observations that midlife women exhibit AD endophenotypes including Aβ accumulation, glucose hypometabolism, and GMV loss in brain regions vulnerable to AD with onset in perimenopause\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These changes are generally attributed to the loss of neuroprotective effects of ovarian sex steroid hormones, E2 in particular\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, recent preclinical work indicates a role for increasing gonadotropin FSH and LH levels in AD pathology in addition to, or independent of the better-characterized effects of estrogenic depletion\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e1, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe menopause transition is characterized not only by a decline in E2 levels but also a rise in the pituitary gonadotropins FSH and LH\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. During a woman’s reproductive years, FSH and LH stimulate ovulation and estrogen production in the ovary, and are inhibited by negative feedback from estrogen levels. In perimenopause, as E2 fluctuates, FSH and LH levels exhibit over 10-fold and 3-fold increases, respectively\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, in response to the loss of ovarian feedback\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. FSH also rises earlier than LH\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, suggesting a possibly earlier role for this hormone in neuronal aging.\u003c/p\u003e \u003cp\u003eIn mechanistic analyses, high serum FSH and LH are associated with cerebral Aβ deposition both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e, with reversible effects after pharmacological suppression\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Recent work demonstrates that daily injections of recombinant human FSH cause a marked acceleration of Aβ as well as tau protein accumulation in triple AD transgenic female mice, leading to subsequent neuroinflammation, synaptic degeneration, and cognitive impairment\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Oophorectomy exacerbated the development of AD-phenotype, whereas FSH blockade prevented accumulation of AD pathology and improved cognition\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Similar findings were obtained via experiments of female mice in an estrogen-replete state, indicating independent effects of FSH from those of E2\u003csup\u003e22\u003c/sup\u003e. In further analyses, FSH and LH were shown to induce neurodegenerative changes through their action on their respective receptors (FSHR and LHR), which in turn promote APP cleavage in a pro-amyloidogenic direction\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e as well as pro-inflammatory IL-1β and IL-6 cytokines expression\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Since FSHR and LHR also regulate structure and a diverse range of functions in the brain, dysregulation of the HPG axis at menopause leading to gonadotropin elevations constitutes a largely underexplored, physiologically relevant risk factor for neurodegeneration. Additionally, FSH increases upregulated low-density lipoprotein receptor-related protein (LRP) in neurons, astrocytes and activated microglia, and LRP binding to ApoE and APP unbalances Aβ metabolism in turn\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, further implicating FSH elevations in AD pathogenesis\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Whether similar mechanisms are active in women remains unknown.\u003c/p\u003e \u003cp\u003eHerein, we took a translational approach to characterize the effects of increasing FSH and LH from pre-menopausal to post-menopausal levels on AD pathology and neurodegenerative biomarkers among women at risk for AD. Our results are consistent with preclinical findings implicating FSH elevations in Aβ deposition, an effect that survived multiple comparison adjustment and masking in frontal areas of post-menopausal women. Additionally, both FSH and LH levels were associated with progressively lower GMV in frontal cortex with onset at the pre-menopausal stage. These results point to the frontal cortex, a brain region subserving higher order cognitive processes such as working memory and executive functions\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, as a main site for gonadotropic action on AD risk. \u003cem\u003eIn vitro\u003c/em\u003e and \u003cem\u003eex vivo\u003c/em\u003e studies show that the frontal cortex is rich in FSHR and LHR in both rodent and human brain\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and both receptors are highly expressed in limbic cortex, an AD vulnerable region in turn. At post-mortem, FSHR expression is higher in frontal regions of AD patients as compared to controls\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, further linking FSH to neurological harm. \u003cem\u003eIn vivo\u003c/em\u003e neuroimaging work also identifies the frontal cortex as a site of early vulnerability to both Aβ deposition and GMV loss during the prodromal phase of AD\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. For women in particular, longitudinal studies reported increasing Aβ load in frontal regions of post-menopausal and peri-menopausal women, with no or limited changes in other regions\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, identifying this area as an early site of AD-related vulnerability in women undergoing menopause.\u003c/p\u003e \u003cp\u003eWhile Aβ effects were limited to frontal cortex, negative associations between FSH and GMV extended to parahippocampal gyrus, precuneus, inferior parietal and fusiform gyrus of post- and peri-menopausal participants. LH levels also correlated with GMV in precuneus across menopause stages. These results may reflect downstream effects of Aβ on neuronal integrity\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, possibly induced by soluble Aβ oligomers, which promote neurotoxicity prior to fibrilization into plaques\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e but are undetectable by means of \u003csup\u003e11\u003c/sup\u003eC-PiB PET. To this point, previous imaging studies have shown distant associations of frontal Aβ uptake and temporo-parietal GMV reductions in AD patients\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Alternatively, gonadotropin-related GMV declines may be due to intracellular neurofibrillary tangles, which have been linked to neuronal loss in limbic and temporo-parietal regions prior to Aβ deposition\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In a recent tau-PET study, post-menopausal women exhibited higher tau deposition in parieto-occipital regions and in middle frontal gyrus as compared to age-controlled men\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. It is unknown whether these differences were related to specific hormone levels.\u003c/p\u003e \u003cp\u003eIn the present study, plasma E2 levels collected at the same time as FSH and LH were not associated with Aβ load across participants or in any menopausal group, but were associated with GMV in orbitofrontal gyrus. This is consistent with the known trophic effects of E2 on synaptic density in frontal regions\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Nonetheless, there was no overlap between frontal areas impacted by E2, FSH and LH, suggesting that these hormones influence neuronal density in frontal cortex with sub-regional specificity. For the purpose of this discussion, the fact that FSH associations with Aβ biomarkers were statistically and anatomically independent of E2 and LH effects lends support to preclinical evidence of an early pathological contribution of this hormone to AD\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Additionally, negative effects of both gonadotropins on GMV in AD-regions beyond frontal cortex, and with onset at the peri-menopause stage, are consistent with the menopause transition being a tipping point for AD risk later in life\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, indicating an optimal window of opportunity for therapeutic intervention in advance of irreversible neurological damage\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. More work is needed to clarify the biological pathways by which FSH exerts negative effects on Aβ-biomarker risk in women, and to unravel the differential action of HPG hormones on neuronal aging and AD. Nonetheless, together with preclinical work, present results identify FSH as a potential female-specific Aβ-lowering therapeutic target, and support testing of therapies that regulate serum gonadotropin levels for AD prevention.\u003c/p\u003e \u003cp\u003eCurrently, estrogen MHT with or without a progestin is the treatment of choice for menopausal symptoms\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and holds promise for AD prevention\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. However, while observational studies generally report favorable effects of MHT on AD risk, clinical trials indicate null effects in early post-menopausal patients and null or negative effects in older post-menopausal women\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Protective effects are generally more consistent among younger women, especially following oophorectomy, with benefits pertaining to early initiation relative to surgery or age of menopause onset\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Besides increasing estrogen levels, MHT induces a dose-related decrease in serum FSH in post-menopausal women\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The neurological effects of decreasing FSH following MHT are not well understood. However, in the Kronos Early Estrogen Prevention Study (KEEPS), after 48 months of randomization to either oral conjugated equine estrogen (o-CEE), transdermal 17β estradiol (tE2) or placebo treatment, decreases in FSH were associated with smaller increases in white matter hyperintensities (WMH) in the tE2 group\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Given the potential impact of FSH on cardiovascular risk\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, more work is warranted to examine whether lowering FSH by means of hormone therapy is a viable strategy to reduce WMH, which are a risk factor for AD in turn\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEstrogen- and progesterone-containing OCP inhibit the release of gonadotropin-releasing hormone (GnRH), suppressing levels of FSH and LH, and preventing follicular development and ovulation in turn\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. OCP can be used to alleviate symptoms of menopause at the perimenopausal stage\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, representing another therapeutic option for controlling gonadotropin effects on AD risk. Generally, MRI studies show greater GMV in OCP users compared to never-users, although results are not always consistent\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In an MRI study of middle-aged women at risk for AD, those who took OCP before menopause and MHT for menopause exhibited larger GMV in AD-vulnerable regions, including frontal and parietal cortex, medial temporal lobe, precuneus, and fusiform gyrus, as compared to never-users of both\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Notably, these are some of the same regions displaying negative effects of FSH on GMV in the present study, warranting further investigation.\u003c/p\u003e \u003cp\u003eFurther epidemiological support for a role of gonadotropins in AD is evidenced by a reduction in neurodegenerative disease among prostate cancer patients treated with GnRH agonists\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. GnRH-agonists and antagonists commonly used to treat hormone-sensitive conditions, such as prostate cancer and endometriosis, disrupt physiological GnRH pulses, producing a drop in serum FSH and LH levels\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Preliminary evidence indicates that GnHR agonists reduced Aβ pathology and improved cognition in rodents\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eClinical trials of humanized monoclonal FSH antibodies, which proved effective in reversing AD pathology in mice\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, are also warranted. Additionally, emerging research is investigating whether healthy ovarian tissue cryopreservation may restore pre-menopausal steroid hormone levels in post-menopausal women\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Initial studies show that serum FSH dramatically decreases after ovarian tissue transplantation, inducing the resumption of folliculogenesis in grafts\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, thus possibly delaying the onset of menopause and associated neuropathological changes. Finally, the administration of platelet-rich plasma with recombinant FSH to the ovaries was shown to restore ovarian function, at least temporarily, in early post-menopausal women\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. While this technique is aimed at prolonging a woman's fertility period\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, it may also hold promise for prevention of menopause-associated AD risk.\u003c/p\u003e \u003cp\u003eStrengths and Limitations\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first \u003cem\u003ein vivo\u003c/em\u003e brain imaging study to investigate associations of FSH and LH levels on AD risk in women. We focused on carefully screened clinically healthy midlife women, ages 40–65 years, with no comorbidities or incidental findings. All participants received thorough clinical and cognitive exams, laboratory tests, menopause assessments, and brain imaging.\u003c/p\u003e \u003cp\u003eFrom a methodological perspective, we capitalized on the menopause transition as a natural experiment of increasing FSH and LH levels by examining statistically powered groups of women at different menopausal stages. FSH and LH levels are progressively higher from pre-menopausal through post-menopausal stages, and show uncorrelated patterns and greater differentiation starting at perimenopause, which enabled us to test for differential associations with AD biomarker outcomes using a cross-sectional design. We used state-of-the-art voxel-based analysis paired with age correction procedures and stringent statistical reporting criteria, while using a translational approach to ensure that our results were both statistically and biologically valid. Results were significant after multi-variable correction for age, APOE-4 status, modality-specific confounders, plasma E2 levels, menopause type, and hormone therapy use.\u003c/p\u003e \u003cp\u003eWe chose this study design because the timing of menopause is highly variable, with a median age at menopause of 51 years, and a range of 40–58 years\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Longitudinal studies may require 10 or more years of follow-ups to capture simultaneous changes in hormones and AD biomarkers. While studies of oophorectomy ideally reduce follow-up times, the procedure is often performed as a result of medical conditions affecting the ovaries, and is associated with more severe neuropathological outcomes than spontaneous menopause\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. However, given the cross-sectional nature of this study, a causal link between gonadotropin levels and AD biomarkers cannot be unequivocally established. Longitudinal studies are warranted to clarify whether FSH and LH effects on AD biomarkers are predictive of future dementia, and to test for differential effects of surgical and spontaneous menopause.\u003c/p\u003e \u003cp\u003eVery little work has been done to compare FSH, LH and E2 effects on AD risk, which may have led to underestimating the role of the gonadotropins. In this study, associations between FSH, LH and AD biomarkers were independent of E2 levels, as determined via a stringent subtraction analysis. While all hormones were associated with GMV in frontal cortex, associated clusters exhibited no anatomical overlap, indicating synergistic but regionally independent effects on neuronal density in this region. As previously discussed, FSH was associated with Aβ load whereas LH and E2 were not, suggesting an earlier contribution of FSH to emerging AD pathology in midlife. While both FSH and LH levels raise in perimenopause, differences in control mechanisms may account for the earlier effects of FSH. First, FSH has greater sensitivity to the inhibition of HPG axis hormones such as inhibin B, whose serum levels fall during the early perimenopausal phase independent of changes in E2\u003csup\u003e28\u003c/sup\u003e. Secondly, while GnRH pulse frequencies regulate both FSH and LH secretion, secondary regulatory factors specific to FSH release exist\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, since FSH and LH measures are intercorrelated and this is a cross-sectional analysis, we cannot fully parse out their effects. We offer that present results provide preliminary data for future hypothesis-driven studies aimed at assessing differential timeline and effects of sex hormones on AD biomarker risk before and after menopause.\u003c/p\u003e \u003cp\u003eFinally, gonadotropin levels were not significantly associated with global cognition or memory scores. This may be due to the fact that the post-menopausal group did not exhibit impaired or diminished cognitive performance as compared to the other groups, consistent with observations that, while self-reports of poor memory and concentration are common in women of menopausal age\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, menopause itself is not associated with clinically significant deficits\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. However, all our participants had at least 12 years of education, and cognitive test results within norms by age and education, which may have hindered detection of associations between hormonal levels and cognition. It is also possible that our testing battery may not be sufficiently demanding to detect early effects of gonadotropin-related AD pathology on cognitive function. Future studies with larger cohorts of midlife women with different educational levels and socioeconomical backgrounds are needed to address these questions.\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eOur results provide novel evidence for emergence of gonadotropin-related AD endophenotypes in midlife women at risk for AD, with FSH levels being the main predictor of Aβ load. Outcomes are consistent with preclinical work implicating rising gonadotropin levels in women’s greater lifetime risk of AD, and provide novel hormonal therapeutic targets for precision medicine strategies for AD prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis study was supported by grants from NIH/NIA (P01AG026572, R01AG05793, R01AG0755122), NIH/NCATS UL1TR002384, the Cure Alzheimer\u0026rsquo;s Fund, the Women\u0026rsquo;s Alzheimer\u0026rsquo;s Movement; and philanthropic support to the WCM Alzheimer\u0026rsquo;s Prevention Program.\u0026nbsp;\u003c/p\u003e"},{"header":"Online Methods","content":"\u003ch2\u003eParticipants and Data\u003c/h2\u003e\n\u003cp\u003eThis is a natural history, non-interventional study of cognitively normal women ages 40-65 years\u0026nbsp;carrying risk factors for late-onset Alzheimer\u0026rsquo;s Disease (AD) such as a family history and/or APOE-4 genotype. We focused on women at different menopausal stages\u0026nbsp;(pre-menopause, peri-menopause, and post-menopause) as a natural experiment of increasing follicle stimulating hormone (FSH) levels across the menopause transition. Participants were recruited at the Weill Cornell Medicine (WCM) Alzheimer\u0026rsquo;s Prevention Program between 2018-2022 by self-referral, flyers, and word of mouth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur inclusion and exclusion criteria have been previously described\u003csup\u003e8-13\u003c/sup\u003e. Briefly, all participants had Montreal Cognitive Assessment (MoCA) score \u003cu\u003e\u0026gt;\u003c/u\u003e26 and normal cognitive test performance by age and education. Exclusion criteria included medical conditions that may affect brain structure or function (e.g., stroke, any neurodegenerative diseases, major psychiatric disorders, hydrocephalus, demyelinating disorders such as Multiple Sclerosis, intracranial mass, and infarcts on Magnetic Resonance (MRI)), use of psychoactive medications, and contraindications to MRI or Positron Emission Tomography (PET) imaging. All participants received medical, neurological, laboratory, cognitive and volumetric MRI exams within 6 months of each other. Half of the participants also received \u003csup\u003e11\u003c/sup\u003eC-Pittsburgh Compound B (PiB) PET to measure\u0026nbsp;A\u0026beta; load.\u003c/p\u003e\n\u003cp\u003eThe patients\u0026rsquo; sex was determined by self-report.\u0026nbsp;APOE-4 genotype was determined using standard qPCR procedures\u003csup\u003e8-13\u003c/sup\u003e. Participants carrying one or two copies of the APOE-4 allele were grouped together as ApoE-4 carriers, and compared to non-carriers. A family history of late-onset AD was elicited using standardized questionnaires.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStandard protocol approvals, registrations, and patient consents\u003c/h2\u003e\n\u003cp\u003eAll methods were carried out in accordance with relevant guidelines and regulations.\u0026nbsp;All experimental protocols were approved by the\u0026nbsp;WMC Institutional Review Board. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCognitive measures\u003c/h2\u003e\n\u003cp\u003eParticipants underwent a cognitive testing battery assessing memory [Rey Auditory Verbal Learning Test (RAVLT), Wechsler Memory Scale logical memory delayed recall], executive function [Trail Making Test, FAS], and language [object naming, animal naming]\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e8-13\u003c/sup\u003e. Cognitive measures were scaled to standard deviations and centered at 0. A composite memory score was obtained by z-scoring each memory test and averaging across measures. A global cognition score was obtained by z-scoring the remaining tests and averaging within and across the three domains.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eHormonal panel\u003c/h2\u003e\n\u003cp\u003eEach participant received a blood draw by venipuncture after an overnight fast. Samples were shipped overnight to CLIA-certified Boston Heart Diagnostics [Framingham, MA] and analyzed on a Roche Cobas e801 analytical unit for immunoassay tests using Electrochemiluminescence technology (ECL) [Roche Diagnostics; Basel, Switzerland].\u0026nbsp;FSH and LH were assessed through\u0026nbsp;electrochemiluminescence sandwich immunoassay (ELISA)\u0026nbsp;with a measuring range of 0.3‑200 mIU/mL for both hormones. Estradiol (E2) was assessed through competitive immunoassay\u0026nbsp;with a measuring range of 18.4‑11010 pmol/L (5‑3000 pg/mL).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMenopause assessments\u003c/h2\u003e\n\u003cp\u003eDetermination of menopausal status was based on the Stages of Reproductive Aging Workshop (STRAW) criteria\u003csup\u003e53\u003c/sup\u003e with hormone laboratory assessments as supportive criteria\u003csup\u003e13\u003c/sup\u003e. Participants were classified as pre-menopausal (regular cycler), peri-menopausal (irregular cyclers with an interval of amenorrhea \u003cu\u003e\u0026gt;\u003c/u\u003e60 days or \u003cu\u003e\u0026gt;\u003c/u\u003e2 skipped cycles) and post-menopausal (absence of menstrual cycle for \u003cu\u003e\u0026gt;\u003c/u\u003e12 months)\u003csup\u003e13\u003c/sup\u003e. A history of hysterectomy and/or oophorectomy before menopause was assessed through review of surgical history.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eHormone therapy\u003c/h2\u003e\n\u003cp\u003eSemi-standardized questionnaires were used to obtain information on history of menopause hormone therapy (MHT) and oral contraceptive (OCP) usage. This information was used to classify participants as current vs. past or never users of MHT or OCP\u003csup\u003e13\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eImage Acquisition and Analysis\u003c/h2\u003e\n\u003cp\u003eAll brain scans were acquired following standardized procedures\u003csup\u003e8-13\u003c/sup\u003e\u003csup\u003e.\u0026nbsp;\u003c/sup\u003eParticipants received a 3D volumetric T\u003csub\u003e1\u003c/sub\u003e-weighted MRI scan on a 3.0 T GE MR 750 Discovery scanner (General Electric, Waukesha, WI) [Brain Volume Imaging (BRAVO); 1x1x1 mm resolution, 8.2 ms repetition time (TR), 3.2 ms echo time (TE), 12\u0026deg; flip angle, 25.6 cm field of view (FOV), 256x256 matrix with ARC acceleration] using a 32-channel head coil. The \u003csup\u003e11\u003c/sup\u003eC-PIB PET scan was acquired using\u0026nbsp;a Siemens BioGraph\u0026nbsp;mCT 64-slice\u0026nbsp;PET/CT operating in 3D mode [70 cm transverse FOV, 16.2 cm axial FOV]. Summed PET images were obtained 60-90 min post-injection of 15 mCi of \u003csup\u003e11\u003c/sup\u003eC-PiB and\u0026nbsp;corrected for attenuation, scatter and decay. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImage analysis was performed using a fully automated image processing pipeline\u003csup\u003e8-13\u003c/sup\u003e. MRI and\u0026nbsp;PET scans were realigned using the Normalized\u0026nbsp;Mutual Information routine of Statistical Parametric Mapping (SPM12)\u003csup\u003e54\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eimplemented in Matlab R2018a (MathWorks; Natick,MA).\u0026nbsp;Each\u0026nbsp;PET scan was co-registered to the corresponding T1-weighted MRI scan. MRI scans were spatially normalized to\u0026nbsp;normalized\u0026nbsp;to the\u0026nbsp;\u003cem\u003etemplate\u003c/em\u003e-normalized tissue probabilistic map (TPM) image included in SPM12, conforming\u0026nbsp;to\u0026nbsp;the Montreal Neurological Institute (MNI) space, using voxel-based morphometry (VBM). VBM processing included image segmentation, Jacobian modulation, high-dimensional warping (DARTEL)\u0026nbsp;of the segments, and application of an 8mm full-width at half maximum smoothing kernel\u003csup\u003e55\u003c/sup\u003e. Gray matter (GM) segments were retained for statistical analysis. The MRI-coregistered PET scans were then spatially normalized to the TPM image using subject-specific transformation matrices using the MRI as the anchor, and smoothed using an 8-mm FWHM filter\u003csup\u003e54\u003c/sup\u003e.\u0026nbsp;SPM12 was also used to obtain total intracranial volume (TIV) as the sum of gray, white and cerebrospinal fluid volume for each subject\u003csup\u003e55\u003c/sup\u003e. Cerebellar gray matter PiB uptake was extracted using the automated anatomical labeling (AAL3) atlas\u003csup\u003e56\u003c/sup\u003e and WFU PickAtlas 2.4\u003csup\u003e57\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCovariates\u003c/h2\u003e\n\u003cp\u003eBrain imaging analyses were adjusted by age, menopause status, and modality-specific confounders (MRI TIV; cerebellar PiB\u0026nbsp;uptake). Cognitive analyses were adjusted by age and education (years). For exposures showing\u0026nbsp;significant associations with outcome measures, we further examined\u0026nbsp;as confounders: APOE-4 status (carrier vs. non-carrier); OCP\u0026nbsp;(user vs. non-user)\u0026nbsp;status for the pre- and peri-menopausal groups; MHT status (user vs. non-user) for peri- and post-menopausal groups; and menopause type (surgical vs. spontaneous) for the post-menopausal group.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eAnalyses were performed in SPSS v.25,\u0026nbsp;R v.4.2.0\u0026nbsp;and SPM12.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical measures were examined using general linear models or chi-squared tests as appropriate. Cohort characteristics are described using mean (standard deviation) and n, percentage (%), stratified by exposure group.\u003c/p\u003e\n\u003cp\u003eWe conducted several analyses to address the independent effects of FSH and LH levels from age and menopause status according to published methods\u003csup\u003e12,58\u003c/sup\u003e: (a) we used box plots and frequency diagrams to confirm that we had sufficient overlap among women of different menopause statuses (Table 1) which enabled us to examine the effects of FSH and LH separately from additional effects of menopause status; (b)\u0026nbsp;included age and menopause status as covariates in analyses of the entire sample; (c) tested for associations between FSH, LH and AD biomarkers within each menopausal group, also adjusting by age; and (d) conducted a stringent subtraction analysis\u003csup\u003e24\u003c/sup\u003e to test for unique contributions of the gonadotropins to AD biomarkers independent of each other and additional confounders, as described below.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFSH and LH associations with AD biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used voxel-based multivariate linear regressions with post-hoc \u003cem\u003et\u003c/em\u003e-contrasts to test for associations between exposures (FSH and LH) and AD biomarker outcomes (A\u0026beta;\u0026nbsp;load and GMV) across all participants, adjusting by confounders. In presence of significant main outcomes, we tested for associations between exposures and outcomes within each menopausal group. Statistical maps were obtained at p\u0026lt;0.05, cluster-level corrected for Family-Wise Type Error (FWE)\u003csup\u003e59\u003c/sup\u003e within a binary masking image consisting of \u003cem\u003ea priori\u003c/em\u003e defined regions with known vulnerability to AD (AD\u003csub\u003eMASK\u003c/sub\u003e). These included bilateral cortical (inferior and superior parietal lobule; inferior, middle and superior temporal gyrus; inferior, orbital, middle, medial and superior frontal gyrus; fusiform gyrus; anterior, middle and posterior cingulate gyrus; precuneus)\u0026nbsp;and subcortical areas (thalamus and medial temporal regions, including hippocampus, amygdala, parahippocampal gyrus, entorhinal and perirhinal cortex)\u003csup\u003e60\u003c/sup\u003e. In analysis of PiB data, medial temporal regions were excluded from the mask. The AD\u003csub\u003eMASK\u003c/sub\u003e was set as an explicit (inclusive) mask to conservatively restrict analysis to voxels within the mask to increase interpretability and reduce the number of voxel-wise comparisons, thus increasing sensitivity to detecting true effects and limiting the potential for false positives\u003csup\u003e24\u003c/sup\u003e.\u0026nbsp;Cluster extent was set at \u003cu\u003e\u0026gt;\u003c/u\u003e16 voxels (e.g. twice the FWHM).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnatomical location of regions reaching significance was described using Talairach coordinates after conversion from MNI space. Biomarker measures were extracted from peak clusters using a volume-of-interest (VOI) approach\u0026nbsp;using MarsBar 0.45 [https://marsbar-toolbox.github.io/download.html]\u0026nbsp;for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo characterize the correlation between FSH and LH levels in selected brain regions and outcomes of interest, correlation graphs are presented both for the overall study sample as well as by menopause status. The stratified analysis was performed to investigate hypothesized differences in the strength of the correlations throughout menopause. Stratification by menopause status also mitigates the effects of its confounding on the overall correlations, since menopause status is known to be associated both with FSH and LH levels, and the outcomes of interest. As age, APOE-4 status, menopause type and hormone therapy status may also impact these relationships, all correlations also adjust for these variables, as appropriate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE2 associations with AD biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe above procedures were used to test for associations between plasma E2 levels and AD biomarkers\u0026nbsp;at p\u0026lt;0.05, cluster-level FWE corrected within the AD\u003csub\u003eMASK\u003c/sub\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubtraction analysis: testing for independent effects of each hormone on AD biomarkers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the interest of being maximally conservative,\u0026nbsp;we conducted a stringent subtraction analysis\u003csup\u003e24\u003c/sup\u003e in the framework of SPM12 to\u0026nbsp;determine whether FSH and LH had entirely independent associations with A\u0026beta;\u0026nbsp;load and GMV from each other and from E2. This was accomplished by\u0026nbsp;testing for associations between each exposure and AD biomarkers \u003cem\u003eexcluding\u003c/em\u003e all regional contributions of the other exposures. To this aim, we\u0026nbsp;first identified brain regions with statistically significant associations between each exposure and each outcome\u0026nbsp;at p\u0026lt;0.05, cluster-level\u0026nbsp;FWE corrected\u0026nbsp;in the AD\u003csub\u003eMASK\u003c/sub\u003e, as described above. Brain regions with significant associations between FSH, LH, E2 and the outcomes were then saved as binary masks (FSH\u003csub\u003eMASK\u003c/sub\u003e, LH\u003csub\u003eMASK\u003c/sub\u003e, E2\u003csub\u003eMASK\u003c/sub\u003e). These binary masks were set as explicit masks at the t-map generation step\u003csup\u003e24\u003c/sup\u003e and\u0026nbsp;SPM12 was employed to evaluate the significant independent associations between each exposure and the outcomes excluding all voxels in the explicit masks of the other exposures, adjusting by clinical and modality-specific confounders.\u0026nbsp;For example, in testing of associations between FSH and GMV,\u0026nbsp;LH\u003csub\u003eMASK\u003c/sub\u003e and E2\u003csub\u003eMASK\u003c/sub\u003e were set as explicit masks, and analysis of associations between FSH and GMV was restricted to the remaining regions (e.g. after masking out all regions showing LH-GMV and E2-GMV associations). For each exposure, significant associations are reported in brain regions that survive the masking and the multiple comparisons adjustment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations between FSH, LH and cognition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used linear regressions to test for associations between FSH, LH and cognitive scores, overall and within each menopause group, adjusting by confounders, at p\u0026lt;0.05.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e2022 Alzheimer's disease facts and figures. \u003cem\u003eAlzheimers Dement\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 700\u0026ndash;789 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1002/alz.12638\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1002/alz.12638\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarter, C. 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Neurology \u003cb\u003e12\u003c/b\u003e, 207\u0026ndash;216 (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org:10.1016/S1474-4422(12)70291-0\u003c/span\u003e\u003cspan address=\"https://doi.org:10.1016/S1474-4422(12)70291-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2351642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2351642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMenopause has been implicated in women’s greater life-time risk for Alzheimer’s disease (AD) due to its disruptive action on multiple neurobiological mechanisms resulting in amyloid-β deposition and synaptic dysfunction.While these effects are typically attributed to declines in estradiol, mechanistic analyses implicate pituitary gonadotropins, follicle-stimulating hormone (FSH) and luteinizing hormone (LH), in AD pathology. In transgenic mouse models of AD, increasing FSH and LH accelerate amyloid-β deposition, while inhibiting these hormones prevents emergence of AD lesions and neurodegeneration. Herein, we take a translational approach to show that, among midlife women at risk for AD, FSH elevations over the menopause transition are associated with higher amyloid-β burden, and both FSH and LH increases are associated with lower gray matter volume in AD-vulnerable brain regions. Results were independent of age, hormone therapy usage, and plasma estradiol levels. These findings provide novel therapeutic targets for sex-based precision medicine strategies for AD prevention.\u003c/p\u003e","manuscriptTitle":"Elevated gonadotropin levels are associated with increased biomarker risk of Alzheimer’s disease in midlife women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-12-14 20:09:04","doi":"10.21203/rs.3.rs-2351642/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d444f3c-8bd1-482f-a3ea-5b114cbfb458","owner":[],"postedDate":"December 14th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":17661736,"name":"Biological sciences/Neuroscience/Neural ageing"},{"id":17661737,"name":"Health sciences/Diseases/Neurological disorders/Neurodegenerative diseases/Alzheimer's disease"}],"tags":[],"updatedAt":"2023-07-11T16:17:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-12-14 20:09:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2351642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2351642","identity":"rs-2351642","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}