Symptom Patterns in Premature Ovarian Insufficiency Differ Across Age of Diagnosis and Aetiology: Implications for Neural Mechanisms and Brain Health

Symptom Patterns in Premature Ovarian Insufficiency Differ Across Age of Diagnosis and Aetiology: Implications for Neural Mechanisms and Brain Health | 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 Research Article Symptom Patterns in Premature Ovarian Insufficiency Differ Across Age of Diagnosis and Aetiology: Implications for Neural Mechanisms and Brain Health L F Naysmith This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8280682/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 Objective Premature ovarian insufficiency, loss of ovarian hormone function before age 40, impacts physical, cognitive, and psychological wellbeing. However, symptom patterns are still poorly characterised, restricting our ability to investigate how early hypoestrogenism shapes symptom severity and contributes to long-term neural changes. Study design Using an online survey (N = 155), we examined whether; i) pre-diagnosis symptoms in idiopathic POI varied by age of diagnosis, and ii) current symptoms differed across POI aetiologies (idiopathic vs known cause). Results Pre-diagnosis symptoms typically persisted for 2–5 years. Age at diagnosis predicted overall symptom burden at pre-diagnosis (β = 0.29–0.42, p < 0.024); those diagnosed at 31–40 years reported higher physical (OR = 4.38, 95% CI: 1.17–16.32, p = 0.028) and emotional burden (OR = 2.84, 95% CI: 1.14–7.04, p = 0.025) than those ≤ 20 years. Structural equation modelling showed that older age was associated with stronger, more coherent symptom clustering. Next, POI with known cause presented more frequent and severe cognitive symptoms (OR = 2.45–2.62, 95% CI: 1.05–6.32, p < 0.040) and anxiety (OR = 3.76, 95% CI: 1.46–9.68, p = 0.006) than idiopathic cases. Hormone treatment status, smoking, and alcohol intake also influenced current symptom reporting. Conclusion Together, these findings underscore age at diagnosis and POI aetiology as central to symptom organisation and experience, highlighting implications for long-term neural trajectories and cohort stratification in future studies. premature ovarian insufficiency oestrogen deficiency clinical symptom presentation brain health neuroimaging Figures Figure 1 Introduction Premature ovarian insufficiency (POI), the loss of ovarian function before age 40, affects 3.7% of the global population [ 1 ]. POI impacts physical and psychological wellbeing, reduces fertility and carries long-term consequences for bone, cardiovascular, and brain health [ 2 ], but it remains an under-researched area of health. Genetic, autoimmune, metabolic, and iatrogenic causes have been shown to contribute to POI, yet idiopathic cases still comprise 70–90% of cases [ 3 , 4 ]. Clinically, POI presents as primary/secondary amenorrhea, or oligomenorrhea, resulting from elevated gonadotropins and low oestradiol [ 5 ]. Symptoms also extend beyond menstrual disruption, encompassing vasomotor, psychological, cognitive, urogenital and sexual changes [ 6 ]. These symptoms reflect systemic and central effects of oestrogen deficiency, thus, when oestrogen levels decline in POI, neuroendocrine regulation is altered. The resulting hypoestrogenic symptoms likely arise from impaired ovarian-neural oestrogen signalling within regions rich in oestrogen receptors, such as the hypothalamus, prefrontal cortex, and hippocampus [ 7 ]. Loss of ovarian function and subsequent oestrogen depletion disrupts widespread neural circuits and therefore should be understood through a neuroendocrine lens. This applies to a range of hypoestrogenic symptoms, for example, vasomotor symptoms, like hot flashes, involving co-activation of hypothalamic, superior frontal, insula and cingulate regions [ 8 , 9 ]. Moreover, whilst cardiovascular [ 10 – 12 ] and bone consequences [ 13 – 16 ] of POI are well-documented, neural consequences remain unclear. There is an increased risk of neurodegenerative disease in POI [ 17 ], perhaps reflecting impaired neurosteroid production [ 18 ], but neuroimaging research underpinning neural vulnerability in POI is sparse. Whilst bilateral oophorectomy over 40 points to risks such as Alzheimer’s disease [ 19 ], surgical POI studies are limited. For idiopathic POI, Yuan et al. [ 20 ] recently observed structural and functional neural changes associated POI, providing novel insight into elevated risk of neurodegenerative disease in POI. The relationship between POI symptom presentation and factors that shape brain vulnerability, such as age at diagnosis, type of POI, and hormone therapy (HT), remains poorly characterised. Understanding how these factors influence symptom patterns is essential to inform the design of neuroimaging studies, identify neural trajectories associated with different POI aetiologies, and ultimately clarify how early hypoestrogenic changes may contribute to long-term neural alterations. Using a self-reported online survey, this study aimed to, i) characterise symptom reporting in idiopathic POI before diagnosis (prior to the initiation HT) specifically, whether age at diagnosis influenced symptom reporting and symptom burden. Guidelines for POI in adolescence and early adulthood indicate age differences in symptom presentation, with paediatric and adolescents with delayed puberty, primary amenorrhea and low bone density [ 21 ]. Thus, it was hypothesised that hypoestrogenic symptoms would be less likely in those aged ≤ 20 and they would be likely to report fewer symptoms, but overall symptom burden would not differ compared to older age groups (21–30, 31–40). Additionally, the study sought to ii) examine differences in current symptom frequency and severity according to POI aetiology (idiopathic; known cause) HT status, and lifestyle factors such as smoker status and alcohol. Participants with a known cause of POI were expected to report more frequent and severe POI symptoms [ 22 , 23 ], compared to idiopathic POI. HT use was expected to reduce likelihood of frequent and severe physical symptoms [ 24 ], whereas smoking and excessive alcohol were expected to increase symptom reporting [ 25 ], particularly physical symptoms [ 26 ]. Methods All procedures were conducted in accordance with the Declaration of Helsinki. This study received ethical approval from the Health Faculties (Purple) Research Ethics Committee at King’s College London (HR-24/25-48999). Participants Participants were recruited through posters where they were able to access the survey. Participants were eligible if they had a confirmed diagnosis of POI at or before age 40, were aged 18 years or older, and were currently residing in the United Kingdom. Measures Data were collected via the POI and the Brain survey , a self-administered online survey using the King’s College London Qualtrics platform (www.qualtrics.kcl.ac.uk). All participants provided informed consent prior to participation. Participants reported the aetiology of their POI diagnosis, indicating whether the cause was i) unknown (idiopathic), ii) related to medical treatment or surgery, iii) associated with an autoimmune condition, or iv) due to a known genetic cause. Alternatively, they could report being unsure about POI diagnosis. For analytical purposes, participants were grouped into idiopathic POI cases or known cause POI cases (iatrogenic, autoimmune, or genetic causes). The latter group was combined due to small sample size. For those reporting medical aetiologies, participants could select more than one underlying cause (endometriosis, BRCA mutation, cancer, fibroids, or other medical indication). Individuals who self-reported using any HT were classified as current HT users, while those reporting no HT were classified as non-users. The survey did not collect information on previous HT or date of initiation; thus, HT was analysed in relation to current symptoms only. Participants reported their age at the time of POI diagnosis, which was subsequently categorised into three groups for analysis: ≤20, 21-30 years, and 31-40 years. Additional demographic information was collected, including current age, ethnicity, and highest level of education. Lifestyle factors, such as smoking status and excessive alcohol consumption, were also recorded and treated as binary variables (yes/no) for analyses. Pre-diagnosis symptoms (hot flashes, brain fog, poor memory, mood swings, vaginal dryness, sleep difficulties, fatigue, palpitations, anxiety, low mood, loss of libido, and weight changes) were each coded as a binary variable for symptom presence/absence. Participants also reported the duration of these symptoms prior to idiopathic diagnosis using an ordinal scale (<6 months, 6-12 months, 1-2 years, 2-5 years, over 5 years) and whether they suspected that these symptoms were related to POI before receiving their diagnosis (yes/no/maybe). Current symptoms (brain fog, poor memory, anxiety, low mood, fatigue, hot flashes, palpitations) were assessed based on the past two weeks. Participants indicated the frequency with which they had experienced each symptom (“Daily”, “Most days”, “Occasionally”, “Once”, or “Never”). Those who experienced symptoms at least once were subsequently asked to rate the severity of each symptom on a scale from 1 “not noticeable at all” to 10 “extremely severe.” The survey also explored participants lived experiences of POI, including the adequacy of medical guidance, information and support after receiving a POI diagnosis. Social support questions were included to probe the impact of POI on relationships with partners, family, or friends. Professional/workplace support questions were included for the adequacy of support received at a participant’s workplace. Additional resources and support categories were identified by participants as most helpful for themselves and others experiencing POI. Statistical analyses Cohort data were processed and analysed in Jamovi version 2.7.7 using the R editor module. All analyses were considered exploratory, consequently, interpretation focuses on effect sizes and confidence intervals in addition to significance thresholds. Cohort analysis Demographic characteristics of the cohort are included in Table 1. Differences in current age, age at diagnosis, and HT status between idiopathic POI and known cause POI were assessed using independent t-tests. Pre-diagnosis symptom patterns: Idiopathic POI All survey responses were reviewed for completeness. No outliers were excluded, as extreme responses reflected clinically meaningful symptom experiences. Missing data were handled listwise, with participants excluded only from analyses where relevant data were missing. Pre-diagnosis symptom patterns were analysed among participants with idiopathic POI only, this removes interference from identifiable underlying causes (genetic, autoimmune, or iatrogenic) which may confound interpretations of pre-diagnosis symptom patterns. An exploratory factor analysis (EFA) was conducted on 12 pre-diagnosis POI symptoms (see Measures ) using minimum residual extraction and oblimin rotation identifying distinct symptom factors. For each factor, a composite score was calculated as the sum of binary symptom endorsements (presence/absence) of the items loading on that factor. Composite scores were standardised (z-scores) to facilitate comparison across domains with different numbers of items. Each standardised composite score reflects relative symptom burden within its respective domain, independent of the number of items. Higher scores reflect greater symptom burden within each domain. To examine differences in symptom presence based on age of diagnosis (three levels: ≤20, 21-30 years, and 31-40 years), a chi square test of independence analysis was conducted. Following a significant outcome, post hoc tests of standardised residuals were examined to characterise the presence/absence of each symptom domain by diagnosis age. Binomial logistic regression analyses were used to examine the main effects of age at diagnosis on predicting the likelihood of reporting each symptom domain prior to diagnosis. Ethnicity was included as covariate due to its known differences in hypoestrogenic symptom presentation in menopause [27]. Due to small cell sizes in several non-White ethnic groups (see Table 1; collectively representing <10% of the sample), ethnicity was coded as White vs. non-White for regression analyses. For each model, the ≤20 age group was set as the reference category, and odds ratios represent comparisons of each older age group (21-30, 31-40) with this reference. Estimates represent the log odds of reporting symptoms (reference level: presence) vs not reporting symptoms (absence). The extent of pre-diagnosis symptom burden across age at diagnosis groups was examined using Kruskal-Wallis tests, due to violations of normality (Shapiro-Wilk p <0.001 for all domains). Levene’s test confirmed homogeneity of variances. For domains showing significant Kruskal-Wallis results, post-hoc pairwise comparisons were conducted using Dunn’s test with Bonferroni correction. Effect sizes are reported. To capture overall symptom burden, a single latent factor was specified in the structural equation modelling (SEM) framework, with standardised symptom composite scores. This latent factor represents the shared variance among the pre-diagnosis symptom domains to illustrate overall symptom burden before idiopathic POI diagnosis. Model fit was evaluated using standard indices (CFI, TLI, RMSEA, SRMR). Factor loadings and standardised estimates are reported in the Results. To examine whether overall symptom burden differed by age at diagnosis, and to explore potential moderation of symptom domain-specific effects by age, interaction effects between age at diagnosis and each symptom domain were evaluated. Three separate SEM models were run for each domain (cognitive, physical, emotional distress) to assess whether the association between that domain and overall symptom burden varied across age groups. Main effects of age and symptom domains were estimated in all models, and fit indices and standardised estimates are reported in the Results. Current symptom patterns: Idiopathic and known cause POI Ordinal logistic regression was performed on individuals reporting each of the seven symptoms (see Measures ) in the past two weeks. Estimates represent the log-odds of reporting symptoms at different frequency levels, ranging from the least to most frequent (e.g., 'Once', ‘Occasionally’, ‘Most days’, ‘Daily’). Main effects were reported for age at diagnosis, current age, HT status, smoker status, excessive alcohol use, and ethnicity. All categorical predictors were dummy-coded using the reference categories reported with Table 2. Ordinal logistic regression was then performed on the individuals who reported each symptom item in the past two weeks to model the odds of experiencing more severe symptoms. Some symptoms had very few participants endorse extreme severity ratings (e.g. 1, 10). To reduce sparse cell counts and improve model stability, severity levels were collapsed into three categories: low severity (rated 1-3), moderate severity (rated 4-6), and high severity (7-10). Thus, modelling the odds of experiencing more severe symptoms (“high”, “moderate”, “low”). This preserves the ordinal structure and clinical interpretation of symptom severity while addressing sparsity in extreme categories. As in the frequency models, main effects were examined for age at diagnosis, current age, HT status, smoker status, excessive alcohol use, and ethnicity. Sensitivity analyses using the original 10-level scale yielded similar results (see Supplementary Information ). Dummy-coded reference levels are reported with Table 2. Those who reported ‘Never’ at each symptom item were excluded in the frequency and severity of symptom analyses. Lived experience Descriptive statistics were provided to illustrate the percentage of the cohort in response to lived experience questions on medical guidance and information, social support, and professional/workplace support. Results Cohort analysis A total of 220 participants consented to the survey. Based on inclusion criteria, 14 were excluded for not having a POI diagnosis, four for being older than 40 at age of diagnosis, and 18 for not currently living in the United Kingdom. Of the remaining 184 participants (83.60%), 159 completed the entire survey (72.30%). Four participants were removed for indicating “Not sure” regarding their POI diagnosis, leaving a final analytic sample of 155 participants (70.50%). The mean current age was 35.65, and the mean age of diagnosis was 28.78. The majority were White (91.00%) and currently using HT (91.00%). A total of 132 participants (85.20%) were self-reported as idiopathic POI cases, and 23 participants (14.80%) reported surgical or medical aetiologies, including autoimmune, genetic, and iatrogenic causes. Current age ( t (153)=0.348, p =0.728), mean age of diagnosis ( t (153)=0.493, p =0.623), and current hormonal treatment status ( t (153)=-0.846, p =0.399) did not differ by POI aetiology (idiopathic, known cause). See Table 1 for all demographic information. Pre-diagnosis symptom patterns: Idiopathic POI 109 participants (82.60%) recalled symptom duration prior to diagnosis and the median duration of symptoms was 2-5 years. Of the 129 participants (97.70%) who reported experiencing symptoms prior to their idiopathic POI diagnosis, 73.60% did not suspect their symptoms were related to POI, 4.70% did, and 21.70% were unsure. Factor retention was guided by parallel analysis and inspection of the scree plot (elbow method). The EFA yielded a three-factor solution, with most items loading clearly onto three distinct factors; cognitive symptoms (brain fog: 0.59; memory problems: 0.88; and fatigue: 0.65), physical symptoms (hot flashes: 0.51; vaginal dryness: 0.68; sleep difficulties: 0.42; loss of libido: 0.58), and emotional distress (anxiety: 0.64, low mood: 0.66). Internal consistency was 0.77, 0.68, and 0.65, respectively. Emotional distress was comprised of two items, so interpretations for this domain should be made with caution. Three items (palpitations, low mood, weight changes) did not strongly load onto these factors (>0.40) and were removed from the analysis. See Supplementary Information for assumption checks and variance of EFA. The presence and absence of cognitive symptoms ( χ² (2)= 3.47, p =0.177), physical symptoms ( χ² (2)= 5.05, p =0.080), and emotional distress ( χ² (2)= 5.68, p =0.058) did not significantly differ by age at diagnosis. Compared with participants aged ≤20 at diagnosis, those aged 31-40 were at higher odds for experiencing physical symptoms (OR= 4.38, p =0.028, 95% CI: 1.17, 16.32), and emotional distress at pre-diagnosis (OR= 2.84, p =0.025, 95% CI: 1.14, 7.04) compared to those aged ≤20. There was no difference in experiencing physical symptoms or emotional distress at pre-diagnosis between those aged 21-30 and ≤20 at diagnosis ( p >0.069). No significant differences were observed for experiencing cognitive symptoms at pre-diagnosis between those aged 21-30 and ≤20 at diagnosis ( p =0.375), or aged 31-40 and ≤20 at diagnosis ( p =0.075). Physical symptom burden differed significantly with diagnosis age ( χ ²(2)=2.63, p =0.006, ε² =0.074), with participants aged 31-40 ( p =0.004), but not 21-30 ( p =0.226), reporting greater physical symptom burden than those aged ≤20. No significant differences were observed for cognitive symptoms ( χ ²(2)=4.46, p =0.108) or emotional distress ( χ ²(2)=4.80, p =0.092) by age at diagnosis. For the SEM main effects model, cognitive, physical, and emotional distress domains created a composite latent factor of overall symptom burden. Standardised factor loadings ranged from 0.56 to 0.68, with moderate associations between each domain and the latent construct. This indicates that the three symptom domains reflect partially distinct but related components of a general symptom burden. The internal consistency of this composite was acceptable (Cronbach’s α=0.65). Model fit was excellent ( χ ²(4) = 3.56, p = 0.470; CFI= 1.00; TLI= 1.01; RMSEA= 0.00; SRMR= 0.019). All three symptom domains loaded positively and significantly onto the latent overall symptom burden (see Table 3). Standardised loadings indicated moderate to strong associations, with cognitive symptoms showing the strongest association with overall symptom burden compared to physical symptoms and emotional distress. Diagnosis age also significantly predicted overall symptom burden. Compared to the ≤20 group, both the older groups reported higher overall symptom burden (see Table 3), with small to moderate associations. Latent SEM was also used to evaluate whether age at diagnosis moderated domain-specific effects on overall symptom burden. All three interaction models demonstrated good fit (see Supplementary Information for model statistics). As shown in the main effects model, in all interaction models, older age groups showed higher overall burden relative to the ≤20 group (see Table 3). Compared to the ≤20 group, the association between all domain-level symptom scores and overall burden was significant and stronger in both the 21-30 group and the 31-40 group, demonstrating consistent domain-specific age moderation effects Current symptom patterns: Idiopathic and known cause POI Known cause POI was associated with higher odds of more frequent brain fog (OR= 2.62, p =0.025, 95% CI: 1.14, 6.20), poor memory (OR= 2.45, p =0.040, 95% CI: 1.05, 5.86), and anxiety (OR=3.76, p =0.006, 95% CI: 1.49, 10.04), compared to idiopathic POI. Additionally, smokers had higher odds of more frequent anxiety (OR= 2.80, p =0.010, 95% CI: 1.30, 6.23), compared to non-smokers. There were no significant differences in the frequency of low mood, fatigue, hot flashes, or palpitations, based on POI aetiology, HT status, current age, ethnicity, smoker status, or excessive alcohol use. Known cause POI was associated with higher odds of more severe brain fog (OR= 2.93, p =0.028, 95% CI: 1.15, 8.03) and anxiety (OR=2.63, p =0.042, 95% CI: 1.06, 7.02) compared to idiopathic POI. Smokers had higher odds of more severe poor memory (OR=2.44, p =0.019, 95% CI: 1.11, 5.53), than non-smokers. HT users were at higher odds of more severe poor memory (OR=3.26, p =0.050, 95% CI: 1.03, 11.51) and palpitations (OR=10.31, p =0.014, 95% CI: 1.78, 80.29) than non-users. Older age was associated with lower odds of more severe palpitations (OR=0.93, p =0.014, 95% CI: 0.88, 0.98), whereas excessive alcohol use was associated with higher odds of more severe palpitations (OR=7.41, p =0.034, 95% CI: 1.25, 56.48). Ethnicity was associated with differences in poor memory severity, with non-White participants experiencing higher odds (OR=3.90, p =0.028, 95% CI: 1.20, 14.21), compared to White participants. There were no significant differences in severity for low mood, fatigue, or hot flashes across POI aetiology, HRT status, age, smoking, alcohol use, or ethnicity. Table 2 includes all details from both ordinal logistic regressions. Lived experience 72.90% of the cohort indicated that they had not received sufficient support from their healthcare providers since their POI diagnosis. Regarding information about HT use and the health risks associated with untreated POI, 40.80% reported not receiving adequate guidance, 8.80% were unsure, and 50.30% felt they had received sufficient information. Participants reported even greater gaps in information related to cognitive health, with 81.00% stating that they had not received any guidance, 4.80% unsure, and only 14.30% reporting that they had received relevant information. 70.30% reported that their relationships had become strained following diagnosis, 5.90% reported improvements, and 20.60% reported no change, with the remainder preferring not to answer. 36.80% felt adequately supported by family and friends during their POI diagnosis and transition, 35.50% reported partial support, and 27.70% indicated that they did not feel supported. 52.60% reported not receiving adequate support from their employer, while 29.70% indicated that they had received sufficient support. The remaining participants reported that this question was not applicable to their situation. Participants identified several key areas needed for additional support for those with a POI diagnosis (see Figure 2), including more medical guidance (92.30%), more mental health support (76.80%), greater accessibility to HT (61.90%), more fertility support (64.50%), more community support groups (56.80%), better workplace accommodations (41.30%), and more non-hormonal treatment options (27.10%). Discussion From survey responses of 155 participants diagnosed with POI, the current study demonstrated that pre-diagnosis symptom patterns in idiopathic POI, prior to HT use, are likely influenced by age. This may contribute to diagnostic delays, particularly during adolescence and early adulthood. Symptom reporting in POI also varies according to POI aetiology, HT status, and lifestyle factors such as smoking and excessive alcohol use. Collectively, these findings characterise the heterogeneity of POI symptom presentation and provide a framework for future studies to investigate how symptom trajectories from pre-diagnosis to post-diagnosis, may relate to neural changes and the mechanisms underlying hypoestrogenic symptoms. Firstly, diagnostic delays in idiopathic POI remain substantial, with a median time of 2–5 years from symptom onset to formal diagnosis in this cohort. Most participants (74%) did not suspect their symptoms were related to POI and most (73%) reported a lack of adequate support after diagnosis from healthcare providers. This underscores major gaps in both patient awareness and clinical recognition [ 4 ]. Comparable diagnostic delays from 18 months have previously been reported, with adolescent diagnoses taking significantly longer than adult diagnoses [ 28 ]. Delayed diagnosis postpones initiation of HT [ 29 ], thereby increasing the risk of osteoporosis, cardiovascular disease, and psychological disorders [ 30 – 32 ]. With the global prevalence of POI now estimated at 3.7% [ 1 ], there is a pressing need for improved diagnostic awareness and early recognition of pre-diagnostic symptom patterns to ensure timely initiation of HT to prevent long-term health complications. At pre-diagnosis, previous research indicates that menstrual cycle changes appear more common and consistent in idiopathic POI, as hypoestrogenic symptoms are heterogenous and likely influenced by factors, including age of diagnosis. Although age differences were not observed in the presence of pre-diagnosis symptoms in idiopathic POI, the likelihood and extent of symptom burden varied markedly. Diagnosis between ages 31–40 was associated with a three- to fourfold increase in physical symptoms and emotional distress, as well as higher physical and overall symptom burden, compared with diagnosis at ≤ 20. The SEM results deepen this interpretation by showing that age at diagnosis not only predicts greater overall symptom burden but also increases the extent to which each symptom domain contributes to the overall symptom burden. For individuals diagnosed at over age 21, cognitive, physical and emotional symptoms contributed more strongly and coherently to their overall symptom burden, compared with those ≤ 20. This moderation may indicate that symptom domains become more tightly interrelated with increasing age at diagnosis, forming a more unified and impactful symptom profile. Importantly, moderation in the SEM reflects differences in how strongly each symptom domain contributes to the latent construct across age groups, rather than differences in symptom severity alone. This distinction adds nuance by showing that age at diagnosis may shape how different symptoms relate to one another, not just the severity. An important avenue for future investigation concerns the mechanisms through which age at diagnosis shapes the organisation and interrelatedness of pre-diagnosis symptom domains. The current findings may suggest that younger individuals experience pre-diagnosis symptoms that are more diffuse or less synchronised, which may contribute to under-recognition and longer diagnostic delays of POI in adolescence and early adulthood [ 28 ]. Conversely, greater coherence of symptom domains among those diagnosed at an older age may reflect age-dependent neural or physiological processes that shape how hypoestrogenic symptoms manifest and interact. Emerging neuroimaging evidence lends support to this possibility. Although menopause-specific quality of life (MENQOL) scores did not differ by age, Yuan et al. [ 20 ] observed age-dependent neural profiles in POI prior to HT initiation. Compared to age-matched controls, younger participants (diagnosed age 20–35) showed greater global and regional reductions, whereas older participants (diagnosed age 36–40) showed increases in frontal grey matter volume. Both POI groups exhibited reduced olfactory and limbic connectivity, compared to controls, but the younger group showed increased frontotemporal connectivity and higher network centrality, which was thought to be consistent with compensatory reorganisation. Yuan et al. [ 20 ] hypothesised that younger age at diagnosis may be associated with greater neural disruption and, potentially, subsequent stronger compensatory neural change. These findings highlight age at diagnosis as a key factor shaping pre-diagnosis symptom patterns in idiopathic POI. Younger individuals tend to show less integrated and more variable symptom profiles, which may contribute to diagnostic delays, as subtle hypoestrogenic symptoms and menstrual irregularities can be overlooked or attributed to adolescent maturation [ 33 – 35 ]. This heterogeneity underscores the clinical relevance of age for diagnosis [ 36 ] and has important implications for neuroimaging research. Differences in symptom burden and interrelatedness across age groups may reflect distinct neural trajectories, and accounting for age of diagnosis when designing studies can guide participant stratification and interpretation of neural changes associated with hypoestrogenic symptoms prior to HT initiation [ 4 , 7 , 20 ]. Building on the limited neuroimaging evidence supporting the increased neurodegenerative risk in POI [ 17 ], the current survey emphasises the need to consider POI aetiology when examining symptom trajectories, as abrupt (iatrogenic) and gradual (idiopathic) oestrogen loss likely have distinct neural consequences. Examining these trajectories is essential for identifying the neural correlates of rapid versus progressive ovarian-neural disruption and how these patterns shape early symptom expression. Consistent with previous reports, hypoestrogenic symptoms were experienced as more sudden and severe following bilateral oophorectomy [ 22 , 23 ], reflecting rapid disruption of ovarian-neural signalling. In the current study, individuals with known cause POI reported more frequent and severe cognitive symptoms and anxiety compared to those with idiopathic POI. Bilateral oophorectomy prior to spontaneous menopause has also been linked to increased risk of cognitive impairment, stroke, and Parkinson’s disease [ 37 – 39 ]. Indeed, Alzheimer’s-related markers, including elevated tau in the context of higher Aβ, were observed in those undergoing surgery before age 46 but not at age 46–49 [ 40 ]. Taken together, these findings highlight the importance of stratifying future research cohorts by POI aetiology to better understand symptom trajectories (including with HT) and long-term cognitive risk in neuroimaging research. However, due to small sample size, known cause POI included iatrogenic, autoimmune and genetic cause. Further investigation of symptom presentation is required using larger samples. Interestingly, HT users were more likely to report more severe poor memory and palpitations than non-users, although frequency of reporting did not differ. While this may appear counterintuitive, it may reflect a combination of factors, such as individuals with more severe symptoms may be more likely to seek treatment, despite HT being prescribed in POI primarily to support long-term health rather than solely for symptom management. These observations underscore the value of incorporating patient-reported outcomes in clinical practice to monitor symptom-specific responses and guide individualised HT regimens. Future neuroimaging research should examine how HT use, timing, and duration relate to brain health trajectories after diagnosis such as whether the timing of HT relative to loss of ovarian function engages different neural systems or compensatory responses across age groups. Based on the current findings and recent Yuan et al. [ 20 ], might age at diagnosis shape neural trajectories and is there is a critical window in which HT provides maximal neural benefit in POI. This will be essential for interpreting imaging findings and optimising clinical management. Lifestyle factors such as smoking and excessive alcohol intake also affected symptom reporting in POI. In line with the European Society of Human Reproduction and Embryology [ 41 ] guidelines, these findings encourage maintaining a healthy lifestyle to promote better long-term bone health [ 42 ] and to prevent symptom exacerbation and reducing efficacy of HRT [ 25 ]. Finally, the survey highlighted necessary improvements in POI healthcare. POI is associated with increased comorbidities and higher mortality when untreated [ 43 ], thus diagnostic delays and limited awareness within the medical community further contribute to poor patient outcomes and lack of medical guidance, as observed in the current study. Mental health support should be an integral part of POI management [ 28 ]. Those with POI are three to four times more likely to experience anxiety and depression [ 44 ], particularly when diagnosis is delayed [ 28 ]. Following diagnosis, many patients report depression, stress, and reduced life satisfaction [ 45 , 46 ]. Thus, mental health support should be prioritised throughout diagnosis and treatment, with personalised care plans addressing both physical and psychological wellbeing. This study is not without limitations. Firstly, self-reported symptom data are subject to recall bias, particularly the retrospective pre-diagnosis symptom patterns. Future research could integrate with POI clinics to capture symptom patterns prior to HT and longitudinally follow patients after HT initiation to explore neural trajectories and associations with hypoestrogenic symptom profiles. Next, initiation of HT was not recorded. These data are important for assessing symptom trajectory after diagnosis and should be examined in future research, such as neuroimaging studies to assess longitudinal impact of HT on POI brain health. Moreover, in the instance of delayed diagnosis, as observed in the current study, it is of interest to assess the impact of untreated POI, such as reduction in bone density [ 21 ], and to probe better timing of HT. This will contribute to our understanding of a “critical window” which will inform POI guidelines, rather than being taken from menopause guidelines. Finally, although SEM illustrates age of diagnosis moderating pre-diagnosis symptoms in idiopathic POI, longitudinal research is needed to infer causality or temporal ordering. Conclusion This survey highlights how age at diagnosis, POI aetiology, and HT status shape symptom trajectories in POI, underscoring the need for greater clinical awareness to support earlier diagnosis and timely HT initiation. Future research should stratify cohorts by these factors to capture heterogeneity in pre-diagnosis symptoms and to clarify how HT modifies symptom patterns after diagnosis. Such stratification is particularly important for neuroimaging studies aiming to link symptom profiles, particularly age-related differences, to underlying neural mechanisms and long-term vulnerability. Integrating symptom-based markers with imaging outcomes will help develop mechanistic models of hypoestrogenic effects on the brain. This work was funded by King’s College London Early Career Research Award. Declarations Competing interests: LN has declared that no competing interests exist. Data availability: Data is available upon reasonable request to author. Acknowledgments: Thank you to the Daisy Network, Professor Adam Hampshire, and Benjamin Goody for their support. References Golezar, S., et al., The global prevalence of primary ovarian insufficiency and early menopause: a meta-analysis. Climacteric, 2019. 22 (4): p. 403-411. Nash, Z. and M. Davies, Premature ovarian insufficiency. BMJ, 2024. 384 : p. e077469. Nelson, L.M., Clinical practice. Primary ovarian insufficiency. N Engl J Med, 2009. 360 (6): p. 606-14. Panay, N., et al., Premature ovarian insufficiency: an International Menopause Society White Paper. Climacteric, 2020. 23 (5): p. 426-446. Welt, C.K., Primary ovarian insufficiency: a more accurate term for premature ovarian failure. Clin Endocrinol (Oxf), 2008. 68 (4): p. 499-509. Kapoor, E., Premature Ovarian Insufficiency. Curr Opin Endocr Metab Res, 2023. 28 . Brinton, R.D., et al., Perimenopause as a neurological transition state. Nat Rev Endocrinol, 2015. 11 (7): p. 393-405. Freedman, R.R., Menopausal hot flashes: mechanisms, endocrinology, treatment. J Steroid Biochem Mol Biol, 2014. 142 : p. 115-20. Freedman, R.R., et al., Cortical activation during menopausal hot flashes. Fertil Steril, 2006. 85 (3): p. 674-8. Roeters van Lennep, J.E., et al., Cardiovascular disease risk in women with premature ovarian insufficiency: A systematic review and meta-analysis. Eur J Prev Cardiol, 2016. 23 (2): p. 178-86. Zhu, Y., et al., Assessment of knowledge, understanding and awareness of Chinese women clinical staff towards menopause hormone therapy: a survey study. J Obstet Gynaecol, 2023. 43 (1): p. 2171779. Honigberg, M.C., et al., Association of Premature Natural and Surgical Menopause With Incident Cardiovascular Disease. JAMA, 2019. 322 (24): p. 2411-2421. Sullivan, S.D., P.M. Sarrel, and L.M. Nelson, Hormone replacement therapy in young women with primary ovarian insufficiency and early menopause. Fertil Steril, 2016. 106 (7): p. 1588-1599. Szeliga, A., M. Maciejewska-Jeske, and B. Meczekalski, Bone health and evaluation of bone mineral density in patients with premature ovarian insufficiency. Prz Menopauzalny, 2018. 17 (3): p. 112-116. Nguyen, H.H., F. Milat, and A.J. Vincent, New insights into the diagnosis and management of bone health in premature ovarian insufficiency. Climacteric, 2021. 24 (5): p. 481-490. Jones, A.R., et al., Bone health in women with premature ovarian insufficiency/early menopause: a 23-year longitudinal analysis. Hum Reprod, 2024. 39 (5): p. 1013-1022. Vujović, S., et al., Alzheimer’s Disease and Premature Ovarian Insufficiency. Endocrines, 2023. 4 (2): p. 250-256. Wang, Q., et al., Epigenetic dysregulation of steroidogenesis and neuroactive steroid deficiency in premature ovarian insufficiency: implications for neurodegenerative risk. Biomark Res, 2025. 13 (1): p. 147. Calvo, N., et al., Cognitive and brain health in women with early bilateral salpingo-oophorectomy: Implications for risk, resilience, and subjective cognitive decline. Alzheimers Dement, 2025. 21 (8): p. e70454. Yuan, S., et al., Brain structural alterations in young women with premature ovarian insufficiency: Implications for dementia risk. Alzheimers Dement, 2025. 21 (3): p. e70111. Kanj, R.V., et al., Evaluation and Management of Primary Ovarian Insufficiency in Adolescents and Young Adults. J Pediatr Adolesc Gynecol, 2018. 31 (1): p. 13-18. Benshushan, A., et al., Climacteric symptoms in women undergoing risk-reducing bilateral salpingo-oophorectomy. Climacteric, 2009. 12 (5): p. 404-9. Gibson-Helm, M., H. Teede, and A. Vincent, Symptoms, health behavior and understanding of menopause therapy in women with premature menopause. Climacteric, 2014. 17 (6): p. 666-73. Khan, S.J., et al., Vasomotor Symptoms During Menopause: A Practical Guide on Current Treatments and Future Perspectives. Int J Womens Health, 2023. 15 : p. 273-287. Cui, J. and Y. Wang, Premature ovarian insufficiency: a review on the role of tobacco smoke, its clinical harm, and treatment. J Ovarian Res, 2024. 17 (1): p. 8. Kwon, R., et al., Alcohol Consumption Patterns and Risk of Early-Onset Vasomotor Symptoms in Premenopausal Women. Nutrients, 2022. 14 (11). Kochersberger, A., et al., The association of race, ethnicity, and socioeconomic status on the severity of menopause symptoms: a study of 68,864 women. Menopause, 2024. 31 (6): p. 476-483. Bakhsh, H., An Evidence-Based Approach to the Management of Primary Ovarian Insufficiency in Adolescents and Young Women. Life (Basel), 2025. 15 (9). Minis, E., et al., Primary Ovarian Insufficiency: Time to Diagnosis and a Review of Current Literature. Clin. Exp. Obstet. Gynecol. , 2022. 49 (6). Popat, V.B., et al., Bone mineral density in estrogen-deficient young women. J Clin Endocrinol Metab, 2009. 94 (7): p. 2277-83. Webber, L., et al., HRT for women with premature ovarian insufficiency: a comprehensive review. Hum Reprod Open, 2017. 2017 (2): p. hox007. Meczekalski, B., et al., Neuroendocrine disturbances in women with functional hypothalamic amenorrhea: an update and future directions. Endocrine, 2024. 84 (3): p. 769-785. Ramireddy, N. and F.S. Mohideen, Premature Ovarian Insufficiency: Management from a Primary Care perspective. International Journal of Medical Reviews and Case Reports, 2020. 4 (10): p. 127. Coulam, C.B., S.C. Adamson, and J.F. Annegers, Incidence of premature ovarian failure. Obstet Gynecol, 1986. 67 (4): p. 604-6. Anasti, J.N., Premature ovarian failure: an update. Fertil Steril, 1998. 70 (1): p. 1-15. van Zwol-Janssens, C., et al., Depressive symptoms in women with premature ovarian insufficiency (POI): a cross-sectional observational study. Menopause, 2025. Rocca, W.A., et al., Increased risk of cognitive impairment or dementia in women who underwent oophorectomy before menopause. Neurology, 2007. 69 (11): p. 1074-83. Rocca, W.A., et al., Hysterectomy, oophorectomy, estrogen, and the risk of dementia. Neurodegener Dis, 2012. 10 (1-4): p. 175-8. Stopieńm, R., Neurological health and premature ovarian insufficiency – pathogenesis and clinical management. Prz Menopauzalny, 2018. 17 (3): p. 120-123. Kantarci, K., et al., Premenopausal bilateral oophorectomy and Alzheimer's disease imaging biomarkers later in life. Alzheimers Dement, 2025. 21 (7): p. e14469. Eshre, A.C., et al., Evidence-based guideline: Premature Ovarian Insufficiency. Fertil Steril, 2025. 123 (2): p. 221-236. Kiriakova, V., et al., Management of bone health in women with premature ovarian insufficiency: Systematic appraisal of clinical practice guidelines and algorithm development. Maturitas, 2019. 128 : p. 70-80. Blumel, J.E., et al., Is premature ovarian insufficiency associated with mortality? A three-decade follow-up cohort. Maturitas, 2022. 163 : p. 82-87. Xi, D., et al., The risk of depressive and anxiety symptoms in women with premature ovarian insufficiency: a systematic review and meta-analysis. Arch Womens Ment Health, 2023. 26 (1): p. 1-10. Liao, K.L., N. Wood, and G.S. Conway, Premature menopause and psychological well-being. J Psychosom Obstet Gynaecol, 2000. 21 (3): p. 167-74. Li, X.T., et al., Health-related quality-of-life among patients with premature ovarian insufficiency: a systematic review and meta-analysis. Qual Life Res, 2020. 29 (1): p. 19-36. Tables Table 1. Demographic information of cohort. Group, n Whole sample, 155 Idiopathic POI, 132 Known cause POI, 23 Mean age (SD; age range) 35.60 (7.70; 19.00-57.00) 35.70 (7.60; 19.00-57.00) 35.10 (7.90; 19.00-49.00) Mean age at diagnosis (SD; age range) 28.70 (7.80; 13.00-40.00) 28.90 (7.80; 13.00-40.00) 28.00 (7.30; 16.00-40.00) Age group, n Under 20 33 28 5 21-30 43 34 9 31-40 79 70 9 Aetiology of POI 1 , n Autoimmune disease 7 - 7 Genetic condition 5 - 5 Iatrogenic/other medical 11 - 11 Endometriosis 1 - 1 Fibroids 0 - 0 BRCA mutation 3 - 3 Cancer 3 - 3 Other 5 - 5 Treatments leading to POI, n Bilateral oophorectomy 4 - 4 Pelvic surgery 4 - 4 Pelvic radiation 1 - 1 Chemotherapy 1 - 1 Other 1 - 1 Current Hormonal Treatment use, (% of total sample) 2 91.00 90.20 95.70 HRT 86.50 84.80 95.70 Hormonal contraception 12.30 12.90 8.70 Testosterone replacement 18.70 18.90 17.40 Other 5.20 6.10 0.00 None 9.00 9.80 4.30 Ethnicity, % Asian 3.90 3.00 8.70 Black 0.60 0.80 4.30 White 91.00 91.70 87.00 Mixed 3.90 3.80 0.00 Not Stated 0.60 0.80 0.00 Lifestyle factors, % Current smoker 20.00 19.70 21.70 Excessive alcohol consumption 4.50 5.30 0.00 Thyroid condition 6.50 3.80 21.70 Neurodegenerative disease 1.30 1.60 0.00 1 Participants could select more than one underlying cause of iatrogenic POI. 2 Participants could select more than one type of hormonal treatment; percentages are based on the total group (not only those receiving treatment). Table 2. Percentage of symptoms reported and predictors of symptom frequency and severity over the past two weeks with odds ratio (OR) and p -value. Odds ratios indicate the odds of experiencing more severe or more frequent symptoms, with reference categories set from least severe/frequent to most severe/frequent. Symptom (% reported) POI aetiology OR p -value HT status OR p- value Smoker OR p -value Alcohol OR p -value Current age OR p -value Ethnicity OR p -value Frequency of symptoms in the past two weeks Brain fog (94.20) 2.62* 0.025* 0.61 0.361 2.08 0.068 0.37 0.183 1.00 0.819 0.95 0.930 Poor memory (95.50) 2.45* 0.040* 1.88 0.260 1.91 0.106 1.30 0.745 1.02 0.311 1.26 0.661 Anxiety (92.30) 3.76* 0.006* 0.63 0.303 2.80* 0.010* 0.83 0.794 0.99 0.706 0.60 0.342 Low mood (91.60) 1.68 0.220 0.47 0.154 1.80 0.139 1.59 0.582 1.01 0.826 1.00 0.994 Fatigue (91.60) 2.11 0.149 0.82 0.736 1.59 0.276 0.44 0.313 0.99 0.685 1.50 0.454 Hot flashes (60.00) 2.92 0.067 1.25 0.749 1.08 0.868 2.00 0.475 1.00 0.889 1.72 0.429 Palpitations (52.30) 1.84 0.287 2.46 0.328 1.53 0.410 2.22 0.396 0.99 0.776 0.87 0.832 Severity of symptoms in the past two weeks Brain fog 2.94* 0.028* 0.82 0.721 2.01 0.080 1.04 0.956 1.02 0.290 3.05 0.073 Poor memory 1.76 0.204 3.29* 0.050* 2.44* 0.029* 1.70 0.490 1.02 0.343 3.90* 0.028* Anxiety 2.63* 0.042* 1.46 0.496 2.20 0.061 1.83 0.449 0.97 0.122 1.42 0.544 Low mood 1.89 0.156 1.20 0.733 1.11 0.79 1.43 0.667 1.02 0.412 2.14 0.206 Fatigue 1.96 0.195 1,20 0.758 1.23 0.629 0.27 0.092 0.98 0.411 0.71 0.532 Hot flashes 2.09 0.228 0.97 0.961 1.76 0.239 0.60 0.606 1.02 0.501 0.840 0.818 Palpitations 1.27 0.677 10.31* 0.014* 1.30 0.602 7.41* 0.034* 0.93* 0.014* 0.81 0.777 * p < 0.05. 95% confidence intervals (CI) for significant ORs are reported in the text. Reference levels: Idiopathic POI, HT non-users, smoker, excessive alcohol use, White. Table 3. Main and interaction effects of age at diagnosis and symptom domains on overall symptom burden at pre-diagnosis in idiopathic POI from latent structural equation modelling (SEM). Predictor Standardised estimate p -value Unstandardised estimate Standard error Main effects model predicting overall symptom burden Age 21-30 0.29 0.024 0.34 0.12 Age 31-40 0.42 0.001 0.43 0.15 Cognitive symptoms 0.68 <0.001 1.00 0.00 Physical symptoms 0.62 <0.001 1.13 0.22 Emotional distress 0.56 <0.001 1.45 0.28 Interaction models predicting overall symptom burden Age 21-30 * cognitive symptoms 0.52 <0.001 0.99 0.09 Age 31-40 * cognitive symptoms 0.70 <0.001 0.99 0.07 Age 21-30 * physical symptoms 0.57 <0.001 0.44 0.09 Age 31-40 * physical symptoms 0.66 <0.001 0.43 0.09 Age 21-30 * emotional distress 0.48 <0.001 0.43 0.09 Age 31-40 * emotional distress 0.73 <0.001 0.43 0.09 Age groups are dummy-coded with ≤20 years as the reference category. Estimates indicate differences relative to this group. Additional Declarations The authors declare no competing interests. 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11:29:02","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138505,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8280682/v1/dabe01b616a87ee9da9588dd.html"},{"id":97894618,"identity":"a41396fb-2ecf-42f3-806e-d67cfcfd050c","added_by":"auto","created_at":"2025-12-10 15:32:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69786,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage (%) of participants (n=155) endorsing areas where additional support is needed following a POI diagnosis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8280682/v1/81ac697713c8e3a4c106a0bc.png"},{"id":98622565,"identity":"7f027a11-937a-4b53-b6a3-4dd500c0c8ff","added_by":"auto","created_at":"2025-12-19 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Neural Mechanisms and Brain Health\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePremature ovarian insufficiency (POI), the loss of ovarian function before age 40, affects 3.7% of the global population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. POI impacts physical and psychological wellbeing, reduces fertility and carries long-term consequences for bone, cardiovascular, and brain health [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], but it remains an under-researched area of health.\u003c/p\u003e\u003cp\u003eGenetic, autoimmune, metabolic, and iatrogenic causes have been shown to contribute to POI, yet idiopathic cases still comprise 70\u0026ndash;90% of cases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Clinically, POI presents as primary/secondary amenorrhea, or oligomenorrhea, resulting from elevated gonadotropins and low oestradiol [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Symptoms also extend beyond menstrual disruption, encompassing vasomotor, psychological, cognitive, urogenital and sexual changes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These symptoms reflect systemic and central effects of oestrogen deficiency, thus, when oestrogen levels decline in POI, neuroendocrine regulation is altered. The resulting hypoestrogenic symptoms likely arise from impaired ovarian-neural oestrogen signalling within regions rich in oestrogen receptors, such as the hypothalamus, prefrontal cortex, and hippocampus [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLoss of ovarian function and subsequent oestrogen depletion disrupts widespread neural circuits and therefore should be understood through a neuroendocrine lens. This applies to a range of hypoestrogenic symptoms, for example, vasomotor symptoms, like hot flashes, involving co-activation of hypothalamic, superior frontal, insula and cingulate regions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, whilst cardiovascular [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and bone consequences [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] of POI are well-documented, neural consequences remain unclear. There is an increased risk of neurodegenerative disease in POI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], perhaps reflecting impaired neurosteroid production [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], but neuroimaging research underpinning neural vulnerability in POI is sparse. Whilst bilateral oophorectomy over 40 points to risks such as Alzheimer\u0026rsquo;s disease [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], surgical POI studies are limited. For idiopathic POI, Yuan et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] recently observed structural and functional neural changes associated POI, providing novel insight into elevated risk of neurodegenerative disease in POI.\u003c/p\u003e\u003cp\u003eThe relationship between POI symptom presentation and factors that shape brain vulnerability, such as age at diagnosis, type of POI, and hormone therapy (HT), remains poorly characterised. Understanding how these factors influence symptom patterns is essential to inform the design of neuroimaging studies, identify neural trajectories associated with different POI aetiologies, and ultimately clarify how early hypoestrogenic changes may contribute to long-term neural alterations.\u003c/p\u003e\u003cp\u003eUsing a self-reported online survey, this study aimed to, i) characterise symptom reporting in idiopathic POI before diagnosis (prior to the initiation HT) specifically, whether age at diagnosis influenced symptom reporting and symptom burden. Guidelines for POI in adolescence and early adulthood indicate age differences in symptom presentation, with paediatric and adolescents with delayed puberty, primary amenorrhea and low bone density [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, it was hypothesised that hypoestrogenic symptoms would be less likely in those aged\u0026thinsp;\u0026le;\u0026thinsp;20 and they would be likely to report fewer symptoms, but overall symptom burden would not differ compared to older age groups (21\u0026ndash;30, 31\u0026ndash;40).\u003c/p\u003e\u003cp\u003eAdditionally, the study sought to ii) examine differences in current symptom frequency and severity according to POI aetiology (idiopathic; known cause) HT status, and lifestyle factors such as smoker status and alcohol. Participants with a known cause of POI were expected to report more frequent and severe POI symptoms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], compared to idiopathic POI. HT use was expected to reduce likelihood of frequent and severe physical symptoms [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], whereas smoking and excessive alcohol were expected to increase symptom reporting [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], particularly physical symptoms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAll procedures were conducted in accordance with the Declaration of Helsinki. This study received ethical approval from the Health Faculties (Purple) Research Ethics Committee at King\u0026rsquo;s College London (HR-24/25-48999).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were recruited through posters where they were able to access the survey. Participants were eligible if they had a confirmed diagnosis of POI at or before age 40, were aged 18 years or older, and were currently residing in the United Kingdom.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData were collected via the \u003cem\u003ePOI and the Brain survey\u003c/em\u003e, a self-administered online survey using the King\u0026rsquo;s College London Qualtrics platform (www.qualtrics.kcl.ac.uk). All participants provided informed consent prior to participation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eParticipants reported the aetiology of their POI diagnosis, indicating whether the cause was i) unknown (idiopathic), ii) related to medical treatment or surgery, iii) associated with an autoimmune condition, or iv) due to a known genetic cause. Alternatively, they could report being unsure about POI diagnosis. For analytical purposes, participants were grouped into idiopathic POI cases or known cause POI cases (iatrogenic, autoimmune, or genetic causes). The latter group was combined due to small sample size. For those reporting medical aetiologies, participants could select more than one underlying cause (endometriosis, BRCA mutation, cancer, fibroids, or other medical indication).\u003c/p\u003e\n\u003cp\u003eIndividuals who self-reported using any HT were classified as current HT users, while those reporting no HT were classified as non-users. The survey did not collect information on previous HT or date of initiation; thus, HT was analysed in relation to current symptoms only.\u003c/p\u003e\n\u003cp\u003eParticipants reported their age at the time of POI diagnosis, which was subsequently categorised into three groups for analysis: \u0026le;20, 21-30 years, and 31-40 years. Additional demographic information was collected, including current age, ethnicity, and highest level of education. Lifestyle factors, such as smoking status and excessive alcohol consumption, were also recorded and treated as binary variables (yes/no) for analyses.\u003c/p\u003e\n\u003cp\u003ePre-diagnosis symptoms (hot flashes, brain fog, poor memory, mood swings, vaginal dryness, sleep difficulties, fatigue, palpitations, anxiety, low mood, loss of libido, and weight changes) were each coded as a binary variable for symptom presence/absence. Participants also reported the duration of these symptoms prior to idiopathic diagnosis using an ordinal scale (\u0026lt;6 months, 6-12 months, 1-2 years, 2-5 years, over 5 years) and whether they suspected that these symptoms were related to POI before receiving their diagnosis (yes/no/maybe).\u003c/p\u003e\n\u003cp\u003eCurrent symptoms (brain fog, poor memory, anxiety, low mood, fatigue, hot flashes, palpitations) were assessed based on the past two weeks. Participants indicated the frequency with which they had experienced each symptom (\u0026ldquo;Daily\u0026rdquo;, \u0026ldquo;Most days\u0026rdquo;, \u0026ldquo;Occasionally\u0026rdquo;, \u0026ldquo;Once\u0026rdquo;, or \u0026ldquo;Never\u0026rdquo;). Those who experienced symptoms at least once were subsequently asked to rate the severity of each symptom on a scale from 1 \u0026ldquo;not noticeable at all\u0026rdquo; to 10 \u0026ldquo;extremely severe.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003eThe survey also explored participants lived experiences of POI, including the adequacy of medical guidance, information and support after receiving a POI diagnosis. Social support questions were included to probe the impact of POI on relationships with partners, family, or friends. Professional/workplace support questions were included for the adequacy of support received at a participant\u0026rsquo;s workplace. Additional resources and support categories were identified by participants as most helpful for themselves and others experiencing POI.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCohort data were processed and analysed in Jamovi version 2.7.7 using the R editor module. All analyses were considered\u0026nbsp;exploratory, consequently, interpretation focuses on\u0026nbsp;effect sizes and confidence intervals in addition to significance thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCohort analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDemographic characteristics of the cohort are included in Table 1. Differences in current age, age at diagnosis, and HT status between idiopathic POI and known cause POI were assessed using independent t-tests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePre-diagnosis symptom patterns: Idiopathic POI\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll survey responses were reviewed for completeness. No outliers were excluded, as extreme responses reflected clinically meaningful symptom experiences. Missing data were handled\u0026nbsp;listwise, with participants excluded only from analyses where relevant data were missing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePre-diagnosis symptom patterns were analysed among participants with idiopathic POI only, this removes interference from identifiable underlying causes (genetic, autoimmune, or iatrogenic) which may confound interpretations of pre-diagnosis symptom patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn exploratory factor analysis (EFA) was conducted on 12 pre-diagnosis POI symptoms (see \u003cem\u003eMeasures\u003c/em\u003e) using minimum residual extraction and oblimin rotation identifying distinct symptom factors. For each factor, a composite score was calculated as the sum of binary symptom endorsements (presence/absence) of the items loading on that factor.\u0026nbsp;Composite scores were standardised (z-scores) to facilitate comparison across domains with different numbers of items. Each standardised composite score reflects relative symptom burden within its respective domain, independent of the number of items. Higher scores reflect greater symptom burden within each domain.\u003c/p\u003e\n\u003cp\u003eTo examine differences in symptom presence based on age of diagnosis (three levels: \u0026le;20, 21-30 years, and 31-40 years), a chi square test of independence analysis was conducted. Following a significant outcome, post hoc tests of standardised residuals were examined to characterise the presence/absence of each symptom domain by diagnosis age.\u003c/p\u003e\n\u003cp\u003eBinomial logistic regression analyses were used to examine the main effects of age at diagnosis on predicting the likelihood of reporting each symptom domain prior to diagnosis. Ethnicity was included as covariate due to its known differences in hypoestrogenic symptom presentation in menopause [27]. Due to small cell sizes in several non-White ethnic groups (see Table 1; collectively representing \u0026lt;10% of the sample), ethnicity was coded as White vs. non-White for regression analyses.\u0026nbsp;For each model, the \u0026le;20 age group was set as the reference category, and odds ratios represent comparisons of each older age group (21-30, 31-40) with this reference. Estimates represent the log odds of reporting symptoms (reference level: presence) vs not reporting symptoms (absence).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe extent of pre-diagnosis symptom burden across age at diagnosis groups was examined using Kruskal-Wallis tests, due to violations of normality (Shapiro-Wilk \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001 for all domains).\u0026nbsp;Levene\u0026rsquo;s test confirmed homogeneity of variances. For domains showing significant Kruskal-Wallis results,\u0026nbsp;post-hoc pairwise comparisons\u0026nbsp;were conducted using\u0026nbsp;Dunn\u0026rsquo;s test with Bonferroni correction. Effect sizes are reported.\u003c/p\u003e\n\u003cp\u003eTo capture overall symptom burden, a single latent factor was specified in the structural equation modelling (SEM) framework, with standardised symptom composite scores. This latent factor represents the shared variance among the pre-diagnosis symptom domains to illustrate overall symptom burden before idiopathic POI diagnosis. Model fit was evaluated using standard indices (CFI, TLI, RMSEA, SRMR). Factor loadings and standardised estimates are reported in the Results.\u003c/p\u003e\n\u003cp\u003eTo examine whether overall symptom burden differed by age at diagnosis, and to explore potential moderation of symptom domain-specific effects by age, interaction effects between age at diagnosis and each symptom domain were evaluated. Three separate SEM models were run for each domain (cognitive, physical, emotional distress) to assess whether the association between that domain and overall symptom burden varied across age groups. Main effects of age and symptom domains were estimated in all models, and fit indices and standardised estimates are reported in the Results.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCurrent symptom patterns: Idiopathic and known cause POI\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOrdinal logistic regression was performed on individuals reporting each of the seven symptoms (see \u003cem\u003eMeasures\u003c/em\u003e) in the past two weeks. Estimates represent the log-odds of reporting symptoms at different frequency levels, ranging from the least to most frequent (e.g., \u0026apos;Once\u0026apos;, \u0026lsquo;Occasionally\u0026rsquo;, \u0026lsquo;Most days\u0026rsquo;, \u0026lsquo;Daily\u0026rsquo;). Main effects were reported for age at diagnosis, current age, HT status, smoker status, excessive alcohol use, and ethnicity. All categorical predictors were dummy-coded using the reference categories reported with Table 2.\u003c/p\u003e\n\u003cp\u003eOrdinal logistic regression was then performed on the individuals who reported each symptom item in the past two weeks to model the odds of experiencing more severe symptoms. Some symptoms had very few participants endorse extreme severity ratings (e.g. 1, 10). To reduce sparse cell counts and improve model stability, severity levels were collapsed into three categories: low severity (rated 1-3), moderate severity (rated 4-6), and high severity (7-10). Thus, modelling the odds of experiencing more severe symptoms (\u0026ldquo;high\u0026rdquo;, \u0026ldquo;moderate\u0026rdquo;, \u0026ldquo;low\u0026rdquo;). This preserves the ordinal structure and clinical interpretation of symptom severity while addressing sparsity in extreme categories. As in the frequency models, main effects were examined for age at diagnosis, current age, HT status, smoker status, excessive alcohol use, and ethnicity. Sensitivity analyses using the original 10-level scale yielded similar results (see \u003cem\u003eSupplementary Information\u003c/em\u003e). Dummy-coded reference levels are reported with Table 2.\u0026nbsp;Those who reported \u0026lsquo;Never\u0026rsquo; at each symptom item were excluded in the frequency and severity of symptom analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLived experience\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were provided to illustrate the percentage of the cohort in response to lived experience questions on medical guidance and information, social support, and professional/workplace support.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eCohort analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 220 participants consented to the survey. Based on inclusion criteria, 14 were excluded for not having a POI diagnosis, four for being older than 40 at age of diagnosis, and 18 for not currently living in the United Kingdom. Of the remaining 184 participants (83.60%), 159 completed the entire survey (72.30%). Four participants were removed for indicating “Not sure” regarding their POI diagnosis, leaving a final analytic sample of 155 participants (70.50%).\u003c/p\u003e\n\u003cp\u003eThe mean current age was 35.65, and the mean age of diagnosis was 28.78. The majority were White (91.00%) and currently using HT (91.00%). A total of 132 participants (85.20%) were self-reported as idiopathic POI cases, and 23 participants (14.80%) reported surgical or medical aetiologies, including autoimmune, genetic, and iatrogenic causes. Current age (\u003cem\u003et\u003c/em\u003e(153)=0.348, \u003cem\u003ep\u003c/em\u003e=0.728), mean age of diagnosis (\u003cem\u003et\u003c/em\u003e(153)=0.493, \u003cem\u003ep\u003c/em\u003e=0.623), and current hormonal treatment status (\u003cem\u003et\u003c/em\u003e(153)=-0.846, \u003cem\u003ep\u003c/em\u003e=0.399) did not differ by POI aetiology (idiopathic, known cause). See Table 1 for all demographic information.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePre-diagnosis symptom patterns: Idiopathic POI\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e109 participants (82.60%) recalled symptom duration prior to diagnosis and the median duration of symptoms was 2-5 years. Of the 129 participants (97.70%) who reported experiencing symptoms prior to their idiopathic POI diagnosis, 73.60% did not suspect their symptoms were related to POI, 4.70% did, and 21.70% were unsure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFactor retention\u0026nbsp;was guided by\u0026nbsp;parallel analysis and inspection of the\u0026nbsp;scree plot (elbow method).\u0026nbsp;The EFA yielded a three-factor solution, with most items loading clearly onto three distinct factors; cognitive symptoms (brain fog: 0.59; memory problems: 0.88; and fatigue: 0.65), physical symptoms (hot flashes: 0.51; vaginal dryness: 0.68; sleep difficulties: 0.42; loss of libido: 0.58), and emotional distress (anxiety: 0.64, low mood: 0.66). Internal consistency was 0.77, 0.68, and 0.65, respectively. Emotional distress was comprised of two items, so interpretations for this domain should be made with caution. Three items (palpitations, low mood, weight changes) did not strongly load onto these factors (\u0026gt;0.40) and were removed from the analysis. See \u003cem\u003eSupplementary Information\u003c/em\u003e for assumption checks and variance of EFA.\u003c/p\u003e\n\u003cp\u003eThe presence and absence of cognitive symptoms (\u003cem\u003eχ²\u003c/em\u003e(2)= 3.47, \u003cem\u003ep\u003c/em\u003e=0.177), physical symptoms (\u003cem\u003eχ²\u003c/em\u003e(2)= 5.05, \u003cem\u003ep\u003c/em\u003e=0.080), and emotional distress (\u003cem\u003eχ²\u003c/em\u003e(2)= 5.68, \u003cem\u003ep\u003c/em\u003e=0.058) did not significantly differ by age at diagnosis.\u003c/p\u003e\n\u003cp\u003eCompared with participants aged ≤20 at diagnosis, those aged 31-40 were at higher odds for experiencing physical symptoms (OR= 4.38, \u003cem\u003ep\u003c/em\u003e=0.028, 95% CI: 1.17, 16.32), and emotional distress at pre-diagnosis (OR= 2.84, \u003cem\u003ep\u003c/em\u003e=0.025, 95% CI: 1.14, 7.04) compared to those aged ≤20. There was no difference in experiencing physical symptoms or emotional distress at pre-diagnosis between those aged 21-30 and ≤20 at diagnosis (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.069). No significant differences were observed for experiencing cognitive symptoms at pre-diagnosis between those aged 21-30 and ≤20 at diagnosis (\u003cem\u003ep\u003c/em\u003e=0.375), or aged 31-40 and ≤20 at diagnosis (\u003cem\u003ep\u003c/em\u003e=0.075).\u003c/p\u003e\n\u003cp\u003ePhysical symptom burden differed significantly with diagnosis age (\u003cem\u003eχ\u003c/em\u003e²(2)=2.63, \u003cem\u003ep\u003c/em\u003e=0.006, ε² =0.074), with participants aged 31-40 (\u003cem\u003ep\u003c/em\u003e=0.004), but not 21-30 (\u003cem\u003ep\u003c/em\u003e=0.226), reporting greater physical symptom burden than those aged ≤20. No significant differences were observed for cognitive symptoms (\u003cem\u003eχ\u003c/em\u003e²(2)=4.46, \u003cem\u003ep\u003c/em\u003e=0.108) or emotional distress (\u003cem\u003eχ\u003c/em\u003e²(2)=4.80, \u003cem\u003ep\u003c/em\u003e=0.092) by age at diagnosis.\u003c/p\u003e\n\u003cp\u003eFor the SEM main effects model, cognitive, physical, and emotional distress domains created a composite latent factor of overall symptom burden. Standardised factor loadings ranged from 0.56 to 0.68, with moderate associations between each domain and the latent construct. This indicates that the three symptom domains reflect partially distinct but related components of a general symptom burden. The internal consistency of this composite was acceptable (Cronbach’s α=0.65). Model fit was excellent (\u003cem\u003eχ\u003c/em\u003e²(4) = 3.56, \u003cem\u003ep\u003c/em\u003e= 0.470; CFI= 1.00; TLI= 1.01; RMSEA= 0.00; SRMR= 0.019). All three symptom domains loaded positively and significantly onto the latent overall symptom burden (see Table 3). Standardised loadings indicated moderate to strong associations, with cognitive symptoms showing the strongest association with overall symptom burden compared to physical symptoms and emotional distress.\u003c/p\u003e\n\u003cp\u003eDiagnosis age also significantly predicted overall symptom burden. Compared to the ≤20 group, both the older groups reported higher overall symptom burden (see Table 3), with small to moderate associations.\u003c/p\u003e\n\u003cp\u003eLatent SEM was also used to evaluate whether age at diagnosis moderated domain-specific effects on overall symptom burden. All three\u0026nbsp;interaction models demonstrated good fit (see Supplementary Information for model statistics). As shown in the main effects model, in all interaction models, older age groups showed higher overall burden relative to the ≤20 group\u0026nbsp;(see Table 3). Compared to the ≤20 group, the\u0026nbsp;association between all domain-level symptom scores and overall burden was significant and stronger in both the 21-30 group and the 31-40 group, demonstrating\u0026nbsp;consistent domain-specific age moderation effects\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCurrent symptom patterns: Idiopathic and known cause POI\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eKnown cause POI was associated with higher odds of more frequent brain fog (OR= 2.62, \u003cem\u003ep\u003c/em\u003e=0.025, 95% CI: 1.14, 6.20), poor memory (OR= 2.45, \u003cem\u003ep\u003c/em\u003e=0.040, 95% CI: 1.05, 5.86), and anxiety (OR=3.76, \u003cem\u003ep\u003c/em\u003e=0.006, 95% CI: 1.49, 10.04), compared to idiopathic POI. Additionally, smokers had higher odds of more frequent anxiety (OR= 2.80, \u003cem\u003ep\u003c/em\u003e=0.010, 95% CI: 1.30, 6.23), compared to non-smokers. There were no significant differences in the frequency of low mood, fatigue, hot flashes, or palpitations, based on POI aetiology, HT status, current age, ethnicity, smoker status, or excessive alcohol use.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKnown cause POI was associated with higher odds of more severe brain fog (OR= 2.93, \u003cem\u003ep\u003c/em\u003e=0.028, 95% CI: 1.15, 8.03) and anxiety (OR=2.63, \u003cem\u003ep\u003c/em\u003e=0.042, 95% CI: 1.06, 7.02) compared to idiopathic POI. Smokers had higher odds of more severe poor memory (OR=2.44, \u003cem\u003ep\u003c/em\u003e=0.019, 95% CI: 1.11, 5.53), than non-smokers. HT users were at higher odds of more severe poor memory (OR=3.26, \u003cem\u003ep\u003c/em\u003e=0.050, 95% CI: 1.03, 11.51) and palpitations (OR=10.31, \u003cem\u003ep\u003c/em\u003e=0.014, 95% CI: 1.78, 80.29) than non-users. Older age was associated with lower odds of more severe palpitations (OR=0.93, \u003cem\u003ep\u003c/em\u003e=0.014, 95% CI: 0.88, 0.98), whereas excessive alcohol use was associated with higher odds of more severe palpitations (OR=7.41, \u003cem\u003ep\u003c/em\u003e=0.034, 95% CI: 1.25, 56.48). Ethnicity was associated with differences in poor memory severity, with non-White participants experiencing higher odds (OR=3.90, \u003cem\u003ep\u003c/em\u003e=0.028, 95% CI: 1.20, 14.21), compared to White participants. There were no significant differences in severity for low mood, fatigue, or hot flashes across POI aetiology, HRT status, age, smoking, alcohol use, or ethnicity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 includes all details from both ordinal logistic regressions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLived experience\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e72.90% of the cohort indicated that they had not received sufficient support from their healthcare providers since their POI diagnosis. Regarding information about HT use and the health risks associated with untreated POI, 40.80% reported not receiving adequate guidance, 8.80% were unsure, and 50.30% felt they had received sufficient information. Participants reported even greater gaps in information related to cognitive health, with 81.00% stating that they had not received any guidance, 4.80% unsure, and only 14.30% reporting that they had received relevant information.\u003c/p\u003e\n\u003cp\u003e70.30% reported that their relationships had become strained following diagnosis, 5.90% reported improvements, and 20.60% reported no change, with the remainder preferring not to answer. 36.80% felt adequately supported by family and friends during their POI diagnosis and transition, 35.50% reported partial support, and 27.70% indicated that they did not feel supported.\u003c/p\u003e\n\u003cp\u003e52.60% reported not receiving adequate support from their employer, while 29.70% indicated that they had received sufficient support. The remaining participants reported that this question was not applicable to their situation.\u003c/p\u003e\n\u003cp\u003eParticipants identified several key areas needed for additional support for those with a POI diagnosis (see Figure 2), including more medical guidance (92.30%), more mental health support (76.80%), greater accessibility to HT (61.90%), more fertility support (64.50%), more community support groups (56.80%), better workplace accommodations (41.30%), and more non-hormonal treatment options (27.10%).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFrom survey responses of 155 participants diagnosed with POI, the current study demonstrated that pre-diagnosis symptom patterns in idiopathic POI, prior to HT use, are likely influenced by age. This may contribute to diagnostic delays, particularly during adolescence and early adulthood. Symptom reporting in POI also varies according to POI aetiology, HT status, and lifestyle factors such as smoking and excessive alcohol use. Collectively, these findings characterise the heterogeneity of POI symptom presentation and provide a framework for future studies to investigate how symptom trajectories from pre-diagnosis to post-diagnosis, may relate to neural changes and the mechanisms underlying hypoestrogenic symptoms.\u003c/p\u003e\u003cp\u003eFirstly, diagnostic delays in idiopathic POI remain substantial, with a median time of 2\u0026ndash;5 years from symptom onset to formal diagnosis in this cohort. Most participants (74%) did not suspect their symptoms were related to POI and most (73%) reported a lack of adequate support after diagnosis from healthcare providers. This underscores major gaps in both patient awareness and clinical recognition [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Comparable diagnostic delays from 18 months have previously been reported, with adolescent diagnoses taking significantly longer than adult diagnoses [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDelayed diagnosis postpones initiation of HT [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], thereby increasing the risk of osteoporosis, cardiovascular disease, and psychological disorders [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. With the global prevalence of POI now estimated at 3.7% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], there is a pressing need for improved diagnostic awareness and early recognition of pre-diagnostic symptom patterns to ensure timely initiation of HT to prevent long-term health complications.\u003c/p\u003e\u003cp\u003eAt pre-diagnosis, previous research indicates that menstrual cycle changes appear more common and consistent in idiopathic POI, as hypoestrogenic symptoms are heterogenous and likely influenced by factors, including age of diagnosis. Although age differences were not observed in the presence of pre-diagnosis symptoms in idiopathic POI, the likelihood and extent of symptom burden varied markedly. Diagnosis between ages 31\u0026ndash;40 was associated with a three- to fourfold increase in physical symptoms and emotional distress, as well as higher physical and overall symptom burden, compared with diagnosis at \u0026le;\u0026thinsp;20.\u003c/p\u003e\u003cp\u003eThe SEM results deepen this interpretation by showing that age at diagnosis not only predicts greater overall symptom burden but also increases the extent to which each symptom domain contributes to the overall symptom burden. For individuals diagnosed at over age 21, cognitive, physical and emotional symptoms contributed more strongly and coherently to their overall symptom burden, compared with those\u0026thinsp;\u0026le;\u0026thinsp;20. This moderation may indicate that symptom domains become more tightly interrelated with increasing age at diagnosis, forming a more unified and impactful symptom profile. Importantly, moderation in the SEM reflects differences in how strongly each symptom domain contributes to the latent construct across age groups, rather than differences in symptom severity alone. This distinction adds nuance by showing that age at diagnosis may shape how different symptoms relate to one another, not just the severity.\u003c/p\u003e\u003cp\u003eAn important avenue for future investigation concerns the mechanisms through which age at diagnosis shapes the organisation and interrelatedness of pre-diagnosis symptom domains. The current findings may suggest that younger individuals experience pre-diagnosis symptoms that are more diffuse or less synchronised, which may contribute to under-recognition and longer diagnostic delays of POI in adolescence and early adulthood [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Conversely, greater coherence of symptom domains among those diagnosed at an older age may reflect age-dependent neural or physiological processes that shape how hypoestrogenic symptoms manifest and interact.\u003c/p\u003e\u003cp\u003eEmerging neuroimaging evidence lends support to this possibility. Although menopause-specific quality of life (MENQOL) scores did not differ by age, Yuan et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] observed age-dependent neural profiles in POI prior to HT initiation. Compared to age-matched controls, younger participants (diagnosed age 20\u0026ndash;35) showed greater global and regional reductions, whereas older participants (diagnosed age 36\u0026ndash;40) showed increases in frontal grey matter volume. Both POI groups exhibited reduced olfactory and limbic connectivity, compared to controls, but the younger group showed increased frontotemporal connectivity and higher network centrality, which was thought to be consistent with compensatory reorganisation. Yuan et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] hypothesised that younger age at diagnosis may be associated with greater neural disruption and, potentially, subsequent stronger compensatory neural change.\u003c/p\u003e\u003cp\u003eThese findings highlight age at diagnosis as a key factor shaping pre-diagnosis symptom patterns in idiopathic POI. Younger individuals tend to show less integrated and more variable symptom profiles, which may contribute to diagnostic delays, as subtle hypoestrogenic symptoms and menstrual irregularities can be overlooked or attributed to adolescent maturation [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This heterogeneity underscores the clinical relevance of age for diagnosis [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and has important implications for neuroimaging research. Differences in symptom burden and interrelatedness across age groups may reflect distinct neural trajectories, and accounting for age of diagnosis when designing studies can guide participant stratification and interpretation of neural changes associated with hypoestrogenic symptoms prior to HT initiation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBuilding on the limited neuroimaging evidence supporting the increased neurodegenerative risk in POI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], the current survey emphasises the need to consider POI aetiology when examining symptom trajectories, as abrupt (iatrogenic) and gradual (idiopathic) oestrogen loss likely have distinct neural consequences. Examining these trajectories is essential for identifying the neural correlates of rapid versus progressive ovarian-neural disruption and how these patterns shape early symptom expression.\u003c/p\u003e\u003cp\u003eConsistent with previous reports, hypoestrogenic symptoms were experienced as more sudden and severe following bilateral oophorectomy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], reflecting rapid disruption of ovarian-neural signalling. In the current study, individuals with known cause POI reported more frequent and severe cognitive symptoms and anxiety compared to those with idiopathic POI.\u003c/p\u003e\u003cp\u003eBilateral oophorectomy prior to spontaneous menopause has also been linked to increased risk of cognitive impairment, stroke, and Parkinson\u0026rsquo;s disease [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Indeed, Alzheimer\u0026rsquo;s-related markers, including elevated tau in the context of higher Aβ, were observed in those undergoing surgery before age 46 but not at age 46\u0026ndash;49 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Taken together, these findings highlight the importance of stratifying future research cohorts by POI aetiology to better understand symptom trajectories (including with HT) and long-term cognitive risk in neuroimaging research. However, due to small sample size, known cause POI included iatrogenic, autoimmune and genetic cause. Further investigation of symptom presentation is required using larger samples.\u003c/p\u003e\u003cp\u003eInterestingly, HT users were more likely to report more severe poor memory and palpitations than non-users, although frequency of reporting did not differ. While this may appear counterintuitive, it may reflect a combination of factors, such as individuals with more severe symptoms may be more likely to seek treatment, despite HT being prescribed in POI primarily to support long-term health rather than solely for symptom management. These observations underscore the value of incorporating patient-reported outcomes in clinical practice to monitor symptom-specific responses and guide individualised HT regimens.\u003c/p\u003e\u003cp\u003eFuture neuroimaging research should examine how HT use, timing, and duration relate to brain health trajectories after diagnosis such as whether the timing of HT relative to loss of ovarian function engages different neural systems or compensatory responses across age groups. Based on the current findings and recent Yuan et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], might age at diagnosis shape neural trajectories and is there is a critical window in which HT provides maximal neural benefit in POI. This will be essential for interpreting imaging findings and optimising clinical management.\u003c/p\u003e\u003cp\u003eLifestyle factors such as smoking and excessive alcohol intake also affected symptom reporting in POI. In line with the European Society of Human Reproduction and Embryology [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] guidelines, these findings encourage maintaining a healthy lifestyle to promote better long-term bone health [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and to prevent symptom exacerbation and reducing efficacy of HRT [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFinally, the survey highlighted necessary improvements in POI healthcare. POI is associated with increased comorbidities and higher mortality when untreated [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], thus diagnostic delays and limited awareness within the medical community further contribute to poor patient outcomes and lack of medical guidance, as observed in the current study.\u003c/p\u003e\u003cp\u003eMental health support should be an integral part of POI management [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Those with POI are three to four times more likely to experience anxiety and depression [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], particularly when diagnosis is delayed [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Following diagnosis, many patients report depression, stress, and reduced life satisfaction [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Thus, mental health support should be prioritised throughout diagnosis and treatment, with personalised care plans addressing both physical and psychological wellbeing.\u003c/p\u003e\u003cp\u003eThis study is not without limitations. Firstly, self-reported symptom data are subject to recall bias, particularly the retrospective pre-diagnosis symptom patterns. Future research could integrate with POI clinics to capture symptom patterns prior to HT and longitudinally follow patients after HT initiation to explore neural trajectories and associations with hypoestrogenic symptom profiles. Next, initiation of HT was not recorded. These data are important for assessing symptom trajectory after diagnosis and should be examined in future research, such as neuroimaging studies to assess longitudinal impact of HT on POI brain health. Moreover, in the instance of delayed diagnosis, as observed in the current study, it is of interest to assess the impact of untreated POI, such as reduction in bone density [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and to probe better timing of HT. This will contribute to our understanding of a \u0026ldquo;critical window\u0026rdquo; which will inform POI guidelines, rather than being taken from menopause guidelines. Finally, although SEM illustrates age of diagnosis moderating pre-diagnosis symptoms in idiopathic POI, longitudinal research is needed to infer causality or temporal ordering.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis survey highlights how age at diagnosis, POI aetiology, and HT status shape symptom trajectories in POI, underscoring the need for greater clinical awareness to support earlier diagnosis and timely HT initiation. Future research should stratify cohorts by these factors to capture heterogeneity in pre-diagnosis symptoms and to clarify how HT modifies symptom patterns after diagnosis. Such stratification is particularly important for neuroimaging studies aiming to link symptom profiles, particularly age-related differences, to underlying neural mechanisms and long-term vulnerability. Integrating symptom-based markers with imaging outcomes will help develop mechanistic models of hypoestrogenic effects on the brain.\u003c/p\u003e\n\u003cp\u003eThis work was funded by King\u0026rsquo;s College London Early Career Research Award.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCompeting interests: LN has declared that no competing interests exist.\u003c/p\u003e\n\u003cp\u003eData availability: Data is available upon reasonable request to author.\u003c/p\u003e\n\u003cp\u003eAcknowledgments: Thank you to the Daisy Network, Professor Adam Hampshire, and Benjamin Goody for their support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGolezar, S., et al., \u003cem\u003eThe global prevalence of primary ovarian insufficiency and early menopause: a meta-analysis.\u003c/em\u003e Climacteric, 2019. \u003cstrong\u003e22\u003c/strong\u003e(4): p. 403-411.\u003c/li\u003e\n\u003cli\u003eNash, Z. and M. Davies, \u003cem\u003ePremature ovarian insufficiency.\u003c/em\u003e BMJ, 2024. \u003cstrong\u003e384\u003c/strong\u003e: p. e077469.\u003c/li\u003e\n\u003cli\u003eNelson, L.M., \u003cem\u003eClinical practice. Primary ovarian insufficiency.\u003c/em\u003e N Engl J Med, 2009. \u003cstrong\u003e360\u003c/strong\u003e(6): p. 606-14.\u003c/li\u003e\n\u003cli\u003ePanay, N., et al., \u003cem\u003ePremature ovarian insufficiency: an International Menopause Society White Paper.\u003c/em\u003e Climacteric, 2020. \u003cstrong\u003e23\u003c/strong\u003e(5): p. 426-446.\u003c/li\u003e\n\u003cli\u003eWelt, C.K., \u003cem\u003ePrimary ovarian insufficiency: a more accurate term for premature ovarian failure.\u003c/em\u003e Clin Endocrinol (Oxf), 2008. \u003cstrong\u003e68\u003c/strong\u003e(4): p. 499-509.\u003c/li\u003e\n\u003cli\u003eKapoor, E., \u003cem\u003ePremature Ovarian Insufficiency.\u003c/em\u003e Curr Opin Endocr Metab Res, 2023. \u003cstrong\u003e28\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eBrinton, R.D., et al., \u003cem\u003ePerimenopause as a neurological transition state.\u003c/em\u003e Nat Rev Endocrinol, 2015. \u003cstrong\u003e11\u003c/strong\u003e(7): p. 393-405.\u003c/li\u003e\n\u003cli\u003eFreedman, R.R., \u003cem\u003eMenopausal hot flashes: mechanisms, endocrinology, treatment.\u003c/em\u003e J Steroid Biochem Mol Biol, 2014. \u003cstrong\u003e142\u003c/strong\u003e: p. 115-20.\u003c/li\u003e\n\u003cli\u003eFreedman, R.R., et al., \u003cem\u003eCortical activation during menopausal hot flashes.\u003c/em\u003e Fertil Steril, 2006. \u003cstrong\u003e85\u003c/strong\u003e(3): p. 674-8.\u003c/li\u003e\n\u003cli\u003eRoeters van Lennep, J.E., et al., \u003cem\u003eCardiovascular disease risk in women with premature ovarian insufficiency: A systematic review and meta-analysis.\u003c/em\u003e Eur J Prev Cardiol, 2016. \u003cstrong\u003e23\u003c/strong\u003e(2): p. 178-86.\u003c/li\u003e\n\u003cli\u003eZhu, Y., et al., \u003cem\u003eAssessment of knowledge, understanding and awareness of Chinese women clinical staff towards menopause hormone therapy: a survey study.\u003c/em\u003e J Obstet Gynaecol, 2023. \u003cstrong\u003e43\u003c/strong\u003e(1): p. 2171779.\u003c/li\u003e\n\u003cli\u003eHonigberg, M.C., et al., \u003cem\u003eAssociation of Premature Natural and Surgical Menopause With Incident Cardiovascular Disease.\u003c/em\u003e JAMA, 2019. \u003cstrong\u003e322\u003c/strong\u003e(24): p. 2411-2421.\u003c/li\u003e\n\u003cli\u003eSullivan, S.D., P.M. Sarrel, and L.M. Nelson, \u003cem\u003eHormone replacement therapy in young women with primary ovarian insufficiency and early menopause.\u003c/em\u003e Fertil Steril, 2016. \u003cstrong\u003e106\u003c/strong\u003e(7): p. 1588-1599.\u003c/li\u003e\n\u003cli\u003eSzeliga, A., M. Maciejewska-Jeske, and B. Meczekalski, \u003cem\u003eBone health and evaluation of bone mineral density in patients with premature ovarian insufficiency.\u003c/em\u003e Prz Menopauzalny, 2018. \u003cstrong\u003e17\u003c/strong\u003e(3): p. 112-116.\u003c/li\u003e\n\u003cli\u003eNguyen, H.H., F. Milat, and A.J. Vincent, \u003cem\u003eNew insights into the diagnosis and management of bone health in premature ovarian insufficiency.\u003c/em\u003e Climacteric, 2021. \u003cstrong\u003e24\u003c/strong\u003e(5): p. 481-490.\u003c/li\u003e\n\u003cli\u003eJones, A.R., et al., \u003cem\u003eBone health in women with premature ovarian insufficiency/early menopause: a 23-year longitudinal analysis.\u003c/em\u003e Hum Reprod, 2024. \u003cstrong\u003e39\u003c/strong\u003e(5): p. 1013-1022.\u003c/li\u003e\n\u003cli\u003eVujović, S., et al., \u003cem\u003eAlzheimer\u0026rsquo;s Disease and Premature Ovarian Insufficiency.\u003c/em\u003e Endocrines, 2023. \u003cstrong\u003e4\u003c/strong\u003e(2): p. 250-256.\u003c/li\u003e\n\u003cli\u003eWang, Q., et al., \u003cem\u003eEpigenetic dysregulation of steroidogenesis and neuroactive steroid deficiency in premature ovarian insufficiency: implications for neurodegenerative risk.\u003c/em\u003e Biomark Res, 2025. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 147.\u003c/li\u003e\n\u003cli\u003eCalvo, N., et al., \u003cem\u003eCognitive and brain health in women with early bilateral salpingo-oophorectomy: Implications for risk, resilience, and subjective cognitive decline.\u003c/em\u003e Alzheimers Dement, 2025. \u003cstrong\u003e21\u003c/strong\u003e(8): p. e70454.\u003c/li\u003e\n\u003cli\u003eYuan, S., et al., \u003cem\u003eBrain structural alterations in young women with premature ovarian insufficiency: Implications for dementia risk.\u003c/em\u003e Alzheimers Dement, 2025. \u003cstrong\u003e21\u003c/strong\u003e(3): p. e70111.\u003c/li\u003e\n\u003cli\u003eKanj, R.V., et al., \u003cem\u003eEvaluation and Management of Primary Ovarian Insufficiency in Adolescents and Young Adults.\u003c/em\u003e J Pediatr Adolesc Gynecol, 2018. \u003cstrong\u003e31\u003c/strong\u003e(1): p. 13-18.\u003c/li\u003e\n\u003cli\u003eBenshushan, A., et al., \u003cem\u003eClimacteric symptoms in women undergoing risk-reducing bilateral salpingo-oophorectomy.\u003c/em\u003e Climacteric, 2009. \u003cstrong\u003e12\u003c/strong\u003e(5): p. 404-9.\u003c/li\u003e\n\u003cli\u003eGibson-Helm, M., H. Teede, and A. Vincent, \u003cem\u003eSymptoms, health behavior and understanding of menopause therapy in women with premature menopause.\u003c/em\u003e Climacteric, 2014. \u003cstrong\u003e17\u003c/strong\u003e(6): p. 666-73.\u003c/li\u003e\n\u003cli\u003eKhan, S.J., et al., \u003cem\u003eVasomotor Symptoms During Menopause: A Practical Guide on Current Treatments and Future Perspectives.\u003c/em\u003e Int J Womens Health, 2023. \u003cstrong\u003e15\u003c/strong\u003e: p. 273-287.\u003c/li\u003e\n\u003cli\u003eCui, J. and Y. Wang, \u003cem\u003ePremature ovarian insufficiency: a review on the role of tobacco smoke, its clinical harm, and treatment.\u003c/em\u003e J Ovarian Res, 2024. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 8.\u003c/li\u003e\n\u003cli\u003eKwon, R., et al., \u003cem\u003eAlcohol Consumption Patterns and Risk of Early-Onset Vasomotor Symptoms in Premenopausal Women.\u003c/em\u003e Nutrients, 2022. \u003cstrong\u003e14\u003c/strong\u003e(11).\u003c/li\u003e\n\u003cli\u003eKochersberger, A., et al., \u003cem\u003eThe association of race, ethnicity, and socioeconomic status on the severity of menopause symptoms: a study of 68,864 women.\u003c/em\u003e Menopause, 2024. \u003cstrong\u003e31\u003c/strong\u003e(6): p. 476-483.\u003c/li\u003e\n\u003cli\u003eBakhsh, H., \u003cem\u003eAn Evidence-Based Approach to the Management of Primary Ovarian Insufficiency in Adolescents and Young Women.\u003c/em\u003e Life (Basel), 2025. \u003cstrong\u003e15\u003c/strong\u003e(9).\u003c/li\u003e\n\u003cli\u003eMinis, E., et al., \u003cem\u003ePrimary Ovarian Insufficiency: Time to Diagnosis and a Review of Current Literature.\u003c/em\u003e Clin. Exp. Obstet. Gynecol. , 2022. \u003cstrong\u003e49\u003c/strong\u003e(6).\u003c/li\u003e\n\u003cli\u003ePopat, V.B., et al., \u003cem\u003eBone mineral density in estrogen-deficient young women.\u003c/em\u003e J Clin Endocrinol Metab, 2009. \u003cstrong\u003e94\u003c/strong\u003e(7): p. 2277-83.\u003c/li\u003e\n\u003cli\u003eWebber, L., et al., \u003cem\u003eHRT for women with premature ovarian insufficiency: a comprehensive review.\u003c/em\u003e Hum Reprod Open, 2017. \u003cstrong\u003e2017\u003c/strong\u003e(2): p. hox007.\u003c/li\u003e\n\u003cli\u003eMeczekalski, B., et al., \u003cem\u003eNeuroendocrine disturbances in women with functional hypothalamic amenorrhea: an update and future directions.\u003c/em\u003e Endocrine, 2024. \u003cstrong\u003e84\u003c/strong\u003e(3): p. 769-785.\u003c/li\u003e\n\u003cli\u003eRamireddy, N. and F.S. Mohideen, \u003cem\u003ePremature Ovarian Insufficiency: Management from a Primary Care perspective.\u003c/em\u003e International Journal of Medical Reviews and Case Reports, 2020. \u003cstrong\u003e4\u003c/strong\u003e(10): p. 127.\u003c/li\u003e\n\u003cli\u003eCoulam, C.B., S.C. Adamson, and J.F. Annegers, \u003cem\u003eIncidence of premature ovarian failure.\u003c/em\u003e Obstet Gynecol, 1986. \u003cstrong\u003e67\u003c/strong\u003e(4): p. 604-6.\u003c/li\u003e\n\u003cli\u003eAnasti, J.N., \u003cem\u003ePremature ovarian failure: an update.\u003c/em\u003e Fertil Steril, 1998. \u003cstrong\u003e70\u003c/strong\u003e(1): p. 1-15.\u003c/li\u003e\n\u003cli\u003evan Zwol-Janssens, C., et al., \u003cem\u003eDepressive symptoms in women with premature ovarian insufficiency (POI): a cross-sectional observational study.\u003c/em\u003e Menopause, 2025.\u003c/li\u003e\n\u003cli\u003eRocca, W.A., et al., \u003cem\u003eIncreased risk of cognitive impairment or dementia in women who underwent oophorectomy before menopause.\u003c/em\u003e Neurology, 2007. \u003cstrong\u003e69\u003c/strong\u003e(11): p. 1074-83.\u003c/li\u003e\n\u003cli\u003eRocca, W.A., et al., \u003cem\u003eHysterectomy, oophorectomy, estrogen, and the risk of dementia.\u003c/em\u003e Neurodegener Dis, 2012. \u003cstrong\u003e10\u003c/strong\u003e(1-4): p. 175-8.\u003c/li\u003e\n\u003cli\u003eStopieńm, R., \u003cem\u003eNeurological health and premature ovarian insufficiency \u0026ndash; pathogenesis and clinical management.\u003c/em\u003e Prz Menopauzalny, 2018. \u003cstrong\u003e17\u003c/strong\u003e(3): p. 120-123.\u003c/li\u003e\n\u003cli\u003eKantarci, K., et al., \u003cem\u003ePremenopausal bilateral oophorectomy and Alzheimer\u0026apos;s disease imaging biomarkers later in life.\u003c/em\u003e Alzheimers Dement, 2025. \u003cstrong\u003e21\u003c/strong\u003e(7): p. e14469.\u003c/li\u003e\n\u003cli\u003eEshre, A.C., et al., \u003cem\u003eEvidence-based guideline: Premature Ovarian Insufficiency.\u003c/em\u003e Fertil Steril, 2025. \u003cstrong\u003e123\u003c/strong\u003e(2): p. 221-236.\u003c/li\u003e\n\u003cli\u003eKiriakova, V., et al., \u003cem\u003eManagement of bone health in women with premature ovarian insufficiency: Systematic appraisal of clinical practice guidelines and algorithm development.\u003c/em\u003e Maturitas, 2019. \u003cstrong\u003e128\u003c/strong\u003e: p. 70-80.\u003c/li\u003e\n\u003cli\u003eBlumel, J.E., et al., \u003cem\u003eIs premature ovarian insufficiency associated with mortality? A three-decade follow-up cohort.\u003c/em\u003e Maturitas, 2022. \u003cstrong\u003e163\u003c/strong\u003e: p. 82-87.\u003c/li\u003e\n\u003cli\u003eXi, D., et al., \u003cem\u003eThe risk of depressive and anxiety symptoms in women with premature ovarian insufficiency: a systematic review and meta-analysis.\u003c/em\u003e Arch Womens Ment Health, 2023. \u003cstrong\u003e26\u003c/strong\u003e(1): p. 1-10.\u003c/li\u003e\n\u003cli\u003eLiao, K.L., N. Wood, and G.S. Conway, \u003cem\u003ePremature menopause and psychological well-being.\u003c/em\u003e J Psychosom Obstet Gynaecol, 2000. \u003cstrong\u003e21\u003c/strong\u003e(3): p. 167-74.\u003c/li\u003e\n\u003cli\u003eLi, X.T., et al., \u003cem\u003eHealth-related quality-of-life among patients with premature ovarian insufficiency: a systematic review and meta-analysis.\u003c/em\u003e Qual Life Res, 2020. \u003cstrong\u003e29\u003c/strong\u003e(1): p. 19-36.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Demographic information of cohort.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eGroup, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eWhole sample, 155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eIdiopathic POI, 132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eKnown cause POI, 23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMean age (SD; age range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e35.60 (7.70; 19.00-57.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e35.70 (7.60; 19.00-57.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e35.10 (7.90; 19.00-49.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMean age at diagnosis (SD; age range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e28.70 (7.80; 13.00-40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e28.90 (7.80; 13.00-40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e28.00 (7.30; 16.00-40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eAge group, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eUnder 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e21-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e31-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eAetiology of POI\u003csup\u003e1\u003c/sup\u003e, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eAutoimmune disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eGenetic condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eIatrogenic/other medical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEndometriosis\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eFibroids\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eBRCA mutation\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eCancer\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eOther\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eTreatments leading to POI, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eBilateral oophorectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003ePelvic surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003ePelvic radiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eCurrent Hormonal Treatment use, (% of total sample)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e91.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e90.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e95.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eHRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e86.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e84.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e95.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eHormonal contraception\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e12.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e12.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eTestosterone replacement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e18.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e18.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e6.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e9.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eEthnicity, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 59px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e91.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e91.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e87.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eNot Stated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Lifestyle factors, %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e19.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e21.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eExcessive alcohol consumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eThyroid condition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e21.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eNeurodegenerative disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eParticipants could select more than one underlying cause of iatrogenic POI. \u003csup\u003e2\u003c/sup\u003eParticipants could select more than one type of hormonal treatment; percentages are based on the total group (not only those receiving treatment).\u003c/p\u003e\n\u003cp\u003eTable 2. Percentage of symptoms reported and predictors of symptom frequency and severity over the past two weeks with odds ratio (OR) and \u003cem\u003ep\u003c/em\u003e-value. Odds ratios indicate the odds of experiencing more severe or more frequent symptoms, with reference categories set from least severe/frequent to most severe/frequent.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"3\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSymptom (% reported)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePOI aetiology OR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHT status OR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoker OR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol OR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent age OR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEthnicity OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e\n \u003cp\u003eFrequency of symptoms in the past two weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBrain fog (94.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.62*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.025*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePoor memory (95.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.45*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.040*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnxiety (92.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.76*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.80*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow mood (91.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFatigue (91.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHot flashes (60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePalpitations (52.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\"\u003e\n \u003cp\u003eSeverity of symptoms in the past two weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBrain fog\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.94*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePoor memory\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.29*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.050*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.44*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.029*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.90*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.63*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.042*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow mood\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHot flashes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePalpitations\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.31*\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.41*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.034*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05. 95% confidence intervals (CI) for significant ORs are reported in the text. Reference levels: Idiopathic POI, HT non-users, smoker, excessive alcohol use, White.\u003c/p\u003e\n\u003cp\u003eTable 3. Main and interaction effects of age at diagnosis and symptom domains on overall symptom burden at pre-diagnosis in idiopathic POI from latent structural equation modelling (SEM).\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eStandardised estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eUnstandardised estimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003eMain effects model predicting overall symptom burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge 21-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge 31-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCognitive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePhysical symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEmotional distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003eInteraction models predicting overall symptom burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge 21-30 * cognitive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge 31-40 * cognitive symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge 21-30 * physical symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge 31-40 * physical symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge 21-30 * emotional distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge 31-40 * emotional distress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAge groups are dummy-coded with \u0026le;20 years as the reference category. Estimates indicate differences relative to this group.\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"King's College London","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":"premature ovarian insufficiency, oestrogen deficiency, clinical symptom presentation, brain health, neuroimaging","lastPublishedDoi":"10.21203/rs.3.rs-8280682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8280682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003ePremature ovarian insufficiency, loss of ovarian hormone function before age 40, impacts physical, cognitive, and psychological wellbeing. However, symptom patterns are still poorly characterised, restricting our ability to investigate how early hypoestrogenism shapes symptom severity and contributes to long-term neural changes.\u003c/p\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eUsing an online survey (N\u0026thinsp;=\u0026thinsp;155), we examined whether; i) pre-diagnosis symptoms in idiopathic POI varied by age of diagnosis, and ii) current symptoms differed across POI aetiologies (idiopathic vs known cause).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePre-diagnosis symptoms typically persisted for 2\u0026ndash;5 years. Age at diagnosis predicted overall symptom burden at pre-diagnosis (β\u0026thinsp;=\u0026thinsp;0.29\u0026ndash;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.024); those diagnosed at 31\u0026ndash;40 years reported higher physical (OR\u0026thinsp;=\u0026thinsp;4.38, 95% CI: 1.17\u0026ndash;16.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) and emotional burden (OR\u0026thinsp;=\u0026thinsp;2.84, 95% CI: 1.14\u0026ndash;7.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) than those\u0026thinsp;\u0026le;\u0026thinsp;20 years. Structural equation modelling showed that older age was associated with stronger, more coherent symptom clustering. Next, POI with known cause presented more frequent and severe cognitive symptoms (OR\u0026thinsp;=\u0026thinsp;2.45\u0026ndash;2.62, 95% CI: 1.05\u0026ndash;6.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.040) and anxiety (OR\u0026thinsp;=\u0026thinsp;3.76, 95% CI: 1.46\u0026ndash;9.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) than idiopathic cases. Hormone treatment status, smoking, and alcohol intake also influenced current symptom reporting.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eTogether, these findings underscore age at diagnosis and POI aetiology as central to symptom organisation and experience, highlighting implications for long-term neural trajectories and cohort stratification in future studies.\u003c/p\u003e","manuscriptTitle":"Symptom Patterns in Premature Ovarian Insufficiency Differ Across Age of Diagnosis and Aetiology: Implications for Neural Mechanisms and Brain Health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 11:28:57","doi":"10.21203/rs.3.rs-8280682/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":"45c19e5b-e1fd-4c00-9213-a3aa2d7671a0","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T11:28:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 11:28:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8280682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8280682","identity":"rs-8280682","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}