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. Author manuscript; available in PMC: 2026 Oct 27.
Published in final edited form as: J Clin Exp Neuropsychol. 2026 Aug 19;48(7):696–707. doi: 10.1080/13803395.2026.2707992

Age at Menopause and Subjective Cognitive Symptoms Predict Digital Cognitive Outcomes at the Gynecological Well-Woman Visit

Teresa Facchetti 1, Jillian L Joyce 2, Sandra Rizer 1, Sarah E Tom 1, Karen Marder 1, Silvia Chapman 1, Mary L Rosser 3, Stephanie Cosentino 1
PMCID: PMC13698039  NIHMSID: NIHMS2214313  PMID: 42616473

Abstract

Introduction:

Women are at increased risk of developing Alzheimer’s Disease (AD), and growing evidence suggests that the menopausal transition may represent a vulnerable window for the development of AD-related pathology. Yet, women are diagnosed with AD later than men. Conducting cognitive screening and integrating information about both cognitive symptoms and age at menopause at routine healthcare visits may help address sex-based disparities in detection and prevention. This study investigated whether subjective cognitive symptoms, in combination with age at menopause, were associated with performance on a digital cognitive task in a sample of postmenopausal women.

Methods:

183 postmenopausal women (mean age = 63.8, range = 45–85) were recruited after their Well-Woman visit. Participants completed the Screener for Cognitive Problems in Everyday Life (SCoPE) to assess subjective cognitive symptoms, followed by a sensitive measure of objective cognition: the Linus Health Digital Clock and Recall (DCR™). Information was also collected on age at menopause. We examined associations of subjective cognitive symptoms and age at menopause (quadratic effect) with digital cognitive performance, adjusting for age, education and depression. Model fit was evaluated using adjusted R2, AIC, and BIC.

Results:

48.1% of women reported one or more cognitive symptoms on the SCoPE. On objective testing, 73.2% scored in the normal range, 20.8% in the borderline range, and 6.0% in the impaired range. SCoPE total score was negatively associated with objective cognitive performance (B = −.12, p =.03), after adjusting for covariates. Age at menopause showed a significant quadratic association with cognitive performance (B = −0.006, p < .001). SCoPE total was not associated with DCR subtests, while age at menopause predicted both Delayed Recall and Clock Drawing.

Conclusion:

Subjective cognitive symptoms and age at menopause were associated with lower performance on a sensitive, objective cognitive test. These findings support routine cognitive screening and suggest that subjective cognitive symptoms as well as age at menopause are associated with lower cognitive scores.

Keywords/search terms: memory screening, subjective cognitive decline, early detection, Alzheimer’s disease, well-woman visit, cognition

Introduction

In the United States, women account for two-thirds of Alzheimer's disease (AD) cases, with women in their 60s facing more than twice the lifetime risk of developing AD as compared to their risk of developing breast cancer (Alzheimer’s Association, 2025). Moreover, women tend to experience a more rapid progression of AD-related cognitive and pathological decline than men (Lee et al., 2018). Emerging evidence suggests that menopausal changes may pose a critical window of vulnerability for cognitive aging in women, as this life stage is characterized by significant hormonal fluctuations and a steep decline in estrogen, potentially contributing to an increased susceptibility to the accumulation of AD-related neuropathology (Scheyer et al., 2018). Furthermore, age at menopause, a key indicator of a woman's lifetime exposure to endogenous sex hormones, is increasingly recognized as an indicator of cognitive health (Georgakis et al.,2016). Despite women’s increased vulnerability to cognitive decline and AD, they are often diagnosed later than men (Emrani & Sundermann, 2025), and cognitive screening remains largely absent from routine women’s healthcare.

The annual Well-Woman gynecological visit - a preventive visit focused on health promotion, disease prevention and age- and risk-based screenings (The American College of Obstetricians and Gynecologists [ACOG], 2018) - provides a valuable opportunity to screen women for cognitive changes during and after menopause. However, traditional cognitive screening assessments may be perceived as impractical in fast-paced clinical environments due to their time-consuming nature and the need for trained examiners (Waldemar et al., 2007). In addition, these assessments may fail to detect the earliest signs of cognitive decline, as traditional screenings were developed to detect conditions such as Mild Cognitive Impairment (MCI) and dementia, and as women's typically strong verbal memory may mask underlying deficits (Mazzoni, 2017).

One potential solution to these barriers is the use of subjective cognitive screening measures which are rapid, cost-effective, and sensitive to cognitive difficulties not yet detectable on traditional cognitive tests. Indeed, a growing body of evidence suggests that subjective cognitive symptoms represent an early marker for AD and other dementias (Jessen et al., 2014). As one example, a study of cognitively normal individuals from a large community cohort found that those who reported cognitive symptoms had a higher risk of developing dementia than those without symptoms (Chapman et al., 2021), with further longitudinal studies suggesting that cognitive decline may become apparent within a year (Jonker et al., 2000). Subjective cognitive symptoms may be particularly useful in predicting cognitive decline in women as compared to men. Indeed, in a large longitudinal study, women with subjective cognitive symptoms faced the fastest rates of cognitive decline (Oliver et al. 2022). Finally, recent work from our lab has demonstrated the feasibility of subjective cognitive screening in fast-paced clinical settings such as the Well-Woman visit (Joyce et al., 2024). Although subjective cognitive symptoms have significant implications, studying them presents challenges. One major challenge is their subjective nature, which makes them susceptible to personal factors, such as mood and depressive symptoms, personality traits, and age-related concerns. These factors can affect the perception and reporting of cognitive changes (Burmester et al., 2016; Buckley et al., 2013).

As such, the extent to which report of subjective cognitive symptoms during routine care reflects objective cognitive function requires investigation. To date, studies examining the relationship between subjective cognitive symptoms and objective cognitive performance have yielded mixed results (Balash et al., 2013; Burmester et al., 2016), likely due to methodological variability across studies, particularly a lack of sensitivity in the objective cognitive assessment methods used (Mitchell et al., 2014). Digital cognitive measures which have emerged in recent years, provide a new means of validating very early subjective cognitive symptoms (Öhman et al., 2021, Libon et al., 2024), proving to be more sensitive than the Mini-Mental State Examination (MMSE) in detecting mild cognitive impairment and dementia (Jannati et al., 2024). The first aim of this study is therefore to examine the extent to which subjective cognitive symptoms in postmenopausal women are associated with performance on a digital cognitive test.

Second, given the potential effect of menopausal changes on cognition and cognitive aging, this study seeks to determine whether information about age at menopause should be integrated with results from subjective cognitive screenings to optimize information about objective cognitive health. Findings from the British National Survey of Health and Development (NSHD) suggest that later age at menopause is associated with better verbal memory performance in later life, even after adjusting for education and socioeconomic status (Kuh et al., 2018). These findings are echoed in large cohort studies, which have also identified premature and early menopause as predictors of lower cognitive performance and faster cognitive decline (Guo et al., 2025; Hao et al., 2023). While the onset of menopause can occur naturally or surgically, studies indicate that its timing, rather than its type, is more closely linked to cognitive decline later in life (Bove et al., 2014; Dobson et al., 2024). However, not all findings are consistent, and some evidence suggests a possible U-shaped association, with both very early and very late menopause linked to poorer cognitive outcomes (Guo et al., 2025).

To date, few studies have explored how subjective cognitive symptoms and age at menopause are associated with objective cognition. The present study investigates the associations between subjective cognitive symptoms, age at menopause, and performance on a digital cognitive screener in a sample of postmenopausal women. We hypothesized that both earlier age at menopause and subjective cognitive symptoms would be associated with poorer cognitive performance.

Methods

Participants and Procedures

A total of 183 postmenopausal women (mean age = 63.8 years, SD = 8.87, range = 45-85) were enrolled in our study. Participants were recruited during their annual Well-Woman visit at Columbia University's Integrated Women's Health Program. Participants either completed their visit immediately after enrolling or were scheduled for a later visit. Participants were recruited from three different outpatient clinics affiliated with Columbia University and located in New York. Eligibility criteria required that participants be at least 45 years of age, not currently pregnant or breastfeeding, and English speakers, given that the assessment was conducted in English. Subjective cognition was evaluated using the Screener for Cognitive Problems in Everyday Life (SCoPE), which is currently administered on paper and takes about five minutes to complete. Objective cognition was assessed using the Linus Health Core Cognitive Evaluation (CCE), a ten-minute, tablet-based assessment. Demographic information was collected, and age at menopause was self-reported by participants (Table 1). Menopause was defined according to standard clinical criteria as the absence of menstruation for at least 12 consecutive months (National Institute on Aging, 2024). Written consent was obtained from all participants prior to their participation in the study, which was approved by the Columbia University Institutional Review Board (IRB#AAAU1778).

Table 1.

Demographic Characteristics of the Study Sample

N=183 Mean ± SD, range or % (n)
Age 63.8 ± 8.87, 45-85
Education (years) 17.0± 2.46, 10-21
Ethnicity
(May endorse more than one)
American Indian or Alaska Native 0.5% (1)
Asian 7.1% (13)
Black, African American or African 7.7% (14)
Hispanic, Latino or Spanish 12% (22)
Middle Eastern or North African 1.1% (2)
White 66.1% (121)
None of these 0.5% (1)
More than one 4.9% (9)
Age at Menopause (years) 50.1± 4.90, 25.00-61.00
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Measures

The Screener for Cognitive Problems in Everyday Life (SCoPE)

Subjective cognition was measured using the SCoPE, a brief six- item screener designed to assess subjective memory and cognitive symptoms while addressing the limitations of existing questionnaires, such as excessive length and reliance on caregiver input. The rationale for item selection has previously been outlined by Joyce et al. (2024) and is summarized in Table 2. Participants were classified as having positive screens if they endorsed one or more screening items, with SCOPE scores ranging from 0 to 8. Scoring was structured as follows: Item 1 was assigned 1 point, with an additional point given if participants reported cognitive changes within the past five years (indicating a deviation from baseline). Item 2 was scored based on the perceived severity of cognitive difficulties, with 2 points for "much worse," 1 point for "somewhat worse," and 0 points for "about the same” or “better”. Items 3, 4, and 5 were scored as 1 point for a "yes" response and 0 points for a "no" response. Item 6 was scored as 1 point for a "yes" response, and participants who had not seen a healthcare provider but were considering doing so also received 1 point. The full scoring criteria are summarized in Table 2.

Table 2.

Summary of the SCOPE, including the rationale for item selection and the scoring framework of the memory screener

Item Answer Rationale
1. Are your memory or thinking abilities worse than others your age?
(Scoring: 0-2)		 ❑ Yes (1)
❑ No (0)		 Age-Anchored memory ratings are more sensitive to memory performance than general or retrospective ratings (Tandetnik, 2015;
Chapman, 2021).
If Yes: How long have you felt this way?
❑ Within the last 5 years (1)
❑ 6-10 years (0)
❑ 10 or more years (0)
2. Please rate your memory and thinking abilities compared to others your age (Scoring: 0-2) ❑ Much worse (2)
❑ Somewhat worse (1)
❑ About the same (0)
❑ Somewhat better (0)
❑ Much better (0)
3. Are you concerned about any recent or ongoing changes to your memory or thinking?
(Scoring: 0-1)		 ❑ Yes (1)
❑ No (0)		 Concern may indicate more severe difficulties and is associated with a higher likelihood of progressing to AD (Jessen et al., 2014).
4. Do any of your family or friends have concerns about your memory or thinking abilities?
(Scoring: 0-1)		 ❑ Yes (1)
❑ No (0)		 In individuals who are unaware of cognitive changes, others’ impressions may be more accurate (Amariglio et al., 2015).
5. Do any problems with memory or thinking make it difficult for you to do things in daily life (e.g., shop, pay bills, drive etc.?)
(Scoring 0-1)		 ❑ Yes (1)
❑ No (0)		 Impairment in daily functioning is a criterion for dementia (Alzheimer’s association, 2021)
6. Have you seen a doctor or other healthcare professional for changes with memory or thinking?
(Scoring: 0-1)		 ❑ Yes (1)
❑ No (0)		 Help-seeking may indicate more severe difficulties and has been associated with a higher likelihood of progressing to AD (Jessen et al., 2014).
If No: Have you considered seeing a healthcare professional for this reason? ❑ Yes (1)
❑ No (0)
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Core Cognitive Evaluation (CCE ™)

The Core Cognitive Evaluation (CCE) consists of the Linus Digital Clock and Recall (DCR) and the Life and Health Questionnaire (LHQ) and has been validated in adults aged 45 years and older (Libon et al., 2024).

Linus Digital Clock and Recall (DCR):

Objective cognition was measured by using the Linus Digital Clock and Recall (DCR), a tablet-based adaptation of the Mini-Cog. The DCR includes three tasks: Immediate Recall, Delayed Recall, and Clock Drawing. The Linus DCR is fast and easy to administer, and in some studies has demonstrated superior performance compared to the MMSE (Jannati et al., 2024). Powered by AI, the Linus DCR captures process-level metrics such as think time versus ink time and inter-item latencies, providing a detailed understanding of the drawing process.

Participants begin with the Immediate Recall task by repeating three words, followed by the Clock Drawing task administered under two conditions: Command (drawing a clock from memory), and Copy (replicating a provided clock). In the final task, Delayed Recall, participants are asked to recall the three previously presented words. Scoring follows a five-point system: one point per correctly recalled word and two points for an accurate clock drawing (ordinal score). Total scores are categorized as follows: 0-1 indicates cognitive impairment, 2-3 is considered borderline, and 4-5 reflects performance within the normal range. Additionally, the software generates a digital clock score on a 0–100 scale. The digital clock score specifically assesses clock-drawing performance, comparing the patient’s performance to normative ranges. Specifically, scores of 75–100 are considered normal, scores of 60–74 are indeterminate (meaning performance cannot be definitively categorized), and scores of 0–59 fall outside the expected range for cognitively healthy individuals. The digital clock score was used for the analyses in the current study.

The Linus Life and Health Questionnaire (LHQ):

The Life and Health Questionnaire (LHQ) is a 32-item digital assessment designed to assess risk factors for dementia. The questionnaire collects information on key risk and protective factors for AD, including general health conditions such as diabetes, hypertension, and history of head injury, as well as lifestyle factors such as diet, physical activity, mood, stress, sleep quality, and social engagement. Each endorsed item is scored as 1, with total scores ranging from 0 to 32. For this study, depressive symptoms were controlled using a single item from the questionnaire: “During the past month, I have often been bothered by feeling down, depressed, hopeless, having little interest or pleasure in doing things”, with responses dichotomously scored from 0 to 1.

Statistical analyses

All statistical analyses were conducted using IBM SPSS Statistics Version 27. Descriptive statistics were computed to summarize SCoPE scores, DCR scores (including total score, immediate and delayed recall, ordinal and digital clock scores), and depression scores within the sample. A Chi-Square test was conducted to examine the association between SCoPE scores (categorical scores) and DCR diagnosis. Nested linear regression models were used to examine predictors of overall cognitive performance. In the first block, covariates (age, education, and depression) were entered. In the second block, SCoPE scores were included as a continuous predictor. In the third block mean-centered age at menopause was added. In the fourth block, a mean-centered quadratic term for age at menopause was entered to test the hypothesized non-linear association with cognitive performance. Model fit was evaluated using adjusted R2, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC), with higher adjusted R2 values and lower AIC and BIC values indicating better fit. Assumption checks confirmed no issues with multicollinearity (VIFs ≤ 1.03). A significance threshold of p < 0.05 was applied for all statistical tests. Additional nested linear regression analyses were conducted to examine the association between SCoPE scores and DCR subtests (delayed recall, and digital clock score). Due to the restricted ranges of the subtests, these analyses used a linear specification of age at menopause and adjusted for the same covariates described above. Given the number of related tests performed and the associated risk of inflated Type 1 error, these analyses are treated as secondary, and results should be interpreted with caution.

Results

The Screener for Cognitive Problems in Everyday Life (SCoPE)

The average endorsement on the SCoPE was 1.14 (SD = 1.54; range = 0–8). In our sample (N = 183), 51.9% (n = 95) of women reported no subjective cognitive symptoms on the SCoPE, while 48.1% (n = 88) endorsed one or more items. Percentages of endorsement for each item in the SCoPE are represented in Figure 1. Item 3 (“Are you concerned about any recent or ongoing changes to your memory or thinking?”) was the item with the highest endorsement rate (n = 64), whilst item 5 (“Do any problems with memory or thinking make it difficult for you to do things in daily life, e.g., shop, pay bills, drive etc.?”) was the item with the lowest endorsement rate (n = 12).

Figure 1.

Figure 1

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Percentages of endorsement (“Yes” and “No” responses) for each item of the SCoPE. Percent values are based on the total sample (N = 183).

Note. The x-axis represents the individual SCoPE items. The "Sum Frequency" indicates how often each item was endorsed

The Linus Health Digital Clock and Recall (DCR)

Participants obtained an average score of 4.06 (SD = 1.29; range = 0–5) on the overall DCR score, 2.93 (SD = 0.26; range = 1–3) on Immediate Recall, 2.61 (SD = 0.79; range = 0–3) on Delayed Recall, and 1.44 (SD = 0.78; range = 0–2) on the ordinal clock score. The digital clock score had a mean of 76.6 (SD = 20.3; range = 7–100). 73.2% of participants (n = 134) scored in the normal range, 20.8% (n = 38) scored in the borderline range, and 6.0% (n = 11) were impaired.

Depressive symptoms

87.9% (n = 160) of participants did not report any feelings of depression, while 12.1% (n = 22) did.

Association between Subjective and Objective cognition

Of women with subjective cognitive symptoms (n = 88), 64.8% (n = 57) scored in the normal range on the DCR, 26.1% (n = 23) were classified as borderline, and 9.0% (n = 8) as impaired (Table 3). A chi-square test showed a significant association between subjective cognitive symptoms and DCR diagnosis, χ2 (2, N = 183) = 6.68, p = .03.

Table 3.

Percentages of individuals with and without subjective cognitive symptoms across different DCR diagnoses

Subjective cognitive
symptoms % (n)		 DCR scores
% (n)
Yes/No Normal Borderline Impaired
No: 51.9% (n = 95) 42.1% (77) 8.2% (15) 1.6% (3)
Yes: 48.1% (n = 88) 31.1% (57) 12.6% (23) 4.4% (8)
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Note: Subjective cognitive concern was operationalized as a binary variable (yes/no) indicating the presence or absence of subjective cognitive symptoms

The Link Between Subjective Cognitive Symptoms and Objective Cognitive Outcomes and the role of Age at Menopause

Overall DCR score

Nested linear regression analyses were conducted to examine the predictors of objective cognitive performance (total DCR scores), including a quadratic specification of age at menopause. Model 1 showed that age and education were positively associated with objective cognition (B = −.02, p = .03 and B = .15, p < .001, respectively), while depression was not (B = .30, p = .28). Model 2 showed that higher SCoPE scores were significantly associated with poorer objective performance (B = −.13, p = .03). Education and age remained significant (B = .15, p < .001 and B = −.02, p = .01), while depression continued to show no significant association. Model 3 included mean-centered age at menopause, which was significantly associated with objective cognition (B = .06, p < .001), as well as SCoPE scores (B = −.12, p = .03), education (B = .14, p < .001) and age (B = −.02, p = .01). The final model included the quadratic menopause-age term, which revealed a significant non-linear negative association between age at menopause and cognitive performance (B = −.006, p < .001). Education (B = .13, p = < .001) and SCoPE scores (B = −.12, p = .03) remained significant predictors in this model, whereas the linear menopause-age term was no longer significant. Each successive hierarchical model explained a significant increase in the variance of objective cognitive performance. The final model accounted for the greatest proportion of the variance (R2 = .26, adjusted R2 = .23). This model showed a better fit compared to the previous models, as indicated by lower AIC (552) and BIC (577) values. See Figure 2.

Figure 2.

Figure 2

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Standardized beta coefficients (β) from hierarchical linear regression models predicting objective cognitive performance (total DCR score). Model 1 included age, education, and depression; Model 2 included SCoPE scores; Model 3 included age at menopause (linear). Standardized coefficients are shown to facilitate comparison of the relative strength of predictors across models. The quadratic menopause-age term (Model 4) is not displayed.

Note. Bars represent standardized beta coefficients (β) for each predictor

To support the interpretation of the quadratic effect, the vertex (i.e., the age at menopause corresponding to the predicted peak in cognitive performance) was calculated using the standard quadratic formula:

Vertex=-Blinear2Bquadratic+mean(age at menopause)

Using the coefficients from the final model (B = 0.018 for mean-centred age at menopause and B = −0.006 for the quadratic term), and the sample mean age at menopause of 50.1 years, the peak was estimated at approximately 51.5 years. See Figure 3.

Figure 3.

Figure 3

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Estimated marginal means of predicted cognitive performance across the observed range of age at menopause, adjusted for subjective cognitive symptoms, chronological age, education, and depressive symptoms. Shaded bands represent 95% confidence intervals.

Delayed Recall

A nested linear regression model with the same structure was employed to examine predictors of delayed recall scores. In Model 1, education significantly predicted performance (B = .07, p = .004), whereas age (B = −.08, p = .223) and depression (B = .26, p = .145) were not significant. Model 2 added age at SCoPE scores (B= −.05, p = .143), which were not significant. Model 3 included age at menopause, which was significantly positively associated with delayed recall (B = .03, p = .008), alongside with education (B = .06, p = .010). The final model accounted for the biggest portion of variance (R2 = .11, adjusted R2 = .08). Compared to the previous models, this model showed a better fit, as indicated by lower AIC (421) and BIC (443) values (Table 5).

Table 5.

Summary of Nested Regression Models

Delayed Recall (DV) Significant Predictors
Model R2 Adjusted R2 AIC BIC F (df1,df2) p
Delayed Recall (DV) Predictor B (95% CI) p
1 .06 .049 426 442 4.07 (5,173) .008 Age ns ns
Education .07 [.02, .11] .004
Depression ns ns
2 .07 .055 426 445 4.96 (4,174) .007 Age ns ns
Education −.07 [.02, .11] .004
Depression ns ns
SCoPE ns ns
3 .11 .088 421 443 4.45 (5,173) <.001 Age ns ns
Education .06 [.01, .10] .01
Depression ns ns
SCoPE ns ns
Age at menopause (linear) .03 [.008, .05] .008
Digital Clock Scores (DV)
1 .14 .131 1542 1558 9.83 (3,172) <.001 Age −.05 [−.88, −.24] <.001
Education 2.49 [1.30-3.65] <.001
Depression ns ns
2 .15 .131 1543 1562 7.60 (4,171) <.001 Age −.05 [−.91, −.26] <.001
Education 2.47 [1.3, 3.64] <.001
Depression ns ns
SCoPE ns ns
3 .19 .170 1536 1558 8.19 (5,170) <.001 Age −.05 [−.88, −.24] <.001
Education 2.21 [1.05, 3.37] <.001
Depression ns ns
SCoPE ns ns
Age at menopause .89 [.31, 1.47] .003
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Digital Clock Scores

An additional nested linear regression model was employed to examine predictors of Digital Clock performance. In Model 1, age was negatively associated with digital clock scores (B = −.05, p < .001) while education showed a positive association (B = 2.49, p < .001) and depression was not significant (B = .04, p =.549). Model 2 added SCoPE scores, which were not associated with digital clock scores (B = −.916, p = .33), while education and age remained significant (B = 2.47, p < .001 and B = −0.59, p <.001, respectively. In Model 3, age at menopause was added to the model and was positively associated with cognition, B = .89, p = .003. The final model accounted for the greatest proportion of the variance (R2 = .19, adjusted R2 = .17). This model showed a better fit compared to the previous models, as indicated by lower AIC (1536) and BIC (1558) values (Table 5).

Discussion

The current study examined the associations between subjective cognitive symptoms, age at menopause, and performance on a sensitive digital cognitive measure in a sample of postmenopausal women seen for their annual Well-Woman visit. Our findings suggest that both subjective cognitive symptoms and age at menopause were independently associated with objective cognitive outcomes. Subjective cognitive symptoms remained significantly associated with poorer cognitive performance even after accounting for demographic factors and depressive symptoms. This association remained robust when age at menopause was considered, supporting the clinical relevance of subjective symptoms as indicators of cognitive vulnerability. Unexpectedly, age at menopause showed a significant non-linear association with objective cognition, indicating that cognitive performance is highest at intermediate menopausal ages and lowest at earlier and later ages. Together, these findings underscore the importance of conducting routine cognitive screenings in midlife and older women and considering both subjective cognitive symptoms and menopause timing when evaluating cognitive health.

In this study, we employed the SCoPE, a brief, freely available subjective cognitive screener developed within our lab and designed specifically for use in fast-paced clinical settings. In our sample, nearly half of participants (48.1%) endorsed one or more items on the SCoPE. Although reported rates of cognitive symptoms vary widely across studies and age groups, ranging from 15% to 81.7% (Joyce et al., 2024; Reuben et al., 2021), this pattern aligns with prior findings showing that many women report attention and memory difficulties during the menopause transition (Greendale et al., 2011), and that these symptoms may persist into the postmenopausal period (Weber et al., 2014). Subjective cognitive symptoms, analyzed as a binary variable, were significantly associated with DCR diagnosis, with the presence of at least one symptom linked to performance in the borderline or impaired range of the DCR. Regression analyses employing continuous SCoPE scores further indicated that a greater degree of subjective cognitive symptoms negatively predicts performance on a digital cognitive measure after adjusting for demographics and clinical factors, with higher SCoPE scores (i.e., more subjective symptoms). Although some women who reported subjective cognitive symptoms performed within the normal range on objective cognitive assessment, this pattern is consistent with the multidimensional nature of subjective cognitive symptoms, which can arise from multiple factors, and, in some cases, precede measurable cognitive impairment (Jessen et al., 2014). Overall, these results support a growing body of work suggesting that subjective cognitive symptoms may represent a marker of cognitive vulnerability, even in the absence of measurable impairment (Mitchell et al., 2014; Jessen et al., 2020). Interestingly, depression did not significantly predict overall cognitive outcomes in our analyses. Even though previous research has emphasized the role of mood and depressive symptoms as confounding or even driving factors of subjective cognitive symptoms (Goveas et al., 2011), our findings reinforce the fact that subjective cognitive symptoms cannot simply be attributed to mood-related factors, and warrant further investigation. Nonetheless, it is important to note that depressive symptoms in our study were assessed using a single item from the LHQ rather than a validated measure, which may have limited out ability to fully observe the potential association between mood and subjective cognitive symptoms.

Subjective cognitive symptoms showed a significant association with overall cognitive performance, but were not significantly associated with individual Linus DCR subtests (i.e., delayed recall, clock drawing). This pattern may indicate that the subjective cognitive screener employed in this study is sensitive to a number of different cognitive symptoms that vary across women, rather than a specific cognitive process. Consistent with previous work, these findings support the practical value of subjective cognitive screening as an accessible tool for identifying individuals who may warrant more comprehensive cognitive assessment (Jessen et al., 2014; Yates et al., 2017).

This study also contributes to a growing body of evidence linking reproductive history, particularly age at menopause, to cognitive outcomes (Kuh et al., 2018). In line with epidemiological data suggesting that longer reproductive lifespan and prolonged exposure to endogenous estrogens may exert neuroprotective effects, particularly in memory-related brain regions (Georgakis et al., 2019; Hao et al., 2023), we hypothesized that earlier menopause would be associated with worse cognitive outcomes. In contrast, results showed that the overall association between age at menopause and objective cognitive performance was non-linear, with cognitive performance highest at intermediate ages of menopause and lowest at both earlier and later ages. Though unexpected, this pattern aligns with recent evidence linking extreme menopausal timing to poorer cognitive outcomes (Guo et al., 2025). Although this result is not yet well understood, one possibility is that very late menopause reflects a longer or atypical menopausal transition, leading to prolonged hormonal instability. Additionally, some studies suggest that although estrogen is neuroprotective in healthy cells, it may become neurotoxic in older ages and in diseased cell populations (Najar et al., 2020).

As a whole, regression analyses revealed that the most comprehensive model - incorporating both age at menopause and SCoPE scores alongside demographic covariates - provided the best fit, as reflected in lower AIC and BIC values. Overall, these findings suggest that subjective cognitive symptoms and reproductive health indicators together provide critical information about objective cognitive function as part of an integrated predictive framework. Our results have several clinical implications. First, they support the feasibility and utility of integrating brief subjective cognitive screeners, such as the SCoPE, into routine women's health visits. Given that nearly half of our sample reported subjective cognitive symptoms and that these symptoms were associated with cognitive outcomes, such screeners may serve as low-cost, scalable tools for identification of cognitive difficulties. While the SCoPE is not intended to function as a standalone diagnostic instrument, our fundings support its utility as a screening measure to identify individuals who may benefit from further evaluation. Overall, these results suggest that the SCoPE may have practical value in detecting early cognitive vulnerability in women aged 45 years and older. Second, age at menopause appears to be a meaningful risk stratifier and should be considered when assessing cognition in postmenopausal women. Screening protocols that incorporate both perceived cognitive health and reproductive history may provide a more comprehensive assessment of cognitive vulnerability than either approach alone.

Despite these strengths, limitations should be acknowledged. The cross-sectional design of our study precludes causal inference and limits the interpretation of temporal relationships among subjective cognitive symptoms, reproductive history, and cognitive performance. Further longitudinal studies are needed to determine whether subjective cognitive symptoms and age at menopause independently or together predict future cognitive decline and conversion to Mild Cognitive Impairment or dementia. Our sample consisted primarily of high-functioning, English-speaking participants with limited demographic variability. Therefore, the findings may not be applicable to populations with more diverse sociodemographic or clinical characteristics. Additionally, although the analyses were theory-driven and focused on prespecified predictors, the lack of formal adjustment for multiple comparisons may increase the risk of Type I error.

Limitations related to menopause data should also be acknowledged. First, age at menopause was self-reported and therefore subject to recall bias, although its use is common and acceptable in population research (e.g., Schmidt- Pokrzywniak & Kluttig, 2016). Second, the present study did not distinguish between type of menopause or include hormone replacement therapy (HRT) in the analyses. This decision was based on evidence suggesting that the increased risk of cognitive decline associated with surgical menopause is primarily due to the earlier and more abrupt loss of ovarian hormones rather than the surgical cause itself (Bove et al., 2014; Dobson et al., 2024), and on the substantial heterogeneity in HRT timing, formulation and duration. Nonetheless, the absence of specific characterization of menopause type and HRT use limits conclusions regarding their potential effects. Future research including larger samples and more detailed characterization of menopause type and treatment exposure may help clarify their independent and combined effects on cognitive outcomes.

In conclusion, our findings highlight the importance of routinely screening women for subjective cognitive symptoms and considering such symptoms as well as age at menopause as risk factors of early cognitive dysfunction in women. Integrating cognitive screenings into gynecological care settings may provide a proactive, accessible strategy for identifying women with current cognitive impairment as week as those at increased risk for cognitive decline.

Table 4.

Summary of Nested Regression Models

Model R2 Adjusted
R2 		AIC BIC F (df1,df2) p Significant predictors
Overall DCR Score (DV) Predictor B (95% CI) p
1 .012 0.10 576 592 7.80 (3,172) <.001 Age −.02 [−.04, −.001] .03
Education .16 [.08,.23] < .001
Depression ns ns
2 .14 .12 573 592 7.17 (4,171) <.001 Age −.02 [−.04, −.005] .01
Education .16 [.08, .23] <.001
Depression ns ns
SCoPE −.13 [−.25, −.01] .03
3 .20 .17 562 585 8.60 (5, 170) <.001 Age −.02 [−.04, −.004] .01
Education .14 [.06, .21] <.001
Depression ns ns
SCoPE −.12 [−.24, −.01] .02
Age at menopause (linear) .06 [.02, .10] <.001
4 .26 .23 552 577 9.88 (6,169) <.001 Age ns ns
Education .13 [−.03, .05] <.001
SCoPE −.12 [−.23, −.01] .03
Age at menopause (quadratic) −.006 [−.009, −.002] <.001
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Declaration of Interest

No competing financial interests exist. This work was supported by the Alzheimer's Association Sex And Gender Award 2023 under Grant [SAGA23/1141640]; National Institutes of Health under Grant [JLJ: NIA T32 AG000037]; the Mary E. Groff Charitable Trust; and the Thompson Family Foundation.

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