Cognitive shadows in perimenopause: linking subjective cognitive decline (SCD) to menopausal symptom severity

Abstract

Objective

As women approach perimenopause, the incidence of Subjective Cognitive Decline (SCD) rises. This study aims to investigate the association between SCD and the severity of perimenopausal symptoms.

Setting

Conducted at The Affiliated Hospital of Guizhou Medical University Menopause Clinic from November 2022 to June 2023. Participants, aged 40-55 years, were classified as perimenopausal using the STRAW + 10 criteria.

Methods

SCD was assessed separately using the Chinese version of the SCD-Q9 scale and the SCD International Working Group (SCD-I) conceptual framework, while perimenopausal symptoms were evaluated with the Modified Kupperman Index (MKI). Linear relationships between MKI scores and SCD-Q9 scores were clarified using both univariate and multivariate linear regression analyses. Additionally, a multivariate Logistic regression analysis was conducted to examine the association between MKI scores and SCD classification based on SCD-I criteria.

Main Outcome Measures

The primary outcomes were the Modified Kupperman Index scores, SCD-Q9 questionnaire scores, and the diagnosis of SCD based on SCD-I criteria.

Results

Among 101 participants, the average MKI score was 18.90 ± 9.74, and the average SCD-Q9 score was 4.57 ± 2.29. Both univariate and multivariate linear regressions demonstrated a positive correlation between these scores. A multivariate Logistic regression analysis, using MKI as the independent variable and SCD-I criteria classification as the dependent variable, revealed a significant positive association.

Conclusions

A notable association exists between SCD and perimenopausal symptoms severity. This underscores the potential clinical importance of addressing perimenopausal symptoms to mitigate SCD risks in women. Further studies should focus on clarifying the causality between these factors.

Keywords:

• Modified Kupperman Index
• perimenopausal women
• perimenopausal symptoms
• Subjective Cognitive Decline (SCD)
• SCD-Questionnaire 9

Introduction

Nearly two-thirds of perimenopausal women report experiencing cognitive decline [1–3]. This perception is more prevalent than among men of the same age or women in their childbearing years [4]. When patients express these concerns, following the recommendations of the International Menopause Society (IMS), clinicians assure them that such experiences are common. They also inform that dementia’s incidence is low before the age of 65 and advise on lifestyle improvements like enhanced socialization and physical activity. However, it remains unclear why subjective cognitive complaints are more prevalent during the perimenopausal period compared to men of equivalent age, why dementia’s prevalence is higher in women over 65 years compared to men [5], and if there’s a correlation between the two [4].

The earliest pathological changes of Alzheimer’s disease, a form of dementia, manifest more than 20 years prior to diagnosis [6]. Notably, initial signs can be detected in middle age. As efforts advance in the prevention of Alzheimer’s disease (AD), subjective cognitive decline (SCD) is widely recognized as an indicator of symptoms in the preclinical stages (Stage 1 and Stage 2). SCD is defined as an individual’s perceived decline in cognition relative to a previous period, even when objective cognitive tests yield normal results [7]. Some studies indicate that about 14% of SCD patients develop dementia, while 27% progress to mild cognitive impairment (from the general population, observed over at least a 4-year span) [8]. The timeframe for progression to dementia varies widely [8].

Many scholars attribute this observed difference to perimenopausal endocrine changes in women [1, 9]. However, several clinical studies challenge this perspective [2, 10]. Recent reports suggest an association between perimenopausal vasodilatory symptoms and poorer outcomes on objective cognitive tests [11, 12]. However, it remains uncertain if other perimenopausal symptoms, or the cumulative symptom burden, correlate with cognitive decline. We hypothesized in this study that the subjective cognitive decline among perimenopausal women correlates with the severity of their perimenopausal symptoms. To our knowledge, this is the first study to evaluate the correlation between the severity of perimenopausal symptoms and SCD in the Chinese population. SCD diagnosis followed the guidelines of the SCD International Working Group (SCD-I) [13] and was cross-validated using the Chinese version of the SCD-Q9 questionnaire, where higher scores denote more severe SCD [14, 15]. We utilized the Modified Kupperman Index (MKI) to evaluate perimenopausal symptoms [16].

Materials and methods

Study design

Cross-sectional study.

Patient selection

This study involved women aged 40–55 years, classified as perimenopausal based on the “Stages of Reproductive Aging Workshop + 10 (STRAW + 10)” [17], and diagnosed at the Menopause Clinic of The Affiliated Hospital of Guizhou Medical University between November 2022 and June 2023. Exclusion Criteria: i. Gynecological Endocrinology Influences: Diagnosis of polycystic ovary syndrome, premature ovarian failure, or history of chemotherapy; Surgically induced menopause; Use of estrogen and progesterone analogues, estrogen receptor blockers, or GnRH-a analogues. ii. Cognitive Function Influences: Diagnosed with conditions such as depression, anxiety, stroke, brain tumor, epilepsy, or Parkinson’s disease; Presence of diabetes mellitus, hypertension, hyperthyroidism, hypothyroidism, moderate to severe anemia, malignant tumors, syphilis, or AIDS; Hearing impairment; Use of medications affecting the central nervous system. Participants were recruited using a promotional poster displayed outside the menopause clinic. This poster outlined the study’s hypothesis, objectives, inclusion and exclusion criteria, and ensured the protection of participants’ privacy. Participants were not offered any financial incentives.

Data collection and experimental methods

Before the study’s commencement, investigators underwent a one-day training session. An operational manual was also provided to ensure standardized explanations for each questionnaire item. The assessments took place in a quiet clinic room, with participants arriving 10 min prior to familiarize themselves with the environment. After signing an informed consent form, eligible participants completed the Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), and Montreal Cognitive Assessment (MoCA) questionnaires. These tools helped exclude individuals with potential depression, anxiety, and Mild Cognitive Impairment (MCI). Subsequently, an SCD assessment, MKI score, and a basic demographic survey were conducted. Blood samples for sex hormone testing are collected early in the morning after fasting for more than 8 h. For menstruating participants, this was done 1-3 days after menstruation onset, while for those who hadn’t menstruated in over two months, sampling occurred on the same or next morning. These specimens were immediately sent to Guizhou Medical University Hospital’s testing center for analysis via electrochemiluminescence.

The Kupperman index, initially developed in the 1950s by Kupperman and colleagues [18], has been adapted for widespread use, notably with a version modified for the Chinese region. This MKI evaluates menopausal symptoms through 13 distinct entries, each scored on a scale from 0 (none) to 3 (very severe) to reflect symptom severity. The symptoms evaluated include Sweating hot flushes, Paresthesia, Insomnia, Nervousness, Melancholia, Vertigo, Fatigue, Arthralgia myalgia, Headache, Heart palpitation, Formication, Urinary tract infection and sexual complaints. In this scoring system, Sweating hot flushes carry a quadruple weight, while Paresthesia, Insomnia, Nervousness, Urinary tract infection, and sexual complaints are doubled in weight. The other symptoms are not weighted, allowing for a cumulative score ranging between 0 and 63.

The assessment of SCD utilized the Chinese version of the validated SCD-Q9 questionnaire [14, 15], comprising nine questions to gauge memory-related issues. The items are as follows: 1. Do you think you have problems with your memory? 2. Do you have difficulty remembering a conversation from a few days ago? 3. Do you have complaints about your memory in the last 2 years? 4. How often is the following a problem for you: Personal dates (e.g. birthdays). 5. How often is the following a problem for you: Phone numbers you use frequently. 6. On a whole, do you think that you have problems remembering things that you want to do or say? 7. How often is the following a problem for you: Going to the store and forgetting what you wanted to buy. 8. Do you think that your memory is worse than 5 years ago? 9. Do you feel you are forgetting where things were placed? Each item is scored 1 point, with options categorized as binary (Yes, No) or ternary (Often, Sometimes, Never), resulting in a total score range of 0 to 9 points.

We assess compliance with SCD using the SCD-I conceptual framework, which define SCD as a persistent decrease in self-reported cognitive function relative to an individual’s previous normal state, unrelated to any sudden incidents. This condition encompasses memory lapses (such as regularly forgetting new information, significant dates, or events, and repetitive questioning), difficulties with language (including challenges in finding the appropriate words for communication or comprehension), and weakened executive function (notably in planning, organizing, sequencing tasks, and problem-solving). In this study, the specific approach for this part entailed thoroughly explaining the concept and scope of SCD, followed by individuals self-assessing whether they experience SCD. Moreover, cognitive abilities, after being adjusted for age, gender, and educational background, must align with the normal range on standardized assessments without fulfilling the criteria for MCI. Additionally, it is essential to exclude any psychoneurological disorders, medical conditions, and effects of medications, which were ruled out by the exclusion criteria of the present study and by the MoCA, SAS, and SDS questionnaires.

Statistical analysis

The data preprocessing constituted the initial step of our analysis, addressing the typical right skewness of laboratory test data through natural logarithmic transformation to ensure appropriate analytical conditions. Following this, a descriptive analysis of the baseline data was conducted, representing continuous variables with their mean ± standard deviation (Mean ± SD) and categorical variables with frequencies and percentages. In the phase of statistical modeling, correlation coefficients and p-values were determined for each variable by employing simple linear regression, taking SCD-Q9 scores as the outcome variable. This analysis was visually supported by forest plots. Additionally, scatter plots were constructed to display the relationship between MKI scores (x-axis) and SCD-Q9 scores (y-axis), with best-fit lines illustrating these associations. Further analytical depth was achieved through multifactorial linear regression modeling, wherein MKI scores were treated as the independent variable and SCD-Q9 scores as the dependent variable. Variables displaying p-values <0.05, aside from MKI, were incorporated as covariates in the adjustment process. Sex hormone levels were excluded from the regression model to eliminate potential covariance, given their substantial correlation with MKI scores. Multifactorial logistic regression analysis was employed to estimate the odds ratios (ORs) and 95% confidence intervals (CIs), as well as p-values, for the variables under consideration. This step used MKI scores as the independent variable and dichotomous outcomes based on the SCD-I conceptual framework as the dependent variables. All statistical analyses, including visualizations, were performed using STATA version 17.0, with a two-sided p-value of less than 0.05 indicating statistical significance.

Result

This study enrolled 110 women. Upon administering the SAS, SDS, and MoCA questionnaires, the analysis showed that four participants met the criteria for depression on the SDS, three on the SAS, and two displayed mild cognitive impairment as per the MoCA criteria. These participants were excluded. Consequently, 101 women completed both the questionnaires and blood collection.

The study evaluated a cohort of women, averaging 47.74 years of age, primarily ranging from 46 to 50 years. The average BMI recorded was 22.47, where 50% of participants had a BMI of 22.06 or less, and 12.9% had a BMI exceeding 25. The SCD-Q9 scores averaged 4.57. The Modified Kupperman Index showed an average score of 18.9 and a median of 17, illustrating significant perimenopausal symptoms in the cohort. Baseline data for the study is provided in Table 1.

Table 1. Baseline characteristics of all participants.

Variables	Total (n = 101)
age (yr)	47.74 ± 2.64
Body Mass Index	22.47 ± 2.72
ln (LH)	3.11 ± 0.87
ln (FSH)	3.79 ± 0.59
ln (Estrogen)	4.05 ± 0.56
ln (Testosterone)	−0.92 ± 0.76
ln (Progesterone)	−1.06 ± 0.47
SCD-Q9 score	4.57 ± 2.29
Modified Kupperman Index	18.90 ± 9.74
Menarche (yr)	13.84 ± 1.68
SCD*—no. (%)
no	38 (0.38)
yes	63 (0.62)
Gravidity—no. (%)
<3^rd	69 (0.68)
> =4rd	32 (0.32)
Parity—no. (%)
<2^nd	68 (0.67)
> =2^nd	33 (0.33)
Race—no. (%)
Han	84 (0.83)
Others	17 (0.17)
Current Marital Status—no. (%)
Living alone	5 (0.05)
Couple household	96 (0.95)
Currently Working—no. (%)
No	43 (0.43)
Yes	58 (0.57)
Education—no. (%)
Upper secondary or less	49 (0.49)
Tertiary	52 (0.51)
Menopause Status—no. (%)
Early-Perimenopause	49 (0.49)
Late-Perimenopause	52 (0.51)

Note: Plus—minus values are means ± SD. The categorical variable SCD, marked with an asterisk (*), is determined based on the SCD-I conceptual framework. The notation ln(var) denotes the natural logarithmic transformation of a variable. Before logarithmic conversion, units for LH (Luteinizing Hormone) and FSH (Follicle-stimulating Hormone) were IU/L, for Estrogen were pmol/L, and for Testosterone and Progesterone were nmol/L. BMI = Body Mass Index; SCD-Q9 score: Score of SCD questionnaire 9 items version; Current marital status includes invisible partnerships; Menopausal Status is defined as one year after the final menstrual period.

In our simple linear regression analysis, we evaluated the influence of various factors on the SCD-Q9 score, using it as the dependent variable, and depicted the results in a forest plot. This analysis revealed a significant positive correlation between the MKI score and SCD-Q9 questionnaire score (Beta = 0.13 [0.09, 0.17]; p < 0.01), without adjusting for other variables’ effects. Additionally, both BMI (Beta = 0.22 [0.06, 0.38]; p < 0.01) and ln(FSH) levels (Beta = 0.86 [0.10, 1.61]; p = 0.03) demonstrated positive correlations with the SCD-Q9 score. Conversely, E2 levels were negatively associated with the SCD-Q9 score (Beta = −1.17 [-1.95, −0.39]; p < 0.01), indicating higher scores in late perimenopause compared to early perimenopause (Beta = 1.27 [0.40, 2.15]; p < 0.01). Refer to Figure 1 for detailed visualizations. Before performing linear regression, a comprehensive review of the MKI scores and SCD-Q9 ratings was conducted by plotting a scatter diagram and fitting a line, as shown in Figure 2. Given the relationships between sex hormone levels, MKI scores, and menopause status, we did not adjust sex hormone levels as covariates. BMI and menopause status were adjusted in the following regression model to account for their impacts.

Figure 1. Forest plot and tabulated summary of variables - beta coefficients, 95% Confidence Intervals, and associated P-values in relation to SCD-Q9 scores.

Note: The forest plot illustrates the correlation coefficient Beta for each variable in relation to SCD-Q9 scores and their 95% confidence intervals. A Beta greater than 0 indicates a positive correlation with SCD-Q9 scores, whereas a Beta less than 0 indicates a negative correlation. The red dashed line represents the reference line at 0.

Figure 2. Scatter plot illustrating the positive correlation between MKI scores and SCD-Q9 scores.

Note: The fitted line in the scatter plot represents the trend of the relationship between MKI scores and SCD-Q9 scores, indicating a positive correlation.

In the multiple linear regression model analyzing the relationship between MKI and SCD-Q9 scores, after adjusting for the above covariates, SCD-Q9 scores were significantly and positively correlated with MKI (Beta = 0.12 [0.08, 0.16]; p < 0.01). The model overall was statistically significant (F (3, 97) = 17.93, p < 0.01, R² = 0.36, Adjusted R² = 0.34). Furthermore, a multifactorial Logistic regression analysis, considering MKI as the independent variable and the presence of SCD (as determined by the SCD-I criteria) as the dependent variable, effectively modeled the association (LR χ²(3) = 40.00, p < 0.01, Pseudo R² = 0.30). After adjusting for body mass index (BMI) and menopause status, MKI was significantly and positively associated with the risk of SCD (OR = 1.18 [1.09, 1.28]; p < 0.01). Both models supported the study’s hypothesis. Tables 2 and 3 summarize these findings.

Table 2. A multivariate linear regression analysis with MKI as the independent variable and SCD-Q9 as the dependent variable.

	Beta	95% CI	Std. error	p-value
Body Mass Index	0.20	(0.06, 0.34)	0.07	<0.01
MKI	0.12	(0.08, 0.16)	0.02	<0.01
Menopause Status	0.38	(–0.41, 1.17)	0.40	0.34

Note: In the multiple linear regression analysis with MKI as the independent variable and SCD-Q9 as the dependent variable, variables other than MKI were included as covariates. The statistical metrics for this model are as follows: F(3, 97) = 17.93, p < 0.01, R² = 0.36, and Adjusted R² = 0.34, indicating the model was successfully constructed.

Table 3. Multifactorial logistic regression analysis with the presence of SCD (determined based on SCD-I criteria) as the dependent variable.

	Odds Ratio	95% CI	Std. error	z	p-value
Body Mass Index	1.25	(1.02, 1.54)	0.13	2.16	0.03
MKI	1.18	(1.09, 1.28)	0.05	4.28	<0.01
Menopause Status	1.46	(0.52, 4.12)	0.77	0.72	0.47

Note: In the logistic regression analysis with MKI as the independent variable and the presence of SCD as the dependent variable, variables other than MKI were included as covariates. The statistical indicators for this model are as follows: LRχ²(3) = 40.00, p < 0.01, Pseudo R² = 0.30, indicating the model was successfully constructed.

Discussion

During perimenopause, a pivotal period in a woman’s life, there are several physiological and psychological changes encountered. Many women confront cognitive challenges, including memory, attention, and decision-making [3, 19]. Our investigation established a clear link between these SCD and the severity of menopausal symptoms, as determined by the SCD-I conceptual framework. We observed a trend: as perimenopausal symptoms intensified, cognitive challenges also increased, aligning with findings from Drogos, Maki, et al. [11, 12].

Previous studies have reported a link between menopausal symptoms and self-reported cognitive issues, yet findings have been mixed. Drogos et al.'s study involved 68 middle-aged women with moderate to severe vasodilatory symptoms who completed the Memory Functioning Questionnaire to assess their subjective memory complaints [11]. They also completed the Menopausal Symptoms Assessment, which indicated an association between subjective memory complaints and vasodilatory symptoms. In contrast, E. S. Mitchell et al.'s retrospective analysis of a Seattle Midlife Women’s Health Study subset, including participants in early and late menopausal transition stages or postmenopause, involved 292 participants [2]. However, this study found no significant link between menopausal transition-related factors and symptoms of inattention or forgetfulness.

The SCD-I conceptual framework’s introduction has standardized the conceptualization and diagnostic criteria for SCD [13], which encompasses a variety of symptoms such as memory loss, concentration difficulties, and executive functioning issues. However, the studies conducted prior to the framework’s release predominantly focused on memory loss alone. These studies did not consider the broader spectrum of SCD symptoms, nor did they factor in the influences of MCI or conditions like depression and anxiety. In contrast, our study, aligned with the SCD-I conceptual framework, led to more accurate and comprehensive conclusions. Additionally, the severity of menopausal symptoms varies with ethnicity [20]. Unlike other studies, ours specifically targeted a Chinese demographic, a population not extensively covered in similar research.

Our study offers an in-depth examination of the link between SCD and the intensity of perimenopausal symptoms, adhering to the SCD-I conceptual framework, which bolsters its credibility. However, it has several limitations. Firstly, its cross-sectional design constrains our ability to discern causal relationships among variables. Secondly, while we collected all consenting patients from the clinic during a specific period, we did not perform a sample size calculation in advance, owing to the limited reference data available for the Asian population. Instead, we conducted a post hoc power analysis using G-power, based on the parameters from the multifactorial linear regression model (Table 2) with an R² of 0.36, a sample size of 101, an α error probability set at 0.05, the number of tested predictors set at 1, and the total number of predictors set at 3. This analysis yielded a Power (1-β error probability) close to 1. For the logistic regression analysis that distinguishes the presence of SCD based on the SCD-I criteria, the proportion of negative events was 0.38. Employing the Events Per Variable (EPV) method with EPV set to 10 indicated that incorporating three variables into the model was appropriate. Thirdly, the recruitment of participants exclusively from a hospital setting, as opposed to a community sample, introduces potential selection bias. Fourthly, reliance on participants’ recollection for information such as the date of the last menstrual period and responses on the modified Kupperman scale may lead to recall bias. Fifthly, our study did not address certain social determinants and environmental factors. Lastly, women experiencing more severe menopausal symptoms might encounter various challenges, including reduced social interaction and physical activity, which could in turn impact cognitive function.

Our findings carry significant clinical implications. Traditionally, it has been thought that the higher incidence of dementia in women is primarily due to their longer average lifespan [5]. However, basic research has demonstrated the neuroprotective effects of estrogen hormones [21–23]. Furthermore, a study published in the journal ‘Nature’ suggested that blocking FSH can improve cognition in Alzheimer’s disease mice, indicating that FSH might influence cognitive changes during perimenopause [24]. Yet, clinical studies have yielded contradictory or inconclusive results [25–28]. Our study, which investigated SCD as a preclinical symptom indicator of AD, uncovered a correlation between the severity of menopausal symptoms in perimenopausal women and the presence of SCD. This aligns with Barrett-Connor et al.'s findings on vasodilatory symptoms. Dementia’s risk factors remain largely elusive. The 2020 report by the Lancet Commission introduced three new risk factors – excessive alcohol consumption, head injury, and air pollution – to the preexisting nine modifiable factors. These twelve factors explain approximately 40% of global dementia cases, yet over half of the risk factors are still unidentified [5]. A cross-sectional study highlighted that dementia risk factors correlate with SCD in individuals over 45 years [29]. Therefore, the detection and management of SCD should be integrated into the healthcare of perimenopausal women, particularly those with more severe symptoms.

In summary, this study underscores the link between subjective cognitive decline and modified Kupperman scores in perimenopausal women. To deepen our understanding of this association, future studies should focus on clarifying the causality between these factors. This objective may be accomplished through longer follow-up durations and the use of specific methods like Mendelian randomization. Healthcare providers, including physicians, should provide increased support to perimenopausal women through lifestyle guidance, cognitive training, and psychological counseling.

Contribution to authorship

Ke Liu handled the statistical analysis and authored the initial draft of the manuscript. Xue Lei executed field studies and oversaw the data input, accomplishing well over half of these duties. Laigang Zhao critically examined and refined the manuscript, ultimately endorsing its final rendition. Both Hangyu Yu and Dian Wang participated in field investigations and managed data cataloging. Lin Yang orchestrated and directed the project, meticulously reviewing the manuscript before giving his final approval. Every author bears responsibility for the content of the published paper.We would like to acknowledge and express our gratitude to Ke Liu, Xue Lei, and Laigang Zhao for their equal and significant contributions to this research, reflecting their roles as co-first authors.

Ethics approval

This study adhered to the principles of the Declaration of Helsinki. The Ethics Committee of The Affiliated Hospital of Guizhou Medical University, People’s Republic of China, approved the study (Approval Number: 2023 no.406).

Acknowledgement

The authors thank all the participants of this work.

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns related to human participant data.

Additional information

Funding

This work was supported by the Guizhou Provincial Science and Technology Projects (qkhjc-ZK[2023]yb351).