Abstract
[Purpose]
This study examined the associations of adiposity indices and physical fitness components with domain-specific cognitive function in postmenopausal women, focusing on hippocampus-dependent mnemonic discrimination.
[Methods]
The study involved 68 postmenopausal women aged ≥ 40 years who were at least one year postmenopausal. Body composition was assessed using dual-energy X-ray absorptiometry, and adiposity indices comprised body fat mass, body fat percentage, fat mass index, fat-to-lean mass ratio, and android-to-gynoid fat ratio. Cognitive function was assessed using the color-word Stroop task and the object and spatial versions of the Mnemonic Discrimination Task. Physical fitness was evaluated based on grip strength, sit-and-reach flexibility, and peak oxygen uptake. Correlation and hierarchical multiple linear regression analyses were performed (n = 61).
[Results]
Adiposity indices were not significantly associated with Stroop performance. Body fat mass and fat mass index were negatively associated with object-version recognition score and spatial-version lure discrimination index, and body fat mass was also negatively associated with spatial-version recognition score. Spatial-version lure discrimination index was positively correlated with flexibility and peak oxygen uptake. In hierarchical regression, adding fat mass index and peak oxygen uptake to age significantly improved the model predicting spatial-version lure discrimination index; peak oxygen uptake was significant at this step. However, flexibility provided no significant additional explanatory power, and no predictor remained significant in the final model.
[Conclusion]
Hippocampus-dependent mnemonic discrimination in postmenopausal women may be related to adiposity and overall fitness status at the level of simple associations; however, the independent contribution of specific fitness components requires further investigation.
Keywords: postmenopause, adiposity, physical fitness, cardiorespiratory fitness, mnemonic discrimination, cognitive function
INTRODUCTION
As the population ages, achieving longevity, accompanied by improved quality of life rather than merely extending survival, has become a central public health challenge. Cognitive decline, a major factor undermining individual independence, increases the demand for care resources. The socioeconomic burden of dementia is substantial and expected to increase further [1,2]. Mild cognitive impairment (MCI) is a high-risk condition that may progress to dementia while representing a window for modifying the trajectory of cognitive decline. A meta-analysis estimated that the global prevalence of MCI among adults aged 50 years or older is approximately 19.7%, with increasing trends observed depending on survey period and study characteristics [3]. Therefore, identifying risk factors associated with cognitive changes in middle and older adults, particularly modifiable factors, is important for developing preventive and management strategies.
Although cognitive health is relevant across the aging population, women require particular consideration, because the biological transition of menopause during midlife may interact with changes in body composition and brain function. According to the Stages of Reproductive Aging Workshop +10 (STRAW+10) criteria, reproductive aging is classified as premenopause, early and late menopausal transition, menopause, and early and late postmenopause [4]. Endocrine changes that occur during this process should be understood as physiological events distinct from chronological aging alone. In Korean women, natural menopause has generally been reported to occur in the late 40s to approximately 50 years [5], indicating that women may spend several decades in the postmenopausal period.
Hormonal changes accompanying menopausal transition may be linked not only to body composition and metabolic alterations but also to changes in brain function. Evidence suggests that menopause is associated with alterations in brain structure, connectivity, energy metabolism, and amyloid-beta deposition [6]. Thus, understanding cognitive health in postmenopausal women requires an integrated consideration of postmenopausal changes in the internal physiological environment, including hormonal, metabolic, and body composition changes, as well as modifiable factors that may contribute to cognitive decline.
One of the most prominent changes observed after menopause is an increase in body fat. A meta-analysis reported that fat percentage and adiposity-related indices tend to worsen during menopausal transition [7]. Adipose tissue is not only an energy storage organ but also an endocrine organ, and physiological changes in adipose tissue after menopause may be closely related to the estrogen milieu and metabolic homeostasis [8]. Accordingly, precise assessment of adiposity beyond body mass index (BMI) is of relevance in postmenopausal women. Five complementary dual-energy X-ray absorptiometry (DXA)-derived indices were considered: body fat mass (BFM) and body fat percentage (BFP) for absolute and relative total adiposity; fat mass index (FMI), a height-adjusted measure recommended for body composition assessment [9]; fat-to-lean mass ratio (FMR), which captures the increase in adiposity and loss of lean tissue that characterize the menopausal transition; and the android-to-gynoid (A/G) ratio, indexing central fat distribution, which is more closely linked to metabolic risk than total adiposity [9]. Because these indices characterize distinct aspects of adiposity, they were examined independently rather than in combination. Accumulating evidence indicates that obesity and increased adiposity may be associated with long-term cognitive decline and the risk of dementia, as shown in systematic reviews and meta-analyses [10]. However, the relationship between adiposity and cognitive function in postmenopausal women is unlikely to be explained by a single factor; other factors such as physical function should be considered. Higher adiposity may be associated with reduced physical activity and muscular function [11] and lower cardiorespiratory fitness [12], which is linked to cognitive performance [13]. Individuals with lower maximal oxygen uptake have an increased future risk of dementia [14]. Nevertheless, these factors have largely been examined independently, leaving two key gaps. First, studies relating adiposity to cognition have relied on BMI, which does not distinguish fat from lean mass and is insensitive to the fat gain and lean tissue loss during menopause; in a multicenter DXA study of 917 Korean peri- and postmenopausal women, many with a normal BMI nonetheless showed excess visceral fat and an android fat distribution [15]. Asian women also have greater abdominal and visceral adiposity than Caucasian women at an equivalent BMI [16], and recent international guidelines recommend measurement of body fat over BMI alone [17]. Second, studies examining fitness and hippocampus-dependent memory have rarely characterized body composition and often relied on self-reported physical activity, which has been unrelated to mnemonic discrimination in young adults, whereas aerobic fitness was directly measured [18]. Moreover, studies linking cardiorespiratory fitness with mnemonic discrimination have included young or mixed-age samples [18,19].
Although pairwise associations among these constructs have been reported, to our knowledge, no previous study has simultaneously assessed DXA-derived adiposity indices, directly measured peak oxygen uptake (VO2peak), and studied both object and spatial versions of hippocampal-dependent mnemonic discrimination in a single cohort of postmenopausal women. Adiposity and fitness may influence hippocampal function through shared and distinct pathways; excess adiposity promotes chronic low-grade inflammation, insulin resistance, and vascular dysfunction, all of which are implicated in hippocampal atrophy and cognitive decline [20], whereas higher cardiorespiratory fitness is associated with exercise-induced neuroplasticity, including upregulation of brain-derived neurotrophic factor and preservation of hippocampal volume [21]. Assessing these factors enables their independent and overlapping contributions to be distinguished. Cognitive function has been assessed using screening tools such as the Mini-Mental State Examination and the Montreal Cognitive Assessment [22,23]; however, these instruments may be affected by ceiling and floor effects as well as educational and cultural factors, limiting their sensitivity for detecting subtle or domain-specific changes [24]. Numerous studies have relied on questionnaire-based physical activity measures, which may not adequately reflect objective physiological capacity. To address these limitations, domain-specific tasks such as the Stroop task (which assesses executive function and inhibitory control [25]) and the Mnemonic Discrimination Task (MDT) (which reflects hippocampus-dependent pattern separation [26]) have been used to detect more cognitive changes. The focus on mnemonic discrimination is supported by the neurobiology of menopause, as estrogen receptors are abundant in the hippocampus and estradiol modulates dentate gyrus neurogenesis and hippocampal synaptic plasticity. Therefore, declining ovarian estradiol may compromise hippocampal integrity beyond chronological aging alone [27]. Because pattern separation depends on the dentate gyrus and its adult-born granule cells [28], and lure discrimination on the MDT provides a sensitive behavioral index of dentate gyrus function [26], mnemonic discrimination may be vulnerable in the estrogen-deprived postmenopausal state.
Accordingly, this study assessed adiposity indices (BFM, BFP, FMI, FMR, and A/G ratio), physical fitness factors (VO2peak, grip strength, and flexibility), and domain-specific cognitive function (Stroop task and MDT) in a single cohort of postmenopausal women. We hypothesized that higher adiposity and lower physical fitness would be associated with poorer performance in domain-specific cognitive tasks.
METHODS
Participants
This study included women aged ≥ 40 years who were at least one year postmenopausal. Overall, 74 participants were recruited. After applying the exclusion criteria, 6 participants were excluded, and data from 68 participants were included in the final analysis (Figure 1). The purpose and procedures of the study were explained to all participants, and written informed consent was obtained before testing. This study conforms to the code of ethics enshrined in the Declaration of Helsinki (published by the World Medical Association) and has been approved by the Institutional Review Board of Incheon National University (Approval No. 7007971-202311-010A).
Figure 1. Participant flow.
Of 74 postmenopausal women assessed for eligibility, 6 did not meet the eligibility criteria, and 68 were included in the descriptive and correlation analyses. Seven participants were excluded from the hierarchical multiple regression because of missing predictor data, which were handled by listwise deletion, yielding an analytic sample of 61. LDI, lure discrimination index; MDTS, spatial version of the Mnemonic Discrimination Task; VO2peak, peak oxygen uptake.
Participants were excluded if they met any of the following criteria: less than 1 year since menopause; current sex-hormone therapy or hormone replacement therapy; current smoking; history of anticancer treatment; history of hysterectomy; psychiatric disease, cognitive impairment, or use of related medications; or musculoskeletal disease that limited exercise performance. The study framework is shown in Figure 2A, and assessments were completed in a single laboratory visit in the order shown in Figure 2B.
Figure 2. Conceptual framework and assessment protocol of the present study.
(A) Conceptual framework. Five adiposity indices derived from dual-energy X-ray absorptiometry and three directly measured physical fitness factors were examined in relation to two domain-specific cognitive outcomes: prefrontal executive control, assessed with the Stroop task, and hippocampaldependent mnemonic discrimination, assessed with the object and spatial versions of the Mnemonic Discrimination Task. Arrows denote the associations examined in this cross-sectional study and do not imply causality; the downward and upward arrows in the right-hand box indicate the direction of the significant bivariate associations observed. (B) Assessment protocol. All assessments were completed during a single laboratory visit in the order shown, and the graded exercise test was administered last to avoid acute effects of exercise on cognitive performance. A/G ratio, android-to-gynoid fat ratio; BFM, body fat mass; BFP, body fat percentage; DXA, dual-energy X-ray absorptiometry; FG, fasting glucose; FMI, fat mass index; FMR, fat-to-lean mass ratio; HbA1c, glycated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; IES, inverse efficiency score; LDI, lure discrimination index; LDL-C, low-density lipoprotein cholesterol; MDT, Mnemonic Discrimination Task; REC, recognition; RT, reaction time; TC, total cholesterol; TG, triglycerides; VO2peak, peak oxygen uptake.
Anthropometry and Body Composition
The participants visited the laboratory wearing light clothing and removed metallic objects, including jewelry, before measurements. Height was measured barefoot using a stadiometer (Samhwa, Seoul, South Korea) with participants standing upright, heels together, and eyes facing forward. Measurements were taken twice, and the mean value was used for analysis. Body weight was measured using a body composition analyzer (InBody 970; Biospace, Seoul, South Korea). Body composition was assessed using DXA (Prodigy, GE Healthcare, USA), and lean mass, fat mass, and fat percentage were obtained. FMI was calculated as fat mass (kg) divided by height squared (m2), and FMR was calculated as fat mass (kg) divided by lean mass (kg). The A/G ratio was calculated as the ratio of android fat mass to gynoid fat mass obtained from DXA. In this study, BFM, BFP, FMI, FMR, and A/G ratio were used as adiposity indices.
Blood Lipids and Glycated Hemoglobin
Fasting glucose, triglycerides, total cholesterol, and high- and low-density lipoprotein cholesterol levels were measured in fingertip whole blood using the Cholestech LDX system (Alere Inc., Waltham, MA, USA), and HbA1c was measured using the Alere Afinion AS100 system (Alere Inc., Waltham, MA, USA), according to the manufacturer’s instructions.
Cognitive Function
Stroop task
A modified computerized color-word matching Stroop task assessed executive function, particularly inhibitive control and cognitive interference related to prefrontal function [25,29,30]. Each trial presented two-line stimuli, and participants judged whether the upper stimulus’s ink color matched the lower stimulus’s semantic meaning. Participants responded using the keyboard (V or N), and the response-key assignment for “same” and “different” judgement was counterbalanced across participants.
The task comprised three conditions: neutral, congruent, and incongruent. In all trials, the upper stimulus appeared in red, blue, yellow, or green, while the lower stimulus was presented in black as one of the four corresponding Korean color words: “빨강”(red), “파랑”(blue), “노랑”(yellow), or “초록”(green). In the neutral condition, the upper stimulus was presented as “XXXX” in colored ink. In the congruent condition, the semantic meaning and ink color were identical (e.g., the word “red” displayed in red). In the incongruent condition, the semantic meaning and ink color of the upper word were mismatched (e.g., the word “blue” displayed in red). The lower word either matched or did not match the ink color, and the participants were required to make a same or different judgment as quickly and accurately as possible.
The task was administered in an event-related design (PsychoPy v2021.1.4) with the three conditions intermixed trial-by-trial, and stimulus parameters followed a previous study from our group on middle-aged Korean women [31]. Although the established protocol for this paradigm typically comprises ten trials per condition [32], each condition was presented 12 times, resulting in a total of 36 trials. The upper stimulus was presented 100 ms before the lower stimulus, and each trial remained on the screen for up to 2 s or until a response was made. Before the task, participants completed practice trials and proceeded to the test only after achieving an accuracy of at least 80%.
The reaction time and error rate were recorded for all trials. Reaction time analyses were performed using correct-response trials only. Stroop interference was calculated as the difference in the mean reaction time between the incongruent and neutral conditions. To account for speed-accuracy trade-off, the inverse efficiency score (IES) was also calculated by dividing the mean reaction time by the proportion of correct responses; lower IES values indicated better performance [33,34].
Mnemonic discrimination task
A computerized MDT was administered to assess hippocampal-dependent pattern separation and mnemonic discrimination [28]. The present study used both the object version of MDT (MDTO) and the spatial version of MDT (MDTS) [35].
During the study phase, participants viewed sequentially presented object stimuli and performed indoor/outdoor classifications using a keyboard. In MDTO, each object was presented at the center of the screen. In the MDTS task, each object was presented at a location on the screen. During the test phase of MDTO, participants judged whether each presented object was the same or different from an object presented during the study phase. During the MDTS test phase, the participants judged whether the location of the same object was the same or different from its location during the study phase. In both MDTO and MDTS, each trial was presented for up to 3 s, with an inter-stimulus interval of 0.5 s. Keyboard responses and reaction times were recorded for each trial.
The MDTO test phase comprised 160 test trials, including 40 repeat target trials, 40 novel foil new trials, and 80 perceptually similar lure trials. The lure trials included 40 high- and 40 low-similarity lures. The MDTS test phase comprised 160 trials: 40 same-location repeat trials and 120 different-location trials, including small-, large-, and corner-shift trials.
The primary MDT outcomes were lure discrimination index (LDI) and recognition score (REC) [35,36]. LDI was calculated as a bias-corrected index of mnemonic discrimination by subtracting the proportion of incorrect ‘different’ responses to repeat target trials from the proportion of correct ‘different’ responses to lure or shifted-location trials. For MDTO, LDI was calculated as p[“different” | lures] - p[“different” | targets], and the LDI values from high- and low-similarity lure conditions were averaged to obtain the MDTO LDI. For MDTS LDI, LDI was calculated as p[“different” | shifted-location trials] - p[“different” | same-location trials], and the LDI values from small- and large-shift trials were averaged to obtain the representative MDTS LDI. Corner-shift trials were not included as representative MDTS LDI.
REC was calculated as the hit rate minus the false-alarm rate. In MDTO, “same” responses to repeat target trials were defined as hits, and “same” responses to novel foil trials were defined as false alarms [35]. In MDTS, “same” responses to same-location repeated trials were defined as hits, and “same” responses to different-location trials were defined as false alarms. Higher LDI and REC values indicated better mnemonic discrimination and recognition performance, respectively.
Physical Fitness
Grip strength and flexibility
Grip strength was measured using a digital hand dynamometer (Baseline Hydraulic Hand Dynamometer, USA). The participants stood upright with their arm fully extended downward and held at approximately 15° from the trunk without contacting the body. Grip strength was measured twice for each hand in alternating order, and the mean of the four measurements was used as the representative value. Bilateral measurements were performed in line with recommendations for grip strength assessment in older adults [37], with repeated measurements used to reduce measurement error [38].
Flexibility was assessed using the sit-and-reach test. Participants sat with knees fully extended and feet against the box, then reached forward slowly with both hands overlapped. The mean of two trials was used for analysis.
Cardiorespiratory fitness
Aerobic capacity (VO2peak) was assessed during a graded exercise test using an electronically braked recumbent cycle ergometer (Corival Recumbent CPET; Lode B.V., Groningen, Netherlands), and respiratory gas exchange was measured using a metabolic analyzer (Quark CPET metabolic cart; COSMED, Rome, Italy). After a 3-min warm-up at 0 W, the workload was increased by 10 W/min, and the participants maintained a cadence of 60 rpm until voluntary exhaustion or maximal effort was achieved [39]. Exercise intensity was monitored using the Borg rating of perceived exertion (RPE) 6-20 scale. Exhaustion was defined as a decrease in cadence of at least 10 rpm sustained for 5 consecutive seconds despite verbal encouragement. Achievement of maximal effort was confirmed using a respiratory exchange ratio ≥ 1.10 or attainment of at least 85% of age-predicted maximal heart rate. After the termination of the test, the participants completed a 3-min cool-down at 0 W.
Statistical Analysis
Continuous variables are presented as mean ± standard deviation (SD). Analyses were performed using SPSS for Windows (version 28.0; IBM SPSS Statistics, IBM Corp., USA). Normality was assessed using the Shapiro-Wilk test. Associations among adiposity indices (BFM, BFP, FMI, FMR, and A/G ratio), physical fitness factors (VO2peak, grip strength, and flexibility), and cognitive function outcomes were examined using correlation analyses. Pearson correlation coefficients were used for normally distributed variables, and Spearman correlation coefficients were used for variables that did not satisfy normality.
Hierarchical multiple linear regression examined the independent associations of adiposity and fitness factors with MDTS LDI, which was significantly correlated with both domains. MDTS LDI was used as the dependent variable. In Model 1, age was used as a covariate. In Model 2, FMI, as an index of adiposity, and VO2peak, as an indicator of cardiorespiratory fitness, were additionally included. In Model 3, flexibility, which showed a significant association with MDTS LDI in simple correlation analysis, was added to examine its additional explanatory power.
Variables were selected using a pre-specified, correlation-guided strategy; only variables significantly correlated with MDTS LDI were eligible for entry, and the number of predictors was kept small given the analytic sample size. A/G ratio and grip strength, which did not correlate with cognitive outcomes, were not assessed. Among the adiposity indices, only BFM and FMI were correlated with MDTS LDI. Because these indices capture overlapping information on fat mass, height-adjusted FMI was retained as the single representative adiposity variable. The final model included age, FMI, VO2peak, and flexibility.
Several confounders were controlled by design rather than by statistical adjustment; women receiving hormone therapy, current smokers, and those with psychiatric or cognitive diseases were excluded at screening and, therefore, did not vary within this sample. Changes in explanatory power at each step were evaluated using the change in R2 (ΔR2) and the F-change statistic. Multicollinearity among independent variables was assessed using the variance inflation factor (VIF). The level of statistical significance was set at p < 0.05. Missing data were confined to VO2peak (n = 7) and flexibility (n = 1). The participant with missing flexibility data also lacked VO2peak; thus, all seven exclusions were attributable to missing VO2peak. Age, FMI, and MDTS LDI were complete for all 68 participants. These values were missing because of an equipment malfunction on the scheduled testing day, which is a technical failure unrelated to the participant characteristics. Listwise deletion yielded a final analytic sample of 61. Depressive symptoms (BDI), sleep quality (PSQI), blood pressure, glycemic status (fasting glucose, HbA1c), global cognitive status (MoCA-K), and time since menopause were also measured and examined independently and jointly in sensitivity analyses, with each variable added separately to the final regression model.
RESULTS
Participant Characteristics
The mean age, mean height, body weight, and BMI of the 68 postmenopausal women were 57.26 ± 5.24 years, 158.25 ± 5.62 cm, 58.09 ± 7.66 kg, and 23.20 ± 2.90 kg/m2, respectively. In terms of body composition, lean mass, fat mass, fat percentage, FMI, and FMR were 35.23 ± 4.18 kg, 20.78 ± 4.39 kg, 36.82 ± 4.18%, 8.32 ± 1.83 kg/m2, and 0.59 ± 0.11, respectively. Cognitive function, physical fitness, and blood variables are presented in Table 1.
Table 1.
Characteristics of participants (n = 68)
| Variables | Value |
|---|---|
| Age (year) | 57.26 ± 5.24 |
| Body composition | |
| Height (cm) | 158.25 ± 5.62 |
| Weight (kg) | 58.09 ± 7.66 |
| BMI (kg/m2) | 23.20 ± 2.90 |
| Lean mass (kg) | 35.23 ± 4.18 |
| BFM (kg) | 20.78 ± 4.39 |
| BFP (%) | 36.82 ± 4.18 |
| FMI (kg/m2) | 8.32 ± 1.83 |
| FMR | 0.59 ± 0.11 |
| A/G ratio | 1.07 ± 0.18 |
| Cognitive function | |
| Stroop task | |
| Neutral RT (s) | 1.10 ± 0.20 |
| Neutral ACC (%) | 95.26 ± 7.21 |
| Neutral ER (%) | 6.19 ± 13.74 |
| Neutral IES (s) | 1.16 ± 0.23 |
| Incongruent RT (s) | 1.33 ± 0.26 |
| Incongruent ACC (%) | 70.26 ± 18.16 |
| Incongruent ER (%) | 30.81 ± 19.99 |
| Incongruent IES (s) | 2.08 ± 0.95 |
| MDT | |
| MDTO LDI | 0.34 ± 0.15 |
| MDTO REC | 0.79 ± 0.22 |
| MDTS LDI | 0.28 ± 0.14 |
| MDTS REC | 0.40 ± 0.17 |
| Physical fitness | |
| Grip strength (kg) | 19.41 ± 4.56 |
| Flexibility (cm) | 13.11 ± 9.32 |
| VO2peak (mL/kg/min) | 20.77 ± 3.51 |
| Lipid profile | |
| TC (mg/dL) | 211.42 ± 44.29 |
| HDL-C (mg/dL) | 59.62 ± 17.76 |
| LDL-C (mg/dL) | 123.76 ± 41.51 |
| TG (mg/dL) | 130.91 ± 73.50 |
| FG (mg/dL) | 102.50 ± 17.68 |
| HbA1c (%) | 5.72 ± 0.60 |
Values are presented as mean ± SD. Owing to equipment malfunction, data were available for 61 participants for peak oxygen uptake and for 67 participants for flexibility; all other variables were available for all 68 participants. A/G ratio, android-to-gynoid fat ratio; ACC, accuracy; BFM, body fat mass; BFP, body fat percentage; BMI, body mass index; ER, error rate; FG, fasting glucose; FMI, fat mass index; FMR, fat-to-lean mass ratio; HbA1c, glycated hemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; IES, inverse efficiency score; LDI, lure discrimination index; LDL-C, low-density lipoprotein cholesterol; MDT, Mnemonic Discrimination Task; MDTO, object version of the Mnemonic Discrimination Task; MDTS, spatial version of the Mnemonic Discrimination Task; REC, recognition; RT, reaction time; TC, total cholesterol; TG, triglycerides.
Correlations among Variables
The correlations of all adiposity indices and physical fitness factors with each cognitive outcome are summarized in Figure 3.
Figure 3. Correlations of adiposity and physical fitness with domain-specific cognitive function.
Each cell shows the correlation coefficient between an adiposity index or physical fitness factor (columns) and a cognitive outcome (rows); warmer colors indicate positive and cooler colors negative coefficients. Pearson coefficients were used when both variables satisfied normality and Spearman coefficients otherwise. A/G ratio, android-to-gynoid fat ratio; BFM, body fat mass; BFP, body fat percentage; FMI, fat mass index; FMR, fat-to-lean mass ratio; IES, inverse efficiency score; LDI, lure discrimination index; MDTO, object version of the Mnemonic Discrimination Task; MDTS, spatial version of the Mnemonic Discrimination Task; REC, recognition; RT, reaction time; VO2peak, peak oxygen uptake.
Associations between Adiposity Indices and Cognitive Function
No significant correlations were observed between adiposity indices (BFM, BFP, FMI, FMR, and A/G ratio) and Stroop task outcomes (p > 0.05; Table 2, Figure 3).
Table 2.
Correlations between adiposity indices and cognitive function (n = 68)
| Variables | BFM | BFP | FMI | FMR | A/G ratio |
|---|---|---|---|---|---|
| Stroop task | |||||
| Stroop interference RT (s) | -0.054 | -0.006 | -0.025 | -0.005 | -0.066 |
| Stroop interference IES (s) | 0.103 | 0.199 | 0.186 | 0.186 | 0.040 |
| MDT | |||||
| MDTO LDI | -0.080 | 0.003 | -0.038 | 0.002 | -0.097 |
| MDTO REC | -0.249* | -0.232 | -0.252* | -0.231 | -0.106 |
| MDTS LDI | -0.319** | -0.233 | -0.294* | -0.222 | -0.207 |
| MDTS REC | -0.244* | -0.121 | -0.178 | -0.115 | -0.150 |
p < 0.05,
p < 0.01.
A/G ratio, android-to-gynoid fat ratio; BFM, body fat mass; BFP, body fat percentage; FMI, fat mass index; FMR, fat-to-lean mass ratio (fat mass/lean mass); IES, inverse efficiency score; LDI, lure discrimination index; MDT, Mnemonic Discrimination Task; MDTO, object version of the Mnemonic Discrimination Task; MDTS, spatial version of the Mnemonic Discrimination Task; REC, recognition; RT, reaction time.
In contrast, significant negative correlations were observed between adiposity indices and selected MDT outcomes. MDTO REC was negatively correlated with BFM (r = -0.249, p < 0.05) and FMI (r = -0.252, p < 0.05). MDTS LDI was negatively correlated with BFM (r = -0.319, p < 0.01) and FMI (r = -0.294, p < 0.05). In addition, MDTS REC showed a significant negative correlation with BFM (r = -0.244, p < 0.05) (Table 2, Figure 3).
Associations between Physical Fitness and Cognitive Function
MDTS LDI showed significant positive correlations with flexibility (r = 0.308, p < 0.05) and VO2peak (r = 0.334, p < 0.01). No other significant correlations were observed between fitness factors and cognitive outcomes (Table 3, Figure 3).
Table 3.
Correlations between physical fitness and cognitive function
| Variables | Flexibility | Grip strength | VO2peak |
|---|---|---|---|
| Stroop task | |||
| Stroop interference RT (s) | 0.032 | -0.109 | 0.026 |
| Stroop interference IES (s) | 0.019 | -0.070 | 0.163 |
| MDT | |||
| MDTO LDI | 0.200 | -0.059 | 0.045 |
| MDTO REC | -0.087 | -0.196 | -0.005 |
| MDTS LDI | 0.308* | -0.023 | 0.334** |
| MDTS REC | 0.239 | -0.132 | 0.205 |
p < 0.05,
p < 0.01.
IES, inverse efficiency score; LDI, lure discrimination index; MDT, Mnemonic Discrimination Task; MDTO, object version of the Mnemonic Discrimination Task; MDTS, spatial version of the Mnemonic Discrimination Task; REC, recognition; RT, reaction time; VO2peak, peak oxygen uptake.
Hierarchical Multiple Linear Regression
Hierarchical multiple linear regression examined the independent associations of adiposity and physical fitness factors with MDTS LDI (n = 61). Model 1, which included age only, was not significant (F(1, 59) = 3.584, p = 0.063, adjusted R2 = 0.041). Model 2, in which FMI and VO2peak were also included, was statistically significant (F(3, 57) = 3.890, p = 0.013, adjusted R2 = 0.126), and the increase in explanatory power compared with Model 1 was significant (ΔR2 = 0.113, ΔF(2, 57) = 3.869, p = 0.027). In Model 2, VO2peak showed a significant positive association with MDTS LDI (β = 0.261, p = 0.049), whereas FMI (β = -0.142, p = 0.284) and age (β = -0.185, p = 0.137) were not significantly associated with MDTS LDI.
Model 3, in which flexibility was added, was also statistically significant (F(4, 56) = 3.430, p = 0.014, adjusted R2 = 0.139). However, the additional increase in explanatory power was not statistically significant (ΔR2 = 0.027, ΔF(1, 56) = 1.871, p = 0.177). In Model 3, flexibility was not significantly associated with MDTS LDI (β = 0.172, p = 0.177), and the association between VO2peak and MDTS LDI observed in Model 2 was no longer significant after adjustment for flexibility (β = 0.207, p = 0.131). All VIF values were 1.270 or lower, indicating no evidence of multicollinearity. The Durbin-Watson statistic was 2.040, indicating that the assumption of independence of residuals was satisfied (Table 4). In sensitivity analyses, adding BDI (9.45 ± 6.58), PSQI (7.03 ± 3.26), systolic blood pressure (113.10 ± 14.46 mmHg), fasting glucose, HbA1c, MoCA-K (25.85 ± 2.50), or time since menopause (6.93 ± 5.01 years) independently to Model 3 did not materially change the results, as none was significantly associated with MDTS LDI (all p ≥ 0.105; ΔR² ≤ 0.038), and the coefficients for FMI, VO2peak, and flexibility were essentially unchanged. Entering BDI, PSQI, systolic blood pressure, and HbA1c levels simultaneously (adjusted R² = 0.153, model p = 0.034) did not alter this pattern (all p > 0.20).
Table 4.
Hierarchical multiple linear regression predicting MDTS LDI
| Model 1 |
Model 2 |
Model 3 |
||||
|---|---|---|---|---|---|---|
| Predictor | β | p | β | p | β | p |
| Age | -0.239 | 0.063 | -0.185 | 0.137 | -0.196 | 0.113 |
| FMI | -0.142 | 0.284 | -0.152 | 0.249 | ||
| VO2peak | 0.261 | 0.049* | 0.207 | 0.131 | ||
| Flexibility | 0.172 | 0.177 | ||||
| Adjusted R2 | 0.041 | 0.126 | 0.139 | |||
| ΔR2 | — | 0.113 | 0.027 | |||
| p for ΔR2 | — | 0.027* | 0.177 | |||
Model 1: age. Model 2: age + FMI + VO2peak. Model 3: age + FMI + VO2peak + flexibility. All VIF < 1.270; Durbin-Watson = 2.040.
p < 0.05.
β, standardized coefficient; ΔR2, R2 change; FMI, fat mass index; LDI, lure discrimination index; MDTS, spatial version of the Mnemonic Discrimination Task; VIF, variance inflation factor; VO2peak, peak oxygen uptake.
DISCUSSION
This study integrated adiposity indices, physical fitness factors, and domain-specific cognitive functions in postmenopausal women. Adiposity indices were not significantly associated with Stroop task performance but were negatively correlated with selected MDT outcomes. MDTS LDI was positively correlated with VO2peak and flexibility. In hierarchical regression, the model incorporating FMI and VO2peak after adjustment for age significantly explained variance in MDTS LDI, with VO2peak showing a significant positive association at this stage. However, in the extended model incorporating flexibility, the additional explanatory power of flexibility was not significant, and neither VO-2peak nor flexibility remained independently associated with MDTS LDI in the final model. These findings suggest simple associations between adiposity, fitness, and hippocampus-dependent mnemonic discrimination; however, the independent contributions of specific fitness components should be interpreted with caution.
Menopause-related changes in hormonal and body composition profiles may influence both metabolic and brain health [7,40-42]. Thus, examining adiposity-cognition associations may provide insight into the links between postmenopausal physiological changes and brain function.
In the present study, adiposity indices were not significantly associated with Stroop task performance but were selectively associated with some MDT outcomes, particularly MDTS LDI. This finding may be interpreted in context of the distinct cognitive processes and neural bases reflected by the Stroop task and the MDT. The Stroop task assesses the ability to inhibit color-word interference and select a target response, and is primarily related to prefrontal executive function and cognitive control [29,43]. However, previous studies have reported inconsistent associations between obesity or adiposity and executive function depending on the task type and participant characteristics [44]. In contrast, MDT more sensitively reflects hippocampus-dependent pattern separation and mnemonic discrimination by assessing the ability to distinguish subtle differences between similar stimuli [26,28,35,36]. Thus, the selective association observed in MDT, but not in Stroop task outcomes, suggests that the relationship between adiposity and cognitive function may not be uniform across all cognitive domains, but may be more apparent in specific domains, such as hippocampus-dependent mnemonic discrimination.
The significant negative correlations between MDTS LDI, BFM, and FMI suggest that greater absolute fat mass and height-adjusted fat mass may be associated with poorer hippocampus-dependent mnemonic discrimination. Pathways proposed to link adiposity to hippocampal integrity, including obesity-related trajectories of brain volume change and insulin resistance-related reductions in hippocampal volume [45,46], provide a potential context. However, none of these intermediate variables was assessed in the present study, and glycemic status, the only metabolic proxy available, did not alter the regression findings. Because the A/G ratio was not significantly associated with MDT outcomes, the present findings should not be overinterpreted as reflecting central fat distribution; they are better interpreted as exploratory evidence of an association between general adiposity indices and MDTS LDI.
Both VO2peak and flexibility were positively correlated with MDTS LDI; cardiorespiratory fitness was a significant predictor of MDTS LDI after adjustment for age and FMI (Model 2). This association did not persist once flexibility was added in Model 3, and no individual predictor was independently associated with MDTS LDI in the final model. Therefore, the observed relationship is a simple association, as the present data do not establish that this association is independent of other fitness components. This is consistent with previous reports linking higher cardiorespiratory fitness to better memory and hippocampus-dependent performance [13,18,19], and that higher levels of midlife fitness are associated with a lower dementia risk [14,47]. Experimental evidence that aerobic exercise can increase hippocampal volume [21] and that acute moderate exercise can improve mnemonic discrimination [48] suggests pathways through which such a relationship might arise, although none was assessed here. Thus, these results are better understood to indicate that overall fitness status is associated with mnemonic discrimination rather than as evidence of a specific contribution of cardiorespiratory fitness.
Flexibility, assessed using the sit-and-reach test, primarily reflects hamstring and lower back mobility [49,50]. Because mind-body and multicomponent programs, such as yoga and Tai Chi, train flexibility together with balance, muscular strength, postural control, and attention, the cognitive benefits reported for such interventions cannot be attributed to flexibility itself [51,52], and the same caveat applies to the cross-sectional association observed here. Consistent with this, the additional explanatory power of flexibility was not significant in Model 3, and neither flexibility nor VO2peak remained independently associated with MDTS LDI. Flexibility is, therefore, best interpreted as an auxiliary marker of general physical function, physical activity participation, and health status, rather than as an independent determinant of hippocampus-dependent mnemonic discrimination.
The attenuation of the VO2peak association following the addition of flexibility is unlikely to reflect severe multicollinearity, as all VIF values in the final model were in the 1-point range; it may more plausibly reflect the limited sample size and the shared explanatory variance among physiologically correlated fitness components.
This study had few limitations. First, the design was cross-sectional and exploratory, and multiple correlation analyses were conducted in a relatively small sample. Therefore, temporal or causal relationships among adiposity, physical fitness, and cognitive function could not be established. Although the final regression model was statistically significant overall, no individual predictor showed an independent association, plausibly reflecting shared explanatory variance among physiologically correlated fitness components and limited statistical power. Accordingly, the findings should be regarded as hypothesis-generating. Second, none of the mechanisms invoked to interpret these associations—chronic low-grade inflammation, insulin resistance, vascular dysfunction, altered hippocampal volume, and estrogen-related changes in dentate gyrus function—was directly assessed. Apart from glycemic status, no intermediate variables were assessed; therefore, these pathways serve as context rather than as tested mechanisms. Third, several measures were limited in scope. Flexibility was assessed using only the sitand-reach test; other components of flexibility and physical function, such as balance and coordination, were not examined. The Stroop task comprised 12 trials per condition (36 in total), consistent with the established event-related protocol [32] and sufficient to reproduce the interference effect at the group level. However, a trial count of this magnitude limits the precision of individual-level Stroop indices and may partly explain the absence of significant associations with Stroop performance. Fat distribution was indexed by the android-to-gynoid ratio, which does not separate visceral from subcutaneous abdominal fat because this ratio was unrelated to any cognitive outcome. However, BFM and FMI were interpreted as relating to overall adiposity rather than to fat distribution; imaging-based assessment of abdominal fat compartments is required to determine whether these associations lead to regional adiposity. Fourth, although depressive symptoms, sleep quality, blood pressure, glycemic status, global cognitive status, and time since menopause were measured and did not alter the findings in sensitivity analyses, educational attainment and habitual physical activity were not systematically assessed. Because VO2peak is strongly influenced by regular exercise participation, the association between cardiorespiratory fitness and MDTS LDI may partly reflect activity behavior. Such confounding would most plausibly inflate rather than mask the observed association, consistent with the cautious interpretation adopted here. Future studies should include education as a covariate and device-based assessment of habitual physical activity and sedentary time. Larger samples, longitudinal designs, and exercise interventions are needed to determine the independent contributions of cardiorespiratory fitness, flexibility, muscular strength, and balance.
Overall, this study examined the associations between adiposity indices, physical fitness factors, and domain-specific cognitive function in postmenopausal women. Adiposity indices were not significantly associated with Stroop task performance but were negatively associated with selected MDT outcomes, particularly MDTS LDI. MDTS LDI was also positively correlated with VO2peak and flexibility. In the hierarchical regression analysis, the model incorporating FMI and VO2peak after adjustment for age explained MDTS LDI, and VO2peak showed a significant association at this stage. However, this association was observed only in the intermediate model and did not persist in the final model, wherein no individual predictor was independently associated with MDTS LDI. These findings suggest that hippocampus-dependent mnemonic discrimination in postmenopausal women may be associated with adiposity and overall fitness status at simpler levels; however, additional research is needed to confirm the independent effects of specific fitness components.
Footnotes
ACKNOWLEDGMENTS
This study was supported by the Ministry of Education of the Republic of Korea and National Research Foundation of Korea (grant number NRF-2023S1A5A2A21084054).
DISCLOSURE OF CONFLICTS OF INTEREST
The authors declare that they have no conflict of interest.
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