Next Article in Journal
Semantic Clustering during Verbal Episodic Memory Encoding and Retrieval in Older Adults: One Cognitive Mechanism of Superaging
Next Article in Special Issue
Cognitive, Emotional, and Daily Functioning Domains Involved in Decision-Making among Patients with Mild Cognitive Impairment: A Systematic Review
Previous Article in Journal
Probing the Bottleneck of Awareness Formed by Foveal Crowding: A Neurophysiological Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Brain Sciences
Volume 14
Issue 2
10.3390/brainsci14020170
brainsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Sex-Specific Association of Body Mass Index with Hippocampal Subfield Volume and Cognitive Function in Non-Demented Chinese Older Adults

by
Shaohui Lin
1,2,†,
Lijuan Jiang
1,†,
Kai Wei
3,4,
Junjie Yang
1,
Xinyi Cao
1,5,* and
Chunbo Li
1,6,7,*
1
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
2
Department of Geriatrics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
3
Department of Traditional Chinese Medicine, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 201108, China
4
Shanghai Institute of Traditional Chinese Medicine for Mental Health, Shanghai 201108, China
5
Clinical Neurocognitive Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
6
Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, China
7
CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai 200030, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Brain Sci. 2024, 14(2), 170; https://doi.org/10.3390/brainsci14020170
Submission received: 2 January 2024 / Revised: 28 January 2024 / Accepted: 1 February 2024 / Published: 8 February 2024
(This article belongs to the Special Issue Advances in Research on Brain Health and Dementia: Prevention and Early Detection of Cognitive Decline and Dementia: Series II)
Browse Figures
Versions Notes

Abstract

:
Recent research suggests a possible association between midlife obesity and an increased risk of dementia in later life. However, the underlying mechanisms remain unclear. Little is known about the relationship between body mass index (BMI) and hippocampal subfield atrophy. In this study, we aimed to explore the associations between BMI and hippocampal subfield volumes and cognitive function in non-demented Chinese older adults. Hippocampal volumes were assessed using structural magnetic resonance imaging. Cognitive function was evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). A total of 66 participants were included in the final analysis, with 35 females and 31 males. We observed a significant correlation between BMI and the hippocampal fissure volume in older females. In addition, there was a negative association between BMI and the RBANS total scale score, the coding score, and the story recall score, whereas no significant correlations were observed in older males. In conclusion, our findings revealed sex-specific associations between BMI and hippocampal subfield volumes and cognitive performance, providing valuable insights into the development of effective interventions for the early prevention of cognitive decline.

1. Introduction

With an increasingly aging population, dementia and cognitive decline have emerged as major public health concerns around the world, magnifying the resulting economic burden on individuals, families, and society [1]. However, there are currently no effective treatments available for dementia. Therefore, there is an urgent need to identify risk factors and investigate effective interventions that could potentially facilitate early prevention of cognitive decline.
In recent years, an increasing number of studies have linked obesity to cognitive decline, particularly in relation to Alzheimer’s disease (AD) [2,3,4,5,6,7]. Notably, a recent study identified midlife obesity as one of the primary modifiable factors strongly correlated with dementia [8]. Maintaining an overweight status (body mass index (BMI) ≥ 25 kg/m2) during midlife was found to increase the risk of cognitive impairment and dementia in later life [9]. Conversely, being overweight in the later stages of life may be associated with a reduced risk of dementia [8,10]. Nevertheless, the precise mechanisms underlying these associations remain unclear [11].
Brain imaging plays a crucial role in examining the mechanisms underlying structural and functional brain abnormalities that occur with aging and dementia. Recently, magnetic resonance imaging (MRI) has become a popular and rapidly advancing tool for investigating the neurobiology underlying variations in cognitive performance related to BMI in humans [12]. Several small-scale studies have found connections between BMI and changes in brain structure [13,14,15,16,17,18,19,20], including, specifically, alterations in the hippocampus [21]. However, there is less consistency in the associations between obesity and subcortical abnormalities [22,23,24,25,26], as well as variations in hippocampal changes across different studies [27,28]. Therefore, further research is needed to better understand the associations between BMI and subcortical regions, with a particular focus on the hippocampus.
During the aging process, the hippocampus is more vulnerable to atrophy than other brain regions [29]. Preserved cognitive function in older individuals has been associated with increased hippocampal volume, even in the presence of substantial AD pathology [30]. However, examining general morphometric changes in the whole hippocampus may not sufficiently elucidate specific neurological deterioration. It has been observed that hippocampal atrophy is more prevalent in certain subfields rather than in the total hippocampal volume, which may be indicative of future cognitive decline [30,31]. The hippocampus is a heterogeneous structure encompassing several interconnected and functionally specialized subfields. These include cornu ammonis (CA) areas 1–4, the granule cell layer of the dentate gyrus (GC-DG), the molecular layer, the subiculum, the fimbria, the hippocampal tail, and the hippocampal fissure. The hippocampal fissure serves as an interstitial space between the dentate gyrus and the subiculum, and its enlargement indicates early stages of hippocampal and medial temporal lobe atrophy [32,33]. Previous studies have demonstrated associations between hippocampal subfield volumes and cognitive function in older adults [34,35]. However, little is known about the correlations between BMI and hippocampal subfield atrophy in Chinese older adults.
Moreover, there are notable disparities in brain structure and cognitive function between males and females. Estrogen has significant and protective effects on cerebrovascular function [36]. Accelerated aging of the ovaries after menopause can cause metabolic dysfunction and cognitive deficits in female mice [37]. Gaining insights into the sex-specific behavior of brain structures during the normal aging process is crucial for tailoring individualized treatments [38]. However, little attention has been paid to the investigation of sex differences in hippocampal subfields in older adults. Ystad et al. [39] demonstrated that hippocampal volumes could serve as predictors of cognitive performance in older females. However, Pruessner et al. [40] and Li et al. [41] reported that age-related hippocampal atrophy was only observed in males. Therefore, further studies are needed to investigate the sex-specific associations between BMI, hippocampal subfield volumes, and cognitive function.
In the present study, we focused on the specific age group of 65–75 years old and used structural MRI to investigate the relationship between BMI and changes in hippocampal subfields, with the aim of revealing the associated sex differences in this relationship in non-demented, community-dwelling older adults.

2. Methods

2.1. Study Design and Participants

This study was approved by the human research ethics committees of Shanghai Mental Health Center (approval number: 2013-40, approval date: 12 June 2014) and Tongji Hospital, Shanghai (approval number: LL (H)-09-04, approval date: 20 February 2009), China. Participants were community-dwelling older adults who lived in neighborhoods located in the Jingan and Putuo Districts of Shanghai. They were recruited through posters, dispatched notices, and broadcasting by local neighborhood committees or service centers. Before participation, written informed consent was obtained from all participants. Inclusion criteria were as follows: individuals aged 65–75 years with more than one year of formal education living independently in the community; no disabilities; no visual, hearing, or communication impairments; no severe psychotic disorders or physical illnesses; and achieving a score of 19 or higher on the Chinese version of the Mini-Mental State Examination (CMMSE) for primary education, or a score of 24 or higher for middle school education and above. It is important to note that the standard cut-off point for CMMSE scores is lower in China due to the comparatively lower level of education [42]. Exclusion criteria included severe cognitive decline; diagnosis of Alzheimer’s disease; major neurological and/or psychiatric disorders, such as brain cancer, cerebral infarction, cerebral hemorrhage, malnutrition, major depressive disorder, or schizophrenia; and history of brain trauma or surgery. BMI was determined based on height and weight records, calculated as weight in kilograms divided by height in meters squared (kg/m2).

2.2. Cognitive Measurement

Cognitive function was assessed in each participant using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) [43], which has demonstrated good validity and reliability in Chinese community-dwelling older adults [44]. The RBANS consists of 12 subtests that produce 5 index scores for the following cognitive domains: immediate memory (list learning and story memory), visuospatial/constructional (figure copying and line orientation), language (picture naming and semantic fluency), attention (digit span and coding), and delayed memory (list recall, list recognition, story recall, and figure recall), in addition to an overall cognitive function score. Global cognition at enrollment was assessed using the Chinese version of the Mini-Mental State Examination (CMMSE), which has been shown to have good validity and reliability in the Chinese population [45]. All cognitive assessments were administered by trained research assistants following the protocol described in the manual.

2.3. MRI Acquisition

All participants underwent imaging on a 3.0 Tesla scanner (Siemens Medical, Erlangen, Germany) with a standard 12-channel head coil using a consistent structural scanning protocol. High-resolution T1-weighted imaging was performed in the sagittal plane using a fast, three-dimensional, gradient-spoiled gradient echo sequence with the following parameters: repetition time, 1900 ms; echo time, 3.43 ms; flip angle, 9°; matrix size, 256 × 256; field of view, 240 × 240 mm2; slice thickness, 1 mm; voxel size, 0.9 × 0.9 × 1.0 mm3; and 160 slices. Each scan took 5 min. Images were reconstructed and visually inspected for major artifacts, including motion, ringing, wrap-around, and neurological abnormalities, before further processing. MRI data processing was performed by a research technician who was blinded to all clinical information.

2.4. Image Preprocessing

The entire hippocampal formation was segmented using the standard FreeSurfer segmentation procedure in the FreeSurfer 6.0 image analysis suite (http://surfer.nmr.mgh.harvard.edu/, accessed on 23 January 2017) [46]. The processing flow included the following steps: motion correction averaging of the two T1-weighted volumes, removal of non-brain tissue using a hybrid watershed/surface deformation procedure [47], automated transformation into the Talairach reference space, and segmentation of subcortical white matter and gray matter structures using a probabilistic brain atlas [46]. In addition, hippocampal subfields were automatically segmented using FreeSurfer 6.0. Following the previous methodology [48], the following 13 subfield volumes were calculated on each side of the hippocampus: CA1, CA2/3, CA4, GC-DG, fimbria, subiculum, pre-subiculum, para-subiculum, molecular layer, hippocampal amygdala transition area (HATA), hippocampal tail, hippocampal fissure, and the whole hippocampus. The segmentation results were visually inspected for errors in all datasets without manual editing. The volumes of the hippocampal subfields and the whole hippocampus are the sum of the left and right hippocampi. To account for differences in head size, we calculated the estimated total intracranial volume (eTIV) to adjust the subfield volumes in the subsequent statistical analysis.

2.5. Statistical Analyses

All statistical analyses were performed using IBM SPSS Statistics version 22 (IBM Corporation, Somers, NY, USA). The statistical significance level was set at p < 0.05. Categorical and continuous variables are presented as numbers and means (standard deviations), respectively. The Mann–Whitney test and Student’s t-test were used to compare variables, such as age, education, and CMMSE score, between females and males. Partial correlation analysis was used to examine the potential association between BMI, hippocampal subfield volumes, and cognitive performance. Additionally, we stratified the total sample by sex and repeated the above models in males and females separately. We then used multivariate linear regression models to further examine the significant associations between BMI and hippocampal subfield volumes and cognitive scores, adjusting for confounders, such as age, sex, and education. Regression coefficients with 95% confidence intervals (β (95% CI)) and the adjusted model fit (adjusted R2) were calculated. False discovery rate (FDR)-corrected p-values were calculated to account for multiple comparisons, as described in previous studies [49].

3. Results

3.1. Demographic and Clinical Characteristics of Participants

The participant enrollment flowcharts are presented in Figure 1. Out of the initial 539 individuals contacted for participation between November 2013 and September 2014, a total of 175 individuals were included in the MRI subsample. Of these, 103 participants were excluded for a variety of reasons; of these, 79 did not provide height and weight measurements, 9 had metal implants, 2 reported claustrophobia, 9 declined the MRI scan, and 4 had scheduling conflicts. As a result, a final sample of 72 participants met the eligibility criteria and completed the MRI scans. However, a further 6 participants had to be excluded due to head movement (n = 2) and abnormal scan findings (n = 4). Thus, after excluding a total of 109 individuals, the final analysis included 66 participants in the present study (Figure 1).
The demographic and clinical characteristics of the participants are presented in Table 1. Of the 66 individuals included in this study, 35 were female and 31 were male. The participants had a mean age of 68.96 (3.04) years and a mean CMMSE score of 28.26 (1.34) points. The mean BMI of the entire sample was 23.44 (3.291), ranging from 16.98 to 32.28. There were no significant differences observed between females and males in terms of age, years of education, CMMSE score, or RBANS total scale score. However, it is important to note that the eTIV volumes were significantly lower in females compared to males (Table 1).

3.2. Association between BMI and Hippocampal Subfield Volumes

The associations between BMI and volumes of hippocampal subfields were examined through partial correlation analysis and multivariate linear regression models. The hippocampal segmentations are shown in Figure 2. The statistical results of volumetric differences between the females and males are presented in Figure 3.
As shown in Table 2, when adjusting for age, sex, education, and eTIV, BMI displayed a significant relationship with a specific subfield, the hippocampal fissure (r = 0.261, p = 0.040) (Figure 4A), in the overall sample. Given the previously reported sex differences in hippocampal structures [48,50,51], we further stratified the analysis by sex. Interestingly, the association between BMI and hippocampal fissure was only significant in older females (r = 0.404, p = 0.022) (Figure 4B), while it was not significant in males (r = 0.164, p = 0.405) (Figure 4C). To further investigate this significant relationship, we employed multivariate linear regression models in females, both unadjusted and adjusted for potential confounders. Supplementary Table S1 presents the results, indicating that after adjusting for age, sex, education, and eTIV, BMI showed a positive association with hippocampal fissures in both the total sample (β = 4.559 (0.209, 8.908), adjusted R2 = 0.253) and in females specifically (β = 7.732 (1.206, 14.258), adjusted R2 = 0.173). However, these findings did not survive correction for multiple comparisons (FDR-corrected p > 0.050) in our exploratory analyses. This could potentially be attributed to the relatively small sample size and limited power to detect underlying associations in this study.

3.3. Association between BMI and Cognitive Functions

Partial correlation analysis and multivariate linear regression models were implemented to investigate the associations between BMI and specific cognitive domains, as evaluated using the RBANS. As shown in Table 3, among older females, BMI exhibited a negative association with the RBANS total scale score (r = −0.368, p = 0.038) (Figure 5A), as well as with two subtest scores: coding (r = −0.375, p = 0.034) (Figure 5B) and story recall (r = −0.407, p = 0.021) (Figure 5C). In contrast, there were no significant associations observed between BMI and other cognitive domains, such as immediate memory, visuospatial/constructional ability, and language, following adjustment (p > 0.050). Furthermore, no notable correlations were found between BMI and RBANS domain index scores in older males (p > 0.050). In the multivariate linear regression models (see Supplementary Table S2), after controlling for potential confounders, BMI was found to be inversely associated with the RBANS total scale score (β = −1.741 (−3.380, −1.101), adjusted R2 = 0.244), coding (β = −0.367 (−0.674, −0.060), adjusted R2 = 0.320), and story recall (β = −1.794 (−3.447, −0.141), adjusted R2 = 0.131) scores. However, the associations between BMI and RBANS scores did not demonstrate statistical significance in multiple FDR-corrected comparison tests (corrected p > 0.05).

4. Discussion

In this cross-sectional study, we examined the associations of BMI with hippocampal subfield volume and cognitive function in non-demented, community-dwelling older adults in China. Our results showed sex-specific associations between BMI and these measures. Specifically, we observed a significant association between BMI and hippocampal fissure volume in older females. We also found a significant relationship between BMI and the RBANS total scale score and two subscale scores, coding and story recall. However, we did not observe any significant correlations between BMI and specific volumes of hippocampal subfields or cognitive scores in older males. These results suggest that a high BMI has a greater effect on hippocampal subfields and cognitive parameters in females than in males.
We observed an association between BMI and specific hippocampal subregions as well as cognitive performance. This association was found to be more significant in females than in males. Previous research studies have also supported these differences [3,17,52,53], indicating that the relationship between BMI and brain structure and cognition varies between sexes. Previous studies have shown that hippocampal volume predicts cognitive performance in older females but not in males [39]. Additionally, obesity independently increases the risk of cognitive impairment [4,54], with a greater effect in females [55,56,57]. There are several potential factors that could explain this sex-specific association. Firstly, the metabolic effects of sex steroid hormones during aging could lead to the production of different metabolites, resulting in age-related brain changes in females at an earlier stage than in males. There are sex hormone receptors, such as androgen receptors, estrogen receptors, and G-coupled protein receptor, in the hippocampus [58]. The expression of estrogen receptors in the hippocampus during aging was higher in older females than in males [59]. Sex differences in the hippocampus, at least in part, relate to steroid hormone manipulations [60]. In addition, differences in fat distribution and body composition between the sexes could explain the discrepant effects of BMI on the hippocampal subfield in older adults [57], creating a more vulnerable environment in females than in males. Genetic factors may also contribute to sex differences in brain structure and cognition [61]. During the development and progression of AD, a sex-specific genetic architecture may emerge with substantial APOE contributions to AD in females, including the sex-biased association between APOE and tau in amyloid-positive subjects [62,63,64]. These findings might suggest a potential opportunity to prevent cognitive decline in females. Timely interventions during this window may help to halt the progression of metabolic deficits and ultimately reduce the risk of dementia in females.
Notably, our findings revealed a significant association between higher BMI and larger hippocampal fissure volumes in non-demented older females. The hippocampal fissure possesses a higher fractional vascular density compared to other subfields of the hippocampus [65], and it is a susceptible subregion for Alzheimer’s disease pathology [66,67]. Enlargement of the hippocampal fissure is indicative of early gray matter atrophy within the hippocampal formation [68,69]. In Alzheimer’s disease, the hippocampal fissure has been identified as the sole hippocampal subfield exhibiting increased volume [70]. We found an association of BMI with hippocampal fissure volume, which has not been reported before. Previous studies have shown that a larger hippocampal fissure correlates with an increased risk of dementia [71] and may serve as a specific indicator of the conversion from MCI to AD [71]. Additionally, strong associations have been documented between the hippocampal fissure and polygenic Alzheimer’s disease risk scores [72]. Hibar DP et al. found that among the top variants in hippocampal subfield volume, Rs61921502 showed the largest effect in the right hippocampal fissure and a strong lateralized effect across right hippocampal subfields [73]. To sum up, BMI, accompanied by enlarged hippocampal fissures, may be one of the early indicators of cognitive impairment in non-demented older individuals, especially in females. However, we did not find significant associations between BMI and total hippocampal volume, as reported in previous studies [74,75]. This discrepancy may be due to the fact that our study participants comprised cognitively normal older individuals without significant hippocampal atrophy.
Consistent with previous research suggesting that being overweight is associated with cognitive decline linked to a decline in cognitive performance among young and middle-aged adults [76,77], the present results extended the existing findings to older age groups. The RBANS total scale score, as well as the delayed memory index and immediate memory index, have been reported to be correlated with hippocampal and global brain capacity in older individuals [78,79]. In addition, brain amyloid plaque density and deposition, which are indicative of AD brain pathology, were significantly related to the RBANS total scale score and all five index scores, with higher scores being associated with less brain amyloid [80]. Our study showed a significant correlation between BMI and cognitive performance in a sample of non-demented, community-dwelling older adults. A systematic review also supports our findings, revealing a negative association between overweight and the risk of cognitive impairment. BMI also negatively affects the cognitive performance of fibromyalgia patients [81]. A cohort study from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) supports the positive associations of overweight status with cognitive function in older adults, particularly in those with a higher plant-based diet index [10]. It should be pointed out that other factors, such as physical activity, may also play a role in modifying the association between BMI and cognition in the elderly. Such factors may amplify the effect of BMI on cognition. However, it is unclear to what extent these factors play a role in brain atrophy and cognitive decline. It is also important to note that differences in study populations and methodologies may contribute to inconsistent results between studies [82,83]. In addition, it should be considered that the association between BMI and cognitive dysfunction may differ between individuals from different racial or ethnic backgrounds [84].
The strength of our study is that we identified the relationship between BMI and a specific subfield of the hippocampus for the first time. To the best of our knowledge, this is the initial investigation to examine the association between BMI and volumes of hippocampal subfields in Chinese community-dwelling older adults. However, our study had several limitations. Firstly, the cross-sectional design of this clinical study could only reveal the association between BMI and volumes of hippocampal subfields as well as specific cognitive domains, but it could not establish a causal relationship between them. Secondly, our data were collected at a single community site, which may limit the generalizability of the study findings. Additionally, the small sample size may have led to a lack of statistical power to detect significant associations and appropriate effect sizes. Therefore, future longitudinal studies with larger sample sizes conducted in different communities are needed to validate our findings. Future research efforts should consider how modifiable risk factors, such as BMI, relate to imaging markers of cerebrovascular pathology and the extent to which they relate to clinical and cognitive outcomes. In addition, further research and longitudinal studies are warranted to explore whether weight control mitigates the risk of hippocampal atrophy in older adults.

5. Conclusions

Our findings exclusively revealed a significant association between BMI, hippocampal fissure, and cognitive performance in older Chinese females, while no such correlation was observed in males. Future longitudinal studies should examine the contribution of BMI to Aβ and tau accumulation, as this will provide additional insight into the extent to which modifiable risk factors contribute to the development of cerebrovascular pathology, as well as a deeper understanding of the mechanisms underlying the sex-specific correlations of BMI with the hippocampus. These findings may help to develop effective sex-stratified preventive and therapeutic interventions to promote hippocampal health and prevent cognitive decline at an early stage.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/brainsci14020170/s1, Table S1. Multivariate regression models assessing the association between BMI and hippocampal subfield volumes. Table S2. Multivariate regression models assessing the association between BMI and cognitive functions in females.

Author Contributions

Conceptualization, C.L. and X.C.; Methodology, S.L. and L.J.; Software, K.W.; Validation, S.L. and J.Y.; Formal Analysis, S.L.; Investigation, L.J. and X.C.; Resources, J.Y.; Data Curation, K.W.; Writing—Original Draft Preparation, S.L. and L.J.; Writing—Review and Editing, S.L. and X.C.; Visualization, L.J.; Supervision, C.L. and X.C.; Project Administration, C.L. and X.C.; Funding acquisition, C.L., X.C., L.J. and K.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (grant number 2022YFC3600600); the National Natural Science Foundation of China (grant number: 82101543, 81901400, 82104981); and the Feixiang Program of Shanghai Mental Health Center (grant number:2022-FX-04, 2022-FX-05).

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the human research ethics committees of the Shanghai Mental Health Center (approval number: 2013-40, approval date: 12 June 2014) and Tongji Hospital, Shanghai (approval number: LL (H)-09-04, approval date: 20 February 2009).

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study. Written informed consent has been obtained from the patient(s) to publish this paper.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available upon request. The data are not publicly available due to patient privacy protection purposes.

Acknowledgments

The authors thank other members of the research group, Yan Cheng, Wei Feng, Yikang Zhu, Huiru Cui, Wei Li, Hongyan Wang, Jiangling Jiang, Zhipei Zhu, and Yiran Wang, who contributed to the data collection.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ren, R.; Qi, J.; Lin, S.; Liu, X.; Yin, P.; Wang, Z.; Tang, R.; Wang, J.; Huang, Q.; Li, J.; et al. The China Alzheimer Report 2022. Gen. Psychiatry 2022, 35, e100751. [Google Scholar] [CrossRef] [PubMed]
  2. Gustafson, D.; Rothenberg, E.; Blennow, K.; Steen, B.; Skoog, I. An 18-year follow-up of overweight and risk of Alzheimer disease. Arch. Int. Med. 2003, 163, 1524–1528. [Google Scholar] [CrossRef] [PubMed]
  3. Elias, M.F.; Elias, P.K.; Sullivan, L.M.; Wolf, P.A.; D’agostino, R.B. Obesity, diabetes and cognitive deficit: The Framingham Heart Study. Neurobiol. Aging 2005, 26 (Suppl. S1), 11–16. [Google Scholar] [CrossRef] [PubMed]
  4. Kivipelto, M.; Ngandu, T.; Fratiglioni, L.; Viitanen, M.; Kåreholt, I.; Winblad, B.; Helkala, E.-L.; Tuomilehto, J.; Soininen, H.; Nissinen, A. Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Arch. Neurol. 2005, 62, 1556–1560. [Google Scholar] [CrossRef] [PubMed]
  5. Whitmer, R.A.; Gunderson, E.P.; Barrett-Connor, E.; Quesenberry, C.P., Jr.; Yaffe, K. Obesity in middle age and future risk of dementia: A 27 year longitudinal population based study. BMJ 2005, 330, 1360. [Google Scholar] [CrossRef] [PubMed]
  6. Anderson, T.; Sharma, S.; Kelberman, M.A.; Ware, C.; Guo, N.; Qin, Z.; Weinshenker, D.; Parent, M.B. Obesity during preclinical Alzheimer’s disease development exacerbates brain metabolic decline. J. Neurochem. 2023. advance online publication. [Google Scholar] [CrossRef] [PubMed]
  7. Ma, Y.; Ajnakina, O.; Steptoe, A.; Cadar, D. Higher risk of dementia in English older individuals who are overweight or obese. Int. J. Epidemiol. 2020, 49, 1353–1365. [Google Scholar] [CrossRef]
  8. Nianogo, R.A.; Rosenwohl-Mack, A.; Yaffe, K.; Carrasco, A.; Hoffmann, C.M.; Barnes, D.E. Risk Factors Associated With Alzheimer Disease and Related Dementias by Sex and Race and Ethnicity in the US. JAMA Neurol. 2022, 79, 584–591. [Google Scholar] [CrossRef]
  9. Nyberg, C.K.; Fjell, A.M.; Walhovd, K.B. Level of body fat relates to memory decline and interacts with age in its association with hippocampal and subcortical atrophy. Neurobiol. Aging 2020, 91, 112–124. [Google Scholar] [CrossRef]
  10. Liang, F.; Fu, J.; Turner-McGrievy, G.; Wang, Y.; Qiu, N.; Ding, K.; Zeng, J.; Moore, J.B.; Li, R. Association of Body Mass Index and Plant-Based Diet with Cognitive Impairment among Older Chinese Adults: A Prospective, Nationwide Cohort Study. Nutrients 2022, 14, 3132. [Google Scholar] [CrossRef]
  11. Qu, Y.; Hu, H.-Y.; Ou, Y.-N.; Shen, X.-N.; Xu, W.; Wang, Z.-T.; Dong, Q.; Tan, L.; Yu, J.-T. Association of body mass index with risk of cognitive impairment and dementia: A systematic review and meta-analysis of prospective studies. Neurosci. Biobehav. Rev. 2020, 115, 189–198. [Google Scholar] [CrossRef]
  12. Li, G.; Hu, Y.; Zhang, W.; Wang, J.; Ji, W.; Manza, P.; Volkow, N.D.; Zhang, Y.; Wang, G.-J. Brain functional and structural magnetic resonance imaging of obesity and weight loss interventions. Mol. Psychiatry 2023, 28, 1466–1479. [Google Scholar] [CrossRef] [PubMed]
  13. Brooks, S.J.; Benedict, C.; Burgos, J.; Kempton, M.J.; Kullberg, J.; Nordenskjöld, R.; Kilander, L.; Nylander, R.; Larsson, E.-M.; Johansson, L.; et al. Late-life obesity is associated with smaller global and regional gray matter volumes: A voxel-based morphometric study. Int. J. Obes. 2013, 37, 230–236. [Google Scholar] [CrossRef] [PubMed]
  14. Debette, S.; Beiser, A.; Hoffmann, U.; DeCarli, C.; O’Donnell, C.J.; Massaro, J.M.; Au, R.; Himali, J.J.; Wolf, P.A.; Fox, C.S.; et al. Visceral fat is associated with lower brain volume in healthy middle-aged adults. Ann. Neurol. 2010, 68, 136–144. [Google Scholar] [CrossRef] [PubMed]
  15. Gunstad, J.; Paul, R.H.; Cohen, R.A.; Tate, D.F.; Spitznagel, M.B.; Grieve, S.; Gordon, E. Relationship between body mass index and brain volume in healthy adults. Int. J. Neurosci. 2008, 118, 1582–1593. [Google Scholar] [CrossRef] [PubMed]
  16. Karlsson, H.K.; Tuulari, J.J.; Hirvonen, J.; Lepomäki, V.; Parkkola, R.; Hiltunen, J.; Hannukainen, J.C.; Soinio, M.; Pham, T.; Salminen, P.; et al. Obesity is associated with white matter atrophy: A combined diffusion tensor imaging and voxel-based morphometric study. Obesity 2013, 21, 2530–2537. [Google Scholar] [CrossRef] [PubMed]
  17. Kurth, F.; Levitt, J.G.; Phillips, O.R.; Luders, E.; Woods, R.P.; Mazziotta, J.C.; Toga, A.W.; Narr, K.L. Relationships between gray matter, body mass index, and waist circumference in healthy adults. Hum. Brain Mapp. 2013, 34, 1737–1746. [Google Scholar] [CrossRef] [PubMed]
  18. Raji, C.A.; Ho, A.J.; Parikshak, N.N.; Becker, J.T.; Lopez, O.L.; Kuller, L.H.; Hua, X.; Leow, A.D.; Toga, A.W.; Thompson, P.M. Brain structure and obesity. Hum. Brain Mapp. 2010, 31, 353–364. [Google Scholar] [CrossRef]
  19. Walther, K.; Birdsill, A.C.; Glisky, E.L.; Ryan, L. Structural brain differences and cognitive functioning related to body mass index in older females. Hum. Brain Mapp. 2010, 31, 1052–1064. [Google Scholar] [CrossRef]
  20. Ward, M.A.; Carlsson, C.M.; Trivedi, M.A.; Sager, M.A.; Johnson, S.C. The effect of body mass index on global brain volume in middle-aged adults: A cross sectional study. BMC Neurol. 2005, 5, 23. [Google Scholar] [CrossRef]
  21. Chenji, S.; Cox, E.; Jaworska, N.; Swansburg, R.M.; MacMaster, F.P. Body mass index and variability in hippocampal volume in youth with major depressive disorder. J. Affect. Disord. 2021, 282, 415–425. [Google Scholar] [CrossRef] [PubMed]
  22. Willette, A.A.; Kapogiannis, D. Does the brain shrink as the waist expands? Ageing Res. Rev. 2015, 20, 86–97. [Google Scholar] [CrossRef] [PubMed]
  23. Tanaka, H.; Gourley, D.D.; Dekhtyar, M.; Haley, A.P. Cognition, Brain Structure, and Brain Function in Individuals with Obesity and Related Disorders. Curr. Obes. Rep. 2020, 9, 544–549. [Google Scholar] [CrossRef] [PubMed]
  24. Subramaniapillai, M.; McIntyre, R.S. A review of the neurobiology of obesity and the available pharmacotherapies. CNS Spectr. 2017, 22 (Suppl. S1), 29–38. [Google Scholar] [CrossRef] [PubMed]
  25. Dekkers, I.A.; Jansen, P.R.; Lamb, H.J. Obesity, Brain Volume, and White Matter Microstructure at MRI: A Cross-sectional UK Biobank Study. Radiology 2019, 292, 270. [Google Scholar] [CrossRef] [PubMed]
  26. Janowitz, D.; Wittfeld, K.; Terock, J.; Freyberger, H.J.; Hegenscheid, K.; Völzke, H.; Habes, M.; Hosten, N.; Friedrich, N.; Nauck, M.; et al. Association between waist circumference and gray matter volume in 2344 individuals from two adult community-based samples. NeuroImage 2015, 122, 149–157. [Google Scholar] [CrossRef] [PubMed]
  27. Whitmer, R.A.; Gustafson, D.R.; Barrett-Connor, E.; Haan, M.N.; Gunderson, E.P.; Yaffe, K. Central obesity and increased risk of dementia more than three decades later. Neurology 2008, 71, 1057–1064. [Google Scholar] [CrossRef]
  28. Yaffe, K.; Kanaya, A.; Lindquist, K.; Simonsick, E.M.; Harris, T.; Shorr, R.I.; Tylavsky, F.A.; Newman, A.B. The metabolic syndrome, inflammation, and risk of cognitive decline. Jama 2004, 292, 2237–2242. [Google Scholar] [CrossRef]
  29. Fotuhi, M.; Do, D.; Jack, C. Modifiable factors that alter the size of the hippocampus with ageing. Nat. Rev. Neurol. 2012, 8, 189–202. [Google Scholar] [CrossRef]
  30. Erten-Lyons, D.; Woltjer, R.L.; Dodge, H.; Nixon, R.; Vorobik, R.; Calvert, J.F.; Leahy, M.; Montine, T.; Kaye, J. Factors associated with resistance to dementia despite high Alzheimer disease pathology. Neurology 2009, 72, 354–360. [Google Scholar] [CrossRef]
  31. Vasta, R.; Augimeri, A.; Cerasa, A.; Nigro, S.; Gramigna, V.; Nonnis, M.; Rocca, F.; Zito, G.; Quattrone, A.; Neuroimaging, F.T.A.D. Hippocampal Subfield Atrophies in Converted and Not-Converted Mild Cognitive Impairments Patients by a Markov Random Fields Algorithm. Curr. Alzheimer Res. 2016, 13, 566–574. [Google Scholar] [CrossRef] [PubMed]
  32. Wang, N.; Zhang, L.; Yang, H.; Luo, X.; Fan, G. Do multiple system atrophy and Parkinson’s disease show distinct patterns of volumetric alterations across hippocampal subfields? An exploratory study. Eur. Radiol. 2019, 29, 4948–4956. [Google Scholar] [CrossRef] [PubMed]
  33. Bastos-Leite, A.; van Waesberghe, J.; Oen, A.; van der Flier, W.; Scheltens, P.; Barkhof, F. Hippocampal sulcus width and cavities: Comparison between patients with Alzheimer disease and nondemented elderly subjects. AJNR Am. J. Neuroradiol. 2006, 27, 2141–2145. [Google Scholar] [PubMed]
  34. Shing, Y.L.; Rodrigue, K.M.; Kennedy, K.M.; Fandakova, Y.; Bodammer, N.; Werkle-Bergner, M.; Lindenberger, U.; Raz, N. Hippocampal subfield volumes: Age, vascular risk, and correlation with associative memory. Front. Aging Neurosci. 2011, 3, 2. [Google Scholar] [CrossRef] [PubMed]
  35. Zammit, A.R.; Ezzati, A.; Zimmerman, M.E.; Lipton, R.B.; Lipton, M.L.; Katz, M.J. Roles of hippocampal subfields in verbal and visual episodic memory. Behav. Brain Res. 2017, 317, 157–162. [Google Scholar] [CrossRef] [PubMed]
  36. Robison, L.S.; Gannon, O.J.; Salinero, A.E.; Zuloaga, K.L. Contributions of sex to cerebrovascular function and pathology. Brain Res. 2019, 1710, 43–60. [Google Scholar] [CrossRef] [PubMed]
  37. Gannon, O.J.; Naik, J.S.; Riccio, D.; Mansour, F.M.; Abi-Ghanem, C.; Salinero, A.E.; Kelly, R.D.; Brooks, H.L.; Zuloaga, K.L. Menopause causes metabolic and cognitive impairments in a chronic cerebral hypoperfusion model of vascular contributions to cognitive impairment and dementia. Biol. Sex Differ. 2023, 14, 34. [Google Scholar] [CrossRef]
  38. Mazure, C.M.; Swendsen, J. Sex differences in Alzheimer’s disease and other dementias. Lancet Neurol. 2016, 15, 451–452. [Google Scholar] [CrossRef]
  39. Ystad, M.A.; Lundervold, A.J.; Wehling, E.; Espeseth, T.; Rootwelt, H.; Westlye, L.T.; Andersson, M.; Adolfsdottir, S.; Geitung, J.T.; Fjell, A.M.; et al. Hippocampal volumes are important predictors for memory function in elderly women. BMC Med. Imaging 2009, 9, 17. [Google Scholar] [CrossRef]
  40. Pruessner, J.C.; Collins, D.L.; Pruessner, M.; Evans, A.C. Age and gender predict volume decline in the anterior and posterior hippocampus in early adulthood. J. Neurosci. 2001, 21, 194–200. [Google Scholar] [CrossRef]
  41. Li, W.; van Tol, M.-J.; Li, M.; Miao, W.; Jiao, Y.; Heinze, H.-J.; Bogerts, B.; He, H.; Walter, M. Regional specificity of sex effects on subcortical volumes across the lifespan in healthy aging. Hum. Brain Mapp. 2014, 35, 238–247. [Google Scholar] [CrossRef]
  42. Li, C.; Wu, W.; Jin, H.; Zhang, X.; Xue, H.; He, Y.; Xiao, S.; Jeste, D.V.; Zhang, M. Successful aging in Shanghai, China: Definition, distribution and related factors. Int. Psychogeriatr. 2006, 18, 551–563. [Google Scholar] [CrossRef] [PubMed]
  43. Randolph, C.; Tierney, M.C.; Mohr, E.; Chase, T.N. The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): Preliminary clinical validity. J. Clin. Exp. Neuropsychol. 1998, 20, 310–319. [Google Scholar] [CrossRef] [PubMed]
  44. Cheng, Y.; Wu, W.; Wang, J.; Feng, W.; Wu, X.; Li, C. Cinical research Reliability and validity of the Repeatable Battery for the Assessment of Neuropsychological Status in community-dwelling elderly. Arch. Med. Sci. 2011, 5, 850–857. [Google Scholar] [CrossRef] [PubMed]
  45. Katzman, R.; Zhang, M.; Wang, Z.; Liu, W.T.; Yu, E.; Wong, S.-C.; Salmon, D.P.; Grant, I. A Chinese version of the Mini-Mental State Examination; impact of illiteracy in a Shanghai dementia survey. J. Clin. Epidemiol. 1988, 41, 971–978. [Google Scholar] [CrossRef] [PubMed]
  46. Fischl, B.; Salat, D.H.; Busa, E.; Albert, M.; Dieterich, M.; Haselgrove, C.; van der Kouwe, A.; Killiany, R.; Kennedy, D.; Klaveness, S.; et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 2002, 33, 341–355. [Google Scholar] [CrossRef] [PubMed]
  47. Ségonne, F.; Dale, A.M.; Busa, E.; Glessner, M.; Salat, D.; Hahn, H.K.; Fischl, B. A hybrid approach to the skull stripping problem in MRI. Neuroimage 2004, 22, 1060–1075. [Google Scholar] [CrossRef] [PubMed]
  48. Jiang, L.; Cao, X.; Jiang, J.; Li, T.; Wang, J.; Yang, Z.; Li, C. Atrophy of hippocampal subfield CA2/3 in healthy elderly men is related to educational attainment. Neurobiol. Aging 2019, 80, 21–28. [Google Scholar] [CrossRef]
  49. Shi, Z.; Cao, X.; Hu, J.; Jiang, L.; Mei, X.; Zheng, H.; Chen, Y.; Wang, M.; Cao, J.; Li, W.; et al. Retinal nerve fiber layer thickness is associated with hippocampus and lingual gyrus volumes in nondemented older adults. Prog. Neuropsychopharmacol. Biol. Psychiatry 2020, 99, 109824. [Google Scholar] [CrossRef]
  50. Murphy, D.G.M.; DeCarli, C.; Mclntosh, A.R.; Daly, E.; Mentis, M.J.; Pietrini, P.; Szczepanik, J.; Schapiro, M.B.; Grady, C.L.; Horwitz, B.; et al. Sex differences in human brain morphometry and metabolism: An in vivo quantitative magnetic resonance imaging and positron emission tomography study on the effect of aging. Arch. Gen. Psychiatry 1996, 53, 585–594. [Google Scholar] [CrossRef]
  51. Neu, S.C.; Pa, J.; Kukull, W.; Beekly, D.; Kuzma, A.; Gangadharan, P.; Wang, L.S.; Romero, K.; Arneric, S.P.; Redolfi, A.; et al. Apolipoprotein E Genotype and Sex Risk Factors for Alzheimer Disease: A Meta-analysis. JAMA Neurol. 2017, 74, 1178–1189. [Google Scholar] [CrossRef]
  52. Elias, M.F.; Elias, P.K.; Sullivan, L.M.; Wolf, P.A.; D’Agostino, R.B. Lower cognitive function in the presence of obesity and hypertension: The Framingham heart study. Int. J. Obes. Relat. Metab. Disord. 2003, 27, 260–268. [Google Scholar] [CrossRef] [PubMed]
  53. Kanaya, A.M.; Lindquist, K.; Harris, T.B.; Launer, L.; Rosano, C.; Satterfield, S.; Yaffe, K. Total and regional adiposity and cognitive change in older adults: The Health, Aging and Body Composition (ABC) study. Arch. Neurol. 2009, 66, 329–335. [Google Scholar] [CrossRef] [PubMed]
  54. Profenno, L.A.; Porsteinsson, A.P.; Faraone, S.V. Meta-analysis of Alzheimer’s disease risk with obesity, diabetes, and related disorders. Biol. Psychiatry 2010, 67, 505–512. [Google Scholar] [CrossRef] [PubMed]
  55. Narayan, K.M.V.; Boyle, J.P.; Thompson, T.J.; Sorensen, S.W.; Williamson, D.F. Lifetime risk for diabetes mellitus in the United States. JAMA 2003, 290, 1884–1890. [Google Scholar] [CrossRef] [PubMed]
  56. Lovejoy, J.C.; Sainsbury, A. The Stock Conference 2008 Working Group Sex differences in obesity and the regulation of energy homeostasis. Obes. Rev. 2009, 10, 154–167. [Google Scholar] [CrossRef]
  57. Harris, G.; Baer, D. Gender differences in body fat utilization during weight gain, loss, or maintenance. In Obesity: Epidemiology, Pathophysiology, and Prevention; CRC Press: Boca Raton, FL, USA, 2007; pp. 239–245. [Google Scholar]
  58. Yagi, S.; Galea, L.A.M. Sex differences in hippocampal cognition and neurogenesis. Neuropsychopharmacology 2019, 44, 200–213. [Google Scholar] [CrossRef] [PubMed]
  59. Foster, T.C. Role of estrogen receptor alpha and beta expression and signaling on cognitive function during aging. Hippocampus 2012, 22, 656–669. [Google Scholar] [CrossRef]
  60. Hamson, D.K.; Roes, M.M.; Galea, L.A. Sex Hormones and Cognition: Neuroendocrine Influences on Memory and Learning. Compr. Physiol. 2016, 6, 1295–1337. [Google Scholar]
  61. Laukka, E.J.; Lövdén, M.; Herlitz, A.; Karlsson, S.; Ferencz, B.; Pantzar, A.; Keller, L.; Graff, C.; Fratiglioni, L.; Bäckman, L. Genetic effects on old-age cognitive functioning: A population-based study. Psychol. Aging 2013, 28, 262–274. [Google Scholar] [CrossRef]
  62. Altmann, A.; Tian, L.; Henderson, V.W.; Greicius, M.D. Sex modifies the APOE-related risk of developing Alzheimer disease. Ann. Neurol. 2014, 75, 563–573. [Google Scholar] [CrossRef] [PubMed]
  63. Guo, L.; Zhong, M.B.; Zhang, L.; Zhang, B.; Cai, D. Sex Differences in Alzheimer’s Disease: Insights From the Multiomics Landscape. Biol. Psychiatry 2022, 91, 61–71. [Google Scholar] [CrossRef] [PubMed]
  64. Hohman, T.J.; Dumitrescu, L.; Barnes, L.L.; Thambisetty, M.; Beecham, G.; Kunkle, B.; Gifford, K.A.; Bush, W.S.; Chibnik, L.B.; Mukherjee, S.; et al. Sex-Specific Association of Apolipoprotein E With Cerebrospinal Fluid Levels of Tau. JAMA Neurol. 2018, 75, 989–998. [Google Scholar] [CrossRef] [PubMed]
  65. Buch, S.; Chen, Y.; Jella, P.; Ge, Y.; Haacke, E.M. Vascular mapping of the human hippocampus using Ferumoxytol-enhanced MRI. NeuroImage 2022, 250, 118957. [Google Scholar] [CrossRef] [PubMed]
  66. Wisse, L.E.; Biessels, G.J.; Heringa, S.M.; Kuijf, H.J.; Luijten, P.R.; Geerlings, M.I. Hippocampal subfield volumes at 7T in early Alzheimer’s disease and normal aging. Neurobiol. Aging 2014, 35, 2039–2045. [Google Scholar] [CrossRef] [PubMed]
  67. Zhao, W.; Wang, X.; Yin, C.; He, M.; Li, S.; Han, Y. Trajectories of the Hippocampal Subfields Atrophy in the Alzheimer’s Disease: A Structural Imaging Study. Front. Neurosci. 2019, 13, 13. [Google Scholar] [CrossRef] [PubMed]
  68. Li, Y.; Yan, J.; Zhu, X.; Zhu, Y.; Qin, J.; Zhang, N.; Ju, S. Increased hippocampal fissure width is a sensitive indicator of rat hippocampal atrophy. Brain Res. Bull. 2018, 137, 91–97. [Google Scholar] [CrossRef] [PubMed]
  69. Malykhin, N.V.; Huang, Y.; Hrybouski, S.; Olsen, F. Differential vulnerability of hippocampal subfields and anteroposterior hippocampal subregions in healthy cognitive aging. Neurobiol. Aging 2017, 59, 121–134. [Google Scholar] [CrossRef]
  70. Iglesias, J.E.; Augustinack, J.C.; Nguyen, K.; Player, C.M.; Player, A.; Wright, M.; Roy, N.; Frosch, M.P.; McKee, A.C.; Wald, L.L.; et al. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. NeuroImage 2015, 115, 117–137. [Google Scholar] [CrossRef]
  71. Izzo, J.; Andreassen, O.A.; Westlye, L.T.; van der Meer, D. The association between hippocampal subfield volumes in mild cognitive impairment and conversion to Alzheimer’s disease. Brain Res. 2020, 1728, 146591. [Google Scholar] [CrossRef]
  72. Murray, A.N.; Chandler, H.L.; Lancaster, T.M. Multimodal hippocampal and amygdala subfield volumetry in polygenic risk for Alzheimer’s disease. Neurobiol. Aging 2021, 98, 33–41. [Google Scholar] [CrossRef]
  73. Hibar, D.P.; Adams, H.H.H.; Jahanshad, N.; Chauhan, G.; Stein, J.L.; Hofer, E.; Renteria, M.E.; Bis, J.C.; Arias-Vasquez, A.; Ikram, M.K.; et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 2017, 8, 13624. [Google Scholar] [CrossRef]
  74. Mestre, Z.; Bischoff-Grethe, A.; Wierenga, C.E.; Jernigan, T.; Eichen, D.M.; Chang, L.; Ernst, T.; Boutelle, K.N. Associations Between Body Weight, Hippocampal Volume, and Tissue Signal Intensity in 12- to 18-Year-Olds. Obesity 2020, 28, 1325–1331. [Google Scholar] [CrossRef] [PubMed]
  75. Hayes, J.P.; Moody, J.N.; Roca, J.G.; Hayes, S.M. Body mass index is associated with smaller medial temporal lobe volume in those at risk for Alzheimer’s disease. Neuroimage Clin. 2020, 25, 102156. [Google Scholar] [CrossRef] [PubMed]
  76. Gunstad, J.; Paul, R.H.; Cohen, R.A.; Tate, D.F.; Gordon, E. Obesity is associated with memory deficits in young and middle-aged adults. Eat. Weight Disord. 2006, 11, e15–e19. [Google Scholar] [CrossRef] [PubMed]
  77. Bell, S.P.; Liu, D.; Samuels, L.R.; Shah, A.S.; Gifford, K.A.; Hohman, T.J.; Jefferson, A.L. Late-Life Body Mass Index, Rapid Weight Loss, Apolipoprotein E ε4 and the Risk of Cognitive Decline and Incident Dementia. J. Nutr. Health Aging 2017, 21, 1259–1267. [Google Scholar] [CrossRef] [PubMed]
  78. Jiang, L.; Cheng, Y.; Li, Q.; Tang, Y.; Shen, Y.; Li, T.; Feng, W.; Cao, X.; Wu, W.; Wang, J.; et al. Cross-sectional study of the association of cognitive function and hippocampal volume among healthy elderly adults. Shanghai Arch. Psychiatry 2014, 26, 280–287. [Google Scholar] [CrossRef] [PubMed]
  79. England, H.B.; Gillis, M.M.; Hampstead, B.M. Hampstead, RBANS memory indices are related to medial temporal lobe volumetrics in healthy older adults and those with mild cognitive impairment. Arch. Clin. Neuropsychol. 2014, 29, 322–328. [Google Scholar] [CrossRef] [PubMed]
  80. Duff, K.; Dixon, A.M.; Embree, L.; Hoffman, J.M. Change on the Repeatable Battery for the Assessment of Neuropsychological Status and its relationship to brain amyloid. J. Clin. Exp. Neuropsychol. 2023, 45, 105–117. [Google Scholar] [CrossRef] [PubMed]
  81. de Guevara, C.M.L.; del Paso, G.A.R.; Fernández Serrano, M.J.; Montoro, C.I. Fibromyalgia Syndrome and Cognitive Decline: The Role of Body Mass Index and Clinical Symptoms. J. Clin. Med. 2022, 11, 3404. [Google Scholar] [CrossRef]
  82. Kaup, A.R.; Mirzakhanian, H.; Jeste, D.V.; Eyler, L.T.; Sæther, S.G.; Rø, A.D.B.; Larsen, J.B.; Vaaler, A.; Kondziella, D.; Reitan, S.K.; et al. A review of the brain structure correlates of successful cognitive aging. J. Neuropsychiatry 2011, 23, 6–15. [Google Scholar] [CrossRef]
  83. Ezzati, A.; Katz, M.J.; Zammit, A.R.; Lipton, M.L.; Zimmerman, M.E.; Sliwinski, M.J.; Lipton, R.B. Differential association of left and right hippocampal volumes with verbal episodic and spatial memory in older adults. Neuropsychologia 2016, 93 Pt B, 380–385. [Google Scholar] [CrossRef]
  84. Bryant, A.N.; Ford, K.-L.; Kim, G. Racial/ethnic variations in the relation between body mass index and cognitive function among older adults. Am. J. Geriatr. Psychiatry 2014, 22, 653–660. [Google Scholar] [CrossRef]
Brainsci 14 00170 g001
Figure 1. Participant enrollment flow chart.
Figure 1. Participant enrollment flow chart.
Brainsci 14 00170 g001
Brainsci 14 00170 g002
Figure 2. Automated segmentation of the hippocampal subfields. CA: cornu ammonis area, GC-DG: granule cell layer of the dentate gyrus, HATA: hippocampus–amygdala transition area.
Figure 2. Automated segmentation of the hippocampal subfields. CA: cornu ammonis area, GC-DG: granule cell layer of the dentate gyrus, HATA: hippocampus–amygdala transition area.
Brainsci 14 00170 g002
Brainsci 14 00170 g003
Figure 3. Comparisons of the volumes (mm3) of the hippocampal subfields between females and males from the cross-sectional sample. Bar graphs present the distributions of the mean hippocampal subfield volumes among participants. Error bars represent the standard deviation. * p < 0.05, ** p < 0.01, *** p < 0.001. Abbreviations: CA: cornu ammonis area, GC-DG: granule cell layer of the dentate gyrus, HATA: hippocampus–amygdala transition area.
Figure 3. Comparisons of the volumes (mm3) of the hippocampal subfields between females and males from the cross-sectional sample. Bar graphs present the distributions of the mean hippocampal subfield volumes among participants. Error bars represent the standard deviation. * p < 0.05, ** p < 0.01, *** p < 0.001. Abbreviations: CA: cornu ammonis area, GC-DG: granule cell layer of the dentate gyrus, HATA: hippocampus–amygdala transition area.
Brainsci 14 00170 g003
Brainsci 14 00170 g004
Figure 4. Correlations between BMI and hippocampal fissure volumes in the overall sample (A), females (B), and males (C).
Figure 4. Correlations between BMI and hippocampal fissure volumes in the overall sample (A), females (B), and males (C).
Brainsci 14 00170 g004
Brainsci 14 00170 g005
Figure 5. Correlations of BMI with the RBANS total scale score (A), coding (B), and story recall (C) in females.
Figure 5. Correlations of BMI with the RBANS total scale score (A), coding (B), and story recall (C) in females.
Brainsci 14 00170 g005
Table 1. Clinical characteristics of study participants.
Table 1. Clinical characteristics of study participants.
CharacteristicsEntire Sample (n = 66)Females (n = 35)Males (n = 31)p a
Age (year)68.90 ± 3.0468.76 ± 2.9669.06 ± 3.170.687
Education (year)12.20 ± 2.8012.09 ± 2.7912.32 ± 2.840.864
BMI (kg/m2)23.44 ± 3.29123.16 ± 2.4023.77 ± 4.090.467
CMMSE28.26 ± 1.3428.43 ± 1.2428.06 ± 1.440.658
RBANS total scale score 95.636 ± 10.9897.51 ± 11.7193.52 ± 9.850.141
 Immediate memory86.35 ± 13.1887.83 ± 12.6784.68 ±13.750.336
 Visuospatial/
 constructional ability102.80 ± 15.96102.46 ± 16.42103.19 ± 15.690.853
 Language97.38 ± 8.5497.74 ± 9.6696.97 ± 7.200.540
 Attention101.48 ± 13.09104.06 ± 14.2198.58 ± 11.230.090
 Delayed memory97.59 ± 12.2299.97 ± 11.6394.90 ± 12.500.197
Whole hippocampus volume (mm3)6642.11 ± 617.066425.65 ± 536.956886.51 ± 617.930.002 *
eTIV (cm3)1487.84 ± 163.391378.04 ± 96.941611.81 ± 131.320.000 *
BMI, body mass index; eTIV, estimated total intracranial volume; CMMSE, Chinese version of the Mini-Mental State Examination; RBANS, Repeatable Battery for the Assessment of Neuropsychological Status. Data are presented as the mean ± standard deviation. a The p-value was based on the Student’s t-test or Mann–Whitney U test, as appropriate. * p < 0.05
Table 2. Partial correlation analysis of BMI with hippocampal subfield volumes.
Table 2. Partial correlation analysis of BMI with hippocampal subfield volumes.
Hippocampal SubfieldsOverall (n = 66) aMales (n = 31) bFemales (n = 35) b
rprprp
CA10.0510.694−0.0960.6290.2860.112
CA2/30.1660.1970.1080.5850.2640.144
CA40.0160.899−0.1320.5050.2490.170
GC–DG−0.0510.692−0.2290.2400.1830.315
Fimbria−0.1670.195−0.0810.680−0.3140.080
Subiculum−0.0630.626−0.2160.2690.2000.272
Presubiculum−0.0530.682−0.0320.8700.0290.877
Parasubiculm−0.0480.712−0.0410.836−0.1130.537
Molecular layer−0.0040.976−0.1580.4230.2630.146
HATA−0.0860.506−0.1560.428−0.0410.823
Hippocampal tail0.0430.741−0.0460.8150.2960.100
Hippocampal fissure0.2610.040 *0.1640.4050.4040.022 *
Whole hippocampus0.0040.974−0.1360.4890.2560.157
CA, cornu ammonis; GC-DG, granule cell layer of the dentate gyrus; HATA, hippocampus–amygdala transition area; eTIV: estimated total intracranial volume. a Includes age, sex, education, and eTIV as covariates. b Includes age, education, and eTIV as covariates. The p-values with “*” indicate a significant correlation.
Table 3. Partial correlation analysis of BMI with cognitive functions measured using RBANS.
Table 3. Partial correlation analysis of BMI with cognitive functions measured using RBANS.
Cognitive FunctionOverall (n = 66) aMales (n = 31) bFemales (n = 35) b
rprprp
Immediate memory0.0490.7080.2140.273−0.1860.308
 List learning0.0560.6670.1440.465−0.0300.869
 Story memory−0.0250.8490.1470.454−0.2770.124
Visuospatial/
constructional ability−0.1390.282−0.0120.951−0.3290.066
 Figure copy−0.2040.112−0.2060.293−0.0940.611
 Line orientation0.0020.9880.1240.528−0.3000.095
Language0.1170.3640.2270.245−0.0710.700
 Picture naming0.0690.5970.2110.282−0.0070.972
 Semantic fluency0.1690.1880.2210.258−0.0530.775
Attention−0.1230.3400.1400.477−0.2520.163
 Digit span−0.1060.412−0.0450.8210.0340.854
 Coding−0.0820.5260.2440.212−0.3750.034 *
Delayed memory−0.0130.9210.1290.514−0.1780.330
 List recall−0.0090.9440.0230.906−0.0560.759
 List recognition−0.0500.6990.1390.481−0.3200.074
 Story recall0.0190.8810.1660.400−0.4070.021 *
 Figure recall0.1260.3300.1760.3690.0030.989
RBANS total scale score−0.0560.6650.2520.196−0.3680.038 *
a Includes age, sex, education, and CMMSE as covariates. b Includes age, education, and CMMSE as covariates. The p-values with “*” indicate a significant correlation.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lin, S.; Jiang, L.; Wei, K.; Yang, J.; Cao, X.; Li, C. Sex-Specific Association of Body Mass Index with Hippocampal Subfield Volume and Cognitive Function in Non-Demented Chinese Older Adults. Brain Sci. 2024, 14, 170. https://doi.org/10.3390/brainsci14020170

AMA Style

Lin S, Jiang L, Wei K, Yang J, Cao X, Li C. Sex-Specific Association of Body Mass Index with Hippocampal Subfield Volume and Cognitive Function in Non-Demented Chinese Older Adults. Brain Sciences. 2024; 14(2):170. https://doi.org/10.3390/brainsci14020170

Chicago/Turabian Style

Lin, Shaohui, Lijuan Jiang, Kai Wei, Junjie Yang, Xinyi Cao, and Chunbo Li. 2024. "Sex-Specific Association of Body Mass Index with Hippocampal Subfield Volume and Cognitive Function in Non-Demented Chinese Older Adults" Brain Sciences 14, no. 2: 170. https://doi.org/10.3390/brainsci14020170

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop