1. Introduction
The aging population faces numerous health issues, with cognitive decline considered one of the most frightening aspects. Cognition is influenced by a variety of factors, and due to the high degree of dietary modifiability, dietary nutrition is thought to have a significant impact on cognition. Recently, several studies have focused on the relationship between mineral intake and cognitive function, suggesting a link between multiple mineral intakes and the risk of cognitive decline [1,2]. However, it cannot be ignored that, due to the complexity of dietary components, multiple minerals coexist and interact with each other. Considering that previous studies have mainly focused on the single effects of several dietary minerals on cognitive function, we used a mixed-effects model to explore the relationship between mineral mixture intake and cognitive function among older adults.
2. Methods
2.1. Study population
This study utilized data from two cycles of the National Health and Nutrition Examination Survey (NHANES) database spanning from 2011 to 2014, with a total of 19,931 participants included. After excluding participants who were <60 years of age, missing information on cognitive tests, valid 24 -h dietary recall, and covariates, 2,089 participants were finally included.
2.2. Assessment of mineral intake and cognitive function
Dietary and supplemental mineral intake was obtained by two nonconsecutive 24-h dietary recall questionnaires. A total of nine minerals (magnesium (Mg), sodium (Na), potassium (K), zinc (Zn), copper (Cu), calcium (Ca), phosphorus (P), iron (Fe), and selenium (Se)) were analyzed in this study. The mineral intake from diet and supplements was calculated as the average of two 24-h recalls. The assessments of cognitive function included the immediate recall test (IRT), one delayed recall test (DRT), the animal fluency test (AFT), and the digit symbol substitution test (DSST). The average of the standardized z-scores of the above four cognitive functions was taken as the global cognition z-scores.
2.3. Statistical analysis
The Bayesian kernel-machine regression (BKMR) model was used to analyze the overall and univariate dose-response relationship between mineral intake and cognitive function. We fitted a BKMR model with 20,000 iterations to (1) calculate the posterior inclusion probability (PIPs) values of the nine mineral intakes to identify the significant components affecting cognitive functions, with a threshold value of 0.5; (2) compare the mineral mixture intakes of the different percentiles with their median mixtures to assess the joint effect; (3) fixing the residual mineral intake at the median to explore the dose-response relationship. The analyses in this study were realized by R software (4.3.1).
3. Results
The BKMR model revealed a significant positive relationship between overall mineral intake and high performance in IRT, DRT, AFT, DSST, and global cognition (Fig. 1A). The PIP values between various mineral intakes and cognitive function were calculated, with Na being more important for DRT, Ca, P, Mg, Zn, K for AFT, P, Mg, Cu, Na, K for DSST, and Mg, Na for global cognition. Univariate exposure-response analyses revealed a positive relationship between Na intake and DRT z-scores; for AFT, Ca, P, Mg, and Zn showed a positive nonlinear dose-response relationship with AFT z-scores, whereas AFT z-scores first increased and then decreased with increasing K intake. P, Mg, Cu, and Na showed a positive nonlinear correlation with DSST z-scores. With an increase in K intake, cognitive performance on the DSST showed an initial improvement followed by a decline. A positive nonlinear dose-response relationship between Na, Mg, and global cognition z-scores was discovered (Fig. 1B).
Fig. 1.
Overall relationship (A) and univariate exposure-response functions (B) between mineral intakes and IRT, DRT, AFT, DSST, and global cognition estimated by Bayesian kernel machine regression (BKMR) model.
Notes: Models were adjusted for age, sex, race, body mass index, smoking status, drinking status, marital status, education level, the ratio of family income to poverty, diabetes, hypertension, caffeine intake, and energy. IRT, Immediate Recall test; DRT, Delayed Recall test; AFT, Animal Fluency test; DSST, Digit Symbol Substitution test; Ca, calcium; P, phosphorus; Mg, magnesium; Fe, iron; Zn, zinc; Cu, copper; Na, sodium; K, potassium; Se, selenium.
4. Discussion
Our study confirmed a positive relationship between mineral mixtures and IRT, DRT, AFT, DSST, and global cognitive function. Na and Mg played important positive roles in enhancing multiple cognitive functions, while with increasing K intake, AFT and DSST z-scores exhibited an inverted "U" shaped change. Mg, as a necessary catalytic substrate for various enzymes, plays a crucial role in DNA replication, DNA repair, and RNA synthesis. Mg deficiency can lead to reduced telomere length accompanied by increased oxidative stress, resulting in cognitive impairment [1]. As for Na, low levels of dietary Na may affect the renin-angiotensin and sympathetic nervous systems [3], increase oxidative stress [4], and adversely affect cognitive functions. Previous studies have shown that K may significantly affect electrophysiological activity and membrane trafficking, thus being involved in the pathophysiology of Alzheimer's disease [5]. This may partly explain the cognitive decline caused by high K intake. It is undeniable that some limitations remain. Due to the inherent deficiency of cross-sectional studies, we were unable to establish a causal relationship. Furthermore, since this study utilized the 24 -h dietary recall method to assess mineral intake, recall bias could potentially distort the findings.
In conclusion, there was a positive correlation between mineral mixture intake and cognitive function improvement. Adequate Mg, Na, and moderate K intakes can improve cognitive function in older adults.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author contributions
Yan Zhuang: Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing. Laifu Li: Software, Writing – review & editing. Jingyan Sun: Writing – review & editing. Yanqi Zhang: Writing – review & editing. Fei Dai: Conceptualization, Supervision.
Ethics approval and consent to participate
This is an observational study. The informed consent procedure was completed by each participant in the NHANES cycles. The data collection for NHANES was approved by the CDC's NCHS Ethics Review Board.
Conflict of interest
There are no conflicts to declare.
Availability of data and materials
NHANES data is publicly available at: https://wwwn.cdc.gov/nchs/nhanes/. The analytical dataset for this study and code can be available upon request.
Acknowledgments
We appreciate Jemo's assistance with the study analysis.
References
- 1.Zhao G., Guo D., Li L., Yang C., Dong J. The association between dietary magnesium intake and telomere length in adults with hypertension. J Nutr Health Aging. 2022;26(11):1010–1015. doi: 10.1007/s12603-022-1856-y. [DOI] [PubMed] [Google Scholar]
- 2.Pan W., Chen H., Ni C., Zong G., Yuan C., Yang M. Sex-specific associations of dietary iron intake with brain iron deposition on imaging and incident dementia: a prospective cohort study. J Nutr Health Aging. 2022;26(10):954–961. doi: 10.1007/s12603-022-1852-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Grassi G., Dell’Oro R., Seravalle G., Foglia G., Trevano F.Q., Mancia G. Short- and long-term neuroadrenergic effects of moderate dietary sodium restriction in essential hypertension. Circulation. 2002;106(15):1957–1961. doi: 10.1161/01.cir.0000033519.45615.c7. [DOI] [PubMed] [Google Scholar]
- 4.Nowak K.L., Yaffe K., Orwoll E.S., Ix J.H., You Z., Barrett-Connor E., et al. Serum sodium and cognition in older community-dwelling men. Clin J Am Soc Nephrol CJASN. 2018;13(3):366–374. doi: 10.2215/CJN.07400717. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Vitvitsky V.M., Garg S.K., Keep R.F., Albin R.L., Banerjee R. Na+ and K+ ion imbalances in Alzheimer’s disease. Biochim Biophys Acta. 2012;1822(11):1671–1681. doi: 10.1016/j.bbadis.2012.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
NHANES data is publicly available at: https://wwwn.cdc.gov/nchs/nhanes/. The analytical dataset for this study and code can be available upon request.