Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) are two significant neurodegenerative disorders that have a profound impact on individuals and society as a whole. The prevalence of MCI and AD has been steadily increasing worldwide, posing a significant public health challenge. According to recent estimates, there were approximately 50 million people living with dementia globally in 2020, with AD accounting for the majority of cases. Furthermore, both MCI and AD are associated with a higher mortality rate and significantly affect the quality of life for affected individuals and their families. The financial burden encompasses direct medical expenses, long-term care services, and the indirect costs of caregiving and productivity loss. As the prevalence of these conditions continues to rise, the economic impact on healthcare systems and society as a whole is expected to escalate.
The causes and factors contributing to the development of MCI and AD are complex and multifactorial. While genetic and environmental factors play a role in disease susceptibility, lifestyle and dietary habits have emerged as potential modifiable risk factors. Micronutrients, including vitamins, minerals, and dietary antioxidant compounds, including vitamins A, C, D, E, and selenium, have garnered significant attention due to their potential role in neuroprotection and cognitive function. The absorption and utilization of micronutrients are essential for maintaining brain health and optimal cognitive function. Deficiencies or imbalances in these micronutrients have been implicated in the pathogenesis of neurodegenerative diseases, including MCI and AD. Furthermore, the impact of micronutrients extends beyond MCI and AD, as they have been investigated in relation to the prevention and management of various other diseases, including cardiovascular disease, cancer, and age-related macular degeneration. Understanding the potential role of micronutrients in neurodegenerative diseases requires careful examination and consideration of the existing scientific evidence. However, the relationship between micronutrient status and cognitive impairment is still not fully understood, and previous research has yielded conflicting results. Some studies utilized large open databases and cohort studies to investigate the association between dietary micronutrient intake and cognitive function or risk of cognitive decline in older adult populations. Devarshi et al. (2023) analyzed data from 1,618 NHANES participants aged 60 and older, finding higher intakes of vitamins C, D, E, folate, and carotenoids were associated with better cognitive performance, especially memory and executive function. Devore et al. (2010) used data from 5,395 Rotterdam Study participants aged 55 and older, revealing that higher intakes of vitamins C and E were associated with a reduced risk of cognitive decline and Alzheimer’s disease over time. Xu et al. (2024) analyzed data from 2,305 CHNS participants aged 65 and older, concluding that higher intakes of vitamins B6, B12, and folate were associated with better cognitive performance, particularly in memory and executive function tests. These findings highlight the potential benefits of specific micronutrient intakes on cognitive health and reducing the risk of age-related cognitive impairment across diverse populations. However, other studies have found no significant association or even contradictory results, indicating that the impact of these antioxidants on cognitive decline may be more complex and multifaceted. Compared to the previous studies, the current study has several distinguishing features: Study Population; while some of the cited studies looked at cognitively normal or at-risk elderly populations, the current study specifically focuses on individuals already diagnosed with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). This allows for a more direct examination of the role of micronutrients in disease progression and severity. Individual Micronutrient Analysis; Many of the previous studies analyzed dietary patterns, food groups, or a limited set of nutrients. The current study takes a more granular approach by investigating the associations between cognitive ability and a broad range of specific micronutrients, such as omega-3 fatty acids, carotenoids, dietary antioxidant compounds, including vitamins A, C, D, E, and selenium, and individual carotenoid compounds like alpha-carotene, beta-carotene, and lycopene. Rigorous Assessment Methods: The current study employs validated tools for dementia diagnosis (NINCDS–ADRDA criteria) and disease severity assessment (Functional Assessment Staging Tool), as well as a comprehensive dish-based food frequency questionnaire for dietary assessment. This rigorous methodology enhances the reliability and precision of the findings. Focus on Disease Progression: While some previous studies looked at the risk of cognitive impairment or dementia, the current study specifically examines the association between micronutrient intake and the progression of cognitive decline in individuals already diagnosed with MCI and AD. This approach contributes to a better understanding of the role of micronutrients in the disease course.
In light of the aforementioned considerations, this article aims to explore the relationship between micronutrients and cognitive ability in an elderly population with MCI and AD. By examining individual diet components rather than overall dietary patterns, this study seeks to shed light on the specific micronutrients that may influence disease progression.
Methods
Study design and participants
A cross-sectional study was conducted between September 2020 and January 2021.The study protocol was in accordance with the Statement on Strengthening Observational Study Reporting in Epidemiology (STROBE). The Ethics Committee of the Tehran University of Medical Sciences approved the study (IR.TUMS.MEDICINE.REC.1400.893).
Participants were recruited from two memory clinics in the Department of Cognitive Neurology and Neuropsychiatry of Roozbeh Hospital and the Department of Geriatrics of Ziaeian Hospital both affiliated with Tehran University of Medical Sciences. The diagnosis of Alzheimer’s disease (AD) was based on the well-established NINCDS–ADRDA (National Institute of Neurological and Communicative Disorders, Stroke-Alzheimer’s Disease and Related Disorders Association) criteria, which are widely used clinical diagnostic criteria for AD. The severity and extent of Alzheimer’s disease progression were assessed using the Functional Assessment Staging Tool (FAST), which is a reliable method for evaluating performance deterioration in Alzheimer’s patients. The inclusion criteria were as follows: individuals aged 60 years or older, the permission from the patient’s caregivers, and willingness to participate. Patients with Alzheimer’s disease (AD) who had confounding underlying conditions, such as another neurodegenerative disease, anoxic brain injury, stroke, or inability to understand or speak Persian, were excluded from the study. The second researcher assessed the eligibility of participants, and informed consent was obtained from the caregivers before participation.
Dietary assessment
Participants completed a 142-item Willett-format dish-based semi-quantitative food frequency questionnaire (FFQ) specifically developed and validated for Iranian adults. The FFQ covered various foods and dishes in the typical Iranian diet. Each food item had nine frequency response options, and portion sizes were also recorded. Based on the FFQ, daily nutrient intake was calculated by multiplying the consumption frequency of each food item by the nutrient content. Nutrient intakes were calculated based on the US Department of Agriculture (USDA) national nutrient database and Iran’s Food Composition Tables. The data obtained from the FFQ were converted to grams using the home scale guide and analyzed by Nutritionist 4 software (NUT4) modified for Iranian foods. To adjust for total caloric intake, linear regression models were used to calculate the residuals of nutrient intake. These residuals were standardized and used for all subsequent analyses.
Anthropometric assessments
Anthropometric measurements were performed according to the methodology provided by the World Health Organization. The Seca Clara 803 digital hand scale with an accuracy of 0.01 g was used for weighing. Height was measured using a shoeless Seca (Stadiometer) with a sensitivity of 0.1 cm (Seca, Germany). Participants body mass index is also calculated using the appropriate formula (BMI= (weight (kg))/ (height (m2)).
Dementia assessment
The diagnosis of Alzheimer’s disease (AD) was based on the NINCDS–ADRDA (National Institute of Neurological and Communicative Disorders, Stroke-Alzheimer’s Disease and Related Disorders Association) criteria. Mild cognitive impairment (MCI) was diagnosed according to standard research criteria. The severity and extent of Alzheimer’s disease progression were assessed using the Functional Assessment Staging Tool (FAST) score 3-6B, a reliable method for evaluating performance deterioration in Alzheimer’s patients. The Persian version of FAST was used, which recognizes seven progressive stages of cognitive decline in Alzheimer’s disease.
Covariate assessment
Sociodemographic and lifestyle characteristics were assessed as potential confounders. These included age, gender, race/ethnicity, marital status, years of education, body mass index (BMI), drug abuse, and smoking status. Age is a crucial covariate as it is strongly associated with the risk and progression of cognitive decline, MCI, and Alzheimer’s disease. The study population had a mean age of 74.1 years, and age varied among different severity groups, with the Moderate Dementia group having the highest mean age of 76.2 years. The study included both male and female participants (54 males and 51 females in the AD group, 23 of each sex in the MCI group). Gender differences in AD risk and progression have been reported in previous studies, making it an important covariate to control for. While not explicitly mentioned in the results, this factor can influence dietary habits, genetic predisposition to AD, and access to healthcare, potentially affecting disease progression and severity. This can be an indicator of social support, which may influence cognitive health and disease management in elderly populations. Educational attainment is often associated with cognitive reserve, which can affect the onset and progression of cognitive decline and AD. The study reported a mean BMI of 25.9, with variations among severity groups. BMI can influence nutritional status and overall health, potentially affecting cognitive function and disease progression. Substance use can impact cognitive function and potentially interact with disease progression in AD. Smoking is a known risk factor for various health conditions and may influence cognitive decline and AD progression. Physical activity Assessed using the Godin leisure-time exercise questionnaire, physical activity levels can affect overall health and potentially influence cognitive function and disease progression in AD. The Charlson Comorbidity Index, which includes information on various medical conditions, was used to assess comorbidities. The study reported an average caloric intake of 1600 kcal/day, with variations among severity groups. Total caloric intake can influence nutritional status and overall health, potentially affecting cognitive function and disease progression.
Statistical analysis
Descriptive statistics were used to summarize the characteristics of the study population. Continuous variables were presented as mean ± standard deviation (SD), while categorical variables were described as frequency and percentage. To assess the association between micronutrient intake and dementia progression, Spearman’s rank correlation coefficient was used. This non-parametric test was chosen due to the ordinal nature of the dementia progression measure (FAST score). Micronutrient intakes were adjusted for total calorie intake using the residual method prior to analysis to account for potential confounding by overall energy intake. Multiple regression analyses were conducted to predict the progression of dementia. The assumptions of multiple regression were checked, including linearity (assessed through scatterplots), independence of residuals (Durbin-Watson statistic), homoscedasticity (plot of standardized residuals against predicted values), and absence of multicollinearity (Variance Inflation Factor < 10). To handle missing data, multiple imputation was used. This method involves creating multiple plausible imputed datasets, analyzing each dataset separately, and then pooling the results using Rubin’s rules. This approach helps to reduce bias and increase precision compared to complete case analysis. Effect sizes for correlations were interpreted according to Cohen’s guidelines (1988): small (0.1–0.3), medium (0.3–0.5), or large (> 0.5). All statistical analyses were performed using SPSS version 26. A p-value of < 0.05 was considered statistically significant. To account for multiple comparisons, we applied the Benjamini-Hochberg procedure to control the false discovery rate. Power calculations were performed post-hoc using G*Power 3.1. With our sample size of 105 participants, we had 80% power to detect a medium effect size (f² = 0.15) in our multiple regression analysis with 7 predictors at α = 0.0.