Summary
Objectives:
This study aimed to analyse the correlation between zinc intake and superoxide dismutase (SOD) with cognitive function in the elderly. Thus, the role of zinc as a structural component of SOD, along with SOD's contribution to cognitive function, can be established.
Methods:
This cross-sectional study was conducted at Panti Sosial Tresna Wredha Budi Mulia 1 in Jakarta, Indonesia, from July to September 2024. Elderly men and women were included. Zinc intake was assessed using the Semi-Quantitative Food Frequency Questionnaire, while plasma SOD activity was measured by spectrophotometer. Cognitive function was assessed using the Montreal Cognitive Assessment-Indonesia version. Data were analysed using bivariate testing and multivariate linear regression.
Results:
A total of 85 subjects were included with a median age of 69 years (61.2% were female and 38.8% male). The majority (72.9%) had primary-level education and 87.1% had chronic diseases. The mean physical activity score was 75.4 ± 39.8, the mean body mass index was 21.8 ± 3.9 kg/m2 and the median daily calorie intake was 1,927.8 kcal/day (range: 1,341.7–2,167.0 kcal/day). No correlation was found between zinc intake and cognitive function. However, a significant correlation (P = 0.006, r = 0.296) was observed between SOD levels and cognitive function, with multivariate analysis indicating that gender, education and SOD accounted for 15.9% of the variance in cognitive function.
Conclusion:
This study found no significant correlation between zinc intake and cognitive function. However, SOD activity were significantly correlated with cognitive function in the elderly.
Keywords: Zinc, Superoxide Dismutase, Cognition, Elderly, Indonesia
Advances in Knowledge
This study found no correlation between zinc intake and cognitive function.
However, significant correlation was observed between superoxide dismutase (SOD) levels and cognitive function.
Multivariate analysis indicated that gender, education and SOD accounted for 15.9% of the variance in cognitive function.
Application to Patient Care
The findings of this study could help reduce health vulnerabilities, especially cognitive disfunction in old age, and ultimately improve the quality of life among the growing elderly population.
1. Introduction
The proportion of the elderly population worldwide continues to increase each year. In 2020, the global population of individuals aged over 60 years reached 1.4 billion and is projected to grow to 2.1 billion by 2050. By 2030, it is estimated that at least 1 in 6 people in the world will be 60 years or older.1 Since 2021, Indonesia has been transitioning into an aging population structure, with elderly individuals constituting 10.18% of the population in Jakarta in 2023.2 The elderly can become a developmental challenge if they are not productive, as they are more likely to face health issues, lose economic productivity and require caregiver support.2,3 As people age, natural declines in physiological and cognitive functions increase their risk of dementia and mortality.4 According to the World Health Organization, in 2021, there were approximately 65.6 million elderly people worldwide with impaired cognitive function.5
A factor contributing to cognitive impairment is zinc deficiency, which can be caused by insufficient zinc intake or reduced absorption. Aging is characterised by impaired immune response and chronic systemic inflammation, both of which can be improved with zinc supplementation.6 Zinc deficiency plays a significant role in the aging of the immune system.7 By supplementing with zinc, the increased zinc levels in older mice match those of younger mice, resulting in beneficial changes in immune markers, such as a reduction in monocyte chemoattractant protein-1 and a rapid increase in T lymphocyte (CD4+) markers.7 Zinc also decreases the activation-induced production of interferon, IL-17 and tumour necrosis factor in lymph node cells. These findings suggest that enhancing zinc levels could potentially alleviate age-related immune issues and decrease chronic inflammation.7 In addition, zinc seems to affect the nucleotide oligomerisation domain-like receptor protein (NLRP3) inflammasome, potentially slowing cognitive decline and the advancement of Alzheimer's disease.7
Zinc is also a structural component of superoxide dismutase (SOD), an enzyme found in the cytoplasm and mitochondria of cells that helps combat oxidative stress.8 Deficiency in SOD, which is crucial for managing oxidative stress, has been linked to various neurological diseases and cognitive impairment.8,9 Many studies have explored the relationship between reactive oxygen species (ROS) and cognitive function, as ROS is a primary cause of oxidative stress. The oxidative damage caused by excessive ROS is believed to be a key factor in cognitive impairment and age-related neurodegeneration.10 Superoxide has been demonstrated to play an important role in several pathological manifestations in aging animals.11 Therefore, understanding the correlation between zinc intake, SOD activity and cognitive function in the elderly is crucial to prevent cognitive impairment. This knowledge could help reduce health vulnerabilities in old age and improve quality of life among the growing elderly population. Therefore, this study aimed to analyse the correlation between zinc intake and SOD activity with cognitive function in the elderly.
2. Methods
This cross-sectional study was conducted at the Tresna Werdha Social Home Budi Mulia 1 in Jakarta, Indonesia, from July to September 2024. Men and women aged 60 years and older residing in nursing homes in Jakarta who agreed to participate were included. Participants were excluded if they had acute or severe pain, blood coagulation disorders or a history of smoking or alcohol consumption within 1 year prior to the study. Participants were selected using a consecutive sampling method, with data collected through probability proportional to size. The sample size was calculated using a correlation equation with a correlation coefficient (r) of 0.3, resulting in a total of 85 participants.
Data collection involved interviewing participants about their characteristics, including age, gender, chronic diseases and education. Age and gender data were obtained from identification cards, while information about chronic diseases and education was sourced from existing records at the nursing home. Food intake was assessed using the food record or food diary method. Anthropometric screening included measurements of weight and height. A calibrated digital scale (Seca GmbH, Hamburg, Germany) was used to measure body weight, while knee height was measured with a knee height calliper positioned between the heel and the proximal patella. Knee height results were then converted to estimated body height in cm using the following formulas:
For men: Height = (1.924 × knee height) + 69.38
For women: Height = (2.225 × knee height) + 50.25
Body mass index (BMI) was calculated by dividing body weight in kg by height in m2.
Physical activity was assessed using the physical activity scale for the elderly (PASE) questionnaire. A higher score indicates an improvement in the respondent's physical activity. Zinc intake was assessed using the validated Semi-Quantitative Food Frequency Questionnaire (SQ-FFQ). Participants or caregivers identified food types and quantities consumed, indicated which kitchen utensils were used and had food items photographed. Enumerators then determined which foods contained zinc. The assessment was done over a 1-month period and the data were analysed using the NutriSurvey 2007 programme. Total plasma SOD activity was measured using a colorimetric assay, with 5 mL of blood collected from the antecubital fossa. The Montreal Cognitive Assessment-Indonesia version (MoCA-Ina) was used to assess cognitive function, including visuospatial executive function, naming, memory, attention, language, abstraction, delayed recall and orientation tests. To assess visuospatial executive function, participants were instructed to follow a sequence of numbers and draw objects or cubes for which a maximum of 5 points could be given. For naming, participants identified objects in a picture, earning up to 3 points. Memory was evaluated by asking participants to repeat specific words twice, with a 5-minute interval, for a total of 2 points. Attention was assessed by having participants read a list of numbers and tap their hands each time they heard the letter ‘A’, for a maximum of 6 points. Language assessment involved repeating 2 pre-prepared sentences and listing at least 11 words starting with the letter ‘F’, for a maximum of 3 points. Abstraction was tested by identifying similarities between objects, such as ‘banana–orange’ or ‘train–bicycle’, earning 1 point. For delayed recall, participants had to remember the words without prompting, scoring up to 5 points. Orientation was evaluated by asking respondents to state the date, month, year, day, place and city, with a maximum of 6 points. In addition, participants with fewer than 12 years of education received 1 extra point.
Statistical analysis was performed using the Statistical Package for Social Sciences (SPSS) software, Version 26 (IBM Corp., Armonk, New York, USA). Pearson or Spearman correlation tests were used and multivariate analysis was conducted with linear regression. The Kolmogorov–Smirnov normality test was utilised for data with numerical scales, such as zinc intake, SOD activity and cognitive function. Data that were normally distributed (P > 0.05) were presented as mean ± standard deviation. Data that were not normally distributed (P < 0.05) were presented as median (range). Categorical data, including gender, education, chronic diseases, physical activity, BMI and total calorie intake were presented as proportions (frequency distribution).
3. Results
A total of 85 participants ranging from 60 to 91 years old (median = 69 years) were included. The majority were female (n = 52, 61.2%) and the remaining participants (n = 33, 38.8%) were male. Most participants had a primary education (n = 62, 72.9%) and only a small proportion (n = 3, 3.5%) had a higher education background. The majority of participants (n = 74, 87.1%) had chronic diseases. The mean physical activity score, based on the PASE questionnaire was 75.4 ± 39.8, and the mean BMI was 21.8 ± 3.9 kg/m2. Total calorie intake ranged from 1,341.7 kcal per day to 2,167.0 kcal/day (median = 1,927.8 kcal/day) [Table 1].
Table 1.
Characteristics of elderly participants included in this study (N = 85).
| Characteristic | n (%) |
|---|---|
| Median age in years (range) | 69 (60–91) |
| Sex | |
| Male | 33 (38.8) |
| Female | 52 (61.2) |
| Education | |
| No schooling | 13 (15.3) |
| Primary education | 62 (72.9) |
| Secondary education | 7 (8.2) |
| Higher education | 3 (3.5) |
| Chronic disease | |
| None | 11 (12.9) |
| Yes | 74 (87.1) |
| Schizophrenia | 29 (34.1) |
| Asthma | 3 (3.5) |
| Hypertension | 40 (47.1) |
| Uric acid | 21 (24.7) |
| Dyslipidaemia | 6 (7.1) |
| Diabetes mellitus | 7 (8.2) |
| Gastritis | 1 (1.2) |
| Vertigo | 1 (1.2) |
| Parkinson’s disease | 1 (1.2) |
| Dermatitis | 1 (1.2) |
| Heart disease | 1 (1.2) |
| Physical activity | |
| No active | 75 (88.2) |
| Active | 10 (11.8) |
| Mean physical activity ± SD | 75.4 ± 39.8 |
| BMI grades in kg/m2 | |
| Underweight | 19 (22.4) |
| Normal | 42 (49.4) |
| Overweight | 9 (10.6) |
| Obesity | 15 (17.6) |
| Mean BMI ± SD | 21.8 ± 3.9 |
| Total calorie intake in kcal/day | |
| Inadequate | 20 (23.5) |
| Adequate | 65 (76.5) |
| Median calorie intake (range) | 1927.8 (1341.7–2167.0) |
SD = standard deviation; BMI = body mass index.
The majority of study participants (78.8%) had insufficient zinc intake, with a median of 7.6 mg/day. SOD activity ranged from 2.4–19.0 U/mL, with a median of 13.1 U/mL. The mean cognitive function score, based on the MoCA-Ina questionnaire, was 15.6 ± 8.3, with 72% of participants showing cognitive decline and 13% exhibiting normal cognitive function Table 2.
Table 2.
Zinc intake, superoxide dismutase level and cognitive function results from this study (N = 85).
| Variable | n (%) |
|---|---|
| Zinc intake in mg/day | |
| Inadequate | 67 (78.8) |
| Adequate | 18 (21.2) |
| Median zinc intake (range) | 7.6 (5.3–8.2) |
| Median SOD in U/mL (range) | 13.1 (2.4–19.0) |
| Cognitive function | |
| Decrease | 72 (84.7) |
| Normal | 13 (15.3) |
| Mean cognitive function ± SD | 15.6 ± 8.3 |
SOD = superoxide dismutase; SD = standard deviation.
Zinc intake based on age ranged from 5.3 mg/day to 8.2 mg/day, with a median intake of 7.6 mg/day. By gender, the mean zinc intake was higher in females (7.4 ± 0.8 mg/day) than males (7.3 ± 0.7 mg/day). Regarding education level, subjects with a primary education had a mean zinc intake of 7.3 ± 0.7 mg/day, while those with higher education had a slightly higher mean intake of 7.8 ± 0.2 mg/day. Participants with chronic diseases had a mean zinc intake of 7.4 ± 0.7 mg/day, while those without chronic diseases had a lower mean intake of 7.1 ± 1.0 mg/day. In terms of physical activity, participants with no active physical activity had a mean zinc intake of 7.4 ± 0.8 mg/day, compared to 7.0 ± 0.5 mg/day in those who were physically active. When considering BMI, participants with a normal BMI had a mean zinc intake of 7.3 ± 0.7 mg/day, while those classified as overweight had a slightly higher mean intake of 7.7 ± 0.5 mg/day. Finally, participants with an adequate total calorie intake had a mean zinc intake of 7.6 ± 0.6 mg/day, whereas those with a deficient total calorie intake had a lower mean intake of 6.6 ± 0.7 mg/day Table 3.
Table 3.
Overview of zinc intake based on participant’s characteristics (N = 85).
| Variable | Zink intake in mg/day ± SD |
|---|---|
| Age in year (range) | 7.6 (5.3–8.2) |
| Sex | |
| Male | 7.3 ± 0.7 |
| Female | 7.4 ± 0.8 |
| Education | |
| No schooling | 7.4 ± 0.8 |
| Primary education | 7.3 ± 0.7 |
| Secondary education | 7.4 ± 0.9 |
| Higher education | 7.8 ± 0.2 |
| Chronic disease | |
| None | 7.1 ± 1.0 |
| Yes | 7.4 ± 0.7 |
| Physical activity | |
| No active | 7.4 ± 0.8 |
| Active | 7.0 ± 0.5 |
| BMI in kg/m2 | |
| Underweight | 7.0 ± 0.8 |
| Normal | 7.3 ± 0.7 |
| Overweight | 7.7 ± 0.5 |
| Obesity | 7.8 ± 0.5 |
| Total calorie intake in kcal/day | |
| Less | 6.6 ± 0.7 |
| Adequate | 7.6 ± 0.6 |
SD = standard deviation; BMI = body mass index.
The Spearman correlation test was used to analyse the correlation between zinc intake and SOD activity, as well as between zinc intake and SOD activity with cognitive function. Statistical analysis indicated no significant correlation between zinc intake and SOD activity (r = –0.014; P = 0.896) or between zinc intake and cognitive function (r = –0.083; P = 0.449). However, statistical analysis revealed a weak but significant correlation between SOD activity and cognitive function (r = 0.296; P = 0.006) Table 4.
Table 4.
Correlation of zinc intake and superoxide dismutase level with cognitive function (N = 85).
| SOD | Cognitive function | |||
|---|---|---|---|---|
|
|
|
|||
| Variable | r | P value* | r | P value* |
| Zinc intake in mg/day | –0.014 | 0.896 | –0.083 | 0.449 |
| SOD in U/mL | 0.296 | 0.006 | ||
SOD = superoxide dismutase; r = correlation coefficient. *Spearman correlation test.
This study also included an analysis of variables that may influence cognitive function, such as age, gender, education level, chronic disease, physical activity, BMI and total calorie intake. There was no significant correlation between these variables and cognitive function. However, variables with P < 0.25 were included in the multivariate analysis as potential confounding factors Table 5.
Table 5.
Correlation of confounding variables with cognitive function (N = 85).
| Variable | Cognitive function ± SD | P value |
|---|---|---|
| Age | r = 0.044 | 0.691* |
| Sex | 0.072† | |
| Male | 17.7 ± 7.6 | |
| Female | 14.4 ± 8.5 | |
| Education | 0.106‡ | |
| No schooling | 11.1 ± 7.0 | |
| Primary education | 16.0 ± 8.3 | |
| Secondary education | 19.3 ± 9.1 | |
| Higher education | 19.7 ± 7.6 | |
| Chronic disease | 0.354† | |
| None | 13.5 ± 9.8 | |
| Yes | 16.0 ± 8.1 | |
| Physical activity | 0.248† | |
| Not active | 15.3 ± 8.1 | |
| Active | 18.5 ± 9.7 | |
| BMI in kg/m2 | 0.270‡ | |
| Underweight | 12.7 ± 8.0 | |
| Normal | 16.8 ± 8.2 | |
| Overweight | 14.1 ± 8.7 | |
| Obese | 17.1 ± 8.5 | |
| Total calorie intake in kcal/day | 0.641† | |
| Less | 16.4 ± 8.9 | |
| Adequate | 15.4 ± 8.2 |
SD = standard deviation; BMI = body mass index; r = correlation coefficient. *Using Spearman Test.†Using independent T-Test. ‡Using one-way ANOVA.
Bivariate tests revealed that there was no significant correlation between zinc intake and cognitive function. However, a weak correlation was discovered between SOD activity and cognitive function, leading to a decision to conduct further analysis to investigate this correlation while adjusting for confounding factors, such as gender, physical activity and SOD activity. The multivariate analysis involved selecting independent variables with a P ≤ 0.200 and r ≥ 0.200 from the bivariate analysis. As a result, gender, education and SOD activity were included in the advanced analysis using multiple linear regression Table 6. After adjusting for confounding variables, the multivariate test results revealed a weak but significant relationship between SOD and cognitive function (P = 0.006). The adjusted coefficient of determination (adjusted R2) of 0.159 indicated that 15.9% of the variation in cognitive function was influenced by gender, education and SOD, while the remaining 84.1% was influenced by other factors.
Table 6.
Correlation of superoxide dismutase with cognitive function after adjusting for confounding variables (N = 85).
| Variable | β | 95% CI | P value |
|---|---|---|---|
| Intercept | 3.324 | –7.541 to 14.188 | 0.544* |
| Gender | –3.722 | –7.107 to –0.337 | 0.032 |
| Education | 3.742 | 1.052 to 6.431 | 0.007 |
| SOD in U/mL | 0.834 | 0.239 to 1.429 | 0.007 |
SOD = superoxide dismutase; β = parameter estimation; CI = confidence interval. *Multiple linear regression multivariate analysis test.
4. Discussion
This study found that 72% of the participants exhibited cognitive decline. Dhakal and Bobrin explained that while cognitive deficits are more common in the elderly, not all older adults experience them.12 There was a higher proportion of female than male participants, which is consistent with data from the Central Statistics Agency. The agency reports that elderly people in Indonesia represented more than 11.75% of the population in 2023, 52.28% of whom were women and 47.72% men.2 Kim's study found that women exhibited more cognitive decline than men.13 Regarding education level, 72.9% of the participants had a primary education, aligning with Central Statistics Agency data indicating that, on average, elderly Indonesians have completed up to grade 5 of elementary school or its equivalent.2 A study by Kim and Park found that low education was a risk factor that affects cognitive function.14
Chronic disease data in this study were collected through interviews with the participants, cross-referenced with nursing home records and categorised based on the presence or absence of chronic diseases. The study revealed that only 12.9% of participants did not have any chronic diseases, while the remaining participants had one or more chronic diseases. However, these findings were not supported by routine physical and laboratory examinations, leaving the severity and duration of the diseases unclear. Oxidative stress, which is triggered by elevated ROS can damage various cellular molecules such as proteins, carbohydrates, fats and DNA. This damage has the potential to accelerate aging and contribute to degenerative diseases.15
Schneider et al. found that vascular disease risk factors were associated with cognitive function in a large multi-ethnic sample of elderly individuals, but were not associated with cognitive decline over time.16 Among these risk factors, diabetes and stroke demonstrated the strongest association with cognitive function.16 Physical activity data were gathered using the PASE questionnaire, with an average score of 75.4. A higher PASE value indicates a higher level of physical activity in the elderly. However, this average was lower than that reported by Singh et al., who found a PASE score of 167.91 among elderly individuals in Malaysia. Singh et al. found that the elderly had irregular physical activity. The nutritional status of the participants in this study was assessed through BMI, with a mean of 21.8 kg/m2. A 1–2% decrease in resting metabolic rate per decade after the age of 20 is associated with aging. This decline is associated with a decrease in fat-free mass, which can reach 25% by the age of 75–80 and lead to overweight or obesity along with sarcopenia in the elderly. To better understand fat composition, a detailed body composition analysis should be examined.18
Vidoni et al. found that a low BMI was associated with normal cognitive function and mild cognitive impairment.19 Total calorie intake was estimated from food records over 2 working days and 1 day off, with a median intake of 1,927.8 kcal/day. According to the 2019 Indonesian Dietary Recommendation, based on age and gender, elderly males should aim for an intake of 1,600–2,150 kcal/day, while elderly females should aim for 1,400–1,800 kcal/day.20 Yeh et al. found that a high intake of total energy, fat and carbohydrates was associated with subjective cognitive decline.21 Zinc intake was evaluated using a SQ-FFQ and compared to the 2019 Indonesian Dietary Recommendation based on age and gender. The median zinc intake among participants was 7.6 mg/day, which falls below the recommended intake of 8–9 mg/day for their demographic. The low zinc intake in the participants of this study may be attributed to poor absorption or limited food variety in the elderly diet, which is often influenced by reduced chewing ability and appetite. Foods high in zinc, such as oysters and red meat, are seldom consumed by the elderly participants.20
Low zinc intakes ranging from 4.3–4.6 mg/day were also found in the study by Ruangritchanul et al.22 Mean zinc intake, based on subject characteristics such as age, gender, education, chronic disease, physical activity, BMI and total calorie intake, ranged from 5.3 mg/day to 8.2 mg/day. These results indicate that zinc intake among participants in the abovementioned study did not meet the 2019 Indonesian Dietary Recommendation of 8–9 mg/day.20
Ruangritchanul et al. found that daily zinc intake across all age groups did not meet the recommended levels.22 Male participants had a lower zinc intake than females, and those with only primary education also had a lower intake. In addition, they found that most elderly individuals had an insufficient daily zinc intake, with low serum zinc concentrations particularly observed in females and those with ≤12 years of education. No significant differences in daily zinc intake were observed between participants with and without chronic diseases or between those with active and inactive physical activity levels. Proper management of comorbidities may help prevent low serum zinc levels. Zinc is a crucial micronutrient for the elderly, supporting physical health and reducing bone fragility. Deficiency in zinc can lead to reduced appetite and malnutrition. In the current study, low zinc intake was also observed in participants with an underweight BMI and those with a deficient total calorie intake.
Abeywickrama et al. reported that elderly individuals with low and normal BMI had similar average zinc intakes, with no significant differences being observed.23 Similarly, Lay et al. found that dietary zinc intake and total energy intake were directly proportional.24 In their study, venous blood was taken for plasma SOD examination and the median value was found to be 13.1 U/mL. This result is significantly lower than that reported by Zang et al. who found a median SOD value of 164 U/mL in early cognitive impairment within 2 weeks after stroke and 174 U/mL in late cognitive impairment 3 months after stroke.9 Their study, which included 187 patients diagnosed with mild acute ischaemic stroke, measured serum SOD activity using pyrogallol autoxidation.9 The median SOD value found by the current study was lower than that reported by Mao et al., namely 57.4 U/mL in 2,224 participants with higher frequent meat intake.25 Mao et al. measured SOD activity using the T-SOD assay kit based on hydroxylamine method. However, their result is higher than that reported by Fasna et al., who found a mean SOD level of 1.01 U/mL in a study of 50 elderly participants.26 Fasna et al. measured SOD levels using a UV-VIS spectrophotometer (Systronics 118). Differences in population demographics, dietary patterns and measurement methods may contribute to the discrepancies in SOD levels. SOD, an enzyme with copper ions and zinc at its active site, plays a crucial role in reducing oxidative stress. Decreased SOD activity has been associated with early cognitive impairment and pathophysiological changes, potentially leading to conditions such as stroke.10
The current study found no significant correlation between zinc intake and SOD activity. The lack of correlation may be due to potential recall bias in zinc intake assessment, impaired zinc absorption caused by age-related physiological decline in the gastrointestinal tract and the presence of phytate, which can hinder absorption. Phytate forms mixed salts with mineral cations due to its high density of negatively charged phosphate groups, playing an important role in mineral storage.27 Serum zinc levels may be a more reliable indicator of zinc status than dietary intake. However, Pinontoan et al. also found no significant correlation between serum zinc levels and erythrocyte SOD activity.28 The relationship between zinc status and SOD activity is inconsistent in both animal and human studies. Mariani et al. reported a weak negative correlation between plasma zinc concentration and erythrocyte SOD activity.29
Zinc is a structural component of the SOD found in the cytoplasm of cells.8 A cause of impaired cognitive function is linked to zinc deficiency. Markiewicz-Zukowska et al.'s study found that 28% of elderly individuals with zinc deficiency had mild cognitive impairment.30 Their study found no significant correlation between zinc intake, SOD activity and cognitive function. The inconsistency in results may be due to the wide range of methods used to measure zinc intake. Yaffe et al.'s study was a randomised controlled trial that investigated the effects of antioxidants, with or without zinc and copper, on cognitive function in non-demented elderly individuals.31 Their results revealed that, compared to a placebo, treatment with antioxidants and/or zinc and copper did not have a significant impact on cognitive function in the late stages. In addition, there was no significant difference in the incidence of cognitive impairment across all treatment groups.31 The lack of correlation may be attributed to other factors that influence cognitive function, such as chronic inflammation, antioxidant status and DNA damage. Nevertheless, zinc is an essential ion required for the physiological functions of all cells, in addition to being an important structural component.32 It plays a role in redox regulation, enzyme activity, gene transcription, energetic metabolism, the cell cycle, cell migration and invasion, apoptosis and proliferation and interacts with metallothionein.33 An imbalance in zinc levels can potentially lead to significant health consequences, including the development of various diseases.34 Banks et al. found that zinc deficiency is closely associated with cognitive impairment, memory deficits and other diseases.35
In the current study, a weak but significant correlation was found between SOD activity and cognitive function, which is consistent with previous studies that have linked reduced SOD levels to cognitive decline, especially in Alzheimer's disease models. SOD deficiency has been shown to accelerate amyloid β oligomerisation, memory loss and vascular damage, all of which contribute to neuronal injury and cognitive impairment.36 SOD is a group of oxidoreductases that catalyse the conversion of superoxide radicals into hydrogen peroxide and oxygen, helping to mitigate oxidative stress, inflammation and aging-related neurodegeneration.9
Fracassi et al. studied the correlation between ROS and cognitive function, finding that most of the observed correlations were influenced by aging.10 To determine the relationship between zinc intake, SOD and cognitive function, multivariate tests were conducted to adjust for potential confounding factors. Gender, education and SOD were included in the multivariate analysis, as they had a P value of ≤ 0.200 and r ≥ 0.200 in the bivariate analysis. The multivariate analysis revealed that gender, education and SOD had a weak but significant effect on cognitive function, explaining 15.9% of the variation in cognitive function. However, most of the variation in cognitive function remains unexplained. Levine et al. found that women tend to have better baseline cognitive abilities than men, including memory, executive function and global cognition.37 However, women also experience a more rapid rate of cognitive decline. In line with this research, there is a significant correlation between education and cognitive function. Goncalves et al. found that individuals with higher education are less likely to experience cognitive impairment than those without formal education.38 This supports the findings of the current study, which also demonstrates a significant correlation between education and cognitive function.
The current study is the first to explore the association between zinc intake, SOD activity and cognitive function, which enhances the importance of the findings. Moreover, previous research has not investigated zinc intake – a crucial structural component of the SOD enzyme in the cytoplasm – among the elderly residing in nursing homes in Jakarta. The evaluation of zinc intake may be influenced by the reliance on interviews with elderly participants, who encountered difficulties in recalling specific food types and precise portion sizes during the SQ-FFQ interview. To minimise recall bias, the researcher employed trained intake enumerators with a background in nutrition and utilised food photo-books to help participants to report their dietary intake more accurately on 2 week-days and 1 day off. However, this analysis did not consider several crucial factors that influence cognitive function, such as psychosocial aspects (e.g., depression and loneliness), genetic predispositions (e.g., APOE genotype) and micronutrient interactions (e.g., copper, iron and selenium). Therefore, further research is needed to consider those factors in the analysis of cognitive function, as well as to stratify the participants based on factors such as education, health status and socioeconomic background.
5. Conclusion
This study did not find any significant correlation between zinc intake and cognitive function. However, a significant correlation was found between SOD activity and cognitive function. Multivariate analysis revealed that gender, education and SOD accounted for 15.9% of the variance in cognitive function. Future research should focus on analysing zinc levels and their correlation with SOD and cognitive function. Longitudinal studies should be conducted to explore interventions such as zinc supplementation and incorporate a wider range of oxidative stress markers.
Authors' Contribution
Rima Khuzaimah: Formal Analysis, Writing-original draft, Writing-review & editing, Data curation, Visualization, Resources. Fiastuti Witjaksono : Supervision, Investigation, Methodology, Validation. Novi Silvia Hardiany: Supervision, Conceptualization, Investigation, Validation, Funding acquisition, Writing-review & editing, Resources
Acknowledgement
All authors wish to express appreciation to the management of Panti Sosial Tresna Wredha Budi Mulia 1 Jakarta for granting permission for the residents to participate in this research. Moreover, the authors extend their sincere gratitude to the Directorate of Research and Development Universitas Indonesia for funding this research.
Ethics Statement
Institutional ethical approval was obtained from the health research ethics committee of the Faculty of Medicine Universitas Indonesia (KET-820/UN2.F1/ETIK/PPM.00.02/2024). Informed consent was obtained from the participants.
Conflict of Interest
The authors declare no conflicts of interest.
Funding
Directorate of Research and Development Universitas Indonesia for funding the Postgraduate grant (Grant number: NKB-80/UN2.RST/HKP.05.00/ 2024).
Data Availability
Data is available upon reasonable request from the corresponding author.
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Associated Data
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Data Availability Statement
Data is available upon reasonable request from the corresponding author.