https://orcid.org/0009-0004-6834-2100
Figures
Abstract
Purpose
Studying the effects of uric acid levels on cognitive function and quantifying the dose-response relationship.
Methods
Based on PubMed and Embase search terms, we identified prospective cohort studies that included blood uric acid as a risk factor and cognitive impairment as a result up to September 2022. We extracted pooled relative risks (RRs) and corresponding 95% confidence intervals (CIs).
Results
Nine reports (including 488,915 participants and 5516 cognitive impairment cases) with median follow-up of 8.8–22 years were eligible for analyses. Compared with lowest category of blood uric acid concentration, the combined RR of cognitive impairment events in the highest classification was 0.81 (95% CI: 0.70–0.92, P < 0.001). Dose-response analysis of eight reports (including 484,297 participants and 5059 cognitive impairment cases) showed that there was no evidence of a curvilinear relationship between blood uric acid levels and cognitive impairment (P = 0.51 for nonlinear relationship). The summary RR of cognitive impairment for an increase of 1 mg/dL blood uric acid level was 0.98 (95% CI: 0.95–1.00; linear trend P = 0.07, I2 = 67.1%, heterogeneity P < 0.05). There was also a linear negative association between blood uric acid levels and cognitive impairment risk in the male subgroup analysis (RR = 0.97, 95% CI: 0.95–0.99, P < 0.05).
Citation: Liu Q, Peng M, Yang T, Si G (2023) Uric acid levels and risk of cognitive impairment: Dose-response meta-analysis of prospective cohort studies. PLoS ONE 18(11): e0293832. https://doi.org/10.1371/journal.pone.0293832
Editor: Lai Kuan Lee, Universiti Sains Malaysia, MALAYSIA
Received: April 17, 2023; Accepted: October 19, 2023; Published: November 2, 2023
Copyright: © 2023 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: our article was funded by the National Natural Science Foundation of China (81473631), Natural Science Foundation of Shandong Province (ZR2020MH349), and Traditional Chinese Medicine Science and Technology Development Project of Shandong (2021Q115).
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: CI, Confidence interval; MCI, Mild cognitive impairment; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RR, Relative risk; UA, Uric acid; WHO, World Health Organization
1 Introduction
Cognitive impairment is an umbrella term for impairment of one or more cognitive domains which may be influenced by various causes and may range from mild cognitive impairment (MCI) to different stages of dementia [1, 2]. With the increase in population aging, cognitive impairment has become a global public health concern, causing serious social and economic losses [3, 4]. MCI is estimated to affect approximately 25% of people aged 80–84 years, with a cumulative incidence of dementia of 14.9% in people aged 65 years and older at 2 years of follow-up [5]. An estimated 55 million people were living with dementia in 2019, it is predicted that by 2050, this number will reach 139 million, according to the updated data from the World Health Organization (WHO) [6]. With the full implementation of the seven priority action areas of the WHO Global Action Plan on the Public Health Response to Dementia, more attention has been paid to prevent disease progression and delay its progression by controlling modifiable risk factors [1, 7, 8]. Among the known risk factors, blood uric acid (UA) has attracted particular attention.
The body produces UA through purine metabolism and it has both oxidation-promoting and anti-oxidant properties. It has been found that UA may be involved in mechanisms such as oxidative stress and inflammatory responses associated with cognitive impairment and acts as a double-edged sword for cognitive function in the brain [9]. Some observational studies suggest that high UA levels may have deleterious effects on brain health, including the development of cerebral small vessel disease and cognitive decline [10, 11]. However, studies have also indicated that serum UA plays a neuroprotective role in the diseases of Alzheimer’s and Parkinson’s and that low blood UA levels are not only associated with faster disease progression, but are also indicative of malnutrition [12–15]. Appropriate increases in UA levels within the normal range can instead delay the onset and progression of cognitive impairment [16]. Notably, many vascular risk factors and diseases have also been associated with UA, which may predispose people to cognitive impairment [2]. This may also, to some extent, alter the relations between UA levels and poor cognitive outcomes. In addition, recent studies suggest that the impact of UA levels on cognitive function may vary according to gender and dementia subtype, but with variable results [17, 18].
The above cumulative findings develop new approaches to identifying and treating cognitive impairment risk factors and highlight the potential for blood UA management in the prevention of cognitive impairment. However, the correlation between blood UA and cognitive impairment remains controversial, although extensive research had been utilized to explore the above relationship. Furthermore, studies have examined whether blood UA is associated with cognitive impairment in published systematic reviews [19, 20], but not specifically within prospective cohort studies, and no relevant dose-response analyses have been evaluated. Therefore, we sought to quantify the relationship between blood UA levels and cognitive impairment by conducting a dose-response meta-analysis of prospective cohort studies.
2 Material and methods
2.1 Search strategy
From inception until September 2022, we searched PubMed and Embase for prospective cohort studies investigating UA levels and cognitive impairment (including MCI and dementia of all types). The meta-analysis was prospectively registered in INPLASY (2022100111). Both MESH subject headings and free terms were employed in the search (S1 and S2 Tables). Furthermore, a thorough review of the references of the relevant original articles was also undertaken to uncover further related studies. It was not restricted in any way in terms of language. Two researchers independently scrutinized all the retrieved articles and preliminary eligibility was carefully reviewed in accordance with titles, abstracts, and text in full when necessary. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement was followed in our study [21].
2.2 Study selection
The included studies must meet the following criteria: 1) Prospective cohort studies; 2) Cognitive impairment (including all types of dementia and MCI) as a specific outcome, based on certain standardized diagnostic criteria (Table 1); 3) The investigators classified UA concentrations into at least three categories and provided relative risks (RRs) with 95% confidence intervals (CIs) for each. Furthermore, we excluded studies that lacked sufficient data, reviews, editorials, and non-human studies. Studies that were reported more than once were compiled based on the results with the longest follow-up.
2.3 Data extraction and quality evaluation
Two authors (QQL and TTY) independently extracted data from each study using standard extraction formats. Each study was analyzed for the following factors: author, publication year, study name, study location, gender and age of participants, diagnostic criteria, size of the sample (number of participants and incidents), UA levels, cognitive impairment (endpoint), follow-up year and RRs (95% CIs) for different UA levels.
Studies were evaluated according to Newcastle-Ottawa quality criteria [22]. Studies with quality scores of 0 to 3, 4 to 6, and 7 to 9 were considered low quality, moderate quality, and high quality, respectively. When the study had multiple adjusted models, we extracted the model that reflected the most adjustment for potential confounders. To resolve differences by consensus, the review process was guided by group consensus and a third reviewer was consulted (MP).
2.4 Statistical analysis
In this study, the hazard ratios were considered equivalent to the RRs, and the RRs with 95% CI were considered to be the effect sizes of all studies. Any outcomes stratified by gender were considered to be two separate reports[23]. A dose-response relationship was investigated by using the generalized least squares regression method of Greenland and Longnecker and Orsini and colleagues [24, 25]. UA, number of reported cases, total participants, and RRs with 95% CIs were extracted for each stratum according to this method.
The RR corresponded to the mean/median of the UA level categories for each stratum in this meta-analysis. If they were not accounted for, the mean of the lower and upper limits per category was used as the dose [26]. If the highest/lowest UA level category was open, in order to calculate the midpoint, half the width of the adjacent categories were added (highest category) and subtracted (lowest category) [26]. If no numbers are available for cases or non-cases per category, the method of Chêne et al was used to supply an approximation based on cases in total and RR for per category [27, 28]. In addition, we assessed potential curvilinear relationships between UA levels and cognitive impairment using four-node restricted cubic splines at the 5%, 35%, 65% and 95% percentile distributions.
Statistical heterogeneity between studies was assessed by the I2 test. A low value of 25%, a medium value of 50%, and a high value of 75% were considered [29]. When heterogeneity was negligible, we used a fixed-effect model (Mantel–Haenszel method) and when it was significant, we used a random-effect model (DerSimonian and Laird method). Overall effects were examined using forest plots. Publication bias was assessed using Egger’s regression asymmetry test and Begg’s test. Stratified analyses were also conducted based on gender, age, location of the study, the length of follow-up, the number of participants, dementia type, and diabetes status. Stata version 13.0 of the software (Stata Corp, College Station, TX, USA) was used to perform all statistical analyses. Statistical significance was determined by P < 0.05.
3 Results
3.1 Literature search
The results of the literature search and selection are shown in Fig 1. We retrieved 606 articles in PubMed and 486 articles in Embase before September 30th, 2022. After excluding duplicate papers (n = 53) and studies that failed to satisfy the inclusion criteria (n = 984), the remaining 55 articles appeared relevant to this meta-analysis. Twenty-two articles published were excluded due to less than three UA categories after reviewing these publications in their entirety. We also excluded 22 studies because they were interventional or retrospective studies. Due to insufficient data, four articles were excluded. Another two articles were excluded because they were meeting abstracts. Three of the six articles examined men and women separately in the final meta-analysis. Overall, this meta-analysis included six articles and nine independent reports. We compared the blood UA levels of the highest and lowest categories based on nine studies, and in eight studies, the impact of every 1 mg/dL of blood UA level was analyzed.
3.2 Study characteristics
Tables 1–4 show extracted information from the included studies. A median follow-up of 8.8 to 22 years was provided for 488,915 participants with 5516 cognitive impairments. Of the total cohort, four studies were conducted in Europe and the others were from China and the United States [30–35]. The research quality evaluation results (scores 0–9) of all studies were ≥7 (high quality), with an average score of 7.2 (Table 5).
3.3 Meta-analysis
3.3.1 Relationship between blood UA levels and cognitive impairment.
Six articles with nine reports were considered to provide sufficient data for the analysis of blood UA levels and cognitive impairment relationships [30–35]. Compared with the lowest category of blood UA level, the combined RR of cognitive impairment events in the highest group was 0.81 (95% CI: 0.70–0.92, P < 0.001) (Fig 2). Results of the research showed no significant heterogeneity (P = 0.051, I2 = 48.2%).
3.3.2 Dose-response analysis.
Five articles with eight reports were considered in this dose-response analysis of blood UA levels and cognitive impairment events [31–35]. UA levels did not appear to be associated with the risk of cognitive impairment in a curvilinear manner (P = 0.51 for nonlinear relationship; Fig 3). The summary RR of cognitive impairment for an increase of 1 mg/dL blood UA level was 0.98 (95% CI: 0.95–1.00; linear trend P = 0.07, I2 = 67.1%, heterogeneity P < 0.05) (Fig 4). The regression tests of Begg’s and Egger’s did not demonstrate substantial publication bias (P = 0.902 and P = 0.962). In addition, we conducted a sensitivity analysis to assess whether outcomes were affected by the excluded studies, and found no substantial changes in the results (Fig 5).
3.3.3 Subgroup analysis.
Subgroup analyses were conducted based on gender, age, location of the study, the length of follow-up, dementia type, diabetes status, and number of participants, to explore the potential source of heterogeneity (Table 6). Fig 6 shows that in the male cohort, blood UA levels and cognitive impairment risk exhibited a linear negative relationship (RR = 0.97, 95% CI: 0.95–0.99, P < 0.05). This meta-analysis of blood UA levels found a negatively linear relationship between UA levels and cognitive impairment in individuals who were followed for fewer than ten years (RR = 0.96, 95% CI: 0.95–0.98, P < 0.001) (Fig 7). When stratified according to participants, if the number of participants in total was more than 2000, the incidence risk had a linear negative correlation (RR = 0.98, 95% CI: 0.97–1.00, P < 0.05) (Fig 8). In any of the three subgroup analyses, there was no evidence of significant heterogeneity (P > 0.05) (Figs 9–11).
4 Discussion
4.1 Principal findings
To the best of our knowledge, the first dose-response meta-analysis conducted on prospective cohort studies investigating UA concentrations and cognitive impairment was presented in our study. Based on the nine prospective cohort studies, a significant correlation was identified between blood UA levels and risk of cognitive impairment in the comparison of the highest versus lowest group. The remarkable relationships disappeared when we conducted a dose-response analysis. However, studies including only male participants, with more than 2000 volunteers and followed up for less than 10 years, demonstrated that a reduction in cognitive impairment was associated with higher blood UA levels. Nevertheless, this should be evaluated further.
4.2 Results in relation to other studies
Our findings identified that the risk of cognitive impairment is not significantly correlated with blood UA levels. A meta-analysis of 5575 participants pointed out that there is a potential risk of Alzheimer’s disease and Parkinson’s dementia associated with low concentrations of blood UA, but this does not apply to vascular dementia [19]. Another meta-analysis of 1128 cases and 2498 controls found that high UA level in blood was a protective element for Alzheimer’s disease [20]. Different study types, study sample size, cognitive impairment diagnosis, and not well-controlled confounders may have contributed to this discrepancy.
In clinical practice, the normal levels of serum UA range from 2 mg/dL to 7 mg/dL. In hyperuricemia, the serum urate concentration is above 7mg/dL, while in hypouricemia, it is below 2mg/dL [36, 37]. To date, most cohort studies have examined the relationship between UA levels and cognitive trajectories. A national retrospective cohort study of older adults conducted in Korea found that there was a lower risk of dementia among patients with gout compared to the general population, regardless of the type of dementia [38]. This discovery provides lateral support for the beneficial neuroprotective effects of hyperuricemia, which are similar to some of our conclusions. Further study showed that both serum UA baseline levels and dynamic changes may affect cognitive trajectories. Although stable or moderate increases of serum UA levels may benefit cognition, hyperuricemia persisting for a long time can negatively impact cognition [39]. However, the observations of Liu et al. find that both high and low blood UA levels are correlated with an increased risk of cognitive impairment, supporting a U-shaped relationship between these associations [40]. Our study includes the populations with hyperuricemia, normal UA levels and hypouricemia. The included studies lack stratification data of the three populations mentioned above, so we cannot consider them as subgroup variables for further analysis. Future studies may be specifically designed to increase the diversity of the study population to solve this problem.
4.3 Interpretation of the results
We further considered the reasons for lack of an overall relationship between blood UA levels and cognitive impairment. First, it is well known that blood UA levels are associated with obesity, alcohol consumption, smoking, physical activity, hypertension, diabetes and other elements [10, 41, 42], that are also risk factors for cognitive impairment. Despite our selection of the most adjusted models, the influence of residual confounding factors on the basis of the results could not be excluded. We lacked an assessment of nutritional status, which might play a partial role in blood UA levels and cognitive function [43]. Studies have also demonstrated that people with low blood UA levels tend to have poorer nutritional status [44, 45], which increases the risk of cognitive impairment [46]. Furthermore, although "one-third" of the total amount of urate in the body comes from dietary purines [47], we cannot ignore the influence of dietary patterns on UA levels. Dietary interventions are considered important for gout prevention [48]. A meta-analysis showed that plant-based dietary patterns were associated with lower UA levels, while animal-based dietary patterns were associated with higher UA levels [49]. Further studies have shown that in populations with normal UA levels, UA concentrations in short- and long-term dieters remain within the normal range even when only plant-based dietary patterns are used [50]. However, due to the wide variation in dietary habits among people born in different countries, geographic regions, and generations, as well as changes in dietary habits during long-term follow-up, most of the included studies lacked data on diet and UA levels, which could be further analyzed in the future by refining the study protocols.
Second, deteriorating health and death are the main hallmarks of the aging process [51]. A potential explanation might be that survival bias reduced the likelihood of people with different levels of blood UA surviving into later life, both inhibiting the development of cognitive impairments and affecting the true assessment of cognitive characteristics in later life. Most of our studies did not consider the character of survival bias in the development of cognitive impairment, which brought bias to the research results.
Third, blood UA levels might modulate cognitive function in a different way. UA has been shown to be a catalyst for human intellectual development has been proven [52], which is due to its anti-oxidant properties and potential neuroprotection [9, 53]. Previous studies have indicated that UA has both oxidation and reduction properties based on differences in the physicochemical environment [11, 54]. UA not only plays a role in oxidative stress as a bioactive pro-inflammatory factor, but also protects neurons under certain conditions and exhibits antioxidant properties [55–57]. Pathological changes in neurodegeneration and cerebrovascular injuries play a synergistic or additive role in the course of cognitive impairment [58, 59]. Several studies, including observational studies and animal experiments, have illustrated that the progression of cognitive impairment is closely linked to oxidative stress and inflammatory responses [59–63], and that blood UA levels are associated with these mechanisms [10, 64–66]. Redox homeostasis is important to maintain normal cellular physiological function [67, 68]. One possible interpretation is that physiological levels of blood UA plays a neuroprotective role in the development of cognitive disorders; however, when UA values are elevated to a certain range and remain high for a long time, the neuroprotective effect might be obscured by the increased vascular risk associated with high blood UA levels.
Fourth, we assessed baseline UA levels and cognitive impairment correlations. Most studies have only measured baseline UA levels, and the role of UA might depend on the duration of its levels. Changes in UA levels occur over the course of the prolonged follow-up [69], and chronically higher UA levels could be associated with cognitive decline [39]. Recently, a community-based cohort study suggested that the interaction between genotype of apolipoprotein E4 and additional genetic risk factors modified the risks of dementia [70]. Similarly, Lee and colleagues found that higher serum UA may interact with apolipoprotein E4 to alleviate cognitive decline in female patients with MCI [71]. The influence of genetic factors in the incidence of cognitive impairment should be further explored.
Furthermore, various cognitive domains may be affected by alterations in UA levels [16, 72]. Huang et al found that low levels (within normal range) of plasma UA had a potentially detrimental effect on cognitive function in the executive domain in adults without hyperuricemia [15]. Yuan et al showed that lower UA levels were associated with poorer performance in situational memory and mental status [73]. A recent cross-sectional study showed that higher UA was associated with poorer performance in the domain of visual memory, but not in the domain of reaction speed [74]. However, some of the studies we included indicated that increased UA levels were correlated with enhanced memory function [30, 35]. We did not explore this further as the included studies were not subdivided regarding the association of UA with different cognitive domains.
Studies in men have shown a significant negative association between blood UA levels and cognitive impairment. Similar to our results, another prospective study suggests that the effect of blood UA levels on spontaneous brain activity and cognitive function varies by gender [75]. Sex hormones may play a vital role in this connection [76]. It is believed that the loss of estrogen plays a role in age-related cognitive changes in neurobiology [77, 78]. Cognitive function of women in a certain age interval may benefit from estrogen supplementation [79]. Higher plasma estrogen levels in women may lead to increased clearance of serum UA by the kidneys, weakening the protective effect of UA on cognition [78, 80]. The specific mechanism needs to be explored further. Our study also shows that the notable relationship vanished when restricted to studies with longer follow-up times. Due to the lack of survival data, a competing risk analysis could not be conducted, which may have biased the results. The power to detect the latent relationship could be increased with a larger sample size. Therefore, an analysis of more than 2000 participants may exhibit remarkable results.
4.4 Strengths and limitations
The strengths of the study should be acknowledged. First, a meta-analysis involving prospective cohort studies with long follow-up periods and large sample sizes was conducted, which enabled us to quantitatively evaluate the relationship between blood UA levels and cognitive impairment. Second, we included studies with fully adjusted models to decrease confounding bias. Third, a dose-response analysis was conducted to explore the potential nonlinear relations.
Our findings also need to be interpreted by considering several limitations. First, causality cannot be established, owing to the natural defection of observational studies. Second, although we brought the fully adjusted models into the analysis, the residual confounders cannot be completely ignored. Third, most of the studies provided baseline blood UA levels. However, the concentration of blood UA may change during the long follow-up time. Further reports with repeated blood UA levels are warranted. Lastly, the significant relationship observed in the subgroup analyses should be explained carefully and verified in future studies.
5 Conclusion
We found that cognitive impairment did not appear to be correlated with blood UA levels. Higher blood UA levels are related to a decline in cognitive impairment in males. A larger sample size, longer follow-up time and dynamic observation of blood UA levels are needed to confirm these findings. Future directions: 1) Studies should be conducted in hyperuricemia, normal UA levels and hypouricemia populations separately to better quantify the effect of UA levels on cognitive function and to provide guidelines for the management of UA in different populations; 2) To Investigate the effects of UA on different cognitive domains; 3) Large prospective cohort studies with long-term follow-up should be conducted in order to eliminate as much as possible the effects of residual confounders such as dietary patterns, nutritional status and survival data.
Acknowledgments
We would like to thank the researchers who conducted the original studies included in this meta-analysis.
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