Abstract
Background
Cognitive dysfunction is prevalent among the elderly diagnosed with chronic kidney disease (CKD). Low protein diets are used for retarding the progression of CKD in clinical practice. Nonetheless, the impact of dietary protein consumption on cognitive function in this population remains uncertain.
Methods
We recruited 2306 participants (≥60 years) from 2011 to 2014 National Health and Nutrition Examination Survey (NHANES). 24-h dietary recall questionnaire was utilized to evaluate protein intake. Cognitive function was measured using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Participants’ characteristics were analyzed, and the interaction between protein consumption and CKD on cognitive impairment were analyzed using a logistic regression model.
Results
We divided participants into three groups based on CKD stages: no CKD, CKD stage G1 to G2 (19%), and CKD stage G3 to G5 (18%). The average protein intake was 0.97 g/(kg·d). In the higher protein intake group, CKD stages G1 to G2 elevated the risk of immediate memory impairment (OR: 2.441, 95% Cl: 1.161–5.132 for protein consumption in 1.0-1.2g/(kg·d); OR: 2.225, 95% Cl: 1.015–4.876 for protein consumption in >1.2 g/(kg·d)). However, no similar resuts were observed in the lower protein intake group. In addition, the interaction between CKD status and protein intake on immediate memory was statistically significant (p = .041).
Conclusion
A higher probability of cognitive impairment in the elderly with early-stage CKD may be linked to higher protein intake. Low protein diets may be a potential strategy to release cognitive impairment in the elderly with early-stage CKD.
Keywords: Dietary protein intake, chronic kidney disease, cognitive function, elderly, NHANES
1. Introduction
Cognition involves psychological and social behaviors, including learning, memory, language, reasoning, mood, and emotions. Abnormalities in these processes can cause severe learning and memory disorders, accompanied by aphasia, apraxia, agnosia, and other pathological processes, leading to cognitive impairment [1]. By the year 2050, it is projected that approximately 130 million individuals will be impacted by dementia, with a global rise in the prevalence of cognitive decline [2]. Older adults are particularly susceptible to the onset of cognitive impairment and dementia [3,4]. In addition, early screening for cognitive disorders allows measuring the incidence of these disorders in different populations and implementing preventive interventions [5,6]. Chronic kidney disease (CKD) is a progressive disease correlated with a higher incidence of cognitive impairment [7,8]. However, the mechanisms underlying cognitive decline in CKD are incompletely understood and may be due to CKD-associated diseases, including hyperhomocysteinemia [9], neurodegeneration [7,8,10], and cardiovascular and cerebrovascular diseases [11].
Most CKD patients develop end-stage renal disease within a span of several years to decades, necessitating costly therapeutic interventions such as dialysis and kidney transplantation for survival [12,13]. There are approximately 800 million people with CKD worldwide, accounting for 11-13% of the global population, posing a significant health burden. In addition, CKD is linked to a heightened susceptibility to cardiovascular diseases and cerebrovascular events [11], tumors [14], and psychiatric disorders [7,8,10]. Therefore, in individuals with CKD, the primary objective of therapeutic interventions is to retard the advancement of CKD toward end-stage kidney disease (ESKD). The treatment of CKD involves pharmaceutic drugs, nutritional support, and dialysis, throughout the disease course (stages G1-G5). Dietary therapies involving low protein intake are essential to retard the advancement to ESKD. In addition, low protein diets reduce complications and mortality in the population diagnosed with CKD [15–17].
Although many studies have attempted to reveal the relationship between dietary patterns and cognitive function, the results of a systematic review indicate that the impact of different healthy dietary patterns on cognitive function in the population still remains highly controversial [18]. Furthermore, the effect of protein intake on cognitive function varies depending on the population [19–21]. Currently, there is a lack of research elucidating the relationship between dietary protein intake and cognitive function in elderly individuals with CKD. This research assessed the influence of dietary protein consumption on cognitive function in elderly individuals diagnosed with CKD, utilizing data from the National Health and Nutrition Examination Survey (NHANES) database.
2. Methods
2.1. Study population and data source
This present cross-sectional investigation comprised a total of 2306 individuals aged ≥60 years, who were selected from the 2011–2012 and 2013–2014 NHANES cycles. NHANES is a comprehensive survey that evaluates the health and nutritional condition of both children and adults residing in the United States. NHANES selects different sample populations for each cycle and completes data collection for each study individual within a specified time frame. NHANES does not conduct follow-up on the study individuals. Data on demographic characteristics, socioeconomic and nutritional status, and laboratory tests were analyzed. The exclusion criteria were age <60 years and participants with missing data on cognitive function, laboratory tests (serum creatinine, urine albumin and creatinine), nutrient intake, and other covariates. This research received ethical approval from the Ethics Committee of the National Center for Health Statistics. Written informed consent was obtained from all participants.
2.2. Cognitive function
Cognitive function was measured using word learning and recall modules from the Consortium to Establish a Registry for Alzheimer’s disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). CREAD was an organization funded by Alzheimer’s Disease Research Centers in 1986 to develop a brief neuropsychological assessment battery which had been widely adopted on cognitive status, especially in the elderly [22]. Immediate and delayed learning of new verbal information (memory subdomain) was assessed using the CERAD Word Learning subtest. The assessment comprised of three successive learning trials to evaluate immediate memory, followed by a delayed recall test to assess delayed memory. Participants were given instructions to orally recite a set of 10 unrelated words and subsequently recall as many words as they could. The AFT measures categorical verbal fluency, which is a crucial aspect of executive function. Participants were instructed to generate the names of as many animals as they could within a time limit of 1 min. The DSST was employed to assess processing speed, sustained attention, and working memory. The test comprises pairs of digits and symbols, followed by a series of digits. Participants were directed to transcribe the corresponding symbols into the 133 adjacent boxes within a two-minute timeframe. Given the absence of a universally accepted criterion for defining cognitive impairment in these assessments, the cutoff point for identifying impairment was determined as the lowest quartile of the scores, as described previously [23,24]. These cutoff points were adjusted for age because age significantly impacts cognitive function [25] (Supplementary Table 3).
2.3. CKD stage
The staging of chronic kidney disease (CKD) is determined by the estimated glomerular filtration rate (eGFR) and the ratio of urinary albumin to urinary creatinine (UACR). The eGFR is calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, which is based on the blood creatinine concentration, gender, race, and age (Supplementary Table 2) [26]. Blood creatinine is measured using the JAFFE rate method by the DxC800 modular chemistry system. Detailed detection methods refer to https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/BIOPRO_G.htm. Urinary albumin and urinary creatinine are measured using a solid-phase fluorescent immunoassay and the Roche/Hitachi Modular P Chemistry Analyzer, respectively. Detailed detection methods refer to https://wwwn.cdc.gov/Nchs/Nhanes/2011-2012/ALB_CR_G.htm. The CKD stages were defined based on eGFR and the urinary albumin-to-creatinine ratio (UACR) as follows: non-CKD (eGFR ≥60 mL/min/1.73 m2 and UACR <30 mg/g), CKD stages G1 or G2 (eGFR ≥60 mL/min/1.73 m2 and UACR ≥30 mg/g), and CKD stages G3-G5 (eGFR <60 mL/min/1.73 m2) [27].
2.4. Dietary protein intake
A report from the Food and Nutrition Board of the Institute of Medicine has shown that 0.8 g/(kg·d) of protein meets the daily requirement for 97.5% of the healthy adult population [28]. High protein intake (1.0–1.2 g/(kg·d)) is recommended for older adults to maintain muscle mass [29]. Based on their protein intake levels (in g/(kg·d)), the participants were categorized into four groups: <0.8, 0.8–1.0, 1.0–1.2, and >1.2. Data on protein intake were collected by 24-h recalls using the US Department of Agriculture Automated Multiple-Pass Method.
2.5. Covariates
Information regarding demographic characteristics, lifestyle factors, and health status was gathered. Demographic information included age, sex, race (non-Hispanic white, non-Hispanic black, Mexican American, non-Hispanic Asians, other Hispanics, other races), education (secondary and high school, university degree), and the poverty impact ratio. Lifestyle information included smoking status (never smoker, former smoker, current smoker), and alcohol consumption (none, 1–5, 5–10, or >10 drinks/month). Health information included body mass index (BMI), history of hypertension (systolic blood pressure >130 mmHg or diastolic blood pressure >80 mmHg), diabetes (hemoglobin A1C concentration ≥6.5%, fasting plasma glucose ≥126 mg/dL, or self-reported diabetes), stroke and depression (none, mild to moderate, moderate to severe). The depression status of participants was evaluated using the Patient Health Questionnaire-9 (PHQ-9) [30].
2.6. Statistical analysis
The patients were divided into three subgroups: non-CKD, CKD stages G1-G2, and CKD stages G3-G5. Continuous variables that followed a normal distribution were represented using mean values and standard deviations, while categorical variables were expressed as percentages. To assess the significance of variations in means for continuous variables and categorical variables, analysis of variance and Pearson’s χ2 test were employed, respectively. The mobile examination center sample weights were used, and the results represented the US civilian population [31]. Unweighted characteristics are shown in Supplementary Table 1. We evaluated the cognitive function of participants with different CKD stages. To evaluate the influence of CKD status on cognitive function, a weighted logistic regression model was employed, controlling for potential confounding factors, including gender, age, BMI, education, smoking, alcohol consumption, medical history of hypertension, stroke, and depression. The interaction between CKD status and protein intake was analyzed. The unadjusted logistic regression model is shown in Supplementary Table 5. We developed weighted linear regression models (Supplementary Table 6), excluded participants with depression (Supplementary Table 7) and included participants who were initially excluded from the logistic regression model because of incomplete data on covariates for sensitive analysis (Supplementary Table 8). The robustness of the observed significant relationship between protein intake, CKD status, and cognitive function was confirmed in the sensitivity analysis. The statistical analysis was performed using R software version 4.2.2. All statistical tests were two-tailed, and statistical significance was defined as p-values less than .05.
3. Results
3.1. Baseline characteristics of the participants
Among 19931 subjects, 2306 were included in the study after applying the exclusion criteria (Figure 1). The demographic and clinical characteristics of the study participants are presented in Table 1 and Supplementary Table 1 (unweighted). In our participants, 21.8% were aged ≥80 years, 51% were non-Hispanic white, and 50.7% were female. In addition, the participants with CKD were more likely to be smokers and have lower levels of protein intake, alcohol intake, and education, and a higher risk of hypertension, diabetes, stroke, and depression.
Figure 1.
Flow chart of the screening process for the selection of eligible participants.
Table 1.
Characteristics of participants aged 60 years and older from the NHANES (2011–2014).
| Characteristics | Overall (N = 2306) |
Non-CKD (N = 1458) |
CKD stage G1-G2 (N = 433) |
CKD stage G3-G5 (N = 415) |
p-value |
|---|---|---|---|---|---|
| Age group, n (%) | <.0001 | ||||
| 60–69 years | 1177(51.04) | 867(61.64) | 179(40.50) | 131(32.38) | |
| 70–79 years | 634(27.49) | 370(24.23) | 122(28.49) | 142(36.92) | |
| 80+ years | 495(21.47) | 221(14.13) | 132(31.00) | 142(30.69) | |
| Sex, n (%) | .47 | ||||
| male | 1137(49.31) | 713(47.45) | 226(46.63) | 198(43.69) | |
| female | 1169(50.69) | 745(52.55) | 207(53.37) | 217(56.31) | |
| Race, n (%) | <.001 | ||||
| Non-Hispanic White | 1175(50.95) | 728(81.06) | 207(74.14) | 240(81.91) | |
| Non-Hispanic Black | 516(22.38) | 288(6.48) | 109(10.87) | 119(10.95) | |
| Mexican American | 200(8.67) | 148(3.68) | 38(4.51) | 14(1.49) | |
| Non-Hispanic Asian | 157(6.81) | 117(3.68) | 28(3.72) | 12(1.58) | |
| Other Hispanic | 225(9.76) | 158(3.73) | 44(4.18) | 23(2.00) | |
| Other/multiracial | 33(1.43) | 19(1.35) | 7(2.58) | 7(2.06) | |
| Education, n (%) | <.001 | ||||
| ≤High school graduate | 1077(46.7) | 634(33.79) | 241(48.68) | 202(37.20) | |
| ≥Collage | 1229(53.3) | 824(66.21) | 192(51.32) | 213(62.80) | |
| PIR, mean (SD) | 3.17(0.08) | 3.30(0.09) | 2.71(0.11) | 3.06(0.12) | <.001 |
| Body mass index (BMI), mean (SD) |
29.06(0.23) | 28.75(0.29) | 29.72(0.41) | 29.68(0.36) | .06 |
| Smoking status, n (%) | .01 | ||||
| Never Smoker | 1130(49) | 745(51.01) | 188(44.50) | 197(49.68) | |
| Former Smoker | 887(38.46) | 543(37.82) | 169(40.52) | 175(43.44) | |
| Current Smoker | 289(12.53) | 170(11.17) | 76(14.98) | 43(6.88) | |
| Alcohol consumption, n (%) | .04 | ||||
| Non-drinker | 699(30.31) | 422(24.71) | 144(34.36) | 133(28.01) | |
| 1–5 drinks/month | 1125(48.79) | 712(48.28) | 210(46.49) | 203(50.25) | |
| 5–10 drinks/month | 108(4.68) | 74(6.04) | 18(3.23) | 16(4.24) | |
| 10+ drinks/month | 374(16.22) | 250(20.96) | 61(15.92) | 63(17.50) | |
| Hypertension, n (%) | <.0001 | ||||
| No | 496(21.51) | 379(29.79) | 52(13.27) | 65(15.99) | |
| Yes | 1810(78.49) | 1079(70.21) | 381(86.73) | 350(84.01) | |
| Diabetes, n (%) | <.0001 | ||||
| No | 1638(71.03) | 1125(82.23) | 219(57.22) | 294(73.65) | |
| Yes | 668(28.97) | 333(17.77) | 214(42.78) | 121(26.35) | |
| Stroke, n (%) | .001 | ||||
| No | 2154(93.41) | 1388(95.56) | 388(90.02) | 378(91.86) | |
| Yes | 152(6.59) | 70(4.44) | 45(9.98) | 37(8.14) | |
| Depression, n (%) | .002 | ||||
| Non-depression | 1748(75.8) | 1140(81.00) | 291(71.32) | 317(78.12) | |
| Mild to Moderate | 483(20.95) | 279(17.00) | 118(23.05) | 86(20.35) | |
| Moderately Sever to Sever | 75(3.25) | 39(2.01) | 24(5.63) | 12(1.53) | |
| Protein intake at different level, n (%) |
.003 | ||||
| <0.8 | 965(41.85) | 555(35.49) | 207(48.09) | 203(46.03) | |
| 0.8–1.0 | 466(20.21) | 306(21.96) | 90(23.48) | 70(17.09) | |
| 1.0–1.2 | 362(15.7) | 235(17.36) | 60(11.37) | 67(18.60) | |
| >1.2 | 513(22.25) | 362(25.20) | 76(17.06) | 75(18.28) | |
| Dietary protein intake, mean (SD) | 0.97(0.01) | 1.00(0.01) | 0.87(0.02) | 0.91(0.03) | <.0001 |
Normally distributed continuous variables were displayed as means and standard deviations (SDs), categorical variables were displayed as proportions. Analysis of Variance (ANOVA) were used for comparison of Continuous variables and Pearson’s χ2 test were used for comparison of categorical variables. The mobile examination center sample weights were accounted and the results represented the US civilian resident population.
3.2. CKD status and cognitive function
Performance on the cognitive function of participants with and without CKD are shown in Figure 2. People with CKD performed worse than the non-CKD population in the four cognitive tests, and people with CKD stage G1 or G2 performed worse than those with stages G3, G4, or G5, and the difference was significant in the four cognitive function tests (Supplementary Table 4). In the weighted logistic regression model unadjusted for covariates, CKD increased the risk of cognitive impairment, and the risk was higher in CKD stages G1 and G2 than in stages G3-G5 (Supplementary Table 5). In the adjusted multivariate logistic regression model, CKD stages G1 and G2 increased the risk of immediate memory impairment by 47.4% (OR: 1.474, 95% Cl: 1.027–2.117, p = .037) and the risk of cognitive impairment (according to the DSST) 122.4% (OR: 2.224, 95% Cl: 1.648–3.057, p < .01) but did not significantly affect delayed memory and verbal fluency. CKD stages G3-G5 were not associated with cognitive impairment in the four domains (Table 2).
Figure 2.
Cognitive function test scores by CKD stage among participants ≥60 years old from the NHANES (2011–2014) (overall (N = 2306); non-CKD (N = 1458); CKD stage G1-G2 (N = 433); CKD stage G3-G5 (N = 415)).
Table 2.
CKD status and cognitive function impairment by levels of dietary protein intake among participants aged 60 years and older from the NHANES (2011–2014).
| non-CKD (N = 1458) | CKD stage G1-G2 (N = 433) |
CKD stage G3-G5 (N = 415) |
|||||
|---|---|---|---|---|---|---|---|
| Character | OR (95%Cl) | p value | OR (95%Cl) | p value | p for trend p for interaction | p for interaction | |
| CERAD immediate recall | .041 | ||||||
| Total | ref | 1.474(1.027, 2.117) | 0.037 | 1.042(0.672, 1.617) | 0.844 | 0.486 0.041 | |
| <0.8 | ref | 1.259(0.736, 2.155) | 0.377 | 1.002(0.581, 1.727) | 0.994 | 0.83 | |
| 0.8–1.0 | ref | 1.009(0.475, 2.144) | 0.981 | 0.885(0.397, 1.973) | 0.749 | 0.97 | |
| 1.0–1.2 | ref | 2.441(1.161, 5.132) | 0.022 | 2.174(0.770, 6.137) | 0.132 | 0.101 | |
| >1.2 | ref | 2.225(1.015, 4.876) | 0.046 | 0.505(0.168, 1.516) | 0.206 | 0.01 | |
| CERAD delayed recall | .228 | ||||||
| Total | ref | 1.333(0.932, 1.907) | 0.108 | 1.219(0.800, 1.857) | 0.333 | 0.252 0.228 | |
| <0.8 | ref | 1.172(0.741, 1.854) | 0.473 | 1.486(0.948, 2.332) | 0.080 | 0.08 | |
| 0.8–1.0 | ref | 1.198(0.518, 2.771) | 0.652 | 0.474(0.239, 0.942) | 0.035 | 0.86 | |
| 1.0–1.2 | ref | 1.031(0.418, 2.540) | 0.944 | 1.449(0.582, 3.610) | 0.401 | 0.421 | |
| >1.2 | ref | 2.062(0.862, 4.935) | 0.098 | 1.040(0.451, 2.398) | 0.921 | 0.254 | |
| The Animal Fluency test (AFT) | .187 | ||||||
| Total | ref | 1.275(0.920, 1.767) | 0.135 | 1.362(0.966, 1.918) | 0.074 | 0.043 0.187 | |
| <0.8 | ref | 1.396(0.911, 2.139) | 0.117 | 1.654(0.995, 2.751) | 0.052 | 0.036 | |
| 0.8–1.0 | ref | 0.579(0.266, 1.263) | 0.156 | 0.864(0.369, 2.025) | 0.720 | 0.153 | |
| 1.0–1.2 | ref | 2.070(1.003, 4.268) | 0.049 | 1.198(0.400, 3.584) | 0.731 | 0.512 | |
| >1.2 | ref | 1.381(0.562, 3.393) | 0.457 | 1.381(0.594, 3.213) | 0.429 | 1 | |
| The Digit Symbol Substitution test (DSST) | .097 | ||||||
| Total | Ref | 2.244(1.648, 3.057) | <0.0001 | 1.295(0.789, 2.125) | 0.284 | 0.056 0.097 | |
| <0.8 | Ref | 1.598(1.131, 2.259) | 0.011 | 1.204(0.637, 2.276) | 0.545 | 0.353 | |
| 0.8–1.0 | Ref | 2.532(1.106, 5.797) | 0.030 | 1.388(0.409, 4.713) | 0.576 | 0.033 | |
| 1.0–1.2 | Ref | 7.882(2.482, 25.024) | 0.002 | 1.026(0.235, 4.484) | 0.971 | 0.315 | |
| >1.2 | Ref | 2.250(0.953, 5.312) | 0.063 | 1.206(0.539, 2.696) | 0.628 | 0.223 | |
The logistic regression model was adjusted by age, gender, BMI, education, smoking, alcohol intake, hypertension, diabetes, stroke, depression. Sampling weights were considered in logistic regression analyses to obtain nationally representative estimates.
3.3. The association between CKD status and cognitive function differed depending on dietary protein intake
The association between CKD status and immediate memory impairment differed depending on protein intake in participants with and without CKD (p for interaction = 0.041). In the population on a high protein diet, compared with the absence of CKD, CKD stages G1 and G2 increased the risk of immediate memory impairment (OR: 2.441, 95% Cl: 1.161–5.132 for a protein intake of 1.0–1.2 g/(kg·d); OR: 2.225, 95% Cl: 1.015–4.876 for an intake of >1.2 g/(kg·d)) (Table 2). The increase in the risk of short-term memory impairment was not significant in the group on a low protein diet. The effect of the interaction between CKD and protein intake was not significant for the other cognitive domains.
4. Discussion
This study utilized data from the NHANES 2011–2014 to investigate the correlation between cognitive function and dietary protein intake among elderly individuals diagnosed with CKD. The results showed that CKD stages G1 and G2 were strongly associated with worse cognitive performance in immediate memory and executive and processing function. In turn, there was no significant correlation between CKD stages G3–G5 and the level of cognitive impairment and no significant association between CKD status and delayed memory.
The association between CKD status and cognitive performance differed depending on protein intake in patients with CKD stage G1 to G2 compared with those without CKD. In the group on a high protein diet, the participants with CKD stage G1 to G2 had poorer immediate memory than those without CKD; however, this association was not found among subjects on a low protein diet. These results indicate that high protein intake is a risk factor for impaired immediate memory in elderly individuals with CKD stages G1 to G2, while low protein intake may improve this adverse impact.
Cognitive health is a public health concern [32]. With the aging of society, the morbidity and mortality of CKD and the risk of cognitive impairment are increasing in countries with long life expectancy [33]. This study found that CKD increased the risk of impairment of memory, executive function, information processing speed, and sustained attention, in line with the previous literature [34–36]. A prospective cohort study demonstrated that kidney dysfunction was associated with higher cognitive impairment, even in subjects with early-stage CKD [37]. A community-based cross-sectional study with 923 individuals showed that early-stage CKD reduced cognitive performance and cognitive function [38]. The results of prospective studies from Germany and the United States found that CKD severity was positively associated with the degree of cognitive impairment [36,37]. Nonetheless, this relationship was not significant in our study, and cognitive function was marginally worse in subjects with CKD stages G1 or G2 than in those with stage G3 - G5. Some previous studies have also reported no significant correlation between eGFR and cognitive impairment [39], and the reasons for this phenomenon seem difficult to explain. For this study, one possible explanation is that the non-dialysis population with CKD stage G4 and G5 was relatively small (Supplementary Table 9), which may have underestimated the relationship between protein intake, advanced CKD, and cognitive function. In the future, researchers can further investigate the relationship between CKD and cognitive impairment in the elderly by expanding the sample size of CKD stage G4–G5, or validating through multiple databases.
Low protein diets are widely used for treating CKD before dialysis in stages G3–G5 to delay disease progression. The evidence from the PROT-AGE Study Group suggests that a low-protein diet (0.6 g/(kg·d)) can delay the progression of CKD in elderly patients with advanced CKD (eGFR < 30 mL/min/1.73 m2), and adequate energy supplementation can effectively prevent the occurrence of malnutrition [40]. Protein intake is associated with cardiovascular health, metabolism, cognition, and other functions in older adults [41–43]. Nonetheless, in the elderly population, there remains a lack of consensus regarding the connection between protein consumption and cognitive performance. For instance, a study found that protein intake in the elderly with dementia living in nursing homes in France was significantly higher than those without dementia [19]. A cross-sectional study based on NHANES showed that higher protein intake from milk and dairy products was related to poorer cognitive performance [43]. In addition, the Mediterranean diet, characterized by low protein intake, is linked with a lower likelihood of incident cognitive impairment, better cognitive performance, and lower learning and memory decline in middle-aged and elderly people [44–46].
Animal studies provide indirect evidence of the detrimental effects of high protein intake on cognitive function. High protein diets worsen spatial memory deficits in rats with cirrhosis [47]. Furthermore, brain weight in mice with Alzheimer-like pathology consuming a high protein diet was smaller than that of controls, and neuronal density and size in hippocampal subregions tended to be lower in the former [48]. We found that older people with CKD stage G1 or G2 on a high protein diet had poorer memory function than those without CKD. In addition, the relationship between CKD and memory function could be reversed by low protein intake. In contrast, other studies showed that high protein intake in patients with psychiatric disorders was associated with less cognitive decline [20,21]. Thus, further subgroup studies are necessary to address inconsistencies in the results.
The previous study found that compared with the low protein intake, people who had high protein intake had higher concentrations of inflammatory markers in peripheral blood [49,50]. In addition, CKD patients have systemic inflammation [51]. Past studies have suggested that inflammation may be related to AD and cognitive impairment. Inflammatory models of neurodegeneration showed that higher circulating inflammation levels would cause cognitive decline [52]. Therefore, a high protein diet may affect the cognitive function of patients with early stages of CKD by aggravating the inflammation.
Elevated levels of homocysteine in the bloodstream could be a contributing factor to the detrimental impact of high level of protein consumption on cognitive decline. A cross-sectional study found that the population with a high protein diet had higher blood concentration of homocysteine [53]. In addition, the albumin excretion rate is strongly associated with fasting plasma homocysteine levels [54], and increased homocysteine level is a risk factor for Alzheimer’s disease and dementia [55–57]. Homocysteine increases the risk of cognitive impairment by promoting thrombosis and endothelial dysfunction [58,59]. Further, homocysteine has neurotoxic effects by activating the N-methyl-D-aspartate receptor [60]. Nonetheless, the underlying mechanisms are unclear.
This study indicated the potential benefits of low protein diets on cognitive function in elderly patients with early-stage CKD. However, present dietary treatments focus on non-dialysis patients with CKD stages G3–G5. Thus, additional prospective studies are necessary to assess the effects of low protein diets on cognitive function in patients with early-stage CKD.
However, it cannot be ignored that protein intake may be related to overall food intake, and food intake is related to calorie intake. We found that there are only limited studies attempting to reveal the relationship between calorie intake and cognitive function, and the conclusions are inconsistent. A cross-sectional study from Korea showed that participants aged 70–84 years who had calorie intake below the recommended levels were more likely to have cognitive impairment, mainly characterized by memory decline [61]. A 6-month RCT study showed that calorie restriction did not change the cognitive function of the participants [62]. However, animal experiments have found that calorie restriction is associated with reduced beta-amyloid deposition, hippocampal synaptic adaptation, and vascular health, which are the pathophysiological basis for improving cognitive function [63]. Therefore, future research needs to consider controlling calorie intake to reduce bias caused by differences in calorie intake.
This study has limitations. First, causal relationships between CKD, protein intake, and cognitive function were not evaluated, future cohort studies and RCT studies may compensate for this limitation. Second, unknown confounders were not controlled in logistic regression. Third, it is difficult to measure protein intake levels accurately because of recall bias. Future studies can test such as urine nitrogen excretion or quantitation of nitrogen appearance to quantitatively analyze protein intake, in order to obtain more accurate data on protein intake.
The study has strengths. First, the study based on the NHANES database increased sample representativeness through weighted analysis. Second, the chosen indicators allowed analyzing multiple cognitive function domains. Third, the CKD-EPI formula estimates the glomerular filtration rate by race, sex, and age, and the CKD stage was identified using eGFR and ACR, which ensured the accurate identification of people with early CKD stages.
5. Conclusion
Renal impairment has been linked to cognitive deterioration in the geriatric population, while low protein diets can potentially reduce memory impairment in this population. These data may help develop effective nutritional management strategies for cognitive impairment in elderly population diagnosed as CKD. To prevent cognitive dysfunction, clinicians should consider using low protein diets in patients with CKD stage G1 or G2. Furthermore, older adults with CKD on a high protein diet should be screened for cognitive impairment.
Supplementary Material
Acknowledgments
We thank to the National Center for Health Statistics at the CDC for their excellent work in collecting the NHANES data and for making it accessible to the public.
Funding Statement
This study was supported by the Special Fund for Medical and Health Talents of Jilin Province [JLSWSRCZX2023-33].
Authors contributions
JD.H and MD.S conceptualized the research topic and framework; JD.H and YH.W identified the methodological details of the research; JD.H, Y.Y and HM.L used statistical software for data analysis; JD.H, Y.Y wrote the main manuscript text; JD.H and MD.S conducted Writing-review and editing. All authors reviewed the manuscript.
Ethical approval
The CDC/NCHS Research Ethics Review Board granted approval for the detailed methods and protocols employed in the NHANES study. These procedures, including informed consent protocols for participants, can be accessed on the CDC.gov website. All methods adhered to the applicable guidelines and regulations. Since the data used in this study are publicly available, it was exempt from ethical review involving human subjects.
Disclosure statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Data availability statement
All data can be downloaded from the NHANES website (https://www.cdc.gov/nchs/nhanes/about_nhanes.htm).
References
- 1.Battle DE. Diagnostic and statistical manual of mental disorders (DSM). Codas. 2013;25(2):1–9. doi: 10.1590/s2317-17822013000200017. [DOI] [PubMed] [Google Scholar]
- 2.Prince M. World Alzheimer report 2015: the global impact of dementia. London: Alzheimer’s Disease International; 2015. [Google Scholar]
- 3.Johnson EL, Krauss GL, Kucharska-Newton A, et al. Dementia in late-onset epilepsy: the atherosclerosis risk in communities study. Neurology. 2020;95(24):e3248–e56. doi: 10.1212/WNL.0000000000011080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Johnson EL, Krauss GL, Lee AK, et al. Association between midlife risk factors and late-onset epilepsy: results from the atherosclerosis risk in communities study. JAMA Neurol. 2018;75(11):1375–1382. doi: 10.1001/jamaneurol.2018.1935. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Mark RE, Brehmer Y.. Preclinical Azheimer’s dementia: a useful concept or another dead end? Eur J Ageing. 2022;19(4):997–1004. doi: 10.1007/s10433-022-00735-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Anderson ND. State of the science on mild cognitive impairment. J Gerontol B Psychol Sci Soc Sci. 2020;75(7):1359–1360. doi: 10.1093/geronb/gbaa040. [DOI] [PubMed] [Google Scholar]
- 7.Tang X, Han YP, Chai YH, et al. Association of kidney function and brain health: a systematic review and meta-analysis of cohort studies. Ageing Res Rev. 2022;82:101762. doi: 10.1016/j.arr.2022.101762. [DOI] [PubMed] [Google Scholar]
- 8.Kim DS, Kim SW, Gil HW.. Emotional and cognitive changes in chronic kidney disease. Korean J Intern Med. 2022;37(3):489–501. doi: 10.3904/kjim.2021.492. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Moon H, Ko HJ, Kim AS.. Hyperhomocysteinemia concurrent with metabolic syndrome is independently associated with chronic kidney disease among community-dwelling adults in an urban Korean population. Int J Environ Res Public Health. 2020;17(18):6810. doi: 10.3390/ijerph17186810. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Xie Z,Tong S, Chu X, et al. Chronic kidney disease and cognitive impairment: the kidney-brain axis. Kidney Dis. 2022;8(4):275–285. doi: 10.1159/000524475. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Spence JD, Urquhart BL.. Cerebrovascular disease, cardiovascular disease, and chronic kidney disease: interplays and influences. Curr Neurol Neurosci Rep. 2022;22(11):757–766. doi: 10.1007/s11910-022-01230-6. [DOI] [PubMed] [Google Scholar]
- 12.Kidney Disease: Improving Global Outcomes (KDIGO) CKD-MBD Work Group . KDIGO clinical practice guideline for the diagnosis, evaluation, prevention, and treatment of chronic kidney disease-mineral and bone disorder (CKD-MBD). Kidney Int Suppl. 2009;(113):S1–S130. [DOI] [PubMed] [Google Scholar]
- 13.Rebollo-Rubio A, Morales-Asencio JM, Pons-Raventos ME, et al. Review of studies on health related quality of life in patients with advanced chronic kidney disease in Spain. Nefrologia. 2015;35(1):92–109. doi: 10.3265/Nefrologia.pre2014.Jul.12133. [DOI] [PubMed] [Google Scholar]
- 14.Bukavina L, Bensalah K, Bray F, et al. Epidemiology of renal cell carcinoma: 2022 update. Eur Urol. 2022;82(5):529–542. doi: 10.1016/j.eururo.2022.08.019. [DOI] [PubMed] [Google Scholar]
- 15.Hahn D, Hodson EM, Fouque D.. Low protein diets for non-diabetic adults with chronic kidney disease. Cochrane Database Syst Rev. 2020;10(10):CD001892. doi: 10.1002/14651858.CD001892.pub5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Chewcharat A, Takkavatakarn K, Wongrattanagorn S, et al. The effects of restricted protein diet supplemented with ketoanalogue on renal function, blood pressure, nutritional status, and chronic kidney disease-mineral and bone disorder in chronic kidney disease patients: a systematic review and meta-analysis. J Ren Nutr. 2020;30(3):189–199. doi: 10.1053/j.jrn.2019.07.005. [DOI] [PubMed] [Google Scholar]
- 17.Rhee CM, Ahmadi SF, Kovesdy CP, et al. Low-protein diet for conservative management of chronic kidney disease: a systematic review and meta-analysis of controlled trials. J Cachexia Sarcopenia Muscle. 2018;9(2):235–245. doi: 10.1002/jcsm.12264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Townsend RF, Logan D, O’Neill RF, et al. Whole dietary patterns, cognitive decline and cognitive disorders: a systematic review of prospective and intervention studies. Nutrients. 2023;15(2):333. doi: 10.3390/nu15020333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Jesus P, Desport JC, Massoulard A, et al. Nutritional assessment and follow-up of residents with and without dementia in nursing homes in the limousin region of France: a health network initiative. J Nutr Health Aging. 2012;16(5):504–508. doi: 10.1007/s12603-012-0017-0. [DOI] [PubMed] [Google Scholar]
- 20.Dickerson F, Gennusa JV, Stallings C, et al. Protein intake is associated with cognitive functioning in individuals with psychiatric disorders. Psychiatry Res. 2020;284:112700. doi: 10.1016/j.psychres.2019.112700. [DOI] [PubMed] [Google Scholar]
- 21.Yeh TS, Yuan C, Ascherio A, et al. Long-term dietary protein intake and subjective cognitive decline in US men and women. Am J Clin Nutr. 2022;115(1):199–210. doi: 10.1093/ajcn/nqab236. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Fillenbaum GG, Mohs R.. CERAD (Consortium to Establish a Registry for Alzheimer’s Disease) neuropsychology assessment battery: 35 years and counting. J Alzheimers Dis. 2023;93(1):1–27. doi: 10.3233/JAD-230026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Chen SP, Bhattacharya J, Pershing S.. Association of vision loss with cognition in older adults. JAMA Ophthalmol. 2017;135(9):963–970. doi: 10.1001/jamaophthalmol.2017.2838. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Wang R, Wang W, Hu P, et al. Association of dietary vitamin D intake, serum 25(OH)D(3), 25(OH)D(2) with cognitive performance in the elderly. Nutrients. 2021;13(9):3089. doi: 10.3390/nu13093089. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ritchie SJ, Hill WD, Marioni RE, et al. Polygenic predictors of age-related decline in cognitive ability. Mol Psychiatry. 2020;25(10):2584–2598. doi: 10.1038/s41380-019-0372-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–612. doi: 10.7326/0003-4819-150-9-200905050-00006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Stevens PE, Levin A.. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825–830. doi: 10.7326/0003-4819-158-11-201306040-00007. [DOI] [PubMed] [Google Scholar]
- 28.Council N, Council NR.. Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein, and amino acids (macronutrients). P. 2005. [DOI] [PubMed] [Google Scholar]
- 29.Traylor DA, Gorissen SHM, Phillips SM.. Perspective: protein requirements and optimal intakes in aging: are we ready to recommend more than the recommended daily allowance? Adv Nutr. 2018;9(3):171–182. doi: 10.1093/advances/nmy003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kroenke K, Spitzer RL, Williams JB.. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. doi: 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Chen TC, Parker JD, Clark J, et al. National health and nutrition examination survey: estimation procedures, 2011-2014. Vital Health Stat 2. 2018;2(177):1–26. [PubMed] [Google Scholar]
- 32.Iadecola C, Duering M, Hachinski V, et al. Vascular cognitive impairment and dementia: JACC scientific expert panel. J Am Coll Cardiol. 2019;73(25):3326–3344. doi: 10.1016/j.jacc.2019.04.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Ortiz A, Mattace-Raso F, Soler MJ, et al. Ageing meets kidney disease. Age Ageing. 2022;51(8):523–526. [DOI] [PubMed] [Google Scholar]
- 34.Thornton WL, Shapiro RJ, Deria S, et al. Differential impact of age on verbal memory and executive functioning in chronic kidney disease. J Int Neuropsychol Soc. 2007;13(2):344–353. doi: 10.1017/S1355617707070361. [DOI] [PubMed] [Google Scholar]
- 35.Abdel-Kader K, Dew MA, Bhatnagar M, et al. Numeracy skills in CKD: correlates and outcomes. Clin J Am Soc Nephrol. 2010;5(9):1566–1573. doi: 10.2215/CJN.08121109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Seidel UK, Gronewold J, Volsek M, et al. The prevalence, severity, and association with HbA1c and fibrinogen of cognitive impairment in chronic kidney disease. Kidney Int. 2014;85(3):693–702. doi: 10.1038/ki.2013.366. [DOI] [PubMed] [Google Scholar]
- 37.Khatri M, Nickolas T, Moon YP, et al. CKD associates with cognitive decline. J Am Soc Nephrol. 2009;20(11):2427–2432. doi: 10.1681/ASN.2008101090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Elias MF, Elias PK, Seliger SL, et al. Chronic kidney disease, creatinine and cognitive functioning. Nephrol Dial Transplant. 2009;24(8):2446–2452. doi: 10.1093/ndt/gfp107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Joosten H, Izaks GJ, Slaets JP, et al. Association of cognitive function with albuminuria and eGFR in the general population. Clin J Am Soc Nephrol. 2011;6(6):1400–1409. doi: 10.2215/CJN.05530610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Bauer J, Biolo G, Cederholm T, et al. Evidence-based recommendations for optimal dietary protein intake in older people: a position paper from the PROT-AGE study group. J Am Med Dir Assoc. 2013;14(8):542–559. doi: 10.1016/j.jamda.2013.05.021. [DOI] [PubMed] [Google Scholar]
- 41.Mittendorfer B, Klein S, Fontana L.. A word of caution against excessive protein intake. Nat Rev Endocrinol. 2020;16(1):59–66. doi: 10.1038/s41574-019-0274-7. [DOI] [PubMed] [Google Scholar]
- 42.Hruby A, Jacques PF.. Protein intake and human health: implications of units of protein intake. Adv Nutr. 2021;12(1):71–88. doi: 10.1093/advances/nmaa097. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Li Y, Li S, Wang W, et al. Association between dietary protein intake and cognitive function in adults aged 60 years and older. J Nutr Health Aging. 2020;24(2):223–229. doi: 10.1007/s12603-020-1317-4. [DOI] [PubMed] [Google Scholar]
- 44.Capurso C. Whole-grain intake in the mediterranean diet and a low protein to carbohydrates ratio can help to reduce mortality from cardiovascular disease, slow down the progression of aging, and to improve lifespan: a review. Nutrients. 2021;13(8):2540. doi: 10.3390/nu13082540. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Moustafa B, Trifan G, Isasi CR, et al. Association of mediterranean diet with cognitive decline among diverse hispanic or latino adults from the hispanic community health study/study of latinos. JAMA Netw Open. 2022;5(7):e2221982. doi: 10.1001/jamanetworkopen.2022.21982. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Ballarini T, Melo van Lent D, Brunner J, et al. Mediterranean diet, Alzheimer disease biomarkers and brain atrophy in old age. Neurology. 2021;96(24):e2920-32–e2932. doi: 10.1212/WNL.0000000000012067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Méndez-López M, Méndez M, Arias J, et al. Effects of a high protein diet on cognition and brain metabolism in cirrhotic rats. Physiol Behav. 2015;149:220–228. doi: 10.1016/j.physbeh.2015.05.038. [DOI] [PubMed] [Google Scholar]
- 48.Pedrini S, Thomas C, Brautigam H, et al. Dietary composition modulates brain mass and solubilizable Abeta levels in a mouse model of aggressive Alzheimer’s amyloid pathology. Mol Neurodegener. 2009;4:40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Giordano M, Lucidi P, Ciarambino T, et al. Effects of dietary protein restriction on albumin and fibrinogen synthesis in macroalbuminuric type 2 diabetic patients. Diabetologia. 2008;51(1):21–28. doi: 10.1007/s00125-007-0874-y. [DOI] [PubMed] [Google Scholar]
- 50.Wright CS, Zhou J, Sayer RD, et al. Effects of a high-protein diet including whole eggs on muscle composition and indices of cardiometabolic health and systemic inflammation in older adults with overweight or obesity: a randomized controlled trial. Nutrients. 2018;10(7):946. doi: 10.3390/nu10070946. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Andersen K, Kesper MS, Marschner JA, et al. Intestinal dysbiosis, barrier dysfunction, and bacterial translocation account for CKD-related systemic inflammation. J Am Soc Nephrol. 2017;28(1):76–83. doi: 10.1681/ASN.2015111285. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Bourassa K, Sbarra DA.. Body mass and cognitive decline are indirectly associated via inflammation among aging adults. Brain Behav Immun. 2017;60:63–70. doi: 10.1016/j.bbi.2016.09.023. [DOI] [PubMed] [Google Scholar]
- 53.Xiao Y, Zhang Y, Wang M, et al. Dietary protein and plasma total homocysteine, cysteine concentrations in coronary angiographic subjects. Nutr J. 2013;12(1):144. doi: 10.1186/1475-2891-12-144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Chico A, Pérez A, Córdoba A, et al. Plasma homocysteine is related to albumin excretion rate in patients with diabetes mellitus: a new link between diabetic nephropathy and cardiovascular disease? Diabetologia. 1998;41(6):684–693. doi: 10.1007/s001250050969. [DOI] [PubMed] [Google Scholar]
- 55.Seshadri S, Beiser A, Selhub J, et al. Plasma homocysteine as a risk factor for dementia and Alzheimer’s disease. N Engl J Med. 2002;346(7):476–483. doi: 10.1056/NEJMoa011613. [DOI] [PubMed] [Google Scholar]
- 56.Garcia A, Zanibbi K.. Homocysteine and cognitive function in elderly people. CMAJ. 2004;171(8):897–904. doi: 10.1503/cmaj.1031586. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Wald DS, Kasturiratne A, Simmonds M.. Serum homocysteine and dementia: meta-analysis of eight cohort studies including 8669 participants. Alzheimers Dement. 2011;7(4):412–417. doi: 10.1016/j.jalz.2010.08.234. [DOI] [PubMed] [Google Scholar]
- 58.Fassbender K, Mielke O, Bertsch T, et al. Homocysteine in cerebral macroangiography and microangiopathy. Lancet. 1999;353(9164):1586–1587. doi: 10.1016/S0140-6736(99)00309-8. [DOI] [PubMed] [Google Scholar]
- 59.Wright CB, Paik MC, Brown TR, et al. Total homocysteine is associated with white matter hyperintensity volume: the Northern Manhattan Study. Stroke. 2005;36(6):1207–1211. doi: 10.1161/01.STR.0000165923.02318.22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Lipton SA, Kim WK, Choi YB, et al. Neurotoxicity associated with dual actions of homocysteine at the N-methyl-D-aspartate receptor. Proc Natl Acad Sci U S A. 1997;94(11):5923–5928. doi: 10.1073/pnas.94.11.5923. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Park JM, Lee J, Kim Y, et al. Calorie intake and cognitive function in the elderly: data from the Korean Frailty and Aging Cohort Study (KFACS). J Nutr Health Aging. 2019;23(10):930–936. doi: 10.1007/s12603-019-1249-z. [DOI] [PubMed] [Google Scholar]
- 62.Martin CK, Anton SD, Han H, et al. Examination of cognitive function during six months of calorie restriction: results of a randomized controlled trial. Rejuvenation Res. 2007;10(2):179–190. doi: 10.1089/rej.2006.0502. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Elias A, Padinjakara N, Lautenschlager NT.. Effects of intermittent fasting on cognitive health and Alzheimer’s disease. Nutr Rev. 2023;81(9):1225–1233. doi: 10.1093/nutrit/nuad021. [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.
Supplementary Materials
Data Availability Statement
All data can be downloaded from the NHANES website (https://www.cdc.gov/nchs/nhanes/about_nhanes.htm).