Next Article in Journal
Correction: Tayebati et al. Choline and Choline alphoscerate Do Not Modulate Inflammatory Processes in the Rat Brain. Nutrients 2017, 9, 1084
Previous Article in Journal
Dietary Patterns in Children with Neurodevelopmental Disorders
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 16
Issue 15
10.3390/nu16152461
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between Dietary Acid Load and Chronic Kidney Disease in the Chinese Population: A Comprehensive Analysis of the China Health and Nutrition Survey (2009)

by
Shurui Wang
1,2,
Xiaohong Fan
2,
Xixi Zheng
2,
Peng Xia
2,
Haiou Zou
1,
Zhaofeng Zhang
3,* and
Limeng Chen
2,*
1
School of Nursing, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
2
Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
3
Department of Nutrition & Food Hygiene, School of Public Health, Peking University Health Science Center, Haidian District, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Nutrients 2024, 16(15), 2461; https://doi.org/10.3390/nu16152461
Submission received: 24 June 2024 / Revised: 21 July 2024 / Accepted: 23 July 2024 / Published: 29 July 2024
(This article belongs to the Collection Clinical Nutrition in Kidney Disease)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background: Dietary acid load (DAL) is closely related to several chronic diseases. However, the link between DAL and chronic kidney disease (CKD) remains scarce and without data from the Chinese populations whose diet is quite different from people in Western countries. Methods: This study evaluated DAL by potential renal acid load (PRAL) and net endogenous acid production (NEAP). We clarified the relationship between DAL and CKD by logistic regression analysis based on data from the China Health and Nutrition Survey (CHNS). Results: The final analysis included 7699 individuals, of whom 811 (11.44%) were CKD patients. Although there was no notable link between PRAL and CKD, higher NEAP levels were independently correlated with CKD. As NEAP values rise, so does CKD prevalence. This trend remains highly significant even after adjustments. In subgroup analyses, the relationship between NEAP and CKD was more consistent in the elderly and subjects with a waistline of less than 82 cm and those without diabetes and heart disease. RCS analysis further confirmed the clear linear relationship between the OR of CKD and NEAP score. Conclusions: This study highlighted that higher NEAP was positively correlated with the risk of CKD.

1. Introduction

In the preceding twenty years, the incidence and mortality rates of CKD have gradually increased worldwide. According to The Lancet, in 2019, there were 697.5 million patients with CKD globally, constituting 9.1% of the world’s population from 1990 to 2017 [1]. This prevalence surpasses non-communicable chronic diseases such as metabolic disorders, cardio-cerebrovascular disease, bronchopneumonia, and gastroenteritis [2]. Even more remarkably, 90% of adults affected by CKD are unaware of their condition, and half of those with diminished kidney function who are not undergoing dialysis are oblivious to the presence of CKD. Therefore, the early blocking of the progress of CKD is beneficial to reducing the morbidity and mortality of ESRD.
Recently, besides pharmacological therapies, medical nutrition and dietary intervention were recognized as effective strategies for preventing and treating CKD. Many studies have confirmed that diet interventions are convenient, inexpensive, and practical methods in CKD therapies [3,4]. The appropriate intake of proteins, phosphorus, sodium, and potassium could effectively prevent or improve the symptoms of insufficient renal function, diminishing proteinuria, maintaining good nutritional status, and avoiding malnutrition and metabolic disorders [5,6,7]. These metabolic disturbances comprise a cluster of dysfunctions, including protein-energy wasting, malnutrition, and progressive decreases in muscle function, all of which might accelerate CKD progression and heighten the incidence of complications, including frailty, cardiovascular disease, disturbed blood control, infectious complication, and the mortality of CKD and ESRD patients [8]. Intuitively, the disorders of acid–base balance are the most common complication of CKD and aggravate gastrointestinal symptoms [9]. There is compelling evidence that DAL is the primary determinant of the acid–base balance and systemic pH clinical research has shown a positive correlation between DAL and metabolic syndromes, chronic inflammation, hypertension, and hyperuricemia [10,11,12,13], which all contribute to CKD progression.
In animal experiments, DAL is associated with increased protein intake, leading to a reduction in renal medullary and muscle pH accompanied by an elevation in ammonia concentration. Clinical studies indicated that diets rich in fruits and vegetables buffered DAL, attenuating kidney injury and slowing GFR decline [14,15]. Tanushree demonstrated that the prevalence of kidney failure was threefold higher for people with higher DAL than those with lower DAL [16]. Crews suggested that the effect of DAL on CKD patients may vary by ethnicity and race-related perceptions, which suggests that these clinical outcomes in Western countries, as described in previous studies, may not be beneficial to the Asia population [17]. Unfortunately, no investigation has focused on the relationship between DAL and CKD in the Chinese population.
To fill this gap, we conducted this study based on cross-sectional data from the CHNS, which collected extensive community-level data from 1989 to 2015 in China [18]. Therefore, this study aims to determine the association between DAL and CKD in a large Chinese population. The results will provide new perspectives on the diagnosis and prognosis of CKD patients while also developing innovative strategies and public health policies to enhance the quality of life and life expectancy of the elderly population.

2. Materials and Methods

2.1. Data Collection and Participants

This study utilized openly accessible data from the CHNS. This study utilized a multi-stage random clustering method to gather comprehensive data on the socioeconomic status, healthcare utilization, dietary habits, and nutritional status of a consistent cohort over time. Finally, we selected approximately 7200 households, with a total population exceeding 30,000 individuals.
We gathered essential particulars, health-related data, socioeconomic status, diet and exercise information derived from the 2009 database, and exclusively biological data. A total of 7749 participants were recruited from 18,805 individuals in this study, excluding individuals with incomplete information, missing variables, no EGFR data, and those under 18 years old (Figure 1).
All the participants provided informed consent. The study was approved by the Carolina Population Center at the University of North Carolina and the National Institute of Nutrition and Health Research at the Chinese Center.

2.2. Predictors—Dietary Acid Load Estimations

The dietary information was gathered by instructing the participants to record their daily food intake. Strictly speaking, the participants should document the types and quantities of food they consume within 24 h, covering two workdays and one weekday. The nutrient and energy intake were measured based on the Chinese Food Composition Tables as follows [14,19]:
PRAL (mEq/d) = 0.4888 × protein (g/d) + 0.0366 × phosphorus (mg/d) − 0.0205 × potassium (mg/d) − 0.0125 × calcium (mg/d) − 0.0263 × magnesium (mg/d);
NEAP (mEq/d) = (54.5 × protein (g/d) ÷ potassium (mEq/d)) − 10.2.

2.3. Outcomes—Definition of CKD

The estimated glomerular filtration rate (eGFR) was determined using the CKD Epidemiology Collaboration formula [20]. CKD was characterized by an eGFR < 60 mL/min/1.73 m2 [21].

2.4. Statistical Analysis

Continuous variables were summarized as mean (SD) or median (IQR), and categorical variables as frequencies. Group comparisons utilized Student’s t-test, Fisher’s exact test, or chi-square test, as appropriate.
Both univariate and multilevel logistic regression analyses were used to identify the association between CKD and DAL. We categorized the individuals into four groups according to the quartile distribution (Q1, Q2, Q3, and Q4); the odds ratios (ORs) were calculated based on the quartiles of the DAL score. Then, we conducted five models; model 1: unadjusted for covariates; model 2: adjusted for demographic variables; model 3: adjusted for hypertension, diabetes, heart disease, and fraction. Model 4: adjusted for hypertension, diabetes, heart disease, and fraction based on model 2; model 5: adjusted for energy, fat, cholesterol, CHO, and water. We also performed a subgroup analysis according to gender, age, and the absence of comorbidities (diabetes, hypertension, and heart disease). In the end, a restricted cubic spline (RCS) analysis was employed to demonstrate the relationship between CKD and DAL. Significance was set at p < 0.05.

3. Results

3.1. Baseline Characteristics

In this study, a total of 7749 individuals were included (Figure 1), and the general characteristics of the participants are presented in Table 1. In all, 895 patients (11.55%) were diagnosed with CKD with a mean age of 50.2 ± 15.0 years, and 22.7% had a high school or higher education. About one-third of them had a history of tobacco smoking (31.06%) or alcohol intake (32.68%). The underlying diseases included hypertension (13.06%), diabetes (2.79%), and cardiovascular disease (0.94%).
The average PRAL and NEAP were 21 (interquartile range, 13–28) mEq/d and 78 (65–92) mEq/d, with higher NEAP in the CKD patients than the non-CKD participants (80 (67, 95) mEq/d vs. 77 (65, 92) mEq/d, p = 0.001, Table 1)). The mean energy intake was approximately 1656 (1354, 1985) Kcal/day and less potassium (K), phosphorus (P), magnesium (Mg), and higher levels of fat and calcium (Ca) intake in the CKD patients than in the non-CKD population, but no difference in protein, phosphorus (P), and cholesterol between the two groups (Table 2).

3.2. Association between DAL and CKD

There was no significant association between CKD and PRAL in the five multivariate logistic regression models adjusted by different covariates (Table 3). The relationship between NEAP and CKD was shown in five logistic regression models, and an elevated level of NEAP was an independent association factor for CKD risk in the adjusted models 2, 3, 4, and 5 (Table 4).

3.3. Subgroup Analysis for the Association between DAL and CKD

In the subgroup analyses, there was no significant association between PRAL and the risk of CKD with the factors (gender, age, waist, BMI, and comorbidities, including diabetes, hypertension, and heart disease) responsible for potential heterogeneity (Figures S1A and S2A). However, we observed that the positive correlation between NEAP and CKD appeared stronger in those aged ≥ 60 years with lower waist circumference and BMI < 24 kg/m2. Further, in the subgroup without heart disease (Q3: OR 1.30, 95% CI 1.01–1.69; Q4: OR 1.32, 95% CI 1.00–1.75) and diabetes 1.32 (95% CI, 1.01–1.72) and 1.34 (95% CI, 1.01–1.78) participants were more likely to suffer from CKD after adjusting for all the confounders, but not in subjects with or without cardiovascular disease (Figure S2B).

3.4. RCS Analysis of DAL and Risk of CKD

The RCS analysis showed non-linear associations between the OR values of CKD and PRAL (p > 0.05; Figure 2A) based on the multivariate logistic proportional hazards model. However, the NEAP score increased the risk of CKD (p < 0.05), which revealed a clear linear relationship between the OR values of CKD and NEAP with the cutoff of 77.99 mEq/day (Figure 2B).

3.5. Dietary Intakes and CKD Risk in an Energy-Adjusted Model

In the energy-adjusted model, we found that protein and fat increased the risk of CKD by 16% (OR: 1.16, 95% CI: 1.06–1.27) for each 10 g increased intake, and each 100 g fat and carbohydrate intake may increase the risk of CKD by 95% (OR: 1.95, 95% CI: 1.41–2.68) and 21% (OR: 1.23, 95% CI: 1.08–1.4) respectively. In addition, calcium was also observed to increase the risk of CKD by 20% for each 100 g increased intake. However, for every 100 mg reduction in energy-adjusted phosphorus and magnesium intake, the risk of CKD may decrease by 17% and 18%, respectively (Figure 3).

4. Discussion

As the first multicenter national cross-sectional study in China, we first identified a significant correlation between elevated levels of NEAP and an increased risk of CKD. In the subgroup analyses, the correlation between NEAP and CKD was more consistent in the elderly, waist < 82 cm, and those without diabetes, and heart diseases. The RCS analysis further indicated a clear linear relationship between the OR of the CKD and NEAP score. In addition, we confirmed that higher consumption of carbohydrates, fat, and protein was correlated with an increased risk of CKD.
Dietary intake is a crucial factor influencing the acid–base balance in individuals with chronic diseases. A substantial body of research has developed various methods to estimate DAL based on dietary intake measurements including endogenous acid production, gastrointestinal (GI) alkali absorption, NEAP, net acid excretion NAE, and PRAL [22,23,24,25]. The most prevalent methodologies for assessing acid–base balance are NEAP and the diet-dependent component of net acid excretion, referred to as PRAL [25,26], both of which assess acid–base status based on food components such as calcium, phosphorus, magnesium, protein, and potassium. NEAP, calculated from the ratio of dietary protein to potassium content, indicates the equilibrium between acid and base precursors in the body. In this study, we first observed that NEAP was positively significantly correlated with the prevalence of CKD in the Chinese population. It filled the gap of Chinese data in this controversial field. This conclusion was inconsistent with previous studies from European and American countries and some Asian regions. Clinical research from Johns Hopkins University suggested that a higher NEAP was correlated with a higher CKD incidence independent of sociodemographic, clinical, and lifestyle factors in a population-based sample [27]. The African American Study drew similar conclusions that higher estimated NEAP predicts a faster decline in directly measured I125 iothalamate GFR [28]. Kabasawa and colleagues from Japan demonstrated that elevated NEAP levels were correlated with the presence of albuminuria, which serves as a marker for CKD [29]. In Banerjee’s cross-sectional cohort study, higher NEAP value was strongly associated with progression to ESRD (HR 3.04 (1.58 to 5.86)) [16]. However, Pike and colleagues found no associations between the NEAP score and CKD progression in any APOL1 genotype [30]. Scialla and colleagues observed that a higher NEAP was correlated with the increased consumption of meat and poultry and decreased intake of fruits and vegetables, but it was not linked to the progression of CKD. However, elevated NEAP estimated from urine biomarkers (NEAPUrine) was correlated with a higher risk of CKD in individuals without diabetes [31].
Then, we further conducted sensitivity analyses to see if our results were robust. In this study, we found that older adults and individuals with smaller waist circumferences tended to have higher acid load. Elderly individuals often experience a reduction in muscle mass, insufficient nutritional intake, decreased metabolic efficiency, weakened buffering system function, and chronic diseases [32,33,34]. These factors contribute to a diminished ability to buffer acids, resulting in an increased acid load in the body [34,35]. Additionally, people with smaller waist circumferences often have less muscle mass and fat reserves, which can lead to disturbances in acid–base balance, and are more susceptible to the effects of NEAP [36,37]. These findings underscore the significance of body composition and muscle mass in evaluating dietary acid load and its health repercussions, particularly in vulnerable elderly groups. In addition, we also found that CKD patients without diabetes and heart disease may be more susceptible to the effects of NEAP. This result is consistent with a previous cross-sectional study in Korea, which suggested that the relationship between eNEAP and CKD appeared stronger in those without obesity, diabetes, and heart disease [38]. These studies demonstrated that in CKD patients without diabetes or heart disease complications, effectively controlling DAL levels contributes significantly to reducing the risk of CKD.
We also confirmed that the people with higher intakes of carbohydrates, fat, and protein might be at an increased risk of CKD, consistent with the majority of the previously published studies [7]. High protein, fat, and carbohydrate load might contribute to proteinuria and the disorder of electrolytic and acid–base balance, resulting in glomerular hyperfiltration and CKD progression [39,40,41,42,43]. Rebholz et al. demonstrated that a DASH-style diet, a dietary pattern high in fruits, vegetables, low-fat, and carbohydrates, was a protective factor for CKD, and this pattern gives evidence for dietary approaches to the prevention and treatment of CKD [44]. Similar findings from research on the Mediterranean diet (MD), which is characterized by the regular intake of plant-based foods, indicate that it can lower the levels of homocysteine, microalbuminuria, total cholesterol, and phosphorus while increasing vitamin B12 levels in the early stages of CKD [45]. In conclusion, a traditional diet rich in plant-based proteins supports glomerular hemodynamics and offers protective benefits for renal function [46].
This study had several limitations. Firstly, as a cross-sectional study, it could not prove the cause-and-effect relations between DAL and CKD. Secondly, although PRAL and NEAP are widely used indicators of overall DAL, it is calculated by diet but not from the directly measured data. Thirdly, since dietary assessment came from the recall of food intake by the participants, recall bias could not be avoided, although we tried several ways to minimize it. Fourthly, due to the presence of some missing data (sampling framework, weight information, serum calcium, and serum phosphorus) in this population-based study, the conclusions may have certain limitations when applied to a broader population, particularly the Chinese population.

5. Conclusions

In conclusion, this study is the first to evaluate the relationship between DAL and CKD in the Chinese population. The graphical summary of the study is shown in Figure 4. It concluded that higher NEAP positively correlated with the prevalence of CKD, either with or without covariate adjustment. It may provide novel insights and strategies to improve the standard of living and life expectancy of the elderly population.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu16152461/s1, Figure S1: Subgroup analysis examined the relationship between DAL and the risk of CKD; Figure S2: Subgroup analysis examined the relationship between DAL and the risk of CKD.

Author Contributions

Research idea and study design: S.W., X.F., X.Z., P.X., H.Z. and L.C.; data analysis/interpretation: S.W., X.F., Z.Z. and L.C.; statistical analysis: S.W. and X.F.; supervision or mentorship: X.Z. and L.C.; writing—original draft preparation, S.W.; writing—review and editing, S.W., Z.Z. and L.C.; supervision, S.W., X.F. and L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partially supported by grants from the National Science and Technology Major Project (2022ZD0116003 to C.L.), CAMS Innovation Fund for Medical Sciences (CIFMS 2021-I2M-1-003 to C.L.), National High-Level Hospital Clinical Research Funding (2022-PUMCH-B-019, to C.L.), and the Capital Exemplary Research Wards Project (BCRW202001 to C.L.).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Ethical approval was secured, and informed consent was acquired from all the participants engaged in the research.

Data Availability Statement

This study involved the examination of datasets that are accessible to the public. The relevant data can be located at the following source: https://www.cpc.unc.edu/projects/china/data (accessed on 1 August 2023).

Acknowledgments

We express our gratitude to the National Institute of Nutrition and Food Safety and the China Center for Disease Control and Prevention for their support, as well as the Carolina Population Center at the University of North Carolina, Chapel Hill.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kovesdy, C.P. Epidemiology of chronic kidney disease: An update 2022. Kidney Int. Suppl. 2022, 12, 7–11. [Google Scholar] [CrossRef] [PubMed]
  2. Drawz, P.; Rahman, M. Chronic kidney disease. Ann. Intern. Med. 2015, 162, ITC1–ITC16. [Google Scholar] [CrossRef] [PubMed]
  3. Jacobs, D.R., Jr.; Gross, M.D.; Tapsell, L.C. Food synergy: An operational concept for understanding nutrition. Am. J. Clin. Nutr. 2009, 89, 1543s–1548s. [Google Scholar] [CrossRef]
  4. MacLaughlin, H.L.; Friedman, A.N.; Ikizler, T.A. Nutrition in Kidney Disease: Core Curriculum 2022. Am. J. Kidney Dis. 2022, 79, 437–449. [Google Scholar] [CrossRef] [PubMed]
  5. Hariharan, D.; Vellanki, K.; Kramer, H. The Western Diet and Chronic Kidney Disease. Curr. Hypertens. Rep. 2015, 17, 16. [Google Scholar] [CrossRef]
  6. Harambat, J.; Kunzmann, K.; Azukaitis, K.; Bayazit, A.K.; Canpolat, N.; Doyon, A.; Duzova, A.; Niemirska, A.; Sözeri, B.; Thurn-Valsassina, D.; et al. Metabolic acidosis is common and associates with disease progression in children with chronic kidney disease. Kidney Int. 2017, 92, 1507–1514. [Google Scholar] [CrossRef]
  7. Ikizler, T.A.; Burrowes, J.D.; Byham-Gray, L.D.; Campbell, K.L.; Carrero, J.J.; Chan, W.; Fouque, D.; Friedman, A.N.; Ghaddar, S.; Goldstein-Fuchs, D.J.; et al. KDOQI Clinical Practice Guideline for Nutrition in CKD: 2020 Update. Am. J. Kidney Dis. 2020, 76, S1–S107. [Google Scholar] [CrossRef]
  8. Nitta, K.; Tsuchiya, K. Recent advances in the pathophysiology and management of protein-energy wasting in chronic kidney disease. Ren. Replace. Ther. 2016, 2, 4. [Google Scholar] [CrossRef]
  9. Kraut, J.A.; Madias, N.E. Metabolic Acidosis of CKD: An Update. Am. J. Kidney Dis. 2016, 67, 307–317. [Google Scholar] [CrossRef]
  10. Zhang, M.; Ye, C.; Wang, R.; Zhang, Z.; Huang, X.; Halimulati, M.; Sun, M.; Ma, Y.; Zhang, Z. Association between Dietary Acid Load and Hyperuricemia in Chinese Adults: Analysis of the China Health and Nutrition Survey (2009). Nutrients 2023, 15, 1806. [Google Scholar] [CrossRef]
  11. Mangano, K.M.; Walsh, S.J.; Kenny, A.M.; Insogna, K.L.; Kerstetter, J.E. Dietary acid load is associated with lower bone mineral density in men with low intake of dietary calcium. J. Bone Min. Res. 2014, 29, 500–506. [Google Scholar] [CrossRef] [PubMed]
  12. Haghighatdoost, F.; Najafabadi, M.M.; Bellissimo, N.; Azadbakht, L. Association of dietary acid load with cardiovascular disease risk factors in patients with diabetic nephropathy. Nutrition 2015, 31, 697–702. [Google Scholar] [CrossRef] [PubMed]
  13. Han, E.; Kim, G.; Hong, N.; Lee, Y.H.; Kim, D.W.; Shin, H.J.; Lee, B.W.; Kang, E.S.; Lee, I.K.; Cha, B.S. Association between dietary acid load and the risk of cardiovascular disease: Nationwide surveys (KNHANES 2008-2011). Cardiovasc. Diabetol. 2016, 15, 122. [Google Scholar] [CrossRef] [PubMed]
  14. Remer, T.; Dimitriou, T.; Manz, F. Dietary potential renal acid load and renal net acid excretion in healthy, free-living children and adolescents. Am. J. Clin. Nutr. 2003, 77, 1255–1260. [Google Scholar] [CrossRef] [PubMed]
  15. Silva, L.; Moço, S.A.; Antunes, M.L.; Ferreira, A.S.; Moreira, A.C. Dietary Acid Load and Relationship with Albuminuria and Glomerular Filtration Rate in Individuals with Chronic Kidney Disease at Predialysis State. Nutrients 2021, 14, 170. [Google Scholar] [CrossRef] [PubMed]
  16. Banerjee, T.; Crews, D.C.; Wesson, D.E.; Tilea, A.M.; Saran, R.; Rios-Burrows, N.; Williams, D.E.; Powe, N.R. High Dietary Acid Load Predicts ESRD among Adults with CKD. J. Am. Soc. Nephrol. 2015, 26, 1693–1700. [Google Scholar] [CrossRef] [PubMed]
  17. Crews, D.C.; Banerjee, T.; Wesson, D.E.; Morgenstern, H.; Saran, R.; Burrows, N.R.; Williams, D.E.; Powe, N.R. Race/Ethnicity, Dietary Acid Load, and Risk of End-Stage Renal Disease among US Adults with Chronic Kidney Disease. Am. J. Nephrol. 2018, 47, 174–181. [Google Scholar] [CrossRef] [PubMed]
  18. Zhang, B.; Wang, H.; Du, S. China Health and Nutrition Survey, 1989–2019. In Encyclopedia of Gerontology and Population Aging; Gu, D., Dupre, M.E., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 1–6. [Google Scholar]
  19. Frassetto, L.A.; Todd, K.M.; Morris, R.C., Jr.; Sebastian, A. Estimation of net endogenous noncarbonic acid production in humans from diet potassium and protein contents. Am. J. Clin. Nutr. 1998, 68, 576–583. [Google Scholar] [CrossRef] [PubMed]
  20. Levey, A.S.; Stevens, L.A.; Schmid, C.H.; Zhang, Y.L.; Castro, A.F., 3rd; Feldman, H.I.; Kusek, J.W.; Eggers, P.; Van Lente, F.; Greene, T.; et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 2009, 150, 604–612. [Google Scholar] [CrossRef]
  21. Chen, T.K.; Knicely, D.H.; Grams, M.E. Chronic Kidney Disease Diagnosis and Management: A Review. JAMA 2019, 322, 1294–1304. [Google Scholar] [CrossRef]
  22. Scialla, J.J.; Anderson, C.A. Dietary acid load: A novel nutritional target in chronic kidney disease? Adv. Chronic Kidney Dis. 2013, 20, 141–149. [Google Scholar] [CrossRef] [PubMed]
  23. Sebastian, A.; Frassetto, L.A.; Sellmeyer, D.E.; Merriam, R.L.; Morris Jr, R.C. Estimation of the net acid load of the diet of ancestral preagricultural Homo sapiens and their hominid ancestors. Am. J. Clin. Nutr. 2002, 76, 1308–1316. [Google Scholar] [CrossRef] [PubMed]
  24. Frassetto, L.A.; Lanham-New, S.A.; Macdonald, H.M.; Remer, T.; Sebastian, A.; Tucker, K.L.; Tylavsky, F.A. Standardizing terminology for estimating the diet-dependent net acid load to the metabolic system. J. Nutr. 2007, 137, 1491–1492. [Google Scholar] [CrossRef] [PubMed]
  25. Remer, T.; Manz, F. Estimation of the renal net acid excretion by adults consuming diets containing variable amounts of protein. Am. J. Clin. Nutr. 1994, 59, 1356–1361. [Google Scholar] [CrossRef] [PubMed]
  26. Remer, T.; Manz, F. Potential renal acid load of foods and its influence on urine pH. J. Am. Diet. Assoc. 1995, 95, 791–797. [Google Scholar] [CrossRef] [PubMed]
  27. Rebholz, C.M.; Coresh, J.; Grams, M.E.; Steffen, L.M.; Anderson, C.A.; Appel, L.J.; Crews, D.C. Dietary Acid Load and Incident Chronic Kidney Disease: Results from the ARIC Study. Am. J. Nephrol. 2015, 42, 427–435. [Google Scholar] [CrossRef] [PubMed]
  28. Scialla, J.J.; Appel, L.J.; Astor, B.C.; Miller, E.R., 3rd; Beddhu, S.; Woodward, M.; Parekh, R.S.; Anderson, C.A. Net endogenous acid production is associated with a faster decline in GFR in African Americans. Kidney Int. 2012, 82, 106–112. [Google Scholar] [CrossRef]
  29. Kabasawa, K.; Hosojima, M.; Takachi, R.; Nakamura, K.; Ito, Y.; Saito, A.; Sawada, N.; Tsugane, S.; Tanaka, J.; Narita, I. Association of estimated dietary acid load with albuminuria in Japanese adults: A cross-sectional study. BMC Nephrol. 2019, 20, 194. [Google Scholar] [CrossRef]
  30. Pike, M.; Stewart, T.G.; Morse, J.; Ormsby, P.; Siew, E.D.; Hung, A.; Abdel-Kader, K.; Ikizler, T.A.; Lipworth, L.; Robinson-Cohen, C. APOL1, Acid Load, and CKD Progression. Kidney Int. Rep. 2019, 4, 946–954. [Google Scholar] [CrossRef]
  31. Scialla, J.J.; Asplin, J.; Dobre, M.; Chang, A.R.; Lash, J.; Hsu, C.Y.; Kallem, R.R.; Hamm, L.L.; Feldman, H.I.; Chen, J.; et al. Higher net acid excretion is associated with a lower risk of kidney disease progression in patients with diabetes. Kidney Int. 2017, 91, 204–215. [Google Scholar] [CrossRef]
  32. Morley, J.E.; Argiles, J.M.; Evans, W.J.; Bhasin, S.; Cella, D.; Deutz, N.E.; Doehner, W.; Fearon, K.C.; Ferrucci, L.; Hellerstein, M.K.; et al. Nutritional recommendations for the management of sarcopenia. J. Am. Med. Dir. Assoc. 2010, 11, 391–396. [Google Scholar] [CrossRef] [PubMed]
  33. Landi, F.; Calvani, R.; Tosato, M.; Martone, A.M.; Ortolani, E.; Savera, G.; Sisto, A.; Marzetti, E. Anorexia of Aging: Risk Factors, Consequences, and Potential Treatments. Nutrients 2016, 8, 69. [Google Scholar] [CrossRef] [PubMed]
  34. Frassetto, L.; Morris, R.C., Jr.; Sellmeyer, D.E.; Todd, K.; Sebastian, A. Diet, evolution and aging—The pathophysiologic effects of the post-agricultural inversion of the potassium-to-sodium and base-to-chloride ratios in the human diet. Eur. J. Nutr. 2001, 40, 200–213. [Google Scholar] [CrossRef] [PubMed]
  35. Adeva, M.M.; Souto, G. Diet-induced metabolic acidosis. Clin. Nutr. 2011, 30, 416–421. [Google Scholar] [CrossRef] [PubMed]
  36. Cunha, P.; Paciência, I.; Rufo, J.C.; Mendes, F.C.; Farraia, M.; Barros, R.; Silva, D.; Delgado, L.; Padrão, P.; Moreira, A.; et al. Dietary Acid Load: A Novel Nutritional Target in Overweight/Obese Children with Asthma? Nutrients 2019, 11, 2255. [Google Scholar] [CrossRef] [PubMed]
  37. Williams, R.S.; Kozan, P.; Samocha-Bonet, D. The role of dietary acid load and mild metabolic acidosis in insulin resistance in humans. Biochimie 2016, 124, 171–177. [Google Scholar] [CrossRef]
  38. Ko, B.J.; Chang, Y.; Ryu, S.; Kim, E.M.; Lee, M.Y.; Hyun, Y.Y.; Lee, K.B. Dietary acid load and chronic kidney disease in elderly adults: Protein and potassium intake. PLoS ONE 2017, 12, e0185069. [Google Scholar] [CrossRef] [PubMed]
  39. Truche, A.S.; Bailly, S.; Fabre, O.; Legrand, R.; Zaoui, P. A Specific High-Protein Weight Loss Program Does Not Impair Renal Function in Patients Who Are Overweight/Obese. Nutrients 2022, 14, 384. [Google Scholar] [CrossRef] [PubMed]
  40. Garofalo, C.; Borrelli, S.; Minutolo, R.; Chiodini, P.; De Nicola, L.; Conte, G. A systematic review and meta-analysis suggests obesity predicts onset of chronic kidney disease in the general population. Kidney Int. 2017, 91, 1224–1235. [Google Scholar] [CrossRef]
  41. Ko, G.J.; Rhee, C.M.; Kalantar-Zadeh, K.; Joshi, S. The Effects of High-Protein Diets on Kidney Health and Longevity. J. Am. Soc. Nephrol. 2020, 31, 1667–1679. [Google Scholar] [CrossRef]
  42. Kamper, A.L.; Strandgaard, S. Long-Term Effects of High-Protein Diets on Renal Function. Annu. Rev. Nutr. 2017, 37, 347–369. [Google Scholar] [CrossRef] [PubMed]
  43. Kalantar-Zadeh, K.; Kramer, H.M.; Fouque, D. High-protein diet is bad for kidney health: Unleashing the taboo. Nephrol. Dial. Transpl. 2020, 35, 1–4. [Google Scholar] [CrossRef] [PubMed]
  44. Rebholz, C.M.; Crews, D.C.; Grams, M.E.; Steffen, L.M.; Levey, A.S.; Miller, E.R., 3rd; Appel, L.J.; Coresh, J. DASH (Dietary Approaches to Stop Hypertension) Diet and Risk of Subsequent Kidney Disease. Am. J. Kidney Dis. 2016, 68, 853–861. [Google Scholar] [CrossRef] [PubMed]
  45. Noce, A.; Marrone, G.; Jones, G.W.; Di Lauro, M.; Zaitseva, A.P.; Ramadori, L.; Celotto, R.; Mitterhofer, A.P.; Di Daniele, N. Nutritional Approaches for the Management of Metabolic Acidosis in Chronic Kidney Disease. Nutrients 2021, 13, 2534. [Google Scholar] [CrossRef]
  46. Kontessis, P.; Jones, S.; Dodds, R.; Trevisan, R.; Nosadini, R.; Fioretto, P.; Borsato, M.; Sacerdoti, D.; Viberti, G. Renal, metabolic and hormonal responses to ingestion of animal and vegetable proteins. Kidney Int. 1990, 38, 136–144. [Google Scholar] [CrossRef]
Nutrients 16 02461 g001
Figure 1. Flow chart of research participant selection.
Figure 1. Flow chart of research participant selection.
Nutrients 16 02461 g001
Nutrients 16 02461 g002
Figure 2. RCS of the correlation between the CKD risk (ORs) and PRAL (A) and NEAP (B). Pink solid line indicates cut-off value.
Figure 2. RCS of the correlation between the CKD risk (ORs) and PRAL (A) and NEAP (B). Pink solid line indicates cut-off value.
Nutrients 16 02461 g002
Nutrients 16 02461 g003
Figure 3. Forest plot of the relationship between dietary intakes and CKD risk (energy-adjusted model).
Figure 3. Forest plot of the relationship between dietary intakes and CKD risk (energy-adjusted model).
Nutrients 16 02461 g003
Nutrients 16 02461 g004
Figure 4. Graphical summary of this study.
Figure 4. Graphical summary of this study.
Nutrients 16 02461 g004
Table 1. Subject characteristics based on CKD status.
Table 1. Subject characteristics based on CKD status.
Variable Overall (n = 7749)Non-CKD (n = 6854)CKD (n = 895)p
Age (y) 50.2 ± 15.047.9 ± 13.967.9 ± 10.8<0.001
GenderMale3639 (46.96%) 3281 (47.87%) 358 (40%) <0.001
EducationPrimary3417 (44.1%) 2785 (40.63%) 632 (70.61%) <0.001
Middle2573 (33.2%) 2436 (35.54%) 137 (15.31%)
High886 (11.43%) 834 (12.17%) 52 (5.81%)
Vocational520 (6.71%) 468 (6.83%) 52 (5.81%)
University353 (4.56%) 331 (4.83%) 22 (2.46%)
MarryUnmarried1163 (15.01%) 914 (13.34%) 249 (27.82%) <0.001
Married6586 (84.99%) 5940 (86.66%) 646 (72.18%)
LocationRural4758 (61.04%)4316 (62.97%)442 (49.39%)<0.001
Urban2991 (38.6%)2538 (37.03%)453 (50.61%)
AlcoholYes2532 (32.68%)2359 (34.42%)173 (19.33%)<0.001
SmokingYes2407 (31.06%)2176 (31.75%)231 (25.81%)<0.001
Hip (cm) 94.4 ± 7.894.4 ± 7.894.0 ± 8.50.056
Waist (cm) 82.6 ± 10.382.4 ± 10.283.8 ± 10.7<0.001
BMI (kg/m2) 23.3 ± 3.423.3 ± 3.423.3 ± 3.8 0.410
BMI (kg/m2)<18.5490 (6.32%)411 (6.0%)79 (8.83%)0.005
(18.5, 23.9)4186 (54.02%)3717 (54.23%)469 (52.4%)
(24, 27.9)715 (9.23%)622 (9.07%)93 (10.39%)
≥282358 (30.43%)2104 (30.7%) 254 (28.38%)
HypertensionYes1012 (13.06%)732 (10.68%)280 (31.28%)<0.001
DiabetesYes216 (2.79%)152 (2.22%)64 (7.15%)<0.001
Heart DiseaseYes73 (0.94%)44 (0.64%) 29 (3.24%) <0.001
FractionYes365 (4.71%)310 (4.52%)55 (6.15%)0.036
Urea (mg/dL) 32 (26, 38) 31 (26, 37) 37 (31, 44) <0.001
Ua (mg/dL) 4.96 (3.98, 6.05) 4.84 (3.90, 5.92) 5.85 (4.97, 6.96) <0.001
CRP (mg/L) 1.00 (0.00, 2.00) 1.00 (0.00, 2.00) 2.00 (1.00, 4.00) <0.001
eGFR (mL/min/1.73 m2) 76 (67, 85) 78 (70, 87) 55 (49, 58) <0.001
PRAL (mEq/d) 21 (13, 28) 21 (13, 28) 21 (13, 28) 0.808
NEAP (mEq/d) 78 (65, 92) 77 (65, 92) 80 (67, 95) <0.001
Notes: PRAL: potential renal acid load; NEAP: net endogenous acid production.
Table 2. Energy and nutrient intakes (energy-adjusted, per 1000 kcal).
Table 2. Energy and nutrient intakes (energy-adjusted, per 1000 kcal).
NutrientsOverall (n = 7749)Non-CKD (n = 6854)CKD (n = 895)p
Energy (Kcal)1656 (1354, 1985)1659 (1355, 1988)1634 (1344, 1967)0.238
Carbohydrate (g)152 (129, 176)153 (129, 177)150 (127, 172)0.006
Protein (g)37 (32, 44)37 (32, 44)37 (33, 45)0.736
Fat (g)25 (15, 35)25 (15, 35)26 (17, 36)0.004
Cholesterol (mg)209 (101, 343)210 (102, 343)202 (95, 342)0.774
Carbohydrate (g)152 (129, 176)153 (129, 177)150 (127, 172)0.006
Calcium (mg)180 (142, 241)179 (141, 240)185 (147, 249)0.006
Phosphorus (mg)536 (478, 602)536 (480, 602)524 (463, 599)0.002
Potassium (mg)922 (777, 1107)924 (780, 1109)906 (744, 1103)0.012
Magnesium (mg)154 (133, 182)155 (134, 183)150 (128, 177)0.001
Water (g)489 (385, 620)490 (386, 620)484 (381, 618)0.349
Note: variables are expressed as median (IQR).
Table 3. ORs and 95% CI for CKD risk based on PRAL.
Table 3. ORs and 95% CI for CKD risk based on PRAL.
PRALModel 1Model 2Model 3Model 4Model 5
OR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)p
Q1 1 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref)
Q2 1.02 (0.84, 1.25) 0.839 1.04 (0.82, 1.32) 0.723 1.03 (0.84, 1.26) 0.778 1.04 (0.82, 1.32) 0.715 1.02 (0.79, 1.31) 0.887
Q3 1.08 (0.89, 1.31) 0.452 1.15 (0.91, 1.45) 0.244 1.08 (0.88, 1.32) 0.462 1.15 (0.91, 1.45) 0.243 1.11 (0.85, 1.45) 0.441
Q4 1.02 (0.84, 1.24) 0.844 1.15 (0.91, 1.46) 0.247 0.98 (0.80, 1.21) 0.873 1.14 (0.90, 1.45) 0.275 1.08 (0.77, 1.52) 0.665
Notes: model 1: without covariate adjustment; model 2: adjusted for demographic data; model 3: adjusted for hypertension, diabetes, heart disease, and fraction; model 4: adjusted for hypertension, diabetes, heart disease, and fraction based on model 2; model 5: adjusted for energy, fat, cholesterol, CHO, and water.
Table 4. ORs and 95% CI for CKD risk based on NEAP.
Table 4. ORs and 95% CI for CKD risk based on NEAP.
NEAPModel 1Model 2Model 3Model 4Model 5
OR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)pOR (95% CI)p
Q11 (ref) 1 (ref) 1 (ref) 1 (ref) 1 (ref)
Q21.04 (0.85, 1.28)0.7131.07 (0.84, 1.36)0.5841.07 (0.87, 1.32)0.5311.09 (0.86, 1.39)0.4821.07 (0.83, 1.37)0.6
Q31.18 (0.97, 1.45)0.1011.33 (1.05, 1.69)0.0171.19 (0.97, 1.47)0.0971.32 (1.04, 1.67)0.0231.33 (1.03, 1.72)0.029
Q41.37 (1.13, 1.67)0.0021.32 (1.04, 1.66)0.021.34 (1.09, 1.64)0.0051.30 (1.03, 1.64)0.031.33 (1.01, 1.76)0.041
Notes: model 1: without covariate adjustment; model 2: adjusted for demographic data; model 3: adjusted for hypertension, diabetes, heart disease, and fraction; model 4: adjusted for hypertension, diabetes, heart disease, and fraction based on model 2; model 5: adjusted for energy, fat, cholesterol, CHO, and water.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, S.; Fan, X.; Zheng, X.; Xia, P.; Zou, H.; Zhang, Z.; Chen, L. Association between Dietary Acid Load and Chronic Kidney Disease in the Chinese Population: A Comprehensive Analysis of the China Health and Nutrition Survey (2009). Nutrients 2024, 16, 2461. https://doi.org/10.3390/nu16152461

AMA Style

Wang S, Fan X, Zheng X, Xia P, Zou H, Zhang Z, Chen L. Association between Dietary Acid Load and Chronic Kidney Disease in the Chinese Population: A Comprehensive Analysis of the China Health and Nutrition Survey (2009). Nutrients. 2024; 16(15):2461. https://doi.org/10.3390/nu16152461

Chicago/Turabian Style

Wang, Shurui, Xiaohong Fan, Xixi Zheng, Peng Xia, Haiou Zou, Zhaofeng Zhang, and Limeng Chen. 2024. "Association between Dietary Acid Load and Chronic Kidney Disease in the Chinese Population: A Comprehensive Analysis of the China Health and Nutrition Survey (2009)" Nutrients 16, no. 15: 2461. https://doi.org/10.3390/nu16152461

APA Style

Wang, S., Fan, X., Zheng, X., Xia, P., Zou, H., Zhang, Z., & Chen, L. (2024). Association between Dietary Acid Load and Chronic Kidney Disease in the Chinese Population: A Comprehensive Analysis of the China Health and Nutrition Survey (2009). Nutrients, 16(15), 2461. https://doi.org/10.3390/nu16152461

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop