
==== Front
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10.1080/0886022X.2024.2399742
2399742
Version of Record
Research Article
Chronic Kidney Disease and Progression
L-shaped association between dietary niacin intake and chronic kidney disease among adults in the USA: a cross-sectional study
Q. Li and W. Lan
Dietary Niacin Intake Associated with Chronic Kidney Disease
https://orcid.org/0009-0008-5288-8706
Li Qishu
https://orcid.org/0009-0002-1181-7545
Lan Wei
Department of Nephrology, Guangzhou Twelfth People’s Hospital, Guangzhou, Guangdong, China
Supplemental data for this article can be accessed online at https://doi.org/10.1080/0886022X.2024.2399742.

CONTACT Wei Lan 13826000366@163.com Department of Nephrology, Guangzhou Twelfth People’s Hospital, Guangzhou, Guangdong 510630, China.
5 9 2024
2024
5 9 2024
46 2 23997426 5 2024
3 8 2024
28 8 2024
KnowledgeWorks Global Ltd.4 9 2024
published online in a building issue4 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Background

Chronic kidney disease (CKD), which has become a global public health issue, is associated with mitochondrial dysfunction. Niacin is a necessary coenzyme for mitochondrial energy metabolism. However, the association between dietary niacin intake and CKD remains uncertain. This study aimed to investigate the association between dietary niacin intake and CKD in American adults.

Methods

This is a cross-sectional study. 25,608 individuals aged ≥20 years from the National Health and Nutrition Examination Survey from 2007 to 2018 were involved.

Dietary niacin intake was estimated based on 24-hour dietary recalls conducted by trained personnel. CKD was determined by an estimated glomerular filtration rate (eGFR) (<60 ml/min/1.73 m2) or a urinary albumin-to-creatinine ratio (ACR) (≥30mg/g). The association between dietary niacin intake and CKD was investigated using multivariable logistic regression analysis.

Results

Of 25,608 participants, 17.14% (4388/25,608) had CKD. Compared to individuals with lower niacin intake (quartile [Q]1, ≤15.30 mg/day), those with higher niacin intake in Q2 (15.31–22.07 mg/day), Q3 (22.08–31.09 mg/day), and Q4 (≥31.10 mg/day) exhibited adjusted odds ratios for CKD of 0.89 (95% confidence interval [CI]:0.81–0.99, p = 0.024), 0.83 (95% CI:0.75–0 .92, p < 0 .001), and 0.83 (95% CI:0.75–0.93, p = 0.001) respectively. The relationship between dietary niacin intake and CKD among U.S. adults follows an L-shaped pattern, with an inflection point at approximately 28.04 mg/day.

Conclusions

These results suggest an L-shaped association between dietary niacin intake and CKD. Individuals with low dietary niacin intake levels should be alert to the risk of CKD.

Keywords

Dietary niacin intake
chronic kidney disease
cross-sectional study
L-shaped
NHANES
The author(s) reported there is no funding associated with the work featured in this article.
==== Body
pmcIntroduction

Chronic kidney disease (CKD) poses a significant health threat to approximately 10% of the world’s population [1,2]. It is a major concern for society and the healthcare system [3]. The prevalence of CKD among Medicare beneficiaries in the United States is approximately 14.5%, with a higher incidence among older individuals [4]. Recent studies have shown that nutrients are associated with CKD [5–7]. With the increase of these nutrients, they may lead to the occurrence of CKD [7]. However, some nutrients may also reduce the prevalence of CKD [5,6].

Niacin is a necessary coenzyme for mitochondrial energy metabolism [8]. Insufficient dietary niacin intake can disrupt the process of mitochondrial respiration [9]. Mitochondrial dysfunction has been identified as a major factor that contribute to the risk of CKD [10]. Niacin has a unique and beneficial impact on factors influencing the decline in glomerular filtration rate (GFR), including triglycerides, high-density lipoprotein cholesterol, oxidative stress, and reducing phosphate absorption [11]. These effects may potentially slow the decline in GFR, ultimately improving the prognosis of CKD [12–14]. Therefore, it is important for CKD patients to have an adequate intake of niacin. Based on previous clinical studies [15–17], it appears that patients with CKD may benefit from the use of niacin. A recent study in Japan suggests that dietary niacin intake is important for preventing CKD in middle-aged and elderly Japanese individuals with genetic variations [18]. Nevertheless, there is a lack of research on the correlation between dietary niacin intake and CKD in the general population.

We used data from the National Health and Nutrition Examination Survey (NHANES) to investigate the association between dietary niacin intake and CKD. Furthermore, we assessed the dose-response relationship between dietary niacin intake and CKD.

Methods

This is a cross-sectional study, and the data were obtained from the NHANES from 2007 to 2018 [19]. Our research involved participants who were older than 20 years and had undergone an interview. We did not include pregnant women or individuals with incomplete data on CKD, dietary niacin intake, or covariates.

The NHANES was carried out by the Centers for Disease Control and Prevention, aimed to assess the health and nutritional condition of non-institutionalized Americans through a survey employing stratified multistage probability sampling [20]. This project involved the collection of demographic and comprehensive health information through household visits, screenings, and laboratory tests facilitated by a mobile examination center (MEC). The NHANES obtained authorization from the Ethics Review Committee of the National Center for Health Statistics (NCHS), and all participants in the group provided written informed consent before joining. No further approval from the Institutional Review Board was required to conduct this secondary analysis. The NHANES data can be available for retrieval via the NHANES website (http://www.cdc.gov/nchs/nhanes.htm) on 14 May 2023.

Chronic kidney disease

We determined the presence of CKD by assessing whether a participant had an eGFR below 60 mL/min/1.73 m2 or a urinary albumin-to-creatinine ratio equal to or exceeding 30 mg/g [21]. The Chronic Kidney Disease Epidemiology Collaboration equation was utilized to estimate the eGFR [22]. The equation was 142 * min (SCr/κ, 1) ^α * max (SCr/κ, 1) ^ (-1.200) * 0.9938^Age * 1.012 [if female]. (SCr (Serum creatinine)) = mg/dL, κ = 0.7 (females) or 0.9 (males), α = −0.241 (females) or −0.302 (males), min = indicates the minimum of SCr/κ or 1, max = indicates the maximum of SCr/κ or 1, age = years). All variables used for the calculations are available in the NHANES database.

Dietary niacin assessment

Trained investigators conducted interviews with participants during the NHANES dietary survey to gather information about the specific types and amounts of food and beverage intake over a 24-h period. The collected data from the NHANES were coded employing the Food Intake Analysis System and the USA Department of Agriculture survey nutrition database, which subsequently converted them into overall nutrient [23]. To gain a comprehensive understanding of the dietary survey methodology, the manuals for NHANES Dietary Interviewers offer a comprehensive guide to the procedures involved [24]. Participants were divided into four groups based on the quartiles of dietary niacin intake.

Potential covariates

The assessment of relevant potential covariates was evaluated on the basis of existing literature [11, 25,26], including sex, age, race/ethnicity, marital status, educational level, family income, smoking status, physical activity, hypertension, diabetes, cancer, stroke, coronary heart disease, calorie intake, protein intake, carbohydrate intake, fat intake, the use of dietary supplements, body mass index (BMI), serum phosphorus, total cholesterol, and high density lipoprotein cholesterol(HDL-C). Race/ethnicity were classified as Mexican American, non-Hispanic black, non-Hispanic white, or other races. Marital status was categorized as living with a partner, living alone or married. Educational level was classified based on the number of years of education completed, with options including less than 9 years, between 9 and 12 years, or more than 12 years. Family income was divided into three categories based on the poverty income ratio (PIR): low (PIR ≤ 1.3), medium (PIR > 1.3 to 3.5), and high (PIR > 3.5) in line with a report by the US government [27]. Smoking status was defined based on established definitions from previous studies [28], which distinguished between individuals who had never smoked (having consumed fewer than 100 cigarettes), those currently smoking, and former smokers (who had quit after smoking more than 100 cigarettes). Physical activity involved into three distinct categories: sedentary, moderate, and vigorous. Moderate physical activity refers to individuals participating in modest sports, fitness activities, or casual leisure pursuits, with slightly elevated respiration or heart rate for a minimum of 10 consecutive minutes over the course of the preceding month. Vigorous physical activity was characterized by intense sports, fitness activities, or recreational activities that significantly increased respiration or heart rate, such as basketball or running, for at least 10 consecutive minutes within the past 30 days. The identification of previous diseases (hypertension, diabetes, stroke, cancer and coronary heart disease) was on the basis of questionnaire inquiries regarding whether the participant had been informed of these conditions by a doctor in the past years. BMI was determined using a standardized method that relied on weight and height measurements. To gather participants’ nutritional information over a 24-h period, including total calorie intake, protein intake, carbohydrate intake, and fat intake. Dietary supplement usage was determined based on inquiries about the consumption of nutritional supplements and medications within the previous month. Phosphorus levels were measured using the ammonium molybdate color-forming reagent method. Total cholesterol levels were assessed through an enzymatic assay, whereas, HDL-C levels were measured using a magnesium sulfate/dextran solution.

Statistical analyses

This study aimed to examine publicly accessible datasets through a secondary analysis. The proportions (%) were used to represent categorical variables, whereas continuous variables were characterized by either mean (standard deviation, SD) or median (interquartile range, IQR) depending on their distribution. One-way analyses of variance were used to compare the groups for variables that followed a normal distribution. Kruskal-Wallis tests were employed for variables exhibiting a skewed distribution to compare groups. Additionally, chi-square analyses were conducted to examine the categorical variables. Univariate analysis is used to assess the relationship between potential covariates and CKD. Multivariable logistic regression analysis was employed to control for confounding factors and evaluate the independent association between dietary niacin intake and CKD, estimating odds ratios (ORs) along with 95% confidence intervals (95% CIs). After eliminating some variables with colinearity, we included adjustment variables in the model based on clinical significance and previous literature [5–7]. Model 1 was controlled for sociodemographic factors including sex, age, race/ethnicity, marital status, educational level, and family income. Model 2 was additionally controlled for variables such as physical activity, smoking status, total cholesterol, HDL-C, serum phosphorus, BMI, and the use of dietary supplements. Model 3 included further adjustments for hypertension, diabetes, coronary heart disease, cancer, and stroke. The study also assessed potential modifications in the association between dietary niacin intake and CKD based on various factors, including sex, age (20–64 years vs. ≥65 years old), marital status (married or cohabiting vs. living alone), educational attainment (up to 12 years of education vs. more than 12 years of education), family income (low vs. medium or high), and BMI (<25 Kg/m2 vs. ≥25 Kg/m2). Heterogeneity within different subgroups was evaluated using multivariate logistic regression analysis, and likelihood ratio tests were conducted to examine the interaction effects between subgroups and dietary niacin intake. Regression analysis using restricted cubic splines (RCS) was conducted using four knots positioned at specific points of dietary niacin intake. The median dietary niacin intake was chosen as the reference point. This analysis aimed to evaluate the linearity and dose-response curve between dietary niacin intake and CKD while controlling for variables in Model 3. Furthermore, a two-piece-wise logistic regression model with smoothing was employed to determine the threshold association between dietary niacin intake and CKD, adjusting for variables included in Model 3. To detect points of inflection, we utilized likelihood-ratio tests and employed bootstrap resampling. To ensure the reliability of our findings, we conducted sensitivity analyses by excluding individuals with energy intake levels outside the range of 500 to 5000 kcal/day. All statistical analyses were conducted utilizing R 4.2.2 (http://www.R-project.org, The R Foundation, Shanghai, China) and Free Statistics software version 1.8. FreeStatistics is a software package provides intuitive interfaces for most common analyses and data visualization. It uses R as the underlying statistical engine, and the graphical user interface (GUI) is written in Python. The analysis declared statistical significance for two-tailed tests with a p-value of less than 0.05.

Results

Study population

A total of 59,842 individuals were interviewed, of whom 25,072 were aged <20 years. Exclusions were made for pregnant women (n = 372) and participants with missing data on CKD (n = 3,482), dietary niacin intake (n = 2,082), and covariates (n = 3,226). Consequently, this cross-sectional study analyzed a final sample size of 25,608 NHANES participants from the years between 2007 and 2018. Figure 1 presents a comprehensive overview of the inclusion and exclusion process.

Figure 1. Flowchart of the study.

Baseline characteristics

The baseline characteristics were showed based on the quartiles of dietary niacin intake are presented in Table 1. Among them, 4,388 (17.14%) had CKD. The average age was 49.5 (17.5) years, and 13,010 (50.8%) were female. Individuals with higher niacin intake were more likely to be younger; male; in a marital or cohabiting relationship; of non-Hispanic white ethnicity; never smokers; with higher levels of education; and experiencing lower rates of hypertension, diabetes, coronary heart disease, stroke, and cancer. Additionally, they exhibited greater intake of calories, proteins, carbohydrates, and fats than those with lower niacin intake.

Table 1. Population characteristics by categories of dietary niacin intake.

Characteristic	Niacin Intake, mg/day	
Total	Q1
(≤15.30)	Q2
(15.31–22.07)	Q3
(22.08–31.09)	Q4
(≥31.10)	p Value	
No.	25608	6400	6401	6403	6404	 	
Sex, n (%)	 	 	 	 	 	< 0.001	
 Male	12598 (49.20)	1947 (30.42)	2648 (41.37)	3374 (52.69)	4629 (72.28)	 	
 Female	13010 (50.80)	4453 (69.58)	3753 (58.63)	3029 (47.31)	1775 (27.72)	 	
Age(year), Mean (SD)	49.49(17.47)	52.73(17.86)	50.80(17.53)	49.38(17.24)	45.06(16.31)	< 0.001	
Race/ethnicity, n(%)	 	 	 	 	 	< 0.001	
 Non-Hispanic white	11189 (43.69)	2578 (40.28)	2786 (43.52)	2972 (46.42)	2853 (44.55)	 	
 Non-Hispanic black	5206 (20.33)	1479 (23.11)	1270 (19.84)	1193 (18.63)	1264 (19.74)	 	
 Mexican American	3768 (14.71)	934 (14.59)	914 (14.28)	953 (14.88)	967 (15.10)	 	
 Others	5445 (21.26)	1409 (22.02)	1431 (22.36)	1285 (20.07)	1320 (20.61)	 	
Education level (year), n (%)	 	 	 	 	 	< 0.001	
 < 9	2402 (9.38)	802 (12.53)	663 (10.36)	518 (8.09)	419 (6.54)	 	
 9–12	9329 (36.43)	2515 (39.30)	2284 (35.68)	2208 (34.48)	2322 (36.26)	 	
 >12	13877 (54.19)	3083 (48.17)	3454 (53.96)	3677 (57.43)	3663 (57.20)	 	
Marital status, n (%)	 	 	 	 	 	< 0.001	
 Married or living with a partner	15324 (59.84)	3482 (54.41)	3859 (60.29)	4032 (62.97)	3951 (61.70)	 	
 Living alone	10284 (40.16)	2918 (45.59)	2542 (39.71)	2371 (37.03)	2453 (38.30)	 	
Family income, n (%)	 	 	 	 	 	< 0.001	
 Low	8147 (31.81)	2382 (37.22)	2023 (31.60)	1829 (28.56)	1913 (29.87)	 	
 Medium	9638 (37.64)	2430 (37.97)	2436 (38.06)	2423 (37.84)	2349 (36.68)	 	
 High	7823 (30.55)	1588 (24.81)	1942 (30.34)	2151 (33.59)	2142 (33.45)	 	
Dietary supplements taken, n (%)	12982 (50.70)	3281 (51.27)	3316 (51.80)	3297 (51.49)	3088 (48.22)	< 0.001	
Body mass index (kg/m2), Mean (SD)	29.30(6.98)	29.53(7.10)	29.38(6.88)	29.22(7.02)	29.09(6.91)	0.002	
Serum phosphorus(mmol/L), Mean (SD)	1.20(0.18)	1.21(0.19)	1.20(0.18)	1.20(0.18)	1.19(0.18)	< 0.001	
High density lipoprotein cholesterol(mmol/L), Mean (SD)	1.36(0.41)	1.40(0.44)	1.38(0.41)	1.36(0.40)	1.32(0.41)	< 0.001	
Total cholesterol(mmol/L), Mean (SD)	4.98(1.07)	5.02(1.08)	4.98(1.07)	4.99(1.07)	4.91(1.06)	< 0.001	
Physical activity, n (%)	 	 	 	 	 	< 0.001	
 Sedentary	13170 (51.43)	3664 (57.25)	3402 (53.15)	3253 (50.80)	2851 (44.52)	 	
 Moderate	6695 (26.14)	1702 (26.59)	1753 (27.39)	1688 (26.36)	1552 (24.23)	 	
 Vigorous	5743 (22.43)	1034 (16.16)	1246 (19.47)	1462 (22.83)	2001 (31.25)	 	
Smoking status, n (%)	 	 	 	 	 	< 0.001	
 Never	14153 (55.27)	3658 (57.16)	3650 (57.02)	3525 (55.05)	3320 (51.84)	 	
 Current	6261 (24.45)	1458 (22.78)	1576 (24.62)	1642 (25.64)	1585 (24.75)	 	
 Former	5194 (20.28)	1284 (20.06)	1175 (18.36)	1236 (19.30)	1499 (23.41)	 	
Hypertension, n (%)	7549(29.48)	2140 (33.44)	2013 (31.45)	1824 (28.49)	1572 (24.55)	< 0.001	
Diabetes, n (%)	3314(12.94)	967 (15.11)	904 (14.12)	784 (12.24)	659 (10.29)	< 0.001	
Coronary heart disease, n (%)	1056(4.12)	312 (4.88)	291 (4.55)	249 (3.89)	204 (3.19)	< 0.001	
Stroke, n (%)	962(3.76)	351 (5.48)	255 (3.98)	206 (3.22)	150 (2.34)	< 0.001	
Cancer, n (%)	2451(9.57)	687 (10.73)	692 (10.81)	625 (9.76)	447 (6.98)	< 0.001	
Calorie intake (kcal/d), Mean (SD)	2114.02(999.59)	1332.68(525.14)	1843.27(580.33)	2242.63(700.44)	3036.88(1158.16)	< 0.001	
Protein intake (g/d), Mean (SD)	81.38(43.12)	44.45(18.29)	67.05(19.88)	87.00(24.58)	126.98(49.23)	< 0.001	
Carbohydrate intake (g/d), Mean (SD)	254.05 (126.54)	173.25(80.37)	228.58(90.63)	267.88(103.20)	346.42(151.09)	< 0.001	
Fat intake (g/d), Mean (SD)	80.83(47.22)	49.99(26.74)	70.67(31.24)	86.60(38.39)	116.02(58.55)	< 0.001	
Chronic kidney disease, n (%)	4388 (17.14)	1418 (22.16)	1171 (18.29)	997 (15.57)	802 (12.52)	< 0.001	

Relationship between dietary niacin intake and CKD

The analysis revealed that CKD was associated with age, sex, marital status, ethnicity; educational attainment; smoking status; intake of dietary supplements; BMI; levels of high-density lipoprotein (HDL) cholesterol and total cholesterol; physical activity level; presence of hypertension or diabetes; and history of coronary heart disease, stroke, or cancer. Additionally, the intake of calories, proteins, carbohydrates, and fats was associated with CKD (Supplementary Table S1).

When examining dietary niacin intake as a continuous variable, we observed a inverse relationship between the dietary niacin intake and CKD in the non-adjusted crude model (OR: 0.83, 95% CI: 0.81–0.86; p < 0.001). After accounting for potential confounding factors, further adjustments did not yield statistically significant changes. Following the same adjustment, a notable inverse association was found between the dietary intake of niacin and CKD when analyzing the quartiles of niacin intake. Compared to individuals with lower niacin intake (Q1, ≤15.30 mg/day), those with higher dietary intake of niacin in Q2 (15.31–22.07 mg/day), Q3 (22.08–31.09 mg/day), and Q4 (≥31.10 mg/day) exhibited adjusted odds ratios (OR) for CKD of 0.89 (95% confidence interval [CI]:0.81–0.99, p = 0.024), 0 .83(95% CI:0 .75–0 .92, p < 0 .001), and 0 .83 (95% CI:0 .75–0 .93, p = 0 .001), respectively (Table 2).

Table 2. Associations between dietary niacin intake and chronic kidney disease in the multiple regression model.

Variable	Total
(n)	Events
n (%)	Crude model	Model 1	Model 2	Model 3	
OR (95%CI)	p Value	OR (95%CI)	p Value	OR (95%CI)	p Value	OR (95%CI)	p Value	
Dietary niacin (per 10 mg/ day)a	25,608	4388(17.14)	0.83 (0.81–0.86)	<0.001	0.95 (0.93–0.98)	0.001	0.96 (0.93–0.99)	0.004	0.96 (0.94–0.99)	0.012	
Dietary niacin levels quartiles(mg/d)	 	 	 	 	 	 	 	 	 	 	
Q1(≤15.30)	6400	1418 (22.16)	1(Ref)	 	1(Ref)	 	1(Ref)	 	1(Ref)	 	
Q2(15.31–22.07)	6401	1171 (18.29)	0.79 (0.72–0.86)	<0.001	0.91(0.83–1.00)	0.042	0.90 (0.82–0.99)	0.038	0.89 (0.81–0.99)	0.024	
Q3(22.08–31.09)	6403	997 (15.57)	0.65(0.59–0.71)	<0.001	0.82 (0.75–0.91)	<0.001	0.83 (0.75–0.91)	<0.001	0.83 (0.75–0.92)	<0.001	
Q4 (≥31.10)	6404	802(12.52)	0.50 (0.46–0.55)	<0.001	0.82 (0.74–0.91)	<0.001	0.83 (0.75–0.92)	0.001	0.83 (0.75–0.93)	0.001	
P for trend	 	 	 	<0.001	 	<0.001	 	<0.001	 	<0.001	
Model 1: Adjusted for sociodemographic variables (sex, age, race/ethnicity, educational level, marital status, and family income).

Model 2: Adjusted for Model 1 + physical activity, smoking status, body mass index, total cholesterol, high-density lipoprotein cholesterol, serum phosphorus, and dietary supplements.

Model 3: Adjusted for Model 2 + hypertension, diabetes, stroke, coronary heart disease, and cancer.

Abbreviations: OR: odds ratio; 95% CI: 95% confidence interval.

a Multiplicative (0.1-fold) transformed values.

Stratified analyses based on additional variables

To examine whether there were any factors that modified the association between the dietary intake of niacin and CKD, stratified analysis was conducted in various subgroups. After analyzing the data based on sex, age, marital status, educational attainment, family income, and BMI, no statistically significant interactions were found within any of the subgroups (Figure 2). This indicates that we did not find any factors that could modified the investigated effects.

Figure 2. The association between dietary niacin intake (per 10 mg/day) and chronic kidney disease according to basic features. Except for some variables with colinearity and the stratification component itself, each stratification factor was adjusted for all other variables (age, sex, marital status, race/ethnicity, family income, education level, smoking status, physical activity, diabetes, hypertension, stroke, cancer, coronary heart disease, body mass index, total cholesterol, high density lipoprotein cholesterol, serum phosphorus and dietary supplements).

Restricted cubic spline regression analysis and threshold analysis

The association between dietary niacin intake and CKD displayed a non-linear (p = 0.001) L-shaped curve within the RCS (Figure 3). In the threshold analysis, participants with a niacin intake of less than 28.04 mg/day had an OR of developing CKD at 0.99 (95% CI:0.98–0.99, p < 0.001), as shown in Table 3. This implies that there is a 1% reduction in the risk of CKD for every 1 mg increase in daily niacin intake through diet. There was no notable association found between the niacin intake in the diet and CKD among individuals who had a daily intake of niacin ≥28.04 mg/day (Table 3). This suggests that the association between increased dietary niacin intake and decreased risk of CKD is no longer evident.

Figure 3. Association between dietary niacin intake and chronic kidney disease odds ratio. Solid and dashed lines represent the predicted value and 95% confidence intervals. The median dietary niacin intake was chosen as the reference point. They were adjusted for age, sex, race/ethnicity, marital status, family income, education level, smoking status, physical activity, diabetes, hypertension, coronary heart disease, cancer, stroke, body mass index, total cholesterol, high density lipoprotein cholesterol, serum phosphorus and dietary supplements. Abbreviation: CKD: chronic kidney disease.

Table 3. Threshold effect analysis of the relationship of niacin intake with chronic kidney disease.

Threshold of Niacin Intake	Adjusted Model	
 	OR (95% CI)	p value	
<28.04 mg/day	0.99 (0.98–0.99)	<0.001	
≥28.04 mg/day	1.00(1.00–1.01)	0.433	
Log-likelihood ratio test	 	0.001	

Sensitivity analysis

After removing participants with excessively high or low energy intake, a total of 25,073 participants were included in the analysis, after excluding those with excessively high or low energy intake. The association between niacin intake and CKD was consistent. In comparison to individuals with the lowest niacin intake (Q1 ≤ 15.30 mg/day), those in Q2 (15.31–22.07 mg/day), Q3 (22.08–31.09 mg/day), and Q4 (≥31.10 mg/day) had adjusted ORs of 0.89 (95% CI:0.81–0.99, p = 0.024), 0.83 (95% CI:0.75–0.92, p < 0.001), and 0.83 (95% CI:0.74–0.93, p = 0.001), respectively, regarding the link between consuming niacin through diet and CKD (see Supplementary Table S2).

Discussion

In this extensive study on American adults, a noteworthy association was found between dietary niacin intake and CKD. This relationship followed an L-shaped pattern, indicating that the risk of CKD decreased as niacin intake increased until it reached an inflection point at approximately 28.04 mg/day.

Niacin is a precursor to nicotinamide adenine dinucleotide (NAD+)[29], and therefore niacin supplementation is considered a method to increase NAD + levels in the body. The kidney is one of the organs with the highest cellular levels of NAD + [30]. Cellular control of NAD + plays a crucial role in kidney metabolism and bioenergetic homeostasis [31,32]. A study conducted by Cho et al. [33] revealed significant reductions in protein excretion over a 24-h period and deceleration of GFR decline among niacin-treated rats with partial nephrectomy. Another study [34] conducted by Poyan et al. has shown that the supplementation of niacinamide (another NAD + precursor) is associated with a lower likelihood of acute kidney injury (AKI) occurrence. However, the use of NAD + enhancers in preventing the progression of CKD has yielded conflicting results. In a study [35] involving 205 CKD patients, the intake effects of lanthanum carbonate, niacinamide or niacinamide combined with lanthanum carbonate or placebo over 12 months were investigated. The results showed no significant changes in the estimated GFR mean values relative to baseline among these patients across the study groups. Another study [36] suggested that NAD + augmentation may be beneficial in delaying the progression of CKD. Currently, niacin is limited in clinical use due to adverse reactions such as skin flushing, nausea, and diarrhea. However, increasing niacin intake through diet helps avoid these occurrences. A recent study [18] in Japan demonstrated that niacin intake in the diet had a preventive effect on CKD in a specific genetic variant of the Japanese elderly population. However, further investigations into the potential link between niacin intake in the diet and CKD in the general population are lacking. The NHANES presents a distinctive opportunity to explore whether there is indeed an association between dietary intake of niacin and CKD, as well as to examine the potential dose-response relationship between them.

The impact of diet on CKD has been reported in the literature. Healthy dietary habits and adequate intake of nutrients may be associated with lower incidence and progression rates of CKD. A cross-sectional study [37] conducted in Taiwan found a strong negative correlation between vegetarian diets and the prevalence of CKD. Results from a systematic review [38] suggested that a vegetarian diet could improve renal filtration function in CKD patients. Joo YS et al. [5] found that low zinc intake in the diet may increase the risk of CKD in individuals with normal kidney function. A prospective study [7] indicated that increased consumption of total red and processed meat was associated with an increased risk of incident CKD. In our study, the association between dietary niacin intake and CKD exhibited an L-shaped trend; thus, enhancing the niacin intake in the diet appears to have a beneficial effect on preventing CKD, reaching a peak in individuals with sufficient niacin intake. Specifically, for individuals with a daily dietary intake < 28.04 mg/day, the likelihood of CKD decreases as niacin intake increases. However, this risk reduction plateaued in participants whose dietary intake exceeded 28.04 mg/day. According to dietary guidelines for Americans [39], the intake of niacin is recommended as follows: 16 mg per day for adult men and 14 mg per day for adult women. Nutritional recommendations for patients with CKD may differ from those for healthy adults. The updated KDOQI clinical practice guideline for nutrition in CKD [40] do not specify a particular recommended intake for niacin, but our study found that a niacin threshold of below 28.04 mg/day is associated with an increased risk of CKD. Although this is above the daily recommended amount for healthy adults, it is still below the tolerable upper intake level (UL) of 35 mg/day for adults, indicating that this level of intake is considered safe.

Foods such as meat, milk, fish, peanuts, and enriched flour products are rich sources of niacin [8]. Based on current statistical analysis, a balanced diet appears to be helpful in preventing CKD. The original Mediterranean diet, which is abundant in legumes, whole grains, fruits, olive oil, nuts, seafood, fish, and vegetables, with moderate wine consumption, is likely to provide high niacin content [41] and has been linked to a lower risk of CKD and improved survival in individuals with CKD, as observed in a population-based cohort of older men [42]. Conversely, the American diet, also referred to as the Western diet, tends to be deficient in niacin and is characterized by excessive consumption of red meat, animal fat, desserts, and sweets, and limited consumption of freshly harvested fruits, vegetables, and low-fat dairy products, which increases the risk of CKD [43]. For instance, individuals who adhere to a Western-style diet are more prone to experiencing elevated levels of urinary albumin excretion, ranging from moderate to severe. They are also more prone to witnessing a swift decrease in GFR (≥3 mL/min/1.73 m2/year) compared with those who do not eat a Western-style diet [44]. Further prospective studies [45,46] in the United States, where a Western diet is prevalent, have suggested that adherence to a niacin-rich Mediterranean diet has significant benefits for kidney function. Therefore, we speculated that a diet rich in niacin may contribute to a decrease risk of CKD.

Our study had several strengths. First, our sample size was large compared with previous studies of a similar nature. Second, we considered the presence of nonlinearity and explored this aspect further. Third, although this observational study was prone to potential confounding factors, we employed rigorous statistical adjustments to minimize the remaining confounding factors. Finally, we approached the target independent variable as both continuous and categorical, thereby minimizing unpredictability in the process of data analysis and bolstering the reliability of our findings.

This study had several limitations. First, owing to the inherent limitations of cross-sectional studies, it was not possible to establish a causal relationship between niacin and CKD. Second, the findings of this study were based on a survey conducted among adults in the United States; whether these findings can be generalized to other populations remains unclear. Third, dietary niacin intake data were collected using a 24-h recall method, which may be prone to recall bias. Nonetheless, the food frequency survey yielded less comprehensive data on the specific varieties and amounts of food ingested than the 24-h recall [47,48]. Fourth, protein is the main source of niacin. Many people with CKD in the NHANES cohort may have been advised to reduce their protein intake. As data on this situation is unavailable, we are unable to conduct further analysis. Fifth, as our study is a retrospective study based on the NHANES data, some covariates (such as tryptophan intake affecting niacin status) were unavailable. Therefore, we were unable to include them as potential confounding factors in the models for analyses. Further validation is required in future studies.

Conclusion

The association between dietary niacin intake and CKD in adults residing in the United States exhibited an L-shaped pattern. The findings revealed an inflection point at approximately 28.04 mg/day. These results suggest the importance of considering the association between dietary niacin intake and CKD.

Supplementary Material

raw data.csv

Revised_Supplementary_Table_S1 new.docx

Supplementary Table S2.docx

Acknowledgements

We are gratefully to Dr. Jie Liu of the Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, for his contribution to the statistical support, study design consultations, and comments regarding the manuscript. We appreciate Dr. JinBao Ma of the Department of Drug-resistant Tuberculosis, West Section of HangTian Avenue, Xi’an Chest Hospital, for statistics, study design consultations, and comments of the manuscript.

Ethics approval and consent to participate

The NHANES obtained authorization from the Ethics Review Committee of the National Center for Health Statistics (NCHS), and all participants in the group provided written informed consent before joining. No further approval from the Institutional Review Board was required to conduct this secondary analysis.

Author contributions

W.L. designed research; Q.L. analyzed data; Q.L. wrote the main manuscript text; W.L. revised the manuscript.

Consent for publication

The publication has obtained consent from all participants.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The raw data is provided in the supplementary information files.
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