Sex Differences in the Association Between Dietary Inflammatory Index and Chronic Kidney Disease in U.S. Adults: Insights from NHANES 2011–2018”

doi:10.21203/rs.3.rs-4973250/v1

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Sex Differences in the Association Between Dietary Inflammatory Index and Chronic Kidney Disease in U.S. Adults: Insights from NHANES 2011–2018”

https://doi.org/10.21203/rs.3.rs-4973250/v1

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Background

studies on the association between dietary inflammatory index (DII) and chronic kidney disease (CKD) are limited. We aimed to examine the association between DII and CKD among U.S. adults with particular attention paid to sex differences.

Methods

A total of 19317participants were included in this study. The exposure variable was DII, which was calculated based on overall inflammatory effect scores. The outcome was CKD, defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m² or urinary albumin/creatinine (uACR) ≥ 30mg/g.

Results

The mean (SD) of age our study participants was 47.84 (18.35); and the mean (SD) of DII was 1.50 (1.91) (median 1.74). In multivariate logistic regression analysis, we observed that the OR value (95%CI) of CKD is 1.19 (1.14, 1.23), 1.12 (1.06, 1.19), and 1.16 (1.06, 1.27) in models 1, 2 and 3 for Per SD increment of DII. Compared with Participants with Q1(DII < 0.15), the adjusted ORs for participants in Q2 (0.15 ≤ DII < 1.74), Q3 (1.74 ≤ DII < 3.02) and Q4 (≥ 3.02)were 1.46 (95% CI: 1.14–1.88), 1.55 (95% CI: 1.20–1.99) and 1.52 (95% CI: 1.17, 1.98) ( p for trend < 0.05), respectively. However, this study observed that the independent positive correlation between DII and CKD appeared in women rather than men.

Conclusions

A higher level of DII is significantly associated with the increased prevalence of CKD, and sex can modify the relationship between them.

Health sciences/Diseases

Health sciences/Medical research

Health sciences/Nephrology

Health sciences/Risk factors

dietary inflammatory index

chronic kidney disease

sex differences

females

As a global public health problem, chronic kidney disease (CKD) is related to serious morbidity, mortality and high utilization rate of medical resources[1]. According to the analysis of global disease burden research in 2017, there are 697.5 million CKD patients worldwide, with an estimated prevalence rate of 9.1%; And 1.2 million patients died of CKD[2]. Traditional risk factors of CKD are hypertension, diabetes, obesity and proteinuria[3, 4]. Accordingly, besides controlling the above risk factors, new and effective strategies are needed to prevent the occurrence and development of CKD and related complications.

There is a close relationship between the pathological process of chronic kidney disease and chronic inflammation[5]. Previous epidemiological studies have shown that the increase of inflammatory indicators will significantly increase the risk of CKD and related cardiovascular events (CVD)[6–9]. As a matter of fact, previous studies have consistently proved that dietary factors can affect chronic inflammation[10, 11]. So, shivappa et al. quantified the dietary components of different diets and developed a tool to determine the potential inflammation of individual diet[12], which was verified by inflammatory biomarkers in different populations[13–15]. However, studies related to dietary inflammatory index (DII) and CKD are limited, with only two studies[16, 17]. And the relationship between them has not been explored in different genders. Therefore, the purpose of this study is to evaluate the relationship between DII and CKD, and whether gender can modify the relationship between them.

Study Design and population

This cross-sectional study was performed using aggregated data from 8 years (2011–2018) of NHANES, conducted by the Centers for Disease Control and Prevention (CDC). The project adopted a stratified multistage sampling method and was an ongoing repeated cross-sectional study to assess the health and nutrition status of adults and children in the United States[18]. The NHANES survey began in 1960s, mainly including interviews and physical examinations.The study has been conducted every two years since 1999; About 5,000 participants are selected for data collection every year[19]. Before the investigation, all participants provided written informed consent. And this survey was approved by the Ethics Review Board of the National Center for Health Statistics. More detailed information can be obtained from https://www.cdc.gov/nchs/nhanes/index.htm.

A total of 22,789 adults aged ≥ 18 in NHANES from 2011 to 2018 with dietary questionnaires and blood samples were selected. The participants with missing values of DII (n = 2121), uACR (n = 331) and eGFR (n = 1020) were excluded. Finally, a total of 19317 participants were included in the final analysis (Fig. 1).

Data Collection

Trainers collect socio-demographic and lifestyle information, including age, sex, race, poverty income ratio, smoking, alcohol consumption, disease history and medication history, through standardized family interview questionnaires. Anthropometric indicators include height, weight and blood pressure (BP). Body height and weight were collected without shoes and thick clothes, and measured with a medical scale. Body mass index (BMI), in kg/m², was calculated as the weight divided by the height squared. According to the standard blood pressure measurement protocol recommended by American Heart Association at that time, mercury sphygmomanometer was used to measure blood pressure. Three blood pressure readings were obtained continuously from the same arm. This study defined systolic blood pressure and diastolic blood pressure (DBP) as the average of three blood pressure measurements.

Every subject was asked to do an overnight rapid venous blood sample from all study participants. The DcX800 method is used to measure the albumin concentration as a bichromatic digital endpoint method. Using Roche Hitachi 717 and 912 analyzers (Hitachi, Tokyo, Japan), total cholesterol (TC), triglyceride (TG) and high density lipoprotein cholesterol (HDL-C) cholesterol were measured by enzymatic method. Creatine were assessed using automated biochemical profiling (Beckman Synchron LX20; Beckman Coulter Inc, Fullerton, California, United States). Low-density lipoprotein cholesterol (LDL-C) is calculated from measured values of total cholesterol, triglycerides, and HDL-cholesterol. Urine albumin was measured using solid-phase fluorescent immunoassay, and urine creatinine was measured using the Roche/Hitachi Modular P Chemistry Analyzer in 2011 and Roche/Hitachi Cobas 6000 chemistry analyzer in 2013; urine albumin and creatinine levels were standardized and calibrated with the gold standard method according to the recommendations of the National Health and Nutrition Examination Survey. Urine albuminto-creatinine ratio (uACR) = urine albumin/urine creatinine. According to Roche Hitachi 911 analyzer, fasting plasma glucose (FPG) was measured by hexokinase method. The formula used for the estimated glomerular filtration rate (eGFR) was the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation[20]. Diabetes mellitus was defined as self-reported physician diagnosis of diabetes or FPG concentration ≥ 7.0 mmol/L or use of glucose-lowering drugs. Further details of data collection can be found in https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.

Defnition of the DII and CKD

The exposure variable in this study is the dietary inflammation index. DII score is based on the pro-inflammatory and anti-inflammatory properties of 45 different food ingredients to assess the impact of diet on inflammation. This index was developed through a systematic review of 2,000 published research articles[12]. DII scores contain positive and negative values, positive values represent pro-inflammatory diets, while negative values correspond to more anti-inflammatory diets[12]. For this analysis, we calculated the DII value by the average nutritional intake on the first day of 24-hour meal recall. Such food components include energy, carbohydrate; protein; total fat; dietary fiber; cholesterol;saturated, monounsaturated, and polyunsaturated fatty acids; ω-3 and ω-6 polyunsaturated fatty acids; vitamins A, B1, B2, B3(niacin), B6, B12, C, D, and E; folic acid; alcohol; beta-carotene;caffeine; iron; magnesium; zinc; and selenium[21, 22]. According to the above food components, we calculate the DII score. DII calculation formula: Zscore = [(daily mean intake − global daily mean intake)/standard deviation]; Zscore¹ = Zscore → (converted to a percentile score)× 2 − 1; DII = X Zscore1×the inflammatory effect score of each dietary component. The outcome was CKD, defined as eGFR less than 60 mL/min/1.73 m² or uACR of 30mg/g or higher[23].

Statistical Analysis

A two-tailed P < 0.05 was regarded as statistically significant. The statistical packages R (http://www.r-project.org) and Empower (R) (www.empowerstats.com) were used to perform all statistical analyses.

Baseline characteristics are presented as means ± SDs for continuous variables and proportions for categorical variables according to DII quartiles. ANOVA or Chi-squared tests were used to compare the significant differences in population characteristics. Multivariable logistic regression models were used to examine the association between DII and the prevalence of CKD among all participant, males and females. Covariates were included as potential confounders in the final multivariate logistic regression models if they changed the estimates of DII for CKD by more than 10% [24]or were known as traditional risk factors for CKD. We built sequential regression models as follows: Model 1 was a crude model. Model 2 was adjusted for age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking, alcohol intake. Model 3 was adjusted for age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking, alcohol intake, FPG, albumin, TG, TC, HDL, LDL, diabetes mellitus, antihypertensive drugs, lipoprotein-lowering drugs, glucose-lowering drugs. We used a generalized additive model and a fitted smoothing curve (penalized spline method) to assess the dose-response association between DII and the prevalence of CKD. In addition, possible modifications of the association between DII and the prevalence of CKD were assessed for the following variables: age (< 65 vs. ≥65, years), BMI (< 25 vs. ≥25, kg/m2), race (non-hispanic white vs. non-hispanic black vs. mexican american vs. other hispanic vs. other races), current smoking (never vs. former vs. now), alcohol intake (< 3 vs. ≥3, drinks per day), and diabetes mellitus (no vs. yes). Potential interactions were examined by including the interaction terms into the logistic regression models.

Study participants and baseline characteristics

As described in Fig. 1 19317 participants from 4 NHANES cycles were included in the final study. Of the 19371 participants, 9413 (48.73%) were males, 3100 (16.22%) were diabetes, and 3332 (17.25%) was CKD. The mean (SD) of age was 47.84 (18.35); and the mean (SD) of DII was 1.50 (1.91) (median 1.74). Baseline characteristics of study participants according to the quartiles of DII are showed in Table 1. Participants with Q4(≥ 3.02) of DII were more likely to be females, to be non-hispanic white, to be current smokers, to have higher educational levels, to have higher values in BMI, and SBP, to have higher rate diabetes mellitus, to have higher rate the usage of antihypertensive drugs, lipoprotein-lowering drugs and glucose-lowering drugs; and to have lower values in DBP, poverty income ratio, TC, HDL-C, albumin, and eGFR than those of the other three groups. At the same time, we observed the no differences between DII quartiles in age, alcohol intake, FPG, TG, LDL-C, and uACR (all P > 0.05).

Table 1

Baseline characteristics of study participants
Variable^a	DII Quartiles				P value
Variable^a	Q1(< 0.15)	Q2(0.15to < 1.74)	Q3(1.74to < 3.02)	Q4(≥ 3.02)	P value
Participants	4829	4829	4829	4830
Males, N(%)	2920 (60.47%)	2579 (53.41%)	2147 (44.46%)	1767 (36.58%)	< 0.001
Age,year	47.82 ± 17.62	47.73 ± 18.17	48.13 ± 18.51	47.68 ± 19.09	0.623
BMI, kg/m²	28.36 ± 6.60	29.06 ± 6.88	29.52 ± 7.17	30.07 ± 7.63	< 0.001
Race					< 0.001
Non-Hispanic White, N(%)	1861 (38.54%)	1806 (37.40%)	1822 (37.73%)	1845 (38.20%)
Non-Hispanic Black, N(%)	832 (17.23%)	948 (19.63%)	1154 (23.90%)	1319 (27.31%)
Mexican American, N(%)	733 (15.18%)	782 (16.19%)	661 (13.69%)	568 (11.76%)
Other Hispanic, N(%)	491 (10.17%)	520 (10.77%)	509 (10.54%)	501 (10.37%)
Other races, N(%)	912 (18.89%)	773 (16.01%)	683 (14.14%)	597 (12.36%)
Current smoking, N(%)	679 (14.23%)	806 (16.88%)	971 (20.39%)	1140 (24.03%)	< 0.001
alcohol intake, drinks per day	2.76 ± 2.65	2.70 ± 2.31	2.73 ± 2.43	2.65 ± 2.80	0.399
SBP, mmHg	122.79 ± 17.45	123.29 ± 17.53	124.40 ± 18.30	124.58 ± 19.61	< 0.001
DBP, mmHg	70.81 ± 11.53	70.53 ± 11.69	70.60 ± 11.92	70.07 ± 12.29	0.023
Poverty income ratio	2.79 ± 1.70	2.56 ± 1.63	2.41 ± 1.59	2.13 ± 1.53	< 0.001
FPG, mg/dL	109.06 ± 35.14	109.33 ± 34.79	110.01 ± 35.32	111.00 ± 37.46	0.245
TC, mg/dL	188.52 ± 40.74	189.17 ± 41.17	190.58 ± 42.78	187.95 ± 42.07	0.014
TG, mg/dL	119.09 ± 99.92	117.48 ± 95.89	120.48 ± 134.09	113.87 ± 85.36	0.165
HDL-C, mg/dL	53.33 ± 15.89	53.15 ± 16.03	53.37 ± 15.98	52.42 ± 15.79	0.012
LDL-C,mg/dL	110.83 ± 35.03	111.07 ± 35.49	112.26 ± 35.41	111.07 ± 35.70	0.529
Albumin, g/L	42.91 ± 3.48	42.51 ± 3.57	42.14 ± 3.55	41.75 ± 3.55	< 0.001
eGFR, mL/min/1.73 m²	96.45 ± 22.34	96.46 ± 23.71	95.47 ± 24.91	95.38 ± 26.52	0.034
uACR, mg/g	36.75 ± 292.22	44.94 ± 396.79	49.92 ± 366.37	48.56 ± 298.07	0.226
Diabetes mellitus^$	704 (14.73%)	740 (15.51%)	793 (16.58%)	863 (18.04%)	< 0.001
Antihypertensive drugs	1363 (28.23%)	1433 (29.67%)	1532 (31.72%)	1590 (32.92%)	< 0.001
Lipoprotein-lowering drugs	906 (18.76%)	948 (19.63%)	1003 (20.77%)	1003 (20.77%)	0.035
Glucose-lowering drugs	525 (10.87%)	564 (11.68%)	614 (12.71%)	618 (12.80%)	0.009
^aData are presented as number (%) or mean ± standard deviation

Abbreviation: DII, Dietary inflammatory index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG: fasting plasma glucose;TC total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate; ACR,albumin-to-creatinine ratio;

^$diabetes mellitus was defined as self-reported physician diagnosis of diabetes or FPG concentration ≥7.0 mmol/L or use of glucose-lowering drugs.

Associations between DII and CKD

The relationship between DII and CKD are shown in Table 2. In multivariate logistic regression analysis, we observed that the OR value (95%CI) of CKD is 1.19 (1.14, 1.23), 1.12 (1.06, 1.19), and 1.16 (1.06, 1.27) in models 1, 2 and 3 for Per SD increment of DII. For sensitivity analysis, we changed DII from continuous variable to classified variable. Compared with Participants with Q1(DII < 0.15), the adjusted ORs for participants in Q2 (0.15 ≤ DII < 1.74), Q3 (1.74 ≤ DII < 3.02) and Q4 (≥ 3.02)were 1.46 (95% CI: 1.14–1.88), 1.55 (95% CI: 1.20–1.99) and 1.52 (95% CI: 1.17, 1.98) ( p for trend < 0.05), respectively after adjusted for age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking, alcohol intake, FPG, albumin, TG, TC, HDL, LDL, diabetes mellitus, antihypertensive drugs, lipoprotein-lowering drugs, glucose-lowering drugs. The smoothing curve showed that a positive association existed between DII and the prevalence of CKD in US adults (Fig. 2). However, this study observed that the independent positive correlation between DII and CKD appeared in women rather than men.(Table 2, Fig. 3).

Table 2

Relative odds of CKD according to DII in different models¹.
DII	Events (%)	CKD OR (95%CI), P value
DII	Events (%)	Model 1	Model 2	Model 3
Per SD increment	3332(17.25%)	1.19 (1.14, 1.23) < 0.0001	1.12 (1.06, 1.19) 0.0001	1.16 (1.06, 1.27) 0.002
Quartile
Q1(< 0.15)	682 (14.12%)	1	1	1
Q2(0.15to < 1.74)	767 (15.88%)	1.15 (1.03, 1.28) 0.016	1.18 (1.00, 1.39) 0.05	1.46 (1.14, 1.88) 0.003
Q3(1.74to < 3.02)	907 (18.78%)	1.41 (1.26, 1.57) <0.0001	1.35 (1.14, 1.58) 0.0004	1.55 (1.20, 1.99) 0.0007
Q4(≥ 3.02)	976 (20.21%)	1.54 (1.38, 1.71) <0.0001	1.37 (1.15, 1.62) 0.0003	1.52 (1.17, 1.98) 0.002
P for trend		< 0.0001	< 0.0001	< 0.0001
Male
Per SD increment	1580(16.79%)	1.20 (1.14, 1.27) < 0.0001	1.12 (1.03, 1.22) 0.007	1.07 (0.94, 1.22) 0.275
Quartile
Q1(<-0.21)	337 (14.32%)	1	1	1
Q2(-0.21to < 1.26)	347 (14.75%)	1.03 (0.88, 1.22) 0.679	0.99 (0.78, 1.25) 0.929	1.22 (0.85, 1.75) 0.275
Q3(1.26to < 2.66)	402 (17.08%)	1.23 (1.05, 1.44) 0.009	1.31 (1.04, 1.64) 0.023	1.33 (0.93, 1.89) 0.117
Q4(≥ 2.66)	494 (20.99%)	1.59 (1.36, 1.85) < 0.0001	1.36 (1.08, 1.73) 0.010	1.33 (0.93, 1.91) 0.122
P for trend		< 0.0001	0.001	0.109
Female
Per SD increment	1752(1769%)	1.16 (1.10, 1.23) < 0.0001	1.11 (1.02, 1.21) 0.0139	1.25 (1.09, 1.44) 0.0011
Quartile
Q1(< 0.63)	361 (14.58%)	1	1	1
Q2(0.63to < 2.14)	421 (17.00%)	1.20 (1.03, 1.40) 0.020	1.08 (0.85, 1.37) 0.534	1.37 (0.94, 2.01) 0.099
Q3(2.14to < 3.27)	486 (19.63%)	1.43 (1.23, 1.66) < 0.0001	1.25 (0.99, 1.58) 0.059	1.66 (1.15, 2.40) 0.007
Q4(≥ 3.27)	484 (19.55%)	1.42 (1.23, 1.65) < 0.0001	1.26 (0.99, 1.60) 0.061	1.73 (1.18, 2.52) 0.005
P for trend		< 0.0001	0.029	0.003
¹Values are ORs (95% CIs) unless otherwise indicated. CKD, chronic kidney disease; DII, Dietary inflammatory index.
Model 1 was adjusted for none.
Model 2 was adjusted for age, sex (only for overall population), race, poverty income ratio, BMI, SBP, DBP, current smoking, alcohol intake.
Model3 was adjusted for age, sex (only for overall population), race, poverty income ratio, BMI, SBP, DBP, current smoking, alcohol intake, FPG, albumin, TG, TC, HDL, LDL, diabetes mellitus, antihypertensive drugs, lipoprotein-lowering drugs, glucose-lowering drugs.

Subgroup analyses

A stratified analysis was evaluated the relationship between DII (continuous variable) and the prevalence CKD in various subgroups (Fig. 3). The effect of DII on CKD showed no significant difference in the following subgroups: age (< 65 vs. ≥65, years), BMI (< 25 vs. ≥25, kg/m²), race (non-hispanic white vs. non-hispanic black vs. mexican american vs. other hispanic vs. other races), current smoking (never vs. former vs. now), alcohol intake (< 3 vs. ≥3, drinks per day), and diabetes mellitus (no vs. yes) in both groups (all P for interactions > 0.05) after adjustment for age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking, alcohol intake, FPG, albumin, TG, TC, HDL, LDL, diabetes mellitus, antihypertensive drugs, lipoprotein-lowering drugs, glucose-lowering drugs except for the stratifying variable (Fig. 4).

A cross-sectional study involving 19317 participants was conducted in this study. The results show that the positive correlation between DII and CKD is independent of the following covariates such as age, gender, race, poverty-income ratio, body mass index, SBP, DBP, current smoking, alcohol intake, FPG, albumin, triglyceride, total cholesterol, high density lipoprotein, low density lipoprotein, diabetes, antihypertensive drugs, lipoprotein-lowering drugs and hypoglycemic drugs. There is a linear positive correlation between DII and CKD in women, not men.

Previous studies related to DII and CKD are limited, with only two studies. Rouhani and his colleagues[16] evaluated the association between DII and renal function indicators and the progression of kidney disease in 221 patients with CKD; This study shows that DII has nothing to do with renal function indexes (blood urea nitrogen (BUN) and serum creatinine (Cr) as well as eGFR) in the fully adjusted model; however, higher DII scores are significantly related to the increased risk of CKD progression ( OR:2.12 ; 95%CI: 1.05, 4.26). The large cross-sectional study conducted by Mazidi[17]et al. included 21 649 participants. The purpose of this study was to investigate the relationship between DII and renal function and CKD prevalence among American adults. The main conclusions are as follows: the increase of DII level is positively correlated with the increase of urinary albumin, uric acid, creatinine and the decrease of eGFR, and the prevalence of CKD in the highest DII quartile was 29% higher than that in the lowest DII quartile. Pro-inflammatory diet is related to decreased renal function and high prevalence of CKD.

We found that our results are consistent with the above studies. However, this study found the gender difference between DII and CKD for the first time. In our study, we found that the positive correlation between DII and CKD prevalence only appeared in women, not men. As shown in Table 2, in the fully adjusted model, the prevalence of CKD increased by 25% for every standard deviation of DII level of female participants. When DII was transformed into a categorical variable, the participants in the lowest group Q1(DII < 0.63) served as the reference group, and the participants in the highest group Q4 (DII ≥ 3.27) increased the risk of CKD by 73% (P for trend < 0.05). Among men, DII has no relationship with CKD, whether it is a continuous variable or a categorical variable.

For the time being, the underlying pathological process between DII and CKD is not clear, so here's a guess. Pathological and physiological process of CKD is based on low-grade chronic inflammation[5]. Inflammation, coagulation dysfunction and neutrophil-endothelial cell interaction are considered to play a role in the occurrence of kidney damage, which may lead to chronic kidney damage[25]. It may be that diet plays an important role in regulating chronic inflammation [26]and kidney health. The design of DII is used to evaluate different anti-inflammatory and pro-inflammatory dietary components and quantify dietary inflammation. Therefore, with the increase of DII, the risk of CKD prevalence rate is significantly increased. From Table 1, we can see that women's DII level, BMI, TC, HDL, LDL level are higher than those of men, but the drug use rate of glucose-lowering drugs and lipoprotein-lowering drugs of women is lower than that of men. Studies have shown that the level of CPR in American women is much higher than that in men [27], so it is more likely that they will have chronic inflammatory reaction. Further investigation is needed to determine the biological and psychosocial factors behind these gender differences.

There are several limitations in our research. First of all, this study is a cross-sectional study, and the causal relationship between DII and CKD cannot be obtained. Secondly, the results may be confused by unmeasured factors, such as structural or functional kidney abnormalities, and some environmental or occupational exposures. Finally, this study was carried out in the American population, and the results could not be extended to other populations.

In summary, Pro-inflammatory diet patterns measured by DII may affect the prevalence of CKD in American adults. The linear positive correlation between DII and CKD is observed only in women, not men. More research is needed to confirm our results and evaluate the possible gender differences in the association between DII and CKD.

Practical Application

Our study quantifies dietary inflammation using the DII and examines its association with CKD. Additionally, significant gender disparities are identified, providing valuable insights for tailoring dietary interventions in male and female patients at risk of CKD.

Acknowledgments

A special thanks to all of the NHANES participants who freely gave their time to make this and other studies possible.

Authors' Contributions

XFX and HBH participated in literature search, study design, data collection, data analysis, data interpretation, and wrote the manuscript. HBH, JJY conceived of the study, and participated in its coordination, data collection and analysis. MXL participated in study design and provided the critical revision. All authors read and approved the final manuscript.

Availability of data and materials

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.

Funding: Not applicable.

Conflict of Interests: The authors declare that they have no conflict of interest.

Ethical approval and consent to participate: All procedures performed in studies involving human participants were following the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All participants provided written informed consent. And this survey was approved by the Ethics Review Board of the National Center for Health Statistics.

Informed consent: Informed consent was obtained from all individual participants included in the study

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No competing interests reported.

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You are reading this latest preprint version

Sex Differences in the Association Between Dietary Inflammatory Index and Chronic Kidney Disease in U.S. Adults: Insights from NHANES 2011–2018”

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

Study Design and population

Data Collection

Defnition of the DII and CKD

Statistical Analysis

RESULTS

Study participants and baseline characteristics

Associations between DII and CKD

Subgroup analyses

Discussion

Conclusions

Declarations

References

Additional Declarations

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Version 1