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 States17. 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 year18. 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.
Participants from the general population who took part in NHANES from 2011 to 2018 were included, with the following exclusion criteria: (1) Age under 18; (2) Failure to complete the dietary questionnaire and provide blood samples; (3) Missing data for DII, uACR, and eGFR. Ultimately, a total of 19,317 participants were included in the final analysis (Fig. 1).
Flow chart of study participants.
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. Cardiovascular disease and stroke are derived from the following questionnaire data: presence or absence of coronary heart disease/presence or absence of angina pectoris/presence or absence of myocardial infarction/presence or absence of stroke/presence or absence of congestive heart failure. Diagnosis of hypertension is determined based on the answers to the questionnaire about the presence or absence of hypertension. 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 articles11. DII scores contain positive and negative values, positive values represent pro-inflammatory diets, while negative values correspond to more anti-inflammatory diets11. 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 selenium19,20. Based on the above food ingredients, we calculate the DII score. DII calculation formula: Zscore = [(average daily intake − global average daily intake)/standard deviation]; Zscore1 = Zscore → (converted to percentile)× 2 − 1; the DII is obtained by multiplying Zscore1 with the inflammation effect score of each food ingredient and then summing the results. The global intake standard values, standard deviations and inflammation effect scores are provided in the study by Shivappa N et al. i.e21. The diagnostic criteria for chronic kidney disease (CKD) are based on international guidelines: estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2 and/or proteinuria of at least 30 mg/g22.
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) (http://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%23or 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, subgroup analyses were conducted to further verify the gender difference in the prevalence of DII and CKD and to assess whether differences also existed in other subgroup variables, including: age (< 65 vs. ≥ 65, years), BMI (< 25 vs. ≥ 25, kg/m2), Gender (Male vs. Female), 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.
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- Source: https://www.nature.com/articles/s41598-024-78307-4