https://orcid.org/0000-0002-9345-4081
Figures
Abstract
Background
With cardiovascular diseases standing as a leading cause of mortality worldwide, the interplay between diet-induced inflammation, as quantified by the Dietary Inflammatory Index (DII), and heart failure biomarker NT-proBNP has not been investigated in the general population.
Methods
This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004, encompassing 10,766 individuals. The relationship between the DII and NT-proBNP levels was evaluated through multivariable-adjusted regression models. To pinpoint crucial dietary components influencing NT-proBNP levels, the LASSO regression model was utilized. Stratified analyses were then conducted to examine the associations within specific subgroups to identify differential effects of the DII on NT-proBNP levels across diverse populations.
Results
In individuals without heart failure, a unit increase in the DII was significantly associated with an increase in NT-proBNP levels. Specifically, NT-proBNP levels rose by 9.69 pg/mL (95% CI: 6.47, 12.91; p < 0.001) without adjustments, 8.57 pg/mL (95% CI: 4.97, 12.17; p < 0.001) after adjusting for demographic factors, and 5.54 pg/mL (95% CI: 1.75, 9.32; p = 0.001) with further adjustments for health variables. In participants with a history of heart failure, those in the second and third DII quartile showed a trend towards higher NT-proBNP levels compared to those in the lowest quartile, with increases of 717.06 pg/mL (95% CI: 76.49–1357.63, p = 0.030) and 855.49 pg/mL (95% CI: 156.57–1554.41, p = 0.018). Significant interactions were observed in subgroup analyses by age (<50: β = 3.63, p = 0.141; 50–75: β = 18.4, p<0.001; >75: β = 56.09, p<0.001), gender (men: β = 17.82, p<0.001; women: β = 7.43, p = 0.061),hypertension (β = 25.73, p<0.001) and diabetes (β = 38.94, p<0.001).
Citation: Ma T-C, Gao F, Liu X-L, Wang C-X, Liu Q, Zhou J (2024) Association between dietary inflammatory index and NT-proBNP levels in US adults: A cross-sectional analysis. PLoS ONE 19(6): e0304289. https://doi.org/10.1371/journal.pone.0304289
Editor: Vincenzo Lionetti, Scuola Superiore Sant’Anna, ITALY
Received: October 13, 2023; Accepted: May 9, 2024; Published: June 5, 2024
Copyright: © 2024 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data used in this study are publicly available in the NHANES database, https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.
Funding: The study was funded by the Yan'an Science and Technology Plan Project (Grant No. 2022SLSFGG-025).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.The authors declare no conflicts of interest and confirm the originality of the work.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
Over recent decades, cardiovascular diseases (CVD) have risen to become a leading global health concern. The World Health Organization identifies CVD as the primary cause of mortality worldwide, responsible for millions of fatalities each year [1,2]. The surge in heart failure (HF) cases, propelled by an aging demographic, highlights the critical need for extensive research into cardiovascular health. However, HF frequently develops insidiously, with significant symptoms emerging only upon the occurrence of severe health episodes. Over time, the heart experiences alterations due to myocardial cell remodeling. In older individuals, this remodeling can result in a progressive deterioration of heart function, ultimately leading to HF [3]. Therefore, identifying risk factors for HF is essential for its prevention, early diagnosis, and management.
Chronic inflammation is recognized as a pivotal risk factor in the intricate causality of CVDs [4,5]. Recent investigations into the Dietary Inflammatory Index (DII) have revealed a significant association between the DII and cardiovascular health [6,7]. The DII evaluates the inflammatory impact of dietary components, offering insights into chronic inflammation and the risk of CVD [8]. Since its inception in 2009 and subsequent update in 2014, the DII has individually tested the diet-induced inflammatory potential [9,10]. The DII objective is to provide a comprehensive analysis of the inflammatory effects of various dietary components, thereby determining the overall inflammatory potential of the diet.
Conventionally, the diagnosis of HF has relied on symptoms and sign evaluation. However, the last two decades have seen N-terminal pro B-type natriuretic peptide (NT-proBNP) measurement emerge as a superior diagnostic criterion for HF [11,12]. NT-proBNP, a sensitive indicator of cardiac strain and dysfunction, is closely associated with the incidence and severity of HF. Its role extends to monitoring therapeutic outcomes and predicting the disease prognosis [13–16]. offering a dynamic and precise measure of cardiac health status beyond conventional diagnostic practices.
This study aimed to investigate the association between the DII and NT-proBNP levels utilizing data from the National Health and Nutrition Examination Survey (NHANES, 1999–2004).
2 Methods
The NHANES protocol and stored serum research program were approved by the National Center for Health Statistics Ethics Review Board, and written informed consent was obtained from all participants. All data were anonymized during the collection and processing stages, ensuring that
neither the authors nor the research team had access to any information that could potentially identify
individual participants.
Study design
This study sourced data from the NHANES, a comprehensive database maintained by the Centers for Disease Control and Prevention (CDC) [17] that encompasses health and medical records of over 120,000 U.S. civilians from 1998 to 2022. The dataset contains over 1,000 variables, including demographic information, results from physical examinations, and laboratory tests.
This study integrated data from three NHANES cycles (1999–2000, 2001–2002, and 2003–2004) to evaluate NT-proBNP levels in participants at a mobile examination center, with blood samples preserved for future analysis. Participants were excluded based on the following criteria: out-of-range NT-proBNP levels (n = 365), incomplete dietary data (n = 528), self-reported pregnancy (n = 627), and absence of HF diagnosis data (n = 43), the study population was narrowed down to 10766 individuals, comprising 5475 women and 5313 men. In the women cohort, 2,552 participants were under 50, 2117 were aged between 50 and 75, and 784 were over 75. Among the men, 2426 were younger than 50, 2159 were within the 50 and 75 age range, and 728 were older than 75. The flowchart (Fig 1) was developed to outline the participant selection.
Assessment of NT-proBNP levels
Serum samples from the 1999–2004 NHANES cycles were analyzed for NT-proBNP levels, with the assays conducted between 2018 and 2020 at the University of Maryland School of Medicine’s laboratory. The NHANES Biospecimen Program provided protocols for sample handling, including details on processing and storage [18].NT-proBNP measurements utilized the Roche Cobas e601 analyzer (Elecsys, Roche Diagnostics), adhering to established detection limits (5 pg/mL to 35,000 pg/mL) and demonstrating a coefficient of variation of 3.1% at a low concentration (46 pg/mL) and 2.7% at a high concentration (32,805 pg/mL). A consistent detection threshold was employed to guarantee the accuracy and reliability of the measurements [19]. Furthermore, prior research has demonstrated the stability of NT-proBNP in samples stored over extended periods [20–22].
Assessment of dietary inflammatory index
The DII was the critical measure for evaluating the inflammatory potential of participants’ diets. The DII quantifies dietary inflammatory potential, ranging from anti-inflammatory to pro-inflammatory effects. This metric, thoroughly validated by Shivappa et al [10], was applied to dietary data collected through 24-hour nutritional recalls at the Mobile Examination Center (MEC). Shivappa and colleagues have demonstrated that using a maximum of 30 food parameters is sufficient to maintain the predictive accuracy of the DII for diet-related inflammation [23]. Participants’ intake of foods and beverages was meticulously recorded, with the United States Department of Agriculture (USDA) Food and Nutrient Database for Dietary Studies used to quantify nutrient content. The DII calculation in this research was based on 27 dietary components known to modulate inflammation, including energy, protein, carbohydrate, fiber, total fatty acid, total saturated fatty acid, monounsaturated and polyunsaturated fatty acids, cholesterol, a range of vitamins and minerals including vitamin A, β-carotene, vitamin B1, vitamin B2, niacin, vitamin B6, folate, vitamin B12, vitamin C, vitamin E, magnesium, iron, zinc, and selenium, caffeine, alcohol, and n-3 and n-6 fatty acids. Each participant’s dietary intake was matched to a comprehensive database, encompassing global average daily intake figures and their standard deviations for each dietary component across 45 populations worldwide. The intake of each nutrient was contrasted with its global average and subsequently divided by the standard deviation to calculate the Z-scores for each nutrient. These Z-scores were converted into percentiles, centered around zero by doubling and then subtracting one, yielding scores from -1 to +1. Each centered score was multiplied by its respective dietary component’s inflammation effect score to calculate specific DII scores for each food parameter [10]. The overall DII score for an individual was calculated by aggregating these specific DII scores. Within this research, the range of the DII scores spanned from a minimum of -4.28 to a maximum of 5.18.
Assessment of study participants’ characteristics
Sociodemographic characteristics captured details including age, gender, ethnic and racial identity (classified as Non-Hispanic White, Mexican American, Non-Hispanic Black, and other) and physical measurements such as waist circumference and BMI. Medical and lifestyle elements brought into the analysis included hypertension—ascertained by a SBP of 140 mmHg or more, or a DBP of 90 mmHg or more, or the administration of antihypertensive drugs. The criterion for diabetes included a self-reported diagnosis or an HbA1c exceeding 6.5%. The diagnosis of hyperlipidemia rested on having total cholesterol readings of 200 mg/dL or higher, or the use of specific medications, or a previously established diagnosis. Renal health was gauged using the updated Chronic Kidney Disease Epidemiology Collaboration formula. Individuals presenting an ACR above 30 mg/g or an eGFR below 60 mL/min/1.73m2 were classified as CKD patients, as per the comprehensive definitions [24]. Cardiovascular conditions were determined from self-disclosed histories of medically confirmed conditions such as congestive heart failure. Smoking behaviors adhered to the NCHS’s categorical divisions: never, former, or current smokers. Alcohol consumption delineations encompassed non-drinkers (fewer than 12 lifetime alcoholic beverages), previous drinkers (a year or more of abstinence after previous consumption), light drinkers (a daily intake of up to one drink for women and two for men), moderate drinkers (daily intake of up to three drinks for women and four for men), and heavy drinkers (daily intake of four or more drinks for women and five or more for men) measured against the previous year’s consumption patterns.
Statistical analysis
Statistical analyses adhered to the protocols established by the CDC [25]. The analysis accounted for the study complex survey design by integrating appropriate sample weights, ensuring the statistical integrity and representativeness of the findings. Continuous and categorical variables were examined using the Student’s t-test and chi-square test, respectively, with outcomes reported as actual numbers and weighted percentages. DII scores were categorized into quartiles (Q1: DII ≤ 0.321; Q2: 0.321 < DII ≤ 1.752; Q3: 1.752 < DII ≤ 2.816; Q4: DII > 2.816) to explore the association between DII and NT-proBNP levels.To investigate the effect of an HF history, analyses without adjustment and multivariable-adjusted regression models evaluated differences in continuous variables across DII score quartiles, with results presented as beta coefficients and standard errors. For DII and NT-proBNP level correlation, three regression models were established: a crude model without adjustments, Model I adjusted for age, gender, and race/ethnicity, and Model II further adjusted for hypertension, diabetes,chronic kidney disease (CKD). Stratified analyses were conducted based on age groups (<50 years, 50–75 years, >75 years), gender, race/ethnicity (white or non-white), and the presence of comorbid conditions such as HF, CKD, hypertension, diabetes, and BMI categories (<25, ≥25). These analyses aimed to investigate potential significant interactions between the DII and NT-proBNP levels. To identify the most effective dietary predictors of NT-proBNP levels and to mitigate multicollinearity, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model was applied. This approach optimized predictive accuracy by reducing the influence of less significant variables. Cross-validation, involving the subdivision of the dataset into ten portions for repetitive training and evaluation, was employed to refine the model, leading to the selection of optimal parameters. A curve was plotted to depict model performance across a range of lambda values during the cross-validation process. This plot was generated to identify the lambda value that resulted in the most negligible cross-validation error, a measure known as "minimum deviance." Statistical analyses were executed using the R statistical software package (The R Foundation; version 4.2.0). Statistical significance was set at a P value of <0.05.
3 Results
Baseline characteristics of participants
Table 1 presents the baseline characteristics of the study population, stratified by HF status. Of 10,766 participants, 374 reported a history of HF. The mean age of those with HF was approximately 64.77 ± 1.16 years, with a gender distribution of 48.26% women and 51.74% men. Racial and ethnic backgrounds were predominantly non-Hispanic whites (76.87%), with the remainder being Mexican Americans (3.23%), non-Hispanic blacks (10.76%), and other races (9.14%). The prevalence of CKD, hypertension, hyperlipidemia, and diabetes was significantly higher in the HF group compared to the non-HF group (all p < 0.001). Furthermore, smoking status and drinking habits also differed significantly between the two groups (both p < 0.001), with a higher proportion of former smokers and former drinkers in the HF group.
BMI and waist circumference were significantly higher in the HF group compared to the non-HF group (both p < 0.001). Additionally, the mean DII score was significantly higher in the HF group (1.87 ± 0.09) compared to the non-HF group (1.39 ± 0.04) (p < 0.001).NT-proBNP levels were markedly elevated in the HF group (1035.53 ± 112.38 pg/mL) compared to the non-HF group (115.94 ± 4.88 pg/mL, p < 0.001). Moreover, the HF group had significantly lower serum albumin and HDL-C levels, and higher glycohemoglobin, and C-reactive protein levels (all p < 0.001). LDL-C levels were lower in the HF group (p = 0.012), but total cholesterol levels did not differ significantly between the two groups (p = 0.092).
Similarly, to explore the dietary factors that contributed to the difference of DII between two groups, each component score of DII was presented in S1 Table. We found that the HF group had significantly lower DII scores for energy, protein, carbohydrate, total fatty acid, saturated fatty acid, vitamin B12, selenium, iron, and zinc (P<0.001) compared to the non-heart failure group. On the other hand, the HF group had significantly higher DII scores for dietary fiber, polyunsaturated fatty acids, N-6 fatty acids, vitamin A, vitamin B1, vitamin B2, vitamin B6, vitamin E, folate, niacin, magnesium, and alcohol (P<0.05). In addition, S2 Table shows the differences between the two groups separately for each actual dietary component.
Associations between Dietary Inflammatory Index (DII) and NT-proBNP levels
Spline smoothing curves illustrated a positive correlation between the DII and NT-proBNP levels (Fig 2). Our regression analyses, presented in Table 2, assessed the association between DII and NT-proBNP concentrations. For participants without a history of HF, an incremental unit in DII was associated with a significant elevation in NT-proBNP levels across different models: an increase of 9.69 pg/mL in the crude model (95% confidence interval [CI]: 6.47, 12.91; p < 0.001), 8.57 pg/mL in Model I (95% CI: 4.97, 12.17; p < 0.001), and 5.54 pg/mL in Model II (95% CI: 1.75, 9.32; p = 0.001) with further adjustments. When DII scores were categorized into quartiles, individuals in the highest quartile (DII≥2.816) had significantly higher NT-proBNP levels compared to those in the lowest quartile (DII<0.321) 43.99pg/mL in the crude model (95%CI: 24.26–63.73, p<0.001), 38.22pg/mL in Model Ⅰ (95%CI: 16.05–60.38, p = 0.001), and 25.18 pg/mL in Model Ⅱ (95%CI: 3.04–47.32, p = 0.027).
(A) Each dot represents an individual sample. (B) The solid red line indicates the smooth fitting curve between DII and NT-proBNP levels (log-transformed, pg/dL), whereas the dotted blue line represents 95% confidence intervals of the fitting.
In individuals with HF, no significant association was found between DII scores and NT-proBNP levels in the crude model (β = 73.46, 95%CI: -72.99–219.90, p = 0.317), Model Ⅰ (β = 52.8, 95%CI: -79.85–185.46, p = 0.424), or Model Ⅱ (β = 106.16, 95%CI: -52.50–264.82, p = 0.182). However, notable differences in NT-proBNP levels were observed among participants in the second and third quartiles compared to those in the lowest quartile in Model II, with increases of 717.06 pg/mL (95% CI: 76.49–1357.63, p = 0.030) and 855.49 pg/mL (95% CI: 156.57–1554.41, p = 0.018), respectively.
Stratified analysis
This study conducted subgroup analyses (Fig 3) across various predefined strata to identify potential effect modifications between characteristics and outcomes. In our study, age, gender, race, HF, CKD, BMI, hypertension, and diabetes were assessed as potential effect modifiers to determine if the association with the outcome varied significantly across different groups. Across all strata, the association between the DII and NT-proBNP levels was consistently positive (positive β coefficients), and most comparisons within each stratum reached significance (p < 0.05). Every unit increase in DII corresponded to an average rise of 14.32 pg/mL in NT-proBNP levels (95% CI: 9.77, 18.86; p < 0.001). This correlation was robust in older adults, significantly over 75 years (β = 56.09; 95% CI: 29.02, 83.17; p < 0.001), highlighting a significant age-related interaction with the DII and NT-proBNP relationship (p < 0.001). Gender differences were apparent, with a significant association observed in men (β = 17.82; 95% CI: 11.25, 24.38; p < 0.001), whereas the association was insignificant in women (β = 7.43; 95% CI: -0.18, 15.04; p = 0.061), and an interaction by gender was observed (p = 0.041). Racial and ethnic analysis indicated positive correlations with NT-proBNP levels for both White and Non-White groups, though no significant interaction was found between these groups (p = 0.593). For individuals with a history of HF, the trend suggested a potential though insignificant association between higher DII and elevated NT-proBNP levels (β = 73.46; 95% CI: -72.47, 219.39; p = 0.321), with no significant interaction observed (p = 0.381). In those with CKD, a significant positive association was observed (β = 36.39; 95% CI: 4.12, 68.65; p = 0.031) alongside a borderline considerable interaction (p = 0.052). The relationship across BMI categories consistently showed a positive trend, though without significant interaction (p = 0.111). A pronounced association was observed in individuals with hypertension (β = 25.73; 95% CI: 13.34, 38.12; p < 0.001), with a significant interaction effect (p = 0.002). Diabetes status further influenced the associations, showing a substantial correlation in those with diabetes (β = 38.94; 95% CI: 22.10, 55.79; p < 0.001) and a significant interaction by diabetes status (p = 0.012).
Identification of critical dietary factors related to NT-proBNP
A LASSO penalized regression was constructed to identify dietary components intimately associated with NT-proBNP, incorporating 27 dietary factors and three covariates: gender, age, and race or ethnicity (Fig 4). The LASSO technique enhances the robustness of the model by introducing an L1 penalty to the regression, effectively reducing specific coefficients to zero. This approach facilitates feature selection from highly correlated variables, improving the model’s interpretability and predictive performance. By eliminating fewer contributory variables, the model enhances its generalization capacity and mitigates the risk of overfitting. The optimization of the loss function, combined with the L1 penalty, leads to a model that selectively excludes non-essential predictors, as depicted in Fig 4A.Out of n = 27 dietary variables constituting the DII scores, n = 17 show an interaction to NT-proBNP levels in whole population, such as: Protein, carbohydrates, fiber, monounsaturated and polyunsaturated fatty acids, vitamin A, β-carotene, vitamin B1, niacin, folate, vitamin E, magnesium, iron, selenium, caffeine, alcohol, and n-3 fatty acids, which were identified as the dietary factors substantially correlated with NT-proBNP levels (log-transformed, pg/dL).
(A) The coefficient shrinkage process of all 27 dietary components and 3 covariates (sex, age, and race/ethnicity), we represent the changes in coefficients of different features under various levels of shrinkage by drawing lines of different colors. (B) A 10-fold cross-validation of the LASSO regression model. LASSO, least absolute shrinkage and selection operator.
4 Discussion
This comprehensive, nationally representative cross-sectional study explored the association between the DII and NT-proBNP levels, employing a robust analytical methodology and leveraging a significant sample size. Through applying LASSO penalized regression, dietary factors significantly associated with NT-proBNP levels were identified. Our analysis consistently revealed a positive association between DII and NT-proBNP across various demographic and clinical groups, with a particularly pronounced effect in older adults. This finding highlights the potential of dietary choices on biomarkers of HF, suggesting that nutritional adjustments could play a crucial role in preserving cardiovascular health. Specifically, our results yielded an average increase of 14.32 pg/dL in NT-proBNP for each unit increment in DII (95% CI: 9.77, 18.86; p < 0.001), providing substantive insights into the dietary impact on cardiovascular health, particularly from an inflammatory standpoint.
Beyond focusing on individual foods or nutrients, the complexity of daily diets encompasses combinations of multiple foods and nutrients. In recent years, the significance of diet quality scores and overall dietary patterns, including Healthy Eating Index (HEI), DII, Mediterranean diet (MedDiet), Dietary Approaches to Stop Hypertension (DASH) diet, and vegetarian diets, have been increasingly recognized [26–29]. While previous research on dietary patterns in the context of HF prevention and treatment has primarily focused on the MedDiet and DASH diet [30,31], the role of chronic inflammation in CVD progression has been emphasized [32–34]. Within this framework, dietary patterns emerge as modifiable elements capable of inducing pro- or anti-inflammatory effects. The rich NHANES dataset offers a valuable resource for further investigating this intriguing issue [35].
Our findings are consistent with Liu et al.’s study [36], identifying a positive correlation between the DII and HF(odds ratio [OR] = 1.110, 95% CI: 1.060, 1.163). However, our study extends to investigating the association of DII levels with the cardiovascular health biomarker NT-proBNP rather than solely focusing on HF diagnoses. This approach allows for a more nuanced investigation into the association of diet with cardiovascular health at the biomarker level. Previous studies have highlighted the prevalence of elevated NT-proBNP levels among U.S. adults, even in the absence of a CVD history [37]. Moreover, elevated NT-proBNP levels have been significantly associated with all-cause and CVD mortality among individuals with CVD [38]. Our analysis of the cohort provides deeper insights into the potential mechanisms underlying HF.
Meanwhile, our findings corroborate the case-control study by Jalal Moludi et al. [39], which found that higher DII scores were associated with higher NT-proBNP levels. However, our study benefits from a considerably larger sample size, encompassing 10,766 participants, significantly surpassing Jalal Moludi’s study (n = 229). Furthermore, our study’s demographic includes a nationally representative sample of Americans, contrasting the Asian population focus in Jalal Moludi’s research. This diversity underscores the global relevance of the DII-cardiovascular health connection across different populations, considering variations in regional, racial, lifestyle, and other factors.
Furthermore, subgroup analyses revealed significant interactions between DII and NT-proBNP levels concerning age, gender, hypertension, and diabetes (P for interaction <0.05). Age emerges as a critical factor; with advancing age, the immune system experiences changes that may enhance inflammatory responses [40–42]. Chronic inflammation, mediated by diet and imbalance between pro- and anti-inflammatory factors, could contribute to endothelial injury, vascular remodeling, atherosclerosis, and insulin resistance. These conditions may precipitate alterations in the cardiovascular system, influencing NT-proBNP levels.
The role of gender in this dynamic is significant. Physiological differences, hormone levels, and lifestyle variations between men and women can affect inflammatory responses and cardiovascular function [43,44]. For example, estrogen influences inflammation, and the distinct impacts on cardiovascular metabolism and function suggest that dietary, inflammation, and cardiovascular health interactions manifest differently across sexes [45]. This finding indicates a need for further investigation into the mechanisms behind these sex-specific differences, which would inform targeted nutritional and lifestyle recommendations to enhance cardiovascular health in both sexes.
Chronic conditions such as CKD, hypertension, and diabetes play distinct roles in influencing inflammation status and cardiovascular functionality, potentially impacting NT-proBNP levels. CKD may result in an accumulation of inflammatory markers due to the kidneys’ diminished capacity to eliminate metabolites and inflammatory substances [46]. Furthermore, hypertension and diabetes are linked to endothelial dysfunction, which fosters inflammation [47,48]. Moreover, hypertension could trigger cardiac hypertrophy and changes in hemodynamics, influencing NT-proBNP production [49]. Despite variations in absolute values among these subgroups, the correlation between DII and NT-proBNP remained consistent.
Furthermore, our study did not identify a significant association between DII and NT-proBNP levels among individuals with HF in the linear regression model (p > 0.05), although the effect size indicated a considerable difference (β = 73.46, 95% CI: -72.47, 219.39). However, analysis of specific DII quartiles revealed significant associations in the second (Q2) and third quartile (Q3), suggesting that moderate to high levels of dietary inflammation may have particularly adverse effects in patients with heart failure. This effect became more apparent in Model II, which adjusts for interaction terms and potential confounders, indicating that correct adjustments are necessary to reveal complex interactions and true effects that may be masked by other factors. Furthermore, the limited sample size (n = 374) of the HF group and the use of weighting adjustments may have limited the representativeness and statistical power of the model, advising a cautious interpretation of these results.
Previous studies have highlighted the association between DII scores and CVD morbidity, emphasizing the significant impact of dietary inflammation on health [50]. Our analysis identifies proteins, carbohydrates, dietary fibers, monounsaturated fatty acids, polyunsaturated fatty acids, vitamins A, β-carotene, vitamin B1, niacin, folate, vitamin E, magnesium, iron, selenium, caffeine, alcohol, and n-3 fatty acids as vital dietary factors impacting NT-proBNP levels. For instance, protein and amino acid supplementation may improve muscle mass and functional capacity in patients with HF. However, evidence is still emerging on its effects across different types of HF, such as HF with preserved ejection fraction (HFpEF) [51,52]. Low-carbohydrate diets have demonstrated positive outcomes for patients with diabetic cardiomyopathy, improving symptoms and overall quality of life associated with HF. In a randomized controlled trial, patients with diabetic cardiomyopathy who followed a low-carbohydrate diet exhibited significant weight and systolic blood pressure reductions over 16 weeks [53]. Moreover, studies indicate the beneficial role of polyunsaturated fatty acids, particularly n-3 fatty acids, in cardiovascular health. Their anti-inflammatory and antioxidant properties are credited with lowering the risk of CVD and HF [54,55]. Dietary fiber, particularly from plant sources, plays a crucial role in cardiac metabolic stability, contributing to a reduced risk of HF. This benefit is assumed to stem from fiber’s ability to modulate inflammatory biomarkers [56].
The role of micronutrients such as iron, magnesium, and selenium in HF prognosis is critical. Iron deficiency, prevalent among patients with HF, has been linked to increased cardiovascular hospitalization rates, particularly in those with HF with reduced fraction(HFrEF) [57,58]. Magnesium’s role is pivotal in developing HF [59], with deficiencies exacerbating the condition. Selenium’s antioxidant properties are vital for tissue protection against oxidative stress [60]. Vitamins A and β-carotene, thiamine (B1), niacin (B3), folate, and vitamin E are crucial for HF management and overall cardiovascular health, though their specific effects warrant further investigation. The association between alcohol intake and HF risk is nuanced, with some studies suggesting potential cardiovascular benefits from moderate alcohol consumption. though evidence remains inconclusive regarding a direct causal relationship [61,62].
This study identified a positive correlation between the DII and NT-proBNP levels. However, the precise biological mechanisms linking these two factors remain elusive. Furthermore, the cross-sectional nature of our study design precluded inferences about causal relationships, necessitating further longitudinal cohort studies to validate our findings. Moreover, reliance on self-reported dietary data collected through 24-hour dietary recalls introduced the potential for measurement inaccuracies and misclassification, possibly not reflecting habitual dietary intake accurately. The absence of data on cardiac function, such as electrocardiogram or echocardiogram results, constrains our understanding of the association between NT-proBNP levels and subclinical cardiac health. Finally, despite the substantial size of our study population, the possibility of residual confounding due to unmeasured variables remains a limitation that could still influence our findings.
5 Conclusion
This study identified a positive correlation between the DII and NT-proBNP levels, suggesting a robust link between pro-inflammatory diets and increased heart failure biomarkers, with implications for dietary modifications in cardiovascular risk management.
Supporting information
S1 Table. Comparison of each component of DII scores among all participants.
https://doi.org/10.1371/journal.pone.0304289.s001
(DOCX)
S2 Table. Comparison of each actual dietary component of DII among all participants.
https://doi.org/10.1371/journal.pone.0304289.s002
(DOCX)
Acknowledgments
Our sincere appreciation is directed towards the participants of the NHANES database; their role was indispensable to the study’s progress.
References
- 1. Mensah GA, Fuster V, Murray CJL, Roth GA. Global Burden of Cardiovascular Diseases and Risks, 1990–2022. J Am Coll Cardiol. 2023;82(25):2350–473. Epub 2023/12/14. pmid:38092509.
- 2. Mensah GA, Roth GA, Fuster V. The Global Burden of Cardiovascular Diseases and Risk Factors: 2020 and Beyond. J Am Coll Cardiol. 2019;74(20):2529–32. Epub 2019/11/16. pmid:31727292.
- 3. McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Böhm M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2022;24(1):4–131. Epub 2022/01/28. pmid:35083827.
- 4. Adamo L, Rocha-Resende C, Prabhu SD, Mann DL. Reappraising the role of inflammation in heart failure. Nat Rev Cardiol. 2020;17(5):269–85. Epub 2020/01/24. pmid:31969688.
- 5. Silvestre-Roig C, Braster Q, Ortega-Gomez A, Soehnlein O. Neutrophils as regulators of cardiovascular inflammation. Nat Rev Cardiol. 2020;17(6):327–40. Epub 2020/01/31. pmid:31996800.
- 6. Zhou N, Xie ZP, Liu Q, Xu Y, Dai SC, Lu J, et al. The dietary inflammatory index and its association with the prevalence of hypertension: A cross-sectional study. Front Immunol. 2022;13:1097228. Epub 2023/02/07. pmid:36741368; PubMed Central PMCID: PMC9893776.
- 7. Mao Y, Weng J, Xie Q, Wu L, Xuan Y, Zhang J, et al. Association between dietary inflammatory index and Stroke in the US population: evidence from NHANES 1999–2018. BMC Public Health. 2024;24(1):50. Epub 2024/01/04. pmid:38166986; PubMed Central PMCID: PMC10763382.
- 8. Hariharan R, Odjidja EN, Scott D, Shivappa N, Hébert JR, Hodge A, et al. The dietary inflammatory index, obesity, type 2 diabetes, and cardiovascular risk factors and diseases. Obes Rev. 2022;23(1):e13349. Epub 2021/10/29. pmid:34708499.
- 9. Cavicchia PP, Steck SE, Hurley TG, Hussey JR, Ma Y, Ockene IS, et al. A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J Nutr. 2009;139(12):2365–72. Epub 2009/10/30. pmid:19864399; PubMed Central PMCID: PMC2777480.
- 10. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–96. Epub 2013/08/15. pmid:23941862; PubMed Central PMCID: PMC3925198.
- 11. McKie PM, Burnett JC Jr. NT-proBNP: The Gold Standard Biomarker in Heart Failure. J Am Coll Cardiol. 2016;68(22):2437–9. Epub 2016/12/03. pmid:27908348.
- 12. Schmitt W, Rühs H, Burghaus R, Diedrich C, Duwal S, Eissing T, et al. NT-proBNP Qualifies as a Surrogate for Clinical End Points in Heart Failure. Clin Pharmacol Ther. 2021;110(2):498–507. Epub 2021/02/26. pmid:33630302; PubMed Central PMCID: PMC8360001 development of heart failure treatment.
- 13. Januzzi JL, van Kimmenade R, Lainchbury J, Bayes-Genis A, Ordonez-Llanos J, Santalo-Bel M, et al. NT-proBNP testing for diagnosis and short-term prognosis in acute destabilized heart failure: an international pooled analysis of 1256 patients: the International Collaborative of NT-proBNP Study. European heart journal. 2006;27(3):330–7. pmid:16293638
- 14. de Bold AJ, Bruneau BG, Kuroski de Bold ML. Mechanical and neuroendocrine regulation of the endocrine heart. Cardiovasc Res. 1996;31(1):7–18. Epub 1996/01/01. pmid:8849584.
- 15. Echouffo-Tcheugui JB, Zhang S, Daya N, McEvoy JW, Tang O, Juraschek SP, et al. NT-proBNP and All-Cause and Cardiovascular Mortality in US Adults: A Prospective Cohort Study. J Am Heart Assoc. 2023;12(11):e029110. Epub 2023/05/26. pmid:37232235; PubMed Central PMCID: PMC10382006.
- 16. Hamatani Y, Nagai T, Shiraishi Y, Kohsaka S, Nakai M, Nishimura K, et al. Long-Term Prognostic Significance of Plasma B-Type Natriuretic Peptide Level in Patients With Acute Heart Failure With Reduced, Mid-Range, and Preserved Ejection Fractions. Am J Cardiol. 2018;121(6):731–8. Epub 2018/02/06. pmid:29394996.
- 17.
Cetners for disease control and prevention about the National health and nutrition examination survey. NHANES. https://www.cdc.gov/nchs/nhanes/index.htm. Accessed 1 September 2023.
- 18.
National Health and Nutrition Examination Survey Biospecimen Program.Serum, Plasma, and Urine Specimens. https://www.cdc.gov/nchs/nhanes/biospecimens/biospecimens.htm. Accessed 1 September 2023.
- 19. Centers for Disease Control. National Health and Nutrition Examination Survey. Data Codebook. 1999–2004. https://wwwn.cdc.gov/Nchs/Nhanes/1999-2000/SSBNP_A.htm#Analytic_Notes. Accessed 1 September 2023.
- 20. van der Merwe DE, Henley R, Lane G, Field R, Frenneaux M, Dunstan F, et al. Effect of different sample types and stability after blood collection of N-terminal pro-B-type natriuretic peptide as measured with Roche Elecsys system. Clin Chem. 2004;50(4):779–80. Epub 2004/03/27. pmid:15044344.
- 21. Cauliez B, Guignery J, Marinier S, Mariau I, Lavoinne A. Two-year stability of NT-proBNP in frozen samples using the Roche Elecsys system. Ann Clin Biochem. 2008;45(Pt 3):318–9. Epub 2008/05/17. pmid:18482923.
- 22. Parrinello CM, Grams ME, Couper D, Ballantyne CM, Hoogeveen RC, Eckfeldt JH, et al. Recalibration of blood analytes over 25 years in the atherosclerosis risk in communities study: impact of recalibration on chronic kidney disease prevalence and incidence. Clin Chem. 2015;61(7):938–47. Epub 2015/05/09. pmid:25952043; PubMed Central PMCID: PMC4782184.
- 23. Shivappa N, Steck SE, Hurley TG, Hussey JR, Ma Y, Ockene IS, et al. A population-based dietary inflammatory index predicts levels of C-reactive protein in the Seasonal Variation of Blood Cholesterol Study (SEASONS). Public Health Nutr. 2014;17(8):1825–33. Epub 2013/10/11. pmid:24107546; PubMed Central PMCID: PMC3983179.
- 24. Cheung AK, Chang TI, Cushman WC, Furth SL, Hou FF, Ix JH, et al. Executive summary of the KDIGO 2021 Clinical Practice Guideline for the Management of Blood Pressure in Chronic Kidney Disease. Kidney Int. 2021;99(3):559–69. Epub 2021/02/28. pmid:33637203.
- 25.
NHANES Tutorials. https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx. Accessed 20 September 2022.
- 26. Shams-White MM, Pannucci TE, Lerman JL, Herrick KA, Zimmer M, Meyers Mathieu K, et al. Healthy Eating Index-2020: Review and Update Process to Reflect the Dietary Guidelines for Americans,2020–2025. J Acad Nutr Diet. 2023;123(9):1280–8. Epub 2023/05/19. pmid:37201748.
- 27. Da Silva R, Bach-Faig A, Quintana BR, Buckland G, de Almeida MDV, Serra-Majem L. Worldwide variation of adherence to the Mediterranean diet, in 1961–1965 and 2000–2003. Public health nutrition. 2009;12(9A):1676–84. pmid:19689839
- 28. Filippou CD, Tsioufis CP, Thomopoulos CG, Mihas CC, Dimitriadis KS, Sotiropoulou LI, et al. Dietary Approaches to Stop Hypertension (DASH) Diet and Blood Pressure Reduction in Adults with and without Hypertension: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Adv Nutr. 2020;11(5):1150–60. Epub 2020/04/25. pmid:32330233; PubMed Central PMCID: PMC7490167.
- 29. Kahleova H, Levin S, Barnard ND. Vegetarian Dietary Patterns and Cardiovascular Disease. Prog Cardiovasc Dis. 2018;61(1):54–61. Epub 2018/05/26. pmid:29800598.
- 30. Papadaki A, Martínez-González M, Alonso-Gómez A, Rekondo J, Salas-Salvadó J, Corella D, et al. Mediterranean diet and risk of heart failure: results from the PREDIMED randomized controlled trial. Eur J Heart Fail. 2017;19(9):1179–85. Epub 2017/01/31. pmid:28133855.
- 31. Wickman BE, Enkhmaa B, Ridberg R, Romero E, Cadeiras M, Meyers F, et al. Dietary Management of Heart Failure: DASH Diet and Precision Nutrition Perspectives. Nutrients. 2021;13(12). Epub 2021/12/29. pmid:34959976; PubMed Central PMCID: PMC8708696.
- 32. Dick SA, Epelman S. Chronic Heart Failure and Inflammation: What Do We Really Know? Circ Res. 2016;119(1):159–76. Epub 2016/06/25. pmid:27340274.
- 33. Paulus WJ, Tschöpe C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol. 2013;62(4):263–71. Epub 2013/05/21. pmid:23684677.
- 34. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505–22. Epub 2018/08/02. pmid:30065258; PubMed Central PMCID: PMC6146930.
- 35. Yang P, Rooney MR, Wallace AS, Kim H, Echouffo-Tcheugui JB, McEvoy JW, et al. Associations between diet quality and NT-proBNP in US adults, NHANES 1999–2004. American Journal of Preventive Cardiology. 2023:100528.
- 36. Liu Z, Liu H, Deng Q, Sun C, He W, Zheng W, et al. Association Between Dietary Inflammatory Index and Heart Failure: Results From NHANES (1999–2018). Front Cardiovasc Med. 2021;8:702489. Epub 2021/07/27. pmid:34307508; PubMed Central PMCID: PMC8292138.
- 37. Echouffo-Tcheugui JB, Zhang S, McEvoy JW, Juraschek SP, Fang M, Ndumele CE, et al. Insulin Resistance and N-Terminal Pro-B-Type Natriuretic Peptide Among Healthy Adults. JAMA Cardiol. 2023;8(10):989–95. Epub 2023/09/06. pmid:37672260; PubMed Central PMCID: PMC10483384 Roche Diagnostics during the conduct of the study and personal fees from Siemens Healthineers, Becton Dickinson, QuidelOrtho, Beckman Coulter, Sphingotech, and PixCell Medical outside the submitted work. Dr Selvin reported receiving grants from the National Institutes of Health (NIH) unrelated to this work. No other disclosures were reported.
- 38. Daya NR, McEvoy JW, Christenson RH, Tang O, Foti K, Juraschek SP, et al. Prevalence of Elevated NT-proBNP and its Prognostic Value by Blood Pressure Treatment and Control. Am J Hypertens. 2023;36(11):602–11. Epub 2023/07/17. pmid:37458697; PubMed Central PMCID: PMC10570660.
- 39. Moludi J, Shivappa N, Alisgharzadeh S, Hébert JR, Alizadeh M. Dietary Inflammatory Index Is Related to Heart Failure Risk and Cardiac Function: A Case-Control Study in Heart Failure Patients. Front Nutr. 2021;8:605396. Epub 2021/04/24. pmid:33889592; PubMed Central PMCID: PMC8056025.
- 40. Brüünsgaard H, Pedersen BK. Age-related inflammatory cytokines and disease. Immunol Allergy Clin North Am. 2003;23(1):15–39. Epub 2003/03/21. pmid:12645876.
- 41. Barbé-Tuana F, Funchal G, Schmitz CRR, Maurmann RM, Bauer ME. The interplay between immunosenescence and age-related diseases. Semin Immunopathol. 2020;42(5):545–57. Epub 2020/08/05. pmid:32747977; PubMed Central PMCID: PMC7398288.
- 42. Jenny NS. Inflammation in aging: cause, effect, or both? Discov Med. 2012;13(73):451–60. Epub 2012/06/30. pmid:22742651.
- 43. Gilliver SC. Sex steroids as inflammatory regulators. J Steroid Biochem Mol Biol. 2010;120(2–3):105–15. Epub 2010/01/05. pmid:20045727.
- 44. Appelman Y, van Rijn BB, Ten Haaf ME, Boersma E, Peters SA. Sex differences in cardiovascular risk factors and disease prevention. Atherosclerosis. 2015;241(1):211–8. Epub 2015/02/12. pmid:25670232.
- 45. Nasser SA, Afify EA, Kobeissy F, Hamam B, Eid AH, El-Mas MM. Inflammatory Basis of Atherosclerosis: Modulation by Sex Hormones. Curr Pharm Des. 2021;27(18):2099–111. Epub 2021/01/23. pmid:33480335.
- 46. Silverstein DM. Inflammation in chronic kidney disease: role in the progression of renal and cardiovascular disease. Pediatr Nephrol. 2009;24(8):1445–52. Epub 2008/12/17. pmid:19083024.
- 47. Savoia C, Sada L, Zezza L, Pucci L, Lauri FM, Befani A, et al. Vascular inflammation and endothelial dysfunction in experimental hypertension. Int J Hypertens. 2011;2011:281240. Epub 2011/09/15. pmid:21915370; PubMed Central PMCID: PMC3170891.
- 48. Sena CM, Pereira AM, Seiça R. Endothelial dysfunction—a major mediator of diabetic vascular disease. Biochim Biophys Acta. 2013;1832(12):2216–31. Epub 2013/09/03. pmid:23994612.
- 49. Lukowicz TV, Fischer M, Hense HW, Döring A, Stritzke J, Riegger G, et al. BNP as a marker of diastolic dysfunction in the general population: Importance of left ventricular hypertrophy. Eur J Heart Fail. 2005;7(4):525–31. Epub 2005/06/01. pmid:15921790.
- 50. Ho FK, Wirth MD, Parra-Soto S, Boonpor J, Zhou Z, Petermann-Rocha F, et al. Dose-Response Associations of Dietary Inflammatory Potential With Health Outcomes: A Prospective Cohort Study of 198,265 UK Biobank Participants. Curr Probl Cardiol. 2023;48(9):101774. Epub 2023/05/01. pmid:37121456.
- 51. Rozentryt P, von Haehling S, Lainscak M, Nowak JU, Kalantar-Zadeh K, Polonski L, et al. The effects of a high-caloric protein-rich oral nutritional supplement in patients with chronic heart failure and cachexia on quality of life, body composition, and inflammation markers: a randomized, double-blind pilot study. J Cachexia Sarcopenia Muscle. 2010;1(1):35–42. Epub 2011/04/09. pmid:21475692; PubMed Central PMCID: PMC3060643.
- 52. Deutz NE, Matheson EM, Matarese LE, Luo M, Baggs GE, Nelson JL, et al. Readmission and mortality in malnourished, older, hospitalized adults treated with a specialized oral nutritional supplement: A randomized clinical trial. Clin Nutr. 2016;35(1):18–26. Epub 2016/01/23. pmid:26797412.
- 53. Kleissl-Muir S, Owen A, Rasmussen B, Zinn C, Driscoll A. Effects of a low carbohydrate diet on heart failure symptoms and quality of life in patients with diabetic cardiomyopathy: A randomised controlled trial pilot study. Nutr Metab Cardiovasc Dis. 2023;33(12):2455–63. Epub 2023/10/06. pmid:37798235.
- 54. Djoussé L, Cook NR, Kim E, Bodar V, Walter J, Bubes V, et al. Supplementation With Vitamin D and Omega-3 Fatty Acids and Incidence of Heart Failure Hospitalization: VITAL-Heart Failure. Circulation. 2020;141(9):784–6. Epub 2019/11/12. pmid:31709816; PubMed Central PMCID: PMC7054158.
- 55. Tavazzi L, Maggioni AP, Marchioli R, Barlera S, Franzosi MG, Latini R, et al. Effect of n-3 polyunsaturated fatty acids in patients with chronic heart failure (the GISSI-HF trial): a randomised, double-blind, placebo-controlled trial. Lancet. 2008;372(9645):1223–30. Epub 2008/09/02. pmid:18757090.
- 56. Marques FZ, Nelson E, Chu PY, Horlock D, Fiedler A, Ziemann M, et al. High-Fiber Diet and Acetate Supplementation Change the Gut Microbiota and Prevent the Development of Hypertension and Heart Failure in Hypertensive Mice. Circulation. 2017;135(10):964–77. Epub 2016/12/09. pmid:27927713.
- 57. Graham FJ, Pellicori P, Kalra PR, Ford I, Bruzzese D, Cleland JGF. Intravenous iron in patients with heart failure and iron deficiency: an updated meta-analysis. Eur J Heart Fail. 2023;25(4):528–37. Epub 2023/02/25. pmid:36823953.
- 58. Docherty KF, Welsh P, Verma S, De Boer RA, O’Meara E, Bengtsson O, et al. Iron Deficiency in Heart Failure and Effect of Dapagliflozin: Findings From DAPA-HF. Circulation. 2022;146(13):980–94. Epub 2022/08/17. pmid:35971840; PubMed Central PMCID: PMC9508991.
- 59. Voultsos P, Bazmpani MA, Papanastasiou CA, Papadopoulos CE, Efthimiadis G, Karvounis H, et al. Magnesium Disorders and Prognosis in Heart Failure: A Systematic Review. Cardiol Rev. 2022;30(6):281–5. Epub 2021/05/19. pmid:34001688.
- 60. Bomer N, Grote Beverborg N, Hoes MF, Streng KW, Vermeer M, Dokter MM, et al. Selenium and outcome in heart failure. Eur J Heart Fail. 2020;22(8):1415–23. Epub 2019/12/07. pmid:31808274; PubMed Central PMCID: PMC7540257.
- 61. Soukoulis V, Dihu JB, Sole M, Anker SD, Cleland J, Fonarow GC, et al. Micronutrient deficiencies an unmet need in heart failure. J Am Coll Cardiol. 2009;54(18):1660–73. Epub 2009/10/24. pmid:19850206.
- 62. McKeag NA, McKinley MC, Harbinson MT, Noad RL, Dixon LH, McGinty A, et al. The effect of multiple micronutrient supplementation on left ventricular ejection fraction in patients with chronic stable heart failure: a randomized, placebo-controlled trial. JACC Heart Fail. 2014;2(3):308–17. Epub 2014/06/24. pmid:24952700.