[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Article in Journal
Molecular Mechanism of Tocotrienol-Mediated Anticancer Properties: A Systematic Review of the Involvement of Endoplasmic Reticulum Stress and Unfolded Protein Response
Next Article in Special Issue
Dietary Strategies to Reduce Triglycerides in Women of Reproductive Age: A Simulation Modelling Study
Previous Article in Journal
Human Milk Composition and Nutritional Status of Omnivore Human Milk Donors Compared with Vegetarian/Vegan Lactating Mothers
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 15
Issue 8
10.3390/nu15081857
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

The Relationship between the Dietary Inflammatory Index (DII) and Metabolic Syndrome (MetS) in Middle-Aged and Elderly Individuals in the United States

by
Qilong Zhao
1,
Xinyue Tan
1,
Zhenni Su
1,
Habasi Patrick Manzi
1,
Li Su
1,
Zhenchuang Tang
2,* and
Ying Zhang
1,*
1
School of Public Health, Lanzhou University, Lanzhou 730000, China
2
Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Nutrients 2023, 15(8), 1857; https://doi.org/10.3390/nu15081857
Submission received: 13 March 2023 / Revised: 8 April 2023 / Accepted: 11 April 2023 / Published: 12 April 2023
(This article belongs to the Special Issue Health Research Methodology on Diet and Dietary Intake)
Browse Figures
Figure 1
<p>Flow diagram of participants in the study.</p> "> Figure 2
<p>Association of DII with MetS and its components. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.</p> "> Figure 3
<p>The restricted cubic spline for the associations of DII with MetS and its components. (<b>A</b>) Metabolic syndrome; (<b>B</b>) Raised FG; (<b>C</b>) Raused TG; (<b>D</b>) Raised WC; (<b>E</b>) Reduced HDL-C; (<b>F</b>) Raised BP. Knots were placed at the 5th, 35th, 65th, and 95th percentiles of the DII distribution. Results were adjusted for age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.</p> "> Figure 4
<p>Subgroup analysis of association between DII and MetS as a whole and its components among different gender groups. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Q1 [−5.21, −1.33), Q2 [−1.33, 0.10), Q3 [0.10, 1.52), Q4 [1.52, 4.43) for male; Q1 [−4.71, −0.54), Q2 [−0.54, 1.10), Q3 [1.10, 2.37), Q4 [2.37, 4.66) for female.</p> "> Figure 5
<p>Subgroup analysis of association between DII and MetS as a whole and its components among different age groups. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Q1 [−5.21, −1.10), Q2 [−1.10, 0.60), Q3 [0.60, 2.09), Q4 [2.09, 4.23) for the age range of 45 to 60 years; Q1 [−4.71, −0.89), Q2 [−0.89, 0.66), Q3 [0.66, 1.99), Q4 [1.99, 4.66) for age over 60 years.</p> "> Figure 6
<p>Generalized linear regression analysis of DII levels and indicators of MetS. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, total triglycerides; FPG, fasting blood sugar; OGTT, oral glucose tolerance test. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Green: No adjustments were made; Red: Adjusted for confounding factors.</p> "> Figure 7
<p>Quantile regression estimation coefficient plot of DII level versus biochemical index of MetS. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.</p> ">
Versions Notes

Abstract

:
(1) Background: With the aging demographic shift in society, there is a growing number of middle-aged and elderly individuals affected by metabolic syndrome (MetS), a risk factor contributing to all causes of mortality. Inflammation plays a crucial role in the development of MetS. This study aims to examine the correlation between MetS and pro-inflammatory diets in middle-aged and elderly individuals, utilizing the Dietary Inflammation Index (DII) as a measure. (2) Methods: Data were extracted from the 2007–2016 National Health and Nutrition Examination Survey (NHANES) database for individuals who were 45 years of age or older. The DII was determined for each participant through 24-h dietary recall interviews. The relationship between DII and MetS was assessed using binary logistic regression analysis, and the association between DII and MetS-related indicators was further explored through generalized linear model (GLM) and quantile regression analysis. (3) Results: A total of 3843 middle-aged and elderly individuals were included in the study. After controlling for confounding factors, the highest quartile of DII was associated with a higher risk of MetS (OR Q 4 : Q 1 = 1.339; 95% CI: 1.013, 1.769; p for trend = 0.018). The top DII quartile also increased the risk of reduced HDL-C (OR Q 4 : Q 1 = 1.499; 95% CI: 1.005, 2.234; p for trend = 0.048) and raised FG (OR Q 4 : Q 1 = 1.432; 95% CI: 1.095, 1.873; p for trend = 0.010) compared to the lowest DII quartile. The levels of DII were found to be positively correlated with BMI ( β = 0.258, p = 0.001), FPG ( β = 0.019, p = 0.049), TG ( β = 2.043, p = 0.013), waistline ( β = 0.580, p = 0.002), and negatively correlated with HDL-C ( β = −0.672, p = 0.003). (4) Conclusions: In middle-aged and elderly individuals in the United States, a high DII score has been linked to the presence of MetS, low HDL-C, and hyperglycemia. Therefore, dietary recommendations for the middle-aged and elderly should focus on reducing the DII by choosing foods rich in antioxidants, dietary fiber, and unsaturated fatty acids.

1. Introduction

Metabolic syndrome (MetS) is a condition characterized by central obesity, high blood pressure, high blood sugar, and abnormal lipid levels, which is closely associated with cardiovascular disease, diabetes, and fatty liver disease [1]. With advancements in socioeconomic conditions and alterations in human lifestyles, the occurrence of MetS is on the rise and has become one of the most pressing health concerns in modern society [2]. The incidence of MetS globally is continuously increasing, with an estimated one-third to two-thirds of adults suffering from it [3]. A meta-analysis of 15 representative countries in the Asia-Pacific region also found that the prevalence of MetS is as high as 20% and continues to increase annually [4]. The U.S. Centers for Disease Control and Prevention reported in 2017 that approximately one-third of Americans are affected by MetS [5]. According to the World Health Organization (WHO), the prevalence of metabolic syndrome in middle-aged and elderly individuals ranges from 11% to 43% (median 21%), while the National Cholesterol Education Program (NCEP) reports it to be between 23% to 55% (median 31%) [6,7]. Furthermore, MetS in middle-aged and elderly individuals is a substantial risk factor for cardiovascular morbidity and mortality [8]. Therefore, there is an urgent need for preventative measures to reduce the incidence of MetS among middle-aged and elderly individuals.
The inflammatory response is a multifaceted biological process that involves several cytokines, such as interleukin-6 (IL-6), tumor necrosis factor- α (TNF- α ), and C-reactive protein (CRP). If this process becomes dysregulated, it can result in chronic inflammation and tissue damage [9]. Current research demonstrates that MetS arises from chronic low-grade inflammation in the body, which is strongly linked to the incidence of both diabetes and hypertension [10,11,12]. Extensive studies have highlighted the significant impact of diet on the body’s inflammatory response and have demonstrated that inadequate dietary habits can contribute to the development of MetS [13,14,15,16]. For example, dietary components such as unsaturated fatty acids, fiber, and vitamins have been shown to reduce body inflammation and lower the risk of MetS, while supersaturated fatty acids, high cholesterol, and high carbohydrate intake may contribute to inflammation and increase the likelihood of MetS [17]. As a result, Examining the risk factors for MetS can be achieved by considering the inflammatory effects of one’s diet. The Dietary Inflammatory Index (DII), which was created by Shivappa et al. in 2009 and refined through a scoring system in 2013, is a credible measure of the extent to which dietary factors contribute to an individual’s inflammatory response [18]. Currently, DII has been increasingly employed in the fields of nutrition and epidemiological research as a method of evaluating an individual’s dietary-related inflammation. In this matter, numerous studies have assessed the correlation between MetS and DII in various demographic groups. A positive association has been observed in Spain between DII and body fat percentage, waist circumference, and body mass index (BMI) among individuals with an increased risk of cardiovascular disease [19]. Research on police officers has revealed that those with a higher DII are more likely to have MetS [20]. A prospective study in France on the SU.VI.MAX population showed that the DII is positively correlated with the risk of MetS [21]. The same results were still found in studies in Iran and South Korea [22,23].
While previous studies have shown the correlation between DII and MetS in both general and specific populations, research on middle-aged and elderly groups is limited. Additionally, some studies have failed to establish a definitive link between DII and MetS [24,25]. The decline in basal metabolic rate (BMR) that commonly occurs in middle-aged and older adults, in combination with inadequate physical activity, weakened immune system function, and prolonged medication use, increases the risk of developing MetS in this population [26,27]. Furthermore, with the aging of the American population, it is estimated that nearly 50% of individuals over 60 years of age have MetS, making it a major challenge for public health care systems [28]. Thus, it is important to evaluate the relationship between DII and MetS in elderly Americans and the relationship between specific components of MetS.

2. Materials and Methods

2.1. Data Sources

The data utilized in this study were acquired from the National Health and Nutrition Examination Survey (NHANES), which was accessed for free on the official NHANES website. The NHANES survey is conducted biannually by the Centers for Disease Control and Prevention (CDC) and employs multi-stage complex sampling to ensure a representative sample [29].

2.2. Study Population

The study sample was selected based on the following criteria: inclusion criteria included all samples aged ≥45 years in the NHANES database from 2007–2016, while exclusion criteria were (1) pregnant women, (2) participants with serious diseases (e.g., malignancy, coronary heart disease, etc.), and (3) participants with missing data on key indicators such as demographic, laboratory and questionnaire data, and dietary data. A total of 3843 study subjects were included after applying these criteria. (Figure 1 and supplementary file).

2.3. Measurement

2.3.1. Definition of MetS

According to the National Cholesterol Education Program (NCEP) guidelines from 2005, MetS is defined as having three or more of the following characteristics [5,30]: (1) Raised fasting blood glucose (Raised FG), fasting blood glucose >5.6 mmol/L (100 mg/dL) or drug treatment for raised blood glucose; (2) Reduced HDL cholesterol (Reduced HDL-C), HDL cholesterol <1.3 mmol/L (50 mg/dL) in women, <1.0 mmol/L (40 mg/dL) in men or drug treatment for reduced HDL cholesterol; (3) Raised blood triglycerides (Raised TG), blood triglycerides >1.7 mmol/L (150 mg/dL) or drug treatment for raised triglycerides; (4) Raised waist circumference (Raised WC), waist circumference >88 cm (women) or >102 cm (men); (5) Raised blood pressure (Raised BP), blood pressure >130/85 mmHg or drug treatment for raised blood pressure.

2.3.2. Dietary Inflammation Index Calculation

The dietary database’s nutrient information of the first day was selected as the accurate nutrient intake for each participant in the study. A total of 27 nutrients, including total energy, fat, saturated fat, monounsaturated fat, polyunsaturated fat, ALA (octadecatrienoic acid), EPA (eicosapentaenoic acid), DHA (docosahexaenoic acid), docosapentaenoic acid, linoleic acid (octadecadienoic acid), arachidonic acid (eicosatetraenoic acid), protein, carbohydrates, fiber, alcohol, cholesterol, niacin, vitamin (A, B 1 , B 2 , B 6 , B 12 , C, D, E), iron, zinc, selenium, magnesium, folic acid, beta carotene, and caffeine were considered in the calculation of the DII. The n-3 fatty acids were equal to the sum of ALA, EPA, DHA, and docosapentaenoic acid, and the n-6 fatty acids were equal to the sum of linoleic acid and arachidonic acid.
The average and variability of each nutrient were obtained from the World Diet Standards Library and used to transform the Z-scores of the respective nutrient into Z-transformed scores. The Z-transform fraction of each nutrient was then transformed into percentiles, and the resulting distribution for each nutrient level was made symmetrical around 0 (zero) by doubling the transformed percentiles and subtracting 1. The bounds of this distribution are −1 (maximum anti-inflammatory) and +1 (maximum pro-inflammatory). The final DII score is obtained by multiplying each nutrient level by its respective corresponding inflammatory fraction and summing the result [18].

2.3.3. Other Covariates

Covariates considered in the analysis included sociodemographic factors such as gender (male/female), age (middle-aged: 45 to 60, elderly: over 60), race (Hispanic White, Non-Hispanic White, Mexican American, Non-Hispanic Black, and other races), education (less than 9th grade, 9th to 11th grade, high school graduate/GED or equivalent, some college or AA degree, college graduate or higher), and poverty level expressed as poverty–income ratio (PIR) (PIR ≤ 1, 1 < PIR ≤ 1.99, 2 < PIR ≤ 3.99, and PIR > 4). In addition, behavioral factors including smoking status (defined as smoking more than 100 cigarettes, with all others considered non-smokers), alcohol consumption (defined as having more than 12 drinks in the past year, with all others considered non-drinkers), and sedentary behavior (total sedentary time of less than 3 h, 3 to 5.9 h, and 6 h or more) were considered.

2.4. Statistical Analysis Methods

Data description and statistical analysis in this study utilized complex weighting through the use of the “survey” package in R software. Categorical variables were described by their frequency and proportion, and differences were tested using the Rao–Scott χ 2 test. Quantitative data that were not normally distributed were described as median (25th percentile, 75th percentile), and group comparisons were performed using the Wilcoxon rank sum test. A binary logistic regression model was employed to examine the relationship between quartile groupings of the DII and MetS and its components, and the median of each quartile grouping of DII was used to complete the linear trend test associated with the disease. To explore potential differences based on gender and age, separate analyses were conducted for gender (males and females) and age (45∼60 years and over 60 years). The association between DII levels and the risk of MetS and its components was further explored through restricted cubic spline analysis with four knots at the 5th, 35th, 65th, and 95th percentiles of its distribution. A generalized linear model was employed to evaluate the relationship between DII levels and biochemical indicators of MetS, while quantile regression models were utilized to assess the strength of the relationship between DII levels and MetS-related parameters at different quantile points of each indicator.
Potential confounding variables were taken into account throughout the analysis, including gender (male or female), age (45∼60 or over 60), race (Hispanic White, Non-Hispanic White, Non-Hispanic Black, Mexican American, and other races), education (less than 9th grade, 9∼11th grade, high school graduate/GED, some college or AA degree, or college graduate or higher), poverty–income ratio (PIR less than or equal to 1, 1 less than PIR less than or equal to 1.99, 2 less than PIR less than or equal to 3.99, or PIR greater than 4), smoking (yes or no), drinking (yes or no), and sedentary behavior (total sedentary time of less than 3 h, 3 to 5.9 h, or 6 h or more). The analysis was conducted using R 4.2.0, and a p-value less than 0.05 was considered statistically significant.

3. Results

3.1. The DII Quartile Feature Distribution of the Study Object

The DII scores range from −5.20 to 4.66, with negative scores indicating anti-inflammatory diets and positive scores indicating pro-inflammatory diets. The first quartile, which ranges from −5.20 to −1.00, represents the group with the most anti-inflammatory diets. The second quartile, which ranges from −1.00 to 0.63, represents the group with moderately anti-inflammatory diets. The third quartile, which ranges from 0.63 to 2.05, represents the group with moderately pro-inflammatory diets. The fourth quartile, which ranges from 2.05 to 4.66, represents the group with the most pro-inflammatory diets.
Table 1 presents the distribution of the characteristics of the study participants based on their quartile of the DII score. Variables including age, smoking habits, sedentary behavior, TC, LDL-C, HDL-C, FPG, and waist circumference showed significant differences in their distribution among the DII quartile groups. The proportion of women increased progressively from a minimum of 50.9% in the Q1 group to the Q4 group, while the proportion of men declined from 38.34% in the same group. The proportion of individuals with a college degree or higher education decreased in the DII grouping, with the highest proportion of high school graduates or equivalent being in the Q4 group. The distribution of non-Hispanic black people in the DII group was most pronounced. Participants with a poverty–income ratio (PIR) less than 2 showed an increasing trend in the DII grouping, while those with a PIR greater than 2 showed the opposite. The proportion of individuals with the MetS also increased, with 38.25% of the Q4 group being affected. Furthermore, participants in the Q4 group had higher levels of SBP, DBP, TG, OGTT, and BMI values.

3.2. Association of Dietary Inflammatory Levels with MetS

The binary logistic regression model was employed to evaluate the correlation between DII and the presence of MetS as a whole, as well as its individual components (Figure 2). After controlling for sociodemographic factors and behavioral information, the group with high DII(Q4) was found to increase the risk of MetS by 1.339 times (OR Q 4 : Q 1 = 1.339, 95% CI: 1.013, 1.769, p-trend = 0.018), compared to the group with the lowest DII (Q1). When considering the components of MetS individually, the results showed that DII was statistically significant in its association with reduced HDL-C (OR Q 4 : Q 1 = 1.499, 95% CI: 1.005, 2.234, p-trend = 0.048) and raised FG (OR Q 4 : Q 1 = 1.432, 95% CI: 1.095, 1.873, p-trend = 0.010). Furthermore, the spline regression models demonstrated a significant increase in the risk of both MetS and its individual components with increasing DII scores (Figure 3).
To evaluate the potential influence of sex and age on our study, we conducted separate analyses stratified by gender and age (Figure 4 and Figure 5). Our results showed that, after controlling for other factors, the risk of MetS (OR Q 4 : Q 1 = 1.783; 95% CI: 1.171, 2.715; p-trend = 0.008) and its component of raised FG (OR Q 4 : Q 1 = 1.591; 95% CI: 1.045, 2.422; p-trend = 0.049), increased significantly in the high DII group (Q4) compared to the low DII group (Q1) among females. Meanwhile, the high dietary inflammation group also exhibited an elevated risk of reduced HDL-C (OR Q 4 : Q 1 = 1.732; 95% CI: 1.013, 2.962; p-trend = 0.047) among males. In the analysis of different age groups, the highest DII group was associated with an increased risk of MetS (OR Q 4 : Q 1 = 1.415; 95% CI: 1.039, 1.928; p-trend = 0.045), and its component of raised TG (OR Q 4 : Q 1 = 1.6; 95% CI: 1.118, 2.29; p-trend = 0.011), raised BP (OR Q 4 : Q 1 = 1.617; 95% CI: 1.09, 2.399; p-trend = 0.038), and raised FG (OR Q 4 : Q 1 = 1.59; 95% CI: 1.181, 2.139; p-trend = 0.014) in individuals aged over 60 years. However, in the 45∼60 age group, a significant association between high DII and reduced HDL-C (OR Q 4 : Q 1 = 1.704; 95% CI: 1.074, 2.705; p-trend = 0.033) was observed, while such an association was not observed in the group aged >60 years.

3.3. Association of DII Levels with Biochemical Markers of MetS

A generalized linear regression model was utilized to establish the correlation between levels of DII and indicators of MetS (Figure 6). Upon adjusting for variables such as age, gender, ethnicity, education, poverty–income ratio, smoking, alcohol consumption, and sedentary time, a statistically significant difference (p < 0.05) was found between the DII level and the remaining five metrics, excluding SBP, DBP, OGTT, TC, and LDL-C. Specifically, there was a positive correlation between DII levels and BMI ( β = 0.258, p = 0.001), FPG ( β = 0.019, p = 0.049), TG ( β = 2.043, p = 0.013), waistline ( β = 0.580, p = 0.002), and a negative correlation with HDL-C ( β = −0.672, p = 0.003). Additionally, the quantile regression model analyzed the association between MetS-related indicators and various quantile points of the DII level (Figure 7). The results were consistent with those of the generalized linear regression, showing a gradually increasing trend in the positive correlation between DII levels and BMI, waistline, FPG, and TG, and a gradual decrease in the correlation with HDL-C. The findings suggest that a pro-inflammatory diet increases the risk of obesity, hyperglycemia, and hyperlipidemia.

4. Discussion

In this study, we investigated the association between DII scores and MetS in middle-aged and older adults using data from the National Health and Nutrition Examination Survey (NHANES). Following adjustment for confounding factors, our study revealed that individuals aged 45 years or older, with high DII scores, were at a greater risk of developing MetS, decreased levels of HDL-C, and elevated FG compared to those with low DII scores. Gender and age-specific analyses revealed that women with high DII scores had a greater likelihood of experiencing MetS and raised FG, while men with high DII scores had an increased risk of reduced HDL-C. Furthermore, DII scores were linked to a greater likelihood of MetS and its constituent components of raised TG, raised BP, and raised FG in individuals aged 60 years and above. Finally, we employed generalized linear regression and quantile regression models to evaluate the correlation between DII and MetS-related markers and found a notable relationship between DII scores and BMI, waist circumference, FPG, TG, and HDL-C levels. Our findings support the use of DII scores as a dietary assessment tool and underscore the importance of dietary interventions in preventing MetS.
Elevated levels of inflammatory markers, including IL-6, C-reactive protein (CRP), and TNF- α , have been associated with higher levels of DII in the bloodstream [31,32]. This suggests that reducing the DII through dietary intervention may prevent the development and progression of MetS. Our findings are consistent with other studies conducted in various countries, including Columbia, Mexico, South Korea, Croatia, China, and Iran [23,33,34,35,36,37]. Previous research has shown that a Western-style diet high in fat, salt, and refined carbohydrates increases the risk of inflammatory diseases, including MetS [38,39]. In contrast, diets with low DII scores, such as the DASH and Mediterranean diets, have been associated with improved health [40,41,42,43]. In addition, various dietary patterns with different DII scores can also affect the composition of the intestinal microbiota and ultimately impact overall health [44]. For instance, a pro-inflammatory diet such as the Western diet is associated with lower gut microbial diversity and reduced probiotic abundance, while an anti-inflammatory diet such as the Mediterranean diet has been found to have higher gut microbial diversity and increased probiotic abundance [45,46]. Therefore, the results indicate that middle-aged and older individuals can prevent and mitigate the onset and progression of MetS by improving their dietary structure. Our study also found that middle-aged and older women with higher DII levels were more prone to MetS and hyperglycemia. This is due to the fact that women are more prone to weight gain physiologically, particularly during menopause when levels of estrogen in the body decrease, leading to increased fat storage. With age, the distribution of fat within the female body also changes, shifting from the lower limbs to the abdominal area, causing an increase in abdominal fat [47,48]. Additionally, insulin sensitivity decreases in women during menopause, leading to weakened glucose control and a slowing of glucose metabolism [49,50]. Studies have also found that women tend to be less physically active than men and are also more susceptible to overeating due to lifestyle stress and emotional factors [51]. These factors all increase the risk of women developing MetS. To prevent the onset of MetS, women need to pay more attention to preventing it by maintaining healthy dietary and exercise habits. Moreover, the study found that high DII scores were associated with an increased risk of MetS in individuals aged 60 years and older, but not in those aged 45 to 60 years. Aging is also associated with chronic low-grade inflammation, which may exacerbate the inflammatory effects of a high DII diet, leading to a higher risk of developing MetS [52,53]. In contrast, individuals aged 45 to 60 years may be more able to control their dietary habits and physical activity levels, which could help to counteract the negative effects of a high DII diet. However, it is important to note that our study only found an association between DII and MetS, and further research is needed to determine causality and to explore the mechanisms behind the observed association.
In addition, in our investigation, we sought to examine the relationship between indicators of MetS and DII scores through both linear and quantile regression models. Results showed that among all MetS indicators, DII was significantly linked with BMI, FPG, TG, HDL-C, and waistline. In a study conducted in East Azerbaijan, Iran, it was found that individuals in the highest quartile of DII scores had significantly elevated levels of FBG (OR = 2.56, 95% CI = 1.00 to 7.05) [23]. Meanwhile, in a Dutch cohort study, it was observed that increasing DII levels were associated with elevated fasting blood glucose concentrations ( β = 0.09, 95% CI = 0.01, 0.17) [20]. However, a study conducted in Luxembourg found no correlation between DII and serum glucose levels [19]. The disparities in the correlation between DII scores and MetS indicators may stem from disparities in the study population and variations in the techniques employed for dietary evaluation and the foods taken into account for DII calculation. Studies have shown that high DII diets contain large amounts of processed foods, which are rich in saturated fatty acids and trans-fatty acids and high in sugar, affecting the secretion of gut hormones and digestive enzymes within the intestine, suppressing insulin secretion, and leading to an increase in fat storage in the body [54]. In addition, high DII diets cause an imbalance in gut microbiota, leading to an overgrowth of pathogenic bacteria and the production of large amounts of endotoxins, which enter the circulation system and cause low-grade inflammation, leading to metabolic disturbance in the body [55]. Therefore, a low DII diet pattern is positively impactful on the health of middle-aged and elderly individuals.
To our knowledge, this is the first study to investigate the association between DII scores and MetS-related markers in middle-aged and elderly individuals aged 45 or above in the United States. Despite some limitations, such as its cross-sectional design that impedes any causal inference, possible errors in dietary recall and covariate data due to self-reported information, the likelihood of unknown confounding factors that are not present in the NHANES database, and the potential lack of generalizability to other countries due to inter-country differences, the study results are based on a large, diverse sample of non-institutionalized participants and may be generalizable to the overall U.S. population.
In conclusion, this study’s findings offer evidence for promoting healthy dietary practices among middle-aged and elderly individuals to deter the onset of MetS and provide a theoretical foundation for disease prevention and treatment that encompasses dietary management, specifically for those in the pre-disease stage. Further, in-depth prospective studies are required to establish a causal relationship between an inflammatory diet and the development of MetS and to examine the role of dietary composition in the etiology of the disease. Ultimately, strategies should be devised for the widespread prevention and treatment of chronic illnesses.

5. Conclusions

In the United States, among middle-aged and elderly individuals, a high DII score was found to be correlated with the presence of MetS, reduced HDL-C, and raised FG. Therefore, dietary recommendations for the middle-aged and elderly in the United States should focus on reducing the DII by choosing foods rich in antioxidants, dietary fiber, and unsaturated fatty acids, such as vegetables, fruits, nuts, whole grains, deep-sea fish, flaxseeds, etc. Additionally, they should reduce their intake of processed foods, such as high sugar, high salt, high-fat foods, and carbohydrates with high calorie levels.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15081857/s1, The supplementary file contains the raw data for this study.

Author Contributions

Conceptualization, Y.Z., Q.Z. and X.T.; methodology, Q.Z., Z.T. and Y.Z.; software, Q.Z. and Z.S.; validation, Q.Z., X.T., L.S. and H.P.M.; data curation, Q.Z.; writing—original draft preparation, Q.Z. and Z.S.; writing—review and editing, L.S. and H.P.M.; visualization, Z.T. and Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (project number: 82173497) and the Fundamental Research Funds for the Central Universities (Lanzhou, China; project numbers: lzujbky-2021-ct20).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the National Centre for Health Statistics.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original data for the conclusion of this paper can be obtained from the National Health and Nutrition Examination Survey (NHANES) website (https://www.cdc.gov/nchs/nhanes/index.htm (accessed on 6 June 2022)) or by contacting the author.

Acknowledgments

We express our gratitude to the team at the National Center for Health Statistics of the Centers for Disease Control for their efforts in designing, collecting, and compiling the NHANES data and developing the public database.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Heindel, J.J.; Blumberg, B.; Cave, M.; Machtinger, R.; Mantovani, A.; Mendez, M.A.; Nadal, A.; Palanza, P.; Panzica, G.; Sargis, R.; et al. Metabolism disrupting chemicals and metabolic disorders. Reprod. Toxicol. 2017, 68, 3–33. [Google Scholar] [CrossRef] [PubMed]
  2. Xu, X.; Zeng, J.; Yang, W.; Dong, T.; Zhang, X.; Cheng, S.; Zhou, X.; Zhou, M.; Niu, L.; Yi, G.; et al. Prevalence of metabolic syndrome among the adult population in western China and the association with socioeconomic and individual factors: Four cross-sectional studies. BMJ Open 2022, 12, e052457. [Google Scholar] [CrossRef] [PubMed]
  3. O’Neill, S.; O’Driscoll, L. Metabolic syndrome: A closer look at the growing epidemic and its associated pathologies. Obes. Rev. 2015, 16, 1–12. [Google Scholar] [CrossRef] [PubMed]
  4. Ranasinghe, P.; Mathangasinghe, Y.; Jayawardena, R.; Hills, A.; Misra, A. Prevalence and trends of metabolic syndrome among adults in the asia-pacific region: A systematic review. BMC Public Health 2017, 17, 101. [Google Scholar] [CrossRef]
  5. Saklayen, M.G. The global epidemic of the metabolic syndrome. Curr. Hypertens. Rep. 2018, 20, 12. [Google Scholar] [CrossRef] [PubMed]
  6. Denys, K.; Cankurtaran, M.; Janssens, W.; Petrovic, M. Metabolic syndrome in the elderly: An overview of the evidence. Acta Clin. Belg. 2009, 64, 23–34. [Google Scholar] [CrossRef]
  7. Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z. The metabolic syndrome. Lancet 2005, 365, 1415–1428. [Google Scholar] [CrossRef]
  8. Mazloomzadeh, S.; Zarandi, F.K.; Shoghli, A.; Dinmohammadi, H. Metabolic syndrome, its components and mortality: A population-based study. Med. J. Islam. Repub. Iran 2019, 33, 11. [Google Scholar] [CrossRef]
  9. Xiong, P.; Zhang, F.; Liu, F.; Zhao, J.; Huang, X.; Luo, D.; Guo, J. Metaflammation in glucolipid metabolic disorders: Pathogenesis and treatment. Biomed. Pharmacother. 2023, 161, 114545. [Google Scholar] [CrossRef]
  10. León-Pedroza, J.I.; González-Tapia, L.A.; del Olmo-Gil, E.; Castellanos-Rodríguez, D.; Escobedo, G.; González-Chávez, A. Low-grade systemic inflammation and the development of metabolic diseases: From the molecular evidence to the clinical practice. Cirugía Cir. 2015, 83, 543–551. [Google Scholar] [CrossRef]
  11. Reddy, P.; Lent-Schochet, D.; Ramakrishnan, N.; McLaughlin, M.; Jialal, I. Metabolic syndrome is an inflammatory disorder: A conspiracy between adipose tissue and phagocytes. Clin. Chim. Acta 2019, 496, 35–44. [Google Scholar] [CrossRef]
  12. Maiorino, M.; Bellastella, G.; Giugliano, D.; Esposito, K. From inflammation to sexual dysfunctions: A journey through diabetes, obesity, and metabolic syndrome. J. Endocrinol. Investig. 2018, 41, 1249–1258. [Google Scholar] [CrossRef]
  13. Diwan, B.; Sharma, R. Nutritional components as mitigators of cellular senescence in organismal aging: A comprehensive review. Food Sci. Biotechnol. 2022, 31, 1089–1109. [Google Scholar] [CrossRef]
  14. Laouali, N.; Mancini, F.R.; Hajji-Louati, M.; El Fatouhi, D.; Balkau, B.; Boutron-Ruault, M.C.; Bonnet, F.; Fagherazzi, G. Dietary inflammatory index and type 2 diabetes risk in a prospective cohort of 70,991 women followed for 20 years: The mediating role of BMI. Diabetologia 2019, 62, 2222–2232. [Google Scholar] [CrossRef]
  15. Pérez-Martínez, P.; Mikhailidis, D.P.; Athyros, V.G.; Bullo, M.; Couture, P.; Covas, M.I.; de Koning, L.; Delgado-Lista, J.; Diaz-Lopez, A.; Drevon, C.A.; et al. Lifestyle recommendations for the prevention and management of metabolic syndrome: An international panel recommendation. Nutr. Rev. 2017, 75, 307–326. [Google Scholar] [CrossRef]
  16. Johansson-Persson, A.; Ulmius, M.; Cloetens, L.; Karhu, T.; Herzig, K.H.; Önning, G. A high intake of dietary fiber influences C-reactive protein and fibrinogen, but not glucose and lipid metabolism, in mildly hypercholesterolemic subjects. Eur. J. Nutr. 2014, 53, 39–48. [Google Scholar] [CrossRef]
  17. Khalil, M.; Shanmugam, H.; Abdallah, H.; John Britto, J.S.; Galerati, I.; Gómez-Ambrosi, J.; Frühbeck, G.; Portincasa, P. The Potential of the Mediterranean Diet to Improve Mitochondrial Function in Experimental Models of Obesity and Metabolic Syndrome. Nutrients 2022, 14, 3112. [Google Scholar] [CrossRef]
  18. Shivappa, N.; Steck, S.E.; Hurley, T.G.; Hussey, J.R.; Hébert, J.R. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014, 17, 1689–1696. [Google Scholar] [CrossRef]
  19. Ruiz-Canela, M.; Zazpe, I.; Shivappa, N.; Hébert, J.R.; Sanchez-Tainta, A.; Corella, D.; Salas-Salvado, J.; Fito, M.; Lamuela-Raventós, R.M.; Rekondo, J.; et al. Dietary inflammatory index and anthropometric measures of obesity in a population sample at high cardiovascular risk from the PREDIMED (PREvencion con DIeta MEDiterranea) trial. Br. J. Nutr. 2015, 113, 984–995. [Google Scholar] [CrossRef]
  20. Wirth, M.; Burch, J.; Shivappa, N.; Violanti, J.M.; Burchfiel, C.M.; Fekedulegn, D.; Andrew, M.E.; Hartley, T.A.; Miller, D.B.; Mnatsakanova, A.; et al. Association of a dietary inflammatory index with inflammatory indices and the metabolic syndrome among police officers. J. Occup. Environ. Med. Coll. Occup. Environ. Med. 2014, 56, 986. [Google Scholar] [CrossRef]
  21. Neufcourt, L.; Assmann, K.; Fezeu, L.; Touvier, M.; Graffouillère, L.; Shivappa, N.; Hébert, J.; Wirth, M.; Hercberg, S.; Galan, P.; et al. Prospective association between the dietary inflammatory index and metabolic syndrome: Findings from the SU. VI. MAX study. Nutr. Metab. Cardiovasc. Dis. 2015, 25, 988–996. [Google Scholar] [CrossRef] [PubMed]
  22. Kim, H.Y.; Lee, J.; Kim, J. Association between dietary inflammatory index and metabolic syndrome in the general Korean population. Nutrients 2018, 10, 648. [Google Scholar] [CrossRef] [PubMed]
  23. Nikniaz, L.; Nikniaz, Z.; Shivappa, N.; Hébert, J.R. The association between dietary inflammatory index and metabolic syndrome components in Iranian adults. Prim. Care Diabetes 2018, 12, 467–472. [Google Scholar] [CrossRef] [PubMed]
  24. Ghorabi, S.; Esteghamati, A.; Azam, K.; Daneshzad, E.; Sadeghi, O.; Salari-Moghaddam, A.; Azadbakht, L.; Djafarian, K. Association between dietary inflammatory index and components of metabolic syndrome. J. Cardiovasc. Thorac. Res. 2020, 12, 27. [Google Scholar] [CrossRef] [PubMed]
  25. Namazi, N.; Larijani, B.; Azadbakht, L. Dietary inflammatory index and its association with the risk of cardiovascular diseases, metabolic syndrome, and mortality: A systematic review and meta-analysis. Horm. Metab. Res. 2018, 50, 345–358. [Google Scholar] [CrossRef]
  26. Furman, D.; Campisi, J.; Verdin, E.; Carrera-Bastos, P.; Targ, S.; Franceschi, C.; Ferrucci, L.; Gilroy, D.W.; Fasano, A.; Miller, G.W.; et al. Chronic inflammation in the etiology of disease across the life span. Nat. Med. 2019, 25, 1822–1832. [Google Scholar] [CrossRef]
  27. Miles, L. Physical activity and health. Nutr. Bull. 2007, 32, 314–363. [Google Scholar] [CrossRef]
  28. Aguilar, M.; Bhuket, T.; Torres, S.; Liu, B.; Wong, R.J. Prevalence of the metabolic syndrome in the United States, 2003–2012. JAMA 2015, 313, 1973–1974. [Google Scholar] [CrossRef]
  29. Ahluwalia, N.; Dwyer, J.; Terry, A.; Moshfegh, A.; Johnson, C. Update on NHANES dietary data: Focus on collection, release, analytical considerations, and uses to inform public policy. Adv. Nutr. 2016, 7, 121–134. [Google Scholar] [CrossRef]
  30. Zafar, U.; Khaliq, S.; Ahmad, H.U.; Manzoor, S.; Lone, K.P. Metabolic syndrome: An update on diagnostic criteria, pathogenesis, and genetic links. Hormones 2018, 17, 299–313. [Google Scholar] [CrossRef]
  31. Kurklu, N.S.; Torun, N.K.; Kucukcetin, I.O.; Akyol, A. Is there a relationship between the dietary inflammatory index and metabolic syndrome among adolescents? J. Pediatr. Endocrinol. Metab. 2020, 33, 495–502. [Google Scholar] [CrossRef]
  32. Galland, L. Diet and inflammation. Nutr. Clin. Pract. 2010, 25, 634–640. [Google Scholar] [CrossRef]
  33. Camargo-Ramos, C.M.; Correa-Bautista, J.E.; Correa-Rodríguez, M.; Ramírez-Vélez, R. Dietary inflammatory index and cardiometabolic risk parameters in overweight and sedentary subjects. Int. J. Environ. Res. Public Health 2017, 14, 1104. [Google Scholar] [CrossRef]
  34. Canto-Osorio, F.; Denova-Gutierrez, E.; Sánchez-Romero, L.M.; Salmerón, J.; Barrientos-Gutierrez, T. Dietary inflammatory index and metabolic syndrome in Mexican adult population. Am. J. Clin. Nutr. 2020, 112, 373–380. [Google Scholar] [CrossRef]
  35. Kim, M.; Sohn, C. Analysis of dietary inflammatory index of metabolic syndrome in Korean: Data from the health examinee cohort (2012–2014). Korean J. Hum. Ecol. 2016, 25, 823–834. [Google Scholar] [CrossRef]
  36. Kenđel Jovanović, G.; Pavičić Žeželj, S.; Klobučar Majanović, S.; Mrakovcic-Sutic, I.; Šutić, I. Metabolic syndrome and its association with the Dietary Inflammatory Index (DII)® in a Croatian working population. J. Hum. Nutr. Diet. 2020, 33, 128–137. [Google Scholar] [CrossRef]
  37. Li, R.; Zhan, W.; Huang, X.; Zhang, Z.; Zhou, M.; Bao, W.; Li, Q.; Ma, Y. Association of dietary inflammatory index and metabolic syndrome in the elderly over 55 years in Northern China. Br. J. Nutr. 2022, 128, 1082–1089. [Google Scholar] [CrossRef]
  38. Uddin, M.H.; Rumman, M. Advancing age, influence of dietary sugars, salts, and fats on chronic diseases and metabolic disorders. In Dietary Sugar, Salt and Fat in Human Health; Elsevier: Amsterdam, The Netherlands, 2020; pp. 25–65. [Google Scholar]
  39. Syauqy, A.; Hsu, C.Y.; Rau, H.H.; Chao, J.C.J. Association of dietary patterns with components of metabolic syndrome and inflammation among middle-aged and older adults with metabolic syndrome in Taiwan. Nutrients 2018, 10, 143. [Google Scholar] [CrossRef]
  40. Asadi, Z.; Ghaffarian Zirak, R.; Yaghooti Khorasani, M.; Saedi, M.; Parizadeh, S.M.; Jafarzadeh-Esfehani, R.; Khorramruz, F.; Jandari, S.; Mohammadi-Bajgiran, M.; Zare-Feyzabadi, R.; et al. Dietary inflammatory index is associated with healthy eating index, alternative healthy eating index, and dietary patterns among Iranian adults. J. Clin. Lab. Anal. 2020, 34, e23523. [Google Scholar] [CrossRef]
  41. Wirth, M.D.; Hébert, J.R.; Shivappa, N.; Hand, G.A.; Hurley, T.G.; Drenowatz, C.; McMahon, D.; Shook, R.P.; Blair, S.N. Anti-inflammatory Dietary Inflammatory Index scores are associated with healthier scores on other dietary indices. Nutr. Res. 2016, 36, 214–219. [Google Scholar] [CrossRef]
  42. Kastorini, C.M.; Milionis, H.J.; Esposito, K.; Giugliano, D.; Goudevenos, J.A.; Panagiotakos, D.B. The effect of Mediterranean diet on metabolic syndrome and its components: A meta-analysis of 50 studies and 534,906 individuals. J. Am. Coll. Cardiol. 2011, 57, 1299–1313. [Google Scholar] [CrossRef] [PubMed]
  43. Phillips, C.M.; Chen, L.W.; Heude, B.; Bernard, J.Y.; Harvey, N.C.; Duijts, L.; Mensink-Bout, S.M.; Polanska, K.; Mancano, G.; Suderman, M.; et al. Dietary inflammatory index and non-communicable disease risk: A narrative review. Nutrients 2019, 11, 1873. [Google Scholar] [CrossRef] [PubMed]
  44. Strasser, B.; Wolters, M.; Weyh, C.; Krüger, K.; Ticinesi, A. The effects of lifestyle and diet on gut microbiota composition, inflammation and muscle performance in our aging society. Nutrients 2021, 13, 2045. [Google Scholar] [CrossRef] [PubMed]
  45. Shivappa, N.; Wirth, M.D.; Murphy, E.A.; Hurley, T.G.; Hébert, J.R. Association between the Dietary Inflammatory Index (DII) and urinary enterolignans and C-reactive protein from the National Health and Nutrition Examination Survey-2003–2008. Eur. J. Nutr. 2019, 58, 797–805. [Google Scholar] [CrossRef]
  46. Singh, R.K.; Chang, H.W.; Yan, D.; Lee, K.M.; Ucmak, D.; Wong, K.; Abrouk, M.; Farahnik, B.; Nakamura, M.; Zhu, T.H.; et al. Influence of diet on the gut microbiome and implications for human health. J. Transl. Med. 2017, 15, 73. [Google Scholar] [CrossRef]
  47. Ko, S.H.; Jung, Y. Energy metabolism changes and dysregulated lipid metabolism in postmenopausal women. Nutrients 2021, 13, 4556. [Google Scholar] [CrossRef]
  48. Monteleone, P.; Mascagni, G.; Giannini, A.; Genazzani, A.R.; Simoncini, T. Symptoms of menopause—global prevalence, physiology and implications. Nat. Rev. Endocrinol. 2018, 14, 199–215. [Google Scholar] [CrossRef]
  49. Slopien, R.; Wender-Ozegowska, E.; Rogowicz-Frontczak, A.; Meczekalski, B.; Zozulinska-Ziolkiewicz, D.; Jaremek, J.D.; Cano, A.; Chedraui, P.; Goulis, D.G.; Lopes, P.; et al. Menopause and diabetes: EMAS clinical guide. Maturitas 2018, 117, 6–10. [Google Scholar] [CrossRef]
  50. Mumusoglu, S.; Yildiz, B.O. Metabolic syndrome during menopause. Curr. Vasc. Pharmacol. 2019, 17, 595–603. [Google Scholar] [CrossRef]
  51. Elran Barak, R.; Shuval, K.; Li, Q.; Oetjen, R.; Drope, J.; Yaroch, A.L.; Fennis, B.M.; Harding, M. Emotional eating in adults: The role of sociodemographics, lifestyle behaviors, and self-regulation—findings from a US National Study. Int. J. Environ. Res. Public Health 2021, 18, 1744. [Google Scholar] [CrossRef]
  52. Falzone, L.; Libra, M.; Polesel, J. Dietary inflammatory index in ageing and longevity. Centen. Ex. Posit. Biol. 2019, 71–86. [Google Scholar]
  53. Orchard, T.S.; Lohman, M.C.; Kopec, R.E. Inflammatory potential of diet and aging. In Diet, Inflammation, and Health; Elsevier: Amsterdam, The Netherlands, 2022; pp. 565–607. [Google Scholar]
  54. Bays, H.; Kothari, S.N.; Azagury, D.E.; Morton, J.M.; Nguyen, N.T.; Jones, P.H.; Jacobson, T.A.; Cohen, D.E.; Orringer, C.; Westman, E.C.; et al. Lipids and bariatric procedures part 2 of 2: Scientific statement from the American Society for Metabolic and Bariatric Surgery (ASMBS), the National Lipid Association (NLA), and Obesity Medicine Association (OMA). Surg. Obes. Relat. Dis. 2016, 12, 468–495. [Google Scholar] [CrossRef]
  55. Zheng, J.; Hoffman, K.L.; Chen, J.S.; Shivappa, N.; Sood, A.; Browman, G.J.; Dirba, D.D.; Hanash, S.; Wei, P.; Hebert, J.R.; et al. Dietary inflammatory potential in relation to the gut microbiome: Results from a cross-sectional study. Br. J. Nutr. 2020, 124, 931–942. [Google Scholar] [CrossRef]
Nutrients 15 01857 g001 550
Figure 1. Flow diagram of participants in the study.
Figure 1. Flow diagram of participants in the study.
Nutrients 15 01857 g001
Nutrients 15 01857 g002 550
Figure 2. Association of DII with MetS and its components. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.
Figure 2. Association of DII with MetS and its components. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.
Nutrients 15 01857 g002
Nutrients 15 01857 g003 550
Figure 3. The restricted cubic spline for the associations of DII with MetS and its components. (A) Metabolic syndrome; (B) Raised FG; (C) Raused TG; (D) Raised WC; (E) Reduced HDL-C; (F) Raised BP. Knots were placed at the 5th, 35th, 65th, and 95th percentiles of the DII distribution. Results were adjusted for age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.
Figure 3. The restricted cubic spline for the associations of DII with MetS and its components. (A) Metabolic syndrome; (B) Raised FG; (C) Raused TG; (D) Raised WC; (E) Reduced HDL-C; (F) Raised BP. Knots were placed at the 5th, 35th, 65th, and 95th percentiles of the DII distribution. Results were adjusted for age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.
Nutrients 15 01857 g003
Nutrients 15 01857 g004 550
Figure 4. Subgroup analysis of association between DII and MetS as a whole and its components among different gender groups. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Q1 [−5.21, −1.33), Q2 [−1.33, 0.10), Q3 [0.10, 1.52), Q4 [1.52, 4.43) for male; Q1 [−4.71, −0.54), Q2 [−0.54, 1.10), Q3 [1.10, 2.37), Q4 [2.37, 4.66) for female.
Figure 4. Subgroup analysis of association between DII and MetS as a whole and its components among different gender groups. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Q1 [−5.21, −1.33), Q2 [−1.33, 0.10), Q3 [0.10, 1.52), Q4 [1.52, 4.43) for male; Q1 [−4.71, −0.54), Q2 [−0.54, 1.10), Q3 [1.10, 2.37), Q4 [2.37, 4.66) for female.
Nutrients 15 01857 g004
Nutrients 15 01857 g005 550
Figure 5. Subgroup analysis of association between DII and MetS as a whole and its components among different age groups. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Q1 [−5.21, −1.10), Q2 [−1.10, 0.60), Q3 [0.60, 2.09), Q4 [2.09, 4.23) for the age range of 45 to 60 years; Q1 [−4.71, −0.89), Q2 [−0.89, 0.66), Q3 [0.66, 1.99), Q4 [1.99, 4.66) for age over 60 years.
Figure 5. Subgroup analysis of association between DII and MetS as a whole and its components among different age groups. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Q1 [−5.21, −1.10), Q2 [−1.10, 0.60), Q3 [0.60, 2.09), Q4 [2.09, 4.23) for the age range of 45 to 60 years; Q1 [−4.71, −0.89), Q2 [−0.89, 0.66), Q3 [0.66, 1.99), Q4 [1.99, 4.66) for age over 60 years.
Nutrients 15 01857 g005
Nutrients 15 01857 g006 550
Figure 6. Generalized linear regression analysis of DII levels and indicators of MetS. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, total triglycerides; FPG, fasting blood sugar; OGTT, oral glucose tolerance test. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Green: No adjustments were made; Red: Adjusted for confounding factors.
Figure 6. Generalized linear regression analysis of DII levels and indicators of MetS. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, total triglycerides; FPG, fasting blood sugar; OGTT, oral glucose tolerance test. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior. Green: No adjustments were made; Red: Adjusted for confounding factors.
Nutrients 15 01857 g006
Nutrients 15 01857 g007 550
Figure 7. Quantile regression estimation coefficient plot of DII level versus biochemical index of MetS. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.
Figure 7. Quantile regression estimation coefficient plot of DII level versus biochemical index of MetS. Adjusted for confounding factors such as age, gender, race, education, poverty–income ratio, smoking, alcohol consumption, and sedentary behavior.
Nutrients 15 01857 g007
Table
Table 1. DII quartile characteristic distribution of the basic situation of the research subjects [n (%) or M(P25, P75)].
Table 1. DII quartile characteristic distribution of the basic situation of the research subjects [n (%) or M(P25, P75)].
CharacteristicsAll (n = 3843)Q1 (n = 817)Q2 (n = 936)Q3 (n = 992)Q4 (n = 1098)p-Value
Range−5.20∼4.66−5.20∼−1.00−1.00∼0.630.63∼2.052.05∼4.66
Sex
Male1887 (49.1)506 (61.93)502 (53.63)458 (46.17)421 (38.34)<0.001
Female1956 (50.9)311 (38.07)434 (46.37)534 (53.83)677 (61.66)
Age
45∼601777 (46.24)403 (49.33)449 (47.97)447 (45.06)478 (43.53)0.4
≥602066 (53.76)414 (50.67)487 (52.03)545 (54.94)620 (56.47)
Education
Less than 9th grade407 (10.59)62 (1.58)91 (9.74)111 (11.19)143 (13.02)<0.001
9∼11th grade499 (12.98)67 (8.20)97 (10.36)136 (13.71)199 (18.12)
High school graduate881 (22.92)134 (21.22)199 (21.26)235 (23.69)313 (28.51)
Some college or AA degree1069 (27.82)233 (30.23)283 (30.23)284 (28.63)269 (24.50)
College graduate or above987 (25.68)321 (28.42)266 (28.41)226 (22.78)174 (15.85)
Race
Mexican American406 (11.97)96 (11.75)132 (14.10)112 (11.29)120 (10.93)0.006
Other Hispanic407 (10.59)73 (12.70)92 (9.83)126 (12.70)116 (10.56)
Non-Hispanic White2039 (53.06)459 (52.12)509 (54.38)517 (52.12)554 (50.46)
Non-Hispanic Black660 (17.17)103 (17.44)134 (14.32)173 (17.44)250 (22.77)
Other Race277 (7.21)86 (6.45)69 (7.37)64 (6.45)58 (5.28)
PIR
<1619 (16.11)80 (9.79)128 (13.68)180 (18.15)231 (21.04)<0.001
1∼1.99966 (25.14)183 (22.40)200 (21.37)242 (24.40)341 (31.06)
2∼3.991051 (27.35)175 (21.42)270 (28.85)292 (29.44)314 (28.60)
≥41207 (31.41)379 (46.39)338 (36.11)278 (28.02)212 (19.31)
Smoking
Yes1917 (49.88)398 (48.71)466 (49.79)493 (49.70)560 (51.00)0.8
No1926 (50.12)419 (51.29)470 (50.21)499 (50.30)538 (49.00)
Alcohol
Yes2772 (72.13)643 (78.70)722 (77.14)713 (71.88)694 (63.21)<0.001
No1071 (27.87)174 (21.30)214 (22.86)279 (28.12)404 (36.79)
Sedentary
<3 h575 (14.96)109 (13.34)137 (14.64)157 (15.83)172 (15.66)0.2
3∼5.9 h1421 (36.98)283 (34.64)359 (38.35)382 (38.51)397 (36.16)
≥6 h1847 (48.06)425 (52.02)440 (47.01)453 (45.67)529 (48.18)
Metabolic syndrome
Non-Mets2558 (66.56)581 (71.11)658 (70.30)641 (64.62)678 (61.75)0.005
Mets1285 (33.44)236 (28.89)278 (29.70)351 (35.38)420 (38.25)
SBP (mmHg)124 (114,136)122 (114,134)122 (112,134)124 (114,138)124 (114,136)0.04
DBP (mmHg)70 (64,78)70 (62,76)70 (64,76)72 (64, 78)72 (64,78)0.01
TC (mg/dL)202 (176,230)199 (173,228)202 (176,226)203 (177,234)203 (178,232)0.3
TG (mg/dL)106 (75,151)103 (72,149)105 (71,144)105 (76,158)111 (81,155)0.03
LDL-C (mg/dL)120 (98,145)119 (94,140)118 (97,145)120 (98,147)122 (103, 146)0.5
HDL-C (mg/dL)54 (45,67)53 (45,65)55 (46,68)54 (46,66)53 (44,67)0.4
FPG (mmol/L)5.61 (5.27,6.05)5.60 (5.27,6.00)5.60 (5.25,5.99)5.66 (5.22,6.05)5.66 (5.32,6.10)0.3
OGTT (mmol/L)6.43 (5.16,8.10)6.27 (5.11,7.88)6.27 (5.16,7.88)6.38 (5.11,8.05)6.71 (5.32,8.54)0.002
BMI (kg/m 2 )27.6 (24.4,31.6)26.9 (24.0,30.8)27.3 (24.3,31.3)27.8 (24.7,31.9)28.1 (24.9,32.8)0.009
Waistline (cm)98.9 (90.1,108.9)98.5 (89.9,108.2)98.3 (90.3,108.4)98.4 (89.0,108.3)100.0 (90.9,110.4)0.4
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; TC, total cholesterol; TG, total triglycerides; FPG, fasting blood sugar; OGTT, oral glucose tolerance test; PIR, poverty–income ratio. Data are presented as n (%) for categorical data, medians (interquartile ranges) for nonparametrically distributed data. Variables with p-values less than 0.05 were highlighted in bold font.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhao, Q.; Tan, X.; Su, Z.; Manzi, H.P.; Su, L.; Tang, Z.; Zhang, Y. The Relationship between the Dietary Inflammatory Index (DII) and Metabolic Syndrome (MetS) in Middle-Aged and Elderly Individuals in the United States. Nutrients 2023, 15, 1857. https://doi.org/10.3390/nu15081857

AMA Style

Zhao Q, Tan X, Su Z, Manzi HP, Su L, Tang Z, Zhang Y. The Relationship between the Dietary Inflammatory Index (DII) and Metabolic Syndrome (MetS) in Middle-Aged and Elderly Individuals in the United States. Nutrients. 2023; 15(8):1857. https://doi.org/10.3390/nu15081857

Chicago/Turabian Style

Zhao, Qilong, Xinyue Tan, Zhenni Su, Habasi Patrick Manzi, Li Su, Zhenchuang Tang, and Ying Zhang. 2023. "The Relationship between the Dietary Inflammatory Index (DII) and Metabolic Syndrome (MetS) in Middle-Aged and Elderly Individuals in the United States" Nutrients 15, no. 8: 1857. https://doi.org/10.3390/nu15081857

APA Style

Zhao, Q., Tan, X., Su, Z., Manzi, H. P., Su, L., Tang, Z., & Zhang, Y. (2023). The Relationship between the Dietary Inflammatory Index (DII) and Metabolic Syndrome (MetS) in Middle-Aged and Elderly Individuals in the United States. Nutrients, 15(8), 1857. https://doi.org/10.3390/nu15081857

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

Article Metrics

Back to TopTop