Abstract
Background and Aim
Research on the correlation between Dietary Inflammation Index (DII) and stroke is limited. Patients with high body mass index (BMI), as a high-risk group for stroke, require attention. Therefore, we aimed to examine the interactive effects of dietary inflammation and BMI on the risk of stroke among adults in the United States.
Methods and Results
Overall, 9,384 participants were included in this study. The exposure variable was the DII, which was calculated based on the overall inflammatory effect score, and the outcome was stroke. Overall, there was a positive relationship between DII (as a continuous variable) and stroke. Increased level of DII was significantly associated with increased risk of stroke (odds ratio [OR]: 1.21, 95% CI: 1.06–1.38), which was enhanced by higher BMI (OR: 1.35, 95% CI: 1.15–1.58) with significant additive interactions. There was a significant secondary interaction of copresence of BMI ≥25 kg/m2, systolic blood pressure (SBP) ≥140 mmHg, and high DII for increased stroke, with a further increase in OR to 1.41 (1.19–1.67).
Conclusions
This cross-sectional study shows that the relationship between higher DII levels and the significant increase in stroke prevalence was further amplified in patients with SBP ≥140 mmHg and higher BMI.
Key words: Dietary inflammatory index, stroke, US adult, BMI, interactive effects
Introduction
Diet is well known to play a vital role in regulating chronic inflammation (1, 2, 3). In previous studies, pro-inflammatory and anti-inflammatory diets were often defined by dietary patterns, including the traditional Western and Mediterranean diets. People have conducted much research on dietary patterns because they better describe the actual eating habits of a population, and they take into account the possible interaction between nutrients. The traditional Western diet is rich in red meat, refined grains, butter, processed meat, high-fat dairy products, sweets, desserts, potatoes, eggs, hydrogenated fats, and sugary drinks, which are known to increase the levels of inflammatory markers, whereas the Mediterranean diet, which is rich in olive oil, nuts, fruits, vegetables, whole grains, and fish, can reduce the levels of inflammatory markers (4, 5). It is difficult to quantify the level of inflammation using the above-mentioned dietary patterns; therefore, researchers at the University of South Carolina proposed the concept of the dietary inflammation index (DII) (6). This is a new and verified tool that can quantify the inflammatory potential of a diet and is used to evaluate the overall pro-inflammatory and anti-inflammatory characteristics of individual diet structures. The evaluation includes 45 dietary components, of which 9 components (energy, carbohydrate, protein, total fat, cholesterol, saturated fatty acids, trans fatty acids, iron, and vitamin B12) have pro-inflammatory characteristics, and the other 36 components have anti-inflammatory characteristics. In addition, there is abundant evidence that DII is closely associated with inflammatory markers (7, 8, 9, 10). A pro-inflammatory diet indicates that the DII score of the overall diet structure is greater than zero, and vice versa.
DII has been widely used in clinical research in recent years; cancer is the most common disease (11, 12, 13), with increased research focus on cardiovascular diseases (CVD) (14, 15). A systematic review and meta-analysis reported that a higher DII score significantly increased the risk of CVD by 35%(16). Numerous studies have shown that DII scores are positively correlated with CVD events (4, 5, 17). Stroke, the second leading cause of death due to CVD in the world, is also the primary cause of disability (18, 19). Moreover, patients with high body mass index (BMI) are a high-risk group for stroke; hence, the BMI of patients is especially worthy of attention. Numerous studies have shown that an elevated BMI significantly increases the risk of stroke (20, 21, 22). Paying attention to dietary indicators in this population can prevent the occurrence of diseases; however, no research has been focused on this population. Consequently, this study aimed to explore the interactive effects of the DII and BMI on the risk of stroke among adults in the United States and further evaluate whether there are possible effect modifiers.
Methods
Study Design and Population
This cross-sectional study was performed using aggregated data from 2011 to 2018 from the National Health and Nutrition Examination Survey (NHANES) conducted by the Centers for Disease Control and Prevention (CDC). The project adopted a stratified multistage sampling method and is an ongoing repeated cross-sectional study, which assesses the health and nutritional status of adults and children in the United States (23). The NHANES began in the 1960s, mainly including interviews and physical examinations and has been conducted every 2 years since 1999; approximately 5,000 participants are selected for data collection every year (23, 24, 25). Before the investigation, all participants provided written informed consent. This survey was approved by the Ethics Review Board of the National Center for Health Statistics. More detailed information can be obtained from https://www.cdc.gov/nchs/nhanes/index.htm.
The inclusion criteria for this cross-sectional study were adults aged >18 years who were not pregnant. The exclusion criteria were as follows: 1. Lack of DII data; 2. Lack of stroke data; and 3. Lack of BMI data. Ultimately, 9,384 participants were included in the data analysis (Figure S1).
Data Collection
Trainers collected sociodemographic and lifestyle information, including age, sex, race, poverty income ratio, smoking, disease history, and medication history through standardized family interview questionnaires. Anthropometric indicators included height, weight, and blood pressure (BP). Body height and weight were collected without shoes and thick clothes and measured using a medical scale. BMI (in kg/m2) was calculated as weight divided by height squared. According to the standard BP measurement protocol recommended by the American Heart Association, a mercury sphygmomanometer was used to measure BP. Three BP readings were obtained continuously from the same arm. This study defined systolic and diastolic blood pressure (SBP and DBP) as the average of three BP measurements.
All study participants were asked to provide an overnight rapid venous blood sample. The DcX800 method was used to measure the albumin concentration as a bichromatic digital endpoint method. Using Roche Hitachi 717 and 912 analyzers (Hitachi, Tokyo, Japan), total cholesterol (TC) was measured by enzymatic method. Creatine levels were assessed by automated biochemical profiling (Beckman Synchron LX20; Beckman Coulter Inc., Fullerton, California, United States). According to Roche Hitachi 911 analyzer, fasting plasma glucose (FPG) was measured using the hexokinase method. The formula used for the estimated glomerular filtration rate (eGFR) was the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation (26). Further details of data collection can be found at https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.
Definition of the DII and stroke
The exposure variable in this study was the DII. The DII score was based on the pro-inflammatory and anti-inflammatory properties of 45 different food ingredients used to assess the impact of diet on inflammation. This index was developed based on a systematic review of 2,000 published research articles (6). DII scores contain positive and negative values; positive values represent pro-inflammatory diets, while negative values correspond to anti-inflammatory diets (6). The DII calculation formula is as follows: Z score = [(daily mean intake — global daily mean intake)/standard deviation]; Zscore1 = Z score → (converted to a percentile score)× 2 — 1; DII = ΣZscore1×the inflammatory effect score of each dietary component. We collected meal data through a single 24-hour meal recall, while 27 nutrients were used for the calculation of the DII score including carbohydrate; protein; total fat; dietary fiber; cholesterol; saturated, monounsaturated, and polyunsaturated fatty acids; ω-3 and ω-6 polyunsaturated fatty acids; vitamins A, B1, B2, B3 (niacin), B6, B12, C, D, and E; folic acid; alcohol; beta-carotene; caffeine; iron; magnesium; zinc; and selenium (6, 8). Importantly, even if fewer than 30 nutrients were suitable for calculating the amount of DII, it was still available (6, 27). A stroke was the outcome variable; the research collected the stroke events by questionnaire and asked the following questions: “Has a doctor or other health professional ever told you that you had a stroke?” We categorized participants who reported stroke based on the more recent event. The self-reported measures of stroke are reasonably accurate in the United States general population and have been used in prior epidemiological studies using data from NHANES (28, 29).
Statistical Analysis
Baseline characteristics are presented as mean ± SD for continuous variables and proportions for categorical variables according to DII quartiles among different BMI groups. Analysis of variance or chi-square tests was used to compare the significant differences in population characteristics. Multivariable logistic regression models were used to examine the association between DII and the prevalence of stroke among all participants and different BMI groups (BMI <25 vs. BMI ≥25 kg/m2). We built sequential regression models as follows: Model 1 was a crude model; model 2 was adjusted for age, sex, race, poverty income ratio, BMI, SBP, and DBP; and model 3 was adjusted for age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking status, FPG, TC, HDL, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, and glucose-lowering drugs. We used a generalized additive model and a fitted smoothing curve (penalized spline method) to assess the dose-response association between the DII score and the prevalence of stroke among all participants and different BMI groups. In addition, possible modifications of the association between the DII score and the prevalence of stroke among the different BMI groups were assessed for the following variables: sex, age, race, current smoking, SBP, and DBP. Potential interactions were examined by including interaction terms in logistic regression models.
A two-tailed P <0.05 was regarded as statistically significant. Statistical packages R (http://www.r-project.org) and Empower (R) (www.empowerstats.com) were used to perform all statistical analyses.
Figure 1.
Association between DII and the prevalence of stroke
A linear association between DII and the prevalence of stroke was found (P < 0.05). The solid line and dashed lines represent the estimated values and their corresponding 95% confidence interval. Adjustment factors included age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking, FPG, TC, HDL, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, glucose-lowering drugs.
Results
Study participants and baseline characteristics
Of the 9,384 participants, 4,582 (48.83%) were men and 4802 (51.17%) were women. The average age of the participants was 49.79 ± 17.54 years. The mean value of DII among participants was 1.53 ±1.90. The number of patients with stroke was 373 (3.97%).
Tables 1 and 2 show the sociodemographic characteristics of the study population according to DII quartiles of both BMI groups. We found no statistical differences in age, BMI, DBP, FPG, TC, HDL, antihypertensive drugs, or lipid-lowering drugs among participants with BMI <25 kg/m2 in the quartiles of DII. There were fewer males, more non-Hispanic whites, fewer smokers, and more patients with diabetes in the group with a higher BMI. The DII score was positively correlated with SBP but negatively correlated with the poverty income ratio and eGFR (Table 1). Table 2 shows that most of the characteristics evaluated differed between the DII quartiles, including lifestyle (current smoking), BMI, race, SBP, DBP, TC, and medication history (antihypertensive drugs, lipoprotein-lowering drugs, and glucose-lowering drugs)(p<0.001). Participants with a higher DII were female, non-Hispanic white, and current smokers; they had higher BMI and SBP; higher use of antihypertensive, lipoprotein-lowering, and glucose-lowering drugs; and lower poverty income ratio, TC, DBP, and eGFR values. Moreover, participants in the DII quartiles showed no statistical differences in age, HDL-C, or FPG.
Table 1.
Baseline characteristics of study participants among BMI <25 kg/m2
| Variablea |
DII Quartiles |
P value |
|||
|---|---|---|---|---|---|
| Q1(<−0.03) | Q2(−0.03 to<1.53) | Q3(1.53 to<2.93) | Q4(≥2.93) | ||
| Participants | 681 | 680 | 681 | 681 | |
| Males, N(%) | 395 (58.00%) | 350 (51.47%) | 307 (45.08%) | 251 (36.86%) | <0.001 |
| Age,year | 47.69 ± 17.85 | 46.20 ± 18.51 | 47.25 ± 19.12 | 46.61 ± 19.57 | 0.472 |
| BMI, kg/m2 | 22.18 ± 2.02 | 22.09 ± 1.98 | 22.13 ± 2.02 | 22.03 ± 2.12 | 0.593 |
| Race | <0.001 | ||||
| Non-Hispanic White, N(%) | 259 (38.03%) | 269 (39.56%) | 271 (39.79%) | 264 (38.77%) | |
| Non-Hispanic Black, N(%) | 100 (14.68%) | 101 (14.85%) | 123 (18.06%) | 168 (24.67%) | |
| Mexican American, N(%) | 49 (7.20%) | 47 (6.91%) | 58 (8.52%) | 41 (6.02%) | |
| Other Hispanic, N(%) | 54 (7.93%) | 62 (9.12%) | 52 (7.64%) | 56 (8.22%) | |
| Other races, N(%) | 219 (32.16%) | 201 (29.56%) | 177 (25.99%) | 152 (22.32%) | |
| Current smoking, N(%) | 137 (20.15%) | 145 (21.35%) | 120 (17.65%) | 104 (15.27%) | <0.001 |
| SBP, mmHg | 119.31 ± 18.53 | 119.63 ± 18.83 | 121.21 ± 18.72 | 122.54 ± 22.06 | 0.009 |
| DBP, mmHg | 68.36 ± 10.96 | 69.11 ± 10.74 | 68.24 ± 11.45 | 68.54 ± 12.13 | 0.511 |
| Poverty income ratio | 2.92 ± 1.75 | 2.66 ± 1.65 | 2.50 ± 1.62 | 2.25 ± 1.58 | <0.001 |
| FPG, mg/dL | 101.77 ± 29.76 | 101.11 ± 27.19 | 100.51 ± 23.01 | 102.67 ± 32.25 | 0.56 |
| TC, mg/dL | 187.19 ± 38.92 | 186.21 ± 41.01 | 186.68 ± 39.50 | 185.98 ± 41.09 | 0.953 |
| HDL-C, mg/dL | 61.55 ± 17.52 | 61.59 ± 16.55 | 62.36 ± 17.81 | 60.30 ± 18.22 | 0.211 |
| eGFR, mL/min/1.73 m2 | 97.33 ± 21.10 | 100.42 ± 22.18 | 97.10 ± 23.92 | 97.31 ± 25.99 | 0.031 |
| stroke | 20 (2.94%) | 19 (2.79%) | 21 (3.08%) | 29 (4.26%) | 0.406 |
| Antihypertensive drugs | 18 (2.64%) | 14 (2.06%) | 16 (2.35%) | 23 (3.38%) | 0.267 |
| Lipoprotein-lowering drugs | 100 (14.68%) | 88 (12.94%) | 107 (15.71%) | 115 (16.89%) | 0.198 |
| Glucose-lowering drugs | 31 (4.55%) | 33 (4.85%) | 52 (7.64%) | 45 (6.61%) | 0.043 |
a. Data are presented as number (%) or mean±standard deviation; Abbreviation: DII, Dietary inflammatory index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG: fasting plasma glucose;TC total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate
Table 2.
Baseline characteristics of study participants among BMI ≥25 kg/m2
| Variablea |
DII Quartiles |
P value | |||
|---|---|---|---|---|---|
| Q1(<0.31) | Q2(0.31 to<1.86) | Q3(1.86 to<3.09) | Q4(≥3.09) | ||
| Participants | 1665 | 1665 | 1665 | 1666 | |
| Males, N(%) | 1043 (62.64%) | 880 (52.85%) | 757 (45.47%) | 599 (35.95%) | <0.001 |
| Age,year | 50.36 ± 15.93 | 50.67 ± 16.80 | 50.93 ± 16.83 | 51.88 ± 17.86 | 0.055 |
| BMI, kg/m2 | 31.47 ± 6.13 | 32.15 ± 6.15 | 32.36 ± 6.25 | 33.04 ± 6.76 | <0.001 |
| Race | <0.001 | ||||
| Non-Hispanic White, N(%) | 634 (38.08%) | 665 (39.94%) | 615 (36.94%) | 655 (39.32%) | |
| Non-Hispanic Black, N(%) | 310 (18.62%) | 350 (21.02%) | 443 (26.61%) | 482 (28.93%) | |
| Mexican American, N(%) | 315 (18.92%) | 274 (16.46%) | 247 (14.83%) | 214 (12.85%) | |
| Other Hispanic, N(%) | 193 (11.59%) | 190 (11.41%) | 197 (11.83%) | 184 (11.04%) | |
| Other races, N(%) | 213 (12.79%) | 186 (11.17%) | 163 (9.79%) | 131 (7.86%) | |
| Current smoking, N(%) | 222 (13.33%) | 271 (16.29%) | 354 (21.29%) | 390 (23.44%) | <0.001 |
| SBP, mmHg | 124.69 ± 17.12 | 125.21 ± 17.56 | 126.21 ± 18.28 | 126.58 ± 19.90 | 0.011 |
| DBP, mmHg | 71.77 ± 11.56 | 71.06 ± 11.57 | 71.26 ± 12.05 | 69.95 ± 12.55 | <0.001 |
| Poverty income ratio | 2.76 ± 1.70 | 2.52 ± 1.63 | 2.31 ± 1.54 | 2.08 ± 1.48 | <0.001 |
| FPG, mg/dL | 113.31 ± 37.88 | 113.90 ± 36.89 | 115.12 ± 40.18 | 115.37 ± 40.19 | 0.378 |
| TC, mg/dL | 190.43 ± 42.18 | 190.70 ± 41.21 | 193.28 ± 43.79 | 188.99 ± 41.97 | 0.038 |
| HDL-C, mg/dL | 50.31 ± 14.19 | 51.45 ± 15.49 | 51.45 ± 14.86 | 50.84 ± 14.21 | 0.081 |
| eGFR, mL/min/1.73 m2 | 94.98 ± 20.82 | 94.03 ± 22.94 | 93.10 ± 24.35 | 92.06 ± 26.38 | 0.004 |
| stroke | 38 (2.28%) | 58 (3.48%) | 86 (5.17%) | 102 (6.12%) | <0.001 |
| Antihypertensive drugs | 108 (6.49%) | 129 (7.75%) | 148 (8.89%) | 128 (7.68%) | 0.005 |
| Lipoprotein-lowering drugs | 398 (23.90%) | 408 (24.50%) | 414 (24.86%) | 425 (25.51%) | 0.034 |
| Glucose-lowering drugs | 251 (15.08%) | 256 (15.38%) | 283 (17.00%) | 293 (17.59%) | 0.021 |
a. Data are presented as number (%) or meantstandard deviation; Abbreviation: DII, Dietary inflammatory index; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG: fasting plasma glucose;TC total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration rate
Associations between DII and stroke
Figure 2 shows the dose-response association between the DII score and the prevalence of stroke. Overall, there was a positive relationship between DII (as a continuous variable) and stroke. When DII levels were examined as a continuous variable in model 3, for one SD increase in the DII, adjusted odds ratio (OR) was 1.21 (95% CI: 1.06–1.38). Consistently, when DII was assessed as quartiles, compared with participants in quartiles 1 (<0.19), the adjusted ORs (95% CI) for stroke in quartile 2 (0.19 to <1.77), quartile 3 (1.77 to <3.06), and quartile 4 (≥3.06) were 1.13 (0.77–1.65), 1.25 (0.87–1.80), 1.46 (1.02–2.09), respectively (Table 2). The highest DII score was associated with an increased prevalence of stroke. However, there were different relationships between the DII score and stroke in different BMI groups. As shown in Figure 3, DII had no relation with stroke in patients with BMI <25, while BMI showed a linear positive correlation with stroke in patients with BMI ≥25. Multivariate logistic regression results were also consistent with the fitting curve of Figure 3. Particularly, among participants with BMI <25, the prevalence of stroke did not increase with increased DII level and the decline was not significant (OR: 0.96, 95% CI: 0.75–1.65). However, among participants with BMI ≥25, there was a significant positive correlation between DII and stroke (OR: 1.35, 95% CI: 1.15–1.58). Furthermore, the prevalence of stroke increased gradually with an increase in the DII score (P for trend=0.0007). There was an interaction between BMI and DII (P for interaction=0.020).
Figure 2.
Association between DII and the prevalence of stroke among different BMI groups
A linear association between DII and the prevalence of stroke among different BMI group was found (P < 0.05). The solid line and dashed line represent the estimated values among the participants of BMI <25, kg/m2 and BMI≥25, kg/m2, respectively. Adjustment factors included age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking, FPG, TC, HDL, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, glucose-lowering drugs.
Figure 3.
Stratified Analyses by Potential Modifiers of the Association between DII and the prevalence of stroke among different BMI groups*
(A) BMI<25,kg/m2 (B) BMI≥25, kg/m2.*Each subgroup analysis adjusted for age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking, FPG, TC, HDL, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, glucose-lowering drugs except for the stratifying variable.
Table 3.
Relative odds of stroke according to DII in different models1
| DII | Events (%) | Stroke, OR (95%CI), P value | ||
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | ||
| All participants | ||||
| Per SD increment | 373(3.97%) | 1.46 (1.30, 1.64) | 1.30 (1.14, 1.47) | 1.21 (1.06, 1.38) |
| Quartile | ||||
| Q1(<0.19) | 57 (2.43%) | 1 | 1 | 1 |
| Q2(0.19 to<1.77) | 80(3.41%) | 1.42 (1.00, 2.00) | 1.19 (0.83, 1.72) | 1.13 (0.77, 1.65) |
| Q3(1.77 to<3.05) | 105(4.48%) | 1.88 (1.36, 2.61) | 1.49 (1.05, 2.11) | 1.25 (0.87, 1.80) |
| Q4(>3.05) | 131(5.58%) | 2.38 (1.73, 3.26) | 1.73 (1.22, 2.44) | 1.45 (1.02, 2.08) |
| P for trend | <0.0001 | 0.0007 | 0.029 | |
| BMI<25, kg/m2 | ||||
| Per SD increment | 89(3.27%) | 1.21 (0.97, 1.50) | 1.07 (0.84, 1.36) | 0.96 (0.75, 1.24) |
| Quartile | ||||
| Q1(<−0.03) | 20 (2.94%) | 1 | 1 | 1 |
| Q2(−0.03 to<1.53) | 19 (2.79%) | 0.95 (0.50, 1.80) | 0.79 (0.40, 1.58) | 0.79 (0.39, 1.62) |
| Q3(1.53 to<2.93) | 21 (3.08%) | 1.05 (0.56, 1.96) | 0.84 (0.43, 1.63) | 0.71 (0.35, 1.42) |
| Q4(≥2.93) | 29 (4.26%) | 1.47 (0.82, 2.63) | 1.09 (0.57, 2.08) | 0.85 (0.43, 1.70) |
| P for trend | 0.164 | 0.734 | 0.620 | |
| BMI≥25, kg/m2 | ||||
| Per SD increment | 284(4.26%) | 1.55 (1.35, 1.77) | 1.41 (1.22, 1.64) | 1.35 (1.15, 1.58) |
| Quartile | ||||
| Q1(<0.31) | 38 (2.28%) | 1 | 1 | 1 |
| Q2(0.31 to<1.86) | 58 (3.48%) | 1.55 (1.02, 2.34) | 1.29 (0.83, 2.01) | 1.25 (0.79, 1.99) |
| Q3(1.86 to<3.09) | 86 (5.17%) | 2.33 (1.58, 3.44) | 1.92 (1.26, 2.92) | 1.73 (1.12, 2.69) |
| Q4(≥3.09) | 102 (6.12%) | 2.79 (1.91, 4.08) | 2.13 (1.41, 3.22) | 1.94 (1.26, 2.98) |
| P for trend | <0.001 | <0.001 | 0.0007 | |
| P value for interaction* | 0.046 | 0.051 | 0.020 | |
1. Values are ORs (95% CIs) unless otherwise indicated. DII, Dietary inflammatoiy index; Model 1 was adjusted for none; Model 2 was adjusted for age, sex, race, poverty income ratio, BMI, SBP, DBP; Model3 was adjusted for age, sex, race, poverty income ratio, BMI, SBP, DBP, current smoking, FPG, TC, HDL, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, glucose-lowering drugs; *P value for interaction test: 2-way interaction of BMI and DII (continuous) on stroke.
Subgroup analyses
Stratified analyses were performed among different BMI groups (BMI <25 vs. BMI ≥25, kg/m2) to assess the association between DII and the prevalence of stroke in various subgroups (Figure 3). In the two BMI groups, sex, age, race, smoking status, and DBP did not influence the association between the DII score and the prevalence of stroke (P for all interactions>0.05). However, as shown in Figure 3B, there was a more significant positive correlation between DII and stroke in patients who were overweight and in the SBP ≥140 mmHg group (OR: 1.41, 95% CI: 1.19–1.67).
Discussion
This study aimed to explore the association between DII and stroke among adults from the NHANES. We observed that the participants from the United States with higher DII scores had a 47% higher prevalence of stroke, which was attenuated after multivariable adjustment (21% higher prevalence of stroke). There was a significant relationship between higher BMI, DII, and stroke. Furthermore, our stratified analysis in different BMI groups showed that the relationship between higher DII levels and the significant increase in stroke prevalence was further amplified in patients with SBP ≥140 mmHg and in those who were overweight.
Previous studies have mostly discussed the relationship between DII and CVD (4, 5, 14, 15, 17). Ana et al. conducted a prospective cohort study (4) that included 7,216 participants with no cardiovascular history and high cardiovascular risk. The study aimed to explore the relationship between DII and the incidence of CVD. The results showed that a pro-inflammatory diet was associated with a high risk of cardiovascular clinical events (HR, 1.22; 95% CI: 1.06–1.40). In the Mashhad stroke and heart atherosclerosis disorder research population, Asadi evaluated the relationship between DII and CVD, and the results showed that there was no correlation between DII and CVD (15). The relationship between high DII levels and CVD was discussed in a large Korean Genome and Epidemiology Study Health Examination (KoGES_HEXA), and the results showed that only men with high DII scores were closely related to the increased risk of CVD events; furthermore, compared with the lowest group of DII, the participants in the highest group of DII had a significantly increased risk of stroke by 2.06 times (HRQuintile 5 vs. 1 2.06; 95% CI: 1.07–3.98) (14). Ramallal (17) and Bodén (5) showed that with an increase in DII score, the risk of CVD events increases significantly.
Previous research on DII and stroke is consistent with our current research, though we observed more interesting findings. No sex-related differences were observed in the present study. Higher DII scores significantly increased the prevalence of stroke in both men and women. Furthermore, from the subgroup analysis, we found that there was no difference in the incidence of stroke between men and women; therefore, sex did not change the relationship between them. However, in participants who were overweight or obese, the positive correlation between a higher DII score and the prevalence of stroke was more significant. According to Ruiz-Canela et al., obesity is closely related to DII, and after controlling for the influence of inflammation, DII significantly increases with BMI (30). As BMI increases, DII scores also increase significantly; therefore, the prevalence of stroke also increases significantly. It is possible that BMI is an intermediary factor between diet, low-grade chronic inflammation, and inflammation-related diseases, and is not just a confounding factor. Body fat produces a metabolic environment that promotes inflammation, and BMI is positively correlated with inflammatory markers (31, 32). Moreover, this study found that in patients who were overweight with SBP ≥140 mmHg, the significant positive correlation between DII and prevalence of stroke can be amplified. Several previous studies have shown that hypertension is closely related to the risk of stroke, especially SBP, which is an independent predictor of stroke risk (33, 34, 35, 36). Thus, we found that the combination of high SBP, BMI, and DII significantly increased the risk of stroke. Baseline characteristics showed that the association between higher poverty and lower DII persisted across both weight groups (Tables 1 and 2). Higher poverty appears to be associated with having more basic foods that are healthier than those that are more expensive. The association between fewer glucose-lowering drugs and a lower DII score is also worth mentioning; this indicates that a lower DII also has value here.
The mechanism of interaction between DII and stroke may be owing to the relationship between inflammatory reactions and atherosclerosis, because atherosclerosis is the main cause of CVD, including stroke, and it is known as an inflammatory process affecting the medium and large blood vessels (37, 38). Moreover, inflammation involves immune cells and may affect the cardiovascular system through oxidative stress and foam cell formation (39, 40).
This study had some limitations that need to be addressed. First, this was a cross-sectional study, and it was essentially impossible to determine a causal relationship. This finding warrants further investigation. Second, dietary information was obtained from one-time recall surveys, which may not accurately reflect an individual's usual diet. However, some studies have shown that 24-hour recalls of daily dietary intake may be sufficient for evaluation (41). Third, the number of participants included a large group (≥18 years old). Furthermore, this study was conducted in an American population, and the results cannot be extended to other populations.
Conclusions
This study showed that the DII score may have a dose-response relationship with stroke in adults in the United States. Notably, the prevalence of stroke increased with a rise in the DII. Moreover, the positive correlation between the DII score and stroke was more significant in patients who were overweight and obese with elevated SBP. This study suggests that attention should be paid to the dietary structure of patients in clinical practice to prevent the occurrence and development of stroke, especially in patients who are overweight and have an elevated SBP (people at a high risk of stroke). The Mediterranean diet is an anti-inflammatory diet (low DII), and there are multiple evidence that the Mediterranean diet can reduce the risk of stroke (42, 43, 44); hence, we recommend the Mediterranean diet for people at high risk of stroke.
Funding
This work was supported by grants from Science and technology project of Education Department of Jiangxi Province (No. GJJ210193).
Acknowledgments
A special thanks to all of the NHANES participants who freely gave their time to make this and other studies possible.
Authors' Contributions
Yumeng Shi participated in literature search, study design, data collection, data analysis, data interpretation, and wrote the manuscript. Yumeng Shi and Wei Zhouconceived of the study, and participated in its design, coordination, data collection and analysis. Wei Zhou participated in study design and provided the critical revision. All authors read and approved the final manuscript.
Conflict of Interests
The authors declare that they have no conflict of interest.
Availability of data and materials
Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.
Ethical approval
All procedures performed in studies involving human participants were following the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Electronic Supplementary Material
Supplementary material is available in the online version of this article at https://doi.org/10.1007/s12603-023-1904-1.
Supplementary material, approximately 98.3 KB.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material, approximately 98.3 KB.
Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.