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Dietary inflammatory index and acute myocardial infarction in a large Italian case–control study

Dietary inflammatory index and acute myocardial infarction in a large Italian case–control study Abstract Background Diet and inflammation have been implicated to play a role in the incidence of acute myocardial infarction (AMI). Methods In this Italian case–control study conducted between 1995 and 2003, we explored the association between the dietary inflammatory index (DIITM) and AMI. Cases were 760 patients, below age 79 years, with a first episode of nonfatal AMI and controls were 682 patients admitted to hospital for acute conditions unrelated to diet. The DII was computed based on dietary intake assessed using a reproducible and validated 78-item food frequency questionnaire. Odds ratios (OR) were estimated through logistic regression models adjusting for age, sex, total energy intake, tobacco, body mass index, hypertension, hyperlipidemia and other recognized confounding factors. Results Higher DII scores (i.e., indicating a more pro-inflammatory diet) were associated with increased likelihood of AMI when expressed both as continuous (ORcontinuous=1.14, 95% confidence interval, CI:1.05, 1.24; one-unit increase in DII score corresponding to ≈9% of the range of DII) and as quartiles (ORQuartile4vs1= 1.60, 95%, CI 1.06, 2.41; P-trend = 0.02). Stratified analyses produced slightly stronger associations between DII and AMI among women, ≥60 years, never smokers, subjects with history of hypertension and subjects with no family history of AMI, however, in the absence of heterogeneity across strata. Conclusion A pro-inflammatory diet as indicated by higher DII scores is associated with increased likelihood of AMI. Introduction Acute myocardial infarction (AMI) is a leading cause of mortality in Western countries.1 Major recognized risk factors for AMI are abnormal lipids, smoking, hypertension, diabetes, (abdominal) obesity, psychosocial factors, dietary components and the absence of regular physical activity.2 Considerable evidence has been gathered over the past few years linking increased AMI risk with chronic inflammation, which also underlies many AMI risk factors, such as atherosclerosis, diabetes, obesity and smoking.3,4 Dietary components such as fruits and vegetables have been inversely related to coronary heart disease,5 including AMI.6 In contrast, Western dietary patterns (high in fried foods, salty snacks, eggs and meat) have been associated with higher levels of c-reactive protein (CRP), interleukin-6 (IL-6) and fibrinogen7 and incident AMI.6 On the other hand, the Mediterranean diet—typical in Mediterranean countries8 and characterized by a high consumption of whole-grains, fruit and vegetables, fish and olive oil, a low consumption of meat and butter and a moderate alcohol and dairy products consumption—has been associated with lower levels of inflammation9 and AMI.10 The literature-derived dietary inflammatory index (DIITM) was developed to assess the inflammatory potential of an individual’s diet.11 Higher DII scores indicate increasing inflammatory potential of diet. The DII has been validated with various inflammatory markers, including CRP,12 IL-6 and tumor necrosis factor.13 Pro-inflammatory diets, as indicated by higher DII scores, have been positively associated with cardiovascular disease (CVD) incidence and mortality,14–18 as well as with various cancer outcomes.19–22 This large case–control study conducted in Italy,10,23 provides us the opportunity to examine the association between DII scores and AMI. Our working hypothesis is that increasing inflammatory potential of diet is associated with increased risk of AMI. Methods Patients and study design Data were derived from a case–control study of non-fatal AMI, conducted in the greater Milan area in northern Italy between 1995 and 2003.10,23 Cases were 760 patients (580 men, 180 women; median age 61 years, range 19–79 years) admitted to a network of general hospitals in the area with a first episode of non-fatal AMI (defined according to the World Health Organization criteria, International Classification of Disease, ICD-9 410). Controls were 682 patients (439 men, 243 women; median age 59 years, range 16–79 years) admitted to the same hospitals as cases for a wide spectrum of acute conditions, related neither to AMI risk factors nor dietary modifications. Patients with previous AMI or other cardiovascular diseases, including arrhythmic disease, ischemic disease and stroke were not included. Cardiovascular history was self-reported and checked against the medical file records of the current hospitalization. Among controls, 30% had traumas, 25% non-traumatic orthopedic disorders, 18% acute surgical conditions, 18% eye, nose, throat or teeth disorders and 9% miscellaneous other illnesses unrelated to diet. Less than 5% of the cases and controls refused the interview. Data collection For both cases and controls, data were collected by trained interviewers during hospital stay, using a structured questionnaire, administered face-to-face, including self-reported information on socio-demographic and anthropometric factors, tobacco smoking, physical activity, other lifestyle habits, medical history and history of AMI in first degree relatives. Cholesterol levels were obtained from clinical records. Both height and weight were self-reported by the participants. A reproducible and validated food frequency questionnaire (FFQ)24–26 including 78 questions on foods or food groups and 5 questions on alcoholic beverages, was used to assess the patients’ usual diet prior to AMI (for cases) or hospital admission (for controls). Participants were asked to indicate the average weekly frequency of consumption of each dietary item; occasional intake (lower than once a week, but at least once a month) was coded 0.5 unit per week. Nutrient and total energy intake was determined using an Italian food composition database.27,28 We also computed the intake of flavonoids by using food composition data published by the US Department of Agriculture (USDA).29,30 In order to compute the DII score, dietary information for each study participant was first linked to the regionally representative database that provided a robust estimate of a mean and a standard deviation for each of the 45 parameters (i.e. foods, nutrients and other food components) considered in the DII definition.11 These parameters then were used to derive the subject’s exposure relative to the standard global mean as a z-score, derived by subtracting the mean of the globally representative database from the amount reported and dividing this value by the parameter’s standard deviation. To minimize the effect of ‘right skewing’, this value was converted to a centred percentile score, which was computed by doubling the raw percentile score and then subtracting 1. This score was then multiplied by the respective food parameter effect score (derived from a literature review on the basis of 1943 articles).11,31 All of these food parameter-specific DII scores were then summed to create the overall DII score for every subject in the study. Higher scores indicate a pro-inflammatory diet while lower scores indicate a more anti-inflammatory diet. The DII computed on this study’s FFQ includes data on 30 of the 45 possible food parameters comprising the DII: carbohydrates, proteins, fats, alcohol, fibers, cholesterol, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, omega 3, omega 6, niacin, thiamin, riboflavin, vitamin B6, iron, zinc, vitamin A, vitamin C, vitamin D, vitamin E, folic acid, beta carotene, anthocyanidins, flavan3ols, flavonols, flavanones, flavones, isoflavones and tea. The DII was analysed by quartiles of exposure computed among controls. Distributions of characteristics across quartiles of DII for controls and cases were computed and differences were analysed using the chi-square test. Differences in food groups across quartiles of DII for controls and cases were determined using ANOVA, and differences between the means in highest quartile of DII and lowest quartile were examined using t-test. Odds ratios (OR), and the corresponding 95% confidence intervals (CI) were estimated using unconditional logistic regression models including terms for age (quinquennia), sex, years of education (<7; 7–11; ≥12 years), tobacco smoking (never; former; current: <15; ≥15 cigarettes/day), body mass index (sex-specific tertiles among controls; upper limits men/women (kg/m2): 24.5/23.7, 27.4/27.3), occupational physical activity at age 30–39 (strenuous, average, standing, mainly sitting), coffee consumption (<10,10–20, ≥21 cups/week), history of hypertension (no, yes), history of hyperlipidemia (no, yes), history of diabetes (no, yes), family history of AMI in first-degree relatives (no, yes) and total energy intake (quintiles among controls). The DII also was included in the models as continuous variables, with one unit difference in DII equal to ≈9% range of DII in this study (i.e. −6.22 to +5.45). Inclusion in the model of serum cholesterol level did not substantially modify the estimates, and was therefore not included in the final models. Because caffeine is one of the 45 DII parameters, we performed a sensitivity analysis excluding coffee as covariate and including caffeine in the DII definition. Tests for linear trend were performed using the median value within each quartile as an ordinal variable. Stratified analyses were carried out according to sex, age, education, tobacco smoking, history of hypertension and family history of AMI in first degree relatives and heterogeneity across strata was tested computing the difference in the −2 log likelihood of the models with and without the interaction terms. Statistical analyses were performed using SAS® 9.3 (SAS Institute Inc., Cary, NC). Results Cases were more often smokers than controls, more often reported a personal history of hypertension and diabetes, and a family history of AMI in first-degree relatives.10 The mean DII value was 0.40 (standard deviation, SD ± 1.80, range −6.22 to +5.35) among cases and 0.14 (SD ± 1.81, range −4.46 to +5.45) among controls, indicating a more pro-inflammatory diet for cases. Characteristics of subjects across quartiles of DII are provided for controls and cases in table 1 and Supplementary table S1, respectively. Controls in the highest quartile of DII were more likely to be females, sedentary, to consume less coffee, to not have history of hyperlipidemia, whereas there was not difference across other factors (table 1). Cases in the highest quartile of DII were younger, more likely to be females, be current smokers, be physically active and not have history of hyperlipidemia, whereas no other significant difference across other factors was observed (Supplementary table S1). Table 1 Participants’ characteristics across quartiles of dietary inflammatory index (DII) among 682 controls. Italy, 1995–2003 Characteristics  DII quartiles    −4.46, −1.38  −1.37, −0.09  −0.08, 1.09  1.10, 5.45  P valuea  Age (years)      Mean ± SD  58 ± 10  57 ± 10  58 ± 12  56 ± 13  0.26    No. (%)  No. (%)  No. (%)  No. (%)    Sex          0.03      Male  123 (71.93)  114 (67.06)  101 (59.41)  101 (59.06)        Female  48 (28.07)  56 (32.94)  69 (40.59)  70 (40.94)    Education (years)b          0.57      <7  70 (41.18)  84 (50.91)  75 (44.38)  79 (47.02)        7–11  64 (37.65)  45 (27.27)  58 (34.32)  55 (32.74)        >11  36 (21.18)  36 (21.82)  36 (21.30)  34 (20.24)    Tobacco smoking          0.10      Never smokers  60 (35.09)  78 (45.88)  78 (45.88)  72 (42.11)        Ex-smokers  56 (32.75)  48 (28.24)  46 (27.06)  38 (22.22)        Current smokers                    <15 cigarettes/day  17 (9.94)  13 (7.65)  18 (10.59)  28 (16.37)            ≥15 cigarettes/day  38 (22.22)  31 (18.24)  28 (16.47)  33 (19.20)    Body mass index (tertiles)b,c          0.18      I  53 (30.99)  50 (29.41)  54 (31.95)  70 (41.18)        II  55 (32.16)  67 (39.41)  56 (33.14)  49 (28.82)        III  63 (36.84)  53 (31.18)  59 (34.91)  51 (30.00)    Occupational physical activity at age 30–39          0.003      Mainly sitting  15 (8.77)  25 (14.71)  29 (17.06)  35 (20.47)        Standing  49 (28.65)  37 (21.76)  37 (21.76)  58 (33.92)        Average  64 (37.43)  60 (35.29)  69 (40.59)  41 (23.98)        Strenuous  43 (25.15)  48 (28.24)  35 (20.59)  37 (21.64)    Coffee (cups/week)          0.002      <10  42 (24.56)  44 (25.88)  59 (34.71)  71 (41.52)        10–20  57 (33.33)  50 (29.41)  59 (34.71)  40 (23.29)        >20  72 (42.11)  76 (44.71)  52 (30.59)  60 (35.09)    History of hypertension          0.55      No  127 (74.27)  128 (75.29)  123 (72.35)  135 (78.95)        Yes  44 (25.73)  42 (24.71)  47 (27.65)  36 (21.05)    History of diabetes          0.20      No  162 (94.74)  156 (91.76)  165 (97.06)  161 (94.15)        Yes  9 (5.26)  14 (8.24)  5 (2.94)  10 (5.85)    History of hyperlipidemia          0.01      No  112 (65.50)  121 (71.18)  124 (72.94)  139 (81.29)        Yes  59 (34.50)  49 (28.82)  46 (27.06)  32 (18.71)    Family history of AMI          0.39      No  134 (78.36)  136 (80.00)  143 (84.12)  144 (84.21)        Yes  37 (21.64)  34 (20.00)  27 (15.88)  27 (15.79)    Characteristics  DII quartiles    −4.46, −1.38  −1.37, −0.09  −0.08, 1.09  1.10, 5.45  P valuea  Age (years)      Mean ± SD  58 ± 10  57 ± 10  58 ± 12  56 ± 13  0.26    No. (%)  No. (%)  No. (%)  No. (%)    Sex          0.03      Male  123 (71.93)  114 (67.06)  101 (59.41)  101 (59.06)        Female  48 (28.07)  56 (32.94)  69 (40.59)  70 (40.94)    Education (years)b          0.57      <7  70 (41.18)  84 (50.91)  75 (44.38)  79 (47.02)        7–11  64 (37.65)  45 (27.27)  58 (34.32)  55 (32.74)        >11  36 (21.18)  36 (21.82)  36 (21.30)  34 (20.24)    Tobacco smoking          0.10      Never smokers  60 (35.09)  78 (45.88)  78 (45.88)  72 (42.11)        Ex-smokers  56 (32.75)  48 (28.24)  46 (27.06)  38 (22.22)        Current smokers                    <15 cigarettes/day  17 (9.94)  13 (7.65)  18 (10.59)  28 (16.37)            ≥15 cigarettes/day  38 (22.22)  31 (18.24)  28 (16.47)  33 (19.20)    Body mass index (tertiles)b,c          0.18      I  53 (30.99)  50 (29.41)  54 (31.95)  70 (41.18)        II  55 (32.16)  67 (39.41)  56 (33.14)  49 (28.82)        III  63 (36.84)  53 (31.18)  59 (34.91)  51 (30.00)    Occupational physical activity at age 30–39          0.003      Mainly sitting  15 (8.77)  25 (14.71)  29 (17.06)  35 (20.47)        Standing  49 (28.65)  37 (21.76)  37 (21.76)  58 (33.92)        Average  64 (37.43)  60 (35.29)  69 (40.59)  41 (23.98)        Strenuous  43 (25.15)  48 (28.24)  35 (20.59)  37 (21.64)    Coffee (cups/week)          0.002      <10  42 (24.56)  44 (25.88)  59 (34.71)  71 (41.52)        10–20  57 (33.33)  50 (29.41)  59 (34.71)  40 (23.29)        >20  72 (42.11)  76 (44.71)  52 (30.59)  60 (35.09)    History of hypertension          0.55      No  127 (74.27)  128 (75.29)  123 (72.35)  135 (78.95)        Yes  44 (25.73)  42 (24.71)  47 (27.65)  36 (21.05)    History of diabetes          0.20      No  162 (94.74)  156 (91.76)  165 (97.06)  161 (94.15)        Yes  9 (5.26)  14 (8.24)  5 (2.94)  10 (5.85)    History of hyperlipidemia          0.01      No  112 (65.50)  121 (71.18)  124 (72.94)  139 (81.29)        Yes  59 (34.50)  49 (28.82)  46 (27.06)  32 (18.71)    Family history of AMI          0.39      No  134 (78.36)  136 (80.00)  143 (84.12)  144 (84.21)        Yes  37 (21.64)  34 (20.00)  27 (15.88)  27 (15.79)    AMI, acute myocardial infarction. a P value for ANOVA and Chi-square test were used for continuous and categorical variables, respectively. b The sum does not add up to the total because of some missing values. c Sex-specific tertiles (upper limits men/women (kg/m2): 24.5/23.7, 27.4/27.3). Distribution of 10 food groups across quartiles of DII are provided for controls and cases in table 2 and Supplementary table S2, respectively. Controls in quartile 4 of DII had significantly lower servings of fruit, vegetables and fish and had significantly higher servings of sugar and desserts and nearly significant higher levels of cereals compared to controls in quartile 1 (table 2). Similarly, cases in quartile 4 of DII had significantly lower servings of fruit, vegetables and fish and had significantly higher servings of sugar, cereals and desserts, and nearly significant higher levels of pork compared to cases in quartile 1 of DII (Supplementary table S2). Table 2 Distribution of servings of food groups across quartiles of dietary inflammatory index (DII) among 682 controls (mean ± standard deviation). Italy, 1995–2003   DII quartiles (range of DII scores)        −4.46, −1.38  −1.37, −0.09  −0.08, 1.09  1.10, 5.45  P valuea  P valueb  Servings/week                  Fruit  23.08 ± 9.18  19.73 ± 8.71  14.07 ± 6.90  9.34 ± 6.65  <0.0001  <0.0001      Vegetables  12.97 ± 4.23  10.38 ± 4.16  8.64 ± 3.76  6.30 ± 3.90  <0.0001  <0.0001      Fish  2.16 ± 1.15  1.94 ± 1.04  1.67 ± 1.12  1.51 ± 0.87  <0.0001  <0.0001      Egg  1.49 ± 1.08  1.33 ± 1.08  1.37 ± 1.12  1.67 ± 1.77  0.21  0.28      Coffee  19.96 ± 11.88  20.12 ± 11.99  19.51 ± 12.26  20.66 ± 14.75  0.73  0.63      Cheese  4.08 ± 1.93  4.40 ± 2.23  4.03 ± 1.97  4.09 ± 2.59  0.66  0.96      Pork  2.96 ± 1.52  3.47 ± 2.32  3.33 ± 2.09  3.30 ± 2.81  0.25  0.17      Sugar  26.94 ± 25.69  31.77 ± 28.78  33.88 ± 27.46  35.09 ± 34.07  0.008  0.01      Cereals  25.34 ± 10.48  27.63 ± 12.23  29.49 ± 13.12  27.65 ± 12.66  0.04  0.08      Desserts  4.51 ± 4.88  5.18 ± 4.95  6.12 ± 6.74  7.73 ± 10.73  <0.0001  0.0004    DII quartiles (range of DII scores)        −4.46, −1.38  −1.37, −0.09  −0.08, 1.09  1.10, 5.45  P valuea  P valueb  Servings/week                  Fruit  23.08 ± 9.18  19.73 ± 8.71  14.07 ± 6.90  9.34 ± 6.65  <0.0001  <0.0001      Vegetables  12.97 ± 4.23  10.38 ± 4.16  8.64 ± 3.76  6.30 ± 3.90  <0.0001  <0.0001      Fish  2.16 ± 1.15  1.94 ± 1.04  1.67 ± 1.12  1.51 ± 0.87  <0.0001  <0.0001      Egg  1.49 ± 1.08  1.33 ± 1.08  1.37 ± 1.12  1.67 ± 1.77  0.21  0.28      Coffee  19.96 ± 11.88  20.12 ± 11.99  19.51 ± 12.26  20.66 ± 14.75  0.73  0.63      Cheese  4.08 ± 1.93  4.40 ± 2.23  4.03 ± 1.97  4.09 ± 2.59  0.66  0.96      Pork  2.96 ± 1.52  3.47 ± 2.32  3.33 ± 2.09  3.30 ± 2.81  0.25  0.17      Sugar  26.94 ± 25.69  31.77 ± 28.78  33.88 ± 27.46  35.09 ± 34.07  0.008  0.01      Cereals  25.34 ± 10.48  27.63 ± 12.23  29.49 ± 13.12  27.65 ± 12.66  0.04  0.08      Desserts  4.51 ± 4.88  5.18 ± 4.95  6.12 ± 6.74  7.73 ± 10.73  <0.0001  0.0004  a P values were obtained from ANOVA. b P values were obtained from t test to test if the mean in quartile 4 is different from that of quartile 1. Table 3 shows the OR of AMI, and the corresponding 95% CI, according to DII score. Subjects in the highest DII score quartile had a 60% increased odds for AMI compared to subjects in the lowest quartile (ORQuartile4vs1= 1.60, 95% CI = 1.06, 2.41; P-trend = 0.02). The OR of AMI was 1.14 (95% CI: 1.05, 1.24) for one-unit increase in DII score (i.e., ≈9% of the range of DII). After excluding coffee as a covariate in the model and including caffeine in the DII definition, the OR of AMI was 1.12 (95% CI: 1.03, 1.21) for one-unit increase in DII score. Table 3 Odds ratios (OR) of acute myocardial infarction and corresponding 95% confidence intervals (CI) according to dietary inflammatory index (DII) among 760 cases and 682 controls. Italy, 1995–2003   DII quartiles, OR (95% CI)  P value for trend  DII continuousd  −4.46, −1.38  −1.37, −0.09  −0.08,1.09  1.10,5.45  Cases/Controls  154/171  187/170  194/170  225/171    760/682  Model 1a  1 b  1.30 (0.94, 1.79)  1.47 (1.05, 2.07)  1.84 (1.27, 2.67)  0.001  1.15 (1.07, 1.24)  Model 2c  1 b  1.26 (0.88, 1.79)  1.47 (1.01, 2.12)  1.60 (1.06, 2.41)  0.02  1.14 (1.05, 1.24)    DII quartiles, OR (95% CI)  P value for trend  DII continuousd  −4.46, −1.38  −1.37, −0.09  −0.08,1.09  1.10,5.45  Cases/Controls  154/171  187/170  194/170  225/171    760/682  Model 1a  1 b  1.30 (0.94, 1.79)  1.47 (1.05, 2.07)  1.84 (1.27, 2.67)  0.001  1.15 (1.07, 1.24)  Model 2c  1 b  1.26 (0.88, 1.79)  1.47 (1.01, 2.12)  1.60 (1.06, 2.41)  0.02  1.14 (1.05, 1.24)  a Adjusted for age, sex, and total energy intake. b Reference category. c Model 1 additionally adjusted for education (<7; 7–11; ≥12 years), tobacco smoking (never; former; current: <15; ≥25 cigarettes/day), body mass index (sex-specific tertiles among controls. Upper limits men/women (kg/m2): 24.5/23.7, 27.4/27.3), occupational physical activity at age 30–39 (strenuous, average, standing, mainly sitting), coffee consumption (<10, 10–20, ≥21 cups/week), history of hypertension, history of hyperlipidemia, history of diabetes and family history of acute myocardial infarction in first-degree relatives. d One unit increase equals to ≈9% range of DII in this study (−6.22 to +5.45). Table 4 shows the OR of AMI according to DII score in strata of selected covariates. Stronger associations—although in the absence of significant heterogeneity (P > 0.10)—were observed among females (ORQuartile4vs1= 2.13, 95% CI 0.90, 5.06), subjects aged ≥60 years (ORQuartile4vs1= 1.88, 95% CI 1.06, 3.34), never smokers (ORQuartile4vs1= 2.12, 95% CI 0.98, 4.58), with history of hypertension (ORQuartile4vs1= 4.23, 95% CI 1.76, 10.18) and individuals with no family history of AMI (ORQuartile4vs1= 1.75, 95% CI 1.08, 2.83). Table 4 Odds ratios (OR) of acute myocardial infarction and corresponding 95% confidence intervals (CI) according to quartiles of dietary inflammatory index (DII), in strata of selected covariates, Italy, 1995–2003   Cases/controls  DII quartiles, OR (95% CI)a  Ptrend  pinteraction  −4.46, −1.38  −1.37, −0.09  −0.08,1.09  1.10,5.45  Sex              0.21      Male  580/439  1b  1.06 (0.71, 1.59)  1.44 (0.93, 2.23)  1.46 (0.90, 2.39)  0.08        Female  180/243  1b  2.11 (0.93, 4.78)  1.70 (0.74, 3.88)  2.13 (0.90, 5.06)  0.20    Age (years)              0.19      <60  338/355  1b  0.99 (0.56, 1.66)  1.10 (0.64, 1.90)  1.18 (0.65, 2.15)  0.54        ≥60  422/327  1b  1.64 (0.99, 2.70)  1.76 (1.05, 2.96)  1.88 (1.06, 3.34)  0.05    Education (years)              0.81      <7  321/308  1b  1.21 (0.70, 2.08)  1.00 (0.55, 1.83)  1.59 (0.83, 3.03)  0.20        ≥7  428/364  1b  1.30 (0.80, 2.10)  1.76 (1.08, 2.86)  1.55 (0.90, 2.69)  0.09    Tobacco smoking              0.48      Never smokers  235/288  1b  1.51 (0.78, 2.91)  1.54 (0.78, 3.06)  2.12 (0.98, 4.58)  0.07        Ever smokers  525/394  1b  1.26 (0.82, 1.94)  1.68 (1.07, 2.63)  1.75 (1.08, 2.85)  0.02    Hypertension              0.13      No  519/513  1b  1.13 (0.74, 1.72)  1.29 (0.83, 2.00)  1.22 (0.76, 1.98)  0.38        Yes  241/169  1b  1.91 (0.95, 3.86)  2.74 (1.25, 6.00)  4.23 (1.76, 10.18)  0.001    Family history of AMI              0.78      No  511/557  1b  1.27 (0.84, 1.94)  1.51 (0.98, 2.33)  1.75 (1.08, 2.83)  0.02        Yes  249/125  1b  1.23 (0.61, 2.45)  1.25 (0.57, 2.75)  1.29 (0.55, 3.02)  0.57      Cases/controls  DII quartiles, OR (95% CI)a  Ptrend  pinteraction  −4.46, −1.38  −1.37, −0.09  −0.08,1.09  1.10,5.45  Sex              0.21      Male  580/439  1b  1.06 (0.71, 1.59)  1.44 (0.93, 2.23)  1.46 (0.90, 2.39)  0.08        Female  180/243  1b  2.11 (0.93, 4.78)  1.70 (0.74, 3.88)  2.13 (0.90, 5.06)  0.20    Age (years)              0.19      <60  338/355  1b  0.99 (0.56, 1.66)  1.10 (0.64, 1.90)  1.18 (0.65, 2.15)  0.54        ≥60  422/327  1b  1.64 (0.99, 2.70)  1.76 (1.05, 2.96)  1.88 (1.06, 3.34)  0.05    Education (years)              0.81      <7  321/308  1b  1.21 (0.70, 2.08)  1.00 (0.55, 1.83)  1.59 (0.83, 3.03)  0.20        ≥7  428/364  1b  1.30 (0.80, 2.10)  1.76 (1.08, 2.86)  1.55 (0.90, 2.69)  0.09    Tobacco smoking              0.48      Never smokers  235/288  1b  1.51 (0.78, 2.91)  1.54 (0.78, 3.06)  2.12 (0.98, 4.58)  0.07        Ever smokers  525/394  1b  1.26 (0.82, 1.94)  1.68 (1.07, 2.63)  1.75 (1.08, 2.85)  0.02    Hypertension              0.13      No  519/513  1b  1.13 (0.74, 1.72)  1.29 (0.83, 2.00)  1.22 (0.76, 1.98)  0.38        Yes  241/169  1b  1.91 (0.95, 3.86)  2.74 (1.25, 6.00)  4.23 (1.76, 10.18)  0.001    Family history of AMI              0.78      No  511/557  1b  1.27 (0.84, 1.94)  1.51 (0.98, 2.33)  1.75 (1.08, 2.83)  0.02        Yes  249/125  1b  1.23 (0.61, 2.45)  1.25 (0.57, 2.75)  1.29 (0.55, 3.02)  0.57    AMI, acute myocardial infarction. a Adjusted for age, sex, education (<7; 7–11; ≥12 years), tobacco smoking (never; former; current: <15; ≥25 cigarettes/day), body mass index (sex-specific tertiles among controls. Upper limits men/women (kg/m2): 24.5/23.7, 27.4/27.3), occupational physical activity at age 30–39 (strenuous, average, standing, mainly sitting), coffee consumption (<10,10–20, ≥21 cups/week), history of hypertension, history of hyperlipidemia, history of diabetes, family history of acute myocardial infarction in first-degree relatives, and total energy intake, when appropriate. b Reference category. Discussion In this large Italian case–control study we investigated the association between inflammatory potential of diet and AMI. We observed a 60% excess AMI risk among individuals with a pro-inflammatory diet as expressed by high DII scores. There have been several reports on the association between diet and AMI in the present case–control study.10,23,32–36 A high adherence to Mediterranean diet was inversely associated with AMI.10 In relation to nutrients, high intakes of anthocyanidins,23 folates, vitamin B6,33 fiber,34 iron35 also have been found to be inversely associated with AMI. The dietary non-enzymatic antioxidant capacity, measured through three different assays as the ferric reducing-antioxidant power (i.e. Trolox equivalent antioxidant capacity and total radical-trapping antioxidant parameter), also was inversely associated with AMI.36 No association was observed with high glycemic load and glycemic index, but slightly increased ORs were observed for glycemic index in elderly people and in association with overweight.32 Our findings are consistent with some previous results for DII and incident CVD as well as CVD mortality.14–18 In the prospective PREDIMED study in Spain, increased risk of CVD was observed across the quartiles (i.e. with increasing inflammatory potential): HR (quartile4 vs. 1) = 1.73 (95% CI 1.15–2.60).18 In an Australian cohort, men with a pro-inflammatory diet at baseline were twice as likely to experience a CVD event over the study period (OR 2.00; 95% CI 1.03–3.96).16 However, no association was observed in a couple of other studies.14,37 One of the possible mechanisms through which the observed positive association between DII and AMI is through attraction and migration of inflammatory cells into vascular tissue by cytokines (interleukin-1 [IL-1], tumor necrosis factor-α [TNF-α]).38 These cytokines also induce the expression of cellular adhesion molecules, which mediate adhesion of leukocytes to the vascular endothelium.39 With regard to strengths and limitations of the present study, cases and controls were interviewed in the same hospitals and came from the same geographical area; participation was almost complete; patients admitted for chronic conditions or diseases related to known risk factors for AMI or modification of diet were excluded from the comparison group; and the FFQ was satisfactorily valid and reproducible.24–26 We accounted for major risk factors for AMI in the analyses, such as physical activity, BMI, smoking, coffee drinking and energy intake. Another major strength of the present study is the strict definition of AMI and validated of the endpoint, because all cases were admitted to reference centres for heart diseases. Also, the DII score, which takes into account both pro- and anti-inflammatory food parameters, reflects the relationship of the inflammatory potential of diet to affect AMI risk than would single nutrients or diet components considered individually. A potential limitation of the DII is the use of the US flavonoid food composition database to describe the Italian diet; however, this is unlikely to have introduced spurious associations because the same food composition database was applied to the intakes of both cases and controls, and imprecise classification of exposure is likely to lead to an attenuation of any real association. A further limitation is that we derived DII scores from 30 of the potential 45 food and nutrient items that can be used to compute this index; however, other published studies that rely on FFQ date also derive DII scores from a sub-optimal number of items, and the ability to still detect significant associations.12 Moreover, some of the missing food parameters such as saffron, ginger and turmeric are consumed infrequently in this population; so, non-availability of these parameters may not have played a major impact. In conclusion, this study indicates a detrimental role of a pro-inflammatory diet, as measured by higher DII scores, on AMI among Italians through a process of inflammation. Acknowledgements This study was partly supported by a grant from the Italian Foundation for Research on Cancer. Drs. Shivappa and Hébert were supported by grant number R44DK103377 to CHI from the United States National Institute of Diabetes and Digestive and Kidney Diseases. Conflict of interest: None declared. Disclosure: Dr. James R. Hébert owns controlling interest in Connecting Health Innovations LLC (CHI), a company planning to license the right to his invention of the dietary inflammatory index (DII) from the University of South Carolina in order to develop computer and smart phone applications for patient counseling and dietary intervention in clinical settings. Dr. Nitin Shivappa is an employee of CHI. Key points Dietary Inflammatory Index is a tool that measures the inflammatory potential of individual’s diet. This Italian case–control study provided an opportunity to examine the association between inflammatory potential of diet and AMI. The results from this study indicate a detrimental role of a pro-inflammatory diet on AMI, which is consistent with the role of inflammation in AMI. References 1 Sanchis-Gomar F , Perez-Quilis C, Leischik R, Lucia A. Epidemiology of coronary heart disease and acute coronary syndrome. Ann Transl Med  2016; 4: 256. Google Scholar CrossRef Search ADS PubMed  2 Yusuf S , Hawken S, Ounpuu S, et al.   Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet  2004; 364: 937– 52. Google Scholar CrossRef Search ADS PubMed  3 Hansson GK . Inflammation, atherosclerosis, and coronary artery disease. N Engl J Med  2005; 352: 1685– 95. Google Scholar CrossRef Search ADS PubMed  4 Yeh ET , Anderson HV, Pasceri V, Willerson JT. C-reactive protein: linking inflammation to cardiovascular complications. Circulation  2001; 104: 974– 5. Google Scholar CrossRef Search ADS PubMed  5 Dauchet L , Amouyel P, Hercberg S, Dallongeville J. Fruit and vegetable consumption and risk of coronary heart disease: a meta-analysis of cohort studies. J Nutr  2006; 136: 2588– 93. Google Scholar CrossRef Search ADS PubMed  6 Iqbal R , Anand S, Ounpuu S, et al.   Dietary patterns and the risk of acute myocardial infarction in 52 countries: results of the INTERHEART study. Circulation  2008; 118: 1929– 37. Google Scholar CrossRef Search ADS PubMed  7 Johansson-Persson A , Ulmius M, et al.   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  2013; 7: 7. 8 Pelucchi C , Galeone C, Negri E, et al.   Trends in adherence to the Mediterranean diet in an Italian population between 1991 and 2006. Eur J Clin Nutr  2010; 64: 1052– 6. Google Scholar CrossRef Search ADS PubMed  9 Smidowicz A , Regula J. Effect of nutritional status and dietary patterns on human serum C-reactive protein and interleukin-6 concentrations. Adv Nutr  2015; 6: 738– 47. Google Scholar CrossRef Search ADS PubMed  10 Turati F , Pelucchi C, Galeone C, et al.   Mediterranean diet and non-fatal acute myocardial infarction: a case-control study from Italy. Public Health Nutr  2015; 18: 713– 20. Google Scholar CrossRef Search ADS PubMed  11 Shivappa N , Steck SE, Hurley TG, et al.   Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr  2014; 17: 1689– 96. Google Scholar CrossRef Search ADS PubMed  12 Shivappa N , Steck SE, Hurley TG, 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: 1825– 33. Google Scholar CrossRef Search ADS PubMed  13 Tabung FK , Steck SE, Zhang J, et al.   Construct validation of the dietary inflammatory index among postmenopausal women. Ann Epidemiol  2015; 25: 398– 405. Google Scholar CrossRef Search ADS PubMed  14 Ramallal R , Toledo E, Martínez-González MA, et al.   Dietary inflammatory index and incidence of cardiovascular disease in the SUN cohort. PLoS One  2015; 10: e0135221. Google Scholar CrossRef Search ADS PubMed  15 Shivappa N , Steck SE, Hussey JR, et al.   Inflammatory potential of diet and all-cause, cardiovascular, and cancer mortality in National Health and Nutrition Examination Survey III Study. Eur J Nutr  2015; 1– 10. 16 O’Neil A , Shivappa N, Jacka FN, et al.   Pro-inflammatory dietary intake as a risk factor for CVD in men: a 5-year longitudinal study. Br J Nutr  2015; 114: 2074– 82. Google Scholar CrossRef Search ADS PubMed  17 Graffouillere L , Deschasaux M, Mariotti F, et al.   Prospective association between the Dietary Inflammatory Index and mortality: modulation by antioxidant supplementation in the SU.VI.MAX randomized controlled trial. Am J Clin Nutr  2016; 103: 878– 85. Google Scholar CrossRef Search ADS PubMed  18 Garcia-Arellano A , Ramallal R, Ruiz-Canela M, et al.   Dietary inflammatory index and incidence of cardiovascular disease in the PREDIMED study. Nutrients  2015; 7: 4124– 38. Google Scholar CrossRef Search ADS PubMed  19 Shivappa N , Bosetti C, Zucchetto A, et al.   Association between dietary inflammatory index and prostate cancer among Italian men. Br J Nutr  2015; 113: 278– 83. Google Scholar CrossRef Search ADS PubMed  20 Shivappa N , Bosetti C, Zucchetto A, et al.   Dietary inflammatory index and risk of pancreatic cancer in an Italian case–control study. Br J Nutr  2015; 113: 292– 8. Google Scholar CrossRef Search ADS PubMed  21 Shivappa N , Hebert JR, Polesel J, et al.   Inflammatory potential of diet and risk for hepatocellular cancer in a case-control study from Italy. Br J Nutr  2016; 115: 324– 31. Google Scholar CrossRef Search ADS PubMed  22 Shivappa N , Hebert JR, Zucchetto A, et al.   Dietary inflammatory index and endometrial cancer risk in an Italian case-control study. Br J Nutr  2016; 115: 138– 46. Google Scholar CrossRef Search ADS PubMed  23 Tavani A , Spertini L, Bosetti C, et al.   Intake of specific flavonoids and risk of acute myocardial infarction in Italy. Public Health Nutr  2006; 9: 369– 74. Google Scholar CrossRef Search ADS PubMed  24 Franceschi S , Barbone F, Negri E, et al.   Reproducibility of an Italian food frequency questionnaire for cancer studies. Results for specific nutrients. Ann Epidemiol  1995; 5: 69– 75. Google Scholar CrossRef Search ADS PubMed  25 Decarli A , Franceschi S, Ferraroni M, et al.   Validation of a food-frequency questionnaire to assess dietary intakes in cancer studies in Italy. Results for specific nutrients. Ann Epidemiol  1996; 6: 110– 8. Google Scholar CrossRef Search ADS PubMed  26 Franceschi S , Negri E, Salvini S, et al.   Reproducibility of an Italian food frequency questionnaire for cancer studies: results for specific food items. Eur J Cancer  1993; 29A: 2298– 305. Google Scholar CrossRef Search ADS PubMed  27 Salvini S , Parpinel M, Gnagnarella P, Maisonneuve P, Turrini A. Banca dati di composizione degli alimenti per studi epidemiologici in Italia. Istituto Europeo di Oncologia. Milano 1998. 28 Gnagnarella P , Parpinel M, Salvini S, et al.   The update of the Italian Food Composition Database. J Food Comp Anal  2004; 6// 17: 509– 22. Google Scholar CrossRef Search ADS   29 Iowa State University Database on the Isoflavone Content of Foods, Release 1.3, 2002. Beltsville, MD: USDA: 2002. 30 USDA Database for the Flavonoid Content of Selected Foods . Beltsville, MD: USDA: 2003. 31 Cavicchia PP , Steck SE, Hurley TG, et al.   A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J Nutr  2009; 139: 2365– 72. Google Scholar CrossRef Search ADS PubMed  32 Tavani A , Bosetti C, Negri E, et al.   Carbohydrates, dietary glycaemic load and glycaemic index, and risk of acute myocardial infarction. Heart  2003; 89: 722– 6. Google Scholar CrossRef Search ADS PubMed  33 Tavani A , Pelucchi C, Parpinel M, et al.   Folate and vitamin B(6) intake and risk of acute myocardial infarction in Italy. Eur J Clin Nutr  2004; 58: 1266– 72. Google Scholar CrossRef Search ADS PubMed  34 Negri E , La Vecchia C, Pelucchi C, et al.   Fiber intake and risk of nonfatal acute myocardial infarction. Eur J Clin Nutr  2003; 57: 464– 70. Google Scholar CrossRef Search ADS PubMed  35 Tavani A , Gallus S, Bosetti C, et al.   Dietary iron intake and risk of non-fatal acute myocardial infarction. Public Health Nutr  2006; 9: 480– 4. Google Scholar PubMed  36 Rossi M , Praud D, Monzio Compagnoni M, et al.   Dietary non-enzymatic antioxidant capacity and the risk of myocardial infarction: a case-control study in Italy. Nutr Metab Cardiovas Dis  2014; 24: 1246– 51. Google Scholar CrossRef Search ADS   37 Vissers LE , Waller MA, van der Schouw YT, Hebert JR, Shivappa N, Schoenaker DA, et al.   The relationship between the dietary inflammatory index and risk of total cardiovascular disease, ischemic heart disease and cerebrovascular disease: Findings from an Australian population-based prospective cohort study of women. Atherosclerosis  2016. 38 Willerson JT , Ridker PM. Inflammation as a cardiovascular risk factor. Circulation  2004; 109(21 Suppl 1): II2– 10. 39 Pasceri V , Willerson JT, Yeh ET. Direct proinflammatory effect of C-reactive protein on human endothelial cells. Circulation  2000; 102: 2165– 8. Google Scholar CrossRef Search ADS PubMed  © The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The European Journal of Public Health Oxford University Press

Dietary inflammatory index and acute myocardial infarction in a large Italian case–control study

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Oxford University Press
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© The Author 2017. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
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1101-1262
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1464-360X
DOI
10.1093/eurpub/ckx058
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Abstract

Abstract Background Diet and inflammation have been implicated to play a role in the incidence of acute myocardial infarction (AMI). Methods In this Italian case–control study conducted between 1995 and 2003, we explored the association between the dietary inflammatory index (DIITM) and AMI. Cases were 760 patients, below age 79 years, with a first episode of nonfatal AMI and controls were 682 patients admitted to hospital for acute conditions unrelated to diet. The DII was computed based on dietary intake assessed using a reproducible and validated 78-item food frequency questionnaire. Odds ratios (OR) were estimated through logistic regression models adjusting for age, sex, total energy intake, tobacco, body mass index, hypertension, hyperlipidemia and other recognized confounding factors. Results Higher DII scores (i.e., indicating a more pro-inflammatory diet) were associated with increased likelihood of AMI when expressed both as continuous (ORcontinuous=1.14, 95% confidence interval, CI:1.05, 1.24; one-unit increase in DII score corresponding to ≈9% of the range of DII) and as quartiles (ORQuartile4vs1= 1.60, 95%, CI 1.06, 2.41; P-trend = 0.02). Stratified analyses produced slightly stronger associations between DII and AMI among women, ≥60 years, never smokers, subjects with history of hypertension and subjects with no family history of AMI, however, in the absence of heterogeneity across strata. Conclusion A pro-inflammatory diet as indicated by higher DII scores is associated with increased likelihood of AMI. Introduction Acute myocardial infarction (AMI) is a leading cause of mortality in Western countries.1 Major recognized risk factors for AMI are abnormal lipids, smoking, hypertension, diabetes, (abdominal) obesity, psychosocial factors, dietary components and the absence of regular physical activity.2 Considerable evidence has been gathered over the past few years linking increased AMI risk with chronic inflammation, which also underlies many AMI risk factors, such as atherosclerosis, diabetes, obesity and smoking.3,4 Dietary components such as fruits and vegetables have been inversely related to coronary heart disease,5 including AMI.6 In contrast, Western dietary patterns (high in fried foods, salty snacks, eggs and meat) have been associated with higher levels of c-reactive protein (CRP), interleukin-6 (IL-6) and fibrinogen7 and incident AMI.6 On the other hand, the Mediterranean diet—typical in Mediterranean countries8 and characterized by a high consumption of whole-grains, fruit and vegetables, fish and olive oil, a low consumption of meat and butter and a moderate alcohol and dairy products consumption—has been associated with lower levels of inflammation9 and AMI.10 The literature-derived dietary inflammatory index (DIITM) was developed to assess the inflammatory potential of an individual’s diet.11 Higher DII scores indicate increasing inflammatory potential of diet. The DII has been validated with various inflammatory markers, including CRP,12 IL-6 and tumor necrosis factor.13 Pro-inflammatory diets, as indicated by higher DII scores, have been positively associated with cardiovascular disease (CVD) incidence and mortality,14–18 as well as with various cancer outcomes.19–22 This large case–control study conducted in Italy,10,23 provides us the opportunity to examine the association between DII scores and AMI. Our working hypothesis is that increasing inflammatory potential of diet is associated with increased risk of AMI. Methods Patients and study design Data were derived from a case–control study of non-fatal AMI, conducted in the greater Milan area in northern Italy between 1995 and 2003.10,23 Cases were 760 patients (580 men, 180 women; median age 61 years, range 19–79 years) admitted to a network of general hospitals in the area with a first episode of non-fatal AMI (defined according to the World Health Organization criteria, International Classification of Disease, ICD-9 410). Controls were 682 patients (439 men, 243 women; median age 59 years, range 16–79 years) admitted to the same hospitals as cases for a wide spectrum of acute conditions, related neither to AMI risk factors nor dietary modifications. Patients with previous AMI or other cardiovascular diseases, including arrhythmic disease, ischemic disease and stroke were not included. Cardiovascular history was self-reported and checked against the medical file records of the current hospitalization. Among controls, 30% had traumas, 25% non-traumatic orthopedic disorders, 18% acute surgical conditions, 18% eye, nose, throat or teeth disorders and 9% miscellaneous other illnesses unrelated to diet. Less than 5% of the cases and controls refused the interview. Data collection For both cases and controls, data were collected by trained interviewers during hospital stay, using a structured questionnaire, administered face-to-face, including self-reported information on socio-demographic and anthropometric factors, tobacco smoking, physical activity, other lifestyle habits, medical history and history of AMI in first degree relatives. Cholesterol levels were obtained from clinical records. Both height and weight were self-reported by the participants. A reproducible and validated food frequency questionnaire (FFQ)24–26 including 78 questions on foods or food groups and 5 questions on alcoholic beverages, was used to assess the patients’ usual diet prior to AMI (for cases) or hospital admission (for controls). Participants were asked to indicate the average weekly frequency of consumption of each dietary item; occasional intake (lower than once a week, but at least once a month) was coded 0.5 unit per week. Nutrient and total energy intake was determined using an Italian food composition database.27,28 We also computed the intake of flavonoids by using food composition data published by the US Department of Agriculture (USDA).29,30 In order to compute the DII score, dietary information for each study participant was first linked to the regionally representative database that provided a robust estimate of a mean and a standard deviation for each of the 45 parameters (i.e. foods, nutrients and other food components) considered in the DII definition.11 These parameters then were used to derive the subject’s exposure relative to the standard global mean as a z-score, derived by subtracting the mean of the globally representative database from the amount reported and dividing this value by the parameter’s standard deviation. To minimize the effect of ‘right skewing’, this value was converted to a centred percentile score, which was computed by doubling the raw percentile score and then subtracting 1. This score was then multiplied by the respective food parameter effect score (derived from a literature review on the basis of 1943 articles).11,31 All of these food parameter-specific DII scores were then summed to create the overall DII score for every subject in the study. Higher scores indicate a pro-inflammatory diet while lower scores indicate a more anti-inflammatory diet. The DII computed on this study’s FFQ includes data on 30 of the 45 possible food parameters comprising the DII: carbohydrates, proteins, fats, alcohol, fibers, cholesterol, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, omega 3, omega 6, niacin, thiamin, riboflavin, vitamin B6, iron, zinc, vitamin A, vitamin C, vitamin D, vitamin E, folic acid, beta carotene, anthocyanidins, flavan3ols, flavonols, flavanones, flavones, isoflavones and tea. The DII was analysed by quartiles of exposure computed among controls. Distributions of characteristics across quartiles of DII for controls and cases were computed and differences were analysed using the chi-square test. Differences in food groups across quartiles of DII for controls and cases were determined using ANOVA, and differences between the means in highest quartile of DII and lowest quartile were examined using t-test. Odds ratios (OR), and the corresponding 95% confidence intervals (CI) were estimated using unconditional logistic regression models including terms for age (quinquennia), sex, years of education (<7; 7–11; ≥12 years), tobacco smoking (never; former; current: <15; ≥15 cigarettes/day), body mass index (sex-specific tertiles among controls; upper limits men/women (kg/m2): 24.5/23.7, 27.4/27.3), occupational physical activity at age 30–39 (strenuous, average, standing, mainly sitting), coffee consumption (<10,10–20, ≥21 cups/week), history of hypertension (no, yes), history of hyperlipidemia (no, yes), history of diabetes (no, yes), family history of AMI in first-degree relatives (no, yes) and total energy intake (quintiles among controls). The DII also was included in the models as continuous variables, with one unit difference in DII equal to ≈9% range of DII in this study (i.e. −6.22 to +5.45). Inclusion in the model of serum cholesterol level did not substantially modify the estimates, and was therefore not included in the final models. Because caffeine is one of the 45 DII parameters, we performed a sensitivity analysis excluding coffee as covariate and including caffeine in the DII definition. Tests for linear trend were performed using the median value within each quartile as an ordinal variable. Stratified analyses were carried out according to sex, age, education, tobacco smoking, history of hypertension and family history of AMI in first degree relatives and heterogeneity across strata was tested computing the difference in the −2 log likelihood of the models with and without the interaction terms. Statistical analyses were performed using SAS® 9.3 (SAS Institute Inc., Cary, NC). Results Cases were more often smokers than controls, more often reported a personal history of hypertension and diabetes, and a family history of AMI in first-degree relatives.10 The mean DII value was 0.40 (standard deviation, SD ± 1.80, range −6.22 to +5.35) among cases and 0.14 (SD ± 1.81, range −4.46 to +5.45) among controls, indicating a more pro-inflammatory diet for cases. Characteristics of subjects across quartiles of DII are provided for controls and cases in table 1 and Supplementary table S1, respectively. Controls in the highest quartile of DII were more likely to be females, sedentary, to consume less coffee, to not have history of hyperlipidemia, whereas there was not difference across other factors (table 1). Cases in the highest quartile of DII were younger, more likely to be females, be current smokers, be physically active and not have history of hyperlipidemia, whereas no other significant difference across other factors was observed (Supplementary table S1). Table 1 Participants’ characteristics across quartiles of dietary inflammatory index (DII) among 682 controls. Italy, 1995–2003 Characteristics  DII quartiles    −4.46, −1.38  −1.37, −0.09  −0.08, 1.09  1.10, 5.45  P valuea  Age (years)      Mean ± SD  58 ± 10  57 ± 10  58 ± 12  56 ± 13  0.26    No. (%)  No. (%)  No. (%)  No. (%)    Sex          0.03      Male  123 (71.93)  114 (67.06)  101 (59.41)  101 (59.06)        Female  48 (28.07)  56 (32.94)  69 (40.59)  70 (40.94)    Education (years)b          0.57      <7  70 (41.18)  84 (50.91)  75 (44.38)  79 (47.02)        7–11  64 (37.65)  45 (27.27)  58 (34.32)  55 (32.74)        >11  36 (21.18)  36 (21.82)  36 (21.30)  34 (20.24)    Tobacco smoking          0.10      Never smokers  60 (35.09)  78 (45.88)  78 (45.88)  72 (42.11)        Ex-smokers  56 (32.75)  48 (28.24)  46 (27.06)  38 (22.22)        Current smokers                    <15 cigarettes/day  17 (9.94)  13 (7.65)  18 (10.59)  28 (16.37)            ≥15 cigarettes/day  38 (22.22)  31 (18.24)  28 (16.47)  33 (19.20)    Body mass index (tertiles)b,c          0.18      I  53 (30.99)  50 (29.41)  54 (31.95)  70 (41.18)        II  55 (32.16)  67 (39.41)  56 (33.14)  49 (28.82)        III  63 (36.84)  53 (31.18)  59 (34.91)  51 (30.00)    Occupational physical activity at age 30–39          0.003      Mainly sitting  15 (8.77)  25 (14.71)  29 (17.06)  35 (20.47)        Standing  49 (28.65)  37 (21.76)  37 (21.76)  58 (33.92)        Average  64 (37.43)  60 (35.29)  69 (40.59)  41 (23.98)        Strenuous  43 (25.15)  48 (28.24)  35 (20.59)  37 (21.64)    Coffee (cups/week)          0.002      <10  42 (24.56)  44 (25.88)  59 (34.71)  71 (41.52)        10–20  57 (33.33)  50 (29.41)  59 (34.71)  40 (23.29)        >20  72 (42.11)  76 (44.71)  52 (30.59)  60 (35.09)    History of hypertension          0.55      No  127 (74.27)  128 (75.29)  123 (72.35)  135 (78.95)        Yes  44 (25.73)  42 (24.71)  47 (27.65)  36 (21.05)    History of diabetes          0.20      No  162 (94.74)  156 (91.76)  165 (97.06)  161 (94.15)        Yes  9 (5.26)  14 (8.24)  5 (2.94)  10 (5.85)    History of hyperlipidemia          0.01      No  112 (65.50)  121 (71.18)  124 (72.94)  139 (81.29)        Yes  59 (34.50)  49 (28.82)  46 (27.06)  32 (18.71)    Family history of AMI          0.39      No  134 (78.36)  136 (80.00)  143 (84.12)  144 (84.21)        Yes  37 (21.64)  34 (20.00)  27 (15.88)  27 (15.79)    Characteristics  DII quartiles    −4.46, −1.38  −1.37, −0.09  −0.08, 1.09  1.10, 5.45  P valuea  Age (years)      Mean ± SD  58 ± 10  57 ± 10  58 ± 12  56 ± 13  0.26    No. (%)  No. (%)  No. (%)  No. (%)    Sex          0.03      Male  123 (71.93)  114 (67.06)  101 (59.41)  101 (59.06)        Female  48 (28.07)  56 (32.94)  69 (40.59)  70 (40.94)    Education (years)b          0.57      <7  70 (41.18)  84 (50.91)  75 (44.38)  79 (47.02)        7–11  64 (37.65)  45 (27.27)  58 (34.32)  55 (32.74)        >11  36 (21.18)  36 (21.82)  36 (21.30)  34 (20.24)    Tobacco smoking          0.10      Never smokers  60 (35.09)  78 (45.88)  78 (45.88)  72 (42.11)        Ex-smokers  56 (32.75)  48 (28.24)  46 (27.06)  38 (22.22)        Current smokers                    <15 cigarettes/day  17 (9.94)  13 (7.65)  18 (10.59)  28 (16.37)            ≥15 cigarettes/day  38 (22.22)  31 (18.24)  28 (16.47)  33 (19.20)    Body mass index (tertiles)b,c          0.18      I  53 (30.99)  50 (29.41)  54 (31.95)  70 (41.18)        II  55 (32.16)  67 (39.41)  56 (33.14)  49 (28.82)        III  63 (36.84)  53 (31.18)  59 (34.91)  51 (30.00)    Occupational physical activity at age 30–39          0.003      Mainly sitting  15 (8.77)  25 (14.71)  29 (17.06)  35 (20.47)        Standing  49 (28.65)  37 (21.76)  37 (21.76)  58 (33.92)        Average  64 (37.43)  60 (35.29)  69 (40.59)  41 (23.98)        Strenuous  43 (25.15)  48 (28.24)  35 (20.59)  37 (21.64)    Coffee (cups/week)          0.002      <10  42 (24.56)  44 (25.88)  59 (34.71)  71 (41.52)        10–20  57 (33.33)  50 (29.41)  59 (34.71)  40 (23.29)        >20  72 (42.11)  76 (44.71)  52 (30.59)  60 (35.09)    History of hypertension          0.55      No  127 (74.27)  128 (75.29)  123 (72.35)  135 (78.95)        Yes  44 (25.73)  42 (24.71)  47 (27.65)  36 (21.05)    History of diabetes          0.20      No  162 (94.74)  156 (91.76)  165 (97.06)  161 (94.15)        Yes  9 (5.26)  14 (8.24)  5 (2.94)  10 (5.85)    History of hyperlipidemia          0.01      No  112 (65.50)  121 (71.18)  124 (72.94)  139 (81.29)        Yes  59 (34.50)  49 (28.82)  46 (27.06)  32 (18.71)    Family history of AMI          0.39      No  134 (78.36)  136 (80.00)  143 (84.12)  144 (84.21)        Yes  37 (21.64)  34 (20.00)  27 (15.88)  27 (15.79)    AMI, acute myocardial infarction. a P value for ANOVA and Chi-square test were used for continuous and categorical variables, respectively. b The sum does not add up to the total because of some missing values. c Sex-specific tertiles (upper limits men/women (kg/m2): 24.5/23.7, 27.4/27.3). Distribution of 10 food groups across quartiles of DII are provided for controls and cases in table 2 and Supplementary table S2, respectively. Controls in quartile 4 of DII had significantly lower servings of fruit, vegetables and fish and had significantly higher servings of sugar and desserts and nearly significant higher levels of cereals compared to controls in quartile 1 (table 2). Similarly, cases in quartile 4 of DII had significantly lower servings of fruit, vegetables and fish and had significantly higher servings of sugar, cereals and desserts, and nearly significant higher levels of pork compared to cases in quartile 1 of DII (Supplementary table S2). Table 2 Distribution of servings of food groups across quartiles of dietary inflammatory index (DII) among 682 controls (mean ± standard deviation). Italy, 1995–2003   DII quartiles (range of DII scores)        −4.46, −1.38  −1.37, −0.09  −0.08, 1.09  1.10, 5.45  P valuea  P valueb  Servings/week                  Fruit  23.08 ± 9.18  19.73 ± 8.71  14.07 ± 6.90  9.34 ± 6.65  <0.0001  <0.0001      Vegetables  12.97 ± 4.23  10.38 ± 4.16  8.64 ± 3.76  6.30 ± 3.90  <0.0001  <0.0001      Fish  2.16 ± 1.15  1.94 ± 1.04  1.67 ± 1.12  1.51 ± 0.87  <0.0001  <0.0001      Egg  1.49 ± 1.08  1.33 ± 1.08  1.37 ± 1.12  1.67 ± 1.77  0.21  0.28      Coffee  19.96 ± 11.88  20.12 ± 11.99  19.51 ± 12.26  20.66 ± 14.75  0.73  0.63      Cheese  4.08 ± 1.93  4.40 ± 2.23  4.03 ± 1.97  4.09 ± 2.59  0.66  0.96      Pork  2.96 ± 1.52  3.47 ± 2.32  3.33 ± 2.09  3.30 ± 2.81  0.25  0.17      Sugar  26.94 ± 25.69  31.77 ± 28.78  33.88 ± 27.46  35.09 ± 34.07  0.008  0.01      Cereals  25.34 ± 10.48  27.63 ± 12.23  29.49 ± 13.12  27.65 ± 12.66  0.04  0.08      Desserts  4.51 ± 4.88  5.18 ± 4.95  6.12 ± 6.74  7.73 ± 10.73  <0.0001  0.0004    DII quartiles (range of DII scores)        −4.46, −1.38  −1.37, −0.09  −0.08, 1.09  1.10, 5.45  P valuea  P valueb  Servings/week                  Fruit  23.08 ± 9.18  19.73 ± 8.71  14.07 ± 6.90  9.34 ± 6.65  <0.0001  <0.0001      Vegetables  12.97 ± 4.23  10.38 ± 4.16  8.64 ± 3.76  6.30 ± 3.90  <0.0001  <0.0001      Fish  2.16 ± 1.15  1.94 ± 1.04  1.67 ± 1.12  1.51 ± 0.87  <0.0001  <0.0001      Egg  1.49 ± 1.08  1.33 ± 1.08  1.37 ± 1.12  1.67 ± 1.77  0.21  0.28      Coffee  19.96 ± 11.88  20.12 ± 11.99  19.51 ± 12.26  20.66 ± 14.75  0.73  0.63      Cheese  4.08 ± 1.93  4.40 ± 2.23  4.03 ± 1.97  4.09 ± 2.59  0.66  0.96      Pork  2.96 ± 1.52  3.47 ± 2.32  3.33 ± 2.09  3.30 ± 2.81  0.25  0.17      Sugar  26.94 ± 25.69  31.77 ± 28.78  33.88 ± 27.46  35.09 ± 34.07  0.008  0.01      Cereals  25.34 ± 10.48  27.63 ± 12.23  29.49 ± 13.12  27.65 ± 12.66  0.04  0.08      Desserts  4.51 ± 4.88  5.18 ± 4.95  6.12 ± 6.74  7.73 ± 10.73  <0.0001  0.0004  a P values were obtained from ANOVA. b P values were obtained from t test to test if the mean in quartile 4 is different from that of quartile 1. Table 3 shows the OR of AMI, and the corresponding 95% CI, according to DII score. Subjects in the highest DII score quartile had a 60% increased odds for AMI compared to subjects in the lowest quartile (ORQuartile4vs1= 1.60, 95% CI = 1.06, 2.41; P-trend = 0.02). The OR of AMI was 1.14 (95% CI: 1.05, 1.24) for one-unit increase in DII score (i.e., ≈9% of the range of DII). After excluding coffee as a covariate in the model and including caffeine in the DII definition, the OR of AMI was 1.12 (95% CI: 1.03, 1.21) for one-unit increase in DII score. Table 3 Odds ratios (OR) of acute myocardial infarction and corresponding 95% confidence intervals (CI) according to dietary inflammatory index (DII) among 760 cases and 682 controls. Italy, 1995–2003   DII quartiles, OR (95% CI)  P value for trend  DII continuousd  −4.46, −1.38  −1.37, −0.09  −0.08,1.09  1.10,5.45  Cases/Controls  154/171  187/170  194/170  225/171    760/682  Model 1a  1 b  1.30 (0.94, 1.79)  1.47 (1.05, 2.07)  1.84 (1.27, 2.67)  0.001  1.15 (1.07, 1.24)  Model 2c  1 b  1.26 (0.88, 1.79)  1.47 (1.01, 2.12)  1.60 (1.06, 2.41)  0.02  1.14 (1.05, 1.24)    DII quartiles, OR (95% CI)  P value for trend  DII continuousd  −4.46, −1.38  −1.37, −0.09  −0.08,1.09  1.10,5.45  Cases/Controls  154/171  187/170  194/170  225/171    760/682  Model 1a  1 b  1.30 (0.94, 1.79)  1.47 (1.05, 2.07)  1.84 (1.27, 2.67)  0.001  1.15 (1.07, 1.24)  Model 2c  1 b  1.26 (0.88, 1.79)  1.47 (1.01, 2.12)  1.60 (1.06, 2.41)  0.02  1.14 (1.05, 1.24)  a Adjusted for age, sex, and total energy intake. b Reference category. c Model 1 additionally adjusted for education (<7; 7–11; ≥12 years), tobacco smoking (never; former; current: <15; ≥25 cigarettes/day), body mass index (sex-specific tertiles among controls. Upper limits men/women (kg/m2): 24.5/23.7, 27.4/27.3), occupational physical activity at age 30–39 (strenuous, average, standing, mainly sitting), coffee consumption (<10, 10–20, ≥21 cups/week), history of hypertension, history of hyperlipidemia, history of diabetes and family history of acute myocardial infarction in first-degree relatives. d One unit increase equals to ≈9% range of DII in this study (−6.22 to +5.45). Table 4 shows the OR of AMI according to DII score in strata of selected covariates. Stronger associations—although in the absence of significant heterogeneity (P > 0.10)—were observed among females (ORQuartile4vs1= 2.13, 95% CI 0.90, 5.06), subjects aged ≥60 years (ORQuartile4vs1= 1.88, 95% CI 1.06, 3.34), never smokers (ORQuartile4vs1= 2.12, 95% CI 0.98, 4.58), with history of hypertension (ORQuartile4vs1= 4.23, 95% CI 1.76, 10.18) and individuals with no family history of AMI (ORQuartile4vs1= 1.75, 95% CI 1.08, 2.83). Table 4 Odds ratios (OR) of acute myocardial infarction and corresponding 95% confidence intervals (CI) according to quartiles of dietary inflammatory index (DII), in strata of selected covariates, Italy, 1995–2003   Cases/controls  DII quartiles, OR (95% CI)a  Ptrend  pinteraction  −4.46, −1.38  −1.37, −0.09  −0.08,1.09  1.10,5.45  Sex              0.21      Male  580/439  1b  1.06 (0.71, 1.59)  1.44 (0.93, 2.23)  1.46 (0.90, 2.39)  0.08        Female  180/243  1b  2.11 (0.93, 4.78)  1.70 (0.74, 3.88)  2.13 (0.90, 5.06)  0.20    Age (years)              0.19      <60  338/355  1b  0.99 (0.56, 1.66)  1.10 (0.64, 1.90)  1.18 (0.65, 2.15)  0.54        ≥60  422/327  1b  1.64 (0.99, 2.70)  1.76 (1.05, 2.96)  1.88 (1.06, 3.34)  0.05    Education (years)              0.81      <7  321/308  1b  1.21 (0.70, 2.08)  1.00 (0.55, 1.83)  1.59 (0.83, 3.03)  0.20        ≥7  428/364  1b  1.30 (0.80, 2.10)  1.76 (1.08, 2.86)  1.55 (0.90, 2.69)  0.09    Tobacco smoking              0.48      Never smokers  235/288  1b  1.51 (0.78, 2.91)  1.54 (0.78, 3.06)  2.12 (0.98, 4.58)  0.07        Ever smokers  525/394  1b  1.26 (0.82, 1.94)  1.68 (1.07, 2.63)  1.75 (1.08, 2.85)  0.02    Hypertension              0.13      No  519/513  1b  1.13 (0.74, 1.72)  1.29 (0.83, 2.00)  1.22 (0.76, 1.98)  0.38        Yes  241/169  1b  1.91 (0.95, 3.86)  2.74 (1.25, 6.00)  4.23 (1.76, 10.18)  0.001    Family history of AMI              0.78      No  511/557  1b  1.27 (0.84, 1.94)  1.51 (0.98, 2.33)  1.75 (1.08, 2.83)  0.02        Yes  249/125  1b  1.23 (0.61, 2.45)  1.25 (0.57, 2.75)  1.29 (0.55, 3.02)  0.57      Cases/controls  DII quartiles, OR (95% CI)a  Ptrend  pinteraction  −4.46, −1.38  −1.37, −0.09  −0.08,1.09  1.10,5.45  Sex              0.21      Male  580/439  1b  1.06 (0.71, 1.59)  1.44 (0.93, 2.23)  1.46 (0.90, 2.39)  0.08        Female  180/243  1b  2.11 (0.93, 4.78)  1.70 (0.74, 3.88)  2.13 (0.90, 5.06)  0.20    Age (years)              0.19      <60  338/355  1b  0.99 (0.56, 1.66)  1.10 (0.64, 1.90)  1.18 (0.65, 2.15)  0.54        ≥60  422/327  1b  1.64 (0.99, 2.70)  1.76 (1.05, 2.96)  1.88 (1.06, 3.34)  0.05    Education (years)              0.81      <7  321/308  1b  1.21 (0.70, 2.08)  1.00 (0.55, 1.83)  1.59 (0.83, 3.03)  0.20        ≥7  428/364  1b  1.30 (0.80, 2.10)  1.76 (1.08, 2.86)  1.55 (0.90, 2.69)  0.09    Tobacco smoking              0.48      Never smokers  235/288  1b  1.51 (0.78, 2.91)  1.54 (0.78, 3.06)  2.12 (0.98, 4.58)  0.07        Ever smokers  525/394  1b  1.26 (0.82, 1.94)  1.68 (1.07, 2.63)  1.75 (1.08, 2.85)  0.02    Hypertension              0.13      No  519/513  1b  1.13 (0.74, 1.72)  1.29 (0.83, 2.00)  1.22 (0.76, 1.98)  0.38        Yes  241/169  1b  1.91 (0.95, 3.86)  2.74 (1.25, 6.00)  4.23 (1.76, 10.18)  0.001    Family history of AMI              0.78      No  511/557  1b  1.27 (0.84, 1.94)  1.51 (0.98, 2.33)  1.75 (1.08, 2.83)  0.02        Yes  249/125  1b  1.23 (0.61, 2.45)  1.25 (0.57, 2.75)  1.29 (0.55, 3.02)  0.57    AMI, acute myocardial infarction. a Adjusted for age, sex, education (<7; 7–11; ≥12 years), tobacco smoking (never; former; current: <15; ≥25 cigarettes/day), body mass index (sex-specific tertiles among controls. Upper limits men/women (kg/m2): 24.5/23.7, 27.4/27.3), occupational physical activity at age 30–39 (strenuous, average, standing, mainly sitting), coffee consumption (<10,10–20, ≥21 cups/week), history of hypertension, history of hyperlipidemia, history of diabetes, family history of acute myocardial infarction in first-degree relatives, and total energy intake, when appropriate. b Reference category. Discussion In this large Italian case–control study we investigated the association between inflammatory potential of diet and AMI. We observed a 60% excess AMI risk among individuals with a pro-inflammatory diet as expressed by high DII scores. There have been several reports on the association between diet and AMI in the present case–control study.10,23,32–36 A high adherence to Mediterranean diet was inversely associated with AMI.10 In relation to nutrients, high intakes of anthocyanidins,23 folates, vitamin B6,33 fiber,34 iron35 also have been found to be inversely associated with AMI. The dietary non-enzymatic antioxidant capacity, measured through three different assays as the ferric reducing-antioxidant power (i.e. Trolox equivalent antioxidant capacity and total radical-trapping antioxidant parameter), also was inversely associated with AMI.36 No association was observed with high glycemic load and glycemic index, but slightly increased ORs were observed for glycemic index in elderly people and in association with overweight.32 Our findings are consistent with some previous results for DII and incident CVD as well as CVD mortality.14–18 In the prospective PREDIMED study in Spain, increased risk of CVD was observed across the quartiles (i.e. with increasing inflammatory potential): HR (quartile4 vs. 1) = 1.73 (95% CI 1.15–2.60).18 In an Australian cohort, men with a pro-inflammatory diet at baseline were twice as likely to experience a CVD event over the study period (OR 2.00; 95% CI 1.03–3.96).16 However, no association was observed in a couple of other studies.14,37 One of the possible mechanisms through which the observed positive association between DII and AMI is through attraction and migration of inflammatory cells into vascular tissue by cytokines (interleukin-1 [IL-1], tumor necrosis factor-α [TNF-α]).38 These cytokines also induce the expression of cellular adhesion molecules, which mediate adhesion of leukocytes to the vascular endothelium.39 With regard to strengths and limitations of the present study, cases and controls were interviewed in the same hospitals and came from the same geographical area; participation was almost complete; patients admitted for chronic conditions or diseases related to known risk factors for AMI or modification of diet were excluded from the comparison group; and the FFQ was satisfactorily valid and reproducible.24–26 We accounted for major risk factors for AMI in the analyses, such as physical activity, BMI, smoking, coffee drinking and energy intake. Another major strength of the present study is the strict definition of AMI and validated of the endpoint, because all cases were admitted to reference centres for heart diseases. Also, the DII score, which takes into account both pro- and anti-inflammatory food parameters, reflects the relationship of the inflammatory potential of diet to affect AMI risk than would single nutrients or diet components considered individually. A potential limitation of the DII is the use of the US flavonoid food composition database to describe the Italian diet; however, this is unlikely to have introduced spurious associations because the same food composition database was applied to the intakes of both cases and controls, and imprecise classification of exposure is likely to lead to an attenuation of any real association. A further limitation is that we derived DII scores from 30 of the potential 45 food and nutrient items that can be used to compute this index; however, other published studies that rely on FFQ date also derive DII scores from a sub-optimal number of items, and the ability to still detect significant associations.12 Moreover, some of the missing food parameters such as saffron, ginger and turmeric are consumed infrequently in this population; so, non-availability of these parameters may not have played a major impact. In conclusion, this study indicates a detrimental role of a pro-inflammatory diet, as measured by higher DII scores, on AMI among Italians through a process of inflammation. Acknowledgements This study was partly supported by a grant from the Italian Foundation for Research on Cancer. Drs. Shivappa and Hébert were supported by grant number R44DK103377 to CHI from the United States National Institute of Diabetes and Digestive and Kidney Diseases. Conflict of interest: None declared. Disclosure: Dr. James R. Hébert owns controlling interest in Connecting Health Innovations LLC (CHI), a company planning to license the right to his invention of the dietary inflammatory index (DII) from the University of South Carolina in order to develop computer and smart phone applications for patient counseling and dietary intervention in clinical settings. Dr. Nitin Shivappa is an employee of CHI. Key points Dietary Inflammatory Index is a tool that measures the inflammatory potential of individual’s diet. This Italian case–control study provided an opportunity to examine the association between inflammatory potential of diet and AMI. The results from this study indicate a detrimental role of a pro-inflammatory diet on AMI, which is consistent with the role of inflammation in AMI. References 1 Sanchis-Gomar F , Perez-Quilis C, Leischik R, Lucia A. Epidemiology of coronary heart disease and acute coronary syndrome. 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Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

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The European Journal of Public Health – Oxford University Press

Published: Feb 1, 2018

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