Conclusion Long term dietary intake of gluten was not associated with risk of coronary heart disease. However, the avoidance of gluten may result in reduced consumption of beneficial whole grains, which may affect cardiovascular risk. The promotion of gluten-free diets among people without celiac disease should not be encouraged.

Results During 26 years of follow-up encompassing 2 273 931 person years, 2431 women and 4098 men developed coronary heart disease. Compared with participants in the lowest fifth of gluten intake, who had a coronary heart disease incidence rate of 352 per 100 000 person years, those in the highest fifth had a rate of 277 events per 100 000 person years, leading to an unadjusted rate difference of 75 (95% confidence interval 51 to 98) fewer cases of coronary heart disease per 100 000 person years. After adjustment for known risk factors, participants in the highest fifth of estimated gluten intake had a multivariable hazard ratio for coronary heart disease of 0.95 (95% confidence interval 0.88 to 1.02; P for trend=0.29). After additional adjustment for intake of whole grains (leaving the remaining variance of gluten corresponding to refined grains), the multivariate hazard ratio was 1.00 (0.92 to 1.09; P for trend=0.77). In contrast, after additional adjustment for intake of refined grains (leaving the variance of gluten intake correlating with whole grain intake), estimated gluten consumption was associated with a lower risk of coronary heart disease (multivariate hazard ratio 0.85, 0.77 to 0.93; P for trend=0.002).

Short of strict gluten avoidance, people may reduce gluten in their diet owing to beliefs that this practice carries general health benefits. 14 The reasons for gluten reduction likely relate to the perception that gluten carries adverse health effects. One national survey showed a steep rise in interest in this diet in recent years, and by 2013 nearly 30% of adults in the US reported that they were trying to minimize or avoid gluten. 15 Concerns exist that a gluten-free or gluten restricted diet may be nutritionally suboptimal, 16 and gluten-free substitute foods cost considerably more than their counterparts that contain gluten. 17 18 Despite the rising trend in gluten restriction, no long term, prospective studies have assessed the relation of dietary gluten with the risk of chronic conditions such as coronary heart disease in people without celiac disease. Thus, using prospective, validated data on dietary intake collected over 20-30 years, we examined the association of estimated long term intake of gluten with the development of incident coronary heart disease (fatal or non-fatal myocardial infarction).

On the basis of evidence that gluten may promote inflammation in the absence of celiac disease or non-celiac gluten sensitivity, 5 concern has arisen in the medical community and lay public that gluten may increase the risk of obesity, metabolic syndrome, neuropsychiatric symptoms, and cardiovascular risk among healthy people. 6 7 8 9 10 As a result, diets that limit gluten intake have gained popularity. 11 12 In an analysis of the National Health and Nutrition Examination Survey (NHANES), most people adhering to a gluten-free diet did have a diagnosis of celiac disease. 3 Moreover, in a follow-up analysis of NHANES, adoption of a gluten-free diet by people without celiac disease rose more than threefold from 2009-10 (prevalence 0.52%) to 2013-14 (prevalence 1.69%). 13

Gluten, a storage protein in wheat, rye, and barley, triggers inflammation and intestinal damage in people with celiac disease. 1 People with intestinal or extra-intestinal symptoms triggered by gluten but who do not meet formal criteria for celiac disease may have non-celiac gluten sensitivity, a clinical entity with an as yet uncharacterized biological basis. 2 Celiac disease, which is present in 0.7% of the US population, 3 is associated with an increased risk of coronary heart disease, which is reduced after treatment with a gluten-free diet. 4

Methods

Study population The Nurses’ Health Study (NHS) is a prospective cohort of 121 700 female nurses from 11 US states who were enrolled in 1976. The Health Professionals Follow-up Study (HPFS) is a prospective cohort of 51 529 male health professionals from all 50 states who were enrolled in 1986. Participants in NHS and HPFS have been followed via biennial self administered questionnaires on health and lifestyle habits, anthropometrics, environmental exposures, and medical conditions. In 1986, diet in both cohorts was assessed with a validated 136 item semiquantitative food frequency questionnaire. Among the 73 666 women in NHS and 49 934 men in HPFS who completed a food frequency questionnaire in 1986, we excluded participants if they reported implausible daily energy intake (<600 or >3500 kcal/d for women and <800 or >4200 kcal/d for men) or missing gluten data (NHS 48; HPFS 39); a diagnosis of myocardial infarction, angina, or stroke or coronary artery bypass graft surgery (NHS 4015; HPFS 2647); or cancer (NHS 4689; HPFS 1785). Participants were specifically asked about a history of celiac disease in 2014; we excluded from this analysis anyone who reported a previous diagnosis of celiac disease (NHS 200; HPFS 160). After these exclusions, 64 714 women and 45 303 men were available for analysis. Return of the mailed questionnaire was considered to imply informed consent.

Measurement of exposure and outcome In both cohorts, diet was assessed in 1986, 1990, 1994, 1998, 2002, 2006, and 2010. For each food item, participants were asked about the frequency with which they consumed a commonly used portion size for each food over the previous year; available responses ranged from never or less than once a month to six or more times a day. We calculated nutrients by using the Harvard T. H. Chan School of Public Health nutrient database, which was updated every two to four years during the period of food frequency questionnaire distribution.19 We used year specific nutrient tables for ingredient level foods. Previous validation studies have shown that the derivation of nutrient values correlates highly with nutrient intake as measured by one week food diaries in women and men.2021 For each of these two cohorts, we derived the quantity of gluten consumed. We calculated the quantity of gluten on the basis of the protein content of wheat, rye, and barley based on recipe ingredient lists from product labels provided by manufacturers or cookbooks in the case of home prepared items. Previous studies have used conversion factors of 75% or 80% when calculating the proportion of protein content that comprises gluten; we used the more conservative estimate of 75%.222324 Although gluten’s proportion of total protein may be more variable for rye and barley than for wheat,25 we used the same conversion factor for all three grains, consistent with previous studies.2223 Although trace amounts of gluten can be present in oats and in condiments (for example, soy sauce), we did not calculate gluten on the basis of these items as the quantity of gluten is much lower than that in cereals and grains and the contribution to total gluten intake would be negligible.26 In 1986 the five largest contributors to gluten in both cohorts were dark bread, pasta, cold cereal, white bread, and pizza (supplementary table A). Previous validation studies within these cohorts found that the Pearson correlation coefficients between the number of servings of these items reported on food frequency questionnaires and that reported on seven day dietary records ranged from 0.35 (pasta) to 0.79 (cold cereal) for women and from 0.37 (dark bread) to 0.86 (cold cereal) for men.2728 A separate validation study of this food frequency questionnaire found that this method of measuring vegetable (that is, plant based) protein intake, of which gluten is the major contributor, correlated highly with that measured in seven day dietary records (Spearman correlation coefficient 0.66).29 We divided cohort participants into fifths of estimated gluten consumption, according to energy adjusted grams of gluten per day. We obtained energy adjusted values by regression using the residual method, as described previously.30 To quantify long term dietary habits, we used cumulative averages through the questionnaires preceding the diagnosis of coronary heart disease, death, or the end of follow-up.31 For example, we calculated cumulative average estimated gluten intake in 1994 by averaging the daily consumption of gluten reported in 1986, 1990, and 1994. We treated cumulative average estimated gluten intake as a time varying covariate. For participants with missing dietary data, we used the most recent previous dietary response on record. Because the development of a significant illness may cause a major change in dietary habits, and so as to reduce the possibility of reverse causality, we suspended updating dietary response data for participants who developed diabetes, cardiovascular disease (including stroke, angioplasty, or coronary artery bypass graft surgery), or cancer. For such patients, the cumulative average dietary gluten value before the development of this diagnosis was carried forward until the end of follow-up.32 The primary outcome of incident coronary heart disease consisted of a composite outcome of non-fatal myocardial infarction or fatal myocardial infarction. For all participants who recorded such a diagnosis, we requested and reviewed medical records. We classified myocardial infarctions meeting World Health Organization criteria, which require typical symptoms plus either diagnostic electrocardiographic findings or elevated cardiac enzyme concentrations, as definite, and we considered myocardial infarctions requiring hospital admission and corroborated by phone interview or letter only as probable. Deaths were identified from state vital records and the National Death Index or reported by participants’ next of kin. We classified coronary heart disease deaths by examining autopsy reports, hospital records, or death certificates. Fatal coronary heart disease was confirmed via medical records or autopsy reports or if coronary heart disease was listed as the cause of death on the death certificate and there was previous evidence of coronary heart disease in the medical records. We designated as probable those cases in which coronary heart disease was the underlying cause on the death certificate but no previous knowledge of coronary heart disease was indicated and medical records concerning the death were unavailable. We considered definite and probable myocardial infarction together as our primary outcome, as we have previously found that results were similar when probable cases were excluded.33

Statistical analyses Patients were followed from 1986 until the development of coronary heart disease, death, or the end of follow-up in 2012 (June 2012 for NHS; January 2012 for HPFS). We tested for the association between cumulative average gluten intake and the development of coronary heart disease, comparing each fifth of gluten intake with the lowest fifth. We used Cox proportional hazards models conditioning on age in months and follow-up cycle to calculate age adjusted and multivariable adjusted hazard ratios and 95% confidence intervals. We first generated these estimates in each cohort and tested for heterogeneity of the associations by meta-analysis of aggregate data using the Q statistic. Because we did not observe any significant heterogeneity for the association of gluten with coronary heart disease in the two cohorts (P for heterogeneity>0.10), we then did a pooled analysis combining the participants of NHS and HPFS and estimated the hazard ratios by using Cox modeling stratified by study cohort. We tested the assumption of proportional hazards by testing the interaction term between gluten intake and the period of follow-up and found no violations of this assumption (P>0.05). We tested the hypothesis that increasing amounts of energy adjusted dietary gluten is associated with an increased risk of coronary heart disease. Our main model included non-dietary and dietary covariates, constructed a priori. Non-dietary covariates consisted of age, race (white, non-white), body mass index (by fifth), height (in inches), history of diabetes, regular (at least twice weekly) use of aspirin and non-steroidal anti-inflammatory drugs, current use of 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors (statins), current use of a multivitamin, smoking history (pack years), parental history of myocardial infarction, history of hypertension, history of hypercholesterolemia, use of physical activity as measured in metabolic equivalents (METs) per week, and (in NHS) menopausal status and menopausal hormone use. Dietary covariates were energy adjusted and consisted of daily consumption of alcohol (grams), trans fats (grams), red meats (servings), processed meats (servings), polyunsaturated fats (grams), fruits (servings), and vegetables (servings). We did several secondary analyses, constructed a priori. Firstly, because gluten is a component of both refined grains and whole grains, which are each purported to be associated with coronary heart disease, we used multivariable models examining the association between estimated gluten intake and coronary heart disease with additional adjustment for refined grain consumption and whole grain consumption. Secondly, we did stratified analyses by age (<65 v ≥65 years), body mass index (<25 v ≥25), physical activity (<18 v ≥18 MET-hours/week), and smoking status (current v never v past smoking). Thirdly, we separately considered the outcomes of fatal and non-fatal myocardial infarction. Fourthly, we considered the possibility that an association of estimated gluten intake with coronary heart disease may be evident only when extreme levels of intake are considered; we therefore examined participants according to tenths (instead of fifths) of gluten intake. Fifthly, because identification and treatment of risk factors for coronary heart disease may have changed over time, we repeated the primary analysis, restricting the time period first to 1986-97 and then to 1998-2012. Sixthly, instead of suspending dietary updates on the diagnosis of cardiovascular disease, diabetes, or cancer (as we did for the primary analysis), we repeated the primary analysis, updating dietary responses regardless of the development of these conditions. Finally, in addition to these a priori analyses, we did post-hoc analyses, including each of the following additional dietary variables in our full model: the Alternate Healthy Eating Index score, percentage protein, percentage total fat, and intake of dairy, saturated fatty acids, monounsaturated fatty acids, sodium, and dietary fiber. We used SAS version 9.4 for all analyses and considered two sided P values of <0.05 to be statistically significant.