Dietary Fiber Reduces Peripheral Arterial Disease Risk in Men: study

We prospectively evaluated the relationship between dietary fiber and Peripheral arterial disease risk (PAD) among 46,032 men, aged 40 to 75 y, in 1986. Subjects answered a vascular disease questionnaire and completed a validated 131-item food frequency questionnaire, and were free of PAD, cardiovascular disease and diabetes. During 12 y of follow-up 308 incident PAD cases were documented.

After adjusting for age, smoking, hypertension, hypercholesterolemia, family history of early coronary heart disease, alcohol consumption, BMI, physical activity and energy intake, PAD risk in each quintile of cereal fiber intake compared with the lowest quintile was 0.69, 95% CI 0.49–0.97 for quintile 2; 0.65, 95% CI 0.45–0.94 for quintile 3; 0.68, 95% CI 0.47–0.98 for quintile 4; and 0.67, 95% CI 0.47–0.97 for quintile 5. In a nonlinear model the overall inverse association (P = 0.02) and nonlinear components (P = 0.03) were significant. Fruit, vegetable and total fiber intakes were not associated with PAD risk. These results suggest an inverse association between cereal fiber intake and PAD risk in men. Increasing cereal fiber intake may prevent PAD.

Peripheral arterial disease (PAD) is a major cause of morbidity, and people with PAD are more than four times as likely to die of any cause over 2 y from diagnosis than those without. Severe disease often requires surgery, including limb amputation, and operative mortality is high. PAD results mainly from atherosclerotic narrowing of the blood vessel lumen and shares some risk factors with coronary heart disease (CHD) and stroke, including diabetes, hypertension and cigarette smoking. There is little direct evidence, however, for the role of diet. An inverse association between fiber and PAD has been suggested in a cross-sectional study, a case-control study and a cohort study conducted among Finnish smokers. This is biologically plausible because soluble fiber reduces LDL. Cereal fiber intake specifically has been inversely associated with the risk of CHD and diabetes, and has been shown to favorably impact total cholesterol, LDL and fasting serum insulin. Therefore, we prospectively examined the association of dietary fiber intake with incident PAD in a large cohort of men followed for 12 y.

The Health Professionals Follow-up Study began in 1986 when 51,529 U.S. male health professionals, aged 40 to 75 y, volunteered to participate in the study (29,683 dentists, 4185 pharmacists, 3745 optometrists, 1600 podiatrists and 10,098 veterinarians; 531 African-American and 877 Asian-American). The participants received questionnaires at baseline and biennially to determine lifestyle and medical conditions, and validated food frequency questionnaires (FFQ) every 4 y to determine diet.

We excluded men with a history of PAD, CHD, stroke and diabetes, because they may have changed their diets following disease, and men with inadequate dietary data (reported energy intake <3352 or >17598 kJ, or >70 unanswered of 131 items in the FFQ) resulting in 46,032 men in this analysis. The study was approved by the Human Subjects Committee of the Harvard School of Public Health.

Case ascertainment.
If a participant reported intermittent claudication or surgery for PAD during follow-up, we requested permission to review his medical record to confirm the diagnosis and the date of occurrence of the disease. Cases of PAD were considered definite if the medical record contained either a report of surgery for PAD, ankle systolic blood pressure index (ABPI) < 0.80, a physician diagnosis, or an angiogram or Doppler ultrasound reporting 50% or more obstruction of at least one artery plus symptoms in the ipsilateral limb. Participants who confirmed the diagnosis of PAD by letter or over the telephone, but without available medical records, were considered probable PAD cases.

Diet and exposure information.

Diet was assessed by a validated FFQ every 4 y starting in 1986. For each food the FFQ contains a commonly used unit or portion, e.g., one apple or one cup of cooked spinach. The respondent was requested to estimate how often on average he consumed a unit or portion during the previous year. There were nine possible responses, ranging from never or less than once a month up to six or more times per day. Nutrient (including fiber) intake was estimated by multiplying the mean nutrient content of the specified unit or portion by the number of times it was consumed. The nutrient content of foods was estimated from the Harvard University Food Composition Database that is derived from USDA sources, manufacturers’ information and data from peer-reviewed literature. Whole grain foods classified as described by Jacobs et al.  and Liu et al., included brown rice, dark breads, whole grain breakfast cereal, cooked cereal, popcorn, wheat germ, bran and other grains. The validity of the FFQ has been described elsewhere. When compared with diet records the FFQ was found to be a good measure of breakfast cereal intakes (r = 0.86), dark bread (r = 0.77) and dietary fiber (r = 0.68). Fiber intakes for total energy were adjusted by regression analysis as described elsewhere. Briefly, we calculated a residual from a linear regression model with fiber as the dependent variable and total energy as the independent variable. The residual was added to the mean fiber intake for that population. Energy-adjusted intake of cereal fiber, for example, was interpreted as the composition of cereal fiber in the diet independent of the total quantity of food eaten.

The enrollment and follow-up questionnaires requested information on age, smoking, diagnosed or treated hypertension, hypercholesterolemia, angina, supplement use, weight and physical activity. Subjects were asked the average amount of time they spent per week on various physical activities including walking, jogging, running, bicycling, calisthenics, aerobics, machine rowing, swimming, squash, racquetball and tennis. From this information weekly energy expenditure in metabolic equivalent task-hours (MET) were calculated. BMI was computed by dividing weight (kg) by the squared height (m2) for every 2-y follow-up period.

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Anwar T. Merchant, Frank B. Hu, Donna Spiegelman, Walter C. Willett, Eric B. Rimm and Alberto Ascherio

Department of Nutrition, Harvard School of Public Health;
Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine;
Departments of Epidemiology and Biostatistics, Harvard School of Public Health;
The Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115

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