Session: ASIP Last-Chance Poster Viewing - Molecular and Cellular Pathobiology of Cancer
(918.7) Polyunsaturated fatty acids may decrease cancer risk in rural midwestern post-menopausal women on vitamin D and calcium supplementation
Monday, April 4, 2022
11:45 AM – 12:45 PM
Location: Exhibit/Poster Hall A-B - Pennsylvania Convention Center
Poster Board Number: D78
Mariah Jackson (University of Nebraska Medical Center), Kathleen Angell (University of Nebraska Medical Center), Lynette Smith (University of Nebraska Medical Center), Joan Lappe (Creighton University), Laura Armas (University of Nebraska Medical Center), Diane Ehlers (University of Nebraska Medical Center), Christopher DAngelo (University of Nebraska Medical Center), Corrine Hanson (University of Nebraska Medical Center)
Presenting Author University of Nebraska Medical Center Omaha, Nebraska
Objectives: Determine the extent to which fatty acid intakes at baseline are independently associated with cancer development by final study visit in a cohort of rural midwestern post-menopausal women. Our hypothesis was higher polyunsaturated fatty acid (PUFA) intake at baseline will be protective in cancer development.
Methods: This study was a secondary analysis of participants enrolled in a randomized controlled trial evaluating the effect of a four-year vitamin D and calcium supplementation intervention (2000 IU/d vitamin D3 and 1500 mg/d calcium) on cancer development in rural, midwestern post-menopausal women from June 2009-August 2015. The primary outcome was any-type cancer assessed at 6-month intervals over four years. Intakes of five fatty acid categories (saturated fat, monounsaturated fatty acids (MUFAs), PUFA, omega (n)-3 fatty acids, n-6 fatty acids) and seven individual fatty acids (linoleic acid, linolenic acid, stearidonic acid, arachidonic acid, eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA)) were evaluated via a Block Food Frequency Questionnaire at baseline. Multivariable logistic regression models, adjusted for body mass index (BMI), age, hormone replacement therapy, physical activity score, smoking status and treatment, were utilized to determine if consumption levels of fatty acids at baseline were associated with development of cancer. Interaction with treatment group was examined.
Results: There were 2,109 participants available for analysis; 109 developed cancer. The majority of participants (99.5%) were White, with a mean age of 65.1 years and mean BMI of 29.9 kg/m2. Participants who developed cancer were significantly older (Cancer cases: 67.9 years, No cancer: 65 years; plt;0.001) and had lower BMIs (Cancer cases: 28.4 kg/m2, No Cancer: 30.0 kg/m2; p= 0.015). There were no differences in demographics between treatment and placebo groups. In adjusted logistic regression models, PUFAs were associated with an increased risk of cancer development in the placebo group [OR 1.04; 95% CI (1.00, 1.08); p=0.048]. In the vitamin D and calcium treatment group, PUFA [OR 0.95; 95% CI (0.90, 1.00); p=0.042], linoleic acid [OR 0.93; 95% CI (0.88, 1.00); p=0.035], DHA [OR 0.98; 95% CI (0.97, 1.00); p=0.045] and n-6 fatty acids [OR 0.93; 95% CI (0.88, 1.00); p=0.036] were found to decrease the odds of cancer development.
Conclusions: When combined with vitamin D and calcium supplementation, PUFAs, including DHA, linoleic acid, and grouped n-6 fatty acids, may be protective against cancer development in rural midwestern post-menopausal women. In the absence of vitamin D and calcium supplementation, PUFA intake may increase odds of developing cancer. Further research should investigate fatty acid intake in the context of healthy diet patterns, including adequate vitamin D and calcium, for cancer prevention.
Support or Funding Information
The original study was funded by the National Cancer Institute and Creighton University internal funding. No additional funding was used for this secondary analysis.
Table 1: Demographics of Total Study Population and by Cancer Status; Table 2: Adjusted Associations Between Fatty Acids and Cancer Diagnosis Using Logistic Regression