Seungyoun Jung1, Youjin Je2, Edward L Giovannucci3, Bernard Rosner4, Shuji Ogino5, Eunyoung Cho6. 1. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. 2. Department of Food and Nutrition, Kyung Hee University, Seoul, South Korea. 3. Departments of Nutrition Epidemiology, and. 4. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA Biostatistics, Harvard School of Public Health, Boston, MA. 5. Epidemiology, and Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA; and. 6. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA Department of Dermatology, The Warren Alpert Medical School of Brown University, Providence, RI eunyoung.cho@channing.harvard.edu.
Abstract
BACKGROUND: One-carbon metabolism, which is crucial in DNA synthesis and genomic stability, is an interrelated network of biochemical reactions involved in several dietary and lifestyle factors. The development of the homocysteine score using these factors may be useful to reflect the status of one-carbon metabolism in large epidemiologic studies without biologic samples to measure homocysteine directly. OBJECTIVE: The aim of this study was to develop an homocysteine score that reflects one-carbon metabolism better than individual dietary or lifestyle factors. METHODS: We divided 2023 participants with measured plasma total homocysteine data in the Nurses' Health Study and the Health Professionals Follow-Up Study into training (n = 1619) and testing (n = 404) subsets. Using multivariable linear regression, we selected lifestyle determinants of plasma homocysteine in the training set and derived the homocysteine score weighted by the β coefficient for each predictor. The validation of the homocysteine score was assessed using the plasma homocysteine in the independent samples of the training set. RESULTS: In the training set, smoking, multivitamin use, and caffeine, alcohol, and dietary and supplemental folate intake were significant independent determinants of plasma homocysteine in multivariable linear regression (P ≤ 0.01) and were included in the derivation of the homocysteine score. The Pearson correlation of the homocysteine score with plasma homocysteine was 0.30 in the testing subset (P < 0.001). The homocysteine score was positively associated with the plasma homocysteine concentration in the testing subset and in an independent population of women; the mean difference of plasma homocysteine concentration between the extreme quintiles of homocysteine score ranged from 0.83 μmol/L to 1.52 μmol/L. Population misclassification either from the lowest quintile of plasma homocysteine into the highest quintile of the homocysteine score or from the highest quintile of plasma homocysteine into the lowest quintile of the homocysteine score was ≤12%. CONCLUSION: These data indicate that the homocysteine score may be used with relatively inexpensive and simple questionnaires to rank an individual's one-carbon metabolism status when homocysteine data are not available.
BACKGROUND:One-carbon metabolism, which is crucial in DNA synthesis and genomic stability, is an interrelated network of biochemical reactions involved in several dietary and lifestyle factors. The development of the homocysteine score using these factors may be useful to reflect the status of one-carbon metabolism in large epidemiologic studies without biologic samples to measure homocysteine directly. OBJECTIVE: The aim of this study was to develop an homocysteine score that reflects one-carbon metabolism better than individual dietary or lifestyle factors. METHODS: We divided 2023 participants with measured plasma total homocysteine data in the Nurses' Health Study and the Health Professionals Follow-Up Study into training (n = 1619) and testing (n = 404) subsets. Using multivariable linear regression, we selected lifestyle determinants of plasma homocysteine in the training set and derived the homocysteine score weighted by the β coefficient for each predictor. The validation of the homocysteine score was assessed using the plasma homocysteine in the independent samples of the training set. RESULTS: In the training set, smoking, multivitamin use, and caffeine, alcohol, and dietary and supplemental folate intake were significant independent determinants of plasma homocysteine in multivariable linear regression (P ≤ 0.01) and were included in the derivation of the homocysteine score. The Pearson correlation of the homocysteine score with plasma homocysteine was 0.30 in the testing subset (P < 0.001). The homocysteine score was positively associated with the plasma homocysteine concentration in the testing subset and in an independent population of women; the mean difference of plasma homocysteine concentration between the extreme quintiles of homocysteine score ranged from 0.83 μmol/L to 1.52 μmol/L. Population misclassification either from the lowest quintile of plasma homocysteine into the highest quintile of the homocysteine score or from the highest quintile of plasma homocysteine into the lowest quintile of the homocysteine score was ≤12%. CONCLUSION: These data indicate that the homocysteine score may be used with relatively inexpensive and simple questionnaires to rank an individual's one-carbon metabolism status when homocysteine data are not available.
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