Research LettersAre imprecise methods obscuring a relation between fat and breast cancer?
Summary
Pooled analyses of cohort studies show no relation between fat intake and breast-cancer risk. However, food-frequency questionnaire (FFQ) methods used in these studies are prone to measurement error. We assessed diet with an FFQ and a detailed 7-day food diary in 13 070 women between 1993 and 1997. We compared 168 breast-cancer cases incident by 2000 with four matched controls. Risk of breast cancer was associated with saturated-fat intake measured with the food diary (hazard ratio 1·22[95% CI 1·06–1·40], p=0·005, per quintile increase in energy-adjusted fat intake), but not with saturated fat measured with the FFQ (1·10[0·94–1·29], p=0·23). Dietary measurement error might explain the absence of a significant association between dietary fat and breastcancer risk in cohort studies.
References (5)
- GR Howe et al.
Dietary factors and risk of breast-cancer: combined analysis of 12 case control studies.
J Natl Cancer Inst
(1990) - SA Smith-Warner et al.
Types of dietary fat and breast cancer: a pooled analysis of cohort studies.
Int J Cancer
(2001)
Cited by (344)
Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake
2024, American Journal of Clinical NutritionMetabolomics has the potential to enhance dietary assessment by revealing objective measures of many aspects of human food intake. Although metabolomics studies indicate that hundreds of metabolites are associated with dietary intake, correlations have been modest (e.g., r < 0.50), and few have been evaluated in controlled feeding studies.
The aim of this study was to evaluate associations between metabolites and weighed food and beverage intake in a controlled feeding study of habitual diet.
Healthy postmenopausal females from the Women’s Health Initiative (N = 153) were provided with a customized 2-wk controlled diet designed to emulate their usual diet. Metabolites were measured by liquid chromatography tandem mass spectrometry in end-of-study 24-h urine and fasting serum samples (1293 urine metabolites; 1113 serum metabolites). We calculated partial Pearson correlations between these metabolites and intake of 65 food groups, beverages, and supplements during the feeding study. The threshold for significance was Bonferroni-adjusted to account for multiple testing (5.94 × 10−07 for urine metabolites; 6.91 × 10−07 for serum metabolites).
Significant diet–metabolite correlations were identified for 23 distinct foods, beverages, and supplements (171 distinct metabolites). Among foods, strong metabolite correlations (r ≥ 0.60) were evident for citrus (highest r = 0.80), dairy (r = 0.65), and broccoli (r = 0.63). Among beverages and supplements, strong correlations were evident for coffee (r = 0.86), alcohol (r = 0.69), multivitamins (r = 0.69), and vitamin E supplements (r = 0.65). Moderate correlations (r = 0.50–0.60) were also observed for avocado, fish, garlic, grains, onion, poultry, and black tea. Correlations were specific; each metabolite correlated with one food, beverage, or supplement, except for metabolites correlated with juice or multivitamins.
Metabolite levels had moderate to strong correlations with weighed intake of habitually consumed foods, beverages, and supplements. These findings exceed in magnitude those previously observed in population studies and exemplify the strong potential of metabolomics to contribute to nutrition research.
The Women’s Health Initiative is registered at clinicaltrials.gov as NCT00000611.
Carbon Isotope Ratios of Plasma and RBC Fatty Acids Identify Meat Consumers in a 12-Week Inpatient Feeding Study of 32 Men
2022, Journal of NutritionMolecular stable isotope ratios are a novel type of dietary biomarker with high sensitivity and specificity for certain foods. Among these, fatty acid carbon isotope ratios (CIRs) have strong potential but have not been investigated as dietary biomarkers.
We evaluated whether fatty acid CIRs and mass proportions were associated with meat, fish, and sugar-sweetened beverage (SSB) intake.
Thirty-two men [aged 46.2 ± 10.5 y; BMI (kg/m2): 27.2 ± 4.0] underwent a 12-wk inpatient dietary intervention at the National Institute of Diabetes and Digestive and Kidney Diseases in Phoenix, Arizona. Men were randomly assigned to 1 of 8 dietary treatments varying the presence/absence of dietary meat, fish, and SSBs in all combinations. Fatty acid CIRs and mass proportions were measured in fasting blood samples and adipose tissue biopsies that were collected pre- and postintervention. Dietary effects were analyzed using multivariable regression and receiver operating characteristic AUCs were calculated using logistic regression.
CIRs of the several abundant SFAs, MUFAs and arachidonic acid (20:4n-6) in plasma were strongly associated with meat, as were a subset of these fatty acids in RBCs. Effect sizes in plasma ranged from 1.01‰ to 1.93‰ and were similar but attenuated in RBCs. Mass proportions of those fatty acids were not associated with diet. CIRs of plasma dihomo-γ-linolenic acid (20:3n–6) and adipose palmitic acid (16:0) were weakly associated with SSBs. Mass proportions of plasma odd-chain fatty acids were associated with meat, and mass proportions of plasma EPA and DHA (20:5n–3 and 22:6n–3) were associated with fish.
CIRs of plasma and RBC fatty acids show promise as sensitive and specific measures of dietary meat. These provide different information from that provided by fatty acid mass proportions, and are informative where mass proportion is not. This trial is registered at www.clinicaltrials.gov as NCT01237093.
Perspective: Big Data and Machine Learning Could Help Advance Nutritional Epidemiology
2021, Advances in NutritionThe field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex exposure, and residual confounding. The objective of this perspective article is to highlight how developments in big data and machine learning can help address these challenges. New methods of collecting 24-h dietary recalls and recording diet could enable larger samples and more repeated measures to increase statistical power and measurement precision. In addition, use of machine learning to automatically classify pictures of food could become a useful complimentary method to help improve precision and validity of dietary measurements. Diet is complex due to thousands of different foods that are consumed in varying proportions, fluctuating quantities over time, and differing combinations. Current dietary pattern methods may not integrate sufficient dietary variation, and most traditional modeling approaches have limited incorporation of interactions and nonlinearity. Machine learning could help better model diet as a complex exposure with nonadditive and nonlinear associations. Last, novel big data sources could help avoid unmeasured confounding by offering more covariates, including both omics and features derived from unstructured data with machine learning methods. These opportunities notwithstanding, application of big data and machine learning must be approached cautiously to ensure quality of dietary measurements, avoid overfitting, and confirm accurate interpretations. Greater use of machine learning and big data would also require substantial investments in training, collaborations, and computing infrastructure. Overall, we propose that judicious application of big data and machine learning in nutrition science could offer new means of dietary measurement, more tools to model the complexity of diet and its relations with diseases, and additional potential ways of addressing confounding.
Assessment of polyunsaturated fatty acids: A self-report and biomarker assessment with a racially and ethnically diverse sample of women
2021, Prostaglandins Leukotrienes and Essential Fatty AcidsCitation Excerpt :The possibility of FFQ inaccuracy is problematic because erroneous dietary assessment may distort our understanding of diet-disease relationships [3]. Currently, the utility of using FFQs in epidemiological studies continues to be disputed [4,7]. Some researchers caution against using FFQs to predict disease due to the abundant risks of inaccurate reporting as compared to alternative ways of measuring diet, such as food diaries or food records [8,9].
Polyunsaturated fatty acids (PUFAs) play an important role in human health, influencing chronic disease and mortality. Food Frequency Questionnaires (FFQs) are widely used to assess self-reported diet, but they can be subject to a variety of errors. Accordingly, an accurate assessment of diet is crucial in nutrition research. This study examined the association between a widely-used self-report measure of PUFAs (Diet History Questionnaire-II: DHQ-II) with the proportion of PUFA in red blood cell (RBC) membranes, and examined whether this relationship was moderated by race/ethnicity. In a racially and ethnically diverse sample of 132 female participants (Mage = 21.97±3.98, range 18 to 42 years), bivariate correlations and linear regression analyses demonstrated associations between DHQ-II and proportion of nutrients in RBCs for omega-3 fatty acids EPA (r = 0.39, ß = 0.38, p < .01), DHA (r = 0.48, ß = 0.47, p < .01), and EPA+DHA (r = 0.51, β = 0.49, p < .01). No associations were found for omega-3 fatty acid ALA or omega-6 fatty acids LA or ARA. DHQ-II and RBC associations for EPA, DHA, and EPA+DHA were moderated by race/ethnicity, controlling for age. Self-report of EPA was most consistent with RBC proportions for Caucasian individuals, and less consistent for Black/African American individuals. Self-reports of DHA and EPA+DHA were most consistent with RBC proportions for Caucasian individuals, and less consistent for Black/African American individuals and Hispanic/Latina individuals, although still statistically significant. No associations were detected for Hispanic/Latina individuals (for EPA only), Asian/Pacific Islanders or individuals of mixed/other descent. The present study found that when compared to PUFA biomarkers, the DHQ-II did not assess PUFAs consistently across all racial/ethnic groups in this sample of women. Further research is needed to determine what factors contribute to weak or lacking correlations between reported fat intake and corresponding values in RBCs, including but not limited to recall errors, underestimations of fatty acids in food composition databases, insufficient DHQ-II assessment of fatty acids in general and from particular cultures, and genetic differences in fat metabolism.
Does the association between patterns of fruit and vegetables and metabolic syndrome incidence vary according to lifestyle factors and socioeconomic status?
2020, Nutrition, Metabolism and Cardiovascular DiseasesThe aim of this study is to investigate the association between the identified patterns of fruits and vegetables and metabolic syndrome (MetS) incidence, and to investigate whether lifestyle factors and socioeconomic status modify the effect of the patterns on MetS risk.
We prospectively studied 1915 participants of the Tehran Lipid and Glucose Study, who were aged 19–74 years and followed up for dietary assessment using a validated, semi-quantitative food frequency questionnaire. After adjustment for confounding factors, total vegetable intake was inversely related to the risk of MetS. Total fruit and total fruit and vegetable were not associated with MetS risk. We identified four major patterns of fruits and vegetables by factor analysis: “fresh fruit pattern”, “vegetable pattern”, “dried fruit and cruciferous vegetable pattern”, and “potatoes and fruit juice pattern”. “Vegetable pattern” was negatively associated with MetS risk, and “potatoes and fruit juice pattern” increased the risk of MetS. Among participants with weight gain <7% during follow-up, all four identified patterns reduced MetS risk. When stratified by smoking, “vegetable pattern” and “dried Fruit and cruciferous vegetable pattern” lowered MetS risk among non-smokers. Stratification based on education resulted in MetS risk reduction across tertiles of “fresh fruit pattern” and “vegetable pattern”. First and second tertiles of “dried fruit and cruciferous vegetable pattern” lowered MetS risk among educated participants, compared to the reference.
The reduction in MetS risk caused by fruits and vegetables intake depends on the modifying effect of lifestyle and socioeconomic factors.
Identification of 102 Correlations between Serum Metabolites and Habitual Diet in a Metabolomics Study of the Prostate, Lung, Colorectal, and Ovarian Cancer Trial
2020, Journal of NutritionMetabolomics has proven useful for detecting objective biomarkers of diet that may help to improve dietary measurement. Studies to date, however, have focused on a relatively narrow set of lipid classes.
The aim of this study was to uncover candidate dietary biomarkers by identifying serum metabolites correlated with self-reported diet, particularly metabolites in underinvestigated lipid classes, e.g. triglycerides and plasmalogens.
We assessed dietary questionnaire data and serum metabolite correlations from 491 male and female participants aged 55–75 y in an exploratory cross-sectional study within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO). Self-reported intake was categorized into 50 foods, food groups, beverages, and supplements. We examined 522 identified metabolites using 2 metabolomics platforms (Broad Institute and Massachusetts General Hospital). Correlations were identified using partial Pearson's correlations adjusted for age, sex, BMI, smoking status, study site, and total energy intake [Bonferroni-corrected level of 0.05/(50 × 522) = 1.9 × 10-6]. We assessed prediction of dietary intake by multiple-metabolite linear models with the use of 10-fold crossvalidation least absolute shrinkage and selection operator (LASSO) regression.
Eighteen foods, beverages, and supplements were correlated with ≥1 serum metabolite at the Bonferronicorrected significance threshold, for a total of 102 correlations. Of these, only 5 have been reported previously, to our knowledge. Our strongest correlations were between citrus and proline betaine (r = 0.55), supplements and pantothenic acid (r = 0.46), and fish and C40:9 phosphatidylcholine (PC) (r = 0.35). The multivariate analysis similarly found reasonably large correlations between metabolite profiles and citrus (r = 0.59), supplements (r = 0.57), and fish (r = 0.44).
Our study of PLCO participants identified many novel food-metabolite associations and replicated 5 previous associations. These candidate biomarkers of diet may help to complement measures of self-reported diet in nutritional epidemiology studies, though further validation work is still needed.