Dataset Information

Development of the anti-cancer food scoring system 2.0: Validation and nutritional analyses of quantitative anti-cancer food scoring model.

ABSTRACT: BACKGROUND/OBJECTIVES:We have previously designed the anti-cancer food scoring model (ACFS) 1.0, an evidence-based quantitative tool analyzing the anti-cancer or carcinogenic potential of diets. Analysis was performed using simple quantitative indexes divided into 6 categories (S, A, B, C, D, and E). In this study, we applied this scoring model to wider recipes and evaluated its nutritional relevance. MATERIALS/METHODS:National or known regional databases were searched for recipes from 6 categories: Korean out-dining, Korean home-dining, Western, Chinese, Mediterranean, and vegetarian. These recipes were scored using the ACFS formula and the nutrition profiles were analyzed. RESULTS:Eighty-eight international recipes were analyzed. All S-graded recipes were from vegetarian or Mediterranean categories. The median code values of each category were B (Korean home-dining), C (Korean out-dining), B (Chinese), A (Mediterranean), S (vegetarian), and D (Western). The following profiles were correlated (P < 0.05) with ACFS grades in the univariate trend analysis: total calories, total fat, animal fat, animal protein, total protein, vitamin D, riboflavin, niacin, vitamin B12, pantothenic acid, sodium, animal iron, zinc, selenium, and cholesterol (negative trends), and carbohydrate rate, fiber, water-soluble fiber, vitamin K, vitamin C, and plant calcium (positive trends). Multivariate analysis revealed that animal fat, animal iron, and niacin (negative trends) and animal protein, fiber, and vitamin C (positive trends) were statistically significant. Pantothenic acid and sodium showed non-significant negative trends (P < 0.1), and vitamin B12 showed a non-significant positive trend. CONCLUSION:This study provided a nutritional basis and extended the utility of ACFS, which is a bridgehead for future cancer-preventive clinical trials using ACFS.

SUBMITTER: Hong YJ

PROVIDER: S-EPMC6997147 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Development of the anti-cancer food scoring system 2.0: Validation and nutritional analyses of quantitative anti-cancer food scoring model.

Hong Yeo-Jin YJ Kim Jeongseon J Lee Hye Yoon HY Rim Chai Hong CH

Nutrition research and practice 20190909 1

<h4>Background/objectives</h4>We have previously designed the anti-cancer food scoring model (ACFS) 1.0, an evidence-based quantitative tool analyzing the anti-cancer or carcinogenic potential of diets. Analysis was performed using simple quantitative indexes divided into 6 categories (S, A, B, C, D, and E). In this study, we applied this scoring model to wider recipes and evaluated its nutritional relevance.<h4>Materials/methods</h4>National or known regional databases were searched for recipes ...[more]

PMID: 32042372

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Project description:PurposeWe attempted to explore the prognostic value of baseline inflammatory and nutritional biomarkers at diagnosis in patients with early-stage breast cancer and develop a novel scoring system, the inflammatory-nutritional prognostic score (INPS).Patients and methodsWe collected clinicopathological and baseline laboratory data of 1259 patients with early-stage breast cancer between December 2010 and November 2012 from Sun Yat-sen University Cancer Center. Eligible patients were randomly divided into training and validation cohorts (n = 883 and 376, respectively) in a 7:3 ratio. We selected the most valuable biomarkers to develop INPS by the least absolute shrinkage and selection operator (LASSO) Cox regression model. A prognostic nomogram incorporating INPS and other independent clinicopathological factors was developed based on the stepwise multivariate Cox regression method. Then, we used the concordance index (C-index), calibration plot, and time-dependent receiver operating characteristic (ROC) analysis to evaluate the prognostic performance and predictive accuracy of the predictive nomogram.ResultsFour inflammatory-nutritional biomarkers, including neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), prognostic nutritional index (PNI), and albumin-alkaline phosphatase ratio (AAPR), were selected using the LASSO Cox analysis to construct INPS, which remained an independent prognostic indicator per the multivariate Cox regression analysis. Patients were stratified into low- and high-INPS groups based on the cutoff INPS determined by the maximally selected rank statistics. The prognostic model for overall survival consisting of INPS and other independent clinicopathological indicators showed excellent discrimination with C-indexes of 0.825 (95% confidence interval [CI]: 0.786-0.864) and 0.740 (95% CI: 0.657-0.822) in the training and validation cohorts, respectively. The time-dependent ROC curves showed a higher predictive accuracy of our prognostic nomogram than that of traditional tumor-node-metastasis staging.ConclusionBaseline INPS is an independent indicator of OS in patients with early-stage breast cancer. The INPS-based prognostic nomogram could be used as a practical tool for individualized prognostic predictions.

Project description:OBJECTIVES:The PROMIS-Preference (PROPr) score is a recently developed summary score for the Patient-Reported Outcomes Measurement Information System (PROMIS). PROPr is a preference-based scoring system for seven PROMIS domains created using multiplicative multi-attribute utility theory. It serves as a generic, societal, preference-based summary scoring system of health-related quality of life. This manuscript evaluates construct validity of PROPr in two large samples from the US general population. METHODS:We utilized 2 online panel surveys, the PROPr Estimation Survey and the Profiles-Health Utilities Index (HUI) Survey. Both included the PROPr measure, patient demographic information, self-reported chronic conditions, and other preference-based summary scores: the EuroQol-5D (EQ-5D-5L) and HUI in the PROPr Estimation Survey and the HUI in the Profiles-HUI Survey. The HUI was scored as both the Mark 2 and the Mark 3. Known-groups validity was evaluated using age- and gender-stratified mean scores and health condition impact estimates. Condition impact estimates were created using ordinary least squares regression in which a summary score was regressed on age, gender, and a single health condition. The coefficient for the health condition is the estimated effect on the preference score of having a condition vs. not having it. Convergent validity was evaluated using Pearson correlations between PROPr and other summary scores. RESULTS:The sample consisted of 983 respondents from the PROPr Estimation Survey and 3,000 from the Profiles-HUI survey. Age- and gender-stratified mean PROPr scores were lower than EQ-5D and HUI scores, with fewer subjects having scores corresponding to perfect health on the PROPr. In the PROPr Estimation survey, all 11 condition impact estimates were statistically significant using PROPr, 8 were statistically significant by the EQ-5D, 7 were statistically significant by HUI Mark 2, and 9 were statistically significant by HUI Mark 3. In the Profiles-HUI survey, all 21 condition impact estimates were statistically significant using summary scores from all three scoring systems. In these samples, the correlations between PROPr and the other summary measures ranged from 0.67 to 0.70. CONCLUSIONS:These results provide evidence of construct validity for PROPr using samples from the US general population.

Dataset Information

Development of the anti-cancer food scoring system 2.0: Validation and nutritional analyses of quantitative anti-cancer food scoring model.

Publications

Development of the anti-cancer food scoring system 2.0: Validation and nutritional analyses of quantitative anti-cancer food scoring model.

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