METHODS: Therefore, using linear programming, this study is aimed to develop a healthy and balanced menu with minimal cost in accordance to individual needs that could in return help to prevent cancer. A cross sectional study involving 100 adults from a local university in Kuala Lumpur was conducted in 3 phases. The first phase is the data collection for the subjects, which includes their socio demographic, anthropometry and diet recall. The second phase was the creation of a balanced diet model at a minimum cost. The third and final phase was the finalization of the cancer prevention menu. Optimal and balanced menus were produced based on respective guidelines of WCRF/AICR (World Cancer Research Fund/ American Institute for Cancer Research) 2007, MDG (Malaysian Dietary Guidelines) 2010 and RNI (Recommended Nutrient Intake) 2017, with minimum cost.
RESULTS: Based on the diet recall, most of subjects did not achieve the recommended micronutrient intake for fiber, calcium, potassium, iron, B12, folate, vitamin A, vitamin E, vitamin K, and beta-carotene. While, the intake of sugar (51 ± 19.8 g), (13% ± 2%) and sodium (2585 ± 544 g) was more than recommended. From the optimization model, three menus, which met the dietary guidelines for cancer prevention by WCRF/AICR 2007, MDG 2010 and RNI 2017, with minimum cost of RM7.8, RM9.2 and RM9.7 per day were created.
CONCLUSION: Linear programming can be used to translate nutritional requirements based on selected Dietary Guidelines to achieve a healthy, well-balanced menu for cancer prevention at minimal cost. Furthermore, the models could help to shape consumer food choice decision to prevent cancer especially for those in low income group where high cost for health food has been the main deterrent for healthy eating.
METHODS AND FINDINGS: This prospective analysis included 471,495 adults from the European Prospective Investigation into Cancer and Nutrition (EPIC, 1992-2014, median follow-up: 15.3 y), among whom there were 49,794 incident cancer cases (main locations: breast, n = 12,063; prostate, n = 6,745; colon-rectum, n = 5,806). Usual food intakes were assessed with standardized country-specific diet assessment methods. The FSAm-NPS was calculated for each food/beverage using their 100-g content in energy, sugar, saturated fatty acid, sodium, fibres, proteins, and fruits/vegetables/legumes/nuts. The FSAm-NPS scores of all food items usually consumed by a participant were averaged to obtain the individual FSAm-NPS Dietary Index (DI) scores. Multi-adjusted Cox proportional hazards models were computed. A higher FSAm-NPS DI score, reflecting a lower nutritional quality of the food consumed, was associated with a higher risk of total cancer (HRQ5 versus Q1 = 1.07; 95% CI 1.03-1.10, P-trend < 0.001). Absolute cancer rates in those with high and low (quintiles 5 and 1) FSAm-NPS DI scores were 81.4 and 69.5 cases/10,000 person-years, respectively. Higher FSAm-NPS DI scores were specifically associated with higher risks of cancers of the colon-rectum, upper aerodigestive tract and stomach, lung for men, and liver and postmenopausal breast for women (all P < 0.05). The main study limitation is that it was based on an observational cohort using self-reported dietary data obtained through a single baseline food frequency questionnaire; thus, exposure misclassification and residual confounding cannot be ruled out.
CONCLUSIONS: In this large multinational European cohort, the consumption of food products with a higher FSAm-NPS score (lower nutritional quality) was associated with a higher risk of cancer. This supports the relevance of the FSAm-NPS as underlying nutrient profiling system for front-of-pack nutrition labels, as well as for other public health nutritional measures.
METHODS AND STUDY DESIGN: Eating behaviors namely Cognitive Restraint, Uncontrolled Eating and Emotional Eating scores were assessed by the Three Factor Eating Questionnaire-R18. The preference/intake frequency/craving of 26 common high-fat Malaysian foods was assessed using a 7-point hedonic scale. Anthropometric measurements were taken and Taq1 gene polymorphisms were genotyped by PCR-Restriction Fragment Length Polymorphism using DNA extracted from mouthwash samples.
RESULTS: The overall minor allele frequencies of Taq1A, Taq1B and Taq1D according to ethnicities (Chinese/Indian) were 0.37/0.29, 0.39/0.28, 0.06/0.30, respectively; genotype and allele distributions of Taq1B and Taq1D were significantly different between ethnicities. Eating behaviorscores were not significantly different between gender and ethnicities. Those with A1 or B1 allele had lower Cognitive Restraint score and higher Uncontrolled Eating score, while those with A1/A1 or B1/B1 genotype had higher fast food preference. D1 allele was associated with increased starchy food craving and mamak (Malaysian Indian-Muslim) food preference, but not eating behavior scores. All three gene variants were not associated with obesity and adiposity.
CONCLUSION: Taken together, we posit that three DRD2 Taq1 gene polymorphisms influence the eating behavior and preference/intake frequency/craving of certain high-fat foods in Malaysian adults, but their role in obesity and adiposity is still inconclusive and needs further investigation.