Association between dietary patterns and risk of breast cancer in Chinese female population: a latent class analysis

Epidemiological breast cancer prediction by country: A novel machine learning approach

Breast cancer remains a significant contributor to cancer-related deaths among women globally. We seek for this study to examine the correlation between the incidence rates of breast cancer and newly identified risk factors. Additionally, we aim to utilize machine learning models to predict breast cancer incidence at a country level. Following an extensive review of the available literature, we have identified a range of recently studied risk factors associated with breast cancer. Subsequently, we gathered data on these factors and breast cancer incidence rates from numerous online sources encompassing 151 countries. To evaluate the relationship between these factors and breast cancer incidence, we assessed the normality of the data and conducted Spearman's correlation test. Furthermore, we refined six regression models to forecast future breast cancer incidence rates. Our findings indicate that the incidence of breast cancer is most positively correlated with the average age of women in a country, as well as factors such as meat consumption, CO2 emissions, depression, sugar consumption, tobacco use, milk intake, mobile cells, alcohol consumption, pesticides, and oral contraceptive use. As for prediction, the CatBoost Regressor successfully predicted future breast cancer incidence with an R squared value of 0.84 ± 0.03. An increased incidence of breast cancer is mainly associated with dietary habits and lifestyle. Our findings and recommendations can serve as a baseline for developing educational programs intended to heighten awareness amongst women in countries with heightened risk.

Exploring the traditional Chinese diet and its association with health status-a systematic review

CONTEXT

Increased adherence to a traditional Chinese diet (TCD) could reduce the increasing prevalence of noncommunicable diseases. Currently, there is no consistent definition of the TCD in the literature, and its associations with health outcomes have not yet been identified.

OBJECTIVE

This systematic review aimed to assess the definition of the TCD, in the literature, and to evaluate whether the TCD, as described, is associated with health outcomes.

DATA SOURCES

Fourteen databases were searched up to April 25, 2022.

DATA EXTRACTION

Three reviewers (in pairs) independently screened and extracted data. A modified risk-of-bias tool was used to assess the quality of the studies assessing the TCD definition; the Newcastle-Ottawa Scale and the Cochrane Risk-of-Bias tool were used to assess the quality of the observational studies and randomized controlled trials assessing associations between the TCD and health outcomes.

DATA ANALYSIS

Ninety-nine studies were identified that assessed the TCD definition. In at least 75% of the studies, rice and leafy vegetables were consistently reported as food groups that characterize the TCD; the most frequently cited food items were white rice, spinach, bokchoy, and cabbage. Fish and seafood, pork, and pork products were consistently reported in studies exclusively referring to the TCD consumed in southern China (n = 21 studies), whereas wheat and wheat products were commonly reported in studies focusing on northern China (n = 14 studies). Fifteen studies reported on the quantities of food groups that are characteristic of the TCD, but their findings were inconsistent. Of the 99 studies, 54 assessed associations with health outcomes. The TCD was overall inversely associated with obesity risk and weight gain, while relationships between the TCD and other health outcomes were inconsistent.

CONCLUSION

Further studies are needed to determine the quantities of foods consumed in the TCD and to establish a consistent definition for further exploration of the TCD's potential role in preventing non-communicable diseases.

Comparison of habitual and meal-specific dietary patterns identified by latent class analysis and confirmatory factor analysis in adults: A cross-sectional study

OBJECTIVE

The aim of this study was to compare characteristics of habitual and meal-specific dietary patterns identified by latent class analysis (LCA) and confirmatory factor analysis (CFA).

METHODS

Participants included 778 adults selected for the present cross-sectional study from local health care centers in Tehran, Iran. Three 24-h dietary recalls evaluated dietary intake. LCA was used to group study participants into exclusive subgroups of individuals with similar patterns of dietary intake. CFA was applied to identify patterns of habitual and meal-specific dietary intake. Analysis of variance was used to compare the average scores of habitual and meal-specific CFA-derived dietary patterns across classes identified by LCA.

RESULTS

Using habitual dietary intake, CFA grouped correlated food items into three major factors: fruits and vegetables, mixed, and Western dietary patterns. LCA grouped study participants with similar patterns of habitual intake into four subgroups of individuals: fruits and vegetables, mixed, Western, and low consumer classes. LCA-fruits and vegetables, LCA-Western, and LCA-mixed classes had, respectively, higher mean scores of CFA-fruits and vegetables, CFA-Western, and CFA-mixed dietary patterns compared with other classes (P < 0.001). Similar findings were observed for meal-specific dietary intake, where classes identified by LCA had the highest mean scores of their corresponding dietary pattern identified by CFA.

CONCLUSION

Habitual and meal-specific classes identified by LCA were well characterized by the dietary patterns derived by CFA, suggesting that LCA may be an appropriate statistical approach to classify study participants with similar patterns of intake into exclusive subgroups of individuals.

The association between dietary pattern and visceral adiposity index, triglyceride-glucose index, inflammation, and body composition among Iranian overweight and obese women

The aim of the present study was to investigate the association between dietary patterns, derived through latent class analysis (LCA), with visceral adiposity index (VAI), Triglyceride-Glucose Index (TyG), inflammation biomarkers, and body composition in overweight and obese Iranian women. For this cross-sectional study, dietary exposure was assessed using a validated 147-item semi-quantitative food frequency questionnaire (FFQ). Dietary patterns were derived through LCA. Binary logistic was performed to test the associations of dietary patterns with VAI, TyG, inflammation biomarkers, and body composition. Health centers in Tehran, Iran. 376 obese and overweight women, aged > 18 years. Two dietary patterns were identified using LCA modeling: healthy and unhealthy. Women in the unhealthy class were characterized by higher consumption of fast food, sweetened beverages, grains, unhealthy oils, butter and margarine, and snacks. Compared with the healthy class, the unhealthy class was associated with an increased risk of higher fasting blood sugar (FBS) (OR = 6.07; 95% CI: 1.33-27.74, P value = 0.02), c-reactive protein (CRP) (OR = 1.72; 95% CI: 1.05-2.80; P value = 0.02), and lower fat free mass index (FFMI) (OR = 0.56; 95% CI: 0.35-0.88, P value = 0.01), after adjusting for confounders. We found that adherence to an unhealthy dietary pattern was associated with decreased FFMI and increased FBS and CRP using LCA, but not with the rest of the variables. Further studies should be conducted to confirm the veracity of these findings.

Dietary patterns and breast cancer risk, prognosis, and quality of life: A systematic review

Background

Statistics indicate that the morbidity of breast cancer is increasing globally, and its (overall figures) incidence has now surpassed that of lung cancer for the first time. The relation between a whole dietary pattern, rather than of a single food or nutrient, and breast cancer (BC) should be examined for findings to capture the complexities of diet and the potential for synergism between dietary components. Hence, the effects of dietary patterns on breast cancer have recently attracted increasing attention.

Objective

To systematically review the effects of dietary patterns on breast cancer risk, prognosis, and quality of life in survivors.

Methods

This systematic review was conducted following PRISMA guidelines and was registered in PROSPERO. Data from Ovid, China Biomedical Literature Database, Wanfang Data Knowledge Service Platform, CNKI, PubMed, Weipu, The Cochrane Library, Duxiu Data, ProQuest, Embase, Web of Science, and Scopus Database were retrieved and evaluated.

Results

A total of 47 studies that investigated the association between eating patterns and breast cancer were identified. Ten studies evaluated the effect of the model on treatment outcome and prognosis of breast cancer and two cross-sectional studies examined the influence of dietary patterns on quality of life. The resulting favorable dietary patterns were shown to regulate metabolic biomarkers, antioxidants, anti-inflammatory agents, and protective genes, and inhibit cell proliferation and invasion.

Conclusion

Numerous studies have examined the effects of healthy eating, plant-based, anti-inflammation, low-fat, and other favorable dietary patterns in relation to breast cancer. However, few studies reported significant associations and the studies had limitations, suggesting that the current findings should be interpreted with caution.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, CRD4202 2350171.

Association Between Dietary Patterns and Plasma Lipid Biomarker and Female Breast Cancer Risk: Comparison of Latent Class Analysis (LCA) and Factor Analysis (FA)

Background: Diet research focuses on the characteristics of "dietary patterns" regardless of the statistical methods used to derive them. However, the solutions to these methods are both conceptually and statistically different. Methods: We compared factor analysis (FA) and latent class analysis (LCA) methods to identify the dietary patterns of participants in the Chinese Wuxi Exposure and Breast Cancer Study, a population-based case-control study that included 818 patients and 935 healthy controls. We examined the association between dietary patterns and plasma lipid markers and the breast cancer risk. Results: Factor analysis grouped correlated food items into five factors, while LCA classified the subjects into four mutually exclusive classes. For FA, we found that the Prudent-factor was associated with a lower risk of breast cancer [4th vs. 1st quartile: odds ratio (OR) for 0.70, 95% CI = 0.52, 0.95], whereas the Picky-factor was associated with a higher risk (4th vs. 1st quartile: OR for 1.35, 95% CI = 1.00, 1.81). For LCA, using the Prudent-class as the reference, the Picky-class has a positive association with the risk of breast cancer (OR for 1.42, 95% CI = 1.06, 1.90). The multivariate-adjusted model containing all of the factors was better than that containing all of the classes in predicting HDL cholesterol (p = 0.04), triacylglycerols (p = 0.03), blood glucose (p = 0.04), apolipoprotein A1 (p = 0.02), and high-sensitivity C-reactive protein (p = 0.02), but was weaker than that in predicting the breast cancer risk (p = 0.03). Conclusion: Factor analysis is useful for understanding which foods are consumed in combination and for studying the associations with biomarkers, while LCA is useful for classifying individuals into mutually exclusive subgroups and compares the disease risk between the groups.