Skip to main content Accessibility help
×
Home
Hostname: page-component-55597f9d44-pgkvd Total loading time: 1.019 Render date: 2022-08-14T01:12:09.786Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true

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

Published online by Cambridge University Press:  01 December 2020

Shang Cao
Affiliation:
Department of Epidemiology and Health Statistics, Southeast University, Dingjiaqiao Road 87, Nanjing210009, Jiangsu, China
Shurong Lu
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Jinyi Zhou
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Zheng Zhu
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Wei Li
Affiliation:
Department of Epidemiology and Health Statistics, Southeast University, Dingjiaqiao Road 87, Nanjing210009, Jiangsu, China
Jian Su
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Hao Yu
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Wencong Du
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Lan Cui
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Yunqiu Dong
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China Department of Health Promotion and Chronic Non-Communicable Disease Control, Wuxi Center for Disease Control and Prevention, Wuxi, China
Yun Qian
Affiliation:
Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China Department of Health Promotion and Chronic Non-Communicable Disease Control, Wuxi Center for Disease Control and Prevention, Wuxi, China
Ming Wu
Affiliation:
Department of Epidemiology and Health Statistics, Southeast University, Dingjiaqiao Road 87, Nanjing210009, Jiangsu, China Department of Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
Pingmin Wei*
Affiliation:
Department of Epidemiology and Health Statistics, Southeast University, Dingjiaqiao Road 87, Nanjing210009, Jiangsu, China
*
*Corresponding author: Email mpw1963@126.com
Rights & Permissions[Opens in a new window]

Abstract

Objective:

To determine if specific dietary patterns are associated with breast cancer (BC) risk in Chinese women.

Design:

Latent class analysis (LCA) was performed to identify generic dietary patterns based on daily food-frequency data.

Setting:

The Chinese Wuxi Exposure and Breast Cancer Study (2013–2014).

Participants:

A population-based case–control study (695 cases, 804 controls).

Results:

Four dietary patterns were identified, Prudent, Chinese traditional, Western and Picky; the proportion in the controls and cases was 0·30/0·32/0·16/0·23 and 0·29/0·26/0·11/0·33, respectively. Women in Picky class were characterised by higher extreme probabilities of non-consumption of specific foods, the highest probabilities of consumption of pickled foods and the lowest probabilities of consumption of cereals, soya foods and nuts. Compared with Prudent class, Picky class was associated with a higher risk (OR = 1·42, 95 % CI 1·06, 1·90), while the relevant association was only in post- (OR = 1·44, 95 % CI 1·01, 2·05) but not in premenopausal women. The Western class characterised by high-protein, high-fat and high-sugar foods, and the Chinese traditional class characterised by typical consumption of soya foods and white meat over red meat, both of them showed no difference in BC risk compared with Prudent class did.

Conclusions:

LCA captures the heterogeneity of individuals embedded in the population and could be a useful approach in the study of dietary pattern and disease. Our results indicated that the Picky class might have a positive association with the risk of BC.

Type
Research paper
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

A multitude of in-vitro studies had reported the effect of individual-specific dietary components on breast cancer (BC)(Reference Gandini, Merzenich and Robertson1). Nutritional studies have historically been focusing on specific nutrients or foods in isolation and oversimplified the complexity of foods(Reference Michels, Mohllajee and Roset-Bahmanyar2,Reference Kerr, Anderson and Lippman3) . The high degree of intercorrelation among various nutrients and foods makes it difficult to attribute effects to a single independent component; the interpretation and application of results were limited(Reference Hu4,Reference Tapsell5) . Now in nutrition epidemiology, the critical concept of food synergy has been convinced that nutrients exist in a purposeful biological sense in food, delivering their combinations reflect biological functionality.

Creating dietary patterns that inherently account for interactions among micronutrients and estimate overall dietary effects may provide a more robust approach for determining associations between disease and diet(Reference Gleason, Boushey and Harris6,Reference Castello, Buijsse and Martin7) . In general, dietary patterns are typically derived using two main approaches, ‘a priori approach’ by using a predefined dietary pattern and fitting the data into the indices, as Diet Quality Index(Reference Kant8,Reference Ocke9) , or ‘a posteriori approach’ by data-driven statistical reduction techniques explores dietary patterns, as cluster analysis, factor analysis (FA) and principal component analysis(Reference Ocke9,Reference Newby and Tucker10) . Both ‘a priori’ and ‘a posteriori’ approaches had been widely used for defining dietary patterns associated with various health outcomes. As Mediterranean dietary pattern measured by the compliance with ‘a priori’ defined dietary indices was associated with reducing BC risk(Reference Sofi, Cesari and Abbate11,Reference Bloomfield, Koeller and Greer12) , the Western dietary pattern derived by FA was linked with increasing the risk of BC(Reference Albuquerque, Baltar and Marchioni13Reference Grosso, Bella and Godos15). Although various methods have been developed to explore dietary patterns in different populations, there were still some challenges in accurately identifying dietary patterns(Reference Edefonti, Randi and La Vecchia16Reference Uzhova, Woolhead and Timon18), including reducing the complex multidimensional nutritional data down to meaningful observed dietary patterns, dealing with the heterogeneity of individuals embedded in the studied population and classifying each participant into a specific dietary pattern(Reference Nurius and Macy19,Reference von Eye and Bergman20) .

Latent class analysis (LCA) is a person-centred, data-driven analytic approach(Reference Uzhova, Woolhead and Timon18,Reference Lanza and Rhoades21) . LCA identifies the patterns of relations among a set of observed variables and classifies similar individuals into specific latent classes, which leads subjects to highly similar in class but uniquely different from the members of other classes(Reference Leech, Worsley and Timperio22). Compared with ‘variable-oriented’ approaches, as FA/principal component analysis, that characterise the overall sample, ‘person-centred’ approaches model distinct configurations of heterogeneity within the sample, which could identify distinct, unknown patterns in subtypes of individuals from multidimensional data, capturing heterogeneity within and between groups. Moreover, LCA allows adjustment for covariates, quantifies member class probability and assessment of the goodness of fit(Reference Miettunen, Nordstrom and Kaakinen23,Reference DiStefano and Kamphaus24) .

Therefore, the study aims to use LCA to identify distinct classes of dietary patterns in Chinese women and evaluate whether specific dietary patterns are associated with BC risk and whether these associations will be affected by menopause status.

Methods

Study design and subjects

Chinese Wuxi Exposure and Breast Cancer Study (2013–2014) is a population-based case–control study. The subjects were all women who lived in Wuxi city, Jiangsu Province, China, for more than 5 years, as previously reported(Reference Lu, Qian and Huang25). According to the cancer registration system, newly diagnosed BC patients within 1 year were selected as the case group. All cases were identified according to the International Classification of Diseases for Oncology (ICD-10, code C50). Patients with secondary or recurrent BC were excluded. For those with multiple incident cancers, we only included those with BC as the first diagnosed original malignancy. Controls were selected from the local population registry system and matched to the cases by the same residence area and age (range of ±5 years), excluding individuals with any cancer history.

From November 2013 to November 2014, a total of 1410 newly diagnosed BC cases were identified, 1072 cases meeting the inclusion criteria and 818 of them were recruited in the current study, the response rate was 76·3 % (818/1072). Moreover, 1072 controls were selected and 935 of them participated, with a response rate of 87·2 % (935/1072), conducted as a frequency-matched case–control study (Fig. 1). The study protocol was approved by the Institutional Review Boards of Jiangsu CDC, and written informed consent was obtained from all subjects.

Fig. 1Flow chart of the participants, the Chinese Wuxi Exposure and Breast Cancer Study

Data collection

Demographic, lifestyle characteristics, menstrual and reproductive events, dietary intake, disease history and physical activity-related data were collected during the person-to-person interviews conducted by trained interviewers. Anthropometric measures were obtained by trained personnel following a standard protocol. The usual diet was assessed by a validated, semi-quantitative FFQ, which included 149 items along with the recipes commonly used in China, a detailed description given(Reference Zhao, Hasegawa and Chen26). Physical activity was measured by referencing the Global Physical Activity Questionnaire(Reference Armstrong and Bull27). Nutrient and energy intake were calculated through the Chinese Food Composition Database (2018, 6th version).

Dietary patterns derived by latent class analysis

Dietary intake assessment included whether the food was consumed, consumption frequency (times per day/week/month/year) and the average amount of food consumption at each time. The 149 food items in the FFQ were classified into eighteen predefined food groups based on similarities in nutrient profile and culinary usage, including rice/flour, cereals, fried foods, red meat, poultry, aquatic products, eggs, milk, fruits, vegetables, soya foods, nuts, cakes, sugar strengthened beverage, fresh juice, soft drink, pickled foods and coffee.

LCA identifies the number of ‘latent’ classes that describe the association between manifest variables. LCA estimates two key sets of parameters for categorical outcomes and covariates, respectively(Reference Fahey, Thane and Bramwell28): conditional probabilities (or called response probability) of observer indicators under a given class (e.g., the probability of red meat consumption among women adhering to Western class) and the regression coefficients predicting class membership (e.g., to differentiate between two subjects under the same energy intake but with different types of food consumption per day, as 250 g/d fire food and 1000 g/d rice respectively, with same 8368 kJ/d (2000 kcal/d)). Two sets of parameters were given to predict the posterior probability of belonging to each class for each subject(Reference Wedel and Kamakura29).

Since the probability of food items consumption often had a typical spike at 0 for non-consumers and constrained between 0 and 1 for consumers, we categorised the derived food group consumption into four levels: tertiles of non-zero consumption and no consumption (calculated from controls). Because there were < 20 % of women consumed sugar strengthened beverage, fresh juice, soft drink or coffee, we set the consumption of these foods as binary variables (consumed or no). While rice/flour was consumed almost ubiquitously, there were only tertiles of consumption and no non-consumption category.

LCA was used to derive dietary patterns of food groups based on the dietary data from controls, adjusted for energy intake (kcal/d). The dietary classes were interpreted and named according to the conditional probabilities of food group intake. The number of classes was determined using the Bayesian information criterion, Lo-Mendel-Rubin likelihood ratio (LMR) test and entropy value to identify the best-fitting model with statistical fit balance and parsimony. Finally, we predicted the probabilities of class membership for controls and cases, assigning them to the specific latent class (dietary pattern) for which the probability is the highest.

Statistical analysis

All analyses were stratified by menopausal status at diagnosis for cases or enrollment for controls. Women were considered to be postmenopausal as an absence of menstruation in the past 12 months.

Associations between exposures (dietary pattern as a nominal predictor) and the outcomes (BC) were estimated in terms of adjusted OR and corresponding 95 % CI using the logistic regression model. All models were adjusted for age at diagnosis for cases or enrollment for controls (by years), area (urban, rural), education (ordered as illiterate and primary, middle and high school, university and above), tobacco smoking (no or yes: including smoking and second-hand smoking $\, \ge \,$ 3d/week), tea intake (no or yes: $\, \ge \,$ 3 d/week), alcohol intake (no or yes: $\, \ge $ 3 d/week), moderate physical activity (min/d), oral contraceptives use (no or yes: current use or ever use), hormone replacement therapy (HRT) (no or yes: current use or ever use), family history of BC (no or yes: in a first-degree relative), history of benign breast disease (no or yes: including lactation mastitis, plasma cell mastitis, cyclomastopathy, fibroadenoma of breast, galactocele), age at menarche (by years), parity (ordered as 0, 1, 2 or ${\rm{\;}} \ge $ 3), age at first full-term delivery (by years), breast-feeding (no or yes), height (by cm), BMI (kg/m2) and energy intake (kcal/d). Postmenopausal stratification analysis was further adjusted for the menopausal age (by years). Furthermore, to examine whether the association between dietary patterns and BC risk was affected by well-established or suspected non-dietary BC risk factors, we conducted stratified analysis and interaction test by selected covariates which can cause a change in the OR of interest by at least 10 %, including age, education, tobacco smoking, alcohol intake, HRT, age at menarche, age at first full-term delivery, menopausal age, family history of BC, history of benign breast disease, parity, energy intake, BMI and height (stratification based on the median of controls distribution). The P for interaction was calculated by the likelihood ratio test.

Finally, to assess the impact of classification quality, we also excluded women with low predicted probabilities of the class membership (< (K–1)/K in LCA with K classes) for their assigned dietary class as a sensitivity analysis.

The LCA was conducted using MPLUS (V8.3; Muthén & Muthén), and other statistical analyses were conducted using R version 4.0.0 (The R Project for Statistical Computing, USA; https://www.r-project.org/). All P values quoted were 2-sided, and < 0·05 was considered as statistically significant.

Results

Of the participants interviewed (818 cases, 935 controls), we excluded seventy-seven cases and seventy-five controls because of extreme values in total energy intake (< 500 or > 5000 kcal) and forty-six cases and fifty-six controls missing information on adjusting covariant variables. No significant difference among demographic characteristics was found between excluded participants and remained ones. The results presented here were based on 695 cases and 804 controls who have complete information on FFQ and possible covariates included for an adjustment. The education level of the cases lower than that of the controls, the overweight rate, family history of BC and history of benign breast disease of cases was higher than that of the controls (P < 0·01) (Table 1).

Table 1 Characteristics of cases and controls for analysis, stratified by menopausal status

* P-values are calculated based on the χ 2 test or t test.

Dietary patterns derived by latent class analysis

Latent class models were fitted for two to six classes, and finally, four dietary pattern classes were chosen. Because when the LCA model retains four classes, the value of Bayesian information criterion is the smallest, the Lo-Mendel-Rubin likelihood ratio test reaches a significant level and the entropy value (0·836) is ideal. The proportion of each class after dividing into four categories is also balanced. Therefore, based on the balance of statistical fit and parsimony, we believe that the four-class model is appropriate (see online supplementary material, Supplemental Table 1). Figure 2 shows the conditional probabilities of Chinese women taking each food group in each class. A food group in high (third tertile) consumption (Fig. 2a) had a probability close to 1 in a given latent class, suggesting women in that class were likely to take more of that specific food. The food group in no consumption (Fig. 2b) with a probability close to 1 indicated that women took food less often (see online supplementary material, Supplemental Table 2). We named the four chosen classes as Prudent diet consumers, Western diet consumers, Chinese traditional diet consumers and Picky diet consumers.

Fig. 2Food consumption level conditional probabilities of dietary pattern class. (a) High consumption conditional probability of dietary pattern class. (b) No conditional consumption probability of dietary pattern class

Characteristics of dietary patterns

The Prudent class was characterised by a high probability of consuming healthy foods such as cereals, aquatic products, fruits, vegetables, soya foods and nuts. The Chinese traditional class was featured by the preference of white meat (as poultry) over red meat and the general willingness to take soya foods, with the lowest probabilities in non-consumption of the specific foods. Western class shows the highest probability of consuming a high-protein, high-fat and high-sugar foods such as fried meat or eggs, cakes, soft drinks, coffee, as well as soya foods. Compared with other classes, women in the Picky class were characterised by higher extreme probabilities of non-consumption of specific foods. In addition, women in the Picky class showed the highest probabilities of consumption of pickled foods and the lowest probabilities of consumption of cereals, soya foods and nuts (see online supplementary material, Supplemental Table 3). Overall, by conditional probability, 29·0, 31·9, 15·7 and 23·4 % of women were characterised by Prudent class, Chinese traditional class, Western class and Picky class, respectively. Women characterised by Western class demonstrated a significantly higher amount of energy intake than those the other three classes did (Western: 2226·71 $ \pm \,$ 414·84 kcal/d, Chinese traditional: 1630·53 $\, \pm \,$ 283·57 kcal/d, Prudent: 1860·84 $ \pm \,$ 280·99 kcal/d, Picky: 1555·98 $\, \pm $ 272·89 kcal/d). Additionally, we compared the socio-demographic characteristics within the identified latent classes and found significant differences between the classes (see online supplementary material, Supplemental Table 4).

Association between dietary pattern and breast cancer risk

Regarding the Prudent class as the reference group, the Picky class showed an independent risk effect on BC with an OR of 1·46 (95 % CI 1·01, 2·05) among postmenopausal women, whereas Western class (OR = 0·87, 95 % CI 0·54, 1·43) and Chinese traditional class (OR = 0·90, 95 % CI 0·63, 1·29) showed no difference, while no relevant association was found in premenopausal women (Table 2). Because the associations of BC risk with LCA-driven dietary patterns were not found among premenopausal women, stratified analysis and interaction tests were restricted only to the postmenopausal women. As shown in Table 3, we found that the associations of dietary patterns with BC risk were affected by some well-established or suspected non-dietary BC risk factors, that is, the Picky class was strongly associated with increased risk of BC among women who were age < 60 (years), drink alcohol, never use of HRT, age of menarche $\, \ge $ 16 (years), age at first full-term delivery $ \ge $ 25(years), menopausal age $\, \ge $ 55, had a history of benign breast disease, parity < 2 or height < 155 cm.

Table 2 OR and 95% CI for the association of latent class analysis (LCA)-derived dietary patterns and breast cancer risk

* OR were derived from the logistic regression model.

Adjusted for age, area, education, tobacco smoking, tea intake, alcohol intake, moderate physical activity, oral contraceptives use, hormone replacement therapy, family history of breast cancer, history of benign breast disease, age at menarche, parity, age at first full-term delivery, breast-feeding, height, BMI, energy intake, postmenopausal additional adjusted for the menopausal age.

P for interaction between dietary patterns and menopause was derived from a likelihood ratio test.

Table 3 Associations between postmenopausal breast cancer risk and latent class analysis (LCA)-derived dietary patterns, stratified by suspected non-dietary breast cancer risk factors

* Adjusted for age, area, education, tobacco smoking, tea intake, alcohol intake, moderate physical activity, oral contraceptives use, hormone replacement therapy, family history of breast cancer, history of benign breast disease, age at menarche, parity, age at first full-term delivery, breast-feeding, height, BMI, energy intake, menopausal age.

P for interaction between dietary patterns and non-dietary BC risk factors was derived from a likelihood ratio test.

Note: in this study, not all the food groups had a third tertile level (e.g., sugar strengthened beverage, fresh juice, etc. were binary) as the title states. And for food groups that had four levels, there is one non-consumption category.

Sensitivity analysis

The sensitivity analysis of dietary classification quality was assessed by excluding women (20·98 % of controls, 27·48 % of cases) with a low predicted probability (< 0·75) of the class membership. However, none of the associations between dietary classes and BC risk changed substantially.

Finally, we analysed the impact of selection bias caused by non-responders on the results; the possible selection bias would not change the existing conclusions. Through telephone follow-up, we investigated the reasons for non-respondents and fill them with random sampling from existing samples based on their characteristics, using fully conditional specification multivariate imputation by the chained equations method(Reference Van Buuren, Brand and Groothuis-Oudshoorn30) (see online supplementary material, Supplemental ‘Selection bias analysis’).

Discussion

In the current study, we applied a novel approach LCA to identify the generic dietary patterns in the Chinese female population and evaluated their associations with the risk of BC. We found that the Picky class contributed an additional risk to BC (OR = 1·42, 95 % CI 1·06, 1·90), while the relevant association was only in post- (OR = 1·44, 95 % CI  1·01, 2·05) but not in premenopausal women. The Western class and Chinese traditional classes were not associated with beneficial or adverse effects on BC risk compared with the Prudent class.

In nutritional epidemiology, data-driven methods such as FA/principal component analysis were widely used for nutritional data reduction, but the challenges in accurately identifying dietary patterns across population still exist. For these ‘variable-oriented’ methods, food items are grouped according to the degree of association between each other, operating by partitioning variance between measured variables(Reference Santos, Gorgulho and Castro31,Reference Varraso, Garcia-Aymerich and Monier32) . However, in most of the cases, the asymmetric distribution of variables reflecting food consumption caused by the heterogeneity of individuals in the diet will hinder the full capture of generic dietary patterns in the studied population(Reference Edefonti, Randi and La Vecchia16,Reference Jacques and Tucker17) . Because this type of method performs on the square of simple correlation coefficients between variables, skewed distribution results in the sum of squares of simple correlation coefficients between variables being much smaller than the sum of squares of partial correlation coefficients, and the lower variance contribution will make it challenging to capture the information about the relationship between the variables of interest(Reference MacCallum, Zhang and Preacher33).

Therefore, considering that the source of heterogeneity is from the individuals of the studied population rather than the diet measurement variables themselves, studying ‘person-centred’ instead of ‘variable-oriented’ may be more effective(Reference Rabe-Hesketh and Skrondal34). We compared the results of LCA and FA based on the same data sets (online supplementary material, Supplemental ‘Comparison between LCA and FA’). We found that the classification of dietary patterns was roughly similar to the previous study based on the FA approach, which demonstrates LCA and FA identified similar dietary patterns when presented with the same data set. However, heterogeneity embedded in the study population leads to an unreliable result of FA; a low original variance (45·21 %) may affect obtained findings. In contrast, LCA with an ideal entropy value (0·836) ensures the accuracy of classification (> 90 %). Additionally, we compared the alternate Mediterranean Diet score to the highly data-driven LCA results; as ‘a priori’ approach, it could better capture specific dietary characteristics under an identified actual dietary pattern. We found that the alternate Mediterranean Diet score indices seemed similar to the Prudent dietary pattern in terms of its correlation with specific foods (online supplementary material, Supplementary ‘Comparison between LCA and DQI’). The comparison demonstrates that under the premise that LCA is conducive to identifying the heterogeneity in different subpopulations, it also has a good performance in understanding the combination of food consumption and capturing the diet characteristics in a specific dietary pattern.

The results of the association between dietary patterns and BC risk were not consistent in Western population(Reference Gandini, Merzenich and Robertson1,Reference Albuquerque, Baltar and Marchioni13Reference Grosso, Bella and Godos15) . Inverse associations with Prudent dietary pattern and positive associations with the Western dietary pattern of BC risk have been found in many studies. However, some studies also reported contradictory findings(Reference Gandini, Merzenich and Robertson1,Reference Albuquerque, Baltar and Marchioni13Reference Grosso, Bella and Godos15) . Similarly, the current research on Asian women’s dietary patterns and BC risk has not reached a consistent conclusion(Reference Butler, Wu and Wang35Reference Cui, Dai and Tseng39). In our study, no difference between BC risk and Prudent class, Western class or Chinese traditional class was found. Although the characteristics of the three dietary patterns were different, we have found some commonalities. Compared with Picky class, the probability of soya foods intake in Prudent, Chinese and Western was relatively higher. Soya isoflavones may reduce the risk of BC by preferentially binding the oestrogen-dependent mechanism of oestrogen receptor-b relative to oestrogen receptor-α (Reference Strom, Hartman and Foster40), as well as the oestrogen-independent mechanism of inhibiting the nuclear transcription factor ${\rm{\kappa }}$ B DNA binding activity and the Akt signalling pathway(Reference Gong, Li and Nedeljkovic-Kurepa41). We did not find Western class had a positive association with BC risk in the current study, a possible reason for that might be the relatively high consumption of polyphenols foods such as soya foods and nuts in the Chinese women of Western class. Besides, Chinese women’s average red meat intake (19·32 g/1000 kcal/d) was much lower than that among Asian Americans (34·5 g/1000 kcal/d)(Reference Wu, Yu and Tseng42), which was different from a typical Western diet pattern among Western populations.

What deserves attention in the current study is the Picky class. We first found women’s compliance with this dietary pattern was with a higher BC risk among postmenopausal women compared with those of Prudent class. Women in Picky class were characterised by higher extreme probabilities of non-consumption of specific foods, the highest probabilities in consumption of pickled foods and the lowest probabilities in consumption of cereals, soya foods and nuts. Therefore, we suspected that the high BC risk of Picky class might come from an imbalance diet that could lead to loss of certain vital nutrients and high consumption of pickled foods that are prone to inflammation.

For BC, the observed heterogeneity of risk affected by menopausal status was particularly substantial, as it hinted at relative contributions of oestrogens, progesterone, insulin and insulin-like growth factor 1 (IFG-1) in mediating the association(Reference Keum, Greenwood and Lee43). The meaningful findings were concentrated in the postmenopausal women population; while during the postmenopausal period, oestrogens appear to be a dominant driver. Therefore, a potential biological mechanism may explain the null finding among premenopausal women: the ovaries are a predominant site of oestrogen synthesis in the premenopausal period; the additional contribution of diet factors to the circulating pool of oestrogens (i.e., oestrone, oestradiol, oestriol) may be negligible. Not only the amount of oestrogens from adipocytes is far smaller, but also the form of oestrogens (i.e., oestrone rather than oestradiol) is less biologically potent(Reference Nelson and Bulun44). However, the conclusion of the association between dietary patterns and breast cancer differed by the menopausal status was not consistent(Reference van den Brandt and Schulpen45); also, this meaningless premenopausal association may be because of the sample size, a small sample size reduces the power of the study and increase the margin of error, more in-depth and cross-validation studies are needed. Furthermore, among postmenopausal women, we found that the association between diet and BC risk could be affected by some well-established or potential non-dietary risk factors, the interaction between these factors and compliance with Picky dietary pattern associated with BC risk appears to be complicated and some factors increased the risk of association between Picky dietary pattern and BC risk, but some others weakened this association. Notably, we found that the Picky pattern was at higher risk in non-alcohol drinkers and non-HRT users, implying that some strong independent risk factors for BC may mask the association between diet and BC risk. For example, oestrogens appear to be a dominant driver of BC in postmenopausal women. In the absence of excess oestrogens from the ovaries and HRT, variation in oestrogen levels because of the different dietary patterns may be sufficient to distinguish the risk of BC. In contrast to HRT users, exogenous oestrogens from HRT may raise plasma oestrogens to the extent that endogenous oestrogens from the diet have a little incremental effect. Further work should assess associations of BC risk and the concentrations of these nutrients in plasma, which may be more predictive for vivo situation and interpretative for disease risk and biological mechanism.

However, several limitations also should be noted. First, data were collected from a case–control study, which might be partially influenced by the biases inherent in case–control designs, included selection bias, recall bias, residual confounding and reverse causality. We only included newly diagnosed BC patients and design the question carefully to reduce the recall bias, and the dietary preference was collected based on composite measures in which it was less likely to cause information bias on specific foods/food groups. We try to reduce the influence of residual confounding on conclusions through stratified analysis. Also, we analysed the potential effect of selection bias in the current study, and it does not change the conclusion. Another limitation is the lack of information on the receptor status of breast tumours, which might lead to an underestimated impact of diet on BC risk. However, some studies reported that the associations between dietary patterns and BC risk did not change substantially by receptor status(Reference Castello, Boldo and Perez-Gomez46,Reference Harris, Willett and Vaidya47) . Lastly, the results obtained through LCA tend to be highly data-driven and require cross-validation with other independent samples in the future.

Conclusions

In conclusion, we found that LCA is a useful approach to capture dietary patterns within complex dietary data of high inter-individual variation and to derive interpretable dietary patterns suitable for associating with health outcomes. Our findings further support the hypothesis that the combinations of specific dietary factors protect against BC and may be involved in the mechanisms of action on breast carcinogenesis.

Acknowledgements

Acknowledgements: We appreciate all study participants for their contributions. The authors thank the entire data collection team. Incident BC cases and controls for the current study were collected by Wuxi Center for Disease Control and Prevention, Jiangsu Center for Disease Control and Prevention. Financial support: The current study was supported by the World Cancer Research Fund (2011/RFA/473) and Wuxi Young Medical Talents (QNRC035). Conflict of interest: No. Authorship: The authors’ responsibilities were as follows: M.W., S.C., S.R.L. and P.M.W.: designed and conducted the study; J.Y.Z., Z.Z., L.W., J.S., H.Y., W.D., L.C., Y.Q.D. and Y.Q.: developed plant-based diet indices and data collection; S.C. and M.W.: performed the statistical analyses and drafted the manuscript; S.C., M.W., P.M.W. and Z.Z.: interpreted the data, critically revised the manuscript and had full responsibility for the analyses and interpretation of the data; S.C.: full access to all study data; and all authors: read and approved the final manuscript. Ethics of human subject participation: The current study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects/patients were approved by the Jiangsu Center for Disease Control and Prevention ethical committee. Written informed consent was obtained from all subjects/patients. All participants signed written informed consent.

Supplementary material

For supplementary material accompanying this paper visit https://doi.org/10.1017/S1368980020004826

Footnotes

These authors contributed equally to this work.

References

Gandini, S, Merzenich, H, Robertson, C et al. (2000) Meta-analysis of studies on breast cancer risk and diet: the role of fruit and vegetable consumption and the intake of associated micronutrients. Eur J Cancer 36, 636646.CrossRefGoogle ScholarPubMed
Michels, KB, Mohllajee, AP, Roset-Bahmanyar, E et al. (2007) Diet and breast cancer: a review of the prospective observational studies. Cancer 109, 27122749.CrossRefGoogle ScholarPubMed
Kerr, J, Anderson, C & Lippman, SM (2017) Physical activity, sedentary behaviour, diet, and cancer: an update and emerging new evidence. Lancet Oncol 18, e457e471.CrossRefGoogle ScholarPubMed
Hu, FB (2002) Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol 13, 39.CrossRefGoogle ScholarPubMed
Tapsell, LC (2014) Foods and food components in the Mediterranean diet: supporting overall effects. BMC Med 12, 100.CrossRefGoogle ScholarPubMed
Gleason, PM, Boushey, CJ, Harris, JE et al. (2015) Publishing nutrition research: a review of multivariate techniques – part 3: data reduction methods. J Acad Nutr Diet 115, 10721082.CrossRefGoogle ScholarPubMed
Castello, A, Buijsse, B, Martin, M et al. (2016) Evaluating the applicability of data-driven dietary patterns to independent samples with a focus on measurement tools for pattern similarity. J Acad Nutr Diet 116, 19141924 e1916.CrossRefGoogle ScholarPubMed
Kant, AK (1996) Indexes of overall diet quality: a review. J Am Diet Assoc 96, 785791.CrossRefGoogle ScholarPubMed
Ocke, MC (2013) Evaluation of methodologies for assessing the overall diet: dietary quality scores and dietary pattern analysis. Proc Nutr Soc 72, 191199.CrossRefGoogle ScholarPubMed
Newby, PK & Tucker, KL (2004) Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 62, 177203.CrossRefGoogle ScholarPubMed
Sofi, F, Cesari, F, Abbate, R et al. (2008) Adherence to Mediterranean diet and health status: meta-analysis. BMJ 337, a1344.CrossRefGoogle ScholarPubMed
Bloomfield, HE, Koeller, E, Greer, N et al. (2016) Effects on health outcomes of a Mediterranean diet with no restriction on fat intake: a systematic review and meta-analysis. Ann Intern Med 165, 491500.CrossRefGoogle ScholarPubMed
Albuquerque, RC, Baltar, VT & Marchioni, DM (2014) Breast cancer and dietary patterns: a systematic review. Nutr Rev 72, 117.CrossRefGoogle ScholarPubMed
Brennan, SF, Cantwell, MM, Cardwell, CR et al. (2010) Dietary patterns and breast cancer risk: a systematic review and meta-analysis. Am J Clin Nutr 91, 12941302.CrossRefGoogle ScholarPubMed
Grosso, G, Bella, F, Godos, J et al. (2017) Possible role of diet in cancer: systematic review and multiple meta-analyses of dietary patterns, lifestyle factors, and cancer risk. Nutr Rev 75, 405419.CrossRefGoogle ScholarPubMed
Edefonti, V, Randi, G, La Vecchia, C et al. (2009) Dietary patterns and breast cancer: a review with focus on methodological issues. Nutr Rev 67, 297314.CrossRefGoogle ScholarPubMed
Jacques, PF & Tucker, KL (2001) Are dietary patterns useful for understanding the role of diet in chronic disease? Am J Clin Nutr 73, 12.CrossRefGoogle ScholarPubMed
Uzhova, I, Woolhead, C, Timon, CM et al. (2018) Generic meal patterns identified by latent class analysis: insights from NANS (National Adult Nutrition Survey). Nutrients 10, 310.CrossRefGoogle Scholar
Nurius, PS & Macy, RJ (2008) Heterogeneity among violence-exposed women: applying person-oriented research methods. J Interpers Violence 23, 389415.CrossRefGoogle ScholarPubMed
von Eye, A & Bergman, LR (2003) Research strategies in developmental psychopathology: dimensional identity and the person-oriented approach. Dev Psychopathol 15, 553580.CrossRefGoogle ScholarPubMed
Lanza, ST & Rhoades, BL (2013) Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci 14, 157168.CrossRefGoogle ScholarPubMed
Leech, RM, Worsley, A, Timperio, A et al. (2017) Temporal eating patterns: a latent class analysis approach. Int J Behav Nutr Phys Act 14, 3.CrossRefGoogle ScholarPubMed
Miettunen, J, Nordstrom, T, Kaakinen, M et al. (2016) Latent variable mixture modeling in psychiatric research – a review and application. Psychol Med 46, 457467.CrossRefGoogle ScholarPubMed
DiStefano, C & Kamphaus, RW (2006) Investigating subtypes of child development: a comparison of cluster analysis and latent class cluster analysis in typology creation. Educ Psychol Meas 66, 778794.CrossRefGoogle Scholar
Lu, S, Qian, Y, Huang, X et al. (2017) The association of dietary pattern and breast cancer in Jiangsu, China: a population-based case-control study. PLoS One 12, e0184453.CrossRefGoogle ScholarPubMed
Zhao, W, Hasegawa, K & Chen, J (2002) The use of food-frequency questionnaires for various purposes in China. Public Health Nutr 5, 829833.CrossRefGoogle ScholarPubMed
Armstrong, T & Bull, F (2006) Development of the World Health Organization Global Physical Activity Questionnaire (GPAQ). J Public Health 14, 6670.CrossRefGoogle Scholar
Fahey, MT, Thane, CW, Bramwell, GD et al. (2007) Conditional Gaussian Mixture modelling for dietary pattern analysis. J Royal Stat Society Ser A (Stat Soc) 170, 149166.CrossRefGoogle Scholar
Wedel, M & Kamakura, WA (2002) Mixture Regression Models. New York: Cambridge University Press.CrossRefGoogle Scholar
Van Buuren, S, Brand, JP, Groothuis-Oudshoorn, CG et al. (2006) Fully conditional specification in multivariate imputation. J Stat Comput Simul 76, 10491064.CrossRefGoogle Scholar
Santos, RO, Gorgulho, BM, Castro, MA et al. (2019) Principal Component Analysis and Factor Analysis: differences and similarities in Nutritional Epidemiology application. Rev Bras Epidemiol 22, e190041.CrossRefGoogle ScholarPubMed
Varraso, R, Garcia-Aymerich, J, Monier, F et al. (2012) Assessment of dietary patterns in nutritional epidemiology: principal component analysis compared with confirmatory factor analysis. Am J Clin Nutr 96, 10791092.CrossRefGoogle ScholarPubMed
MacCallum, RC, Zhang, S, Preacher, KJ et al. (2002) On the practice of dichotomization of quantitative variables. Psychol Methods 7, 1940.CrossRefGoogle ScholarPubMed
Rabe-Hesketh, S & Skrondal, A (2008) Classical latent variable models for medical research. Stat Methods Med Res 17, 532.CrossRefGoogle ScholarPubMed
Butler, LM, Wu, AH, Wang, R et al. (2010) A vegetable-fruit-soy dietary pattern protects against breast cancer among postmenopausal Singapore Chinese women. Am J Clin Nutr 91, 10131019.CrossRefGoogle ScholarPubMed
Shin, S, Saito, E, Inoue, M et al. (2016) Dietary pattern and breast cancer risk in Japanese women: the Japan Public Health Center-based Prospective Study (JPHC Study). Br J Nutr 115, 17691779.CrossRefGoogle Scholar
Kojima, R, Okada, E, Ukawa, S et al. (2017) Dietary patterns and breast cancer risk in a prospective Japanese study. Breast Cancer 24, 152160.CrossRefGoogle Scholar
Zhang, CX, Ho, SC, Fu, JH et al. (2011) Dietary patterns and breast cancer risk among Chinese women. Cancer Causes Control 22, 115124.CrossRefGoogle ScholarPubMed
Cui, X, Dai, Q, Tseng, M et al. (2007) Dietary patterns and breast cancer risk in the shanghai breast cancer study. Cancer Epidemiol Biomarkers Prev 16, 14431448.CrossRefGoogle ScholarPubMed
Strom, A, Hartman, J, Foster, JS et al. (2004) Estrogen receptor beta inhibits 17beta-estradiol-stimulated proliferation of the breast cancer cell line T47D. Proc Natl Acad Sci USA 101, 15661571.CrossRefGoogle ScholarPubMed
Gong, L, Li, Y, Nedeljkovic-Kurepa, A et al. (2003) Inactivation of NF-kappa B by genistein is mediated via Akt signaling pathway in breast cancer cells. Oncogene 22, 47024709.CrossRefGoogle ScholarPubMed
Wu, AH, Yu, MC, Tseng, CC et al. (2009) Dietary patterns and breast cancer risk in Asian American women. Am J Clin Nutr 89, 11451154.CrossRefGoogle ScholarPubMed
Keum, N, Greenwood, DC, Lee, DH et al. (2015) Adult weight gain and adiposity-related cancers: a dose-response meta-analysis of prospective observational studies. J Natl Cancer Inst 107, djv088.CrossRefGoogle Scholar
Nelson, LR & Bulun, SE (2001) Estrogen production and action. J Am Acad Dermatol 45, S116S124.CrossRefGoogle ScholarPubMed
van den Brandt, PA & Schulpen, M (2017) Mediterranean diet adherence and risk of postmenopausal breast cancer: results of a cohort study and meta-analysis. Int J Cancer 140, 22202231.CrossRefGoogle ScholarPubMed
Castello, A, Boldo, E, Perez-Gomez, B et al. (2017) Adherence to the Western, Prudent and Mediterranean dietary patterns and breast cancer risk: MCC-Spain study. Maturitas 103, 815.CrossRefGoogle ScholarPubMed
Harris, HR, Willett, WC, Vaidya, RL et al. (2017) An adolescent and early adulthood dietary pattern associated with inflammation and the incidence of breast cancer. Cancer Res 77, 11791187.CrossRefGoogle ScholarPubMed
Figure 0

Fig. 1 Flow chart of the participants, the Chinese Wuxi Exposure and Breast Cancer Study

Figure 1

Table 1 Characteristics of cases and controls for analysis, stratified by menopausal status

Figure 2

Fig. 2 Food consumption level conditional probabilities of dietary pattern class. (a) High consumption conditional probability of dietary pattern class. (b) No conditional consumption probability of dietary pattern class

Figure 3

Table 2 OR and 95% CI for the association of latent class analysis (LCA)-derived dietary patterns and breast cancer risk

Figure 4

Table 3 Associations between postmenopausal breast cancer risk and latent class analysis (LCA)-derived dietary patterns, stratified by suspected non-dietary breast cancer risk factors

Supplementary material: File

Cao et al. supplementary material

Cao et al. supplementary material

Download Cao et al. supplementary material(File)
File 440 KB
You have Access
1
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

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

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

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

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

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

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *