Background: Early age at menarche is associated with risk of several chronic diseases. Prospective study on the association between dietary pattern and timing of menarche is sparse. We examined whether dietary patterns prior to the menarche onset were prospectively associated with menarcheal age in Chinese girls. Methods: One thousand one hundred eighteen girls aged 6–13 y in the China Health and Nutrition Survey (CHNS) with three-day 24-h recalls and information on potential confounders at baseline were included in the study. Dietary patterns were identified using principal component analysis. Age at menarche was self-reported at each survey. Cox proportional hazard regression models were performed to examine the associations of premenarcheal dietary patterns and menarcheal timing. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Results: Three major dietary patterns were identified: modern dietary pattern, animal food pattern, and snack food pattern. After adjustment for age at baseline, region, ethnicity, maternal education level, energy intake at baseline, and body mass index Z-score at baseline, girls in the highest quartile of modern dietary pattern score had a 33% higher probability of experiencing menarche at an earlier age than those in the lowest quartile (HR: 1.33, 95% CI: 1.002–1.77, p for trend = 0.03). No significant association was found for the animal food pattern or snack food pattern. Conclusions: Higher adherence to modern dietary pattern during childhood is associated with an earlier menarcheal age. This association was independent of premenarcheal body size.
Keywords: Dietary patterns; Menarche; Cohort study; China health and nutrition survey
Early age at menarche is a risk factor for insulin resistance [[
Large numbers of observational studies have addressed the role of dietary factors for menarche onset. Girls with higher intakes of fat [[
In recent decades, China has experienced a remarkable transition in the structure of food consumption, such as increased consumption of animal-source foods, food away from home, and declining consumption of coarse grains and legumes [[
We used data from the recent seven waves (1997, 2000, 2004, 2006, 2009, 2011 and 2015) of the CHNS, an ongoing longitudinal cohort study which was started in 1989. Details on the study protocol have been described elsewhere [[
Between the 1997 and the 2015 survey, there were 2259 girls with plausible data on menarche. Of these, 1434 girls aged 6–13 y who had baseline dietary information and at least one follow-up visit of menarche onset thereafter were included. We excluded 9 girls who provided dietary records with extremely low or high total energy intake values (< 400 kcal/d or > 4000 kcal/d) [[
Graph: Fig. 1 Flow chart for the study sample
Dietary intake data of girls in the CHNS were collected by trained investigators using three consecutive 24-h recalls. When girls were 12 years or older, they were asked to recall their consumption of all foods and beverages. For girls < 12 y of age, their parents or guardians provided the information on food consumption at home, while girls provided the dietary intake information away from home. Food models and picture aids were used to improve the accuracy of the portion-size estimates [[
Dietary intake data were divided into 18 categories (Table 1) based on their similarity in nutrient profiles and Chinese Food Composition Tables. Principal components analysis (PCA) was conducted to identify dietary patterns at baseline through the PROC FACTOR procedure in SAS software (version 9.3, SAS Institute Inc., Cary, NC, USA.). Results of the Kaiser-Meyer-Olkin test (0.65) and the Bartlett's test of sphericity (p < 0.0001) indicated that the present food intake data were suitable for factor analysis. Factors were rotated orthogonally to simplify the interpretation. Based on the eigenvalues (> 1), the inspection of scree plot, and the interpretability of the factors, three factors (dietary patterns) were retained. The factor loadings represent the correlations of each food group with the corresponding dietary pattern. Food groups with factor loadings > 0.30 or < − 0.30 were considered to be strongly associated within a pattern, and thus were selected to describe the dietary patterns. Labeling of the factors was primarily descriptive, and was based on our interpretation of the pattern structures [[
Food or food groups used in the dietary pattern analysis
Food or food groups Food items Cereals Rice, noodle, steamed bun, corn, barley, millet, brown rice, black rice Tubers and starches Potato, sweet potato, cassava, konjac powder, vermicelli Legumes and its products Dried legumes, tofu, soya-bean milk, dried bean curd, mung bean, red bean Vegetables Root vegetable, leguminous vegetable and sprout, cucurbitaceous and solanaceous vegetable, steam, leafy and flowering vegetable, aquatic vegetable Fungi and algae Mushroom, agaric, tremella, laver, sea-tangle Fruits Kernel fruit, drupe fruit, berry, orange fruit, tropic fruit, melons Nuts Walnut, melon seeds, cashew, hazelnut, almond, pistachio Meat and its products Pork, beef, mutton, rabbit meat, processed pork, sausage Poultry and its products Chicken, duck, goose, turkey, pigeon Dairy products Milk, dried milk, yoghurt, cheese Eggs Chicken egg, duck egg, goose egg, partridge egg Fish and shellfish Fish, shrimp, crab, shellfish Ethnic foods and cakes Pancake, tangyuan, spring rolls, mooncake, tea-oil tree, mung bean cake Fast foods Hamburger, sandwich, hotdog, chips, instant noodles, bread, biscuit, snacks Beverages Carbonated drink, fruit juice, vegetable juice, milk drink, vegetable protein drink, tea drink, powdered drink, popsicle and ice cream Sugar and preserves Lollipops, hard candy, chocolate, filled candy, honey, preserved fruit Fats and oils Animal fat, vegetable oils Condiments Sauce, vinegar, catsup, fermented soybean curd, pickles, spice, salt
Girls aged 8 years or older and/or their parents were asked whether menarche had already occurred during each survey, and if they had, the month and year of their first menstrual period was recorded. If girls provided different menarcheal ages in different survey years, only the first reported menarcheal age in the panel data were used for analysis to reduce potential recall bias. For the present analysis, our outcome of interest was the time for the participants to experience menarche. Thus, for girls who experienced menarche during the follow-up survey, the observation time interval was from baseline to age of first menstrual period. For girls who did not reach menarche during the follow-ups, they were censored at the last follow-up visit, i.e. the observation time was from baseline to the last follow-up visit date.
Detailed information on participants' socio-demographic characteristics was collected using a structured questionnaire at baseline, including birth year, ethnicity (Han and minority), residency (urban and rural), region (northeastern area: Liaoning, Heilongjiang; east coast area: Beijing, Jiangsu, Shandong, Shanghai; central area: Henan, Hubei, Hunan; and western area: Chongqing, Guangxi, Guizhou), household income (continuous variable), and maternal education level (illiterate, primary school, middle school, high school, technical or vocational degree, and college degree or higher).
Anthropometric measurements of the participants were performed at each visit by trained research assistants according to standard procedures, with the girls dressed in underwear only and barefoot. Height and weight were measured to the nearest 0.1 cm and 0.1 kg, respectively. Body Mass Index (BMI) was calculated as weight divided by the square of height (kg/m
All statistical analyses were performed with SAS procedures (version 9.3, 2011, SAS Institute Inc., Cary, NC, USA.). Results were considered statistically significant when a two-sided p-value < 0.05.
We performed time-to-event analysis to investigate the prospective relevance of dietary pattern scores at baseline with the event of menarche using Cox proportional hazard regression models (PROC PHREG procedure in SAS software), which appropriately account for both the information on age at menarche from postmenarcheal girls and the censoring information from premenarcheal girls. The independent variables in the Cox proportional hazard regression models were the quartiles of each dietary pattern factor score. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated by comparing the second, third and fourth quartiles to the first quartile (as the reference category) of each dietary pattern factor score. Also, the associations between the three dietary pattern scores on a continuous scale and menarche onset were examined. Three models were used in our study: model 1 adjusted for age at baseline, region, ethnicity and maternal education level; model 2 further adjusted for energy intake at baseline. As we were interested in the potential mediating effect of body size on diet-menarche relations, we further adjusted for BMI Z-score at baseline in model 3.
Considering household income is a most frequently used proxy of socioeconomic status in investigating diet-menarche relations, we conducted sensitivity analysis with substitution of household income per capita (continuous variable) for maternal educational level in order to obtain more comparable results.
General characteristics of the study sample are shown in Table 2. Girls included in the present analyses (n = 1118) were 8.3 ± 1.8 years old at baseline. Among them, 711 participants (63.6%) reported menarche during follow-up, and 407 participants (36.4%) were censored at the time of last follow-up visit. Overall, the participants were followed up for 4.0 ± 1.8 years after study baseline. The mean length of follow-up was longer for girls who had reached menarche (4.1 ± 1.8 y) during the follow-up than their counterparts who were censored (3.8 ± 1.7 y). The mean menarcheal age from 711 postmenarcheal girls was 12.7 ± 1.2 years. Age at menarche did not differ between the 711 girls and the 656 postmenarcheal girls who also had data on menarcheal age but were excluded from the final analysis due to lack of dietary intake at baseline, socio-demographic and anthropometric data (p = 0.53).
General characteristics of the CHNS participants in the present study
Characteristics Values (mean ± SD/n (%)) n 1118 postmenarcheal girls during follow-up, n 711 (63.6) Age at baseline a, y 8.3 ± 1.8 Years of follow-up, y 4.0 ± 1.8 Wave (baseline survey year) 1997 488 (43.6) 2000 170 (15.2) 2004 177 (15.8) 2006 66 (5.9) 2009 100 (8.9) 2011 117 (10.5) Region b Northeastern area 199 (17.8) East coast 210 (18.8) Central area 359 (32.1) Western area 350 (31.3) Residency Urban 343 (30.7) Rural 775 (69.3) Ethnicity Han 950 (85.0) Minority 168 (15.0) Maternal education level Illiterate 194 (17.4) Primary school 283 (25.3) Middle school 404 (36.1) High school 140 (12.5) Technical or vocational degree 45 (4.0) College degree or higher 52 (4.7) Energy intake at baseline, kcal/d 1546 ± 495 Protein, % of energy 12.4 ± 3.0 Fat, % of energy 28.1 ± 11.8 Carbohydrate, % of energy 59.5 ± 12.3 Weight, kg 25.9 ± 7.3 Height, cm 127.2 ± 12.4 BMI Z-score at baseline c −0.04 ± 1.22
The factor loadings for the three main dietary patterns are shown in Table 3. Factor 1 (the modern dietary pattern) was characterized by high intakes of fast foods, dairy products, fruits and eggs, and low intakes of cereals, vegetables, and condiments. Factor 2 (the animal food pattern) was loaded heavily for meat, poultry, fish and shellfish. Factor 3 (the snack food pattern) was marked by high intakes of nuts, beverages, ethnic foods and cakes, and legumes. These three dietary patterns explained 27.2% of the total variation in dietary intake (13.1, 7.5 and 6.6% for factor 1, factor 2 and factor 3, respectively).
Orthogonally rotated factor loadings for three dietary patterns derived from principal components analysis
Food or food groups Factor 1: Modern Factor 2: Animal food Factor 3: Snack food Cereals 0.10 −0.12 Tubers and starches 0.06 −0.21 0.00 Legumes −0.11 −0.06 Vegetables 0.21 −0.13 Fungi and algae 0.14 0.30 −0.04 Fruits 0.21 0.11 Nuts −0.11 0.10 Meat and its products 0.10 0.05 Poultry and its products 0.21 −0.08 Dairy products 0.25 0.19 Eggs 0.25 0.22 Fish and shellfish −0.02 0.17 Ethnic foods and cakes 0.25 −0.03 Fast foods 0.20 −0.11 Beverages 0.15 0.03 Sugar and preserves 0.24 0.11 −0.07 Fats and oils 0.01 −0.20 0.15 Condiments 0.22 0.17 % Variance explained 13.1% 7.5% 6.6%
Cox proportional hazard regression models for the associations between the three dietary pattern scores at baseline and age at menarche are presented in Table 4. There was a positive association between the modern dietary pattern score and probability of earlier menarche, which remained significant when the potential mediator BMI Z-score at baseline was included in the final model (adjusted HR in model 3: 1.13, 95% CI: 1.03–1.24). After adjustment for age at baseline, region, ethnicity, maternal education level, energy intake at baseline, and BMI Z-score at baseline (model 3), girls in the highest quartile of modern dietary pattern score had a 33% higher probability of experiencing menarche at an earlier age than those in the lowest quartile (adjusted HR: 1.33, 95% CI: 1.002–1.77, p for trend = 0.03). However, no significant association was observed for animal food pattern or snack food pattern with timing of menarche. In sensitivity analyses, replacing maternal education level with household income per capita did not change these results (data not shown).
Cox proportional hazard regression models of three dietary patterns and timing of menarche among 1118 girls in the CHNS
Model 1 b Model 2 c Model 3 d Quartile 1 1.00 1.00 1.00 Quartile 2 0.98 (0.79, 1.21) 1.04 (0.83, 1.30) 1.03 (0.82, 1.28) Quartile 3 1.19 (0.95, 1.50) 1.27 (0.98, 1.65) 1.24 (0.96, 1.60) Quartile 4 1.23 (0.95, 1.59) 1.34 (1.01, 1.78) 1.33 (1.002, 1.77) Dietary pattern score (continuous) 1.13 (1.03, 1.23) 1.14 (1.04, 1.25) 1.13 (1.03, 1.24) 0.03 0.02 0.03 Quartile 1 1.00 1.00 1.00 Quartile 2 1.05 (0.84, 1.30) 1.07 (0.85, 1.33) 1.06 (0.85, 1.32) Quartile 3 1.00 (0.80, 1.25) 1.02 (0.81, 1.28) 1.01 (0.80, 1.27) Quartile 4 1.12 (0.89, 1.41) 1.14 (0.88, 1.47) 1.12 (0.87, 1.45) Dietary pattern score (continuous) 1.03 (0.95, 1.11) 1.03 (0.95, 1.12) 1.03 (0.95, 1.12) 0.49 0.52 0.54 Quartile 1 1.00 1.00 1.00 Quartile 2 0.99 (0.81, 1.23) 1.00 (0.81, 1.23) 1.00 (0.81, 1.24) Quartile 3 0.91 (0.73, 1.13) 0.91 (0.73, 1.13) 0.91 (0.74, 1.14) Quartile 4 0.85 (0.68, 1.06) 0.85 (0.68, 1.06) 0.85 (0.68, 1.06) Dietary pattern score (continuous) 0.93 (0.85, 1.02) 0.93 (0.85, 1.02) 0.93 (0.85, 1.01) 0.12 0.12 0.13
In the present study, we identified three major dietary patterns in the years preceding onset of menarche among Chinese girls: modern dietary pattern, animal food pattern and snack food pattern. We found that higher adherence to modern dietary pattern was associated with higher odds of experiencing menarche at an earlier age. This association was independent of potential sociodemographic confounders and premenarcheal body size. However, no significant association was found for animal food pattern or snack food pattern.
To our knowledge, our study is the first prospective study of the associations between dietary patterns and timing of menarche in a Chinese population. Compared with studies focusing on single nutrients or foods, dietary pattern takes the interactions of nutrients or foods into account, and thus could have important public health implications because overall patterns of dietary intake might be easier for the public to translate into daily diets. It serves as a complementary approach to traditional analysis, and evidence could be enhanced when the results from multiple lines of research (i.e. nutrients, foods, and dietary patterns) are consistent [[
The modern dietary pattern we identified showed some similarities with results previously reported by Zhang et al. [[
It is of note that the animal food pattern was not associated with age at menarche in the present study, although most of the previous studies have shown that girls with high intake of animal protein (especially red meat) reached menarche at an earlier age [[
Our study has several strengths, including the prospective design and the representative sample from four different regions in China. Dietary intake data were collected by using a validated three-day 24-h dietary recalls. A further advantage lies in the use of dietary pattern analysis, which examines the effects of diet as a whole, and might be much easier for the public to interpret or translate into diets. In addition, the comprehensive and detailed data allowed us to simultaneously take a number of potential confounders or mediators into account and thus to reliably examine the association between dietary patterns and menarcheal timing.
Some limitations should be mentioned. First, menarche represents a relatively late stage of pubertal development. Although using menarcheal age as an indicator of puberty timing is reliable, the effects of dietary patterns on earlier events of pubertal development may differ from those on age at menarche. Future work should address the relevance of dietary patterns and early stage of puberty, such as the age at take-off (ATO, i.e. the age at minimal height velocity) [[
Our data suggest that girls with higher adherence to modern dietary pattern experienced menarche at an earlier age. This association was independent of body mass. Our finding provides evidence to support the recommendation to have a balanced diet for prepubertal girls in China. Further research is needed to address the prospective effects of dietary patterns on earlier stage of pubertal development in Chinese girl population, and to determine the underlying biologic mechanisms.
This study was supported by the National Natural Science Foundation of China (81673158), International Cooperation Project of Science and Technology Department of Sichuan Province (19GJHZ0171), and International Cooperation Project of Chengdu Science and Technology Bureau (2019-GH02–00058-HZ).
This research uses data from the China Health and Nutrition Survey (CHNS). We thank the National Institute for Nutrition and Food Safety, China Center for Disease Control and Prevention, Carolina Population Center, the University of North Carolina at Chapel Hill, the NIH and the Fogarty International Center, NIH, for providing financial support for the CHNS data collection and analysis of files from 1989 to 2015 and future surveys.
G.C. contributed to the conception and design of the study. R.D. conducted data analysis and wrote the manuscript. Y. C., T. Q., R.D., M.C., L.Z. and Y.G. contributed to analysis and interpretation of the data. All authors have critically reviewed the manuscript for important intellectual content, and given approval of the final version for publication.
The data supporting the findings of this study are available from CHNS (https://
The CHNS study was approved by the Institutional Review Board at the University of North Carolina and the National Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention. All parents provided written informed consent for their children's participation in the survey.
Not applicable.
The authors declare that they have no competing interests.
• CHNS
- China Health and Nutrition Survey
• BMI
- Body mass index
• FCT
- Food Composition Tables
• PCA
- Principal components analysis
• HR
- Hazard ratio
• CI
- Confidence interval
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