Longitudinal adherence to a dietary pattern and risk of depressive symptoms: the Furukawa Nutrition and Health Study

Accepted Manuscript Title: Longitudinal adherence to a dietary pattern and risk of depressive symptoms: the furukawa nutrition and health study Author: Takako Miki, Masafumi Eguchi, Shamima Akter, Takeshi Kochi, Keisuke Kuwahara, Ikuko Kashino, Huanhuan Hu, Isamu Kabe, Norito Kawakami, Akiko Nanri, Tetsuya Mizoue PII: DOI: Reference:

S0899-9007(17)30257-5 https://doi.org/10.1016/j.nut.2017.10.023 NUT 10081

To appear in:

Nutrition

Received date: Revised date: Accepted date:

26-7-2017 30-9-2017 29-10-2017

Please cite this article as: Takako Miki, Masafumi Eguchi, Shamima Akter, Takeshi Kochi, Keisuke Kuwahara, Ikuko Kashino, Huanhuan Hu, Isamu Kabe, Norito Kawakami, Akiko Nanri, Tetsuya Mizoue, Longitudinal adherence to a dietary pattern and risk of depressive symptoms: the furukawa nutrition and health study, Nutrition (2017), https://doi.org/10.1016/j.nut.2017.10.023. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Longitudinal adherence to a dietary pattern and risk of depressive symptoms: the

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Furukawa Nutrition and Health Study

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Short running head: Dietary pattern and the risk of depressive symptoms

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Takako Miki M.P.H. a, b, c,*, Masafumi Eguchi M.D. d, Shamima Akter Ph.D. a, Takeshi Kochi

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M.D. d, Keisuke Kuwahara Ph.D. a, e, Ikuko Kashino Ph.D. a, Huanhuan Hu Ph.D. a, Isamu

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Kabe M.D., Ph.D. d, Norito Kawakami M.D., Ph.D. b, Akiko Nanri Ph.D. a, Tetsuya Mizoue

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M.D., Ph.D. a

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a

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for Global Health and Medicine, Tokyo, Japan.

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b

Department of Mental Health, Graduate School of Medicine, The University of Tokyo.

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c

Research Fellow of Japan Society for the Promotion of Science.

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d

Department of Health Administration, Furukawa Electric Corporation, Tokyo, Japan.

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e

Teikyo University Graduate School of Public Health, Tokyo, Japan.

Department of Epidemiology and Prevention, Center for Clinical Sciences, National Center

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Author contributions

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T. Mizoue and A.N. designed the research; T. Miki, M.E., S.A., T.K., K.K., I. Kashino, H.H.,

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I. Kabe, A.N., and T. Mizoue conducted the research; T. Miki performed statistical analysis,

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wrote the manuscript, and had primary responsibility for the final content; and all authors

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were involved in revision and approved the final version of the manuscript.

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Word count: 4192 (excluding title page, abstract, references, and tables) 1 Page 1 of 30

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Number of references: 40

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Number of Tables: 2

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*

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Takako Miki

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Department of Mental Health, Graduate School of Medicine,

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The University of Tokyo, Japan

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7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.

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Tel: +81-3-5841-3522, Fax: +81-3-5841-3592,

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E-mail address: [email protected] (Takako Miki)

Corresponding author:

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Highlights:

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• We prospectively studied the association of dietary pattern with depressive risk.

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• A stable high dietary pattern scores was related to a lower risk of depression.

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• Those with improved dietary pattern scores were linked to a lower depressive risk.

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Abstract 246 words

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Objective: We explored the association of 3-year adherence to a dietary pattern based on

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nutrients that may be related to mood with the development of depressive symptoms in

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Japanese employees.

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Research Methods & Procedures: Participants were 903 employees, free from depressive

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symptoms at baseline and who attended the 3-year followup. Participants with depressive

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symptoms were defined as those with a score ≥ 16 on the Center for Epidemiologic Studies

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Depression Scale. Dietary patterns were derived using reduced-rank regression at baseline

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and at the 3-year follow-up survey using a validated, self-administered diet history

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questionnaire. Based on changes in dietary pattern scores between baseline and follow-up

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surveys, participants were categorized into four groups: maintained high scores, improved

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scores, decreased scores, and maintained low scores. Logistic regression was used to estimate

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odds ratios of depressive symptoms according to changes in dietary pattern scores.

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Results: Maintaining high or improving adherence to a diet rich in vegetables, mushrooms,

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seaweeds, soybean products, green tea, potatoes, fruits, and fish and low in rice over 3 years

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was associated with a decreased risk of depressive symptoms. The multivariable-adjusted

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odds ratio (95% confidence intervals) of developing depressive symptoms for maintained

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high scores vs maintained low scores was 0.57 (0.35-0.93) and for improved scores vs

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maintained low scores was 0.54 (0.29-1.01). The association with the severe depressive status

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was more pronounced. 3 Page 3 of 30

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Conclusion: Maintaining high or improving adherence to a dietary pattern derived by

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reduced-rank regression is associated with a lower risk of depression among Japanese

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employees.

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Keywords: depression risk; dietary patterns; prospective studies; Japanese; reduced-rank

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regression

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Introduction

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Depression is a common mental health problem in the general population that reduces

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work productivity, lowers quality of life, and increases mortality [1]. Several previous studies

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have reported associations between individual nutrients or foods and depressive symptoms

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[2-9]. However, single-nutrient or food analysis in relation to disease is challenging because

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many nutrients are highly correlated with each other and this analysis may not capture the

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interactive or synergistic effects among nutrients [10]. To overcome these limitations, dietary

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pattern analysis has emerged as a complementary approach that better reflects the complexity

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of diet in daily life and its relation with disease [10]. Two main methodologies facilitate the

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identification of dietary pattern—a priori defined methods (e.g., diet quality score) and

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exploratory methods (e.g., principal component analysis: PCA)—but neither method

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incorporates existing evidence of the pathway from diet to the specific disease [11].

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To address this issue, a new a posteriori method, reduced-rank regression (RRR), has

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been proposed [12]. The great advantage of RRR is that it identifies dietary pattern based on

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disease-specific evidence (e.g., several nutrients or biomarkers) that have been linked to the

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outcome of primary interest [12]. To our knowledge, RRR has been applied to examine the

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prospective association between dietary pattern and depression risk in United States [13] and

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Italy [14], but not in Asia. Because of the marked difference in dietary habits between Eastern

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and Western populations [15], however, dietary patterns in Western populations and their

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association with depression symptoms [13, 14] might not apply to Eastern populations.

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Given that the diets of individuals change over time, the use of dietary assessment at a

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single time point is unable to capture dietary changes over time [16]. Repeated assessment of

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diet over a follow-up period should provide a better estimate of dietary status [16]. One study

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used a cumulative average of dietary intake [13], and another measured dietary intake only at 5 Page 5 of 30

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baseline [14].

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Previously, we identified dietary patterns using nutrients that may be related to mood

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as response variables and found a significant inverse association with depressive symptoms

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among Japanese employees [17]. To confirm the cross-sectional observation prospectively,

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we examined the association of adherence to an RRR-derived dietary pattern over 3 years

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with the risk of new onset of depressive symptoms in the same cohort of Japanese workers.

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Materials and methods

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Study procedure

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As part of the Japan Epidemiology Collaboration on the Occupational Health Study,

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the Furukawa Nutrition and Health Study, a nutritional epidemiological survey, was

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conducted at the time of a periodic health examination among workers of two works of a

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manufacturing company and its affiliated companies in Chiba and Kanagawa Prefectures.

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This survey was conducted at baseline (in April 2012 and May 2013) and at a 3-year

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follow-up session (in April 2015 and May 2016). Prior to the health check-up, all employees

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(n = 2828) in these companies were invited to take part in the survey and asked to fill out two

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types of survey questionnaires (one for diet and the second for overall health-related lifestyle

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in general). The study protocol was approved by the Ethics Committee of the National Center

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for Global Health and Medicine, Japan, and secondary analysis of the Furukawa Nutrition

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and Health Study data was approved by the ethics committee of the University of Tokyo. The

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workers’ identities were anonymized and survey data obtained by this study were only

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available for research. In the baseline survey, of 2828 health check-up attendees, 2162 agreed

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to participate (response rate, 76%). Of these, 1354 (62.6%) also responded to the 3-year

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follow-up survey. At the time of the health check-up, the research staff checked the 6 Page 6 of 30

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questionnaires for completeness and, when necessary, clarified with the respondents. We

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obtained additional data including the results of anthropometric and biochemical

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measurements and information on disease history.

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Participants

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Among the 1354 participants participating in both baseline and 3-year follow-up

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surveys, we excluded 11 participants with missing data on the questionnaires used, the Brief

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Self-administered Diet History Questionnaire (BDHQ) [18] or the Center for Epidemiological

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Studies Depression Scale (CES-D) score [19, 20] at either baseline or follow-up. We then

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excluded 60 participants with a history of the following diseases: cancer (n = 13);

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cardiovascular disease (n = 13); chronic hepatitis (n = 1); kidney disease, including nephritis

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(n = 3); pancreatitis (n = 3); and mental disorders, such as depression and anxiety disorder (n

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= 29). Some participants had ≥ 2 of these diseases. Of the remaining 1283, we further

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excluded 350 participants with depressive symptoms (CES-D ≥ 16) at baseline and 19

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participants who had missing data on covariates of the present analysis. We additionally

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excluded 11 participants who reported extreme total energy intake (more than mean ± 3

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standard deviations) at either the baseline or follow-up survey. Ultimately, a total of 903

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participants (804 men and 99 women aged 19–68 years) were available for analysis (Figure 1).

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Additionally, to assess for reverse causality, we examined the prospective association between

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depressive symptoms at baseline (CES-D < 16 or ≥ 16) and continuous DP1 scores at

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follow-up study by using 1243 participants without excluding participants with depressive

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symptoms (CES-D ≥ 16) at baseline and missing data on CES-D score at follow-up in the

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above main sample.

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Assessment of depressive symptoms

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Depressive symptoms were assessed using a Japanese version [19] of the CES-D scale

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[20], which was incorporated into the lifestyle questionnaire at baseline and at the 3-year

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follow-up survey. This scale consists of 20 items addressing 6 typical symptoms of

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depression experienced during the preceding week, including depressed mood, guilt or

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worthlessness, helplessness or hopelessness, psychomotor retardation, loss of appetite, and

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sleep disturbance. Each item is scored on a scale of 0 to 3 according to the frequency of the

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symptom, and the scores are then summed to give the total CES-D score, ranging from 0–60.

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The criterion validity of the CES-D scale has been well established in both Western [20] and

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Japanese [19] subjects. Participants with a CES-D score ≥ 16 are considered to have

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depressive symptoms [20]. A cutoff of ≥ 19, which has been suggested as more suitable for

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Japanese workers [21], was also assessed.

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Dietary assessment

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Dietary habits during the preceding one-month period were assessed using the BDHQ

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at baseline and 3 years [18]. The questionnaire consists of five sections: 1) intake frequency

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of 46 food and non-alcoholic beverage items; 2) daily intake of rice and miso soup; 3)

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frequency of alcohol drinking and amount of consumption of five alcoholic beverages per

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typical drinking occasion; 4) usual cooking methods; and 5) general dietary behavior. Dietary

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intakes for 58 food and beverage items, energy, and selected nutrients were estimated using

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an ad hoc computer algorithm for the BDHQ [22], with reference to the Standard Tables of

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Food Composition in Japan [23, 24]. According to a validation study of the BDHQ using

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16-day weighted dietary records as standard, correlation coefficients were > 0.40 for the

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intake of many foods, beverages [22], and nutrients used in our study [18]. 8 Page 8 of 30

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Assessment of dietary pattern

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Dietary pattern scores were determined using RRR analysis [12]. Briefly, RRR

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identifies linear functions of food groups (i.e., the dietary patterns) that maximally explain the

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variation of response variables. We selected the following six nutrients as response variables:

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folate [2], vitamin C [3, 4], magnesium [5], calcium [5], iron [6], and zinc [6], which were

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same as used in our previous cross-sectional study using RRR [17]. Biological and

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epidemiological evidence suggests that these nutrients may be potentially protective factors

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for depression in Japanese and other populations [2-6, 25-30]. In addition to these nutrients,

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we conducted a sensitivity analysis using additional nutrients that may be related to mood

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included as response variables (i.e., vitamin D [7], vitamin B6 [8], vitamin B12 [8], and n-3

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polyunsaturated fatty acids [9]). Dietary patterns related to the intake of these nutrients were

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derived on the basis of 52 food and beverage items, excluding six items (sugar added to

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coffee and black tea, three items usually added during cooking [salt, oil, and sugar], table salt

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and salt-containing seasoning at the table, and soup consumed with noodles). The nutrients

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selected as response variables and the 52 food and beverage items were energy-adjusted (via

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the density method) prior to the RRR.

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Explained percentage of variations in nutrients (response variables) and food and

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beverage items by extracted dietary patterns and Pearson correlation coefficients between

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nutrients and dietary patterns were computed. Of the six factors extracted, the first factor,

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namely dietary pattern 1 (DP1), obtained by RRR, accounted for the largest amount of

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variation among the nutrients. Therefore, we selected scores for DP1 as a target dietary

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pattern in this study. The dietary pattern score represents how much each participant adhered

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to the dietary pattern: the higher the score, the more closely the participant’s diet conformed 9 Page 9 of 30

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to the dietary pattern. We created the DP1 scores at baseline according to the above

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procedures and we used the coefficients of the baseline DP1 scores to calculate the

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corresponding DP1 scores at 3-year followup.

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To assess the changes in DP1 scores from baseline to 3 years, scores of the DP1 at

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baseline and follow-up periods were categorized as high or low according to the median

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value of the DP1 scores at each survey. Participants were then grouped into four categories

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based on changes scores from baseline to 3 years. Participants remaining in the high DP1

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group at both baseline and follow-up were categorized as “maintained high scores.”

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Participants who moved from a low group at baseline to a high group, or vice versa, at

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follow-up were categorized as “improved scores” or “decreased scores,” respectively.

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Participants who remained in the low group at both surveys were categorized as “maintained

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low scores.”

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Other variables

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Marital status, job grade, night and rotating shift work, overtime work, smoking, sleep

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duration, physical activity during work and housework or in commuting to work, and

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leisure-time physical activity were asked about in the survey questionnaire. Physical activity

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during work and housework or in commuting and leisure time were expressed as the sum of

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metabolic equivalents (METs) multiplied by the duration of time (in hours) across physical

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activities with different levels. Psychological work environment was assessed via the Job

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Content Questionnaire [31], and job strain score was calculated according to the standard

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procedure. Body height and weight were assessed to the nearest 0.1 cm and 0.1 kg,

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respectively, in a standardized procedure with participants wearing light clothes and without

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shoes. Body mass index was calculated as weight in kilograms divided by the square of 10 Page 10 of 30

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height in meters (kg/m2).

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Statistical analysis

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Differences in baseline characteristics between participants with and without

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depressive symptoms at follow-up were assessed using an independent t-test (continuous

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variable) and a χ2-test (categorical variable). To examine the prospective association between

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changes in DP1 scores and the risk of depressive symptoms, we performed multiple logistic

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regression analysis and calculated the odds ratios (ORs) and 95% confidence intervals (CIs)

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of depressive symptoms for changes in DP1 scores, using the maintained low scores as

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reference. The first model was adjusted for age (in years, continuous), sex, and works (survey

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in April 2012 or in May 2013). The second model was further adjusted for marital status

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(married or other), job grade (low, middle, or high), night or rotating shift work (yes or no),

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overtime work (< 10 hours/month, 10 to < 30 hours/month, or ≥ 30 hours/month), job strain

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(quartile), physical activity at work and housework or in commuting to work (< 3 METs-hour

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/day, 3 to < 7 METs-hour/day, 7 to < 20 METs-hour/day, or ≥ 20 METs-hour/day),

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leisure-time physical activity (not engaged, > 0 to < 3 METs-hour/week, 3 to < 10

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METs-hour/week, or ≥ 10 METs-hour/week), smoking (never-smoker, quitter, current

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smoker consuming < 20 cigarettes/day, or current smoker consuming ≥ 20 cigarettes/day),

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sleep duration (< 6 hours/day, 6 to 6.9 hours/day, or ≥ 7 hours/day), body mass index

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(continuous), and total energy intake (kcal/day, continuous). In addition, CES-D scores at

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baseline (continuous) were further adjusted for (model 3). All models were adjusted for

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baseline values of all potential confounding variables (described above). Additionally, we

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repeated the above analysis for the tertiles of baseline DP1 scores, using the lowest tertile

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category as a reference (n = 909). Furthermore, in order to examine the issue of reverse 11 Page 11 of 30

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causality, we used the baseline CES-D score (CES-D < 16 or ≥ 16) as a predictor in a

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multiple linear regression analysis, with the continuous DP1 scores at follow-up survey as

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outcome (n = 1243). A two-sided P value < 0.05 was considered statistically significant in all

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analyses. The RRR analyses were performed using Statistical Analysis System (SAS)

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software version 9.4 (SAS Institute, Cary NC, USA), while all the other analyses were

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performed using Stata version 12.1 (StataCorp, College Station TX, USA).

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Results

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DP1 scores were highly correlated with each nutrient (folate, vitamin C, magnesium,

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calcium, iron, and zinc) selected as response variables at both baseline and followup. All

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Pearson correlation coefficients were ≥ 0.70 (Supplemental Tables 1). We derived the dietary

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pattern (DP1) characterized by a high intake of vegetables, mushrooms, seaweeds, soybean

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products, green tea, potatoes, fruits, and fish, and a low intake of rice using RRR at baseline

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and 3 years (details in supplemental Tables 2).

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Table 1 shows the baseline characteristics of study participants according to their

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depressive status at followup. Of 903 participants without depressive symptoms at baseline,

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152 participants (16.8%) were newly identified as having depressive symptoms at the

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follow-up survey 3 years later. Compared with participants who did not have depressive

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symptoms at follow-up survey, those with depressive symptoms were more likely to be in a

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low-ranking job position; and had higher CES-D score at baseline and total energy intake but

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lower DP1 scores at followup and absolute 3-year changes in DP1 scores.

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The ORs of depressive symptoms according to 3-year changes in DP1 scores are

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shown in Table 2. In an age-, sex-, and workplace-adjusted model (model 1), participants who

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maintained high DP1 scores as well as those who improved DP1 scores over the follow-up 12 Page 12 of 30

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period had lower odds of developing depressive symptoms compared with those who

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maintained low DP1 scores at both baseline and 3 years. Further adjustment for other

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covariates (model 2) somewhat attenuated the association. The multivariable-adjusted odds

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ratios (95% CI) of developing depressive symptoms were 0.57 (0.35–0.93; P = 0.024:

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maintained high scores vs maintained low scores) and 0.54 (0.29–1.01; P = 0.053: improved

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scores vs maintained low scores). A stronger association was observed when the higher cutoff

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point (CES-D ≥ 19) was used in the definition of depressive symptoms. The corresponding

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values for depressive symptoms (CES-D ≥ 19) were 0.45 (0.24–0.84; P = 0.013: maintained

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high scores vs maintained low scores) and 0.36 (0.15–0.87; P = 0.024: improved scores vs

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maintained low scores), respectively. On the other hand, participants whose DP1 scores

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decreased over time had non-significant increased odds of developing depressive symptoms

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compared with those maintained low DP1 scores over the 3-year period. The results were

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virtually unchanged after additional adjustment for baseline CES-D score (model 3), except

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for those who maintained high scores (the estimate became statistically not significant). No

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measurable association of baseline DP1 scores with subsequent development of depressive

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symptoms was observed.

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In the sensitivity analysis including the additional nutrients that may be related to

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mood (i.e., vitamin D, vitamin B6, vitamin B12, and n-3 polyunsaturated fatty acids) as

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response variables, we identified a dietary pattern close to the DP1 (details in supplemental

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Tables 3 and 4) and confirmed a slightly stronger inverse association for those who

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maintained high DP1 scores and improved their scores (Supplemental Tables 5). These

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findings remained statistically significant after further adjustment for baseline CES-D score

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(data not shown). To explore reverse causality, we examined the prospective association

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between depressive symptoms at baseline (CES-D < 16 or ≥ 16) and continuous DP1 scores 13 Page 13 of 30

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at follow-up study, but CES-D score at baseline did not predict DP1 scores at follow-up

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survey (Supplemental Table 6).

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Discussion

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In this prospective analysis, we found that maintaining high or improving adherence to

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a diet rich in vegetables, mushrooms, seaweeds, soybean products, green tea, potatoes, fruits,

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and fish and low in rice over a period of 3 years was associated with a lower risk of

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depressive symptoms among Japanese employees. This is among few studies addressing the

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longitudinal association between dietary patterns derived by RRR and depressive symptoms

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assessed over time.

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Our longitudinal finding of an inverse relationship between maintaining high or

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improving adherence to the DP1 over time with the risk of depressive symptoms generally

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agrees with prior results in Australia, which showed that participants who maintained a high

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diet quality score had lower odds of depression compared with those who maintained a low

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score, but showed no clear association between baseline diet score and the risk of depression

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after the exclusion of participants with a history of depression [32]. A British study found

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participants who maintained high healthy diet scores or improved scores had lower odds of

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subsequent recurrent depressive symptoms compared with those maintained low scores, and

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also that baseline diet scores were inversely related to recurrent depressive symptoms in

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women but not men, though the relationship with baseline scores was weaker compared with

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those for maintained high or improved scores [33]. Not only participants who maintained

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high dietary scores but also improved scores had lower odds of depressive symptoms

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compared with those maintaining low scores, suggesting the possibility that current diet had a

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more substantial influence on the development of depressive symptoms than previous diet. 14 Page 14 of 30

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Further prospective studies with dietary assessment at multiple points will be required to

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elucidate this issue.

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Up to now, dietary patterns similar to DP1 have been shown to be associated with

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decreased depression. A prior Japanese study using PCA dietary pattern analysis has reported

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a significantly decreased prevalence of depressive symptoms associated with a dietary pattern

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featuring a high intake of vegetables, fruit, mushrooms, and soy products [34]. Another

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Japanese cross-sectional study using PCA found that a diet rich in vegetables, mushrooms,

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and seaweed was inversely associated with depressive symptoms [35]. Moreover, in a prior

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meta-analysis of observational studies including four cohort studies that used a priori

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methods (e.g., diet quality scores) and a posteriori approaches (e.g., PCA) in the

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identification of dietary patterns, a healthy diet pattern, characterized by a high intake of

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fruits, vegetables, fish, and whole grains, was inversely associated with depression [36]. The

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results of recent updated meta-analysis also suggested that healthy pattern rich in fruit,

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vegetables, whole grain, fish, olive oil, low-fat dairy and antioxidants and low in animal

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foods was related with a decreased risk of depression [37]. This consistency underscores the

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protective role of diet rich vegetables, mushrooms, seaweeds, soybean products, green tea,

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potatoes, fruits, and fish and low in rice against depressive symptoms.

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Contrary to expectation, we did not detect a measurable relationship between dietary

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pattern at baseline and subsequent development of depressive symptoms after 3 years. The

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null finding from a prospective analysis was inconsistent with that from our prior

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cross-sectional analysis, which found inverse association between DP1 scores and depressive

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symptoms at baseline survey [17]. This discrepancy raises the possibility of reverse causality

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(i.e., a low DP1 score could be the consequence of depressive symptoms, rather than the

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cause). In fact, the diet-mood relationship has been suggested to be bi-directional [38]. Thus, 15 Page 15 of 30

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we examined the prospective association of CES-D score at baseline as predictor with the

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DP1 scores at follow-up survey as outcome, but found no association between them. Another

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possibility might be that the protective association of DP1 at baseline with subsequent

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development of depressive symptoms might be masked by dietary changes during the

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follow-up period (a total of 27.5% of participants in the present study changed their status in

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respect to exposure due to changes in diet during the followup as described below); that is,

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those who improved DP1 scores during the followup showed decreased risk of depressive

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symptoms. On the other hand, those who decreased DP1 scores had non-significant increased

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depressive risk. A previous report suggested that using baseline measurements may

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underestimate the true association of dietary exposure with the outcome disease [39].

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Although multiple exposure assessment increases accuracy via its ability to protect against

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reverse causation and accounts for changes in diet during followup, most prior cohort studies

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used only dietary measurement at baseline to examine the association of dietary pattern with

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the risk depressive symptoms [40], and thus the association may be underestimated.

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The inverse relationship of maintaining high or improving adherence to a Japanese

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diet (DP1) with the risk of depressive symptoms in the present study is supported by

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biological evidence of nutrients that may be related to mood selected as response variables in

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the present study. Folate is involved in the metabolism of monoamines such as serotonin, and

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may protect the brain via reducing homocysteine, which has a neurotoxic effect [25]. In

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addition, reactive oxygen species and defective antioxidant defenses are thought to be

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implicated in the underlying pathophysiology of depression [26]. Thus, vitamins with

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antioxidant properties, such as vitamin C, may protect against depression. Furthermore,

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magnesium is a voltage-dependent blocker of the N-methyl-D-aspartate (NMDA) channel

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[27] and calcium activates tryptophan hydroxylase in the serotonin synthesis [28]. Moreover, 16 Page 16 of 30

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iron is involved in the synthesis of neurotransmitters such as dopamine and serotonin [29]

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and zinc might modulate mood via enhancing the function of the serotonergic system or

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inhibiting the function of the NMDA receptor complex [30]. These nutrients may individually

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as well as jointly protect against depression through multiple pathways.

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The strengths of this study include prospective design using RRR, use of validated

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methodologies to assess nutrient intake (i.e., the BDHQ), and controlling for a large range of

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confounders with work-related factors. Additionally, the strength of our study was the use of

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measurements at baseline and at 3-year followup, providing a better estimate of exposure

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status compared with dietary assessment at only one time point. Our study also has some

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limitations. First, the large loss to followup (37.4% did not participate at followup) may have

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introduced selection bias. However, we confirmed that the baseline characteristics of subjects

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participating and not participating in the followup were generally similar. Second, dietary

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intake assessed at baseline and 3 years might not accurately represent the long-term habitual

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diet. Third, although we adjusted for a range of potential confounding variables, we cannot

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rule out the possibility that the observed associations were due to unmeasured confounders

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and residual confounding. Fourth, because the present study was conducted among workers

369

in a Japanese manufacturing company, caution is required in generalizing the finding. Finally,

370

our exposure variables were defined as changes in RRR dietary pattern scores from baseline

371

to follow-up survey after 3 years and the outcome was defined as the development of

372

depressive symptoms at 3 years. Thus, we were not able to assess exposure status that

373

absolutely preceded the outcome, and this inability limited the drawing of causal inference

374

even though participants were free from depressive symptoms at the beginning of present

375

cohort study. Nevertheless, the consistency of our findings with those of prior prospective

376

cohort studies [32, 33] lends credence to these findings. 17 Page 17 of 30

377 378

Conclusion

379

In conclusion, this prospective study suggests that maintaining high or improving adherence

380

to a diet rich in vegetables, mushrooms, seaweeds, soybean products, green tea, potatoes,

381

fruits, and fish and low in rice over a period of 3 years is associated with a lower risk of

382

depressive symptoms among Japanese employees. Further long-term prospective or

383

intervention studies are required to examine whether maintaining or improving adherence to

384

this diet can decrease depressive symptoms.

385 386

Conflict of interest

387

The authors declare no conflicts of interest. M.E., T.K., and I. Kabe are health professionals

388

employed by the Furukawa Electric Corporation.

389 390

Acknowledgments

391

We thank Fumiko Zaizen, Rie Ito, Hiroko Tsuruoka, and Akiko Makabe (Furukawa

392

Electric Corporation) and Ayami Kume, Sachiko Nishihara, Yuho Mizoue, Saeko Takagiwa,

393

Maki Konishi, Rika Osawa, and Yuriko Yagi (National Center for Global Health and

394

Medicine) for their help in data collection.

395

This study was supported by the Grant-in-Aid for Japan Society for the Promotion of

396

Science (JSPS) Fellows Number 17J00166, JSPS KAKENHI Grant Numbers 25293146,

397

25702006,

398

Cardiovascular Diseases and Diabetes Mellitus (15ek0210021h0002) from the Japan Agency

399

for Medical Research and Development, and the Industrial Health Foundation.

Practical

Research

Project

for

Life-Style

related

Diseases

including

18 Page 18 of 30

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[8] Skarupski KA, Tangney C, Li H, Ouyang B, Evans DA, Morris MC. Longitudinal association of vitamin B-6, folate, and vitamin B-12 with depressive symptoms among older adults over time. The American Journal Of Clinical Nutrition. 2010;92:330-5. [9] Sanchez-Villegas A, Henríquez P, Figueiras A, Ortuño F, Lahortiga F, Martínez-González MA. Long chain omega-3 fatty acids intake, fish consumption and mental disorders in the SUN cohort study. Eur J Nutr. 2007;46:337-46. [10] Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13:3-9. [11] Michels KB, Schulze MB. Can dietary patterns help us detect diet-disease associations? Nutr Res Rev. 2005;18:241-8. [12] Hoffmann K, Schulze MB, Schienkiewitz A, Nothlings U, Boeing H. Application of a new statistical method to derive dietary patterns in nutritional epidemiology. Am J Epidemiol. 2004;159:935-44. [13] Lucas M, Chocano-Bedoya P, Shulze MB, Mirzaei F, O'Reilly EJ, Okereke OI, et al. Inflammatory dietary pattern and risk of depression among women. Brain Behav Immun. 2014;36:46-53. [14] Vermeulen E, Stronks K, Visser M, Brouwer IA, Schene AH, Mocking RJ, et al. The association between dietary patterns derived by reduced rank regression and

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depressive symptoms over time: the Invecchiare in Chianti (InCHIANTI) study. Br J Nutr. 2016;115:2145-53. [15] Zhou BF, Stamler J, Dennis B, Moag-Stahlberg A, Okuda N, Robertson C, et al. Nutrient intakes of middle-aged men and women in China, Japan, United Kingdom, and United States in the late 1990s: the INTERMAP study. J Hum Hypertens. 2003;17:623-30. [16] Willett W. Nutritional epidemiology, 2nd edn. Oxford University Press, New York. 1998. [17] Miki T, Kochi T, Kuwahara K, Eguchi M, Kurotani K, Tsuruoka H, et al. Dietary patterns derived by reduced rank regression (RRR) and depressive symptoms in Japanese employees: The Furukawa nutrition and health study. Psychiatry Res. 2015;229:214-9. [18] Kobayashi S, Honda S, Murakami K, Sasaki S, Okubo H, Hirota N, et al. Both comprehensive and brief self-administered diet history questionnaires satisfactorily rank nutrient intakes in Japanese adults. J Epidemiol. 2012;22:151-9. [19] Shima S, Shikano T, Kitamura T, Asai M. New self-rating scale for depression. Jpn J Clin Psychiatry. 1985;27:717–23 (in Japanese). [20] Radloff LS. The CES-D scale: a self-report depression scale for research in the

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general population. Appl Psychol Meas. 1977;1:385-401. [21] Wada K, Tanaka K, Theriault G, Satoh T, Mimura M, Miyaoka H, et al. Validity of the Center for Epidemiologic Studies Depression Scale as a screening instrument of major depressive disorder among Japanese workers. Am J Ind Med. 2007;50:8-12. [22] Kobayashi S, Murakami K, Sasaki S, Okubo H, Hirota N, Notsu A, et al. Comparison of relative validity of food group intakes estimated by comprehensive and brief-type self-administered diet history questionnaires against 16 d dietary records in Japanese adults. Public Health Nutr. 2011;14:1200-11. [23] Science and Technology Agency. Standard Tables of Food Composition in Japan. Tokyo: Ministry of Education, Culture, Sports, Science and Technology; 2010 (in Japanese). [24] Standard Tables of Food Composition in Japan, fatty acids section. 5th revised and enlarged ed. Printing Bureau of the Ministry of Finance, Tokyo.Science and Technology Agency, 2005a. [25] Bottiglieri T. Homocysteine and folate metabolism in depression. Prog Neuropsychopharmacol Biol Psychiatry. 2005;29:1103-12. [26] Bilici M, Efe H, Koroglu MA, Uydu HA, Bekaroglu M, Deger O. Antioxidative enzyme activities and lipid peroxidation in major depression: alterations by

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antidepressant treatments. J Affect Disord. 2001;64:43-51. [27] Bresink I, Danysz W, Parsons CG, Mutschler E. Different binding affinities of NMDA receptor channel blockers in various brain regions--indication of NMDA receptor heterogeneity. Neuropharmacology. 1995;34:533-40. [28] Knapp S, Mandell AJ, Bullard WP. Calcium activation of brain tryptophan hydroxylase. Life Sci. 1975;16:1583-93. [29] Beard JL, Connor JR, Jones BC. Iron in the brain. Nutr Rev. 1993;51:157-70. [30]

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dietary guidelines and future depressive symptoms: evidence for sex differentials in the Whitehall II study. Am J Clin Nutr. 2013;97:419-27. [34] Nanri A, Kimura Y, Matsushita Y, Ohta M, Sato M, Mishima N, et al. Dietary patterns and depressive symptoms among Japanese men and women. Eur J Clin Nutr. 2010;64:832-9. [35] Suzuki T, Miyaki K, Tsutsumi A, Hashimoto H, Kawakami N, Takahashi M, et al. Japanese dietary pattern consistently relates to low depressive symptoms and it is modified by job strain and worksite supports. J Affect Disord. 2013;150:490-8. [36] Lai JS, Hiles S, Bisquera A, Hure AJ, McEvoy M, Attia J. A systematic review and meta-analysis of dietary patterns and depression in community-dwelling adults. Am J Clin Nutr. 2014;99:181-97. [37] Li Y, Lv MR, Wei YJ, Sun L, Zhang JX, Zhang HG, et al. Dietary patterns and depression risk: A meta-analysis. Psychiatry Res. 2017;253:373-82. [38] Gibson EL. Emotional influences on food choice: sensory, physiological and psychological pathways. Physiol Behav. 2006;89:53-61. [39] Hoevenaar-Blom MP, Spijkerman AM, Boshuizen HC, Boer JM, Kromhout D, Verschuren WM. Effect of using repeated measurements of a Mediterranean style diet on the strength of the association with cardiovascular disease during 12 years: the

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Doetinchem Cohort Study. Eur J Nutr. 2014;53:1209-15. [40] Sanhueza C, Ryan L, Foxcroft DR. Diet and the risk of unipolar depression in adults: systematic review of cohort studies. Journal of human nutrition and dietetics : the official journal of the British Dietetic Association. 2013;26:56-70.

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2828 employees who invited in the 2012-13 baseline survey 666 participants who did not consent to baseline survey

808 participants who did rate not 76%) attend or consent to follow-up 2162 participants who took part in baseline survey (response survey 1354 participants who took part in the 2015-16 follow-up survey (follow-up rate 62.6 %)

11 participants with missing data on either exposure (BDHQ) or outcome (CES-D) at either baseline or follow-up 60 participants with a history of severe diseases at baseline 350 participants with depressive symptoms (CES-D≥16) at baseline 19 participants with missing data on covariates at baseline 11 participants who reported extreme total energy intake at either baseline or follow-up (Some participants had two or more conditions for exclusion.)

26 Page 26 of 30

903 participants (804 men and 99 women aged 19–68 years at baseline)

Figure 1. Flowchart of study protocol.

27 Page 27 of 30

Table 1 Baseline characteristics of the study population according to their depressive status at follow-up survey Subjects with Subjects without * depressive symptoms (n = depressive symptoms † (n P value ‡ 152) = 751) Age (mean ± s.d., year) 40.8 ± 9.2 42.1 ± 9.4 0.12 Sex (women, %) 7.2 11.7 0.11 Works (survey in April 2012, %) 51.3 58.2 0.12 Marital status (married, %) 75.0 69.2 0.16 Job grade (low, %) 77.6 67.5 0.014 Night or rotating shift work (yes, %) 25.0 18.6 0.07 Overtime work (≥30 hours/month, %) 23.7 24.4 0.86 Job strain (mean ± s.d.) 0.48 ± 0.10 0.47 ± 0.11 0.09 Physical activity at work and housework or in commuting to work 27.6 20.8 0.06 (≥20 METs-hours/day, %) Leisure-time physical activities (≥10 METs-hours/week, %) 26.3 30.2 0.34 33.6 27.4 0.13 Smoking status (current, %) Alcohol drinking (current §, %) 52.0 53.7 0.70 Sleep duration (<6 hours/day, %) 40.8 34.9 0.17 Body mass index (mean ± s.d., kg/m2) 23.4 ± 3.1 22.9 ± 3.1 0.054 e CES-D score at baseline (mean ± s.d) 10.9 ± 3.5 7.9 ± 3.9 <0.001 DP1 scores (mean ± s.d) At baseline (survey in April 2012 and in May 2013) -0.06 ±1.23 0.01 ± 1.28 0.56 At follow-up (survey in April 2015 and in May 2016) -0.47 ± 1.72 0.09 ± 2.13 0.0022 Absolute 3-year change -0.41 ± 1.35 0.08 ± 1.76 0.0010 Daily dietary intake (mean ± s.d) Total energy (kcal/day) 1893 ± 509 1751 ± 460 <0.001 Folate (μg/1000 kcal) 165 ± 56 166 ± 59 0.80 Vitamin C (mg/1000 kcal) 52 ± 23 51 ± 24 0.71 Magnesium (mg/1000 kcal) 125 ± 24 126 ± 25 0.58 28 Page 28 of 30

Calcium (mg/1000 kcal) 231 ± 78 236 ± 86 0.56 Iron (mg/1000 kcal) 3.8 ± 0.9 3.8 ±1.0 0.55 Zinc (mg/1000 kcal) 4.1 ± 0.6 4.2 ± 0.6 0.11 Abbreviations: s.d., standard deviation; METs, Metabolic Equivalents; DP1 scores, Dietary Pattern 1 scores; CES-D score, Center for Epidemiologic Studies Depression scale score. Subjects with a Center for Epidemiologic Studies Depression scale score ≥16. Subjects with a Center for Epidemiologic Studies Depression scale score <16. ‡ For continuous variables, independent t test was used; for categorical variables, χ2-test was used. § Alcohol consumption of at least one day per week. *

†

Table 2 Odds ratios and 95% confidence intervals for depressive symptoms according to 3 year change in DP1 scores* Subjects with/without depressive symptoms

Model 1 †

Maintained low scores

69/258

1.00 (ref)

Improved scores

15/109

0.53 (0.29-0.97)

0.040

0.54 (0.29-1.01)

0.053

Decreased scores

32/92

1.33 (0.82-2.16)

0.25

1.40 (0.83-2.36)

0.21

Maintained high scores

36/292

0.50 (0.32-0.79)

0.003

0.57 (0.35-0.93)

0.024

3 year change in DP1scores

P value *

Model 2 ‡

P value *

CES-D (15/16) 1.00 (ref)

29 Page 29 of 30

CES-D (18/19) Maintained low scores

41/286

1.00 (ref)

1.00 (ref)

Improved scores

7/117

0.44 (0.19-0.999)

0.050

0.36 (0.15-0.87)

0.024

Decreased scores

19/105

1.31 (0.72-2.36)

0.38

1.18 (0.62-2.23)

0.61

Maintained high scores

20/308

0.51 (0.29-0.90)

0.019

0.45 (0.24-0.84)

0.013

Abbreviations: DP1 scores, Dietary Pattern 1 scores; CES-D, Center for Epidemiologic Studies Depression scale; ref, reference. * Based on multiple logistic regression analysis. † Adjusted for baseline values of age (year, continuous), sex, and works (survey in April 2012 or in May 2013). ‡ Adjusted for baseline values of age (year, continuous), sex, works (survey in April 2012 or in May 2013), marital status (married or other), job grade (low, middle, or high), night or rotating shift work (yes or no), overtime work (<10 hours/month, 10 to <30 hours/month, or ≥30 hours/month), job strain (quartile), physical activity at work and housework or in commuting to work (<3 METs-hours/day, 3 to <7 METs-hours/day, 7 to <20 METs-hours/day, or ≥20 METs-hours/day), leisure-time physical activity (not engaged, >0 to <3 METs-hours/week, 3 to <10 METs-hours/week, or ≥10 METs-hours/week), smoking (never-smoker, quitter, current smoker consuming <20 cigarettes/day, or current smoker consuming ≥20 cigarettes/day), sleep duration (<6 hours/day, 6 to 6.9 hours/day, or ≥7 hours/day), body mass index (kg/m2, continuous), and total energy intake (kcal/day, continuous).

30 Page 30 of 30