Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults

Minami Sugimoto; Kentaro Murakami; Aya Fujiwara; Keiko Asakura; Shizuko Masayasu; Satoshi Sasaki

doi:10.1371/journal.pone.0240803

. 2020 Oct 23;15(10):e0240803. doi: 10.1371/journal.pone.0240803

Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults

Minami Sugimoto ¹, Kentaro Murakami ², Aya Fujiwara ^2,³, Keiko Asakura ⁴, Shizuko Masayasu ⁵, Satoshi Sasaki ^1,^2,^*

Editor: Nicoletta Righini⁶

PMCID: PMC7584234 PMID: 33095787

Abstract

Objectives

A growing number of Western studies have been exploring sustainable and healthy dietary patterns that target to reduce diet-related greenhouse gas emissions (GHGE) and to achieve nutritional needs. However, research is limited among Asian populations, where food sources for diet-related GHGE differ from those in Western populations. This study aimed to investigate associations between diet-related GHGE and the prevalence of inadequate nutritional intake.

Methods

A cross-sectional study was carried out among 392 healthy Japanese volunteers aged 20–69 years. Dietary intake was assessed by four-non-consecutive day diet record. Diet-related GHGE was estimated using the Global Link Input-Output model and adjusted for energy intake by residual method. Prevalence of inadequacy was defined as a percentage of participants with nutrient intake outside the Tentative Dietary Goal for Preventing Lifestyle-Related Disease or below the Estimated Average Requirement defined by the Dietary Reference Intakes (DRIs) for Japanese. The association between diet-related GHGE and the prevalence of inadequacy of the usual intake of each nutrient was examined using logistic regression models.

Results

Participants with higher diet-related GHGE had overall better adherence to the DRIs. Intakes of all selected nutrients were positively associated with diet-related GHGE, except for carbohydrate, total fat, and saturated fat. With increasing quartile of diet-related GHGE, the prevalence of inadequacy decreased for protein, dietary fiber, potassium, vitamins A, B-6, and C, thiamin, riboflavin, calcium, magnesium, iron, and zinc, while that for sodium increased.

Conclusions

Diets with lower diet-related GHGE did not have better adherence to the DRIs compared to diets with higher diet-related GHGE among Japanese adults. Drastic dietary change or other strategies such as improving the food system would be needed to achieve a sustainable and healthy diet among Japanese.

Introduction

In the context of growing interest for sustainable healthy diets [1] and climate change, many epidemiological studies have been focused on individual dietary choice and greenhouse gas emissions (GHGE) related to the diet [2–21]. Individual dietary choices would consequently affect emissions from the food sectors because it drives food systems, which contribute 21–37% global GHGE when pre- and post-production stages are included [22]. Thus, it is expected that achieving sustainable healthy dietary choices at individual level would reduce GHGE from food sector.

Most of previous observational studies have shown inverse associations between diet-related GHGE and adequacy of nutrient intake [3] and overall diet quality [4–9], while a few showed positive or null associations [2, 7]. On the other hand, a series of scenario studies have shown that modeled healthy diets meeting dietary guidelines did not always improve diet-related GHGE [10–12]. However, these previous studies mainly come from Western countries [2–18], while research is limited among Asian countries including Japan, where meat intake is lower than Western countries [19–21].

Japan was the sixth-largest greenhouse gas emitter in 2016 [23]. The Japanese government advocated the strategies “to reduce greenhouse gas emissions 80% by 2050 as its long-term goal [24].” The environmental dimension of diet has not been included in this statement nor mentioned in the dietary guidelines. It might be the result of a lack of evidence about the environmental impact of Japanese diet, including diet-related GHGE. Japanese dietary habits have long been of interest to researchers from other countries from a nutrition and health standpoint [25, 26], while its environmental aspects have been rarely investigated.

The contemporary Japanese diet is typically high in refined grains, seaweeds, vegetables, fish, legumes and low in whole grains, nuts and seeds, dairy products, sugar-sweetened beverage, and processed and unprocessed red meats [27–29]. At the nutrient level, it is characterized by a high intake of sodium and a low intake of dietary fiber, calcium, and saturated fat [29, 30]. Major dietary sources of GHGE among Japanese included meat (19.7%), fish and seafood (13.8%), and cereals (13.1%) [19]. They are quite different from those observed in Western countries, where a considerably large proportion of diet-related GHGE is explained by meat (31.3–56.6%) and dairy products (13.1–25.2%), with a small contribution of fish and seafood (2.1–9.2%) and cereals (2.1–10.5%) [6, 13–18]. Adverse health effects of higher meat intake have been reported in a population with high meat intake [31], while negative or no associations between meat intake and mortality from cardiovascular disease or cancer were generally found in Asian population with low meat intake [31, 32]. Hence, the association between diet-related GHGE and dietary intake among Japanese could differ from that observed in Western studies. Therefore, this study aimed to investigate associations between diet-related GHGE and the prevalence of inadequate nutritional intake in Japanese adults.

Materials and methods

Study design and participants

This cross-sectional study was based on data from healthy Japanese adults aged 20–69 years. Data collection was conducted in 20 study areas covering 23 of 47 prefectures between February and March 2013. Details of the study have been reported elsewhere [33, 34]. The primary objective of this survey was to estimate sodium and potassium excretion using biomarker and to identify food sources of sodium and potassium. First, 199 dietitians working in separate welfare facilities were recruited as research dietitians supporting the survey. Next, the research dietitians recruited participants from their co-workers or family members of co-workers with stratifying by sex and by five 10-year age bands (20–29, 30–39, 40–49, 50–59, and 60–69 years). The number of participants was targeted to be 40 adults in each study area to allow for statistical analysis stratified by sex, age, body mass index (BMI; in kg/m²), and physical activity. The exclusion criteria were: (i) licensed dietary or medical provider, (ii) residence in the prefecture or adjacent prefecture in which the facility was located for less than 6 months, (iii) individuals who were under diet therapy prescribed by a doctor or dietitian at the time of the study or within 1 y before the study, (iv) pregnant or lactating women, and (v) individuals who had history of hospitalization for diabetes education. Of those 800 adults recruited, nine adults withdrew from the survey. In total, 791 adults participated. To reduce the burden to the participants and the research dietitians, half of the participants (n = 400) were also asked to complete diet records. A total of 392 adults (196 men and 196 women) completed diet records and were included in the present analysis. BMI was calculated based on measured weight and height. The participants’ occupation, educational background, and smoking habits were assessed using a questionnaire (S1 Appendix in S1 File).

This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Ethics Committee of the University of Tokyo, Faculty of Medicine (approval number: 10005, approval date: January 7, 2013). Written informed consent was obtained from all participants.

Dietary assessment

Dietary intake was assessed by four-non-consecutive-day diet records. The assessment days consisted of three working days and one day off. All participants were provided with digital kitchen scale (KD-812WH; Tanita, Tokyo, Japan), measuring spoon, measuring cup, a manual for the diet record, and recording sheets and instructed how to weigh and record foods and beverages consumed. Each participant was asked to weigh and record all food and beverages consumed on the four assessment days using the provided equipments and recording sheet. When weighing was difficult (e.g. eating out), the restaurant’s name, name of dishes, and an estimated amount of leftovers were reported. Pictures of food and beverages were also provided by some participants but not mandatory. All recorded foods and beverages were assigned food item numbers according to the Standard Tables of Food Composition in Japan, Fifth Revised and Enlarged Edition [35]. All records were checked twice (by the research dietitians at each facility and trained dietitian staff at the survey center). The research dietitian contacted the participants to clarify any ambiguities or missing data in the recording sheets. Daily intakes of foods, energy, and nutrients were estimated based on the Standard Tables of Food Composition in Japan in which free sugar content of each food item was added based on published sources [36, 37] due to the insufficiency of data for free sugars in original food composition tables.

To evaluate the accuracy of the reported energy intake (EI), the ratio of EI to basal metabolic rate (BMR) (EI:BMR) was compared to the Goldberg cut-off [38]. For this, the average EI of the four-assessment-days was used. BMR was calculated using the sex-specific equation for Japanese population based on age, weight, and height. Assuming sedentary lifestyle for all subjects because of a lack of objective information on physical activity in this study (physical activity level: 1.55 for men and 1.56 for women), under-, plausible-, and over-reporters were defined as having EI:BMR <1.02, 1.02–2.35, and >2.35 for men, and <1.03, 1.03–2.36, and >2.36 for women, respectively. Under- and over- reporters were identified in this study but were not excluded from the analysis to avoid bias for that exclusion. Assuming sedentary lifestyle for all subjects because of a lack of objective information on physical activity in this study (physical activity level: 1.55 for men and 1.56 for women), under-, plausible-, and over-reporters were defined as having EI:BMR <1.02, 1.02–2.35, and >2.35 for men, and <1.03, 1.03–2.36, and >2.36 for women, respectively [39, 40].

Greenhouse gas emissions of food and drinks

GHGE of foods and drinks were estimated using the food GHGE database based on the Global Link Input-Output (GLIO) model [41]. The detailed description of the database development was written elsewhere [19]. Briefly, the GLIO model includes 804 economic sectors in Japan and 230 foreign countries and regions describing the relationship between their production and consumption systems. Production-based GHGE values for 354 food items were calculated from the GLIO model. Then, the values of 354 foods and drinks were systematically linked to food items from the Standard Tables of Food Composition in Japan [35]. Diet-related GHGE was calculated by multiplying the GHGE value for food items and the mean food intake of the four assessment days.

Estimation of usual nutrient and food intakes and greenhouse gas emissions

The usual dietary intake of nutrients, foods, and diet-related GHGE were estimated with the Multiple Source Method (MSM) [42, 43]. For each participant, four-day measurements of food intake, nutrient intake, or diet-related GHGE data were imported to the MSM program. In the MSM, usual intake for each participant was calculated as following three steps: calculating the probability of eating a certain food or nutrient on a random day was estimated for each individual, estimating the usual amount of food or nutrient intake on a consumption day, and multiplying resulting numbers from former two steps by each other. The usual percent of energy from protein, fat, carbohydrate, saturated fat, and free sugar was estimated by the MSM after calculation of percent energy from these nutrients for each assessment day.

Assessment of nutrient intake inadequacy

For the nutrient intake inadequacy assessment, the estimated usual nutrient intakes were compared with age- and sex-specific reference values in the Dietary Reference Intakes (DRIs) for Japanese [44], as described in previous studies [45, 46]. In brief, several types of reference values are set according to their purposes in the Japanese DRIs. The Estimated Average Requirement (EAR) is defined as “the estimated intake amount that meets the requirements of 50% of the individuals belonging to an age or sex group [47].” The Tentative Dietary Goal for Preventing Lifestyle-Related Disease (DG) is defined as “the nutrient intake amount Japanese people should aim for, to prevent lifestyle-related diseases, in the foreseeable future [47].” The DGs are provided for macronutrient balance (% of energy from protein, total fat, saturated fat, and carbohydrates), dietary fiber, sodium, and potassium. For seven nutrients with DGs, participants whose intake levels were outside the range of the corresponding DG values were considered inadequate. For nutrients with EAR except for iron, participants whose intake was lower than the EAR were considered inadequate. Because of the strongly skewed distribution of iron requirements among menstruating women [48, 49] and the lack of information about menopause among female participants, less than 9.3 mg/d of iron intake (probability of inadequacy >50% for menstruating women whose bioavailability of iron is 15%) [50] was considered as inadequate among women under 50 years old using a probability method. For free sugar, the participants whose intake was 5% energy or more were deemed to be inadequate based on the conditional recommendation advocated by the World Health Organization (WHO) [51] because there is no reference value for free sugar in Japanese DRIs. The reference values are shown in S1 Table in S1 File.

We excluded biotin, iodine, selenium, chromium, and molybdenum for which EAR is set from the present analysis because of insufficiency of food composition tables for these nutrients in Japan.

Statistical analysis

All statistical analyses were performed with the SAS statistical software, version 9.4 (SAS Institute Inc., Cary, NC, USA). All reported P-values were two-tailed, with a P-value < 0.05 considered statistically significant. The mean and standard deviation (SD) of the usual nutrient intake and prevalence of inadequacy among all participants were described. Because energy intake was highly correlated with both nutrient intakes and diet-related GHGE, usual diet-related GHGE was adjusted for energy by residual method. Energy-adjusted diet-related GHGE was computed as the residuals from the regression model, with total energy intake as the independent variable and absolute diet-related GHGE as the dependent variable [52]. The participants were divided into sex-specific quartiles (i.e. divided separately by sex) according to the usual diet-related GHGE (g CO₂-eq/d), and then mixed for further analysis as our sample size was small, and the analysis by sex showed similar results (S2–S6 Tables in S1 File).

Linear regression models were constructed to examine the association between diet-related GHGE (g CO₂-eq/d) and age and BMI using the median value for each quartile as a continuous variable. Mean differences of age and BMI among quartile groups were also tested by one-way analysis of variance (ANOVA) with the post hoc Bonferroni test. The chi-square test was used to test differences in living area, occupation, educational background, and smoking habits.

Linear regression models were constructed to examine the association between diet-related GHGE (g CO₂-eq/d) and the usual nutrition intake using the median value for each quartile as a continuous variable. Differences between quartiles were also assessed using one-way ANOVA with post hoc Bonferroni’s test. Next, using the logistic regression, the odds ratio (OR) and 95% confidence interval (CI) for inadequacy were calculated for each quartile of diet-related GHGE, with the lowest quartile category used as the reference. Further, linear trends of OR were tested with increasing levels of diet-related GHGE by assigning each subject the median value for the category and modeling this value as a continuous variable. For the nutrients of which the prevalence of inadequacy among all participants was less than 10%, the odds ratio and linear trends of OR were not analyzed due to a small prevalence of each quartile group. In addition, the association between diet-related GHGE and the usual food intake was examined using linear regression models and one-way ANOVA with post hoc Bonferroni’s test.

Results

The basic characteristics of the participants are described in Table 1. The mean age was 44.5 years and mean BMI was and 23.3 kg/m². Around 80% of the participants worked on clerical or nursing care. The prevalence of under-reporters (defined as having EI:BMR <1.02 and for men and <1.03 for women) was 3.6% (nine men and five women) and that of over-reporters (defined as having EI:BMR >2.35 for men and >2.36 for women) was 2.3% (three men and six women). Participants with higher diet-related GHGE were significantly older than those with lower diet-related GHGE.

Table 1. Basic characteristics of participants according to quartile (Q) of diet-related GHGE (g CO₂-eq/day) among 392 Japanese adults^a.

	All (n = 392)	Q1 (n = 98)	Q2 (n = 98)	Q3 (n = 98)	Q4 (n = 98)	P^c
	All (n = 392)	3241 (2992, 3550)^b	3757 (3450, 4044)^b	4191 (3844, 4511)^b	4833 (4420, 5198)^b	P^c
Age (years)	44.5 ± 13.4	39.6 ± 13.1^*	44.9 ± 13.6^†^‡	44.1 ± 13.1^*^†	49.4 ± 11.9^‡	<.0001
Body mass index (kg/m²)	23.3 ± 3.6	23.0 ± 3.8	23.6 ± 4.0	22.9 ± 3.1	23.7 ± 3.6	0.39
Living area (%)						0.02
Hokkaido and Tohoku	15.1	21.4	18.4	7.1	13.3
Kanto	20.2	20.4	24.5	22.4	13.3
Hokuriku and Tokai	9.4	10.2	10.2	12.2	5.1
Kinki	15.1	13.3	7.1	16.3	23.5
Chugoku and Shikoku	20.2	18.4	23.5	21.4	17.3
Kyusyu and Okinawa	20.2	16.3	16.3	20.4	27.6
Occupation (%)						0.003
Clerical	41.8	35.7	40.8	49.0	41.8
Nursing care	41.8	52.0	41.8	33.7	39.8
Medical assistant	3.1	2.0	0.0	9.2	1.0
Cooking assistant	6.1	4.1	7.1	3.1	10.2
Others	7.1	6.1	10.2	5.1	7.1
Educational background (%)						0.82
Junior high school or other	2.6	5.1	1.0	2.0	2.0
Senior high school	26.5	26.5	27.6	26.5	25.5
Vocational school or junior college	36.7	38.8	37.8	33.7	36.7
University or graduate school	34.2	29.6	33.7	37.8	35.7
Smoking habit (%)						0.70
Nonsmoker	56.1	54.1	58.2	56.1	56.1
Past smoker	18.1	15.3	15.3	19.4	22.4
Current smoker	25.8	30.6	26.5	24.5	21.4

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GHGE, greenhouse gas emissions; CO₂-eq, carbon dioxide equivalents.

*†‡ Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

^a Participants (196 men and 196 women) were divided into quartiles by usual diet-related GHGE separately by sex, and then combined for analysis. Usual diet-related GHGE was calculated using the Multiple Source Method [42, 43] and then adjusted for energy intake by residual method. Values are means ± SDs unless otherwise indicated.

^b Usual diet-related GHGE (g CO₂-eq/d): median (25th, 75th percentiles).

^c Trend of association was examined for age and body mass index using a linear regression model with the median value of diet-related GHGE in each quartile as a continuous variable. χ2 test was used for categorical variables.

The overall prevalence of inadequacy is shown in Table 2. Of seven nutrients with DG, over 60% of the participants did not meet the DRIs for saturated fat, dietary fiber, sodium, and potassium. In particular, 90% of the participants did not meet the DRIs for dietary fiber and sodium. Similarly, 72% of participants did not meet the WHO’s conditional recommendation on free sugar. Of 14 nutrients with EAR, the inadequacy prevalence for protein, niacin, vitamin B-12, folate, and copper was less than 10%, while the prevalence for vitamins A, thiamine, and calcium was nearly 60%.

Table 2. Usual nutrient intake according to quartile (Q) of diet-related GHGE (g CO₂-eq/day) among 392 Japanese adults (aged 20–69 y)^a.

	All (n = 392)			Q1 (n = 98)	Q2 (n = 98)	Q3 (n = 98)	Q4 (n = 98)	P for trend^c
		Participants outside or below reference value (%)		3241 (2992, 3550)^b	3757 (3450, 4044)^b	4191 (3844, 4511)^b	4833 (4420, 5198)^b	P for trend^c
Energy (kcal/d)	2115 ± 434	-		2111 ± 467	2109 ± 438	2092 ± 441	2149 ± 390	0.58
Nutrients with DG		<DG	>DG
Protein (% energy)	14.1 ± 1.4	31.9	-	13.2 ± 1.3^*	14.0 ± 1.2^*^†	14.4 ± 1.4^†^‡	14.9 ± 1.2^‡	<.0001
Total fat (% energy)	27.9 ± 3.8	2.3	28.1	27.5 ± 3.9	28.6 ± 4.2	27.5 ± 3.5	27.9 ± 3.5	0.13
Saturated fat (% energy)	8.1 ± 1.6	-	75.3	7.9 ± 1.8	8.3 ± 1.7	8.0 ± 1.6	8.1 ± 1.3	0.28
Carbohydrate (% energy)	53.6 ± 5.4	22.7	1.0	55.2 ± 5.0^*	53.5 ± 5.4^*^†	53.0 ± 5.7 ^*^†	52.5 ± 4.9^†	0.003
Dietary fiber (g/d)	13.9 ± 4.1	91.8	-	12.4 ± 3.7^*	13.8 ± 4.1^*	13.9 ± 3.2^*^†	15.6 ± 4.5^†	<.0001
Sodium (g NaCl equivalent/d)	10.2 ± 2.4	-	92.6	9.7 ± 2.3^*	10.0 ± 2.3^*^†	10.0 ± 2.1^*^†	10.9 ± 2.8^†	0.002
Potassium (mg/d)	2638 ± 663	62.5	-	2292 ± 575^*	2578 ± 583^†	2659 ± 585^†	3021 ± 698^‡	<.0001
Nutrient with the World Health Organization’s conditional recommendation		≥5% Energy
Free sugar (% energy)	6.9 ± 3.0	71.6		7.3 ± 3.9	6.4 ± 2.3	7.4 ± 3.0	6.6 ± 2.5	0.04
Nutrients with EAR		<EAR (%)
Protein (g/d)	74.0 ± 15.7	1.5		68.2 ± 14.5^*	73.4 ± 15.7^*^†	74.5 ± 15.6^*^†	79.8 ± 15.0^†	<.0001
Vitamin A (μg RAE/d)	524 ± 213	61.5		431 ± 146^*	525 ± 230^†	542 ± 160^†	596 ± 262^†	<.0001
Thiamin (mg/d)	1.0 ± 0.2	60.2		0.9 ± 0.2^*	1.0 ± 0.2^*^†	1.0 ± 0.2^*^†	1.0 ± 0.2^†	0.0008
Riboflavin (mg/d)	1.3 ± 0.3	29.6		1.2 ± 0.3^*	1.3 ± 0.3^*^†	1.3 ± 0.3^*^†	1.4 ± 0.3^†	<.0001
Niacin (mg/d)	18.7 ± 4.8	4.3		16.3 ± 4.4^*	17.8 ± 3.8^*^†	19.4 ± 4.9^†^‡	21.2 ± 4.7^‡	<.0001
Vitamin B-6 (mg/d)	1.3 ± 0.3	24.5		1.1 ± 0.3^*	1.2 ± 0.3^*^†	1.3 ± 0.3^†	1.5 ± 0.3^‡	0.0008
Vitamin B-12 (μg/d)	6.3 ± 2.6	0.3		5.1 ± 2.0^*	6.3 ± 2.4^†	6.5 ± 2.6^†	7.2 ± 2.8^†	<.0001
Folate (μg/d)	367 ± 124	6.1		304 ± 106^*	357 ± 120^†	374 ± 115^†	433 ± 123^‡	<.0001
Vitamin C (mg/d)	111 ± 45	31.1		90 ± 37^*	108±38^*^†	113 ± 41^†	135±50^‡	<.0001
Calcium (mg/d)	509 ± 155	68.4		467 ± 158^*	521 ± 155^*^†	502 ± 137^*^†	547 ± 162^†	0.003
Magnesium (mg/d)	287 ± 75	41.1		258 ± 77^*	281 ± 68^*	288 ± 71^*^†	319 ± 74^†	<.0001
Iron (mg/d)^d	8.3 ± 2.0	33.7		7.4 ± 2.0^*	8.1 ± 1.9^*^†	8.3 ± 1.8^†^‡	9.2 ± 2.0^‡	<.0001
Zinc (mg/d)	8.6 ± 2.0	37.2		8.0 ± 1.9^*	8.6 ± 2.3^*^†	8.5 ± 1.9^*^†	9.3 ± 1.8^†	<.0001
Copper (mg/d)	1.2 ± 0.3	1.8		1.1 ± 0.3^*	1.2 ± 0.3^*^†	1.2 ± 0.3^*^†	1.3 ± 0.3^†	0.003

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GHGE, greenhouse gas emission; CO₂-eq, carbon dioxide equivalents; DG, Tentative Dietary Goal for Preventing Lifestyle-related Diseases; EAR, Estimated Average Requirement; RAE, retinol activity equivalent.

*†‡Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

^a Participants (196 men and 196 women) were divided into quartiles by usual diet-related GHGE separately by sex, and then combined for analysis. Usual nutrient intake and diet-related GHGE were calculated using the Multiple Source Method [42, 43]. Diet-related GHGE was adjusted for energy intake by residual method. Values are means ± SDs unless otherwise indicated.

^b Usual diet-related GHGE (g CO₂-eq/d): median (25th, 75th percentiles).

^c Trend of association was examined using a linear regression model with the median value in each quartile as a continuous variable.

^d Probability approach was used to assess inadequacy for iron intake.

There was no association between diet-related GHGE and EI (Table 2). For nutrients, the diet-related GHGE was inversely associated with carbohydrate intake and positively with intakes of all the remaining nutrients, except for total fat and saturated fat. Analysis by one-way ANOVA with post hoc Bonferroni test showed similar results. There was no significant difference between quartile groups for total fat, saturated fat, and free sugars. On the other hand, higher quartile groups generally had lower intake for carbohydrate but higher intakes for other nutrients than lower quartile groups.

The overall adherence to the DG and EAR was better among participants in the higher diet-related GHGE quartile compared to participants in the lower quartile (Table 3). The prevalence of inadequacy for protein, dietary fiber, potassium, vitamins A, B-6, and C, thiamine, riboflavin, calcium, magnesium, iron, and zinc decreased with increasing quartile of the diet-related GHGE. Conversely, the prevalence of inadequate sodium intake was increased with increasing quartile, but the prevalence in the lowest quartile was not low: 89% and 98% of the participants had intake above the recommendations in lowest and highest quartile group.

Table 3. Odds ratios for inadequate nutrient intake compared to DRIs reference value according to the quartile (Q) of usual diet-related GHGE (g CO₂-eq/day) among 392 Japanese adults (aged 20–69 y)^a.

	Diet-related GHGE	Inadequate/adequate intake participants (n)^b	OR (95% CI)
Nutrients with Tentative Dietary Goal for Preventing Lifestyle-related Diseases
Protein	Q1	54/44	1 (ref)
	Q2	31/67	0.24 (0.13-0.47)
	Q3	28/70	0.21 (0.11-0.41)
	Q4	12/86	0.07 (0.03-0.18)
			P for trend^c <.0001
Total fat	Q1	30/68	1 (ref)
	Q2	38/60	1.44 (0.80-2.59)
	Q3	23/75	0.70 (0.37-1.31)
	Q4	28/70	0.91 (0.49-1.68)
			P for trend^c=0.35
Saturated fat	Q1	71/27	1 (ref)
	Q2	75/23	1.24 (0.65-2.36)
	Q3	73/25	1.11 (0.59-2.09)
	Q4	76/22	1.31 (0.69-2.52)
			P for trend^c=0.48
Carbohydrate	Q1	21/77	1 (ref)
	Q2	24/74	1.19 (0.61-2.32)
	Q3	23/75	1.12 (0.57-2.20)
	Q4	25/73	1.26 (0.65-2.44)
			P for trend^c=0.55
Dietary fiber	Q1	94/4	1 (ref)
	Q2	90/8	0.48 (0.14-1.65)
	Q3	93/5	0.79 (0.21-3.04)
	Q4	83/15	0.24 (0.08-0.74)
			P for trend^c=0.01
Sodium	Q1	87/11	1 (ref)
	Q2	89/9	1.25 (0.49-3.17)
	Q3	91/7	1.64 (0.61-4.43)
	Q4	96/2	6.07 (1.31-28.15)
			P for trend^c=0.01
Potassium	Q1	78/20	1 (ref)
	Q2	65/33	0.51 (0.27-0.96)
	Q3	65/33	0.51 (0.27-0.96)
	Q4	37/61	0.16 (0.08-0.30)
			P for trend^c <.0001
Nutrient with the World Health Organization’s conditional recommendation
Free sugar	Q1	69/29	1 (ref)
	Q2	70/28	1.05 (0.57-1.95)
	Q3	73/25	1.23 (0.66-2.30)
	Q4	69/29	1.00 (0.54-1.85)
			P for trend^c=0.92
Nutrients with Estimated Average Requirement
Vitamin A	Q1	75/23	1 (ref)
	Q2	56/42	0.41 (0.22-0.76)
	Q3	61/37	0.51 (0.27-0.94)
	Q4	49/49	0.31 (0.17-0.57)
			P for trend^c=0.0006
Thiamine	Q1	71/27	1 (ref)
	Q2	61/37	0.63 (0.34-1.15)
	Q3	60/38	0.60 (0.33-1.10)
	Q4	44/54	0.31 (0.17-0.56)
			P for trend^c=0.0001
Riboflavin	Q1	48/50	1 (ref)
	Q2	29/69	0.44 (0.24-0.79)
	Q3	24/74	0.34 (0.18-0.62)
	Q4	15/83	0.19 (0.10-0.37)
			P for trend^c <.0001
Vitamin B-6	Q1	47/51	1 (ref)
	Q2	24/74	0.35 (0.19-0.65)
	Q3	17/81	0.23 (0.12-0.44)
	Q4	8/90	0.10 (0.04-0.22)
			P for trend^c<.0001
Vitamin C	Q1	50/48	1 (ref)
	Q2	32/66	0.47 (0.26-0.83)
	Q3	28/70	0.38 (0.21-0.69)
	Q4	12/86	0.13 (0.07-0.28)
			P for trend^c <.0001
Calcium	Q1	72/26	1 (ref)
	Q2	69/29	0.86 (0.46-1.60)
	Q3	69/29	0.86 (0.46-1.60)
	Q4	58/40	0.52 (0.29-0.96)
			P for trend^c =0.03
Magnesium	Q1	58/40	1 (ref)
	Q2	40/58	0.48 (0.27-0.84)
	Q3	40/58	0.48 (0.27-0.84)
	Q4	23/75	0.21 (0.11-0.39)
			P for trend^c <.0001
Iron	Q1	46/52	1 (ref)
	Q2	33/65	0.51 (0.29-0.91)
	Q3	32/66	0.51 (0.29-0.91)
	Q4	21/77	0.28 (0.15-0.52)
			P for trend^c <.0001
Zinc	Q1	48/50	1 (ref)
	Q2	38/60	0.66 (0.37-1.16)
	Q3	36/62	0.61 (0.34-1.07)
	Q4	24/74	0.34 (0.18-0.62)
			P for trend^c=0.0005

Open in a new tab

GHGE, greenhouse gas emission; CO₂-eq, carbon dioxide equivalents. DRIs, Dietary Reference Intakes for Japanese.

^a Participants (196 men and 196 women) were divided into quartiles by usual diet-related GHGE separately by sex, and then combined for analysis. Usual diet-related GHGE was calculated using the Multiple Source Method (MSM) [42, 43] and then adjusted for energy intake by residual method.

^b Inadequate intake was defined by comparing usual intake with reference values derived from Dietary Reference Intakes for Japanese 2020 except for iron intake for women aged under 50 years old and free sugar. For iron intake among women aged <50 years, less than 9.3 mg/d [50] was considered inadequate. For free sugar, the World Health Organization’s conditional recommendation (<5% energy) was used.

^c Logistic regression models were used with the median value in each quartile category of diet-related GHGE as a continuous variable.

The diet-related GHGE was positively associated with the intakes of potato, vegetables, mushrooms, seaweeds, fish and seafood, meat, tea and coffee, and seasonings, while negatively associated with cereals, fat and oils, and sweetened beverages (Table 4). Analysis by one-way ANOVA with post hoc Bonferroni test also found similar results.

Table 4. Usual food intake (g/d) according to quartile (Q) of usual diet-related GHGE (g CO₂-eq/day) among 392 Japanese adults (aged 20–69 y)^a.

	All (n = 392)	Q1 (n = 98)	Q2 (n = 98)	Q3 (n = 98)	Q4 (n = 98)	P for trend^c
	All (n = 392)	3241 (2992, 3550)^b	3757 (3450, 4044)^b	4191 (3844, 4511)^b	4833 (4420, 5198)^b	P for trend^c
Cereals	449±133	481±155^*	459±136^*^†	425±123^†	430±106^†	0.008
Potatoes	45±17	42±16^*	45±15^*	42±14^*	52±19^†	<.0001
Sugar	15±10	15±11	14±8	16±10	16±9	0.43
Pulses	56±34	50±33^*	57±32^*^†	56±33^*^†	62±37^†	0.08
Nuts	3±5	3±5	3±4	4±5	3±3	0.88
Vegetables	245±93	196±72^*	238±79^†	252±79^†	296±110^‡	<.0001
Fruits	86±77	74±79	85±70	84±68	99±88	0.14
Mushroom	15±10	12±8^*	14±9^*^†	16±11^†^‡	18±12^‡	<.0001
Seaweeds	5±4	4±3^*	5±4^*^†	6±4^†	6±5^†	0.01
Fish and seafood	40±21	31±16^*	41±20^†	43±22^†	47±22^†	<.0001
Meat	94±39	86±33^*	90±39^*^†	97±40^*^†	103±42^†	0.01
Beef	16±11	11±7^*	13±10^*^†	17±10^†^†	23±14^‡	<.0001
Pork	34±17	34±16	35±17	34±17	36±17	0.83
Chicken	31±15	31±13	29±15	33±17	30±15	0.21
Processed meat products	12±9	12±7	13±9	13±10	11±7	0.21
Egg	40±14	40±14	39±16	39±14	41±12	0.77
Milk and dairy food products	98±82	93±83^*^†	117±93^*	86±64^†	97±82^*^†	0.05
Fat and oils	21±7	22±7^*	22±7^*	20±7^*^†	19±5^†	0.0004
Confectioneries	42±27	43±31	41±23	45±29	40±22	0.57
Alcoholic beverages	132±224	123±217	89±143	166±231	149±279	0.09
Tea and coffee	599±355	510±308^*	560±341^*	612±362^*^†	713±380^†	0.0005
Sweetened beverages	39±73	58±98^*	34±59^*^†	39±79^*^†	24±41^†	0.01
Seasonings	119±68	84±34^*	106±49^*^†	121±57^†	167±89^‡	<.0001
Water	515±325	456±266	511±286	563±374	531±357	0.13

Open in a new tab

GHGE, greenhouse gas emissions; CO₂-eq, carbon dioxide equivalents.

*†‡Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

^a Participants (196 men and 196 women) were divided into quartiles by usual diet-related GHGE separately by sex, and then combined for analysis. Usual food intake and diet-related GHGE were calculated using the Multiple Source Method [42, 43]. Diet-related GHGE was adjusted for energy intake by residual method. Values are means ± SDs.

^b Usual diet-related GHGE (g CO₂-eq/d): median (25th-75th percentiles).

^c Trend of association was examined using a linear regression model with the median value in each quartile as a continuous variable.

Discussion

To our knowledge, this is the first study to evaluate the association between diet-related GHGE and nutritional adequacy among Japanese adults. Our result would be useful to develop future public policies or dietary guidelines to encourage sustainable healthy dietary choices. We observed overall lower prevalence of inadequacy among higher diet-related GHGE quartiles compared to lower quartiles. The participants in higher quartiles had a higher intake of almost all nutrients examined. Thus, according to the current Japanese diet, diet-related GHGE was positively associated with nutrition adequacy. Our result is inconsistent with observational studies from Western countries, where relatively consistent inverse associations between diet-related GHGE and nutritional adequacy or diet-quality scores were found [3–9] with a few exceptions [2, 7]. These inconsistent findings between this study and Western studies could be at least partly explained by difference in food intake and major food sources of diet-related GHGE. In Western countries, a few food groups such as meat and dairy products predominantly contributed to diet-related GHGE. For example, contributions of meat and dairy products were estimated at 31.3%-38.4% and 11%-25%, respectively, in European countries [14, 16, 17] and 56.6% and 18.3%, respectively, in the US [18]. Studies from the Netherlands [17], the US [6], and Ireland [14] have shown consistent positive associations between diet-related GHGE and intakes of energy-dense foods such as alcoholic beverages [14, 17], and fats [6, 14, 17] as well as meat [6, 14, 17] and dairy products [6, 14, 17]. In Japan, meat was also the top contributor to diet-related GHGE (19.6%), followed by fish/seafood (13.8%) and cereals (13.1%) [19]. Nevertheless, it should be noted that the percentage contribution of meat was lower than that in the Western countries. In addition, the contribution of dairy products (4.6%) was low [19]. In relation to diet-related GHGE, intakes of vegetables, fish/seafood, and meat showed positive associations, while those of cereals and fat and oils showed inverse associations. In this regard, intakes of nutrient-dense foods and moderate meat intake would be associated with higher intakes of protein and micronutrients as well as diet-related GHGE among Japanese, while dietary patterns in Western countries characterized by higher intake of meat and dairy products would be associated with both higher diet-related GHGE and lower nutrient intake or diet quality [3–9].

On the contrary to observational studies, previous scenario studies in European populations showed that healthy dietary pattern complying dietary guidelines would not always reduce diet-related GHGE [10–12]. These results suggest that there would be trade-offs between nutrient intake and diet-related GHGE when achieving further improvement of nutrition intake, although healthier diet would associate with lower diet-related GHGE within the current observed diet in Western populations. On the other hand, our study suggests that just shifting to the diets with lower diet-related GHGE that were currently observed could not achieve sufficient nutritional adequacy among Japanese. A drastic dietary change would be needed to achieve both a reduction in the diet-related GHGE and an improvement in the nutritional adequacy. Even though meat and fish were major contributors to diet-related GHGE among Japanese [19], a previous modeling study for Japanese aiming at achieving nutritional goals reported a need for increasing intake of meat and fish in young adults [53]. Thus, there would be a trade-off between impact to diet-related GHGE and contribution to micronutrient intake by animal-based foods among Japanese. It is not known what amount of intake of animal-based food should be recommended for Japanese when both nutritional benefits and impact for diet-related GHGE are taken into account. Further research is needed to design optimized dietary patterns to achieve nutrition goals as well as reduction of diet-related GHGE.

Apart from intakes of animal-based foods, dietary modification would be needed for reducing sodium intake among Japanese. In the present study, prevalence of inadequate sodium intake was generally high and increased with increasing quartiles of diet-related GHGE. Similarly, previous Japanese studies reported that one of three or four dietary patterns identified had the lowest prevalence of inadequacy for fifteen nutrients and the highest prevalence for sodium [45, 46]. These results suggest that participants who seem to have favorable dietary patterns do not always comply with a lower sodium diet. Thus, because seasonings is the top food source of sodium (61.7% for men and 62.9% for women) [33] and the fifth largest contributor of diet-related GHGE (9.4%) among Japanese [19], reducing seasonings intake is demanded in population level for both aspects of healthy diets and diet-related GHGE.

Several limitations of the present study warrant mention. First, the participants in this study were not randomly sampled from the general Japanese population. The participants would be more health-conscious than the general Japanese population. In addition, most of the participants were working at welfare facilities. Moreover, proportion of urban and rural area was not considered at recruitment although regional differences in dietary habits were considered at sampling. Thus, further research in a national representative sample would be needed. Second, our relatively small sample size (n = 396) would limit the power to detect moderate associations with statistical significance. In addition, the estimated usual intake distributions of nutrients and foods would be uncertain due to the small sample size [54]. Nevertheless, significant associations were generally observed between diet-related GHGE and intakes of nutrient and food. Although our sample size might be sufficient to detect the difference between quartiles of diet-related GHGE, further studies with larger sample sizes would be needed. Third, the system boundaries considered in the GHGE database were limited to the production stage due to the lack of quality data to develop the GHGE database for Japanese diet including the whole life cycle. Fourth, both nutritional adequacy and diet-related GHGE were estimated based on the self-reported dietary assessment method, which is prone to measurement errors due to, for example, changes in dietary habits during the assessment period, under-recording, under-eating, and intentional, or unintentional misreporting [39]. Given that a positive association between EI and diet-related GHGE is widely reported [8, 13], misreporting of EI may have a substantial influence on the estimation of diet-related GHGE. However, diet-related GHGE was adjusted for EI by the residual method to avoid a confounding effect of energy intake on associations between diet-related GHGE and nutritional adequacy. In addition, the effect of energy-misreporting would be small in this study because similar results have shown when under- and over-reporters were excluded from the analysis (S7–S10 Tables in S1 File). Thus, any potential effect of energy misreporting was minimized in our study. Fifth, due to a lack of objective information on physical activity, bias can be present in the identification of under- and over-reporters. Nevertheless, the potential effect of energy-misreporting in this study would be small. Finally, this study was conducted from February to March. Several previous studies have reported seasonal differences in intakes among Japanese adults [55–58]. Thus, this limited period for the survey might have produced some bias in assessing the usual intake.

In conclusion, there was an overall better nutritional adequacy observed in the participants with higher diet-related GHGE when compared to individuals with lower GHGE among Japanese adults. This suggests that current dietary choices should be drastically changed to achieve sustainable diets. Further studies are needed to design sustainable diets.

Supporting information

S1 File

(DOCX)

Click here for additional data file.^{(141.3KB, docx)}

Acknowledgments

We thank the dietitians who supported the survey in each welfare facility for their valuable contribution and Editage (www.editage.jp) for English language editing.

Data Availability

Data cannot be made publicly available as the database contains sensitive and identifying information. Restrictions were imposed by the Ethics Committee of the University of Tokyo, Faculty of Medicine. The non-author point of contacts for data access are as follows: 1) the Department of Social and Preventive Epidemiology, Division of Health Sciences and Nursing, Graduate School of Medicine, University of Tokyo (email: nutrepibox@m.u-tokyo.ac.jp) and 2) the Ethics Committee of the University of Tokyo, Faculty of Medicine (email: ethics@m.u-tokyo.ac.jp). Interested researchers may also contact the corresponding author Satoshi Sasaki (stssasak@m.u-tokyo.ac.jp).

Funding Statement

The present study was supported by a Health and Labour Sciences Research Grant (H23-Jyunkankitou (seishuu)-ippan-001) from the Ministry of Health, Labour and Welfare, Japan and a Grant-in-Aid for Japan Society for the Promotion of Science Fellows (18J21618) from the Japan Society for the Promotion of Science.

References

1.FAO. Biodiversity and sustainable diets-united against hunger. Food Agric Organ United Nations. Rome, Italy: Food and Agriculture Organization of the United Nations; 2010.
2.Vieux F, Soler L, Touazi D, Darmon N. High nutritional quality is not associated with low greenhouse gas emissions in self-selected diets of French adults. Am J Clin Nutr. 2013;97: 569–83. 10.3945/ajcn.112.035105 [DOI] [PubMed] [Google Scholar]
3.Sjörs C, Hedenus F, Sjölander A, Tillander A, Bälter K. Adherence to dietary recommendations for Swedish adults across categories of greenhouse gas emissions from food. Public Health Nutr. 2017;20: 3381–3393. 10.1017/S1368980017002300 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Murakami K, Livingstone MBE. Greenhouse gas emissions of self-selected diets in the UK and their association with diet quality: is energy under-reporting a problem? Nutr J. 2018;17: 27 10.1186/s12937-018-0338-x [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Biesbroek S, Verschuren WMM, Boer JMA, Van De Kamp ME, Van Der Schouw YT, Geelen A, et al. Does a better adherence to dietary guidelines reduce mortality risk and environmental impact in the Dutch sub-cohort of the European Prospective Investigation into Cancer and Nutrition? Br J Nutr. 2017;118: 69–80. 10.1017/S0007114517001878 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Rose D, Heller MC, Willits-Smith AM, Meyer RJ. Carbon footprint of self-selected US diets: nutritional, demographic, and behavioral correlates. Am J Clin Nutr. 2019;109: 526–534. 10.1093/ajcn/nqy327 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Mertens E, Kuijsten A, Geleijnse JM, Boshuizen HC, Feskens EJM, Van’T Veer P. FFQ versus repeated 24-h recalls for estimating diet-related environmental impact. Nutr J. 2019;18: 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Monsivais P, Scarborough P, Lloyd T, Mizdrak A, Luben R, Mulligan AA, et al. Greater accordance with the Dietary Approaches to Stop Hypertension dietary pattern is associated with lower diet-related greenhouse gas production but higher dietary costs in the United Kingdom. Am J Clin Nutr. 2015;102: 138–145. 10.3945/ajcn.114.090639 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Seconda L, Baudry J, Allès B, Boizot-Szantai C, Soler LG, Galan P, et al. Comparing nutritional, economic, and environmental performances of diets according to their levels of greenhouse gas emissions. Clim Change. 2018;148: 155–172. 10.1007/s10584-018-2195-1 [DOI] [Google Scholar]
10.Vieux F, Perignon M, Gazan R, Darmon N. Dietary changes needed to improve diet sustainability: are they similar across Europe? Eur J Clin Nutr. 2018;72: 951–960. 10.1038/s41430-017-0080-z [DOI] [PMC free article] [PubMed] [Google Scholar]
11.van Dooren C, Tyszler M, Kramer GF, Aiking H. Combining low price, low climate impact and high nutritional value in one shopping basket through diet optimization by linear programming. Sustain. 2015;7: 12837–12855. 10.3390/su70912837 [DOI] [Google Scholar]
12.van de Kamp ME, van Dooren C, Hollander A, Geurts M, Brink EJ, van Rossum C, et al. Healthy diets with reduced environmental impact?–The greenhouse gas emissions of various diets adhering to the Dutch food based dietary guidelines. Food Res Int. 2018;104: 14–24. 10.1016/j.foodres.2017.06.006 [DOI] [PubMed] [Google Scholar]
13.Temme EHM, Toxopeus IB, Kramer GFH, Brosens MCC, Drijvers JMM, Tyszler M, et al. Greenhouse gas emission of diets in the Netherlands and associations with food, energy and macronutrient Intakes. Public Health Nutr. 2014;18: 2433–2445. 10.1017/S1368980014002821 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Hyland JJ, Henchion M, McCarthy M, McCarthy SN. The climatic impact of food consumption in a representative sample of Irish adults and implications for food and nutrition policy. Public Health Nutr. 2017;20: 726–738. 10.1017/S1368980016002573 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Vieux F, Darmon N, Touazi D, Soler LG. Greenhouse gas emissions of self-selected individual diets in France: Changing the diet structure or consuming less? Ecol Econ. 2012;75: 91–101. 10.1016/j.ecolecon.2012.01.003 [DOI] [Google Scholar]
16.Mertens E, Kuijsten A, van Zanten HH, Kaptijn G, Dofková M, Mistura L, et al. Dietary choices and environmental impact in four European countries. J Clean Prod. 2019;237: 117827 10.1016/j.jclepro.2019.117827 [DOI] [Google Scholar]
17.Biesbroek S, Bueno-de-Mesquita HB, Peeters PHM, Verschuren WM, van der Schouw YT, Kramer GFH, et al. Reducing our environmental footprint and improving our health: greenhouse gas emission and land use of usual diet and mortality in EPIC-NL: a prospective cohort study. Environ Health. 2014;13: 27 10.1186/1476-069X-13-27 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Heller MC, Willits-Smith A, Meyer R, Keoleian GA, Rose D. Greenhouse gas emissions and energy use associated with production of individual self-selected US diets. Environ Res Lett. 2018;13: 044004 10.1088/1748-9326/aab0ac [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Sugimoto M, Murakami K, Asaskura K, Masayasu S, Sasaki S. Diet-related greenhouse gas emission and major food contributor among Japanese adults: comparison of different calculation methods. Public Health Nutr. 2020; 1–11. Online ahead of print. 10.1017/S1368980019004750 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Green RF, Joy EJM, Harris F, Agrawal S, Aleksandrowicz L, Hillier J, et al. Greenhouse gas emissions and water footprints of typical dietary patterns in India. Sci Total Environ. 2018;643: 1411–1418. 10.1016/j.scitotenv.2018.06.258 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Song G, Li M, Fullana-i-palmer P, Williamson D, Wang Y. Dietary changes to mitigate climate change and benefit public health in China. Sci Total Environ. 2017;577: 289–298. 10.1016/j.scitotenv.2016.10.184 [DOI] [PubMed] [Google Scholar]
22.Mbow C, Rosenzweig C, Barioni LG, Benton TG, Herrero M, Krishnapillai M, et al. Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Nature. 2019. [Google Scholar]
23.Olivier J, Schure K, Peters J. Trends in global CO2 and total greenhouse gas emissions. The Hague; 2017. http://www.pbl.nl/sites/default/files/cms/publicaties/pbl-2017-trends-in-global-co2-and-total-greenhouse-gas-emis-sons-2017-report_2674.pdf.
24.Government of Japan. The plan for global warming countermeasure (in Japanese). 2016 [cited 30 Jul 2019]. http://www.env.go.jp/press/files/jp/102816.pdf
25.Sasaki S. The value of the National Health and Nutrition Survey in Japan. Lancet. 2011;378: 1205–6. 10.1016/S0140-6736(11)61220-8 [DOI] [PubMed] [Google Scholar]
26.Ikeda N, Saito E, Kondo N, Inoue M, Ikeda S, Satoh T, et al. What has made the population of Japan healthy? Lancet. 2011;378: 1094–105. 10.1016/S0140-6736(11)61055-6 [DOI] [PubMed] [Google Scholar]
27.Imamura F, Micha R, Khatibzadeh S, Fahimi S, Shi P, Powles J, et al. Dietary quality among men and women in 187 countries in 1990 and 2010: a systematic assessment. Lancet Glob Heal. 2015;3: e132–e142. 10.1016/S2214-109X(14)70381-X [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Micha R, Khatibzadeh S, Shi P, Andrews KG, Engell RE, Mozaffarian D. Global, regional and national consumption of major food groups in 1990 and 2010: A systematic analysis including 266 country-specific nutrition surveys worldwide. BMJ Open. 2015;5: e008705 10.1136/bmjopen-2015-008705 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Ministry of Health Labour and Welfare. The National Health and Nutrition Survey in Japan, 2013 (in Japanese). [cited 20 Aug 2019]. http://www.mhlw.go.jp/bunya/kenkou/kenkou_eiyou_chousa.html
30.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. 10.1038/sj.jhh.1001605 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Yip CSC, Lam W, Fielding R. A summary of meat intakes and health burdens. Eur J Clin Nutr. 2018;72: 18–29. 10.1038/ejcn.2017.117 [DOI] [PubMed] [Google Scholar]
32.Nagao M, Iso H, Yamagishi K, Date C, Tamakoshi A. Meat consumption in relation to mortality from cardiovascular disease among Japanese men and women. Eur J Clin Nutr. 2012;66: 687–693. 10.1038/ejcn.2012.6 [DOI] [PubMed] [Google Scholar]
33.Asakura K, Uechi K, Masayasu S, Sasaki S. Sodium sources in the Japanese diet: difference between generations and sexes. Public Health Nutr. 2015;19: 2011–23. 10.1017/S1368980015003249 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Asakura K, Uechi K, Sasaki Y, Masayasu S, Sasaki S. Estimation of sodium and potassium intakes assessed by two 24 h urine collections in healthy Japanese adults: a nationwide study. Br J Nutr. 2014;112: 119–205. 10.1017/S0007114514001779 [DOI] [PubMed] [Google Scholar]
35.Science and Technology Agency. Standard Tables of Food Composition in Japan. (in Japanese). 7th editio Printed Bureau of Ministry of Finance; 2015. [Google Scholar]
36.Fujiwara A, Murakami K, Asakura K, Uechi K, Sugimoto M, Wang H-C, et al. Association of free sugar intake estimated using a newly-developed food composition database with lifestyles and parental characteristics among Japanese children aged 3–6 years: DONGuRI Study. J Epidemiol. 2018;29: 414–423. 10.2188/jea.JE20180036 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Fujiwara A, Murakami K, Asakura K, Uechi K, Sugimoto M, Wang H-C, et al. Estimation of starch and sugar intake in a Japanese population based on a newly developed food composition database. Nutrients. 2018;10: 1474 10.3390/nu10101474 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Black AE. The sensitivity and specificity of the Goldberg cut-off for EI:BMR for identifying diet reports of poor validity. Eur J Clin Nutr. 2000;54: 395–404. 10.1038/sj.ejcn.1600971 [DOI] [PubMed] [Google Scholar]
39.Livingstone MBE, Black AE. Markers of the validity of reported energy intake. J Nutr. 2003;133: 895S–920S. 10.1093/jn/133.3.895S [DOI] [PubMed] [Google Scholar]
40.Murakami K, Livingstone MBE. Prevalence and characteristics of misreporting of energy intake in US adults: NHANES 2003–2012. Br J Nutr. 2015;145: 2715–24. 10.1017/S0007114515002706 [DOI] [PubMed] [Google Scholar]
41.Nansai K, Kagawa S, Kondo Y, Suh S, Inaba R, Nakajima K. Improving the completeness of product carbon foodprints using a global link input-output model: The case of Japan. Econ Syst Res. 2009;9: 267–290. 10.1080/09535310903541587 [DOI] [Google Scholar]
42.Harttig U, Haubrock J, Knüppel S, Boeing H. The MSM program: Web-based statistics package for estimating usual dietary intake using the multiple source method. Eur J Clin Nutr. 2011;65: S87–S91. 10.1038/ejcn.2011.92 [DOI] [PubMed] [Google Scholar]
43.Haubrock J, Nöthlings U, Volatier J-L, Dekkers A, Ocké M, Harttig U, et al. Estimating usual food intake distributions by using the Multiple Source Method in the EPIC-Potsdam Calibration Study. J Nutr. 2011;141: 914–920. 10.3945/jn.109.120394 [DOI] [PubMed] [Google Scholar]
44.Ministry of Health Labour and Welfare. Dietary Reference Intakes for Japanese, 2020. (in Japanese). 2020.
45.Saito A, Matsumoto M, Hyakutake A, Saito M, Okamoto N, Tsuji M. The frequency of cooking dinner at home and its association with nutrient intake adequacy among married young-to-middle-aged Japanese women: the POTATO Study. J Nutr Sci. 2019. 10.1017/jns.2019.9 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Murakami K, Okubo H, Livingstone MBE, Fujiwara A, Asakura K, Uechi K, et al. Adequacy of usual intake of Japanese children aged 3−5 years: A nationwide study. Nutrients. 2018;10: 1150 10.3390/nu10091150 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Ministry of Health Labour and Welfare. Dietary Reference Intakes for Japanese, 2015. 2015.
48.FAO/WHO. Requirements of Vitamin A, Iron, Folate and Vitamin B12. Report of a Joint FAO/WHO Expert Consultation. Rome, Italy; 1988.
49.Institute of Medicine Food and Nutrition Board. Dietary Reference Intakes: Applications in Dietary Assessment. Washington, DC: National Academies Press; 2000. [PubMed] [Google Scholar]
50.WHO/FAO. Guidelines on Food Fortification with Micronutrients. Geneva, Switzerland; 2006. [Google Scholar]
51.WHO. Guideline: Sugars intake for adults and children. World Heal Organ; Geneva, Switzerland; 2015. 978 92 4 154902 8 [Google Scholar]
52.Willett W. Implications of Total Energy Intake for Epidemiologic Analyses Third edit Nutritional Epidemiology. Third edit. Oxford University Press; 2012. p. 274. [Google Scholar]
53.Okubo H, Sasaki S, Murakami K, Yokoyama T, Hirota N, Notsu A, et al. Designing optimal food intake patterns to achieve nutritional goals for Japanese adults through the use of linear programming optimization models. Nutr J. 2015;14: 57 10.1186/s12937-015-0047-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Souverein OW, Dekkers AL, Geelen A, Haubrock J, de Vries JH, Ocké MC, et al. Comparing four methods to estimate usual intake distributions. Eur J Clin Nutr. 2011;65: S92–S101. 10.1038/ejcn.2011.93 [DOI] [PubMed] [Google Scholar]
55.Tokudome Y, Imaeda N, Nagaya T, Ikeda M, Fujiwara N, Sato J, et al. Daily, weekly, seasonal, within- and between-individual variation in nutrient intake according to four season consecutive 7 day weighed diet records in Japanese female dietitians. J Epidemiol. 2002;12: 85–92. 10.2188/jea.12.85 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Mori S, Saito K, Wakasa Y. Studies on annual fluctuation of food intake in female college students. Japanese J Nutr Diet. 1981;39: 243–368. 10.1093/mnras/183.3.341 [DOI] [Google Scholar]
57.Owaki A, Takatsuka N, Kawakami N, Shimizu H. Seasonal variations of butrient intake assessed by 24 hour recall method. Japanese J Nutr Diet. 1996;54: 11–18. [Google Scholar]
58.Taguchi C, Kishimoto Y, Takeuchi I, Tanaka M, Iwashima T, Fukushima Y, et al. Estimated dietary polyphenol intake and its seasonal variations among Japanese University students. J Nutr Sci Vitaminol (Tokyo). 2019;65: 192–195. 10.3177/jnsv.65.192 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0240803.r001

Decision Letter 0

Nicoletta Righini

29 Jul 2020

PONE-D-20-15820

Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults

PLOS ONE

Dear Dr. Sasaki,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The study is interesting and presents novel data for an understudied geographical region. However, the authors need to make some changes before the manuscript can be published. Both reviewers indicate specific useful comments/recommendations that need to be addressed, especially concerning details in the methods section and data analysis. The writing is for the most part clear, but the ms needs to be thoroughly checked for accuracy, typos, and other mistakes.

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2.Thank you for including the following ethics statement on the submission details page:

'This study was conducted according to the guidelines laid down in the Declaration of

Helsinki and all procedures involving research study participants were approved by the

Ethics Committee of the University of Tokyo, Faculty of Medicine (approval number:

10005, approval date: January 7, 2013). Written informed consent was obtained from

all participants.'

Please also include this information in the ethics statement in the Methods section of your manuscript.

3. Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

4. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

Additional Editor Comments (if provided):

Line 165-66 - Change to: 'Because energy intake WAS highly correlated...'.

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors described an interesting cross-sectional study aimed at investigating potential associations between diet-related GHG emissions and the diet in a convenience sample of Japanese adults. As stated by the authors, the study addresses the environmental dietary dimension which has been poorly investigated so far in the Japanese population compared to the health-related implications.

General comments

The writing is not fully clear, thus the authors should consider further editing of the English language to improve the form. Furthermore, the statistical approach that has been used lacks of post-hoc analysis that would be able to test the differences between the quartiles in which the respondents have been divided.

Specific comments

Introduction

Page 2, line 46: please make sure that the provided percentage range (i.e. 23-37%) is consistent with that indicated in the reference. It should be 21-37%.

Methods

Please rename the paragraph title as “Materials and methods”, consistently with the submission guidelines reported on the Journal website.

In this section there are some missing information, as detailed below.

Please add to the heading Page 3, line 82: the authors should specify the criteria used to determine the sample size for the recruitment (i.e. 400 people).

Page 4, lines 92-101: within the dietary assessment paragraph, please specify if the participants were trained to provide accurate dietary records. Furthermore, the authors should specify how they managed data misreporting (e.g. if the respondents were contacted after data collection and asked to provide explanation of eventual mistakes made during dietary recording).

Page 4, line 100: The authors should maybe comment on potential inconsistencies due to the use of two different food composition databases on which the energy and nutritional analyses were made.

Page 4, line 103: please amend the typo “brinks” in “drinks” and add “of” between emission and food.

Page 5, line 117: please substitute “;” with “:”.

Page 6, line 141: please delete “women” as it is repeated twice (line 140 and 141).

Page 6, lines 149-151: the authors might evaluate to move this part at the end of the “Study design and participants” section.

Page 6, lines 152-159: The authors might consider to move this part to the paragraph “Dietary assessment” for a more logical manuscript organisation.

Page 6-7: The authors should specify in the “Statistical analysis” section the statistical texts applied to compare the quartiles reported in Table 1 (e.g. 2 test).

Page 6, line 167: Please provide a brief explanation about the “residual method” used to adjust usual diet-related GHG emissions for energy.

As mentioned above in the general comments, the authors should apply a post-hoc analysis to test the differences between the quartiles in which the respondents have been divided. Indeed, the p for trend evaluation that has been used is not enough to provide such information.

Results

Page 11, line 219. Please, amend the text by substituting “carbohydrates” with “energy”. Indeed, according to the Table 2, a positive association can be observed between diet-related GHGe and CHO intake, while no association (p=0.58) can be observed for energy.

Page 11, lines 226-227: Please substitute “;” with “:” and add “the” before “lowest”.

Page 13, line 243: For consistency, please move the detail referred to the approach used to define iron intake inadequacy to line 240, as specified for free sugar intake.

Page 13, Table 4: please amend the number referred to the total sample. It should be n= 392.

Discussion

Page 14, lines 267 and 269: The sentence starting with “In contrast” is not actually opposite to the previous sentence. Please make sure about the provided statements from line 265 to line 271.

Page 15, lines 283 and 293: please add a reference.

Page 15, lines 293-295 and page 16, lines 314-317: As the sample of respondents is not representative of the whole Japanese adult population, the authors should comment on the generalisation of their findings to this population, taking into account also the background characteristics of the sample of respondents (e.g. family income, rural/urban residence, ecc).

Page 16, line 325-326: please clarify the sentence as it is not fully understandable.

Reviewer #2: This is an interesting, carefully planned, and well written baseline study on the relation between individual diets and associated green house gas emissions in a section of the Japanese population. It was surprising, though, that no previous baseline study was available for Japan, as noted by the authors. It was also interesting how, in the case of the population sampled, a better nutrition implied larger emissions.

I would recommend providing more information on the larger study in the Methods section, rather than just indicating that the details are published elsewhere (although both invoked references appear to be open access, at least at the time of writing this review), which will give an overview to readers, before committing to reading an additional two papers.

I would also recommend giving the manuscript a final parse to catch very few existing idiosyncratic language errors, which can be distracting (for example, insert "the" in Line 62: "Recently, THE Japanese..."; Line 72: it should be "found" rather than "founded"; Line 89: what do authors mean by "educational admission").

Under the dietary assessment more details on the way food was logged are needed: did people have to measure their food? take photographs, just write down what was consumed? Also, which equipment was provided.

Line 184: This should refer to Table 1, not table 2. In the same paragraph, please provide better definitions for under- and over-reporters.

Line 222: Should "quantile" be "quartile"?

Finally, although strictly outside of the scope of the study, but mentioned by the authors in the introduction, it would be great if they could provide some discussion about the tensions between individual choice and population-wide actions "encouraged" by public policies.

**********

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Reviewer #1: No

Reviewer #2: Yes: Erick de la Barrera

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PLoS One. 2020 Oct 23;15(10):e0240803. doi: 10.1371/journal.pone.0240803.r002

Author response to Decision Letter 0

15 Aug 2020

Reply to the Editors’ and Reviewers’ comments for the manuscript: PONE-D-20-15820

(Title: Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults)

We thank the editor and the reviewers for their very helpful comments on our paper. We have revised the manuscript by addressing each comment point-by-point as described below. All amendments and changes are highlighted in the manuscript using red font. We trust these changes will resolve any confusion and remedy the shortcomings of the paper.

Journal Requirements:

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Authors’ response:

We have revised the title page to meet the style requirements.

Page 1, line 4-23:

“Minami Sugimoto1, Kentaro Murakami2, Aya Fujiwara2, 3, Keiko Asakura4, Shizuko Masayasu5, and Satoshi Sasaki1, 2*

1 Department of Social and Preventive Epidemiology, Division of Health Sciences and Nursing, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.

2 Department of Social and Preventive Epidemiology, School of Public Health, University of Tokyo, Tokyo, Japan.

3 Department of Nutritional Epidemiology and Shokuiku, National Institute of Biomedical Innovation, Health and Nutrition, Tokyo, Japan

4 Department of Environmental and Occupational Health, School of Medicine, Toho University, Tokyo, Japan.

5Ikurien-Naka, Ibaraki 311-0105, Japan.

*Corresponding author

E-mail: stssasak@m.u-tokyo.ac.jp; sasakicrs@m.u-tokyo.ac.jp

Page 4, line 86:

Materials and Methods

2.Thank you for including the following ethics statement on the submission details page:

'This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving research study participants were approved by the Ethics Committee of the University of Tokyo, Faculty of Medicine (approval number: 10005, approval date: January 7, 2013). Written informed consent was obtained from all participants.'

Please also include this information in the ethics statement in the Methods section of your manuscript.

Authors’ response:

We have added the following information in the Method section.

Page 4, line 107-110:

Authors’ response:

We have added questions to assess demographic variables including occupation, educational background, and smoking habits as “S1 Appendix.” (see S1 Appendix in “Supporting information”)　We have not included a questionnaire because the primary focus of our manuscript is not to develop a questionnaire or assessment tool.

Authors’ response:

We have shown the analytical results by sex and results excluded under- and over- reporters in the supporting material. (see Revised Supplemental Tables, S2-S10).

Page 7, line 189-190:

…and the analysis by sex showed similar results (S2-S6 Tables).

Page 18, line 368-370:

In addition, the effect of energy-misreporting would be small in this study because similar results have shown when under- and over-reporters were excluded from the analysis (S7-S10 Tables).

Page 25, line 559-581:

Supporting information

S1 Appendix. Questions for demographic variables

S1 Table. Sex- and age-specific Diet reference intakes values for Japanese population

S2 Table. Basic characteristics of participants according to quartile (Q) of diet-related GHGE (g CO2-eq/day) among 196 Japanese men and 196 women (aged 20-69 y)

S3 Table. Usual nutrient intake according to quartile (Q) of diet-related GHGE (g CO2-eq/day) among 196 Japanese men (aged 20-69 y)

S4 Table. Usual nutrient intake according to quartile (Q) of diet-related GHGE (g CO2-eq/day) among 196 Japanese women (aged 20-69 y)

S5 Table. Odds ratios for inadequate nutrient intake compared to DRI reference value according to the quartile (Q) of usual diet-related GHGE (g CO2-eq/day) among 196 Japanese men and 196 women (aged 20-69 y)

S6 Table. Usual food intake (g/d) according to quartile (Q) of usual diet-related GHGE (g CO2-eq/day) among 196 Japanese men and 196 women (aged 20-69 y)

S7 Table. Basic characteristics of participants according to quartile (Q) of diet-related GHGE (g CO2-eq/day) among 369 Japanese adults with plausible energy intake

S8 Table. Usual nutrient intake according to quartile (Q) of diet-related GHGE (g CO2-eq/day) among 369 Japanese adults (aged 20-69 y) with plausible energy intake

S9 Table. Odds ratios for inadequate nutrient intake compared to DRI reference value according to the quartile (Q) of usual diet-related GHGE (g CO2-eq/day) among 369 Japanese adults (aged 20-69 y) with plausible energy intake

S10 Table. Usual food intake (g/d) according to quartile (Q) of usual diet-related GHGE (g CO2-eq/day) among 369 Japanese adults (aged 20-69 y) with plausible energy intake

Additional Editor Comments (if provided):

Line 165-66 - Change to: 'Because energy intake WAS highly correlated...'.

Authors’ response:

We have revised the text.

Page 7, line 183-184:

Because energy intake was highly correlated…

Reviewer #1:

The authors described an interesting cross-sectional study aimed at investigating potential associations between diet-related GHG emissions and the diet in a convenience sample of Japanese adults. As stated by the authors, the study addresses the environmental dietary dimension which has been poorly investigated so far in the Japanese population compared to the health-related implications.

Authors’ response:

Thank you for your comments.

General comments

Authors’ response:

We have revised the English language as follows. In addition, we have added post-hoc analysis in Tables 1, 2 and 4 and accordingly made a number of revisions in “Materials and methods” and “Results” sections as shown below. (also see Tables 1, 2, and 4 in the revised manuscript)

Grammatical corrections:

Page 3, line 60-62:

However, these previous studies mainly come from Western countries [2-18], while research is limited among Asian countries including Japan, where meat intake is lower than Western countries [11–13].

Page 3, line 70:

Recently, the Japanese diet has been characterized by…

Page 3, line 79-81:

…while negative or no associations between meat intake and mortality from cardiovascular disease or cancer were generally found in Asian population with low meat intake [31,32].

Page 6, line 153-155:

The usual percent of energy from protein, fat, carbohydrate, saturated fat, and free sugar was estimated by the MSM after calculation of percent energy from these nutrients for each assessment day.

Page 7, line 183-184:

Because energy intake was highly correlated with both nutrient intakes and diet-related GHGE,…

Page 12, line 249-251:

Page 16, line 331:

Western populations

Page 17, line 349-352:

Thus, because seasonings is the top food source of sodium (61.7% for men and 62.9% for women) [33] and the fifth largest contributor of diet-related GHGE (9.4%) among Japanese [11], reducing seasonings intake is demanded in population level for both aspects of healthy diets and diet-related GHGE.

Page 18, line 365:

misreporting of EI may have a substantial influence

Additional statistical test:

Page 7, line 193-194:

Mean differences of age and BMI among quartile groups were also tested by one-way analysis of variance (ANOVA) with the post hoc Bonferroni test.

Page 8, line 198-199:

Differences between quartiles were also assessed using one-way ANOVA with post hoc Bonferroni’s test.

Page 8, line 205-207:

In addition, the association between diet-related GHGE and the usual food intake was examined using linear regression models and one-way ANOVA with post hoc Bonferroni’s test.

Page 9, line 219-220:

*†‡Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

Page 11, line 240:

*†‡Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

Page 12, line 252-255:

There was no significant difference between quartile groups for total fat, saturated fat, and free sugars. On the other hand, higher quartile groups generally had lower intake for carbohydrate but higher intakes for other nutrients than lower quartile groups.

Page 14, line 282-283:

Analysis by one-way ANOVA with post hoc Bonferroni test also found similar results.

Page 15, line 287-288:

*†‡Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

Specific comments

Introduction

Page 2, line 46: please make sure that the provided percentage range (i.e. 23-37%) is consistent with that indicated in the reference. It should be 21-37%.

Authors’ response:

We have checked the reference and revised the percentage range as suggested.

Page 2, line 53:

which contribute 21-37% global

Methods

Please rename the paragraph title as “Materials and methods”, consistently with the submission guidelines reported on the Journal website.

Authors’ response:

We have revised the paragraph title.

Page 4, line 85:

Materials and Methods

In this section there are some missing information, as detailed below.

Please add to the heading Page 3, line 82: the authors should specify the criteria used to determine the sample size for the recruitment (i.e. 400 people).

Authors’ response:

In accordance with the comment, we have made the following revision.

Page 4, line 87-106:

This cross-sectional study was based on data from healthy Japanese adults aged 20–69 years. Data collection was conducted in 20 study areas covering 23 of 47 prefectures between February and March 2013. Details of the study have been reported elsewhere [33,34]. The primary objective of this survey was to estimate sodium and potassium excretion using biomarker and to identify food sources of sodium and potassium. First, 199 dietitians working in separate welfare facilities were recruited as research dietitians supporting the survey. Next, the research dietitians recruited participants from their co-workers or family members of co-workers with stratifying by sex and by five 10-year age bands (20–29, 30–39, 40– 49, 50–59, and 60–69 years). The number of participants was targeted to be 40 adults in each study area to allow for statistical analysis stratified by sex, age, body mass index (BMI; in kg/m2), and physical activity. The exclusion criteria were: (i) licensed dietary or medical provider, (ii) residence in the prefecture or adjacent prefecture in which the facility was located for less than 6 months, (iii) individuals who were under diet therapy prescribed by a doctor or dietitian at the time of the study or within 1 y before the study, (iv) pregnant or lactating women, and (v) individuals who had history of hospitalization for diabetes education. Of those 800 adults recruited, nine adults withdrew from the survey. In total, 791 adults participated. To reduce the burden to the participants and the research dietitians, half of the participants (n=400) were also asked to complete diet records. A total of 392 adults (196 men and 196 women) completed diet records and were included in the present analysis. BMI was calculated based on measured weight and height. The participants’ occupation, educational background, and smoking habits were assessed using a questionnaire (S1 Appendix).

Authors’ response:

In accordance with the comment, we have made the following revision.

Page 5, line 112- page 6, 120:

Page 5, line 123-124:

The research dietitian contacted the participants to clarify any ambiguities or missing data in the recording sheets.

Page 4, line 100: The authors should maybe comment on potential inconsistencies due to the use of two different food composition databases on which the energy and nutritional analyses were made.

Authors’ response:

Free sugar intake was not calculated based on an independent database from the “Standard Tables of Food Composition in Japan” but based on the “Standard Tables of Food Composition in Japan” with additional values for free sugar content. Fujiwara et al [ref 37 and 38] added free sugar content to each item in the “Standard Tables of Food Composition in Japan” based on published sources because the “Standard Tables of Food Composition in Japan” does not originally include the content of free sugars. We have made the following revision to make it clear.

Page 5, line 124-127:

Daily intakes of foods, energy, and nutrients were estimated based on the Standard Tables of Food Composition in Japan in which free sugar content of each food item was added based on published sources [37,38] due to the insufficiency of data for free sugars in original food composition tables.

Page 4, line 103: please amend the typo “brinks” in “drinks” and add “of” between emission and food.

Authors’ response:

We have revised the text in the manuscript.

Page 5, line 137:

Greenhouse gas emissions of food and drinks

Page 5, line 117: please substitute “;” with “:”.

Authors’ response:

We have revised the text in the manuscript.

Page 6, line 150:

…as following three steps: calculating…

Page 6, line 141: please delete “women” as it is repeated twice (line 140 and 141).

Authors’ response:

We have revised the text in the manuscript.

Page 7, line 172-173:

…among women under 50 years old women using…

Page 6, lines 149-151: the authors might evaluate to move this part at the end of the “Study design and participants” section.

Authors’ response:

Thank you for the comments. We moved them at the end of the “Study design and participants” section as suggested.

Page 5, line 104-106:

BMI was calculated based on measured weight and height. The participants’ occupation, educational background, and smoking habits were assessed using a questionnaire (S1 Appendix).

Page 6, lines 152-159: The authors might consider to move this part to the paragraph “Dietary assessment” for a more logical manuscript organisation.

Authors’ response:

We have moved this part to “Dietary assessment.”

Page 5, line 128-136:

To evaluate the accuracy of the reported energy intake (EI), the ratio of EI to basal metabolic rate (BMR) (EI:BMR) was compared to the Goldberg cut-off [39]. For this, the average EI of the four-assessment-days was used. BMR was calculated using the sex-specific equation for Japanese population based on age, weight, and height. Assuming sedentary lifestyle for all subjects because of a lack of objective information on physical activity in this study (physical activity level: 1.55 for men and 1.56 for women), under-, plausible-, and over-reporters were defined as having EI:BMR <1.02, 1.02-2.35, and >2.35 for men, and <1.03, 1.03-2.36, and >2.36 for women, respectively. Under- and over- reporters were identified in this study but were not excluded from the analysis to avoid bias for that exclusion [40,41].

Page 6-7: The authors should specify in the “Statistical analysis” section the statistical texts applied to compare the quartiles reported in Table 1 (e.g. 2 test).

Authors’ response:

In accordance with the comment, we have made the following revision in the “Materials and Methods” section.

Page 7, line 191-195:

Linear regression models were constructed to examine the association between diet-related GHGE (g CO2-eq/d) and age and BMI using the median value for each quantile as a continuous variable. Mean differences of age and BMI among quartile groups were also tested by one-way analysis of variance (ANOVA) with the post hoc Bonferroni test. The chi-square test was used to test differences in living area, occupation, educational background, and smoking habits.

Page 6, line 167: Please provide a brief explanation about the “residual method” used to adjust usual diet-related GHG emissions for energy.

Authors’ response:

We have added the explanation about the “residual method.”

Page 7, line 185-187:

Energy-adjusted diet-related GHGE was computed as the residuals from the regression model, with total energy intake as the independent variable and absolute diet-related GHGE as the dependent variable [53].

Authors’ response:

In accordance with the comment, we have added post-hoc analysis and accordingly made revision in “Materials and methods” and “Results” sections, and Tables 1, 2, and 4. (see Tables 1, 2, and 4 in the revised manuscript)

Page 7, line 193-194:

Mean differences of age and BMI among quartile groups were also tested by one-way analysis of variance (ANOVA) with the post hoc Bonferroni test.

Page 8, line 198-199:

Differences between quartiles were also assessed using one-way ANOVA with post hoc Bonferroni’s test.

Page 8, line 205-207:

In addition, the association between diet-related GHGE and the usual food intake was examined using linear regression models and one-way ANOVA with post hoc Bonferroni’s test.

Page 9, line 219-220:

*†‡Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

Page 11, line 240:

*†‡Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

Page 12, line 252-255:

Page 14, line 282-283:

Analysis by one-way ANOVA with post hoc Bonferroni test also found similar results.

Page 15, line 287-288:

*†‡Maen values within a row with different symbols were significantly different between the quartile group by post hoc Bonferroni’s test (P<0.05).

Results

Authors’ response:

In accordance with the comment, we have made the following revision

Page 13, line 249-251:

Page 11, lines 226-227: Please substitute “;” with “:” and add “the” before “lowest”.

Authors’ response:

We have revised the manuscript.

Page 12, line 261-262:

the prevalence in the lowest quartile was not low: 89% and 98% of the participants had intake above the recommendations in lowest and highest quartile group.

Page 13, line 243: For consistency, please move the detail referred to the approach used to define iron intake inadequacy to line 240, as specified for free sugar intake.

Authors’ response:

We have added the detail referred to the approach to define inadequacy of iron intake and have revised the next sentence accordingly. To avoid repetition, we deleted the footnote “d.”

Page 14, line 271-277:

b Inadequate intake was defined by comparing usual intake with reference values derived from Dietary Reference Intakes for Japanese 2020 except for iron intake for women under 50 years old and free sugar. For iron intake among women aged <50 years, less than 9.3 mg/d [51] was considered inadequate. For free sugar, the World Health Organization’s conditional recommendation (<5% energy) was used.

c Logistic regression models were used with the median value in each quartile category of diet-related GHGE as a continuous variable.

d Probability approach was used to assess inadequacy for iron intake.

Page 13, Table 4: please amend the number referred to the total sample. It should be n= 392.

Authors’ response:

We have revised the number of the total sample in Table 4. (See Table 4 in the revised the manuscript)

Discussion

Page 14, lines 267 and 269: The sentence starting with “In contrast” is not actually opposite to the previous sentence. Please make sure about the provided statements from line 265 to line 271.

Authors’ response:

We have revised the words as follows.

Page 15, line 304-307:

Thus, according to the current Japanese diet, diet-related GHGE was positively associated with nutrition adequacy. Our result is inconsistent with observational studies from Western countries, where relatively consistent inverse associations between diet-related GHGE and nutritional adequacy or diet-quality scores were found [3,14–19] with a few exceptions [2,17].

Page 15, lines 283 and 293: please add a reference.

Authors’ response:

We have added references to the latter part of this sentence. We did not add a reference in the former part because it was a summary of our study.

Page 16, line 321-325:

In this regard, intakes of nutrient-dense foods and moderate meat intake would be associated with higher intakes of protein and micronutrients as well as diet-related GHGE among Japanese, while dietary patterns in Western countries characterized by higher intake of meat and dairy products would be associated with both higher diet-related GHGE and lower nutrient intake or diet quality [3,14–19].

Authors’ response:

The generalizability of our sample was low because most of them were working at the welfare facilities. In addition, the proportion of urban or rural residence and educational background was not considered at the recruitment. Further, information for family income was not obtained. Thus, we have revised the manuscript as shown below.

Page 17, line 354- 358:

The participants would be more health-conscious than the general Japanese population. In addition, most of the participants were working at welfare facilities. Moreover, proportion of urban and rural area was not considered at recruitment although regional differences in dietary habits were considered at sampling. Thus, further research in a national representative sample would be needed.

Page 16, line 325-326: please clarify the sentence as it is not fully understandable.

Authors’ response:

We have revised the manuscript and added the results when under- and over-reporters were excluded as Supplemental Tables. (see the Tables in revised “Supporting information”)

Page 18, line 366-370:

However, diet-related GHGE was adjusted for EI by the residual method to avoid a confounding effect of energy intake on associations between diet-related GHGE and nutritional adequacy. In addition, the effect of energy-misreporting would be small in this study because similar results have shown when under- and over-reporters were excluded from the analysis (S7-S10 Tables).

Reviewer #2:

This is an interesting, carefully planned, and well written baseline study on the relation between individual diets and associated green house gas emissions in a section of the Japanese population. It was surprising, though, that no previous baseline study was available for Japan, as noted by the authors. It was also interesting how, in the case of the population sampled, a better nutrition implied larger emissions.

Authors’ response:

Thank you for your comments.

Authors’ response:

We have added more information for “Study design and participants” and “Dietary assessment” sections.

Page 4, line 87-104:

Study design and participants

This cross-sectional study was based on data from healthy Japanese adults aged 20–69 years. Data collection was conducted in 20 study areas covering 23 of 47 prefectures between February and March 2013. Details of the study have been reported elsewhere [33,34]. The primary objective of this survey was to estimate sodium and potassium excretion using biomarker and to identify food sources of sodium and potassium. First, 199 dietitians working in separate welfare facilities were recruited as research dietitians supporting the survey. Next, the research dietitians recruited participants from their co-workers or family members of co-workers with stratifying by sex and by five 10-year age bands (20–29, 30–39, 40– 49, 50–59, and 60–69 years). The number of participants was targeted to be 40 adults in each study area to allow for statistical analysis stratified by sex, age, body mass index (BMI; in kg/m2), and physical activity. The exclusion criteria were: (i) licensed dietary or medical provider, (ii) residence in the prefecture or adjacent prefecture in which the facility was located for less than 6 months, (iii) individuals who were under diet therapy prescribed by a doctor or dietitian at the time of the study or within 1 y before the study, (iv) pregnant or lactating women, and (v) individuals who had history of hospitalization for diabetes education. Of those 800 adults recruited, nine adults withdrew from the survey. In total, 791 adults participated. To reduce the burden to the participants and the research dietitians, half of the participants (n=400) were also asked to complete diet records. A total of 392 adults (196 men and 196 women) completed diet records and were included in the present analysis.

Page 4, line 112-page 5, 127:

Dietary intake was assessed by four-non-consecutive-day diet records. The assessment days consisted of three working days and one day off. All participants were provided with digital kitchen scale (KD-812WH; Tanita, Tokyo, Japan), measuring spoon, measuring cup, a manual for the diet record, and recording sheets and instructed how to weigh and record foods and beverages consumed. Each participant was asked to weigh and record all food and beverages consumed on the four assessment days using the provided equipments and recording sheet. When weighing was difficult (e.g. eating out), the restaurant’s name, name of dishes, and an estimated amount of leftovers were reported. Pictures of food and beverages were also provided by some participants but not mandatory. All recorded foods and beverages were assigned food item numbers according to the Standard Tables of Food Composition in Japan, Fifth Revised and Enlarged Edition [36]. All records were checked twice (by the research dietitians at each facility and trained dietitian staff at the survey center). The research dietitian contacted the participants to clarify any ambiguities or missing data in the recording sheets. Daily intakes of foods, energy, and nutrients were estimated based on the Standard Tables of Food Composition in Japan in which free sugar content of each food item was added based on published sources [37,38] due to the insufficiency of data for free sugars in original food composition tables.

We have revised the language errors as follows.

Page 3, line 70:

Recently, the Japanese diet has been characterized by…

Page 3, line 79-81:

…while negative or no associations between meat intake and mortality from cardiovascular disease or cancer were generally found in Asian population with low meat intake [31,32]

Line 89: what do authors mean by "educational admission").

Authors’ response:

"Educational admission" is a kind of hospitalization for patients especially of diabetes with poor glycemic control. During hospitalization, patients are taught about proper treatment for their condition, motivate them to improve their lifestyle habits. We have made a revision in the text not to use the word "educational admission."

Page 4, 100-101:

(v) individuals who had history of hospitalization for diabetes education.

Authors’ response:

We have added information about the dietary assessment. They measured their food and just write down them. Some participants took pictures of food and attached them as supporting information about food although it was not mandatory.

Page 4, line 112-page 5, 127:

Dietary intake was assessed by four-non-consecutive-day diet records. The assessment days consisted of three working days and one day off. All participants were provided with digital kitchen scale (KD-812WH; Tanita, Tokyo, Japan), measuring spoon, measuring cup, a manual for the diet record, and recording sheets and instructed how to weigh and record foods and beverages consumed. Each participant was asked to weigh and record all food and beverages consumed on the four assessment days using the provided equipments and recording sheet. When weighing was difficult (e.g. eating out), the restaurant’s name, name of dishes, and an estimated amount of leftovers were reported. Pictures of food and beverages were also provided by some participants but not mandatory. All recorded foods and beverages were assigned food item numbers according to the Standard Tables of Food Composition in Japan, Fifth Revised and Enlarged Edition [36]. All records were checked twice (by the research dietitians at each facility and trained dietitian staff at the survey center). The research dietitian contacted the participants to clarify any ambiguities or missing data in the recording sheets. Daily intakes of foods, energy, and nutrients were estimated based on the Standard Tables of Food Composition in Japan in which free sugar content of each food item was added based on published sources [37,38] due to the insufficiency of data for free sugars in original food composition tables.

Line 184: This should refer to Table 1, not table 2. In the same paragraph, please provide better definitions for under- and over-reporters.

Authors’ response:

We have revised the text. We also added the definition of under- and over- reporters although they were described in the “Method and material” section.

Page 8, line 209:

The basic characteristics of the participants are described in Table 1.

Page 8, line 211-213:

The prevalence of under-reporters (defined as having EI:BMR <1.02 and for men and <1.03 for women) was 3.6% (nine men and five women) and that of over-reporters (defined as having EI:BMR >2.35 for men and >2.36 for women) was 2.3% (three men and six women).

Line 222: Should "quantile" be "quartile"?

Authors’ response:

We have revised the text.

Page 12, line 256-262:

Authors’ response:

In accordance with your comment, we have shortly discussed the tensions between individual choice and recommendation in the “Discussion” section.

Page 16, line 300-301:

Attachment

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PLoS One. doi: 10.1371/journal.pone.0240803.r003

Decision Letter 1

Nicoletta Righini

23 Sep 2020

PONE-D-20-15820R1

Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults

PLOS ONE

Dear Dr. Sasaki,

The authors did a good job responding to the reviewers' queries and now the manuscript has significantly improved. Please just take care of a few minor comments, after which the ms can be accepted.

Please submit your revised manuscript by Nov 07 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Nicoletta Righini, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Please check the spelling of MEAN in several Tables (e.g., 1, 4..). Currently it appears as MAEN

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: I was a reviewer also of the first version of the manuscript. The authors significantly improved the quality of the manuscript and addressed the previous comments. Only minor revisions remain to be considered before publication.

Introduction

Page 3, line 65-66: The sentence “The dietary aspects have not been included in this statement nor mentioned in the dietary guidelines” is understandable, however it could be slightly modified to increase the readability and clarity by substituting “The dietary aspects” with, for example, “the dietary environmental dimension”.

Page 3, line 70-73: the authors should consider to change a bit the sentence to improve the form by substituting “while” (line 71) with “and” or deciding to divide this long sentence in two parts.

Material and methods

Thank you for adding information about the criteria used to determine the sample size and for providing details on the larger study. Thank you also for providing information about data collection and data management. This information is needed for data replicability and study clarity. Furthermore, thank you for adding the post hoc analysis to compare the quartile groups.

Page 7, line 192 and page 8, line 200: Should “quantile” be “quartile”?

Discussion

Page 16, line 316-319: please check the accuracy of the sentence. Meat contribution to diet-related to GHGE has been indicated as 19.6%. This percentage is the highest compared to those referred to the other food groups. As a consequence, meat contribution should not be mentioned together with dairy products (4.6%), but together with cereals (13.1%), vegetables/fruits (7.6%), and fish/seafood (13.8%). Once rectified, the sentence will be compliant with what properly mentioned at page 17, line 334 and 335.

Page 18, line 368-370: The authors should mention the lack of objective information on physical activity as a limitation of the study in the discussion section. Indeed, bias can be present in the identification of under- and over- reporters, even though potential effect of energy-misreporting would be small. Another limitation is the relatively limited sample size that should be highlighted.

**********

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Reviewer #1: No

PLoS One. 2020 Oct 23;15(10):e0240803. doi: 10.1371/journal.pone.0240803.r004

Author response to Decision Letter 1

29 Sep 2020

Reply to the Editors’ and Reviewers’ comments for the manuscript: PONE-D-20-15820R1

(Title: Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults)

We thank the editor and the reviewers for their very helpful comments on our paper. We have revised the manuscript by addressing each comment point-by-point as described below. All amendments and changes are highlighted in the manuscript using a red font. We trust these changes will resolve any confusion and remedy the shortcomings of the paper.

Reviewer #1:

I was a reviewer also of the first version of the manuscript. The authors significantly improved the quality of the manuscript and addressed the previous comments. Only minor revisions remain to be considered before publication.

Authors’ response:

Thank you for your comments.

Introduction

Authors’ response:

Based on your comments, we have revised the text as follows.

“The environmental dimension of diet has not been included in this statement nor mentioned in the dietary guidelines.” (page 3, line 65-66)

Authors’ response:

In accordance with the comment, we have decided to divide this sentence into two parts; one for food consumption and another for nutrient intakes.

“The contemporary Japanese diet is typically high in refined grains, seaweeds, vegetables, fish, legumes and low in whole grains, nuts and seeds, dairy products, sugar-sweetened beverage, and processed and unprocessed red meats [27–29]. At the nutrient level, it is characterized by a high intake of sodium and a low intake of dietary fiber, calcium, and saturated fat [29,30].” (page 3, line 70-73)

Material and methods

Authors’ response:

Thank you for your comments.

Page 7, line 192 and page 8, line 200: Should “quantile” be “quartile”?

Authors’ response:

Thank you for finding our careless typographical errors, which have been corrected as shown below.

“using the median value for each quartile” (page 7, line 195)

“using the median value for each quartile” (page 8, line 200)

“inadequacy were calculated for each quartile of diet-related GHGE” (page 8, line 203)

“c Trend of association was examined using a linear regression model with the median value in each quartile as a continuous variable” (page 16, line 297-298)

Discussion

Authors’ response:

In accordance with the comment, we have made the following revision. In line with the revision of the sentences that you pointed out, we have revised some sentences in the same paragraph.

“In Japan, meat was also the top contributor to diet-related GHGE (19.6%), followed by fish/seafood (13.8%) and cereals (13.1%)[11]. Nevertheless, it should be noted that the percentage contribution of meat was lower than that in the Western countries. In addition, the contribution of dairy products (4.6%) was low [11]. In relation to diet-related GHGE, intakes of vegetables, fish/seafood, and meat showed positive associations, while those of cereals and fat and oils showed inverse associations.” (page 17, line 319-323)

Authors’ response:

In accordance with the comment, we have added the limitation for sample size and a lack of observational measure for physical activity level.

“Second, our relatively small sample size (n = 396) would limit the power to detect moderate associations with statistical significance. In addition, the estimated usual intake distributions of nutrients and foods would be uncertain due to the small sample size [54]. Nevertheless, significant associations were generally observed between diet-related GHGE and intakes of nutrient and food. Although our sample size might be sufficient to detect the difference between quartiles of diet-related GHGE, further studies with larger sample sizes would be needed. Third,…” (page 17, line 360- page 18, 366)

“Fourth, both nutritional adequacy and diet-related…” (page 18, 368)

“Fifth, due to a lack of objective information on physical activity, bias can be present in the identification of under- and over-reporters. Nevertheless, the potential effect of energy-misreporting in this study would be small.” (page 18, line 378-380)

Attachment

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PLoS One. doi: 10.1371/journal.pone.0240803.r005

Decision Letter 2

Nicoletta Righini

5 Oct 2020

Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults

PONE-D-20-15820R2

Dear Dr. Sasaki,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Nicoletta Righini, PhD

Academic Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0240803.r006

Acceptance letter

Nicoletta Righini

14 Oct 2020

PONE-D-20-15820R2

Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults

Dear Dr. Sasaki:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Nicoletta Righini

Academic Editor

PLOS ONE

Associated Data

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Supplementary Materials

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Data Availability Statement

[pone.0240803.ref001] 1.FAO. Biodiversity and sustainable diets-united against hunger. Food Agric Organ United Nations. Rome, Italy: Food and Agriculture Organization of the United Nations; 2010.

[pone.0240803.ref002] 2.Vieux F, Soler L, Touazi D, Darmon N. High nutritional quality is not associated with low greenhouse gas emissions in self-selected diets of French adults. Am J Clin Nutr. 2013;97: 569–83. 10.3945/ajcn.112.035105 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref003] 3.Sjörs C, Hedenus F, Sjölander A, Tillander A, Bälter K. Adherence to dietary recommendations for Swedish adults across categories of greenhouse gas emissions from food. Public Health Nutr. 2017;20: 3381–3393. 10.1017/S1368980017002300 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref004] 4.Murakami K, Livingstone MBE. Greenhouse gas emissions of self-selected diets in the UK and their association with diet quality: is energy under-reporting a problem? Nutr J. 2018;17: 27 10.1186/s12937-018-0338-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref005] 5.Biesbroek S, Verschuren WMM, Boer JMA, Van De Kamp ME, Van Der Schouw YT, Geelen A, et al. Does a better adherence to dietary guidelines reduce mortality risk and environmental impact in the Dutch sub-cohort of the European Prospective Investigation into Cancer and Nutrition? Br J Nutr. 2017;118: 69–80. 10.1017/S0007114517001878 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref006] 6.Rose D, Heller MC, Willits-Smith AM, Meyer RJ. Carbon footprint of self-selected US diets: nutritional, demographic, and behavioral correlates. Am J Clin Nutr. 2019;109: 526–534. 10.1093/ajcn/nqy327 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref007] 7.Mertens E, Kuijsten A, Geleijnse JM, Boshuizen HC, Feskens EJM, Van’T Veer P. FFQ versus repeated 24-h recalls for estimating diet-related environmental impact. Nutr J. 2019;18: 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref008] 8.Monsivais P, Scarborough P, Lloyd T, Mizdrak A, Luben R, Mulligan AA, et al. Greater accordance with the Dietary Approaches to Stop Hypertension dietary pattern is associated with lower diet-related greenhouse gas production but higher dietary costs in the United Kingdom. Am J Clin Nutr. 2015;102: 138–145. 10.3945/ajcn.114.090639 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref009] 9.Seconda L, Baudry J, Allès B, Boizot-Szantai C, Soler LG, Galan P, et al. Comparing nutritional, economic, and environmental performances of diets according to their levels of greenhouse gas emissions. Clim Change. 2018;148: 155–172. 10.1007/s10584-018-2195-1 [DOI] [Google Scholar]

[pone.0240803.ref010] 10.Vieux F, Perignon M, Gazan R, Darmon N. Dietary changes needed to improve diet sustainability: are they similar across Europe? Eur J Clin Nutr. 2018;72: 951–960. 10.1038/s41430-017-0080-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref011] 11.van Dooren C, Tyszler M, Kramer GF, Aiking H. Combining low price, low climate impact and high nutritional value in one shopping basket through diet optimization by linear programming. Sustain. 2015;7: 12837–12855. 10.3390/su70912837 [DOI] [Google Scholar]

[pone.0240803.ref012] 12.van de Kamp ME, van Dooren C, Hollander A, Geurts M, Brink EJ, van Rossum C, et al. Healthy diets with reduced environmental impact?–The greenhouse gas emissions of various diets adhering to the Dutch food based dietary guidelines. Food Res Int. 2018;104: 14–24. 10.1016/j.foodres.2017.06.006 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref013] 13.Temme EHM, Toxopeus IB, Kramer GFH, Brosens MCC, Drijvers JMM, Tyszler M, et al. Greenhouse gas emission of diets in the Netherlands and associations with food, energy and macronutrient Intakes. Public Health Nutr. 2014;18: 2433–2445. 10.1017/S1368980014002821 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref014] 14.Hyland JJ, Henchion M, McCarthy M, McCarthy SN. The climatic impact of food consumption in a representative sample of Irish adults and implications for food and nutrition policy. Public Health Nutr. 2017;20: 726–738. 10.1017/S1368980016002573 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref015] 15.Vieux F, Darmon N, Touazi D, Soler LG. Greenhouse gas emissions of self-selected individual diets in France: Changing the diet structure or consuming less? Ecol Econ. 2012;75: 91–101. 10.1016/j.ecolecon.2012.01.003 [DOI] [Google Scholar]

[pone.0240803.ref016] 16.Mertens E, Kuijsten A, van Zanten HH, Kaptijn G, Dofková M, Mistura L, et al. Dietary choices and environmental impact in four European countries. J Clean Prod. 2019;237: 117827 10.1016/j.jclepro.2019.117827 [DOI] [Google Scholar]

[pone.0240803.ref017] 17.Biesbroek S, Bueno-de-Mesquita HB, Peeters PHM, Verschuren WM, van der Schouw YT, Kramer GFH, et al. Reducing our environmental footprint and improving our health: greenhouse gas emission and land use of usual diet and mortality in EPIC-NL: a prospective cohort study. Environ Health. 2014;13: 27 10.1186/1476-069X-13-27 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref018] 18.Heller MC, Willits-Smith A, Meyer R, Keoleian GA, Rose D. Greenhouse gas emissions and energy use associated with production of individual self-selected US diets. Environ Res Lett. 2018;13: 044004 10.1088/1748-9326/aab0ac [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref019] 19.Sugimoto M, Murakami K, Asaskura K, Masayasu S, Sasaki S. Diet-related greenhouse gas emission and major food contributor among Japanese adults: comparison of different calculation methods. Public Health Nutr. 2020; 1–11. Online ahead of print. 10.1017/S1368980019004750 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref020] 20.Green RF, Joy EJM, Harris F, Agrawal S, Aleksandrowicz L, Hillier J, et al. Greenhouse gas emissions and water footprints of typical dietary patterns in India. Sci Total Environ. 2018;643: 1411–1418. 10.1016/j.scitotenv.2018.06.258 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref021] 21.Song G, Li M, Fullana-i-palmer P, Williamson D, Wang Y. Dietary changes to mitigate climate change and benefit public health in China. Sci Total Environ. 2017;577: 289–298. 10.1016/j.scitotenv.2016.10.184 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref022] 22.Mbow C, Rosenzweig C, Barioni LG, Benton TG, Herrero M, Krishnapillai M, et al. Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Nature. 2019. [Google Scholar]

[pone.0240803.ref023] 23.Olivier J, Schure K, Peters J. Trends in global CO2 and total greenhouse gas emissions. The Hague; 2017. http://www.pbl.nl/sites/default/files/cms/publicaties/pbl-2017-trends-in-global-co2-and-total-greenhouse-gas-emis-sons-2017-report_2674.pdf.

[pone.0240803.ref024] 24.Government of Japan. The plan for global warming countermeasure (in Japanese). 2016 [cited 30 Jul 2019]. http://www.env.go.jp/press/files/jp/102816.pdf

[pone.0240803.ref025] 25.Sasaki S. The value of the National Health and Nutrition Survey in Japan. Lancet. 2011;378: 1205–6. 10.1016/S0140-6736(11)61220-8 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref026] 26.Ikeda N, Saito E, Kondo N, Inoue M, Ikeda S, Satoh T, et al. What has made the population of Japan healthy? Lancet. 2011;378: 1094–105. 10.1016/S0140-6736(11)61055-6 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref027] 27.Imamura F, Micha R, Khatibzadeh S, Fahimi S, Shi P, Powles J, et al. Dietary quality among men and women in 187 countries in 1990 and 2010: a systematic assessment. Lancet Glob Heal. 2015;3: e132–e142. 10.1016/S2214-109X(14)70381-X [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref028] 28.Micha R, Khatibzadeh S, Shi P, Andrews KG, Engell RE, Mozaffarian D. Global, regional and national consumption of major food groups in 1990 and 2010: A systematic analysis including 266 country-specific nutrition surveys worldwide. BMJ Open. 2015;5: e008705 10.1136/bmjopen-2015-008705 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref029] 29.Ministry of Health Labour and Welfare. The National Health and Nutrition Survey in Japan, 2013 (in Japanese). [cited 20 Aug 2019]. http://www.mhlw.go.jp/bunya/kenkou/kenkou_eiyou_chousa.html

[pone.0240803.ref030] 30.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. 10.1038/sj.jhh.1001605 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref031] 31.Yip CSC, Lam W, Fielding R. A summary of meat intakes and health burdens. Eur J Clin Nutr. 2018;72: 18–29. 10.1038/ejcn.2017.117 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref032] 32.Nagao M, Iso H, Yamagishi K, Date C, Tamakoshi A. Meat consumption in relation to mortality from cardiovascular disease among Japanese men and women. Eur J Clin Nutr. 2012;66: 687–693. 10.1038/ejcn.2012.6 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref033] 33.Asakura K, Uechi K, Masayasu S, Sasaki S. Sodium sources in the Japanese diet: difference between generations and sexes. Public Health Nutr. 2015;19: 2011–23. 10.1017/S1368980015003249 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref034] 34.Asakura K, Uechi K, Sasaki Y, Masayasu S, Sasaki S. Estimation of sodium and potassium intakes assessed by two 24 h urine collections in healthy Japanese adults: a nationwide study. Br J Nutr. 2014;112: 119–205. 10.1017/S0007114514001779 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref035] 35.Science and Technology Agency. Standard Tables of Food Composition in Japan. (in Japanese). 7th editio Printed Bureau of Ministry of Finance; 2015. [Google Scholar]

[pone.0240803.ref036] 36.Fujiwara A, Murakami K, Asakura K, Uechi K, Sugimoto M, Wang H-C, et al. Association of free sugar intake estimated using a newly-developed food composition database with lifestyles and parental characteristics among Japanese children aged 3–6 years: DONGuRI Study. J Epidemiol. 2018;29: 414–423. 10.2188/jea.JE20180036 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref037] 37.Fujiwara A, Murakami K, Asakura K, Uechi K, Sugimoto M, Wang H-C, et al. Estimation of starch and sugar intake in a Japanese population based on a newly developed food composition database. Nutrients. 2018;10: 1474 10.3390/nu10101474 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref038] 38.Black AE. The sensitivity and specificity of the Goldberg cut-off for EI:BMR for identifying diet reports of poor validity. Eur J Clin Nutr. 2000;54: 395–404. 10.1038/sj.ejcn.1600971 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref039] 39.Livingstone MBE, Black AE. Markers of the validity of reported energy intake. J Nutr. 2003;133: 895S–920S. 10.1093/jn/133.3.895S [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref040] 40.Murakami K, Livingstone MBE. Prevalence and characteristics of misreporting of energy intake in US adults: NHANES 2003–2012. Br J Nutr. 2015;145: 2715–24. 10.1017/S0007114515002706 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref041] 41.Nansai K, Kagawa S, Kondo Y, Suh S, Inaba R, Nakajima K. Improving the completeness of product carbon foodprints using a global link input-output model: The case of Japan. Econ Syst Res. 2009;9: 267–290. 10.1080/09535310903541587 [DOI] [Google Scholar]

[pone.0240803.ref042] 42.Harttig U, Haubrock J, Knüppel S, Boeing H. The MSM program: Web-based statistics package for estimating usual dietary intake using the multiple source method. Eur J Clin Nutr. 2011;65: S87–S91. 10.1038/ejcn.2011.92 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref043] 43.Haubrock J, Nöthlings U, Volatier J-L, Dekkers A, Ocké M, Harttig U, et al. Estimating usual food intake distributions by using the Multiple Source Method in the EPIC-Potsdam Calibration Study. J Nutr. 2011;141: 914–920. 10.3945/jn.109.120394 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref044] 44.Ministry of Health Labour and Welfare. Dietary Reference Intakes for Japanese, 2020. (in Japanese). 2020.

[pone.0240803.ref045] 45.Saito A, Matsumoto M, Hyakutake A, Saito M, Okamoto N, Tsuji M. The frequency of cooking dinner at home and its association with nutrient intake adequacy among married young-to-middle-aged Japanese women: the POTATO Study. J Nutr Sci. 2019. 10.1017/jns.2019.9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref046] 46.Murakami K, Okubo H, Livingstone MBE, Fujiwara A, Asakura K, Uechi K, et al. Adequacy of usual intake of Japanese children aged 3−5 years: A nationwide study. Nutrients. 2018;10: 1150 10.3390/nu10091150 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref047] 47.Ministry of Health Labour and Welfare. Dietary Reference Intakes for Japanese, 2015. 2015.

[pone.0240803.ref048] 48.FAO/WHO. Requirements of Vitamin A, Iron, Folate and Vitamin B12. Report of a Joint FAO/WHO Expert Consultation. Rome, Italy; 1988.

[pone.0240803.ref049] 49.Institute of Medicine Food and Nutrition Board. Dietary Reference Intakes: Applications in Dietary Assessment. Washington, DC: National Academies Press; 2000. [PubMed] [Google Scholar]

[pone.0240803.ref050] 50.WHO/FAO. Guidelines on Food Fortification with Micronutrients. Geneva, Switzerland; 2006. [Google Scholar]

[pone.0240803.ref051] 51.WHO. Guideline: Sugars intake for adults and children. World Heal Organ; Geneva, Switzerland; 2015. 978 92 4 154902 8 [Google Scholar]

[pone.0240803.ref052] 52.Willett W. Implications of Total Energy Intake for Epidemiologic Analyses Third edit Nutritional Epidemiology. Third edit. Oxford University Press; 2012. p. 274. [Google Scholar]

[pone.0240803.ref053] 53.Okubo H, Sasaki S, Murakami K, Yokoyama T, Hirota N, Notsu A, et al. Designing optimal food intake patterns to achieve nutritional goals for Japanese adults through the use of linear programming optimization models. Nutr J. 2015;14: 57 10.1186/s12937-015-0047-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref054] 54.Souverein OW, Dekkers AL, Geelen A, Haubrock J, de Vries JH, Ocké MC, et al. Comparing four methods to estimate usual intake distributions. Eur J Clin Nutr. 2011;65: S92–S101. 10.1038/ejcn.2011.93 [DOI] [PubMed] [Google Scholar]

[pone.0240803.ref055] 55.Tokudome Y, Imaeda N, Nagaya T, Ikeda M, Fujiwara N, Sato J, et al. Daily, weekly, seasonal, within- and between-individual variation in nutrient intake according to four season consecutive 7 day weighed diet records in Japanese female dietitians. J Epidemiol. 2002;12: 85–92. 10.2188/jea.12.85 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0240803.ref056] 56.Mori S, Saito K, Wakasa Y. Studies on annual fluctuation of food intake in female college students. Japanese J Nutr Diet. 1981;39: 243–368. 10.1093/mnras/183.3.341 [DOI] [Google Scholar]

[pone.0240803.ref057] 57.Owaki A, Takatsuka N, Kawakami N, Shimizu H. Seasonal variations of butrient intake assessed by 24 hour recall method. Japanese J Nutr Diet. 1996;54: 11–18. [Google Scholar]

[pone.0240803.ref058] 58.Taguchi C, Kishimoto Y, Takeuchi I, Tanaka M, Iwashima T, Fukushima Y, et al. Estimated dietary polyphenol intake and its seasonal variations among Japanese University students. J Nutr Sci Vitaminol (Tokyo). 2019;65: 192–195. 10.3177/jnsv.65.192 [DOI] [PubMed] [Google Scholar]

PERMALINK

Association between diet-related greenhouse gas emissions and nutrient intake adequacy among Japanese adults

Minami Sugimoto

Kentaro Murakami

Aya Fujiwara

Keiko Asakura

Shizuko Masayasu

Satoshi Sasaki

Roles

Abstract

Objectives

Methods

Results

Conclusions

Introduction

Materials and methods

Study design and participants

Dietary assessment

Greenhouse gas emissions of food and drinks

Estimation of usual nutrient and food intakes and greenhouse gas emissions

Assessment of nutrient intake inadequacy

Statistical analysis

Results

Table 1. Basic characteristics of participants according to quartile (Q) of diet-related GHGE (g CO2-eq/day) among 392 Japanese adultsa.

Table 2. Usual nutrient intake according to quartile (Q) of diet-related GHGE (g CO2-eq/day) among 392 Japanese adults (aged 20–69 y)a.

Table 3. Odds ratios for inadequate nutrient intake compared to DRIs reference value according to the quartile (Q) of usual diet-related GHGE (g CO2-eq/day) among 392 Japanese adults (aged 20–69 y)a.

Table 4. Usual food intake (g/d) according to quartile (Q) of usual diet-related GHGE (g CO2-eq/day) among 392 Japanese adults (aged 20–69 y)a.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Nicoletta Righini

Roles

Author response to Decision Letter 0

Decision Letter 1

Nicoletta Righini

Roles

Author response to Decision Letter 1

Decision Letter 2

Nicoletta Righini

Roles

Acceptance letter

Nicoletta Righini

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Table 1. Basic characteristics of participants according to quartile (Q) of diet-related GHGE (g CO₂-eq/day) among 392 Japanese adults^a.

Table 2. Usual nutrient intake according to quartile (Q) of diet-related GHGE (g CO₂-eq/day) among 392 Japanese adults (aged 20–69 y)^a.

Table 3. Odds ratios for inadequate nutrient intake compared to DRIs reference value according to the quartile (Q) of usual diet-related GHGE (g CO₂-eq/day) among 392 Japanese adults (aged 20–69 y)^a.

Table 4. Usual food intake (g/d) according to quartile (Q) of usual diet-related GHGE (g CO₂-eq/day) among 392 Japanese adults (aged 20–69 y)^a.