Higher Nutritional Adequacy Is Associated With Lower All-Cause Mortality: Findings From the J-MICC Cohort Study

Present Address:

Kotone Tanaka 1,2 Emailtanaka-rt8@kuhs.ac.jp Emailprotonk@med.nagoya-u.ac.jp

Megumi Tsubota-Utsugi 3 Emailtsubota.megumi.ey@teikyo-u.ac.jp

Sho Nakamura 2✉,3

Kanami Tsuno 2 Emailk.tsuno-wm4@kuhs.ac.jp

Yasutake Tomata 1 Emailtoomata-5h0@kuhs.ac.jp

Kazushige Ide 5 Emailide.k@chiba-u.jp

Hiroto Narimatsu 2,4,6 Emailhiroto-narimatsu@umin.org

An Tran Quyen 2 Emailtranquyenan@gmail.com

Jun Otonari 7 Emailjotti_song@yahoo.co.jp

Hiroaki Ikezaki 8 Emailharameg@cc.saga-u.ac.jp Emailikezaki.hiroaki.149@m.kyushu-u.ac.jp

Megumi Hara 9 Emailn-taka@koto.kpu-m.ac.jp

Yuichiro Nishida 9 Emailynishida@cc.saga-u.ac.jp Emailpsychogenomics@gmail.com

Takashi Tamura 10 Emailttamura@med.nagoya-u.ac.jp

Mako Nagayoshi 10 Emailmnagayoshi@med.nagoya-u.ac.jp

Rieko Okada 10 Emailrieokada@med.nagoya-u.ac.jp

Yoko Kubo 10

Isao Oze 11 Emaili_oze@aichi-cc.jp

Hidemi Ito 1 Emailhidemi@aichi-cc.jp

Nobuaki Michihata 1 Emailnmichihata@chiba-cc.jp

Yohko Nakamura 1 Emailynakamur@chiba-cc.jp

Shiroh Tanoue 1 Emailtanoue@m.kufm.kagoshima-u.ac.jp

Rie Ibusuki 1 Emailiburie@m2.kufm.kagoshima-u.ac.jp

Sadao Suzuki 1 Emailssuzuki@med.nagoya-cu.ac.jp

Takeshi Nishiyama 1

Etsuko Ozaki 1 Emailozaki@koto.kpu-m.ac.jp

Satomi Tomida 1 Emailsatomida@koto.kpu-m.ac.jp

Kiyonori Kuriki 1 Emailkuriki@u-shizuoka-ken.ac.jp

Naoyuki Takashima 1

Naoko Miyagawa 1 Emailnaocom@belle.shiga-med.ac.jp

Sakurako Katsuura-Kamano 1 Emailskamano@tokushima-u.ac.jp

Takeshi Watanabe 1 Emailwatanabe.takeshi.2@tokushima-u.ac.jp

Kenji Wakai 1 Emailwakai@med.nagoya-u.ac.jp

Keitaro Matsuo 1✉ Phone+81-46-828-2813 Emailkmatsuo@aichi-cc.jp Emailresearch@nakasho.org

1 School of Nutrition and Dietetics, Faculty of Health and Social Services Kanagawa University of Human Services 1-10-1 Heiseicho 238-8522 Yokosuka Kanagawa Japan

2 School of Health Innovation Kanagawa University of Human Services Research Gate Building TONOMA-CHI2, 3-25-10 Tonomachi, Kawasaki-ku 210-0821 Kawasaki Kanagawa Japan

3 Department of Hygiene and Public Health Teikyo University School of Medicine 2-11-1 Kaga, Itabashi-ku 173-8605 Tokyo Japan

4 Cancer Prevention and Cancer Control Division Kanagawa Cancer Center Research Institute 2-3-2 Nakao, Asahi-ku 241-8515 Yokohama Kanagawa Japan

5 Department of Community Building for Well-being, Center for Preventive Medical Sciences Chiba University 1-33 Yayoicho, Inage-ku 263-8522 Chiba Chiba Japan

6 Department of Genetic Medicine Kanagawa Cancer Center 2-3-2 Nakao, Asahi-ku 241-8515 Yokohama Kanagawa Japan

7 Department of Psychosomatic Medicine, Graduate School of Medical Sciences Kyushu University 3-1-1 Maidashi, Higashi-ku 812- 8582 Fukuoka Japan

8 Department of Comprehensive General Internal Medicine, Faculty of Medical Sciences Kyushu University 3-1-1 Maidashi, Higashi-ku 812-8582 Fukuoka Japan

9 Department of Preventive Medicine, Faculty of Medicine Saga University 5-1-1 Nabeshima 849-8501 Saga Japan

10 Department of Preventive Medicine Nagoya University Graduate School of Medicine 65 Tsurumai-cho, Showa-ku 466-8550 Nagoya Japan

11 Division of Cancer Epidemiology and Prevention Aichi Cancer Center Research Institute 1-1 Kanokoden, Chikusa-ku 464-8681 Nagoya Japan

12 Department of Descriptive Cancer Epidemiology Nagoya University Graduate School of Medicine 65 Tsurumai-cho, Showa-ku 466- 8550 Nagoya Japan

13 Cancer Prevention Center Chiba Cancer Center Research Institute 666-2 Nitona-cho, Chuo-ku 260-8717 Chiba Japan

14 Department of Epidemiology and Preventive Medicine Kagoshima University Graduate School of Medical and Dental Sciences 8-35-1 Sakuragaoka 890-8544 Kagoshima Japan

15 Department of International Island and Community Medicine Kagoshima University Graduate School of Medical and Dental Sciences 8-35-1 Sakuragaoka 890-8544 Kagoshima Japan

16 Department of Public Health Nagoya City University Graduate School of Medical Sciences 1 Kawasumi, Mizuho-cho, Mizuho-ku 467-8601 Nagoya Japan

17 Department of Epidemiology for Community Health and Medicine Kyoto Prefectural University of Medicine 465 Kajii-cho, Kamigyo-ku 602- 8566 Kyoto Japan

18 Department of Endocrine and Breast Surgery Kyoto Prefectural University of Medicine 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku 602- 8566 Kyoto Japan

19 Laboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences University of Shizuoka 52-1 Yada, Suruga- ku 422-8526 Shizuoka Japan

20 NCD Epidemiology Research Center Shiga University of Medical Science Seta-Tsukiwacho 520-2192 Otsu Shiga Japan

21 Department of Preventive Medicine and Public Health Keio University School of Medicine 35 Shinanomachi, Shinjuku-ku 160-8582 Tokyo Japan

22 Department of Public Health Shiga University of Medical Science Seta Tsukiwacho 520-2192 Otsu Japan

23 Department of Preventive Medicine Tokushima University Graduate School of Biomedical Sciences 3-18-15 Kuramoto-cho 770-8503 Tokushima Japan

24 Department of Cancer Epidemiology Nagoya University Graduate School of Medicine 65 Tsurumai-cho, Showa-ku 466-8550 Nagoya Japan

Kotone Tanaka^a,b, Megumi Tsubota-Utsugi^c, Sho Nakamura^b,c, Kanami Tsuno^b, Yasutake Tomata^a, Kazushige Ide^e, Hiroto Narimatsu^b,d,f, An Tran Quyen^b, Jun Otonari^g, Hiroaki Ikezaki^h, Megumi Haraⁱ, Yuichiro Nishidaⁱ, Takashi Tamura^j, Mako Nagayoshi^j, Rieko Okada^j, Yoko Kubo^j, Isao Oze^k, Hidemi Ito^k,l, Nobuaki Michihata^m, Yohko Nakamura^m, Shiroh Tanoueⁿ, Rie Ibusuki^o, Sadao Suzuki^p, Takeshi Nishiyama^p, Etsuko Ozaki^q, Satomi Tomida^q,r, Kiyonori Kuriki^s, Naoyuki Takashima^q,t, Naoko Miyagawa^u,v, Sakurako Katsuura-Kamano^w, Takeshi Watanabe^w, Kenji Wakai^j, Keitaro Matsuo^k,x

Author Affiliations:

^aSchool of Nutrition and Dietetics, Faculty of Health and Social Services, Kanagawa University of Human Services, 1-10-1 Heiseicho, Yokosuka, Kanagawa 238–8522, Japan

^bSchool of Health Innovation, Kanagawa University of Human Services, Research Gate Building TONOMA-CHI2, 3-25-10 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210–0821, Japan

^cDepartment of Hygiene and Public Health, Teikyo University School of Medicine, 2-11-1 Kaga, Itabashi-ku, Tokyo 173–8605, Japan

^dCancer Prevention and Cancer Control Division, Kanagawa Cancer Center Research Institute, 2-3-2 Nakao, Asahi-ku, Yokohama, Kanagawa 241–8515, Japan

^eDepartment of Community Building for Well-being, Center for Preventive Medical Sciences, Chiba University, 1–33 Yayoicho, Inage-ku, Chiba, Chiba 263–8522, Japan

^fDepartment of Genetic Medicine, Kanagawa Cancer Center, 2-3-2 Nakao, Asahi-ku, Yokohama, Kanagawa 241–8515, Japan

^gDepartment of Psychosomatic Medicine, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812–8582, Japan

^hDepartment of Comprehensive General Internal Medicine, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812–8582, Japan

ⁱDepartment of Preventive Medicine, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga 849–8501, Japan

^jDepartment of Preventive Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466–8550, Japan

^kDivision of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, 1–1 Kanokoden, Chikusa-ku, Nagoya 464–8681, Japan

^lDepartment of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466–8550, Japan

^mCancer Prevention Center, Chiba Cancer Center Research Institute, 666-2 Nitona-cho, Chuo-ku, Chiba 260–8717, Japan

ⁿDepartment of Epidemiology and Preventive Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890–8544, Japan

^oDepartment of International Island and Community Medicine, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima 890–8544, Japan

^pDepartment of Public Health, Nagoya City University Graduate School of Medical Sciences, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya 467–8601, Japan

^qDepartment of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 602–8566, Japan

^rDepartment of Endocrine and Breast Surgery, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kawaramachi-Hirokoji, Kamigyo-ku, Kyoto 602–8566, Japan

^sLaboratory of Public Health, Division of Nutritional Sciences, School of Food and Nutritional Sciences, University of Shizuoka, 52 − 1 Yada, Suruga-ku, Shizuoka 422–8526, Japan

^tNCD Epidemiology Research Center, Shiga University of Medical Science, Seta-Tsukiwacho, Otsu, Shiga 520–2192, Japan

^uDepartment of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160–8582, Japan

^vDepartment of Public Health, Shiga University of Medical Science, Seta Tsukiwacho, Otsu 520–2192, Japan

^wDepartment of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima 770–8503, Japan

^xDepartment of Cancer Epidemiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya 466–8550, Japan

Author Email Addresses:

Kotone Tanaka: tanaka-rt8@kuhs.ac.jp

Megumi Tsubota-Utsugi: tsubota.megumi.ey@teikyo-u.ac.jp

Kanami Tsuno: k.tsuno-wm4@kuhs.ac.jp

Yasutake Tomata: toomata-5h0@kuhs.ac.jp

Kazushige Ide: ide.k@chiba-u.jp

Hiroto Narimatsu: hiroto-narimatsu@umin.org

An Tran Quyen: tranquyenan@gmail.com

Jun Otonari: jotti_song@yahoo.co.jp

Hiroaki Ikezaki: ikezaki.hiroaki.149@m.kyushu-u.ac.jp

Megumi Hara: harameg@cc.saga-u.ac.jp

Yuichiro Nishida: ynishida@cc.saga-u.ac.jp

Takashi Tamura: ttamura@med.nagoya-u.ac.jp

Mako Nagayoshi: mnagayoshi@med.nagoya-u.ac.jp

Rieko Okada: rieokada@med.nagoya-u.ac.jp

Yoko Kubo: protonk@med.nagoya-u.ac.jp

Isao Oze: i_oze@aichi-cc.jp

Hidemi Ito: hidemi@aichi-cc.jp

Nobuaki Michihata: nmichihata@chiba-cc.jp

Yohko Nakamura: ynakamur@chiba-cc.jp

Shiroh Tanoue: tanoue@m.kufm.kagoshima-u.ac.jp

Rie Ibusuki: iburie@m2.kufm.kagoshima-u.ac.jp

Sadao Suzuki: ssuzuki@med.nagoya-cu.ac.jp

Takeshi Nishiyama: psychogenomics@gmail.com

Etsuko Ozaki: ozaki@koto.kpu-m.ac.jp

Satomi Tomida: satomida@koto.kpu-m.ac.jp

Kiyonori Kuriki: kuriki@u-shizuoka-ken.ac.jp

Naoyuki Takashima: n-taka@koto.kpu-m.ac.jp

Naoko Miyagawa: naocom@belle.shiga-med.ac.jp

Sakurako Katsuura-Kamano: skamano@tokushima-u.ac.jp

Takeshi Watanabe: watanabe.takeshi.2@tokushima-u.ac.jp

Kenji Wakai: wakai@med.nagoya-u.ac.jp

Keitaro Matsuo: kmatsuo@aichi-cc.jp

Corresponding Author

Sho Nakamura

School of Health Innovation, Kanagawa University of Human Services, Research Gate Building TONOMA-CHI2, 3-25-10 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210–0821, Japan

research@nakasho.org

Phone: +81-46-828-2813

Abstract

Background

Although nutrient intake has been associated with mortality, little is known about the effect of nutritional balance in the diet on mortality. We evaluated the association between a nutritionally balanced diet and all-cause, cancer, and cardiovascular disease (CVD) mortality in a Japanese cohort. We hypothesized that participants with nutritionally adequate diets would have lower risks of all-cause, cancer, and CVD mortality.

Methods

In the longitudinal Japan Multi-Institutional Collaborative Cohort Study, we included 65,407 participants (mean age: 55.0 years; 52.0% women) without cancer and CVD at baseline. The nutritional adequacy score, obtained using a validated food frequency questionnaire, was calculated based on the intake of eight beneficial nutrients and two nutrients to be restricted. A Cox proportional hazard model was used to estimate the hazard ratios and 95% confidence intervals of each quintile of the nutritional adequacy score, in relation to all-cause and cause-specific mortality, using the first quintile as reference.

Results

After adjusting for age and other potential confounding factors, the nutritional adequacy score was linearly and significantly associated with lower all-cause, cancer, and CVD mortality risks in men (p < 0.001, p = 0.001, p = 0.04, respectively); no association was found in women (p = 0.74, p = 0.50, p = 0.96, respectively). Results of the subgroup analyses that excluded participants who died within 3 years of baseline and those with disease remained largely unchanged from the main results. In the Japanese population, a more nutritionally balanced diet was significantly linearly associated with a decreased all-cause mortality risk in men.

Conclusions

These findings may facilitate the establishment of dietary recommendations for enhancing life expectancy in Asia.

Keywords:

diet quality

nutrient intake

nutrient density

nutritional adequacy

mortality

Background

According to the Global Burden of Diseases, Injuries, and Risk Factors Study, many noncommunicable diseases are largely attributable to modifiable lifestyle-related risk factors, and diet is expected to contribute substantially to decreasing their incidence and related mortality [1]. Each country’s dietary reference intakes provide evidence-based nutritional requirements specific to its context as a guide for the adequate intake of each nutrient to prevent diseases and promote health [2–4].

Traditionally, studies in the nutritional epidemiology field have focused on the intake of individual nutrients that increase/decrease disease or mortality rates (e.g., the excessive intake of saturated fatty acids increases the risk of cardiovascular disease [CVD]) [5]). The diet is a complex combination of various nutrients that contribute to human health in additive and synergistic ways [4]. Therefore, examining the relationship between nutritional balance in the overall diet and health outcomes is important. However, epidemiological evidence for the association between nutritional balance in the overall diet and all-cause/cause-specific mortality is limited.

Nutrient density–based indicators are widely used to determine the nutritional balance of the overall diet. For instance, Cano-Ibáñez et al. examined lifestyle factors associated with nutritional balance in the overall diet using nutrient density–based indicators [6]. In another study, Murakami et al. compared nutrient density–based indices between Japanese and American populations to examine differences in nutrient intake trends [7]. However, in these studies, it was assumed that adequate overall nutrient sufficiency indicates good health without confirming whether overall sufficiency contributes to actual health outcomes. Only one study examined the association between the intake of several nutrients and all-cause mortality in 20,602 participants from the American general population [8]; however, the researchers examined individual nutrients and did not evaluate the diet as a whole.

We hypothesized that participants with nutritionally adequate diets would have a lower risk of all-cause, cancer, and CVD mortality. Therefore, in the present study, we examined the association between nutritional balance in the overall diet and all-cause, cancer, and CVD mortality risks in the general population aged 35–69 years in Japan. The present study is expected to advance the field of nutritional epidemiology by providing evidence on whether a nutritionally balanced diet—evaluated as a whole rather than as individual nutrients—is associated with long-term mortality outcomes.

By focusing on nutrient adequacy at the overall dietary level and its relationship to cause-specific mortality, the findings may support the development of more comprehensive dietary guidelines and public health policies that emphasize balanced nutrient intake patterns, not merely the avoidance or promotion of single nutrients.

Methods

Study design and population

The Japan Multi-Institutional Collaborative Cohort (J-MICC) study (dataset version 20220807) was conducted between February 2004 and March 2014 [9]. The J-MICC study involved the collection and analysis of genetic and clinical data from the general Japanese population to detect and confirm gene–environment interactions related to lifestyle-associated diseases. The age range of this cohort was 35–69 years, and participants were enrolled upon responding to study announcements, attending health checkup examinations commissioned by their local governments, visiting local health checkup centers, or visiting a cancer center. The details of the J-MICC study have been previously described [10].

Figure 1 shows a flowchart of the selection of participants for the present study. From the 13 research sites, 92,525 individuals were recruited for the J-MICC study. Among the research sites, one where the general population was not surveyed (n = 9,101) and two with < 11 years of follow-up data were excluded (n = 6,938). From a total of 76,486 individuals at 10 sites, 4405 were excluded for the following reasons: incomplete responses on food intake (n = 219), extreme levels of total energy intake (2.5% above or below the range for all participants; n = 3,794), lost to follow-up or a follow-up period of 0 d (n = 372), and a diagnosis of cancer (n = 5,154) or CVD (n = 1,880) at baseline. Finally, 65,047 participants (52.0% women; mean age 55.0 years) were included in the present study.

Fig. 1

Participant flowchart.

Dietary assessment

A validated food frequency questionnaire (FFQ) was used to assess the average intake of 46 foods over the previous year at baseline. The details have been discussed in a previous paper [11–15]. In brief, participants were asked to indicate how often they had consumed given amounts of each food on average during the previous year. Response options for each item were divided into eight categories ranging from “never/rarely” to “three or more times per day.” The options for staple foods and alcohol were categorized into six groups ranging from “never/rarely” to “every day;” for these items, a standard portion size was given, and the participants were asked to specify their portion size (e.g., one cup or one slice of bread) relative to the standard. The daily nutrient intakes were calculated by multiplying the daily consumption frequency of each food by the nutrient content of the selected portion size and summing these values for all foods. The energy and nutrient intakes from the FFQ were calculated according to the Japanese food composition tables (fourth edition) [16, 17].

Nutritional adequacy score

The nutritional adequacy score was defined as the percentage of the daily actual intake relative to the Dietary Reference Intakes for Japanese (2020) [18]. The nutritional adequacy score comprises eight beneficial nutrients (protein; fiber; vitamins A, C, and E; potassium; iron; and calcium) and two nutrients that should be restricted (saturated fatty acids and sodium), based on the Nutrient Rich Food Index 9.3 [19–21], which has been associated with all-cause mortality [22]. The intakes of magnesium (which was not calculated in the J-MICC study) and added sugar (which is not listed in the Dietary Reference Intakes for Japanese [2020]) [18] were excluded.

The nutritional adequacy score was calculated as follows.

nutritional adequacy score = (∑nutritional adequacy of eight beneficial nutrients) − (∑nutritional adequacy of two nutrients to be restricted) (1)

The density of each nutrient was calculated using the following equation:

(nutritional adequacy of each nutrient component) = (daily intake value) / (age–sex–specified daily reference value) ×100% (2)

Each daily intake value was standardized according to the daily energy requirement using the density method, except for saturated fatty acid intake (for which an energy percentage standard was adopted). According to the Recommended Dietary Allowance in the Dietary Reference Intakes for Japanese (2020) [18], dietary reference values were adopted for each sex and age group based on normal physical activity levels (Supplementary Table 1). Moreover, the percentage of the dietary reference values for each nutrient was capped at 100% to avoid overestimating the high intake of a single nutrient. The total nutritional adequacy score ranged from − 200 to 800, with a higher score indicating a better nutritional balance in the overall diet.

Following this procedure, participants were divided into quintiles according to their nutritional adequacy score, with the lowest quintile used as the reference category. Calculation methods similar to those used in the present study have been previously reported [23–30].

Follow-up and outcomes

Information on the residence [27–30] and vital status of the participants was obtained from residential registers from the baseline survey (February 2004 to March 2013) to December 2019 (eight sites) and December 2020 (two sites). The cause of death was confirmed by means of death certificates and classified using the International Classification of Diseases 10th Revision (ICD-10).

The primary endpoint was mortality from all causes, cancer (ICD-10: C00–D48), and CVD (ICD-10: I00–I99). Each participant was followed up until the occurrence of one of the following censoring events: death, loss to follow-up owing to relocation from the study area, or the end of the follow-up period. As all sites were followed up for at least 11 years, 11 years was defined as the follow-up period to align the number of follow-up years at each site.

Covariates

All data were obtained using self-administered questionnaires. Based on previous studies, we selected the following potential confounding variables: age, sex, data collection site, body mass index (body mass index [BMI]; <18.5 kg/m², 18.5 to < 25 kg/m², 25 to < 30 kg/m², and > 30 kg/m²), smoking status (never smoked, currently smoking, and smoked in the past), alcohol consumption status (currently consuming alcohol, consumed alcohol in the past, and not consuming alcohol), physical activity (estimated based on the International Physical Activity Questionnaire [10, 31]; moderate-to-vigorous physical activity scores), stroke (defined as a history of stroke), hypertension (defined as medication use, a history of hypertension, or both), diabetes (defined as medication use, a history of diabetes, or both), dyslipidemia (defined as medication use, a history of hypercholesterolemia, or both), educational attainment (lower than high school graduate, university graduate or above, and others), vitamin supplement intake (defined as using vitamin, calcium, or mineral supplements at least once per week for at least 1 year), and total energy intake.

Statistical analyses

All analyses were performed based on sex owing to the different distributions of dietary intake and lifestyle characteristics. Descriptive statistics in Tables 1 and 2 are presented as means ± standard deviations for continuous variables and numbers with corresponding percentages for categorical variables. Multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between the quintiles of the nutritional adequacy score and risk of mortality from all causes, cancer, and CVD were calculated using a Cox regression model. The proportional hazard assumption was assessed by calculating scaled Schoenfeld residuals, which did not reveal any violations. The analysis based on quintiles was conducted to examine the linear trends in the associations.

Table 1

Participant characteristics according to quintiles of nutritional adequacy score in Japanese men: the J-MICC study
Characteristics	Nutritional adequacy score quintile
	Q1	Q2	Q3	Q4	Q5
Age at baseline (years), mean (SD)	55.0 (9.3)	55.0 (9.3)	54.9 (9.5)	55.5 (9.4)	57.1 (9.0)
Body mass index, n (%)
< 18.5 kg/m²	3510 (64.4)	3622 (66.4)	3601 (66.1)	3722 (68.3)	3732 (68.5)
18.5 to < 25.0 kg/m²	132 (2.4)	140 (2.6)	125 (2.3)	122 (2.2)	113 (2.1)
25.0 to < 30.0 kg/m²	1575 (28.9)	1515 (27.8)	1553 (28.5)	1445 (26.5)	1476 (27.1)
> 30.0 kg/m²	232 (4.3)	173 (3.2)	171 (3.1)	159 (2.9)	129 (2.4)
Missing	2 (0.0)	1 (0.0)	1 (0.0)	3 (0.1)	1 (0.0)
Smoking status, n (%)
Never smoked	1408 (25.8)	1600 (29.4)	1599 (29.3)	1696 (31.1)	1753 (32.2)
Currently smoking	1972 (36.2)	1792 (32.9)	1737 (31.9)	1526 (28.0)	1257 (23.1)
Smoked in the past	2065 (37.9)	2056 (37.7)	2113 (38.8)	2226 (40.8)	2436 (44.7)
Missing	6 (0.1)	3 (0.1)	2 (0.0)	3 (0.1)	5 (0.1)
Alcohol consumption status, n (%)
Currently consuming alcohol	4307 (79.0)	4253 (78.0)	4187 (76.8)	4156 (76.2)	4018 (73.7)
Consumed alcohol in the past	163 (3.0)	137 (2.5)	166 (3.0)	174 (3.2)	237 (4.3)
Not consuming alcohol	976 (17.9)	1057 (19.4)	1094 (20.1)	1115 (20.5)	1188 (21.8)
Missing	5 (0.1)	4 (0.1)	4 (0.1)	6 (0.1)	8 (0.1)
Physical activity (MVPA min/day), mean (SD)	15.7 (14.8)	14.5 (13.7)	14.0 (13.6)	13.5 (12.7)	14.1 (13.0)
Stroke, n (%)	112 (2.1)	98 (1.8)	100 (1.8)	119 (2.2)	149 (2.7)
Missing	3 (0.1)	8 (0.1)	5 (0.1)	3 (0.1)	5 (0.1)
Hypertension, n (%)	1340 (24.6)	1300 (23.8)	1204 (22.1)	1263 (23.2)	1308 (24.0)
Missing	7 (0.1)	7 (0.1)	8 (0.1)	6 (0.1)	5 (0.1)
Diabetes, n (%)	460 (8.4)	425 (7.8)	434 (8.0)	522 (9.6)	544 (10.0)
Missing	6 (0.1)	5 (0.1)	4 (0.1)	8 (0.1)	3 (0.1)
Dyslipidemia, n (%)	822 (15.1)	835 (15.3)	871 (16.0)	991 (18.2)	965 (17.7)
Missing	10 (0.2)	10 (0.2)	8 (0.1)	11 (0.2)	4 (0.1)
Educational attainment, n (%)
Lower than high school graduate	2293 (42.1)	2101 (38.5)	1933 (35.5)	1770 (32.5)	1703 (31.2)
University graduate or above	1295 (23.8)	1534 (28.1)	1771 (32.5)	1972 (36.2)	1858 (34.1)
Other	498 (9.1)	485 (8.9)	464 (8.5)	407 (7.5)	396 (7.3)
Missing	1365 (25.0)	1331 (24.4)	1283 (23.5)	1302 (23.9)	1494 (27.4)
Vitamin supplement intake, n (%)	480 (8.8)	577 (10.6)	624 (11.4)	690 (12.7)	827 (15.2)
Missing	12 (0.2)	11 (0.2)	8 (0.1)	6 (0.1)	10 (0.2)
Total energy intake (kcal/day), mean (SD)	2010.5 (260.2)	1893.4 (260.3)	1831.1 (270.5)	1792.8 (276.6)	1842.2 (259.5)
Staple foods (g/day), mean (SD)	578.3 (132.8)	529.4 (125.1)	500.4 (124.1)	478.5 (127.8)	503.3 (118.7)
Potatoes (g/day), mean (SD)	8.5 (6.8)	10.9 (8.5)	12.8 (9.5)	15.8 (11.7)	22.1 (17.0)
Confectionery (g/day), mean (SD)	13.1 (16.1)	14.1 (14.2)	14.9 (13.7)	16.1 (15.1)	17.3 (17.3)
Fats and oils (g/day), mean (SD)	12.8 (7.8)	14.0 (8.1)	14.7 (8.2)	15.5 (8.7)	16.2 (9.5)
Beans (g/day), mean (SD)	43.3 (27.0)	51.3 (29.2)	54.0 (29.1)	59.7 (32.2)	73.5 (38.9)
Fish and seafood (g/day), mean (SD)	42.6 (26.2)	48.7 (26.1)	52.7 (28.7)	56.7 (30.7)	61.8 (31.7)
Meats (g/day), mean (SD)	34.1 (21.6)	36.9 (21.5)	37.4 (20.6)	38.2 (22.1)	35.5 (20.0)
Eggs (g/day), mean (SD)	17.7 (14.3)	19.0 (14.0)	19.2 (13.7)	20.3 (14.3)	21.2 (15.1)
Dairy (g/day), mean (SD)	68.8 (91.6)	82.2 (95.0)	100.5 (101.8)	111.5 (101.1)	112.7 (90.6)
Green and yellow vegetables (g/day), mean (SD)	28.6 (15.6)	40.2 (19.5)	51.0 (24.6)	67.4 (34.0)	96.8 (50.0)
Other vegetables (g/day), mean (SD)	33.9 (19.5)	43.3 (22.5)	50.8 (24.8)	62.6 (31.7)	84.6 (44.1)
Fruits (g/day), mean (SD)	23.9 (27.1)	33.8 (33.5)	42.4 (38.5)	54.0 (46.8)	73.5 (57.2)
Mushrooms (g/day), mean (SD)	3.3 (3.0)	4.3 (3.7)	5.1 (4.2)	6.6 (5.3)	9.7 (7.3)
Seaweeds (g/day), mean (SD)	0.8 (0.9)	1.1 (1.1)	1.3 (1.3)	1.7 (1.5)	2.5 (1.9)
MVPA, moderate-to-vigorous physical activity; SD, standard deviation.

Table 2

Participant characteristics according to quintiles of nutritional adequacy score in Japanese women: the J-MICC study
Characteristics	Nutritional adequacy score quintile
	Q1	Q2	Q3	Q4	Q5
Age at baseline (years), mean (SD)	52.2 (9.7)	53.6 (9.4)	54.4 (9.2)	56.0 (9.0)	57.4 (8.5)
Body mass index, n (%)
< 18.5 kg/m²	5267 (69.7)	5496 (72.7)	5546 (73.4)	5555 (73.5)	5541 (73.3)
18.5 to < 25.0 kg/m²	696 (9.2)	635 (8.4)	649 (8.6)	625 (8.3)	685 (9.1)
25.0 to < 30.0 kg/m²	1312 (17.4)	1204 (15.9)	1195 (15.8)	1180 (15.6)	1164 (15.4)
> 30.0 kg/m²	277 (3.7)	213 (2.8)	164 (2.2)	187 (2.5)	161 (2.1)
Missing	7 (0.1)	10 (0.1)	4 (0.1)	11 (0.1)	8 (0.1)
Smoking status, n (%)
Never smoked	6140 (81.2)	6420 (84.9)	6599 (87.3)	6699 (88.6)	6856 (90.7)
Currently smoking	785 (10.4)	586 (7.8)	486 (6.4)	385 (5.1)	291 (3.8)
Smoked in the past	629 (8.3)	546 (7.2)	468 (6.2)	468 (6.2)	404 (5.3)
Missing	5 (0.1)	6 (0.1)	5 (0.1)	6 (0.1)	8 (0.1)
Alcohol consumption status, n (%)
Currently consuming alcohol	2980 (39.4)	2892 (38.3)	2816 (37.3)	2748 (36.4)	2431 (32.2)
Consumed alcohol in the past	165 (2.2)	120 (1.6)	116 (1.5)	122 (1.6)	140 (1.9)
Not consuming alcohol	4407 (58.3)	4538 (60.0)	4618 (61.1)	4679 (61.9)	4974 (65.8)
Missing	7 (0.1)	8 (0.1)	8 (0.1)	9 (0.1)	14 (0.2)
Physical activity (MVPA min/day), mean (SD)	14.3 (12.9)	13.8 (12.1)	14.1 (12.0)	14.3 (11.8)	14.6 (12.1)
Stroke, n (%)	92 (1.2)	75 (1.0)	54 (0.7)	93 (1.2)	96 (1.3)
Missing	4 (0.1)	8 (0.1)	11 (0.1)	4 (0.1)	10 (0.1)
Hypertension, n (%)	1115 (14.8)	1120 (14.8)	1158 (15.3)	1260 (16.7)	1374 (18.2)
Missing	10 (0.1)	18 (0.2)	4 (0.1)	16 (0.2)	19 (0.3)
Diabetes, n (%)	232 (3.1)	200 (2.6)	215 (2.8)	269 (3.6)	319 (4.2)
Missing	13 (0.2)	12 (0.2)	3 (0.0)	12 (0.2)	12 (0.2)
Dyslipidemia, n (%)	1025 (13.6)	1136 (15.0)	1241 (16.4)	1394 (18.4)	1531 (20.3)
Missing	16 (0.2)	18 (0.2)	15 (0.2)	22 (0.3)	29 (0.4)
Educational attainment, n (%)
Lower than high school graduate	3150 (41.7)	3047 (40.3)	2811 (37.2)	2715 (35.9)	2696 (35.7)
University graduate or above	622 (8.2)	674 (8.9)	807 (10.7)	750 (9.9)	746 (9.9)
Other	1851 (24.5)	1908 (25.2)	1902 (25.2)	1807 (23.9)	1715 (22.7)
Missing	1936 (25.6)	1929 (25.5)	2038 (27.0)	2286 (30.2)	2402 (31.8)
Vitamin supplement intake, n (%)	1114 (14.7)	1159 (15.3)	1300 (17.2)	1452 (19.2)	1482 (19.6)
Missing	15 (0.2)	20 (0.3)	16 (0.2)	24 (0.3)	19 (0.3)
Total energy intake (kcal/day), mean (SD)	1614.8 (235.9)	1538.9 (198.4)	1529.9 (203.8)	1505.8 (213.5)	1568.0 (203.4)
Staple foods (g/day), mean (SD)	409.1 (102.0)	373.9 (82.7)	361.1 (84.1)	340.4 (91.7)	377.6 (84.2)
Potatoes (g/day), mean (SD)	12.8 (9.7)	15.9 (11.2)	19.5 (13.1)	24.8 (17.2)	28.7 (19.9)
Confectionery (g/day), mean (SD)	22.7 (24.4)	21.4 (19.1)	22.0 (18.7)	22.7 (19.8)	25.3 (22.6)
Fats and oils (g/day), mean (SD)	14.8 (8.2)	15.3 (8.0)	16.0 (8.2)	16.9 (9.4)	16.8 (9.7)
Beans (g/day), mean (SD)	45.0 (27.6)	53.6 (28.9)	59.0 (31.3)	73.5 (40.2)	74.0 (38.1)
Fish and seafood (g/day), mean (SD)	41.2 (23.1)	47.1 (25.4)	50.2 (25.0)	57.5 (30.4)	56.6 (27.6)
Meats (g/day), mean (SD)	40.5 (22.8)	39.9 (22.1)	40.7 (21.8)	41.4 (23.7)	37.2 (23.7)
Eggs (g/day), mean (SD)	18.6 (13.6)	19.9 (13.3)	20.5 (13.2)	21.4 (14.4)	20.3 (14.1)
Dairy (g/day), mean (SD)	103.3 (108.6)	128.0 (106.9)	144.3 (108.2)	159.7 (110.0)	147.5 (105.9)
Green and yellow vegetables (g/day), mean (SD)	42.3 (20.8)	58.2 (26.2)	73.5 (32.1)	102.0 (51.7)	122.2 (59.7)
Other vegetables (g/day), mean (SD)	48.9 (24.0)	61.4 (27.5)	73.2 (31.3)	94.1 (45.3)	108.8 (52.2)
Fruits (g/day), mean (SD)	36.4 (38.4)	54.9 (46.5)	70.6 (55.3)	92.0 (68.0)	105.2 (75.0)
Mushrooms (g/day), mean (SD)	5.6 (4.6)	7.1 (5.1)	8.9 (5.9)	12.3 (8.1)	13.8 (8.8)
Seaweeds (g/day), mean (SD)	1.2 (1.2)	1.5 (1.3)	1.9 (1.5)	2.7 (2.1)	3.1 (2.2)
MVPA, moderate-to-vigorous physical activity; SD, standard deviation.

Table 3

Association between nutritional adequacy score and all-cause, cancer, and cardiovascular disease mortality among Japanese adults: the J-MICC Study

Parameter

Nutritional adequacy score quartile

p-trend

Men

Nutritional adequacy score, median (IQR)

513 (489–528)

555 (548–562)

578 (573–582)

595 (590–599)

615 (609–625)

No. of participants, n

5451

Person-years, mean (SD)

9.5 (2.4)

9.5 (2.5)

9.6 (2.4)

9.7 (2.3)

All-cause mortality

No. of deaths, n

313

293

254

255

253

Model 1 ^a

1.00 (ref)

0.93 (0.79, 1.09)

0.79 (0.67, 0.94)

0.76 (0.65, 0.90)

0.67 (0.57, 0.79)

< 0.001

Model 2 ^b

1.00 (ref)

0.93 (0.79, 1.10)

0.79 (0.67, 0.95)

0.75 (0.65, 0.91)

0.66 (0.57, 0.80)

< 0.001

Cancer

No. of deaths, n

165

145

141

140

Model 1 ^a

1.00 (ref)

0.87 (0.69, 1.08)

0.83 (0.66, 1.04)

0.79 (0.63, 0.99)

0.69 (0.55, 0.86)

0.001

Model 2 ^b

1.00 (ref)

0.89 (0.71, 1.11)

0.85 (0.68, 1.07)

0.82 (0.65, 1.04)

0.72 (0.57, 0.91)

0.001

Cardiovascular disease

No. of deaths, n

Model 1 ^a

1.00 (ref)

1.04 (0.70, 1.54)

0.74 (0.48, 1.15)

0.92 (0.61, 1.38)

0.61 (0.39, 0.95)

0.03

Model 2 ^b

1.00 (ref)

1.07 (0.71, 1.60)

0.76 (0.49, 1.19)

0.94 (0.61, 1.42)

0.61 (0.39, 0.96)

0.04

Women

Nutritional adequacy score, median (IQR)

548 (529–559)

578 (573–582)

593 (590–596)

604 (600–607)

620 (615–627)

No. of participants, n

7559

7558

7559

Person-years, mean (SD)

9.5 (2.4)

9.6 (2.3)

9.7 (2.3)

9.8 (2.3)

9.8 (2.2)

All-cause mortality

No. of deaths, n

149

162

156

142

185

Model 1 ^a

1.00 (ref)

0.98 (0.78, 1.22)

0.88 (0.70, 1.10)

0.71 (0.57, 0.90)

0.84 (0.68, 1.05)

0.59

Model 2 ^b

1.00 (ref)

1.00 (0.80, 1.25)

0.92 (0.73, 1.15)

0.73 (0.58, 0.93)

0.87 (0.70, 1.09)

0.74

Cancer

No. of deaths, n

Model 1 ^a

1.00 (ref)

1.01 (0.74, 1.37)

0.91 (0.67, 1.25)

0.83 (0.61, 1.14)

0.85 (0.63, 1.16)

0.46

Model 2 ^b

1.00 (ref)

1.02 (0.75, 1.39)

0.93 (0.68, 1.27)

0.84 (0.61, 1.15)

0.86 (0.63, 1.17)

0.50

Cardiovascular disease

No. of deaths, n

Model 1 ^a

1.00 (ref)

1.07 (0.63, 1.83)

0.94 (0.54, 1.61)

0.70 (0.40, 1.23)

0.88 (0.52, 1.49)

0.87

Model 2 ^b

1.00 (ref)

1.10 (0.64, 1.88)

1.00 (0.58, 1.73)

0.72 (0.40, 1.28)

0.96 (0.57, 1.64)

0.96

Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals for all-cause, cancer, and CVD mortality across quintiles of the nutritional adequacy score. Model 1 was adjusted for age, sex, and study area; Model 2 was additionally adjusted for body mass index; smoking status; physical activity; alcohol consumption; histories of stroke, hypertension, diabetes, and dyslipidemia; educational attainment; vitamin supplement use; and total energy intake. p for trend was calculated by modeling the median score in each quintile as a continuous variable.

IQR, interquartile range; SD, standard deviation.

CVD, cardiovascular disease.

Additional files

File name: Supplementary_Material

File format: .pdf

Title of data: Supplementary Tables and Nutrient Reference Values

Description of data:

This file includes:

Supplementary Table 1: Daily reference values and recommended intake values for individual nutrients used in the nutritional adequacy score calculation.

Supplementary Table 2: Associations between nutritional adequacy score and all-cause, cancer, and cardiovascular disease mortality excluding early deaths.

Supplementary Table 3: Associations between nutritional adequacy score and all-cause, cancer, and cardiovascular disease mortality excluding participants with baseline diseases.

These tables provide detailed statistical outputs supporting the findings in the main manuscript.

We adjusted for the following potential confounding variables: in Model 1, age, sex, and the data collection site; in Model 2, the variables adjusted for in Model 1 as well as BMI, smoking status, physical activity, alcohol consumption status, stroke, hypertension, diabetes, dyslipidemia, educational attainment, vitamin supplement intake, and total energy intake. p-values for linear trends were calculated using regression models by assigning the median intake value in each nutritional adequacy score category as a continuous variable. Sensitivity analyses were performed, in which we excluded a) participants who died within 3 years of baseline to consider causal reversal and b) participants with disease (stroke, hypertension, diabetes, and dyslipidemia) at baseline to consider the possibility that behavioral changes may have occurred due to disease morbidity.

Possible interactions were tested by introducing a multiplicative term into the main-effects model. We subsequently performed additional subgroup analyses according to the risk factors for CVD, that is, overweight (cut-off BMI, 27 kg/m²) and the absence/presence of hypertension, diabetes, and dyslipidemia, to evaluate the varied effects of overall nutrient balance on mortality risk related to potential risk factors. Multiple imputations were conducted using linear regression (20 times, 10 iterations) to account for the missing data that were assumed to be missing at random. All the variables used in this analysis were included in each imputation model as predictor variables. The estimated parameters were combined using Rubin’s combination method [32]. We assessed for multicollinearity among the explanatory variables and found no issues with the correlation coefficients or variance inflation factors.

All statistical analyses were performed using R version 4.2 (R Foundation for Statistical Computing, Vienna, Austria). The statistical significance level was set at a two-tailed p < 0.05.

Ethical approval

All participants provided written informed consent.

The J-MICC study protocol was approved by the Ethics Committees of Aichi Cancer Center and other participating institutions.

The present study’s protocol was approved by the Ethics Committee of Kanagawa Cancer Center (approval number: 28KEN-36).

All the study procedures adhered to the principles of the Declaration of Helsinki.

Results

During a median follow-up period of 9.6 years, a total of 1368 men and 794 women died; the causes of death were cancer in 732 men (2.7%) and 424 women (1.1%) and CVD in 214 men (0.8%) and 141 women (0.4%).

Baseline characteristics across the quintiles of the nutritional adequacy score are summarized in Table 1 for men and Table 2 for women. No marked differences were determined in age by quintile of the nutritional adequacy score among men, whereas women with higher nutritional adequacy scores were older. Those with higher nutritional adequacy scores were more likely to have never smoked or consumed alcohol. The total energy and staple food intakes of both men and women decreased as their nutritional adequacy score increased from Q1 to Q4, but these intakes increased again in Q5.

Table 3 summarizes the results of the multivariable model (adjusted HR [95% CI]) for all-cause, cancer, and CVD mortality according to the nutritional adequacy score quintile. A higher nutritional adequacy score quintile was significantly associated with a lower risk of all-cause mortality in a dose-dependent manner in men (p-trend < 0.001). However, the nutritional adequacy score was not significantly associated with all-cause mortality in women (p-trend = 0.74). In men, the nutritional adequacy score was inversely and dose-dependently associated with cancer and CVD mortality risk, whereas in women, it was not associated with either cancer or CVD mortality risk.

The results of the sensitivity analyses that excluded participants who died within 3 years of baseline (Supplementary Table 2) and those with disease (Supplementary Table 3) did not differ from the main results. The results also remained unchanged in the stratified analyses according to disease status (data not shown).

Discussion

In this prospective population-based study conducted among Japanese adults, better nutritional balance in the overall diet was associated with a lower risk of all-cause, cancer, and CVD mortality in men but not in women. Thus, our hypothesis was partially supported. These findings highlight the importance of considering sex-specific differences in dietary impacts on long-term health outcomes.

To the best of our knowledge, this was the first study to examine the association of the nutritional balance in the overall diet with mortality risk in Japanese participants. In previous studies, researchers have examined the relationship between the intake of individual nutrients and mortality, which required the analysis of each nutrient’s specific impact. Conversely, in the present study, we evaluated the intake sufficiency of multiple nutrients as a single indicator, allowing for an analysis that considers the interactions between nutrients and the importance of overall balance.

Considering nutritional balance in the overall diet as a single indicator simplifies the interpretation of the results and renders the findings more practically applicable.

The researchers of only one previous study employed nutritional adequacy scores and reported findings that were consistent with ours. In that study, 20,602 American individuals from the general population were followed, and the association between the intake of several nutrients and all-cause mortality was examined. The authors found that intakes of vitamin E, magnesium, iron, dietary fiber, potassium, and essential amino acids that met the Recommended Dietary Allowance (percentage of Recommended Dietary Allowance) or Adequate Intake (percentage of Adequate Intake) were associated with a lower risk of all-cause mortality [8]. These nutrients constitute components of the nutritional adequacy score used in the present study. Another previous study has revealed that a higher intake of nutrient-dense foods, calculated using nutrient profiling, is associated with a lower risk of all-cause but not CVD mortality [22]. Although the researchers in the previous studies did not examine mortality risk by sex and used a different method to calculate nutritional sufficiency, making direct comparisons impossible, the findings of the present study support the conclusion that a well-balanced nutrient intake reduces the risk of all-cause mortality in men. Nevertheless, further studies are required to determine whether a well-balanced nutrient intake reduces the risk of cancer and CVD mortality.

However, the significant associations found in our study may have been influenced by several nutrients that constitute the nutritional adequacy score. For instance, antioxidants such as vitamins A [33] and E [34] inhibit the generation of reactive oxygen species and free radicals, which may help prevent the development of cancer [35] and CVD [36]. Similarly, adequate potassium and reduced salt intakes have blood pressure–lowering effects and reduce the incidence of CVD and death [37]. Dietary fiber reduces CVD-related, adiposity-related, and all-cause mortality risks by lowering cholesterol and blood pressure, exerting anti-inflammatory effects, and improving insulin resistance [38]. Furthermore, saturated fat intake is a risk factor for CVD [38]. Therefore, the results of the present study may include the combined effects of these factors. Moreover, we found that a higher nutritional adequacy score was associated with a higher intake of vegetables, fruit, and legumes. The authors of several previous studies have reported that a higher intake of fruit and vegetables [39–42] or dietary protein [43, 44], such as legumes [41], is associated with all-cause, cancer, and CVD mortality risks. Thus, higher nutritional adequacy scores may be associated with higher intakes of fruit and vegetables as well as plant proteins, which may have influenced the findings on the lower risks of all-cause, cancer, and CVD mortality.

We found no association between the nutritional adequacy score and risk for all-cause and cause-specific mortality among women, suggesting that mortality risk may differ between men and women. One possible explanation is the different distributions of nutritional adequacy scores between men and women. Previous studies have revealed that women tend to have better nutritional adequacy in their diet [6], which aligns with the findings of the present study. Our results revealed that men had lower average nutritional adequacy scores and greater variability (larger standard deviations) in nutritional adequacy than did women. This suggests that women may have smaller interindividual differences in nutritional adequacy than do men. Other factors, such as physiological differences, disease history, and healthcare outcomes, may also play a role [45]. However, the specific mechanisms underlying the sex-related differences in the relationship between nutritional adequacy and mortality remain unknown and require further investigation.

The present study had certain limitations. First, the intakes of magnesium and added sugar were not included in the nutritional adequacy score [19–21]. Second, owing to the use of an FFQ, the calculated absolute nutrient intake may not be accurate [11–15]. As described in previous studies, the FFQ has the disadvantage of underestimation. However, we standardized each daily intake value—except for that of saturated fatty acids—according to the daily energy requirement. Additionally, the nutritional adequacy scores were classified into relative quintiles rather than absolute values. Thus, the underestimation of nutrient intake due to the characteristics of the FFQ is unlikely to have had a significant influence on the results of our study. Third, although we adjusted for multiple potential confounding factors, we could not eliminate residual confounders, such as socioeconomic status [46] and renal disease [47]. Finally, while the association between diet and cancer has been reported to vary by cancer type [48], the detailed association between death and the cause of death was unknown in the present study.

In conclusion, men with a better balance in nutrient intake had a lower risk of all-cause, cancer, and CVD mortality; however, no such association was found in women. Our findings may contribute to the formulation of dietary recommendations designed to enhance life expectancy in Asia. Further research is required on dietary nutritional balance and its association with health outcomes other than mortality.

List of Abbreviations

BMI

Body Mass Index

Confidence Interval

CVD

Cardiovascular Disease

FFQ

Food Frequency Questionnaire

Hazard Ratio

ICD-10

International Classification of Diseases, 10th Revision

J-MICC

Japan Multi-Institutional Collaborative Cohort

List of Supporting Information

Declarations

Ethics approval and consent to participate

All participants provided written informed consent. The study was approved by the Ethics Committee of Kanagawa Cancer Center (approval number: 28KEN-36).

Consent for publication

Not applicable.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by Grants-in-Aid for Scientific Research for Priority Areas of Cancer (grant number 17015018) and Innovative Areas (grant number 221S0001) as well as the Japan Society for the Promotion of Science KAKENHI Grant (grant numbers 16H06277, 22H04923 [CoBiA], and 22K02116) from the Japanese Ministry of Education, Culture, Sports, Science and Technology. The funding sources had no role in the study design; data collection, analysis, or interpretation; manuscript writing; or decision to submit the manuscript for publication.

Author Contribution

Conceptualization: KT, MT, and SN; Resources: JO, HI, MG, YN, TT, MN, RO, YK, IO, HI, MN, NY, ST, RI, EO, ST, KK, NT, NM, SKK, TW, KW, and KM; Methodology: KT, MT, and SN; Data curation: KT, MT, and SN; Writing-original draft: KT, MT, and SN; Writing-review and editing: KT, MT, KI, YT, AQ, and HN. All the authors reviewed and approved the final version of the manuscript to be published.

Acknowledgements

The authors have no acknowledgments to declare.

Clinical trial number

Not applicable.

Electronic Supplementary Material

Below is the link to the electronic supplementary material

Supplementary Material 1

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