Number 64 (June, 2021)

Health andNutrition News

No.64

Contents
The application of AI in health and nutrition ･････････････････････ 2

Nutrient and food group intakes and skeletal muscle index (SMI)
among the Japanese elderly: a secondary analysis of
the National Health and Nutrition Survey, 2017 ･･･････････････････ 3
Hidemi Takimoto
Department of Nutritional Epidemiology and Shokuiku

Effective coverage of medical treatment for hypertension,
diabetes, and dyslipidemia ･････････････････････････････････････ 4
Nayu Ikeda

Section of Population Health Metrics, International Center for Nutrition and Information

Yuko Tousen
Section of Food Safety and Function, Department of Food Function and Labelingo .6 4

A study on the quality evaluation of health foods
with functional claims ･････････････････････････････････････････ 5

Health and Nutrition News N

Michihiro Araki
AI Nutrition Research teamRole of the Brain in the Regulation of Growth and Glucose Metabolism
Takanori Hayashi
Section of Nutritional Therapy, Department of Clinical Nutrition･･･････････････ 6

Does breakfast skipping induce postprandial glucose excursion
in lunch? ･････････････････････････････････････････････････････ 7
Yoichi Hatamoto
Section of Energy Metabolism, Department of Nutrition and Metabolism

A U-Shaped Relationship between the Prevalence of Frailty
and Body Mass Index in Community-Dwelling Japanese Older
Adults: The Kyoto–Kameoka Study ････････････････････････････ 8
Daiki Watanabe
Section of Healthy Longevity Research, Department of Physical Activity Research

National Institutes of Biomedical Innovation, Health and Nutrition

National Institute of Health and Nutrition

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The application of AI in health and nutrition
Michihiro Araki, AI Nutrition Research team
The AI nutritional research field is a research field that aims to apply information processing technology
related to Artificial Intelligence (AI) to the fields of health and nutrition, just as the AI medical and drug
discovery research field does, and was started at our institute in April 2020. In this section, we provide
an overview of AI technology and its application to the fields of health and nutrition.

【Overview of AI】
Currently, the term "AI" is found everywhere, and it is used in a variety of contexts, ranging from simple
data processing to advanced information and knowledge processing. In fact, there are many different
definitions of AI, depending on the person, but how can human intelligence be reproduced in an
artificial and computational manner? I would like to briefly describe the concept of AI from the point ofview of the essentials.
Humans are equipped with various sensory functions to perceive the outside world, such as the fivesenses. How can we translate these sensations into numerical values for the computer as AI? For
example, if we take up taste in the food sector, it is necessary to decompose the process of humanunderstanding of taste and identify the elements to be applied in each phase based on the questions ofwhat is understood and what is not. In other words, there are three phases: the taste sensing phase,taste memory and data accumulation phase, and taste classification and learning phase.
In this case, the AI technology elements required for each phase include data measurement(component, feature, and subjective evaluation and sensing), data accumulation and construction, andinformation processing (machine/deep learning and language processing), which are commonly
required in other fields as well. However, this is an area of research that requires a wide range ofperspectives, as it targets the reproduction of human abilities such as perception, understanding,prediction, and action, even when considering simple examples like this.

【Expansion into AI nutrition】
The above is just an example, but the goal of AI nutritional research is to address various issues in thefield of health and nutrition by providing data measurement (component, feature, and subjective
evaluation, and sensing), data integration and construction, and information processing (machine/deeplearning and language processing) in each phase. We aim to contribute to precision nutrition and theextension of healthy life expectancy by starting from ”small” AI technology and eventually developing
it into “big” AI. On the contrary, since the scope of AI nutrition research is diverse in terms of bothsubject matter and the technology required, it is essential to deepen cooperation with institutions both
inside and outside the institute—such as the International Life Sciences Institute (ILSI Japan)—as wellas with the industry. We want to move forward with an eye toward effectiveness as well.

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Health and Nutrition News No. 64

Nutrient
food
group
intakes
and andskeletal
Nutrientand
and
food
group
intakesskeletalmuscle(SMI)
amongthetheJapaneseelderly: amuscleindex
index
(SMI)
among
Japanesesecondaryof theNational
andand Shokuiku
elderly: a analysis
secondaryanalysisofHealththe
National
Hidemi Takimoto, Departmentof Nutritional
Epidemiology
Nutrition
Survey,2017 Survey, 2017
Health andNutrition

Hidemi Takimoto, Department of Nutritional Epidemiology and Shokuiku【Introduction】
The elderly population is increasing globally, and one in four
people is estimated to be 60 years and older in 2050,
except Africa. In Japan, the proportion of those aged 65
years and older is rising dramatically, from 6.3% in 1965 to
26.6% in 2015. In order to promote healthy aging in thissuper aging society, maintaining muscle mass is one of theimportant factors, as well as adequate nutrition. In order toinvestigate the relationship between muscle mass andnutrition, we conducted this study using the National Healthand Nutrition Survey 2017 data.
【Material and Methods】
Adults aged 60 years and over (797 men and 969 women), whoparticipated in the 2017 National Health and Nutrition Survey, andhad underwent the appendicular skeletal muscle massmeasurement by a multi-frequency biomedical impedance
apparatus（MC-780A）, and completed both the dietary survey
and the blood pressure measurements, were selected foranalysis. Skeletal muscle mass index (SMI)was calculated as
appendicular skeletal muscle mass divided by height squared.
The subjects were grouped according to their SMI quartiles bysex.
【Results】
The subjects’general characteristics are shown in Table 1. In
both men and women, subjects with higher SMI values
were younger, with higher BMI, more likely to be habitual
drinkers, and sleep less than 6 hours a day (all p<0.01).There
were no differences in the proportion of current smokers or thosewith exercise habits. The nutrients and food groups that
subjects in the highest quartile of SMI were consuming
more compared to those in the lowest quartile, are shownin Table 2.
【Conclusions】
As in previous studies, protein intake as well as a balanced
diet consisting of vegetables and meat were shown to beeffective in maintaining muscle mass in the elderly.
Although the current results are from a one-day dietary
record survey, it is important that it produced similar
results. However, we could not confirm whether improving
dietary intakes enhances muscle mass in this study, andwe could not compare the results with young adults. Theseissues should be addressed in future studies.
【Reference】
1)Okada C, Okada E, Takimoto H. Nutrient and food groupintakes and skeletal muscle index in the Japanese elderly: across-sectional analysis of the NHNS 2017.Public Health
Nutrition, 2020
DOI: https://doi.org/10.1017/S1368980020000415
2)Gray-Donald K, St-Arnaud-McKenzie D, Gaudreau P, Morais
JA, Shatenstein B, Payette H. Protein intake protects against
weight loss in healthy community-dwelling older adults. J
Nutr. 2014;144(3):321-326. doi:10.3945/jn.113.184705
3)Hengeveld LM, Wijnhoven HAH, Olthof MR, Brouwer IA,
Harris TB, Kritchevsky SB, et al. Prospective associations ofpoor diet quality with long-term incidence of protein-energy
malnutrition in community-dwelling older adults: the Health,
Aging, and Body Composition (Health ABC) Study. Am J Clin
Nutr.2018; 107(2):155-64.

Table1． General characteristics of the study subjects by quartiles of SMI
Table１． General characteristics of the study subjects by quartiles of SMI＊（）: interquartile range

Table 2．Nutrients and food groups consumed by subjects in the fourth quartile of SMI ****compared to the first quartile
The underlined nutrients and food groups were significantly higher after adjusting for age, drinking habits, smoking, sleep duration, exercise habits,
occupation, and total energy intake.

Health and Nutrition News No. 64

3Effective coverage of medical treatment for
hypertension,diabetes, and dyslipidemia
Nayu Ikeda, Section of Population Health Metrics
International Center for Nutrition and Information【Introduction】

A metric for effective coverage of health interventions was
proposed under a framework for benchmarking health system
performance by the World Health Organization in the early
2000s. Effective coverage has been studied in recent years toassess universal health coverage (UHC) for the Sustainable
Development Goals (SDGs). Hypertension, diabetes, anddyslipidemia are major metabolic risk factors for mortality anddisability from noncommunicable diseases worldwide.1 In thisstudy, we assessed the effective coverage of medication for
hypertension, diabetes, and dyslipidemia in the middle-aged
and older population in Japan.2【References】
１) GBD Risk Factor Collaborators. Global, regional, andnational comparative risk assessment of 84 behavioural,environmental and occupational, and metabolic risks or clustersof risks for 195 countries and territories, 1990-2017: a systematic
analysis for the Global Burden of Disease Study 2017. Lancet
2018; 392: 1923-1994.
２) Ikeda N, et al. Effective coverage of medical treatment for
hypertension, diabetes and dyslipidemia in japan: an analysis of
National Health and Nutrition Surveys 2003-2017.
Journal of Health Services Research & Policy. In press.

【Methods】

We obtained cross-sectional data for individuals aged 40–74
years from the National Health and Nutrition Surveys of 2003–
2017. We defined the treatment need for hypertension,
diabetes, and dyslipidemia using biomarkers, such as systolicblood pressure (SBP), hemoglobin A1c (HbA1c), and nonhigh-density lipoprotein cholesterol (non-HDL-C), equal toor greater than diagnostic thresholds or self-reported
medication use. For individuals needing treatment, we
conducted matching to estimate treatment effects andeffective coverage, defined as the fraction of potential
reductions in biomarkers actually achieved in treatedindividuals.

【Results】

The age-standardized prevalence of treatment need for
hypertension, diabetes, and dyslipidemia remained stable ataround 40%, 7%, and 33%, respectively, from 2003–
2017. Average treatment effects for those treated were 14.8mmHg for SBP, 1.2 percentage points for HbA1c, and 57.9
mg/dL for non-HDL-C. The figure shows that effective
coverage increased between 2003 and 2007 (hypertension:
48.4%, diabetes: 43.8%, dyslipidemia: 86.3%) and 2013–2017(hypertension:76.2%, diabetes:74.7%, dyslipidemia:94.6%).

【Future research】
Effective medication coverage for metabolic risk factors has
improved. Most of the potential reductions in non-HDL-C
have been achieved, while further efforts are necessary to
improve the effectiveness of antihypertensive and antidiabetic
drugs in the Japanese health care system. In future research,
we would like to apply effective coverage to examine theeffects of health care interventions on the prevention ofcardiovascular diseases.

【Acknowledgment】
This study was supported by the Japan Society for the
Promotion of Science [grant numbers 24590785, 18H03063].

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Health and Nutrition News No. 64

Figure. Effective coverage of
medication for hypertension, diabetes
and dyslipidemia in adults aged 40-74
years in Japan
Error bars indicate upper and lower
bounds of 95% confidence intervals.

A study on the quality evaluation ofhealth foods with functional claims
Yuko Tousen, Section of Food Safety and Function
Department of Food Function and Labeling【Introduction】【Results】

The extension of healthy life expectancy is one of the crucial
issues of the super-aging society in Japan. The properuse of health foods (HF), including “Foods for SpecifiedHealth Uses”, appears to be effective in extending
healthy life expectancy. However, products that have notundergone proper efficacy and safety evaluations arebeing sold. Hepatic dysfunction and health damage due
to estrogenic effects have been reported due to theconsumption of certain HF. In 2018, the Food Sanitation Law
was revised in part, and a system of “gathering
information about health hazards from foods including
ingredients needing special attention,” was incorporated;
four such plant-derived ingredients were established in
2019.

Although several HF were selected, we have discussed licoricein this section. Licorice has been sold as HM, HFI, HF, andimported HF. Glabridin was detected in HM, HFI, and HF madefrom Glycyrrhiza glabra or G. inflata (Figure 1). Likewise,
glycyrrhizin was not detected in one HFI or in HF containing
only one HFI (Figure 1). However, glycyrrhizin was detected inother HF, and it was found in almost all imported HF.
Estrogenic activity was detected in some HM, HFI, and HF.
The effects on the activity of the hepatic drug-metabolizing
enzyme, and the weight of the reproductive organs and fat inpost-menopausal model mice might be useful in evaluating
the estrogenic activity of HFI.

【Future direction】

In 2015, the synergy research was initiated by National
Institute of Health and Nutrition (NIH) in cooperation with
Research Center for Medicinal Plant Resources, after
NIH was combined with the National Institute of
Biomedical Innovation. This study was conducted toconfirm the safety and efficacy of HF and establish a quality
evaluation method. Some parts of the study’s findings
are presented in this paper.

Since the recent revision of the Food Sanitation Law,
prevention of health damage has become an important issue.
Quality evaluation of HF, including their safety and efficacy, isrequired in the future. This study verified the safety andefficacy of HF, leading to the maintenance and promotion ofsafe and secure health in people. We will continue to provide
evidence of safety and efficacy from this study the public【Materials and methods】【References】

First, we investigated the HF that showed high frequency ofuse as a health food ingredient (HFI) with limited scientificevidence. Herbal medicines (HM), HFI, and HF wereassessed as follows: 1) differentiation of raw plant species bygenetic analysis, 2) analysis of the functional (glabridin) andmedicinal (glycyrrhizin) ingredients, 3) estrogen activity
assay, and 4) effects on the activity of hepatic drugmetabolizing enzymes in normal female and postmenopausal model mice.

widely.
1) Ishimi Y, et al. Quality evaluation of health foods containing licoricein the Japanese Market. Toxicol Rep. 21;6, 904-913, 2019.
2) Yamauchi J, et al. Detection of health foods indicating the estrogen
activity. J Jpn Soc Nutr Food Sci. 72; 4, 159-163,2019.
3) Tousen Y, et al. Effects of Siberian ginseng on hepatic drug
metabolizing enzymes and bone mineral density in ovariectomized mice.
Jpn J Nutr Diet. 75, 151-163, 2017．

Figure 1. Glabridin and glycyrrhizin content in licorice-based products used as herbal
medicines, health food ingredients, and health foods

Health and Nutrition News No. 64

5Role of the Brain in the Regulationof Growth and Glucose Metabolism
Takanori Hayashi, Section of Nutritional Therapy
Department of Clinical Nutrition【Introduction】

In recent years, the prevalence of type 2 diabetes has
increased considerably worldwide. Hence, it is crucial andimperative to develop strategies for preventing disease
onset and minimizing its complications. Insulin receptor
substrates (IRS) are activated by insulin and insulin-likegrowth factor (IGF)-1 receptors and allow docking ofdownstream effectors. Hence, they are essential for insulin
and IGF signaling. In particular, IRS1 and IRS2 play a keyrole in glucose homeostasis. Our previous study
demonstrated that IRS1 plays an important role in not only
glucose metabolism but also growth in whole-body IRS1knockout mice1). However, the underlying molecularmechanisms and tissues or organs responsible for IRS1mediated growth and glucose homeostasis are unclear.
Since recent studies have revealed that the brain, especiallythe hypothalamus, plays a crucial role in the regulation offood intake and glucose homeostasis, we investigated therole of IRS1 in hypothalamic neurons to elucidate themechanism of IRS1-mediated growth and glucose
homeostasis in the present study.

【Subjects and Methods】

We induced downregulation of IRS1 expression in ahypothalamic cell line by the addition of siRNA. We
analyzed neurite outgrowth, neural proliferation, and mRNA
expression levels in these cells, with or without thestimulation of insulin and IGF-1. We also investigated thephosphorylation of intracellular signaling proteins in thesecells.

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Health and Nutrition News No. 64【Results】
Downregulation of IRS1 was associated with significant
impairment of IGF-1-induced neurite elongation in thesecells. GHRH mRNA expression levels after IGF-1
treatment were also significantly decreased in cells withdownregulated IRS1. There were no differences in neuralproliferation after IGF-1 administration between thecontrol and IRS1-downregulated neurons.
Downregulation of IRS1 was associated with a decrease in
Akt phosphorylation, but not Erk phosphorylation, induced
by IGF-1 in these cells.

【Conclusion and Future Direction】

This study demonstrates that IRS1 plays a role in neurite
outgrowth, but not in the proliferation of GHRHproducing hypothalamic neurons. Our findings suggestthat brain IRS1 regulates both somatic growth and glucose
metabolism via GHRH expression (Figure). We are now
analyzing growth-related parameters and metabolic
factors in brain-specific IRS1 knockout (NIrs1KO) mice.
Further research is necessary to prevent and treat
diabetes.

【References】
1) Tamemoto H et al. Insulin resistance and growth retardationin mice lacking insulin receptor substrate-1. Nature. 372(6502):182-6. 1994

Does breakfast skipping induce
postprandial glucose excursion in lunch?
Yoichi Hatamoto, Section of Energy Metabolism
Department of Nutrition and Metabolism【Introduction】【Results and discussion】

A well-balanced healthy diet is important for health and theprevention of chronic disease. However, recent studies
indicate that not only “what we eat” but also “when weeat” is important and influences the physiological response
and metabolism related to health outcomes 1, 2.

Mean glucose values were not significantly different between
the two trials. However, the peak postprandial glucose value
in skipping breakfast trials was significantly higher than thatin the three-meals trials. In the dinner, postprandial glucose
responses were not significantly different between trials.

Our laboratory reported that a high-fat diet in the morning
has a risk of increasing postprandial glucose level at lunch 2.
Unhealthy dietary habits can have various effects on
cardiometabolic health markers, and skipping breakfast is arisk factor for diabetes and cardiometabolic disorders. Oneof such disorder is postprandial hyperglycemia. We
examined the influence of breakfast-skipping on the
postprandial glucose response3 . (Meal timing and healthwere well documented by Dr. Yamasaki (the Number 61,
February, 2019)).

There are some variations of meal timing in the real world. We
plan to examine and understand the effects of meal timing
on health outcomes.

【Subjects and method】
Nine young and healthy subjects (21.4±1.4 y) performed two
different eating pattern trials: 1) three meals and 2) skipping
breakfast. Blood glucose was measured continuously for
24-hours using a continuous glucose monitoring system(CGMS).

【Future research】【Reference】
1) Sutton EF, Beyl R, Early KS, Cefalu WT, Ravussin E, Peterson CM.
Early time-restricted feeding improves insulin sensitivity, blood pressure,and oxidative stress even without weight loss in men with prediabetes.
Cell metabolism. 2018;27:1212-21. e3.
2) Ando T, Nakae S, Usui C, Yoshimura E, Nishi N, Takimoto H, et al.
Effect of diurnal variations in the carbohydrate and fat composition ofmeals on postprandial glycemic response in healthy adults: a novel insight3) Ogata H, Hatamoto Y, Goto Y, Tajiri E, Yoshimura E, Kiyono K, et al.
Association between breakfast skipping and postprandial hyperglycaemia
after lunch in healthy young individuals. Br J Nutr. 2019;122:431-40.

Figure. Time course of interstitial glucose concentrations over 24 hours.
Data represents means with standard deviations represented by vertical bars (n=9).
Breakfast-skipping induced increased postprandial glucose levels at lunch.

Health and Nutrition News No. 64

7

A U-Shaped Relationship between the Prevalence of
Frailty and Body Mass Index in Community-DwellingJapanese Older Adults: The Kyoto–Kameoka Study
Daiki Watanabe, Section of Healthy Longevity Research
Department of Physical Activity Research【Introduction】【Future direction】

Frailty is a condition in which the function of multiple physiologicalsystems are impaired due to a decrease in homeostatic response tostress1,2. A cross-sectional British epidemiological study showed thatbody mass index (BMI) has a U-shaped relationship withthe prevalence of frailty; it also showed that the BMI ranges
associated with the lowest prevalence of frailty are 18.5–24.93,4 and
25–29.9 kg/m2 5. Although the optimum BMI range may vary fromstudy to study, the current state of knowledge points to the importanceof maintaining a healthy BMI to prevent frailty. However, themean BMI differs between Japanese and British people6, and it isdifficult to extrapolate the results of previous studies to account forJapanese people. With the use of two validated frailty assessment
tools, the present study aimed to investigate the relationship between
the prevalence of frailty and BMI in community-dwelling Japaneseolder adults.

The results indicate that a healthy BMI may reduce the prevalence offrailty, and the risk of frailty should be evaluated in underweight
and overweight people. Middle-aged and older individuals withobesity and metabolic syndrome may already experience frailty.
Therefore, adding BMI-related items to the tools currently used forevaluating frailty among middle-aged and older individuals could
potentially lead to a more robust diagnosis of frailty. The National
Institute of Health and Nutrition is focused on the "prevention offrailty from working generations" and is working in cooperation with
Osaka Prefecture to aim for social implementation.

【Results】

and its association with multimorbidity and mortality: A prospective
analysis of 493 737 UK Biobank participants. Lancet Public Health 2018,
3, e323-e332, doi:10.1016/S2468-2667(18)30091-4.
5) Hubbard, R.E. et al. Frailty, body mass index, and abdominal obesity inolder people. J. Gerontol. A Biol. Sci. Med. Sci. 2010, 65, 377–381,
doi:10.1093/gerona/glp186.
6) Collaboration, N.C.D.R.F. Rising rural body-mass index is the main
driver of the global obesity epidemic in adults. Nature 2019, 569, 260–264,
doi:10.1038/s41586-019-1171-x.

Please refer the article below for details.
Watanabe, D., Yoshida, T., Watanabe, Y., Yamada, Y., Kimura, M.,
and Kyoto-Kameoka Study Group. A U-Shaped Relationship
Between the Prevalence of Frailty and Body Mass Index in
Community-Dwelling Japanese Older Adults: The Kyoto-Kameoka【Methods】
This cross-sectional study used baseline data of 7191 individuals Study. J. Clin. Med. 2020, 9, E1367. doi: 10.3390/jcm9051367.
aged ≥65 years living in Kameoka City, Kyoto, Japan. BMI was
calculated based on self-reported height and body weight and 【Reference】
classified into six categories. The validity of “self-reported” BMI
values was evaluated on a sub-cohort of 1169 subjects. No difference 1) Clegg, A.et al. Frailty in elderly people. Lancet 2013, 381, 752–762,
was observed between the BMI values calculated from actual height doi:10.1016/S0140-6736(12)62167-9.
and weight measurements (22.6 kg/m2 and 23.0 kg/m2). A strong 2) Hoogendijk, E.O. et al.; Fried, L.P. Frailty: Implications for clinicalcorrelation was noted between these two BMI parameters (r = 0.915). practice and public health. Lancet 2019, 394, 1365–1375, doi:10.1016/
S0140-6736(19)31786-6.
Frailty was assessed using two validated assessment tools: the Fried 3)
Brunner, E.J. et al. Midlife contributors to socioeconomic differences
phenotype (FP) model and the Kihon Checklist (KCL). The relationship in frailty during later life: A prospective cohort study. Lancet Public
between frailty and BMI was evaluated using multivariate, restricted Health 2018, 3, e313-e322, doi:10.1016/S2468-2667(18)30079-3.
spline logistic regression.
4) Hanlon, P. et al. Frailty and pre-frailty in middle-aged and older adults
The prevalence of frailty defined using the FP model and KCL for each
BMI category (<18.5, 18.5–19.9, 20.0–22.4, 22.5–24.9, 25.0–27.4,
and ≥27.5 kg/m2) were 25.3% and 55.5%, 19.6% and 37.7%, 14.3%
and 34.2%, 12.4% and 32.6%, 12.6% and 34.3%, and 19.4% and49.2%, respectively. The spline model showed a significant U-shaped
relationship between BMI and the prevalence of frailty defined using
both the KCL and FP models. The BMI range corresponding to thelowest prevalence of frailty was found to be 21.4–25.7 kg/m2 (Figure
1).

Figure 1. Relationship between body mass index (BMI) and frailty defined based on (A,C) the Fried phenotype (FP) model and (B,D) the Kihon
Checklist (KCL), using a restricted cubic spline logistic regression model. Frailty according to the KCL was defined as a score ≥7 out of 25 points,
whereas frailty according to the FP model was defined as a score ≥3 out of 5 points. Solid lines represent odds ratios (ORs) and dashed lines
represent 95% confidence intervals (CIs). The OR was calculated using a BMI reference value of 23.0 kg/m2. The analysis was adjusted for age,sex, region, smoking habit, alcohol consumption, education history, number of drugs taken, family composition, economic status, physicalactivity, presence/absence of dentures, and history of hypertension, stroke, heart disease, diabetes, and dyslipidemia.

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Health and Nutrition News No. 64