ISSN2434-2688

Asian Journal of Dietetics
Vol.2 No.1
2020
Contents
Page

Title and authors

Originals
1-8

Nutritional Care of Preterm Infants: from Global
Guidelines to Local Practice
Shih-Ching Lo, Hsing-Chun Lin, Cheau-Feng Lin

9-14

Validation of a Pediatric Nutrition Screening Tool in
Hospital Outpatients of Myanmar
Lin Ei Phyu, Wantanee Kriengsinyos, Nipa Rojroongwasinkul, Nalinee
Chongviriyaphan, Tippawan Pongcharoen

15-22

Carbohydrate Profile, Total Dietary Fiber and In vitro
Glycemic Index of Taro- Corn and Sweet Potato- CornnoodlesHazel T. Lat, Wilma A. Hurtada, Erlinda I. Dizon, Aimee Sheree A.
Barrion23-30

Effects of Pre-Germinated Brown Rice on Blood Glucose
Concentration of Type 2 Diabetes Mellitus Patients in
VietnamBui Thi Nhung, Le Dang Tuyen, Nguyen Do Van Anh, Nguyen Thi
Hong Ha, Vu Thi Minh Thuc, Vu Anh Linh, Kei Yui, Yukihiko Ito,
Shigeru Yamamoto

Research note
31-38

The Effect of A School-based Nutrition Education Programon Japanese Elementary School Children’s Nutrition
Knowledge and the Revaluation after One and A Half Year.
Mai Matsumoto, Ikumi Goto, Ayumi Masumoto, Azusa Sakamoto, Aki
Saito, Shinji Ikemoto

Letter to editor
39-40

Aetiology of Kwashiorkor then and now – still the Deposed
Child?
Edem M. A Tette, Juliana YarteyEnos

Asian Journal of Dietetics, 2020

ORIGINAL
Nutritional Care of Preterm Infants:from Global Guidelines to Local Practice
Shih-Ching Lo1, 2 *, Hsing-Chun Lin1, 2, Cheau-Feng Lin3, 4
1

Department of Nutrition, Chung Shan Medical University Hospital, Taiwan, R.O.C
Department of Nutrition, Chung Shan Medical University, Taiwan, R.O.C
3
Institute of medicine, Chung Shan Medical University, Taiwan, R.O.C
4
Department of surgery, Chung Shan Medical University Hospital, Taiwan, R.O.C
2(received November 30, 2019)
ABSTRACT Background. Meeting the nutritional needs of preterm infants represents ahuge challenge. According to previous studies, higher intakes of protein and energy in thefirst week of life are linked to improved neurodevelopmental outcomes. However, whether
these global guidelines can be successfully implemented at the local level requires further
study. Methods. This is a retrospective cohort study, for the period from Oct 2016 throughNov 2017. All participating subjects reached 75% of the nutritional targets within one
week of birth as the standard of care. But given the clinical condition constraints, theyalmost inadequate nutrition in the first weeks of life. We conducted a trial involving 122
preterm divided into four groups to determine the most appropriate nutritional strategy:
HCHP (more calories and higher protein intake); LCLP (fewer calories and lower protein
intake); HCLP (more calories and lower protein intake) and LCHP (higher protein intake
and fewer calories). Results. Higher energy intake in the first week after birth was related
to significant decreases in the average duration of hospitalization (p<0.05) and significant
weight gain (p<0.05). Adequate caloric intake is more important than high protein intake.
Furthermore, early aggressive nutritional strategy may help to decreases in length of ICU
stay. (HCLP versus LCLP group, odds ratio 0.022 [95% CI 0.003-0.189]). Conclusion.
Based on the results of this study, we determined the optimal nutritional support forpreterm infants. Protein is an important factor in developmental outcomes and can be usedmost efficiently when associated with adequate caloric intake.
Keywords: Nutritional Requirements; Nutritional Support; Nutrition Therapy; Premature
Infant.
practices for preterm infants include energy
INTRODUCTION
requirements of 110~130 kcal/kg/day and protein
intake of 3.5~4.5 g/kg/day (5). Various disciplines
Early aggressive nutrition is essential for preterm
have contributed specialized expertise to theinfant growth and immunity. In preterm infants, poor
identification of potentially better practices (6).
nutrition is associated with poor head growth andAlthough the Committee on Nutrition of ESPGHAN
persistent small head size results in poor psychomotor
announced appropriate recommendations in 2007, it
and mental skills and high rates of cerebral palsy andwas considered necessary to review them (8). The
autism (1). Preterm infants are at a higher risk of
updated guidelines are consistent with, but not
growth and developmental disabilities compared tocompletely identical to, the major recommendations
their full-term counterparts. Early administration ofprior to 2010. However, whether these global
optimal nutrition to preterm infants lowers the risk ofguidelines can be successfully implemented on a local
adverse health outcomes and improves cognition inlevel requires further study. Differences in medical
adulthood (2). Recommendations for the nutritionalcare and social factors among countries may limit the
management of preterm infants by the European
generalizability of global guidelines (13). The aim ofSociety for Paediatric Gastroenterology, Hepatology
this study is to review and discuss the manner in which
and Nutrition (ESPGHAN), the American Academy ofthe postnatal growth of preterm infants is monitored in
Pediatrics (AAP) and the Taiwan Society of
neonatal intensive care units (NICUs) and to
Neonatology (TSN) Committee on Nutrition include
investigate whether growth and clinical outcomes arehigher intakes of protein and energy in the first week
associated with the adequacy of postnatal nutrientof life, which have been linked to improved
intake.
neurodevelopmental outcomes (3). Amino acids andproteins are key factors for growth. Current
METHODS
recommendations are designed to provide nutrients toapproximate the rate of growth and composition ofThis was a retrospective cohort study, conductedweight gain for a normal fetus of the samefor the period from Oct 2016 to the end of Nov 2017,
postmenstrual age (4). The prevailing nutritionalon preterms admitted to the NICU at local medical*To whom correspondence should be addressed: cshc188@csh.org.tw, mygodway@gmail.com

1

Guidelines and Practices in Preterm Nutrition
center and who met the criteria for inclusion. Those
who died within 7 days were excluded, of which there
were two. Therefore, mortality rate was not considered
an indicator. In addition, infants who remained in thehospital at the end of the study period were excluded(Figure 1). All participating subjects reached 75% of
the nutritional targets within one week of birth as thestandard of care. Due to clinical constraints, almost allreceived inadequate nutrition in the first weeks of life.
The nutritional protocol was to start parenteralnutrition on day 1 and first enteral feed as soon as
possible after birth. Continued provision of
appropriate nutrition (fortified human milk or
premature formula) is important throughout thehospitalization period. Subjects were assigned to one
of four groups based on number of calories and amountof protein ingested daily during the first seven days ofadmission to NICU: high calorie intake group (HCLP)
which received more than 75% of the energy needs (90kcal/kg/day); high protein intake group (LCHP) which
received at least 75% of the protein requirement (3g/kg/day); high calorie and protein intake group(HCHP) which received at least 90 kcal/kg/day with 3
g/kg/day of protein and low calorie and protein intake
group (LCLP group) which received less than 90
kcal/kg/day and less than 3 g/kg/day of protein.
Perinatal and neonatal data were retrieved frommedical records (Figure 1). Weight gain and bloodbiochemical values were collected after two weeks ofnutritional support. Complication rates and lengths of
hospital stay were also analyzed. Finally, we exploredthe impact of nutritional support on the clinicaloutcomes of preterm infants. This study was
conducted according to the guidelines of the
Declaration of Helsinki and all procedures involving
human subjects were approved by the Institutional
Review Board of Chung Shan Medical University
Hospital (CSMUH IRB No: CS-18256).

Figure 1. Enrollment flow chart and study protocol
scores were calculated on the first day and at 7-14 days
after admission. The predictive power for clinical
Characteristics
outcomes included the average duration of
hospitalization (length of stay, LOS) and number of
The Neonatal Therapeutic Intervention Scoringdays in ICU. Furthermore, preterm infants can suffer
System (NTISS) was used to indicate disease severity
from morbidities such as patent ductus arteriosus
in neonates requiring intensive care (14). NTISS
2

Asian Journal of Dietetics, 2020
(PDA), respiratory distress syndrome (RDS) above
grade II, retinopathy of prematurity (ROP),
bronchopulmonary dysplasia (BPD), necrotizing
enterocolitis (NEC), intraventricular hemorrhage(IVH), metabolic bone disease, hypoxic ischemic
encephalopathy(HIE),
congenital
intestinal
malrotation and spontaneous intestinal perforation(SIP) (15). Neonatal morbidities were diagnosed bypediatric specialist and retrieved from medical records.
Complications in this study were defined as the
combination of more than two of these morbidities.
Preterm infants received surgical intervention
including PDA ligation, external ventricular drainage(EVD)/ventriculoperitoneal shunt (V-P shunt)procedure, photoligulation, inguinal hernia repair andenterostomy.

reactive protein levels were within normal range withdecreased inflammation in all groups at 2 weeks.
Unfortunately, information about nutritional status andpossible indexes to be applied to organ function
maturity was limited. Due to insufficient data on serum
albumin, alkaline phosphatase, bilirubin level, γglutamyltransferase and creatine kinase, these could
not be analyzed.
Adequate nutritional support was associated withimproved growth and clinical outcomes and lessextrauterine growth retardation (EUGR) (Table 2).
High energy with low protein intake during the firstweek after birth was associated with significant
decreases in the average duration of hospitalizationand length of ICU stay (HCLP versus LCLP groupboth p<0.01, respectively). Higher energy with higher
protein intake was only associated with significant
decreases in length of ICU stay (Figure 3), indicating
an excellent predictive marker for clinical outcome.
Additionally, HCLP group showed significantly
improved weight gain at 2 weeks when compared with
LCHP group and LCLP group (p<0.05, Table 2).

Statistical Analysis
Continuous variables were summarized as
medians with interquartile ranges (IQRs) and reported
as 25th-75th percentiles. Categorical variable
comparisons were performed with Pearson chi-square
test or Fisher’s exact test. To determine whether theeffect of the intervention on the end points was
influenced by baseline risk factors, p values forinteractions were calculated with one-way ANOVA
and post hoc analysis with Scheffé test with thresholdfor significance of interaction set at p<0.05. For
continuous parameters in non-normal distribution,
Kruskal-Wallis H test was used when the assumptionsof one-way ANOVA were not met. We further
performed Mann-Whitney U test to compare thedifferences between two groups. As the group with thehighest intakes was comprised of infants of the highest
gestational age (GA) and birth body weight (BBW),
confounders were GA and BBW. Following initial
analysis, logistic regression analysis was performedfor variables without statistically significant difference.
Furthermore, we adjusted for the confounders GA andBBW during the nutritional treatment period andclinical outcome. Logistic regression was applied tothe baseline characteristics and clinical outcome. Allanalyses were performed using PASW Statistics 18,
version 18.0.0 (formerly SPSS Statistics).

Although administration of optimal nutrition
resulted in lower risk of more than two pretermcomplications and surgical intervention formorbidities (Adjusted p>0.05), there were significant
differences in ICU stay between HCLP versus LCLP
groups and HCHP versus LCLP groups (Figure 3). For
HCLP versus LCLP group, odds ratio was 0.022 [95%CI 0.003-0.189], p=0.001 and for HCHP versus LCLP
group, odds ratio was 0.044 [95% CI 0.006-0.33],
p=0.002. Moreover, LCLP group showed significantly
increased LOS (HCLP versus LCLP group, odds ratio
0.022 [95% CI 0.002-0.267], p=0.003).
DISCUSSION
Providing more than 75% of the nutritional needs
to preterm infants within 7 days of birth can improve
clinical prognosis. Protein administration is more
effective when number of calories is adequate.
Although survival of premature infants has improved,
there is a continuing need to develop and implement
strategies for reducing the potentially lethal
complications of premature birth (16). Early
aggressive nutritional enteral and parenteral support
may help to improve growth and developmental
outcomes in preterm newborn low birth weight (LBW)
infants (12). In preterm infants, poor postnatal growthis associated with adverse neurocognitive outcomes.
Conversely, rapid postnatal growth is considered a risk
factor for future development of metabolic diseases(17). Therefore, sufficient nutritional support andoptimal nutritional delivery are essential. Proteinenergy balance studies of preterm infants haveprovided data to guide recommendations for proteinenergy intakes for specific short-term goals (18).
Nevertheless, from the results of our study on therelevance of energy it is particularly important topromote protein balance at lower energy intakes, asamino acids are increasingly used for oxidative
metabolism when non-protein energy is limited.
Regardless of protein intake, however, nutritionalbalance requires increased energy intake. A reasonable
explanation for no associations with laboratory data inthis study is that the biochemical values of prematureinfants are monitored daily and corrected immediately.

RESULTSA total of 122 preterm infants of less than 37
weeks' gestational age were eligible for this study. The
basic characteristics of these infants are shown in
Table 1. Infants in the LCLP group had lower GA,
lower BBW, and lower Apgar scores at 1 minute and 5
minutes when compared with infants in the HCLP
group (all p< 0.05). On post hoc analysis with Scheffétest, there were statistically significant differences in
GA between the HCLP and LCHP groups and the
HCLP and LCLP groups. Similarly, there were
statistically significant differences in BBW and Apgar
score at 1 minute. We performed logistic regression
analysis with the significant variables BBW and GA.
After adjusting for significant factors, there were no
significant differences between groups at baseline aspresented in Table 1.
In addition, there were no associations withlaboratory data after 2 weeks of nutritional support,such as hemoglobin, white blood cell count andplatelet count. Similar results were found forelectrolyte balance (data not shown). Median C3

Guidelines and Practices in Preterm Nutrition

Table 1. Baseline characteristics of patients at enrollment
Characteristicsα
HCLP 90kcal
HCHP 90kcal+3g

LCHP 3g

LCLP Nonep value βAdjusted p γ

Number22 (18.1)

19 (15.6)

60 (49.2)

21 (17.2)

－

－

Male

12 (54.5)

7 (36.8)

35 (58.3)

14 (66.7)

0.268

－

GA (wk)35.86 ab(34.1-36.3)

35 c(33.9-35.4)

33.07 a(30.3-34.3)

33.86 bc
(27.4-34.9)

<0.01

0.119

BL (cm)45.3 (42-48.3)

45.5 (42-47.5)

42 (39-46)

42 (35.3-47.5)

0.027

0.121

BBW (kg)

2.300 de(1.863-2.550)

2.020(1.730-2.269)

1.756 d(1.301-2.066)

1.758 e(0.975-2.252)

<0.05

0.66

1
8 (7-8.25) fg
8 (7-8) h
7 (6-8) f
6 (5-7.5) gh
<0.01
0.138
min
5
9 (9-9) i
9 (9-9)9 (7-9)8 (7-9) i
<0.01
0.439
min
α
Continuous variables are presented as median (IQR) and categorical variables as n (%)β
The p values are from Pearson chi-square test for categorical variables and from one way ANOVA or KruskalWallis test for continuous variables.
γ
Adjusted p values are from binary logistic regression analysis after adjusting for significant factors. (Confounding
variables: BBW, GA)*
The same English symbols indicate significant difference
Apgar
Score

Table 2. Clinical outcomes of each group
Intervention
Prognosis assessment
PeriodNutritional therapy groups
HCLP
HCHP
LCHP
LCLP
Actual Calories
104.6
101.5
74.2
57
(kcal/kg BW)(99-113.5)(92.5-117.8)(66.2-79)(51.7-71.7)

p-value<0.01

Adjustedp－

Actual Protein(g/kg BW)

2.56
(2.29-2.67)

3.67(3.38-4.09)

3.74(3.46-4.11)

2.83(2.52-2.95)

<0.01

－

LOS (days)

14.5 a(12.8-21.5)

28(14-37)39.5(27.3-74.3)

61 a(30-97.5)

<0.01

0.025

ICU stay (days)

7.5 b(5-12)
290(171-357)

12 c(7-21)
188.5(65.8-258.3)

23(15.3-51.5)
91.5(36.8-155.8)

46 bc(17-71.5)
101(36-179.5)

<0.01

<0.01

<0.01

0.015

NTISS score(after 7~14
days)
Complications

8 (5-10.5)

9 (7-9)9.5 (7-15)

10 (7-21.3)

0.087

－

3 (14)

5 (26)

28 (47)14 (67)

<0.01

0.287

Surgery0 (0)2 (11)

13 (22)10 (48)

<0.01

0.437

BW gain (gm)*Continuous variables are presented as median (IQR) and categorical variables as n (%)
The same English symbols indicate significant difference*
Adjusted p values are from binary logistic regression analysis after adjusting for significant factors. (Confounding
variables: BBW, GA)*

4

Asian Journal of Dietetics, 2020

Figure 2a. Actual caloric intakes of each group. There were significant increases in HCLP and HCHP group whencompared with LCHP and LCLP groups.

Figure 2b. Actual protein intakes of each group. There were significant increases in HCHP and LCHP group whencompared with HCLP and LCLP groups.
5

Guidelines and Practices in Preterm Nutrition

LOS
HCLP
HCHP
LCHP
LCLP
ICU stay
HCLP
HCHP
LCHP
LCLP

Adjusted-p

OR (95% CI)

0.003
0.160
0.240
Indicator

0.022 (0.002-0.267)
0.284 (0.049-1.644)
0.397 (0.085-1.854)
1

0.001
0.002
0.155
Indicator

0.022 (0.003-0.189)
0.044 (0.006-0.330)
0.292 (0.054-1.591)
1
0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Favor low risk

Figure 3. The relevance of this experiment. HCLP group showed significant improvement in odds ratios of LOS
and ICU stays when compared with LCLP groups.
Previous studies have suggested that increasingenergy intake in the face of static protein intake should
be avoided in the first week of life (19). As there isoften a gap between global guidelines and the resultsof clinical practice, NICUs should develop their own
methods of nutritional support for preterm infantssuited to local conditions and actual patient needs. Our
study of a local practice revealed the optimal
nutritional support strategy for preterm infants.
Greater energy and lipid intake predict increased total
brain and basal nuclei volumes over the course ofneonatal care to term-equivalent age (20). Evidence
linking postnatal weight gain to later adiposity andother cardiovascular disease risk factors in preterm
infants is limited (17). In our study, more than 90
kcal/kg/day with or without 3 g/kg/day protein was
associated with sufficient weight gain (＞150 g/week).
Consistent with the findings of a previous study,emphasis should be on providing optimal energy andprotein during the first week following birth (21).

statistics to minimize bias in determining variables,
but with uncertain validity. From observational studies
there are consistent positive associations between
nutritional support and clinical outcomes. However,there is limited evidence from intervention studies.
Given the above constraints, further studies are needed
to clarify the effects of early nutritional intervention onpreterm infants.
ACKNOWLEDGEMENT

A previous study proposed that infants weighing≥1250 g be fed three times hourly and those weighing<1250 g be fed two times hourly (22). The Society of
Critical Care Medicine (SCCM) and American Society
for Parenteral and Enteral Nutrition (ASPEN)
recommend that enteral feeding protocols be designed
and implemented to increase the overall percentage ofgoal calories provided. For instance, volume-based
feeding protocol or top-down multi-strategy protocol
should be considered (23). Protocols in which 24-houror daily volumes are targeted instead of hourly rates
have been shown to be successful in increasing theoverall percentage of goal energy provided.
Consequently, daily volumes may be applied topreterm feeding protocol for catch-up growth and earlyaggressive nutritional support.

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Sey R, eds. Nutritional Support of the Very Low
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We would like to thank the anonymous reviewers
and the editor for their comments. In addition, we
would like to acknowledge all participating
investigators and the directors of the nutrition andpediatric departments of hospital who served as
scientific advisors. A research assistant fromuniversity school collected data and attending
physicians in the NICU of hospital provided care to thestudy subjects.

The major limitation of this study is that enteralnutrition and parenteral nutrition were not calculated
separately, although each has different requirements.
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Guidelines and Practices in Preterm Nutrition
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56(2):108-113. 2015.
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WA, Laptook AR, et al. Causes and timing ofdeath in extremely premature infants from 2000
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Forsyth S, Godfrey KM, Koletzko B, et al.
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18) Bhatia J, Mena P, Denne S, García C. Evaluationof Adequacy of Protein and Energy. J Pediatr
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AJWM, Reuser JJCM, Goudoever JBV et al. The
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Paediatr 106(2):242-249. 2017.
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Guidelines and Practices in Preterm Nutrition

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Asian Journal of Dietetics, 2020

ORIGINAL
Validation of a Pediatric Nutrition Screening Toolin Hospital Outpatients of Myanmar
Lin Ei Phyu1, Wantanee Kriengsinyos1*, Nipa Rojroongwasinkul1,
Nalinee Chongviriyaphan1, Tippawan Pongcharoen1
1

Institute of Nutrition, Mahidol University, Nakhon Pathom, Thailand(received Jan 20, 2020)

ABSTRACT Background: Nutrition screening is important in identifying children at riskof developing malnutrition. No pediatric nutrition screening tool is previously applied orvalidated in Myanmar. Objective: This study aimed to validate Screening of Risk for
Nutritional Status and Growth (STRONGkids) tool and to analyze the association ofnutrition status with the clinical characteristics of Myanmar pediatric outpatients. Method:
The STRONGkids screening score was calculated and the nutrition risk from the tool wascompared with the WHO growth standards determined by weight and height related zscores. The nutrition status of the participants and its association with clinical factors werealso investigated. Results: A total of 120 children (60 boys, 50%), aged between 1 and12-year-old, were included. The screening tool identified 58.3% of children as
nutritionally-at-risk. It had 90.9% sensitivity and 45% specificity to detect thinness, and
81% sensitivity and 46.5% specificity for stunting. The nutrition risk from the screening
was also significantly associated with the weight, height, and BMI-related WHO z-scores(p < 0.05). Overall, 26.6% of our study children had thinness and/or stunting, and > 5year old children had significantly reduced weight status compared to the younger age
group. Conclusion: This study suggested that the STRONGkids screening tool is asensitive and valid tool that can be used for early detection of malnutrition in Myanmar
pediatric outpatients. The effectiveness of nutrition intervention following screening
should be further investigated.
Keywords: Malnutrition; Pediatric; Nutrition Screening Tool; Myanmar; Anthropometry
INTRODUCTION
Parenteral and Enteral Nutrition (ASPEN) and theEuropean Society of Pediatric Gastroenterology,
Hepatology and Nutrition (ESPGHAN), thus,
recommend the early detection of malnutrition risk by
screening (7). Several nutrition screening andassessment tools have recently been developed, but theagreement regarding the best screening tool has notreached yet (3, 8). Although nutritional screening tools
are developed with pre-specified nutritional
intervention plan, the successful implementation ofthis plan during hospitalization is limited for somepatients due to decreased length of hospital stays. In
contrast, if a screening tool can be applicable to the
outpatient setting, followed by detailed nutritional
assessment, the optimal benefit from timely nutritionintervention can be achieved. Almost all of theprevious screening tools were developed for
hospitalized children and the applicability of thesetools in outpatient population is still needed to beinvestigated.
In the outpatient setting of Myanmar hospitals,
although physicians could recognize the children who
are already malnourished, the lack of a validated
screening tool makes it difficult to diagnose thechildren who are at risk of malnutrition. In addition, adetailed nutritional assessment cannot be performed inevery pediatric outpatient since it is a time-consuming
process which required skills and knowledge inChildhood malnutrition is considered as a global
health concern since it is associated with poor growthand development, as well as reduced educational
outcomes of children and can have negative impacts
on their adulthood (1). The 2018 global malnutrition
report estimated that the prevalence of under-fivemalnutrition in the form of wasting was around 49
million, and stunting was around 149 million (2).
Undernutrition is not only a consequence of prolonged
starvation or food insecurity but also diseases, injuries
or illness. Children with chronic diseases andhospitalized children have a greater risk ofmalnutrition since they have increased energy demand
from the diseases, and reduced nutrient intakes andabsorption from underlying conditions, medications
and, or, inadequate nutritional support during thetreatment (3). On the other hand, malnourishedchildren have an increased risk of infections, poor
healing and disease-associated complications, which
can increase their morbidity and mortality (3, 4).
Therefore, early identification of nutritional risk inchildren is essential in order to prevent from severe
malnutrition and its complications (5, 6). Internationalorganizations such as the American Society for*To whom correspondence should be addressed:
Wantanee Kriengsinyos<wantanee.krieng@mahidol.ac.th>9Pediatric nutrition screening in Myanmar
nutrition. Therefore, there is a probability of missing
children who were at-risk to be malnourished and did
not receive timely nutritional treatment. The
application of nutritional screening tool in outpatient
clinic can detect the children at risk at an early point,and can prevent from consequences of malnutrition.
For the practical application in outpatient clinicalpractice, a malnutrition screening tool should be quick,simple, reliable and easy to understand. Therefore, ourstudy aimed to validate the Screening of Risk for
Nutritional Status and Growth (STRONGkids) tool
which has been reported as an easy-to-use and rapid
screening tool (6, 9), and furthermore, to evaluate thefactors associated with nutrition status in Myanmar
pediatric outpatients.

Nutrition Screening
The caregivers or older children in the study wereinterviewed with the questions in the STRONGkids
nutrition screening tool (9) which includes 1) the
presence of illness with nutrition risk or plan forsurgery, 2) physical appearance by subjective clinicalassessment, 3) indicators of reduced intake such as
gastrointestinal symptoms, pain, reduced food intake,nutritional intervention and presence of pain, and 4)
weight history. The scoring of 1 point was given to any
positive answer the questions except the presence ofunderlying disease and given with the weighted scoreof 2 points. Therefore, the total score for all positive
response is 5 points and the children were categorized
into three groups; high risk (total score ≥ 4), moderaterisk (total score = 1 to 3), and low risk (total score =0).

METHODS

Dietary evaluation

This cross-sectional study was conducted during
February to April 2019 in pediatric outpatient
department of Parami General Hospital, which is a
private medical center located in Yangon, andproviding health care services especially for thechildren. The study was approved by Mahidol
University Central Institutional Review Board (MUCIRB 2019/029.1102).

A single 24-hour dietary recall of the children
during their illness was taken from the caregivers orolder children to estimate the approximate energy
intake. The energy intake of the children wascompared with the age-specific recommended dietary
allowance per day for Southeast Asia (13), in order todecide whether they had an adequate caloric intake (≥75% of RDA) or inadequate caloric intake (< 75% ofRDA) during illness (14).

Validation of nutrition screening tool
In order to validate a screening tool, the nutrition
status based on WHO anthropometric indicators:
weight-for-age (WFA), weight-for-height (WFH),
height-for-age (HFA) and BMI- for-age were chosen
as a trusted criterion standard. Malnutrition as definedby World Health Organization is the presence of either
wasting (WFH z-score <-2SD or BMI-for-age z-score< -2 SD), stunting (HFA z-score < -2 SD) or
underweight (WFA z-score < -2 SD) (10). The patientswith each of these anthropometric z-score of < -2SD
were considered as malnourished, and ≥-2 SD wereconsidered as well-nourished.

Statistical analysis
Descriptive statistics were used for presenting
patient characteristics, anthropometric data and othercategorical variables. Based on the weight and height
related z-scores and the cut-off point of -2 SD for
malnutrition, the sensitivity, specificity, positive
predictive value and negative predictive value of thenutrition screening tool was determined. In thecontingency table, medium and high-risk categories
from the tool were combined as “at-risk” category, andthe low-risk was considered as “not-at risk” category
in order to calculate these diagnostic values of the tool.
The chi-square method, or exact Fisher’s test whenappropriate, was applied to determine the presence ofa significant association between dichotomous
variables such as nutritional risk (at-risk and not-atrisk), age (< 5 years and ≥ 5 years), gender (male,
female) and caloric intake (adequate, inadequate) anddisease status (acute and chronic) with the nutritional
status by WHO z-scores (well-nourished andmalnourished). The agreement of the screening toolwith anthropometry was decided by calculating Cohenκ statistics, with 95% confidence intervals, andinterpreted using value scores by Landis and Koch (15).
The sample size for the validation was calculated by
expecting the Cohen’s kappa coefficient κ value would
be at least 0.4, which was considered to be appropriate
based on previous report (15). With the significancelevel of 5%, power of 90% with two tails, theminimum sample of 62 is required for kappa at 2×2
category, according to sample size calculation
guideline using Cohen’s kappa value by Bujang et. al(16). However, in order to avoid the possibility ofincomplete data, we accounted a doubled sample size
(16). All the statistical calculations were done by using
computer software, IBM SPSS Statistics version 22.0
(IBM Corp. Armonk, NY, USA). The p value <0.05
was considered statistically significant.

Subject selection and data collection
The pediatric outpatients who aged 1 years or older
and whose parents agreed to participated in the study
were included in the study. Critically ill children, andthe children with inability to perform anthropometricmeasurements were excluded. All of the subjects wererecruited by convenient sampling, and data collection
was initiated after getting the informed consent fromthe parents. The application of screening tool andanthropometric assessment were performed on thesame day by two different researchers.
Anthropometry
The weight measurement was done with thechildren on light clothes and recorded to the nearest 10
g, on the electronic scale accurate to at least 100g (11).
Height was recorded to the nearest 0.1 cm, and supine
length was measured for children under 2years of age.
Mid-upper Arm Circumference (MUAC) was
measured in children younger than 5 years old, byusing the measuring tape in the left upper arm of thechild, at the mid-point between olecranon process andacromion. The anthropometric measurements wereclassified as z-scores corresponding to age and sex
according to WHO growth reference, and these were
calculated by using the WHO Anthro version 3.2.2 and
WHO Anthro Plus software (12).
10Asian Journal of Dietetics, 2020
than half (55%) of this outpatient population werecurrently taking multivitamin supplements.

RESULTS
Among the families approached in the outpatient
department during the study period, there were 120
eligible pediatric outpatients (50% males) who
completed both anthropometric assessment andnutrition screening tool. The median age of the patientswas 3.3 years (range between 1 to 10 years). There
were 85 children (70.8%) who aged below 5 years old,and 35 children (29.2%) aged 5 years or older.
Majority of the children (84.2%) were presented withacute illness including seasonal flu and viral orbacterial infections of respiratory tract, urinary tract or
skin, gastroenteritis and others. Only 15.8% of thepatients had chronic disease conditions such ascongenital heart disease, tuberculosis and chronic
respiratory diseases. According to the 24-hr dietary
recall of the children, we found that there were 26
children who had inadequate caloric intake (< 75% of
the recommended daily allowance) during their illness(Table 1). Moreover, it was also observed that more

Prevalence of undernutrition among study
participants
Among the 120 patients studied, the WFH z-score
was determined in 86 children who were 5 years old or
younger. There were 6 children who had wasting(WFH z-score < -2 SD) with one of them being
severely wasted (WFH z-score <-3 SD). The same agegroup was examined for MUAC z-score and nochildren in this group had their MUAC z-score lessthan or equal to -2 SD. The WFA z-scores wascalculated in children younger than 10 years (n=118)
and there were 14 children who were underweighted
and the remaining 88.1% had normal weight. BMIfor- age z-score was also calculated for children of all
age groups and 9.1% of them (n=11) had thinness. It
was also found that 21 children in our study had
stunting (HFA z-score < -2 SD) or chronicmalnutrition. Overall, acute malnutrition was found in
9.1% and chronic malnutrition was diagnosed in
17.5 % of our sample (Table 2).

Table 1. General characteristics of study children
No. (n=120)<228≥2 to <557
≥535
GenderMale
60
Female60
DiseaseAcute101
Chronic
19
Diagnosis
Infection/fever
44
Respiratory
43
Gastrointestinal
21
Cardiac1
Others11
Caloric intake*
Adequate
94
Inadequate (<75% of RDA)
26
*Caloric intake calculated from 24-hr food recall (intake during illness)
RDA, recommended daily allowance
Characteristics
Age (yr)

Table 2. Anthropometric characteristics of the study children
Anthropometric indicator≥-2SD
WFH z-score(n=86)
80(93)
HFA z-score(n=120)
WFA z-score(n=118)

99(82.5)
104(88.1)

%
23.3
47.5
29.2
50
50
84.2
15.8
36.7
35.8
17.5
0.8
9.2
78.3
21.7

Number of children, n (%)< -2SD to -3SD
5(5.8)<-3SD
1(1.2)

19(15.8)10(8.5)2(1.7)
4(3.4)BMI for age z-score(n=120)
109(90.8)7(5.8)
4(3.3)
MUAC z-score (n=86)
86(100)0(0.0)
0(0.0)WFH, weight-for-height; HFA, height-for-age; WFA, weight-for-age; BMI, body mass index; MUAC, mid-upperarm circumference
remaining children had low or no risk of malnutrition.
None of the participants from our study had high risk
Validity of STRONGkid nutrition screening toolof malnutrition. When the nutrition risk was
in hospital outpatient setting
compared to WHO anthropometric indicators, it has
100% sensitivity and 47.5% specificity in identifying
Accordingtonutritionscreeningby
wasting, and 81% sensitivity ad 46.5% specificity in
STRONGkids tool, 58.3% of our study population
identifying stunting. Overall, the tool has an(n=70) had moderate nutrition risk and the11Pediatric nutrition screening in Myanmar
excellent sensitivity (>90%) except the comparison
with HFA z-score (81%), and fair specificity (>45%)
in detecting malnutrition. When compared to WHO
standards of weight for height, weight for age, BMIfor-age and height for age, it was found that thescreening questionnaire had significant association
with wasting, underweight and stunting with p-value< 0.05. However, the kappa agreement between
anthropometry and nutrition risk was still weak (𝜅=0.105 to 0.143) (Table 3).

and HFA z-scores were not significantly different.
However, the older age group had significantly lower
WFA and BMI-for-age z-scores (p < 0.05) than theyounger ones. According to our data, different formsof acute malnutrition such as wasting, underweight
and thinness were more common in boys comparedto girls, 7.7%, 15% and 11.7% respectively. The
percentage of chronic malnutrition or stunting ingirls was more than boys (18.3% compared to
16.7%). However, there was no statisticallysignificant difference in characteristics of thepatients such as sex, and acute or chronic diseasestatus in both well-nourished and malnourishedgroups, except the inadequate caloric intake
calculated from 24-hour dietary recall, which had astatistical association with stunting (p=0.02) (Table
4).

Characteristic of 120 pediatric outpatients inrelation to their nutritional status
Among the under-five years old children (n=85),
7.1% had wasting, 7.1% had underweight and 20%
had stunting according to WHO standards. In thechildren who aged 5 years or older, 24.2% had
underweight, 20% had thinness and 11.4% had
stunting. Between these two age groups, the WFH

Table 3. Cross-classification of nutrition risk from screening and WHO anthropometric standards
Nutrition risk
WFH z-score
WFA z-score
BMI-for-age
HFA(n=86)(n=118)z-score (n=120)z-score(n=120)<-2 SD ≥-2SD<-2 SD≥-2SD<-2 SD≥-2SD<-2 SD≥-2SD
Risk (n)
6
42
13
57
10
60
17
53
No risk (n)
0
38
1
47
1
49
4
46
p-value
0.032b0.007a0.025b
0.02a
Kappa
0.112
0.139
0.105
0.143
Sensitivity
100
92.9
90.9
81
Specificity
47.5
45.2
45
46.5
PPV
12.5
18.6
14.3
24.3
NPV
100
97.9
98
92
a
b
chisquare; fisher’s exact test
WFH, weight-for-height; WFA, weight-for-age; BMI, body mass index; HFA, height-for-age; PPV, positive
predictive value, NPV; negative predictive value

Table 4. Association between clinical characteristics and nutrition status of children
Wasting
UnderweightThinnessAge<5yr (n=85)≥5yr (n=35)
GenderMale (n=60)
Female (n=60)
DiseaseAcute (n=101)
Chronic(n=19)
Caloric intake
Adequate (n=94)
Inadequate (n=26)

Stunting

n (%)

p

n (%)

p

n (%)

p

n (%)

p

6(7.1)
0 (0)1.00

6(7.1)
8(24.2)0.02*

4(4.7)
7(20)

0.01*

17(20)
4(11.4)0.30

3(7.7)
3(6.4)

1.00

9(15)
5(8.6)

0.40

7(11.7)4(6.7)

0.53

10(16.7)
11(18.3)

1.00

6(8)
0(0)

1.00

12(12)
2(11.1)

1.00

10(9.9)1(5.3)

1.00

19(18.8)2(10.5)0.52

4(6.0)
2(10.5)0.61

8(8.7)
6(23.1)0.08

9(9.6)
2(7.7)

1.00

12(12.8)9(34.6)0.02*

p-value for association between categorical variables were derived from Fisher’s exact test

12

Asian Journal of Dietetics, 2020children in Myanmar had wasting and stunting (2).
Although the study was done in a private hospital, there
was a recognizable prevalence of malnutrition inchildren with acute illness. Moreover, we also foundthat although the weight of the children was measured
by the nurses as a routine procedure in outpatient
department, the measurement of height and standard
procedure of plotting anthropometric measurement on
WHO growth charts was not performed routinely.
Therefore, the application of a screening tool not only
can help in detecting the children with nutrition risk, butalso can improve the use of WHO growth chart.
It was also found that there was a statisticallysignificant association (p<0.05) between age of the childwith the malnutrition status according to WFA andBMI-for-age data (Table 4). Children who were older
than 5 years had higher prevalence of underweight andthinness. Similar to our results, the general population
prevalence of underweight in 5-19 years old children
was also high in Myanmar, which was reported as
33.3% in females and 39.9% in males in 2015(2). No
statistical association (p>0.05) was found between sex
and malnutrition status in our study (Table 4). In contrast,
a study performed in Iranian hospitalized children by
Gholampour et al demonstrated that the male sex
constituted the larger proportion of malnourished group(23).
The results of the current study indicated that
15.8% of the participants had chronic disease and 84.2%
had acute illness. It was noticeable that in the group ofchildren with acute fever or other non-specificinfections, there was a relatively high rate of chronicmalnutrition or stunting (Table 4). Rub et al also
reported regarding high malnutrition prevalence which
was found in apparently healthy children, and this iscomparable with our results (21). The evaluation of 24hour food intake of the children revealed that reduced
caloric intake had statistically significant association
with malnutrition risk (Table 4). This indicates that thepatients with acute illness had tendency to bemalnourished due to reduced oral intake. Moreover, wefound that the reduced food intake of patients had
statistical association with stunting (Table 4). However,the food intake was taken from a single day during
illness, and it cannot be used to explain the high rate ofstunting in our population.
There are some limitations in our study. Firstly,
this is a single center study with relatively small sample
size, and our participants may not represent the whole
outpatient population in Myanmar, and thegeneralization of our results is not possible. The crosssectional design of our study does not permit evaluating
the outcome of nutrition intervention in children withhigh or moderate risk. Further research which evaluate
the effect of presence or absence of nutritionintervention following screening in children at-risk isrecommended.

DISCUSSIONIn Myanmar, the applicability of nutrition
screening tool has never been studied in hospitalized
children, or, in children at the outpatient department. To
the best of our knowledge, this is the first study toevaluate the efficiency of a nutrition screening tool in
detecting risk of malnutrition among children in a
hospital outpatient setting of Myanmar.
The STRONGkids nutrition screening tool has
been validated and widely used in many developed anddeveloping countries (17, 18). For the validation of thistool in Myanmar population, we chose theanthropometric measurements which have been used
globally to assess the malnutrition, as a reference
method (6) for comparison. The validation of thescreening tool in our study demonstrated a goodsensitivity and negative predictive value (> 90%) in
detecting different forms of acute malnutrition of thechildren (Table 3). This value was higher than thevalidation of STRONGkids tool in Belgium which
presented 71.9% sensitivity for identifying acutely
malnourished pediatric inpatients (9). Alternatively,
when comparing to WFH z-score, our screening tool
has slightly lower specificity (47.5%) than the study by
Huysentruyt et al which has 49.1% specificity (9).
According to ASPEN, the purpose of the screening in aclinical setting is to filter out the children with no riskof undernutrition, and to find the children who may bebenefitted from full nutritional assessment andintervention (19). Therefore, the higher false positive
rates will only increase the number of patients who
undergo full nutritional assessment and, for the purposeof screening, sensitivity is more important than thespecificity (9).
Previously, few studies had been conducted toinvestigate the value of nutritional screening in pediatricoutpatients, although most of the tool have applied andvalidated for hospitalized children in developed anddeveloping countries. The STRONGkids screening tool
was studied in special schools by Joosten et al with theaim of finding association between subjective healthstatus and nutrition risk, and it was reported thatchildren with nutrition risk had more difficulties in
performing daily activity and increased tendency tohave pain (20). Another tool, the Screening Tool for theAssessment of Malnutrition in Pediatrics (STAMP) was
also applied in 1-6 years old children at an ambulatory
setting, and, in contrast to our screening results which
only had weak agreement (𝜅 00.1) with the reference
method, the STAMP tool had a moderate agreementwith full dietitian assessment (𝜅 =0.47(95% CI 0.240.70)) (21). This can be explained by the use of different
criterion standards for determining nutritional status in
validation of screening tool.
In our study population, 7% of children had
wasting, 9.1% had thinness and 17.5% were stunted
according to WHO growth standards. The overall
prevalence of malnutrition in our participants according
to anthropometry was 26.6%. This percentage is lessthan our neighbor country, India by Gupta et al, where
the prevalence of malnutrition in OPD children of underfive years was reported as 66.4% (22). However, thedata in Myanmar comparable to our study results islimited. Among the participants, 70% of children wereyounger than 5 years old, and wasting and stunting wereidentified in 7.1% and 20% of them respectively (Table2). These prevalence rates were in accordance with therecent community malnutrition report by UNICEF/
WHO in which 6.6% and 29.4% of under 5 years

In conclusion, our study is the first one to validatea simple pediatric nutrition screening tool for detecting
malnutrition in Myanmar hospital-outpatient setting.
The tool was very sensitive and it can detect >90% of
the children with poor nutrition status. The specificityof the screening tool was around 50% and there was asignificant association of nutrition risk calculated fromscreening tool with the nutrition status by WHO growthstandards. Our results demonstrated a recognizable
prevalence of malnutrition among the pediatricoutpatients of Myanmar. Furthermore, the present study
provided public health information regarding disease13

Pediatric nutrition screening in Myanmar
and malnutrition status in a private hospital. Together,
our results encouraged the use of nutrition screening
tool in different hospital settings for early detection of
malnutrition and to reduce the prevalence andconsequences of malnutrition in Myanmar children.

for-age, methods and development. 2006: World
Health Organization.
11) Organization W H. Training course on child
growth assessment. Geneva: WHO: p17-25. 2008.
12) Organization, W.H., WHO Anthro (version 3.2. 2,January 2011) and macros. Geneva: WHO. 2011.

ACKNOWLEDGEMENT

13) Barba C V, Cabrera M I Z. Recommended dietary
allowances harmonization in Southeast Asia. Asia
Pac J Clin Nutr 17: 405-08. 2008.

The author gratefully acknowledged Professor Dr.
Saw Win and hospital administrative committee for thepermission to collect data at Parami General Hospital.
We also grateful to the children and parents for theirkind participation in our study, and the advisors from
Institute of Nutrition, Mahidol University for valuableadvice.

14) Becker P, Carney L N, Corkins M R, Monczka J,
Smith E, Smith S E, Spear B A, White J V,
Nutrition A O, Dietetics, Parenteral A S F,
Nutrition E. Consensus statement of the Academyof Nutrition and Dietetics/American Society for
Parenteral and Enteral Nutrition: indicators
recommended for the identification anddocumentationofpediatric
malnutrition(undernutrition). Nutr Clin Pract 30: 147-61. 2015.

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14

Asian Journal of Dietetics, 2020

ORIGINAL
Carbohydrate Profile, Total Dietary Fiber and In vitro Glycemic Index
of Taro- Corn and Sweet Potato- Corn noodles.
Hazel T. Lat1*, Wilma A. Hurtada2, Erlinda I. Dizon3, Aimee Sheree A. Barrion2
1

Department of Science and Technology-Food and Nutrition Research Institute, Bicutan,
Taguig City, Philippines.
2
Institute of Human Nutrition and Food, College of Human Ecology, University of thePhilippines Los Baños, Philippines.
3
Institute of Food Science and Technology, Food Science Cluster, College of Agriculture,
University of the Philippines Los Baños, Philippines.
(received January 30, 2019)
ABSTRACT Background. The carbohydrate profile and In vitro glycemic index of taro,
sweet potato and IPB var. 6 white corn flours and the noodles from taro-corn and sweet
potato-corn flour mixtures were studied. Results. Processing the flours into noodles
significantly affected its carbohydrate profile (starch and amylose), total dietary fiberand glycemic index. There was a significant reduction (p<0.05) in the starch content
when the flours were processed into noodles, but a significant increase (p<0.05) in theamylose content, total dietary fiber and glycemic index when the flours were processed.
Conclusion. The results of this study may provide opportunities to promote and support
the use of the taro, sweet potato and IPB var. 6 white corn flours into processed foods
like noodles which can help improve its production and utilization potentials.
Keywords: Taro; Sweet Potato; Corn; Dietary Fiber; Glycemic Index

INTRODUCTION
intestine (3), while the glycemic index of foods wasintroduced to provide additional information about
foods and to classify them in terms of their glycemic
response whether foods eaten release glucose rapidly
or in a slow and sustained fashion for a period of time(4). From a nutrition perspective, it is not particularly
important to know the exact GI value of every foodas this will change depending on various factors.
What is important is to be able to identify which
foods are absorbed over a longer period of time (lowGI; GI <55), which foods elicit a rapid surge of
glucose (energy) into the bloodstream (high GI; GI >70), and which are in between (intermediate GI; GI
55-70). Consequently, foods high in carbohydrates
can thus be arranged into these broad categories toguide dietary choices (5).
This study specifically determined thecarbohydrate profile (starch and amylose), total
dietary fiber content and in vitro glycemic index of
the taro [Colocasia esculenta (L.) Schott], sweet
potato [Ipomoea batatas (L.) Poir], IPB var. 6 whitecorn[Zea mays L] flours, and the cooked anduncooked taro-corn and sweet potato-corn noodles todetermine the effect of processing. The results of thisstudy may provide opportunities to promote andsupport the use of the taro, sweet potato and IPB var.
6 white corn flours into processed foods like noodles
which can help improve its production and utilization
potentials.
This study was conducted at IHNF, UPLB from
May to June 2014.

Carbohydrates, such as rice, breads, and noodles
are the major sources of food, because it provides
most of the daily energy needs. Among these, noodles
are popularly known for their ease of preparation,
availability and deemed as a cheap source ofcarbohydrates. Even in disaster situations, noodles
are usually being provided as a source of nourishment.
Noodles are usually prepared using ingredients suchas wheat flour, egg and salt. These ingredientsprovide high energy, but most often, it does notcontain other nutrients that are beneficial for growthand development. That is why, noodles in the country
are starting to be fortified with vitamins and also haveadditional vegetable ingredients. Although, it will begood to try substituting wheat flour with indigenous
flours, for added nutritional and health benefits, suchas taro flour, sweet potato flour, and corn flour.
Tubers and root crops, such as taro, sweet potato
and crops such as corn or maize, are basicallyimportant sources of carbohydrates as an energy
source and as a staple in the tropical and sub-tropicalcountries (1). These products have nutritionallybeneficial components high dietary fiber content andhave a lower glycemic index value (2).
Dietary fiber is an important part of a healthy
diet. It is the remnants of the edible part of plants andanalogous carbohydrates that are resistant todigestion and absorption in the human small intestinewith complete or partial fermentation in the humanlarge*

To whom correspondence should be addressed:
lat.hazel@gmail.com
15

GI of taro sweet potato and corn
their aroma, texture and flavour, and was acceptable
as compared to the other proportions.

METHODS
Experimental Materials
Quality Protein Maize (IPB var. 6) and Sweet
Potato (NSIC 31 variety) were obtained from theInstitute of Plant Breeding, College of Agriculture,
University of the Philippines Los Baños, Laguna.
Taro corms (NSIC V9 variety) were obtained fromthe Philippine Root Crop Research and Training
Center, Visayas State University (VSU), Visca,
Baybay City, Leyte.

Chemical analyses
Starch content was determined using the anthrone
method while the amylose content was determinedusing a colorimetric assay by Williams et al. (6).
Total Dietary Fiber
The total dietary fiber was determined using the
enzymatic-gravimetric method using the total dietaryfiber assay kit (TDF-100 A and TDF – C10) of
Sigma-Aldrich, Inc.

Processing of taro corms and sweet potato roots
The raw taro corms and sweet potato roots
underwent different physical treatments such as
washing, peeling, dicing, soaking, drying and milling
before they were processed into taro and sweet potato
flour. The taro and sweet potato flour was then mixed
with the IPB var. 6 white corn flour and was used in
the preparation of the noodles. It was analyzed for
its carbohydrate profile, total dietary fiber content
and in vitro glycemic index. All the chemical
analyses were done at the Analytical Laboratory of
the Institute of Human Nutrition and Food at the
University of the Philippines Los Baños.

Estimation of Glycemic Index
The kinetics of in vitro starch digestion was
followed by a nonlinear model established by Goni etal. (7).
Statistical analysis
Results of the chemical tests were analyzed using
ANOVA and LSD. All the statistical tests were
performed at 5% level of significance.
RESULTS AND DISCUSSION

Preparation of the taro-corn and sweet
potato-corn noodles
The taro, sweet potato and IPB var. 6 white cornflours were used in the preparation of the noodles.
The recipes were standardized to determine the rightproportion of the ingredients that yielded the mostacceptable product.

Carbohydrate Profile
The taro flour has the highest starch content at
75.22 % followed by the sweet potato flour at
71.45 % and the IPB var. 6 white corn flour at
70.98 %. As for the noodles, the sweet potato-corn
uncooked noodles have higher starch content at
65.32 % as compared to the uncooked taro-cornnoodles at 52.79 %. There was a significant decreasein the starch content when the noodles were cooked,
from 52.79 % starch content of the uncookedtaro-corn noodles to 30.91 % in the cooked taro-cornnoodles, as well as in the sweet potato-corn noodles
at 65.32 % for the uncooked noodles to 55.42 % in
the cooked noodles (Table 1).

Selection of flour proportion for noodle
production
Preliminary study was conducted to produce flat
noodles from the different possible combinations of
the taro-corn flour and sweet potato-corn flour (100:0,
90:10, 80:20,70:30, 60:40, 50:50, 40:60, 30:70, 20:80,
10:90 and 0:100). The 70:30 taro-corn and 70:30
sweet potato-corn noodles were both slightly liked forTable 1. Starch and amylose content of taro, sweet potato, IPB Var. 6 flour and taro-corn and sweet potato-cornnoodles
SampleStarch (%)
Amylose (%)
Taro Flour
Sweet Potato Flour
Quality Protein Maize Flour
Taro-Corn uncooked noodles
Taro-Corn noodles
Sweet potato-Corn uncooked
noodles
Sweet potato-Corn cooked
noodles75.22 + 1.30a
71.45 + 2.24b
70.98 + 0.62b52.79 + 0.76d
46.80 + 0.91e
65.32 + 1.96c

39.75 + 2.33b
18.57 + 0.21f
30.45 + 0.21e
33.52 + 0.58d
36.27 + 1.07c
39.05 + 1.15b55.42 + 1.82f

45.14 + 2.21a

Means that share the same letter within columns are not significantly different at p (<0.05).
Waxy (0-5% amylose); very low (5-12% amylose); low (12-20%); intermediate (20-25%); high (25-33%)

16

Asian Journal of Dietetics, 2020
The taro flour has the highest amylose content at
39.75 % followed by the IPB var. 6 white corn flourat 30.45 % and the sweet potato flour at 18.57 %.
For the noodles, there was a significant increase in
the amylose content after cooking, from 33.52 to
36.27 % in the taro-corn noodles and from 39.05 %amylose content in the uncooked sweet potato-cornnoodles to 45.14 % after cooking. Also, theamylopectin content was measured. The sweet potato
flour has the highest amylopectin content at 40.53 %followed by the IPB var. 6 white corn flour and thetaro flour at 40.53 and 35.47 % respectively.

Total Dietary Fiber
Among the three flours, the sweet potato flour has
the highest total dietary fiber content at 4.74 %,followed by the IPB Var.6 white corn flour and thetaro flour at 2.71 % and 2.63 % respectively. The
total dietary fiber content of the cooked noodles
increased from, 5.02 % to 5.36 % in the cookedtaro-corn noodles while the uncooked sweet
potato-corn noodles also increased from 7.21 % to10.17 % in the cooked sweet potato-corn noodles(Table 2). This could have been due to the fact that anincrease in temperature leads to a breakage of weakbonds between polysaccharide chains.
Also,
glycosidic
linkagesinthedietary fiber
polysaccharides may be broken (9). According toDhingra et al. (10), grinding may affect the hydration
properties, in particular, the kinetics of water uptake
as the result of the increase of surface area, the fiber
hydrates more rapidly. Heating generally changes theratio of the soluble to insoluble fiber. Simple
processes, such as soaking and cooking tend to
modify the composition and availability of nutrients.
This may also modify the plant cell wall material that
may have physiological effects. In a study done inwheat bran, it has been found out that thermal
treatment, such as boiling, cooking or roastingoriginate an increase of total fiber that is not due to
new synthesis but rather to the formation offiber-protein complexes that are resistant to heating
and are qualified as dietary fiber. According to
Pushparaj and Urooj (11) changes in dietary fiber
composition of processed cereal and pulses have beenreported where increase in the total dietary fiber
content could be due to formation of resistant starch.
The amount of total dietary fiber is importantbecause of its influence on glucose absorption,
post-prandial glucose levels, and glycemic index(12).

The amylose content in starch is one of theimportant factors influencing starch pasting and thestrength of starch gel due to its quick retrogradation,
association and interaction to lipids, and amylopectin
form helical complex giving strong gel structures.
Starches with high amylose content were desired for
manufacture of starch noodles (8). Taro has asmooth-textured gel, which would be suitable fornoodle processing, which requires a smooth mouth
feel and avoidance of grainy texture. Also, paststudies have shown that sweet potato flour may
potentially be used in noodle processing and bread
production (2). However, it was also reported thathigh amylose corn starch was not suitable for noodle
making because it was not sufficiently gelatinized at
atmosphere condition, leading to almost no amylose
molecules released to participate into the noodle
structure formation (8). As what was observed in the
preliminary study wherein the 100% corn flour
cannot be formed into noodles. However, if mixed
into starches with high amount of protein, lipid andash, which can reduce the clarity of starch paste andhinder the swelling of the starch granule because of
the high rate formation of amylose-lipid complex.
Nevertheless, the protein and lipid played importantrole in retention of amylose in starch noodles duringcooking, resulting in minimizing cooking loss (8).

Table 2.

Total dietary fiber content
SampleTotal Dietary Fiber (%)

Taro Flour

2.63 + 0.21d

Sweet Potato Flour
Quality Protein Maize Flour

4.74 + 0.59c
2.71 + 0.21d

Taro-Corn uncooked noodles
Taro-Corn noodles

5.02 + 0.24c
5.36 + 0.68c

Sweet potato-Corn uncooked noodles
Sweet potato-Corn cooked noodles

7.21 + 0.86b
10.17 + 0.68a

Means that share the same letter within columns are not significantly different at p (<0.05)

Glycemic Index
The classification of foods based on theirglycemic index has dispelled the repeatedly
suggested dietary notion that carbohydrate-rich foods
have deleterious health effects, and as such,consumption should be limited. In fact, there are
numerous evidence-based studies which dismiss thenegative view of carbohydrate-rich foods and clearly
demonstrate that “not all carbohydrates are createdequal”.
Furthermore,variations
inthephysicochemical properties of complex carbohydrates
have been shown to elicit dissimilar physiologicaleffects when consumed. Also, some complex
17

GI of taro sweet potato and corncarbohydrate-rich foods are undeniably beneficial anddo not cause blood glucose levels to spike any greaterthan simple sugars. However, food preparation isimportant and should be considered, as the method of
cooking can alter the structure and the nature of the
starches (13).

potato-corn noodles, but much more rapid in the caseof white bread and that of the cooked taro-corn andsweet potato-corn noodles, which showed higherhydrolysis index. However, it should be kept in mind
that ingredients included in the noodle formulation
might have also affected starch digestibility (16).

Enzyme hydrolysis of starch molecules to
produce glucose is important to provide energy forplant metabolism, food, feeds and ethanol production.
The starch granules are hydrolyzed at a slower rate by
enzymes than the gelatinized, amorphous starch
molecules. The susceptibility of the starch granules
depends on the granular size, the structure of theamylopectin, the amylose content, the lipid content,
and the reaction pattern of the enzyme. Generally, thelarger the starch granule, are normally digested at a
slower rate than the smaller starch granules because
the larger granules have a smaller relative surface
area for enzyme hydrolysis (14).

The rate of starch hydrolysis in the samples
varied widely suggesting that other factors other thanhydrolytic enzyme activity. It can be observed thatthe cooked noodles hydrolyzed more rapidly ascompared to the uncooked noodles. This is because
cooking greatly increased the rate at which starch canbe hydrolyzed by gelatinizing the starch and making
it more readily available for enzymatic attack (17).
The degree of starch gelatinization is not the only
factor that affects susceptibility of starch to enzyme
action. The digestibility of starch can be affected byseveral factors including the composition andphysical form of the starch, protein-starchinteractions, the physical form of the food, theintegrity of the starch-containing cells, and presenceof anti-nutritional factors (18), while the lower levelsof starch hydrolysis that were observed in the taro,
sweet potato and IPB var 6 white corn flour, may be
attributed to the presence of cell wall and othercomponents such as the dietary fiber and mucilage(19) as what was observed by Bordoloi et al. (20) in
the starch of cooked potatoes.

White bread, used as reference in this study,
showed a digestion value of 75.3 % at 180 minutes(Figure 1), which resembles that reported by Goñi etal. (7), but previous works, such as the one done byGrandfelt et al. (15) as reported by Tovar et al.
(2002) have found that white bread showed adigestion value of 50 %. This could be explained, due
to the different methodologies that were used, such as
the inclusion of pepsin in the hydrolysis and the useof an unrestricted system (7).

Table 3 shows that the reference sample, whitebread has an estimated glycemic index of 94.61. For
the flour samples, the sweet potato flour has thehighest glycemic index at 77.20, followed by the taro
flour and the IPB Var 6 white corn flour at 65.46 and60.79 respectively.
For the noodles, cooking
showed a decrease in the glycemic index of thetaro-corn and sweet potato-corn noodles at 84.92 forthe uncooked taro-corn noodles to 72.24 for thecooked taro-corn noodles, and the uncooked sweet
potato-corn noodles at 88.43 to 74.45 for the cooked
sweet potato-corn noodles.

Among the flours, the IPB Var. 6 white corn flour
has the lowest total starch hydrolysis rate at 29.13 %and the sweet potato flour has the highest rate at
51.66 % as compared to that of the white bread. The
cooked taro-corn and sweet potato-corn noodles also
showed a high starch hydrolysis rate of 67.05 % and62.22 % respectively (Figure 1).
The course of hydrolysis differed greatly among
various samples, being markedly slow in the case of
IPB Var. 6 white corn flour, sweet potato flour, taro
flour and the uncooked taro-corn noodles and sweet
80

Taro

TOTAL STARCH HYDROLYSIS (%)

70
60

Sweet potato

50

Quality Protein Maize

40
Taro-corn uncooked noodles
30
Taro-corn cooked noodles

20

Sweet potato-Corn
uncooked noodles

10
0
0

30

60

90

120

150

TIME (MIN)180

Sweet potato-Corn cooked
noodlesFigure. 1. Average hydrolysis curves of starch in taro flour, sweet potato flour, IPB var. 6 white cornflour (QPM), and cooked and uncooked taro-corn and sweet potato-corn noodles.

18

Asian Journal of Dietetics, 2020
Table 3. Starch hydrolysis and estimated glycemic index (EGI) of flour and noodles
SAMPLEWhite Bread

H90
75.30 + 1.19aC∞
79.29ak
0.03

HI
100 + 0.00a

EGI
94.61 + 0.00a

Taro Flour
Sweet Potato Flour

35.91 + 3.41bd
51.66 + 1.99c

39.34bd
55.46c0.03
0.03

46.91 + 6.11bd
68.28 + 3.80ch

65.46 + 3.35bd
77.20 + 2.08ch

Quality Protein Maize
Flour
Taro-Corn uncooked
noodlesTaro-Corn noodles

29.13 + 5.13d

32.31d0.03

38.38 + 6.71d

60.79 + 3.70d

62.22 + 1.85eh

66.04ec

0.03

82.34 + 0.95e

84.92 +0.52e

44.88 + 0.82fbc

48.43fbc

0.03

59.25 + 2.01fh

72.24 + 1.10fh

Sweet potato 67.05 + 4.12ga
70.93gae
0.03
88.74 + 4.58g
88.43 + 2.52g
corn uncooked noodles
Sweet potato – corn
47.89 + 2.69hc
51.52hcf
0.03
63.27 + 4.25h
74.45 + 2.33h
cooked noodles
Means that share the same letter within columns are not significantly different at p (<0.05)
GI Classification: Low GI <55; intermediate 55-70; High >70;
H90 – Starch hydrolysis at 90 minutes; C∞ - Equilibrium concentration; k- Kinetic Constant; HI- Hydrolysis
Index
the amylose content, high dietary fiber was believed
The sweet potato flour exhibit the highest GI
to reduce the blood glucose response, and thus lower
value among the three flours at 77.20 and also thethe GI value of a food. Dietary fiber influences thelowest amylose content at 18.57 %, while the taro
starch digestibility by changing the microstructure offlour and the IPB var. 6 exhibited an intermediate GI
foods, which decreases the susceptibility of starch tovalue at 65.46 and 60.79 respectively, and also
amylolytic attack, and by limiting water availability,
exhibited a higher amylose content than that of thewhich restricts starch gelatinization (26).
sweet potato flour at 39.75 and 30.45 % for taro and
IPB var. 6 white corn flour respectively. When the
The sweet potato flour has the highest total
flours are made into noodles, it resulted in higher
dietary fiber content and the highest GI value among
amylose content for the sweet potato-corn uncooked
the three flours, followed by the taro and the IPB Var.
and cooked noodles at 39.05 to 45.14 % respectively.
6 QPM white corn flour (Figure 2). This was because
As for the taro-corn noodles, when the noodles were
grinding has an effect on the gross matrix structure of
cooked it resulted in a higher amylose content at
the flour. The grinding process increases the glycemic
33.52 % for the uncooked noodles to 36.27 % for the
response and glycemic index of the flours (27). The
cooked noodles. When the noodles are cooked, theuncooked sweet potato-corn noodles has a lower
amylose content increases, but the GI value decreased,dietary fiber content but have a higher GI value as
from the GI value of 84.92 to 72.24 for the taro-corncompared to the cooked sweet potato-corn noodles.
noodles and the GI value of 88.43 to 74.45 for the
The same can be observed in the taro-corn noodles
sweet potato-corn noodles (Figure 1). Bahado-Singh
wherein there was an increase in the dietary fiber
et al. (13) reported that rice with high amylose
content and a decrease in the GI value after cooking.
content was accompanied by a lower metabolicThis might be due to the higher amylose content ofresponse to and lower GI values. Also, boiling lowers
the cooked noodles as compared to the uncooked
GI values. This may be link to the chemical structurenoodles. This is due to high amylose slowed theof starches - that is the amylose-amylopectin ratio
digestion rate. There is greater hydrogen bonding(21). The amylose content is known to be negatively
between glucose units in amylose molecule than thecorrelated with the starch susceptibility to amylase
amylopectin molecule, thus less exposure tohydrolysis (22). Juliano (23) suggested the
enzymatic digestion (28).
classification of amylose content in rice as waxy
Wolever (29), studied naturally present fiber(0-5 %), very low (5-12 %), low (12-20 %),contents in foods and their subsequent glycemic
intermediate (20-25 %) and high (25-33 %). Waxy
response, and revealed that the total dietary fiber was
starch granules are more susceptible to enzyme
significantly related to the glycemic index of foods.
hydrolysis than intermediate starch granules, and theThis was supported by the study done by Brouns et al.
intermediate starch granules are more susceptible to(27), Riccardi et al. (30) and Allen et al. (12), thatenzyme hydrolysis than high-amylose starch granules
stated that foods that are rich in fiber generally have a(24-25). Concomitantly, greater amounts of resistant
low glycemic index, although not all foods with a low
starches may have been retained in the boiled foods.
glycemic index necessarily have high fiber content,
Furthermore, as these foods cool, the possibility ofas what have been observed between the glycemic
forming resistant starches increases. This occurs as
index values and the total dietary fiber values of thethe starches undergo recrystallization due to theuncooked and cooked noodles.
formation of the intermolecular hydrogen bonds. Also,these resistant starches, after the leaching of freeThus, the presence of amylose and dietary fiber insugars during the boiling process also play a role inthe different samples used affects its glycemic index.
retarding the enzymatic degradation of the starches,
The combined effect of amylose and dietary fiber
thus reducing the glycemic response (13). Also, apartcontributed in the low glycemic index value in thefromcooked taro-corn and sweet potato-corn noodles.

19

Concentration

GI of taro sweet potato and corn

90
80
70
60
50
40
30
20
10
0

Glycemic index
Amylose
Dietary fiber

SamplesFigure 2. Comparison between the glycemic index, amylose content andtotal dietary fiber for each sample.
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CONCLUSION
The results of the study revealed that processing
the taro flour, sweet potato flour, IPB var.6 white cornflour, and the uncooked and cooked taro-corn andsweet potato-corn noodles significantly affected thecarbohydrate profile, specifically, the starch andamylose content, the total dietary fiber content andthe glycemic index. Boiling the noodles significantly
decreased its starch content, and its glycemic index,
while significantly increasing its amylose content andtotal dietary fiber content.
Studies on the shelf life and effect of storage
temperature to the flours, as well as the noodles must
be done. Another area of interest would be theutilization of the taro and sweet potato flours as aningredient in other products to improve its palatability,
especially for children. Also further studies regarding
the cost analysis/profitability in the production of theflours, and its products, like the noodles should be
pursued. Lastly, the use of the taro-based and sweet
potato-based products in feeding programs should betested to know whether it can help improve thenutritional status, and thus preventing malnutrition.
ACKNOWLEDGEMENT
Special thanks to the Department of Science andTechnology- Accelerated Science and Technology
Human
Resource
Development
Program(DOST-ASTHRDP) for the full financial support andscholarship grant; to Dr. Dilberto Ferraren ofPhilippine Root Crop Research and Training Center,
Visayas State University, Visca, Baybay City, Leyte,for providing the taro samples, and to Prof. Felicito
Rodriguez of the Institute of Plant Breeding andInstitute of Human Nutrition and Food, University of
the Philippines, Los Baños for providing the sweet
potato samples.

20

Guidelines and Practices in Preterm Nutrition
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Guidelines and Practices in Preterm Nutrition

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Asian Journal of Dietetics, 2020

ORIGINAL
Effects of Pre-Germinated Brown Rice on Blood Glucose
Concentration of Type 2 Diabetes Mellitus Patients in Vietnam
Bui Thi Nhung1, Le Dang Tuyen1, Nguyen Do Van Anh1, Nguyen Thi Hong Ha2,
Vu Thi Minh Thuc3, Vu Anh Linh2, Kei Yui4, Yukihiko Ito4, Shigeru Yamamoto2*
1

National Institute of Nutrition, Hanoi, Vietnam
Asian Nutrition and Food Culture Research Center, Jumonji University, Saitama, Japan
3
National ENT Hospital, Hanoi, Vietnam
4
FANCL Research Center, Yokohama, Japan2(received December 24, 2019)
ABSTRACT In recent years, type 2 diabetes mellitus (DM) prevalence in Vietnam isincreasing rapidly. Newly diagnosed Vietnamese DM patients were reported to have low
body mass index (BMI) of around 23, and high white rice intake has been identified as
one of the major causes. As a recommended staple food substitute for white rice, brownrice has the disadvantages of hard texture and unfavorable taste. These drawbacks can becompensated in the process of making pre-germinated brown rice (PGBR), by soaking BR
in lukewarm water to make it slightly sprouted. Previous studies have suggested thatsimilar to BR, PGBR is effective in modulating blood glucose and lipid concentrationlevels. In this study, a 4-month PGBR administration was applied to evaluate the efficiency
of PGBR on HbA1c index of Vietnamese type 2 diabetics. The present results suggest that
replacing WR with PGBR may be useful in modulating blood glucose levels in Vietnamesetype 2 DM patients.
Keywords: PGBR; DM; HbA1c; Vietnamese

INTRODUCTION

approximately 23 (7-8). The use of white rice (WR) asa staple food may serve as the major factor, since it has
been demonstrated and distinguished as a highglycemic index (GI) food and a risk factor for DM (1011).
GI was proposed by Jenkins et al in 1980 as anindicator reflecting post-prandial blood glucose
response after digesting foods (12). Since then, astrong relationship between GI and DM has been
observed. It is expected that a carbohydrate-rich dietaccompanied by rapid glucose absorption, i.e. a dietwith high GI value, is strongly associated withincreased risks for diabetes (13-14). Although it mayvary depending on such factors as amylose content or
refining process, the average GI value of WR isgenerally described to be higher than that of whole
wheat cereals. In particular, GI value of WR was 76±7,
62±5 for BR, 30±9 for whole wheat and 29±5 forbarley kernels in an earlier meta-analysis (15). A largescale study with more than 59,000 subjects by Nanri etal linked high WR intake with an increased risk of type
2 DM in Japanese women (16). Although WR has beenvalidated as a high GI food and a DM risk factor, for
centuries it has been the major staple food that laid thefoundation of the traditional Vietnamese diet.
According to the National Nutrition Survey 2009-2010,as much as 67% of total energy intake in the diet of
Vietnamese people comes from white rice, despite thedramatic change in dietary habits in recent decades(17). The long tradition of consuming rice as a staple

The prevalence of type 2 diabetes mellitus (DM) is
increasing worldwide at a dramatic pace. According to
the World Health Organization (WHO), DM ranked 8th
among 10 leading causes of death in the world, having
caused 1.5 million deaths within a year of 2013 (1).
Around 80% of people with diabetes reside in
developing nations, where striking transformation inlifestyle, population structure and growth rates aremajor contributors to the wide spread of the epidemic.
As reported by the International Diabetes Federation(2), the prevalence of type 2 DM in Vietnam has
doubled within a decade, rising from 2.7% (2002) to5.4% (2012). As of 2012, Vietnam was home to nearly
3.3 million DM cases, with more than 54,000 deaths
by DM-related diseases (2). It is anticipated that by
2030, the number of type 2 DM cases in Vietnam will
double in comparison to 2010 (3).
Although DM has been consistently associatedwith obesity, studies have indicated that type 2 DM
develops at a lower body mass index (BMI) range
among Asians as opposed to those of Western
populations, and that at the same BMI, Asian
populations have a higher incidence of DM than their
Western counterparts (4-6). In Vietnam, various
studies in the 1990s and 2000s showed that
Vietnamese DM patients have low BMI of* To whom correspondence should be addressed:
shigeruy@juminji-u.ac.jp
23

PGBR improves HbA1c in Vietnamese with DM
food makes it difficult for Asians, including theVietnamese, to reduce rice intake.
As low GI foods, whole grains such as BR orwhole wheat are often recommended to be consumed
in combination with high GI staple foods in Asian style
diets, namely WR and white bread, in order to restrain
rapid elevation of blood glucose and control metabolic
syndrome risk factors (18). Epidemiologic studies
have demonstrated an inverse association between
risks of metabolic syndrome and whole grain
consumption. Ye et al (19) reviewed 66 papers
published between 1996 and 2012 and reported a 21%
decrease in DM and 21% decrease in heart diseases by
the daily intake of 370g whole grains. Low GI diets
have been known to exert positive effects similar tothat of several anti-hyperglycemic pharmacologicalagents, as suggested by Brand-Miller in a previous
meta-analysis (20). In recent years, significant
evidence has transpired in respect of the influences of
BR on blood glucose and DM risks. A cohort study by
Harvard School of Public Health reviewing 3 studies
on 197,228 subjects found that replacing 50g WR(equivalent to one third of a typical daily serving) withan alternative portion of BR everyday can help lower
risk of type 2 DM by 16% (11).
BR is made by removing the outermost husk of therice grain, whereas WR is made by depriving BR of its
bran and germ portion. Due to the differences inprocessing, WR primarily consists of starch whilst BR
maintains prominent nutrient contents of dietary fiber,
vitamins and minerals (11, 21-22). BR however, has
several problems of tough texture and unpleasantpalatability, causing difficulty in digestion as well as
meal preparation, especially in large quantity. In recent
years, a new type of rice called pre-germinated brownrice (PGBR) has been developed and known toVietnam. PGBR is produced by soaking BR inlukewarm water until slight germination. In PGBR,
nutrient contents of BR are retained, whereas thehardness of the texture is reduced and the taste isconsiderably improved (23). Scientific evidence
showed that the germination process also activates
several bioactive components available in the rice branlike γ-aminobutyric acid (GABA), acylated steryl
glycoside (ASG), or γ-oryzanols (21-23, 36). Similar
to BR, PGBR is a low GI food (GI=54±5) (15).
PGBR has been reported to exert beneficial effects
in preventing rapid elevation of blood glucose
response and plasma insulin concentrations (21-22,
24). A previous study showed that a long-term dietwith PGBR as a staple food reduced both blood
glucose concentration and body weight in Vietnamese
female subjects with impaired glucose tolerance(24).This study was conducted to evaluate theinfluence of PGBR consumption on the control of
HbA1c value in DM patients under long-term antidiabetic medication.

was approved by the Scientific Board and the EthicalCommittee of Vietnam’s National Institute of
Nutrition (NIN).
Over the course of one month, medical database of
more than two hundred DM patients affiliated with
Nam Dinh Endocrinology Hospital were screened for
subject selection. Patients referred to insulin injection
were excluded. Based on the latest health-check andblood test results (with Hemoglobin A1c index of 6.5– 9.5), 70 individuals were invited to participate. A
briefing session was organized in order to provide
potential participants with thorough details of the study,
as well as the prospective beneficial health impacts ofwhole grains and PGBR. In addition, a test meal wasprovided to each person to account their ability ofadaptation with PGBR as a staple food. Two weeks
prior to the study baseline, informed consent was
obtained from each individual. In accordance with theinformed consent, participants were allowed towithdraw from the study at any time and by any reason.
At the end of the subject selection process, a total
number of 60 participants were achieved. To minimize
the drop-out rate, subjects were contacted on weekly
basis to track their rice intake and health statusthroughout the study.
All subjects have been taking oral anti-diabetic drugs
for several years. Administration involves
combination of common anti-hypoglycemic agents,
particularly sulfonylureas (which act by increasing
insulin secretion), intestinal α-glucosidase inhibitors(which delay the digestion and absorption ofcarbohydrates) and thiazolidinediones (which
stimulate insulin activity without inducing insulin
secretion).
Study design
This is a parallel study design. From 60
participants, 30 matched pairs were created according
to age, HbA1c results and physical activity intensity.
Matched pairs were then randomly divided into either
WR group (control group) or PGBR group(intervention group).
Throughout the intervention period of 4 months,
subjects were instructed to consume either WR or
PGBR exclusively as a staple food. Before thebeginning (baseline), at the midpoint (two months
following baseline) and at the end of the study (final),
anthropometric measurements, nutrition survey, andblood biochemical examinations were conducted.
Participants were guided to pursue normal activities ofdaily life, as well as to continue anti-diabetic
medication.
PGBR was produced in Nam Dinh Province
with technological support from FANCL Corp., Japan.
PGBR was prepared by soaking BR in lukewarm waterof around 30°C in 22 hours and dried for preservation.
The rice used in this study was of Tam Thom – afragrant, long-grain rice variety native to the Red River
Delta in Northern Vietnam. PGBR was provided tosubjects of the PGBR group once every two weeks.
Excluding staple food, participants were instructed tocontinue usual diet without any alteration.

METHODS
Setting and study subjects
This study was designed as a parallel study incompliance with the Helsinki Declaration on Humanstudies. It was conducted from December 2014 toApril 2015 in Nam Dinh Endocrinology Hospital of
Nam Dinh province – a coastal administrative division
located 90 km southeast from Hanoi, Vietnam. The
population was approximately 2 million, nearly 18 percent of which living in urban areas. The study protocol

Blood collection
Blood collection was conducted before thebeginning of the study (baseline), at the midpoint andat the end of the study (final). Subjects were asked to
fast for at least 10 hours before taking intravenous
fasting blood samples in early morning. With aseptic
24Asian Journal of Dietetics, 2020
precautions, each participant was subjected to a 6 mL
blood withdrawal for the analyses of glucose, HbA1c,
triacylglycerol (TG), total cholesterol (TC), LDLcholesterol (LDL-C), and HDL-cholesterol (HDL-C).
Serum was separated by centrifuge and stored frozen
at -30°C until analysis. Fasting glucose were done
within 30 mins after blood collection by colorimetricmethod at Nam Dinh Endocrinology Hospital. Other
analyses were carried out at Vietnam’s NIN. Levels ofTC, TG and HDL-C were measured byenzymatic colorimetric method. HbA1c was estimatedby nephelometry method. Laboratory technologists
were blinded to the identity of subjects andintervention status.

the Vietnamese Food Composition Table 2007. Fiberof PGBR was calculated using the data of a previous
study on short grain rice (Japonica variety).
Statistical analysis
Qualitative variables were confirmed for normal
distribution and compared using Student’s t-test(paired and unpaired). Numerical data were presented
as mean±SD. Values of p<0.05 were considered
statistically significant for all analysis results. All
statistical procedures were performed with SPSS
software for Windows version 20.0 (SPSS, Inc.,
Chicago, IL).
RESULTS

Anthropometric measurements
Weight, height, waist and hip circumferences
and body fat percentage were measured three times,
and the mean was used for analysis. Weight and height
were measured while standing, wearing light clothing
and without shoes. BMI was calculated as weight (kg)divided by the square of height (m2). Waist
circumference was measured mid-way between theumbilicus and the iliac crest, whereas hip
circumference was measured at the widest
circumference around the buttocks. Waist-hip ratio(WHR) was calculated as waist circumference (cm)divided by hip circumference (cm). Body fat
percentage was measured by the bioelectrical
impedance method (HBF-351, Omron Co., Kyoto,
Japan).

Two participants from WR group and one fromPGBR group withdrew from the study citing personalreasons. Data of two subjects were excluded for noncompliance. As a result, data of 55 subjects were
presented in this study. Table 1 shows the physicalcharacteristics and blood biochemical parameters of
the subjects at baseline. At the outset of the study,
except for hip circumference and footstep counts, nostatistical differences were identified between two
groups in age, weight, height, BMI, body fat
percentage, waist circumference, WHR, fasting blood
glucose, HbA1c, TG, TC, LDL-C and HDL-C.
Table 2 shows the comparison of physicalcharacteristics and blood biochemical parameters of
the PGBR and WR groups at baseline and final. Body
fat percentage decreased significantly in both groups
at final. Physical activity intensity (represented byfootstep counts) of participants within each groupremained constant during the time of intervention(p=0.206 in the PGBR and p=0.713 in the WR group).
Glucose and HbA1c levels of the PGBR group at final
remarkably declined in comparison with that at
baseline, whereas in the WR group such changes were
not detected.

Nutrition survey
A nutrition survey for 3 non-consecutive days by
the 24h recall method was implemented at baseline
and final. During interviews, a full-size photo albumof common food samples and standard householdmeasures (cups, bowls, etc.) were used to facilitate thesurvey and increase the accuracy of food description.
Energy and nutrient intake were calculated based on

Table 1. Comparison of physical characteristics and blood biochemical parameters between PGBR group and WR
group at baseline.
Variables
PGBR (n=28)
WR (n=27)
p-valueAge (y)
619
597
0.32
Weight (kg)60.068.96
57.8210.38
0.395
Height (m)1.580.08
1.570.08
0.713
BMI
23.92.39
23.22.88
0.328
Body fat (%)
30.06.3
29.346.6
0.698
Waist (cm)85.87.4
84.06.5
0.343
Hip (cm)
91.34.9
88.64.8
0.043
Waist-hip ratio
0.940.6
0.950.36
0.584
Footstep counts
53392667
82514132
0.026
Glucose (mmol/L)
9.171.80
10.01.80
0.089
TC (mmol/L)
5.040.77
4.960.91
0.74
HDL-C (mmol/L)
1.30.135
1.310.16
0.643
LDL-C (mmol/L)
2.820.50
2.840.87
0.937
TG (mmol/L)
2.382.00
2.992.85
0.364
HbA1c (%)
6.920.36
7.030.46
0.349
Data are meanSD. P-values obtained by unpaired t-test. TC: Total cholesterol. HDL-C: High density lipoproteincholesterol, LDL-C: low-density lipoprotein-cholesterol, TG: triacylglycerol, HbA1c: hemoglobin A1c.

25

PGBR improves HbA1c in Vietnamese with DM
Table 2. Comparison of physical characteristics and blood biochemical parameters in PGBR group and WR groupat baseline and final.
PGBR group (n=28)
WR group (n=27)
Variables
Baseline
Finalp-value
BeforeFinal
p-value
Weight (kg)60.068.96
60.188.97
0.531
57.8210.38
57.8010.7
0.964
BMI
23.92.39
23.92.54
0.688
23.22.88
23.22.94
0.864
Body fat (%)
30.06.3
28.56.6<0.00129.346.6
26.986.66<0.001
Waist (cm)85.87.4
86.797.0
0.45
84.06.5
84.036.16
0.935
Hip (cm)
91.34.9
90.655.7
0.114
88.64.8
88.15.5
0.357
Waist-hip ratio
0.940.06
0.960.05
0.007
0.950.036
0.950.04
0.307
Footstep counts
53392667
60643279
0.206
82514132
78573707
0.713
Glucose (mmol/L)
9.171.80
7.962.3
0.021
10.01.80
9.732.83
0.639
TC (mmol/L)
5.040.77
4.750.69
0.05
4.960.91
4.740.71
0.296
HDL-C (mmol/L)
1.30.135
1.220.17
0.071
1.310.16
1.170.16
0.003
LDL-C (mmol/L)
2.820.50
2.840.51
0.851
2.840.87
2.870.57
0.798
TG (mmol/L)
2.382.00
1.750.64
0.083
2.992.85
1.870.92
0.029
HbA1c (%)
6.920.36
6.20.77<0.001
7.030.46
7.120.84
0.626
Data are meanSD. P-values obtained by paired t-test. TC: Total cholesterol. HDL-C: High density lipoproteincholesterol, LDL-C: low-density lipoprotein-cholesterol, TG: triacylglycerol, HbA1c: hemoglobin A1c.

Figure 1. Changes in HbA1c
Different letters denote statistical significance within a group (p<0.05).
*, ** Significant differences between WR and PGBR groups at p<0.05 and p<0.001, respectively.
difference in HbA1c levels was detected among bothgroups at the beginning of the study (p=0.33).
Between-group disparity emerged two months after
the baseline (p=0.001) and was further emphasized at
the final (p<0.001).

Figure 1 shows the changes in HbA1c levels of thetwo groups during the time of intervention. Within one
month and a half after the study implementation,
HbA1c mean value of the PGBR group drasticallydeclined from 6.9 to 6.5, which is the cut-off point for
DM diagnosis as recommended by WHO. At the
middle of the study, HbA1c levels of the PGBR group
dropped to 6.4. Average HbA1c value of the PGBR
group at the end of the study was 6.2, indicating asignificant reduction compared to the baseline(p<0.001). Such decreases were not observed within
the WR group (HbA1c 7.0 and 7.1 as initial and final
values, respectively). In addition, no meaningful

Table 3 shows the energy and nutrient intakes of
the PGBR and WR groups at baseline and final. The
intakes of energy, protein, lipid and carbohydrates
between the two groups were similar. Mean fiber
intake of the PGBR group at final was 10.9 g/day,significantly higher (p=0.05) than that of the WR
group (7.7 g/day), despite the similarity between thetwo values at baseline.

Table 3. Energy and nutrient intakes of PGBR group and WR group at baseline and final.
Baseline
Final
Variables
PGBR
WR
p-value
PGBRWR(n=28)(n=27)(n=28)(n=27)
Energy (kcal)
1485381
1453360
0.758
1448329
1462256
Carbohydrates (g)22863.7
23765.5
0.63
23757.4
23159.4
Protein (g)60.415.3
61.2200.87
6115.4
58.211.6
Lipid (g)35.918.6
29.111.6
0.11
28.79.2
3411.1
Dietary fiber (g)7.94.8
7.93.2
0.959
10.94.6
7.73.2
p-values obtained by unpaired t-test
26

p-value
0.86
0.689
0.44
0.56
0.05

Asian Journal of Dietetics, 2020
DISCUSSION

Fiber may not be the sole factor explaining theresults of the present study. GABA, γ-oryzanol and ASG
are among various bioactive components of which
concentrations were potentiated during the germination
process of PGBR. GABA is the main inhibitory
neurotransmitter produced by pancreatic β-cells andwas demonstrated to induce stimulated insulin secretion(28). In PGBR, GABA concentration is 5-8 times higher
than that of WR (21). Recent studies have shown that
GABA improved hyperglycemia of diabetic mousemodels and promoted β-cell proliferation (28-30). It has
been reported that in grafted human islets, GABA
activates a Ca2+-dependent signaling pathway through
GABAA receptor and GABAB receptor, a process inwhich activation of phosphatidylinositol 3-kinase(PI3K)/AKT signaling is involved. PI3K/AKT is known
to be critical for the modulation of β-cell mass and acts
in response to Glucagon-like peptide-1 (GLP-1) andglucose in rodents (31), which is in accord with findings
by Purwana et al (30) in human islets. It can bepresumed from GABA administration outcomes that the
improvement in blood glucose concentration may bederived from beneficial impacts of GABA in fortifying
human β-cell mass, increasing human insulin
circulation, and decreasing glucagon levels. γ-oryzanol
is another major bioactive components found in BR aswell as PGBR. It is a compound of ferulic acid esters
with phytosterols. Oryzanol (Orz) has been reported toameliorate glucose intolerance and significantly
reduced hypothalamic endoplasmic reticulum (ER)stress in rodents. Hypothalamic ER stress has been
known as a factor in systemic glucose intolerance by
inducing ER stress in the liver and adipose tissue, while
an 8-week oral Orz treatment was confirmed to improveglucose metabolism in high fat diet fed mice (32).
Preclinical findings have shown multiple effects oforyzanol and ferulic acid, including anti-oxidative (3233), anti-hypoadiponectinemia and anti-hyperlipidemia(34-35). Furthermore, ASG contents found in PGBR
bran was reported by Usuki et al to increase Insulin
growth factor-1 (GF-1) generation in pancreatic β-cellsof streptocizotocin-induced diabetic rats, which in turnremarkably reduced hyperglycemia (36). ASG wasconcurrently
demonstratedtorecoverthestreptocizotocin-induced decrease in activities ofhomocysteine-thiolactonase (HTase), an anti-oxidant
enzyme whose activation is associated with decreased
glycolysis in oxidative-stressed cells.

Despite medicine administration, HbA1c levels in
the WR group barely changed during the time of thestudy. On the other hand, HbA1c levels of the PGBR
group dropped drastically, and after two months ofintervention, it was lower than the cut-off value for DM
diagnosis according to WHO (HbA1c≥6.5%).
Significant differences in HbA1c levels between two
groups were evident at the midpoint of the study andwere further expanded at the final. Participants of the
PGBR group were administered the same types of
medicine as those in the WR group, indicating that a
combination of medication treatment and PGBR dietwas more effective than the single administration ofanti-diabetic agents after a few years. The current datasuggest that consuming PGBR in place of WR may be
useful in modulating blood glucose in Vietnamese type
2 DM patients.
WR is the principle staple food consumed in
Vietnam, where 66% of the energy intake wascontributed by rice. As previously reported, the average
BMI of newly diagnosed DM patients in Vietnam wasapproximately 23. High WR intake is considered amajor cause of DM at low BMI in Asian populations,since WR has been classified as a high GI food. To
produce WR, bran and germ of BR is eliminatedthrough milling and refining processes, which
dispossess WR of vital nutrients and phytonutrients,
including dietary fiber, minerals, GABA, oryzanols, andfatty acids (11, 22-23). WR therefore is mainlycomposed of starchy endosperm and is rich in refinedcarbohydrates, which explains the high GI value. BR
and PGBR, on the other hand, retain the dietary fiberrich rice bran, which makes the most prominent
distinction between them and WR. Compared to WR,
BR and PGBR have lower GI values, and have been
demonstrated to attenuate glycemic response (21-23,
35).
It is noteworthy that in the current study, dietaryfiber intakes of the subjects at baseline were around 8
g/day, substantially lower than the recommended intakeof 18-20 g/day by Vietnam’s Ministry of Health (25). In
line with previous papers, the present results revealed amarked difference in dietary fiber intake of the twostudy groups at final, with mean fiber intake of the
PGBR group being significantly higher (10.9 g/day and7.7 g/day in the PGBR and WR group, respectively).
Dietary fiber has been known to be beneficial in
regulating blood glucose and lipid concentrations (2628). In the gastrointestinal tract, fiber passes throughunabsorbed and unchanged. Fiber increases bulk ofintestinal contents and viscosity of digestive solutions,blunting the diffusion of glucose to the intestinal walls,
decelerating the processes of carbohydrate digestion as
well as glucose absorption (27). As a consequence,sugar is absorbed into the bloodstream more slowly, andthe following increment of the post-prandial blood
glucose level is reduced in rapidity. This may be one of
the chief physiological mechanisms correlated with GI
values of foods, giving an explanation for the differencein GI classification among WR, BR and PGBR.
Substitution of PGBR for WR was shown tosignificantly lower the threat of developing diabetes risk
factors, and the credits were given to dietary fiber
contents in PGBR bran (21-22, 24). Therefore, a daily
addition of 3 g fiber in the diet of the PGBR group may
be one of the foremost factors contributing to the HbA1c
improvement found in this study.

Until now, preclinical studies have evaluated effectsof GABA, oryzanol, ASG and other PGBR components
as individual bioactive compounds, at times with muchhigher doses compared to those available in PGBR.
Purwana et al administrated 6-30 mg GABA per 1 mL
drinking water on rodents (30), while GABA
concentration in PGBR was 3-15 mg/100 g rice (21-22).
Nevertheless, PGBR diet administrations haveexhibited significant outcomes as reported by several
previous papers. This suggests that the improvement in
HbA1c found in this study were elicited from thesynergy among various bioactive compounds in
combination with dietary fiber when consumed as awhole food (PGBR), rather than the impacts of any
individual components (37-38). It is likely that theoverall synchronous activities of these compounds, or“food energy” (39), were what optimized thecomprehensive effects, as evidenced by the noteworthy
outcomes of PGBR diet.

27

PGBR improves HbA1c in Vietnamese with DM
As opposed to fasting blood glucose concentrationlevel which is afflicted by daily fluctuations, HbA1c isa more reliable measure for clinical assessment ofglycemic control in DM patients (39). HbA1c is formed
by non-enzymatic attachment of glucose to the Nterminal valine of the hemoglobin β-chain (40). The
concentration of HbA1c depends on both the blood
glucose concentration and the life span of the red blood
cells (RBCs). As human RBCs live in blood circulation
for roughly 100-120 days, HbA1c level reflects the
glucose concentration accumulated over a course of 8–
12 weeks (41). Accordingly, this study was designed tolast 4 months to accurately elucidate the effects of
PGBR on HbA1c levels of the subjects.

5)
6)

7)

Preliminary results in physical activity intensity(represented by footstep counts) of both groups
exhibited particular difference (p=0.026). However,
over the 4-month intervention, both groups showed nosign of any within-group changes in physical activity
intensity (p=0.206 and p=0.713 in PGBR group and WR
group, respectively). Despite the fact that marked
declines in body fat percentage were observed, no
significant difference was found in body weight among
both groups, in contrast with the results of a previous
study (24). As type 2 DM patients, all subjects strictly
follow their individualized diet, as reflected in thesimilar energy and nutrient intake at baseline and final
in both groups. It is widely known that energy balance
is what control body weight. Therefore, the stability inbody weight observed in our data can be explained by
the well balanced energy intake and constant physicalactivity intensity of study participants. A study withsimilar design in younger, non-diabetic subjects has
been planned to corroborate the previous body weight
results.

8)

9)

10)

11)

12)

CONCLUSION
The present study suggests that substitution of WRwith PGBR can improve HbA1c levels of Vietnamesetype 2 DM patients who have been under long-term oral
diabetes treatment. In Vietnam where rice contributes to
67% of the daily caloric intake, the escalation of T2DM
cases could be regulated owing in large part to thewidespread use of PGBR.

13)

14)

15)

ACKNOWLEDGEMENT
The authors are grateful to all the study participants
and workers of Nam Dinh Endocrinology Hospital. This
study was supported by FANCL Research Institute of
FANCL Corp., Japan, Vietnam’s National Institute of
Nutrition, US-Japan Medical Science Program, andAsian Nutrition and Food Culture Research Center,
Jumonji University, Japan.

1)
2)
3)
4)

16)

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PGBR improves HbA1c in Vietnamese with DM

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Asian Journal of Dietetics, 2020

RESEACH NOTE
The Effect of A School-based Nutrition Education Program on Japanese
Elementary School Children’s Nutrition Knowledge and
The Revaluation after One and A Half Year.
Mai Matsumoto1, 2, Ikumi Goto2, Ayumi Masumoto3,
Azusa Sakamoto2, Aki Saito1, Shinji Ikemoto2 *1

Department of Nutritional Epidemiology and Shokuiku, National Institutes of
Bioinnovation, Health and Nutrition., Shinjuku-ku, Tokyo, Japan2
Department of Human Nutrition, SEITOKU University., Matsudo-shi, Chiba, Japan
3
Saitama city., Saitama-shi, Saitama, Japan( received November 30, 2018 )ABSTRACT Background and purpose. Nutrition knowledge is one of the factors
influencing on diet. Children’s nutrition knowledge was reported to increase after theyreceived nutrition education classes. However, there were few reports about thecontinuation of effect after the nutrition education on children’s nutrition knowledge. This
study aim was to evaluate the continued efficacy of a school-based nutrition education
program on children’s nutrition knowledge specializing in food grouping for one and a halfyear among elementary school children using pre-post survey. Methods. Participants wereJapanese elementary school students in second grade (n=53). Two-time nutrition educationlectures on the “three-color food groups” held as part of a school-based seven-monthdietary education program. The changes in children’s nutrition knowledge scores by thenutrition education program of “Three-color food groups” evaluated by a questionnaire
about “Three-color food groups” including two sections of “Color” and “Function” from
Pre-intervention to After-class (immediately following lectures), Post-intervention(immediately after all programs for seven months) and After-a-year-intervention (one year
following the intervention). The Friedman test and Scheffe’s method were used to thoseanalyses. Results. Both “Color” and “Function” scores were higher at After-class,
Post-intervention, and After-a-year-intervention than Pre-intervention (P < 0.001 for all),while the “Function” score declined at Post-intervention and A-year-after-intervention
compared to After-class. Conclusion. A single program specialized in “Color” of“Three-color food groups” may improve knowledge and continue its effect among children
aged 7-8 years, although repeated familiarization may be needed until they understanddifficult or unfamiliar concepts or words such as “Function”.
Keywords: School-based nutrition education, Nutrition knowledge, Elementary schools,Children, Food groups
INTRODUCTION
School age is crucial years for normal physicaland mental development, and the diet and eating
behaviour that develop during these years tend to
persist throughout life (1) is a period in which foodpreference and eating habits are formed. Childhood isa time of greater openness to modification of foodchoice (2), therefore, the eating habits and behaviors
formed in childhood may be difficult to change laterin life (2-4).
Children’s diet is influenced by various factors,
such as their own knowledge (5), their parents’nutrition knowledge (6,7), and home foodenvironment (6,8). In particular, nutrition knowledge
is necessary for making healthier food choices(7,9-11), and it has been found to predict dietary
quality (11). Moreover, the knowledge about foodand its health benefits that children gain in childhood
can influence their dietary choices and preferences
later in life (5,12). For example, a Japanese study
reported that elementary school students with higher
nutrition knowledge consumed more vegetables thanstudents with lower nutrition knowledge (13). Thus,
it might be necessary to obtain nutrition knowledge
during childhood in order to benefit health later inlife.
A number of nutrition-education programs aredesigned to improve nutrition knowledge, with theaim of supporting sound dietary intake within thecommunity or a specific target population (6,8).
Nutrition-education programs that present positive
dietary messages can potentially improve dietarybehavior and increase nutrition knowledge in children(6). In fact, several studies about nutrition education
have been reported. A review reported that
school-based, nutrition education programs can leadto moderate increases in vegetable consumption*To whom correspondence should be addressed: S
Ikemoto: ikemoto@seitoku.ac.jp31

Long-term effect of a school-based nutrition education

among children (14). A UK study reported thatnutrition education improved 5-7 year-olds’ nutrition
knowledge (15). Several studies have reported thatgardening improved preference and consumption offruits and vegetables in younger generations (16-18).
In particular, school-based nutrition education can bedelivered in a way that is easily understood bychildren, and teach the skills and knowledge required
to improve or strengthen healthy eating habits (19).
For example, it is reported that school food policies
may encourage healthy eating and discourage
consumption of unhealthy foods and sweetened
drinks (20,21). The previous study showed thatnutrition education on food groups for young
Japanese children improved their nutrition knowledge(22). Another study reported that nutrition education
on healthy food was effective in helping young
children increase nutrition knowledge (23). That is tosay school-based nutrition education has impacted
improving children’s nutrition knowledge and food
behavior.
The ultimately goal is to increase nutrition
knowledge and positive behavior change, which canhave a beneficial effect on the long-term health ofchildren enrolled in any such nutrition-education
programs (15). However, there are few studies on theeffect of the continuousness of these programs. It is
important to investigate what undermines thecontinuousness of such programs in order toimplement effective interventions. Therefore, the aimof this study was to assess the continued efficacy of aschool-based nutrition education program on 7-8 year
old elementary school children’s nutrition knowledge
for one and a half years.
METHODS
Study design. This before-after trial evaluated aschool-based nutrition education program on

2012
May

nutrition knowledge of “Three-color food groups” forsecond grade students (7-8-year-olds) in a public
elementary school in Saitama city of Japan. The
nutrition-education program was conducted as part ofa school-based, seven-month, dietary education
program.
Pre-intervention
assessmentofnutritionknowledge was conducted using a questionnaire forparticipants in May 2012 (Pre-intervention). The
school-based, dietary education program wasconducted for seven months after the Pre-intervention
assessment. This program included a series ofnutrition-education lectures on the “Three-color foodgroups”, which was conducted twice in October andNovember of 2012. The nutrition knowledge
assessment using the same questionnaire as
Pre-intervention was conducted after the series oflectures (After-class), immediately after all programsfor seven months (Post-intervention), and one year
after the Post-intervention in February 2014
(After-a-year-intervention).
The present study was granted ethical approval by
the ethics committee of Seitoku University inaccordance with the “Helsinki Declaration: EthicalPrinciples for Research Involving Human Subjects”(H24U003).
Subjects. The Figure 1 shows an overview of thenumber of participants in the present study.
Seventy-nine second grade students of an elementary
school in Saitama city in Japan were recruited. There
were no exclusion criteria. A total of 59 students
completed all interventions, including four
nutrition-knowledge
assessments
andthenutrition-education program on “Three-color foodgroups”. Finally, 53 (28 male and 25 female) students
completed all programs.
The nature of the study was explained to theschool principal and homeroom teachers by the

Pre-intervention(n=79)
・Incompletion of the answer (n=7)
・Incompletion of the intervention (n=13)

2012
NovemberAfter-class(n=59)

2013
February

Post-intervention(n=59)

・Transfer of their school (n=6)
2014
February

After-a-year-intervention(n=53)

Figure 1. Overview of the number of participants contained in this study
32

Asian Journal of Dietetics, 2020

researcher and distributed by mail to the parents ofchildren who participated in the study. Writtenconsent forms were obtained from parents before thecommencement of the study.
Intervention
program.
The
school-based,
seven-month dietary education program wascomposed of lectures on six topics: “Breakfast andlife-rhythm”, “Healthy snacking behavior”, “Foodpoisoning”, “Three-color food groups”, “Eatingmanner” and “Seasonal foods”. The program wasdeveloped for the school by teachers and theresearcher, based on the principal components of“Basic Law on Shokuiku” (24), “Basic Program for
Shokuiku Promotion” (25) and “Elementary school
curriculum guideline” (26). The lecture on the“Three-color food groups” was conducted twice,while the others were conducted once. “Three-color
food groups” is a classification system recommended
to be taught in home economics at Japaneseelementary schools for students to easily understandwell-balanced dietary intake (26). According to thesystem, food can be classified into three color groups,
including “Red”, “Green,” and “Yellow” by thefunction of the dominant nutrients in each food. The“Red” food group includes meat, fish, soybeans, andeggs, which are all rich in protein, and work to build
the blood and body. The “Green” food group includes
vegetables and fruits, which are rich in vitamins andminerals, and work to keep the body healthy. The“Yellow” food group includes rice, bread, noodles,
potatoes, and oil, which are rich in carbohydrates orfats, and work as the source of the body’s energy. The
example of lecture about “Three-color food groups”
is shown in Figure 2. The lectures included thedefinition and classification of food groups based on“Three-color food groups” system so the children
could understand the differences of nutritional
functions in the various food groups and their
combination to achieve a well-balanced diet. The
other lectures in the program provided information on
basic dietary behaviors. For example, the lectures on“Breakfast and life-rhythm” and “Healthy snacking
behavior” comprised the roles of eating breakfast andRedsnaking in children’s lifestyle. The lecture on “Foodpoisoning” provided information on preventing foodpoisoning and related behaviors. In the lecture on“Eating manner”, children were taught a good tablemanner, such as how to use chopsticks. The lecture
on “Seasonal food” comprised the best season foreating vegetables and fruits.
The program was completed by a participatory
approach, using a black board and card games aimed
at building students’ nutrition knowledge, whichamounted to seven interventions in total. Each lecture
was given for a 30-minute period and were conductedby the same registered dietitians. Since the durationof a lecture at elementary schools in Japan is 45
minutes, a 30-minute lecture was deemed suitable forthe study participants.
Nutrition Knowledge Questionnaire on the“Three-color Food Groups” for Young Children. A
questionnaire was developed to assess students’nutrition knowledge focusing on the “Three-color
food groups” and based on the contents of thenutrition-education program. The contents of thequestionnaire are shown in Table 1.
The questionnaire consisted of six items asking
students of their nutrition knowledge of “Three-color
food groups”. It took approximately 5 to 10 minutes
to complete the questionnaire, which is a suitable
length of time for children of the participants’ age
group (27). This questionnaire is a multiple-choicetest in which participants were given a choice of fouranswers, plus an option of “I don’t know”. Before thefirst assessment, the components of the questionnaire
were reviewed by the school principal, teachers (n=3),
and registered dietitians (n=2) to ensure its
appropriateness. Internal consistency of thequestionnaire was calculated by using Cronbach’s α
was 0.603 among 25 children, age 6 in the pilot study(data not shown). The questionnaire was conductedby an anonymous self-completed method in eachclass. A researcher read questions aloud to each classbecause it was reported that it was preferable for theproctor to read questions aloud in order to minimize
the impact of reading levels on results (27).

Green

Yellow

Meat, Fish, Egg

Vegetable, Fruit

Rice, Bread,
NoodleBuild the blood andbody

Keep the body healthy

Source of thebody’s energy

Figure 2. The content of lecture about “Three-color food groups” system
33

Long-term effect of a school-based nutrition education

Table 1. Content of the Questionnaire for Children
Category

Subcategory
Color

Three-color
food groups
Function

Question
number
No.1
No.2
No.3
No.4
No.5
No.6

Content"Three-color food groups" Which food is included in the "red group"?
"Three-color food groups" Which food is included in the "green group"?
"Three-color food groups" Which food is included in the "yellow group"?
"Three-color food groups" Which food is the source of energy?
"Three-color food groups" Which food is the source of blood or muscle?
"Three-color food groups" Which food helps maintain proper bodily functions?

Table 2. Anthropometric Data for Students who Participated in Study
All (n=53)

Body height(cm)
Body weight(kg)
Body massindex (kg/m2)

Male (n=28)

Female (n=25)pMedian
value

（IQR）

Median
value

（IQR）

Median
value

（IQR）

120.0

(118.0-125.0)

120.0(118.5-125.5)

121.0(118.0-123.0)

0.713

22.5

(20.0-25.0)

22.0(20.5-25.0)

22.8(20.0-25.0)

0.781

15.3

(14.4-16.9)

15.3(14.1-17.5)

15.1(14.5-16.8)

0.915

The p values were calculated by Mann-Whitney test.
Other Variables. Body weight and body height wereobtained by school health check data. Body massindex (BMI) was calculated as body weight (inkilograms) divided by the square of body height (inmeters).
Statistical analysis. All statistical analyses were
performed using the IBM SPSS statistics software
package (version 22.0, SPSS Inc., Chicago, IL, USA).
The responses of thenutrition-knowledge
questionnaire were converted to scores of ‘1’ and ‘0’
for correct and incorrect answers, respectively. The
answer of “I don’t know” was scored as ‘0’. Scores
were calculated by summing the total number ofcorrect answers. A higher score reflected a higher
knowledge level. The difference of body height,
weight, and BMI between male and female students
participating in the study was compared by the
Mann-Whitney test. The effect of the continuousnessof the nutrition-education program was assessed bycomparison of nutrition knowledge of the“three-color food groups” for a total of fourinterventions, including Pre-intervention, After-class,
Post-intervention, and A-year-after-intervention by
the Friedman test, using Scheffe’s method to compare
the score between each intervention.

RESULTS
Demographic characteristics of the study
participants are presented in Table 2. Median bodyheight, weight, and BMI were not significantly
different between male and female students.
Table 3 shows the scores of the “Three-color foodgroups” knowledge questionnaire at Pre-intervention,
After-class,
Post-intervention,
andAfter-a-year-intervention. The knowledge score of“Color”
wasimprovedat
After-class,
Post-intervention, and A-year-after-intervention from
Pre-intervention, respectively (P < 0.001). The
knowledge score of “Function” of each color was
improved at After-class, in comparison to
Pre-intervention (P < 0.001). The score of “Function”
declinedat
Post-intervention
andA-year-after-intervention from After-class, whilethese scores were significantly higher compared tothe score of Pre-intervention. The difference in sex
did not affect these results (data not shown).

Table 3. Score of Knowledge of Questionnaire to Assessfood groups" (n=53)

Effect of Nutrition Education of the "Three-color

Pre-intervention
Medianvalue"Color""Function"

DISCUSSION
In this study, we examined whether the effect of aschool-based nutrition education program on the
nutrition knowledge of 7-8 year-olds continued for

After-class（IQR）Medianvalue

1.0(1.0-2.0)a1.0(1.0-2.0)aPost-intervention

A-year-afterintervention
Median
（IQR）
value

p

（IQR）

Median
value

（IQR）

3.0

(3.0-3.0)b3.0(3.0-3.0)b3.0(3.0-3.0)b< 0.001

3.0(2.0-3.0)b2.0(2.0-3.0)c2.0(2.0-2.0)c< 0.001

IQR, interquartile range.
The p values were calculated by Friedman test.
abc
There is significant difference between different alphabet in the same row using by Scheffe’s method tocompare the score between each intervention
34

Asian Journal of Dietetics, 2020

one and a half years. To the best of our knowledge,
the present study is the first study to examine thecontinued efficacy of a nutrition-education program
on children’s nutrition knowledge for over a year.
Our findings suggest that that the nutrition education
specialized in “Color” of “Three-color continued has
led to a one and a half year effect among 7-8-year-old
children.
The effect of school-based nutrition education
programs have been examined in various settings. For
example, Connell, et al. and Resnicow, et al. reported
that the evaluation of school health education
improved healthy food behavior for elementary
school students (28,29). In addition, the effect ofschool-based nutrition education specializing in
nutrition knowledge has also been reported. Bargen
reported that the evaluation of school healtheducation showed that 10 to 15 hours were required
to expect “large” effects in a program about specificknowledge (30). Morgan et al. found that a three-hour,school-based nutrition education program was apromising tool to improve 11-12-year-olds’ nutrition
knowledge and food preferences (8). The results ofthe previous studies were similar to our results, whichshowed that a nutrition-education program improved
7-8-year-olds’ nutrition knowledge. Our participants
obtained low scores (median 2 points out of 6) at thebaseline assessment (Pre-intervention), while theyobtained median 5-6 points after the intervention. A
previous study showed that 4-6-year-olds did nothave considerable knowledge on “Three-color foodgroups” used for the intervention (31). Together withthis result, our participants were likely to obtain theknowledge in question from the intervention program,which indicated that nutrition-education programs
focusing on nutrition knowledge may have an effectin improving and establishing nutrition knowledge
for 7-8-year-olds.
For nutrition education, our study focused on thefood grouping and nutrient functions using“Three-color food groups” system, both in education
and assessment of nutrition knowledge. We selected
this classification system because it has been
employed for children to easily understand abalanced diet and is the tool to teach children foodgrouping in Japanese elementary schools. Previous
studies
comprisednutrition-knowledge
questionnaires for young children, including theconcept of food grouping in the view of nutrient
function or healthy food intake (32,33), which madeus assess the knowledge similarly. In this study, theassessment was conducted from two viewpoints:“Color” and “Function”, which represented foodgrouping and nutrient function, respectively. A
number of differences were observed between the
results. Recognition of “Color” was maintained for an
extended time after the improvement realized throughthe intervention. Therefore, a single nutrition
education about “Color” may be appropriate forchildren of this age. However, although recognitionof “Function” of each color improved in the
After-class phase, the score slightly decreased
following that (Post-intervention). A previous study
using “Three-color food groups” reported thatrecognition of “Color” was significantly improved
after an intervention while recognition of “Function”
did not significantly improve among young Japanesechildren (22). Based on these findings in our results,
it might be difficult for children to fully acquire

knowledge on nutritional function. One possible
reason for this could be familiarization after theintervention. Most Japanese elementary schools
provide parents with monthly school-lunch program
letters, which usually include information to classify
the foods used in the lunch menu to the “Three-color
food groups”. After the children received nutrition
education on “Three-color food groups”, it is possiblethat consciousness of the monthly school-lunch
program letter led to the foundation of the knowledge.
On the contrary, it might be too difficult for thechildren to understand the definition of nutritional
function, as the score declined after an extended
period. In fact, nutrition education using “Three-color
food groups” is mostly employed targeting fifth grade(10-11-year-olds) in Japan (34). Our subjects weremuch younger than the targeted children and it ispossible that the words used for the functions were
too complicated to remember. Birch et al. reported
that five to six times more education were needed
until children became familiar with vegetables orvegetable names that were new to them in 1987 (35).
In addition, a study about children’s nutrition
knowledge showed that they had poor nutrition
knowledge about food function and nutrientscontained in food (32, 36). Thus, it may take a longer
period of time for children to understand new ordifficult words, particularly words that are unfamiliar
to them. A follow-up strategy may be needed tomaintain the nutritional knowledge such as“Function” gained in a nutrition-education program.
Therefore, further study will be needed to resolve thisquestion. Additionally, our results showed theimprovement or continuance of nutrition knowledge
did not significantly differ according to sex (data notshown). These results were similar to a previous
study among 5-7-year-old children (15). Thus, thedifferences by sex in children of our participants’ages may have little influence in gaining nutrition
knowledge.
Several limitations of the present study warrant
highlighting. First, the number of subjects was small(n=53). However, several studies of nutrition
education for children were conducted for a similar
number of subjects (37). In addition, participants inthis study were limited to children belonging to a
public elementary school in Saitama city in Japan,
not random samples from the general population. The
participants were therefore likely not representativeof Japanese children. Second, before-after trials
without a control group was used in the present study.
However, there are few studies on the effects ofnutrition education on young children in Japan, andthe effects of improvement and its continuousness fornutrition education were not found. It is said thatsufficient information is needed to complete a
randomized-controlled trial and the effects should be
investigated without a control group before such a
trial is completed (38). Thus, it is important that theeffect of improvement of continuousness on nutrition
education was investigated without a control group,
such as the previous study (37) the present study
completed. Third, the Cronbach’s α value of thequestionnaire was lower in comparison to thereference value of 0.7 (39). Cronbach’s α coefficientis estimated to be higher when the number of items
increases (40). The low α value obtained in thisquestionnaire may be attributed to the small item
numbers. Fourth, in the present study, the same
35

Long-term effect of a school-based nutrition educationquestionnaire was used for each nutrition-knowledge
assessment, and it was possible that children
remembered the correct answers of the questionnaire.
However, according to previous studies reporting
reproducibility of questionnaires of nutrition
knowledge targeting children, our questionnaires
were conducted 2-4 weeks apart because children
were likely to forget the correct answers during thattime (27,41). Our examination was conducted within
at least three-month intervals; as such, we believedthat using the same questionnaire did not influence
the current results. Lastly, we did not ask participants
about household income, family environment, andtheir parents’ nutrition knowledge. It was reported
that household income and family environment
affected nutrition knowledge (42,43), and parents’knowledge made an impact on children’s knowledge(41). Additional studies that include those factors that
influence children’s nutrition knowledge should be
conducted.

7) Worsley A. Nutrition knowledge and foodconsumption: can nutrition knowledge change
food behaviour? Asia Pac J Clin Nutr 11 Suppl 3:
S579-585. 2002.
8) Morgan PJ, Warren JM, Lubans DR, Saunders
KL, Quick GI, Collins CE. The impact ofnutrition education with and without a school
garden on knowledge, vegetable intake andpreferences and quality of school life among
primary-school students. Public Health Nutr
13:1931-1940. 2010.
9) Gibson EL, Wardle J, Watts CJ. Fruit andvegetable consumption, nutritional knowledge
and beliefs in mothers and children. Appetite 31:
205-228. 1998.
10) Wardle J, Parmenter K, Waller J. Nutrition
knowledge and food intake. Appetite 34:269-275. 2000.
11) Axelson ML, Federline TL, Brinberg D. A
meta-analysis of food- and nutrition-related
research. Journal of Nutrition Education. 17:
51-54. 1985.
12) Branen L, Fletcher J. Comparison of College
Students’
Current
EatingHabitsandRecollections of Their Childhood Food Practices.
J Nutr Educ 31: 304-310. 1999.
13) Asakura K, Todoriki H, Sasaki S. Relationshipbetween nutrition knowledge and dietary intake
among primary school children in Japan:
Combined effect of children's and theirguardians' knowledge. J Epidemiol. 27: 483-491.
2017.
14) Howerton MW, Bell BS, Dodd KW, Berrigan D,
Stolzenberg-Solomon
R,
Nebeling
L.
School-based nutrition programs produced amoderate increase in fruit and vegetableconsumption: meta and pooling analyses from 7
studies. J Nutr Educ Behav 39: 186-196. 2007.
15) Warren JM, Henry CJ, Lightowler HJ, Bradshaw
SM, Perwaiz S. Evaluation of a pilot school
programme aimed at the prevention of obesity inchildren. Health Promot Int. 18: 287-296. 2003.
16) Heim S, Stang J, Ireland M. A garden pilot
projectenhancesfruit
andvegetable
consumption among children. J Am Diet Assoc
109: 1220-1226. 2009.
17) McAleese JD, Rankin LL. Garden-based
nutrition education affects fruit and vegetableconsumption in sixth-grade adolescents. J Am
Diet Assoc 107: 662-665. 2007.
18) Hermann JR, Parker SP, Brown BJ, Siewe YJ,
Denney BA, Walker SJ. After-school gardening
improves children's reported vegetable intake
and physical activity. J Nutr Educ Behav 38:
201-202. 2006.
19) Perez-Rodrigo C, Aranceta J. School-based
nutrition education: lessons learned and new
perspectives. Public Health Nutr 4: 131-139.
2001.
20) Bere E, Hilsen M, Klepp KI. Effect of thenationwide free school fruit scheme in Norway.
Br J Nutr 104: 589-594. 2010.
21) Cullen KW, Watson K, Zakeri I. Improvements
in middle school student dietary intake after
implementation of the Texas Public School
Nutrition Policy. Am J Public Health 98: 111-117.
2008.
22) Takao Y, Adachi N, Matsumoto M, Ikemoto S.
The Efficiency of Intervention on Diet andCONCLUSION
In conclusion, a school-based nutrition education
program focused on the “Three-color food groups”
improved nutrition knowledge among 7-8-year-old
Japanese children. Moreover, the obtained knowledge
specialized in “Color” was likely to remain for one
and a half years. Therefore, a single nutrition
education about “Three-color food groups”
specialized in “Color” may be appropriate forchildren of this age. In order for children to gain
knowledge, repeated familiarization may be needed
until they understand difficult or unfamiliar concepts
or words such as “Function”. Future studies areneeded to determine the contents and components ofeffective nutrition-education programs to improve
both nutrition knowledge and dietary behavior ofyoung children further.
ACKNOWLEDGEMENT
The authors would like to express their deepest
appreciation to all the study participants: school
principals, teachers, students and their parents. The
authors have no potential conflicts of interest to
disclose.
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Letter to the Editor
Aetiology of Kwashiorkor then and now – still the Deposed Child?
Dear Editor:
Kwashiokor in 1933, the aetiology remains elusive,probably because there are several causal factors
using different pathways or influencing a common
pathway. Current advances in medicine and genetic
sequencing, more than ever, provide a unique
opportunity to investigate proposed hypotheses,
resolve controversies and discover new concepts
about undernutrition and disease conditions
associated with kwashiorkor and marasmus.

Kwashiorkor, described by Cecily Williams in1933 as the disease of the child “deposed” frombreastfeeding, still poses an enigma to scientists
today as it did then. Almost nine decades after it was
described, there has been a decline in prevalence insome parts of the world, but it remains prevalent inother parts, especially in East Africa where it isreported to be more common than marasmus (1).
Changes in the nomenclature and current treatment
modalities make it seem as if the two conditions are
the same, though distinct pathophysiologicaldifferences exist between the two conditions (2).
Kwashiorkor is a multisystem disease, characterisedby oedema and multiorgan dysfunction resulting
from the body’s inability to maintain cell membrane
integrity, leading to potassium and water loss fromall categories of cells. It is associated with
consumption of monotonous diets, consisting
mainly of maize, cassava and rice (3). Pathologicalchanges such as deposition of fat in the liver, villous
atrophy and anaemia have been observed. However,the primary aetiology is largely unknown at thistime, but the search goes on.
Several factors are implicated in the aetiology ofkwashiorkor, though many have been disputed.
These include prolonged breast feeding, inadequate
protein intake, hypoalbuminemia, intoxication fromaflatoxins, excessive oxidant stress and measles
infection. (3) More recent animal studies haveshown that hepatic steatosis or the fatty liverchanges in kwashiorkor can be prevented by feeding
mice with a maize vegetable diet supplemented withcholine, suggesting that choline insufficiency maybe
a contributory factor (4). Others have observed
DNA hypomethylation and linked this to the slow
turnover of 1-carbon cycle metabolites such as
methionine (5). An increased proportion ofproteobacteria and more frequent cultures offusobacterium from gut microbiota have been foundin Kwashiokor compared to marasmus, supporting
hypothesis that gut microbiota produce toxins thatcause the cell damage. (6) Studies have shown low
levels of antioxidants such as beta carotene in dietsof these children (7).
One hypothesis that remains difficult to discard
is the role of inadequate protein intake andhypoalbuminemia in the aetiology. Semba in his
narrative on “The Rise and Fall of Protein
Malnutrition” provides a succinct historical accountof global efforts made in the past to support thistheory, close the protein gap and the controversies
that ensued. It is striking to note that today, stunting
which is a milder form of malnutrition that affects
millions of children globally, and considered bysome to be a pre-morbid state of kwashiorkor has
been linked to inadequate protein intake withcorrectional efforts aimed at improving protein
intake [8]. So perhaps it is time to reconsider therole of protein or its components in the aetiology of
Kwashiorkor.
Thus, although significant progress has been
made since Dr Cecily William’s description of

Edem M. A Tette1,Juliana YarteyEnos2*
1

Department of Community Health, University of
Ghana Medical School
2
Noguchi Memorial Institute for medical Research,
University of Ghana* Corresponding Author

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7) Kismul H, Van den Broeck J, Lunde TM.
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