Evaluation of the Japanese Metabolic Syndrome Risk Score (JAMRISC): a newly developed questionnaire used as a screening tool for diagnosing metabolic syndrome and insulin resistance in Japan
Environ Health Prev Med
Evaluation of the Japanese Metabolic Syndrome Risk Score (JAMRISC): a newly developed questionnaire used as a screening tool for diagnosing metabolic syndrome and insulin resistance in Japan
Ce Tan 0 1 2 3 4 5 6
Yutaka Sasagawa 0 1 2 3 4 5 6
Ken-ichi Kamo 0 1 2 3 4 5 6
Takehiro Kukitsu 0 1 2 3 4 5 6
Sayaka Noda 0 1 2 3 4 5 6
Kazuma Ishikawa 0 1 2 3 4 5 6
Natsumi Yamauchi 0 1 2 3 4 5 6
Takashi Saikawa 0 1 2 3 4 5 6
Takanori Noro 0 1 2 3 4 5 6
Hajime Nakamura 0 1 2 3 4 5 6
Fumihiko Takahashi 0 1 2 3 4 5 6
Fumihiro Sata 0 1 2 3 4 5 6
Mitsuhiro Tada 0 1 2 3 4 5 6
Yasuo Kokai 0 1 2 3 4 5 6
0 Department of Gastroenterology, Rumoi Municipal Hospital , Rumoi 077-0011 , Japan
1 Center for Medical Education, Sapporo Medical University , Sapporo 060-8556 , Japan
2 Department of General Medicine, Rumoi Municipal Hospital , 2-16-1 Sinonome, Rumoi 077-0011 , Japan
3 Research Institute for Frontier Medicine, Sapporo Medical University , Sapporo 060-8556 , Japan
4 Department of Neurosurgery, Rumoi Municipal Hospital , Rumoi 077-0011 , Japan
5 NPO Rumoi Cohortopia , Rumoi 077-0028 , Japan
6 Department of Cardiology, Rumoi Municipal Hospital , Rumoi 077-0011 , Japan
Objectives To prevent the onset of lifestyle-related diseases associated with metabolic syndrome (MetS) in Japan, research into the development of a useful screening method is strongly desired. We developed a new screening questionnaire (JAMRISC) utilizing a logistic regression model and evaluated its ability to predict the development of MetS, type 2 diabetes and other lifestyle-related diseases in Japanese populace. Methods JAMRISC questionnaire was sent to 1,850 individuals in Rumoi, a small city in Hokkaido. We received a total of 1,054 valid responses. To maximize the target individuals accurately diagnosed with MetS, logistic regression analysis was used to generate a unique metabolic syndrome score calculation formula as taking into consideration the clinical relevance of each question item as individual coefficients.
JAMRISC; Logistic regression model; Questionnaire; Postprandial hyperglycemia; Insulin resistance
Results The results of our comparative research utilizing
both JAMRISC and Finnish Diabetes Risk Score
(FINDRISC) questionnaires revealed the usefulness of
JAMRISC for its ability to detect risks for MetS, pre-MetS,
diabetes, and pre-diabetes. Study of disease risk detection
via JAMRISC questionnaire targeting the 4283 residents of
Rumoi indicated a high detection rate for pre-MetS
(98.8 %), MetS (94.2 %), pre-diabetes (85.1 %) and type 2
diabetes (94.9 %). In addition, JAMRISC was useful not
only as a MetS risk score test, but also as a screening tool
for diagnosing insulin resistance.
Conclusions JAMRISC questionnaire is a useful
instrument for the detection of early risk of not only MetS and
type 2 diabetes but also insulin resistance.
The increased incidence of cardiovascular events
accompanying the increasing number of patients with type 2
diabetes is a global issue requiring urgent measures.
Retinopathy, nephropathy, and neurological disorders are
well-known microvascular complications of type 2
diabetes. However, it has been recently reported that the
development of macrovascular complications leading to
strokes or coronary artery events starts earlier than
previously believed. Namely, postprandial hyperglycemia and
MetS are strongly involved in the onset of cardiovascular
In response to the incidence of lifestyle-related
diseases associated with MetS dramatically increasing due to
lifestyle changes and the rapid aging of the population in
Japan, specific health checkups for MetS for Japanese
residents aged 40–74 years with medical insurance were
made compulsory in April 2008. However, even 7 years
after the introduction of these checkups, examination rates
remain much lower than the original target figures.
Accordingly, it is feared that if the present situation
continues, the specific health checkups will not be as
effective in preventing the onset of lifestyle-related
diseases and reducing medical expenses as previously
projected. To make the health checkups more effective, the
examination rate needs to be greatly increased.
Furthermore, efficient screening methods for risk assessment
need to be introduced. In this study, we first demonstrate
how we developed a new health checkup questionnaire
(JAMRISC). We then explain how it is more effective at
detecting risk in the Japanese populace (as well as
populations in other Asian countries) than FINDRISC health
checkup questionnaire  developed in Finland 12 years
Fig. 1 JAMRISC questionnaire
ago. Finally, we describe how we used JAMRISC
questionnaire when conducting a survey of early risk detection
among residents aged 55–64 years in Rumoi, a small city
in Hokkaido. Results of this survey indicated that
JAMRISC questionnaire was useful for early disease risk
detection and risk stratification.
Subjects and methods
Creation of JAMRISC questionnaire
The questionnaire was composed of eleven items including
age, gender, abdominal circumference (self-reported
measurement around the waist), height and weight. Smoking
and drinking histories were also included in addition to
items related to physical activity, dietary habits, history of
hypertension or hyperglycemia, and family history of
myocardial infarction, stroke, diabetes (Fig. 1).
We conducted the survey from April 2007 through
August 2009 with the cooperation of the residents of
Rumoi City. As a result, we received a total of 1,850
responses (males 1,065; females 785). After excluding
individuals undergoing treatment for a MetS-related
disease and those with missing blood data items, a total
of 1,054 valid responses remained. Of these 1,054
subjects, 163 males (aged 36–80 years; mean age,
57.9 years) and 30 females (aged 39–86 years; mean age,
65.0 years) were diagnosed with MetS. We adopted the
Japanese MetS criteria. Individuals who suffered from
central obesity (waist C85 cm in males, C90 cm in
females) plus C2 of the following three components
were defined as MetS. (1) blood pressure C130/
85 mmHg or taking an antihypertensive, (2) fasting
plasma glucose (FPG) C110 mg/dl, medication for
diabetes, (3) serum high-density lipoprotein-cholesterol
(HDL-C) \40 mg/dl, serum triglyceride C150 mg/dl, or
medication for hyperlipidemia. Individuals who suffered
from central obesity plus at least one of the conditions
among these three components were defined as Pre-MetS
To maximize the number of target individuals
accurately diagnosed with MetS, logistic regression analysis
was used to generate a unique metabolic syndrome score
calculation formula taking into consideration the clinical
relevance of each question item as individual
coefficients. Furthermore, by multiplying the risk (probability
of 0–1) predicted on the basis of this calculation formula
by 100, we were able to create a total metabolic
syndrome score ranging from 0 to 100 in an
From the receiver operating characteristic (ROC)
curve, the cutoff point was set at 20 (sensitivity 0.90;
specificity 0.74), with a score of lower than 20 classified
as ‘‘no risk’’ and a score of 20 or higher classified as ‘‘at
risk.’’ Normally, the cutoff should be set at 50; however,
because of the characteristics of the health checkups, we
set the cutoff at 20 to reduce false negatives and to
secure results with high sensitivity and specificity.
Comparison of the sensitivity and specificity
of JAMRISC and FINDRISC questionnaires
To compare the sensitivity and specificity of JAMRISC
and FINDRISC questionnaires , a sample of 83
subjects (aged 40–60 years), either determined to be healthy
according to results of regular health checkups or
definitively diagnosed with MetS, pre-MetS, type 2
diabetes, or pre-diabetes completed both the questionnaires
Verification of disease risk detection in Rumoi
residents through JAMRISC questionnaire
In October 2009, the questionnaire (JAMRISC) was sent
via post to all 4,283 residents of Rumoi City aged
55–64 years, and responses were received from 1,915
individuals (males 855; females 1060; response rate,
44.7 %). The results indicated that 67.2 % of the subjects
(males 372; females 915; total 1287) had a risk score of
\20 according to the questionnaire, whereas 32.8 % of
subjects (males 483; females 145; total 628) had a risk
score of C20 indicating an ‘‘at risk’’ status. The 628
subjects who had a risk of C20 and the 218 subjects who had a
risk score of \20 were recommended to undergo blood
testing. The 218 subjects were extracted at random from
the 1287 subjects with a risk score of \20 as a control
group. As a result, a total of 846 subjects were
recommended to undergo blood testing.
In accordance with the theory proposed by Matthews
et al. , we also investigated the Homeostasis model
assessment insulin resistance (HOMA-IR), an index for
assessing insulin resistance calculated from FPG and
fasting insulin (FIRI), and Homeostasis model assessment b
cell (HOMA-b), an index that classifies insulin secretory
ability. The insulin resistance index HOMA-IR was
calculated using the formula FPG 9 FIRI 7 405, whereas the
insulin secretory ability index HOMA-b was calculated
using the formula FIRI 9 360/(FPG–63).
Moreover, we adopted the diagnostic criteria of type 2
diabetes reported from the committee of the Japan Diabetes
Society on the classification and diagnostic criteria of
diabetes mellitus in 2010 . Type 2 diabetes is
diagnosed if any of the following criteria are met: (1) FPG level
C126 mg/dl, (2) HbA1c C6.5 % (National
Glycohemoglobin Standardization Program:NGSP). For the
purpose of estimating the frequency of type 2 diabetes, ‘‘type 2
diabetes’’ can be substituted for the determination of
‘‘diabetic type’’ from a single examination. In this study,
Hemoglobin A1c (HbA1c) C6.5% alone can be defined as
‘‘type 2 diabetes.’’ Generally, normal type is defined as
fasting plasma glucose level of \110 mg/dl and 2-h value
of \140 mg/dl in 75 g oral glucose tolerance test (OGTT).
Borderline type (equal to pre-diabetes) is defined as falling
between the type 2 diabetes and normal values. Subjects
with borderline type correspond to the combination of
impaired fasting glucose (IFG), impaired glucose tolerance
(IGT) and mixed type of both IFG and IGT (IFG/IGT)
noted by the World Health Organization (WHO). While
IFG is diagnosed with FPG value of 110–125 mg/dl ,
IGT is diagnosed when both FPG value of \110 mg/dl and
2-h glucose levels of 140–199 mg/dl on OGTT are met
[13, 14]. Mixed type of both is diagnosed when both FPG
value of 110–125 mg/dl and 2-h glucose levels of
140–199 mg/dl are met.
In this study, both 75 g OGTT 2-h plasma glucose levels
and casual plasma glucose level are not measured from the
background of epidemiology and health screening.
Although data were not shown in this study, the OGTT
analysis results of 629 individuals who underwent the test
at Rumoi Municipal hospital revealed that 82.1 % of
individuals with FPG \110 mg/dl, HOMA-b C55 were
equivalent to IGT. Impaired insulin action leads to
postprandial hyperglycemia. Practically, impaired insulin
action is hypo-secretion of insulin from the beta cell of
Langerhans in the pancreas and/or decreased insulin
sensitivity in peripheral tissues. Not only IGT with insulin
resistance but also IFG/IGT and DM with insulin resistance
were matched to the ‘‘postprandial hyperglycemia with
insulin resistance’’. Especially, postprandial hyperglycemia
with insulin resistance was reported, which is closely
related to the risk of cardiovascular diseases. So we
focused on the presence of postprandial hyperglycemia
with insulin resistance. Therefore, we hypothesized that
individuals who met the criteria of HOMA-IR C1.4, FPG
C100 mg/dl, HOMA-b C55 had postprandial
hyperglycemia with insulin resistance. In addition, we
hypothesized that IGT having insulin resistance was diagnosed
when HOMA-IR C1.4, FPG values of 100–109 mg/dl and
We then investigated the correlations between insulin
resistance-related glucose metabolism disorders in which
all of these criteria are met and risk scores are according to
This study was conducted with financial assistance from
Rumoi City long-term care and disease risk early detection
activities as part of the 2009 series of elderly health
promotion activities sponsored by the Ministry of Health,
Labour and Welfare. And then, all these present studies
were approved by the ethics committee of the Rumoi
Municipal Hospital, Rumoi, Hokkaido, Japan. Informed
consent was obtained from all individual participants
included in the study in written form.
Creation of JAMRISC questionnaire
The candidates of variable which affect the occurrence for
MetS are age, gender, abdominal circumference, body
mass index (kg/m2; height and weight), smoking history,
drinking history, physical activity, dietary habits, history of
hypertension, history of hyperglycemia, and family history
of myocardial infarction, stroke and diabetes. From these
Table 1 Evaluation of the clinical relevance of the JAMRISC
question items and creation of the calculation formula of the
JAMRISC total risk score
Coefficients for the question items
Exercises (yes or no) (less than 2h = 1, 2h or more = 0)
Calculation formula of total risk score with the JAMRISC
questionnaire = (1.3369 9 gender ) ? (0.1897 9 abdominal circumference
cm) ? (1.3738 9 history of hypertension) ? (1.5084 9 history of
hyperglycemia / urinary sugar) ? (0.8768 9 exercises yes/no)
candidates, the best combination of the variables in the
logistic regression model was selected using Akaike’s
Information Criterion (AIC).
The set of five variables listed in Table 1 was selected as
the best for explaining the risk probability against
Coefficients in a selected optimized model were
estimated to indicate the clinical relevance of each question
item. When the total number of entries reached 1,054
subjects, the coefficient stabilized, and the score
calculation method was considered completed. The questionnaire
was composed of eleven question items, but only five
explanatory items, namely gender, abdominal
circumference, history of hypertension, history of hyperglycemia,
and exercise habits, were required to calculate risk. The
coefficients for the five items at the time of completion
were as follows: gender, 1.3369 (male = 1, female = 0);
abdominal circumference, 0.1897; history of hypertension,
1.3738; history of hyperglycemia, 1.5084; exercise habit,
yes or no (less than 2 h = 1, 2 h or more = 0), 0.8768.
Accordingly, JAMRISC total risk score was calculated by
linear combination of risk factors weighted by the
estimated parameters in Table 1. By translating the risk
probability to percent scale, the total metabolic syndrome
score ranges from 0 to 100 in an easy-to-understand
manner. Next we created an ROC curve and were able to
achieve a sensitivity of 90 % and specificity of 74 % when
the cutoff point was set at 20, thereby completing
JAMRISC (Fig. 2).
Comparison of the sensitivity and specificity
of JAMRISC and FINDRISC questionnaires
The sensitivity of JAMRISC was high, totaling 100.0 % for
MetS, 90.0 % for pre-MetS, 83.3 % for type 2 diabetes,
and 92.3 % for pre-diabetes. For FINDRISC, the
Table 2 Comparison of risk detection rate between JAMRISC and FINDRISC questionnaires
A sample of 83 subjects (aged 40–60 years) definitively diagnosed as healthy or with MetS, pre-MetS, type 2 diabetes, or pre-diabetes according
to the results of regular health checkups completed both JAMRISC and FINDRISC questionnaires simultaneously
a JAMRISC could detect individuals with any risks related to type 2 diabetes and MetS with a sensitivity of 93.1 % and a specificity of 83.3 %,
whereas FINDRISC offered high specificity (100.0 %), but markedly low sensitivity (27.6 %)
figures were low, totaling 44.4, 0.0, 66.7, and 23.1 %,
respectively. Regarding specificity, the results were
somewhat low for JAMRISC, totaling 72.3 % for MetS,
63.0 % for pre-MetS, 59.7 % for type 2 diabetes, and
65.7 % for pre-diabetes, whereas the values were high for
FINDRISC, totaling 100.0, 89.0, 94.8, and 92.8 %,
respectively. Furthermore, an investigation of whether each
questionnaire could identify individuals at risk for any of
the four pathologies indicated that JAMRISC had a
sensitivity of 93.1 % and specificity of 83.3 %, whereas
FINDRISC had a high specificity of 100.0 % but a markedly
low sensitivity of 27.6 % (Table 2).
Verification of disease risk detection via JAMRISC
targeting the residents of Rumoi
We sent questionnaires to 4,283 residents of Rumoi City
aged 55–64 years (males 2,008; females 2,275) whose data
were extracted from the basic resident register. Valid
responses were received from 855 males (42.6 %) and
1,060 females (46.6 %) with a total response rate of
No significant difference was observed between males
and females concerning the number of questionnaires sent
or responses received. We calculated the risk for the 1,915
Table 3 Timetable of JAMRISC questionnaire utilized to direct disease development risk in Rumoi residents aged 55–64 years
Table 4 Validation of the risk detection rate by the JAMRISC questionnaire for MetS, pre-MetS, type 2 diabetes, and pre-diabetes in 396
subjects that underwent blood testing
a Among the 396 subjects who underwent blood testing, 52 subjects (equivalent to 13.1 %) were diagnosed with MetS, among whom 49
(94.2 %) exhibited the risk scores of C20
b High detection rates were also shown for pre-MetS, type 2 diabetes and pre-diabetes
subjects from whom responses were received and found
that 1,287 subjects (67.2 %) had a risk score of \20,
indicating ‘‘no risk,’’ whereas 628 subjects (32.8 %) had a
risk score of 20 or higher, indicating that they were ‘‘at
risk’’. Among the 1,915 subjects, 217 (11.3 %), 241
(12.6 %), and 170 subjects (8.9 %) had scores of 20–49,
50–89, and 90–100, respectively. The 628 subjects who
had a risk score of C20 and the 218 subjects who had a risk
score of \20 were recommended to undergo blood testing.
As a result, the 298 subjects who had a risk score of C20
and the 98 subjects who had a risk score of C20 underwent
blood testing (Table 3).
As shown in Table 4, study of disease risk detection via
JAMRISC questionnaire indicated a high detection rate for
pre-MetS (98.8 %), MetS (94.2 %), pre-diabetes (85.1 %)
and type 2 diabetes (94.9 %). Furthermore, the results of
blood testing revealed that the mean HOMA-IR was 1.15
for subjects with a questionnaire score less than 20 (males,
Table 5 Correlation between the risk score calculated with JAMRISC and the degree of insulin resistance
a The results of blood testing revealed that the mean HOMA-IR was 1.15 for subjects with a questionnaire score of\20 (males, 32.7 %), 1.67 for
subjects with a score of 20–49 (males, 71.4 %), 1.66 for subjects with a score of 50–89 (males, 83.8 %), and 2.25 for subjects with a score of
90–100 (males, 80.6 %), indicating strong insulin resistance. Accordingly, insulin resistance tended to increase as the risk score increased
b Insulin resistance intensity was set at three levels: HOMA-IR C1.4, HOMA-IR C2.0, HOMA-IR C3.0, and insulin resistance detection rates
were investigated for each risk score. The results indicated that 87.1% of subjects with a risk score of C20 were HOMA-R ]1.4, 91.2 % were
HOMA-IR ]2.0, and 92.3 % were HOMA-IR ]3.0
32.7 %), 1.67 for subjects with a score of 20–49 (males,
71.4 %), 1.66 for subjects with a score of 50–89 (males
83.8 %), and 2.25 for subjects with a score of 90–100
(males 80.6 %), indicating strong insulin resistance.
Accordingly, insulin resistance tended to increase as the
risk score increased. Therefore, insulin resistance intensity
was set at three levels: HOMA-IR C1.4, HOMA-IR C2.0
and HOMA-IR C3.0, and insulin resistance detection rates
were investigated for each risk score. The results indicated
that 87.1 % of subjects with a risk score of C20 were
HOMA-R C1.4, 91.2 % were HOMA-IR C2.0, and 92.3 %
were HOMA-IR C3.0. Accordingly, this demonstrated that
the JAMRISC risk evaluation could be used to determine
insulin resistance with the cutoff point set at 20 and that
even slight resistance as denoted by HOMA-IR C1.4 could
be detected (Table 5).
As shown in Table 6, the rate of subjects with
‘‘postprandial hyperglycemia with insulin resistance’’ which
included IGT, IFG/IGT and type 2 diabetes increased with
increasing risk scores.
From 2003 to 2025, it is projected that there will be a 72 %
increase in type 2 diabetes worldwide [15, 16]. It is also
predicted that the incidence of MetS in addition to type 2 diabetes
will rapidly increase in Japan and other Asian countries
(Korea, China, and India), as well as in developing countries.
Approximately 12 years ago, a simple questionnaire
called FINDRISC that was scored on the basis of the
Framingham Study was developed in Finland in Northern
Europe. In the initial study, it was reported that
development of type 2 diabetes was suppressed in the intervention
group by 58 % compared with the non-intervention group
. The questionnaire was developed to screen
individuals who had a high risk of developing type 2 diabetes in
the future and reduce its onset of incidence through early
intervention . These results were later confirmed in
various countries and the questionnaire is now accepted
and utilized worldwide [19, 20]. FINDRISC was first
developed as a diabetes risk test. Moreover, it has recently
come to be used to assess MetS risk [21, 22]. Accordingly,
this questionnaire could greatly increase the rate at which
people undergo health examinations due to its simplicity,
low cost, and non-invasiveness. However, because the
dietary habits and physique of Japanese people are greatly
different from those of Western people, FINDRISC might
not necessarily be as effective when applied to Japanese
people. The results of our comparative research, which
indicated that FINDRISC risk detection rate was markedly
low, suggest that FINDRISC should be modified to suit
Japanese people and that MetS risk questionnaires should
be developed specifically for the Japanese.
Table 6 Correlation between the rates of subjects with postprandial hyperglycemia with insulin resistance and the risk score calculated with
Although data were not shown in this study, the OGTT analysis results of 629 individuals who underwent the test at Rumoi Municipal hospital
revealed that 82.1% of individuals with FPG \110mg/dl, HOMA-b C55 were equivalent to IGT
In addition, recent epidemiological data in Japan show that subjects with FPG values of 100 to 109mg/dl, which are in the normal range,
develop type 2 diabetes at a higher rate than subjects with FPG values \100mg/dl
Moreover, FPG values of 100 mg/dl are seem to be corresponding to 2-hr values of 140 mg/dl in 75 g OGTT approximately (J. Japan Diab. Soc.
51(3): 281-283, 2008). With those reports as a background, we hypothesized that IGT having insulin resistance was diagnosed when HOMA-IR
C 1.4, FPG values of 100-109mg/dl and HOMA-b C 55
For the purpose of target all subjects exhibited ‘‘postprandial hyperglycemia with insulin resistance’’ within IGT, IFG/IGT and type 2 diabetes,
we decided to describe FPG values of C100 mg/dl
Therefore, we hypothesized that individuals who met the criteria of HOMA-IR C 1.4, FPG C100 mg/dl, HOMA-b C 55 had postprandial
hyperglycemia with insulin resistance
In developing countries in Asia, the incidence of
cardiovascular events is expected to rise dramatically with the
rapid increase in lifestyle-related diseases associated with
MetS and diabetes. In this study, therefore, we developed a
health checkup questionnaire for Japanese people
(JAMRISC) that used a different method from that of FINDRISC
that was able to detect not only type 2 diabetes and MetS,
but also pre-diabetes and pre-Mets conditions with high
Screening with currently available questionnaires,
including FINDRISC, usually involves evaluation with
whole numbers indicating the clinical relevance for each
question item (e.g., 0 point, 1 point, 2 points, and so forth),
and the scores for each item are then totaled to create an
overall risk score. We, however, adopted a method
different from the conventional ones to calculate MetS risk.
First, we conducted a questionnaire survey for a
population in which individuals with MetS (meeting Japanese
criteria) had already been clarified. Next, without revealing
subjects already identified as having MetS, logistic
regression analysis was used to estimate clinical relevance
for each question item to achieve the highest accuracy
possible. After that, the number of participants in the
population was gradually increased and, once the number
of subjects reached 1,054, the risk calculation formula was
completed at the point when question item coefficient
fluctuation decreased and stabilization was achieved.
The JAMRISC questionnaire had eleven question items.
Five of these were explanatory items, and the remaining six
were considered to have been explained by these five
items. It should be noted that abdominal circumference was
not allocated to possible responses such as 85 cm or
approximately 90 cm, but reflected an actual measurement
of abdominal circumference in centimeters and was used to
demonstrate risk transition with continuity.
In general, although screening via questionnaires is
simple, easy to participate in, and can be done at home
because it does not require blood testing, there is a
significant disadvantage that forced its low risk detection.
In contrast, the JAMRISC questionnaire offered high
detection with a sensitivity of 94.2 % for MetS in this
We confirmed that JAMRISC had had a higher
sensitivity than and comparable specificity to FINDRISC. In
addition, we also demonstrated that the JAMRISC
questionnaire could also detect insulin resistance, which occurs
at an even earlier stage in disease progression. The ability
to detect not only pre-diabetes and pre-MetS but also mild
insulin resistance may lead to the prevention of type 2
diabetes, MetS, as well as severe lifestyle-related diseases
such as cardiovascular disease [23, 24], Alzheimer-type
dementia [25, 26], and cancer [27, 28].
Recently, many reports have indicated that insulin
resistance itself is closely related to cardiovascular events
[29–33]. A GAMI study conducted by Ryden et al. [34, 35]
found that one-third of patients hospitalized for acute
myocardial infarction were diagnosed with type 2 diabetes,
one-third had postprandial hyperglycemia diagnosed with
IGT or IFG/IGT, and the remaining third had normal
glucose metabolism. Some of these patients with normal
glucose metabolism may have been in a high-risk group
exhibiting very mild insulin resistance [36, 37]. Therefore,
we decided to use HOMA-R C1.4 as an indicator of ‘‘the
presence of insulin resistance’’ so as to determine the
appearance of even slight insulin resistance and thus
The spread of simple and low-cost methods of
screening with high risk detection sensitivity such as
JAMRISC could contribute to the prevention of the onset
of lifestyle-related diseases associated with MetS and
type 2 diabetes. JAMRISC was found to not only exhibit
high precision for detecting the presence or absence of
risk, but also offered possibilities for stratifying low to
high risk levels, therefore, suggesting that it could be an
extremely useful method for screening the risk of
Acknowledgments We would like to extend our deepest gratitude to
Chiharu Kawano, who offered considerable cooperation in resident
questionnaire analysis, preservation, and management. We would also
like to warmly thank Hokkaido staff members Mikio Saito and
Makoto Urasaki as well as Rumoi City staff member Satoshi Kaino,
who were dispatched to the ‘‘Rumoi Health Station’’ (NPO Rumoi
Cohortopia) and cooperated in our survey work. Finally, we are
grateful to all the staff at NPO Rumoi Cohortopia.
Compliance with ethical standards
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1. Fujishima M , Kiyohara Y , Kato I , Ohmura T , Iwamoto H , Nakayama K , et al. Diabetes and cardiovascular disease in a prospective population survey in Japan, The Hisayama Study . Diabetes . 1996 ; 45 (Supple. 3): s14 - s16 .
2. Hanefeld M , Fischer S , Jullius U , Schulze J , Schwanebeck H , Ziegelasch HJ , et al. Diabetes Intervention Study: risk factors for myocardial infarction and death in newly detected NIDDM: Diabetes Intervention Study, 11-year follow up . Diabetologia . 1996 ; 39 : 1577 - 83 .
3. DECODE study group, the European Diabetes Epidemiology Group . Glucose tolerance and cardiovascular mortality: comparison of fasting and 2-hour diagnostic criteria . Arch Intern Med . 2001 ; 161 : 397 - 405 .
4. McNeill AM , Schmidt MI , Rosamond WD , East HE , Girman CJ , Ballantyne CM , et al. The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study . Diabetes Care . 2005 ; 28 : 385 - 90 .
5. DECODA Study Group. Cardiovascular risk profile assessment in glucose-intolerant Asian individuals-an evaluation of the World Health Organization two-step strategy; The DECODA study (Diabetes Epidemiology: collaborative Analysis of Diagnostic Criteria in Asia) . Diabetic Med . 2002 ; 19 : 549 - 57 .
6. Tominaga M , Eguchi H , Manaka H , Igarashi K , Kato T , Sekikawa A , et al. Impaired glucose tolerance is a risk factor cardiovascular disease, but not impaired fasting glucose: the Funagata Diabetes Study . Diabetes Care . 1999 ; 22 : 920 - 4 .
7. Lindstro¨ m J , Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk . Diabetes Care . 2003 ; 26 : 725 - 31 .
8. Investigative committee of diagnostic criteria for metabolic syndrome. Definition and diagnostic criteria of metabolic syndrome . J Jpn Soc Int Med . 2005 ; 94 : 188 - 203 .
9. http://canadiantaskforce. ca/ctfphc-guidelines/2012-type-2-dia betes/clinician -findrisc/( 2016 .7.19). Canadian Task Force on Preventive Health Care. Type 2 Diabetes-Clinician FINDRISC Canadian Task Force.
10. Matthews DR , Hosker JP , Rudenski AS , Naylor BA , Treacher DF , Turner RC . Homeostasis model assessment: insulin resistance and B-cell function from fasting plasma glucose and insulin concentrations in man . Diabetologia . 1985 ; 28 : 412 - 9 .
11. The Committee of the Japan Diabetes Society on the diagnostic criteria of diabetes mellitus. Report of the Committee on the classification and diagnostic criteria of diabetes mellitus . Diabetol Int . 2010 ; 1 : 2 - 20 .
12. World Health Organization. Definition and diagnosis of diabetes mellitus and Intermediate hyperglycemia: report of a WHO/IDF consultation . World Health Org ( 2006 ).
13. Kosaka K. Various parameters used for the diagnosis of diabetes and for the epidemiological investigation-their characteristics, their mutual relationship and their application . J Jpn Diabetes Soc . 1998 ; 41 : A101 - 5 .
14. Sasaki A , Shimizu T , Hasegawa K. Study of diagnostic criteria for diabetes mellitus from a viewpoint of clinical epidemiology , In: Kosaka K, editor, Diabetology. 1999 : 99 , Shindan to Chiryo sha (Tokyo): 97 - 105 .
15. King H , Aubert RE , Herman WH . Global burden of diabetes , 1995 - 2025 . Diabetes Care . 1998 ; 21 : 1414 - 1431 .
16. O'Rahilly S. Science , medicine, and the future. Non-insulin dependent diabetes mellitus; the gathering storm . BMJ . 1997 ; 314 : 955 - 9 .
17. Tuomilehto J , Lindstoro¨m J, Eriksson JG , Valle TT , Ha¨ma¨la¨inen H, Pirjo I , et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance . N Engl J Med . 2001 ; 344 : 1343 - 50 .
18. Lindstro¨m J , Louheranta A , Mannelin M , Rastas M , Salminen V , Eriksson J , et al. The finnish diabetes prevention study (DPS): lifestyle intervention and 3-year results on diet and physical activity . Diabetes Care . 2003 ; 26 : 3230 - 6 .
19. Soriguer F , Valde´s S , Tapia MJ , Esteva I , Adana MSR , Almaraz MC , et al. Validation of the FINDRISC (FINnish Diabetes Risk Score) for prediction of the risk of type 2 diabetes in a population of southern Spain Pizarra Study . Med Clin (Bare) . 2012 ; 138 : 389 - 90 .
20. Hellgren MI , Petzold M , Bjo¨rkelund C , Wedel H , Jansson PA , Lindblad U. Feasibility of the FINDRISC questionnaire to identify individuals with impaired glucose tolerance in Swedish primary care. A cross-sectional population-based study . Diabet Med . 2012 ; 29 : 1501 - 5 .
21. Makrilakis K , Liatis S , Grammatikou S , Perrea D , Stathi C , Tsiligros P , et al. Validation of the Finnish diabetes risk score (FINDRISC) questionnaire for screening for undiagnosed type 2 diabetes, dysglycaemia and the metabolic syndrome in Greece . Diabetes Metab . 2011 ; 37 : 144 - 51 .
22. Janghorbani M , Adineh H , Amini M. Evaluation of the Finnish Diabetes Risk Score (FINDRISC) as a screening tool for the metabolic syndrome . Rev Diabet Stud . 2013 ; 10 : 283 - 92 .
23. Lakka HM , Laaksonen DE , Lakka TA , Niskanen LK , Kumpussalo E , Tuomilehto J , et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men . JAMA . 2002 ; 288 : 2709 - 16 .
24. Bonora E , Targher G , Formentini G , Calcaterra F , Lombardi S , Marini F , et al. The metabolic syndrome is an independent predictor of cardiovascular disease in Type 2 diabetic subjects . Prospective data from the Verona Diabetes Complications Study . Diabet Med . 2004 ; 21 : 52 - 8 .
25. Matsuzaki T , Sasaki K , Tanizaki Y , Hata J , Fujimi K , Matsui Y , et al. Insulin resistance is associated with the pathology of Alzheimer disease . The Hisayama Study. Neurology . 2010 ; 75 : 764 - 70 .
26. Luchsinger JA , Tang MX , Shea S , Mayeux R. Hyperinsulinemia and risk of Alzheimer disease . Neurology . 2004 ; 63 : 1187 - 92 .
27. Calle EE , Rodriguez C , Walker-Thurmond K , Thun MJ . Overweight, obesity, and mortality from cancer in prospectively studied cohort of U .S. adults. N Engl J Med . 2003 ; 348 : 1625 - 38 .
28. Inoue M , Sobue T , Tsugane S , JPHC Study Group. Impact of body mass index on the risk of total cancer incidence and mortality among middle-aged Japanese: data from a large-scale population-based cohort study-The JPHC Study . Cancer Causes Control . 2004 ; 15 : 671 - 80 .
29. Hanley AJG , Williams K , Stern MP , Haffner SM . Homeostasis model assessment of insulin resistance in relation to the incidence of cardiovascular disease . The San Antonio Heart Study. Diabetes Care . 2002 ; 25 : 1177 - 84 .
30. Bressler P , Bailey SR , Matsuda M , DeFronzo RA . Insulin resistance and coronary artery disease . Diabetologia . 1996 ; 39 : 1345 - 50 .
31. Rewers M , Zaccaro D , D' Agostino R , Haffner S , Saad MF , Selby JV , et al. Insulin sensitivity, insulinemia, and coronary artery disease . Diabetes Care . 2004 ; 27 : 781 - 7 .
32. McFarlane SI , Banerji M , Sowers JR . Insulin resistance and cardiovascular disease . J Clin Endocrinol Metab . 2001 ; 86 : 713 - 8 .
33. Gotoh S , Doi Y , Hata J , Ninomiya T , Mukai N , Fukuhara M , et al. Insulin resistance and the development of cardiovascular disease in a Japanese Community: the Hisayama Study . J Atheroscler Thromb . 2012 ; 19 : 977 - 85 .
34. Norhammar A , Tenerz A , Nilsson G , Hamsten A , Efendic S , Ryde´n L , et al. Glucose metabolism in patients with acute myocardial infarction and no previous diagnosis of diabetes mellitus: a prospective study . Lancet . 2002 ; 359 : 2140 - 4 .
35. Bartnik M , Malmberg K , Norhammar A , Tenerz A , O¨ hrvik J , Ryde´n L. Newly detected abnormal glucose tolerance: an important predictor of long-term outcome after myocardial infarction . Eur Heart J . 2004 ; 25 : 1990 - 7 .
36. Zavaroni I , Bonora E , Pagliara M , Dall'Aglio E , Luchetti L , Buonanno G , et al. Risk factors for coronary artery disease in healthy persons with hyperinsulinemia and normal glucose tolerance . N Engl J Med . 1989 ; 320 : 702 - 6 .
37. Weyer C , Funahashi T , Tanaka S , Tanaka S , Hotta K , Matsuzawa Y , et al. Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia . J Clin Endocrinol Metab . 2001 ; 86 : 1930 - 5 .