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Worldwide Increase in Incidence of Type I Diabetes – The Analysis of the Data on Published Incidence Trends

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Several reports on the incidence of Type I (insulin-dependent) diabetes mellitus have suggested that the incidence is increasing. The aim of this study was to find out whether the incidence is increasing globally or restricted to a selected populations only and to estimate the magnitude of the change in incidence. During 1960 to 1996 37 studies in 27 countries were carried out. To fulfil the inclusion criteria the study periods ranged from 8-32 years. The temporal trend was fitted by linear regression, with the logarithm of the age-standardized incidence as the dependent variable and the calendar year as the independent variable. Then, the regression coefficient (x 100%) is approximately the average relative increase in incidence per year (as percentage). Results from the pooled data from all 37 populations showed that the overall increase in incidence was 3.0% per year (95% CI 2.6; 3.3, p = 0.0001). The statistically significant increase was found in 24 of 37 populations including all high incidence (> 14.6 per 100000 a year) populations. The relative increase was, however, steeper in the populations with a lower incidence. The correlation between logarithm of the incidence and the increase in incidence was r = -0.56, p = 0.0004. The incidence of Type I diabetes is increasing worldwide both in low and high incidence populations. By the year 2010 the incidence will be 50 per 100000 a year in Finland and also in many other populations it will exceed 30 per 100000 a year.
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The aetiology and natural history of Type I (insulin-
dependent) diabetes mellitus are still not known but
both genetics and environmental factors contribute
to the development of the disease [1±3]. Although
HLA genetics have a major role in the aetiology of
Type I diabetes, other genes also contribute to the ge-
netic effect, but the mode of inheritance of the dis-
ease is not clear [4]. The genetic effect contributes
70±75% of the susceptibility to Type I diabetes [5,
6]. Environmental factors possibly initiate or trigger
the process which leads to the destruction of the
beta cells and the onset of diabetes [3, 7, 8].
In the late 1970s epidemiological reports of dia-
betic children for the first time showed a wide geo-
graphical variation in the incidence of Type I diabe-
tes. During the 1960s to the early 1980s the data on
incidence of Type I diabetes were available for a few
populations only, mostly from regions with a high or
intermediate risk for this disease. A large number of
registries had been established since the mid 1980s
worldwide. The lack of standardized data made it dif-
ficult to determine the true magnitude of the world-
wide variation in incidence or time trends [9]. The Di-
Diabetologia (1999) 42: 1395±1403
Articles
Worldwide increase in incidence of Type I diabetes ±
the analysis of the data on published incidence trends
P. Onkamo, S. Väänänen, M. Karvonen, J. Tuomilehto
Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute,
Helsinki, Finland
Ó Springer-Verlag 1999
Abstract
Aims/hypothesis. Several reports on the incidence of
Type I (insulin-dependent) diabetes mellitus have
suggested that the incidence is increasing. The aim
of this study was to find out whether the incidence is
increasing globally or restricted to a selected popula-
tions only and to estimate the magnitude of the
change in incidence.
Methods. During 1960 to 1996 37 studies in 27 coun-
tries were carried out. To fulfil the inclusion criteria
the study periods ranged from 8±32 years. The tem-
poral trend was fitted by linear regression, with the
logarithm of the age-standardized incidence as the
dependent variable and the calendar year as the inde-
pendent variable. Then, the regression coefficient
( ´ 100%) is approximately the average relative in-
crease in incidence per year (as percentage).
Results. Results from the pooled data from all 37 pop-
ulations showed that the overall increase in incidence
was 3.0% per year (95% CI 2.6; 3.3, p = 0.0001). The
statistically significant increase was found in 24 of 37
populations including all high incidence ( > 14.6 per
100 000 a year) populations. The relative increase
was, however, steeper in the populations with a lower
incidence. The correlation between logarithm of the
incidence and the increase in incidence was
r = ±0.56, p = 0.0004.
Conclusion/interpretation. The incidence of Type I di-
abetes is increasing worldwide both in low and high
incidence populations. By the year 2010 the incidence
will be 50 per 100 000 a year in Finland and also in
many other populations it will exceed 30 per 100 000
a year. [Diabetologia (1999) 42: 1395±1403]
Keywords Epidemiology, geographical variation, in-
cidence, increase, modelling, prediction, trend, Type
I diabetes.
Received: 3 May 1999 and in final revised form: 17 August
1999
Corresponding author: J. Tuomilehto, Diabetes and Genetic
Epidemiology Unit, Department of Epidemiology and Health
Promotion, National Public Health Institute, Mannerheimin-
tie 166, FIN-00300 Helsinki, Finland
abetes Epidemiology Research International Group
(DERI) started the collection of the aggregate data
on incidence of Type I diabetes in the late 1970s and
the early 1980s [10]. The efforts of the DERI group
led to an increase in the number of registries on dia-
betic children and to the establishment of the World
Health Organization Project of Childhood Diabetes
(DIAbetes MONdiale) in 1990 [11]. The collabora-
tive research project EURODIAB ACE was estab-
lished also in the late 1980s [12] to gather information
of Type I diabetes in Europe. The latest reviews on
the incidence of Type I diabetes among populations
have indicated that differences in the incidence are
60-fold between the highest and the lowest rates [10,
12±14]. The highest incidence is found in Caucasoid
populations, particularly in northern Europe, and
the lowest rates are found in Asia and South America
[13, 14].
Thus far only one trend analysis of the incidence of
Type I diabetes comparing simultaneously several
but yet a limited number of populations has been car-
ried out by the DERI group [15]. The standardized
procedures agreed upon for the incidence data col-
lected around the world now permit a comparative
assessment of temporal trends among several popula-
tions. We estimated the temporal trends in the inci-
dence of Type I diabetes from incidence data collect-
ed through a systematic literature review. A statistical
analysis of the data was done in order to find out
whether the incidence is increasing globally. Another
objective was to evaluate quantitatively the extent to
which the change in incidence of Type I diabetes dif-
fers among populations.
Materials and methods
Literature search. The literature was searched using MED-
LINE, direct examination of reference lists of the articles,
hand searches of selected journals and published conference
abstracts. By the closing date, 28th February 1999, more than
160 original publications reporting time series of the incidence
of Type I diabetes were found.
Inclusion criteria. The publications were further evaluated
with the strict inclusion criteria in order to choose appropri-
ate studies for the quantitative analysis. The inclusion criteria
were: 1) the study period was 8 years or more, 2) the inci-
dence rates were presented for each year separately, 3) the
number of cases per year was five or more, 4) in the papers
in which the age standardization had been reported, the inci-
dences had been estimated with age standardization accord-
ing to the world population and, 5) Type I diabetes was diag-
nosed according to the WHO definition. Studies comparing
incidence rates estimated with different methods during dif-
ferent periods were excluded. No requirement for a minimum
case-ascertainment level of the data source was made as reli-
able case ascertainment estimation had usually not been
done until the 1990 s. The articles were evaluated by two inde-
pendent reviewers according to the above-mentioned inclu-
sion criteria.
Description of the data. Incidence data were obtained either
from the tables or from the figures in the published articles.
In approximately half of the original articles the annual inci-
dence rates were presented in the tables and in another half in
the figures only. The numbers derived from the figures were
reconfirmed by the second reviewer. For Montreal (Canada),
Allegheny County (USA), Scotland, Auckland (New Zea-
land), Prince Edward Island (Canada), Leicestershire (UK)
and Wielkopolska (Poland) we used the original database of
the DERI Study [15].
Altogether 37 studies from 27 countries met the inclusion
criteria and were included in the analysis (Table 1). The regis-
tration of diabetic children was prospective in most of the
studies. In 30 studies the age of children ranged from 0 to
14 years and in 7 studies from 0 to 15, 16, 17 and 19 years.
The time period of the studies ranged from 8 to 32 years.
The average length of the study period was 14.9 years (medi-
an 14 years). The estimates of the degree of case-ascertain-
ment were high, ranging from 85 to 100%. The degree of as-
certainment remained unspecified only in five studies. The
studies included in the analysis were from the period 1960 to
1996.
Statistical methods. The incidence of Type I diabetes for our
analysis was taken from the individual studies as it was report-
ed in these publications. The incidence for the data obtained
from the DERI Study [15] was calculated per 100 000 people
a year. Age standardization of the rates was done using 5-year
intervals with the proportions 33/100, 33/100 and 34/100 (for
the age groups 0±4 years, 5±9 years, and 10±14 years respec-
tively) as the standard according to the approach by the
DERI Study Group [16], which is the same as the world popu-
lation standard.
The temporal trend for each population was fitted by a sim-
ple linear regression under the assumption of normally distrib-
uted errors, with the age-standardized incidence as dependent
variable expressed on a logarithmic scale and the calendar
year as independent variable: lnl
i
(t) = a
i
+ b
i
t, where l
i
(t) de-
note the age-standardized incidence predicted at year t for
population i; the intercept a
i
is different for each population,
and b
i
is the population specific regression coefficient (the
trend), respectively. In such a multiplicative model the regres-
sion coefficient ( ´ 100 %) is a percentage, approximately be-
ing the average relative increase in incidence per year. The
multiplicative regression model was used because it fitted the
data well. It is commonly used in estimating time trends in inci-
dence and allows a simple interpretation of the regression co-
efficient.
The overall estimate of the relative annual increase was ob-
tained by using a pooled, centralized data set: to start, for each
population the logarithms of the age-standardized incidence
rates and the time points were centralized to make the differ-
ent lengths of the studies and incidence levels more compara-
ble. Then, using the method of least squares, a straight line
constrained to cross the origin of the centralized coordinate
system was fitted to the pooled data set. The regression coeffi-
cient has the same interpretation as in the population-wise
analysis. The analysis was subsequently repeated as weighted
regression, where the residual sum of squares was weighted
with the number of cases in individual studies, to give more
weight for observations with a higher number of cases.
The association between the level of incidence and increase
in incidence was assessed by calculating the correlation coeffi-
cient between the logarithms of incidences in 1983 predicted
by the model and the incidence increases estimated by the mul-
tiplicative model. The year 1983 was chosen because almost all
studies covered it.
P. Onkamo et al.: Worldwide increase in incidence of Type I diabetes1396
Predictions until the year 2010 have been made with both
multiplicative and additive regression models, meaning that
the curve produced by fitting the model to the data is simply
extrapolated to the year 2010. In essence, the multiplicative
model fits an exponential curve to the incidence, whereas the
additive model fits a straight line. The additive regression
model is used here to point out the differences between predic-
tions when using alternative models.
Results
Incidence. The mean incidence of Type I diabetes
among the study populations varied from 0.5 to 30.3
per 100 000 a year during the observation period (Ta-
ble 1). The mean incidences were divided into quar-
tiles: low incidence, less than 6.4 per 100 000 a year
(the lowest 25% of the mean incidences), intermedi-
ate 6.4±14.6 per 100 000 a year (50% of the mean in-
cidences, thus the two intermediate groups com-
bined), and high, more than 14.6 per 100 000 a year
(highest 25% of the mean incidences).
Increase in the incidence of Type I diabetes. The rela-
tive change (% per year) in incidence among individu-
al populations ranged from ±0.2% in Colorado (USA)
to 9.5% in Leicestershire (UK) (Table 2). A statisti-
cally significant increase in incidence was found in
65% (24/37) of the populations. An upward tendency
in incidence not reaching statistical significance was
observed in another 12 populations. Only in one pop-
ulation, Colorado (USA), the trend was slightly, but
not significantly, negative (±0.2% per year) and the
upper limit of the 95% confidence interval show that
an increase of 2.2% per year was possible.
The global trend and the annual increase in the in-
cidence of Type I diabetes were estimated from the
P. Onkamo et al.: Worldwide increase in incidence of Type I diabetes 1397
Table 1. The studies included in the analysis
Country: area Study
period
Length
of study
period
(years)
Age-
group
(years)
Number
of cases
Mean
incidence
(100,000/
year)
Study
design
a)
Degree of
ascertain-
ment
Rates
obtained
from
b)
Reference
Algeria: Oran 1979±1988 10 0±14 173
c)
4.7 P, R ? F 36
Australia: West 1985±1992 8 0±14 350 14.9 P 99.6% T 37
Austria 1979±1993 15 0±14 1551 7.8 P, R > 90% T 15, 38
Bulgaria, East 1974±1995 22 0±14 818 6.3 R, P 98.8% T 39
Canada: Montreal 1971±1985 15 0±14 839 9.3 R 94% T 40
Canada: Prince 1975±1986 19 0±14 115 23.5 P 99% T 41, 42
Edward Island 1990±1993
China: Shanghai 1980±1993 14 0±14 67 0.7 R 85.2% F 43, 44
East-Germany 1960±1989 30 0±19 9581 6.7 P 99% F 45
Estonia 1983±1996 14 0±14 523 10.2 P 92%
d)
96%
e)
T 42, 46
Finland 1965±1996 32 0±14 9047 30.3 P, R 100% T 14, 47
France 1988±1995 8 0±19 1439 8.0 P 96% T 48
Hungary 1978±1987 10 0±14 1060 6.1 P 96.2% T 49
Iceland 1970±1988 19 0±14 120 9.0 P, R 100% F 50
Israel: Yemenite Jews 1965±1993 29 0±17 1665 5.0 P ? F 51
Italy: Turin 1984±1991 8 0±14 227 8.4 R, P 99% F 52
Japan: Hokkaido 1973±1992 20 0±14 396 1.7 P 100% F 53
Latvia 1983±1996 10 0±14 652 7.2 P 80±100% F, T 54, 55
Libya 1981±1990 10 0±14 251 8.7 P ? T 56
Lithuania 1983±1992 10 0±14 215 6.4 P 95±100% F 54
Malta 1980±1996 17 0±14 90 14.7 P ? F 57
New Zealand: Auckland 1977±1987 11 0±15 206 10.1 P 100% T 58
New Zealand: Canterbury 1982±1990 9 0±19 123 12.7 P 100% F 59
Norway 1973±1982 10 0±14 1914 20.8 R 99.4% F 60
Peru: Lima 1985±1994 10 0±14 111 0.5 P 85 % T 61
Poland: Krakow 1987±1994 8 0±14 396 5.9 P 100% T 62
Poland: Rzeszów 1980±1992 13 0±14 122 5.1 P, R 99% F 63
Poland: Wielkopolska 1970±1985 15 0±16 451 4.4 P > 95% T 15, 64
Slovakia 1985±1995 11 0±14 1127 7.5 P 95% T, F 65, 66
Sweden 1978±1992 15 0±14 5831 24.9 P, R 100 % F 3
UK: Leicestershire 1965±1981 17 0±14 248 7.8 P, R 97.6% F 15
UK: Oxford 1985±1995 11 0±14 1037 18.5 P ? T 67
UK: Plymouth 1975±1996 22 0±14 488 14.9 P 95.3% F 68
UK: Scotland 1976±1993 18 0±14 4182 21.6 R > 95% T 15
UK: Yorkshire 1978±1992 15 0±14 1721 14.3 P 97.6% F 69, 70
USA: Allegheny County 1965±1985 21 0±14 1041 14.7 P, R 100 % T 71
USA: Colorado 1978±1988 11 0±17 1376 7.8 P, R 93.3% F 72
USA: Hawaii 1980±1990 11 0±14 113 12.3 P 97% F 73
a)
P = prospective, R = retrospective
b)
F = figure, T = table
c)
families
d
non Estonian
e
Estonian
age-standardized incidence rates using the log-linear
regression (Table 2). The p value of less than 0.05 for
the two-sided test for a non-zero regression coeffi-
cient was regarded as evidence for the trend. The
global annual increase was 3.0% (95% CI 2.59; 3.33,
p = 0.0001) during 1960 to 1996, showing a highly sig-
nificant increasing trend. When the annual incidence
rates were weighted with the number of cases in
each individual study, the increase in incidence was
2.5% (95% CI 2.32; 2.66; p = 0.0001). The estimated
population-wise regression lines illustrate well the in-
creasing trends (Fig.1, 2).
Comparison of increase rates. The relation between
the increase in incidence of Type I diabetes and the
average level of incidence expressed as the incidence
in 1983 estimated from the regression model is shown
in Figure 3. There was a significant inverse associa-
tion between the increase and the logarithm of the
level of incidence (r = ±0.56, p = 0.0004). The associa-
tion indicates that the relative increase was more pro-
nounced in the populations with a low incidence.
Nevertheless, in the five populations with the highest
incidence the increase was also statistically signifi-
cant, varying from 1.2 to 3.2% per year.
The incidence level of Type I diabetes and its in-
crease seemed to be similar in some geographically
adjacent populations. For example in the northern
European countries; Finland, Sweden and Norway
where the incidence of Type I diabetes has been high
for a long time, the increase was 1 to 3% per year.
Adjacent countries around the Baltic Sea, Estonia,
Latvia, Lithuania and Poland with an intermediate
or low incidence (4±10 per 100 000 a year) showed
P. Onkamo et al.: Worldwide increase in incidence of Type I diabetes1398
Table 2. Relative increase in incidence of Type I diabetes in
children aged 14 years or less in 37 populations. The popula-
tions are arranged in descending order according to the rela-
tive increase per year. The 95% confidence interval is given in
the parentheses. P value stands for the two-sided test for a non-
zero regression coefficient
Country: area Mean incidence Increase in incidence %
per year (95% CI)
p value
UK: Leicestershire 7.8 9.5 (6.51; 12.53) 0.0001
Hungary 6.1 8.5 (6.50; 10.42) 0.0001
Algeria: Oran 4.7 7.9 (1.85; 14.00) 0.0338
USA: Hawaii 7.8 7.8 (1.80; 14.87) 0.0315
Peru: Lima 0.5 7.7 (0.97; 16.40) 0.1197
China: Shanghai 0.7 7.4 (2.30; 12.51) 0.0148
Poland: Krakow 5.9 6.8 (2.27; 11.41) 0.0262
New Zealand: Auckland 10.1 6.4 (4.20; 8.52) 0.0003
Australia: West 14.9 6.3 (2.11; 10.50) 0.0259
Libya 8.7 6.3 (0.69; 11.8) 0.0589
Japan: Hokkaido 1.7 5.9 (4.14; 7.63) 0.0001
Slovakia 7.5 5.5 (3.64; 7.41) 0.0003
Poland: Wielkopolska 4.4 4.8 (1.94; 7.66) 0.0054
France 8.0 3.9 (2.85; 4.94) 0.0003
UK: Oxford 18.5 3.7 (1.82; 5.50) 0.0037
Canada: Prince Edward Island 23.5 3.2 (0.33; 6.38) 0.0728
Israel: Yemenite Jews 5.0 3.2 (2.51; 3.88) 0.0001
Norway 20.8 3.2 (1.19; 5.22) 0.0143
Austria 7.8 2.7 (1.58; 3.76) 0.0003
UK: Plymouth 14.6 2.7 (0.91; 4.50) 0.0079
New Zealand: Canterbury 12.7 2.7 (0.05; 10.50) 0.5262
UK: Scotland 21.6 2.5 (1.85; 3.08) 0.0001
East-Germany 6.7 2.4 (1.96; 2.90) 0.0001
Finland 30.3 2.3 (1.98; 2.57) 0.0001
Iceland 9.0 2.3 (2.38; 6.96) 0.3498
Italy: Turin 8.4 2.2 (3.99; 8.35) 0.5150
Bulgaria: East 6.3 2.1 (1.03; 3.15) 0.0010
UK: Yorkshire 14.3 1.9 (0.30; 3.53) 0.0372
Canada: Montreal 9.3 1.6 (0.67; 3.82) 0.1937
USA: Allegheny County 14.7 1.5 (0.21; 2.83) 0.0348
Sweden 24.9 1.2 (0.42; 2.02) 0.0103
Poland: Rzeszów 5.1 1.1 (3.25; 5.40) 0.6339
Lithuania 6.4 1.1 (4.25; 6.41) 0.7023
Latvia 7.2 0.9 (1.90; 3.75) 0.5350
Malta 14.7 0.5 (2.15; 3.19) 0.7078
Estonia 10.2 0.4 (0.96; 1.76) 0.5741
USA: Colorado 12.3 0.2 (2.52; 2.19) 0.8938
Change globally 3.0 (2.59; 3.33) 0.0001
Change globally (weighted by number of cases) 2.5 (2.32; 2.66) 0.0001
an upward course but not a statistically significant
trend in incidence. The increase in incidence in east-
ern Europe varied from 2.1% per year in East Bul-
garia to 8.5% per year in Hungary. In the United
Kingdom the mean incidence ranged from 14.3 to
21.6 and the increase in incidence ranged between
1.9 and 3.7% except for Leicestershire where the
mean incidence was 7.8 with an increase of 9.5%.
The data for Leicestershire, however, were consider-
ably older (from 1965 to 1981) than from other UK
study populations.
Predictions until the year 2010. Since no effective pre-
vention has thus far been invented or is foreseen in
the near future, we used the observed trends to pre-
dict the incidence of Type I diabetes at least until the
year 2010 (Table 3). For the prediction we applied
both linear and exponential models, since the model
of the increase in incidence is not known. The expo-
nential predictions were only calculated for popula-
tions with a study period of at least 14 years. In gener-
al, the linear model produces more conservative pre-
dictions than the exponential model, however, in
those populations where an increase had started dur-
ing the very last years of the observation period, the
exponential model gave more conservative predic-
tions than the linear.
The predictions based on the linear model show
that Finland will still have the highest incidence in
the world (50 per 100 000 a year) in the year 2010, fol-
lowed by Norway, Prince Edward Island (Canada),
western Australia, Scotland (UK), Oxford (UK),
and Sweden. Despite the large relative increases in
the incidence observed in China and Peru, the abso-
lute incidence rates in these countries would still re-
main low, less than 2 per 100 000 a year. Based on
these predictions, in Japan the incidence will be lower
than 5 per 100 000 a year and in Poland, Latvia and
Lithuania the incidence will be under 10 per 100 000
a year.
Discussion
There are also other populations in which an increase
in the incidence of Type I diabetes has been recently
reported such as Croatia, Denmark, Kuwait, the
Netherlands, Russia and Switzerland [17±23]. These
studies, however, did not meet the inclusion criteria
of this study and were not included in our analysis.
To find out whether the rising incidence is really a
global phenomenon, we carried out an analysis of in-
cidence trends among 37 populations worldwide for
which the data had been collected for 8 years or
more. The incidence of Type I diabetes is globally in-
creasing by 3.0% per year (or by 2.5%, when the in-
cidences were weighted by the number of diabetic
children included in the individual studies). Confi-
dence intervals for these estimates were fairly narrow
indicating that these estimates are reliable. Accord-
ing to this estimate, the incidence of Type I diabetes
will be 40% higher in 2010 than in 1998. This is a real-
istic, although a rather frightening, scenario.
P. Onkamo et al.: Worldwide increase in incidence of Type I diabetes 1399
Fig. 1. Trends in incidence of Type I diabetes in European pop-
ulations. AUT: Austria; BGR: Bulgaria; DEU: East-Germany;
EST: Estonia; FIN: Finland; FRA: France; HUN: Hungary;
ICE: Iceland; ITA: Italy (Turin); LVA: Latvia; LTU: Lithuania;
MAT: Malta; NOR: Norway; POLk: Poland (Krakow); POLr:
Poland (Rzeszów); POLw: Poland (Wielkopolska); SVK: Slo-
vakia; SWE: Sweden; UNKl (UK, Leicestershire); UNKo (Ox-
ford); UNKp (Plymouth); UNKs (Scotland); UNKy (York-
shire). The model fitted to the incidence data was a multiplica-
tive regression model with logarithm of the age standardized
incidence as dependent variable, thus the scale of the incidence
is logarithmic when straight lines were used in drawing the re-
gression lines
Fig. 2. Trends in the incidence of Type I diabetes mellitus in
Non-European populations. DZD: Algeria (Oran); AUSw:
Australia (West); CANm: Canada (Montreal); CANp: Canada
(Prince Edward Island); CHN: China (Shanghai); ISR: Israel
(Yemenite Jews); JPN: Japan (Hokkaido); LIY: Libya; NEZa:
New Zealand (Auckland); NEZc: New Zealand (Canterbury);
PER: Peru (Lima); USAa: USA (Allegheny County); USAc:
USA (Colorado); USAh: USA (Hawaii)
The global variation in the incidence of Type I dia-
betes is prominent [10, 13, 14]. It reflects the distribu-
tion of ethnic diversity showing the importance of the
differential genetic susceptibility among populations.
The incidence is higher among Caucasoid popula-
tions than among Mongoloids and Blacks. Within
ethnic groups, however, there are geographical differ-
ences in incidence depending on the admixture be-
tween racial groups and possible environmental ex-
posures [13]. Although most of the populations in-
cluded in this analysis were Caucasoid, statistically
significant increases in incidence were also found
among the Asian populations in China and Japan,
Mestizos in Peru and also among the Polynesians in
Hawaii. In this literature review it was not possible
to account for ethnic differences within populations
because the authors of the original papers had usually
not given detailed information on incidence in differ-
ent ethnic groups. Where the ethnic groups had been
analyzed separately, however, an increase in the inci-
dence had been observed in all groups, but the rate
of increase could vary from one ethnic group to an-
other. Overall, the increase in the incidence of Type
I diabetes does not seem to be restricted to any par-
ticular ethnic group.
In most countries with a low incidence the stan-
dardized incidence data have been collected during a
relatively short period, which may in some cases ex-
plain the large relative change in incidence. The inci-
dence of Type I diabetes possibly has been underesti-
mated in earlier studies because of incomplete case-
ascertainment and death from undiagnosed diabetes.
Among those populations where the study period
was 18 years or more the increase in incidence was
usually low (from 1.5 to 3.2% per year except Japan,
Hokkaido, 5.9%). Therefore, results from several in-
dividual populations showing large increases should
be interpreted cautiously when the number of cases
P. Onkamo et al.: Worldwide increase in incidence of Type I diabetes1400
Fig. 3. Association between the increase in incidence and the
level of incidence of Type I diabetes. The correlation coeffi-
cient between the log-transformed level of incidence predicted
for 1983 and the increase in incidence was ±0.56 (p = 0.0004)
for all populations. The increase in incidence was estimated
from multiplicative regression models. For each population
the level of incidence was calculated as the model-predicted
(fitted) incidence in 1983 according to the multiplicative re-
gression model. Leicestershire, UK, has been excluded. DZD:
Algeria (Oran); AUSw: Australia (West); AUT: Austria;
BGR: Bulgaria; CANm: Canada (Montreal); CANp: Canada
(Prince Edward Island); CHN: China (Shanghai); DEU: East-
Germany; EST: Estonia; FIN: Finland; FRA: France; HUN:
Hungary; ICE: Iceland; ISR: Israel (Yemenite Jews); ITA: Ita-
ly (Turin); JPN: Japan (Hokkaido); LVA: Latvia; LIY: Libya;
LTU: Lithuania; MAT: Malta; NEZa: New Zealand (Auck-
land); NEZc: New Zealand (Canterbury); NOR: Norway;
PER: Peru (Lima); POLk: Poland (Krakow); POLr: Poland
(Rzeszów); POLw: Poland (Wielkopolska); SVK: Slovakia;
SWE: Sweden; UNKo: UK (Oxford); UNKp: UK (Plymouth);
UNKs: UK (Scotland); UNKy: UK (Yorkshire); USAa: USA
(Allegheny County); USAh: USA (Hawaii); USAc: USA
(Colorado)
Table 3. Predicted incidence (100000 per year) of Type I dia-
betes in children aged 14 years or less by the year 2010 accord-
ing to both the multiplicative and additive regression model.
The populations are arranged in descending order according
to the predicted incidence in 2010 based on the additive model.
Only the populations with a study period covering 14 years or
more were included in predictions with the multiplicative mod-
el
Country: area Predicted incidence per
100000/year in 2010
Additive
regression
model
Multiplicative
regression
model
Finland 50.2 57.9
Norway 41.8
Canada: Prince Edward Island 39.2 51.4
Australia: West 36.7
UK: Scotland 34.9 40.0
UK: Oxford 33.0
Sweden 32.2 33.7
New Zealand: Auckland 27.7
New Zealand: Canterbury 25.0
UK: Plymouth 23.4 27.0
USA: Allegheny County 22.5 24.5
USA: Hawaii 21.4
UK: Yorkshire 21.0 23.0
Hungary 20.1
Libya 19.0
Malta 16.8 15.8
Iceland 16.1 16.0
Slovakia 16.0
Algeria: Oran 15.2
Canada: Montreal 14.8 15.0
France 13.7
Poland: Krakow 13.0
Italy: Turin 12.8
Austria 12.8 14.6
East Germany 12.0 15.4
USA: Colorado 12.0
Poland: Wielkopolska 11.8 20.2
Estonia 10.9 11.0
Bulgaria: East 10.3 10.7
Israel: Yemenite Jews 10.1 12.9
Latvia 8.9
Lithuania 7.5
Poland: Rzeszów 6.7
Japan: Hokkaido 4.1 7.9
China: Shanghai 1.7 3.3
Peru: Lima 1.3
is small and the study period short. The analysis of
the pooled data was repeated excluding populations
for which the case ascertainment level was not report-
ed (Algeria, Israel, Libya, Malta and Oxford, UK).
The results were essentially the same as in the analy-
sis using all data: 2.95% per year vs 2.96% per year,
respectively, and for the weighted regression, 2.40%
vs 2.49%, respectively.
There is presently no way to know whether the ob-
served trend in incidence might reflect a change in
the age at onset of diabetes instead of a true rise in
prevalence. The increase in incidence in 0±14-year-
olds might just be a transition of the age at onset
from the age group 15 years or older. Data on inci-
dence trends in older age groups exist from just a
few populations; thus, reliable information a possible
decrease in incidence in young adults is not available.
Our main result is that the incidence is globally in-
creasing in the age group of 0±14-year-olds.
The genetic factors have been shown to be impor-
tant in the susceptibility to Type I diabetes [5, 6]. Al-
though it is possible that the part of the population
genetically predisposed for Type I diabetes is increas-
ing, this increase may have been modest during the
last decades and not alone a sufficient cause for the
observed increase in incidence. The changes in the
genetic code of the human populations are usually
slow. In this analysis even the longest study period
covered only 30 years, which is approximately the
time span of one generation. It is very unlikely that a
three to tenfold increase in the proportion of subjects
with genetic susceptibility to Type I diabetes has tak-
en place in any population during such a short time.
Instead, the penetrance of the susceptibility genes
might be changing. The penetrance is likely to be de-
termined by an interaction between several suscepti-
bility genes and unknown environmental factors [4,
24].
During the recent years much attention has been
paid to the identification and possible control of envi-
ronmental factors which possibly initiate or trigger
the process leading to Type I diabetes. Although
some studies suggest associations between environ-
mental factors such as diet and viral infections with
the risk of Type I diabetes [25±35], their causative
role has not been shown. It is also difficult to show
that any of these environmental factors has changed
in such a way that a continuous global increase in the
incidenceof TypeI diabeteswould beeasily explained.
The incidence of Type I diabetes is increasing
worldwide. Thus far no population has been identi-
fied in which the incidence has significantly de-
creased. The population-based WHO DIAMOND
Project and the EURODIAB study started at the be-
ginning of the 1990s but have not yet reported results
from the long-term progress in the incidence of Type
I diabetes. It seems obvious that in both of these stud-
ies the 10-year monitoring period planned thus far is
too short to produce reliable trend estimations and
predictions for the change of the incidence of Type I
diabetes, especially in countries where incidence is
low. There is a need to continue with the communi-
ty-based registries on Type I diabetes worldwide. Ef-
forts are also needed to identify effective primary
prevention measures for Type I diabetes to stop the
global increase in the incidence of this disease.
Acknowledgements. We thank the Diabetes Epidemiology Re-
search International (DERI) Group for the data for several
countries used in this study.
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P. Onkamo et al.: Worldwide increase in incidence of Type I diabetes 1403
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... Human EVs are ubiquitous and responsible for serious diseases such as poliomyelitis, myocarditis and aseptic meningitis [99]. However, many EV infections cause subclinical or mild disease and are thus underreported, with a small proportion proceeding to clinical identification [100]. More severe EV infection is typically seen in children and neonates, with proposed intrinsic immunity in the adult mature gut moderating the course of infection and preventing viraemia [101]. ...
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For over a century, viruses have left a long trail of evidence implicating them as frequent suspects in the development of type 1 diabetes. Through vigorous interrogation of viral infections in individuals with islet autoimmunity and type 1 diabetes using serological and molecular virus detection methods, as well as mechanistic studies of virus-infected human pancreatic β-cells, the prime suspects have been narrowed down to predominantly human enteroviruses. Here, we provide a comprehensive overview of evidence supporting the hypothesised role of enteroviruses in the development of islet autoimmunity and type 1 diabetes. We also discuss concerns over the historical focus and investigation bias toward enteroviruses and summarise current unbiased efforts aimed at characterising the complete population of viruses (the “virome”) contributing early in life to the development of islet autoimmunity and type 1 diabetes. Finally, we review the range of vaccine and antiviral drug candidates currently being evaluated in clinical trials for the prevention and potential treatment of type 1 diabetes.
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Type 1 diabetes (T1D) is a chronic disease caused by the destruction of pancreatic β cells, which is driven by autoreactive T lymphocytes. It has been described that a high proportion of T1D patients develop other autoimmune diseases (AIDs), such as autoimmune thyroid disease, celiac disease, or vitiligo, which suggests the existence of common etiological factors among these disorders. In this regard, genetic studies have identified a high number of loci consistently associated with T1D that also represent established genetic risk factors for other AIDs. In addition, studies focused on identifying the shared genetic component in autoimmunity have described several common susceptibility loci with a potential role in T1D. Elucidation of this genetic overlap has been useful in identifying key molecular pathways with a pathogenic role in multiple disorders. In this review, we summarized recent advances in understanding the shared genetic component between T1D and other AIDs and discuss how the identification of common pathogenic mechanisms can help in the development of new therapeutic approaches as well as in improving the use of existing drugs.
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A number of animal models of virus-induced diabetes have now been described; Encephalomyocarditis (EMC) virus (4), foot-and-mouth virus (2), and Coxsackie B4 virus (3) have all been found to be diabetogenic in animals. The diabetes described in these reports has usually been mild and transient, and although of great interest, it may not be relevant to the problem of diabetes in man.
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The intakes of nitrate and nitrite of children and their parents from food and drinking water were estimated in a Finnish nation-wide case-control study on the epidemiology of Type 1 diabetes. The study population consisted of 684 case and 595 control children; 548 case-control pairs of fathers; and 620 case-control pairs of mothers. The consumption frequencies of foods which are important sources of nitrate and nitrite were assessed by structured questionnaire. Nitrate and nitrite concentration data were collected from Finnish water works. Diabetic children's and their mothers' daily dietary intake of nitrite was greater compared with that of control children and mothers (for case and control children 0.9 mg vs 0.8 mg, for case and control mothers 0.9 mg vs 0.8 mg, p<0.001). Case mothers compared with control mothers received less (p<0.05) nitrate from their diet. No differences were observed in the intake of nitrate or nitrite from drinking water. Dietary nitrite intake of children (odds ratios and 95% confidence intervals for the second, third, and fourth quartile 1.16, 0.82–1.65; 1.49, 1.06–2.10; 2.32, 1.67–3.24, respectively) and mothers (odds ratios and 95% confidence intervals for the second, third, and fourth quartile 1.15, 0.76–1.74; 1.29, 0.87–1.91; 1.98, 1.35–2.90, respectively) was positively associated with the risk for Type 1 diabetes independently from length of mother's education, child's or mother's age, place of residence or mother's smoking status. The present study gives supporting evidence that dietary nitrites, from which N-nitroso compounds can be formed in foods and in the human body, are associated with the development of Type 1 diabetes in man.
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HLA genotypes were ascertained in 150 families with a diabetic child from the same geographical area. There was preferential zygotic assortment of the paternal HLA A1-B8 haplotype (63--65% compared with the expected 50%) in 69 diabetic families and 33 control families (pooled from elsewhere) who were informative for this haplotype. In diabetic families, the offspring also had an increased incidence of the maternal HLA A2-B15-Cw3 haplotype. Irrespective of which parent contributed the HLA A1-B8 haplotype, there was a significantly increased incidence of male children (63%) who inherited this particular haplotype. This probably explains the known excess of male diabetic children.
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In a case—control design the feeding in infancy of newly diagnosed 7- to 14-year-old diabetic children (n = 426) was compared with that of age- and sex-matched non-diabetic children (n = 426) randomly selected from the Finnish population registry. All 7- to 14-year-old diabetic children diagnosed from September 1986 to the end of April 1989 from all hospitals which treat diabetic children in Finland were invited to participate in the study. Breast-feeding was initiated in almost all children, but during the birth years of this study population (1972–1982), an increase was observed in the duration of breast-feeding (whether alone or in combination with supplementary feeding) and in the age of introduction of supplementary milk feeding. The risk of Type 1 diabetes was decreased in the children who were totally breast-fed for at least 2 months (odds ratio (OR) 0.64, 95% confidence interval (CI) 0.42–0.98) or 3 months (OR 0.67, 95% CI 0.48–0.95) or exclusively breast-fed for at least 2 months (OR 0.60, 95% CI 0.41–0.89) or 3 months (OR 0.63, 95% CI 0.43–0.93). Those children who were younger than 2 months (OR 1.54, 95% CI 1.08–2.18) or 3 months (OR 1.52, 95% CI 1.11–2.08) at the time when supplementary milk feeding was begun had an increased risk of Type 1 diabetes. These associations remained significant after adjusting for the mother's education. The results suggest that early infant feeding patterns are associated with the risk of Type 1 diabetes developing at the age of 7 to 14 years.
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To study the possible temporal association between primary cytomegalovirus infection and the appearance of islet cell autoantibodies or the development of insulin-dependent diabetes mellitus (IDDM) cytomegalovirus antibodies were analysed from follow-up sera of 46 initially non-diabetic siblings of diabetic children who either manifested clinical IDDM (22 siblings) or turned islet cell antibody positive (24 siblings) during the prospective observation (mean follow-up time 2.9 years). Secondly, cytomegalovirus antibodies were analysed during pregnancy in 96 mothers whose child presented with IDDM before the age of 7 years and in 96 control mothers who gave birth to a non-diabetic child. Thirdly, a case-control series including 90 newly-diagnosed young children with IDDM and their 90 control subjects was analysed. No seroconversions were found in cytomegalovirus antibodies during the follow-up of the 46 siblings indicating no temporal association with islet cell antibody seroconversion or manifestation of clinical diabetes. During the follow-up 17 (37%) siblings were constantly seronegative and 29 (63%) seropositive for cytomegalovirus IgG and there was no difference between islet cell antibody positive and negative siblings. Cytomegalovirus IgG and IgM were not different in pregnant mothers who gave birth to a subsequently diabetic child compared to control mothers, or in newly-diagnosed diabetic children compared to control children. Cytomegalovirus IgA was higher in newly-diagnosed diabetic children than in control children (p<0.005). This difference disappeared when only cytomegalovirus IgG positive individuals were analysed. No correlation was found between islet cell antibodies and cytomegalovirus antibodies in newly-diagnosed diabetic patients. The results do not support the hypothesis that primary cytomegalovirus infections could initiate the cascade leading to autoimmune destruction of the beta cells.
Article
Animal models, most notably EMC-induced murine diabetes mellitus, have demonstrated convincingly a juvenile-type of diabetic state in the genetically susceptible host. There is evidence that one site of genetic control in mice relates to differences in viral receptors on the surface of the beta cells that may confer susceptibility or resistance to EMC-virus-induced diabetes. The availability of β cell tissue culture techniques, assuming that such cultivation does not render β cells nonspecifically susceptible to any virus, has potential in screening for possible diabetogenic viruses in man. Animal models of virus-induced diabetes in primates could offer the possibility to study carefully the evolution of diabetic complications. Certain animal models and human studies are consistent with an autoimmune component playing a part in the genesis of JIDM. A viral infection and an autoimmune process are not incompatible with each other: A virus can trigger an autoimmune event and an autoimmune phenomenon can heighten susceptibility to a a diabetogenic virus. The pathologic entity of insulitis in JIDM is compatible with a viral and/or autoimmune process being operative etiologically. Genetic studies of twins concordant and discordant for JIDM suggest an environmental component, such as a viral infection, in the etiology of this type of diabetes. HLA associations have been established with JIDM, but the nature of the gene product(s) of specific HLA haplotypes is not known. Pancreatic islet cell antibodies (ICAb) are associated with JIDM. Whether ICAb are formed in response to a viral or autoimmune process, or, whether they actually play a role in the etiology of JIDM is unclear. Epidemiologic investigations reveal a relationship of JIDM with seasonal patterns and, in certain studies, an association with increased titers of neutralizing antibodies to Coxsackie-B viruses. While a seasonal variation in JIDM can be explained by a concomitant seasonal variation in viral infections, other interpretations (such as dietary and exercise alterations) are also possible. Although these findings, taken together, suggest a role for certain viruses and other environmental factors (drugs, toxins, foods) in the pathogenesis of JIDM, further studies will be necessary to establish unequivocally such relationships and to define the specific mechanisms that are operative.
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EURODIAB ACE is a collaborative European study that was set up to assess incidence of childhood insulin-dependent diabetes mellitus (IDDM) in Europe, test the proposal of a south-north gradient, and to gather information to determine the causes and pathogenesis of the disease. Here, the basic epidemiological results are reported. Newly diagnosed cases of IDDM in children aged up to 15 years were identified prospectively in twenty-four geographically well-defined study regions in Europe and Israel (a total of 16.8 million children) during 1989 and 1990. 3060 cases were identified with estimated ascertainment rates exceeding 90% in all study regions. Age-standardised and sex-standardised incidence rates varied widely, ranging from 4.6 (northern Greece) to 42.9 (two regions in Finland) cases per 100,000 per year. Rates in southern Europe were generally higher than previously assumed, and there was an unexpectedly high incidence in Sardinia, which had the second highest rate (30.2 cases per 100,000 per year) recorded in Europe. Eastern European regions had generally low rates. The collaborative network now established provides a framework for further studies to examine the complex interaction between genetic and environmental factors in the cause and pathogenesis of IDDM.
Article
Through use of primary and secondary data sources for registration and validation, the incidence and prevalence of Type 1 (insulin-dependent) diabetes mellitus in children aged 0-14 years in Iceland has been completely ascertained for the years 1970-1989. The age-adjusted mean annual incidence per 100,000 for the 20-year period was 9.4 (95% confidence interval 7.8-11.3); similar for boys (9.9; 7.7-12.7) and girls (8.8; 6.7-11.5). Between 1970-1979 the incidence was 8.0 (6.0-10.6) and between 1980-1989 it was comparable at 10.8 (8.4-13.8) (p greater than 0.10). By Poisson regression analysis the variation in incidence was related to age at diagnosis (p less than 0.001), while a linear trend for calendar year at diagnosis did not reach statistical significance (p = 0.07). A quadratic curve, however, better described the temporal variation in incidence (p less than 0.05). The total prevalence per 1,000 by the end of 1979 and 1989 was similar, 0.45 (0.30-0.65) and 0.57 (0.40-0.79), respectively. In conclusion, this study confirms that both the incidence and prevalence of childhood Type 1 diabetes in Iceland are low compared to the other Nordic countries. The findings may suggest a causative role for environmental factors that are not related to latitude or ambient temperature.