Article

Assessing Risk for Development of Diabetes in Young Adults

Department of Family Medicine, Medical University of South Carolina, Charleston, South Carolina, United States
The Annals of Family Medicine (Impact Factor: 5.43). 09/2007; 5(5):425-9. DOI: 10.1370/afm.705
Source: PubMed

ABSTRACT

The prevalence of diabetes is increasing to epidemic levels. A multivariable risk score for the development of diabetes has been shown to be predictive for middle-aged adults; however, it is unclear how well it performs in a younger adult population. The purpose of this study was to evaluate a preexisting multivariable risk score for the development of diabetes in a young adult cohort.
We analyzed the Coronary Artery Risk Development in Young Adults (CARDIA), a population-based observational study of participants aged 18 to 30 years recruited in 1985-1986. We observed individuals without diabetes at baseline for 10 years for the development of diabetes (n = 2,543). We computed receiver operating characteristic (ROC) curves for a diabetes risk score composed of the following 6 variables: elevated blood pressure, low high-density lipoprotein cholesterol levels, high triglyceride levels, body mass index, large waist circumference, and hyperglycemia.
The area under the ROC curve was .70 in this population, which was less than the .78 previously found among middle-aged adults. BMI alone (.67) was not significantly different from the risk score. Blacks (.72; 95% CI, .69-.74) and whites (.68; 95% CI, .66-.71) do not significantly differ in the area under the ROC curve for the risk score; however, the area under the ROC curve for BMI is significantly larger for blacks (.69; 95% CI, .66-.72) than for whites (.63; 95% CI, .60-.65).
An established risk score for the development of diabetes among middle-aged persons has limited utility in a younger population. Future research needs to focus on identifying novel factors that may improve the risk stratification for diabetes development among young adults.

Full-text

Available from: Charles J. Everett, Dec 27, 2013
ANNALS OF FAMILY MEDICINE
WWW.ANNFAMMED.ORG
VOL. 5, NO. 5
SEPTEMBER/OCTOBER 2007
425
Assessing Risk for Development
of Diabetes in Young Adults
ABSTRACT
PURPOSE The prevalence of diabetes is increasing to epidemic levels. A multivari-
able risk score for the development of diabetes has been shown to be predictive
for middle-aged adults; however, it is unclear how well it performs in a younger
adult population. The purpose of this study was to evaluate a preexisting multi-
variable risk score for the development of diabetes in a young adult cohort.
METHODS We analyzed the Coronary Artery Risk Development in Young Adults
(CARDIA), a population-based observational study of participants aged 18 to
30 years recruited in 1985-1986. We observed individuals without diabetes at
baseline for 10 years for the development of diabetes (n = 2,543). We computed
receiver operating characteristic (ROC) curves for a diabetes risk score composed
of the following 6 variables: elevated blood pressure, low high-density lipopro-
tein cholesterol levels, high triglyceride levels, body mass index, large waist cir-
cumference, and hyperglycemia.
RESULTS The area under the ROC curve was .70 in this population, which was
less than the .78 previously found among middle-aged adults. BMI alone (.67)
was not signi cantly different from the risk score. Blacks (.72; 95% CI, .69-.74)
and whites (.68; 95% CI, .66-.71) do not signi cantly differ in the area under
the ROC curve for the risk score; however, the area under the ROC curve for BMI
is signi cantly larger for blacks (.69; 95% CI, .66-.72) than for whites (.63; 95%
CI, .60-.65).
CONCLUSION An established risk score for the development of diabetes among
middle-aged persons has limited utility in a younger population. Future research
needs to focus on identifying novel factors that may improve the risk strati ca-
tion for diabetes development among young adults.
Ann Fam Med 2007;5:425-429. DOI: 10.1370/afm.705.
INTRODUCTION
C
onsiderable evidence has been presented on the increased
prevalence of diabetes in the United States,
1
This prevalence has
become so large that diabetes has been termed an epidemic.
1,2
In particular, diabetes is increasingly diagnosed among adolescents and
younger adults.
3,4
One factor thought to be driving the diabetes epidemic
is the increase in obesity.
5-7
Prediction of chronic conditions that have a defi nable onset in adults
can help to guide interventions and health policy development. Prediction
is an important issue, given that diabetes leads to considerable morbid-
ity and mortality, which can be mitigated through early recognition and
treatment.
8
Major risk factors for diabetes have been identifi ed and are
currently used by the American Diabetes Association to guide screening
strategies. Risk scores for diabetes fall into 2 primary categories that are
conceptually distinct. Although risk scores are usually thought to quantify
an individual’s risk of developing disease, as with the Framingham Risk
Score for coronary heart disease, most self-identifi ed diabetes risk scores
do not assess the risk of developing disease; rather, they assess the likeli-
Arch G. Mainous III, PhD
Vanessa A. Diaz, MD, MS
Charles J. Everett, PhD
Department of Family Medicine, Medical
University of South Carolina, Charleston, SC
Confl icts of interest: none reported
CORRESPONDING AUTHOR
Arch G. Mainous III, PhD
Department of Family Medicine
Medical University of South Carolina
295 Calhoun St.
Charleston, SC 29425
mainouag@musc.edu
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hood of having undiagnosed diabetes.
9-14
There are few
measures that assess the risk of developing diabetes.
15,16
Some risk scores for the likelihood of having undi-
agnosed diabetes have been tested in populations
other than the ones in which they were created and
have unfortunately not worked as well.
17,18
Consider-
ing the importance of identifying individuals at risk
for developing diabetes, a strategy for assessing risk of
developing diabetes in young adults has many benefi ts,
including targeted interventions for young adults at
high risk. Thus, the purpose of this study was to evalu-
ate how well a risk score for developing diabetes that
was created with a middle-aged population performs in
a cohort of young adults.
METHODS
This study is based on an analysis of the Coronary
Artery Risk Development in Young Adults (CARDIA),
a population-based observational study of participants
aged 18 to 30 years recruited in 1985-1986. Partici-
pants were recruited in 4 communities: Birmingham,
Alabama; Chicago, Illinois; Minneapolis, Minnesota;
and Oakland, California. Recruitment was stratifi ed
by race (black and white), age (18 to 24 years, and 25
to 30 years), and education (less than high school,
and high school or more). Second (1987-1988), third
(19 9 0 -19 91), fo u r th (19 92-19 93), fth (19 95-19 96), and
sixth examinations (2000-2001) have been completed
in the cohort. The public use data set used for this
study, however, only includes information from the
rst 5 examinations.
For the progression to diabetes analyses, all individu-
als had no indication of diabetes at baseline. This cohort
was comprised of 2,543 persons. A total of 100 persons
out of 2,543 developed diabetes within the 10 years.
Diabetes
Diabetes was de ned by self-report in response to
the question, “Has a doctor or nurse ever said you
had diabetes (high sugar in blood or urine)?” and by
a fasting plasma glucose of ≥126 mg/dL. Although
this biomarker defi nition deviates from the defi nition
in place at baseline (140 mg/dL), we believed that it
was important to use a current defi nition of diabetes,
whether diagnosed or not. This defi nition is also con-
sistent with the diabetes risk score used in this study.
15
Development of diabetes was defi ned as having diabe-
tes at year 10 (examination 5).
Diabetes Risk Score
The risk score used in this study predicts the develop-
ment of diabetes, not the risk of having undiagnosed
diabetes.
15
It was created from an analysis of individu-
als aged 45 to 64 years in Atherosclerosis Risk in Com-
munities (ARIC) study and is based on the metabolic
syndrome.
19
Among individuals without diagnosed
diabetes or fasting plasma glucose ≥126 mg/dL at base-
line, a scoring strategy was developed that included
large waist circumference (>102 cm in men and >88
cm for women), raised blood pressure (>130/85 mm
Hg or antihypertensive medications), low high-density
lipoprotein cholesterol levels (<40 mg/dL for men and
<50 mg/dL for women), high triglyceride levels (>150
mg/dL), body mass index (BMI) of greater than 30
kg/m
2
, and hyperglycemia. Each of the characteristics
are worth 1 point except for hyperglycemia, which can
be worth 2 points if fasting glucose is ≥102 mg/dL or 5
points when fasting glucose ≥111 mg/dL. A score of ≥4
puts an individual at high risk for development of dia-
betes, either diagnosed or undiagnosed.
This particular risk score was chosen for several
reasons. First, it has moderate sensitivity (68%) and
specifi city (75%). The area under the receiver operat-
ing characteristic (ROC) curve was 0.78. Second, it
is computed in a reasonably straightforward manner
without having to use coeffi cients from the ARIC
cohort that may be specifi c to that cohort.
Family History
Family history of diabetes has been shown to be a pre-
dictor of development of diabetes.
20
We defi ned family
history as either a parent having diagnosed diabetes, or
a parent or sibling having diagnosed diabetes.
Data Analysis
We used MedC alc sof tware
21
to compute ROC curve
analyses in an effort to evaluate the ability of the diabe-
tes risk score, as well as other variables, including family
history of diabetes and BMI, to predict development of
diabetes in 10 years. We specifi cally examined the use-
fulness of family history as an alternative to the diabetes
risk score, because family history was not included in
the risk score. We also examined the predictive ability
of BMI by itself, because recent evidence showed that
BMI was as predictive of having undiagnosed diabetes as
the Cambridge Risk Score.
17
The parsimonious bene t
of prediction by means of one easily accessible variable
(eg, BMI) instead of a 6-variable measure would be sub-
stantial. BMI was evaluated in a continuous manner as
well as in a 3-category classifi cation (<25, 25-29.9, ≥30).
To compute the benefi ts of adding family history of
diabetes to BMI, we needed to provide a point score for
the new variable. Thus, we scored 1 point for BMI <25,
2 points for BMI 25-29.99, 3 points for BMI ≥30, and 1
point for family history of diabetes.
Finally, we strati ed the CARDIA cohort by race to
examine the utility of the diabetes risk score within dif-
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ferent racial groups, because recent evidence indicated
that some risk scores for undiagnosed diabetes do not
work equally well in different racial or ethnic groups.
18
RESULTS
Table 1 displays the characteristics of the cohort.
Only 2.1% of this young adult cohort was classifi ed at
baseline as being at high risk for developing diabetes,
whereas 3.9% developed diabetes within 10 years. Fur-
ther, 32.9% of the cohort was overweight or obese at
baseline (BMI ≥25). The proportion of individuals with
a family history of diabetes had a moderate increase
when the defi nition of family history was changed
from parents to parents and siblings.
The area under the ROC curve for the diabetes risk
score in this young adult cohort is not optimal at .70
compared with .78 found in the middle-aged cohort in
the ARIC study (Table 2). A diabetes risk score of 4 or
greater had a sensitivity of 15.0% and a specifi city of
98.4% in the CARDIA participants.
The area under the ROC curve does not increase
signifi cantly when family history is added to the diabe-
tes risk score. Moreover, the 3-category BMI variable is
not signifi cantly different from the multivariable diabe-
tes risk score in the area under the ROC curve, nor is
BMI plus family history of diabetes signifi cantly better
than BMI alone (P = .08).
Table 3 shows the analyses within racial groups.
Blacks (.72; 95% CI, .69-.74) and whites (.68; 95% CI,
.66-.71) do not signifi cantly differ in the area under
the ROC curve for the risk score, as indicated by the
overlapping 95% confi dence intervals. The area under
the ROC curve for BMI is signifi cantly larger for blacks
(.69; 95% CI, .66-.72) than for whites (.63; 95% CI, .60-
.65). Similarly, the area under the ROC curve for BMI
plus family history is signifi cantly larger for blacks (.73;
95% CI, .70-.76) than for whites (.64; 95% CI, .61-.66).
DISCUSSION
As the prevalence of diabetes rises, and more young
adults and adolescents develop diabetes, it is crucial
from a clinical and public health perspective to be able
to identify high-risk populations. The ndings of this
study indicate that a risk score for the development of
diabetes created from a middle-aged population is a
less successful predictor of the development of diabetes
in a younger population.
We chose to assess the diabetes risk score devel-
oped in the ARIC cohort because it is one of the few
measures designed to assess the risk of developing
diabetes rather than the likelihood of having undiag-
nosed diabetes (eg, Cambridge Risk Score). An alter-
native measure to the ARIC diabetes risk score was
considered for evaluation, but it included in the model
history of high blood glucose,” which was defi ned as
Have you ever been told by a health-care professional
that you have diabetes or latent diabetes?”
16
Including
Table 1. Characteristics of the Study Cohort
Characteristic Value
Development of diabetes, % 3.9
Female, % 55.7
Black, % 41.2
Mean age ± SD, y 25.0 ± 3.6
ARIC diabetes risk score ≥4, % 2.1
Body mass index, kg/m
2
<25 , % 67.0
25-29.99, % 22.6
>3 0 , % 10 . 3
Family history of diabetes
Parents, % 13.6
Siblings and parents, % 14.4
ARIC = Atherosclerosis Risk in Communities.
Table 2. Area Under ROC Curve Using a Diabetes
Risk Score, Family History of Diabetes, and BMI to
Predict Development of Diabetes Within 10 Years
Predictors
Area Under
ROC Curve
ARIC diabetes risk score .70
Family history of diabetes
Parents .57*
Siblings and parents .58*
BMI
Continuous .65
3 categories .67
3 categories plus family history
of diabetes (siblings and parents)
.69
ARIC = Atherosclerosis Risk in Communities; BMI = body mass index;
ROC = receiver operating characteristic.
* Area under the curve signi cantly different from that of diabetes risk score.
† Not signi cantly different.
Table 3. Area Under ROC Curve to Predict
Development of Diabetes Within Groups of
White and Black Participants
Predictors White Black
ARIC diabetes risk score .68 .72
BMI, 3 categories .63* .69*
BMI, 3 categories plus
family history of diabetes
(siblings and parents)
.64* .73*
ARIC = Atherosclerosis Risk in Communities; BMI = body mass index;
ROC = receiver operating characteristic.
* Not signi cantly different.
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RISK OF DIABETES IN YOUNG ADULTS
a previous diagnosis of diabetes as a predictor of the
development of diabetes did not seem to be a logi-
cal strategy for identifying individuals at high risk
for developing diabetes. Thus, we believed that the
Lindstrom score was not as useful for evaluation as the
ARIC diabetes risk score.
Multivariable risk scores that are diagnostically
helpful should be clinically less burdensome in the
age of personal digital assistants and electronic health
records and should therefore allow the clinician to go
beyond assessing risk factors singly for development
of disease. In this case, the multivariable diabetes risk
score does not predict the development of diabetes any
better than simply using the BMI. Because the diabetes
risk score includes BMI in addition to 5 other variables,
it would be expected to perform better than BMI alone.
With an area under the ROC curve of .67, however,
BMI as a predictor of the development of diabetes in
young adults is not optimal. Further, the addition of
family history to either BMI or the diabetes risk score
did not signifi cantly improve prediction of the devel-
opment of diabetes. These fi ndings suggest that more
work is needed to create an effective strategy for identi-
fying young adults at high risk for developing diabetes.
Recent evidence has suggested that risk assessment
strategies may need to differ depending on which
racial or ethnic population is being evaluated.
18
The
results reported in this study indicate that although the
diabetes risk score did not differ signifi cantly between
young black and white adults in the prediction of
diabetes, BMI and BMI plus family history did differ
between the 2 groups: BMI plus family history was a
signi cantly more predictive strategy for identifying
risk for the development of diabetes among blacks than
among whites. These racial differences in the relation-
ship of BMI and the development of diabetes may be
due to the interaction of race and diet, as Pereira et
al
22
found that fast-food habits varied by race and sex
and were related to insulin resistance in the CARDIA
study. This fi nding indicates the need to be more
aware of racial and ethnic differences in diabetes risk
and the need to include that awareness in the develop-
ment of diabetes risk assessment strategies. Further
evaluation of the novel factors, including biomarkers,
underlying these differences is also necessary.
There are several limitations to this study. First, the
biomarker diagnosis of diabetes in the CARDIA data
is based on a single fasting glucose test. This strategy,
although common in epidemiological studies, could
potentially underestimate the prevalence of diabetes
associated with isolated postchallenge hyperglyce-
mia, which occurs more commonly in women and
lean populations. It could also overestimate diabetes
prevalence, because a clinical diagnosis of diabetes
in asymptomatic persons requires 2 abnormal fasting
glucose levels. Second, racial differences in the predic-
tive utility of the risk assessment strategies suggest that
evaluating the risk score and other markers may be
enhanced by having a diverse sample of ethnic groups.
The CARDIA study is limited to blacks and whites and
thus does not allow for evaluation with other racial or
ethnic groups. Third, not only is it inherently diffi cult
to improve on conventional risk factors when develop-
ing a scoring system as a prognostic tool, as shown
by Wang et al
23
and discussed by Ware,
24
it is also
dif cult to improve on conventional risk factors when
developing a prognostic tool. Hence, we compared the
ARIC metabolic syndrome (augmented) model with
BMI alone and BMI plus family history of diabetes to
determine whether multivariate diabetes risk factors
performed better than more general risk factors.
In conclusion, an established risk score for the
development of diabetes among middle-aged per-
sons had limited utility in a younger population. The
diabetes risk score had no advantage compared with
BMI alone. Neither BMI nor the risk score, however,
had optimal predictive ability, suggesting that future
research needs to focus on identifying novel factors
that may improve the risk stratifi cation for diabetes
development among young adults.
To read or post commentaries in response to this article, see it
online at http://www.annfammed.org/cgi/current/full/5/5/425.
Submitted December 1, 2006; submitted revised December 6, 2007;
accepted March 18, 2007.
Key words: Diabetes mellitus, type 2; cohort studies; risk factors
Acknowledgments: This study was supported in part by grants
1D12HP00023 from the Health Resources and Services Administration;
grant 1 P30 AG21677 from the National Institute on Aging, as well as a
grant from the Robert Wood Johnson Foundation.
Disclaimer: The Coronary Artery Risk Development in Young Adults
(CARDIA) is conducted and supported by the NHLBI in collaboration
with the CARDIA Study Investigators. This article was prepared using a
limited-access data set obtained by the NHLBI and does not necessarily
re ect the opinions or views of the CARDIA Study or the NHLBI.
References
1. Engelgau MM, Geiss LS, Saaddine JB, et al. The evolving diabetes
burden in the United States. Ann Intern Med. 2004;140(11):945-950.
2. Steinbrook R. Facing the diabetes epidemic—mandatory reporting
of glycosylated hemoglobin values in New York City. N Engl J Med.
2006;354(6):545-548.
3. Prevalence of diabetes and impaired fasting glucose in
adults—United States, 1999-2000. MMWR Morb Mortal Wkly Rep.
2003;52(35):833-837.
4. Duncan GE. Prevalence of diabetes and impaired fasting glu-
cose levels among US adolescents: National Health and Nutri-
tion Examination Survey, 1999-2002. Arch Pediatr Adolesc Med.
2006;160(5):523-528.
Page 4
ANNALS OF FAMILY MEDICINE
WWW.ANNFAMMED.ORG
VOL. 5, NO. 5
SEPTEMBER/OCTOBER 2007
429
RISK OF DIABETES IN YOUNG ADULTS
5. Clinical Guidelines on the Identi cation, Evaluation, and Treatment of
Overweight and Obesity in Adults: The Evidence Report. Bethesda, MD:
National Institutes of Health; 1998. NIH Publication No. 98-4083.
6. Sturm R. Increases in clinically severe obesity in the United States,
1986-2000. Arch Intern Med. 2003;163(18):2146-2148.
7. Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence
and trends in obesity among US adults, 1999-2000. JAMA.
2002;288(14):1723-1727.
8. Vijan S, Stevens DL, Herman WH, Funnell MM, Standiford CJ.
Screening, prevention, counseling, and treatment for the complica-
tions of type II diabetes mellitus. Putting evidence into practice.
J Gen Intern Med. 1997;12(9):567-580.
9. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H,
Kannel WB. Prediction of coronary heart disease using risk factor
categories. Circulation. 1998;97(18):1837-1847.
10. Glumer C, Carstensen B, Sandbaek A, Lauritzen T, Jorgensen T,
Borch-Johnsen K. A Danish diabetes risk score for targeted screen-
ing: the Inter99 study. Diabetes Care. 2004;27(3):727-733.
11. Herman WH, Smith PJ, Thompson TJ, Engelgau MM, Aubert RE.
A new and simple questionnaire to identify people at increased risk
for undiagnosed diabetes. Diabetes Care. 1995;18(3):382-387.
12. Baan CA, Ruige JB, Stolk RP, et al. Performance of a predictive
model to identify undiagnosed diabetes in a health care setting.
Diabetes Care. 1999;22(2):213-219.
13. Grif n SJ, Little PS, Hales CN, Kinmonth AL, Wareham NJ. Diabetes
risk score: towards earlier detection of type 2 diabetes in general
practice. Diabetes Metab Res Rev. 2000;16(3):164-171.
14. Franciosi M, De Berardis G, Rossi MC, et al. Use of the diabetes
risk score for opportunistic screening of undiagnosed diabetes and
impaired glucose tolerance: the IGLOO (Impaired Glucose Toler-
ance and Long-Term Outcomes Observational) study. Diabetes Care.
2005;28(5):1187-1194.
15. Schmidt MI, Duncan BB, Bang H, et al. Identifying individuals at
high risk for diabetes: The Atherosclerosis Risk in Communities
study. Diabetes Care. 2005;28(8):2013-2018.
16. Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool
to predict type 2 diabetes risk. Diabetes Care. 2003;26(3):725-731.
17. Thomas C, Hypponen E, Power C. Type 2 diabetes mellitus in
midlife estimated from the Cambridge Risk Score and body mass
index. Arch Intern Med. 2006;166(6):682-688.
18. Glumer C, Vistisen D, Borch-Johnsen K, Colagiuri S. Risk scores for
type 2 diabetes can be applied in some populations but not all.
Diabetes Care. 2006;29(2):410-414.
19. Executive Summary of The Third Report of The National Choles-
terol Education Program (NCEP) Expert Panel on Detection, Evalu-
ation, And Treatment of High Blood Cholesterol In Adults (Adult
Treatment Panel III). JAMA. 2001;285(19):2486-2497.
20. von Eckardstein A, Schulte H, Assmann G. Risk for diabetes mellitus
in middle-aged Caucasian male participants of the PROCAM study:
implications for the de nition of impaired fasting glucose by the
American Diabetes Association. Prospective Cardiovascular Munster.
J Clin Endocrinol Metab. 2000;85(9):3101-3108.
21. MedCalc for Windows, Statistics for Biomedical Research, Software
Manual. Version 8.1. Mariakerke, Belgium: MedCalc Software.
22. Pereira MA, Kartashov AI, Ebbeling CB, et al. Fast-food habits,
weight gain, and insulin resistance (the CARDIA study): 15-year pro-
spective analysis. Lancet. 2005;365(9453):36-42.
23. Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the
prediction of  rst major cardiovascular events and death. N Engl
J Med. 2006;355(25):2631-2639.
24. Ware JH. The limitations of risk factors as prognostic tools. N Engl
J Med. 2006;355(25):2615-2617.
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  • Source
    • "While choosing a model for prediction of diabetes the availability of risk factor data in the clinical setting, the optimal cut-point to de�ne a positive test, and the simplicity of the model [13] must be considered. Some of previous studies are restricted with respect to age or sex [36, 37]; some used predictors like serum biomarkers, insulin resistance indices or surrogates, or genetic markers, which can only be measured by time-consuming, costly, or invasive testing procedures383940. Complex models were shown to be unlikely to provide such increased predictive performances that �usti�es their complexity [40, 41]; this underscores the view that identi�cation of adverse phenotypic characteristics remains the cornerstone of approaches for predicting the risk of diabetes40414243444546. "
    [Show abstract] [Hide abstract] ABSTRACT: Aims. To provide a yardstick for physicians/patients to efficiently communicate/measure incident diabetes risk. Methods. We included data on 5,960 (3,438 women) diabetes-free adults, aged ≥20 years at baseline who either developed diabetes during two consecutive examinations or completed the followup. Age, systolic blood pressure, family history of diabetes, waist-to-height ratio (WHtR), triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDLD-C), and fasting plasma glucose (FPG) were introduced into an accelerated failure time regression model. Results. Annual diabetes incidence rate was 0.85/1000-person (95% CIs 0.77–0.94). Point-score-system incorporated age (1 point for >65 years), family history of diabetes (4 points), systolic blood pressure (−1 to 3 points), WHtR (−4 to 6 points), TG/HDL-C (1 point for ≥1.5), and FPG (0 to 27 points). Harrell’s C statistic = 0.830 (95% CIs 0.808–0.852) and Hosmer-Lemeshow 𝜒 2 = 9 . 7 (P for lack of fitness = 0.462) indicated good discrimination and calibration. We defined beta-cell age as chronological age of a person with the same predicted risk but all risk factors at the normal levels (i.e., WHtR 0.50, no family history of diabetes, Ln (TG/HDL-C) = 0.531, and FPG = 4.9 (mmol·L−1)). Conclusion. Hereby, we have made it also possible to estimate wide ranges of “beta-cell age” for most chronological ages to assist clinician with risk communication.
    Full-text · Article · Dec 2012
  • Source
    • "Of the many prediction models for incident type 2 diabetes in adulthood, few have included adequate numbers of young adults [4]. In addition, type 2 diabetes prediction models developed in cohorts of middle-aged adults may have inferior performance in younger adults [5]. Type 2 diabetes is heritable [6, 7]. "
    [Show abstract] [Hide abstract] ABSTRACT: Genotype does not change over the life course and may thus facilitate earlier identification of individuals at high risk for type 2 diabetes. We hypothesised that a genotype score predicts incident type 2 diabetes from young adulthood and improves diabetes prediction models based on clinical risk factors alone. The Coronary Artery Risk Development in Young Adults (CARDIA) study followed young adults (aged 18-30 years, mean age 25) serially into middle adulthood. We used Cox regression to build nested prediction models for incident type 2 diabetes based on clinical risk factors assessed in young adulthood (age, sex, race, parental history of diabetes, BMI, mean arterial pressure, fasting glucose, HDL-cholesterol and triacylglyercol), without and with a 38-variant genotype score. Models were compared with C statistics and continuous net reclassification improvement indices (NRI). Of 2,439 participants, 830 (34%) were black and 249 (10%) had a BMI ≥30 kg/m(2) at baseline. Over a mean 23.9 years of follow-up, 215 (8.8%) participants developed type 2 diabetes. The genotype score significantly predicted incident diabetes in all models, with an HR of 1.08 per risk allele (95% CI 1.04, 1.13) in the full model. The addition of the score to the full model modestly improved reclassification (continuous NRI 0.285; 95% CI 0.126, 0.433) but not discrimination (C statistics 0.824 and 0.829 in full models with and without score). Race-stratified analyses were similar. Knowledge of genotype predicts type 2 diabetes over 25 years in white and black young adults but may not improve prediction over routine clinical measurements.
    Full-text · Article · Jul 2012 · Diabetologia
  • Source
    • "Furthermore, there is a lack of information about the consequences of ischemia on plantar foot sensitivity of healthy young adults, since sensory testing in this research area is usually carried out in elderly patients. Due to the increasing number of young adults being diagnosed with vascular diseases every year [14], investigating this population may provide first insight into initial adaptations caused by reduced blood flow in plantar foot sensitivity . Therefore, the goal of the present study was to investigate the effects of short-time ischemia on plantar foot vibration sensitivity of healthy young adults. "
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