Thirty-one novel biomarkers as predictors for clinically incident diabetes.
BACKGROUND
The prevalence of diabetes is increasing in all industrialized countries and its prevention has become a public health priority. However, the predictors of diabetes risk are insufficiently understood. We evaluated, whether 31 novel biomarkers could help to predict the risk of incident diabetes.


RESULTS
The biomarkers were evaluated primarily in the FINRISK97 cohort (n = 7,827; 417 cases of clinically incident diabetes during the follow-up). The findings were replicated in the Health 2000 cohort (n = 4,977; 179 cases of clinically incident diabetes during the follow-up). We used Cox proportional hazards models to calculate the relative risk of diabetes, after adjusting for the classic risk factors, separately for each biomarker. Next, we assessed the discriminatory ability of single biomarkers using receiver operating characteristic curves and C-statistics, integrated discrimination improvement (IDI) and net reclassification improvement (NRI). Finally, we derived a biomarker score in the FINRISK97 cohort and validated it in the Health 2000 cohort. A score consisting of adiponectin, apolipoprotein B, C-reactive protein and ferritin almost doubled the relative risk of diabetes in the validation cohort (HR per one standard deviation increase 1.88, p = 2.8 e-5). It also improved discrimination of the model (IDI = 0.0149, p<0.0001) and reclassification of diabetes risk (NRI = 11.8%, p = 0.006). Gender-specific analyses suggested that the best score differed between men and women. Among men, the best results were obtained with the score of four biomarkers: adiponectin, apolipoprotein B, ferritin and interleukin-1 receptor antagonist, which gave an NRI of 25.4% (p<0.0001). Among women, the best score included adiponectin, apolipoprotein B, C-reactive protein and insulin. It gave an NRI of 13.6% (p = 0.041).


CONCLUSIONS
We identified novel biomarkers that were associated with the risk of clinically incident diabetes over and above the classic risk factors. This gives new insights into the pathogenesis of diabetes and may help with targeting prevention and treatment.
Salomaa
Veikko
V
Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Finland. veikko.salomaa@thl.fi
Havulinna
Aki
A
Saarela
Olli
O
Zeller
Tanja
T
Jousilahti
Pekka
P
Jula
Antti
A
Muenzel
Thomas
T
Aromaa
Arpo
A
Evans
Alun
A
Kuulasmaa
Kari
K
Blankenberg
Stefan
S
D052242:Adiponectin / Q000097:blood; D000328:Adult; D000368:Aged; D001055:Apolipoproteins B / Q000097:blood; D015415:Biomarkers / Q000097:blood; D002097:C-Reactive Protein / Q000032:analysis; D015331:Cohort Studies; D003920:Diabetes Mellitus / Q000175:diagnosis*; D005260:Female; D005293:Ferritins / Q000097:blood; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012372:ROC Curve; D012306:Risk; D012737:Sex Factors
D052242:Adiponectin; D001055:Apolipoproteins B; D015415:Biomarkers; D002097:C-Reactive Protein; D005293:Ferritins
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