Thirty-one novel biomarkers as predictors for clinically incident diabetes
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Thirty-one novel biomarkers as predictors for clinically incident diabetes. / Salomaa, Veikko; Havulinna, Aki; Saarela, Olli; Zeller, Tanja; Jousilahti, Pekka; Jula, Antti; Muenzel, Thomas; Aromaa, Arpo; Evans, Alun; Kuulasmaa, Kari; Blankenberg, Stefan.
in: PLOS ONE, Jahrgang 5, Nr. 4, 09.04.2010, S. 10100.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Thirty-one novel biomarkers as predictors for clinically incident diabetes
AU - Salomaa, Veikko
AU - Havulinna, Aki
AU - Saarela, Olli
AU - Zeller, Tanja
AU - Jousilahti, Pekka
AU - Jula, Antti
AU - Muenzel, Thomas
AU - Aromaa, Arpo
AU - Evans, Alun
AU - Kuulasmaa, Kari
AU - Blankenberg, Stefan
PY - 2010/4/9
Y1 - 2010/4/9
N2 - 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.METHODS AND FINDINGS: 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.
AB - 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.METHODS AND FINDINGS: 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.
KW - Adiponectin/blood
KW - Adult
KW - Aged
KW - Apolipoproteins B/blood
KW - Biomarkers/blood
KW - C-Reactive Protein/analysis
KW - Cohort Studies
KW - Diabetes Mellitus/diagnosis
KW - Female
KW - Ferritins/blood
KW - Humans
KW - Male
KW - Middle Aged
KW - Predictive Value of Tests
KW - Proportional Hazards Models
KW - ROC Curve
KW - Risk
KW - Sex Factors
U2 - 10.1371/journal.pone.0010100
DO - 10.1371/journal.pone.0010100
M3 - SCORING: Journal article
C2 - 20396381
VL - 5
SP - 10100
JO - PLOS ONE
JF - PLOS ONE
SN - 1932-6203
IS - 4
ER -