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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, Vol. 5, No. 4, 09.04.2010, p. 10100.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Salomaa, V, Havulinna, A, Saarela, O, Zeller, T, Jousilahti, P, Jula, A, Muenzel, T, Aromaa, A, Evans, A, Kuulasmaa, K & Blankenberg, S 2010, 'Thirty-one novel biomarkers as predictors for clinically incident diabetes', PLOS ONE, vol. 5, no. 4, pp. 10100. https://doi.org/10.1371/journal.pone.0010100

APA

Salomaa, V., Havulinna, A., Saarela, O., Zeller, T., Jousilahti, P., Jula, A., Muenzel, T., Aromaa, A., Evans, A., Kuulasmaa, K., & Blankenberg, S. (2010). Thirty-one novel biomarkers as predictors for clinically incident diabetes. PLOS ONE, 5(4), 10100. https://doi.org/10.1371/journal.pone.0010100

Vancouver

Salomaa V, Havulinna A, Saarela O, Zeller T, Jousilahti P, Jula A et al. Thirty-one novel biomarkers as predictors for clinically incident diabetes. PLOS ONE. 2010 Apr 9;5(4):10100. https://doi.org/10.1371/journal.pone.0010100

Bibtex

@article{fd2ebbb5a4334752802ed3cbe2ab1f4c,
title = "Thirty-one novel biomarkers as predictors for clinically incident diabetes",
abstract = "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.",
keywords = "Adiponectin/blood, Adult, Aged, Apolipoproteins B/blood, Biomarkers/blood, C-Reactive Protein/analysis, Cohort Studies, Diabetes Mellitus/diagnosis, Female, Ferritins/blood, Humans, Male, Middle Aged, Predictive Value of Tests, Proportional Hazards Models, ROC Curve, Risk, Sex Factors",
author = "Veikko Salomaa and Aki Havulinna and Olli Saarela and Tanja Zeller and Pekka Jousilahti and Antti Jula and Thomas Muenzel and Arpo Aromaa and Alun Evans and Kari Kuulasmaa and Stefan Blankenberg",
year = "2010",
month = apr,
day = "9",
doi = "10.1371/journal.pone.0010100",
language = "English",
volume = "5",
pages = "10100",
journal = "PLOS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "4",

}

RIS

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 -