The potential role of biomarkers in predicting gestational diabetes

Gestational diabetes (GD) is a frequent complication during pregnancy and is associated with maternal and neonatal complications. It is suggested that a disturbing environment for the foetus, such as impaired glucose metabolism during intrauterine life, may result in enduring epigenetic changes leading to increased disease risk in adult life. Hence, early prediction of GD is vital. Current risk prediction models are based on maternal and clinical parameters, lacking a strong predictive value. Adipokines are mainly produced by adipocytes and suggested to be a link between obesity and its cardiovascular complications. Various adipokines, including adiponectin, leptin and TNF&, have shown to be dysregulated in GD. This review aims to outline biomarkers potentially associated with the pathophysiology of GD and discuss the role of integrating predictive biomarkers in current clinical risk prediction models, in order to enhance the identification of those at risk.

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Development of Appropriate Coronary Heart Disease Risk Prediction Models in HIV-Infected Patients

Circulation ◽

10.1161/circulationaha.107.189627 ◽

2008 ◽

Vol 118 (2) ◽

Cited By ~ 27

Author(s):

Morris Schambelan ◽

Peter W.F. Wilson ◽

Kevin E. Yarasheski ◽

W. Todd Cade ◽

Victor G. Dávila-Román ◽

...

Keyword(s):

Coronary Heart Disease ◽

Heart Disease ◽

Risk Prediction ◽

Coronary Heart Disease Risk ◽

Prediction Models ◽

Disease Risk ◽

Risk Prediction Models ◽

Heart Disease Risk

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Cardiovascular Disease Risk Prediction Models and Scores

Current Pharmaceutical Design ◽

10.2174/138161282710210324104510 ◽

2021 ◽

Vol 27 (10) ◽

pp. 1231-1231

Author(s):

George Giannakoulas ◽

MD Ioannis Doundoulakis

Keyword(s):

Cardiovascular Disease ◽

Risk Prediction ◽

Prediction Models ◽

Disease Risk ◽

Cardiovascular Disease Risk ◽

Risk Prediction Models

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Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts

Stroke ◽

10.1161/strokeaha.120.027473 ◽

2020 ◽

Vol 51 (7) ◽

pp. 2095-2102

Author(s):

Eugene Y.H. Tang ◽

Christopher I. Price ◽

Louise Robinson ◽

Catherine Exley ◽

David W. Desmond ◽

...

Keyword(s):

Risk Factors ◽

General Population ◽

Risk Prediction ◽

Prediction Models ◽

Disease Risk ◽

Risk Index ◽

Area Under The Curve ◽

Risk Prediction Models ◽

C Statistic ◽

Dementia Risk

Background and Purpose: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. Methods: Data were harmonized from 4 stroke studies (follow-up range, ≈12–18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. Results: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). Conclusions: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

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