Dataset Information

A Systematic Review of Biomarkers and Risk of Incident Type 2 Diabetes: An Overview of Epidemiological, Prediction and Aetiological Research Literature.

ABSTRACT:

Background

Blood-based or urinary biomarkers may play a role in quantifying the future risk of type 2 diabetes (T2D) and in understanding possible aetiological pathways to disease. However, no systematic review has been conducted that has identified and provided an overview of available biomarkers for incident T2D. We aimed to systematically review the associations of biomarkers with risk of developing T2D and to highlight evidence gaps in the existing literature regarding the predictive and aetiological value of these biomarkers and to direct future research in this field.

Methods and findings

We systematically searched PubMed MEDLINE (January 2000 until March 2015) and Embase (until January 2016) databases for observational studies of biomarkers and incident T2D according to the 2009 PRISMA guidelines. We also searched availability of meta-analyses, Mendelian randomisation and prediction research for the identified biomarkers. We reviewed 3910 titles (705 abstracts) and 164 full papers and included 139 papers from 69 cohort studies that described the prospective relationships between 167 blood-based or urinary biomarkers and incident T2D. Only 35 biomarkers were reported in large scale studies with more than 1000 T2D cases, and thus the evidence for association was inconclusive for the majority of biomarkers. Fourteen biomarkers have been investigated using Mendelian randomisation approaches. Only for one biomarker was there strong observational evidence of association and evidence from genetic association studies that was compatible with an underlying causal association. In additional search for T2D prediction, we found only half of biomarkers were examined with formal evidence of predictive value for a minority of these biomarkers. Most biomarkers did not enhance the strength of prediction, but the strongest evidence for prediction was for biomarkers that quantify measures of glycaemia.

Conclusions

This study presents an extensive review of the current state of the literature to inform the strategy for future interrogation of existing and newly described biomarkers for T2D. Many biomarkers have been reported to be associated with the risk of developing T2D. The evidence of their value in adding to understanding of causal pathways to disease is very limited so far. The utility of most biomarkers remains largely unknown in clinical prediction. Future research should focus on providing good genetic instruments across consortia for possible biomarkers in Mendelian randomisation, prioritising biomarkers for measurement in large-scale cohort studies and examining predictive utility of biomarkers for a given context.

SUBMITTER: Abbasi A

PROVIDER: S-EPMC5082867 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

A Systematic Review of Biomarkers and Risk of Incident Type 2 Diabetes: An Overview of Epidemiological, Prediction and Aetiological Research Literature.

Abbasi Ali A Sahlqvist Anna-Stina AS Lotta Luca L Brosnan Julia M JM Vollenweider Peter P Giabbanelli Philippe P Nunez Derek J DJ Waterworth Dawn D Scott Robert A RA Langenberg Claudia C Wareham Nicholas J NJ

PloS one 20161027 10

<h4>Background</h4>Blood-based or urinary biomarkers may play a role in quantifying the future risk of type 2 diabetes (T2D) and in understanding possible aetiological pathways to disease. However, no systematic review has been conducted that has identified and provided an overview of available biomarkers for incident T2D. We aimed to systematically review the associations of biomarkers with risk of developing T2D and to highlight evidence gaps in the existing literature regarding the predictive ...[more]

PMID: 27788146

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Project description:BackgroundGermany has become an important immigration country and health services need to adopt to meet the needs of an increasingly multicultural population. For public health planning, it is essential to understand the aetiology of health problems among migrant populations. The main objective was to systematically identify, evaluate and synthesise population-based studies that investigated exposure-outcome relationships among migrant groups in Germany.MethodsIn November 2019, we searched PubMed and LIVIVO, and updated this search in November 2020, to identify peer-reviewed publications that fulfilled our eligibility criteria: English or German language; study on disease aetiology among major migrant groups in Germany, according to the latest microcensus; publication date from inception to 01 November 2020 and observational or experimental study designs. For quality appraisal, we used the Critical Appraisal Skills Programme checklists. Outcomes under investigation were categorised according to the WHO major disease groups, and their associations with risk factors were synthesised as a heat map.ResultsOut of 2407 articles retrieved, we included 68 publications with a total number of 864 518 participants. These publications reported on cross-sectional data (n=56), cohort studies (n=11) and one intervention study. The population groups most frequently studied were from the Middle East (n=28), Turkey (n=24), sub-Saharan Africa (n=24), Eastern Europe (n=15) and the former Soviet Union (n=11). The outcomes under study were population group specific. There were consistent associations of demographic and socioeconomic factors with ill health among migrants in Germany.DiscussionIn this systematic review, we observed low risk of bias in two-thirds of the studies. There is an increasing body of evidence for aetiological research on migrants' health in Germany. Still, the directions of associations between a wide range of risk factors and major disease groups seem only partially understood.Prospero registration numberCRD42018085074.

Project description:BackgroundPeople with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures.MethodsA systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test).ResultsForty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52-0.60, 0.50-0.59, and 0.50-0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54-0.73, 0.52-0.67, and 0.59-0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05).ConclusionsIn Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.

Project description:ObjectiveCurrent studies lack a comprehensive understanding of the environmental factors influencing type 2 diabetes, hindering an in-depth grasp of the overall etiology. To address this gap, we utilized network science tools to highlight research trends, knowledge structures, and intricate relationships among factors, offering a new perspective for a profound understanding of the etiology.MethodsThe Web of Science database was employed to retrieve documents relevant to environmental risk factors in type 2 diabetes from 2012 to 2024. Bibliometric analysis using Microsoft Excel and OriginPro provided a detailed scientific production profile, including articles, journals, countries, and authors. Co-occurrence analysis was employed to determine the collaboration state and knowledge structures, utilizing social network tools such as Gephi, Tableau, and R Studio. Additionally, theme evolutionary analysis was conducted using SciMAT to offer insights into research trends.ResultsThe publications and themes related to environmental factors in type 2 diabetes have consistently risen, shaping a well-established research domain. Lifestyle environmental factors, particularly diet and nutrition, stand out as the most represented and rapidly growing topics. Key focal hotspots include sedentary and digital behavior, PM2.5, ethnicity and socioeconomic status, traffic and greenspace, and depression. The theme evolutionary analysis revealed three distinct paths: (1) oxidative stress-air pollutants-PM2.5-air pollutants; (2) calcium-metabolic syndrome-cardiovascular disease; and (3) polychlorinated biphenyls (PCBs)-persistent organic pollutants (POPs)-obesity.ConclusionsDigital behavior signifies a novel approach for preventing and managing type 2 diabetes. The influence of PM2.5 and calcium on oxidative stress and abnormal vascular contraction is intricately linked to microvascular diabetes complications. The transition from PCBs and POPs to obesity underscores the disruption of endocrine function by chemicals, elevating the risk of diabetes. Future studies should explore the connections between environmental factors, microvascular complications, and long-term outcomes in diabetes.

Project description:Rationale & objectiveBiomarkers of kidney disease progression have been identified in individuals with diabetes and underlying chronic kidney disease (CKD). Whether or not these markers are associated with the development of CKD in a general population without diabetes or CKD is not well established.Study designProspective observational cohort.Setting & participantsIn the Atherosclerosis Risk in Communities) study, 948 participants were studied.ExposuresThe baseline plasma biomarkers of kidney injury molecule-1 (KIM-1), monocyte chemoattractant protein-1 (MCP-1), soluble urokinase plasminogen activator receptor (suPAR), tumor necrosis factor receptor 1 (TNFR-1), tumor necrosis factor receptor 2 (TNFR-2), and human cartilage glycoprotein-39 (YKL-40) measured in 1996-1998.OutcomeIncident CKD after 15 years of follow-up defined as ≥40% estimated glomerular filtration rate decline to <60 mL/min/1.73 m2 or dialysis dependence through United States Renal Data System linkage.Analytical approachLogistic regression and C statistics.ResultsThere were 523 cases of incident CKD. Compared with a random sample of 425 controls, there were greater odds of incident CKD per 2-fold higher concentration of KIM-1 (OR, 1.49; 95% CI, 1.25-1.78), suPAR (OR, 2.57; 95% CI, 1.74-3.84), TNFR-1 (OR, 2.20; 95% CI, 1.58-3.09), TNFR-2 (OR, 2.03; 95% CI, 1.37-3.04). After adjustment for all biomarkers, KIM-1 (OR, 1.42; 95% CI, 1.19-1.71), and suPAR (OR, 1.86; 95% CI, 1.18-2.92) remained associated with incident CKD. Compared with traditional risk factors, the addition of all 6 biomarkers improved the C statistic from 0.695-0.731 (P < 0.01) and using the observed risk of 12% for incident CKD, the predicted risk gradient changed from 5%-40% (for the 1st-5th quintile) to 4%-44%.LimitationsBiomarkers and creatinine were measured at one time point.ConclusionsHigher levels of KIM-1, suPAR, TNFR-1, and TNFR-2 were associated with higher odds of incident CKD among individuals without diabetes.Plain-language summaryFor people with diabetes or kidney disease, several biomarkers have been shown to be associated with worsening kidney disease. Whether these biomarkers have prognostic significance in people without diabetes or kidney disease is less studied. Using the Atherosclerosis Risk in Communities study, we followed individuals without diabetes or kidney disease for an average of 15 years after biomarker measurement to see if these biomarkers were associated with the development of kidney disease. We found that elevated levels of KIM-1, suPAR, TNFR-1, and TNFR-2 were associated with the development of kidney disease. These biomarkers may help identify individuals who would benefit from interventions to prevent the development of kidney disease.

Dataset Information

A Systematic Review of Biomarkers and Risk of Incident Type 2 Diabetes: An Overview of Epidemiological, Prediction and Aetiological Research Literature.

Background

Methods and findings

Conclusions

Publications

A Systematic Review of Biomarkers and Risk of Incident Type 2 Diabetes: An Overview of Epidemiological, Prediction and Aetiological Research Literature.

Similar Datasets

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