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Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report.

ABSTRACT: Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (? = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.

SUBMITTER: Ayensa-Vazquez JA

PROVIDER: S-EPMC7290893 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report.

Ayensa-Vazquez Jose Angel JA Leiva Alfonso A Tauler Pedro P López-González Angel Arturo AA Aguiló Antoni A Tomás-Salvá Matías M Bennasar-Veny Miquel M

Journal of clinical medicine 20200520 5

Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze ...[more]

PMID: 32443837

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Project description:ObjectivesThis study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST).Data sourcesBoth PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage.Study selectionArticles published between December 2011 and October 2019 were considered.Data extractionFor each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported.ResultsThe median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively.ConclusionsAmong prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.

Project description:AimTo uncover novel genetic markers that could contribute to predicting hepatocellular carcinoma (HCC) susceptibility in Caucasians.MethodsThe present retrospective case-control study compared genotype frequencies between a cohort of HCC cases and two, independent, HCC-free, age/sex-matched control groups. The HCC cohort comprised 192 homogeneous patients that had undergone orthotopic liver transplantation. The first control group comprised 167 patients that were matched to the HCC cohort for the percentage of hepatitis B (HBV) and/or hepatitis C (HCV) infections. A second control group included 192 virus-free, healthy individuals that were used to evaluate the generalizability of the identified predictive markers. All cases and controls were Caucasian. The three study populations were characterized with a panel of 31 markers derived from 21 genes that encoded key proteins involved in hepatocarcinogenesis-related pathways. The study end-point was to assess the association between genetic variants and HCC onset.ResultsFive genetic markers were identified as risk factors for HCC in high-risk patients infected with HBV/HCV. According to a dominant model, reduced HCC risk was associated with three polymorphisms: ERCC1 rs3212986 (OR = 0.46, 95%CI: 0.30-0.71, P = 0.0005), GST-P1 rs1138272 (OR = 0.41, 95%CI: 0.21-0.81, P = 0.0097), and CYP17A1 rs743572 (OR = 0.50, 95%CI: 0.31-0.79, P = 0.0032). Conversely, according to a recessive model, increased HCC risk was associated with two polymorphisms: XRCC3 rs1799794 (OR = 3.70, 95%CI: 1.02-13.39, P = 0.0461) and ABCB1 rs1128503 (OR = 2.06, 95%CI: 1.18-3.61, P = 0.0111). These associations remained significant in a subgroup analysis, where patients were stratified according to viral status (HBV- or HCV-positive serology). Two variants exhibited a serology-specific effect: ABCB1 rs1128503 (OR = 4.18, 95%CI: 1.55-11.29, P = 0.0048) showed an effect in the HBV-positive subgroup; and ERCC1 rs3212986 (OR = 0.33, 95%CI: 0.18-0.60, P = 0.0003) showed an effect in the HCV-positive subgroup. Among the five markers identified, ERCC1 rs3212986 (OR = 0.43, P < 0.0001) and CYP17A1 rs743572 (OR = 0.73, P = 0.0310) had a different distribution in patients with HCC compared to healthy individuals. With a recursive partitioning approach, we also demonstrated that significant gene-gene interactions between ERCC1 rs3212986, CYP17A1 rs743572, GST-P1 rs1138272, and the previously described UGT1A7*3 predictive marker, played a role in the complex trait of HCC susceptibility.ConclusionWe identified five polymorphisms and interactions that contributed crucially to predicting HCC risk. These findings represented an important step towards improving HCC diagnosis and management.

Project description:The prevalence of type 2 diabetes (T2D) is increasing in the Middle East. However, the genetic risk factors for T2D in the Middle Eastern populations are not known, as the majority of studies of genetic risk for T2D are in Europeans and Asians.All subjects were ?3 generation Qataris. Cases with T2D (n = 1,124) and controls (n = 590) were randomly recruited and assigned to the 3 known Qatari genetic subpopulations [Bedouin (Q1), Persian/South Asian (Q2) and African (Q3)]. Subjects underwent genotyping for 37 single nucleotide polymorphisms (SNPs) in 29 genes known to be associated with T2D in Europeans and/or Asian populations, and an additional 27 tag SNPs related to these susceptibility loci. Pre-study power analysis suggested that with the known incidence of T2D in adult Qataris (22%), the study population size would be sufficient to detect significant differences if the SNPs were risk factors among Qataris, assuming that the odds ratio (OR) for T2D SNPs in Qatari's is greater than or equal to the SNP with highest known OR in other populations.Haplotype analysis demonstrated that Qatari haplotypes in the region of known T2D risk alleles in Q1 and Q2 genetic subpopulations were similar to European haplotypes. After Benjamini-Hochberg adjustment for multiple testing, only two SNPs (rs7903146 and rs4506565), both associated with transcription factor 7-like 2 (TCF7L2), achieved statistical significance in the whole study population. When T2D subjects and control subjects were assigned to the known 3 Qatari subpopulations, and analyzed individually and with the Q1 and Q2 genetic subpopulations combined, one of these SNPs (rs4506565) was also significant in the admixed group. No other SNPs associated with T2D in all Qataris or individual genetic subpopulations.With the caveats of the power analysis, the European/Asian T2D SNPs do not contribute significantly to the high prevalence of T2D in the Qatari population, suggesting that the genetic risks for T2D are likely different in Qataris compared to Europeans and Asians.

Dataset Information

Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report.

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Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report.

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