scholarly journals Evaluation of the Association between Obesity Markers and Type 2 Diabetes: A Cohort Study Based on a Physical Examination Population

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tengfei Yang ◽  
Bo Zhao ◽  
Dongmei Pei
Keyword(s):  
Type 2 Diabetes ◽  
Cohort Study ◽  
Physical Examination ◽  
Roc Curve ◽  
Risk Model ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Visceral Adiposity Index ◽  
Digital Repository

Purpose. To evaluate the predictive effect of different obesity markers on the risk of developing type 2 diabetes in a population of healthy individuals who underwent physical examination and to provide a reference for the early detection of individuals at risk of diabetes. Methods. This retrospective cohort study included 15206 healthy subjects who underwent a physical examination (8307 men and 6899 women). Information on the study population was obtained from the Dryad Digital Repository. Cox proportional risk models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) of different obesity markers, including the lipid accumulation index (LAP), body mass index (BMI), waist-to-height ratio (WHtR), visceral adiposity index (VAI), and body roundness index (BRI) on the development of type 2 diabetes. The effectiveness of each obesity marker in predicting the risk of developing type 2 diabetes was analyzed using the receiver operating characteristic curve (ROC curve) and the area under the curve (AUC). Results. After a mean follow-up of 5.4 years, there were 372 new cases of type 2 diabetes. After correcting for confounding factors such as age, sex, smoking, alcohol consumption, exercise, and blood pressure, Cox proportional risk model analysis showed that elevations in BMI, LAP, WHtR, VAI, and BRI increased the risk of developing type 2 diabetes. The ROC curve results showed that LAP was the best predictor of the risk of developing diabetes, with an AUC (95% CI) of 0.759 (0.752–0.766), an optimal cutoff value of 16.04, a sensitivity of 0.72, and a specificity of 0.69. Conclusion. An increase in the BMI, LAP, WHtR, VAI, and BRI can increase the risk of developing type 2 diabetes, with LAP being the best predictor of this risk.

scholarly journals Serum acylcarnitines and amino acids and risk of type 2 diabetes in a multiethnic Asian population

2020 ◽  
Vol 8 (1) ◽  
pp. e001315
Author(s):  
Samuel H Gunther ◽  
Chin Meng Khoo ◽  
E-Shyong Tai ◽  
Xueling Sim ◽  
Jean-Paul Kovalik ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Amino Acids ◽  
Risk Model ◽  
Characteristic Curve ◽  
Asian Population ◽  
Cox Proportional Hazards ◽  
Diabetes Risk ◽  
Study Program ◽  
Parental History

IntroductionWe evaluated whether concentrations of serum acylcarnitines and amino acids are associated with risk of type 2 diabetes and can improve predictive diabetes models in an Asian population.Research design and methodsWe used data from 3313 male and female participants from the Singapore Prospective Study Program cohort who were diabetes-free at baseline. The average age at baseline was 48.0 years (SD: 11.9 years), and participants were of Chinese, Malay, and Indian ethnicity. Diabetes cases were identified through self-reported physician diagnosis, fasting glucose and glycated hemoglobin concentrations, and linkage to national disease registries. We measured fasting serum concentrations of 45 acylcarnitines and 14 amino acids. The association between metabolites and incident diabetes was modeled using Cox proportional hazards regression with adjustment for age, sex, ethnicity, height, and parental history of diabetes, and correction for multiple testing. Metabolites were added to the Atherosclerosis Risk in Communities (ARIC) predictive diabetes risk model to assess whether they could increase the area under the receiver operating characteristic curve (AUC).ResultsParticipants were followed up for an average of 8.4 years (SD: 2.1 years), during which time 314 developed diabetes. Branched-chain amino acids (HR: 1.477 per SD; 95% CI 1.325 to 1.647) and the alanine to glycine ratio (HR: 1.572; 95% CI 1.426 to 1.733) were most strongly associated with diabetes risk. Additionally, the acylcarnitines C4 and C16-OH, and the amino acids alanine, combined glutamate/glutamine, ornithine, phenylalanine, proline, and tyrosine were significantly associated with higher diabetes risk, and the acylcarnitine C8-DC and amino acids glycine and serine with lower risk. Adding selected metabolites to the ARIC model resulted in a significant increase in AUC from 0.836 to 0.846.ConclusionsWe identified acylcarnitines and amino acids associated with risk of type 2 diabetes in an Asian population. A subset of these modestly improved the prediction of diabetes when added to an established diabetes risk model.

scholarly journals The Finnish Diabetes Risk Score Is Associated with Insulin Resistance and Progression towards Type 2 Diabetes

2009 ◽  
Vol 94 (3) ◽  
pp. 920-926 ◽  
Author(s):  
Peter E. H. Schwarz ◽  
Jiang Li ◽  
Manja Reimann ◽  
Alta E. Schutte ◽  
Antje Bergmann ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Type 2 Diabetes ◽  
Cohort Study ◽  
Glucose Tolerance ◽  
Area Under The Curve ◽  
Diabetes Risk ◽  
Operating Characteristics ◽  
Cross Sectional Survey ◽  
Cross Sectional

Abstract Objective: The Finnish Diabetes Risk Score (FINDRISC) questionnaire is a practical screening tool to estimate the diabetes risk and the probability of asymptomatic type 2 diabetes. In this study we evaluated the usefulness of the FINDRISC to predict insulin resistance in a population at increased diabetes risk. Design: Data of 771 and 526 participants in a cross-sectional survey (1996) and a cohort study (1997–2000), respectively, were used for the analysis. Data on the FINDRISC and oral glucose tolerance test parameters were available from each participant. The predictive value of the FINDRISC was cross-sectionally evaluated using the area under the curve-receiver operating characteristics method and by correlation analyses. A validation of the cross-sectional results was performed on the prospective data from the cohort study. Results: The FINDRISC was significantly correlated with markers of insulin resistance. The receiver operating characteristics-area under the curve for the prediction of a homeostasis model assessment insulin resistance index of more than five was 0.78 in the cross-sectional survey and 0.74 at baseline of the cohort study. Moreover, the FINDRISC at baseline was significantly associated with disease evolution (P < 0.01), which was defined as the change of glucose tolerance during the 3 yr follow-up. Conclusions: The results indicate that the FINDRISC can be applied to detect insulin resistance in a population at high risk for type 2 diabetes and predict future impairment of glucose tolerance.

4-Year Trajectory of Visceral Adiposity Index in the Development of Type 2 Diabetes: A Prospective Cohort Study

2016 ◽  
Vol 69 (2) ◽  
pp. 142-149 ◽  
Author(s):  
Meilin Zhang ◽  
Li Zheng ◽  
Ping Li ◽  
Yufeng Zhu ◽  
Hong Chang ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Visceral Adiposity ◽  
Visceral Adiposity Index ◽  
Prediction Ability ◽  
Cox Regression Analysis ◽  
Adiposity Index

Background/Aims: Our aim was to evaluate whether visceral adiposity index (VAI) could predict the risk of type 2 diabetes (T2D) in different genders and to compare the predictive ability between VAI and other fatness indices. Methods: Four thousand seventy-eight participants including 1,817 men and 2,261 women, aged 18 and older and free of T2D at baseline were enrolled in 2010 and followed up for 4 years. New cases of T2D were identified via the annual medical examination. Cox regression analysis was used to assess the association between VAI and incidence of T2D. Receiver operating characteristic curve and area under the curves (AUC) were applied to compare the prediction ability of T2D between VAI and other fatness indices. Results: During the 4-year follow-up, 153 (8.42%) of 1,817 men and 88 (3.89%) of 2,261 women developed T2D. The multivariable-adjusted hazards ratios for developing T2D in the highest tertile of VAI scores were 2.854 (95% CI 1.815-4.487) in men and 3.551 (95% CI 1.586-7.955) in women. The AUC of VAI was not higher than that of other fatness indices. Conclusions: VAI could predict the risk of T2D among Chinese adults, especially in women. However, the prediction ability of T2D risk for VAI was not higher than that of the other fatness indices.

A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740055 ◽  
Author(s):  
Jiang Xie ◽  
Yan Liu ◽  
Xu Zeng ◽  
Wu Zhang ◽  
Zhen Mei
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Electronic Health Records ◽  
Physical Examination ◽  
Risk Model ◽  
Learning Approach ◽  
Health Records ◽  
Machine Learning Approach

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

scholarly journals Lower Circulating miR-122 Level in Patients with HNF1A Variant-Induced Diabetes Compared with Type 2 Diabetes

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Xiuting Huang ◽  
Siqian Gong ◽  
Yumin Ma ◽  
Xiaoling Cai ◽  
Lingli Zhou ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Liver Neoplasm ◽  
Chinese Patients ◽  
Increased Risk ◽  
A3243g Mutation ◽  
Abnormal Lipid Metabolism

miR-122, the expression of which is regulated by several transcription factors, such as HNF1A, was recently reported to be associated with type 2 diabetes (T2DM) and hepatocellular carcinoma. HNF1A variants can cause diabetes and might be involved in the development of primary liver neoplasm. Differences in miR-122 expression among different types of diabetes have not been studied. This study aimed to investigate differences in serum miR-122 levels in Chinese patients with different forms of diabetes, including T2DM, type 1 diabetes (T1DM), HNF1A variant-induced diabetes (HNF1A-DM), glucokinase variant-induced diabetes (GCK-DM), and mitochondrial A3243G mutation-induced diabetes (MDM). In total, 12 HNF1A-DM patients, 24 gender-, age-, and body mass index-matched (1 : 2) T2DM patients and 24 healthy subjects were included in this study. In addition, 30 monogenic diabetes (11 GCK-DM and 19 MDM) and 17 T1DM patients were included. Fasted blood biochemistry and miR-122 were measured. The results showed that the HNF1A-DM patients had lower miR-122 levels [0.046 (0.023, 0.121)] than T2DM patients [0.165 (0.036, 0.939), P=0.02] and healthy controls [0.249 (0.049, 1.234), P=0.019]. The area under the curve of the receiver operating characteristic curve for miR-122 to discriminate HNF1A-DM and T2DM was 0.687 (95% CI: 0.52–0.86, P=0.07). There was no difference in serum miR-122 among HNF1A-DM, GCK-DM, MDM, and T1DM patients. Lower serum miR-122 is a unique feature of HNF1A-DM patients and might partially explain the increased risk for liver neoplasm and abnormal lipid metabolism in HNF1A-DM patients.

[beta]eta-hydroxybutyrate and risk for incident type 2 diabetes: results from the prevend prospective cohort study

2019 ◽  
Author(s):  
Jose L Flores-Guerrero ◽  
Margery A Connelly ◽  
Dion Groothof ◽  
Eke G Gruppen ◽  
Stephan JL Bakker ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cohort Study ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Incident Type ◽  
Incident Type 2 Diabetes

Hormonal factors and type 2 diabetes risk in women: a 22 year follow-up study on more than 83 000 women from the E3N cohort study

2019 ◽  
Author(s):  
Sopio Tatulashvili ◽  
Gaelle Gusto ◽  
Beverley Balkau ◽  
Emmanuel Cosson ◽  
Fabrice Bonnet ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cohort Study ◽  
Diabetes Risk ◽  
Hormonal Factors ◽  
Follow Up Study
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