Luiz Sérgio F Carvalho1,2,3, Isabela M Benseñor4, Ana C C Nogueira5, Bruce B Duncan6, Maria I Schmidt6, Michael J Blaha7, Peter P Toth7,8, Steven R Jones7, Raul D Santos4,9, Paulo A Lotufo4, Andrei C Sposito10. 1. Data Lab, Clarity Healthcare Intelligence, Jundiaí, SP, Brazil. lsergio@clarityhealth.com.br. 2. Cardiology Division, Faculty of Medical Sciences, State University of Campinas (Unicamp), Campinas, SP, Brazil. lsergio@clarityhealth.com.br. 3. Laboratory of Data for Quality of Care and Outcomes Research, Institute for Strategic Management in Healthcare DF (IGESDF), Brasília, DF, Brazil. lsergio@clarityhealth.com.br. 4. Center for Clinical and Epidemiological Research, University Hospital, University of São Paulo, São Paulo, SP, Brazil. 5. Laboratory of Data for Quality of Care and Outcomes Research, Institute for Strategic Management in Healthcare DF (IGESDF), Brasília, DF, Brazil. 6. Postgraduate Studies Program in Epidemiology, School of Medicine and Hospital de Clínicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil. 7. The Johns Hopkins Ciccarone Center for the Prevention of Heart Disease, Baltimore, MD, USA. 8. Preventive Cardiology, CGH Medical Center, Sterling, IL, USA. 9. Lipid Clinic Heart Institute (InCor), University of São Paulo, Medical School Hospital, São Paulo, SP, Brazil. 10. Cardiology Division, Faculty of Medical Sciences, State University of Campinas (Unicamp), Campinas, SP, Brazil.
Abstract
AIMS/HYPOTHESIS: Type 2 diabetes prevention requires the accurate identification of those at high risk. Beyond the association of fasting serum triacylglycerols with diabetes, triacylglycerol-enriched remnant lipoproteins (TRLs) more accurately reflect pathophysiological changes that underlie progression to diabetes, such as hepatic insulin resistance, pancreatic steatosis and systemic inflammation. We hypothesised that TRL-related factors could improve risk prediction for incident diabetes. METHODS: We included individuals from the Brazilian Longitudinal Study of Adult Health cohort. We trained a logistic regression model for the risk of incident diabetes in 80% of the cohort using tenfold cross-validation, and tested the model in the remaining 20% of the cohort (test set). Variables included medical history and traits of the metabolic syndrome, followed by TRL-related measurements (plasma concentration, TRL particle diameter, cholesterol and triacylglycerol content). TRL features were measured using NMR spectroscopy. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS: Among 4463 at-risk individuals, there were 366 new cases of diabetes after a mean (±SD) of 3.7 (±0.63) years of follow-up. We derived an 18-variable model with a global AUROC of 0.846 (95% CI: 0.829, 0.869). Overall TRL-related markers were not associated with diabetes. However, TRL particle diameter increased the AUROC, particularly in individuals with HbA1c <39 mmol/mol (5.7%) (hold-out test set [n = 659]; training-validation set [n = 2638]), but not in individuals with baseline HbA1c 39-46 mmol/mol (5.7-6.4%) (hold-out test set [n = 233]; training-validation set [n = 933]). In the subgroup with baseline HbA1c <39 mmol/mol (5.7%), AUROC in the test set increased from 0.717 (95% CI 0.603, 0.818) to 0.794 (95% CI 0.731, 0.862), and AUPRC in the test set rose from 0.582 to 0.701 when using the baseline model and the baseline model plus TRL particle diameter, respectively. TRL particle diameter was highly correlated with obesity, insulin resistance and inflammation in those with impaired fasting glucose at baseline, but less so in those with HbA1c <39 mmol/mol (5.7%). CONCLUSIONS/ INTERPRETATION: TRL particle diameter improves the prediction of diabetes, but only in individuals with HbA1c <39 mmol/mol (5.7%) at baseline. These data support TRL particle diameter as a risk factor that is changed early in the course of the pathophysiological processes that lead to the development of type 2 diabetes, even before glucose abnormalities are established. Graphical abstract.
AIMS/HYPOTHESIS: Type 2 diabetes prevention requires the accurate identification of those at high risk. Beyond the association of fasting serum triacylglycerols with diabetes, triacylglycerol-enriched remnant lipoproteins (TRLs) more accurately reflect pathophysiological changes that underlie progression to diabetes, such as hepatic insulin resistance, pancreatic steatosis and systemic inflammation. We hypothesised that TRL-related factors could improve risk prediction for incident diabetes. METHODS: We included individuals from the Brazilian Longitudinal Study of Adult Health cohort. We trained a logistic regression model for the risk of incident diabetes in 80% of the cohort using tenfold cross-validation, and tested the model in the remaining 20% of the cohort (test set). Variables included medical history and traits of the metabolic syndrome, followed by TRL-related measurements (plasma concentration, TRL particle diameter, cholesterol and triacylglycerol content). TRL features were measured using NMR spectroscopy. Discrimination was assessed using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS: Among 4463 at-risk individuals, there were 366 new cases of diabetes after a mean (±SD) of 3.7 (±0.63) years of follow-up. We derived an 18-variable model with a global AUROC of 0.846 (95% CI: 0.829, 0.869). Overall TRL-related markers were not associated with diabetes. However, TRL particle diameter increased the AUROC, particularly in individuals with HbA1c <39 mmol/mol (5.7%) (hold-out test set [n = 659]; training-validation set [n = 2638]), but not in individuals with baseline HbA1c 39-46 mmol/mol (5.7-6.4%) (hold-out test set [n = 233]; training-validation set [n = 933]). In the subgroup with baseline HbA1c <39 mmol/mol (5.7%), AUROC in the test set increased from 0.717 (95% CI 0.603, 0.818) to 0.794 (95% CI 0.731, 0.862), and AUPRC in the test set rose from 0.582 to 0.701 when using the baseline model and the baseline model plus TRL particle diameter, respectively. TRL particle diameter was highly correlated with obesity, insulin resistance and inflammation in those with impaired fasting glucose at baseline, but less so in those with HbA1c <39 mmol/mol (5.7%). CONCLUSIONS/ INTERPRETATION: TRL particle diameter improves the prediction of diabetes, but only in individuals with HbA1c <39 mmol/mol (5.7%) at baseline. These data support TRL particle diameter as a risk factor that is changed early in the course of the pathophysiological processes that lead to the development of type 2 diabetes, even before glucose abnormalities are established. Graphical abstract.
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