ORCID Profile
0000-0002-8783-0984
Current Organisation
Eastern Health
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Publisher: Hindawi Limited
Date: 15-11-2018
DOI: 10.1155/2018/9423965
Abstract: Recovery of acute insulin response (AIR) is shown to be associated with long-term outcomes of patients with early type 2 diabetes treated with short-term intensive insulin therapy (SIIT). However, the complexity of measuring an AIR limits its utility in a real-world clinical setting. The aim of the study was to assess fasting indicators that may estimate recovery of the AIR after SIIT. We enrolled 62 patients with type 2 diabetes mellitus (T2DM) of varying disease duration who had poor glycemic control. Participants were treated with SIIT using insulin pumps to achieve near normoglycemia for 7 days. The AIR before and after the therapy were measured by intravenous glucose tolerance tests. After the therapy, AIR increased from −16.7 (−117.4, 52.4) pmol/L·min to 178.7 (31.8, 390.7) pmol/L·min ( P 0.001 ) while hyperglycemia was alleviated this improvement was observed in all disease duration categories. AIR was almost absent when fasting plasma glucose (FPG) 10 mmol/L, while both AIR ( R = − 0.53 , P 0.001 ) and its improvement from baseline (△AIR, R = − 0.52 , P 0.001 ) were negatively associated with FPG after SIIT when FPG 10 mmol/L. In multivariate analyses, FPG after SIIT and baseline fasting C peptide were independent indicators of both AIR after the therapy and ∆AIR HDL-C after the therapy also predicted AIR after the therapy. We concluded that recovery of the AIR could be obtained in T2DM patients of varying disease duration by SIIT and it could be conveniently estimated using posttreatment fasting plasma glucose and other fasting indicators.
Publisher: JMIR Publications Inc.
Date: 28-07-2020
DOI: 10.2196/16850
Abstract: Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in in iduals with obesity (men: 17.78% [17.05%-18.43%] women: 14.59% [13.99%-15.17%]) compared with that of nonobese in iduals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P .001). A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.
Publisher: JMIR Publications Inc.
Date: 30-10-2019
Abstract: revious conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology. e aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017. e collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model. verall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in in iduals with obesity (men: 17.78% [17.05%-18.43%] women: 14.59% [13.99%-15.17%]) compared with that of nonobese in iduals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% ( i P /i & .001). one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.
Publisher: Hindawi Limited
Date: 29-07-2019
DOI: 10.1155/2019/4828402
Abstract: The absence of nocturnal blood pressure (BP) decline is associated with hypertensive complications. Data regarding circadian BP patterns in patients with aldosterone-producing adenoma (APA) are limited and equivocal. We evaluated the circadian BP profile in patients with APA and its relationship with the circadian aldosterone rhythm. BP in patients with APA and in those with essential hypertension (EH) were assessed through in-hospital 24-h ambulatory blood pressure monitoring. Over a 24-h in-hospital period, plasma aldosterone levels taken at midnight, 0400, 0800, 1200, 1600, and 2000 h were measured. To evaluate a correlation between BP and hormone rhythm, we included 27 patients with APA (APA group) and 27 patients with EH (EH group). Both groups had similar age, sex ratio, body mass index, duration of hypertension, family history of hypertension, and lipid profiles. The day-night BP differences in both patient groups were similar, whether expressed as absolute values or percentages. The proportions of patients with dipping BP profiles were also comparable (APA group, 5 of 27 EH group, 7 of 27 χ 2 = 0.429 P = 0.513). At each time point, APA group plasma aldosterone concentrations (PACs) were higher than those of the EH group. A circadian change in relation to PAC was observed in both groups. A correlation between PAC and BP was statistically nonsignificant in most study patients in either group. Our data indicated that the circadian BP pattern was not associated with a change in PAC levels in patients with APA.
No related grants have been discovered for Jianbin Liu.