Assessment of the Added Value of Intravoxel Incoherent Motion Diffusion-Weighted MR Imaging in Identifying Non-Diabetic Renal Disease in Patients With Type 2 Diabetes Mellitus.

BACKGROUND: Identification of non-diabetic renal disease (NDRD) in patients with type 2 diabetes mellitus (T2DM) may help tailor treatment. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising tool to evaluate renal function but its potential role in the clinical differentiation between diabetic nephropathy (DN) and NDRD remains unclear. PURPOSE: To investigate the added role of IVIM-DWI in the differential diagnosis between DN and NDRD in patients with T2DM. STUDY TYPE: Prospective. POPULATION: Sixty-three patients with T2DM (ages: 22-69 years, 17 females) confirmed by renal biopsy divided into two subgroups (28 DN and 35 NDRD). FIELD STRENGTH/SEQUENCE: 3 T/ T2 weighted imaging (T2WI), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). ASSESSMENT: The parameters derived from IVIM-DWI (true diffusion coefficient [D], pseudo-diffusion coefficient [D*], and pseudo-diffusion fraction [f]) were calculated for the cortex and medulla, respectively. The clinical indexes related to renal function (eg cystatin C, etc.) and diabetes (eg diabetic retinopathy [DR], fasting blood glucose, etc.) were measured and calculated within 1 week before MRI scanning. The clinical model based on clinical indexes and the IVIM-based model based on IVIM parameters and clinical indexes were established and evaluated, respectively. STATISTICAL TESTS: Student's t-test; Mann-Whitney U test; Fisher's exact test; Chi-squared test; Intraclass correlation coefficient; Receiver operating characteristic analysis; Hosmer-Lemeshow test; DeLong's test. P < 0.05 was considered statistically significant. RESULTS: The cortex D*, DR, and cystatin C values were identified as independent predictors of NDRD in multivariable analysis. The IVIM-based model, comprising DR, cystatin C, and cortex D*, significantly outperformed the clinical model containing only DR, and cystatin C (AUC = 0.934, 0.845, respectively). DATA CONCLUSION: The IVIM parameters, especially the renal cortex D* value, might serve as novel indicators in the differential diagnosis between DN and NDRD in patients with T2DM. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Glycated albumin may be a choice, but not an alternative marker of glycated hemoglobin for glycemic control assessment in diabetic patients undergoing maintenance hemodialysis.

BACKGROUND: It has been suggested that glycated hemoglobin (HbA1c) underestimate the actual glycemic control levels in maintenance hemodialysis (MHD) patients, because of anemia and the using of erythropoietin (EPO); it was recommended that glycated albumin (GA) should be an alternative marker. Therefore, the assessment performances of glycemic control were compared between GA and HbA1c in this research by referring to mean plasma glucose (MPG) in diabetes mellitus (DM) patients undergoing MHD or not. METHODS: MPG was calculated according to the data registered at enrollment and follow-up 2 months later and corresponding HbA1c, albumin (ALB), GA, etc. were measured in 280 cases. A case-control study for comparing GA and HbA1c was done among the groups of MHD patients with DM (n=88) and without DM (NDM; n=90), and non-MHD ones with DM (n=102) using MPG for an actual glycemic control standard. RESULTS: In these 3 groups, only for DM patients' (whether undergoing MHD or not), GA and HbA1c correlated with MPG significantly (P < 0.01). Through linear regression analysis, it could be found that the regression curves of GA almost coincided in MHD and non-MHD patients with DM, because the intercepts (2.418 vs. 2.329) and slopes (0.053 vs. 0.057) were very close to each other. On the contrary, regression curves of HbA1c did not coincide in the two groups, because variance of the slopes (0.036 vs. 0.052) were relatively large. Through comparing receiver operating characteristic (ROC) areas under the curve (AUC), it could be understood that the assessment performances of GA and HbA1c in MHD patients were lower than those in non-MHD ones, and assessment performance of HbA1c in MHD patients was better than GA (P < 0.05). In addition, the effects of Hb and EPO dose on HbA1c, or that of ALB on GA were unobvious in our study. CONCLUSIONS: Actual glycemic control level in MHD patients with DM may be underestimated by HbA1c, and it could be avoided by GA; however, glycemic evaluating performance of HbA1c may be still better than that of GA. Therefore, HbA1c should not be replaced completely although GA can be used as a choice to monitor glycemic level.

[Correlation of cardiovascular risk factors versus glomerular filtration rate in healthy population].

OBJECTIVE: To evaluate the relationship between cardiovascular risk factors and glomerular filtration rate in healthy population. METHODS: A community-based cross-sectional study was conducted in Shenyang. The Framingham sex-specific risk equation was employed to evaluate the cardiovascular risk factors of 501 healthy study objects, calculate Framingham risk score and estimate the risk of 10-year coronary heart disease (CHD). A total of 501 study subjects were then divided into 3 groups according to 10-year CHD risk: low risk group (< 10%), moderate risk group (10% -20%) and high risk group ( > 20%). Study subjects were also divided into 5 groups according to age: < or = 44 years old; 45 - 54 years old; 55 - 64 years old; 65 - 74 years old and > or = 75 years old. The Cockcroft-Gault equation (GFR(CG)), abbreviated MDRD equation (GFR(MDRD1)) and modified MDRD equation (GFR(MDRD2)) were used to estimate glomerular filtration rate (GFR). Glomerular filtration rate (GFR) were compared among different risk groups and correlation coefficients between Framingham risk score and glomerular filtration rate calculated. RESULTS: GFR(CC), GFR(MDRD1) and GFR(MDRD2) in the low risk group was [(103 +/- 27) ml x min(-1) GFR(MDRD2) in moderate risk group all decreased [(84 +/- 24) ml x min(-1) x (1.73 m2) (-1), (101 +/- 27) ml x min(-1) x (1.73 m2) (-1), (124 +/- 33)ml x min(-1) (1.73 m2) (-1), all P < 0.01]. GFR(CG), GFR(MDRD1) and GFR(MR(MDRD2) in the high risk group all decreased [(71 +/- 15) ml x min(-1) (1.73 m2) (-1), (88 +/- 15) ml x min(-1) x (1.73 m2)(-1), (109 +/-18)ml x min(-1) x (1.73 m2) (-1), all P < 0.01]. The GFR(CG), GFR(MDRD1) and GFR(MDRD2) in the high risk group all decreased compared with the moderate risk group (P < 0.05). There was a significantly inverse correlation between Framingham risk score and GFR with the Pearson correlation coefficient -0.586 (GFR(CG), P < 0.01) and -0.449 (GFR(MDRD1) and GFR(MDRD2), P < 0.01). CONCLUSION: There is an inverse correlation between cardiovascular risk factors and GFR in healthy population. With the increasing of risk factors and their severity, Framingham risk score increases and GFR decreases.

[Quantitative assessment of healthy People's cardiovascular aging and analysis of the relevant law: study in three cities in Northern China].

OBJECTIVE: To quantitatively assess the progress of cardiovascular aging in the healthy people in North China by non-invasive ways, and to analyze the law of cardiovascular aging. METHODS: 855 healthy people randomly sampled from 3 cities in northern China: Shenyang, Dalian, and Beijing, divided into 4 age groups: < or = 44, 45 - 59, 60 - 74, and > or = 75, underwent interview of the life style and dietary structure, measurement of blood pressure, heart and carotid ultrasonography, blood biochemistry, blood routine, urine routine, and detection of micro-inflammation indexes such as C-reactive protein, fibrinogen (FIB), thrombomodulin, and interleukin-6. Totally 70 items were surveyed. Then the cardiovascular aging assessment equation was constructed and cardiovascular biological aging score (CBAS) was calculated to analyze the law of cardiovascular aging. RESULTS: Seven items including pulse pressure, carotid artery intimal-medial thickness, carotid artery end-diastolic velocity, mitral valve anulus anterior wall A, mitral valve anulus lateral wall E, FIB, total cholesterol were correlated with chronological age significantly (r = 0.63, 0.54, -0.41, 0.45, -0.42, 0.43, and 0.46, all P < 0.01). The CBAS was positively correlated with chronological age (r = 0.85, P < 0.05) with the most rapid change rate in the group aged 45 - 59. CONCLUSION: CAS can be used as an objective index to quantitatively assess the cardiovascular aging. With an acceleration of cardiovascular aging rate after the age 45, the speed of cardiovascular aging in healthy people may be inconstant. The age 45 - 59 may be the key threshold for cardiovascular aging.