ABSTRACT
Objective:To explore the correlation between metabolic syndrome(MS)and early renal function injury in chronic kidney disease(CKD)in the healthy elderly subjects having passed through a healthy physical examination.Methods:These healthy elderly subjects aged ≥ 60 years in the Sichuan Provincial People's Hospital health management center from September 2017 to August 2018 were randomly selected to collect lifestyle information such as disease history and living habits, as well as health information such as height, weight, waist circumference, blood pressure, fasting blood lipid, blood glucose, creatinine, morning urine routine, urinary protein and urinary occult blood.The detection situation of MS and early renal function injury of chronic kidney disease, and the correlation of MS and its abnormal components with early renal function injury of CKD were analyzed.Results:A total of 2 975 subjects, aged from 60 to 92 years old, with mean age of(67.10±5.98)years old, were enrolled.The detection rate of MS and early renal function impairment in CKD was 32.34%(962/2 975)and 28.84%(858/2 975)respectively.The detection rate of early CKD renal function injury in MS patients was 37.63%(362/962), which was higher than that in non-MS patients(24.64%)(496/2 013)( χ2=53.52, P<0.001). The prevalences of CKD and albumin/creatinine ratio(ACR)in MS patients were significantly higher than those in non-MS patients[31.91%(307/962)versus 17.49%(352/2 013)]with the difference being statistically significant( χ2=78.56, P <0.001). Central obesity, elevated blood pressure and elevated blood glucose increased the risk of CKD( OR=1.44, 1.82 and 1.74 respectively, all P< 0.05). Along with the increase of number of metabolic abnormal components, the risk of CKD increased. Conclusions:MS is a risk factor for early renal function impairment in CKD.Early control and treatment of MS are of great significance in reducing the incidence rate of CKD and delaying CKD progression.
ABSTRACT
Objective:To investigate the impact of Bayesian penalized likelihood (BPL) PET reconstruction method on the uptake of 18F-fluorodeoxyglucose (FDG) and signal to noise ratio (SNR) of lungs, liver, aorta and bones. Methods:From March 2019 to June 2019, the 18F-FDG PET/CT images of 60 patients with clinical diagnosed tumors (29 males, 31 females, age: 24-89 (60.4±15.2) years) in Yuhuangding Hospital were retrospectively analyzed. PET images were reconstructed with ordered subset expectation maximization (OSEM), time of flight (TOF)+ point spread function (PSF) and BPL (β=350) algorithms. Volumes of interest (VOIs) were delineated on the right upper lung lobe, the right liver, aortic root and lumbar vertebra. The mean standardized uptake value (SUV mean), maximum standardized uptake value (SUV max), peak of lean body standardized uptake value (SUL peak), standard deviation of standardized uptake value (SUV SD) and the SNR were measured. The percentage of SNR change (%ΔSNR) between the BPL method and non-BPL methods were calculated. The correlations between body mass index (BMI) and %ΔSNR were analyzed by Pearson correlation analysis. One-way analysis of variance and the least significant difference (LSD) t test were used to analyze the data. Results:There were no significant differences of SUV mean and SUL peak in lung, aorta, liver and lumbar vertebra among 3 methods ( F values: 0.04-1.95, all P>0.05). The SUV max in lung, aorta, liver and lumbar vertebra of BPL reconstruction (1.14±0.82, 2.13±0.37, 2.95±0.50 and 2.76±0.87) was significantly lower than those of TOF+ PSF (1.56±0.61, 2.99±0.75, 4.32±0.94 and 4.05±1.48) and OSEM (1.51±0.67, 3.00±0.70, 4.45±1.12 and 3.81±1.06) reconstructions ( F values: 20.59-52.24, all P<0.001) and SUV SD (0.13±0.07, 0.20±0.05, 0.26±0.06, 0.38±0.17) was also significantly lower than those of TOF+ PSF (0.24±0.11, 0.43±0.11, 0.58±0.15, 0.67±0.21) and OSEM (0.21±0.09, 0.42±0.10, 0.58±0.14, 0.63±0.20) reconstructions ( F values: 24.46-124.95, all P<0.001), while the SNR (4.67±1.34, 7.74±2.22, 8.17±1.77, 4.45±1.22) was significantly higher than those of TOF+ PSF (2.54±0.72, 3.55±0.82, 3.77±0.91, 2.49±0.69) and OSEM (2.65±0.64, 3.67±0.80, 3.75±0.87, 2.60±0.67) reconstructions ( F values: 83.04-247.73, all P<0.001). However, there were no significant differences between OSEM and TOF+ PSF reconstructed images in SUL peak, SUV mean, SUV SD and SNR (all P>0.05). In BPL group, SNR increased with the increase of BMI, and there were statistically differences of aortic SNR (7.07±2.21 vs 9.67±2.26) and liver SNR (7.75±1.85 vs 9.32±0.70) between BMI<25 kg/m 2 and BMI≥30 kg/m 2 ( F values: 3.46 and 4.19, both P<0.05). Positive correlations were found between %ΔSNR of lung, aorta, liver and lumbar vertebra in OSEM and TOF+ PSF and BMI ( r value: 0.042-0.354, all P<0.05). Conclusion:In background tissues, BPL algorithm has no significant impact on absolute quantification compared with OSEM and TOF + PSF reconstruction methods but it can significantly improve SNR, especially for the patients with large body weight.