Objective Previous studies on the association between lipid profiles and chronic kidney disease(CKD)have yielded inconsistent results and no defined thresholds for blood lipids.Methods A prospective cohort study inclu...Objective Previous studies on the association between lipid profiles and chronic kidney disease(CKD)have yielded inconsistent results and no defined thresholds for blood lipids.Methods A prospective cohort study including 32,351 subjects who completed baseline and follow-up surveys over 5 years was conducted.Restricted cubic splines and Cox models were used to examine the association between the lipid profiles and CKD.A regression discontinuity design was used to determine the cutoff value of lipid profiles that was significantly associated with increased the risk of CKD.Results Over a median follow-up time of 2.2(0.5,4.2)years,648(2.00%)subjects developed CKD.The lipid profiles that were significantly and linearly related to CKD included total cholesterol(TC),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),TC/HDL-C,and TG/HDL-C,whereas lowdensity lipoprotein cholesterol(LDL-C)and LDL-C/HDL-C were nonlinearly correlated with CKD.TC,TG,TC/HDL-C,and TG/HDL-C showed an upward jump at the cutoff value,increasing the risk of CKD by 0.90%,1.50%,2.30%,and 1.60%,respectively,whereas HDL-C showed a downward jump at the cutoff value,reducing this risk by 1.0%.Female and participants with dyslipidemia had a higher risk of CKD,while the cutoff values for the different characteristics of the population were different.Conclusion There was a significant association between lipid profiles and CKD in a prospective cohort from Northwest China,while TG,TC/HDL-C,and TG/HDL-C showed a stronger risk association.The specific cutoff values of lipid profiles may provide a clinical reference for screening or diagnosing CKD risk.展开更多
Background With the variability in emerging data,guidance on the isolation duration for patients with coronavirus disease 2019(COVID-19)due to the Omicron variant is controversial.This study aimed to determine the pre...Background With the variability in emerging data,guidance on the isolation duration for patients with coronavirus disease 2019(COVID-19)due to the Omicron variant is controversial.This study aimed to determine the predictors of prolonged viral RNA shedding in patients with non-severe COVID-19 and construct a nomogram to predict patients at risk of 14-day PCR conversion failure.Methods Adult patients with non-severe COVID-19 were enrolled from three hospitals of eastern China in Spring 2022.Viral shedding time(VST)was defined as either the day of the first positive test or the day of symptom onset,whichever was earlier,to the date of the first of two consecutively negative PCR tests.Patients from one hospital(Cohort I,n=2033)were randomly grouped into training and internal validation sets.Predictors of 14-day PCR conversion failure were identified and a nomogram was developed by multivariable logistic regression using the training dataset.Two hospitals(Cohort II,n=1596)were used as an external validation set to measure the performance of this nomogram.Results Of the 2033 patients from Cohort I,the median VST was 13.0(interquartile range:10.0-16.0)days;716(35.2%)lasted>14 days.In the training set,increased age[per 10 years,odds ratio(OR)=1.29,95%confidence interval(CI):1.15-1.45,P<0.001]and high Charlson comorbidity index(OR=1.25,95%CI:1.08-1.46,P=0.004)were independent risk factors for VST>14 days,whereas full or boosted vaccination(OR=0.63,95%CI:0.42-0.95,P=0.028)and antiviral therapy(OR=0.56,95%CI:0.31-0.96,P=0.040)were protective factors.These predictors were used to develop a nomogram to predict VST>14 days,with an area under the ROC curve(AUC)of 0.73 in the training set(AUC,0.74 in internal validation set;0.76 in external validation set).Conclusions Older age,increasing comorbidities,incomplete vaccinations,and lack of antiviral therapy are risk factors for persistent infection with Omicron variant for>14 days.A nomogram based on these predictors could be used as a prediction tool to guide treatment and isolation strategies.展开更多
基金supported by the Municipal Science and Technology Program of Wuwei City,China(WW2202RPZ037)the Fundamental Research Funds for the Central Universities in China(Grant No.lzujbky-2018-69).
文摘Objective Previous studies on the association between lipid profiles and chronic kidney disease(CKD)have yielded inconsistent results and no defined thresholds for blood lipids.Methods A prospective cohort study including 32,351 subjects who completed baseline and follow-up surveys over 5 years was conducted.Restricted cubic splines and Cox models were used to examine the association between the lipid profiles and CKD.A regression discontinuity design was used to determine the cutoff value of lipid profiles that was significantly associated with increased the risk of CKD.Results Over a median follow-up time of 2.2(0.5,4.2)years,648(2.00%)subjects developed CKD.The lipid profiles that were significantly and linearly related to CKD included total cholesterol(TC),triglycerides(TG),high-density lipoprotein cholesterol(HDL-C),TC/HDL-C,and TG/HDL-C,whereas lowdensity lipoprotein cholesterol(LDL-C)and LDL-C/HDL-C were nonlinearly correlated with CKD.TC,TG,TC/HDL-C,and TG/HDL-C showed an upward jump at the cutoff value,increasing the risk of CKD by 0.90%,1.50%,2.30%,and 1.60%,respectively,whereas HDL-C showed a downward jump at the cutoff value,reducing this risk by 1.0%.Female and participants with dyslipidemia had a higher risk of CKD,while the cutoff values for the different characteristics of the population were different.Conclusion There was a significant association between lipid profiles and CKD in a prospective cohort from Northwest China,while TG,TC/HDL-C,and TG/HDL-C showed a stronger risk association.The specific cutoff values of lipid profiles may provide a clinical reference for screening or diagnosing CKD risk.
基金the National Natural Science Foundation of China(No.82070604),the Shanghai Municipal Key Clinical Specialty(shslczdzk01103),and the"Chenguang Program"supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission.The study sponsors were not involved in the study design,data collection,data analysis,and data interpretation.
文摘Background With the variability in emerging data,guidance on the isolation duration for patients with coronavirus disease 2019(COVID-19)due to the Omicron variant is controversial.This study aimed to determine the predictors of prolonged viral RNA shedding in patients with non-severe COVID-19 and construct a nomogram to predict patients at risk of 14-day PCR conversion failure.Methods Adult patients with non-severe COVID-19 were enrolled from three hospitals of eastern China in Spring 2022.Viral shedding time(VST)was defined as either the day of the first positive test or the day of symptom onset,whichever was earlier,to the date of the first of two consecutively negative PCR tests.Patients from one hospital(Cohort I,n=2033)were randomly grouped into training and internal validation sets.Predictors of 14-day PCR conversion failure were identified and a nomogram was developed by multivariable logistic regression using the training dataset.Two hospitals(Cohort II,n=1596)were used as an external validation set to measure the performance of this nomogram.Results Of the 2033 patients from Cohort I,the median VST was 13.0(interquartile range:10.0-16.0)days;716(35.2%)lasted>14 days.In the training set,increased age[per 10 years,odds ratio(OR)=1.29,95%confidence interval(CI):1.15-1.45,P<0.001]and high Charlson comorbidity index(OR=1.25,95%CI:1.08-1.46,P=0.004)were independent risk factors for VST>14 days,whereas full or boosted vaccination(OR=0.63,95%CI:0.42-0.95,P=0.028)and antiviral therapy(OR=0.56,95%CI:0.31-0.96,P=0.040)were protective factors.These predictors were used to develop a nomogram to predict VST>14 days,with an area under the ROC curve(AUC)of 0.73 in the training set(AUC,0.74 in internal validation set;0.76 in external validation set).Conclusions Older age,increasing comorbidities,incomplete vaccinations,and lack of antiviral therapy are risk factors for persistent infection with Omicron variant for>14 days.A nomogram based on these predictors could be used as a prediction tool to guide treatment and isolation strategies.
