| Objectives1.Inferring the hidden transmission relationship and evaluating the transmission risk from the bridge population to young students based on the characteristics of the HIV molecular network.2.Developing and validating a risk scoring model for ART based on real-world data of ART in Guangxi and identifying the patients’factors that affect the therapeutic effect of ART according to the risk scores of predictors.3.Comparing the difference in the therapeutic effect of ART among PWH with CRF01＿AE,CRF07＿BC,and CRF08＿BC in Guangxi.Methods1.A total of 8639 HIV-1 protease and reverse-transcriptase genetic（pol）sequences in the HIV sequences database of our laboratory which were sampled from 1997 to 2020 and 206926 HIV-1 pol sequences in the Los Alamos National Laboratories were integrated.The molecular network was constructed,the characteristics of network structure were described,the proportion of local connection and the cross-regional connection was compared,and the influencing factors were analyzed.The homogenous correlation was represented by assortativity.Three network centrality metrics were introduced to estimate the level of risk of HIV transmission from nonstudent to young students.2.PWH who initiated ART from 2003 to 2018 were randomly divided into a development cohort and a validation cohort,at a ratio of 7:3.Cox proportional risk regression model（Cox model）was used to screen predictors and construct a survival risk scoring model.The survival risk scoring model was visualized using a nomogram.The performances of the survival risk scoring model were evaluated by discrimination,calibration,and net benefit.3.PWH with CRF01＿AE,PWH with CRF07＿BC,and PWH with CRF08＿BC were extracted from the database with the real-world data of ART and the HIV-1 pol sequences database in Guangxi.Temporal trends of CD4+T-lymphocyte（CD4）trajectories and proportions of virologic suppression were displayed by fitting the trend line with a scatter plot and locally weighted regression scatter smoothing method and were displayed by the area accumulation diagrams.The Cox model was used to adjust the effect of confounding factors and compare the hazard ratio of occurring clinical events among the three groups.Additionally,the characteristics of drug resistance mutation in HIV strains were analyzed.ResultsPartⅠCharacteristics of HIV molecular network in Guangxi1.After quality control,5996 HIV-1 pol sequences collected from Guangxi were obtained.CRF01＿AE was the most prevalent in Guangxi（51.8%）,followed by CRF07＿BC（20.4%）,CRF08＿BC（16.3%）,CRF55＿01B（3.7%）,unique recombinant forms（7.4%）,B（0.4%）and C（0.1%）.2.All 5996 HIV-1 pol sequences from Guangxi and 1020 HIV-1 pol sequences that were sampled from other regions and directly linked to Guangxi HIV-1 pol sequences were used to construct the HIV molecular network under a threshold of 0.5%pairwise genetic distance.A total of 1886 HIV-1 pol sequences from Guangxi fell within the HIV molecular network,with a clustering rate of31.5%.There were 531 clusters in the HIV molecular network,and the median size of clusters was 2（IQR:2–3）,ranging from 2 to 76.The median degree of nodes was 1（IQR:1–3）,ranging from 1 to 36.5062 edges were in the HIV molecular network.Internal provincial edges accounts for 51.6%,cross-provincial edges 48.2%and international edges 0.4%.3.The group of men who have sex with men（MSM）was 1.77 times（a OR=1.77,95%CI:1.48–2.12,P<0.001）likely to fall within the HIV network than the group of PWH acquired HIV from heterosexual contact（HET）.The group of MSM was 1.87 times（a OR=1.87,95%CI:1.42–2.48,P<0.001）likely to participate in cross-prefectures connection.The group of MSM was 3.28 times（a OR=3.28,95%CI:2.30–4.72,P<0.001）likely to participate in cross-regional links.4.MSM had a high homogenous correlation（assortativity=0.672）,and single people had a moderate homogenous correlation（assortativity=0.468）.The group of 25–49 years old（assortativity=0.239）and the group of 25–24 years old（assortativity=0.335）had a weak homogenous correlation,respectively.The group of private company/government employees（assortativity=0.114）,the group of freelancers/unemployed/retired（assortativity=0.143）,and students（assortativity=0.175）had an extremely weak homogenous correlation.5.A total of 241 nonstudents and 68 young students formed 43 molecular clusters,of which 132 nonstudents are directly linked to students and 109nonstudents are indirectly linked to students.The proportion of men in young students,nonstudents with direct connections to young students（DLS）,and nonstudents with indirect connections to young students（ILS）was 98.5%,92.4%,and 97.2%respectively.The median age of young students,DLS,and ILS were20（IQR:19–22）years,25（IQR:22–30）years,and 28（IQR:24–35）years.There was a significant difference between each group（adjusted by the Benjamini-Hochberg method:P student vs DLS<0.001,P student vs ILS<0.001,P DLS vs ILS<0.001）.The degree centrality,the betweenness centrality,and the eigenvector centrality of young students were 2.0（IQR:1.0–5.0）,0（IQR:0–2.0）,and 0.81（IQR:0.62–1.00）,respectively.The degree centrality,the betweenness centrality,and the eigenvector centrality of DLS were 4.0（IQR:2.0–8.0）,0.6（IQR:0–7.1）,and0.83（IQR:0.61–1.00）.The degree centrality,the betweenness centrality,and the eigenvector centrality of ILS were 2.0（IQR:1.0–4.0）,0（IQR:0–3.3）,and 0.18（IQR:0.01–0.64）,respectively.By pairwise comparison,the degree centrality(adjusted by Benjamini-Hochberg method:P student vs DLS<0.001,P student vs ILS=0.661,P DLS vs ILS<0.001)and the betweenness centrality（adjusted by Benjamini-Hochberg method:P student vs DLS=0.014,P student vs ILS=0.809,P DLS vs ILS=0.020）of DLS are the largest among the 3 groups,the eigenvector centrality of DLS is similar to young students and is higher than ILS（adjusted by Benjamini-Hochberg method:P student vs DLS=0.571,P student vs ILS<0.001,P DLS vs ILS<0.001）.PartⅡDevelopment and validation of survival risk scoring model for ART1.The development cohort included 25388 PWH,of which 1548 PWH died,with a mortality rate of 1.92/100 person-years（95%CI:1.83–2.02）.The validation cohort included 10880 PWH,of which 637 died,with a mortality rate of 1.85/100person-years（95%CI:1.71–2.00）.2.The baseline survival of 1,3,and 5 years in the survival risk scoring model were 95.0%,91.2%,and 87.9%respectively.A total of 14 predictors were selected.Age had the highest theoretical risk score（100 points）,followed by aspartate aminotransferase（58 points）,serum creatinine（43 points）,CD4（32 points）,total bilirubin（30 points）,route of infection（29 points）,body mass index（26 points）,hemoglobin（25 points）,leukocytes（23 points）,gender（20 points）,platelets（13points）,marriage（12 points）,Current HIV symptoms/signs（11 points）and opportunistic infection in the last 3 months（6 points）.3.In the development cohort,the area under the curve（AUC）of 1-,3-,and5 year was 0.81（95%CI:0.80–0.82）,0.79（95%CI:0.77–0.81）and 0.76（95%CI:0.69–0.83）,respectively.In the validation cohort,the AUC was 0.81（95%CI:0.79–0.83）,0.79（95%CI:0.76–0.81）,0.75（95%CI:0.69–0.80）,respectively.4.In the development cohort,the Brier score for 1-,3-,and 5-year was 0.03（95%CI:0.03–0.04）,0.05（95%CI:0.05–0.06）,and 0.07（95%CI:0.07–0.07）,respectively.In the validation cohort,the Brier score of 1-,3-,and 5-year was0.03（95%CI:0.03–0.03）,0.05（95%CI:0.05–0.05）,and 0.07（95%CI:0.06–0.07）,respectively.Both in the development cohort and the validation cohort,every curve was similar and close to the ideal calibration curve（45°diagonal）.5.The decision curve analyses showed that our survival risk scoring model had a high net benefit.PartⅢImpacts of HIV-1 subtype on the therapeutic effect1.A total of 4690 PWH with CRF01＿AE,635 PWH with CRF07＿BC,and625 PWH with CRF08＿BC were included in the study.The mean follow-up time was 6.1 years.2.The median baseline CD4 of the CRF01＿AE group,CRF07＿BC group,and CRF08＿BC group was 153（IQR:42–277）cells/μL,288（IQR:198–409）cells/μL,263（IQR:151–386）cells/μL,respectively.The difference between each group was significant,respective（adjusted by Benjamini-Hochberg method:P CRF01＿AE vs CRF07＿BC<0.001,P CRF01＿AE vs CRF08＿BC<0.001,P CRF07＿BC vs CRF08＿BC<0.001）.The median CD4 in the CRF01＿AE group was significantly lower than that in the CRF07＿BC group at 6,12,24,36,48,60,84,and 108 months(adjusted by Benjamini-Hochberg method:P CRF01AE vs CRF07BC<0.050).One year after the beginning of ART,CD4 in the CRF08＿BC group showed a decreasing trend.At the 84 months after treatment,the median CD4 in the CRF08＿BC group was significantly lower than that in the CRF01＿AE group（adjusted by Benjamini-Hochberg method:P CRF01＿AE vs CRF08＿BC<0.001）.3.The proportion of complete virologic suppression of the CRF08＿BC group was significantly lower than that of the CRF01＿AE group and the CRF07＿BC group at 12,36,48,and 60 months（adjusted by Benjamini-Hochberg method:P CRF01＿AE vs CRF08＿BC<0.050,P CRF07＿AE vs CRF08＿BC<0.050）.4.After adjusting for the covariables,compared with CRF07＿BC,CRF01＿AE was a negative factor of early immune recovery（a HR=0.740;95%CI:0.652–0.841,P<0.001）,immune recovery（a HR=0.787;95%CI:0.670–0.924,P=0.003）.In addition to be a negative relation to early immune recovery（a HR=0.826;95%CI:0.703–0.970,P=0.020）and immune recovery（a HR=0.764;95%CI:0.620–0.943,P=0.012）,CRF08＿BC is also a negative factor of complete virologic suppression（a HR=0.772;95%CI:0.680–0.877,P<0.001）and a risk factor of virologic failure（a HR=2.028;95%CI:1.437–2.863,P<0.001）and immunologic failure（a HR=1.441;95%CI:1.104–1.881,P=0.007）.When analyzing mortality,CRF01＿AE was a robust risk factor for overall mortality（a HR=1.528;95%CI:1.018–2.292,P=0.041）and adjusted mortality（a HR=1.787;95%CI:1.101–2.899,P=0.019）.However,the significantly higher risk effect of CRF08＿BC on mortality than CRF07＿BC could be found 12 months after ART initiation（a HR=1.835;95%CI:1.080–3.119,P=0.025）.5.The prevalence of drug resistance mutation of non-nucleoside reverse transcriptase inhibitor（NNRTI）in the CRF08＿BC group was 7.7%before treatment and 32.6%after treatment,respectively.The prevalence of NNRTI in the CRF01＿AE/CRF07＿BC group was 2.9%before treatment and 18.0%after treatment.The prevalence of NNRTI in the CRF08＿BC group before and after treatment was significantly higher than that in the Non-CRF08＿BC group,respectively（P pretreatment<0.001,P posttreatment<0.001）.Conclusions1.The epidemic of HIV in Guangxi is mainly driven by local transmission and domestic cross-regional transmission.MSM is an important transmission bridge population.The group of nonstudent young MSM is in the center of the young student clusters.They have a high risk of transmitting HIV to young students.2.The survival risk scoring model constructed based on the real-world data of Guangxi ART has the characteristics of high prediction accuracy and a large net benefit.The risk scores in the model suggested that the elderly and those with impaired liver and kidney function have a higher risk of death during the period of ART.3.PWH with CRF01＿AE has a low baseline CD4 and a slow immune recovery,and PWH with CRF08＿BC has a high risk of treatment failure.Both of them have a higher risk of death than PWH with CRF07＿BC. |
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