Derived from our original nomogram study by using the risk variables from multivariable analyses in the derivation cohort of 1383 patients with extranodal NK/T-cell lymphoma, nasal-type (ENKTCL) who were mostly treated with anthracycline-based chemotherapy, we propose an easily used nomogram-revised risk index (NRI), validated it and compared with Ann Arbor staging, the International Prognostic Index (IPI), Korean Prognostic Index (KPI), and prognostic index of natural killer lymphoma (PINK) for overall survival (OS) prediction by examining calibration, discrimination, and decision curve analysis in a validation cohort of 1582 patients primarily treated with non-anthracycline-based chemotherapy. The calibration of the NRI showed satisfactory for predicting 3- and 5-year OS in the validation cohort. The Harrell’s C-index and integrated Brier score (IBS) of the NRI for OS prediction demonstrated a better performance than that of the Ann Arbor staging system, IPI, KPI, and PINK. Decision curve analysis of the NRI also showed a superior outcome. The NRI is a promising tool for stratifying patients with ENKTCL into risk groups for designing clinical trials and for selecting appropriate individualized treatment. Extranodal natural killer/T-cell lymphoma, nasal-type (ENKTCL) is a heterogeneous disease with variable clinical features and prognoses –5. It isThe implementation of an optimal risk classification model for ENKTCL would provide significant advancement in prognostication and would improve outcomes for patients through refined stratification and more relevant information in clinical decision-making. Several models, such as the International Prognostic Index (IPI), Korea Prognostic Index (KPI), and prognostic index of natural killer lymphoma (PINK) have been validated in patients with ENKTCL –18. HoweAs described previously in detail , the ChiIn our original nomogram model , the risThe primary endpoint was overall survival (OS), defined as the time from the start of treatment to death from any cause or to the last follow-up. Progression-free survival (PFS) was defined as the time from the start of treatment to disease progression, relapse, or death. Survivals were estimated by the Kaplan–Meier method and compared with a log-rank test.http://www.r-project.org/).Predicted survival probabilities from the derivation cohort by applying the NRI to the baseline survival estimate at the individual level in the validation cohort, and averaging across each risk group . Then thn = 127).A CONSORT diagram describing the cohort selection is outlined , radiotherapy alone , or combined modality therapy  in our validation cohort. Of the patients who had received non-anthracycline–based chemotherapy, the majority  had received asparaginase- or gemcitabine-containing regimens, and the remaining 154 (11.6%) patients had received platinum- or etoposide-containing regimens. Of early-stage patients who had received definitive radiotherapy, most had received extended involved-site intensity-modulated radiotherapy (IMRT) or three-dimensional conformal radiotherapy (88.4%) and a ≥50 Gy dose (88.6%).The patients had received chemotherapy alone  of the 5-year OS for all patients was lower than that of the IPI (0.148), KPI (0.152), PINK (0.154), and Ann Arbor staging (0.149). Similarly, early-stage patients had higher IBS of the IPI (0.141), KPI (0.140), PINK (0.143), and Ann Arbor staging (0.141) than that of NRI (0.135). The corresponding prediction error curves of all models in the entire cohort and early-stage cohort were shown in Supplementary Fig. We used decision curve analysis to evaluate whether the models can guide treatment in the validation cohort. In the whole validation cohort, the NRI had better utility for clinical decision-making than the other models, with a risk probability of 0.11–0.64 Fig. . The NRIDespite several proposed ENKTCL classification models based on clinical data, there has been no systematic evaluation of these models to date. Using a large multicenter cohort of patients with ENKTCL from the CLCG and North American databases, we verified that a ready-to-use NRI, derived from an original visual nomogram , provideOptimization of risk stratification is important for facilitating prognoses and treatment decisions in a variety of lymphomas , 28–30. Treatment strategies differ notably between early-stage and advanced-stage ENKTCL. Radiotherapy is the backbone of curative intent for localized disease , 8, 11, Although we validated and compared the accuracy of the NRI model using systematic and effective methods, the study has some limitations. First, genomic classifiers were not used, and therefore could not be incorporated into the NRI. However, the NRI may be a promising backbone for more comprehensive risk models integrating molecular and biological markers in the future . Second,In summary, the NRI significantly improves prognostication with respect to the capability for discrimination and the effectiveness of clinical decision-making, and is particularly useful for early-stage patients in the era of modern chemotherapy and radiotherapy. This study provides the basis of prognostic stratification for designing prospective trials of risk-adapted therapies and surveillance strategies.Supplementary Figure legendsSupplementary Figure 1ASupplementary Figure 1BSupplementary Figure 1CSupplementary Figure 1DSupplementary Figure 2Supplementary Figure 3ASupplementary Figure 3BSupplementary Figure 3CSupplementary Figure 3DSupplementary Figure 4ASupplementary Figure 4B


Approximately 70% of mental health disorders appear prior to 25 years of age and can become chronic when ineffectively treated. Individuals between 18 and 25 years old are significantly more likely to experience mental health disorders, substance dependencies, and suicidality. Treatment progress, capitalizing on the tendencies of youth to communicate online, can strategically address depressive disorders.We performed a randomized controlled trial (RCT) that compared online mindfulness-based cognitive behavioral therapy (CBT-M) combined with standard psychiatric care to standard psychiatric care alone in youth (18-30 years old) diagnosed with major depressive disorder.Forty-five participants were randomly assigned to CBT-M and standard care (n=22) or to standard psychiatric care alone (n=23). All participants were provided standard psychiatric care , while participants in the experimental group received an additional intervention consisting of the CBT-M online software program. Interaction with online workbooks was combined with navigation coaching delivered by phone and secure text messaging.P=.01), Quick Inventory of Depressive Symptoms score , Beck Anxiety Inventory score , and Brief Pain Inventory score , while marginal differences were found for the Five Facet Mindfulness Questionnaire–Nonjudging subscale .In a two-level linear mixed-effects model intention-to-treat analysis, significant between-group differences were found for the Beck Depression Inventory-II score (difference –8.54, These results confirm that youth depression can be effectively treated with online CBT-M that can be delivered with less geographic restriction.Clinical Trials.gov NCT03406052; https://www.clinicaltrials.gov/ct2/show/NCT03406052 Approximately 70% of all mental health problems appear before 25 years of age and often become chronic when not treated or ineffectively treated . Such dag=0.27) [Cognitive behavioral therapy (CBT) is the best-validated psychotherapy , and in g=0.27) . Individg=0.27) .and psychiatric treatment as usual (TAU) was superior to psychiatric TAU. Control participants received pharmacotherapy only when deemed appropriate by treating psychiatrists, and the TAU comparison accounted for the standard use of and response to antidepressant medications.These findings motivated a focus on assessing online CBT in patients concurrently receiving standard psychiatric care to examine whether online CBT The key behavioral intervention in this study was online access to 24 CBT workbooks and 56 mindfulness instruction videos that supported metacognitive change and autonomic balance , which hNavigation coaching has been increasingly applied to support the adoption of evidence-based, health-related behaviors as demonstrated in adherence to cancer screening, and exercise and diet regimens . Our expOur study objective was to assess whether online CBT-M with weekly interactions with a coach navigator and standard psychiatric care was superior to standard psychiatric care alone (as workbooks and videos were never provided without coach navigator assistance). We hypothesized that intervention participants would demonstrate significant improvements in primary outcomes when compared to the waitlist controls who received only standard psychiatric TAU.The study was approved by the Research and Ethics Boards of the Centre for Addiction and Mental Health  and York University (Certificate number 2017–154) in Toronto, Canada, and was registered at ClinicalTrial.gov (NCT03406052). This included distinct software platform approval for all privacy and security requirements at CAMH. The study evaluated the efficacy of CBT-M to treat young adults (18-30 years of age) with major depressive disorder. Participants were identified from service wait lists at the CAMH by research coordinators and in the prescreening of new clinic referrals. The investigative team was informed about possible participant eligibility and the client’s clinician was notified. The clinician then asked the client if she/he was willing to meet with a study team member to explore participation. Information about the study was only shared once the clients agreed to meet for potential participation. A biostatistician (GT) performed electronic randomization of participants, assigning study IDs to intervention vs waitlist control participants. Information regarding each study ID with its respective group allocation was transferred onto cards placed in individually sealed, opaque envelopes. After a participant completed baseline questionnaires, the research coordinator opened the next envelope in the sequence to assign the group and respective study ID.The inclusion criteria were: (1) aged 18 to 30 years; (2) Beck Depression Inventory-2 (BDI-II) score of at least mild severity, with no upper limit (BDI-II score≥14) ; (3) MinAll participants were provided standard psychiatric care, operationally defined as one monthly session of TAU and mostly pharmacotherapy focused. Experimental participants received the additional CBT-M program content (workbooks and videos) accessed online through a software platform developed by NexJ Health, Inc. The platform, NexJ Connected Wellness (NCW), has unique properties that facilitated participant use. Interactions with the online workbooks were combined with navigation coaching , delivered as phone and text message exchanges. Each participant was also given a Fitbit-HR Charge 2 that assessed physical steps and 24-hour heart rate in 5-second durations combined with the NexJ Health, Inc software that permitted daily monitoring.The intervention content was built on two prior successful web-based CBT-M RCTs with students -17 and oThe primary outcome measure was the BDI-II , and theAll self-report measures and the HDRS-24 interviews were conducted at the same CAMH Ambulatory Service setting. The HDRS-24 interview rater was blinded to intervention and control conditions for the trial duration.We used a two-level linear mixed-effects model to compare the difference in the rate of change regarding outcome scores between the intervention and control groups, accounting for the repeated measurement nature of the data. A full information maximum-likelihood method was used to deal with missing data . Age, seData obtained from participants during study visits were deidentified and stored as electronic case reporting forms (CRFs) on the CAMH REDCap system, with the CRF paper copies stored in a secure, locked cabinet. Participant characteristics are summarized via descriptive statistics in A mean of 4.7 participants were enrolled per month. The intervention and TAU retention differed markedly as 91% (20/22) of intervention participants were retained compared to only 39% (9/23) of the TAU group . Of the 14 control dropouts, 9 dropped out shortly after the baseline assessment and 5 dropped out following completion of midterm, 3-month measures. Of the 2 intervention dropouts, both dropped out shortly after enrollment (prior to 3-month measures). The between-group retention differences were significant at 3 months (P=.04) and 6 months (P=.001).In the two-level linear model intention-to-treat analysis, between-group BDI-II, QIDS, BAI, BPI, and FFMQ–Nonjudging subscale differences were statistically significant .d=1.90 and Hedges g=1.82) and large on the QIDS (Cohen d=1.43 and Hedges g=1.38). All effect sizes were large and two were at or above 1.6 (Cohen d), typically calculated as two times a large effect size.In the within-group differences, participants who completed the intervention (n=20) demonstrated significant reductions in depressive and anxiety symptoms as measured by changes in BDI-II (P<.001), BAI (P<.001), QIDS (P<.001), and (blinded) HDRS (P<.001) scores from pre- to postintervention . The effThe online CBT-M intervention was beneficial, given significant between-group differences in depression , anxiety (BAI), pain (BPI), and mindfulness . Other notable between-group observations involved a 9% dropout rate in the intervention group that significantly differed from the 61% dropout rate in the TAU control group (the 61% dropout rate was estimated at ~14% above the mean for CAMH TAU). This difference suggests that the intervention had positive effects on participant retention. The intervention sample included a large subgroup with severe depression , 50% of whom were in remission (BDI<14) at the final (6-month) assessment. Of the 6 participants who exhibited moderate depression, 5 achieved remission, and of the 3 study participants with mild depression, 2 achieved remission.Given the CBT-M intervention, it was notable that between-group differences were found in the Nonjudging subscale of the FFMQ that assesses the excess self-critical thinking associated with distress . InterveSignificant between-group differences were found in self-reported chronic pain as indicated in the BPI scale. The inclusion of a pain assessment reflects recent findings about the high comorbidity prevalence in depression with respect to chronic pain  and the Although attrition in psychiatric treatment has been linked to early improvements associated with medication changes , this exSignificant study strengths included the control comparison with a standard-care psychiatry intervention, delivered at the same institution, independently versus in combination with the experimental behavioral intervention. This resulted in detailed records of how pharmacological and behavioral interventions interacted, assisting estimations of independent and combined benefits. The study further controlled for the intervention-related placebo effects observed in 35%-40% of RCT participants exposed to TAU conditions . This waIn recent meta-analyses focused on CBT-M delivery for depression, multiple CBT modalities have been assessed, notably individual, group, telephone-based, and guided self-help, all of which appear to be significantly more effective than waitlist and care-as-usual control conditions, and unguided self-help . These aKey limitations of our study include a lack of participant blinding and the limited power associated with a small sample size. However, the HDRS assessment was undertaken by a rater blinded to group allocation. Although the between-group differences on the HDRS were trending toward significance (P=.09), they were not statistically significant. A final limitation is that the study psychiatrists administering TAU to control and intervention participants were not blind to which participants were in the intervention versus control groups, and this might have led to biased treatment.Future studies comparing CBT-M and standard-care psychiatry would benefit from larger sample sizes, more complete blinding, and extended follow up after intervention conclusion . Despite these limitations, the results indicate that online CBT-M combined with TAU psychiatric treatment was an effective treatment for major depressive disorder and led to significantly greater reductions in BDI-II scores than TAU psychiatry alone.


LDLR), apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9) and low density lipoprotein receptor adaptor protein 1 (LDLRAP1) genes in some selected African populations. Nineteen SNPs were selected from publicly available African whole genome sequence data based on functional prediction and allele frequency. SNPs were genotyped in 1000 participants from the AWI-Gen, study selected from the extremes of LDL-cholesterol level distribution (500 with LDL-cholesterol>3.5 mmol/L and 500 with LDL-cholesterol<1.1 mmol/L). The minor alleles at five of the six associated SNPs were significantly associated (P<0.05) with lower LDL-cholesterol levels: LDLRAP1 rs12071264  and rs35910270 ; APOB rs6752026 ; LDLR: rs72568855 ; and PCSK9 rs45613943 . The minor allele of the sixth variant was associated with higher LDL-cholesterol levels: APOB rs679899 . A replication analysis in the Africa America Diabetes Mellitus (AADM) study found the PCSK9 variant to be significantly associated with low LDL-cholesterol levels (Beta = -0.10). Since Africans generally have lower LDL-cholesterol levels, these LDL-cholesterol associated variants may be involved in adaptation due to unique gene-environment interactions. In conclusion, using a limited number of potentially functional variants in four genes, we identified significant associations with lower LDL-cholesterol levels in sub-Saharan Africans.Non-communicable diseases, including cardiovascular diseases (CVDs), are increasing in African populations. High serum low density lipoprotein cholesterol (LDL-cholesterol) levels are a known risk factor for CVDs in European populations, but the link remains poorly understood among Africans. This study investigated the associations between serum LDL-cholesterol levels and selected variants in the low density lipoprotein receptor ( The incidence of non-communicable diseases (NCDs) is increasing in Africa  and in 2LDLR, APOB, PCSK9 or LDLRAP1 [Investigating genetic associations with LDL-cholesterol levels among sub-Saharan Africans is the focus of this study. LDL-cholesterol levels have a multifactorial aetiology, where environmental factors and variants in many different genes contribute to determining levels of LDL-cholesterol ,7. We aiLDLR, APOB, PCSK9 and LDLRAP1 [Several genome-wide association studies (GWASs) have been performed to identify variants associated with LDL-cholesterol levels –17. Howe LDLRAP1 . Studies LDLRAP1 –25. HoweAfrica is generally a genetic data-scarce region; therefore, large studies on the genetic aetiology of LDL-cholesterol level variation in African populations are few. The genes chosen for this study were based on data from previous associations in European populations and we genotyped selected variants. Ideally, in an African setting, sequence based-approaches are preferable as they have the potential to identify novel African genetic contributions to the variation in lipid levels.The aim of this study was to investigate whether selected genetic variants in genes previously associated with LDL-cholesterol levels show association in African populations. We identified five variants associated with low LDL-cholesterol levels and one variant associated with high LDL-cholesterol levels. The African American Diabetes Mellitus (AADM) study was used as a replication cohort ,27. GiveAfrica, Wits-INDEPTH Partnership for GENomic studies), an established project investigating genomic and environmental factors that influence cardio-metabolic disease risk in rural and urban Africans [Participants came from six sites in four African countries: Kenya, South Africa, Ghana and Burkina Faso. They were selected from the H3Africa AWI-Gen Collaborative Center  was calculated from height and weight measurements. Classification of diabetes was guided by the standards set by the American Diabetes Association [The data collection for the AWI-Gen study is described by Ali, l., 2018 . Brieflyociation . It was ociation , where fLDLR, APOB, PCSK9 and LDLRAP1 were identified using publicly available whole genome sequence (WGS) data from African participants in the 1000 Genomes Project (KGP) and the African Genome Variation Project (AGVP). The variants were selected on the basis of in silico functional prediction and allele frequency in African populations and genotyped in a group of 1000 AWI-Gen participants, half with low and half with high LDL-cholesterol levels. The variants were tested for association in a case:control study design with high (cases) compared to low (controls) LDL-cholesterol levels, correcting for multiple testing and considering potential confounders.Variants in The four genes are known to be associated with monogenic FH, and in some cases also with multifactorial LDL-cholesterol levels. Variants in these genes were extracted in VCF file format from WGS data of African population samples available from KGP and the AGVP. A region including the genomic sequence of each gene, plus a 1000bp flanking region on either side, was screened for variants. A total of 975 individuals from eight African populations were included in the investigation: 655 WGS from KGP and 320 WGS from AGVP.A total of 3541 variants were identified. The variants were functionally annotated using CADD  and Ense2>0.4) [To increase the power of the association analysis, only variants that were observed in at least six of the eight populations were chosen. Furthermore, the variants were filtered in two stages. Firstly, variants with at least one deleterious score, and being either a missense, start/stop, gain/loss, exonic or regulatory variant, were selected. Secondly, variants with a minor allele frequency (MAF) in African populations (according to dbSNP) of between 10% and 45% were selected to boost the power of the analysis. Linkage disequilibrium (LD) was assessed for the selected variants and no pairs were in strong LD  .The Agena Bioscience MassARRAY genotyping platform was used to genotype 19 selected SNPs. This service was provided by Inqaba Biotech in Pretoria, South Africa. The DNA used for the genotyping was obtained from the Biobank based at the Sydney Brenner Institute for Molecular Bioscience (SBIMB), Johannesburg, South Africa. The DNA concentration for each of the 1000 samples was normalised to ~30 ng/μl and ~10 μl DNA was provided. The MassARRAY system software was used to test whether variants of interest are likely to be successfully genotyped.-5 and MAF <0.01 were excluded from further analysis. Quality control measures were derived from Marees et al., (2018) [PLINK 1.9  was used, (2018)  with sliWe used logistic regression analysis using 14 SNPs in the four genes of interest. Associations were corrected for multiple testing using the Benjamini-Hochberg method. All associations with P<0.05 after adjustment were considered significant. The odds ratios (OR) and 95% confidence intervals (CI) were calculated using the major allele (A2) as a reference for all associations. The logistic regression analysis was adjusted for variables that were identified as potential covariates, namely: sex, BMI, fasting glucose levels and geographical origin of participants.A simple additive PRS for lower LDL-cholesterol was calculated using six variants (P<0.05) that were significant after adjusting for covariates . A frequ−6 and MAF >0.01. SNPs that passed quality control were used as the basis for imputation. Imputation of all samples was done with the African Genome Resources Haplotype Reference Panel using the Sanger Imputation Server [Replication analysis of the six variants associated with LDL-cholesterol was performed in the AADM study. This ongoing genetic epidemiology study of diabetes and related traits has been described previously ,27. Brien Server . Analysin Server , the firn Server . StatistA Chi-squared test was used to determine whether there was a significant difference between males and females with regard to LDL-cholesterol levels. The variables age, BMI, fasting glucose levels and LDL-cholesterol levels were all tested for normality. None of the variables fit a normal distribution, and therefore a Mann-Whitney U test was used to determine whether there was a significant difference between the cases and controls. STATA was used for these statistical tests .There are more females in the low LDL-cholesterol group, and the high LDL-cholesterol group was characterised by a higher BMI and higher fasting glucose levels .The distribution of the LDL-cholesterol values in the group with low LDL-cholesterol ranged from 0.4–1.2 mmol/L. The high LDL-cholesterol group had LDL-cholesterol levels ranging from 3.7–14.2 mmol/L. Two individuals were excluded from the analyses due to very high fasting LDL-cholesterol levels of 14.2 mmol/L and 8.23 mmol/L as they may have a monogenic FH aetiology.In total, 29 variants were selected for genotyping, but only 19 remained after assay design for final genotyping. Of these, five variants failed quality control parameters , leaving 14 variants to be analysed  and LDLRAP1 rs12071264 (OR: 0.54) and rs35910270 (OR: 0.78), PCSK9 rs45613943 (OR: 0.72), LDLR rs72658855 (OR: 0.47). Only one variant was significantly associated with increased levels of LDL-cholesterol: APOB rs679899 (OR: 1.41). A forest plot was generated using the 14 variants from An allelic association  found siIn the PRS, “risk” is depicted by lower LDL-cholesterol . TherefoLDLRAP1), did not pass quality control filters in the replication dataset and was not included in the replication analysis. PCSK9 variant rs45613943 was associated with lower LDL-cholesterol in the AADM data . There was no association between rs12071264 or rs72658855 with LDL-cholesterol in the AADM study.We evaluated our associated variants in an independent sample of West and East Africans drawn from the AADM study ; variants in APOB have been known to cause both low and high LDL-cholesterol levels [PCSK9 [LDLRAP1 variants were associated with low LDL-cholesterol levels. LDLRAP1 rs12071264 is located in intron 5, close to a splice site [LDLRAP1 rs35910270 is in the 3’UTR and is common in both European (47%) and African (42%) populations.Mutations in the genes investigated also contribute to monogenic dyslipidaemias. Deleterious mutations in se of FH ,45,46; ll levels ,47–49; as [PCSK9 –52. In tice site , and couAPOB were associated with LDL-cholesterol levels. rs6752026, a missense variant in exon 5, was associated with lower LDL-cholesterol levels. The proline to serine change is predicted to be deleterious by both SIFT and PolyPhen, however, it has not been previously associated with LDL-cholesterol levels. This variant occurs at very low frequencies in European populations (~0.1%) but is common in Africans (~11%). The second, rs679899, is an alanine to valine missense variant associated with higher LDL-cholesterol levels. It is common in European populations (86%), but less common in African populations (13%). The evidence for the deleterious nature of the variant is conflicting with PolyPhen predicting it to be possibly damaging, while SIFT predicts it to be tolerated. One variant in PCSK9, rs45613943, was associated with low LDL-cholesterol levels and was also significantly associated with low LDL-cholesterol levels in an African replication cohort , strengthening its association with low LDL-cholesterol levels across several African populations. The variant allele occurs at low frequencies in European populations (5%) but is more common in Africa (~29%). This is likely to be a loss of function variant, as loss of function of the PCSK9 enzyme increases the number of LDL receptors returning to the surface of the cell, but further functional studies would be required to assess the effect of the variant on the function of the protein. Interestingly, one variant in LDLR, rs72568855, was associated with low LDL-cholesterol levels and this variant has not been reported in European populations.Two variants in PCSK9 variant, rs45613943, is a regulatory variant. This may decrease transcription, resulting in less production of protein and an increased turnover of the LDL receptors, thereby reducing the serum levels of LDL-cholesterol. The APOB rs679899 rare allele was the only variant that was significantly associated with high LDL-cholesterol levels in this study.Associations with four of the variants would not have been detected in studies with participants from Europe as they appear to be African-specific or extremely rare in Europeans. In all cases, the rare allele was associated with lower LDL-cholesterol. This may suggest that the allelic variants, excluding rs45613943, have a gain of function impact, or are in close LD with functional variants that contribute to decreased LDL-cholesterol levels in Africans. The Participants from East, West and South Africa have an LDL-cholesterol distribution favouring lower LDL-cholesterol levels and interestingly, the rare alleles at five loci  showed aThe LDL-cholesterol distribution in African populations is generally considered to be lower, compared to non-African populations; therefore, it is counter intuitive that the common alleles at the five associated variants would associate with higher LDL-cholesterol levels in Africans. For five of the six significantly associated variants identified in this study, the major alleles were associated with higher LDL-cholesterol levels. Although this may suggest that the normal distribution of LDL-cholesterol levels in African populations would be expected to be higher, the rare alleles in rs6752026, rs12071264, rs35910270 and rs72658855 may have some gain of function effect that associates them with lower LDL-cholesterol levels.In addition, gene-environment interactions could play a role, and low-fat diets and high physical activity could also contribute to lower LDL-cholesterol levels in African populations. However, as African populations become more urbanised, a more western lifestyle will follow, which could increase LDL-cholesterol, especially in those with a genetic predisposition for high LDL-cholesterol levels .Detecting hyperlipidaemia early in individuals and administering treatment and lifestyle changes can reduce the number of CVD related events, and subsequently reduce the health burden among Africans . PrecisiEven though the AWI-Gen participants were all African, they were multi-ethnic with an uneven distribution across geographic regions in West, East and South Africa. This could have caused a bias due to population sub-structure, despite adjusting for study site (as proxy for ethnicity) in the logistic regression analysis. Nonetheless, since lipid data on African populations are limited, this study serves as a starting point for subsequent research endeavours on understanding genetic associations with LDL-cholesterol levels in African populations.Due to funding limitations, only a small number of variants were tested per gene. Ideally, a more representative set of markers to capture all the haplotype blocks across each gene (to account for lower linkage disequilibrium in African populations) would have provided a more accurate indication of the association of variants in these genes with LDL-cholesterol levels. A GWAS analysis for LDL-cholesterol in the AWI-Gen study is in progress.This study used a basic logistic regression approach to analysing variants, unlike the linear mixed model used by the replication study. Glucose levels were not adjusted for in the replication study.LDLR, APOB, PCSK9 and LDLRAP1) known to be involved in lipid metabolism. The study identified five variants associated with low LDL-cholesterol levels and one variant associated with high LDL-cholesterol levels. Using a different cohort from West Africa, we replicated the association of PCSK9 rs45613943C with low LDL-cholesterol. These data suggest allelic association differences with LDL-cholesterol levels across African populations, which may be influenced by gene-environment interactions.In selected African populations in four sub-Saharan African countries, we investigated the association of variants in four genes (S1 File(MAP)Click here for additional data file.S2 File(PED)Click here for additional data file.S3 File(TXT)Click here for additional data file.


The risk model was developed using Lasso regression. The clinical data were analyzed by univariate and multivariate Cox regression analysis. Gene Ontology (GO) and KEGG enrichment analysis were performed in the DEGs genes between the high-risk and low-risk subgroups. We identified six survival-related genes, EMP1, TPM1, NRP2, FGFR1, CAVIN1, and LATS2, found in the DEG analyses of both, tumor-paracancerous and paracancerous-normal differentially expressed data sets. Then, the patients were classified into two clusters, which can be distinguished by specific clinical characteristics. A three-gene risk prediction model  was constructed in patients within cluster 1. The model was applied to categorize cluster 1 patients into high-risk and low-risk subgroups. The prognostic risk score was considered as an independent prognostic factor. The six identified survival-related genes can be used in molecular characterization of a specific subtype of bladder cancer. This subtype had distinct clinical features of T (topography), N (lymph node), stage, grade, and survival status, compared to the other subtype of bladder cancer. Among the six identified survival-related genes, three-genes, EMP1, FGFR1, and CAVIN1, were identified as potential independent prognostic markers for the specific bladder cancer subtype with clinical features described.This study was conducted to identify genes that are differentially expressed in paracancerous tissue and to determine the potential predictive value of selected gene panel. Gene transcriptome data of bladder tissue was downloaded from UCSC Xena browser and NCBI GEO repository, including GTEx (the Genotype-Tissue Expression project) data, TCGA (The Cancer Genome Atlas) data, and GEO (Gene Expression Omnibus) data. Differentially Expressed Genes (DEGs) analysis was performed to identify tumor-DEGs candidate genes, using the intersection of tumor-paracancerous DEGs genes and paracancerous-normal DEGs genes. The survival-related genes were screened by Kaplan–Meier (KM) survival analysis and univariable Cox regression with the cutoff criteria of KM < 0.05 and cox  The most common pathological type of bladder cancer is transitional cell carcinoma, with non-muscle-invasive bladder cancers (NMIBCs), and muscle-invasive bladder cancers (MIBCs). The treatment outcomes are very different for bladder cancer patients with MIBCs and NMIBCs2. MIBCs are usually associated with less favorable prognosis, lower 5-year survival rates, cancer progression, and metastasis. Patients with NMIBCs have much higher expected 5-year survival rate. However, NMIBCs could progress into MIBCs3. We conducted in silico study to improve the accuracy in prediction of 5-year patient survival rates for both types of bladder cancer.Bladder cancer is a malignant tumor with high morbidity and high mortality. The steadily-rising incidence and prevalence make bladder cancer one of the most common urogenital cancers in the word. There were 549,393 new cases and 199,922 deaths reported worldwide in 20184. Paracancerous tissue, the tissue adjacent to a solid tumor, is often used as a histologically normal, control sample in research studies on tumors. However, a recent study reported that the paracancerous tissue had quite distinct characteristics compared to either tumor tissue or histologically normal healthy tissue, suggesting a unique intermediate state between healthy tissue and tumor5. Additional essential information might be obscured when paracancerous tissue was used as a control instead of using healthy normal tissue.Analysis of differentially expressed genes between conditions is an integral part of understanding the molecular basis of phenotypic variation in biology, including diseasesIn this study, we aimed to understand the differences in gene expression between tumor-adjacent and healthy tissue, and to determine potential prognostic value of the genes differentially expressed in paracancerous tissue. We first obtained the survival-related genes from RNA expression interaction of tumor-paracancerous DEGs genes and paracancerous-normal DEGs genes. Subsequently, we classified the bladder cancer patients with six survival-related genes by the consistent clustering algorithm and Lasso cox regression. The clusters and subgroups of patients were systematically analyzed with their clinicopathological features, and the difference between subgroups was analyzed by GO and KEGG enrichment.For gene transcriptome data, we analyzed a total of 411 cases of TCGA bladder cancer, 19 cases of TCGA paracancerous tissue, nine cases of GTEx healthy bladder, 36 cases of GEO bladder cancer, 29 cases of GEO paracancerous tissue, and five cases of GEO healthy bladder. There were only 345 cases of available TCGA clinical information. Among the clinical information, the metastasis could not be measured in more than half of the cases (176 cases). No available clinical information on GEO data was found.Using Limma and edgeR packages, 1413 tumor-paracancerous DEGs RNA sequences and 3223 paracancerous-normal DEGs RNAs were identified in the TCGA-GTEx dataset, and 771 tumor-paracancerous DEGs RNAs and 2963 paracancerous-normal DEGs RNAs in the GEO dataset. After overlapping all data, a total of 60 genes were selected as potential tumor-DEGs genes.p < 0.001).The expression of six survival-related genes in the TCGA database was shown in Fig. Based on the expression of the survival-related genes in TCGA, patients were clustered into two groups by consistent clustering Fig. . The prip < 0.001). The overall survival rate of cluster 2 was significantly higher than cluster 1 , while CAVIN1 was less expressed in the high-risk group (p < 0.05).The clinical characteristics and the expression of EMP1, FGFR1, and CAVIN1 were also compared between the two subgroups Fig. . The subp values of risk score were all <0.001, and the HR values were >1.The Hazard ratio (HR) of different clinical features (with the exception of M) in the two subgroups was further analyzed. HR of grade and its 95% confidence interval could not be obtained because there was only high grade in this cluster. As shown in Fig. p-value < 0.05.Eleven DEGs genes were selected from the differential gene analysis between the high-risk and low-risk subgroups of the cluster 1 Fig. . After t3. Most research focused on bladder cancer has been carried out using paracancerous tissue as normal control. However, it has been suggested that some additional information might be obscured when using paracancerous tissue as a control5.Bladder cancer is one of the most common urogenital cancers in the word. It carries a high risk of morbidity and mortality and is one of the main causes of death. It is acknowledged that NMIBCs has a higher survival rate than MIBCs, and the survival rate increases when it progresses to MIBCsThe goal of our study was to reveal the potential value of paracancerous tissue in predicting the prognosis of bladder cancer patients. Sixty DEGs were identified in paracancerous tissue comparing to both, tumor tissue and normal control. Since the GEO data had no clinical information, the survival analysis and clinical correlation analysis were conducted in TCGA data. Lastly, we identified six survival-related genes  and used this gene-set to group the bladder cancer patients into two clusters, while three survival-related genes  were used to estimate the prognostic risk for the patients grouped in cluster 1.11. EMP1 belongs to the peripheral myelin protein 22-kDa (PMP22) gene family. It promotes glioblastoma progression through the PI3K/AKT/mTOR signaling pathway6, and the unusual expression of EMP1 has been revealed in most tumor types12. TPM1 gene is a member of a tumor-associated protein family (the tropomyosin family) - it plays an important role in tumor-specific variations of actin filament aggregation via stress fiber modulation and actin cytoskeleton modification7. LATS2 gene belongs to the LATS tumor suppressor family. It encodes a serine/threonine protein kinase, reported to be involved in Hippo signaling pathway11. Similarly, the essential caveolar gene CAVIN1 is also involved in the Hippo pathway10. It appears the Hippo signaling pathway might be associated with the particular cluster 1 of bladder cancer. However, further experimental verification is needed.All six selected survival-related genes  were reported to play a positive role in disseminating cancer cells or invasion in many kinds of cancers14. NRP2 is a member of the membrane-associated neuropilin family and has been reported to be expressed during macrophage differentiation and facilitated tumor growth8. The NRP2 gene is reported as a prognostic indicator for reduced survival in bladder cancer patients15. FGFR1 is a member of the fibroblast growth factor receptor (FGFR) family, and is known to be involved in the progression of different cancer types, including bladder cancer9.Moreover, NRP2 and FGFR1 expressions were explicitly associated with cancer survival in bladder cancerp < 0.001, Fig. p < 0.001, Fig. 11.In our analysis, all six survival-related genes were expressed at significantly lower levels in tumor tissue p < 0.00, Fig. 1.p < 0.001) and relative lower expression of CAVIN1 (p < 0.05) in the high-risk subgroup suggested that the risk was associated with altered expression of these three genes. The results of univariate and multivariate Cox regression analysis indicated that the risk score obtained by our model can be used as an independent prognostic factor for overall survival in a specific cluster of bladder cancer patients  and HPGD (15-hydroxyprostaglandin dehydrogenase) were involved in the negative regulation of cell cycle. While the component of extracellular exosome were associated to the expression levels of TMPRSS2 (transmembrane serine protease 2), KRT13 (keratin 13), IGF2 (insulin-like growth factor 2), MFAP4 (microfibril associated protein 4), GDF15 (growth differentiation factor 15), and HPGD.16. Inhibition of the gene expression of IGF2 and IGF2-mediated PI3K/AKT/mTOR signaling pathway could suppress the development of bladder cancer17. DNA methylation of GDF15 was significantly high in bladder cancer. Therefore, this gene could be used as an epigenetic biomarker to identify bladder cancer18. DNA demethylation of GDF15 would upregulate its expression, which led to a suppression of cell proliferation, invasion, and tumorigenesis in bladder carcinoma cells19. It was also reported that methylation of KRT13 had a close relationship with high-grade non-invasive bladder cancer20. That is to say, lower expressions of IGF2, GDF15, and KRT13 was associated with lower cell proliferation, invasion, and tumorigenesis of bladder cancer. These findings were consistent with our results, in which IGF2, GDF15, and KRT13 expression were significantly decreased in the low-risk subgroup. Interestingly, it was reported that an increase of HPGD would suppress cell proliferation and invasion in bladder cancer21, which implied that patients with a high expression of HPGD might have a better survival rate. However, in our analysis, an increased expression of HPGD was found in the high-risk subgroup, with a worse overall survival rate.Among these enriched genes, IGF2 is reported to be frequently overexpressed in bladder cancerThe current study has identified promising models for characterization of a specific subtype of bladder cancer and independent estimation of the prognostic risk in this particular subtype. However, the generalizability of these results is subject to evident limitations. One limitation of this study is that our sample size for normal control was too small, some potential association could not be revealed. Another source of limitation is that we did not have experimental verification for the findings in this study. Therefore, further research with larger sample sizes of normal control and more experiments is warranted.While there are limitations in our study, there are also undoubted strengths. We do believe that this set of six identified survival-related genes could be a useful tool for identification of a particular subtype of bladder cancer. This subtype of bladder cancer had quite different clinical features of T, N, stage, grade, and survival status from the other subtype of bladder cancer. The survival rate of patients was negative related to the expressions of the six survival-related genes. In this particular subtype, the prognosis of bladder cancer can be independently evaluated by a risk model composed of EMP1, FGFR1, and CAVIN1 genes. The high-risk and low-risk subgroups, which were classified by the 3-gene prognostic factor model, might have a different biological process on the negative regulation of cell cycle and a different cellular component in extracellular exosome. These might be the reason account for their different prognostic factors.22. The latest clinical data of TCGA was downloaded from GDC (Genomic Data Commons), and patients with unavailable clinical information were excluded. After combining the data from GTEx and TCGA, genes were selected by FPKM > 0 for further analysis.The Fragments per Kilobase of transcript per Million mapped reads (FPKM) for the bladder cancer RNA-Seq data from TCGA and GTEx were downloaded from the UCSC Xena browser platform23. Datasets were merged according to the Ensembl IDs. The filtering criteria for the merged dataset were raw counts > 0 and CPM > 1. Next, the gene annotation was performed with “hsapiens_gene_ensembl” dataset by the biomaRt R package25.GEO RNA-Seq raw data of bladder cancer (GSE133624) and normal healthy bladder in GSE120795 were downloaded from the NCBI GEO SRA database. After quality control for the sequence data, raw counts were obtained by alignment to “Homo_sapiens.GRCh38.96.gtf” by the read-count program featureCounts26 and edgeR  R package28 were used to identify the tumor-paracancerous DEGs genes and paracancerous-normal DEGs genes respectively in the FPKM and count data. In the screening, the FDR value < 0.05 and |logFC | > 1 cutoff criterion was applied. Tumor-DEGs genes were obtained by overlapping the tumor-paracancerous DEGs genes and paracancerous-normal DEGs genes from TCGA-GTEx and GEO datasets. The high-low risk DEGs genes (DEGs genes between high-risk subgroup and low-risk subgroup) were also identified by the Limma package, using FDR value < 0.05, and |logFC | > 1 cutoff criteria.The Limma (Linear Models for Microarray Data) R package30. The cutoff criterion was set to KM < 0.05 and cox p-value < 0.05 for screening of the survival-related gene at overall survival.Patient’s survival time and status, combined with tumor-DEGs genes, were used for the KM survival analysis and univariable Cox regression, performed with survival R package31 was executed to evaluate the stability of clustering by the consistent clustering algorithm. Small clusters were excluded in the clustering data. The final clustering number of samples was determined according to the slow growth rate of cumulative distribution function (CDF) value and high correlation within the group.ConsensusClusterPlus R package26. The ggplot2 R package32 was used to visualize the results.PCA for all Tumor-DEGs genes was performed by Limma R package33. The risk genes were identified among the survival-related genes through the minimum standard, and their coefficients were calculated. The best penalty parameter λ, related to the minimum ten times cross-verification in the training set, was selected to obtain the risk score. Consistent with the median risk score, patients were further divided into high-risk and low-risk subgroups. Using the survivalROC R package34, the area under the Receiver Operating Characteristic (ROC) curve was calculated to detect the accuracy of the predictive survival model35.For the six survival-related genes, the Lasso regression algorithm was executed for each of the two patient clusters to develop a potential risk model and to evaluate the risk characteristics by using the glmnet R packageχ2 test. The potential independent prognostic factor was explored in stage, gender, age, TNM  classification, and risk score by univariate and multivariate Cox regression analysis. These programs were executed using the survival R package30.The survival analysis was performed and the survival curves in different groups were compared by the log-rank test. The prognostic factors  and the risk genes in those groups/subgroups were investigated and compared by the http://david.abcc.ncifcrf.gov/home.jsp)37.Biological function of high-/low-risk DEGs genes of cluster 1 was evaluated with GO and KEGG enrichment analyses, performed using DAVID Functional Annotation Bioinformatics Microarray Analysis method .The expression of survival-related genes in tumor tissues and paracancerous tissues was compared by one-way ANOVA. The clinical characteristics and survival-related genes of different groups were compared by the


Are there racial differences in receipt and duration of conservative management  among veterans with low-risk and intermediate-risk prostate cancer?In this cohort study of 51 543 veterans with low-risk and intermediate-risk prostate cancer, African American veterans were less likely than White veterans to receive conservative management, and African American patients who received conservative management were more likely to receive definitive therapy within 5 years of diagnosis.The findings of this study suggest that conservative management for low-risk and intermediate-risk prostate cancer may be less durable for African American veterans compared with White veterans. This cohort study examines the association of African American race with the receipt and duration of conservative management  in the Veterans Health Administration from 2004 to 2018. Conservative management  is a guideline-based strategy for men with low-risk and intermediate-risk prostate cancer. However, use of conservative management is controversial for African American patients, who have worse prostate cancer outcomes compared with White patients.To examine the association of African American race with the receipt and duration of conservative management in the Veterans Health Administration (VA), a large equal-access health system.This cohort study used data from the VA Corporate Data Warehouse for 51 543 African American and non-Hispanic White veterans diagnosed with low-risk and intermediate-risk localized node-negative prostate cancer between January 1, 2004, and December 31, 2013. Men who did not receive continuous VA care were excluded. Data were analyzed from February 1 to June 30, 2020.All patients received either definitive therapy  or conservative management .Receipt of conservative management and (for patients receiving conservative management) time from diagnosis to definitive therapy.P < .001) and intermediate-risk disease  were less likely to receive conservative management than White veterans. Compared with White veterans, African American veterans with low-risk disease  and intermediate-risk disease  who received conservative management were more likely to receive definitive therapy within 5 years of diagnosis .The median (interquartile range) age of the 51 543 veterans in our cohort was 65 (61-70) years, and 14 830 veterans (28.8%) were African American individuals. Compared with White veterans, African American veterans were more likely to have intermediate-risk disease (18 988 [51.7%] vs 8526 [57.5%]), 3 or more comorbidities (15 438 [42.1%] vs 7614 [51.3%]), and high disability-related or income-related needs (9078 [24.7%] vs 4614 [31.1%]). Overall, 20 606 veterans (40.0%) received conservative management. African American veterans with low-risk disease (adjusted relative risk, 0.95; 95% CI, 0.92-0.98; In this study, conservative management was less commonly used and less durable for African American veterans than for White veterans. Prospective trials should assess the comparative effectiveness of conservative management in African American men with prostate cancer. Among men who received conservative management, we also examined race-based differences in time to definitive therapy, a metric assessing the duration and effectiveness of conservative management.STROBE) reporting guideline.14 This study was approved by the Corporal Michael J. Crescenz VA Medical Center institutional review board with a waiver of informed consent owing to the use of deidentified data.This study followed the Strengthening the Reporting of Observational Studies in Epidemiology , a national database housing inpatient, outpatient, laboratory, procedure, and pharmacy encounters throughout the VA as well as fee-basis care received in non-VA facilities and covered by the VA.International Classification of Diseases, Ninth Revision, Clinical Modification code for prostate cancer. We identified veterans with low-risk  and intermediate-risk  localized prostate cancer who had a diagnosis date between January 1, 2004, and December 31, 2013, and who had no diagnosis code of prostate cancer in the year before diagnosis.17 Patients with missing clinical stage were excluded; patients with clinical stage TX disease were included if they met PSA and Gleason criteria for low-risk or intermediate-risk disease. As we were interested only in comparative associations between African American and White men, we excluded men whose race, defined by self-report and/or the CDW registry, was not classified as African American or non-Hispanic White. Patients who were younger than 21 years or had no structured inpatient or outpatient data were also excluded. Previous studies have suggested that White veterans are more likely to seek non-VA care than African American veterans.18 To approximate a cohort receiving VA-only care, we excluded patients with no recorded use of VA inpatient or outpatient health services in the year prior to diagnosis and who did not have documented follow-up with a VA primary care physician within each of the years that they were alive during the 5-year follow-up period. An illustration of our cohort selection is found in eFigure 1 in the We identified veterans as having prostate cancer if they had a diagnosis of prostate cancer in the registry and had at least 1 VA encounter with an 2 Patients classified as receiving conservative management were further categorized into active surveillance  or watchful waiting.2 Time to definitive therapy was defined as the time from the date of diagnosis to the date of definitive therapy, which was defined using dates from the registry or procedure codes. Follow-up was terminated at the earliest date of definitive therapy or death; for all others, follow-up was terminated 5 years after the diagnosis date.The coprimary outcomes were receipt of conservative management (in the full cohort) and time to definitive treatment (in the subcohort of patients who received conservative management). Receipt of conservative management was defined as the absence of definitive therapy , as defined in the CDW registry and by procedure codes and pharmacy claims , as defined in the CDW, was the exposure of interest in all multivariable analyses. Other variables included in multivariable analyses were age at diagnosis, pre-treatment PSA level (continuous), time period of diagnosis, veteran enrollment priority group ,2 tests for categorical variables, Cochran-Armitage test for ordinal variables, and t tests and Wilcoxon-Mann-Whitney tests for continuous variables.All data were extracted on December 31, 2019, and statistical analysis was conducted between February 1 to June 30, 2020. The distribution of demographic and clinical variables was summarized for all patients with low-risk and intermediate-risk prostate cancer and stratified by race. The distribution of variables was compared using χTo estimate the relative risk of receiving conservative management among patients with low-risk and intermediate-risk prostate cancer, we fit separate log-binomial regression modes for patients with low-risk and intermediate-risk prostate cancer at diagnosis, adjusted for all covariates, with the outcome being the receipt of conservative management. To estimate the risk of definitive treatment associated with race, we fit separate multivariable Cox proportional hazards regressions for patients with low-risk and intermediate-risk disease who received conservative management, adjusted for all covariates, with the outcome being receipt of definitive therapy. The proportional hazard assumption was tested with Kaplan-Meier curves, in which race was the categorical variable. In a secondary analysis, we calculated the difference in restricted mean survival time to receipt of definitive therapy at 5 years between African American and White veterans who received conservative management. In another secondary analysis estimating how the association between race and receipt of definitive treatment varies by conservative management strategy, we fit separate multivariable Cox proportional hazards models for patients receiving active surveillance vs watchful waiting. To better account for patients who initially elected conservative management but who progressed within 1 year of diagnosis and to minimize bias owing to inaccurate representation of the conservative management group, we performed a sensitivity analysis varying the definition of conservative management as the absence of definitive therapy from diagnosis to 6 months after the date of diagnosis.P < .05 was considered statistically significant.Statistical analyses were performed using SAS Enterprise Guide version 7.15 (SAS Institute). A 2-sided Among 51 543 veterans, the median (interquartile range) age was 65 (61-70) years, 14 830 (28.8%) were African American individuals, 24 029 (46.6%) had a low-risk disease at the time of diagnosis, and 5109 (9.9%) died during the follow-up period . The meaP < .001) and intermediate-risk  prostate cancer had a lower likelihood of receiving conservative management than White veterans. Higher age and higher comorbidity burden were associated with significantly higher likelihood of conservative management in univariable analyses of the low-risk and intermediate-risk cohorts  received conservative management; 12 069 of 24 029 veterans (50.2%) with low-risk and 8537 of 27 514 (31.0%) with high-risk localized prostate cancer received conservative management, respectively. During the study period, rates of receipt of conservative management in veterans with low-risk prostate cancer increased for both African American and White veterans but remained stable among those with intermediate-risk prostate cancer  (P < .001). Sensitivity analyses that used a shorter time window from diagnosis to define conservative management demonstrated similar results  received definitive therapy during the 5-year follow-up. The median (interquartile range) time to definitive treatment was 719 (498-1085) days for African American men and 787 (524-1178) days for White men. The 5-year risk of receiving definitive therapy during the follow-up period was higher for African American veterans compared with White veterans  B. Lower  < .001) . The resP < .001; intermediate-risk disease: aHR, 1.56; 95% CI, 1.06-2.29; P = .03) .Of 20 606 veterans who received conservative management, 2522 (12.2%) (1966 of 12 069 [16.3%] with low-risk disease and 556 of 8537 [6.5%] with intermediate-risk disease) received active surveillance. The proportion of veterans receiving active surveillance did not meaningfully differ between African American and White men with low-risk disease (490 of 2902 [16.9%] vs 1476 of 9167 [16.1%]) or intermediate-risk disease (129 of 2292 [5.6%] vs. 427 of 6245 [6.8%]). Among veterans receiving active surveillance, the likelihood of receiving definitive therapy was higher for African American patients compared with White patients  B. Among Using longitudinal national VA data, we demonstrated that African American veterans were slightly less likely to receive conservative management than White veterans (adjusted relative risk of 0.95 for patients with low-risk disease and of 0.92 for patients with intermediate-risk disease) with localized prostate cancer. Among patients receiving conservative management, African American veterans had a higher risk of receiving definitive therapy than White veterans (aHR of 1.71 for low-risk disease and 1.46 for intermediate-risk disease) within 5 years of diagnosis.26 Such long follow-up is important given the natural history of localized prostate cancer.28 The nature of our data set, which contains both claims and electronic health record information from a large equal-access national population, is unique. Finally, the VA is an important setting to study racial differences in prostate cancer outcomes because African American representation is much higher in the veteran population than in the Medicare population.29 There are several findings worth emphasis.There are several strengths to this analysis. To our knowledge, this analysis is the largest to date to study the duration of conservative management in a large, equal-access health system and has a similar follow-up time compared with other population-wide analyses of conservative management, including those using a Medicare population.7 However, it has been difficult to disentangle the role that unmeasured nonclinical confounders  play compared with potential racial differences in the biology of low-risk prostate cancer.30First, our study is among the first to show that among men who receive conservative management, the time to definitive therapy is shorter in African American patients compared with White patients with low-risk and intermediate-risk prostate cancer. Our analysis controls for various socioeconomic confounders, including driving distance, area-level deprivation, and income and disability status, and studies a population within an equal-access health care system with a high representation of African American patients. This race-based difference persisted across different mechanisms of conservative management . Previous analyses have found an increased risk of mortality among African American men with low-risk prostate cancer, even when controlling for socioeconomic status.33 which could result in a faster need for definitive therapy. Indeed, we found that African American veterans who received active surveillance, which involves regular PSA checks and biopsies and may control for race-specific differences in follow-up, still had a faster time to definitive therapy. Alternatively, differences in treatment preferences, patient-level or clinician-level risk tolerance, or trust in the health system among African American veterans could also have contributed to this finding. Further qualitative and retrospective work among African American men receiving conservative management and their clinicians could elucidate these mechanisms. Racial differences should be further studied in the context of a prospective trial, given that they may warrant the consideration of differential management strategies for African American men with low-risk or intermediate-risk prostate cancer.The mechanism behind the shorter duration of conservative management in African American men is unclear, given that we were unable to obtain PSA levels or pathology results prior to definitive therapy in the conservative management cohort. Recent studies have suggested that men of African descent may have an inherent biological susceptibility toward more aggressive low-risk and intermediate-risk prostate cancer,3 The rate of uptake of conservative management was similar to what has been reported in a previous analysis of conservative management trends in veterans with low-risk prostate cancer. This analysis found that African American race was associated with slightly higher odds of receipt of conservative management.2 In contrast, we found that African American patients had a slightly lower relative risk of receiving conservative management, although the low magnitude of this difference is unlikely to be clinically meaningful. The difference in these 2 VA-based studies may be because our cohort consisted of veterans who received consistent VA-based care, who may have different preferences regarding primary management strategies compared with veterans who seek non-VA care. Additionally, while prior studies have primarily examined trends in conservative management in patients with low-risk disease,34 we also examined trends in patients with intermediate-risk prostate cancer and found lower uptake of conservative management among African American patients. This is important given the increasing recognition that certain patients with intermediate-risk prostate cancer can receive conservative management with low long-term mortality risk.35 Our findings are consistent with prior research in Medicare populations, which also found lower uptake of active surveillance among African American patients, even when controlling for socioeconomic status.36 Lower rates of conservative management among African American men may be because of patient and/or clinician preference and should be explored through further qualitative work.Second, our study adds to prior literature in both veteran and nonveteran populations suggesting that conservative management has become the predominant management strategy for men with low-risk prostate cancer but remains low for men with intermediate-risk prostate cancer.37 our data set did capture primary treatment and procedure codes from fee-basis care provided at non-VA hospitals and thus identified procedures  done at non-VA hospitals that were paid through the VA. Furthermore, we attempted to control for receipt of non-VA care by identifying a cohort of veterans who primarily used VA care in all years of the follow-up period. Rates of receipt of conservative management in our study were nearly identical to rates described in a previously published analysis of veterans with prostate cancer that did link data to Medicare claims.2 Second, there may be systematic clinical differences between veterans and nonveterans owing to differential rates of screening and timing of detection of prostate cancer at the VA.39 Thus, our findings may not generalize to patients who receive their care primarily outside the VA. Third, we were unable to differentiate between unfavorable and favorable intermediate-risk prostate cancer in our data set. Conservative management is not a guideline-based management strategy in unfavorable intermediate-risk prostate cancer, and thus, it is possible that we have underestimated rates of receipt of guideline-based conservative management in the intermediate-risk cohort. However, findings regarding racial disparities were consistent between low-risk and intermediate-risk cohorts in our analysis, indicating that these disparities may exist in intermediate-risk disease as well. Fourth, our data set of routinely collected data did not contain information regarding prostate volume, which may also factor into decisions regarding pursuing active surveillance.40This study has limitations. First, our population was limited to veterans receiving primary treatment and active follow-up within the VA. While previous analyses have suggested that this may underestimate the degree of racial differences in laboratory testing,In this cohort study, we found that observed rates of conservative management increased at similar rates for African American and White veterans with low-risk disease but increased at much slower rates in veterans with intermediate-risk disease. After adjusting for demographic, diagnosis, tumor-specific, and socioeconomic covariates, African American patients were less likely to receive conservative management. Furthermore, the duration of conservative management was markedly lower for African American patients receiving either active surveillance or watchful waiting. Future prospective research should study the effectiveness of conservative management in African American men with low-risk and intermediate-risk prostate cancer to determine whether race-specific recommendations regarding conservative management are warranted.


Objective: This study aimed to investigate the brain functional alterations with resting-state functional magnetic resonance imaging (rs-fMRI) in older patients with knee osteoarthritis (KOA) before and after total knee arthroplasty (TKA) and to assess the causal relationship of the brain function and neuropsychological changes.Methods: We performed rs-fMRI to investigate brain function of 23 patients aged ≥65 with KOA and 23 healthy matched controls. Of the KOA patients, 15 completed postoperative rs-fMRI examinations. Analyzes of the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) were used to estimate differences in brain functional parameters between KOA patients, postoperative patients, and the controls. The relationship between changes of pre- and post-surgical status in ALFF and neuropsychological test results was analyzed.Results: Compared with the controls, all patients with KOA exhibited decreased ALFF in the default mode network  and increased ALFF in the bilateral amygdala and cerebellum posterior lobe before surgery (P < 0.001). Altered ALFF persisted in the same brain regions 1 week postoperatively. The decreased ALFF in the left precuneus gyrus and middle temporal gyrus was found after surgery when compared with preoperative data (P < 0.01). Preoperatively, the KOA patients exhibited increased FC between the left precuneus gyrus and the right supplementary motor area compared to the controls (P < 0.001), but this connectivity became no significant difference after TKA. The left Cerebelum_9 was found to have decreased FC with the right precuneus gyrus postoperatively (P < 0.001) although this was not significantly different before surgery. The significantly altered ALFF values were not correlated with changes in cognitive assessment scores.Conclusion: In older patients with end-stage KOA, functional alterations in important brain regions were detected with the persistence and further changes observed at an early stage after knee replacement. Our data further our understanding of brain functional abnormalities and cognitive impairment in older patients following knee replacement, which may provide therapeutic targets for preventive/treatment strategy to be developed.Trial registration: Clinical Trial Registration: http://www.chictr.org.cn/index.aspx, ChiCTR1800016437; Registered June 1, 2018. Knee osteoarthritis (KOA) is a common disease in elderly people worldwide; chronic pain and movement limitation caused by the disease seriously negate their quality of life . It has Brain imaging is relatively objective, and indeed, a neuroimaging study has shown that patients with KOA have structural brain alterations, especially in the prefrontal cortex, which is associated with cognitive decline compared with healthy controls . Lewis eGiven that the number of cases of KOA receiving TKA and showing cognitive impairment are increasing in an aging society, it is essential to know the integrated functional differences of the brain regions related to cognition in KOA patients before surgery and at the early stage after TKA compared to healthy matched controls. The aim of the current study is to investigate functional brain alterations in older patients with KOA by analyzing ALFF and FC to examine the further changes in brain function at the early stage after TKA and to determine the relationships between ALFF alterations and the neuropsychological changes following TKA.The protocol was approved by the institutional review board of Xuanwu Hospital, Capital Medical University (Code: 2018-047), and then registered on the Chinese clinical trial registration (ChiCTR1800016437). After obtaining written informed consent in accordance with the Declaration of Helsinki, 30 patients aged ≥65 years with KOA who had intact daily living activities and underwent TKA between August 2018 and July 2019 at our institute and 26 matched controls were screened for recruitment . The diaAll patients received spinal anesthesia through a median or para-median approach using a 26 or 27 G needle with 2.0 ml 0.5% bupivacaine at the L3/4, and no intravenous sedatives were administered throughout. TKA was performed in a standard manner for all patients by the same surgeon group. A tourniquet was applied during surgery set to 250 mmHg during surgery and deflated just before wound closure. All patients received local infiltration anesthesia of a total of 100 ml 0.2% ropivacaine and 0.5 mg adrenaline in the surgical cutting area before incision closure. Upon completion of the surgery, all patients received a multimodal pain relief regimen postoperatively, including oral acetaminophen 1,000 mg and oral celecoxib 200 mg every 12 h. In addition, a continuous femoral nerve block for 3 days used a 20-ml bolus injection of 0.2% ropivacaine followed by a continuous infusion of 6 ml/h. A bolus injection of 6 ml was given per patient as needed. Furthermore, a single injection of subgluteal sciatic nerve block was administered with 25 ml of 0.2% ropivacaine once immediately after surgery.Baseline general cognitive data were collected from all participants using the Montreal Cognitive Assessment (MoCA) system, and then detailed neuropsychological tests were carried out by the same neuropsychologist. The tests included the verbal fluency test, auditory verbal learning test , shape trails test–B, and clock drawing test . Particular attention was paid to memory and executive ability, which are commonly seen to decline after TKA , 18. AllAll 23 healthy matched controls received fMRI scan once; 23 patients had fMRI scan 1 day before surgery. Due to drop off or refused surgery, only 15 patients had fMRI 7 days after surgery .2, flip angle = 90°, section thickness = 4 mm, acquisition matrix = 64 × 64, a total of 28 slices covering the whole brain). Three-dimensional T1-weighted magnetization-prepared rapid-gradient echo sagittal images were collected using the following parameters: repetition time = 1900 ms, echo time = 2.2 ms, inversion time (TI) = 900 ms, FA = 9°, resolution = 256 × 256 matrix, a total of 176 slices with a thickness of 1.0 mm, and voxel size = 1 × 1 × 1 mm.MRI data were obtained using a Clinical 3 Tesla whole body MR imager  with a 32-channel head coil. The head coil was fitted with foam padding and headphones to minimize head motion and reduce scanner noise. During scanning, participants were asked to hold their head still, relax with their eyes closed, and try not to think of anything in particular. rs-fMRI images were generated using a rapid-gradient echo-planar imaging sequence , which is a python-based pipeline tool involving AFNI, ANTs, FSL, and custom python code. The whole analysis was accelerated and simplified through a cloud platform .Data processing was performed using the Configurable Pipeline for Analysis of Connectomes  with linear and nonlinear registrations; (5) the brain area was categorized into gray matter, white matter, and cerebrospinal fluid; (6) individual participant tissue segmentations were constrained by tissue priors from the standard space provided with FSL.Structural processing included the following steps: (1) Images were de-obliqued; (2) images were reoriented into right-to-left, posterior-to-anterior, inferior-to-superior orientation; (3) skull stripping was performed; (4) individual skull-stripped brains were normalized to Montreal Neurological Institute (MNI) 152 stereotactic space  the first 10 time points were removed; (2) slice-time correction was performed; (3) images were de-obliqued, (4) images were reoriented into right-to-left, posterior-to-anterior, inferior-to-superior orientation; (5) motion correction was performed to averaged images to obtain motion parameters; (6) skull stripping was performed; (7) global mean intensity was normalized to 10,000; (8) functional images were registered to anatomical space using linear transformation, white-matter boundary-based transformation, and the prior white-matter tissue segmentation from FSL; (8) motion artifacts were removed using ICA-AROMA with partial component regression ; and (9)The time series for each voxel was filtered between 0.01 and 0.1 Hz, transformed into the frequency domain by fast Fourier transform and then the square root was calculated for each frequency of the power spectrum, and the averaged square root  was obtained for each voxel. The previous anatomical-to-standard-space registration was applied to ALFF images to transform them into standard space. Smoothing  and Z-score standardization were applied to registered ALFF images.The regions of interest were selected as seeds for whole-brain FC analysis from the significant results of ALFF images from comparison of 23 patients and healthy matched controls. The FC for each voxel was defined as the Pearson's correlation between the time series within that voxel and the averaged times series in the seed. Then, FC images were registered to standard space. Smoothing (fwhm = 6) and Fisher-Z transformation were applied to the registered FC images.t test was conducted to assess differences in age, education level, clinical data, and neuropsychological assessment using intergroup comparisons. Categorical data were compared using the two-tailed chi-square test. A paired-sample t test was used to evaluate cognitive differences pre- and post-operation in the 15 patients.SPSS  was used to analyze data. A two-sample post hoc analysis, and Gaussian random field multiple comparison correction with p values (voxel-wise p < 0.001 and cluster-wise p < 0.025 for each tail) was applied for pairwise analysis. The whole analysis was accelerated and simplified through the cloud platform mentioned above.Analysis of variance was applied for the analysis of ALFF and FC images of 23 patients, healthy matched controls, and 15 postoperative patients, using statistical tools in DPABI . Scheffet test was applied to analyze ALFF images and whole-brain FC images of the postoperative 15 patients compared to their preoperative status, using statistical tools in DPABI. Gaussian random field multiple comparison correction with p values (voxel-wise p < 0.01 and cluster-wise p < 0.025 for each tail) was applied for analysis. The whole analysis was accelerated and simplified through the cloud platform mentioned above.A paired-sample p value of < 0.05 to be a significant difference.Relationships between ALFF values of 23 patients in the significantly altered brain regions and their clinical data were analyzed with Pearson's correlation analysis. Furthermore, the brain regions that revealed significant differences in ALFF in pre- and postoperative comparisons were selected as a mask, and we extracted the mean ALFFs for each patient in these masks. We calculated the ΔALFF and Δcognitive assessment score, which represented the changes in ALFF and neuropsychological tests, respectively, before and after TKA. Then, Pearson's correlation analysis was used to study the relationship between ΔALFF in these brain regions and Δcognitive assessment score. The statistical threshold was set at a ps > 0.05). Based on the physical function scores of the WOMAC (mean score: 34.9 ± 10.7), all KOA patients experienced motor functional impairment due to KOA (p > 0.05).Thirty patients and 26 controls were recruited. One patient with KOA was excluded from the research due to liver cancer as revealed by presurgical medical examination; one patient with KOA was excluded due to meningioma as revealed by MRI. Three healthy controls and five patients with KOA were excluded due to unqualified imaging, resulting from poor scanning compliance. Finally, we enrolled 23 patients  and 23 age-, gender-, and education-matched controls  in this study. Due to 2 patients refusing surgery and 6 patients dropping out from the research, 15 patients ultimately underwent surgery and postoperative neuropsychological assessment and MRI scan . The deme to KOA . Althougp < 0.05). However, there were no differences in other neuropsychological tests (p > 0.05). The paired two-sample t test revealed that no significant difference was found in terms of neuropsychological assessment although the results were better after surgery when comparing the pre- and post-surgical results of the 15 patients who underwent TKA (p > 0.05).There was no difference in the basic cognitive status (MoCA) in 23 patients and 15 postoperative patients compared to the controls. Compared with the controls, all AVLT tests were significantly lower in 23 patients, and only long-term recall of AVLT was significantly lower after surgery in 15 patients . Decreased and increased ALFF were found in the same brain regions at day 7 after TKA in the 15 postoperative patients , Table 2post hoc analysis revealed FC differences when comparing the 23 KOA patients and 15 postoperative patients to the healthy matched controls (When the left precuneus gyrus and left Cerebelum_9 (cerebellum posterior lobe) were selected as the regions of interest for seed-based connectivity analysis, controls . AlthougAs for paired-sample analysis in seed-based connectivity analysis, no differences in the 15 patients between the pre- and post-operative status were found based on those two regions of interest.p > 0.05).No relationships were found between ALFF value in the altered brain regions and clinical data, such as physical functional scores. Further, the Pearson's correlation analysis of the association between the changes of neuropsychological data, and ALFF of the significantly altered brain regions before and after TKA in the 15 patients revealed no significant correlations between ΔALFF and Δcognitive assessment score  Brain low-frequency oscillations were decreased in the DMN and increased in the bilateral amygdaloid and cerebellum posterior lobe in the older patients with KOA compared with healthy matched controls. (2) One week after TKA, altered ALFF persisted in the same brain regions compared to controls. (3) Significantly decreased ALFF in the left precuneus gyrus and new-onset decline in the left middle temporal gyrus were identified in a paired comparison of post- and presurgical status. (4) Disordered FC was recovered, but a newly decreased FC after TKA was detected compared to controls, and no FC differences were found between pre- and post-operation. (5) No relationships were found between ALFF changes in significantly altered brain regions and the changes of neuropsychological assessment. These findings suggest that, in older patients with end-stage KOA, brain functional abnormalities were detected with the most abnormalities persisting in the same brain regions, and further low-frequency oscillation changes in specific regions were observed at the early stage after joint replacement. The ALFF may have more potential value for the detection of brain changes after TKA prior to FC and neuropsychological assessment.Many algorithms have been developed to analyze fMRI data, such as independent component analysis, seed correlation analysis, regional homogeneity, and ALFF , 22, 23,The precuneus gyrus is a key part of the DMN and primarily involved in highly integrated work, including episodic memory retrieval, visuospatial attention, and self-processing operations . The angper se and worth investigating in future study.Previous studies report that the prevalence of cognitive decline 1 week after TKA in the older patients was not low , 6 althoThe use of a single analytical method for rs-fMRI might mean that some important information is missed. Therefore, we analyzed FC, which defines the temporal correlation of the blood oxygen level-dependent signals of distinct brain areas to reflect the information integration between brain areas . The regIn this study, some slight improvements of cognition were found after surgery through a comparison of all KOA and postoperative patients to healthy matched controls and a comparison of patients in pre- and post-surgical status. Such performance improving in neuropsychological testing can be attributable to a learning or practice effect, especially for repeated testing at only a 1-week interval . A few sThe current research has some limitations. First, it is difficult to fully exclude confounders of pre-and post-TKA on the ALFF/FC changes in the present study, such as the surgical trauma, pain, and medications taken after surgery. Therefore, a strict control group or randomized controlled trial–type study would be helpful to rule out the exact causes of these changes in the future. Second, our results, particularly the correlation between altered brain function and cognitive changes, were derived from a relative small sample size, and therefore, the robustness of this correlation is not known. Third, long-term follow-up was not done, and therefore, brain function changes after TKA in the long term is not known. Fourth, the participants in the healthy control group were only tested once. Considering that the neurological status of the healthy controls was almost unchanged over months, we did not carry out second MRI examinations in this group. In addition, some patients, particularly if they experienced no discomfort after surgery, were not compliant with follow-up scans. Similarly, it was difficult to obtain second scans for healthy volunteers in the short research period. That made the different sample sizes before and after surgery, especially for postoperative patients, which might yield result bias.In older patients with end-stage KOA, low-frequency brain oscillations revealed a decrease in the DMN and an increase in the amygdala and the posterior lobe of the cerebellum with persistence in the same brain regions and further changes in left precuneus gyrus and middle temporal gyrus observed at the early stage after knee replacement. Although disordered FC backed to no difference, a newly disturbed connectivity occurred after joint arthroplasty. Despite coincident alterations in brain function and cognitive assessment, there did not appear to be any association between the two, which may be very likely due to low power of small sizes in our study. Nevertheless, our data demonstrated that rs-fMRI with the ALFF and FC algorithm may have practical value for detecting brain function changes superior to neuropsychological assessment and may be useful to uncover the mechanism of cognitive decline before and at the early stage after TKA in older patients with KOA.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Institutional Review Board of Xuanwu Hospital, Capital Medical University, Code: 2018-047. The patients/participants provided their written informed consent to participate in this study.FL, ZGQ, DQM, and TLW contributed conception and design of the study. FYL and MD organized the database. ZGQ performed the statistical analysis. FL and GWL wrote the first draft of the manuscript. GLC, ZL, WX, and HQF wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To confirm the relationship between sex and the progression of Coronavirus Disease-19 (COVID-19), and its potential mechanism, among severe patients. For this retrospective study, we included 168 consecutive severe patients with pathogen-confirmed COVID-19 who were hospitalized between January 16th and February 4th, 2020, at Tongji Hospital in Wuhan, China. Clinical characteristics, laboratory parameters, and outcomes were compared and analyzed between males and females. In the present study, we analyzed 168 severe patients with COVID-19, including 86 males and 82 females, and 48 patients (28.6%) were diagnosed as critically ill. Of 86 male patients, 12.8% (11/86) died and 75.6% (65/86) were discharged; of 82 female patients, 7.3% (6/82) died and 86.6% (71/82) were discharged. Eleven laboratory parameters showed significant differences between male and female patients, and six of them were higher during the whole clinical course in patients who died than in patients who were discharged. In adjusted logistic regression analysis, males with comorbidities presented a higher risk of being critically ill than males without comorbidities . However, this association attenuated to null in female patients . A similar sex-specific trend was observed in the relation between age and critically ill conditions. We highlighted sex-specific differences in clinical characteristics and prognosis. Male patients appeared to be more susceptible to age and comorbidities. Sex is an important biological variable that should be considered in the prevention and treatment of COVID-19. Overall, our results highlight sex-specific differences in clinical characteristics and prognosis to COVID-19, which are consistent with the sex-bias observed in other COVID-19 related studies. We also provided more evidence and explored the underlying mechanism with epidemiological analysis. Six candidate laboratory indicators with significant sex differences were considered as potential mediators affecting the prognosis of COVID-19. The age-specific risk distributions of COVID-19 showed a positive correlation between the prognosis and age/ comorbidities, which was significantly affected by sex. We also emphasize that sex is a biological variable that should be considered in the prevention and therapy of COVID-19. It is our hope that these methods may serve as a guide to the clinician in providing timely and specific therapy. Obviously, future studies are warranted to elucidate the different pathways and cellular responses between women and men. The key challenge is how best to use this disparity for providing adequate protection in both males and females. More than 20,000 people have died from COVID-19 so far. What also seems to be emerging among these numbers is a clear sex difference in who is contracting and dying from the virus. Several epidemiological studies observed possible differences in the progression of disease between different sexes [In December 2019, the first coronavirus disease-19 (COVID-19) cases associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were identified in Wuhan, Hubei, China . As of MP = .0001) and hospital discharge rates (P = .0014). When comparing the mortality and hospital discharge rates between women and men, men had a trend toward a higher risk of mortality and a lower hospital discharge rate (P < .0001), which implies that underlying diseases might influence the severity of COVID-19 .P < .05, P < .05). Therefore, we analyzed the sex-specific dynamic changes in selected laboratory parameters during the course of COVID-19 in patients who died , which was consistent with the finding of previous studies –9. Elevewho died  and in pwho died . As showwho died .P < .0001). The results from binary logistic regression analysis presented a higher risk of critically ill progression in the elderly aged 80 and above than in young and middle-aged individuals aged < 59 years old . The relation between comorbidities and critically ill conditions was assessed with appropriate adjustments for covariates including age, respiratory symptoms, days from illness onset to the first admission and the pathogens identified. A similar sex-specific trend was observed  were graded as critically ill cases and were regarded as patients with poor prognosis. The age-specific risk distributions of COVID-19 showed a positive correlation between poor prognosis and age . We also analyzed the sex-specific dynamic changes in selected laboratory parameters during the course of COVID-19 in patients who died and in patients who were discharged . Written informed consent was not obtained because the data were analyzed retrospectively and anonymously.th and February 4th, 2020, at Tongji Hospital in Wuhan, China. The clinical characteristics  were monitored up to March 24th, 2020, the final date of follow-up. The median duration of follow-up for all patients was 27 days, with accurate follow-up and few missing data. As one of the designated hospitals, Tongji Hospital is responsible for the treatment for critically ill COVID-19 patients in line with the arrangements of the local government. Therefore, according to the COVID-19 diagnosis and treatment plan issued by the National Health Commission, all patients included were graded as severe cases or critically ill cases. Clinical and demographic data of the confirmed cases were collected from their medical records.For this retrospective study, we included 168 consecutive severe patients with COVID-19 who were hospitalized between January 16All clinical information  was obtained by reviewing the medical records of patients. After proper training, the data collection was conducted by two individuals independently. EpiData 3.1 was used for data entry and management. The data entry quality was checked by the double data entry verification method to reduce data entry errors. All patients were identified and classified according to the sixth edition of the COVID-19 diagnosis and treatment plan issued by the National Health Commission. Based on the published guide, doctors in Hubei Province should use CT scans to make a clinical diagnosis of suspected coronavirus infections. Real-time reverse transcriptase polymerase chain reaction  testing kits were then used to confirm coronavirus infection. In this study, all patients included were verified as positive for SARS-CoV-2 infection in throat swabs or bronchoalveolar lavage fluid analyzed by real-time RT-PCR. The specific operation methods were followed according to the instructions and were consistent with other literature ,5.2 /FiO2 (the inspired oxygen fraction) ≤ 300 mmHg. Critically ill cases are defined as patients who met any of the following criteria: (a) developed respiratory failure requiring intubation, (b) presented with shock, and (c) developed other organ failure or were admitted to the ICU.According to the COVID-19 diagnosis and treatment plan mentioned above, the clinical classifications of COVID-19 were classified into four main types: mild cases, moderate cases, severe cases, and critically ill cases. Severe cases are defined as patients who met any of the following criteria: (a) with respiratory distress syndrome, respiratory rates ≥ 30/min; (b) the finger oxygen saturation measured after 5 minutes of rest was ≤93%; and (c) PaOP < .05 was considered statistically significant.All analyses were conducted with SAS software version 9.4. The measurement data were expressed as the mean and standard deviation (SD) or median and interquartile range (IQR) values. The comparison between the two groups was performed by independent group t-tests when the measurement data were normally distributed. Otherwise, Mann-Whitney U tests were applied. Enumeration data were summarized as frequency rates and percentages. Intergroup comparison of enumeration data was performed using chi-squared tests or Fisher’s exact tests. Binary logistic regression models were adopted to analyze the influence of age in different age groups or with/without comorbidity on the risk of progression to critically ill cases.