NavSTAR trial, hospital patients receiving SUD consultation who were randomly assigned to patient navigation services for 3 months post-discharge had fewer readmissions compared to usual care.Facilitating opioid agonist treatment (OAT) for opioid use disorder (OUD) is an important role of hospital substance use disorder (SUD) consultation services. In the N=314). Associations between OAT initiation and linkage, and patient demographics, housing status, comorbid SUD diagnoses, recent substance use, and study condition were examined using multinomial and dichotomous logistic regression.This secondary analysis examined hospital-based OAT initiation (pre-randomization) and community-based OAT linkage (post-discharge) among NavSTAR trial participants with OUD (p=0.02), while participants who received buprenorphine were more likely to report homelessness . Compared to participants initiating methadone, participants initiating buprenorphine were more likely to be non-White  and to report prior buprenorphine treatment . OAT linkage within 30-days post-discharge was associated with hospital-based buprenorphine initiation  and patient navigation intervention .Overall, 57.6% initiated OAT during hospitalization . Compared to participants not initiating OAT, participants who received methadone were more likely to be female (Relative Risk Ratio [RRR]=2.05, 95% CI=1.11, 3.82, OAT initiation differed by sex, race, and housing status. Hospital-based OAT initiation and patient navigation were independently associated with linkage to community-based OAT. Hospitalization is a reachable moment to begin OAT to alleviate withdrawal and facilitate treatment continuity post-discharge. In 2020, there was over 93,000 estimated drug overdose deaths, with nearly 75% involving an opioid . Opioid Opioid use and related harms such as intravenous use are associated with several health conditions that result in hospitalization, including endocarditis, cellulitis, and soft tissue infections . MoreoveHospital providers are equipped to assess for OUD and initiate treatment. Unfortunately, providers may be unaware that they are able to initiate OAT in the hospital or may feel uncomfortable doing so due to limited training . In recoNavigation to Avoid Rehospitalization (NavSTAR) study, a randomized controlled trial (RCT) that examined the effectiveness of patient navigation services in reducing hospital readmissions. The NavSTAR study found that patient navigation services reduced inpatient admissions and emergency department visits through 12-month follow-up. To build upon the evidence base for hospital-based initiation of OAT and OUD treatment linkage post-discharge, the current study examined participant characteristics, hospital-based OAT initiation, and post-discharge treatment linkage and retention among a subgroup of NavSTAR trial participants with OUD. Specifically, the aims of the current study were to examine the association between participant and service characteristics and (1) initiation of OAT in the hospital (any initiation and by medication type) [occurring prior to study enrollment/randomization], and (2) linkage to community-based OAT [post-discharge].The current study is a secondary analysis from the 2The current study is a secondary subgroup analysis of data collected in the NavSTAR study, which compared a patient navigation intervention to usual care among 400 medical or surgical inpatients with comorbid alcohol, cocaine, and/or opioid use disorder. All participants in the study received services from and were referred to the study by a hospital-based SUD consultation service. Detailed methods and outcomes are reported elsewhere . The stu2.1The NavSTAR study was conducted at the University of Maryland Medical Center (UMMC), a large academic hospital in Baltimore, MD. UMMC operates a SUD consultation service that has been in continuous operation for several decades . The serParticipants randomized to the NavSTAR patient navigation condition received usual care from the SUD consultation service, as described above, in addition to patient navigation services delivered by Masters-level patient navigators. A navigator met with patients at the bedside to conduct an initial assessment of needs and barriers to care and develop a post-discharge plan. The patient navigator provided proactive care coordination services for up to three months post-discharge. These services aimed to link participants to available resources in the community for SUD treatment, recommended medical care, and basic needs. Patient navigators worked to resolve identified internal and external barriers . Navigators were also able to access a modest support fund to cover costs as needed .2.2All participants were patients hospitalized between March 2015 and May 2018 who received a SUD consultation. Participants were recruited from various hospital services including Internal Medicine , Trauma, Neurology and Surgery. For study inclusion, patients were required to: 1) be 18 or older; 2) have Baltimore City residency (or homeless); 3) meet DSM-5 criteria for alcohol, cocaine, and/or opioid use disorder; and 4) be willing and able to provide informed consent. Patients were excluded if they: 1) were enrolled in SUD treatment at the time of the index hospitalization; 2) were pregnant or in the hospital's Labor and Delivery unit; 3) had a planned discharge to hospice or long-term inpatient care (discharges to acute or sub-acute skilled nursing facilities were eligible); or 4) were hospitalized for a suicide attempt. Patients who received a consultation and were provided routine services from the SUD consultation service were referred to research staff for eligibility screening. After determining eligibility and obtaining informed consent, research staff completed a baseline interview at the bedside. Following the baseline interview, participants were randomly assigned to one of two study conditions: (1) usual care or (2) the NavSTAR patient navigation intervention.n=314; 78.5% of the entire sample).For the current study, the analytic sample consists of participants in both study conditions who met DSM-5 criteria for OUD at baseline , including admission and discharge diagnoses, housing status, and opioid pain medication.2.3.1Research staff abstracted data from SUD consultation records on OAT initiation with methadone and buprenorphine, which occurred prior to study enrollment and randomization. We defined OAT initiation as cases where (1) the SUD consultation made a recommendation for OAT to the medical team and (2) it was confirmed that participants received OAT medication during their hospitalization. Of note, hospital-based methadone and buprenorphine doses were adjusted based on a patient's symptoms and other clinical considerations. Further, hospital-based OAT initiation occurred prior to the delivery of the NavSTAR intervention.2.3.2Self-reported data on community-based treatment entry were collected during follow-up interviews conducted at 3-, 6-, and 12-months post-discharge. Specifically, participants recounted details of any community-based treatment episodes after discharge, including information on treatment entry dates, type of treatment setting, whether medication was provided, and whether the participant was still engaged in treatment at the time of the interview.We defined community-based OAT linkage if the participant (1) reported a treatment start date within 30 days of discharge from the index hospitalization and (2) reported receiving buprenorphine or methadone for OUD as part of that treatment episode. We classified 30-day treatment retention using self-reported data on the number of days the participant engaged in community-based OAT following linkage to a community-based program post-discharge.At the time of this study, there was no established stand-alone bridge clinic for post-discharge treatment. However, the hospital system operates an opioid treatment program (OTP) that provides methadone and buprenorphine, as well as another non-OTP program that offers buprenorphine. These programs are within several blocks of the hospital and provide next-day appointments for patients referred from the SUD consultation service. Patients discharged on Friday or Saturday (and unable to be admitted to the OTP until Monday) were seen in the hospital's psychiatric emergency department where they were administered methadone. The consultation service also has established relationships with other outpatient programs (OTP and non-OTP) in other areas of the city which will see patients the next day for methadone. Prescriptions for buprenorphine are provided to patients to bridge them to their intake if not next day. The hospital system does not have the capacity to provide direct follow-up for hospital patients once discharged, though patients with direct referrals from the SUD consultation service can be prioritized for admission into the system's community-based programs.2.4We used descriptive statistics to examine OAT initiation across participant characteristics. We used a multivariable, multinomial logistic regression model to compare participants not initiated on OAT to participants initiated on either methadone or buprenorphine, respectively, using sex, race, age, homelessness, co-morbid SUD diagnoses , days of illicit opioid use, injection drug use, and previous treatment history as explanatory variables. The focus of the multinomial logistic regression was to characterize differences between participants initiated on either medication type compared to participants who did not receive medication, as well as differences between participants initiated on buprenorphine compared to participants initiated on methadone. In addition to considering participant characteristics, we described clinical aspects that could influence OAT initiation, such as pain management with opioid analgesics and medical diagnoses.We used a multivariable logistic regression model to examine linkage to community-based OAT within 30 days of discharge, using the above explanatory variables, as well as hospital-based OAT initiation  and study condition . Study intervention condition was included as a predictor because linkage to community-based OAT post-discharge may have been related to the intervention, as facilitating linkage to SUD treatment was a core function of the patient navigators. Furthermore, we extended the model to consider the interaction between hospital-based OAT initiation and study condition. Finally, we used descriptive statistics to examine rates of 30-day retention in community-based OAT. All statistical analyses were conducted in Stata Version 16.33.1SD=12.3). Over half of the sample (60.2%) had co-morbid cocaine use disorder, while 21.7% had co-morbid alcohol use disorder. Nearly two-thirds (64.3%) reported recent injection drug use and 46.8% reported experiencing homelessness at baseline. Over half of the sample  were initiated on OAT, with 36.3% initiated on methadone and 21.3% initiated on buprenorphine (occurring prior to study enrollment/randomization). Approximately half of the sample were randomized to receive the patient navigation intervention post-discharge .Of the 314 participants with OUD, most (92.7%) met criteria for severe OUD (≥ 6 diagnostic symptoms) and reported opioid withdrawal symptoms in the past 30 days (>95%). Descriptive statistics of the analytic sample are shown in 3.2RRR]=2.05, 95% CI=1.11, 3.82, p=0.02), and to report greater days of heroin/illicit street opioid use  and non-medical prescription opioid (NMPO) use  in the 30 days prior to the index hospitalization. There were no other significant differences between the group initiated on methadone (compared to the group not initiated) for the remaining explanatory variables.Results of the multinomial logistical regression are reported in RRR=2.40, 95% CI=1.01, 5.70, p=0.046), to report experiencing homelessness , and to report greater days of heroin/illicit street opioid use in the 30 days prior to the index hospitalization .Compared to participants not initiated on OAT, participants initiated on buprenorphine were more likely to be non-White  and to report previous buprenorphine treatment experience . Compared to participants who initiated methadone, participants initiated on buprenorphine reported fewer days of NMPO use .Compared to participants who initiated methadone, participants who initiated buprenorphine were more likely to be non-White (3.3n=133), prescription opioids for pain  were noted by the SUD consultation team for 77.4%, compared to 18.2% for participants who did initiate OAT (p<0.001). Of participants on opioid analgesics, 76% were not initiated on OAT, 21% initiated methadone, and <3% initiated buprenorphine (p<0.001).We considered several other factors that could affect OAT initiation, including pain management, discharge diagnosis, patient interest, and benzodiazepine misuse. Among individuals not initiated on OAT (n=8), hepatic issues (n=8), ongoing pain (n=5), and other medical complications (n=3). However, actual OAT initiation patterns did not differ from the larger sample for these cases.Two of the authors (CW and RPS) reviewed participants’ discharge diagnoses, identifying several medical conditions that could influence OAT initiation, or choice of medication: cardiac issues/QTc prolongation  did not express interest in OAT during hospitalization. Three other participants (2.3%) were recommended for OAT during their admission but left against medical advice prior to receiving medication.Past 30-day non-medical use of sedatives (including benzodiazepines) was reported by 14% of the sample, but at low frequency on average (≤3/30 days). While not included in the main analyses due to small numbers, additional exploratory analyses found that sedative misuse was not associated with either any OAT initiation, or medication type.3.4n=251) and provided data on community-based OAT.Approximately 80% of the sample completed at least one follow-up interview following the index hospitalization  and if they were randomized to the patient navigation intervention condition . Participants with co-morbid alcohol use disorder also were more likely to link with community-based OAT within 30 days of discharge . Compared to no medication, there was no significant association between methadone initiation during the index hospitalization and linkage to community-based OAT . However, there was a significant association of any medication initiation (either buprenorphine or methadone) with community-based OAT linkage (joint 2-degree-of-freedom contrast compared to no medication: χ2(2) =10.85, p=0.004). In changing the reference category to examine initiation with buprenorphine versus methadone, this comparison did not reach statistical significance (p=0.06).Results of the logistic regression examining linkage to community-based OAT within 30-days post-discharge are presented in 2(2) =1.21, p=0.55], indicating that the patient navigation intervention was not differentially effective in facilitating treatment linkage based on whether the participant started OAT in the hospital. Rather, both in-hospital OAT initiation and patient navigation services post-discharge were independently associated with linkage to community-based OAT.An extension of the model testing an interaction effect between hospital OAT initiation and intervention condition found the interaction to be non-significant [2-degree-of-freedom contrast: χn=79), 88.6% were engaged for at least 30-days (n=70). Bivariate comparisons using χ2 tests showed no statistically significant differences in rates of 30-day retention based on receipt of hospital-based OAT (83.3% no OAT vs. 86.2% methadone vs. 96.2% buprenorphine) or the patient navigation intervention .Among the subsample of participants who reported linkage to OAT . Additionally, this sample reported high rates of injection drug use, frequent drug use days, and homelessness. Thus, this sample represents vulnerable, high-risk patients who could benefit from hospital-based OAT initiation, as such care could reduce risks related to injection drug use or infectious disease and promote better quality of life . ExaminiWithin this sample, participants who were initiated on buprenorphine during hospitalization were nearly four times more likely to enter community-based OAT within 30 days of discharge compared to participants not initiated on OAT. This is consistent with other studies that have found that OAT initiation in hospital settings is associated with increased engagement in outpatient care  and highThe study's patient navigation intervention was also independently associated with increased odds of community-based treatment engagement, when controlling for other factors. The patient navigation intervention primarily focused on SUD treatment linkage; however, navigators routinely resolved challenges ranging from acute medical needs to transportation barriers that may have increased participant readiness to engage in SUD treatment once these problems were addressed. Given the competing needs of patients during acute hospitalizations, hospital systems should consider utilizing addiction-focused specialists in discharge planning and transitional care to promote this continuity. There is a high need for targeted interventions for hospitalized patients with SUDs, such as patient-centered case management services, that can facilitate linkage to community-based treatment upon discharge and provide motivational and logistic support for individuals who are newly engaged in treatment and at risk of early treatment discontinuation.In this sample, co-morbid alcohol use disorder was associated with increased likelihood of community-based OAT linkage, which is in contrast with other studies that have found associations between alcohol use disorder diagnosis and treatment non-adherence in general populations . ResearcWe also identified sociodemographic differences in the hospital-based OAT initiation. First, we found that that women were more likely to be initiated on methadone, a contrast to extant research showing that women are likely to experience multiple barriers to treatment access and are less likely to seek care . HospitaSecond, compared to participants who received methadone, participants who received buprenorphine during hospitalization were substantially more likely to be non-White. This contrasts with research that has found lower access to buprenorphine treatment in non-White individuals. Several factors are associated with decreased access to treatment among racial and ethnic minority groups, including clinician attitudes and bias and policies that limit accessibility . As noteFinally, similar to other studies of OAT initiation in hospital settings, this study found that experiencing homelessness was positively associated with OAT initiation, specifically buprenorphine . This coWhile this study identified certain patient characteristics that were associated with OAT initiation and which medication people started, the overarching finding of greatest importance is that most participants were able to successfully initiate OAT with either buprenorphine or methadone during a medical hospitalization. Among participants who did not initiate OAT, most were receiving opioids for analgesia . Opioid analgesics  can complicate the logistics of OAT initiation with respect to choice of medication, dosage, and frequency. This sample was a medically complex, socially vulnerable one characterized by a wide range of presenting medical problems, for which multiple comorbidities were common. Our findings highlight the important role of SUD consultation services in facilitating OAT in the hospital setting.5This study has several limitations. First, we were likely unable to account for all variables that influence OAT initiation during hospitalization or linkage post-discharge, and many that we considered were measured imperfectly. While we obtained information on the important issues of opioid analgesia (which may be a complicating factor to initiating OAT) and discharge diagnoses, our data abstraction did not capture other factors that could affect treatment decision-making and discharge planning, such as nuances of patients’ medical conditions or specific practices surrounding initiation of opioid agonist medications such as dose and timing. Moreover, because recruitment occurred from 2016-2018, the study was conducted during the early transition to fentanyl in the illicit opioid supply. There is emerging evidence of precipitated withdrawal among patients using fentanyl when starting buprenorphine treatment . Thus, iSecond, the study was conducted in a large, urban teaching hospital with a well-established SUD consultation service, which may limit generalizability of these findings for hospitals without such specialized capacity, or hospitals with less mature or under-resourced SUD consultation services.Third, outcome data on linkage to and retention in community-based OAT was self-reported by participants and may be subject to error or bias, for example, due to recall error or social desirability.Finally, the sample of patients who enrolled in the NavSTAR trial exhibited high rates of severe OUD, which may not generalize to populations with less severe OUD. While a comprehensive accounting of the clinical decision process for OAT initiation is beyond the scope of this study, such treatment decisions are likely a complex interplay of patient characteristics, patient/provider preferences, underlying medical conditions , other treatments received during the hospitalization , and prospects for OAT continuity upon discharge.6This study highlights that many hospitalized patients who meet diagnostic criteria for OUD are appropriate for OAT and can be initiated on such treatment during hospitalization. Moreover, initiating OAT in the hospital, as well as providing patient navigation services post-discharge, are independently associated with considerably higher rates of linkage to OAT in the community within 30-days post-discharge. Hospitalization offers a critical opportunity to expand access to OAT for patients with comorbid OUD to alleviate withdrawal symptoms and to facilitate continuity of care after discharge. Specialty SUD consultation services are poised to play an important role in helping to address the opioid crisis. Inpatient care for hospitalized patients with OUD can be complex, thus it is important to identify patterns and disparities in treatment delivery in order to optimize care for high-risk populations.Unrelated to the present study, JG is part owner of COG Analytics and has received research funding  from Indivior. He is also Principal Investigator of a NIDA-funded study that is receiving medication in kind from Alkermes and Indivior. RPS has consulted with Verily Life Sciences.NavSTAR study, designed the study protocol, assisted with the data analyses and contributed to the writing of the manuscript. C Welsh was the director of the addiction consultation service and contributed to the writing of the manuscript. KE O'Grady provided guidance on the data analyses. RP Schwartz and SG Mitchell contributed to the study design and manuscript preparation. All authors contributed to the interpretation of the findings and critically reviewed the final manuscript. All authors approved the final manuscript.CD Nordeck managed the data collection for the study, conducted literature review and statistical analysis for this manuscript, and drafted the manuscript. J Gryczynski acted as the Principal Investigator for the None related to the presented research.Unrelated to the present study, J Gryczynski is part owner of COG Analytics and has received research funding  from Indivior. J Gryczynski and RP Schwartz are involved in a separate NIDA-funded study that is receiving free study medication from Alkermes and Indivior (unrelated to the present study). RP Schwartz has consulted with Verily Life Sciences. The remaining authors report no conflicts of interest.


To evaluate the benefit of combining polygenic risk scores with the QCancer-10  prediction model for non-genetic risk to identify people at highest risk of colorectal cancer.Population based cohort study.Data from the UK Biobank study, collected between March 2006 and July 2010.434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts.Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event  analyses were used to evaluate risk model performance in men and women.Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 , and top age and sex adjusted C statistic of 0.733  in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone.Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population. Colorectal cancer is the fourth most common cancer in the UK, with increasing incidence in younger ages and countries with historically lower rates.Both genetic and non-genetic factors contribute to an individual’s risk of colorectal cancer. Some non-genetic factors are modifiable. The top performing risk model by non-genetic factors in external validation is QCancer-10 ,Genetic variants known to predispose to colorectal cancer are mostly single nucleotide polymorphisms identified as significantly associated with riskin genome-wide association studies (GWAS). Genetic risk can be summarised in a polygenic risk score (PRS). Most PRSs have used a limited set  of significantly associated single nucleotide polymorphisms, with genotypes weighted by predicted effect sizes.Integrated models for colorectal cancer, which have combined GWAS significant PRS with non-genetic risk factors, generally do better than non-genetic models or PRS alone.We conducted a development and validation study of PRS and integrated PRS-epidemiological models to predict risk of colorectal cancer in a set of UK individuals of bowel cancer screening age. We followed the PRS-Reporting Standards (PRS-RS) and Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines for PRS and prediction modelling.We used the UK Biobank study to derive and validate our risk models, under application number 8508.The primary outcome in all models was colorectal cancer diagnosis, identified through self-report at UK Biobank study enrolment visit and International Classification of Diseases-9  and International Classification of Diseases-10 (C18-C20) codes in linked cancer and death registries and hospital data. For PRS development and evaluation in logistic regression models, we included incident and prevalent cases, with the remaining cohort used as controls. For time-to-event analysis by use of Cox proportional hazards models, we excluded prevalent cases with a diagnosis of colorectal cancer before cohort entry. Follow-up began at the date of enrolment and was censored at the earliest of date of incident colorectal cancer, loss to follow-up, death, or end of available registry follow-up .We calculated age specific and directly standardised colorectal cancer incidence rates in UK Biobank overall and for the Integrated Modelling Cohort used to derive integrated QCancer-10+PRS risk models, and compared these with Office for National Statistics 2013 cancer registry data for England (chosen as the approximate mid-point of available UK Biobank follow-up).We did a meta-analysis of summary data from 14 colorectal cancer GWAS cohorts , to provide association effect sizes of single nucleotide polymorphisms  correction.We evaluated six PRS models from three broad approaches to PRS development (supplementary methods). Firstly, we used a standard PRS (hereafter GWAS significant), which comprised a manually curated list of 50 sentinel single nucleotide polymorphisms shown in GWAS meta-analyses of European data,We show the quality control measures per person for the genetic data and sample exclusions for each modelling cohort. We used imputed dosage data from UK Biobank, and restricted single nucleotide polymorphisms to those included in the HapMap3 reference dataset and those present in the base GWAS data. After quality control, 1 104 409 single nucleotide polymorphisms were available for PRS development .PRSs were developed in the derivation dataset, which included participants of white British ancestry (identified through self-reported ethnic group and genetic information)xy statistic to assess discrimination, in addition to Royston’s D statistic and separation of Kaplan-Meier curves across four risk groups 2 was used in logistic regression models and Royston and Sauerbrei's R2D in Cox proportional hazards models to assess variance explained, and R2 attributable to the PRS was calculated by R2 (full model) minus R2 (reference model). These measures were evaluated over the follow-up time of the cohort for Cox proportional hazards models. Scaled Brier scores . We used the C statistic  and Somers’ DBefore external validation, models were adjusted for optimism. The optimism adjusted calibration slope was used as a global shrinkage factor to adjust the regression coefficients, and the intercept or baseline survival function was re-estimated (by refitting the model with the adjusted linear predictor as an offset).Coding of QCancer-10 predictors in UK Biobank was matched as closely as possible to the original model.The integrated modelling cohort used for QCancer-10 validation and development of integrated models comprised all individuals with imputed genetic data passing quality control, excluding the individuals in the PRS training cohort (supplementary methods), and with complete QCancer-10 predictor data. Since missingness was less than 5% for all predictors (supplementary table S3), we used complete case analysis. Sample size adequacy for integrated model development was calculated following Riley et alhttps://www.qcancer.org/15yr/colorectal/. We then developed integrated models including the risk score from QCancer-10 plus either the top performing genome-wide PRS  or the GWAS significant PRS, with PRS adjusted for genotyping array and the first four principal components from UK Biobank, using Cox models, developed in men and women separately. Inspection of Schoenfeld residuals showed that the proportional hazard assumption held. We evaluated the use of multiple fractional polynomials to model the predictors, ultimately using a fractional polynomial term to model the genome-wide PRS in the model for women. We assessed possible interactions between the predictors by visual inspection of plots of marginal effects of the QCancer-10 risk score across PRS values and examining the prognostic strength and significance of interaction terms based on Wald χ2statistics.We validated QCancer-10 performance in UK Biobank, and recalibrated the model for the UK Biobank dataset through recalibration-in-the-large. Full QCancer-10 model specification is available at We used the same metrics to assess the original QCancer-10 model and QCancer-10+PRS model performance as described for Cox PRS models, with paired t tests to compare models as previously described. Confidence intervals and internal validation used 500 bootstrap samples. We undertook a sensitivity analysis excluding people diagnosed within two years of recruitment to evaluate possible reverse causality. Prespecified subgroup analyses for QCancer-10 and QCancer-10+PRS included people with a first degree family history of colorectal cancer, analysis by self-reported ethnic group (minority ethnic participants compared with white participants), and calibration by age. As we observed some miscalibration of PRS models in some age groups, we also evaluated performance of QCancer-10 and QCancer-10+PRS models across three age groups .Model sensitivities, specificities, detection rates, and false positive rates were calculated at centile thresholds for absolute risk and relative risk. Relative risks were calculated relative to an individual of the same age and sex, mean PRS (by sex), mean principal components, body mass index of 25, white ethnic group, mean Townsend score, and no other colorectal cancer risk factors.t ÷ (1−Pt)), where N is the number of individuals in the integrated modelling cohort and Pt is the probability (or risk) threshold .A full evaluation of the clinical usefulness of PRS and the integrated risk score in a population screening setting is complex because the assessment must take into account participation rates, screening frequency, method used , criteria used to select participants for colonoscopy , and success at preventing colorectal cancer by removal of premalignant lesions. We could, however, consider a simplified situation, in which we assume a single colonoscopy at the start of an eight year follow-up period would detect all colorectal cancers and relevant premalignant lesions, with participant benefit in those cases. We then captured the complex benefits arising from this screening in a simple but standard measure of net benefit obtained using QCancer-10 and QCancer-10+PRS models to select individuals for screening colonoscopy: NB=(true positives÷N)–(PWe plotted net benefit and unnecessary interventions avoided (which represents true negatives) across relevant risk thresholds over eight years of follow-up in decision curves. We reported values for net benefit, unnecessary interventions avoided, and test trade-off at a range of prespecified risk thresholds .The concept and design of this study was informed by discussions with individuals at Bowel Cancer UK, including patient representatives. Several members of the public reviewed the paper and provided feedback.v 127.8) cases in men per 100 000 person years at risk and 73.9 (v 80.7) cases in women.We report the study profile of included participants in the quality controlled dataset for PRS development and validation , compris2 of 6.3% (5.9 to 6.8%) (Of the six PRS models assessed (supplementary fig S4), LDpred2-grid had the highest odd ratios per standard deviation of PRS  . A weak to 6.8%) . Performto 6.8%) .In the geographical validation cohort, discrimination and explained variation improved compared with the test cohort for all PRS models. LDpred2-derived models performed best, and all genome-wide models showed improved performance over the GWAS significant model . All modIn subgroup analyses of logistic regression models , discrimination and explained variation were better in men; models were better fitted in women but underpredicted risk to a greater extent, particularly in higher risk groups. Discrimination and explained variation were poorer in individuals with a first degree family history of colorectal cancer, with models systematically underpredicting risk across PRS risk groups. All models tended to underpredict risk across age groups, with more marked miscalibration in the 55-59 years age group due to a step in observed risk (supplementary fig S8). PRS performance was poor in the minority ethnic validation cohort . Models Comparative demographics of the original QCancer-10 derivation cohortGiven the similarities in performance of LDpred2-grid and LDpred2-grid-sp models, we selected LDpred2-grid-sp as the top performing genome-wide PRS for integrated modelling with QCancer-10, favouring sparsity . We found no evidence of interactions between QCancer-10 and PRS terms in the models .Cox models combining the QCancer-10 risk score with LDpred2 sparse grid model (QCancer-10+LDP), and the GWAS significant PRS (QCancer-10+GWS) both outperformed QCancer-10 . Figure Models predicting risk in men had better discrimination and explained more of the variation in risk than models for women . CalibraQCancer-10+LDP consistently provided the best risk prediction. By way of illustrating a possible clinical use of the model, enhanced screening is frequently offered for people with at least one first degree relative with colorectal cancer, corresponding to an about 2.2-fold increased risk.Illustrative decision curve analyses supported the findings that, across a range of threshold probabilities, QCancer-10+LDP gave greater net benefit than did QCancer-10+GWS and QCancer-10, for both men and women, and predicted a greater number of interventions avoided across clinically relevant thresholds . Taking We have developed and validated new prediction models for colorectal cancer that combine phenotypic risk with genome-wide PRS.The incremental benefit of PRS over use of QCancer-10 alone is modest. The increase in C statistic is 0.04 .Of the PRS methods evaluated, LDpred2-grid and LDpred2-grid-sp models had the highest discrimination, explained more of the variation in risk, and were well calibrated. The improvement in performance between the derivation and validation cohorts when using the PRS models probably results from lower genetic homogeneity in the validation cohort. Evaluation of the PRS in a geographically external cohort shows portability of the PRS models. The geographical validation cohort was well matched in age to the derivation cohort but had a higher proportion of women and prevalence of colorectal cancer was higher at 1.79% compared with 1.51% in the derivation cohort. All models tended to underpredict risk in the top risk group in the geographical validation cohort, probably due to demographic differences between the cohorts, which improved with recalibration. This miscalibration was greater for women, which might reflect a greater difference in demographics and cancer risk between derivation and validation cohorts for women compared with men. In addition, we noted some miscalibration by age due to a jump in observed risk in the 55-64 year age groups (supplementary figs S8 and S12). This increase could be due to early detection of prevalent colorectal cancer on entry to the bowel screening programme, which begins at 50 in Scotland compared with 60 in the rest of the UK during the study period. This miscalibration was not seen in the integrated modelling cohort. We would expect performance for northern European individuals in the general population to be similar to that of the validation cohort.Our PRS findings are in line with a recent study in which a PRS derived using LDpred software (an earlier version of LDpred2) out-performed both machine learning approaches and a 140 GWAS significant single nucleotide polymorphism PRS.The UK Biobank study provides a large sample size, extensive phenotyping, data completeness, and linkage to external datasets. In general, UK Biobank represents a healthy population with, for example, lower prevalence of smoking and of most self-reported health conditions than in national Health Survey for England data.Firstly, because the UK Biobank study has a lower incidence of disease than does the general population of screening age, sensitivity is expected to be higher in the screening population, which is at higher risk.Further limitations of our study might include unknown differences in the demographics of the contributing base GWAS datasets and UK Biobank. Additionally, we did not include mendelian colorectal cancer syndromes in the genetic model, probably resulting in poorer calibration in people with a family history of colorectal cancer.In existing cancer screening programmes in the general population, the risk models perform at a level that might be clinically useful.Colorectal cancer is arguably the best placed of all cancers to benefit from stratified screening. Although we have shown that risk stratification in some form is likely in principle to improve resource use and performance of colorectal cancer screening, the added benefit of adding PRS to QCancer-10 is modest, and we find no clear justification for implementing PRS based risk stratification at present. Risk assessment, particularly PRS, also has the potential to reduce screening participation and widen existing health disparities. Thus, if the potential clinical benefit of our integrated risk model is deemed acceptable to policy makers, a thorough real world evaluation of both QCancer-10 and QCancer-10+PRS, including cost effectiveness, should be undertaken before implementation. We contend that an introduction of risk stratified screening for colorectal cancer or other common cancers is premature without a full assessment alongside current screening methods in a cohort representative of the screening population.Risk stratification based on genetic or environmental risk factors could improve cancer screening outcomesNo previously published study has examined integrated models combining genome-wide polygenic risk scores and non-genetic risk factors beyond ageQCancer-10  is the top performing non-genetic risk prediction model for colorectal cancerAdding PRS to the QCancer-10  risk prediction model modestly improves performance and clinical benefit, with greatest gain from the LDpred2 genome-wide PRSThe challenges and costs of implementing stratification based on polygenic risk scores in population screening might not be justified by the incremental benefit over QCancer-10 aloneDetailed real world evaluation, including value added to screening regimens, economic assessment, and effects on participant participation are needed before PRS implementation


Oral manifestations and lesions could adversely impact the quality of people’s lives. COVID-19 infection may interact with smoking and the impact on oral manifestations is yet to be discovered.The aim of this study was to assess the self-reported presence of oral lesions by COVID-19-infected young adults and the differences in the association between oral lesions and COVID-19 infection in smokers and non-smokers.This cross-sectional multi-country study recruited 18-to-23-year-old adults. A validated questionnaire was used to collect data on COVID-19-infection status, smoking and the presence of oral lesions  using an online platform. Multi-level logistic regression was used to assess the associations between the oral lesions and COVID-19 infection; the modifying effect of smoking on the associations. vs 7.5%, p = 0.009) and change in taste  were greater in COVID-19 infected than non-infected persons. The association between COVID-19-infection and dry mouth was stronger among smokers than non-smokers  while the association with change in taste was stronger among non-smokers .Data was available from 5,342 respondents from 43 countries. Of these, 8.1% reported COVID-19-infection, 42.7% had oral manifestations and 12.3% were smokers. A significantly greater percentage of participants with COVID-19-infection reported dry mouth and change in taste than non-infected participants. Dry mouth (11.1% Dry mouth and changed taste may be used as an indicator for COVID-19 infection in low COVID-19-testing environments. Smoking may modify the association between some oral lesions and COVID-19-infection. The COVID-19 pandemic has adversely affected people’s lives and caused multiple clinical manifestations  includinFurthermore, the public health response to COVID-19 pandemic affected the mental health of many individuals because of the induced psychological distress resulting from the loss of job, uncertainties about income, prolonged isolation and poor access to social support . ShutdowTobacco use causes oral manifestations due to the harmful chemicals and nicotine released when tobacco is burned in the oral cavity . TobaccoThere is growing evidence about the association between COVID-19 and the occurrence of oral manifestations . There aTherefore, the aim of this study was to determine the association between the self-reported presence of oral manifestations in young adults from several countries and COVID-19 infection, and how this association may be modified by smoking. The null hypothesis of the study is that there is no significant difference in the self-reported presence of oral manifestations between participants infected and not infected with COVID-19, and that this difference is not modified by smoking status.This was a cross-sectional study that collected data from 43 countries between August 2020 to January 2021 . EthicalThe sample size for the study was estimated to address the study aims. For the first aim, we assumed a prevalence of oral manifestations ranging from 5% to 90% in different countries, with 5% margin of error and 95% confidence level. The required sample size ranged from 73 to 383. This was increased to 167 and 460 to accommodate a non-response rate of 20% . For theParticipants were included in the study if they were young adults aged 18–23-year-old and consented to participate. Participants had to be able to read the language of the survey and have access to an electronic device connected to the internet to be able to respond to the survey.The questionnaire was constructed in Arabic and translated by native dentists into French, Malay, Turkish and English. The content validity index (CVI) was calculated  for eachThe core team  invited collaborators to recruit data from their countries/regions. Each collaborator received the study protocol, the ethical approval in addition to the customized link and details about required number of participants. We used a snowball sampling technique where country collaborators asked individuals in their network to further circulate the questionnaire link among those they knew.Data were collected using an electronic questionnaire that comprised two sections: the first section asked about the participants’ gender, medical problems the participant had, their COVID-19 status (confirmed COVID-19 infection based on antigen or PCR test), and if they smoked. The second section asked if participants had any of the eight listed oral lesion or conditions including dental caries, stained teeth, gingival inflammation, dry mouth, change in taste, leukoplakia, burns or ulcers, or hairy tongue.p values were calculated. Significance was set at the 5% level.The associations between oral manifestations and COVID-19 and smoking status were assessed using the chi square test. Multilevel binary logistic regression was used to assess the relationship between each oral lesion and COVID-19 infection status, adjusted for gender, history of medical problems and smoking. Country of residence was entered into the model as random effect variable. Multilevel binary logistic regression was also used to assess whether smoking modified the association between oral manifestations and COVID-19 infection status after adjusting for gender and a history of medical problems with country entered as random effect variable. IBM SPSS for Windows version 22.0  was used for statistical analysis. Adjusted odds ratios (AOR), confidence intervals (CI) and The CVI for the English and Arabic versions of the questionnaire was 0.87 based on feedback from nine dentists, 0.97 for the Turkish version (seven dentists), 0.80 for the Malay version (five dentists) and 0.88 for the French version (five dentists).Complete responses were available from 5,342 young adults from 43 countries . Table 1p < 0.05). A significantly higher proportion of participants who were smokers than those who are not smokers had stained teeth, gingival inflammation, dry mouth, change in taste, leukoplakia, burns or ulcers and hairy tongue (p < 0.05).p = 0.02).p > 0.05) except for the association between COVID-19 infection and change in taste where the association was non-significantly (p = 0.86) stronger among non-smokers than smokers.For the other oral manifestations, the odds for the association between the presence of manifestations and COVID-19 infection were higher among smokers than non-smokers although the effect modification was not significant (The study showed that persons with COVID-19 infection had higher frequency of reporting dry mouth, change in taste, leukoplakia, and hairy tongue. However, after controlling for gender, having medical problems and smoking status, none of these associations was statistically significant, and the strongest association among them was for the change in taste. This suggests that underlying medical conditions and smoking may predispose persons infected with COVID-19 to these oral conditions accounting at least partly for the strength of the association between COVID-19 infection and the presence of oral manifestations or condition. Smoking significantly modified the association between COVID-19 infection and stained teeth where a directly proportional association was observed only among smokers and there was no association among non-smokers. The association between COVID-19 infection and dry mouth was stronger in smokers than in non-smokers whereas the association with change in taste was stronger among non-smokers than smokers. The null hypothesis can thus be rejected.Our findings have implications for identifying oral manifestations associated with COVID-19 and the detection of COVID-19 infection among young adults especially in settings where the uptake of COVID-19 testing is low. In these settings, dentist’s screening for oral manifestations should have a high index of suspicion for COVID-19 when adolescents and young persons who smoke or have health problems, have oral the reported manifestations.Similar to prior studies, we observed salivary gland-related oral manifestations such as dry mouth, and taste alteration in COVID-19 infected patients . The salThe higher odds for a change in taste in association with COVID-19 infection than the other oral manifestations corroborate the results of a systematic review that showed a significant association between taste disorders and COVID-19 infection . This taStreptococcus mutans in the mouth of smokers hence promoting the formation of dental plaque that gets stained by cigarette contents (The study showed that smoking modified the association between COVID-19 infection and teeth staining where the odds ratios were significantly higher in smokers than in non-smokers although these associations were not statistically significant. Nicotine significantly increases the number of contents . Smokerscontents . Unlike We also observed that the association between alteration in taste and COVID-19 infection was greater in non-smokers than smokers. COVID-19 infection may cause chemosensory impairment that affects taste sensation , and smoThe present study has some limitations. First, its cross-sectional design cannot prove causation but only suggest association. Second, self-reporting may under or overestimate the presence of some oral manifestations, although this may be the only method to assess the presence of some conditions such as change in taste. Also, a recent study compared clinical evaluation with self-reported oral conditions and showed a positive association between both reports . Future Smoking may confound the association between COVID-19 infection and the presence of some oral manifestations and modify the association with others. Policy makers, clinicians and health educators may need to take these associations into consideration when designing interventions.10.7717/peerj.13555/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.13555/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.13555/supp-3Supplemental Information 3Click here for additional data file.


Tooth decay is one of the most prevalent diseases worldwide, and efficient tooth brushing with a fluoride-containing dentifrice is considered fundamental to caries prevention. Fluoride-containing dentifrices have been extensively studied in relation to enamel resistance to demineralization. Arginine (Arg) has also been proposed as a promising prebiotic to promote pH buffering through ammonia production. Here, we present the first metagenomic  and metatranscriptomic (RNAseq of the same community) analyses of human dental plaque to evaluate the effect of brushing with fluoride (Fl) and a Fl+Arg containing dentifrices on oral microbial composition and activity. Fifty-three patients were enrolled in a longitudinal clinical intervention study with two arms, including 26 caries-active and 27 caries-free adults. After a minimum 1-week washout period, dental plaque samples were collected at this post-washout baseline, 3 months after the use of a 1450-ppm fluoride dentifrice, and after 6 months of using a 1450-ppm fluoride with 1.5% arginine dentifrice.There was a shift in both the composition and activity of the plaque microbiome after 3 months of brushing with the fluoride-containing toothpaste compared to the samples collected at the 1-week post-washout period, both for caries-active and caries-free sites. Although several caries-associated bacteria were reduced, there was also an increase in several health- and periodontitis-associated bacteria. Over 400 genes changed proportion in the metagenome, and between 180 and 300 genes changed their expression level depending on whether caries-free or caries-active sites were analyzed. The metagenome and metatranscriptome also changed after the subjects brushed with the Fl+Arg dentifrice. There was a further decrease of both caries- and periodontitis-associated organisms. In both caries-free and caries-active sites, a decrease of genes from the arginine biosynthesis pathway was also observed, in addition to an increase in the expression of genes associated with the arginine deiminase pathway, which catabolizes arginine into ammonia, thereby buffering acidic pH. Bacterial richness and diversity were not affected by either of the two treatments in the two arms of the study.Our data demonstrate that long-term use of both assayed dentifrices changes the bacterial composition and functional profiles of human dental plaque towards a healthier microbial community, both in caries-free and caries-active sites. This observation was especially apparent for the Fl+Arg dentifrice. Thus, we conclude that the preventive benefits of tooth brushing go beyond the physical removal of dental plaque and that the active ingredients formulated within dentifrices have a positive effect not only on enamel chemistry but also on the metabolism of oral microbial populations.Video AbstractThe online version contains supplementary material available at 10.1186/s40168-022-01338-4. Caries is a multi-factorial disease caused by microbial-derived acidic pH, which demineralizes the enamel . It is cAlthough there is solid clinical evidence of the beneficial effects of fluoride and arginine dentifrices, the availability of functional studies that evaluate the bacterial composition and activity in vivo is scarce. Regarding this, the new “omics” techniques now allow researchers to functionally characterize a microbial community  based onIn the current work, subjects were given a new toothbrush and instructed to brush 2×/day with a fluoride-containing dentifrice for a week to homogenize the differences in oral microbiota caused by the differences in oral hygiene habits and dentifrice use prior to the study. Then, they were asked to brush with the same fluoride dentifrice for 3 months to further harmonize the microbiota before introduction to the Fl+Arg dentifrice. The Fl+Arg dentifrice was used for the remaining 6 months of the study to investigate the additional effects of arginine over the microbiota Fig. . In thisbaseline) and patients were asked to continue toothbrushing with the dentifrice with fluoride for 3 months after which the same teeth were sampled again (fluoride). Participants were then asked to brush with a Fl+Arg dentifrice for 6 months, after which plaque collection was repeated at the same sites.The effect of fluoride and Fl+Arg dentifrices over the taxonomic composition, genetic potential, and functional activity of the oral microbiota was studied in 26 caries-active (CA) and 27 caries-free (CF) patients Fig. A. Fiftee6 initial reads per sample from which 20 ± 14% were human, and 57 ± 8.7% of the remaining, bacterial reads were annotated. In the MTT datasets, an average of 5.2 × 106 reads per sample were initially sequenced. Of those, 9.2 ± 11.7% belonged to the host, and 78.41 ± 20.1% of the remaining reads were ribosomal. Finally, 32.7 ± 13.2% (0.2 × 106) of the remaining reads were annotated.In order to establish a threshold to filter out low-coverage samples, we performed rarefaction analyses in the 16S rRNA sequencing (16S), the metagenome (MTG), and the metatranscriptome (MTT) datasets, by plotting the sequencing coverage against the estimated number of bacterial species, as well as to the estimated number of gene functions in the KEGG database. The curves showed that taxonomical and functional genetic richness stabilized after 10k, 100k, and 50k reads for the 16S, MTG, and MTT datasets, respectively  and CF (30.3 ± 5%) patients  and combined accounted for 50–60% of the active microbiota. The proportion of these genera in the whole (DNA-based) and the active (RNA-based) microbiota differed substantially, suggesting that bacterial activity at a given moment may not correlate with its overall presence in the community and that its activity may change considerably through time. In fact, there was no significant correlation in bacterial composition between the MTG and the MTT datasets for the top 15 most abundant genera . These results are in agreement with previous reports which showed that microbial communities may significantly vary when comparing MTG and MTT data ). Similarly, the same pathway was under-represented and under-expressed after brushing with Fl+Arg. In total, 57 out of 63 genes annotated as lipopolysaccharide biosynthesis were under-represented or repressed after Fl+Arg treatment in the MTG and MTT datasets when compared to brushing with fluoride dentifrice alone  and histidine metabolism. Histidine metabolism genes were over-expressed after brushing for 3 months with the fluoride dentifrice in comparison with the samples taken at baseline and were also over-represented and over-expressed after brushing with the Fl+Arg in comparison with those taken after 3 months of fluoride toothbrushing. This suggests that histidine metabolism was increased as subjects continued to brush with the fluoride dentifrice and increased even more after brushing with the Fl+Arg dentifrice. Thus, given the interconnection between the catabolic routes of different amino acids, arginine supplementation could also influence other amino acids’ metabolic pathways. This is also confirmed by the observation that glutamate ABC transporters  were over-represented after brushing with the Fl+Arg dentifrice.Regarding the genes over-represented after brushing for 3 months with the fluoride dentifrice vs baseline, 171 were detected in CA whereas 242 were detected in CF individuals , spermidine/putrescine transport system ATP-binding protein , and l-cysteine transport system substrate-binding protein . Given that the arginolytic pathway can lead to putrescine, this represents another example of how arginine metabolism can influence other connected pathways.Interestingly, in CF patients, there were several amino acid-related transporters that were under-represented after the Fl+Arg treatment such as branched-chain amino acid transport system permease protein (manX and manZ) were only over-expressed in CA patients whereas cellobiose-specific components (starch-related) were exclusively over-expressed in CF (celA and celB).Regarding the genes codifying for phosphotransferase systems for sugars, only one component was over-represented in CA whereas 8 were over-represented in CF after the Fl+Arg treatment. At the transcriptional level, 4 and 6 genes were over-expressed after the treatment in CA and CF patients, respectively. Interestingly, fructose- and mannose-specific components (nuoGFMHDN) were under-expressed whereas nine subunits of V/A-type H+/Na+-transporting ATPase (ntpABCDEFHIK) (8 with a fold change > 2) were over-expressed after the 6-month Fl+Arg treatment. Both complexes play a central role in cellular energy production, and the data suggest that arginine addition changed which system was preferably used for bacteria ATP production. At the transcriptional level, there was also a significant over-expression of ethanolamine utilization proteins (eutLMHQBTS) after the arginine treatment.Other functions potentially relevant for biofilm formation are quorum sensing (QS) pathways. There were significant differences in both MTG and MTT datasets, with 13 QS genes over-represented or over-expressed after 3 months of brushing with the fluoride dentifrice and 18 QS genes over-represented or over-expressed after brushing with the Fl+Arg dentifrice  prior the study start, whose effect could be eliminated after entering the study, when these kinds of products were not allowed. MTT data indicated that Neisseria, Prevotella, and Porphyromonas species were not only more abundant but also more active after 3 months of brushing with the fluoride dentifrice. Interestingly, even though 16S rRNA datasets showed differences at the compositional level when compared with MTG, the differences detected after the 3-month brushing period supported the MTG results. It has to be borne in mind that PCR of the 16S rRNA gene introduces a bias by under-amplifying bacteria with a sequence divergence at the primer binding sites [Actinomyces. Thus, our data suggest that previous 16S-based studies of dental plaque have consistently underestimated the levels of Actinomyces in dental plaque, as in our MTG dataset the levels of this organism are over 40%  because participants may have been using a fluoride product before enrolling in the study and because volunteers participating in a clinical study normally improve their hygiene habits and skills during the study period. In order to ameliorate these confounding effects, clinical trials normally include a “washout period” of 1–2 weeks to homogenize oral hygiene product effects. Our study also included this standard homogenization period and was specially designed to test the effect of arginine by extending the period of brushing using a regular  fluoride-containing dentifrice. The results obtained show a taxonomical and functional alteration in the oral microbiota when patients use a dentifrice with fluoride for 3 months compared to baseline, indicating that standard short washout periods may not be sufficient to control for the abovementioned confounding effects upon entering a clinical study. This long-term, cumulative effect of dentifrice use has also been observed after the use of oral hygiene products with stannous ions, although in this case, only taxonomic changes were studied within the salivary microbiome . In the  authors , 37. On ng sites , with a ng sites  or with ng sites . Thus, tSelenomonas noxia) were increased. The usual commensal members of the oral microbiota, including some health-associated organisms such as Neisseria and Rothia were favored after brushing with Fl+Arg. In addition, the MTG analysis of bacterial abundance after brushing with Fl+Arg suggested that arginine addition increased arginolytic bacteria. Thus, future work should study whether nitrate-reducing bacteria, which also produce ammonia [Treatment with the dentifrice-containing fluoride and 1.5% arginine also induced changes at the taxonomical and functional levels. Firstly, the periodontitis-associated bacteria which had increased after 3 months of brushing with fluoride dentifrice (vs baseline) were significantly reduced, although other potential periodontal pathogens  or the different methodologies (16S gene probe arrays vs 16S rRNA sequencing).Our results generally agree with those from a previous study that compared the effects of an arginine-containing toothpaste in the microbiota of CA and CF individuals using 16S microarrays . In theifluoride treatment by itself increased periopathogenic bacteria whereas brushing with the Fl+Arg dentifrice decreased these organisms but enhanced the growth of other potential periopathogens such as Selenomonas and Treponema species. This could be due to a reduction in acidogenicity of the biofilm at the expense of a more protein-rich environment, and the potential preference for proteolytic or alkalophilic communities should be further studied in the future. In this regard, it is generally accepted that a cariogenic plaque is normally saccharolytic and acidophilic whereas a periodontopathogenic plaque appears to be more proteolytic and alkalophilic, and therefore, the two microbial ecosystems leading to these two conditions could be favored by different external factors, ranging from salivary pH to dietary protein and carbohydrate intake [Interestingly, an increase of potential periopathogens observed after brushing with fluoride and Fl+Arg dentifrices has been previously reported . In our e intake , 42. It e intake , could ie intake .These results might bring up a question on whether dentifrices should combine fluoride and arginine. Considering that arginine alone provides pH buffering capacity once the environment is acidic but does not initially prevent acidification of the plaque, and given that fluoride has been shown to inhibit several glucolytic pathways , 15, theActinomyces or Corynebacterium and also the mentioned changes in the arginolytic pathway.The MTG is well-known to represent the metabolic and genetic potential of a community and is expected to be more stable throughout the day whereas a metranscriptome provides a snapshot of the bacterial activity and gene expression at a specific sampling time , 27. ForCorynebacterium matruchotti could be relevant as it plays an instrumental role in biofilm architecture [Actinomyces species are commensal organisms in the oral cavity and are generally more active in dental plaque of healthy individuals in comparison with those with caries [Actinomyces genus can invade oral mucosa and enter the subcutaneous tissue causing actinomycosis [A. gerencseriae, have been detected at higher activity levels in root caries lesions [A. gerencseriae was significantly reduced (p = 0.02) after brushing with the regular fluoride and Fl+Arg dentifrices. Finally, Neisseria and Rothia, which increased after the use of both dentifrices, are nitrate reducers generally associated with health in comparison with periodontitis patients [Among the commensal microorganisms that increased after brushing with both dentifrices, itecture , 45. Acth caries , 47. Howomycosis , and othomycosis , 49, 50  lesions . Interespatients –54 and ipatients .Brushing with Fl+Arg dentifrices had an effect not only on arginine-related genes but also on many others, especially those related to amino acid metabolism. Compared to the 3-month samples, brushing with the Fl+Arg dentifrice changed the transport of several amino acids and glutamate, the PTS systems of several sugars, and promoted the utilization of ethanolamine as well as changed the oxidative phosphorylation complex.E. faecalis after fluoride exposure [S. mutans, which has the AgDS pathway, have proven that ammonia production through the AgDS and the consequent alkalinization is smaller than ADS [ureC) after arginine treatment [ureC was over-represented in CA patients after treatment with the Fl+Arg dentifrice which could contribute to pH buffering by urease-dependent ammonia production [Regarding the ethanolamine utilization proteins, a similar pattern was found in pure cultures of exposure , and thethan ADS , 57. Thethan ADS , 58, 59 than ADS , which coduction . In addioduction , 35. FinOur data show that a prolonged toothbrush with either a fluoride- or Fl+Arg-containing dentifrice had an overall positive effect on the composition and functional profile of the dental plaque microbiota. The changes observed were beneficial for caries prevention since health-associated microbes appear to be favored by these treatments. Moreover, the addition of arginine into the dentifrice increased the abundance of arginolytic bacteria and the expression of genes involved in the arginolytic route, confirming that ammonia production through this pathway is activated and may contribute to reducing plaque acidogenicity. Altogether, the data indicate that the active ingredients in dentifrices, apart from having a chemical effect on tooth tissues, appear to be beneficial for caries prevention from a microbiological point of view, as a consequence of a taxonomic and functional shift associated with oral health. However, the potential increase of known periodontal pathogens must be further studied to determine the potential effect of dentifrices’ active ingredients on periodontal health. We acknowledge that the key to maintaining oral health is to minimize dysbiosis by following a healthy oral hygiene regimen. For some, this may mean using an oral hygiene product specially designed to manage caries or gum inflammation, and our data indicate that active ingredients in dentifrices, such as arginine, improve oral biofilm bacterial composition. We therefore propose that, in line with current views supporting the need of advancing in personalized dentistry tools , dentifrbaseline . Patients then received a dentifrice with 1.5% arginine and 1450-ppm fluoride as a sodium MFP and an insoluble calcium compound, which they used for an additional period of 6 months. After 6 months, a third set of samples was collected and termed Fl+Arg. Sampling at all time points was repeated a week after the initial visit to have a replicate of samples collected. Replicates were combined to obtain enough nucleic acid concentration to perform the metagenome and metatranscriptome analyses. The same CA or CF teeth were sampled at every collection time point. Patients were asked to visit the clinic at least 12 h after their last teeth brushing, meal, or drink (anything but water). All reported adverse effects during the study were not related to treatment.An overview of the study is shown in Fig. ine Fig. A. At thiGiven that fluoride could have a long-term effect on bacterial communities, standard washout periods (1–2 weeks) may not be sufficient to eliminate fluoride as a confounding factor in testing the effect of arginine. Therefore, baseline samples were collected after the standard washout period of two weeks, and then a 3-month pretreatment with fluoride was selected as a more robust period for studying the effect of additional arginine, given that the arginine-containing toothpaste used also contained fluoride. The length (6 months) of the treatment with arginine-containing toothpaste was based on previous clinical trials where this period was sufficient to provide a clinical benefit. The study was registered on ClinicalTrials.gov with reference number NCT05138978.RNA was extracted using the Ambion mirVana miRNA isolation kit  and modified to include enzymatic digestion with lysozyme, lysostaphin, and mutanolysin plus bead beating. DNA was eluted from the organic phase of the RNA extraction and then ethanol precipitated.16S rRNA gene libraries were created as described by Caporaso et al. , employiOne hundred fourteen sample libraries were prepared using the Celero DNA-Seq kit  and sequenced on the Illumina NextSeq platform using 2 × 150 bp 550 High Output Kits. The remainder of the samples were not sequenced because of quality issues.One hundred eleven samples were prepared as RNA-Seq libraries using the Ovation RNA SoLO kit . Eleven 150-bp paired-end libraries were sequenced on the Illumina MiSeq Platform during the optimization phase while the remaining libraries were sequenced as 150-bp paired-end libraries on the Illumina NextSeq Platform.No rRNA depletion methods were used. According to our own experience and reported studies, the commercially available kits can introduce a significant bias in mRNA proportions . TherefoFor 16S rRNA analyses, we used the pipeline of dada2  as previcutadapt), short reads, reads with high percentage of ambiguous bases, and low mean quality reads were trimmed (using prinseq). Once cleaned, R1 and R2 metagenomic paired reads were joined using Flash [For both metagenomic (MTG) and metatranscriptomic (MTT) reads, adapters  to the human genome . AlignedMTG reads were assembled into contigs using Megahit  and openFor the annotation of the MTT dataset, reads were aligned with bowtie2 to a manually curated database. This database was based on the Human Oral Microbial Database (HOMD)  with the5 for MTG samples and 5 × 104 for MTT samples). The Vegan library of R [R language was used for statistical computing  to perfoary of R  was usedary of R , and divAdditional file 1: Additional Tables 1–17. Additional tables are separated in sheets in a single excel file. The first line in each sheet contains the title of each additional table.Additional file 2: Additional Figure 1. Relationship between sequencing effort and bacterial species and gene functions in human dental plaque. Rarefaction curves of all samples used for the current study when using metagenomic (A and B) or metatranscriptomic (C and D) data represent the number of species (left) and functions (right) detected relative to the number of sequence reads. Gene functions were calculated based on the KEGG database. Thresholds of 1x105 reads/sample from the MTG and 5x104 reads/sample for the MTT were used for annotation and subsequent analysis. Rarefaction curves at species level when samples were sequenced using 16S rRNA sequencing approach were also studied (E). Finally, the curves using metagenomics were also analyzed at species level when only species above 0.1% were considered (F).Additional file 3: Additional Figure 2. Differences in bacterial abundance between metagenomic (MTG) and metatranscriptomic (MTT) data. The percentage of reads annotated to the top-20 most abundant genera is presented for both datasets in caries-active (CA) and caries-free (CF) samples. Baseline samples were collected after the washout period.Additional file 4: Additional Figure 3. Differences in bacterial composition at genus level between datasets. (A) Bar graphs show the proportion (%) of the top-20 most abundant members of supragingival plaque microbiota at Baseline (post-washout period) and after Fluoride and Fluoride + Arginine treatments. At each time point, data are shown for the three available datasets . CA and CF individuals are also differentiated. (B) PCA plot of samples from the CA and CF individuals at the three time points using 16S rRNA sequencing or MTG.Additional file 5: Additional Figure 4. Number of species and genes detected (N), Chao1 and ACE richness indexes and Shannon diversity index for each study group. Data are shown for all three available datasets . Baseline samples were collected after the washout period. Those comparisons which were significant  according to Wilcoxon test are indicated with *.Additional file 6: Additional Figure 5. Changes in the levels of total and transcriptionally active species caused by toothbrushing with a fluoride toothpaste. Each dot represents a bacterial species that was significantly under- or over-represented in the 16S rRNA sequencing, metagenomic (MTG) or metatranscriptomic (MTT) datasets after 3 months of using a fluoride dentifrice. The abundance (y-axis) and the fold change  is shown for each bacterial species. Dots corresponding to species over-represented at Baseline are colored in blue and those over-represented after treatment with fluoride are colored in orange. Species differentially represented in caries-active sites (CA) are at the left panels while those for caries-free individuals (CF) are at the right panels.Additional file 7: Additional Figure 6. Presence of arginolytic and non-arginolytic bacteria at the different sample collection times. Bacterial species were separated into argynolytic and non-argynolytic (Non-arcA) according to the presence of the arcA gene. The abundance of each group was calculated at each time point. Data are separately shown for Caries-Active (A) and Caries-Free (B) individuals. Species abundance was normalized by the number of reads (R), by the length  of the gene and by the size of the dataset (in Megabasepairs).Additional file 8: Additional Figure 7. Metagenomic and metatranscriptomic functional profiles correlation. The overlap between over-represented (MTG) and over-expressed (MTT) genes are represented as Venn diagrams. The genes that were over-represented and over-expressed are shown in the overlapping region for each comparison: Baseline vs Fluoride (left panel) and Fluoride vs Fluoride+Arg (right panel).Additional file 9: Additional Figure 8. Changes in bacterial communities’ gene content and activity after Fluoride treatment. Each dot represents a gene that was significantly under- or over-represented in the metagenomics (MTG) or metatranscriptomic (MTT) datasets after 3-month use of fluoride dentifrice. The abundance/expression level in the MTG/MTT datasets  and the fold change (RLM before/RLM after) are shown for each gene. Dots corresponding to genes over-represented in baseline are colored in blue and those over-represented after Fluoride treatment are depicted in orange. Genes differentially represented in caries-active sites (CA) and Caries-free individuals (CF) are shown at the top and lower panels, respectively.Additional file 10: Additional Figure 9. Changes in bacterial communities’ gene content and activity after Fluoride + Arginine treatment. Each dot represents a gene that was significantly under- or over-represented in the metagenomics (MTG) or metatranscriptomic (MTT) datasets after 6-month use of Fl+Arg dentifrice. The abundance/expression level in the MTG/MTT  and the fold change (RLM before/RLM after) are shown for each gene. Dots corresponding to genes over-represented after fluoride treatment are labeled in orange and those in green correspond to genes over-represented after Fl+Arg treatment. Genes differentially represented in Caries-Active sites (CA) and Caries-Free individuals (CF) are shown at the top and lower panels, respectively. AgDS, agmatine deiminase system; ADS, arginine deiminase system; NQR, NADH:quinone oxidoreductase; Na+ATPase, V/A-type H+/Na+-transporting ATPase; PTS, phosphotransferase system.Additional file 11: Additional Figure 10. Effect of genome-size normalization on taxonomic composition. The abundance of the top-15 genera for the MTG dataset was normalized considering not only the size of the dataset but also by the corresponding genome size (Mbp).Additional file 12. Additional Material and Methods.