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Outcome | death, ’ disease, cancer, CVD, Deaths, Death | CVD, CANCER, SECONDARY | The primary outcome of the study was all-cause mortality and secondary outcomes included CVD mortality, cancer mortality, and other-cause mortality. The SUN cohort keeps a close and permanent follow-up that provides continuously updated information about participants’ disease incidence and death. Deaths are reported by next of kin, work colleagues, and professional associations (such as alumni). In addition, we checked the Spanish National Death Index, and the National Statistics Institute ( | PMC10195723 |
Other covariates | Participants provided additional information at baseline on sociodemographic characteristics, medication use, personal and family history of medical conditions, and adherence to the Mediterranean diet (MedDiet) [ | PMC10195723 | ||
Statistical analysis | cancer, death | REGRESSION, CANCER, CVD | Baseline characteristics for all participants and OBS components were described according to OBS quartiles, expressed as means with standard deviations for numerical variables or percentages for categorical variables. Pearson product–moment correlation coefficients were calculated between dietary OBS components.Cox proportional regression models were fitted with age as the underlying time variable to assess the risk of all-cause mortality, CVD death, cancer mortality, and other causes of death across OBS quartiles. Follow-up for each participant was calculated from the date the baseline questionnaire was returned to the date of death reported or the last questionnaire was received, whichever came first. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) across OBS quartiles for all-cause and cause-specific mortality, using the lowest OBS quartile (Q1) as the reference for all models. In addition, linear trend tests were performed by assigning medians to each quintile and treating it as a continuous variable.After conducting crude analyses, we fitted different models to control for potential confounders for the effect of the OBS on mortality risk: We stratified our analyses by sex, age (< 60 and ≥ 60 years), and presence of chronic conditions (< 1 and ≥ 1), and we assessed effect modification between these variables and OBS quartiles by testing an interaction product-term (3 degrees of freedom) with the maximum likelihood ratio test. We additionally evaluated the specific contribution of each of the individual components of the OBS (treated as continuous variables) to the association with all-cause mortality by removing one component at a time from the total score and including the same component in the model as a covariate. Lastly, multiple sensitivity analyses were performed to test the robustness of the findings by repeating the multivariable-adjusted Cox regression models under different scenarios: excluding participants with < 2 years of follow-up before March 2017: participants deceased (All analyses were conducted with Stata version 15.0 (StataCorp, College Station, TX). All | PMC10195723 |
Results | PMC10195723 | |||
Contribution of the individual components of the OBS | tachycardia, cardiovascular disease, aneurysm, cancer, stroke, diabetes, angina pectoris, atrial fibrillation, depression, hypertension, dyslipidemia | PULMONARY EMBOLISM, MYOCARDIAL INFARCTION, CARDIOVASCULAR DISEASE, ANEURYSM, CARDIOVASCULAR DISEASES, DEEP VEIN THROMBOSIS, CANCER, STROKE, RECRUITMENT, INTERMITTENT CLAUDICATION, CARDIAC INSUFFICIENCY, DIABETES, ATRIAL FIBRILLATION, EVENTS, HYPERTENSION, DYSLIPIDEMIA | After removing each of the OBS components one at a time and adjusting for the removed component using it as a covariate, the differences in mortality risk estimates remained significant and barely changed (a maximum difference of 2% with respect to the primary OBS analysis) (Fig. All-cause mortality hazard ratios (HRs) and 95% confidence intervals (CIs) per 1-point increment associated with the oxidative balance score (OBS) and after alternate subtraction of each of its dietary components. All models were adjusted for age (underlying variable), family history of cardiovascular diseases (dichotomous), following special diet at baseline (dichotomous), marital status (married, single and others). Mediterranean adherence (continuous), prevalent cancer (dichotomous), prevalent depression (dichotomous), prevalent cardiovascular disease* (dichotomous), prevalent diabetes (dichotomous), prevalent dyslipidemia (dichotomous), prevalent hypertension (dichotomous), sex (dichotomous), total energy intake (continuous), use of aspirin (dichotomous), years of higher education (continuous), corresponding subtracted component, and stratified by deciles of age and recruitment period (6 categories). *Prevalent cardiovascular disease was considered as having at least one of the following events before entering the cohort: aneurysm, angina pectoris, atrial fibrillation, cardiac insufficiency, coronary bypass, deep vein thrombosis, intermittent claudication, myocardial infarction, pulmonary embolism, stroke, or tachycardia | PMC10195723 |
Sensitivity analyses | Consistent with the primary analyses, all point estimates showed an inverse association between the OBS and risk of all-cause and cause-specific mortality for all the sensitivity analyses. Results remained similar in most scenarios, and in some models, the inverse association became stronger, suggesting a robust association between the OBS and mortality risk (Additional File 1: Figure s1). Noteworthy, the OBS association with other-cause mortality became significant in most of the proposed scenarios. | PMC10195723 | ||
Discussion | cancer | CVD, CANCER, OXIDATIVE STRESS, OXIDATIVE STRESS | We investigated the association of overall oxidative balance with all-cause, CVD, cancer, and other-cause mortality risk among nearly 20,000 middle-aged Spanish adults in a Mediterranean cohort. We used a novel holistic score based on 12 a priori selected dietary and non-dietary lifestyle pro- and antioxidants exposures to represent the overall oxidative balance status of an individual in a comprehensive manner. Our results suggested a statistically significant strong inverse association between the OBS and all-cause and cause-specific mortality. In addition, our stratified analyses suggested that women, older participants, and those without any chronic conditions may experience lower mortality risk when achieving a better antioxidant balance status, although no significant interaction effect was observed.Oxidative stress is a well-studied topic in research, with the theory of aging having been proposed decades ago [Our study may help better understand the beneficial effect of a combined set of dietary and lifestyle factors on the prevention of mortality centered on their potential to reduce oxidative stress. Based on our results, the combination of non-smoking, low consumption or abstinence of alcohol, regular physical activity, and maintaining a normal BMI has a strong antioxidant effect, which could be helpful toward the prevention of premature mortality. Moreover, following a dietary pattern rich in antioxidant compounds may further contribute to this preventive effect. In this regard, identification of individuals with an adequate oxidant balance provides a novel approach to design multidimensional interventions aimed at improving dietary patterns accompanied by healthy lifestyle behavior changes. A shift from a unidimensional to a more multidimensional approach with dietary and lifestyle interventions may be warranted in the current nutritional epidemiology.Certain limitations of our study should be acknowledged. First, information used to construct the OBS was collected at baseline, and participants may have modified their dietary and lifestyle exposures throughout follow-up. However, we were not able to update the OBS because a new FFQ was collected only after 10 years of follow-up, other lifestyle factors were not available in this follow-up questionnaire, and the number of participants with information available at this time was limited. Second, our OBS may not capture certain antioxidant factors, such as total fat, certain PUFAS (ω-6 and ω-3), saturated fatty acids, vitamin D, folate, calcium, and fiber; however, diverse discrepancies arose about the inclusion of these factors in OBSs [ | PMC10195723 |
Conclusions | CVD | Our findings suggest that greater antioxidant balance status is associated to lower premature mortality, including CVD and cancer-related mortality. Individuals exposed to both antioxidant dietary and lifestyle exposures may potentially experience the lowest all-cause and cause-specific mortality risk. Moreover, the results of the present study support the utility of our OBS to capture potential correlations and synergies between the antioxidant components that single assessments may not capture. Efforts to prevent premature mortality should be focused on recommending dietary patterns rich in antioxidant compounds together with healthy lifestyle behaviors. | PMC10195723 | |
Additional information | The findings of the study have been presented as a virtual oral presentation at the American Society for Nutrition meeting, NUTRITION 2021 LIVE ONLINE, and the presentation has been recognized as finalist for the American Society for Nutrition’s Emerging Leaders in Nutrition Science Abstract Recognition Award Program. | PMC10195723 | ||
Supplementary Information | Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 237 KB)Irene Talavera-Rodriguez and Cesar I. Fernandez-Lazaro have contributed equally to this work. | PMC10195723 | ||
Acknowledgements | More extensive acknowledgments are included in the online Additional File 1. | PMC10195723 | ||
Author contributions | ITR and CIFL contributed equally to this work. ITR, AHR, MAM and MRC participated in the design research; ITR, CIFL, and MRC conducted research; ITR, CIFL, and MRC analyzed data or performed statistical analysis; ITR, CIFL, MSH, MRC wrote the manuscript; AHR, CG, MSP, CFA, and MAM revised the manuscript and provided critical edits; MRC had primary responsibility for final content; and all authors read and approved the final manuscript. | PMC10195723 | ||
Funding | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The SUN Project has received funding from the Spanish Government-Instituto de Salud Carlos III, and the European Regional Development Fund (FEDER) (RD 06/0045, CIBER-OBN, Grants PI10/02658, PI10/02293, PI13/00615, PI14/01668, PI14/01798, PI14/01764, PI17/01795, and G03/140), the Navarra Regional Government (27/2011, 45/2011, 122/2014), the Government Delegation for the National Drug Plan (2020/ 021) and the University of Navarra. Maria Soledad Hershey receives ERC training-grant support (T42 OH008416). | PMC10195723 | ||
Data availability | Data described in the manuscript, code book, and analytic code will be made available upon request pending application and approval. | PMC10195723 | ||
Declarations | PMC10195723 | |||
Conflict of interest | The authors declare that there is no conflict of interest regarding the publication of this article. | PMC10195723 | ||
Ethical approval | The present study has been conducted in accordance with the Declaration of Helsinki, with the approval of the Institutional Review Board of the University of Navarra. | PMC10195723 | ||
Consent to participate | All participants provided informed consent by responding to the first questionnaire. | PMC10195723 | ||
Consent for publication | No applicable. | PMC10195723 | ||
References | PMC10195723 | |||
Methods | First, we analysed differential gene expression in six lymphoblastoid cell lines (LCLs) before and after incubation with busulfan. Second, we used WES data from 87 HSCT patients and estimated the association with SOS at the SNP and the gene levels. We then combined the results of the expression and the association analyses into an association statistic at the gene level. We used an over-representation analysis to functionally characterize the genes that were associated with a significant combined test statistic. | PMC10075428 | ||
Results | After treatment of LCLs with busulfan, 1708 genes were significantly up-, and 1385 down-regulated. The combination of the expression experiment and the association analysis of WES data into a single test statistic revealed 35 genes associated with the outcome. These genes are involved in various biological functions and processes, such as “ | PMC10075428 | ||
Conclusions | RARE DISEASES | This novel data analysis pipeline integrates two independent omics datasets and increases statistical power for identifying genotype-phenotype associations. The analysis of the transcriptomics profile of cell lines treated with busulfan and WES data from HSCT patients allowed us to identify potential genetic contributors to SOS. Our pipeline could be useful for identifying genetic contributors to other rare diseases where limited power renders genome-wide analyses unpromising. | PMC10075428 | |
Trial registration | For the clinical dataset: Clinicaltrials.gov: | PMC10075428 | ||
Data Availability | Hepatic Sinusoidal Obstruction Syndrome | BLOOD | The data underlying the results presented in the study were included in a previously published article and its supplementary information files: Ansari M, Petrykey K, Rezgui MA, Del Vecchio V, Cortyl J, Ralph R-O et al. Genetic Susceptibility to Hepatic Sinusoidal Obstruction Syndrome in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation. Biol Blood Marrow Transplant 2020; 26: 920–927. The raw data cannot be shared publicly because of information that could compromise the privacy of research participants. Data are available from the CANSEARCH Research platform in pediatric oncology and hematology, University of Geneva, Geneva, Switzerland (contact via e-mail: | PMC10075428 |
1. Introduction | SINUSOIDAL OBSTRUCTION SYNDROME, COMPLICATIONS, COMPLICATION | Sinusoidal obstruction syndrome (SOS) of the liver is a serious, sometimes life-threatening complication of chemotherapies and hematopoietic stem cell transplantation (HSCT) [Various studies have reported germline genetic variants in association with SOS. Most of these studies have used a candidate gene approach. They identified several genetic determinants of SOS in genes that encode for detoxification enzymes, particularly glutathione S transferases such as Since a majority of studies addressing complications of HSCT and similar other rare conditions lack the power to identify genetic predictors due to the small sample size, we used a strategy that is built on the strong link between the exposure to busulfan and the onset of SOS [ | PMC10075428 | |
2. Materials and methods | PMC10075428 | |||
2.1. Design of the study | We developed a data analysis pipeline which includes the following steps:First, we performed a differential gene expression analysis of LCLs before and after | PMC10075428 | ||
Flowchart of the transcriptomic data analysis pipeline in 6 lymphoblastic cell lines with and without | Legend: BAM, Binary Alignment Map; DEG, differentially expressed genes; RNA, ribonucleic acid; SAM, Sequence Alignment Map; WES, whole-exome sequencing. | PMC10075428 | ||
2.2 Transcriptomic analysis of lymphoblastoid cell lines | We collected RNA sequencing data on LCLs (Coriell Cell Repository, Camden, NJ, USA) before and after exposure to busulfan. Briefly, we performed true-seq RNA-sequencing of mRNA extracted from six LCLs (GM7056, GM12239, GM12762, GM12057, GM12489, and GM12546) pre and post 48h of 100 μM busulfan treatment. The chosen concentration of busulfan represents 80% of cell viability in our set of LCLs with an average 50% inhibitory concentration of 415,1+/-203,3 μM. To avoid having non-genetic cell-to-cell variability in gene expression which could be introduced by the culturing condition, we fixed a limit on the maximum number of cell culture medium passages of 15 times.Library construction and sequencing were performed at the iGE3 Genomics Platform–CMU, Geneva, Switzerland, using the Illumina HiSeq 2000 library preparation kit. The differential expression analysis was performed with DESeq2 [ | PMC10075428 | ||
2.3. Data filtering on WES genotype data | We used the unfiltered whole-exome sequencing data, of which analyses were published previously [ | PMC10075428 | ||
2.4. Tests of association of WES data with clinical outcome | Our clinical outcome SOS was defined using the modified Seattle criteria, as outlined in the description of the clinical dataset [We used the Versatile Gene-based Association Study-2 (VEGAS2) software [ | PMC10075428 | ||
2.5. Combined test of transcriptomic and WES data | We computed a gene-based score, and its associated p-value, by combining the results of the differential gene expression analysis (To assign equal weights to the 2 experiments into the combined statistic, we rescaled the p-values derived from the LCL expression analysis to have the same range as the p-values calculated from the WES data, before converting them to Z-scores. We used Bonferroni correction for multiple testing. | PMC10075428 | ||
2.6. Over-representation analysis | The functional characterization of the genes that show a significant difference in expression in LCLs after treatment with busulfan was performed using an over-representation analysis (ORA). We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) [ | PMC10075428 | ||
2.7. Polygenic risk scores | ∑We | REGRESSION | We computed polygenic risk scores (PRSs), for the cases and controls, as the sum of the number of risk alleles carried at each locus (∑We used for the estimations of the PRSs the SNPs with the p-value of the association test using the logistic regression model < 0.05, and located within the boundaries of the genes (including the UTRs) for which the combined test was significant after a Bonferroni correction for multiple testing. | PMC10075428 |
3. Results | PMC10075428 | |||
3.1. Transcriptomic analysis of lymphoblastoid cell lines | death, Death, necrosis | GRAFT-VERSUS-HOST DISEASE, DISEASE, ADHESION, NECROSIS | We identified 3093 genes that showed significant differential expression in the LCLs after Among the gene sets that were enriched in up-regulated genes, we found pathways involved in “Cell growth and death” (p53 signaling pathway, Apoptosis, Necroptosis; Regulation by c-FLIP, Caspase activation via Death Receptors in the presence of ligand, RIPK1-mediated regulated necrosis), “immune system” and “immune disease” (Graft-versus-host disease, NOD-like receptor signaling pathway, Allograft rejection; Interferon alpha/ beta/ gamma, Interleukin-2 / -4/ -10/ -13/ -35 signaling), “Signal transduction” (TNF, NF-kappa B, Phosphatidylinositol, FoxO, JAK-STAT) and “Signaling molecules and interaction” (Cell adhesion molecules, Cytokine-cytokine receptor interaction, ECM-receptor interaction; Signaling by KIT in disease, Among the down-regulated gene sets, we identified pathways involved in | PMC10075428 |
3.2. Data filtering on WES genotype data | We used the unfiltered whole-exome sequencing data from our previous publication [ | PMC10075428 | ||
3.3. Association tests in the clinical WES dataset | The analysis at the SNP level using a standard chi-squared test identified 60 variants associated with SOS after Benjamini and Hochberg (1995) step-up false discovery rate (FDR) control [At the gene-level, we identified 12 genes that reached significance after Benjamini and Hochberg FDR control | PMC10075428 | ||
3.4. Combined test, Over-representation analysis and polygenic risk score | We identified 35 genes that were found to be significant for the test which combined the results of the differential gene expression analysis in LCLs and the gene-based test for an association in the WES dataset after Bonferroni correction for multiple testing | PMC10075428 | ||
Graphical representation of the relationship between the p-values of the combined test (color coded), the lymphoblastoid cell line expression test (x-axis), and the whole-exome sequencing association test (y axis): We see that the majority of the genes that are associated with a significant combined test statistic show very low p values for the expression test and that the p-value of the combined test increases with the p-value of the association test. | cancer, death | CANCER, HAND INFECTION | The functional characterization of these genes using the Reactome database identified 14 significantly enriched pathways The KEGG analysis identified 16 enriched pathways. These pathways were involved in “cell growth and death” (p53 signaling pathway, cellular senescence), “signaling molecules and interaction” (cytokine-cytokine receptor interaction), cancer, and infection ( | PMC10075428 |
Over representation analysis using CPDB.org incorporating 35 genes associated with sinusoidal obstruction syndrome identified through a combined test statistic. | The polygenic risk score computed using 209 SNPs from the 35 genes identified through combined expression and association analyses at the gene level showed a significantly different distribution between the cases and the controls (p-value = 8.506e-07; | PMC10075428 | ||
4. Discussion | death | ADVERSE EVENTS, INFLAMMATORY RESPONSE | In this study, we identified new potential genetic determinants of SOS at the SNP, gene, and pathway levels using a new multi-omics data analysis pipeline integrating data from two different experiments. We analyzed In the LCLs treated with busulfan, we found several upregulated pathways, which are mainly involved in the inflammatory response, signaling by interleukins and interferon-gamma, We identified new genetic markers in significant association with SOS, using the WES data, after the application of filtering steps to increase the homogeneity of the genetic dataset. Our data workflow reduced the number of individuals from the unfiltered WES dataset by 34.5% and the number of SNPs by 12.4%. Our pipeline thus generated a more homogenous sample at the cost of a smaller sample size. This workflow allowed us to identify through the analyses at the gene level 12 genes in significant association with SOS. None of these genes was identified in the previously published study, where a different strategy was used with a focus on single SNPs and functional variants [Existing data suggest that the association of the currently identified genes with SOS could be meaningful. The combined test statistic allowed to integrate the expression results and the association metrics into a single statistic measure. We identified 35 genes that were found to be associated with SOS after correction for multiple testing. None of these genes was previously investigated for an association with SOS. The functional analysis of these genes, using the KEGG database revealed that many are involved in the The functional analysis using the Reactome database identified pathways mostly involved in programmed cell death. Additionally, gene sets linked to the integrin cell surface interactions were found (We estimated polygenic risk scores for the cases and the controls that were included in this study, using the SNPs that were found within the boundaries of the 35 genes that were associated with a significant combined test statistic. The difference in the distributions of the PRSs between the cases and the controls is highly significant. Classically, PRSs are estimated in another cohort than the one that was used to estimate the association metrics with the phenotype of interest. Here, our goal was to estimate the magnitude of differences in scores between our cases and our controls. The difference in scores between cases and controls was highly significant and underscores the notion that the SNPs we used to estimate those scores are good candidates for further validation. In a future study, selecting those SNPs that contributed most to the PRS should be tested or some of the SNPs could be further prioritized, choosing some specific functional categories, i.e. non-synonymous variants.The pipeline we developed and used in this study allowed us to identify genes and pathways in association with a rare clinical outcome with a limited number of patient samples, where machine learning-based approaches are inapplicable [There are some limitations to our approach that are associated with the assumptions that are made in the setup of this study. First, the hypothesis that the genes that are dysregulated in LCLs after busulfan exposure are more likely to carry variants which are modifiers of busulfan toxicity might not be true. The importance of transcriptomic changes after drug exposure was shown previously to correlate with adverse events after drug exposure [In this paper, we proposed a novel approach to unravel the genetic determinants of SOS by combining | PMC10075428 |
Supporting information | PMC10075428 | |||
Clinical data of 87 patients included in this study (adapted from Ansari et al. BBMT, 2020, with permission). | (PDF)Click here for additional data file. | PMC10075428 | ||
Differential gene expression analysis in lymphoblastoid cell lines; sorted by expression change. | (PDF)Click here for additional data file. | PMC10075428 | ||
Over-representation analysis of differentially expressed genes in lymphoblastoid cell lines, sorted by strength of association. | (PDF)Click here for additional data file. | PMC10075428 | ||
List of SNPs that are found to be in significant association with VOD, after correction for multiple testing. | The SNPs are sorted by chromosome and position. Functional annotations of the variants were retrieved from ENSEMBL (Genome assembly: GRCh37.p13). The r2 column shows the r2 measure of linkage disequilibrium between each SNP and the following SNP in the table, in the CEU population from the 1,000 Genomes phase 3 data, as reported in ENSEMBL.(PDF)Click here for additional data file. | PMC10075428 | ||
Whole-exome sequencing association analysis of 57 individuals (11 cases with sinusoidal obstruction syndrome and 46 controls), genes with the strongest association, sorted by adjusted association metric. | (PDF)Click here for additional data file. | PMC10075428 | ||
Combined association analysis of differential gene expression in lymphoblastoid cell lines and whole-exome sequencing in 57 individuals; genes sorted by adjusted association metric. | (PDF)Click here for additional data file. | PMC10075428 | ||
Over-representation analysis of differentially expressed genes in a combined test statistic integrating differential expression data of lymphoblastoid cell lines after exposure to busulfan and a whole-exome association analysis. | (PDF)Click here for additional data file. | PMC10075428 | ||
Abbreviations | Acute lymphoblastic leukemiaConfidence intervalCentral nervous systemDeoxyribonucleic acidFalse discovery rateHematopoietic stem cell transplantationHardy–Weinberg equilibriumLymphoblastoid cell lineLinkage disequilibriumKyoto Encyclopedia of Genes and GenomesMinor allele frequencyOver-representation analysisPolygenic risk scoresRibonucleic acidSingle nucleotide polymorphismSinusoidal obstruction syndromeUntranslated regionVersatile Gene-based Association Study-2Whole-exome sequencing | PMC10075428 | ||
References | PMC10075428 | |||
Objectives | To develop and probe the first computerised decision-support tool to provide antidepressant treatment guidance to general practitioners (GPs) in UK primary care. | PMC9990646 | ||
Design | BLIND | A parallel group, cluster-randomised controlled feasibility trial, where individual participants were blind to treatment allocation. | PMC9990646 | |
Setting | South London NHS GP practices. | PMC9990646 | ||
Participants | depressive disorder, treatment-resistant | Ten practices and eighteen patients with treatment-resistant current major depressive disorder. | PMC9990646 | |
Interventions | Practices were randomised to two treatment arms: (a) treatment-as-usual, (b) computerised decision support tool. | PMC9990646 | ||
Results | ADVERSE EVENTS, RECRUITMENT | Ten GP practices participated in the trial, which was within our target range (8–20). However, practice and patient recruitment were slower than anticipated and only 18 of 86 intended patients were recruited. This was due to fewer than expected patients being eligible for the study, as well as disruption resulting from the COVID-19 pandemic. Only one patient was lost to follow-up. There were no serious or medically important adverse events during the trial. GPs in the decision tool arm indicated moderate support for the tool. A minority of patients fully engaged with the mobile app-based tracking of symptoms, medication adherence and side effects. | PMC9990646 | |
Conclusions | RECRUITMENT | Overall, feasibility was not shown in the current study and the following modifications would be needed to attempt to overcome the limitations found: (a) inclusion of patients who have only tried one Selective Serotonin Reuptake Inhibitor, rather than two, to improve recruitment and pragmatic relevance of the study; (b) approaching community pharmacists to implement tool recommendations rather than GPs; (c) further funding to directly interface between the decision support tool and self-reported symptom app; (d) increasing the geographic reach by not requiring detailed diagnostic assessments and replacing this with supported remote self-report. | PMC9990646 | |
STRENGTHS AND LIMITATIONS OF THIS STUDY | The Antidepressant Advisor tool was incorporated into an existing general practitioner (GP) healthcare record system for ease of use by GPs.We were unable to recruit a sufficient number of participants to estimate effect sizes for future trials.The eligibility criteria for participants to have tried two antidepressants before entering the study limited the number of eligible participants. | PMC9990646 | ||
Introduction | depression | The last 70 years have seen the development of a wide range of antidepressants. In UK primary care, three first-line antidepressants are primarily used for the treatment of depression (fluoxetine, sertraline and citalopram), all of which are Selective Serotonin Reuptake Inhibitors.One way to provide structured treatment guidelines is through algorithms which incorporate various patient characteristics and allocate treatments most likely to be effective (Harrison Hence, at the time of publishing the study protocol, to the authors’ knowledge there was no scientifically evaluated and pragmatic stepped antidepressant decision support tool in UK primary care. | PMC9990646 | |
Study objectives | RECRUITMENT | Our objectives were to describe (a) the recruitment of GP practices and enrolment of patients, (b) baseline patient characteristics, (c) report the prespecified feasibility outcomes and (d) provide descriptive summaries of the chosen clinical outcomes. | PMC9990646 | |
Methods | PMC9990646 | |||
Design | The Antidepressant Advisor Study (ADeSS) was a feasibility cluster-randomised clinical trial of a computerised decision support system for antidepressant prescribing in UK primary care. The trial was randomised at the GP practice level, where GP practices formed the clusters. At each practice, a single GP could participate at any given time. However, if a GP left the practice, a replacement GP from that practice could take their place. Most outcome measures were based on individual patient measures who were recruited to be seen by participating GPs. | PMC9990646 | ||
Eligibility criteria | depressive syndrome | Inclusion criteria for GP practices were: (a) up to one GP/practice participating at any time; located within one of the study’s South East London areas; and (b) using EMIS electronic health record software. Inclusion criteria for patients in addition to being registered at one of the participating practices were: (a) age ≥18, (b) at least moderately severe major depressive syndrome on Patient Health Questionnaire (PHQ-9; a score of ≥15),Exclusion criteria for patients were: (a) inability to consent to the study, (b) unstable medical condition (assessed based on in-depth screening visit), (c) currently being treated by mental health specialist, (d) high suicide risk (assessed with Mini International Neuropsychiatric Interview suicidality screen), | PMC9990646 | |
Recruitment of GP practices | RECRUITMENT | The GP practice recruitment period was from September 2018 until March 2020, when the study had to be stopped due to the COVID-19 pandemic. During this time, 70 GP practices in Lambeth were approached as well as several other practices in South East London. Of these, 20 (29%) were recruited into the study and randomised. Of the 20 randomised practices, 10 practices (from a single Research and Development office) were withdrawn from the study shortly after randomisation and prior to training. The withdrawn practices initially expressed an interest in participating but subsequently failed to respond to all attempts to contact. | PMC9990646 | |
Recruitment of patients | Patients at participating GP practices were enrolled in three stages: first, a search was conducted (via EMIS) to identify potentially eligible patients from among those registered at each GP practice. Second, patients meeting initial screening criteria were sent a letter inviting them to participate in the study and attend a prescreening assessment (conducted online or by phone). Third, patients who met eligibility criteria assessed at prescreening were then invited to attend a face-to-face screening interview where further eligibility criteria were assessed. Please refer to the trial protocol for details regarding these procedures. | PMC9990646 | ||
Measures | PMC9990646 | |||
Feasibility outcomes | ADVERSE EVENT, RECRUITMENT, ADVERSE EVENT | The primary feasibility outcome was:The number and percentage of patients lost to follow-up.Secondary feasibility outcomes were:GP adherence to the algorithm for each completed patient rated by a trial clinician (0=none of recommended steps implemented; 1=less than 50% of recommended steps implemented; 2=50% or more of recommended steps implemented; 3=100% of recommended steps implemented).Average patient adherence to prescribed medications based on EMIS electronic prescribing records.Adverse event (AE) and serious adverse event (SAE) rates (grade and relationship to intervention).Patient adherence to GP attendance measured by % of attended GP visits out of scheduled visits on EMIS over treatment period.Recruitment rates.Average GP satisfaction with decision support tool (intervention arm; after GP completion of study).DSM-IV Social and Occupational Functioning Assessment ScaleMaudsley Visual Analogue Mood Scale (MVAS) on final visit, while modelling baseline score. | PMC9990646 | |
Primary clinical outcome measure | Depressive | Self-rated Quick Inventory of Depressive Symptomatology sum score (QIDS-SR16 | PMC9990646 | |
Secondary clinical outcome measures | Depressive, Anxiety, Montgomery-Asberg Depression | Depressive symptoms were assessed by the Montgomery-Asberg Depression Rating Scale (MADRSClinical Global Impression (CGI) scale,Generalised Anxiety Disorder-7Body mass index at follow-up assessment adjusting for baseline score. | PMC9990646 | |
Exploratory clinical outcome measures | app).Average | MEDICATION SIDE EFFECTS | Average score for medication side effects on Frequency, Intensity, Burden of Side Effects Rating (FIBSER)Average % of adherence to prescribed antidepressant medication (self-report via mobile app).Average Maudsley Modified Patient Health Questionnaire-9 (MM-PHQ-9) | PMC9990646 |
Health economic measures | psychiatric | Service use as determined on EMIS including psychiatric referrals and referrals to study psychiatrist, as well as time to psychiatric referral; also primary care consultation rates.Service use; self-reported using a modified version of the Adult Service Use Schedule.Quality of life using the EQ-5D-3LRefer to the published protocol for further details
| PMC9990646 | |
Interventions | SIDE EFFECTS | ADeSS was a two-arm cluster-randomised study. GP practices were allocated either to the intervention arm (herein ‘Decision tool’) or the control arm (herein ‘treatment-as-usual’; TAU). In the Decision tool arm, patients received treatment from GPs who were using the computerised decision support tool. The tool assisted with antidepressant prescriptions and prompted GPs to review patients’ medications and change them if ineffective. The algorithm and technical requirements of the tool are described in the trial protocol.Patients meeting the above eligibility criteria and consenting to participate in the study attended the participating GP in their practice for treatment over 14 weeks. Patients received the intervention or control based on the arm that their GP practice was allocated to. Side effects were assessed for each week of the treatment period via the mobile app using the FIBSERFor patients who were not able to use the mobile app (eg, incompatible phone or other technical difficulties), weekly FIBSER scores were collected by the study team via telephone/email. Follow-up assessments took place at 15–18 weeks after the baseline interview. | PMC9990646 | |
Statistical analyses | depression | REGRESSION | The primary feasibility outcome (#1, ‘Number of patients lost to follow-up’) was summarised with frequencies and percentages with exact 95% CIs (Clopper-Pearson methodSeveral continuous outcomes (#8–9; clinical outcomes #13–14, #16–17, #20) were analysed using linear regression models where the dependent variable was the follow-up score and each model included (a) a dummy variable representing treatment allocation (1=‘Decision tool’; 0=‘TAU’) and (b) the baseline score. To account for the clustering of patients within GP practices, SEs were adjusted using a sandwich estimatorFor individual scales, we used published guidance on how to handle missing items. Where such guidance was not available, scales were pro-rated for individuals where 20% or less of items were missing. FIBSER scores were summarised based on item three (‘Burden’: ‘In the past week, how much have the side effects to your medications for depression interfered with your day-to-day activities?’) of the scale (see No participants were missing information at baseline, therefore, no imputation was carried out. Participants with missing follow-up information were excluded from regression models. No sensitivity analyses or subgroup analyses were performed. 95% CIs were treated as underpowered and not used as the basis for inferential conclusions. P values were not presented for any analyses. Analyses were conducted using R V.4.0.4. | PMC9990646 |
Patient and public involvement | The study was supported by service users which provide input to the study. We have scheduled regular meetings with our service users and one of our service user representatives has read our trial protocol publication and commented on it before submission. Due to the pandemic we have not been able to run wider public engagement workshops. We have finalised a lay summary report with our service user representatives for distributing to all participants of the study. | PMC9990646 | ||
Results | SECONDARY | Results for study outcomes below are ordered conceptually rather than by importance or whether they were related to feasibility or explored clinical outcomes for future trials. For our health economic results showing the absence of secondary care use in our sample (see Supplementary Results and Supplementary Table A, B, C). | PMC9990646 | |
Patient enrolment and baseline characteristics | PMC9990646 | |||
Number of patients enrolled per month | Hypomania | Each practice enrolled between 0 and 4 patients during the enrolment period, a mean of one per month (95% Poisson CI 0.59 to 1.58). Consolidated Standards of Reporting Trials (CONSORT) flow diagram. A CONSORT diagram describing the participant flow and exclusion from prescreening to follow-up for the trial. GP, general practitioner; PHQ-9, Patient Health Questionnaire-9; WHO-CIDI, WHO Composite International Diagnostic Interview.Consolidated Standards of Reporting Trials (CONSORT) flow diagram continued. A CONSORT diagram describing the participant flow and exclusion from prescreening to follow-up for the trial. GP, general practitioner; MINI, Mini International Neuropsychiatric Interview; HCL, Hypomania Checklist-16. | PMC9990646 | |
GP-based outcomes | PMC9990646 | |||
Number of GP practices recruited per month | RECRUITMENT | When including all recruited GP practices, a total of 20 practices were recruited. A breakdown of GP recruitment by month is presented in Supplementary Table D. The mean number of practices recruited per month was 1.11 (95% Poisson CI 0.68 to 1.72). When including only GP practices that participated in the study (ie, excluding those that were withdrawn very shortly after randomisation), a total of 10 practices were recruited, 0.56 per month (95% Poisson CI 0.27 to 1.02). | PMC9990646 | |
GP satisfaction with tool-assisted consultation flow and outcome | GPs in the Decision tool arm were invited to complete a satisfaction survey at the end of their time in the trial. There were five GP practices in the Decision tool arm, although only four of these practices saw at least one patient and the practice not having any eligible patients also did not return a GP satisfaction questionnaire. Only 3/5 practices overall responded to the survey.The responses from the survey are presented in Supplementary Table E. To summarise:2/5 GPs found the decision tool to be ‘Possibly helpful’, 1/5 ‘Definitely helpful’ and 2/5 did not respond.3/5 found the tool ‘Slightly easy’ or ‘Easy’ to use; 2/5 did not respond.3/5 indicated that they ‘Weakly support’ recommending that the EMIS Antidepressant Advisor tool be used in future clinical practice; 2/5 did not respond. | PMC9990646 | ||
GP adherence to the algorithm | Supplementary Table F presents, for each GP in the Decision tool arm, the number of patients in each adherence category (and percent, relative to total number of patients seen by the GP). These data present a mixed picture. While for 4/7 patients the algorithm was ‘Fully implemented’ by the GPs, for 3/7 patients GPs implemented ‘None of the recommended steps’. It is important to note that while some GPs may have chosen to not implement the algorithm, some will have not implemented for reasons outside their control. For example, information required for the algorithm (weekly MM-PHQ-9) was not always available and some patients did not accept proposed changes in their medication. | PMC9990646 | ||
Loss to follow-up (primary feasibility outcome) | Only 1/18 (5.6%; 95% CI 0.1 to 27.3) participants failed to attend their follow-up interview at 15/18 weeks after baseline. The single patient not attending follow-up was in the TAU arm. In total, five patients had their follow-up interview outside the 15–18 weeks window because of difficulties contacting patients or scheduling the interview. This was despite offering remote video or phone consultations as an alternative to face-to-face consultations as per protocol which allowed us to continue follow-up visits throughout the pandemic. | PMC9990646 | ||
Adverse events | nausea, tiredness | ADVERSE EVENTS | Most adverse events were attributable to expected antidepressant side effects, such as tiredness, loss of libido and nausea (see also Supplementary Table G). No SAEs were recorded during the trial. In total, there were eight mild AEs (affecting eight patients) and two moderate AEs (affecting one patient). There was an equal number of mild AEs in the Decision tool and TAU arms. The two moderate AEs were recorded for a patient in the TAU arm; no moderate AEs were recorded in the Decision tool arm. | PMC9990646 |
Score for medication side effects during follow-up | 5/18 patients were not using the mobile app and their weekly FIBSER scores were collected by telephone or email. FIBSER completion rates in the first week were good (83% overall), but this fell in subsequent weeks and around 20%–30% of patients completed FIBSER during the final weeks of the treatment period (Supplementary Table H). For 5/18 patients, no FIBSER scores were recorded in any week of the treatment period. For the remaining 13, mean scores item 3 (‘Burden’) were similar at 2.2 and 2.1 for the Decision tool and TAU arms, respectively. A score of 2 corresponds to the category ‘Minimal interference’. | PMC9990646 | ||
Patient adherence to treatment based on EMIS electronic prescribing records | Supplementary Table J presents the percentage of scheduled GP appointments that were attended by patients (including phone consultations) over the treatment period. This information was collected from EMIS records for 15/18 patients. One patient did not attend follow-up (and therefore, EMIS data were not extracted); two further patients attended their follow-up interview but EMIS data could not be extracted. Overall, most patients attended most scheduled appointments. Of patients with EMIS data (15/18), nearly 100% of scheduled appointments were attended. | PMC9990646 | ||
Adherence to prescribed antidepressant measured via mobile app | Uptake of the mobile app was low and some patients who initially agreed to use the app experienced technical difficulties (eg, unable to log in, missed notifications). Initial inspection of the data indicated that there were insufficient reports of daily adherence to summarise this outcome. Therefore, we report data completeness among enrolled patients. The number of doses of prescribed antidepressants could not be analysed as this data was not available from EMIS. Data completeness is presented in | PMC9990646 | ||
GP practice effect on primary clinical outcome | The ICC for QIDS-SR16 at follow-up (among 17 patients at 9 practices) was 0.07, indicating that 7.3% of variance in patient scores was attributable to differences between GP practices, after taking into account treatment allocation and baseline score. However, the 95% bootstrap CIs ranged from <0.001 to 0.76, highlighting the high degree of uncertainty in this estimate. | PMC9990646 | ||
Discussion | depression, depressive symptoms | RECRUITMENT | This trial investigated the feasibility of a cluster-randomised design to study a novel computerised Antidepressant Advisor tool for UK primary care which was developed as part of this study. While the loss to follow-up rate (the primary feasibility outcome) was very low and the software implementation of our algorithm was successful and raised no safety issues, both GP practice and patient recruitment were slower than anticipated, resulting in a much smaller sample size than planned. The GP practice recruitment strategy was partially successful, in that GPs were interested in the study but the recruitment rate was slow, largely due to our restriction of only being able to recruit in South London. A national recruitment strategy with remote consultations and/or online self-assessment would have greatly increased our speed of practice recruitment. Most practices were recruited via Clinical Research Network staff, who assisted in advertising to GP practices and setting up training for recruited GPs. This is similar to findings reported by the STAR*D trial that sites where clinical research coordinators played a key role were more likely to be enrolled into the study.Our patient recruitment strategy was successful in that an expected percentage of patients expressed their interest in taking part in the study (8% of those contacted). Patients’ interest in the study and perception of it as worthwhile was also supported by the low loss-to-follow-up rate, as well as informal feedback. However, the number of patients eligible for the study limited the recruitment pace. This limitation was apparent both during searches in EMIS electronic records (0.3% of patients found to be eligible) and from eligibility of patients at prescreening (53% eligible). A large factor in limiting the pool of eligible patients in EMIS was the criterion to have previously taken an antidepressant different to their current one. Indeed, an exploratory EMIS eligibility search showed that, when the requirement for patients to have taken a previous antidepressant different to their current antidepressant was removed, the number of eligible patients increased fivefold. Additionally, the main reason for exclusion at prescreening was a PHQ-9 score of at least 15, comprising 55% of exclusions and one may question whether one may use a lower cut-off score in future.GP satisfaction with the advisor tool was moderate, but due to our low sample size, it is difficult to draw firm conclusions around the usability and acceptance of the tool in primary care. GP satisfaction is a crucial criterion for successful implementation of the advisor tool in a definitive trial, therefore additional feedback would be required before progressing. Independently of GPs’ priorities, given the large treatment gaps for depression confirmed in a recent paper, there was a consensus for introducing decision support systems as one of a set of recommendations to improve the fact that only a minority of patients with depression receive guideline-based care.The mobile app enabled regular reporting of MM-PHQ-9 scores to GPs for use in treatment. However, almost half of patients did not use the app at all and, among those who did use it, only around half completed their weekly MM-PHQ-9 scores. Collecting MM-PHQ-9 and FIBSER scores via phone was very time-consuming and would not be scalable to a larger study. There are several potential reasons for the lack of use of the app. The app was not available to download directly from the Apple App Store and had to be downloaded via another app as a test version, which introduced additional complexities. Additionally, patients regularly reported technical errors where the app stopped working and needed to be updated or re-downloaded.One limitation of our study was the lack of a more in-depth qualitative evaluation of user perspectives on the decision support system as well as the mobile app and future trials could embed this into further optimisation of their design. One of the main limitations of the trial was its geographical limitation to South London and disruption due to the COVID-19 pandemic. A future trial, should expand the geographical reach for recruitment to increase the sample size and to employ remote assessments, without the need for in depth in-person diagnostic assessments. Another limitation was the reliance of the study on GPs, who are struggling with their workloads, to run the decision tool. It may be beneficial for future studies to use other health practitioners such as pharmacists, who could share the burden of using the tool with GPs. Indeed, the Royal Pharmaceutical Society has emphasised the important role pharmacists can play in providing treatment and improving patient outcomes as part of Primary Care Networks.Low uptake of the mobile app meant GPs often lacked the necessary information to run the advisor tool. A recent systematic review and meta-analysis found that trials of apps for depressive symptoms which incorporated human feedback had lower dropout rates.The main implications of this feasibility trial are that while computerised decision support tools for antidepressant prescribing are technically feasible and well placed to address important treatment gaps in UK primary care, their implementation is unlikely to be feasible by solely relying on GPs without additional case management, for example, by community pharmacists or prescribing nurses. Our study highlights that many patients remained on one antidepressant even if they had not sufficiently responded and that switching even to a second alternative was often not implemented. | PMC9990646 |
Supplementary Material | PMC9990646 | |||
Reviewer comments | PMC9990646 | |||
Data availability statement | Data are available upon reasonable request. We have not obtained consent for sharing pseudonymised data and will therefore only be able to share fully anonymised data such as scores on standardised instruments via the King’s Open Research Data System ( | PMC9990646 | ||
Ethics statements | PMC9990646 | |||
Patient consent for publication | Not applicable. | PMC9990646 | ||
Ethics approval | This study involves human participants and was approved by London - Camberwell St Giles Research Ethics Committee, reference number: 17/LO/2074. Participants gave informed consent to participate in the study before taking part. | PMC9990646 | ||
References | PMC9990646 | |||
1. Introduction | CORONAVIRUS INFECTION | This research aimed to evaluate the effects of high-dose cholecalciferol (VDThe new coronavirus infection (COVID-19) is a global pandemic that has aggressively propagated worldwide [In this manner, vitamins D (VD) and C, in addition to zinc supplementations, are recommended by the Jordan Ministry of Health as a part of the treatment protocol for COVID-19 patients. VD increases innate cellular immunity and provides direct antibacterial activity against various microorganisms, including enveloped and nonenveloped viruses [Consequently, raising 25-hydroxyvitamin D (25OHD) levels by 1,25(OH)2D3 (VDA previous study [ | PMC10005440 | |
2. Materials and Methods | PMC10005440 |
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