Monitoring biomarkers using machine learning (ML) may determine glioblastoma treatment response. We systematically reviewed quality and performance accuracy of recently published studies.Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy, we extracted articles from MEDLINE, EMBASE and Cochrane Register between 09/2018–01/2021. Included study participants were adults with glioblastoma having undergone standard treatment , and follow-up imaging to determine treatment response status . Using Quality Assessment of Diagnostic Accuracy Studies Two/Checklist for Artificial Intelligence in Medical Imaging, we assessed bias risk and applicability concerns. We determined test set performance accuracy . We used a bivariate random-effect model to determine pooled sensitivity, specificity, area-under the receiver operator characteristic curve (ROC-AUC). Pooled measures of balanced accuracy, positive/negative likelihood ratios (PLR/NLR) and diagnostic odds ratio (DOR) were calculated. PROSPERO registered (CRD42021261965).Eighteen studies were included (1335/384 patients for training/testing respectively). Small patient numbers, high bias risk, applicability concerns (particularly confounding in reference standard and patient selection) and low level of evidence, allow limited conclusions from studies. Ten studies  included in meta-analysis gave 0.769 (0.649-0.858) sensitivity [pooled (95% CI)]; 0.648 (0.749-0.532) specificity; 0.706 (0.623-0.779) balanced accuracy; 2.220 (1.560-3.140) PLR; 0.366 (0.213-0.572) NLR; 6.670 (2.800-13.500) DOR; 0.765 ROC-AUC.ML models using MRI features to distinguish between progression and mimics appear to demonstrate good diagnostic performance. However, study quality and design require improvement. T1-weighted MRI images, which reflects the non-specific disruption of the blood-brain barrier  promoter methylation. As with pseudoprogression, there is a need to distinguish radiation necrosis from true progression at the time of increased contrast enhancement because, again, there is substantial potential clinical impact. In particular, if there is true progression the treating clinical team typically would initiate second-line surgery or therapies. However, the decision making can only be made retrospectively with current treatment response evaluation guidelines . A review of such neuro-oncology studies up to September 2018 showed that the evidence is relatively low level, given that it has usually been obtained in single centers retrospectively and often without hold-out test sets , 12. TheThe aim of the study is to systematically review and perform a meta-analysis of diagnostic accuracy of ML-based treatment response monitoring biomarkers for glioblastoma patients using recently published peer-reviewed studies. The study builds on previous work to incorporate the rapidly growing body of knowledge in this field , 16, proThis systematic review and meta-analysis are registered with PROSPERO CRD42021261965). The review was organized in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy (PRISMA-DTA)  incorpor21261965.Pseudoresponse (bevacizumab-related response mimic), an important concern in the United States where it is licensed, was not the focus of the systematic review and meta-analysis.Recommendations were followed to perform a sensitive search (with low precision), including the incorporation of subject headings with exploded terms, and without any language restrictions . Search Study participants included were adult glioblastoma patients treated with a standard care regimen  who underwent follow-up imaging to determine treatment response status , and designated as the target condition of the systematic review).Studies were excluded if they focused on pediatrics, pseudoresponse, or had no ML algorithm employed in the extraction or selection of features, or in classification/regression.The ML model determined the treatment response outcome, and was designated as the index test of the systematic review. Either clinicoradiological follow up or histopathology at re-operation or a combination of both, were designated as the reference standard of the systematic review. The bibliography of each included article was checked manually for other relevant studies.A neuroradiologist, T.C.B., and a data scientist, A.C., with 16 and 2 years, respectively, of experience in neuroimaging applied to neuro-oncology, independently performed the literature search and selection.For every study, risk of bias as well as concerns regarding applicability, were assessed by applying QUADAS 2 methodology  alongsidT2-w lesion enlargement  used in the published study was appropriate. Under the standard care regimen, contrast-enhancing lesions enlarging due to pseudoprogression typically occur within 0-6 months after radiotherapy, whereas contrast-enhancing lesions enlarging due to radiation necrosis typically occur beyond this 6 month window, according to the evidence. When “post-treatment related effects” (PTRE) is employed as a term for treatment response outcome, the phenomena of pseudoprogression and radiation necrosis are both included , 24. Theargement ; permittargement , 27. ThiA neuroradiologist , T.C.B., and a data scientist, A.C., with 16 and 2 years, respectively, of experience in neuroimaging applied to neuro-oncology, independently performed the data extraction and quality assessment. Discrepancies between the two reviewers were considered at a research meeting chaired by a third neuroradiologist , A.A-B. (8 years experience of neuroimaging applied to neuro-oncology), until a consensus was reached.Based on the published study data, 2 x 2 contingency tables were made for hold-out test sets from which the principal diagnostic accuracy measures of sensitivity  and specificity were calculated. The area under the receiver operating characteristic curve (ROC-AUC) values and confidence intervals were extracted in studies where these were published. Additional secondary outcome measures of balanced accuracy, precision  and F1-score were also determined from the contingency tables. In those studies where there was a discrepancy in the principal diagnostic accuracy measures and the accessible published study raw data, this was highlighted. If both internal and external hold-out test sets were published in a study, the principal diagnostic accuracy measures for the external test set alone were calculated. In studies without hold-out test sets, “no test set” was recorded  and the The principal diagnostic accuracy measures of sensitivity  and specificity were subject to meta-analysis. We determined two pooled primary measures of accuracy: the true positive rate  and the false negative rate (1-specificity). A bivariate random-effect model , which aThe parameters of this model also allowed us to plot the summary ROC (SROC) curve and determine the summary ROC-AUC. Using a resampling approach , the modThe meta-analysis was conducted by a statistician, M.G., with 15 years of relevant experience. All the statistical analyses were performed in R (v 3.6.1). The R package mada (v 0.5.10)  was usedMost studies of prognostic imaging biomarkers in glioblastoma predict the outcome measure of overall survival using baseline images. Nonetheless, we found a small group of studies using ML models that predicted the outcome measure of future treatment response using baseline images. The studies were examined using identical methodology to that applied to monitoring biomarkers.In all, 2362 citations fulfilled the search criteria of which the full text of 57 potentially eligible articles were reviewed  employed a combination of clinicoradiological follow up and histopathology at re-operation, to distinguish true progression from a mimic. A few individual studies employed one reference standard for one decision (true progression) and another reference standard for the alternative decision (mimic); this and other idiosyncratic rules led to a high risk of bias in terms of the reference standard used, as well as how patients were selected, in several studies.Most studies only analyzed imaging features alone  whereas the remainder incorporated additional non-imaging features. A third of studies  used deep learning methodology to derive features .A third of studies did not have hold-out test sets  and instead the performance accuracy was determined using training data through cross-validation ; the third study in this subgroup was retrospective. One study applied genomic alterations alone as features to predict future MRI treatment response. All studies  were in the highest class for risk of bias in terms of the reference standard, patient selection and index test QUADAS 2 categories . Ten  remaining studies were subject to further analyses. Forest plots of sensitivity and specificity  and true positive rates (sensitivity) shown in The derived pooled measures of balanced accuracy = 0.706 (0.623-0.779); positive likelihood ratio = 2.220 (1.560-3.140); negative likelihood ratio = 0.366 (0.213- 0.572); diagnostic odds ratio = 6.670 (2.800-13.500).To date, available evidence is relatively low level  for deteLimitations encompassed three main areas. First, the reference standards used in all studies resulted in a high risk of bias and concerns about applicability. With the exception of the prognostic biomarker subgroup of studies, all the studies were retrospective, which increased the risk of confounding. Confounding factors, in relation to imaging follow-up and pathology at re-operation reference standards, were second-line drug therapy and cessation of temozolomide, all of which were rarely considered. Likewise, the use of corticosteroids was rarely considered despite being a confounding factor in relation to the imaging follow-up reference standard. If unaccounted for, an increase in corticosteroid dose may cause false negative treatment response. Some authors provided a statement within their methodology that they followed RANO guidelines  which ifSecond, patient selection was problematic and is associated with confounding. For example, patients receiving second-line drug therapy should have been excluded as response assessment may be altered. It is also noteworthy that astrocytoma, IDH-mutant, grade 4 are biologically and prognostically distinct from glioblastomas, IDH-wildtype . VariablThird, hold-out test sets should be used for diagnostic accuracy assessment in ML studies  as it isImaging reference standards, especially RANO trial guidelines  and lateAs well as the scenario described above highlighting the upslope effect of PTRE , 27, anoAnother limitation of this systematic review is that pathology at re-operation, where used as a reference standard, is typically not an entirely reliable reference standard for two reasons . First, Publication bias may also have affected the range of diagnostic accuracy of the monitoring biomarkers included in this systematic review and meta-analysis. Related to this, the exclusion of pre-prints and non-peer reviewed material may exacerbate publication bias. In particular, given that some in the data science community may not submit their work in peer-reviewed journals as peer review is relatively slow compared to the speed at which data science develops, it is plausible that publication bias relates to the make-up of the researcher team. For example, more clinically-orientated teams may be more inclined to publish in a peer reviewed journal compared to more data science-orientated teams.T1-weighted sequences as contrast-enhancing hyperintense regions  to monitor disease progression in glioma patients . HoweverSecond, another promising approach is to use advanced imaging techniques . The lasA third approach is to use ML, whether applied to conventional structural MRI, advanced imaging techniques or a combination of both imaging and non-imaging features. Indeed, an advantage of machine learning applied to MRI is that wide data can be handled relatively easily  which miNonetheless, overall there appears to be good diagnostic performance of ML models using MRI features to differentiate between progressive disease and mimics. For now, if ML models are to be used they may be best confined to the centers where the data was obtained from, badged as research tools and undergo further improvement.Concordant with a previous review of studies published up to Sept 2018 , the diaThe results demonstrate that glioblastoma treatment response monitoring biomarkers using ML are promising but are still at the early development stage and are not yet ready to be integrated into clinical practice. All studies would benefit from the improvements in methodology described above. Methodological profiles or standards might be developed through consortiums such as the European Cooperation in Science and Technology (COST) Glioma MR Imaging 2.0 (GliMR)  initiatiIn future studies, it would be beneficial to perform analytical validation using external hold-out tests as epitomized by several studies in the current review. Using larger datasets which include a wider range of tumors and mimics as well as parameters from different sequences, manufacturers and coils, and thereby reduce overfitting, would also improve future studies. Multidisciplinary efforts and multicenter collaborations are therefore necessary . HoweverPredictions can also be made more informative through the modelling of prediction uncertainty including the generation of algorithms that would “know when they don’t know” what to predict .Further downstream challenges for clinical adoption will be the completion of clinical validation  as well Non-technical challenges relate to patient data safety and privacy issues; ethical, legal and financial barriers to developing and distributing tools that may impact a patient’s treatment course; medical device regulation; usability evaluation; clinical acceptance and medical education around the implementation of CDS software , 82. MedA range of ML-based solutions primed as glioblastoma treatment response monitoring biomarkers may soon be ready for clinical adoption. To ensure clinical adoption, it would be beneficial during the development and validation of ML models that studies include large, well-annotated datasets where there has been meticulous consideration of the potential for confounding.The original contributions presented in the study are included in the article/TB: experimental design and implementation, analysis and interpretation of the data, performance accuracy statistical analysis, writing of draft manuscript, approval of the final version. AC, AR, AAB, CD, HS, AL, AM, BA, NM, JL, FV, KA, SO, MM: the implementation, analysis, and interpretation of the data and approval of the final version. MG: implementation, analysis and interpretation of the data, meta-analysis statistical analysis, writing of draft manuscript, approval of the final version. All authors contributed to the article and approved the submitted version.This research was supported by the Wellcome/EPSRC Centre for Medical Engineering (WT 203148/Z/16/Z)  which includes open access fees, The Royal College of Radiologists (TB) and King’s College Hospital Research and Innovation (TB).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method—heterogeneous graph attention network (‘HGAT–AMR’)—to predict anti-tuberculosis (TB) drug resistance. The HGAT–AMR model is able to accommodate incomplete phenotypic profiles, as well as provide ‘attention scores’ of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is ‘paying attention to’ in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs , and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from  Antimicrobial resistance (AMR) is a global public health challenge that is impacting the way infections are treated. Mycobacterium tuberculosis (MTB), which kills more people each year than any other single pathogen, is not an exception. Timely prediction of resistance or susceptibility of a bacterial sample (or ‘isolate’) to anti-TB drugs is key to personalised treatment. As bacterial AMR is usually genetically encoded. There are two major ways to make a fast prediction of AMR for previously unseen isolates using genetic data : (i) makOur dataset includes 13 402 isolates that were tested to up to 11 drugs, including four first-line drugs: INH, RIF, ethambutol (EMB) and pyrazinamide (PZA). The drug-susceptibility testing (DST) was performed in either liquid culture or on solid medium, or both. All lineages were represented in , 11. TheThe details of DNA sequencing are provided in . NucleotWe propose a heterogeneous graph attention network to predict AMR (HGAT–AMR) to deal with the heterogeneous genetic data with variable-length. Graph neural networks operate on graph-structured data and can effectively capture the structural information encapsulated in graphs and generate more effective embedding. The graph attention network is one of a major branch of graph neural networks . AttentikatG, rpoB and pncA. In this study, those 23 genes were investigated that constituted the assembled SNPs catalogue for the available tested drugs [The HGAT–AMR takes network-like data (i.e. a graph) as input, in which graph nodes correspond to isolates and to SNPs, and in which graph edges correspond to the relationship between the isolates and SNPs. Each node has different number of neighbouring nodes e.g. each isolate has different number of mutations, and each SNP occurs different number isolates. We term this undirected network-like input the ‘SNP graph’. In the MTB dataset, there are 24 types of node including: (i) isolates and (ii) SNPs of 23 types, where SNP nodes of the same type correspond to SNPs in the same gene. ed drugs . No edgeFor any given isolate, different mutations within different genes may carry information of varying importance when determining drug-resistance. To capture this hierarchical importance at both node—(SNP) and type—(gene) level, we applied a building block, the HGAT module , to be dFor an undirected and unweighted graph Type-level attention Given a specific node Node-level attention Given a node networks  module wBased on the HGAT module, we constructed a deep graph learning model, HGAT–AMR  and several conventional ML methods. The details of the definition and implementation of the DA were the same as described in Yang et al.  and whicThe initialisation of HGAT–AMR involves two components: (i) initialising SNPs’ embedding and (ii) initialising isolates’ embedding.SNPs’ embedding initialisation. We compare genetic samples and mutation features to sentences and words in natural language, respectively. In the MTB dataset, we consider SNPs as equivalent to words and isolates (comprising SNPs) as equivalent to sentences (comprising words). Like word embedding in natural language processing, we first compute the SNP co-occurrence matrix based on point-wise mutual information (PMI). Then, we factorise the matrix using sparse singular value decomposition. The result of this procedure is an embedding vector of fixed size for each SNP, where every element is continuous (please see parameters of PMI-based embedding in Supplement B). We note that this embedding vector for a SNP comprises real  numbers, it being the result of factorising a matrix of PMI values .Isolates’ embedding initialisation. The vector for each isolate is the aggregation of the embedding vectors for all of the SNPs present in the isolate. Naturally, the number of SNPs within an isolate will vary between isolates, and so the aggregator will accordingly take varying numbers of embedding vectors for different isolates. Common choices for the aggregator functions used to combine the multiple embedding vectors for the isolate’s SNPs into a single vector to represent that isolate include (i) the average, (ii) minimum, or (iii) maximum. In this study, we used an aggregator function of the maximum to initialise these representatives ‘feature vectors’ for each isolate node in the input graph. The evaluation of the other aggregator functions could be explored in future work, although our experiments are largely insensitive to such a choice.ML comparators. For each model, the phenotypes to individual drugs for the isolates were used for classification in single-label learning, whereas those for multiple drugs were used for classification in multi-label learning. The conventional ML models were optimised via conventional cross-validation methods by choosing the values of the various hyperparameters using grid search. All comparator ML methods were trained in a conventional supervised manner HGAT–AMR. The output of Lth layer of HGAT–AMR, the embeddings of isolate nodes are then fed to a softmax layer for classification. During the training, the cross-entropy loss over training data with the L2-norm is applied. Subsets for the supervised/transductive learning. We first constructed four different subsets of our database for individual drugs , which were used for single-label learning; the HGAT–AMR model trained on these datasets is termed as ‘HGAT–AMRs’ (noting the ‘s’ suffix). We then constructed a fifth subset, where all samples have complete phenotypes of all four drugs, for multi-label learning; the HGAT–AMR model trained on this dataset is named ‘HGAT–AMRm’.Subsets for the inductive learning. We reconfigured the subset for the above supervised multi-label learning by (i) randomly removing the phenotypes of drugs of interest in the existing training set and (ii) augmenting subsets (otherwise comprising completely-labelled data) with any available incompletely-phenotyped samples. The HGAT–AMR model trained in the first experiment is named ‘HGAT–AMRi’ (for ‘inductive’), whereas the second is name ‘HGAT–AMRi-E’ (for ‘Enhanced’). In our later results, we indicate the ratio of the removed phenotypes by the percentage following the HGAT–AMRi e.g. ‘HGAT–AMRi-10%.’Data split and cross-validation. To evaluate various models, we split each subset described above corresponding to examined drugs into training (40%), validation (40%) and testing sets (20%). For multi-label learning, we performed stratified cross-validation, which ensures that the proportion of the two classes (resistant versus susceptible) for every label was approximately the same in each fold. For the ML baseline models, as noted earlier, cross-validation was performed to identify the optimal values of the hyperparameters via standard grid-search that led to the best classification accuracy (Please see details of the hyperparameter set used for grid-search in Supplement B). The DA method did not require training, it being a clinical ‘look-up table’. An early-stopping criterion was applied while training the HGAT–AMR model, such that when the best validation accuracy of the model ceased increasing for at least ten training epochs, training was stopped to avoid over-fitting. For all methods, 20 iterations of the experiments (of 5-fold CV) were carried out, with the average performance of the resulting model being applied to the held-out testing dataset reported, as is conventional practice.Before going on to discuss the advantages offered by our proposed method, we first consider the classification task, in which each model aims to classify resistance/susceptibility for individual drugs. Considering general classification performance, the ML methods outperformed the DA approach for all four drugs except for sensitivity in RIF and specificity in PZA. In the cases of INH, EMB and PZA, the best sensitivities were obtained by the HGAT–AMRi-E . For INH and RIF, the best AUROCs were obtained by two of the HGAT–AMR models, 98.53% by HGAT–AMRi-E and 99.10% by HGAT–AMRs. For the EMB and PZA, the LR generated the best F1-score of 87.37 and 83.95% and the best AUROC of 96.68 and 93.70%. In terms of specificity, the best values were 98.32% of HGAT–AMRm for INH, 98.87% of SVM for RIF, 97.90% of LR for EMB and 95.02% of DA for PZA,. The HGAT–AMRs showed similar results as the LR, especially in the cases of EMB and PZA. This is because they were trained in the same mode i.e. supervised single-label learning. The multi-label learning approach enabled the HGAT–AMR model to increase sensitivity by 0.2% for INH and RIF, and by 3% for EMB and PZA. This could be explained by the genetic patterns corresponding to the resistance co-occurrence that are predictive for these cases. Furthermore, inductive learning allowed the HGAT–AMR models to continue the increment in sensitivity for INH, EMB and PZA, but to decrease for RIF. The inductive learning essentially loosened the dependence of resistance co-occurrence and resistance prediction by introducing incomplete phenotypes for all examined drugs. It implies that the resistance to all four first-line drugs except for RIF could be predicted better if the evidence of resistance co-occurrence is relaxed. For INH and RIF, the differences in all performance measurements across different methods were less than 4%. For EMB and PZA, the significant improvement in sensitivity compared to the DA was obtained by HGAT–AMRi-E, which were 12 and 41%, respectively. This can be explained by (i) associations between SNPs and the resistance to INH or RIF being nearly linear, and this is consistent with the results obtained by the DA method, (ii) associations between SNPs and the resistance to EMB or PZA are more complex and (iii) patterns of resistance co-occurrence mask those SNPs associated with EMB or PZA-resistance.A key advantage of the proposed method, beyond any (modest or otherwise) improvements in classification performance is the interpretable nature of the resulting graph constructed during the training of the HGAT. For the purposes of demonstration and computational efficiency, we applied two-head attention in the cases of INH, RIF and EMB, and four-head attention for PZA. The reason for using four heads with PZA is that the use of two heads was experimentally (not shown here for brevity) found not to capture the resistance patterns associated with PZA, which was assumedly masked by patterns of resistance co-occurrence. Therefore, there are four sets of type-level and node-level attention scores for each node for the two-headed models  and eight sets of attention scores for the four-headed model (PZA). We index attention scores by head and layer: for example, ‘1L1H’ refers to the 1st head of the 1st layer.Averaged type-level attention scores.The plots of averaged type-level attention scores for each drug via single-label learning are referred in Figure S3 of Supplement E. For INH  by removing the phenotypes gradually, as outlined earlier. During the training, the phenotype of each of the four drugs could now be absent. For example, an isolate could have been tested by INH and EMB, but not by RIF and PZA. According to an imbalance ratio for a specific drug, the trans-training set is resampled to construct a new training set. The label-masking ratio gradually increased on each resampled training set, and the model is trained for 100 times at each label-masking ratio. The experiment compared baseline models  and HGAT. The examined models are verified on the same test dataset with a class-imbalance ratio and label-masking ratio. Performance evaluation measurements include sensitivity, precision and AUPCR. It is worth noting that the baseline model is trained with a single-label learning framework, whereas HGAT is trained with multi-labels learning framework. Besides, the optimal threshold for calculating sensitivity and precision is determined by AUROC. Figures In the evaluation of prediction performance, HGAT–AMRi-E outperforms the others in terms of sensitivity for all four drugs except RIF. This could be because the extra subset enables the model to learn more patterns from data with incomplete phenotypes. For RIF, the DA and conventional ML models outperform the proposed models in sensitivity, specificity, and F1-score. This can be explained by (i) ropB being the only gene related to RIF-resistance among the considered genes and (ii) that most of the RIF-resistance-associated SNPs in rpoB have been recognised. In contrast, the proposed model considers all genes and the performance was only lower than the best sensitivity, specificity, and F1-score by up to 1% for RIF. This also testifies to the fact that the attention mechanism of the model is able to capture the RIF-associated genes  and SNPs . Overall, the sensitivity was increased at the expense of reducing specificity. F1-score is a harmonic mean of precision and recall. In the cases of EMB and PZA, the F1-score is lower because the sensitivities were lower and the trade-off between sensitivity and specificity was larger than the cases of INH and RIF. This can be explained by (i) the genetic pattern of drug-resistance co-occurrence dominating the prediction of EMB and PZA, (ii) the possible underestimation of the effect of predictive SNPs associated with EMB and PZA or (iii) a greater preponderance of phenotypic errors for EMB and PZA.The layer-wise type-level averaged attention scores are able to rank the interpretable genes for different drug-resistance predictions. The different attention heads capture different aspects of the predictive genetic patterns. These scores would enable researchers to (i) shortlist effectively the candidates of genetic variables associated with specific drug-resistance by looking into those highly ranked genes and (ii) efficiently identify the candidates of contributed genes given all mutations, especially when predicting drug-resistance without knowing the genes associated with less-studied drugs. Furthermore, the pattern in the layer-wise type-level attentions can be helpful in interpretating genes when the isolates exhibit resistance co-occurrence. For example, all layers and all heads that captured the gene rpoB might explain the high probability that this gene is the only RIF-resistance-associated gene, and the pattern of resistance co-occurrence could minimally influence the RIF-resistance prediction. Conversely, the EMB and PZA-resistance prediction could be highly affected by considering whether the genetic pattern corresponds to resistance co-occurrence.As with conventional feature selection in LR and RF methods, we equivalently generated SNP ranking lists by averaging node-level attention scores. Since the PMI initialisation has removed the most frequent and least frequent words in the corpus , the proposed model will not consider those most and least common SNPs, which aids in the discovery of informative SNPs beyond the most dominant. With our two-level attention structures, the proposed model was inclined to identify the SNPs from the most dominant genes identified by the type-level attention. In the case of INH and EMB, the SNPs associated with resistance were ranked higher by the first layer. For RIF, both the two layers captured several subgroups of RIF-resistance-associated SNPs. For PZA, only 2L3H identified PZA-resistance-associated SNPs, whereas the other attentions captured the SNPs associated with other drugs due to drug-resistance co-occurrence. This list is consistent with the type-level attention scores in Figure Transductive learning is equivalent to standard supervised learning, where each sample (i.e. the nodes in graph model) in the training set has complete annotations. The inductive learning is semi-supervised learning, where not all samples of the training set have annotations and the unlabelled samples are considered to be ‘silent’ for classification. In the framework of multiple label learning, an advantage of the inductive learning would be that each sample in the training set is allowed to have incomplete annotations for all labels. In the context of multiple drug-resistance predictions, the ‘inductive’ training allows the isolates to have phenotypes missed randomly for drugs. As we have discussed, it is common to have incomplete phenotype annotations with respect to the full spectrum of drugs. The inductive learning method allows those samples with incomplete phenotypes to be used as much as possible and reduce information loss. It can be generalised for less tested drugs. For certain drugs, the predictive influence of resistance co-occurrence can be mitigated by randomly removing the phenotype of different drugs. In the case of INH, when the data are highly imbalanced (1:6), the three models’ precision and AUPRC decrease, but the mean and median remain higher than 90%  provided in .Y.Y. and D.C. conceived the experiments, Y.Y. conducted the experiments, Y.Y., S.K, C.W, T.M.W, T.E.A.P and D.W.C. analysed the results. Y.Y. wrote the manuscript. Y.Y., S.K, T.M.W, T.E.A.P, D.W.C. and D.C, CRYPTIC Consortium, and other contributors reviewed the manuscript.CRYPTIC Consortium Derrick W Crook, Timothy EA Peto, A Sarah Walker, Sarah J Hoosdally, Ana L Gibertoni Cruz, Joshua Carter, Sarah Earle, Samaneh Kouchaki, Yang Yang, Timothy M Walker, Philip W Fowler, Daniel Wilson and David A Clifton, University of Oxford; Zamin Iqbal, Martin Hunt and Jeff Knaggs, European Bioinformatics Institute; Daniela M Cirillo, Emanuele Borroni, Simone Battaglia, Arash Ghodousi, Andrea Spitaleri and Andrea Cabibbe, Emerging Bacterial Pathogens Unit, IRCCS San Raffaele Scientific Institute, Milan; Sabira Tahseen, National Tuberculosis Control Program Pakistan, Islamabad; Kayzad Nilgiriwala, Sanchi Shah, Ayan Mandal and Nerges Mistry, The Foundation for Medical Research, Mumbai; Camilla Rodrigues, Priti Kambli, Utkarsha Surve and Rukhsar Khot, P.D. Hinduja National Hospital and Medical Research Centre, Mumbai; Stefan Niemann, Thomas Kohl and Matthias Merker, Research Center Borstel; Harald Hoffmann, Katharina Todt and Sara Plesnik, Institute of Microbiology & Laboratory Medicine, IML red, Gauting; Nazir Ismail, Shaheed Vally Omar, Lavania Joseph Dumisani Ngcamu, Nana Okozi and Shen Yuan Yao, National Institute for Communicable Diseases, Johannesburg; Guy Thwaites, Thuong Nguyen Thuy Thuong, Nhung Hoang Ngoc and Vijay Srinivasan, Oxford University Clinical Research Unit, Ho Chi Minh City; David Moore, Jorge Coronel and Walter Solano, London School of Hygiene and Tropical Medicine and Universidad Peruana Cayetano Heredia, Lima; George F Gao, Guangxue He, Yanlin Zhao, Aijing Ma and Chunfa Liu, China CDC, Beijing; Baoli Zhu, Institute of Microbiology, CAS, Beijing; Ian Laurenson and Pauline Claxton, Scottish Mycobacteria Reference Laboratory, Edinburgh; Robert J Wilkinson, University of Cape Town, Imperial College London and Francis Crick Institute; Anastasia Koch, University of Cape Town; Ajit Lalvani, Imperial College London; James Posey, CDC Atlanta; Jennifer Gardy, University of British Columbia; Jim Werngren, Public Health Agency of Sweden; Nicholas Paton, National University of Singapore; Ruwen Jou, Yu-Xin Xiao, Wan-Hsuan Lin, CDC Taiwan; Lucilaine Ferrazoli, Rosangela Siqueira de Oliveira, Juliana Maira Watanabe Pinhata, Institute Adolfo Lutz, São Paulo. James Millard, Africa Health Research Institute, Durban Rob Warren - University of Stellenbosch, Cape town Annelies Van Rie, University of Antwerp Simon Grandjean Lapierre, Marie-Sylvianne Rabodoarivelo and Niaina Rakotosamimanana, Institut Pasteur de Madagascar Camus Nimmo, University College London Kimberlee Musser and Vincent Escuyer, Wadsworth Center, New York Ted Cohen, Yale UniversityThose contributing to the CRyPTIC consortium: Inaki Comas, Biomedicine Institute of Valencia Maha Farhat, Harvard University Jennifer Gardy, formerly BCCDC and now BMGF Vitali Sintchenko, University of Sydney Vanessa Mathys, Sciensano, Belgian reference laboratory for M. tuberculosis Dick van Soolingen, RIVM, Netherlands Philip Supply, National Center for Scientific Research, France Mark Wilcox, Leeds teaching Hospital NHS Trust Angkana Chaiprasert, Mahidol University, Bangkok, Thailand Irena Arandjelovic, University of Belgrade, SerbiaSupplements-2021-4-8_bbab299Click here for additional data file.


Objective: Inflammation plays important role in atherosclerotic cardiovascular diseases (CVDs), but the interaction between the inflammation and lipid profile is largely unrevealed in humans. Patients with rheumatoid arthritis (RA) suffer from a higher risk of CVDs. Decreased total cholesterol (TC) and high-density lipoprotein (HDL) were prevalent in patients with RA. Anti-tumor necrosis factor (TNF) therapies relieve disease activity and decrease CVDs risk in RA, but their comprehensive effects on the lipid profile are unclear. This study aims to investigate the changes in blood lipid profile along time in the patients with RA accepting anti-TNF therapies by meta-analysis.Methods: The MEDLINE, the Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched for eligible literature. Data of lipids were classified into short-, mid-, and long-term according to treatment duration. Meta-analyses were performed to compare the lipid levels before and after treatments.Results: A total of 44 records and 3,935 patients were included in the meta-analyses. Anti-TNF therapies were associated with significant increase in TC  and HDL (MD): +0.11, +0.12, and +0.11 mmol/l, respectively) in the short-, mid-, and long-term; anti-TNF therapies were associated with increased low-density lipoprotein (LDL) (MD: +0.06 mmol/l) and apolipoprotein A1 (ApoA1) (MD: +0.07 g/l) in the short-term, but not in the mid-term and long-term; triglyceride (TG) and apolipoprotein B (ApoB) do not change significantly in all the periods; proatherosclerotic indexes  tend to decrease in the short- and mid-term, but return to baseline in the long-term after TNF inhibition.Conclusion: Anti-TNF therapies were related to a long-term raised HDL level, which, together with evidence of improved HDL function, may contribute partially to the decreased CVDs risk by TNF inhibition. Inflammation plays important role in atherosclerotic cardiovascular diseases (CVDs). Recently, two anti-inflammatory therapies, i.e., the interleukin-1β (IL-1β) monoclonal antibody, canakinumab and colchicine, have brought encouraging results in reducing the residual risks in atherosclerosis in addition to and independent of lipid-lowering effect . HoweverPatients with rheumatoid arthritis (RA) are at higher risk of CVDs, which can be partially explained by traditional risk factors of CVDs including smoking, hypertension, obesity, and diabetes. Interestingly, lipid profile in the patients with RA changes toward an “atheroprotective” direction. Patients with RA have lower blood total cholesterol (TC), low-density lipoprotein (LDL), and high-density lipoprotein (HDL) compared to the general population. The lower TC and LDL are unexpectedly associated with higher CVD risk, which is called the lipid paradox . Some ofThis study aims to systematically assess the changes in blood lipid profile after short-, mid-, and long-term anti-TNF therapies in patients with RA by meta-analysis of the literature. We also aim to review the evidence of altered lipoprotein functions after TNF inhibition in patients with RA, trying to investigate the possible effects of systemic inflammation inhibition on lipid profile, especially HDL.Refer to Study selection was performed by YL and XR independently. When divergency comes, a third person DP would make the final decision. Studies meet all the following criteria that were included for meta-analysis: (1) prospective studies; (2) with patients diagnosed with RA; (3) including any one of the anti-TNF therapies: infliximab (IFX), etanercept (ETN), adalimumab (ADA), certolizumab, or golimumab; and (4) with mean, SD, sample size, and treatment duration of any one of the following lipid profile both before and after anti-TNF therapies are available : TC, TG, HDL, LDL, TC/HDL (AI), ApoA1, ApoB, ApoB/ApoA1 ratio, or LDL/HDL ratio.Quality assessment was performed by SW and CY independently. When divergency comes, a third person DP would make the final decision. Quality assessment was a combination based on: (1) the Cochrane Collaboration's tool for assessing the risk of bias in randomized trials , (2) theData extraction was performed by YL and XR independently. When inconsistency arose, a third person QM would double check the original reference. Mean ± SD of lipid profile in the patients with RA before (baseline) and after (endpoint) anti-TNF therapies was extracted. When mean ± SD of lipid profile was not available in the articles or from the authors, mean was estimated from median if available  and SD wI2 statistic. Low heterogeneity was defined as I2 < 50% and p > 0.05 (Mean differences (MDs) with corresponding 95% CIs were calculated by using a fixed-effects model when low heterogeneity was indicated; otherwise, meta-analyses were performed by using a random-effects model. Heterogeneity was assessed by using the p > 0.05 . In casep > 0.05 ; the Disp > 0.05 ; age  study obviously influences the results of pooled MD  and remoThe funnel plot, Begg's test, and Egger's test show that the pooled MD of mid-term changes in blood TC, TG, HDL, LDL, AI, ApoA1, and ApoB are at low risk of publication bias .Long-term changes in lipid profile after anti-TNF treatments are summarized in Sensitivity analysis of TG indicates that the study by Popa (Popa-2007)  obviouslThe funnel plot, Begg's test, and Egger's test show that the pooled MD of mid-term changes in blood TC, TG, HDL, and LDL are at low risk of publication bias .Subgroup analyses of changes in all the lipid profiles in the short-, mid, and long-term are summarized in p < 0.05 for HDL short and mid-term changes in We found that RA duration may contribute to the heterogeneity of various changes of the lipids. Subgroup analyses show that meta-analysis of lipid changes in the patients with RA duration ≥ 6 months is usually with decreased heterogeneity compared to the total population. For the patients with disease duration for more than 6 months, mid-term and long-term TNF inhibition are associated with increased HDL  without heterogeneity ; mid-terSubgroup analysis and meta-regression imply that IFX may be associated with a more short-term increase in TC . Meta-reThe current meta-analysis investigates the changes in blood lipid profile after short-term (0–12 weeks), mid-term (13–26 weeks), and long-term (>26 weeks) anti-TNF therapies in patients with RA. This study is important for some reasons. First, it adds meta-analysis of the lipid changes following long-term anti-TNF treatments based on sufficient studies, which provides evidence to evaluate the possible roles of lipid alterations in the favorable CVDs effects of TNF blockade in RA. Second, this study is a state-of-art meta-analysis that systematically analyzes apolipoproteins and several atherogenic lipid ratios including AI, ApoB/ApoA1, and LDL/HDL, which gives an overall view of the lipid changes, since previous studies have shown simultaneous elevations in both “proatherogenic” and “antiatherogenic” lipids. Third, the comprehensive subgroup analysis in this study provides insights into factors that may impact lipid profile after anti-TNF therapies, which are not provided by previous studies. Finally, we observed consistent increases in TC and HDL as the previous meta-analysis , but we The mid and long-term increase in TC after TNF blockade  is primarily possible due to an increase in the antiatherogenic HDL , as we found no significant changes in TG, atherogenic lipids (LDL and ApoB), and atherogenic indexes  after mid and long-term TNF inhibition. In general, lipid profile changes toward an atheroprotective direction. A previous meta-analysis showed a similar increase in TC and HDL in the short- and mid-term after TNF inhibition, but it did not evaluate long-term changes of the lipids . AccordiThe mechanisms behind the increase of TC and HDL upon TNF inhibition are unclear. Basic researches have revealed a complex regulation between TNF and lipid metabolism, involving regulations in both hepatocytes, macrophages, and adipocytes, which need further verification in human . PreviouIn the current meta-analysis, to detect the possible confounding effects from the use of corticosteroids, cDMARDs, and lipid-lowering drugs  that may affect the blood lipids, we included six items toward the use of these drugs in quality assessment , where wWe found that the meta-analyses for lipid changes in the mid and long-term are usually with heterogeneity, which may come from different RA duration as indicated by subgroup analysis and meta-regression. When checking the concomitant uses of corticosteroids, cDMARDs, and lipid-lowering drugs  in studip values  for baseline HDL  significantly induce the hepatic expression of apolipoprotein serum amyloid A (SAA), which replaces ApoA1 on HDL and results in SAA-rich HDL. SAA-rich HDL was eliminated from plasma more rapidly than Apo A1-HDL  and showInflammation also impairs the expression and activity of paraoxonase 1 (PON1), the major component that accounts for the antioxidative capacity of HDL. PON1 hydrolyzes a wide range of substrates including atherogenic lipid peroxides, thus exerting atheroprotective roles. The PON1 single nucleotide polymorphism (SNP) Q192R greatly influences PON1 activity. Individuals carrying the RR allele, which associates with higher PON1 activity, had lower systemic oxidative stress and CVDs risk . In the In contrast to PON1, myeloperoxidase (MPO), a peroxidase enzyme, facilitates ROS production and oxidizes ApoA1 on HDL, leading to dysfunctional HDL and contributing to atherosclerosis. Induced by oxidative stress and inflammation, MPO promoted the development of arthritis in an experimental mouse model . In the Cholesterol efflux capacity inversely correlates with CVDs risk in the general population, independent of HDL level. HDL from the patients with RA exhibited impaired CEC  and a hiCardiovascular disease development in RA is partially due to endothelial dysfunction caused by multiple factors including the elevated proinflammatory cytokines (such as TNF). A meta-analysis including 20 studies demonstrates that anti-TNF treatment improves endothelial function in RA . HDL ameFollowing TNF inhibition, the increase in HDL and ApoA1 is usually more significant in those responsive to TNF blockade , 60, 62,p = 0.063 for quality score, see This study encompasses some limitations. The effects of golimumab or certolizumab on lipids are not investigated in our meta-analysis due to insufficient data. We did not include lipoprotein(a) [Lp(a)] data into the meta-analysis because few studies reported Lp(a) and a mixed presentation of Lp(a) in either mmol/l or mg/dl or logarithmic or original form in the different studies. The DAS28, we collected, is a mixture of results based on either CRP or erythrocyte sedimentation rate (ESR). However, we used a threshold of 5.1  to differentiate the patients with high disease activity. We cannot explain the heterogeneity for long-term change in TC after TNF inhibition  and atherogenic indexes  do not increase after long-term TNF blockade. HDL mostly contributes to the increased TC. Together with evidence that inflammation impairs HDL functions and inflammation reduction is associated with improved HDL functions, it is likely that improvements in HDL quantity and quality at least partially contribute to the reduced CVDs risk by TNF inhibition. Our findings add to evidence that systemic inflammation inhibition may exert a favorable alteration in lipid profile and HDL function in the patients with a highly inflammatory state, thus potentiating to reduce the CVD risk in those patients. However, direct evidence is still required in the future.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.YL and DP come up with the concept and design of the study. YL, XR, SW, CY, and QM performed the data collection. YL performed the statistical analysis, did the interpretation, and drafted the manuscript. DP revised the manuscript. All the authors contributed to the article and approved the submitted version.This study was supported by grants from the National Natural Science Foundation of China (No. 81870336).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


According to the International Myeloma Working Group diagnostic criteria serum free light chain (sFLC) assay is recommended for disease monitoring in oligo-secretory and micromolecular multiple myeloma (MM). However, normo-secretory patients could suffer from oligo-secretory/micromolecular escape at disease relapse, making it difficult to monitor disease relapse with serum protein electrophoresis alone. The possibility of a simple and manageable biochemical exam able to predict either future treatment response at disease relapse or relapse type in the course of treatment could be of great aid. In this work, we retrospectively analyzed the occurrence of oligo-secretory/micromolecular escape at disease relapses and its impact on disease outcome. Moreover, we have retrospectively evaluated the role of sFLC ratio and involved sFLC at disease relapse in a historical real-life single-center cohort of MM patients that underwent at least three lines of treatment.p = 0.0086 and p = 0.0065). Furthermore, both post-iFLC and post-FLCr greater than the pre-established values had a negative impact on PFS of the study cohort; respectively, p = 0.014 and p = 0.0079. Odds ratio analysis evidenced that patients with both involved post-sFLC greater than 138 mg/mL and post-FLCr above 25 at disease relapse had a higher probability of having clinical relapse (respectively p = 0.026 and p = 0.006). Conclusions: Alterations of sFLC values, namely iFLC and FLCr, both prior to treatment initiation and in the course of therapy at every treatment line, could be of aid in relapse evaluation and treatment outcome. We therefore suggest close periodical monitoring of sFLC assay, independently from secretory status.Background: In the era of novel drugs a growing number of multiple myeloma (MM) patients are treated until disease progression. Serum free light chain (sFLC) assay is recommended for disease monitoring in oligo-secretory and micromolecular MM. Methods: In this real-life survey, a total of 130 relapsed/refractory MM patients treated at our center with at least three lines were investigated as a retrospective cohort. Results: The median age at diagnosis was 64 years and more than half of patients were male. A total of 24 patients (18%) had oligo-secretory/micromolecular disease at diagnosis. More than 20% of 106 normo-secretory patients had oligo-secretory/micromolecular escape. In order to evaluate potential role of sFLC assay before (“pre”) and after (“post”) every treatment line, involved serum free light chain values (iFLC) less than 138 mg/mL and serum free light chain ratios (FLCr) <25 were identified by using ROC curve analysis. The analysis of the entire cohort throughout four treatment lines demonstrated a statistically significant negative impact on progression-free survival (PFS) for both involved pre-sFLC and its ratio (respectively  Multiple myeloma (MM) is characterized by the clonal proliferation of malignant plasma cells in the bone marrow and the presence of paraprotein in serum and urine, associated with organ dysfunction . The serIn MM, there is an overproduction of only one type of monoclonal FLC  leading to an imbalance between kappa and lambda sFLC and an increase of involved/uninvolved sFLC ratio (FLCr), that represents a quantitative marker of increased monoclonal (involved) light chain and the decrease of the polyclonal (uninvolved) one. Since sFLC have a considerably shorter lifespan than M-protein—with half-lives around 21 days—they can reveal early response to treatment. Thus, sFLCs represent a pivotal tool for monitoring MM patients, especially in those affected by NSMM in which M-protein is undetectable. Indeed, sFLC evaluation has already been recommended and is currently part of the by IMWG guidelines for MM diagnosis and response assessment . HoweverThere are different methods of measuring FLCs: some are based on the use of monoclonal antibodies, such as N-Latex Siemens and Seralite lateral flow assay, while others are polyclonal antibody-based assays, such as the Freelite. Several studies have compared the available assays showing different results in measuring FLCs, hence it is recommended to use only one assay to monitor patients ,7. In thIn this work, we retrospectively analyzed the occurrence of oligo-secretory/micromolecular escape at disease relapses and its impact on disease outcome. Moreover, we have retrospectively evaluated the role of sFLC ratio and involved sFLC at disease relapse in a historical real-life single-center cohort of MM patients that underwent at least three lines of treatment.Policlinico Catania 1, n.34/2019/PO) and was conducted in accordance with International Conference on Harmonization Guidelines on Good Clinical Practice and the principles of the Declaration of Helsinki.In this real-life survey a total of 130 relapsed/refractory multiple myeloma (RRMM) patients treated at our center between January 2000 and October 2020 were investigated as a retrospective cohort. Data was collected from the medical records of the patients treated with at least three lines of therapy, including novel agents. The study was approved by an independent ethics committee of the coordinating center  scale of performance status (PS). The cohort was also classified according to age, sex, time of diagnosis, type of monoclonal component , secretory status  and number of treatment lines. Secretory status was classified in measurable and unmeasurable disease (divided into oligo-secretory and micromolecular light chains), according to IMWG guidelines ,11. The Characteristics of myeloma disease, such as fluorescent in-situ hybridization (FISH) analysis and elevated LDH level  were evaluated at diagnosis and after every relapse. The Revised International Staging System (R-ISS) was evaluated only at diagnosis. FISH analysis was considered as high risk in the presence of the following alterations present in at least 10% of purified plasma cells: t, t; del 17p13; del 1p32; gain 1q21 together with another cytogenetic alteration. All other cytogenetic alterations were considered as standard risk . Staging® assay, according to the center’s availability. Involved sFLC (iFLC) and involved/uninvolved FLCr were evaluated according to IMWG guidelines , p = 0.006). However, multivariate analysis failed to confirm the statistically significant value of both pre-sFLC and post-sFLC values in terms of overall PFS.Furthermore, both post-iFLC and post-FLCr greater than the abovementioned threshold had a negative impact in terms of PFS of the study cohort; respectively, 09–1.86) C,D. Postp = 0.03 and p = 0.004 in the triplets, respectively, and p = 0.02 in each doublet regimen. As for the post-sFLC values, the impact of post-iFLC and post-FLCr remained significant in the doublets, respectively, p = 0.001 and p = 0.005, while their impact in the triplet regimen was not confirmed (p > 0.05)  .p = 0.0002 and p = 0.0001 in pre-iFLC and pre-FLCr and p = 0.01, while p = 0.0007 for post-iFLC and post-FLCr, respectively. The same result was not confirmed for fixed-duration treatment, where statistical significance was not reached neither for pre- nor for post-sFLC values  in the oligo-secretory/micromolecular cohort. On the other hand, pre-iFLC ≥ 138 mg/mL and pre-FLCr ≥ 25 confirmed statistical significance even in the oligo-secretory/micromolecular cohort, respectively p = 0.03 and p = 0.009, while post-sFLC values did not have an impact in terms of PFS .A total of 10 patients with extramedullary disease at diagnosis or disease relapse were evaluated with sFLC analysis. Given the extremely limited patient subcohort, serum free light chains did not demonstrate statistically significant impact in terms of PFS, neither for pre- nor post-sFLC values at abovementioned cut-offs  and at disease relapse after last therapy (post-sFLC). Involved sFLC value greater than 138 mg/mL value and ratio of 25 or higher were extrapolated by using ROC analysis. The impact of pre-sFLC assay evaluation prior to treatment initiation was used in a retrospective analysis in order to estimate its predictive value on PFS in the case of iFLC and/or FLCr below the cut-off values. Indeed, out of 195 patients in the entire cohort at disease relapse those with pre-iFLC value less than 138 mg/mL had an improved PFS A. The saOn the other hand, post-sFLC assay at disease relapse following last treatment line was analyzed for the purpose of estimating its use in predicting disease relapse and relapse type. The study cohort of 229 patients with post-sFLC values at disease relapse was evaluated, confirming, once again, the negative trend in terms of PFS following last therapy both in those with post-iFLC value greater than 138 mg/mL C and witIn order to evaluate patients with different characteristics in terms of secretory status, aggressive disease and treatment type, different subcohorts were analyzed by using the abovementioned sFLC cut-off values. Pre-sFLC values, involved chains and ratio remained significant in patients independently from treatment type, doublet or triplet, and in continuous treatment, but had no impact in cases of fixed-duration treatment. The lack of impact, in terms of PFS, was confirmed for post-sFLC values in both fixed-duration treatment and triplet regimens, while, in the rest of the treatment subcohorts, PFS was prolonged in cases of iFLC inferior to 138 mg/mL and with a ratio less than 25 . A possiThe study was retrospectively designed, without the availability of serial sFLC assay monitoring in the course of treatment for all patients. Furthermore, limitations, such as an extremely heterogeneous population with disease diagnosis in the last 20 years and different treatment regimens throughout treatment lines, make it difficult to strongly confirm the supposed prognostic impact of sFLC assay. Finally, the abovementioned cut-off values, which were obtained by using an N-Latex Siemens assay, cannot be introduced in every-day practice due to the lack of interchangeability between different sFLC methods, especially in case of involved sFLC.With the growing number of treatment lines and innovative drugs, the dosage of monoclonal component alone could underestimate disease evolution and could be improved by sFLC assay monitoring. In this work we have evidenced that in more than 20% of normo-secretory patients the secretory status switched to oligo-secretory after the first two lines of treatment. We can, therefore, suggest close periodical monitoring of sFLC to evaluate response both prior to and in the course of treatment and search for clinical disease relapse with organ damage.


It is widely known that exposure to residential greenness is beneficial for health. However, few studies have analyzed the association between greenery and Parkinson’s disease (PD). We selected 313,355 participants who matched the inclusion criteria from the National Health Insurance Service-National Sample Cohort, followed up from 2007 to 2015. Residential greenness, represented by the normalized difference vegetation index (NDVI), was obtained from satellite measurements. We estimated hazard ratios of PD associated with a 0.1-unit increase in long-term greenness exposure at the district level for the previous 1 year of each year until a censoring/event occurred, using time-varying Cox proportional hazard models, adjusted for individual- and area-level characteristics. During the 2,745,389 person-years of follow-up, 2621(0.8%) participants developed PD. Exposure to higher levels of residential greenness was found to be associated with a decreased risk of PD incidence : 0.74–0.84). In subgroup analyses, stronger protective effects were observed in participants aged over 50 years, females, overweight/obese participants, non-urban residents, non-smokers, alcoholics, and those with comorbidities. Long-term exposure to greenness was beneficial to incident PD, and our findings could aid in the development of public-health strategies. Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease after Alzheimer’s disease (AD), posing a great socio-economic burden to our aging society . GloballConsidering that 90% of PD cases have no distinct genetic cause , many prTo contribute to the limited evidence on the protective role of residential greenness in PD, we conducted a national-level cohort study to investigate the association between residential greenness and the incidence of PD using a nationwide population-based cohort of South Korean citizens.Data for this nationwide population-based cohort study were obtained from the National Health Insurance Service-National Sample Cohort . The NHIS is a universal insurance provider that was launched in 2000 as a single-payer health insurance system for all citizens of South Korea . The NHIn = 0) and were diagnosed with PD before 2007 (n = 680), those with incomplete address information (n = 1579), and those with incomplete information for any covariates . Finally, 313,355 participants were included in this cohort study  code G20, and medication prescriptions. The incidence of PD was defined as the first healthcare utilization (admission or outpatient visit) due to PD as a primary or secondary diagnosis for 2007–2015, simultaneously accompanied by the prescription of anti-PD medications such as carbidopa and levodopa.Exposure to residential greenness was represented by the satellite-derived NDVI, which has been widely used to estimate the effects of vegetation on mortality  and morbTime-varying Cox proportional hazard regression was conducted to assess the hazard ratios (HRs) and 95% confidence intervals (CIs) of PD incidence associated with annually updated exposure to NDVI from the previous 1 year at baseline. The time-varying model reflects the variability in exposure that changes over time. Survival time was measured in years from baseline (1 January 2007) until PD incidence date, mortality date, or end of follow-up (31 December 2015). 10). Personal exposure to the annual average of PM10 was estimated in the district (Si-Gun-Gu) unit by inverse distance weighting (IDW), which considers the square of the inverse distance between monitoring stations as weight ), those with low BMI , those living in metropolitan cities , ever smokers , rare drinkers , and those without comorbidities . The effect estimates were similar between younger  and older participants , but the association in younger participants was not significant.In stratified analyses, we identified more affected subgroups per a 0.1-unit increase in NDVI  as follop > 0.05) resulted in decreased effect estimates (HR 0.92–0.93)  . The ana92–0.93) . Finallyfindings .In this nationwide population-based cohort study, we investigated the association between long-term exposure to residential greenness and incident PD events after adjusting for demographic, lifestyle, physical, laboratory, and regional characteristics, as well as exposure to ambient air pollution. We found that exposure to higher levels of residential greenness significantly decreased the risk of PD. Stronger inverse associations were observed in participants aged over 50 years, females, overweight/obese participants, non-urban residents, never-smokers, alcohol consumers, and those with comorbidities. Our primary findings were consistent with those of various sensitivity analysesTo the best of our knowledge, our study is the first national-level cohort study on residential greenness and incident PD based on 313,355 nationwide-representative cohort participants. To date, only two epidemiological studies have investigated the association between long-term greenness exposure and PD ,17. UsinRecent studies have studied the neurological effects of residential greenness and reported significant neuroprotective effects, including reduced depression , slower The biological pathways of residential greenness in neurological disorders, including PD, remains unclear; however, greenness exposure would be associated with a lower chance of developing PD. Greenness may encourage higher levels of moderate or vigorous physical activities , such asOur findings of the stronger inverse associations in older participants (≥50 years), overweight/obese participants, alcoholics, and those with comorbidities can be explained by the susceptibility of these subgroups, implying that exposure to greenness could be more advantageous to sensitive subgroups. In the sex-specific analysis, we observed a stronger protective effect in females, which is consistent with the findings of a previous PD study in Ningbo, China . StudiesThe stronger protective effect of greenness in never smokers than ever smokers in our study is consistent with a recent study from Ningbo, China. . The stuWe found a noticeable protective effect of greenery on PD in provinces than in metropolitan cities. Aside from Parkinson’s disease, for which no previous research has been done, various studies have looked into the relationship between urbanity and cancer mortality , generalOur study has several limitations. First, our exposure assessment based on district-level residential addresses may not reflect accurate individual exposure and lead to exposure misclassification, despite the fact that we obtained high spatial resolution greenness measurements from satellites. However, district-level residential greenness assessments may be more accurate in capturing participants’ travel from home to work or other activities. Moreover, the NDVI captured by the satellite could not indicate vegetation type  or greenDespite several limitations, our research has a number of advantages. First, we conducted a national-level cohort analysis based on the government’s largest and most trustworthy national claims database, which contains actual healthcare utilization of Korean citizens. Second, important individual-level covariates were addressed in this study, such as medication history, lifestyle (smoking/drinking/exercise), laboratory findings, and concomitant disorders. Third, we employed spatial interpolation of air pollution to overcome the limitations of monitoring stations concentrated in urban areas to adjust for the primary confounding factor, long-term air pollution exposure. Finally, the results of various stratified studies by important features could aid in the identification of more benefited subpopulations and the development of effective health policies.With no clear treatment for PD, it is important to identify the factors which protect against developing PD. Our study provides scientific evidence that living in higher levels of residential greenness could be beneficial for lowering the risk of PD incidence. With the rapid urbanization and rapid aging of population worldwide, our findings should be useful for policy makers and city planners to establish appropriate public health policies and interventions.


High-quality longitudinal evidence exploring the mental health risk associated with low-quality employment trajectories is scarce. We therefore aimed to investigate the risk of being diagnosed with common mental disorders, substance use disorders, or suicide attempt according to low-quality employment trajectories.A longitudinal register-study based on the working population of Sweden (N=2 743 764). Employment trajectories (2005–2009) characterized by employment quality and pattern  were created. Hazard ratios (HR) were estimated using Cox proportional hazards regression models for first incidence (2010–2017) diagnosis of common mental disorders, substance use disorders and suicide attempt as dependent on employment trajectories.We identified 21 employment trajectories, 10 of which were low quality (21%). With the exception of constant solo self-employment, there was an increased risk of common mental disorders (HR 1.07–1.62) and substance use disorders (HR 1.05–2.19) for all low-quality trajectories. Constant solo self-employment increased the risk for substance use disorders among women, while it reduced the risk of both disorders for men. Half of the low-quality trajectories were associated with a risk increase of suicide attempt (HR 1.08–1.76).Low-quality employment trajectories represent risk factors for mental disorders and suicide attempt in Sweden, and there might be differential effects according to sex – especially in terms of self-employment. Policies ensuring and maintaining high-quality employment characteristics over time are imperative. Similar prospective studies are needed, also in other contexts, which cover the effects of the Covid-19 pandemic as well as the mechanisms linking employment trajectories with mental health. Mental disorders are among the leading disease burdens in middle- and high-income countries such as Sweden . The indEven in countries like Sweden – with strong and influential unions, high rates of collective bargaining agreement coverage and employment protection – PE is cause for concern. The proportions of unemployment as well as temporary employment have been relatively stable since the dramatic increases after the deep recession of 1990. However, in association with more relaxed employment protection legislation on temporary employment, its most precarious form, on-call employment, has continuously increased and replaced the more secure form of substitution positions. Since 2000, temporary and particularly on-call employment has grown in the lowest occupational wage strata, indicating that the lower end of the occupational structure is becoming more precarious .To date, there are several knowledge gaps in terms of PE’s relationship with poor mental health. For one, the association has largely been explored in studies using unidimensional measures of PE and/or a cross-sectional design. Indeed, a recent metanalysis of longitudinal studies found inconclusive results associated with a scarcity of high-quality studies and a lack of comparability between them . FurtherFurthermore, certain workforce subgroups are important to include and analyze properly when studying low-quality employment and health. Trends in precarious forms of self-employment  such as those associated with the gig economy and/or bogus self-employment  are increasing, in the EU as well as in Sweden . Low-quaData-driven approaches such as latent class analysis (LCA) allows for the creation of employment typologies, ie, explorations of how employment conditions such as income level, temporariness, etc, cluster based on similarity. In Sweden  and EuroConsequently, our objective was to investigate the risk of being diagnosed with a common mental disorder, substance use disorder or suicide attempt according to low-quality employment trajectories. We expect that individuals in low-quality employment trajectories characterized by constancy , fluctuation , or directional mobility  are at increased risk of poor mental health.https://www.sjweh.fi/article/3978) figure S1.This is a longitudinal register-study based on the Swedish Work, Illness, and Labour-market Participation (SWIP) cohort . The SWISociodemographic and employment data were collected for 2003–2009 from the Longitudinal Integration Database for Health Insurance and Labor Market Studies register (LISA) . Date ofThe Regional Ethics Board of Stockholm granted ethical permission for the study (no. 2017/1224-31/2 and 2018/1675-32).Employment trajectories spanning 2005 to 2009, characterized by employment quality and pattern , were created for the total population, as well as for women and men separately. This was done in two steps, as briefly described below. Complete details are provided in the supplementary material.In order to extract a class-solution (employment typology) applicable to the entire period of 2005–2009, the Swedish Register-based Operationalization of Precarious Employment (SWE-ROPE)  was implEmployment trajectories were created by combining the employment types and UE (jointly referred to as employment states) across the five years. Trajectories were manually created to enable categorization of individuals in patterns of (i) constancy (spending 4–5 years in an employment state), (ii) fluctuation (spending ≥2 years in two employment states with fluctuating movement in-and-out between these), and (iii) direction of mobility . If trajectories matched several patterns, grouping was done in the following hierarchy: fluctuation, constancy, mobility. The 8230 initial combinations were first reduced to 68 trajectories and, thereafter, further to 21 trajectories. In this process, for analytical feasibility, employment states were merged according to their perceived employment quality: (i) low , (ii) high  and (iii) HMJH, which could not be determined to be either high or low quality in this study and hence kept separate. Similar interpretation of the quality of the employment types has been made in a previous study . Once thFirst incidence of diagnosis occurring 2010–2017 was identified in the NPR by the following ICD-10 codes: (i) common mental disorders – depression (F32-F33), anxiety (F41) and stress-related disorders (F43); substance use disorders – alcohol (F10), opioids (F11), cannabinoids (F12), sedatives or hypnotics (F13), cocaine (F14), other stimulants including caffeine (F15), hallucinogens (F16), volatile solvents (F18), and multiple drug use and other psychoactive substances (F19); (iii) suicide attempt – intentional self-harm (X60-X84) and events of undetermined intent (Y10-Y34)  (supplementary figure S2). Confounders included: (i) age, highest completed education , country of birth , and marital status . These were measured in 2005 ; (ii) individual history of any mental disorder diagnosis  or suicide attempt  with 95% confidence intervals (CI) for the outcomes as dependent on employment trajectories. The trajectory “constant SER” was used as reference in all models. Person-time was calculated from the 1 January 2010, until first incidence of diagnosis, end of follow-up (31 December 2017), or until censoring due to death or emigration, whichever came first. The time-lag between the exposure and outcome assessment was chosen for capturing short-, mid- and long-term effects of the trajectories. One unadjusted model and two confounder-adjusted models were run for each outcome. The first adjusted model included individual characteristics and the second model both individual and parental characteristics. All analyses were conducted for the total population and women and men separately. Furthermore, additional analyses were performed to explore the effect of employment trajectories on five specific outcomes: depression (ICD-10 codes F32-F33), anxiety (F41), stress-related disorders (F43), alcohol disorders (F10), and other drug-related disorders . The RLMCA were run in Mplus version 8.4 , while remaining data management and statistical analysis was conducted with SAS version 9.4.0  and STATA version 16 . Figures were produced in RStudio version 1.2.5033 .We found six employment types in the total population and among women and men. Women and men had similar-sized types of PER and HMJH, whereas most women were in SER and the majority of men in SER/HI. Further, both SSE and BO were more common among men. Women generally had lower income than men across all employment types, although particularly evident in the low-quality employment types of PER and SSE (supplementary table S3).Combining the employment types and UE resulted in 21 employment trajectories , 10 of wIndividuals in low-quality employment trajectories generally had higher incidence of common mental disorders, substance use disorders, and suicide attempt, compared with high-quality employment trajectories, both overall and among women and men respectively see . The excThe risk of being diagnosed with a common mental disorder was increased among individuals in all low-quality employment trajectories, as compared with individuals in constant SER (fully adjusted HR 1.07–1.62), except for constant SSE (HR 0.95). Mobility within low-quality employment, fluctuating low-quality employment and constant UE showed the largest risk estimates . In addition to constant SSE, constant SER/HI and BO were also associated with reduced risk . Men generally had smaller risk estimates compared to women in these trajectories. Additional differences were seen in terms of constant HMJH , mobility between HMJH and low-quality employment, fluctuation in-and-out of low-quality and HMJH (both showed increased risk for women only), and constant SSE (reduced risk for men only). See Low-quality employment trajectories were associated with an increased risk of substance use disorders (HR 1.05–2.19), with the exceptions of constant SSE and mobility between HMJH and low-quality employment. The largest risk estimates were found for constant UE (HR 2.19), fluctuating low-quality employment (HR 1.90) and constant low-quality employment (HR 1.56). Reduced risk was seen among individuals in the constant trajectories of high-quality employment (HR 0.92), HMJH (HR 0.84) and SER/HI (HR: 0.80). However, in the sex-specific analyses, these trajectories were associated with reduced risk in men only. Furthermore, reduced risk was found for men in constant SSE and BO , while the risk was increased among women in these trajectories (HR 1.19 and 1.27). See The risk of suicide attempt was increased for individuals in half of the low-quality trajectories, including constant UE (HR 1.76), low-quality (HR 1.30), PER (HR 1.22), fluctuation in-and-out of low-quality and HMJH (HR 1.21), and downwards mobility from high-quality to low-quality (HR 1.08). Reduced risk was seen for individuals in constant SER/HI (HR 0.86), effects which remained for both women and men. However, constant low-quality and fluctuation in-and-out of low-quality and HMJH was only associated with an increased risk among men, and mobility from high-quality to low-quality only among women. See This study identified 21 employment trajectories across five years in the Swedish labor market. Ten of these trajectories were considered low-quality, accounting for 21% of the cohort. With the exception of constant SSE, all low-quality employment trajectories increased the risk of diagnosis of common mental disorder and substance use disorder. Half of the low-quality trajectories were associated with an increased risk of suicide attempt. The constant trajectories PER, UE and low-quality employment were risk factors for all three outcomes.Contrary to our expectations, only women in constant SSE showed an increased risk of substance use disorders, while there was a tendency for reduced risk in the total population and among men in terms of both substance use and common mental disorders. Women also had increased risk of common mental disorders in trajectories characterized by low-quality employment and HMJH, which men did not.Overall, individuals who experienced low-quality employment trajectories – characterized by patterns of constancy, fluctuation, or mobility – had an increased risk of common mental disorders, substance use disorders and suicide attempt. These findings are in line with the expectations of the study, as well as with previous studies exploring the relationship between employment trajectories and mental health using various measures of employment quality and labor market position , 22–25.In terms of low-quality employment trajectories characterized by fluctuations and mobility, previous Swedish and Swiss studies have found unstable trajectories characterized by multiple transitions to be associated with increased risk of receiving a psychiatric diagnosis  and deprApart from constant SSE, constant low-quality trajectories were risk factors for all outcomes. The association between trajectories of constant low-quality positions, characterized by poor labor market attachment  and long-term difficulties, and poor mental health has been shown in a few previous studies , 22, 26.Among possible mechanisms linking low-quality employment trajectories and poor mental health, we note an accumulation of employment, economic and social factors. Factors include employment strain , job insecurity and other psychosocial exposures in the workplace; income uncertainty and income instability, which may lead to material deprivation as well as have an impact on other social determinants of health ; feelings of precariousness or failure, and social exclusion , 32–34. This study is based on population registers with high validity and low attrition rates , allowinWe are, however, only capturing the effect of low-quality employment trajectories on severe mental disorders as we do not have information about visits to primary care, leading to a possible underestimation of the risk estimates. Also, we could not capture the effect of low-quality employment trajectories on actual suicides due to the low number of cases. Future studies using a case–control approach may be able to explore actual suicide. In addition, some trajectories were characterized by small numbers, which implies that the results should be interpreted with caution due to uncertainty of the estimates. Further, the cohort included individuals with exposure information  across all years, which might have led to an exclusion of eg, precarious employees in trajectories of employment and non-employment, leading to an underestimation of effects. Moreover, SWE-ROPE does not as of yet include all items initially described in Kreshpaj et al , 16. If By considering both a complex range of characteristics of low-quality employment as well as the dynamic nature of individuals’ working life course, this study adds to the literature an exploration of several low-quality employment trajectories, some of them largely unexplored in previous studies, and their association with mental health. The results demonstrate that low-quality employment trajectories characterized by constancy, fluctuations and mobility indeed are risk factors for severe common mental disorders, substance use disorders and suicide attempt, while the high-quality trajectory of standard employment relationship with high income showed reduced risk. We also found that there could be differential mental health effects according to sex, the most notable being in constant solo self-employment. In the light of these results – as well as the development of temporary employment on the Swedish labor market and the harmful effects of the ongoing COVID-19 pandemic on vulnerable employees – policy interventions aiming at ensuring and maintaining high-quality employment characteristics over time and protecting employees from ending up in unemployment or in otherwise disadvantageous employment trajectories are essential. Similar studies are needed in Sweden as well as in other contexts. In particular, prospective cohort studies covering pandemic effects and mechanisms involved in the relationship between poor employment quality and mental health are needed.