Medulloblastomas, highly aggressive neoplasms of the central nervous system (CNS) that present significant heterogeneity in clinical presentation, disease course, and treatment outcomes, are common in childhood. Moreover, patients who survive may be diagnosed with subsequent malignancies during their life or could develop treatment-related medical conditions. Genetic and transcriptomic studies have classified MBs into four subgroups: wingless type (WNT), Sonic Hedgehog (SHH), Group 3, and Group 4, with distinct histological and molecular profiles. However, recent molecular findings resulted in the WHO updating their guidelines and stratifying medulloblastomas into further molecular subgroups, changing the clinical stratification and treatment management. In this review, we discuss most of the histological, clinical, and molecular prognostic factors, as well the feasibility of their application, for better characterization, prognostication, and treatment of medulloblastomas. Medulloblastomas (MBs) are highly aggressive neoplasms of the central nervous system (CNS) and are considered the most common malignant brain tumor in childhood ,2. FirstEpidemiologically, the estimated annual incidence of the tumor in the US is approximately 500 cases per year and the median age of diagnosis is 6–8 years; it is very rarely diagnosed in adults [Since 1990, when the estimated event-free survival (EFS) of MBs was 20–50% , and witHowever, although recent research has mainly focused on a thorough investigation of molecular mechanisms behind MB pathogenesis and their contribution to risk stratification of patients, clinicopathological characteristics are also important factors for both relapse and prognosis and have been involved in recently developed prognostic nomograms .The main scope of our review is to summarize the evidence concerning clinical, pathological, and molecular factors affecting the prognosis of childhood medulloblastomas. We mainly focus on the interconnection between these factors and their role in risk stratification, aiming to prove that appropriate treatment selection in chosen patients is feasible.We searched the literature via PubMed, Scopus, and Cochrane for relevant studies, using specific keywords such as “medulloblastoma”, “prognosis”, and “prognostic factors” from inception to January 2023. Studies referring to clinicopathological and molecular factors associated with the prognosis of childhood medulloblastomas were included, whereas studies referring to adulthood or not referring to prognosis, as well as non-English papers, were excluded from the reviewing process. The primary outcome of our review was to investigate possible interactions between clinical, histologic, and molecular characteristics of MBs and the prognosis of the patients. In addition, the effect of the aforementioned factors on relapse occurrence and their association with post-relapse prognosis were set as secondary outcomes.2, and large-cell/anaplastic histology are considered high-risk features, whereas patients not fulfilling the above criteria are considered standard risk [Before the establishment of molecular subgroups of MBs, patient risk stratification was mainly carried out based on their clinical characteristics, the histological features of the tumor, and the treatment approaches followed. Using the above factors as prognostic parameters, patients are categorized into two discrete subtypes: standard and high risk. Age < 3 years old, residual tumor > 1.5 cmard risk ,12,23.One of the clinical characteristics used for risk stratification is the age of the patient. In general, younger age at diagnosis seem to have a worse prognosis compared to older children and is considered a high-risk feature . More prAnother significant factor that affects the prognosis of childhood MBs and is included in the risk-stratification system is the extent of disease. Chang‘s staging is the main clinical classification system for MB patients and evaluates the local extent (T stage) and the tumor’s dissemination (M stage) ,32. Alth2, is considered a poor prognostic factor, leading to local tumor relapses and worst survival rates. However, the benefit of extensive surgical resections may be counterbalanced for post-surgical neurologically adverse events, ranging from endocrinologic abnormalities to neurologic complications such as cerebellar-mutism syndrome, which are present in a considerable percentage of patients after GTR [Surgical resection comprises an important part of MB treatment plans, and the extent of resection poses a significant prognostic factor. The main goal of resection is the removal of the whole extent of the tumor, with gross total resection (GTR) demonstrating a favorable impact on both PFS and OS, as shown by observational studies and meta-analyses ,26,34,40fter GTR . The latfter GTR ,43,44. CThere are four main histological subtypes of MBs: desmoplastic/nodular (DMN), classic (CMB), MBs with extensive nodularity (MBEN), and large cell/anaplastic (LCA). Each of them is characterized by different histological patterns and is associated with distinct molecular and genetic alterations, exhibiting diverse prognosis ,45,46. MDespite that, the latest WHO classification combines all the above histological subtypes into one category, called histologically defined MBs, which are associated with specific molecular pathways, suggesting the need for a multilayered evaluation of specific tumor characteristics.Except for the above-described clinicopathological and molecular factors, hematological and serum markers are also described in the literature, which can predict the survival of MB patients. Li et al. investigated the role of serum markers on prognosis and revealed that an elevated preoperative neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were detected more frequently in Group 3 and Group 4 MBs and were associated independently with worse PFS and OS . In addiApart from serum markers, recent research has focused on the fast-growing field of radiomics, where the incorporation of image analysis into risk-prediction models, which are based on well-established factors, could evaluate the prognosis of each patient preoperatively and at diagnosis. The latter is of paramount importance, as it would assist with further understanding of the diverse nature of MBs and the designation of appropriate treatment strategies. Until recently, radiomic analysis had managed to indirectly predict the prognosis of patients, as MRI findings were associated with the molecular subgroup of the tumor ,59 or thSVZ was defined. These recent data explain the underlying molecular signatures, biological and clinical overlap, and site of diagnosis that these two groups share [During the last 20 years, knowledge about medulloblastoma biology has fiercely increased, primarily because of the advance of integrated genomics. The first approved classification of medulloblastomas concerning the molecular characteristics was performed by the WHO in 2016, resulting in four different molecular groups of MBs, each of which has a totally different molecular signature from both genetic and epigenetic aspects: WNT (wingless-related integration site) activated, SHH (sonic hedgehog)- activated, Group 3, and Group 4 ,63. The ps share .The intention behind molecular classification is to better relate clinical phenotypes with the tumor biology and to identify new personalized, safer, therapeutic targets. The four main molecular groups describe in a vague manner the disease evolution and outcome. In the updated WHO classification of the CNS tumors, Group 3 and Group 4 are merged into one, called non-WNT/non-SHH MB. It is a very large category, to which the majority of pediatric MB patients belong . The claThe WNT group accounts for 10% of MBs and usually develops in the midline cerebellum but is also capable of spreading to the dorsal brainstem. The WNT group overall has the best prognosis out of all MB groups . MutatioThere is a conflict about the subtypes within the WNT group; some studies have presented evidence for at least two distinct subgroups, whereas other researchers have reported only one ,69,70. OSHH MBs are the most common group in infants, accounting for 25% of all medulloblastoma cases. Usually, it is found in cerebellar hemispheres but can also be found in the midline. It is characterized by mutations or copy-number alterations of SHH-pathway genes. According to Robinson et al., the infant-SHH group should be split into SHH-I and SHH-II, as it seems that the first one is enriched in SUFU aberrations and chromosome 2 gain ,73,74. AGroup 3 has the highest rate of mortality, with only a 58% 5-year OS in children and an even lower, 45% 5-year OS in infants ,65,77. IIn summary, since the recognition of the four core MB groups in 2010, molecular subgrouping has experienced a revolutionary diversification. Nowadays, about 12 molecular subtypes are defined and new data come to light every day. Cavalli et al. analyzed more than 700 primary-MB tissue samples and showed that SHH MBs can be divided into four subclasses, with different copy-number aberrations, activated pathways, and—most importantly—different clinical outcomes. The SHHα subgroup has the worst prognosis of all four subgroups [Medulloblastoma genetic analysis is a key component not only for risk stratification but also for prognosis . CurrentEach molecular subgroup of MBs can be identified by a unique signature of DNA alterations and copy-number variation, abnormal gene transcription, and various post-transcriptional modifications. These characteristics are associated with different clinical phenotypes and, when combined, could have an important prognostic value. TP53 R273H mutation, which lies within the DNA-binding domain of the tumor-suppressor protein Tp53, is responsible for the loss of DNA binding and decreased activation of Tp53 target gene expression. As a result, cells resist apoptosis and an aberrant transcriptional activation and increased cell migration take place. TP53 mutation in the R273H point is associated with poor follow-up in the SHHα subgroup and generally with worse prognosis . In addiOther genes, such as MYCN and GLI2, are frequently found in various cohorts to be over-expressed . Indeed,It may take years before genetic tests based on sequencing technologies can be incorporated as a part of standard care for our patients. Further studies are needed to validate their prognostic merit and lead to tailored therapies that are able to detect measurable, residual disease and prevent the fatal relapse of medulloblastomas.New reliable and affordable methods are needed to help prognosis and to suggest the most appropriate therapy to patients with MBs individually. MiRNAs could be the answer to that, as they have already been used in treating various diseases, including cancer. MicroRNAs (miRNAs or miRs) are small non-coding RNA molecules that have been identified as major regulators of cancer growth, functioning either as oncogenes or tumor suppressors. MiRNAs help tumor cells called oncomiRs to proliferate, but others inhibit cell proliferation and promote cell differentiation, known as tumor-suppressor miRs. MicroRNAs have been implicated in gene-expression regulation through different mechanisms. Usually, based on sequence-specific binding, they can inhibit the translation of messenger RNA (mRNA) or mark mRNA molecules for degradation . Due to Different miRNAs have different molecular signatures in the four distinct groups. It has also been found that downregulation of miR-204 expression is associated with poor survival in children diagnosed with Group 3 or Group 4 MBs . MoreoveThe most important remark about miR-204 is probably that the replacement of downregulated expression levels could limit a tumor’s metastatic potential and thus be a promising therapeutic approach. The improvement of RNA-sequencing platforms has led to the identification of several miRNAs that alter gene regulation and promote cancer and metastasis. Large cohort studies with the neuroblastoma pediatric population revealed the crucial role of miR-383, miR-206, miR-183, miR-128a/b, and miR-133b in WNT MBs, as they are downregulated significantly ,99. IdenIn 2022, Lee et al. showed in vitro that long non-coding RNA SPRIGHTLY promote the MB subgroup 4 . SPRIGHTAccording to Zhang et al., HOTAIR (HOX transcript antisense RNA) is another LncRNA that promotes MB progression . HOTAIR Recurrence of MB  is considered a serious adverse event of the tumor, which is present in approximately 30% of patients and associated with poor prognosis. Recent studies have reported significantly diminished PFS and OS for rMBs , even in the presence of a wide range of therapeutic options, such as re-irradiation, surgery, chemotherapy, and targeted therapies . The aboRegarding prognostic factors of MB relapse, both clinical and histological characteristics of the tumor at diagnosis seem to affect the time and pattern of relapse and post-relapse survival rates. Concerning age, younger patients (<5 years old) have a higher rate of recurrence, and the role of histology is not clear. Although LCA was associated with reduced time to relapse and short time to death post-relapse , Sabel e2 residual disease showed a better EFS compared to STR or residual > 1.5 cm2 [I molecular subtype that had a worse PFS , underlining the importance of an extended molecular subtyping for the better understanding of recurrence risk in iMBs [I and SHHII subgroups. On the contrary, non-DN/MBEN histology or STR within the SHH subgroup were considered poor prognostic factors. On the other hand, Group 3 usually presented with a disseminated relapse pattern [Prospective studies also evaluated the existence of prognostic factors for MB recurrence in infants (iMB) and young children and investigated parameters associated with post-relapse survival (PRS). Gross total resection and the absence of residual disease or <1.5 cm 1.5 cm2 ,113, whe 1.5 cm2 . From a  1.5 cm2 , and fur in iMBs . In conc in iMBs , whereas in iMBs  and ACNS in iMBs  did not  pattern ,118 and  pattern ,118. OthThe observation of different treatment outcomes among MB patients, their diversity in histopathological and molecular characteristics, and the presence of different cells of origin, imply the detection of discrete molecular findings at relapse compared to the primary tumor. However, methylation data analysis performed at relapsed tumor specimens concluded that MBs maintain their primary characteristics also at recurrence (“tumor fidelity”), affecting all aspects of the disease, starting from time to relapse . DiffereIn the SHH MB group, it seems that age is being negatively correlated with differences between gene expression in the primary and in the relapsed tumor. Particularly, the youngest patients with SHH MB, develop relapsed tumors with copy-number variations and supreme transcriptome variability compared to the primary tumor. The majority of relapsed cases are characterized by the loss of 17p and, in a limited number of cases, by new TP53 mutation . This fiRichardson et al. studied more than 100 relapsed MBs, enabling the characterization of the molecular basis in medulloblastoma reoccurrence with the goal of the clinical exploitation of this knowledge. They found that molecular groups and the novel subtypes also remained consistent to the original stratification of the primary tumor. However, it was found that a small subset of Group 4 MBs switched subgroups when relapsing .Hill et al. also proved that MBs show an altered molecular profile at relapse, which is predictive of disease prognosis but cannot be detected earlier. They pointed out the significance of P53-MYC interactions during tumor relapse and their prognostic value as predictors of clinically aggressive disease . These fII subgroup compared to SHHI, in whom significant adverse genetic factors, such as SUFU aberration and isochromosome 2 gain, were detected. In addition, recent clinical trials have been designed  and SJMB12 (NCT01878617)) and enrolled pediatric patients based on both clinical and molecular data.The knowledge acquired from molecular data suggests that clinical risk is not enough for the reliable assessment of relapse risk. The intention behind molecular classification is to better relate clinical phenotypes with the tumor biology and to identify new personalized, safer, therapeutic targets. So far, randomized controlled trials focusing on older children, and SJYC07 including younger children (<3 years old), have evaluated both clinical prognostic factors and molecular subgroups to guide treatment decisions. More precisely, the ACNS0331 study concluded a non-inferior EFS in clinically defined average-risk patients with MB in whom a reduction of radiation-boost volume was preferred compared to posterior-fossa radiation therapy. In addition, that regimen decreased neurocognitive disorders attributed to RT, serving as an additional advantage in the treatment of patients lacking high-risk features. However, molecular subgrouping revealed a specific subgroup with inferior EFS when low-dose CSI was administered compared to the standard dose . In concFor the PNET 5 study, the only criterion for the assignment of patients was β-catenin status. Therefore, children positive for β-catenin endonucleic accumulation were assigned to treatment arm PNET5 MB-LR, whereas children with negative results were assigned to treatment arm PNET 5 MB-SR. It is worth mentioning that the initial diagnostic assessments  needed for inclusion in the study were the same for both treatment arms. An amendment of the protocol permitted the entry of WNT-activated MB patients with high-risk characteristics to the PNET 5 MB WNT-HR study. Likewise, patients with high-risk SHH MB with TP53 mutation  were included in the PNET 5 MB SHH-TP53 study.The second phase II clinical trial, called SJMB12, stratified MB treatment according to clinical risk  and molecular subtype . The main aim was to evaluate whether participants with low-risk WNT tumors could be treated with a lower dose of radiation and mild chemotherapy to achieve the same survival rates with fewer side effects. All children had surgery to remove as much of the primary tumor as was safely possible, radiation therapy, and chemotherapy. However, the amount of therapy (radiation and chemotherapy) was determined by the patient’s treatment stratum. The treatment-stratum assignment had been planned based on the tumor’s molecular subgroup and clinical risk.Both studies are ongoing, and we have to wait for the results to fulfill the research community’s ambitions for more efficient and safe treatment of MBs.Until 2014, MB grading, like general grading in CNS tumors, was based exclusively on histological data. However, since then, a lot has changed in the diagnostics and therapeutics of cancer diseases. Undoubtedly, genomic and transcriptomic analyses shed light on the molecular and genetic pathways involved in the pathogenesis of MBs, evolving risk-stratification systems. Molecular markers can provide powerful prognostic knowledge, and this is why they have been added to the biomarker list of grading and prognosis estimation. Apart from the already-applied technologies, new molecular biomarkers have come into view. Recent studies have revealed the promising prognostic value of various analyses based on genomic approaches that use the cell-free DNA (cfDNA) of the cerebrospinal fluid (CSF) and the different structural variants and point mutations by multiplexed droplet digital PCR (ddPCR) . MoreoveDisparities among patients’ clinical outcomes make the application of further subtypes of MB urgent and have been included in this study. Despite the prognostic value of the recent 2021 WHO molecular classification, it is obvious that there is a great heterogeneity within each of the four groups and their relevant subgroups. Therefore, there is a constant update of downstream classification from studies around the world, and this is only the beginning. As technology improves and permits wider and more complicated data interpretation, a more detailed subgroup analysis will appear. Furthermore, as the new molecular characterization in MBs remains independent and difficult to match with the histological data, we face problems of overlapping associations. That is the case for desmoplastic/nodular MB and MBEN, subtypes of the SHH group .The new era in molecular-based medulloblastoma prognosis and stratification has arrived. It remains to be seen, while awaiting the first sets of data from the ongoing relevant clinical trials, how translational research can be integrated into clinical practice to balance survival with quality of survival.


Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.  •Topic modeling of diseases in UK Biobank identifies clusters of multimorbidity•Priors informed by known disease relationships can improve power•Topic-GWAS finds novel genetic associations and can improve risk prediction•A healthy topic is associated with specific behaviors and genetic variants Zhang et al. develop and apply computational methods to identify clusters of disease multimorbidity among participants in UK Biobank. The characterization of such disease topics identifies novel genetic loci, can improve risk prediction for some disorders, and identifies unique characteristics associated with a healthy, disease-free phenotype. Its prevalence has increased because of a worldwide increase in life expectancy,,,,,,,,,,,,,,Several common multimorbidity patterns, such as a cluster composed of cardiovascular and mental health disorders and a musculoskeletal disease cluster, have been identified from literature reviews.,,The existence of common multimorbidity patterns raises the question of their etiology. One way to approach this question is to analyze the identified multimorbidity patterns together with appropriate omics data to determine the biological pathways involved. These analyses have been made possible with the establishment of biobanks linking individuals’ biological samples and genetic information to their EHR.,,,,,,,,,,Intrinsic to the study of multimorbidity is the joint analysis of multiple disease phenotypes, for which various multitrait GWAS methods have been developed.,,,,,,,,,,,,,,Despite these advances, existing methods all have limitations. First, diagnostic data are often binary in nature, with zeros and ones representing the absence and presence of diseases, yet topic models based on LDA were designed for count data, while algorithms designed for binary dataHere, we develop and validate an analytic framework for the study of multimorbidity, built around “treeLFA” , a statistical model to identify multimorbidity clusters of common diseases based on their co-occurrence patterns and an informed prior for topics. Applying treeLFA to Hospital Episode Statistics (HES) data extracted from UKB, we find multimorbidity clusters in the form of disease “topics.” Performing GWAS on the quantitative traits defined by individuals’ weights for these topics (topic-GWAS), we show that the approach identifies novel loci that correlate in expected ways with several genomic annotations. We also show that topic-GWAS can improve genetic risk prediction for multiple disorders on the test data, in particular immune disorders, and those for which currently few associated loci are known.treeLFA is a topic model designed to identify multimorbidity clusters in the form of topics of diseases from binary diagnosis data. The presence and absence of S disease codes for D individuals are modeled by factoring the D × S Bernoulli probability matrix into a K × S topic matrix (φ) and a D × K topic weight matrix (θ) A. In thid,s = 1) is the probability of disease code s diagnosed for individual d. d,s = 1) is specified by the corresponding entry in the product of matrices Here, W is the input data, and P, and the standard case-control GWAS for the 100 ICD-10 codes on the same population for comparison, as well as the 296 Phecodes mapped from these ICD-10 codes see  to invesFrom topic-GWAS we found 128 independent loci associated with at least one of the 11 topics, while 812 independent loci were associated with at least one of the 100 ICD-10 codes; 82 loci were shared between the sets A. PhecodWe next asked whether topic-GWAS simply identifies loci associated with expert-defined disease clusters, which provide an alternative approach to our data-driven multimorbidity clusters. To address this, we performed GWAS on groups of ICD-10 codes or Phecodes corresponding to internal nodes on the respective ontology. We found that of the 128 topic-associated loci, 41 were not associated with any of the internal or terminal ICD-10 codes; and for Phecodes the corresponding number was 56 E–S3H. ThWe then compared the topic-GWAS results for topics inferred by treeLFA, flatLFA, LDA, and GETM. Similar numbers of loci were identified by treeLFA and flatLFA  for topic weights by applying PRSice-2post hoc clustering of topics, is a computationally efficient strategy for producing a stable and comprehensive set of topics.Before applying treeLFA to more diseases, we considered how to select the number of topics (K), a fundamental problem for topic models. We trained treeLFA models with different numbers of topics  on the top 100 UKB dataset and found that, after clustering posterior samples of topics , the resWe next defined a larger dataset consisting of 436 ICD-10 codes  or large real-world data involving rare diseases (the top 436 UKB dataset). With independent test data, we verified, using both the predictive likelihood and the polygenic risk prediction, that the tree prior improves generalization of the inference, which confirmed the validity of using information in disease classification systems as a supplement to data-driven methods on noisy and sparse real-world data. We also found that treeLFA’s model structure better fits the binary input data, resulting in the identification of an empty  topic and ensuring that topic weights for the remaining disease topics were largely uncorrelated, improving power for downstream topic-GWAS. We also implemented algorithms to optimize hyperparameters of the model and developed a computationally efficient approach to determine the number of meaningful and stable topics.By applying treeLFA to HES data for 436 common diseases in UKB, we identified 40 topics with varied density reflecting combinations of diseases that tend to co-occur. Most inferred topics likely reflect underlying etiology, as indicated by the fact that the large majority (34/40) show genome-wide significant associations with genetic markers, while 20 topics are associated with 80 novel loci that do not reach genome-wide significance in GWASs for single ICD-10 codes and show evidence of functionality using multiple approaches. The active ICD-10 codes in the topics with the most novel associations are mainly from chapter 13 , with substantial contributions also from chapters 4 , 9 (diseases of the circulatory system), and 14 (diseases of the genitourinary system), suggesting that diseases in these chapters often share genetic risk factors.We explored constructing PRSs for an ICD-10 code using topic-GWAS results. We found that, for certain codes, especially codes from chapter 13, this new type of PRS outperforms the standard PRS. This improvement in prediction might result from better estimation of variants’ effect sizes by topic-GWAS, since treeLFA achieves a dimension reduction from disease space to topic space, which results in fewer traits and therefore more “cases” for each one. These results not only are validation for topic-GWAS, but also indicate the potential gain in utilizing multimorbidity in risk prediction for individual diseases.In contrast to topic-GWAS, single-code GWAS on the 463 common diseases resulted in 1,093 significant associations, of which the vast majority (895) were not associated with any topic. These genetic analyses indicate that most genetic associations are driven through links to individual diseases. However, most multimorbidity clusters do have a genetic basis, typically composed of pleiotropic genetic variants affecting risks of multiple active diseases in the cluster. In addition, there are also genetic associations for topics that are difficult to identify by single-code GWAS due to the lack of power. From a biological perspective, the loci uniquely associated with topics might reflect pathways causing multiple diseases in the same multimorbidity cluster. It is also possible that different subtypes of a disease have diverse genetic bases; therefore, some loci can be identified by topic-GWAS only where individuals with the same disease and different co-morbidity are separated. Overall, these observations are a helpful starting point for our pursuit of a deeper understanding of the mechanisms underlying multimorbidity clusters.,,We consistently identified an “empty” topic, as a result of treeLFA’s model configuration. The empty topic provides a way to include healthy people in topic-GWASs, which increases the power for finding associations for other disease topics. In addition, the topic itself showed many genetic associations, many of which (21/35) had not been identified before. It is possible that these variants are associated with risks of multiple diseases, and they were not found before because previous GWASs usually used a negative control group for a specific disease in question, which contained both completely healthy people and people with other diseases. Further analyses of topic-GWAS results indicated lifestyle factors and CNS cell types for the empty topic, a situation similar to the function of genes associated with BMI, obesity, and frailty,,,,,There have been many studies aiming at identifying multimorbidity patterns using various methods.Our work represents real progress in the understanding of multimorbidity, yet also reveals important unsolved challenges. For instance, while we showed that, taken together, the novel genetic associations likely represent true biology, we have not performed individual replications of the findings in this study in independent datasets, and this may be challenging unless the data sources and methods of data collection are comparable to UKB. In addition, some important factors in modeling diagnosis data, such as errors in assigning ICD-10 codes to individuals and the impact of age on multimorbidity patterns, were not addressed. Overall, the problem of inferring disease topics that are stable, tractable, and biologically meaningful across geographies and health-care systems represents a major challenge for future research.g.a.lunter@umcg.nl).Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Gerton Lunter , which factorises a Bernoulli probability matrix (used to model the binary input matrix) into a topic matrix and a topic weight matrix. For treeLFA, each topic is a vector of probability, with each entry parameterising the Bernoulli distribution for one disease code. This is fundamentally different from LDA, which only models diseases that have already occurred to individuals using multinomial distributions across all diseases. As LDA, treeLFA models individuals’ weights for all topics with Multinomial distributions. treeLFA is a Bayesian model, with a Dirichlet prior put on individuals’ topic weights, and two Beta priors put on the probability of active and inactive diseases in topics.01 = P(I = 1|Iparent = 0), ρ11 = P(I = 1|Iparent = 1)) of the Markov process control the sparsity of topics. Small values for both ρ01 and ρ11 give rise to sparse topics (most disease codes in a topic are inactive), while large values for both generate dense topics. ρ11 also controls the clustering of active codes in topics. With a large ρ11, most children codes of an active parent code will be active. As a result, active codes in a topic will gather on the same branch of the tree. On both simulated and real-world datasets, we used small ρ01 and large ρ11. This reflects our belief that in the real world most topics of disease codes should be sparse , and that active disease codes in the same topic tend to come from the same subtree (thus we choose large ρ11).treeLFA incorporates a special prior for topics constructed on a tree structure of disease codes . This prior assumes that disease codes close to each other on the tree are likely to have similar Bernoulli probability in the same topics. This is achieved by running a Markov process on the tree structure of individual diseases. The Markov process chooses active disease codes (disease codes having large probability in a topic) for each topic by generating binary indicator variables for all disease codes  see . The twoThe generative process of treeLFA is briefly described as follow:for k =1…K Sample indicator variable  for s=1…S Sample probability variable for d=1…D Sample topic weight vector  for s =1…S Sample topic assignment variable  Sample disease variable ,Inference on the treeLFA model is performed using partially collapsed Gibbs sampling,The simulation study served two purposes. Firstly, we aimed to verify that treeLFA can accurately infer latent topics from the diagnosis data encoded as binary variables. Secondly, we aimed to compare the performance of treeLFA to two related topic models (flatLFA and LDA) that are briefly introduced below, such that the influence of model structure and treeLFA’s informative prior for topics on the inference can be assessed.flatLFA has the same model space as treeLFA, with the only difference being that flatLFA uses a non-informative prior for topics, which is constructed on a tree structure where all nodes  are placed directly under the common root node. By comparing the performance treeLFA and flatLFA, the contribution of treeLFA’s informative prior for topics to the inference can be assessed.Latent Dirichlet Allocation (LDA01 and large ρ11 on the tree (the hyperparameter setting for treeLFA on the simulated datasets), while the second set of topics are unlikely to be generated by this Markov process. The first set of topics are used to test if the tree structure of codes improves inference accuracy, and the second set of topics test the robustness of treeLFA’s inference when the tree structure of codes is wrongly specified. We manually specified the topics to ensure that they are completely distinct from each other and have strong patterns with respect to the clustering of active codes.We simulate multiple data sets using different topics and hyperparameters to assess the performance of the three topic models  in different situations. We simulate the input data sets in two steps. Firstly, we build a tree structure for 20 disease codes B. The tr01 and large ρ11, since a parent code and all its children codes are always in the same state (either active or inactive). In the second simulation setting, active diseases in topics are not generated according to their adjacency on the tree  and D . A large value for α means that most individuals will have large topic weights spread across topics. By contrast, a small value for α will make topic weights for each individual more concentrated on a single topic. A large α makes the inference difficult, since most individuals are a mixture of multiple topics. Therefore, data sets simulated using large α require larger D for accurate inference. For each hyperparameter and topic setting, we simulate 20 data sets, with each one containing training and testing data of the same size. https://github.com/zhangyd10/treeLFA-demo-CG” or “https://zenodo.org/record/8174980” . The code for treeLFA is made available at “80” see “” section01 and ρ11) of the Markov process are Beta and Beta (treeLFA). For flatLFA only ρ01 will be used on the tree, and its prior is Beta, resulting in approximately the same expected number of active codes in topics as treeLFA. For LDA we try a few different values  for η, the concentration parameter of the Dirichlet prior for topics. We find that with η = 0.01 LDA has the best performance evaluated by the inference accuracy.For the hyperparameters, we provide the true value of α to train the topic models to simplify the inference tasks and focus on the effects of the tree prior. Beta priors for the probability of active and inactive disease codes in topics (φ) are Beta and Beta. Beta priors for the transition probability  as 0, and simulate probability variables (φ) using the Beta prior for inactive disease codes. Topic assignment variables (Z) are randomly sampled for all individuals.For each simulation scenario, ten Gibbs chains were sampled, and 20 posterior samples of hidden variables were collected from each Gibbs chain with an interval of 100 iterations after 15,000 burn-in iterations.ks is the Bernoulli probability of disease code s in topic k. To reorder the inferred topics as the true topics, we match each inferred topic to the true topic that has the highest cosine similarity, in a greedy procedure . The two-sided pairwise Wilcox test is used to test for statistical difference between Two metrics are used to evaluate the models in simulations. The first metric is the inference accuracy, measured with the averaged absolute per disease difference between aligned true and inferred topic vectors:https://icd.who.int/browse10/2016/en). The first layer of the ICD-10 tree is the root node; the second layer is composed of chapters of diseases (e.g. diseases of the respiratory system) coded using capital English letters; the third layer contains blocks of disease categories (e.g. acute upper respiratory infections); the fourth layer contains single disease categories (e.g. acute sinusitis); and lastly, the bottom layer contains sub-categories of diseases, which can be, for instance, the same disease occurring at different sites of the human body, or subtypes of the same disease. In UKB, most diagnosed diseases are encoded using codes on the bottom layer (fifth layer) of the tree. We use the fourth layer of encoding as diagnoses, where we replace all diagnoses in UKB with their parental code in the fourth layer on the tree.The input to treeLFA includes the diagnosis data for individuals in the UK Biobank, and the tree structure for disease codes. The diagnosis dataset is constructed from the Hospital Episode Statistics (HES) data in the UK Biobank (data fields 41202 and 41204) which is coded using the five-layered hierarchical ICD-10 billing system . In the M-step, α is optimised using this single posterior sample of Z collected in the E-step. In the second stage we continue to run the Gibbs-EM algorithm for 200 iterations. In the E-step of each iteration, we run the Gibbs sampler for treeLFA for 200 iterations and collect ten posterior samples of Z in total (19 burn-in Gibbs sampling iterations before the collection of each posterior sample). The reason to have two stages of training is to balance the computational speed with the inference accuracy. In the first stage, we optimise α more frequently and quickly get close to its optimal value. In the second stage, α is more accurately optimised based on multiple posterior samples of Z.After optimising α, we continue to use the collapsed Gibbs sampler to simulate posterior distributions of all hidden variables , with α fixed at values provided by the last iteration of the Gibbs-EM algorithm. 5,000 burn-in iterations are run before the collection of posterior samples of hidden variables of treeLFA. For each topic model, ten Gibbs chains are constructed, and 50 posterior samples are collected with an interval of 100 iterations from each chain.01 and ρ11 of the Markov process on the tree. The hyperparameters for flatLFA are set in the same way as treeLFA. For LDA, the concentration parameters of the Dirichlet priors for topic weights (α) and topics (η) are both initialised as a vector of 0.1. α is not optimised since we find that this has negligible influence on the inference result (inferred topics) and downstream analyses (topic-GWAS).To shorten the training with the Gibbs-EM algorithm, we initialise α as . The first entry in α is much larger than the others, which corresponds to the empty topic that will always be inferred from real-world diagnosis data. We also initialise α in other ways, such as using , and we find that model converge to the same results regardless of the ways of initialization of α, and the optimised α is usually closer to  than other choices. For topic assignment variable Z, we assign the empty topic  to all disease variables of individuals without any diagnosed disease codes. For individuals with at least one diagnosed disease code, all topics are randomly assigned to all disease variables. For topics, all indicator variables I are initialised as 0, and probability variables φ are randomly sampled from Beta. Beta and Beta are used as the prior for φ of inactive and active codes. Beta and Beta are used as the prior for transition probability ρdt is the total number of disease variables assigned with topic t for individual d, and Nd is the total number of disease variables for individual d.The topic weight variable θ is integrated out during the collapsed Gibbs sampling, therefore their posterior samples need to be approximated using posterior samples of Z and α. θ can be computed as in Griffiths and Steyvers:,,,To combine the inference results given by different Gibbs chains, the “identifiability” issue needs to be addressed, since the order of topics in different posterior samples from different chains may not be the same. We combine all posterior samples of topics from all chains together, and cluster topics before taking the average within each cluster. To cluster topics from all samples, we firstly construct a shared nearest neighbour (SNN) graph using the R package “scran”.ch is the number of Gibbs chains for the same treeLFA model, and Nps is the total number of posterior samples taken from each chain. This relatively small value for k  for the construction of the SNN graph. A large k will result in a small number of clusters, while a small k gives rise to a large number of clusters, though some clusters might be alike. Empirically, we use As a part of the model selection strategy introduced in this paper, sometimes treeLFA models set with a large and likely excess number of topics are trained, and multiple near-empty topics  will be inferred. Although the small differences between these near-empty topics are not meaningful, they are usually assigned to different clusters by the Louvain algorithm. To collapse these near-empty topics into a single empty topic, we further apply hierarchical clustering on topics averaged from different clusters given by the Louvain algorithm. During the hierarchical clustering, similar topics are kept being combined until all the remaining topics are significantly different from each other. By visualising the inferred topics using heatmap, one can roughly decide the number of distinct meaningful topics (topics that are not empty or near empty) to keep, and then manually set the number of clusters (topics) to keep for the hierarchical clustering. Alternatively, a threshold for the dissimilarity of topics can be used, such as cosine distance between topic vectors larger than 0.1 or Manhattan distance larger than 1.GETM (graph embedded topic model) is regarded as a LDA based topic model in this study since the occurrence of individual words in documents are modelled with Multinomial distributions. A distinguishing feature of GETM is that it incorporates pre-existing knowledge about diseases using pre-trained embeddings for diseases learned with the “word2vec” algorithm from disease ontology. GETM is included in this study to make the comparative analysis for topic models on real world datasets more thorough and comprehensive.https://github.com/li-lab-mcgill/getm) and the protocol paper for the model.GETM is implemented according to the on-line tutorial  to calculate their correlation with topics. treeLFA and flatLFA topics are normalised such that all entries in a topic sum to one for comparison with LDA/GETM topics. For each topic, the top 5 disease groups which have the largest correlation with it are shown.,To find genetic variants influencing individuals’ risks for topics of diseases, we perform GWAS using inferred topic weights as continuous traits (topic-GWAS) using PLINK2.https://phewascatalog.org/phecodes) In total, there are 296 terminal Phecodes mapped from the 100 ICD-10 codes.For comparison, we also perform GWAS (logistic regression) using the presence and absence of single ICD-10 codes as binary traits (single code GWAS). The inclusion criterion for individuals, SNPs and covariates are the same as topic-GWAS. In addition to ICD-10 codes, we also use terminal Phecodes mapped from the top 100 ICD-10 codes as traits for single code GWAS. Phecodes are defined by systematically grouping terminal ICD-10 codes into more applicable medical terms based on the judgements of clinicians and researchers,GC and the intercept of LDSC output by the algorithm are compared. A large λGC and small intercept for the same trait suggest true polygenicity causing the inflation in P-values, while large values for both λGC and intercept suggest stratification in the population.Inflation in P-values are observed for the topic-GWAS results given by all three topic models. The inflation can either be resulted from true polygenicity of the traits (topic weights), or stratification in the population. To differentiate these two possibilities, we carry out LD score regression (LDSC)−8) found by GWAS, we use the clumping function implemented in PLINK-1.9 and the threshold of r2>0.1 to group SNPs in linkage disequilibrium (LD). We define a locus to be an association for both topic-GWAS and single code GWAS as follows: the significant lead SNP found by one GWAS method can be clumped with a significant lead SNP found by the other method.To define genomic loci from significant SNPs . In other words, we use internal codes  of the two disease classification systems as binary traits for single code GWAS. For instance, if both disease codes A and B are under a common parent code C on the tree, then C will be used as the trait for GWAS, and individuals who are diagnosed with either A or B will be used as cases for the single code GWAS for code C. For the 100 ICD-10 codes there are 68 internal codes, and for the 296 Phecodes there are 136 internal codes.To illustrate the potential reason for only a small proportion of single code associated loci being identified by topic-GWAS, we used a simple simulation. We first simulated five binary genotype variables for 20,000 individuals: In addition to topics inferred by treeLFA, topic-GWAS for flatLFA, LDA and GETM inferred topics are also performed. For LDA and GETM, only individuals with at least one diagnosed disease code are used as input for inference. For topic-GWAS, there are two options to deal with the individuals without any diagnosis. We can either exclude them or include them and give them small random weights for all disease topics. We experiment with both methods and find that excluding the completely healthy individuals results in a larger power for topic-GWAS.https://www.ebi.ac.uk/gwas/), and clumped all SNPs in it to topic-associated lead SNPs (r2>0.5 as threshold). If a topic-associated lead SNP can be clumped, it means that a SNP in LD with it was found by a previous GWAS as significant.We used the GWAS CatalogueIntegrated analysis of GWAS results and functional genomics datasets has gained popularity in recent years.,GSE87112 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE87112). The default setting of FUMA for parameters is used in all the analyses. The clumping of SNPs by FUMA uses the phase 3 1000 Genomes European samples as reference, therefore the results are slightly different from the clumping carried out using UK biobank samples as reference. The two-sided two proportion Z-test is used to test for significant differences between the proportions of two groups, with Bonferroni correction done for the 15 chromHMM states.Three types of functional annotations are used for the validation of topic-associated lead SNPs. First, the three groups of lead SNPs (defined in the above paragraph) are annotated using the 15-core chromatin states predicted by the chromHMM algorithm.Another way to validate topic-GWAS results is to use them for prediction tasks on the test data. Because individual variants’ effects on traits of interest are usually small, polygenic risk scores (PRS) are constructed to aggregate the effects of tens of thousands of variants. With topic-GWAS carried out on the training data, PRS for topic weights (traits of topic-GWAS) are constructed using the software “PRSice-2”We used the testing dataset to evaluate the PRS for topics constructed on the training data. Topic weights of individuals in the testing dataset were inferred by running the Gibbs sampler for treeLFA on them, with φ and α fixed at values learnt from the training data . Ten Gibbs chains were simulated to infer topic weights for individuals in the testing dataset, and 50 posterior samples are collected from each chain, and their average is used in the subsequent analyses. With inferred topic weights, linear models were fit to evaluate the associations of PRS for topics and the corresponding topic weights, using the logit transformed topic weights as response variables, and PRS for topics as independent variables. The heritability of topic weights is estimated using LDSC as a reference.ts), and calculate an individual’s PRS for code s as:dt is the PRS of individual d for topic t (constructed using the topic-GWAS result for topic t). The area under the receiver-operator curve (AUC) is used to evaluate the predictive performance of different types of PRS for the same trait on the testing dataset. To compare the topic-GWAS results for different topics models (such as treeLFA and LDA) under a common criterion, we construct PRS for ICD-10 codes using the topic-GWAS results for different models, and compare the AUC of PRS on the testing dataset.To evaluate topic-GWAS results using single code GWAS results as reference, we construct two types of PRS for single ICD-10 codes using single code and topic-GWAS results, respectively. PRS based on single code GWAS are constructed in the standard way. As for PRS based on topic-GWAS, for code s we extract its probabilities in all topics (φThe larger UKB dataset (top-436 UKB dataset) is constructed in the same way as the top-100 UKB dataset, and contains the diagnostic records of the top 436 most frequent ICD-10 codes from the first 14 chapters of the ICD-10 coding system for all individuals in UKB. These codes are all the ones in UKB with a prevalence of at least 0.001 at the date of selection , corresponding to approximately 500 cases. The prevalence threshold of 0.001 is chosen both for computational reasons (this is roughly the limit of what can be performed using available computing resources) and because there must be sufficient occurrences of diseases from which to discover multi-morbidity clusters. As with the top-100 UKB dataset, we partition the full top-436 UKB dataset into training (80%) and testing (20%) datasets. The top-436 and the top-100 datasets use different partitions for the training and testing datasets.The top-436 UKB dataset is more than three times larger than the top-100 UKB dataset, increasing the computational requirements for training topic models. On the top-100 UKB dataset, treeLFA models with different numbers of topics are trained and compared. We find that when we set an excess number of topics for the model, both inferred topics and topic-GWAS results are stable across different models A. TherefFor the optimization of α, the two-stage training strategy with the GibbsEM algorithm is used again. 1,500 iterations are run in the first stage (with a single posterior sample of Z collected in the E-step), and 350 iterations are run in the second stage (with 10 posterior samples of Z collected in the E-step). 50 posterior samples of hidden variables are collected during the last 50 iterations for Gibbs-EM . For both treeLFA and flatLFA, three Gibbs chains are simulated.α is initialised as . Beta and Beta are used as the prior for φ of inactive and active codes to account for diseases with small prevalence. The remaining hidden variables and hyperparameters are set in the same way as for the top-100 UKB dataset.We find that different Gibbs chains for treeLFA and flatLFA give slightly different inference results on the top-436 UKB dataset, while different posterior samples from the same chain have a very high level of consistency. Considering the variability among the inference results given by different chains, instead of clustering posterior samples of topics from all chains altogether, we cluster posterior samples from different chains separately. Among the 40 inferred topics, 29 were found by all three treeLFA chains, and five were found by two treeLFA chains, suggesting most topics were stably identified from the data A.With the averaged φ and α for different chains, we calculate their predictive likelihood on the testing dataset, and for both treeLFA and flatLFA we retain the chain which has the largest predictive likelihood, and use its inference result as the input for downstream analyses. For each topic inferred by the chain with the largest predictive likelihood, we check the inference results of the other chains, and annotate the topic with the number of chains that infer them to give a reference of its reliability.Since the top-436 UKB dataset is much larger than the top-100 UKB dataset, to increase the inference accuracy for topic weights, after the training with Gibbs-EM algorithm we use Gibbs sampling to re-estimate the topic weights for individuals in the training dataset, which is observed to increase the power of topic-GWAS. φ and α are fixed at values averaged from all posterior samples from the chain with the largest predictive likelihood. As a result, there is no longer an identifiability issue, so the results given by different chains (for the re-estimation of topic weights) can be combined directly. For both treeLFA and flatLFA, ten Gibbs chains are used to re-estimate topic weights, and 50 posterior samples are collected from each chain. Topic weights averaged from these chains are used as the input for topic-GWAS.https://github.com/bulik/ldsc/wiki/Cell-type-specific-analyses.sLDSC is developed based on LDSC, which partitions the heritability of the trait of interest into different sets of genes to identify relevant tissues and cell types. To find the cell types enriched for the heritability of the empty topic, we run sLDSC on the summary statistics of the topic-GWAS result for the empty topic. We use two sets of cell type annotations to run sLDSC: one gene expression dataset (GTEx) and one chromatin dataset (Roadmap Epigenomics) . With the mapped genes, further analyses can be performed. Gene-set enrichment analysis (GSEA) tests for enrichment of different gene sets among a group of genes (e.g. genes mapped from significant SNPs of a trait). We choose genes that are associated with different traits in the GWAS catalogue (e.g. BMI) as the reference gene sets to carry out GSEA for genes mapped from topic-associated SNPs. By doing this, we summarise the major phenotypic associations for topic-associated SNPs found by previous GWAS. The default setting for FUMA is used in all the analyses in this section.All statistical analyses were performed using R 4.2.2. Methodological details can be found in the


IJMS. This issue has brought together a diverse set of research articles that highlight the importance of understanding the molecular signaling pathways involved in stroke. The studies presented have shed light on the molecular mechanisms that drive stroke and have provided insights into potential therapeutic targets for this disease.We have reached the end of the Special Issue on Molecular Signaling in Stroke in The use of rodent models in stroke research has significantly advanced our understanding of stroke pathophysiology and has contributed to the development of new therapies and treatment strategies. However, metabolic and transcriptomic variability in different brain regions must be considered ,2. Filip2+) signaling [2+ plays a crucial role in various cellular processes, including neurotransmitter release, gene expression, excitotoxicity, oxidative stress, inflammation and cell death. Kourti et al., 2022 [After an ischemic stroke, the brain undergoes a complex cascade of biochemical and molecular changes, including alterations in calcium  can impact patient prognosis and survival through two significant factors: inflammation and apoptosis. Toll-like receptor 4 (TLR4) plays a pivotal role in activating the inflammatory pathway, which results in the production and release of inflammatory factors leading to neuronal death ,13. Lua Overall, the research presented in this Special Issue underscores the complex nature of strokes and the importance of a multidisciplinary approach to stroke research. By understanding the molecular signaling pathways involved in strokes, we can develop new and innovative approaches for preventing and treating this devastating disease. We would like to extend our sincere thanks to all the authors, reviewers, and editors who contributed to this Special Issue. We hope that the articles presented here will inspire further research and innovation in the field of strokes and molecular signaling.


ANOS1 (KAL1) gene is responsible for 8% of mutations causing KS. A 17-year-old male presented to our clinic with delayed puberty and hyposmia, along with a family history suggestive of hypogonadism in his maternal uncle. Genetic testing for KS revealed complete exon 3 deletion in the ANOS1 gene. To the best of our knowledge, this specific mutation has not been previously described in the literature.Kallmann syndrome (KS) is a genetically heterogeneous condition characterized by hypogonadotropic hypogonadism with coexisting anosmia or hyposmia along with potential other phenotypic abnormalities depending on the specific genetic mutation involved. Several genetic mutations have been described to cause KS. The KAL1 or ANOS1 gene located in the X chromosome are responsible for 8% of all known genetic mutations of Kallmann syndrome.Missense and frameshift mutations in the ANOS1 gene is a novel mutation, not reported before.Exon 3 deletion is one of the Targeted gene sequencing for hypogonadotropic hypogonadism can be employed based on the phenotypic presentation. Kallmann syndrome (KS) is a genetically heterogeneous disorder characterized by isolated hypogonadotropic hypogonadism (IHH) with anosmia or hyposmia, caused by defective migration of olfactory and gonadotropin-releasing hormone (GNRH) neurons from the olfactory placode region across the cribriform plate to the bulb . The preIHH presents as gonadal failure due to inadequate production of GNRH or gonadotropins from the hypothalamus or pituitary, respectively. Children will typically present with partial or complete lack of pubertal development secondary to deficient GNRH-induced gonadotropin secretion with or without olfactory dysfunction.ANOS1 (Xp22.3), FGF8 (10q24.3), FGFR1 (8p11.2-p11.1), PROK2 (3p13), PROKR2 (20p12.3), NELF (9q34.3), CHD7 (8q12.1-q12.2), WDR11 (10q26.12), HS6ST1(2q14.3), SEMA3A (7q21.11)  with a mid-parental sex-adjusted target height of 176.5 cm (s.d. = −0.4) and BMI above the 97th percentile. On examination, no dysmorphic features were noted . Acanthosis nigricans was noted on his neck. His nasal turbinates appeared erythematous. He was unable to correctly identify the odor on the smell test. Cranial nerves II–XII were intact with symmetric deep tendon reflexes bilaterally. There were no involuntary or mirror movements noted. His testes were bilaterally descended with the prepubertal volume of 3 mL each and pubic hair in Tanner 4 distribution. Measured stretched penile length was 6.5 cm which was expected for his Tanner 1 stage.A 17-year-old male was referred to the pediatric endocrinology clinic due to concerns about delayed puberty. His initial symptoms of adrenarche began at 13 years of age heralded by adult-type body odor, axillary hair followed by deepening of the voice without any facial hair. He reported occasional penile erections weekly. The mother initially denied any pertinent family history. There were no cognitive or behavioral concerns. He was born at 37 weeks via C-section due to breech presentation, one of twins with a birth weight of five pounds two ounces and no history of cryptorchidism. A review of systems was significant for polyuria, polydipsia, and weight gain with no neurological or hypogonadal symptoms. A decreased sense of smell with onset around 10 years of age and a history of symptoms of allergic rhinitis necessitated an ENT evaluation and antiallergic medications were initiated. Past history included tonsillectomy and adenoidectomy for obstructive sleep apnea at 15 years of age. His height was 166.6 cm  vs IHH more likely was considered.Initial diagnostic evaluation is shown in ANOS1 gene consistent with KS  IHH which is more commonly referred to as KS and normosomic IHH (nIHH). KS is thought to be due to embryonic maldevelopment and migration of the GNRH neurons in association with olfactory neurons. Abnormalities in the GNRH neurons seated in the hypothalamus lead to nIHH. Certain genes may have an overlapping pathophysiological role making the differentiation between nIHH and KS blur. There are currently about 50 genes identified in IHH. The presence of specific anomalies and certain syndromic features may help narrow the differential and targeted genetic testing may be employed. Certain genes associated with KS will be discussed in this section.Multiple genes have been associated with KS, but only 30% of KS have known gene mutations . KS is mANOS1 gene is responsible for 8% of all cases of KS  and fibroblast growth factor receptor 1 (FGFR1) mutations have an autosomal dominant inheritance with incomplete penetrance attributing to 10% of all KS cases  are cysteine-rich proteins that are ligands to the G-protein coupled Prokineticin receptor (PROKR). PROK2 and PROKR2 specifically have an essential role in the morphogenesis of the olfactory bulb and GNRH secretion. CHD7, NELF, SEMA3A, WDR11, and HS6ST1 which contribute to only 1–6% of KS. CHD7 mutations causing anosmia and hypogonadotropic hypogonadism (KS) have been described in patients with CHARGE syndrome Other even rare genetic mutations causing KS include Management of hypogonadism in KS should take into consideration the desire of fertility at the time of therapy and beyond. Testosterone replacement therapy is initiated to induce virilization and maintain secondary sexual characteristics as well as to preserve bone mass. Should fertility be desired, sperm retrieval can be done after administration of pulse GNRH therapy or combined FSH/hCG therapy to stimulate spermatogenesis, under the guidance of a fertility specialist .Our patient was started on testosterone replacement therapy with IM testosterone enanthate and slowly titrated up to adult dose for development and maintenance of secondary sexual characteristics. He was eventually switched to Androderm®  patches 4 mg daily and is doing well.ANOS1 gene, located on the short arm of the X chromosome, is the most studied, and several missense, frameshift mutations, and exon deletions have been described in ANOS1-causing KS. Our patient has KS due to a novel mutation, a deletion of exon 3 on the ANOS1 gene.In conclusion, KS is a genetically heterogeneous condition caused by mutations in genes that control morphogenesis, migration, and survival of the olfactory placode and GNRH neurons. Variable presentation includes hypogonadotropic hypogonadism and anosmia or hyposmia. The The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.This work did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.Written informed consent for publication of their clinical details was obtained from the parent of the patient.Drs Arora, Yeliosof, and Chin conceptualized and prepared the manuscript. All authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.


Misclassification of plaque type accounted for majority of false negative findings  which was most prevalent among the mixed plaque . There was calcification within mixed plaques (N = 5) seen by CCTA but missed by OCT. Our findings suggest that CCTA is excellent at identifying coronary plaques except those sub-millimeter in size which likely represent very early atherosclerosis, although the clinical implication of very mild atherosclerosis is yet to be determined.Plaques identified by Coronary CT angiography (CCTA) are important in clinical diagnosis and primary prevention. High-risk plaque features by CCTA have been extensively validated using optical coherence tomography (OCT). However, since their general diagnostic performance and limitations have not been fully investigated, we sought to compare CCTA with OCT among consecutive vessel sections. We retrospectively compared 188 consecutive plaques and 84 normal sections in 41 vessels from 40 consecutive patients referred for chest pain evaluation who had both CCTA and OCT with a median time lapse of 1 day. The distance to reference points were used to co-register between the modalities and the diagnostic performance of CCTA was evaluated against OCT. Plaque categories evaluated by CT were calcified, non-calcified and mixed. The diagnostic performance of CCTA was excellent for detecting any plaque identified by OCT with the sensitivity, specificity, negative and positive predictive values and accuracy of 92%, 98%, 99%, 84% and 93%, respectively. The lower than expected negative predictive value was due to failure of detecting sub-millimeter calcified (≤ 0.25 mm Emerging evidence also suggests that CCTA is promising in identifying plaque features that might be precursors of acute coronary syndrome (ACS) irrespective of coronary stenosis3. In fact, over 50% of adverse cardiac events occur in patients with non-obstructive coronary artery disease during follow up5. Optical Coherence Tomography (OCT) is an intravascular imaging modality that can accurately define plaque composition owing to its superior spatial resolution of 10 μm or less7. There have been a number of studies published comparing high-risk plaque features by CCTA with OCT13. However, the general diagnostic performance of detecting and differentiating coronary plaques by CCTA has not been fully investigated when using OCT as a reference. In this study, we sought to examine the diagnostic accuracy and limitations of CCTA by comparing consecutive coronary plaques identified by OCT to those by CCTA.Coronary computed tomography angiography (CCTA) is a well-established modality for detecting coronary atherosclerosis. In addition, the findings of CCTA provide important diagnostic and prognostic informationWe retrospectively reviewed 42 consecutive cases referred for chest pain evaluation where CCTA was followed by OCT within a median time of 1 day (interquartile range 6 days). All patients presented with stable or chronic coronary artery disease. The decision to perform coronary catheterization following CCTA and to use OCT was at the discretion of the attending physicians. The majority of coronary interventions at our institution were guided by intracoronary imaging with a large proportion guided by OCT. Patients' demographic information was captured prospectively in our CCTA database.CCTA was performed on a 64-row detector scanner (Siemens Sensation Cardiac 64 or GE Lightspeed VCT 64). Images were acquired with electrocardiogram gating and bolus tracking. On average, 65–85 mL iodixanol contrast  was administered using a power injector at an infusion rate of 6 mL/s. Detector collimation was 64 × 0.625 mm, gantry rotation speed 330 ms/rotation, pitch 1.375, and tube voltage 120 kVp with a variable tube current set at a quality reference of 600 mAs. The adaptive iterative reconstruction was implemented and set at 40%. The reconstructions were performed with a kernel generating 0.625-mm-thick slices with an average field of view of 300–360 mm centered at the heart. The images were analyzed using a Vitrea workstation .OCT was performed using the Optis Dragonfly catheter  with longitudinal coverage of 75 mm. The segment of OCT imaging was confirmed using the angiographic co-registration feature of the OCT software. Once the catheter was in place, a contrast flush was injected and OCT image acquired via an automated pullback. Image quality was evaluated for blood swirl, chatter, and adequate distal and proximal ends prior to ending the study. OCT images were analyzed offline on an Abbott workstation.14 guidelines and included calcified, non-calcified and mixed plaque composition. The non-calcified plaque was defined as discrete low attenuation surrounding vessel wall with well-defined border. Plaque features by OCT were defined as calcific, lipidic, fibro or fibro-lipidic, and mixed which included fibro-calcific, lipo-calcific, fibro-lipo-calcific15. For the purpose of comparison, fibrotic, lipidic, and fibro-lipidic were grouped as non-calcified plaque. The analysis was performed for the entire length of OCT image section by section consecutively. The plaques identified by OCT or normal section randomly selected on OCT were then matched to CT. The intimal thickening seen on OCT was not classified as having a plaque.Of the 42 cases, 2 were excluded due to inadequate OCT image quality. Among 40 patients included in the analysis 1 had two vessels imaged by OCT. We evaluated the entire vessel segment available from OCT imaging with comparisons made with CCTA section by section contiguously. The plaque locations were matched between the two modalities using distances to fixed reference points such as side branches. In addition, the plaque in the vicinity was also an important landmark to match. To analyze the plaque, images based on multiplanar reformation and curved planar reformation were used. The vessel section was first defined as having or not having atherosclerotic plaque. If a plaque was present, the plaque type was further characterized by CCTA and OCT. Plaques defined on CCTA were based on the Society of Cardiac Computed TomographyTwo randomly selected sections without plaque were chosen from each vessel analyzed by OCT which were then matched on CCTA. In addition to classification as false positive and/or false negative by CCTA, discordant findings were further graded as misclassification, whereby plaque was present but the plaque type was not appropriately matched. For example, a mixed plaque by OCT that was seen as a calcified plaque by CCTA was considered misclassified. To compare the overall diagnostic performance, the detection of any plaque by CCTA was defined as a concordant finding. The images were initially analyzed by two cardiologists who were reading CCTA and OCT simultaneously to ensure the coregistrations of area of interests. Afterwards, CCTA and OCT were read again by independent readers who were blinded to the findings of the comparing modality.Continuous variables were described as mean ± standard deviation. Categorical variables were presented as percentage. Sensitivity, specificity, and positive and negative predictive values of CCTA to detect plaques and plaque features were determined using OCT findings as the reference standard.All research was performed in accordance with the Declaration of Helsinki. This study was approved by the St. Francis Hospital Institutional Review Board. Informed consent was waived by the St. Francis Hospital Institutional Review Board.There were 40 patients included in this study with an average age of 60 ± 10 years, 26% female. The cardiovascular risk factors were prevalent including 63% (N = 25) hypertension, 18% (N = 7) diabetes, 60% (N = 24) hyperlipidemia, and 33% (N = 13) smoking history.Of the 41 vessels analyzed, 24 were left anterior descending artery, 8 left circumflex or obtuse marginal, and 9 right coronary artery. Plaques extended from left main to left anterior descending coronary artery were included in the latter vessel category. Among 272 coronary vessel sections evaluated, CCTA was able to correctly identify 254 (93%) sections, either as having no plaque (82 out of 84) or having plaque (172 out of 188). The overall sensitivity, specificity, positive and negative predictive values and accuracy for detecting any plaque were 91%, 98%, 99%, 84% and 93%, respectively  with matching plaque types between CCTA and OCT. The discordance was 22% (41/188) summarized in Table 47/188 wi2)  were identified by CCTA Fig.  and 12 w2) Fig. . The senThere were 70 non-calcified plaques, either lipidic, fibrotic or fibro-lipidic by OCT. CCTA correctly identified 61 (87%) of them. The examples of non-calcified plaque by CCTA and OCT are shown in Fig. Among 31 mixed plaques identified by OCT 12 were correctly characterized by CCTA Figs.  and 6. TIn this study we demonstrated excellent diagnostic performance of CCTA in identifying coronary plaque and differentiating plaque types in comparison with OCT when analyzing consecutive plaques. However, CCTA is limited in detecting sub-millimeter calcified or non-calcified plaques likely due to lower spatial resolution relative to OCT, thereby compromising the negative predictive value of CCTA.13. However, the focus of those studies is largely on high-risk plaques or plaque features of culprit lesion during ACS such as positive remodeling, low attenuation, spotty calcification, and napkin ring sign by CCTA5. Despite the higher prevalence seen in patients with ACS, those high-risk features seem to only account for a small number of cases with future ACS in the ICONIC trial16. To date, few study if any have investigated the performance of CCTA in detecting and delineating coronary plaques using OCT as reference standard. Nevertheless, such knowledge is highly relevant as we have now entered an era when atherosclerotic changes identified by CCTA are increasingly used not only to make diagnoses of coronary stenosis but also to guide therapy for primary prevention since such strategies have been shown to provide significant benefits of morbidity and mortality prevention in clinical trial17. In this study we compared CCTA with OCT in consecutive vessel sections to assess plaque of all types and sizes and found CCTA is excellent at detecting any coronary plaque with 93% accuracy. While it is expected that CCTA may miss very small plaques that are seen on OCT due to their 50-fold difference in spatial resolution, it is unclear which of the plaque types and their frequencies are undetectable by CCTA. In our study nearly 9% (16 out of 188) of all plaques were not detectable due to small size (≤ 0.25 mm2) which contributed to a lower than expected negative predictive value of CCTA than previously reported when using ex vivo specimens as reference although tissue distortion or degradation is common in ex vivo tissues which may compromise the accuracy of plaque detection. We believe that these small plaques likely represent the beginning or early phase of coronary atherosclerosis though the clinical significance of sub-millimeter plaque is unknown when found in isolation.There have been a number of publications comparing the diagnostic performance of CCTA with OCT20 adding to the list of established ones such as rupture of non-calcified plaque and thromboembolic risk.The non-detectable small calcifications are not uncommon in our study accounting for nearly 14% of all calcified plaques. The ramifications of such small plaques may be inconsequential as subjects with zero coronary calcium score are generally associated with good outcomes. Nevertheless, the incidence of ACS is not zero and the non-detectable spotty calcification is potentially an unrecognized etiology23. Among all plaque types the low attenuation plaque, commonly associated with lipid-rich necrotic core, is most significantly associated with future risk of acute coronary syndrome25. Furthermore, the risk seems to be higher when the plaque burden is larger. To date, many physicians initiate primary prevention therapy such as statin in patients who are identified as having coronary atherosclerosis by CCTA, which has resulted in much reduced cardiovascular morbidity and mortality22. It is foreseeable that plaque features by CCTA may become the therapeutic targets, and the change of plaque characteristics may serve as a surrogate for treatment efficacy. To that end, it is critical to understand the accuracy of plaque evaluation by CCTA.While coronary plaque detection by CCTA is excellent, the delineation of specific plaque tissue type remains challenging largely owing to the relatively inferior spatial resolution of 0.6 mm when compared with OCT of 0.01 mm. In addition, calcium blooming from CT may further compromise the distinction between non-calcified and calcified compositions. Despite the large difference in spatial resolution, CCTA is highly specific in defining all three major plaque types, making it invaluable in non-invasive coronary plaque characterization, which is important for risk stratification27 allows for detection of very early coronary atherosclerosis in the form of tiny calcification or non-calcified plaque that is beyond the technical limits of CCTA. On the other hand, OCT has its limitations too28. In our study 7% (5 out of 70) of non-calcified plaque was characterized incorrectly by OCT due to failure to detect calcification seen on CCTA. This is an inherent limitation of OCT where relatively poor depth penetration prevents visualization of deep calcium29. In addition, superficial lipid accumulation can mask underlying calcification due to attenuation of light signals30. Nevertheless, OCT not only matches CT on detecting all the isolated calcified plaques but also identifies additional small calcification of sub-millimeter in size.The near histological resolution of OCTWe recognize the limitations of our study. While OCT is an excellent reference standard owing to its near histological resolution it clearly has its limitations in the evaluation of calcification in mixed plaque, although OCT appears to be highly accurate in detecting isolated calcified plaque. While all of the CCTA and OCT evaluations were performed within days they did not occur on the same day. However, it is unlikely to expect significant change in plaque features within a short duration in the absence of ACS. The OCT pull back distance is typically 75 mm which limits the analyzable vessel length. So the analysis of CCTA is confined to the segments that are visualized by OCT. The current study was intended to characterize the basic plaque compositions in the categories of calcified, non-calcified and mixed plaques. Therefore, we did not systematically evaluate high-risk plaque features that have been reported previously. The negative predictive value of CCTA is likely much higher in the general population than reported in the present study due to the high prevalence of coronary atherosclerosis since only cases with advanced atherosclerosis on CCTA are likely to be selected by attending physicians to undergo OCT evaluation. In addition, The CCTA image quality overall is good despite using a 64-row CT scanner. Newer CT technology with higher spatial and temporal resolution may further improve the diagnostic accuracy of CCTA. Our sample size is relatively small which may limit the generalizability. Future larger studies may allow more comprehensive subgroup analyses for each plaque type and more importantly provide prognostication based on accurate plaque characterization.To conclude, CCTA is excellent at characterizing coronary plaques when compared with OCT, but it may miss very early atherosclerosis although the clinical implications of very mild atherosclerosis are yet to be determined.


Acute coronary syndrome or ST-elevation myocardial infarction that develops as a hypersensitive reaction following exposure to an allergen, such as chemicals or bee or wasp stings, is known as Kounis syndrome (KS). Based on angiographic characteristics, three kinds of KS have been identified. Multiple bee stings typically result in localized allergic reactions and anaphylaxis, but they can also occasionally induce severe systemic toxic reactions. Here, a case of KS in a 50-year-old male presented with swelling on the face and upper limbs and breathing difficulties resulting from bee stings which led to myocardial infarction. The risk of KS should be considered by the physician at the primary level in all situations involving multiple bee bites. Kounis syndrome (KS) is a hypersensitive coronary disorder brought on by a variety of illnesses, medications, environmental exposures, diets, and coronary stents . In KS, A 50-year-old male patient reported to casualty of the peripheral center with a history of 100-150 honeybee bites. He was having facial puffiness with swelling on both the upper limbs and difficulty in breathing. On examination, the patient was drowsy with bradycardia and hypotension. He was intubated immediately and respiratory support was given. The patient immediately received steroids, adrenaline, and antihistaminic. The patient became conscious 30 minutes later and mentioned experiencing chest pain. An electrocardiogram (ECG) suggested inferior and posterior wall myocardial infarction Figure . The patIn the tertiary care center, the patient was weaned off from the ventilator on the second day. Antiplatelet (Tab. aspirin 75 mg and Tab. clopidogrel 75 mg) once daily and anticoagulant therapy (Inj. enoxaparin 60 mg subcutaneous) twice daily was given during the hospital stay. The patient underwent coronary angiography on the fourth day. Angiography was suggestive of 80% thrombotic proximal right coronary artery occlusion and 80% stenosis in the left circumflex artery, for which coronary angioplasty was advised Figure . The patCheck angiography was done after 15 days of discharge which was suggestive of the resolution of the right coronary artery thrombus Figure . The patIn India, especially in rural areas, honey bee stings are frequent, particularly in communities where beehives are frequently found. More than 50 bee stings at once signify massive honey bee envenomation . EnvenomThe prevalence of KS among patients hospitalized for allergy, hypersensitivity, and anaphylactic reactions was 1.1% overall , according to the first nationwide epidemiological study on KS conducted in the United States of America . AccordiACS following bee sting is uncommon, commonly misdiagnosed, and infrequently recorded . Recent AMI is one of the most severe and difficult cardiovascular consequences caused by KS. ACS following bee stings is uncommon, commonly misdiagnosed, and infrequently recorded. The risk of KS should be considered by the physician at the primary level in all situations involving multiple bee bites. Awareness should be raised regarding the seriousness of multiple bee stings-induced ACS in general physicians and healthcare professionals working at the primary level. This can be accomplished locally by conducting seminars and providing health information via the media.