According to guidelines, individuals with familial hypercholesterolemia (FH) shall receive lifestyle intervention and intensive lipid-lowering treatment from early in life to reduce the risk of coronary heart disease. Our aim was to study if treatment of FH also could affect risk of lifestyle-related cancer. We presented cumulative incidence of total cancer and lifestyle-related cancer sites in individuals with genetically verified FH (n = 5531) compared with age and sex matched controls (n = 108354). Individuals with FH had 20% lower risk of smoking-related cancer compared with the control population [HR 0.80 ], in particular men with FH at 40–69 years at age of diagnosis with HR 0.69 . The FH population and controls had similar rates of total cancer [HR 0.97 ], cancer related to poor diet [HR 0.82 ], cancer related to physical inactivity [HR 0.93 ], alcohol-related cancer [HR 0.98 ] and cancer related to obesity [HR 1.03 ]. In summary, we found reduced risk of smoking-related cancer in individuals with FH, most likely due to a lower prevalence of smoking. Implications of these findings can be increased motivation and thus compliance to treatment of hypercholesterolemia. Familial hypercholesterolemia (FH) is a disorder usually caused by a mutation in the low density lipoprotein (LDL) receptor gene, causing elevated plasma levels of LDL cholesterol (LDL-C) which leads to increased risk of coronary heart disease (CHD)2. Individuals with FH are therefore recommended lifelong lifestyle intervention and lipid-lowering medication from the age of 8–10 years or from the time of diagnosis3. Consequently, it has been demonstrated that both children and adults with FH have a healthier lifestyle than controls5. Cardiovascular disease (CVD) and cancer are the two main types of non-communicable diseases (NCDs). These diseases share several risk factors such as tobacco use, an unhealthy diet including low fruit and vegetable intake, lack of physical activity, and alcohol use6. Accordingly, lifestyle advice to prevent CVD and cancer overlap6. In the present study, we therefore investigated whether treatment for FH results in lower risk of total and lifestyle-related cancer in people with FH compared with an age and sex matched control population.A combination of lifestyle intervention including dietary advice, physical activity, smoking cessation and lipid-lowering medication, usually statins, is vital in the treatment of hypercholesterolemia8. In brief, since its establishment in 1992 all individuals with genetically verified FH are asked for consent to be included in the registry. The UCCG Registry currently holds 8511 individuals with genetically verified FH9. In the present study, birth year, sex, and date and age at inclusion to the registry were extracted from the UCCG Registry. Inclusion date to the registry was equal to date of genetic diagnosis after 1.1.1992. In total 744 individuals were missing exact inclusion date due to diagnosis prior to 1992 and were assigned inclusion date 1.1.1992. In this study we included 5645 individuals with FH who were registered between 1.1.1992 and 1.3.2014 (deadline of study reservation). The FH population was matched on age and sex on inclusion date to the UCCG Registry, to 112907 controls randomly drawn from the Population Registry of Norway. After excluding subjects for various reasons, as shown in Fig. This study is a prospective matched cohort study comprising of a sample of individuals with genetically verified FH, and age and sex matched controls 1:20). The FH sample was obtained from the Unit for Cardiac and Cardiovascular Genetics (UCCG) Registry for individuals with genotyped FH, at Oslo University Hospital. The UCCG Registry has been described elsewhere:20. The 10.This study was approved by The Regional Committee of Medical and Health Research Ethics for South-Eastern Norway (reference 2011/1343 REK Sør-Øst B), by the Norwegian Data Protection Official at Oslo University Hospital, and complies with the Declaration of Helsinki. Due to the nature of this study, we were granted exemption from the obligation to gain informed consent from the FH population and controls. However, the individuals with FH were informed about the study by a letter of information and were given the opportunity to reject having their information linked to the registries, as previously describedThe FH population and controls were linked  to the Cancer Registry of Norway (CRN) and the Norwegian Cause of Death Registry (NCoDR) in order to obtain information on cancer incidence and mortality (underlying cause of death in the death certificates). Endpoints were defined according to the International Classification of Diseases (ICD), version 10 (ICD-10) and for NCoDR also ICD-9 (1992–1995) codes. All ICD-9 codes were converted to the ICD-10 codes that are shown in Table 6. We identified and categorized cancer types according to World Cancer Research Fund (WCRF) & American Institute for Cancer Research (AIRC) and Cancer Facts & Figures from the American Cancer Society (ACS) who have defined groups of lifestyle-related cancers12. Risk factors that according to WCRF&AICR and ACS have sufficient or strong (either convincing or probable) evidence for causing cancer in humans were included in the study12. Hence, the lifestyle-related cancer sites included smoking-related cancer11, cancer related to poor diet , cancer related to physical inactivity, alcohol-related cancer, and cancer related to body fatness and weight gain12, as shown with corresponding ICD-10 codes in Table As stated earlier, CVD and cancer have several lifestyle-related risk factors in common7, we also performed competing risk regression with deaths treated as competing event in order to take into account that individuals with FH might die from CVD before they get cancer. The results were however very similar  and we therefore only present results from Cox regression. Because lifestyle advice may affect women and men differently, and because age at time of FH diagnosis also may modify the risk of cancer, we performed analyses stratified on sex and age groups . All analyses were performed with Stata version 15 and 5% was used as the significance level for all statistical tests.Baseline for start of follow-up was defined as date of the pathogenic genetic diagnosis for individuals with FH, and the same date was chosen for each of the FH patients’ corresponding controls. Follow-up time was calculated as time from baseline until date of cancer diagnosis or death (from CRN or NCoDR), or 31.12.2017, whichever came first. Cumulative incidence curves for risk of cancer with death treated as competing event were constructed separately for the FH population and the control population using the stcompet-package in Stata. The curves are presented graphically with age as the time scale. Risk of cancer among individuals with FH was compared with age and sex matched controls in terms of hazard ratios (HR) with 95% confidence intervals (95% CI) obtained from Cox proportional hazards regression, stratified on matched risk set to take the matching into account. Because of the already known increased risk of CVD in the FH populationIncidence of total cancer and grouped lifestyle-related cancers for the FH population and the control population are summarized in Table ). In the FH population, the total number of person years of follow-up was 71253 and the median time to cancer diagnosis among those who developed cancer was 8.7 years. In the control population, the corresponding numbers were 1366647 person years and 8.8 years, respectively. As illustrated in Fig. A total of 289 cancer cases were observed in the FH population and 5579 cases in the control population Table . In the In the FH population, we observed 97 cases of smoking-related cancer, whereas 2220 cases were observed in the control population. Individuals with FH had 20% lower risk of smoking-related cancer compared with the control population [HR 0.80 ]. In the age group 40–69 years at baseline, the FH population was 27% less likely to have smoking-related cancer compared with controls, as demonstrated with a HR of 0.73 . The strongest relative reduced risk was observed for men with FH aged 40–69 years at baseline, where an incidence rate per thousand person years of follow-up for smoking-related cancer was 3.94  compared with 2.85  for controls  mainly due to lower risk of smoking-related cancers such as respiratory and lymphatic cancer15.We have previously shown that cancer mortality in individuals with FH is similar to that of the general Norwegian population17. Our results show that men with FH aged 40–69 years at baseline had a 31% lower risk of smoking-related cancer than their matched non-FH controls. The ACS consider smoking a strong risk factor for the cancer sites included in the predefined group of smoking-related cancers11. Hence, the reduced risk observed in the FH group is likely caused by less smoking in FH individuals than in controls. Indeed, in 2014–15, 9% of a subsample of the FH population in the present study were current smokers18, compared with 13% of the general population19 and similar differences are supported by others20. Furthermore, the SAFEHEART study reported that 2404 adult individuals with FH smoked less than their non-affected siblings5. Neil and co-workers also proposed that a reduced cancer mortality in FH might be attributable to the effects of regular medical control, including being given early and continuous lifestyle advice from early in life, resulting in better prognosis15.Lung cancer, one of the largest contributors to smoking-related cancer, is among the most common cancer sites in Norway particularly in men16. However, these results of post hoc analyses and small numbers should be interpreted with caution and more information on risk factors of colorectal cancer is warranted. We cannot rule out the possibility that the FH mutation per se can protect against cancer, but this is less plausible from a current biological point of view.Yet, in this study the incidences of cancer related to poor diet or physical inactivity were not significantly reduced in the FH population, except a tendency towards a reduced risk of cancer related to poor diet in women with FH compared with controls, with a HR of 0.59 . By further investigating the risk of only colorectal cancer, which accounts for the majority of cancer incidents related to poor diet in the Norwegian population, we found a HR of 0.55  in FH women compared with controls . Colorectal cancer is the second most frequent cancer site in Norwegian womenThere was no difference in incidence of alcohol-related cancer, nor cancer related to body fatness and weight gain, between the FH population and the matched controls. In the present study, the mean age at genetic diagnosis and assumed initiation of lifestyle advice and lipid-lowering treatment in the FH cohort was 33 years with a 25-percentile of 17 years and a 75-percentile of 47 years. For those who received the FH diagnosis late in life, any pre-diagnosis unhealthy lifestyle might have already affected their cancer risk, causing any lifestyle changes to have a weaker effect on cancer risk than anticipated. On the other side, it could also be that favorable diet and lifestyle changes are not easier to comply with for individuals with FH than controls.21. A recent summary of several large randomized controlled trials (RCTs) state that there is no association between statins and the risk of cancer22. Yet, studies on very long-term exposure to statins are challenging to conduct. Large metaanalyses of RCTs can provide solid evidence but not for a longer exposure than the typical duration of RCTs of five years or less23. Development of NCDs like cancer and CHD are slow processes24 and data on very long time exposure are sparse. In a subsample (n = 714) of the current FH population 89% (n = 635) used statins and mainly potent statins in maximal dose. Mean duration of treatment was of 13.5 years18. The corresponding duration of statin treatment were mean 11.9 and 16.1 years for normal risk and very high-risk patients, respectively18. The median follow-up time of all individuals with FH in the present study was 12.2 years and the maximum follow-up time was 25 years (from 1992 to 2017). As statins usually are recommended from the time of FH diagnosis, this supports that the current FH population is likely to have been exposed to statins for a considerably longer time than RCTs usually lasts23. In secondary analysis we divided the FH population in two, those diagnosed in 1999 or earlier (n = 1104) and those diagnosed in 2000 or later (n = 4417). Compared with matched controls, HR for the FH cohort diagnosed before or equal to 1999 was 0.97  and for those diagnosed in 2000 or later, the HR was 0.97 . Since statins were approved in 1992, the period 1992–1999 reflects possible long time use of statins compared to the period after 1999. Hence, this study could support the growing amount of evidence stating that use of statins does not increase the risk of cancer.The relationship between statins and cancer has earlier been subject to some debate, especially in the media where negative statin-related news stories have resulted in reduced use of statins in some hypercholesterolemic individuals and accordingly increased risk of CVDStrengths of the present study include the large cohort of genetically verified FH and matched controls and the long follow-up time. Since the FH mutation is inherited randomly from heterozygous parents across socioeconomic strata, there is no need to adjust for socioeconomic status. Furthermore, the use of population-based registries for collection of endpoints ensured almost no loss to follow-up. The inclusion of data from the NCoDR in addition to the CRN, allowed us to take into account deaths as competing events and hence, strengthen our results of the observed cancer risk in the FH population. The number of person years of follow-up was 71253, which is more than any other study investigating cancer risk in individuals with genetically verified FH.9. The low reservation rate (2.5%) to the study however strengthens the representativeness of the results.Limitations include lack of information on medication use including statins, smoking prevalence and frequency, dietary habits, physical activity and other lifestyle factors, and biochemical measures such as LDL-C concentration in the study populations. There were few cancer events in FH group overall (n = 289). A greater number of FH patients or more events in the FH population are therefore likely to increase the precision of the results by reducing the wide CI observed for subgroups within the different lifestyle-related cancer sites. The FH population is likely to be more regularly followed up by physicians compared with the control population, which could cause cancer to be more easily detected in the FH population. This kind of detection bias would bias the incidence of cancer in the FH population upwards and cannot explain why we found a significantly lower risk of smoking-related cancer compared with the control population. It could, however, potentially explain why there was no difference in risk of cancer related to poor diet. Additionally, there might be limitations regarding the individuals who gives their consent to be registered in the UCCG Registry of genetically verified FH, and as only about 1/3 (n = 8511) of all individuals assumed to have FH in Norway (~25000) are diagnosed, the current sample might not reflect the total FH population in NorwayIn summary, a FH mutation and corresponding treatment was associated with reduced risk of smoking-related cancer in a FH population compared with matched controls, most likely due to a lower prevalence of smoking in the FH population. There was no difference in the risk of total cancer and cancer related to poor diet, physical inactivity, alcohol intake, and body fatness and weight gain between the FH population and the control population.Supplementary Material


Parkinson’s disease is one of the most common neurodegenerative illnesses in older persons and the leucine-rich repeat kinase 2 (LRRK2) is an auspicious target for its pharmacological treatment. In this work, quantitative structure–activity relationship (QSAR) models for identification of putative inhibitors of LRRK2 protein are developed by using an in-house chemical library and several machine learning techniques. The methodology applied in this paper has two steps: first, alternative subsets of molecular descriptors useful for characterizing LRRK2 inhibitors are chosen by a multi-objective feature selection method; secondly, QSAR models are learned by using these subsets and three different strategies for supervised learning. The qualities of all these QSAR models are compared by classical metrics and the best models are discussed in statistical and physicochemical terms. Though clinical diagnosis is based on the presence of bradykinesia and other cardinal motor issues, Parkinson disorder is related with many non-motor symptoms that add to overall disability.At present, the pursuit of effective treatments for neurodegenerative disorders is one of the imperative clinical and social needs. The number of patients affected by those pathologies, including Alzheimer’s and Parkinson’s diseases, rise every year, principally in developed countries, directly related with the longer life expectancy. Parkinson’s Disease (PD) is the second most frequent human neurodegenerative disorder in persons over 60 years of age, affecting 1 in 100 people and growing to that affects 2–3% of the population ≥65 years of age. It is linked with Lewy bodies, abnormal aggregates of α-synuclein protein, and loss of dopaminergic neurons in the Genetic and epidemiological studies carried on numerous families in Asia, the United States, and Europe led to discover in 2004 that mutations in a new gene, known as leucine-rich repeat kinase 2 (LRRK2), are a key genetic risk factor for familiar and sporadic PD . NowadayQuantitative structure–activity/property relationship (QSAR/QSPR) modelling has established itself as one of the main computational molecular modelling approaches, with a key role in drug virtual screening or optimization. QSAR models permit to identify relationships between structural information of chemical compounds (molecular descriptors) and a physicochemical or biological property under study. Nowadays, these methods are broadly used as a substitute for experimental studies to predict the activity of the molecules from their structure. In particular, machine learning techniques had become widely used in this field during the last decades .th International Conference on Practical Applications of Computational Biology & Bioinformatics12 . In. In9]. Iormatics . In part22.1Datasets on SMILES format were converted to 3D structures using LigPrep  softwareDifferent conformers and ionization states of the same compounds were reduced in order to keep one 3D structure per initial compound. The selection was made considering the most probable ionization state at physiological pH conditions. This preparation is a crucial step for the following studies and was performed with the aim of obtaining the most suitable 3D structures to further calculate the physicochemical properties of the existing compounds.2.2All the prepared compounds were analysed using Qikprop  module o2.3The first step consisted in dividing the dataset into a training (75%) and a test set (25%) following a stratified division. The next step is based on computing the molecular descriptors of all the dataset using DRAGON software . DRAGON Finally, in order to build the models, WEKA software  was usedNeural Networks (Multiperceptron): A classifier that uses back-propagation to classify instances. The network nodes are all sigmoid, excluding the cases where the class is numeric (in this situation the output nodes become unthresholded linear units).Random Forest: class for constructing a forest of random trees. The random trees for constructing a tree that considers K randomly chosen attributes at each node. Also, it has an option to allow estimation of class probabilities (or target mean in the regression case) based on a hold-out set (back fitting).Random Committee: class for building an ensemble of randomized base classifiers. Each base classifier is built using a different random number of seed (but based on the same data). The final output is a straight average of the predictions obtained by the individual base classifiers.Metrics of the best models are reported in the work and the descriptors present in the best models are analysed in terms of correlation using VIDEAN tool.3QSAR models, both for classification and regression models, learned by a multi-objective feature selection technique and machine learning methods are described. Some of these relevant parameters are depicted in 3.1A total quantity of 3224 descriptors were calculated using Dragon for the complete database. The experiments were executed following the rationale schematized in http://lidecc.cs.uns.edu.ar/pacbb2018.LRRK2/datasets.html.The classification model with higher performance was inferred from the subset M3 by using Random Committee and achieved 87.5% of correct classified samples with a ROC value of 0.91. These results are shown in rd component size directional WHIM index/weighted by I-state.The best model inferred for the regression case has five molecular descriptors ranging a wide variety of different descriptors classes, from 0D molecular descriptors to 3D. For example, molecular weight is a constitutional index, belonging to the class of 0D-descriptors, that is obtained from the chemical formula, as they do not consider the tridimensional structure of the ligands. We have also found ESpm09x descriptor, spectral moment 09 from edge adjacency matrix weighted by edge degrees and IC0 information content index (neighbourhood symmetry of 0-order) and JGI3: mean topological charge index of order 3 that are 2D descriptors from different families. Lastly, the 3D descriptor found in this model is the L3s: 3Concerning the best classification model, includes four descriptors and we have also selected a wide representation of different descriptors families. MW has also been included in this model as a 0D descriptor. A very related descriptor to JGI3 was also used in this model, JGI2, is a 2D descriptor and is related to the charge index of the compounds in the database. To conclude, two 3D descriptors were chosen by DELPHOS that are R2e: R autocorrelation of lag 2/weighted by Sanderson electronegativity andHATs6m: leverage-weighted autocorrelation of lag 6/weighted by mass. Both are GETAWAY descriptors  and are related to electronegativity and mass, key parameters in protein-ligand recognition process for protein inhibition.Additionally, we studied the relationship among the descriptors in statistical terms by using VIDEAN software, which is a visual analytics tool for the analysis of molecular descriptor subsets. It displays the associations and interactions among the molecular descriptors and the target property in statistical terms. The analysis of the pair correlation among the five descriptors that conform the regression model and the four descriptors that are part of the best classification model is presented in In this kind of analysis, the central aim is to detect models with low correlation among descriptors that means low redundant information. In this case, we perceive a clear majority of light tones of links, both blue and orange, that make connection between the descriptors (nodes) present in the models. This fact reveals the low data redundancy in both models that have been selected as the best ones.4Parkinson’s disease is one of the neurodegenerative illnesses with higher impact in elder people around the world. A promising target for its pharmacological treatment is the leucine-rich repeat kinase 2 (LRRK2). Several studies suggest LRRK2 inhibitors can play a key role for preventing neurodegeneration. For this reason, in this paper, QSAR models for virtual screening of putative inhibitors of LRRK2 protein has been designed. These models were obtained by machine learning methods over data from an in-house chemical library.The computational approach used in this paper has two main steps: initially, alternative subsets of molecular descriptors relevant for structural characterization of LRRK2 inhibitors are identified by a multi-objective feature selection method; after that, several QSAR models are learned from these subsets by applying different supervised learning algorithms. The performance of these QSAR models was contrasted by classical metrics.The molecular descriptor subsets related with the regression and classification models that yield the best performances were examined in statistical and physicochemical terms. From the analysis, it is possible to observe that the selected subsets have low cardinality but cover a wide spectrum of the molecular descriptor classes, contributing in this way with meaningful and diverse structural information to the models. Additionally, the visual analytics study reveals that the selected molecular descriptors offer non-redundant information in statistical terms.Nonetheless, even when these QSAR models reach high accuracies, it is important to note that these models were learned from datasets integrated by a reduced quantity of molecules, which can limit the generalization properties of these QSAR models. For this reason, our piece of advice for the potential users of these models is to employ applicability domain methods over their testing compounds before using these models. As future work, we hope to extend the in-house chemical library for LRKK2 in order to improve the generalizability of these findings.


Neisseria meningitidis, the meningococcus, is a prerequisite for invasive meningococcal disease (IMD), a potentially devastating infection that disproportionately afflicts infants and children. Humans are the sole known reservoir for the meningococcus, and it is carried asymptomatically in the nasopharynx of ~10% of the population. Rates of carriage are dependent on age of the host and social and behavioural factors. In the UK, meningococcal carriage has been studied through large, multi-centre carriage surveys of adolescents in 1999, 2000, and 2001, demonstrating carriage can be affected by immunisation with the capsular group C meningococcal conjugate vaccine, inducing population immunity against carriage. Fifteen years after these surveys were carried out, invasive meningococcal disease incidence had declined from a peak in 1999.  The UKMenCar4 study was conducted in 2014/15 to investigate rates of carriage amongst the adolescent population during a period of low disease incidence. The protocols and methodology used to perform UKMenCar4, a large carriage survey, are described here.Carriage of Neisseria meningitidis occurs at a variable rate, with a range of approximately 2% to 30%, dependent on age and exposure to risk factors1. Humans are the sole known reservoir for the meningococcus, and as such it is an obligate human commensal and pathogen. Transmission of meningococci occurs by droplet spread through close contact with an infected individual. Of those who carry meningococci, a very small number, 1–2 per 100,000 in the UK2, will develop invasive meningococcal disease (IMD) with the bacteria invading systemically through the oropharyngeal epithelium, resulting in septicaemia and/or meningitis.Asymptomatic oropharyngeal carriage of the Gram negative diplococcus3. Risk factors that affect carriage include; living in overcrowded settings; passive and active smoking; intimate contact (e.g. kissing); frequenting pubs or clubs; and intercurrent viral respiratory tract infection6. Carriage rates are dynamic and have been observed to rise in UK students starting university, from 6.9% on the first day of university term to 23.1% by day four7. Carriage rates as high as 60–70% have been reported amongst military personnel, with disease outbreaks a common occurrence in both these settings9. There is variation in meningococcal carriage and disease epidemiology globally. For example, historically high IMD incidence in the meningitis belt in Africa, led to carriage studies10 performed by the MenAfriCar consortium in association with the introduction of the conjugate polysaccharide A vaccine in 2010. These surveys identified mean carriage prevalence of 4.5%, lower than high IMD incidence, non-African countries, with the highest rates amongst 5–14-year-olds in the belt12. Risk factors in this setting included living in rural communities and the dry seasonal climate12.A meta-analysis of meningococcal carriage in Europe, North America and Australia, where serogroups B and C IMD predominates, demonstrated increasing carriage with age, with low carriage in young children to 23.7% in 19 year olds, subsequently declining in adulthood to 7.8% in 50 year olds Proteobacteria as two of the core taxonomic groups within the throat of healthy individuals13. In addition, this study found an inverse relationship exists between the presence of Bacteriodetes and Proteobacteria, which includes the genusNeisseria. Studies of meningococcal carriage prevalence have identified an inverse relationship between pathogenic and non-pathogenicNeisseria species15. The studies also indicate that host age, as well as a predominant species, influenced host susceptibility to colonisation and invasion by the pathogenic species,Neisseria meningitidis17. The human nasopharynx and oropharynx are important sites of bacterial colonization supporting a complex and changing microbiota. Awareness and knowledge of the complex association of the microbiota is critical to understanding immune response and preserving human health as well as its relationship to invasive infection. For example, the Human Microbiome Project identified Bacteroidetes and18, where increased meningococcal carriage as a consequence of overcrowding and social interactions was found to result in increased rates of IMD19. There is genotypic and phenotypic diversity amongst the meningococci carried in the oropharynx and certain meningococci are inherently more invasive than others. Many genetic constituents underlay the phenotypic expression of a meningococcus; for example, the function of capsular expression and other virulence factors associated with particular genetic lineages, can be defined by multilocus sequence typing (MLST) as a clonal complex (cc)22. In response to an increase in IMD with associated high mortality due to a serogroup C, hyperinvasive clonal complex ST-11 (C:cc11) the capsular group C meningococcal conjugate vaccine (MCC) was introduced into the UK immunisation schedule in November 1999, with a catch up campaign for all <18 year olds23. Three large multi-centre carriage surveys conducted through the UK Meningococcal Carriage (UKMenCar) consortium, UKMenCar1-3, were carried out in 1999, 2000, and 2001. These surveys assessed oropharyngeal carriage of meningococci and collected risk factor data in over 45,000 adolescents in the UK; and demonstrated that the population immunity induced by the MCC vaccine programme was due to the reduction in carriage of the specific hypervirulent strain C:cc1125.A relationship between asymptomatic carriage and IMD outbreaks has been recognized since pioneering work conducted in the UK in the First World War26. Unlike the hyperendemic periods, which usually represent a clonal expansion of a novel strain, endemic disease is caused by multiple different genetic lineages of meningococci, which are influenced by secular changes22. There has been a progressive fall in overall serogroup B IMD in the UK since 2000–2001, when in England 1614/2343 (69%) cases were due to serogroup B compared with 418/724 (58%) in 2014–2015, predominantly affecting infants and children under five27. The reasons for this decline in serogroup B IMD were unclear, in the absence of any national vaccination programme targeting capsular group B meningococci. Given the changing epidemiology since 1999, this UKMenCar4 study was performed in 2014–2015 to undertake a structured investigation of the difference in adolescent meningococcal carriage between periods of high (1999–2001) and low (2014–2015) IMD incidence using both the phenotypic and genotypic information.In the UK, the greatest burden of endemic IMD has been due to serogroup B since the 1980sThe aims of the UKMenCar4 study were to: i) characterise a representative collection of meningococcal carriage isolates from UK adolescents during a period of low IMD incidence; ii) identify risk factors associated with meningococcal carriage; and iii) to study the relationship between meningococcal carriage and disease during periods of high and low IMD incidence.Neisseria species obtained as part of the UKMenCar4 study, which sampled 21,873 adolescents aged 15–19 years attending educational establishments in eleven sampling sites around the UK between September 2014 and March 2015. These isolates were characterized phenotypically and genotypically in order to:The specific objective was to characterise the(i)Neisseria species present, determining carriage rates forN. meningitidis (the meningococcus) and otherNeisseria species; identify the(ii) establish the meningococcal lineages present using whole genome sequence data;(iii) quantify the meningococcal capsule type prevalence and rates of expression;(iv)https://pubmlst.org/neisseria/ website28. make the data publicly-available via the25. Previous carriage studies conducted during UKMenCar1-3 had estimated carriage in the 15–19-year-old age group at 16.7–18.7%25. A sample size of 18,000 participants was calculated to provide equivalent numbers of meningococci to UKMenCar1-3 studies, assuming a carriage prevalence of 17%25. It was anticipated that around 3000 meningococcal isolates would be collected, allowing the prevalence of rare strains to be determined with good precision, e.g. variants with 1% prevalence, 95% confidence interval 0.85% to 1.15%. Achieving a very large unbiased cohort size required a multi-centre design, involving centres which had a proven recruitment track record. The sites were chosen to provide broad geographical distribution throughout the United Kingdom and encompassing a wide range of demographics. Schools and colleges were approached directly and asked if they would be willing for students in their institution to take part and pre-recruitment numbers were estimated, and details regarding the study were made available on the school/college website and distributed via newsletter where available. To ensure adequate enrolment two additional centres were recruited through the clinical trials network.The UKMenCar4 study was a cross-sectional survey of meningococcal carriage conducted from September 2014 to March 2015. Sample collection was funded by the Wellcome Trust (087622/Z/08/Z) and a Wellcome Trust Institutional Strategic Support Fund and molecular characterization of isolates was funded by the UK Department of Health and National Institute for Health Research (NIHR) Policy Research Programme (PR-ST-0915-10015). This study used the same approach and sampling methodology as UKMenCar1-3 studies, conducted from 1999–2001, in order to ensure comparabilityExtended data, file 1)29 following circulation of study information prior to school visits by study staff 29 was provided in advance of the study for parents to discuss with their children and provided the information required for informed consent. Adolescents in many schools/colleges acted as ‘School Ambassadors’, assisting in practical aspects of the study. The study was approved under the NHS Research Ethics Committee reference 14/SC/1163 .Adolescents and young adults who were 15–19 years old, attending school or college in UK academic years 12 and 13 or equivalent, were invited for oropharyngeal swab collection and completion of a risk factor questionnaire  and incubated within 4 hours) or placed directly into STGG  broth for transport to a local laboratory for plating (within 4–6 hours). If swabs collected into STGG broth could not be plated in the laboratory within 4 hours, they were frozen at -80°C, then thawed and cultured as soon as laboratory capacity allowed. Students completed a questionnaire including demographic information and risk factors for meningococcal carriage. All swabs, plates and/or tubes were labelled with a pre-printed unique participant accession number and cross-referenced to the questionnaire and consent form. 2 for 16–24 hours, a representative colony of putativeNeisseria was sub-cultured onto non-selective Columbia blood agar (CBA) plates to obtain a pure, fresh culture for further characterisation. Following incubation at 37°C, 5% CO2 for a further 16–24 hours, pure sub-cultured colony growth was characterised by Gram stain and oxidase reaction. Gram negative, oxidase positive diplococci were stored in duplicate at -80°C in brain heart infusion and 20% glycerol at the processing site or sent to the University of Oxford, UK. A sample of each recovered putativeNeisseria isolate was sent to the Public Health England Meningococcal Reference Unit (PHE-MRU) in Manchester, UK.After incubation at 37°C, 5% CO30. DNA extraction was performed using the DNA extraction using the Qiagen DNeasy Blood and Tissue Kit . A single colony archived culture was used to avoid selection of unrepresentative phase variants. Purified DNA was quantified and sent to the Oxford Genomics Centre  for whole genome sequencing using the Illumina HiSeq platform.At the PHE-MRU, isolates were revived and phenotypically characterised for capsular types B, C, W, and Y using standard serological techniquesVelvetOptimiser (version 2.2.4) assembly package with an established pipeline31. The assembled high-quality draft genomes were uploaded onto the pubMLST.orgNeisseriadatabase, which includes the Meningococcal Research Foundation Meningococcal Genome Library (MRF-MGL) amongst other meningococcal collections26. Corresponding FASTQ files are available from the European Nucleotide Archive (study referencePRJEB14319). Each genome was annotated usingNeisseria locus tag identifiers for >2,400 individual loci, including loci belonging to the MLST22, ribosomal MLST32, core genome MLST31, capsular genogroup33, and Bexsero® Antigen Sequence Typing (BAST)34 schemes. Using a gene-by-gene analysis approach, isolates that belonged to the genusNeisseria and were determined and speciated; each isolate was then characterised by sequence type (ST), cc, genetic lineage, capsular genogroup, and BAST (protein-based vaccine Bexsero® variants) profile.Short-read sequence data were assembled using Velvet (version 1.2.10) with35. Initially, individual level risk factors for meningococcal carriage will be analysed in single-variable models by using logistic regression. A multi-level modelling approach will then be used with levels defined as: level 1 individuals, level 2 school, and level 3 centre. Univariable analyses will identify risk factors at each level with p<0.2, for inclusion in a multilevel logistic regression model for a multivariable analysis. We will also compare findings between (a) subjects carrying any meningococcus, (b) those carrying a meningococcus associated with invasive disease and (c) those who are not carrying a meningococcus, to allow any specific risk factors for disease associated meningococcal carriage to be identified. The meningococcal population distribution is described using point prevalence for each serogroup with 95% confidence intervals and case-carrier ratios for different strains will be calculated to look for relative virulence between 1999 and 2014 in the carrier population according to circulating disease types. The meningococcal genomes and associated risk factor data will be publicly-available onhttps://pubMLST.org/neisseria on final acceptance and publication of results.Data from the UKMenCar4 survey (2014/15) was analysed alongside results from UKMenCar1-3 surveys  for comparison. The UKMenCar4 dataset was prepared by reconciling the WGS data with participant questionnaires. Samples were excluded if essential questionnaire data were incomplete, or if valid consent was missing. All data and statistical analyses were performed using Intercooled Stata (version 14)Each participant was given a single, unique study number, on pre-printed labels, to reduce errors and to facilitate data linkage and samples. The name of participants and corresponding study number are kept on a separate and secure database. Centres used their preferred method of data capture – paper questionnaires or electronic capture. Data was double entered, verified, and stored at the site where collected prior to incorporation into a central database. Subject associated data is stored securely at the study site and only accessible by authorised study personnel.Neisseria database using the project name “UKMenCar4”.An annual progress report and an end of study final report were produced and participating schools informed. Study data, isolate characterization, and risk data analysis results will be published in peer reviewed scientific journals and provided in conference presentations. The collected information will also be publically accessible via PubMLST.org in theSurvey sites included comprehensive, independent, and voluntary-aided schools, sixth form and technical colleges, mixed- and single-sex schools, day schools and others with boarding facilities, and located in areas with different income bands. Participation was voluntary and those willing to take part were random, with the expectation that the cohort was a representative sample of the communities from which they were obtained, with diversity of gender, ethnic, social, educational, and income status. Throughout the UKMenCar4 study, the management group engaged with charities (Meningitis Research Foundation and Meningitis Now), patient groups , and prospective participants, who were involved in discussions of the study motivation and design. Adolescents aged 15–19 years piloted information sheets and questionnaires with their feedback incorporated, including the addition of a shorter summary information sheet. ‘School Ambassadors’ were involved in ensuring the smooth running of the study on site as well as workshops, laboratory visits, and feedback sessions. Working alongside charities, public awareness information about meningitis was provided including presentations at school assemblies and information cards listing meningitis symptoms and advice. During the collection of UKMenCar4 samples, a three-session course was piloted with a group of 10 year 12 students. The course comprised:(i) a one-hour lecture: ‘At the back of your throat: meningococcal disease and adolescents’, which described the basic biology of the meningococcus and the importance of transmission and population immunity in prevention of meningococcal disease by immunisation;(ii) a one-hour computer practical session: ‘How to be a genome detective’, in which the students were able to use the web-based tools in the MRF-MGL to investigate the epidemiology of meningococcal disease in the UK;(iii) a one-hour tutorial on the implementation of meningococcal vaccines. Working in groups of three with a tutor, students were provided with various documents in advance and were asked to argue from the following perspectives: a patient’s parent; a paediatrician; a government official; a vaccine manufacturer. The tutorials explored the basis of decisions on immunisation and the different conclusions that different individuals might make from the same information.36 and mouse38 models have been developed to address these research questionsin vitro. Despite this, real life population dynamics are relatively easy to measure and monitor through large carriage surveys, though to obtain a sample representative of the whole population, studies need to be of sufficient magnitude and well-resourced.Humans are the sole known reservoir for the meningococcus and, as such, carriage surveys can provide information about the human reservoir and transmission dynamics that have implications for disease epidemiology. Studying the physiological circumstances that promote bacterial invasion rather than colonization is difficult as human challenge models are not appropriate, but both laboratory6. In 2014, the UKMenCar consortium was reconvened in order to conduct a fourth multi-centre carriage survey (UKMenCar4), aiming to provide a large-scale survey of meningococcal carriage at a time of low IMD incidence. The UKMenCar4 study provides an extensive resource for addressing meningococcal epidemiology. Understanding the transmission dynamics and changes in carriage over time, will help to explain the decline in IMD in the UK from 1999–2019. Characterisation of theNeisseria species from this study will provide a definitive molecular description of the prevalence and types of meningococci carried by UK adolescents in 2014/15. In this study an emphasis was placed on maintaining consistency of sampling and analysis. Therefore, UKMenCar4 methodology was conducted as with previous UKMenCar studies to ensure longitudinal comparability. Meningococcal carriage was defined by the presence of at least one colony of typicalNeisseria species morphology on an isolation plate, with reliance on microbiological identification. Further biochemical tests were used to confirm the Gram negative, oxidase positive diplococci and WGS to determineNeisseria species.Information collected in the UKMenCar1-3 studies demonstrated that the MCC vaccine induced population immunity by specifically reducing carriage of C:cc11, and that the high rates of transmission in this age group was a consequence of social behaviour, especially smoking, kissing, and attendance at pubs and clubsNeisseria to be abundant40. Standardized collection and laboratory identification methods that are robust, scalable, and cost-effective for large studies contribute to the overall interpretation and understanding ofNeisseria epidemiology and its diversity. The UKMenCar4 study aimed to collect, identify, and genome sequence all recoveredNeisseria species using methods that allowed comparison with the UKMenCar 1-3 surveys. Literature describing the methods of sampling, identifying, and measuring meningococcal carriage is abundant; however, at the time of writing there was no universally agreed method for carriage studies within the academic community, with choice of protocol based on specific study questions and aims. Following on from the UKMenCar1-3 surveys (1999–2001), large carriage studies were undertaken by the MenAfriCar consortium prior to the introduction of the conjugate polysaccharide A vaccine in 2010 for use in the African meningitis belt. Oropharyngeal as opposed to nasopharyngeal swabbing was performed in 19 of 20 carriage surveys, largely swabbing the posterior pharyngeal wall11. Comparison of swabbing sites between the posterior pharynx behind the uvula and the posterior pharynx behind the uvula and one tonsil, in a direct comparison in Mali, identified that carriage where only one site is swabbed may underestimate carriage rates41. Alternative samples, such as saliva, are rapid, inexpensive, and non-invasive but there are conflicting reports on their effectiveness for detecting carriage ofNeisseria44. The UKMenCar surveys sought to optimize the site of swabbing by taking a sweep of the posterior pharynx from one tonsil to the other.Conventional microbiological approaches to identifying the oropharyngeal microbiota has revealedNeisseria from clinical samples and have been shown to increase reported cases of IMD45. A large multi-centre study in South Australia, studying the impact of 4CMenB vaccine Bexsero® on adolescent carriage, undertook DNA extraction from swabs and real-time PCR screening forporA for identification of meningococci and capsular group, followed by selective culture of PCR positive swabs. This has the benefit of reducing the microbiological time, labour and expense of culturing all oropharyngeal swabs and increasing sensitivity to low colonization density. However, the disadvantages include the inability to identify otherNeisseria species that lackporA, or other targets , such asNeisseria lactamica, or the impact of phase variation on capsular expression46. Both these variables contribute to the understanding of the oropharyngeal microbiota and the complex interactions and relationships between the differentNeisseria species that occupy this niche at different ages17, with implications for carriage and, therefore, disease due toN. meningitidis. A human challenge model ofN. lactamica inoculation demonstrated inhibition ofN. meningitidis colonization16, with potential implications for prevention of IMD, though the nature of the intra-species relationships within the microbiota remain unclear. Comprehensive studies of the interactions within the oropharyngeal microbiota will increase our ability to define risk factors that predispose to carriage and progression to invasive disease, and develop preventative strategies.Once swab samples are obtained, different methods can be used for microbiological identification of meningococci. Some studies have indicated that PCR methods are an alternative means of identifying24 and the UK to evaluate the effect of protein-based vaccines on meningococcal carriage, especially pathogenic meningococci and genogroup B isolates. Such studies can only be powered adequately with knowledge of the background prevalence of carriage of different types of meningococci, information provided by UKMenCar4.Through WGS analysis of carriage isolates, it is possible to determine the antigenic variation of protein antigens present in the protein-based vaccines. Hence, the UKMenCar4 study also serves as a baseline to analyse further changes in meningococcal carriage in the UK after the introduction of 4CMenB vaccine into the infant immunization schedule in September 2015. Lessons learned from UKMenCar1-4 methodology have been important for the efficient design of trials in South AustraliaNeisseria are a highly abundant component of the human pharyngeal microbiota and it remains a challenge to understand how carried populations contribute to human health and what impact carriage has on colonization and prevention of disease caused by the hyperinvasive meningococcal lineages in the vast majority of people. Meningococcal carriage studies have played an important role in developing our understanding of the population biology, genotypic and phenotypic epidemiology, as well as the risk factors and pathogenesis of IMD. Given the epidemiology of IMD is dynamic, through secular changes47 or vaccine interventions25, ongoing surveillance of both carriage and disease is crucial for this infectious disease that contributes to a significant proportion of morbidity and mortality.No underlying data are associated with this article.https://doi.org/10.6084/m9.figshare.8952986.v129.Figshare: UKMenCar4 survey protocol forms.This project contains the following extended data: extended_data_dile_1.pdf.  extended_data_file_2.pdf. Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of theExtended data file 1. Study Questionnaire. Form used to obtain information from study participants regarding social demographics and risk factors known to be associated with carriage of the meningococcus.Extended data file 2. Study Information Sheet. An information sheet describing the survey and the type of studies the data will be supporting. The sheet was given to students prior to their enrolment, to provide ample time to consider the information, and the opportunity to question the Investigator, their GP or other independent parties to decide whether they will participate in the study. Sections highlighted in yellow were adapted to reflect the details of the individual Centres. Neisseria meningitidis, though rare has a rapid onset and is associated with severe outcomes. Vaccines are now available for the five serogroups that cause the majority of disease. These vaccines have mostly been licensed by demonstrating that they induce antibodies that can kill their target pathogen in serum bactericidal assays. This approach has resulted in a reduction of disease rates forN. meningitidis serogroup C (MnC) after the introduction and mass vaccination of children in the UK from 1.8 cases per 100000, prior to introducing the vaccine in 2000, to 0.08 cases per 100000 in 2015/161. The primary risk factor for IMD, is coming into contact with the pathogen, specifically through the oropharyngeal route. Carriage studies conducted after the implementation of the MnC conjugate vaccine demonstrated that it not only reduced disease incident rates, but also reduced the carriage of MnC strains in the population2,3. Thus the vaccine provided direct protection from disease by making antibodies that killed MnC and indirect protection by inducing herd protection by preventing acquisition of the pathogen. Carriage studies, such as UKMenCar4 are an incredibly important approach to indirectly measure the potential effectiveness of vaccines through herd immunity. They can also provide an insight into population dynamics of the pathogen in different environments as a way to better understand additional risk factors for IMD and to identify the optimum ages to vaccinate children prior to them becoming at risk.Invasive meningococcal disease (IMD), caused by4. A major objective for this study is to assess what the carriage dynamics are in a period of low IMD incidence. The submitted protocol provides the justification for the study- size, population, collection methods, data analysis and meningococcal isolate characterization. The protocol also provides information on how the different study sites use student advocates to disseminate information to the student bodies. UKMenCar4 is a cross-sectional survey of 21,873 UK adolescents aged between 15 and 19 years. It was conducted as a follow-on study of those conducted 19 years previously when meningococcal carriage rates were approximately 17% in this age group; and disease rates were 9.28 per 100000 I 15-17 year oldset al discuss how some of the protocol choices were made. A major consideration is the approach that is used to define carriage.N. menigitidis strains are fastidious which has led to the practice of plating out the subjects swabs as soon as they are taken in the field as opposed to transporting them to a laboratory prior to doing the isolation work. In contrast with the advent of sensitive molecular diagnostics many groups have assessed whether the culture approach can be negated by directly analyzing the swab for DNA. Jones et al5 conducted a head to head analysis of carriage and disease isolates using molecular approaches and showed that while the invasive disease isolates (99%) could be readily characterized by PCR genogrouping approaches, only 43% of isolates could be grouped by this approach. Sequencing of these isolates revealed that small changes in the capsular operon was the culprit for this lack of sensitivity, highlighting the importance of collecting the isolate so that more definitive strain characterization can be conducted. This still leaves the question of what may be being missed due to the potential for isolation approaches to be less sensitive than molecular methods. A series of studies in 10 – 25 year old’s (approximately 4000) were recently conducted in the UK6, Canada7 and the Netherlands8 to specifically address this question. The overall meningococcal carriage rates were similar for both methods. In contrast there was a trend forN. menigitidis serogroup B rates to be lower in the UK and Canada for the cultured samples compared to the samples that were not cultured. These studies were designed to pinpoint at what age subjects start to carry meningococcal strains and thus looked at a wider age range than the described UKMenCar4 study. In Canada carriage rates jumped from approximately 8% to 25% as subjects transitioned to a University setting. In the Netherlands carriage rates started to rise in 14- 18 year olds and averaged around 20% for older subjects. In the UK there was on incremental increase of approximately 10% between the 15-16, 17-18 and 19-25 year old age groups from approximately 20% to 40%. The younger age groups had much lower carriage rates; suggesting that from a vaccination perspective it would be important to ensure that children are vaccinated to ensure that they produce an optimal response prior to being exposed to the pathogen if vaccination is indicated. These studies also highlight that although smaller than UKMenCar4, carriage rates may differ from region to region, which should also be taken into consideration when implementing meningococcal vaccine schedules. Bratcher In summary the UKMenCar4 protocol will be sufficient to estimate UK carriage rates in this population. It will not have the advantage to determine what the trends are in the aligning age groups but will provide a useful baseline is meningococcal vaccine schedules are expanded to adolescents in the UK.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Neisseria species, and as surveillance tools.Meningococcal carriage, especially among adolescents, is a critical part of the natural history of meningococcal disease. Adolescents represent the main reservoir of this pathogen, and the changing epidemiology of this disease in the UK and elsewhere is driven by changing patterns of carriage. In addition, the longer term effects of vaccination on rates and patterns of meningococcal carriage and disease are rather uncertain. Comprehensive carriage studies are essential to improve our understanding of both pathogenic and non-pathogenic This protocol describes such a study, the fourth in a series conducted by the UK Meningococcal Carriage consortium, in England and Wales. Significantly, this carriage study also serves as a baseline immediately prior to the introduction of routine infact MenB vaccination in the UK in 2015. As for previous UKMenCar studies, the study aimed to recruit a large and unbiased cohort of adolescents through a multi-centre cross-sectional cohort design. The study team are internationally recognised experts in this field, and the methods were chosen to ensure compatibility with previous UKMenCar studies. I would encourage future studies of this type to consider molecular testing of naso/pharyngeal swab specimens in addition to culture to identify carriers. I was slightly disappointed to see the oft-cited average carriage rate of ~10% appear prominently in the Abstract . The authors also cite  a figure of 4.5% carriage in infants. That analysis did not assume zero carriage at age zero, and the fitted data indicated 4.5% meningococcal carriage at birth - a value I feel is open to question. As the UKMenCar4 study is entirely focused on adolescents, it may be better to emphasise the much higher age-specific carriage rates seen in that age group, rather than repeating the (often misinterpreted!) all-age population average of 10%. Notably, I believe the number of meningococcal whole-genome sequences  that will be generated make this the largest study of it's kind to date.Neisseria isolates is a welcome and valuable objective that may improve understanding of interactions with meningococcus within the oropharynx/nasopharynx. The commitment to also sequence other (non-pathogenic) I look forward with great interest to seeing the data from this study in due course.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.