Chronic back pain (CBP) is heterogenous and identifying sub-groups could improve clinical decision making. Machine learning can build upon prior sub-grouping approaches by using a data-driven approach to overcome clinician subjectivity, however, only binary classification of pain versus no-pain has been attempted to date. In our cross-sectional study, age- and sex-matched participants with CBP (n = 4156) and pain-free controls  from the UkBioBank were included. We included variables of body mass index, depression, loneliness/social isolation, grip strength, brain grey matter volumes and functional connectivity. We used fuzzy c-means clustering to derive CBP sub-groups and Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbour (kNN) and Random Forest classifiers to determine classification accuracy. We showed that two variables  and five clusters were optimal for creating sub-groups of CBP individuals. Classification accuracy was greater than 95% for when CBP sub-groups were assessed only, while misclassification in CBP sub-groups increased to 35–53% across classifiers when pain-free controls were added. We showed that individuals with CBP could sub-grouped and accurately classified. Future research should optimise variables by including specific spinal, psychosocial and nervous system measures associated with CBP to create more robust sub-groups that are discernible from pain-free controls. Chronic pain defined as pain lasting greater than 12 weeks, contribute to the majority of financial2, physical and mental burden in back pain cases3. As high as 90% of back pain cases are classed as non-specific, meaning that there is no clear pathoanatomical cause for the experienced pain4. Heterogenous clinical presentations in spinal tissue, pain processing mechanisms and psychological state have been shown in those with chronic back pain (CBP)5; and provides a strong rationale for the development and implementation of an effective sub-classification approach in clinical practice to improve treatment outcomes for clients with chronic low back pain.Back pain generates a substantial global burden as the leading cause of disability6; A limitation of prior approaches is that they have investigated specific pain mechanisms7 or spinal tissues10 in isolation. This is in contrast to the current understanding of CBP, which is known to be influenced by an interplay between nervous system, psychosocial and physical factors11. Using measures of the central nervous system, psychosocial and physical health together, may allow for more appropriate sub-grouping of back pain individuals11. Machine learning (ML) can analyse data and recognise patterns, which is of particular interest in heterogenous pain presentations12. However, a systematic review of artificial intelligence in low back pain research has showed that only the binary classification  has been completed to date, and there has been no published attempts to sub-classify low back pain clients based on clinical presentation13. Therefore, the next step to implementing such tools in back pain research is to determine if sub-classification in back pain is possible using data science approaches.Previous attempts at back pain sub-classification have had minimal impact on the disease burden.14, which recruited 500,000 individuals across the United Kingdom to track health-related outcomes such as brain function and structure, psychosocial and physical health, presents an opportunity to develop approaches to sub-classification of CBP using data science tools. We have previously analysed differences in brain structure, psychosocial, and physical health in those with acute or chronic and localised or widespread back pain in the UKBioBank15; however, exploring individual differences through data-driven classification could lead to an improved management of the condition. Therefore, the aim of this study was to develop a ML approach for CBP sub-grouping, based on clustering and classification methods using central nervous system, psychosocial and physical factors from the UKBioBank. A secondary aim was to determine the robustness of these sub-groups by adding pain-free individuals into classification methods and to test clustering within each domain .The UKBioBankStep 1: Of the 1502 variables used in this study, 100 were significantly different between CBP and pain-free controls from t-tests after adjustment of p values using the false discovery rate method  for CBP only, the order of importance of features in a CBP-only space was symptoms of loneliness/social isolation, depressive symptoms, FPN-DMN connectivity (edge 1014), grip strength, body mass index, FPN-SMN connectivity (edge 222) and VN-DMN connectivity (edge 44), fronto-orbital cortex, primary motor and somatosensory grey matter volumes  were used for the main clustering analysis  low loneliness/social isolation and moderate depressive symptoms ; (2) low loneliness/social isolation and depressive symptoms ; (3) high loneliness/social isolation and moderate depressive symptoms ; (4) moderate loneliness/social isolation and high depressive symptoms ; and (5) moderate loneliness/social isolation and low depressive symptoms  ;  low lonen = 185; .5%; (4) Step 6: Classifiers were first fit to CBP sub-groups using the seven variables determined important in feature weighing methods. The results showed misclassification error rates of < 1% for SVM , FPN-SMN connectivity (edge 222) and VN-DMN connectivity (edge 44) were ranked, in order of importance, using Laplacian scores , three , four  and five  had higher levels of depressive symptoms than pain-free control, while sub-group two had lower levels of depressive symptoms . For loneliness/social isolation, sub-group two  had lower symptoms, while sub-groups three , four  and five  had greater symptoms. No difference in loneliness/social isolation symptoms were observed between sub-group one and pain-free controls.2, p < 0.001, ES = large) and three  had a higher body mass index than pain-free controls, while sub-group two had a lower value . Sub-groups one  and two  had lower grip strength than pain-free controls. Compared to pain-free controls, sub-group three had higher grip strength .For the three sub-groups derived from physical variables, results from ANCOVA are presented in Table p < 0.001, ES = moderate), while sub-groups two  and three  had lower connectivity compared to pain-free controls. For FPN-SMN connectivity, sub-group one  and three  had lower connectivity, while sub-group two  had higher connectivity compared to pain-free controls. While not used for eventual clustering, for VN-DMN connectivity, sub-groups one  and sub-group three  had higher connectivity than pain-free controls, while no statistically significant difference was observed for sub-group two .Between-group differences for sub-groups derived from brain variables are reported in Table 3, p < 0.001, ES = moderate) and lower in sub-group two  compared to pain-free controls. Primary motor cortex volume was also higher in sub-group one  and lower  in sub-group two compared to pain-free controls. Lastly, even though it was not used for deriving sub-groups, primary somatosensory volume was higher in sub-group  one and lower in sub-group two  compared to pain-free controls.Differences between-groups derived from brain structure are presented in Table Step 8: Feature ranking and clustering was completed in training set then applied to the test data set. Similar feature weight and clustering performances were observed across training and test data sets  for the validation set for SVM, Naïve Bayes, kNN and RF classifiers were > 99% (1.00–1.00), 63.6% (0.58–0.99), 92.3% (0.95–0.99) and > 99% (1.00–1.00), respectively  low loneliness/social isolation and moderate depressive symptoms; (2) low loneliness/social isolation and depressive symptoms; (3) high loneliness/social isolation and moderate depressive symptoms; (4) moderate loneliness/social isolation and high depressive symptoms; and (5) moderate loneliness/social isolation and low depressive symptoms. Furthermore, these groups could be accurately classified using ML approaches when people with CBP were evaluated alone. When pain-free controls were examined as well, the error rate in the models increased, particularly in CBP sub-groups. We interpret the latter finding to indicate that these variables alone are insufficient for predicting who has CBP yet are likely necessary in determining sub-group classification of patients with known CBP.15. However, identifying sub-groups of individuals with back pain has been a goal of clinicians and researchers to improve diagnosis and treatment16, with the aim of improved patient outcomes and reduced financial costs at both the individual and societal level. To build on prior ML research that only used binary classification of back pain versus no-back pain13, our results showed that individuals with CBP could be sub-grouped based on psychosocial factors of symptoms of depression and loneliness/social isolation and attempt classification on these new classes. These results are supported by Backyrd et al.17, who included psychosocial measures only and found four sub-groups of individuals with chronic pain based on hierarchical clustering methods characterised by low and high levels of psychological and social distress. Given back pain is a biopsychosocial issue11, we attempted to include additional brain-related and physical variables in our analyses to derive sub-groups. Even though these differed between CBP and pain-free controls, they were not important for deriving subgroups based on feature weighting and cluster validity methods for the main analyses. However, as shown by our secondary analyses, physical and brain measures may still be important for deriving sub-groups of CBP. Importantly, even though psychosocial variables are often treated as continuous in analyses, they have results at discrete intervals on a measurement scale 18. Given feature weighting and cluster validity methods are based on distances within- and between-groups20, this may mean psychosocial factors are the most important solely due to method of measurement and the potential for greater distances on normalised data scales within- and between-groups. Therefore, future research should: (1) derive sub-groups based on homogenous outcomes across multidimensional domains linked to CBP, and (2) develop methods to account for differences in measurement scales in feature weighting and cluster validity methods.We have previously showed using UKBioBank data that differences in brain structure, psychosocial, and physical health exist depending on if back pain is acute or chronic and localised or widespread17, while research in low back pain had showed clusters based on sensory testing21 and spinal tissue profiles9. However, only one of these studies included pain-free controls for comparison21. For example, prior work in acute low back pain showed that sub-groups may exist based on pressure-pain thresholds and conditioned pain modulation, with differences observed in ANCOVA compared to pain-free controls in all but one cluster 21. In a study without pain-free controls, which assessed sub-groups of low back pain based on MRI morphological findings, a cluster containing 51.6% of 3155 vertebral motion-segments consisted of no or minimal (< 5%) MRI morphological findings9. Even though no ML techniques were used in either study21, it seems the inclusion of a pain-free control arm is paramount for developing meaningful back pain sub-groups. However, in our study, pain-free and back pain status was self-report and therefore it is unclear if pain-free individuals had prior pain. Based on our findings, future research should include a pain-free control arm to identify variables which produce less overlap to create more meaningful CBP sub-groups.The findings from the current study showed that individuals with CBP can be accurately into all the subgroups derived. However, the addition of pain-free controls increased the misclassification of CBP individuals to greater than 35–53% for CBP groups in the main analyses. Prior research has showed sub-groups in chronic pain based on psychosocial distress222. However, a prior longitudinal study showed that those with obesity (body mass index ≥ 30 kg/m2) were 30% more likely to develop low back pain compared to those with a body mass index < 25 kg/m223. Therefore, the importance of body mass index may be in those with CBP who are obese . For grip strength, muscular strength is not considered to be a risk factor for back pain24, however, may be considered a consequence of deconditioning25. In our analyses we showed physical sub-groups two and three have lower grip strength compared to pain-free controls. Therefore, treatment orientated at restoring muscular conditioning may be reserved for sub-groups two and three. Notably, we were unable to assess other key physical parameters, such as intervertebral disc health26, as these measures were not available in the UKBioBank dataset. Clusters of CBP based on body mass index and grip strength exist, however, follow-up with other key physical parameters, such as spine imaging, is required.When examining sub-groups based on physical variables of body mass index and grip strength, we showed that three sub-groups may exist. A systematic review showed no statistically significant relationship on developing chronic low back pain in those with a body mass index above ≥ 25 kg/m27; the default mode network is involved in memory retrieval and internal cognitions, while deactivating during attention demanding external tasks28, and; the sensorimotor network is involved in sensory processing, and could be involved in encoding the location and intensity of pain29. Previous studies have examined connectivity differences in the regions between individuals with chronic pain and pain-free controls33, yet none have attempted sub-grouping. In our analyses, we showed that sub-group one had greater FPN-DMN connectivity compared to pain-free controls and could indicate impairments in the interaction between memory, internal cognitions, goal-driven behaviour and pain. This may explain why some individuals with CBP have impaired movements in the presence of fear and catastrophising34. Sub-group two had higher connectivity in the FPN-SN, while sub-group three had lower connectivity compared to pain-free controls. This could indicate impairments in connectivity between goal-driven behaviours27 and sensory processing of pain29. This may be important for sub-group two given increases in sensorimotor network activity have been shown to have a moderate strength correlation with higher levels of back pain30. However, for both FPN-DMN and FPN-SN connectivity, we were unable to explore the association between clinical outcomes  in our study due to outcome being unavailable in the UKBioBank. Overall, these results indicate that sub-groups of CBP may have impairments in frontoparietal, default mode and sensorimotor networks. Regardless, more research is required to determine the clinical meaningfulness of these findings.For sub-groups based on resting state brain function, we showed that clusters may exist for connectivity between frontoparietal, default mode and sensorimotor networks in those with CBP. These function of these brain networks, while linked, differ; the frontoparietal network is linked to cognitive control of behaviour in a goal-driven manner35. In addition to this, the primary somatosensory cortex grey matter volume differentiated CBP individuals and pain-free controls and was a candidate for clustering. The primary somatosensory cortex has been previously linked to somatosensory aspects of pain encoding35. When assessing the derived sub-groups, sub-group one had higher values in all three brain grey matter volumes, while sub-group two had lower values. For sub-group two, this could indicate impairments in motor, sensory and cognitive/emotional areas related to pain35. Furthermore, given that all brain grey matter volumes were lower, this may indicate impairments with central processing of pain in this sub-group36. However, given we did not assess if the pain was localised or widespread in this study, this should be considered in future research to further identify important brain markers across back pain individuals. Differences in brain grey matter volumes also need to be related to clinical outcomes to determine if they are clinically relevant for treatment. Our study showed that clusters may exist in brain structure in individuals with CBP and should be further explored to determine clinically relevant CBP sub-groups.For sub-groups based on brain structure, we derived two clusters based on the fronto-orbital and primary motor cortex grey matter volumes. The fronto-orbital cortex is linked to cognitive/emotional processing of pain, while the primary motor cortex to motor aspects11 and our study indicates that clinicians could consider differing psychosocial, physical and brain outcomes in individuals they treat for back pain. An approach to integrate this into clinical practice could be to individualise assessment based on findings from subjective assessment11 or use physical/psychosocial screening tools such as the STarTBack tool37 and Orebro Musculoskeletal Questionnaire to identify psychosocial aspects of pain through question responses38. However, even though we could not assess this in our study, prior research has showed that other sensory testing21 and spinal tissue9 factors could also be used to sub-group back pain individuals. Even though some back pain sub-groups may not be clearly separated from pain-free controls, until it is clear which variables best segregate these groups, they should continue to be considered clinically to truly adopt a biopsychosocial approach to treatment. Using such an approach, of targeting psychosocial factors with treatment, may improve patient centred care by identifying and targeting more individualised factors in the treatment of back pain conditions.It is appropriate to consider the potential treatment implications of the sub-groups identified. A biopsychosocial approach is promoted for the treatment of back pain13. Secondly, we had 19,083 participants that could be used to help identify sub-groups of individuals with CBP. Furthermore, we followed up clustering methods with post-hoc tests and relevant adjustments to ensure more robust sub-groups that consider covariates of important variables. For limitations, firstly, there was a lack of spinal tissue measures, which could be important for sub-grouping some individuals with CBP10. Even though spinal tissues outcomes do not always relate to pain, they are a source of nociception39 and need consideration in future studies for a more informative approach to sub-classification40. Secondly, clinical outcomes, such as pain intensity, disability and pain duration, were not available in the UKBioBank. It would be beneficial to relate the sub-groups detected to these clinical characteristics. However, validation of our classification models with the inclusion of pain-free controls suggests future research should determine which variables best discriminate cases from controls for more robust subgroups, regardless of additional pain-related variables. Furthermore, limited psychological variables that can modulate pain and disability were available in the UKBioBank. Future research using classification methods should consider additional psychological variables such as anxiety and pain catastrophizing. Finally, the cross-sectional nature of this study precludes conclusions regarding causality. Future research should use prospective data to determine how sub-groups transition through the stages of back pain.The current study had multiple strengths. Firstly, this was the first study to develop and classify sub-groups of CBP based on psychosocial, physical and nervous system measures using data-analytic approaches21 and changes in spinal tissues10 could be used in addition to psychosocial factors to identify relevant back pain sub-groups. This has the potential to reduce overlap and misclassification in the data; hence, creating more robust sub-groups. Furthermore, studies should investigate combining different outcomes, while also completing sub-domain analyses separately, to better understand how to combine variables in data-analytic method. In addition, future studies should consider relating clinical outcomes (e.g. pain intensity and disability) to new sub-groups. Understanding the relationship between sub-groups and important clinical measures may allow for improved knowledge on how to target outcomes for individuals with back pain.Based on our findings and limitations, future studies should consider which variables they use in design and implementation of attempting accurate sub-classification of back pain individuals with ML approaches. In particular, pain processing mechanismsThis study was the first to use ML algorithms to attempt to divide individuals with CBP into sub-groups based on psychosocial, brain and physical factors. For the main analysis, optimal sub-grouping was determined to require only two psychosocial variables. Secondary analyses indicate that sub-groups based on physical, brain structure and function can also be derived in sub-domains analyses. Misclassification error may indicate that variables used in this study were not optimal to differentiate these sub-groups from pain-free controls. Future research should optimise variables via including spinal tissue , sensory testing (e.g. pressure-pain thresholds) and additional psychosocial variables (e.g. self-efficacy) to create more robust CBP sub-groups that discriminate them from pain-free controls to optimise targeted treatments for the condition.This was a cross-sectional study of the UKBioBank imaging visit (Project ID: 55843). Following the first wave of recruitment between 2006 and 2010, a subset of participants was invited for initial imaging analyses between 2014 and 2018. Participants provided written informed consent prior to data collection. For this sub-study, we used a sample of individuals with chronic back pain and those who were pain-free. The UKBioBank was ethically approved by the NHS National Research Ethics Service  and all experiments were performed in line with relevant guidelines and regulations. The current project was approved by Deakin University Human Research Ethics Committee (project ID: 2020–323).https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=100048) were used to determine pain status. Information on the specific questions can be found here: https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/TouchscreenQuestionsMainFinal.pdf (search SY5). The following groups were included for this sub-study: (a) pain-free, in which participants reported ‘none of the above’ to pain experienced over the month prior; and (b) CBP: participants reporting ‘yes’ to back pain lasting greater than three months. Participants without full neuroimaging datasets were excluded from the sample. For magnetic resonance imaging, individuals with metal implants, such as a pacemaker, were excluded.The UKBioBank initially recruited adults from the United Kingdom between the ages of 40–69 who were able, and consented to, attending an assessment centre for testing. For inclusion, self-report questions available in the UkBioBank 42 measures may be important for individuals with CBP. Psychosocial factors including mental and social health43, and physical factors of body mass index22 and muscular strength44 have also been shown to be associated with CBP.We selected and extracted key variables available in the UKBioBank which have previously shown to be related to CBP. For brain imaging, meta-analyses and systematic reviews show both of structuralhttp://fsl.fmrib.ox.ac.uk/fsl) was used for image pre-processing. Protocols for detailed imaging methods are available on the UKBioBank website . For analyses here, we used volumetric and functional brain analyses.A 3.0-T MRI imaging machine  with a 32-channel receiver head coil was used to scan individuals. FSL packages  to define the location of the peak activations of each node and determine the anatomical location of the connectives45. To further label the anatomical locations of each node to a corresponding functional network (such as the default mode network), we used 7-network parcellation46. Brain coordinates, anatomical locations and corresponding functional networks of connectivity edges used in data-analytic approaches are in Supplementary Table For functional brain analyses, we used partial correlation matrices in the 100-dimension from independent component analysis to assess 1,485 direct connections between sub-regions of functional brain nodes at varying anatomical labels. For important connections in our analyses, we used the Automated Anatomical Labelling (AAL) atlas through MRICron , supplementary motor cortex, post-central gyrus (primary somatosensory cortex) and parietal operculum (secondary somatosensory cortex) were used to determine how these brain volumes differed between groups47. Data from left- and right-hand sides were pooled for the analyses.For structural brain analyses, we used regional grey matter volumes linked to pain processing43. Both measures were collected via questionnaires on a touch screen. Four questions were used to determine current symptoms of depression. From the validated Patient Health Questionnaire, questions assessed frequency of depressed mood, disinterest/unenthusiasm, tenseness/restlessness and tiredness/lethargy over the prior 2 weeks48. For each question, a four-point scale, ranging from not at all, several days, more than half the time to almost every day was set. The total score was summed from four to 16, with higher scores indicating a greater level of current depressive symptoms49. As reported in previous UKBioBank studies, symptoms of social isolation/loneliness were also assessed in the current study50. Social isolation consisted of three questions: (1) “Including yourself, how many people are living together in your household?” (2) “How often do you visit friends or family or have them visit you?” and (3) “Which of the following  do you engage in once a week or more often? You may select more than one.” Loneliness was assessed with two questions: (1) “Do you often feel lonely?” and (2) “How often are you able to confide in someone close to you?” The scale was scored from 0 to 5, where individuals with higher scores were deemed to have greater symptoms of social isolation and loneliness50.We used measures of current depression and loneliness/social isolation symptoms as these were available psychosocial measures in the UKBioBank dataset linked to CBPA Jamar J00105 Hydraulic Hand Dynamometer  measured grip strength for both right- and left-hand sides. The participant was instructed to have their arm by their side with the elbow at 90 degrees, and the wrist in a neutral position (thumb to the ceiling). The participant squeezed the dynamometer for three seconds with encouragement to produce as much force as possible. For both right- and left-hand sides, the maximum grip strength was measured in kilograms of force. The highest grip strength achieved on either the left- or right-hand side was used for analysis.2.Body mass index was used as a measure of adiposity. Height in centimetres was measured using Seca 202 stadiometer . Weight in kilograms was measured using a Tanita BC-418 . Body mass index was then calculated as kg/mStep 1: Initial data preparation was completed in StataMP version 15  to allow for a reduced working file size for subsequent analyses. Individuals who did not complete the first brain imaging visit (instance 2) were removed from the dataset. Following this, the sample was matched one-to-three back pain-to-pain-free control on age and sex using a custom code in software package R version 3.5.1 (www.r-project.org), while preferentially matching for cases without missing data if possible. We selected three controls for matching, given little is gained by including more51. Following matching, 20,352 individuals remained , with 19,083 having full datasets for ML analysis   to determine sub-groups of CBP. Further details on the data-analytic methods used below are available in the Supplementary Methods Step 2: Feature weighting was completed to determine variables which best distinguish CBP from pain-free controls to create the most robust CBP sub-groups. Treating variables as continuous, data was first normalised on a 0–1 scale to allow comparability of different variables and different scales of values. Variable weighting methods of (a) minimum redundancy maximum relevance (MRMR) and (b) a random forest (RF) predictor was compared to determine important features. Following this, pain-free controls were removed from the dataset, to explore clustering of CBP individuals.Step 3: Laplacian scores were used to rank remaining variables in the CBP space in order of importance. The dataspace for clustering was initially explored with two variables and were added into the dataspace in order of importance to determine where the breakpoint of random noise being added to the models.Step 4: To determine the optimal number of variables and clusters in each dataspace, the noise breakpoint was explored using Calinski-Harabasz, Davies-Bouldin and Silhouette cluster evaluation methods with k-means linkage54, as well as fuzzy c-means clustering using entropy values57.Step 5: Following this, new sub-groups of CBP were labelled based on the membership values to each cluster from fuzzy c-means clustering. To evaluate the post-hoc similarity of each data point, on average, to its derived clusters, we used Silhouette Index values to determine the goodness of fit . In addition, we evaluated the average within- and between-cluster distances using the centroids of each cluster. From the average within- and between-cluster distances, we also calculated the discrimination value between clusters as reported here58. Given the potential for differences in effect sizes/variance in outcomes across domains due to the way they are measured, even when they are all scaled to 0–1, we also explored clustering for variables which passed prior steps in each sub-domain of psychosocial, physical, brain function and brain structure separately59.Step 6: Retaining all variables which passed FDR and feature weighting, Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbour (kNN) and RF one-vs-one multi-class classifiers were fit to the new CBP sub-groups to determine how accurately they could be classified. To cross-validate the model, a portion of the training set  was used through tenfold cross-validation . Pain-free controls were readded into the dataset and classifiers were trained and validated again to determine if classification accuracy changed. The reason for completing classification with the addition of pain-free controls was to determine if and what sub-groups were separable from pain-free controls. There is contention surrounding the most important pain-related features, therefore, determining which features separate CBP from pain-free controls is important to guide future research to find specific treatable phenotypes of the condition59. Furthermore, we attempted classification on the sub-groups derived from the main and sub-domain clustering analyses. Area under the received operating curve was calculated for every class following each classifier. Reliability diagrams , histograms of predicted probabilities and calibration metrics  were also determined for each model and class in binary one-vs-rest approach60.Step 7: To further determine where statistical significance existed between each derived sub-group and pain-free controls, an analysis of covariance (ANCOVA) was used on remaining variables using StataSE version 16 . The ANCOVA models were adjusted for age and sex (to account for any remaining variability following matching). p values were adjusted for multiple comparisons using the Tukey HSD method. An alpha level was set to 0.05. Standard formula were used to calculate effect sizes  and relevant confidence intervals between CBP sub-groups and pain-free controls61. Effect sizes were considered small from 0.2–0.49, moderate from 0.5–0.79 and large from 0.8 and above62.Step 8: To further validate the result from our clustering and classification, the processing pipeline was completed in CBP individuals again by splitting the data 50/50 into training and test sets. Firstly, we analysed Laplacian scores in the training set to rank features in order of explaining the most variance. Following this cluster validity methods were repeated in the training set by adding variables in order of Laplacian scores to determine the optimal number of clusters and variables. After this, fuzzy c-means clustering was applied to the training set only. Fuzzy c-means clustering was then applied to the test set ‘out-of-sample’ to determine if clustering performance was similar to the training set. Cluster centroids, within- and between-cluster distances, Silhouette index and discrimination values were compared between clustering in the training and test sets. The classifiers used in Step 6 were then repeated with only the training data being used to train the model. To cross-validate the model, a portion of the training set  was used through tenfold cross-validation. Following this, the trained classification model was applied to the test set and the accuracy and areas under the receiver operating characteristics curve compared to that of the training validation set. Furthermore, to explore the effects of class imbalance on all of our classification models, we balanced the models class prior probabilities uniformly to determine if this altered the classification results63.The UKBioBank was approved ethically by the NHS National Research Ethics Service , with ethical exemption granted by the Deakin University Human Research Ethics Committee (project ID: 2020-323) for this project. All experiments were performed in line with relevant guidelines and regulations.Supplementary Information.


Untargeted multi-omics analysis of plasma is an emerging tool for the identification of novel biomarkers for evaluating disease prognosis, and for developing a better understanding of molecular mechanisms underlying human disease. The successful application of metabolomic and proteomic approaches relies on reproducibly quantifying a wide range of metabolites and proteins. Herein, we report the results of untargeted metabolomic and proteomic analyses from blood plasma samples following analyte extraction by two frequently-used solvent systems: chloroform/methanol and methanol-only. Whole blood samples were collected from participants (n = 6) at University Hospital Sharjah (UHS) hospital, then plasma was separated and extracted by two methods: (i) methanol precipitation and (ii) 4:3 methanol:chloroform extraction. The coverage and reproducibility of the two methods were assessed by ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). The study revealed that metabolite extraction by methanol-only showed greater reproducibility for both metabolomic and proteomic quantifications than did methanol/chloroform, while yielding similar peptide coverage. However, coverage of extracted metabolites was higher with the methanol/chloroform precipitation. Omics studies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have advanced the early diagnosis of diseases . Each ofBlood plasma-based multi-omics research presently dominates the field due to its less-invasive sampling methods and the nature of blood in that it reflects the global metabolic response to many stimuli, such as disease development, applied medication or nutrition, genetic change, and environmental variables ,12. For The most-widely-used sample preparation techniques for LC-MS-based plasma untargeted metabolomics and proteomics typically use protein precipitation by different organic solvents—including methanol , butanolAdditionally, the various solvent extraction systems differ in the number of phases, with uniphasic solutions such as methanol-only, and multiphasic solutions such as that obtained with the MTBE or the long-standing chloroform/methanol method. Biphasic separation techniques are used in the field of multi-omics, the motivation initially being to remove potentially interfering compounds from the downstream analytical method to improve analysis quality, and it was initially designed to extract either pure lipids or polar metabolites . The mos3) extraction, though untargeted metabolomics and proteomics analysis using the identical plasma samples.Due to the above-mentioned specificity of each extraction, multiple aliquots of the same sample are usually required to obtain sufficient material to be analyzed accurately. In addition to the increased effort caused by multiple parallel sample handling, separate extraction protocols per sample also necessitate the use of more sample material, which may be limiting many cases, such as when sample types are rare. Consequently, a reliable and robust extraction method is required to recover the major molecular component from a single sample aliquot. Thus, in this study, we compared the coverage and reproducibility of two extraction methods commonly employed in plasma studies: the single-phase methanol precipitation (MeOH ppt), and biphasic chloroform/methanol  present with higher intensities  in the MeOH:CHCl3 extraction and 8  with improved recovery in the MeOH-only extraction  difference could be observed when comparing the two methods across multiple samples sourced from different individuals. While the observed CV values trended lower in MeOH ppt than MeOH:CHCl3 extraction across the six different plasma samples (mean CV of 0.389594 for MeOH ppt in comparison to MeOH:CHCl3 extraction 0.427405), if any true difference existed, it was exceeded by the inherent biological variation between the samples.In evaluating the quantitative variability of each method, we compared the coefficient of variance  for each identified metabolite for each method and compared the distributions of the CV values between the two methods. Variation in biological replicates includes both biological and technical variation, so biological variability may mask technical reproducibility. Thus, we sought to compare the reproducibility of the technical protocols more precisely by considering quadruplicate extractions for each extraction method from aliquots of a single biological sample.3 extracts, including saccharopine, 2-ketohexanoic acid, glycylproline, rhamnose, N-acetyl-L-tyrosine, heparin, butanone, pregnenolone sulfate, acetic acid, 2-phenylaminoadenosine and diacetyl. An additional 3  were recovered with significantly higher intensities from the MeOH:CHCl3 extracts  with MeOH:CHCl3 extracts when compared to that seen in MeOH-only extracts. Thus, when considering only the merits of the respective protocols and not the variability inherent to the samples, it appears that the MeOH-only extraction shows improved reproducibility with respect to quantitation, while MeOH:CHCl3 extraction appears to yield generally higher recoveries of metabolites, with the same trend being observed with respect to unidentified analyte features, where over 5500 molecular features were detected by the MetaboScape software, as contrasted with >3500 such features in the MeOH-only extraction . Six aliquots (three for each method) from the same biological sample were extracted by either methanol (MeOH) ppt or MeOH:CHClMaxQuant , which ip < 0.0001) less in MeOH-only protein precipitation than was observed from MeOH:CHCl3 precipitation for raw intensity values at both the peptide (0.369 vs. 0.483) and protein levels (0.265 vs. 0.482). However, it was noteworthy that when using the MaxLFQ algorithm for protein quantitation, the variation for protein quantitation was improved for both methods and no such difference could be detected . However, there are many limitations to this method, one of them being that it is harder to automate than SPE due to multiple extraction steps and imprecise phase boundary locations. There are also many disadvantages specifically related to working with chloroform including toxicity, carcinogenicity, and it being hazardous to the environment. MS-based ‘omics that involve chloroform usage also necessitate the use of high-quality plastics and glassware with their associated higher cost, making it a less desirable choice of solvent overall, especially for high-throughput settings ,36.3 method . Box plots were generated using GraphPad Prism 9 and Venn diagrams by using Bioinformatics and Evolutionary Genomics (http://bioinformatics.psb.ugent.be/webtools/Venn/ (accessed on 15 September 2022).MetaboAnalyst (Proteomics Data Processing. To identify the peptides and proteins, the raw data were processed by MaxQuant  1.6.17.03 has higher metabolite recovery but similar proteomics recovery in comparison to MeOH-only, while the technical reproducibility of the MeOH precipitation was enhanced for both metabolomics and proteomics.We compared two commonly-employed extraction methods for simultaneous metabolomics and proteomics analyses in blood plasma, methanol:chloroform and methanol precipitation. This study revealed that MeOH:CHCl


Leveraging local ancestry and haplotype information in genome-wide association studies and downstream analyses can improve the utility of genomics for individuals from diverse and recently admixed ancestries. However, most existing simulation, visualization and variant analysis frameworks are based on variant-level analysis and do not automatically handle these features. We present haptools, an open-source toolkit for performing local ancestry aware and haplotype-based analysis of complex traits. Haptools supports fast simulation of admixed genomes, visualization of admixture tracks, simulation of haplotype- and local ancestry-specific phenotype effects and a variety of file operations and statistics computed in a haplotype-aware manner.https://github.com/cast-genomics/haptools.Haptools is freely available at https://haptools.readthedocs.io.Detailed documentation is available at Bioinformatics online. Existing frameworks for complex trait analysis are typically based on variant-level analysis. However, phenotypic effects may also be mediated by haplotypes  for flexible representation of haplotype-level and other information. These files consist of a collection of haplotypes. Each haplotype is defined by a set of one or more variants and their alleles, and optionally a local ancestry label, that tend to be inherited together on an individual chromosome . Unlike simgenotype utility simulates random mating between individuals of ancestral populations under a user-specified population history model, which defines admixture proportions and the number of generations of admixture. It outputs haplotype breakpoints and genotypes of simulated admixed individuals in VCF or PGEN format. simgenotype is adapted from admix-simu  supported by large existing datasets such as the 1000 Genomes Project (The mix-simu  with minmix-simu . We bencdmixSim2 . While Akaryogram takes breakpoints generated by simgenotype as input and generates a karyogram to visualize chromosome segments. It is adapted from an existing script (g script . Exampletransform produces a VCF file of pseudo-genotypes, in which each haplotype is encoded as a bi-allelic variant record, for a set of haplotypes in .hap format. This operation can facilitate downstream tasks which require variant-level information as input. For example, to perform association testing based on haplotypes, one could first use transform to generate a VCF encoding haplotypes as bi-allelic variants, and then use a standard framework such as PLINK (simphenotype utility simulates phenotypes for complex traits with variant-, haplotype- or local ancestry-specific effects. Causal haplotypes are loaded from a VCF file output by transform and their effect sizes are specified in a .hap file. By default, simphenotype simulates quantitative traits. Users may specify case–control traits by providing disease prevalence, which results in that percentage of individuals with the highest trait value being labeled as cases. We evaluated simphenotype by simulating quantitative traits under various scenarios, including individual causal variants (The variants , local avariants , and hapvariants .Accounting for ancestry and other more complex effects is becoming increasingly critical in association testing and downstream analysis pipelines. Haptools helps fulfill an unmet need to handle ancestry and haplotype level information in a standardized way that is compatible with existing file formats and workflows and is computationally efficient. It enables easily performing tasks such as genotype and phenotype simulation, local ancestry visualization and power analyses which previously have been done primarily using a variety of custom scripts. Overall, haptools will help enable more systematic incorporation of ancestry and haplotype-level features in future workflows.https://haptools.readthedocs.io/en/stable/project_info/example_files.html, and the haptools-paper repository, https://github.com/CAST-genomics/haptools-paper.The datasets used for validation and example generation are available from the haptools documentation page: btad104_Supplementary_DataClick here for additional data file.


This meta-analysis investigated (1) whether ethnic minority and majority members have a neural inter-group bias toward each other, and (2) whether various ethnic groups  are processed in the brain differently by the other respective ethnicities.n = 1211, 50.1% female).A systematic coordinate-based meta-analysis of functional magnetic resonance imaging (fMRI) studies was conducted using Web of Science, PubMed, and PsycINFO . Ethnic majority members, in turn, expressed biased responses toward minority (vs. majority) members in frontal, parietal, temporal, and occipital regions that are known to be involved in e.g., facial processing, attention, and perspective-taking. We also found differences in neural response patterns toward different ethnic groups ; broadest biases in neural response patterns were evident toward Black individuals . Heterogeneity was mostly minor or low.Overall, the findings increase understanding of neural processes involved in ethnicity perception and cognition as well as ethnic prejudices and discrimination. This meta-analysis provides explanations for previous behavioral reports on ethnic discrimination toward minority groups. Ethnic inter-group bias refers to biased mental processing of ethnic in-group and out-group members. It can encompass in-group favoritism or out-group derogation, being either unconscious or conscious . In thisDespite discrimination-reduction efforts, ethnic discrimination has remained evident in the Organization for Economic Co-operation and Development (OECD) countries in 1990–2015 . A systeThis evidence has remained ignored, however, in the neuroscientific models of ethnic inter-group bias and prejudices. That is, the neuroscientific models on the topic have postulated a single model of neural responses toward ethnic in- and out-groups, without paying attention to the specific ethnic group in question, or ethnic minority or majority status . Thus, tTo date, two meta-analyses have examined ethnic inter-group biases in general, without taking into consideration possible differences between ethnic groups or between ethnic minorities and majorities. The meta-analyses showed that ethnic inter-group bias associates with activity patterns in the frontal cortex, insula, right superior temporal gyrus, left superior parietal gyrus, and cerebellum . The finWe conducted a systematic literature search and meta-analysis of functional magnetic resonance imaging (fMRI) studies to investigate blood-oxygenation-level-dependent (BOLD) responses to visually presented ethnic groups in healthy adults. In this study, we used the concept of “ethnic group” to refer a group of individuals sharing distinctive outward physical characteristics (such as skin color or hair texture). More specifically, we investigated (1) whether ethnic minority and majority members have a neural inter-group bias toward each other and (2) whether various ethnic groups  are processed in a biased way by the other ethnicities.The Meta-analyses Of Observational Studies in Epidemiology (MOOSE) Checklist was followed throughout the meta-analysis. Moreover, most recent recommendations for a neuroimaging meta-analysis were followed through the process (when applicable) . The litA PRISMA flowchart on the article selection process can be found in n ≥ 10; adult sample (mean age > 18 years); a non-clinical sample (the subjects did not have any reported diseases or medications); a study design including exposure to ethnic in-group and out-group; a stimulus material including visual exposure to ethnic in-group and out-group members, including their faces (so that skin color could be approximately obtained); study design included face visual material of ethnic in-group and out-group ; the coordinates had been reported in the Talairach Atlas  or the Montreal Neurological Institute (MNI) space; T or Z statistics or p values of the observed BOLD response toward ethnic in-group vs. out-group members were available; the study reported results of whole-brain analyses or analyses with very limited masking. More precisely, we did not include small-volume corrected results or region-of-interest (ROI) based results because a previous review recommended to only include studies with whole-brain results available  software (version 6.21). Further meta-analytical details of the SDM software are available elsewhere . Next, a mean analysis was conducted, representing the weighted mean difference in the fMRI activity  toward in-group vs. out-group. There is evidence that sufficient statistical stability can be reached with 20 imputations . To be ondations . Regardindations . All theFirst, a main analysis was conducted where all the original datasets were included (regardless of ethnic minority/majority status or ethnic in-group). Next, we examined neural inter-group bias separately (a) in subjects belonging to ethnic minorities (responses to ethnic minorities vs. majorities) and (b) in subjects belonging to ethnic majorities (responses to ethnic minorities vs. majorities). The analyses were adjusted for age and gender. https://www.kff.org/other/state-indicator/distribution-by-raceethnicity/). In general, the study countries had obvious minority/majority groups; most common study countries were the US  and China . The study country was defined on the basis of the following information: the region where the subjects were collected, the location of the institutional board for ethical permission, the location of the fMRI imaging device, or (if none of the previous details were available) from the country and region of the authors’ affiliations. We excluded a study in case it included an ethnically heterogeneous sample  and the results had not been reported separately within each ethnic group.When classifying subjects’ ethnic groups, we used similar classifications to those in the original studies: (1) Caucasian/White/European-American, (2) African/African-American/Black, or (3) Asian/Chinese/Korean. Subjects’ ethnic majority or minority status was defined by cross-tabulating (1) subjects’ ethnicity and (2) most recent statistical data available on ethnic minorities and majorities in the study country  vs. BOLD responses to ethnic minority members . Second, we conducted a corresponding sub-group analysis in ethnic minority members. That is, we analyzed the contrast of BOLD responses to ethnic minority members  vs. BOLD responses to ethnic majority members . In these analyses, we excluded studies that compared exposure to two different types of ethnic minorities . Then, we conducted a sub-group meta-analysis Here, we examined whether various ethnic groups  are processed in a biased way by the other ethnicities. The analyses were adjusted for age and gender. (1) When examining inter-group bias toward White target individuals , we included subjects with non-White ethnicity . Specifically, we contrasted the BOLD responses to White vs. non-White target individuals. (2) When examining inter-group bias toward Black target individuals , we included subjects with non-Black ethnicity . In this analysis, we contrasted the BOLD responses toward Black vs. non-Black target individuals. (3) When examining inter-group bias toward Asian target persons , we included subjects with non-Asian ethnicity . More specifically, we contrasted the BOLD responses toward Asian vs. non-Asian target individuals. The analyses were controlled for age and gender.I2 statistics . We used the following interpretations of I2 values: low (<25%), moderate (>50%), and high (>75%). Additionally, the amount of publication bias was evaluated with the metabias tests . There were 39 datasets with samples including majority members ; and 11 datasets with samples including minority members . Additionally, we found altogether 21 datasets with White target individuals ; 21 datasets with Black target individuals ; and 8 datasets with Asian target individuals . Most minority members were newly moved immigrants: they had been living only a short time in the country, and many of them were students of an abroad study program or had not had close contacts with the ethnic out-group before the study  and left median network  when compared to ethnic out-group. No brain region exhibited the opposite activity pattern. There was not significant publication bias, as indicated by a visual inspection of the funnel plots (see p = 0.998–0.994 for the peak voxels). Heterogeneity was minor in the left median network (I2 = 0.256) and low in the left superior parietal gyrus (I2 = 10.034).First, we conducted a meta-analysis including all eligible studies. There was higher activity toward ethnic in-group in the left superior parietal gyrus  in the left superior parietal gyrus and in an undefined brain region (Brodmann area no. 30) . AdditioI2 = 1.565–23.042). Further, the metabias tests did not obtain any statistically significant publication bias (p = 0.995–1.000 for the peak voxels). The funnel plots are available in Heterogeneity was minor or low in all the peak voxels . No brain region showed higher activity toward ethnic minority (vs. majority) members, or vice versa. That is, we did not obtain any neural inter-group bias among ethnic minority members.Then, we examined whether various ethnic groups  are processed in a biased way by the other ethnicities.I2 = 10.613) and moderate in the right and left superior frontal gyri . We found no significant publication bias in the funnel plots or metabias tests (p = 0.978–0.992 for the peak voxels)  target individuals in non-White study subjects (Black or Asian subjects). The findings are presented in els) see .Next, we conducted a meta-analysis on neural inter-group bias toward Black (vs. non-Black) target individuals in non-Black study subjects (White or Asian subjects). There was lower activity toward Black (vs. non-Black) target individuals in the left anterior thalamic projections, left posterior cingulate gyrus, and right parahippocampal gyrus . In addiI2 = 0.535–11.377), except for the peak voxel of the left anterior thalamic projections where we obtained slightly higher but still low heterogeneity (2I = 23.728). The metabias tests did not obtain any significant publication bias . The funnel plots are available in Heterogeneity was minor in all the peak voxels  target individuals in non-Asian study subjects (Black or White subjects). No brain region showed higher activity toward Asian (vs. non-Asian) target individuals, or vice versa. That is, we did not obtain any neural inter-group bias toward Asian (vs. non-Asian) target individuals.This meta-analysis provided a novel perspective to the current literature by investigating the neural basis of ethnic inter-group bias (a) separately among ethnic minority and majority members  and (b) separately toward visually presented Black, White, and Asian individuals. The pooled evidence from the original studies showed that, first, neural inter-group bias was non-evident in ethnic minority members but evident in ethnic majority members. Second, we found neural inter-group bias toward Black and White individuals but not toward Asian individuals in subjects not belonging to the respective ethnic group. Finally, heterogeneity was mostly minor or low in the brain regions related to inter-group bias, and the metabias tests did not identify any significant publication bias. In summary, this study showed that neural inter-group bias is differently expressed among ethnic minority vs. majority members, and toward different ethnic groups .We did not find any neural inter-group bias in ethnic minority members: ethnic minority members did not show different responses to ethnic minority vs. majority members. Hence, in minority members, we obtained neither signs of out-group derogation (such as deactivation of perspective-taking-related brain regions when observing majority members); nor signs of anxiety or threat responses toward ethnic majority members. In the original studies, most minority members were newly moved immigrants: they had been living only a short time in the country, and many of them had not had close contacts with the ethnic out-group beforehand see . It is pThe fusiform gyrus showed more activity toward ethnic minority (vs. majority) members; and also toward Black (vs. non-Black) individuals. The fusiform gyrus constitutes a face-specialized region , shows aOur meta-analysis showed higher activity toward ethnic minority (vs. majority) members in the right inferior frontal gyrus; and also higher activity toward Black (vs. non-Black) individuals in the left median cingulate cortex. The right inferior frontal gyrus is a region of the emotional empathy network , and is Our results also showed, however, that observing Black (vs. non-Black) individuals correlates with higher activity of the right insula, which is commonly interpreted as a stronger disgust reaction . AdditioFinally, we did not find any inter-group bias in the subcortical regions , indicating that there may not be any threat-related reactions when observing ethnic majority or minority members. We found, however, that ethnic majority members showed higher activity toward ethnic majority (than minority) members in the left superior parietal gyrus. It is a part of the dorsal frontoparietal network that is thought to maintain a “salience map” and direct attention to such external stimuli that are salient on the basis of semantic knowledge . Thus, aThe left superior frontal gyrus showed lower activity toward White (vs. non-White) individuals; and also higher activity toward Black (vs. non-Black) individuals. Thus, the left superior frontal gyrus seemed to respond in opposite directions depending on the target individuals’ ethnicity. In general, this brain region is thought to be involved in working memory, especially during high cognitive load and spatial processing . InteresWe found biased responses toward Black (vs. non-Black) individuals in the middle occipital gyrus that is involved in perception of visuospatial objects , and in There was lower activity toward White (vs. non-White) individuals in the left supplementary motor area (SMA). The SMA is involved in the preparation and execution of voluntary movements . Thus, oWe found no signs of neural inter-group bias toward Asian (vs. non-Asian) individuals in subjects with non-Asian ethnicities. This is in accordance with some behavioral studies showing that Asian individuals encounter less racial discrimination in the UK labour market when compared to e.g., African individuals ; and AsiDue to a lack of a sufficient number of original studies, this meta-analysis could not investigate neural inter-group biases between different ethnic minority groups, or inter-group biases toward Arabs, Middle Eastern individuals, or Latinos. Some pieces of evidence suggest that ethnic discrimination is more evident toward Arabs and Middle Eastern than Asian individuals . FurtherIt is necessary to keep in mind that, the brain responses toward particular ethnicities  may differ between individuals from different ethnic groups. In our analyses, it was not possible to examine brain responses toward White/Black/Asian individuals separately among White/Black/Asian individuals (due to limited number of studies in each category). Therefore, results per ethnicity might theoretically reflect the combination of activation clusters linked to the different ethnic out-groups. This remains a research question for future studies.In all included studies, the fMRI tasks consisted of visual  processing of ethnic groups. Nevertheless, there was some degree of variation in the task content between single original studies: for example, whether the stimulus material included video clips or photographs; whether the stimulus material presented only faces  or also a body; and whether subjects were instructed to press a button on the basis of group membership or some other indicator . As a result, each sub-group meta-analysis included a slightly different combination of different fMRI tasks. The fMRI task of each original study is described in Inter-group bias can encompass either in-group favoritism or out-group derogation. In this meta-analysis, however, it was not possible to certainly define whether the biased BOLD responses reflected either in-group favoritism or out-group derogation. This is because, first, most brain regions  are involved in both positive and negative emotions and may also process complex sociocognitive information. Second, when finding a significant difference (such as higher activity toward out- than in-group), it is not possible to deduce whether it refers to elevated activity to out-group or reduced activity to in-group. This is because the brain responses cannot be directly interpreted as reflecting changes in activity level  but merely a difference in activity level.Finally, this meta-analysis was limited to non-clinical study samples with adult-aged subjects. The original datasets were approximately balanced by sex: ca. 48.0–57.2% were female in different analyses of this manuscript. A large proportion of the original samples consisted of young adults, with the mean age of 24.1–32.8 years in our meta-analyses. Consequently, our results cannot be directly generalized to low-educated groups, clinical populations, elderly, adolescents, or children. Inter-group bias may be less evident in children , and morEthnic inter-group bias seems not to constitute a universal pattern of neural responses toward visually presented ethnic in-group and out-group. On the contrary, neural responses to ethnic groups depend on (i) one’s own ethnic minority or majority status and (ii) the target individuals’ ethnic background. Neural inter-group bias appears to be non-evident among ethnic minority members  but clearly visible among majority members in many brain regions . Moreover, this meta-analysis found evidence for distinct response patterns toward different ethnic groups  that may possibly reflect certain sociocultural norms and attitudes. Taken together, this meta-analysis provides novel understanding of the neural mechanisms beyond ethnic inter-group bias and ethnic prejudices.Publicly available datasets were analyzed in this study. This data can be found here: The full statistical data  can be requested from the corresponding author. Additionally, we have provided descriptive information of the included studies in AS conducted the data analysis and wrote the initial manuscript draft. All authors contributed to the study design, interpretation of the data, and writing of the manuscript.


N-glycome of PTMC, and establish nomograms for the diagnosis of PTMC and the prediction of lymph node metastasis (LNM). N-glycome of PTMC  vs. non-CI (NCI)) and matched healthy controls (HC) were quantitatively analyzed based on mass spectrometry. N-glycan traits associated with PTMC/LNM were used to create binomial logistic regression models and were visualized as nomograms. We found serum N-glycome differed between PTMC and HC in high-mannose, complexity, fucosylation, and bisection, of which, four N-glycan traits  were significantly associated with PTMC. The nomogram based on four traits achieved good performance for the identification of PTMC. Two N-glycan traits (CA4 and A2F0S0G) showed strong associations with LNM. The nomogram based on two traits showed relatively good performance in predicting LNM. We also found differences between CI and NCI in several N-glycan traits, which were not the same as that associated with LNM. This study reported serum N-glycosylation signatures of PTMC for the first time. Nomograms constructed from aberrant glycans could be useful tools for PTMC diagnosis and stratification.Non-invasive biomarkers for the diagnosis and prognosis of papillary thyroid microcarcinoma (PTMC) are still urgently needed. We aimed to characterize the  V600E mutational status, and the expression of some intracellular molecules [Papillary thyroid cancer (PTC) is a common endocrine malignancy, with the incidence rising steadily over the past three decades . The incolecules . Timler olecules , whereasClinically, FNAB with cytopathological examination is an effective tool for the diagnosis of thyroid cancer (TC). However, it has limited value for tumors smaller than 10 mm. The American Thyroid Association (ATA) guidelines recommend against the use of FNAB for thyroid nodules smaller than 10 mm, even those with highly suspicious ultrasound patterns ,14. AddiN-glycome has great potential as a biomarker for the diagnosis of cancers, including PTC [N-glycome has potential as a biomarker for predicting LNM of PTMC still needs to be investigated.Glycosylation is one of the most common and important post-translational modifications . Glycosyding PTC ,26,27. Iding PTC , hoping N-glycomic features of healthy controls (HC) and PTMC patients consisting of LNM and non-LNM (NLNM) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). We aimed to explore non-invasion glycan biomarkers and establish nomograms based on glycans for PTMC diagnosis and stratification. Additionally, N-glycome of CI and non-CI (NCI) in PTMC was analyzed to explore associations between CI and LNM. Moreover, based on our previous PTC glycomic study, N-glycome profiles between PTMC and PTC were also investigated to discover their difference.In this study, we investigated serum protein The study enrolled 100 patients diagnosed with PTMC, and 80 HC volunteers. The serum specimen of each individual was consecutively collected from Peking Union Medical College Hospital  between January 2021 and June 2021. The inclusion criteria for the PTMC group were: (a) the maximal diameter of the thyroid nodule ≤ 10 mm on the ultrasound images; (b) age ≥ 18 years; (c) diagnosed based on postoperative pathology, and had no distant metastasis. In the HC group, all volunteers should have normal thyroid function, thyroid ultrasonography, and biochemical and blood coagulation parameters. The exclusion criteria were: (a) a history of blood and infectious diseases, diabetes, impaired glucose tolerance, or autoimmune disorders that required systemic immunosuppressive therapy; (b) a history of pituitary disease or other cancers; (c) a previous history of thyroid or malignancy operations. The clinical and pathological characteristics of the cohort are presented in N-glycans [N-glycan release was performed by the addition of 10 μL of the mixture , followed by incubation at 37 °C overnight. During the derivation procedure, sialic acid residues at the nonreducing ends of the glycan were derivatized to stable end-products , permitting mass-based distinction among the sialic-acid linkage variants [N-glycans were then purified by hydrophilic interaction liquid chromatography solid-phase extraction (HILIC-SPE) micro-tips, and were eventually eluted with MilliQ water according to the previously reported method [A total of 180 serum samples from the cohort (100 PTMC and 80 HC) and 17 quality control samples (5 blanks and 12 replicated serum standards) were randomly distributed into three 96-well plates. According to the protocol described previously, serum glycoproteins from the samples were enzymatically treated to release -glycans . Brieflyvariants . In shord method ,29,30. Thttps://glycopost.glycosmos.org accessed on 22 August 2022) [N-glycan compositions using the GlycoPeakfinder tool of GlycoWorkbench [N-glycan compositions were also confirmed by previous literature [N-glycan compositions was generated for subsequent targeted data extraction. The peak intensities of putative glycan composition were extracted as peak areas (background-corrected) by the composition list and MassyTools. The extracted data were further processed in Microsoft Excel. In the furthering process, 35 out of 131 glycan compositions were excluded after applying the cut-offs of S/N > 9, ppm error < 20, QC score < 25%, and the minimum percentage (>50%) of presence in all spectra of PTMC, HC, or quality control serum samples [N-glycan compositions met the quality criteria and were included in quantitative analysis  . Brieflyst 2022) . The peaorkbench ,34. The terature ,28,29. Aanalysis . ThereafThe average value, standard deviation (SD), and coefficient of variance (CV) of the 12 replicated standard samples were calculated for directly detected and derived glycan traits to evaluate the quality of the data .p = 0.05/91, “91” is the number of derived glycan traits). The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity, specificity, and accuracy of each glycan trait. The derived glycan traits that showed diagnostic or predictive potential in univariate analysis were entered into multivariate analysis. Statistically significant derived glycan traits in multivariate analysis  were used to create visual nomograms, which were tested for goodness of fit using Hosmer–Lemeshow test. The ROC curves were used to evaluate predictive performance. Statistical analyses were performed by SPSS statistics (version 25.0), GraphPad Prism software (version 8.4.3), and R software (version 4.0.3). The GlycoWorkbench software was used for the annotation of glycan structures.Categorical variables were presented as frequencies and percentages. Continuous variables were presented as means ± standard deviations . CompariN-glycans were detected by MALDI-TOF MS. Ninety-six directly detected N-glycan traits passed the quality criteria  of complex-type glycans. Mono-antennary and tetra-antennary N-glycans within complex type (CA1 and CA4) were increased in PTMC compared to HC  and the ratio of high-mannose to hybrid glycans (MHy) were upregulated in PTMC compared to that in HC . Serum Ned to HC . In addied to HC . In addied to HC .p = 0.002, p = 0.042, p = 0.000, P = 0.010, respectively) were found to be strongly associated with PTMC  and the average number of mannoses on high-mannose-type glycans (MM) was upregulated in the LNM compared to that in NLNM (N-glycans within complex type (CA3 and CA4) were increased in LNM compared to NLNM, with the concomitant decrease in diantennary species  were strongly associated with LNM  that were significantly associated with PTMC in multivariate analysis, the logistic regression models were created and visualized as nomograms to provide a quantitative method for the diagnosis of PTMC . The calibration curve indicated a satisfying agreement between predictions performed by the nomogram and actual outcomes in the cohort . The calibration curves of the model demonstrated a satisfying agreement between nomogram and observed outcomes  was upregulated in CI compared to that in NCI, which was mainly due to the increase of galactosylation of the biantennary glycans  discovered in our previous study [N-glycans in PTMC patients (compared to HC) found in the present study were compared. We found three glycan traits  were both dysregulated in PTC and PTMC. Five glycan traits  were only altered in PTMC. Additionally, altered galactosylation, sialylation, and linkage-specific sialylation were only found in PTC. These findings showed that the serum N-glycomes of PTMC and PTC are different. Moreover, we screened out four potential diagnostic biomarkers for PTMC  in the present study (Aberrant us study  and dysrnt study . Two of N-glycomes of PTMC and PTC (diameter > 10 mm). The N-glycomic data of PTC patients  from our previous study [Furthermore, we compared the us study  and thatN-glycomic profiles of PTMC at present. Hence, this study analyzed the released N-glycans from serum samples of HC and PTMC  using MALDI-TOF MS to explore N-glycomic features and potential biomarkers for PTMC diagnosis and stratification.Apart from the cancer genome, deciphering changes in protein glycosylation is vital not only for understanding the mechanisms of tumor occurrence and progression, but also to explore novel biomarkers for diagnosis and prognosis. Despite significant advances in thyroid cancer health care systems, and the mortality rate remaining stable, deeper insight into the underlying mechanisms and discovering the diagnostic and prognostic biomarkers are crucial for thyroid cancer. The affiliation between PTMC and PTC remains a point of controversy due to the aggressive biological behavior and good prognosis of PTMC. So far, several studies regarding the aberrant glycosylation of PTC have been reported in tissues, plasma, and IgG. Due to the underestimation of PTMC in practice, little is known about the N-glycan profiles in PTMC. Our data demonstrate that PTMC patients have elevated levels of high-mannose, complexity, and bisected N-glycans, and decreased levels of fucosylation, compared with HC. Our investigation of the dysregulation of N-glycan patterns in PTMC may point to pathophysiological processes involving multiple proteins, as we discuss below. Early studies showed that the high-mannose glycans and complex-type glycans were increased in breast cancer progression, and glycans took part in cancer progression phases, such as cell proliferation signaling, activating invasion and metastasis, and tumor-promoting inflammation [N-glycans was also increased in our study, which indicated that these glycan structures might be strongly associated with the occurrence and development of PTMC. These findings together with our observations suggest increased levels of high-mannose and complex-type glycans to be key molecular features in PTMC. Additionally, the aberrant expression of bisecting GlcNAc has been observed in many cancer cells, such as breast, pancreatic, and colorectal cancer, and plays a regulatory role in tumor development by altering N-glycans in adhesion molecules and the extracellular matrix [N-glycome patterns of thyroid cancer compared to HC in our previous study, which was partially consistent with our findings in PTMC serum [The present study represents the first comprehensive analysis of serum ammation ,37,38. Cammation . The abur matrix . In the r matrix . FUT4 isr matrix . FUT6 isr matrix . In partMC serum .N-glycan trait was underpowered. Then, we screened out four derived glycan traits  which were significantly associated with PTMC, based on which, a nomogram was established as a model for the diagnosis of PTMC. The AUC value of our nomogram was greater than 0.8, and has superior accuracy. Patients with a total point value of more than 0.746 (cutoff) are more likely to be diagnosed with PTMC. Hence, this nomogram can provide surgeons with a quantitative tool for PTMC diagnosis, assisting existing approaches (ultrasound and FNAB).Though the PTMC-associated glycan traits were identified, the diagnostic accuracy of each N-glycan and LNM. We found an increased abundance of high-mannose  in LNM. Tumor cells likely used aberrant high-mannose to enhance cell adhesion to the vascular wall, further promoting extravasation and metastasis [N-glycans were observed in tumor tissues and module tumor adhesion through attachment to cadherins and integrins. Increased complex branched N-glycans observed in the serum of PTMC with LNM might promote tumor progression and metastasis. What is more, we observed decreased galactosylation in the serum of PTMC with LNM. Theodoratou et al. also found that decreased IgG galactosylation was associated with colorectal cancer prognosis [N-glycans of IgG in ovarian, prostate, gastric, and lung cancers, which were associated with poor prognosis [Considering the good prognosis and indolent behavior of PTMC, the necessity of operation is questioned by certain surgeons and patients. Nevertheless, PTMC patients with central LNM need to undergo an operation. Cervical LNM is present in 17–64% of PTMC. Ultrasound is not sensitive for the detection of LNM, which is mainly due to the location of lymph nodes and interference from vessels and trachea . This ditastasis . Additiorognosis . Severalrognosis ,47,48. Prognosis . We founrognosis ,51. Addirognosis . We hypoN-glycomic profiles of PTMC and PTC (diameter > 10 mm) were compared. To our knowledge, it is the first report to investigate the difference between PTMC and PTC (diameter > 10 mm) at the level of serum N-glycome. Here, the findings indicated that PTMC displayed distinct serum N-glycome patterns (PTMC-specific) compared to PTC (diameter > 10 mm). Therefore, our N-glycomic data indicate that the pathological mechanism of PTMC may not be the same as that of PTC, and PTMC is not the “early stage” of PTC. Further studies will combine clinical data with multi-omics approaches to further explore the underlying mechanisms between PTMC and PTC patients.The affiliation between PTMC and PTC remains controversial. Studies comparing PTMC and PTC (diameter > 10 mm) patients are fewer and less conclusive at present. In this study, the serum N-glycomic results suggest that CI may not be related to LNM, at least at the glycan level. The in-depth mechanisms between CI and LNM still need further investigation.Several studies demonstrated that CI was independently associated with an increased risk of LNM ,53. ConvN-glycan traits, which remains the known challenge in this field, and might be resolved by glycoproteomic analysis. Second, we only explore the association between CI and LNM at serum N-glycome levels, and the underlying mechanisms need to combine multi-omic approaches with clinical datasets to further investigate. Third, we only focused on the association between CI and LNM. Due to the sample size of subgroups based on other risk factors not being large enough, we were unable to conduct a stratified analysis. A stratified analysis of subgroups according to age, sex, and multifocality will be performed in future studies. Fourth, an external validation of the logistic models was not conducted. Studies in large validation cohorts are further required to confirm our findings before applications of the glycan-based nomogram.Though we discovered valuable glycan biomarkers and established nomograms for PTMC diagnosis and stratification, the present study has several limitations. First, the methodology of the study cannot provide detailed information on protein origins of N-glycomic signatures. The predictive nomograms based on serum N-glycome were established for PTMC diagnosis and stratification. We found that CI was not associated with LNM at the level of serum N-glycome. Surgeons can give patients preoperative counseling and guidance based on these nomograms. Further protein-specific studies and validation are required to confirm our findings.In summary, this is the first study focused on PTMC-specific serum


Familial hypercholesterolemia (FH) is a common monogenic disease which is associated with high serum levels of low-density lipoprotein cholesterol (LDL-C) and leads to atherosclerosis and cardiovascular disease (CVD). Early diagnosis and effective treatment strategy can significantly improve prognosis. Recently, non-coding RNAs (ncRNAs) have emerged as novel biomarkers for the diagnosis and innovative targets for therapeutics. Non-coding RNAs have essential roles in the regulation of LDL-C homeostasis, suggesting that manipulation and regulating ncRNAs could be a promising theranostic approach to ameliorate clinical complications of FH, particularly cardiovascular disease. In this review, we briefly discussed the mechanisms and pathophysiology of FH and novel therapeutic strategies for the treatment of FH. Moreover, the theranostic effects of different non-coding RNAs for the treatment and diagnosis of FH were highlighted. Finally, the advantages and disadvantages of ncRNA-based therapies vs. conventional therapies were discussed. Familial hypercholesterolemia (FH) is an autosomal co-dominant disorder that affects people of all ethnic backgrounds. The FH disease is characterized by abnormally high serum levels of low-density lipoprotein cholesterol (LDL-C), and if remains untreated, it can lead to atherosclerosis, cardiovascular diseases (CVDs), and even death . FH is aFH increases the risk of atherosclerosis and CVD, and approximately 2% of the patients with myocardial infarction at <50 years of age have a molecular diagnosis of FH . AdvanceLDLR, APOB, and PCSK9) are usually targeted for next-generation DNA sequencing. This method is the most commonly used one for molecular diagnosis, although bioinformatics analysis of whole-exome sequence data and the use of small non-coding RNAs (ncRNAs) focused on genes related to FH might be the methods of choice in the future , APOB (apolipoprotein B100), and PCSK9 (proprotein convertase subtilisin/kexin type 9) genes  genes . Mutatioe plasma . More thma  has approved four novel LDL-C-lowering drugs for HoFH and HeFH . Some moPCSK9 expression and increased the total cellular LDLR protein and mediated LDL-C uptake by HepG2 cells  for the management of FH disease  used the CRISPR/Cas9 technology to edit the 3 base-pair homozygous deletion in the exon 4 of LDLR gene in the HoFH patient-derived induced pluripotent stem cells /Cas9 system is a novel therapeutic approach for the treatment of monogenic inherited diseases . CRISPR em cells . In the LncRNAs are defined as RNAs longer than 200 nucleotides . The bioThe biogenesis of lncRNAs is a multi-step process, and different mechanisms are involved, including RNaseP digestion (for the generation of mature ends), snoRNP complex formation (for insertion of a cap to the end of lncRNAs), and formation of circular structures . PreviouCircular RNAs (circRNAs) are single stranded RNAs that are covalently looped. There are three types of circRNAs based on different circularizing mechanisms, including exonic circRNAs, intronic circRNAs, and intron-retained circRNAs . UsuallyLncRNAs have various regulatory functions. At the chromatin level, they are involved in dosage compensation, genomic imprinting, chromatin modification, and remodeling . At the circRNAs are important regulatory elements that regulate at both transcription and post-transcription levels. They act as miRNA sponges and compete with endogenous RNAs (ceRNAs). As ceRNAs, they can compete for miRNA-binding sites and regulate mRNA expression . Also, clncRNAs are involved in cholesterol homeostasis and lipid-related diseases such as cardiovascular diseases . Next-geet al. reported that 447 circRNAs were upregulated and 219 circRNAs were downregulated in FH , and other cardiovascular diseases . A studyed in FH . circHRCed in FH .As a therapeutic approach, lncRNAs may serve as key regulators of lipid homeostasis. Here we briefly highlighted various lncRNAs involved in cholesterol metabolism and their potential therapeutic applications ; Table 1MIAT is a long non-coding RNA located on chromosome 22q12.1 and regulates the expression of genes at both transcriptional and post-transcriptional levels  gene  gene . In macr−/− mice . Therefo−/− mice .As a regulator of hepatic sterol content and serum cholesterol levels, LeXis lncRNA orchestrates the crosstalk between liver X receptor and SREBP transcription factors . It was CYP7a1 and ABCA1 in hepatocytes. It is now clear that lnc-HC knockdown improves the total cholesterol, TG, and HDL-cholesterol levels in rats  genes, which increase cholesterol efflux and decrease neutral lipid accumulation. Down-regulation of DYNLRB2 in mice decreased circulating lipids and alleviate atherosclerotic symptoms (DYNLRB2 is a long intergenic non-coding RNA expressed in human macrophages in response to oxidized-LDL (ox-LDL) . In macrsymptoms .As small but vitally regulatory modules, micro-RNAs that vary in length from 18 to 24 nucleotides belong to the ncRNAs class . These mThe hairpin primary miRNA sequence (pri-miRNA) produced at this stage is capable of producing one miRNA or, in some cases, more than one miRNA . Then, iIn addition to canonical rout, there are some atypical miRNA biogenesis pathways that can be relevant to CVD. One class of miRNAs biosynthesized in these non-canonical pathways are miRtrons that are derived from introns. Due to the short length of a hairpin structure, after splicing in the nucleus and export to the cytoplasm, processes of pre-miRNA are done by Dicer instead of Drosha and DGCR8 . DespitemicroRNAs as critical gene regulators can affect the function and developmental processes of human cells. miRNAs negatively regulate target genes expression. By cleaving target mRNA in the RISC complex (miRISC), it completely stops mRNA translation . The GW1in vivo inhibition of mir-148a reverses the negative regulatory effect on LDLR and ABCA1 mRNA  up-regulate the ABCA1 protein in macrophages through down-regulation of miR-33b-5p in HC patients  and FH patients who did not have a cardiovascular incident (FH-nCVE). miR-133a was a marker that was elevated in the FH-CVE population. Because miR-133a is linked to inflammatory cytokines, this could be used as a diagnostic marker for cardiovascular and atherosclerosis in FH patients . NotablysiRNAs (20–30 nucleotide-long) are synthesized artificially and employed as novel therapeutic agents. These RNAs can bind to target mRNAs and help to silence disease-causing genes . As compIn-vitro transcription of siRNAs and shRNAs is another way for synthesizing siRNAs, but due to the immuno-stimulatory effects, the 5′ triphosphate at the end of siRNA must be removed using phosphatases , increased LDLR protein (2 fold), and reduced LDL-C in vivo . Becausein vivo . In healin vivo . Likewisin vivo . Some siin vivo . Anotherin vivo  Rx is another ASO that targeted the Lp(a) protein level in FH patients to reduce the early onset and severity of coronary artery disease (CAD) . In a phAnother ASO,ANGPTL3-LRx, reduces the level of ANGPTL3 protein, which lowers the risk of cardiovascular disease . ANGPTL3Multiple conventional therapies are available to manage LDL-C levels in FH patients. Statins can up-regulate the LDLR level and reduce the LDL-C in HoFH patients lacking functional LDLR. Therefore, they are not generally effective . Combinain vivo since they are a subset of non-coding RNA. On one side, negative charges on miRNA and hydrophilic feature restricted its absorption by cells  the specificity of targets involved in the development of the disease, b) targeted delivery of the drug at the subcellular level, c) stability of ncRNA-based therapeutics, and d) off-target effects . For exaEarly detection and suitable intervention for FH in order to lower LDL is the standard goal to reduce the associated cardiovascular disease burden. Various non-coding RNAs, including lncRNAs, miRNAs, siRNAs, and ASOs interact with and regulate genes and their transcripts in lipid and cholesterol metabolism. As a therapeutic approach, some lncRNAs such as MIAT, GAS5, RP5-833A20.1, LeXis, Lnc-HC, and DYNLRB2 may serve as crucial regulators of cholesterol homeostasis. Small RNA interference drugs are used to silence disease-causing genes. Inclisiran  is an excellent example of siRNAs used to reduce the level of LDL-C in FH. ASOs are another group of ncRNAs synthesized to regulate the level of some proteins involved in the LDL-c metabolism. Mipomersen is a well-known ASO used to inhibit the translation of ApoB mRNA. Also, miRNAs are another class of non-coding RNAs that could be promising tools for the early diagnosis and treatment of FH. Despite the importance and necessity of ncRNAs in preclinical investigations, their efficient and successful utilization in clinical trials has faced some challenges. These data suggest that non-coding RNAs could be promising tools for the early detection and treatment of FH disease, but more research is needed to overcome current challenges.