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Approximately 30% of patients with psoriasis will develop psoriatic arthritis (PsA), leading to a decreased quality of life for the patient caused by increasing disability and additional health complications. The identification of risk factors for the development of PsA would facilitate the development of risk prediction models in which patients with psoriasis at high risk of developing PsA could be targeted in a stratified medicine approach, enabling early intervention and treatment. PsA is known to have a genetic contribution to susceptibility, and the identification of genetic risk factors that differentiate PsA from cutaneous-only psoriasis is a key area of research. This narrative review summarizes the discovery of genetic risk factors and, with the aid of a primer on risk prediction models, discusses their potential role for the classification of PsA risk and diagnosis. All relevant research articles were identified through searches of the PubMed database for literature published up until December 2022. Search terms included psoriatic arthritis, genetic susceptibility, genetic association, genome-wide association study, GWAS, prediction, and polygenic risk score. The current literature reveals considerable overlap between the genetic susceptibility loci for PsA and psoriasis. Several PsA-specific genetic risk factors have been reported, and most notably these implicate the HLA-B and IL23R genes. Efforts to include genetic risk factors in prediction models for the development of PsA have reported good discrimination. Key messages emerging from this narrative are as follows: the limited number of PsA-specific susceptibility loci reported to date suggest larger studies are required, facilitated by international collaboration, to achieve the power to detect further genetic factors; the early promising results for genetic-based risk prediction require further validation in independent datasets; and risk prediction models combining clinical and genetic risk factors have yet to be explored.

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the ‘lowest number of feature subset’ with the ‘maximal average AUC over the nested cross validation’ and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.

Background Constipation is a frequent adverse event associated with opioid medications that can have a considerable impact on patients’ quality of life. In patients who require opioids for pain relief, less is known about the risk conferred by specific opioids given their diverse pharmacology and the effect of daily dose and potency. The aim of the study was to evaluate the comparative risk of severe constipation by opioid type and dose in patients with non-cancer pain admitted to hospital. Methods We conducted a retrospective cohort study using hospital electronic health records in Northwest England between December 1, 2009, and December 31, 2020. Patients who were ≥ 18 years and without a history of cancer were included. Opioid exposure was measured using administered drug information in hospital. The outcome was a severe constipation event defined as administration of an enema or suppository. Incidence rates by opioid use status, type of opioid class and morphine milligram equivalent (MME) per day were calculated, and a Cox regression model was used to determine associations with incident constipation after adjusting for confounders. Results The study included 80,475 eligible patients who were administered an opioid in hospital. Compared to codeine, morphine (HR 1.59, 95% CI 1.45–1.74), oxycodone (HR 1.46, 95% CI 1.32–1.63), fentanyl (HR 1.37, 95% CI 1.14–1.64) and combination opioids (HR 1.85, 95% CI 1.66–2.06) were associated with a higher risk of constipation in the fully adjusted models. Tramadol demonstrated a significantly lower risk compared to codeine (HR 0.80, 95% CI 0.64–1.00). Higher opioid doses of more than ≥ 50 MME/day in comparison to < 50 MME/day were associated with an increased risk of constipation (compared to < 50 MME/day, 50 to < 120 MME/day: HR 1.95, 95% CI 1.78–2.15; ≥ 120 MME/day: HR 1.45, 95% CI 1.32–1.60). Conclusions Morphine, oxycodone, fentanyl and combination opioids administration were associated with a significantly higher risk of severe constipation compared to codeine. Tramadol was associated with the lowest risk of the outcome compared to codeine. Patients on ≥ 50 MME/day experienced a higher risk of severe constipation compared to those on < 50 MME/day. These results can be used to guide better shared decisions with patients to balance benefit and harms of specific opioid types and doses.

Aim:This study aims to further investigate (1) the increased occurrence of coronary artery disease (CAD) in patients with rheumatoid arthritis (RA) and (2) Psoriatic Arthritis (PsA) in patients with cutaneous-only psoriasis (PsC). The main objective is to apply existing methodologies that have been widely used for developing polygenic risk scores and utilizing genetic information, in order to better understand potential causal factors. These methods have not yet been specifically applied to assess the relationship between RA and CAD or PsA and psoriasis. This doctoral project aims to further investigate the intricate associations between multiple health conditions in Musculoskeletal disorders.Methods:First, a systematic literature review was conducted. Second, a genome-wide meta-analyses was conducted on a large number of participants, including patients with PsA, healthy controls, and patients with PsC from the UK biobank. Biological pathways that distinguish between PsA and PsC were identified using Priority Index software. To assess the generalizability of previously published risk prediction models for predicting the development of PsA, external validation techniques were employed. Third, utilizing longitudinal data from the Norfolk Arthritis Register (NOAR), a predictive model for development of CAD was developed using conventional risk factors and multiple CAD Polygenic risk scores by leveraging effect sizes obtained from three extensive genome-wide association studies within a subset of NOAR patients with available genetic information. Cox proportional hazards models were utilized to derive risk equations for evaluating an individual's 10-year likelihood of developing CAD. Finally,a causal relationship was estimated using two-sample MR using large-scale summary-level genetic data.Results: The study identified a novel genome-wide significant susceptibility locus for the development of PsA on chromosome 22q11 (rs5754467; P = 1.61 Ã- 10â^'9) and key pathways that differentiate PsA from PsC, including NF-κB signaling (adjusted P = 1.4 Ã- 10â^'45) and Wnt signaling (adjusted P = 9.5 Ã- 10â^'58). Using NOAR, the inclusion of a CAD meta-GRS improved Harrellâ€TMs C-statistics to 0.79 (95% CI 0.78, 0.80), explaining more of the variance at 81% (95% CI 79, 82%) with a calibration slope of 0.93. A likelihood ratio test indicates that the integrated model is a better fit (p = 0.04). When estimating causal association, the combined odds ratios showed an increase in CAD risk by 1.06 per unit rise in log odds of RA seropositive individuals (95% Confidence interval [1.05-1.07, P = 0.04).Conclusion:The inclusion of genetics in risk assessment has been shown to significantly improve prediction accuracy for patients with PsC and RA. This highlights the importance of further exploring the underlying biological pathways involved in these disorders to gain a more profound understanding and develop robust predictive models that are applicable across diverse populations.

Objectives Pain, fatigue and sleep disturbances are common symptoms in patients with rheumatic and musculoskeletal diseases (RMDs) that may prompt the use of over-the-counter (OTC) supplements, sleep aids and analgesics as self-management strategies. This study evaluated the prevalence of OTC supplements, sleep aids and pain relievers and the financial burden associated with their use in rheumatology. Methods A web-based survey developed with patients was administered in rheumatology clinics in an English hospital. Participants shared demographic information and detailed their use of OTC supplements, sleep aids and pain relief in the past week. The data were analysed using descriptive statistics and logistic regression models to identify influencing factors. Results A total of 876 people consented to participate in the survey. More than half of patients (54.5%) reported daily supplement intake, typically spending £10/month (interquartile range 5–20), ranging up to £200/month. The most commonly administered supplements were vitamin D, multivitamins, vitamin C, vitamin B/B complex and omega-3/-6 supplements, with multiple overlaps. Prescription, OTC or non-prescription pain relief use was reported by 82% of respondents, with sleep aids being used by 13%. Of the 327 patients who took NSAIDs, 165 (50.4%) also reported taking OTC supplements, while among the 131 patients using opioids (20.5%), 66 (50.3%) reported supplement use, some of which have documented interactions. Conclusion The use of OTC supplements, pain relief and sleep aids is common in patients with RMDs. Healthcare professionals should be encouraged to proactively ask about these during consultations, especially from a drug safety perspective, but also to provide timely, reliable advice about such strategies that may be sought by patients. Lay Summary What does this mean for patients? Living with musculoskeletal conditions often means coping with persistent pain, fatigue and sleep disturbances. To manage these symptoms, many people turn to over-the-counter (OTC) medications such as supplements, sleep aids and pain relievers, in addition to their prescribed medications. But how common is this practice and what are the implications for patients and healthcare providers? To understand this, we surveyed patients attending rheumatology clinics. The survey was designed with input from patients and asked participants about their use of these OTC products over the past week. The results revealed that using OTC products to manage symptoms is widespread. More than half of the patients surveyed reported take supplements daily, with vitamin D, multivitamins, vitamin C, B vitamins, omega-3/-6 and turmeric being the most popular. People often took multiple supplements with a financial cost up to £200/week. More than 80% of patients also used some form of pain relief, whether it was prescription, OTC/internet or borrowed from a family member. Concerningly, a considerable number of patients combined OTC supplements with prescription pain medications, including anti-inflammatories and opioids, despite potential interactions. This study highlights the importance of open communication between patients and healthcare providers about OTC product use. Healthcare professionals should proactively ask patients about these products during consultations, not only to ensure safety and manage potential drug interactions, but also to provide evidence-based advice and support patients in making informed decisions about their health.

Background and Objective: Beta-thalassemia major (β-Thal) and compound heterozygote of Sickle β-thalassemia (S-β Thal) are hereditary autosomal recessive disorders resulting from mutations or deletion in β-globin gene cluster. Patients with increased HbF levels having polymorphism at BCL11A site loci have shown clinical significance. The present study aimed to assess the frequency of BCL11A gene polymorphism in a study population of β-Thal, S-β Thal & Controls using Sanger sequencing leading to plot the HbF response of polymorphism with reference to wild type. Methods: The sample size of the study is n=180, groups were divided in Controls, β-thal & S-β thal. One ml blood was drawn from patients and controls to extract DNA for PCR amplification and BCL11A locus genotyping using Sanger sequencing. This study was carried out at Dow Research Institute of Biotechnology and Biomedical Sciences, for one year from March 2021 to February 2022. Results: The HbF response of three groups is hyperbolic with 83 for β-Thal, 16 for S-β Thal and close to zero for controls. The frequency of heterozygous variant GA of BCL11A gene polymorphism is 51%. The frequency of homozygous variant GG is 49%. Complete absence of wild type AA in patient group. The frequency of BCL11A polymorphism in control group was 43% (with male 18% and female 21%) showing wild type status of 57%. Conclusions: The patient groups of SCD and Beta thalassemia are devoid of wild type status. The wild type status of BCL11A is 57% even in control population. Higher level of HbF in B-thalassemia and SCD and B Thalassemia is a cost-effective screening marker before switching to an expensive genotyping testing. doi: https://doi.org/10.12669/pjms.39.6.7183 How to cite this: Soomro N, Wahid M, Mehmood M, Danish SH. Responses of β-thalassemia and compound heterozygote of Sickle/βthalassemia of BCL11A Gene Polymorphism in Pakistani Patients. Pak J Med Sci. 2023;39(6):1788-1792. doi: https://doi.org/10.12669/pjms.39.6.7183 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.