Dataset Information

Living with Systematic Lupus Erythematosus: A Profile of Young Female Patients.

ABSTRACT: The aim is to describe a profile of systemic lupus erythematosus (SLE) patient (socio-demographic data, course of disease, health status, and health care utilization, SLE impact on their life, SLE awareness) and to explore the association of patient's perspective with clinical indicators. Adult patients diagnosed by SLE were recruited in outpatient clinics (n = 76, 88% female, data collected in 2012-2016, Slovakia). The association of patients' perspective (SLE status, health complaints during remission, SLE impact, hospitalizations) with clinical activity (European Consensus Lupus Activity Measurement Index - ECLAM) and inflammatory marker (erythrocyte sedimentation rate - ESR) was assessed by t-test for independent variables and one-way ANOVA. Almost 17.9% of patients reported relapse. During remission, they mostly suffered fatigue and pain. Nearly all patients were on chronic pharmacological treatment. Most of the patients assessed SLE impact on their life as restrictive (56.9%) or very restrictive (23.1%). The most frequent source of information was their physician, and 67.2% reported that they have sufficient information about the disease and its treatment. Only the association of SLE status and hospitalization with clinical activity (ECLAM) and inflammatory marker (ESR) were confirmed. With recent improvements in diagnostics and therapy options, the prognosis for patients with SLE has improved. Nevertheless, the impact of this disease on all areas of a patient's life is extensive.

SUBMITTER: Macejova Z

PROVIDER: S-EPMC7068424 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Living with Systematic Lupus Erythematosus: A Profile of Young Female Patients.

Macejova Zelmira Z Madarasova Geckova Andrea A Husarova Daniela D Zarikova Michaela M Kotradyova Zuzana Z

International journal of environmental research and public health 20200218 4

The aim is to describe a profile of systemic lupus erythematosus (SLE) patient (socio-demographic data, course of disease, health status, and health care utilization, SLE impact on their life, SLE awareness) and to explore the association of patient's perspective with clinical indicators. Adult patients diagnosed by SLE were recruited in outpatient clinics (<i>n</i> = 76, 88% female, data collected in 2012-2016, Slovakia). The association of patients' perspective (SLE status, health complaints d ...[more]

PMID: 32085615

Similar Datasets

Project description:ObjectivesThis systematic review assessed which variables are associated with or are predictors for work participation outcomes in patients with systematic lupus erythematosus (SLE).MethodsA literature search using MEDLINE, The Cochrane Library, Embase and CINAHL was conducted to identify all studies published from inception (1947) to June 2021 on factors related to and/or predicting employment status, absenteeism and/or presenteeism in SLE patients aged ≥18 years. The quality of included articles was assessed using the QUIPS tool. Narrative summaries were used to present the data.ResultsFifteen studies (nine on associations, four on predictions, and two assessing both) were included, encompassing data of 3800 employed patients. Younger age, Caucasian ethnicity, higher educational level, lower disease activity score, shorter disease duration, absence of specific disease manifestations, higher levels of physical functioning and less physical job demands and higher levels of psychological/cognitive functioning were associated with or predicted favorable work outcomes. Older age, non-Caucasian ethnicity, female gender, never being married, poverty, lower educational level, higher disease activity score, longer disease duration, specific disease manifestations, lower levels of physical functioning, more physical job demands and low job control, less job tenure and lower levels of cognitive functioning were associated with or predicted an unfavorable work outcome. Limitations of the evidence were the quality of the studies and the use of heterogeneous outcome measures, applied statistical methods and instruments used to assess work participation.ConclusionWe recommend applying the EULAR points to consider for designing, analysing and reporting on work participation in inflammatory arthritis also to SLE studies on work participation, to enhance the quality and comparability between studies and to better understand the impact of SLE on work participation.Trial registrationregistration in PROSPERO (CRD42020161275; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=161275).

Project description:BackgroundLupus nephritis (LN) is a crucial complication of systemic lupus erythematosus (SLE) and has important clinical implications in guiding treatment. N-glycosylation of immunoglobulin G (IgG) plays a key role in the development of SLE by affecting the balance of anti-inflammatory and proinflammatory responses. This study aimed to evaluate the performance of IgG N-glycosylation for diagnosing LN in a sample of female SLE patients.MethodsThis case-control study recruited 188 women with SLE, including 94 patients with LN and 94 age-matched patients without LN. The profiles of plasma IgG N-glycans were detected by hydrophilic interaction chromatography with ultra-performance liquid chromatography (HILIC-UPLC). A multivariate logistic regression model was used to explore the associations between IgG N-glycans and LN. A diagnostic model was developed using the significant glycans as well as demographic factors. The performance of IgG N-glycans in the diagnosis of LN was evaluated by receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) and its 95% confidence interval (CI) were calculated.ResultsThere were significant differences in 9 initial glycans (GP2, GP4, GP6, GP8, GP10, GP14, GP16, GP18 and GP23) between women with SLE with and without LN (P < 0.05). The levels of sialylated, galactosylated and fucosylated glycans were significantly lower in the LN patients than in the control group, while bisected N-acetylglucosamine (GlcNAc) glycans were increased in LN patients (P < 0.05). GP8, GP10, GP18, and anemia were included in our diagnostic model, which performed well in differentiating female SLE patients with LN from those without LN (AUC = 0.792, 95% CI: 0.727 to 0.858).ConclusionOur findings indicate that decreased sialylation, galactosylation, and core fucosylation and increased bisecting GlcNAc might play a role in the development of LN by upregulating the proinflammatory response of IgG. IgG N-glycans can serve as potential biomarkers to differentiate individuals with LN among SLE patients.

Project description:ObjectivesSystemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women's health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females.MethodsStool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females.ResultsBoth alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value.ConclusionsThis study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

Dataset Information

Living with Systematic Lupus Erythematosus: A Profile of Young Female Patients.

Publications

Living with Systematic Lupus Erythematosus: A Profile of Young Female Patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets