Correlations of baseline neutrophil-lymphocyte ratio with prognosis of patients with lupus nephritis: A single-center experience.

Objective: We aimed to evaluate the correlations among the neutrophil-to-lymphocyte ratio (NLR), lupus nephritis (LN) clinical characteristics, and renal prognosis of patients with LN. Methods: We enrolled 122 patients who were diagnosed with LN at the Rheumatology Department of the People's Hospital, Xinjiang Uygur Autonomous Region from January 2013 to April 2022. We determined the occurrence of renal adverse events in patients with LN by reviewing medical records and follow-up data. Correlations were analyzed using the Spearman test, and the quartile method was applied to classify all of the 122 patients who had completed follow-up into low, medium, and high NLR groups. The Kaplan-Meier survival curve was used to conduct survival analysis, and Cox regression analyses were used to explore possible potential risk factors. Results: The baseline NLR of patients with LN was positively correlated with C-reactive protein (CRP), serum creatinine, blood urea nitrogen, and systemic lupus erythematosus disease activity index scores (P < 0.05) and negatively correlated with estimated glomerular filtration rate (eGFR) and serum albumin (P < 0.05). Patients who completed follow-up were divided into three NLR groups based on their NLR values: 30 in the low (NLR ≤ 2.21), 62 in the medium (NLR > 2.21 and NLR ≤ 6.17), and 30 in the high NLR group (NLR > 6.17). The patient survival time before developing poor renal prognosis was significantly different among the three groups (P < 0.05). High NLR (hazard ratio [HR] = 3.453, 95% confidence interval [CI]: 1.260-9.464), CRP (HR = 1.009, 95% CI: 1.002-1.017), eGFR (HR = 0.979, 95% CI: 0.963-0.995), and 24-h proteinuria values (HR = 1.237, 95% CI: 1.025-1.491) as well as anti-double stranded DNA antibody positivity (HR = 3.056, 95% CI:1.069-8.736) were independent risk factors associated with a poor renal prognosis for patients with LN. Conclusion: The baseline NLR in peripheral blood can be used as a reference index for evaluating renal function and disease activity in patients with LN, and a high NLR has predictive value for the prognosis of patients with LN.

Application of serum SERS technology combined with deep learning algorithm in the rapid diagnosis of immune diseases and chronic kidney disease.

Surface-enhanced Raman spectroscopy (SERS), as a rapid, non-invasive and reliable spectroscopic detection technique, has promising applications in disease screening and diagnosis. In this paper, an annealed silver nanoparticles/porous silicon Bragg reflector (AgNPs/PSB) composite SERS substrate with high sensitivity and strong stability was prepared by immersion plating and heat treatment using porous silicon Bragg reflector (PSB) as the substrate. The substrate combines the five deep learning algorithms of the improved AlexNet, ResNet, SqueezeNet, temporal convolutional network (TCN) and multiscale fusion convolutional neural network (MCNN). We constructed rapid screening models for patients with primary Sjögren's syndrome (pSS) and healthy controls (HC), diabetic nephropathy patients (DN) and healthy controls (HC), respectively. The results showed that the annealed AgNPs/PSB composite SERS substrates performed well in diagnosing. Among them, the MCNN model had the best classification effect in the two groups of experiments, with an accuracy rate of 94.7% and 92.0%, respectively. Previous studies have indicated that the AgNPs/PSB composite SERS substrate, combined with machine learning algorithms, has achieved promising classification results in disease diagnosis. This study shows that SERS technology based on annealed AgNPs/PSB composite substrate combined with deep learning algorithm has a greater developmental prospect and research value in the early identification and screening of immune diseases and chronic kidney disease, providing reference ideas for non-invasive and rapid clinical medical diagnosis of patients.

B1-cell-produced anti-phosphatidylserine antibodies contribute to lupus nephritis development via TLR-mediated Syk activation.

Autoantibodies produced by B cells play a pivotal role in the pathogenesis of systemic lupus erythematosus (SLE). However, both the cellular source of antiphospholipid antibodies and their contributions to the development of lupus nephritis (LN) remain largely unclear. Here, we report a pathogenic role of anti-phosphatidylserine (PS) autoantibodies in the development of LN. Elevated serum PS-specific IgG levels were measured in model mice and SLE patients, especially in those with LN. PS-specific IgG accumulation was found in the kidney biopsies of LN patients. Both transfer of SLE PS-specific IgG and PS immunization triggered lupus-like glomerular immune complex deposition in recipient mice. ELISPOT analysis identified B1a cells as the main cell type that secretes PS-specific IgG in both lupus model mice and patients. Adoptive transfer of PS-specific B1a cells accelerated the PS-specific autoimmune response and renal damage in recipient lupus model mice, whereas depletion of B1a cells attenuated lupus progression. In culture, PS-specific B1a cells were significantly expanded upon treatment with chromatin components, while blockade of TLR signal cascades by DNase I digestion and inhibitory ODN 2088 or R406 treatment profoundly abrogated chromatin-induced PS-specific IgG secretion by lupus B1a cells. Thus, our study has demonstrated that the anti-PS autoantibodies produced by B1 cells contribute to lupus nephritis development. Our findings that blockade of the TLR/Syk signaling cascade inhibits PS-specific B1-cell expansion provide new insights into lupus pathogenesis and may facilitate the development of novel therapeutic targets for the treatment of LN in SLE.

Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome.

The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value.

Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms.

Introduce: Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in severe cases, they can cause lifelong disability and even death. Studies have shown that early diagnosis and treatment are key to improving patient outcomes. Therefore, a rapid and accurate method for rapid diagnosis of diseases has been established, which is of great clinical significance for realizing early diagnosis of diseases and improving patient prognosis. Methods: This study was based on Fourier transform infrared spectroscopy (FTIR) combined with a deep learning model to achieve non-invasive, rapid, and accurate differentiation of AS, RA, OA, and healthy control group. In the experiment, 320 serum samples were collected, 80 in each group. AlexNet, ResNet, MSCNN, and MSResNet diagnostic models were established by using a machine learning algorithm. Result: The range of spectral wave number measured by four sets of Fourier transform infrared spectroscopy is 700-4000 cm-1. Serum spectral characteristic peaks were mainly at 1641 cm-1(amide I), 1542 cm-1(amide II), 3280 cm-1(amide A), 1420 cm-1(proline and tryptophan), 1245 cm-1(amide III), 1078 cm-1(carbohydrate region). And 2940 cm-1 (mainly fatty acids and cholesterol). At the same time, AlexNet, ResNet, MSCNN, and MSResNet diagnostic models are established by using machine learning algorithms. The multi-scale MSResNet classification model combined with residual blocks can use convolution modules of different scales to extract different scale features and use resblocks to solve the problem of network degradation, reduce the interference of spectral measurement noise, and enhance the generalization ability of the network model. By comparing the experimental results of the other three models AlexNet, ResNet, and MSCNN, it is found that the MSResNet model has the best diagnostic performance and the accuracy rate is 0.87. Conclusion: The results prove the feasibility of serum Fourier transform infrared spectroscopy combined with a deep learning algorithm to distinguish AS, RA, OA, and healthy control group, which can be used as an effective auxiliary diagnostic method for these rheumatic immune diseases.

Raman spectroscopy combined with machine learning algorithms for rapid detection Primary Sjögren's syndrome associated with interstitial lung disease.

BACKGROUND: Interstitial lung disease (ILD) is a major complication of Primary Sjögren's syndrome (pSS) patients.It is one of the main factors leading to death. The aim of this study is to evaluate the value of serum Raman spectroscopy combined with machine learning algorithms in the discriminatory diagnosis of patients with Primary Sjögren's syndrome associated with interstitial lung disease (pSS-ILD). METHODS: Raman spectroscopy was performed on the serum of 30 patients with pSS, 28 patients with pSS-ILD and 30 healthy controls (HC). First, the data were pre-processed using baseline correction, smoothing, outlier removal and normalization operations. Then principal component analysis (PCA) is used to reduce the dimension of data. Finally, support vector machine(SVM), k nearest neighbor (KNN) and random forest (RF) models are established for classification. RESULTS: In this study, SVM, KNN and RF were used as classification models, where SVM chooses polynomial kernel function (poly). The average accuracy, sensitivity, and precision of the three models were obtained after dimensionality reduction. The Accuracy of SVM (poly) was 5.71% higher than KNN and 6.67% higher than RF; Sensitivity was 5.79% higher than KNN and 8.56% higher than RF; Precision was 6.19% higher than KNN and 7.45% higher than RF. It can be seen that the SVM (poly) had better discriminative effect. In summary, SVM (poly) had a fine classification effect, and the average accuracy, sensitivity and precision of this model reached 89.52%, 91.27% and 89.52%, respectively, with an AUC value of 0.921. CONCLUSIONS: This study demonstrates that serum RS combined with machine learning algorithms is a valuable tool for diagnosing patients with pSS-ILD. It has promising applications.

Prevalence and risk factors of active tuberculosis in patients with rheumatic diseases: a multi-center, cross-sectional study in China.

Evidence of active tuberculosis (ATB) in patients with rheumatic diseases are research priorities but limited data from China have been reported. Research targeting patients not taking anti-TNF biologics are especially insufficient. We aimed to investigate the prevalence and risk factors of ATB in this at-risk population. We conducted a tertiary hospital-based, multi-center, cross-sectional study by using stratified multi-stage cluster sampling strategy to screen ATB in patients with rheumatic diseases. We estimated the prevalence of ATB in patients with rheumatic diseases and identified risk factors among those who were not taking anti-TNF biologic. A total of 13,550 eligible patients were enrolled, and the result showed the standardized prevalence of ATB according to the composition ratio of various types of rheumatic disease was 882/100000 (95% confidence interval (CI): 706-1057). Multivariable logistic regression analysis in patients not taking anti-TNF biologics showed that the independent risk factors of ATB were having systemic lupus erythematosus (SLE) (OR=2.722, 95% CI: 1.437-5.159, p=0.002), having Behcet's disease (BD) (OR= 5.261, 95% CI: 2.071-13.365, p<0.001), taking azathioprine(AZA) within the past two years (OR=2.095, 95% CI: 0.986-4.450, p=0.054), exposing to glucocorticoids ≥30mg/d for more than four weeks within the past two years (OR=2.031, 95% CI: 1.247-3.309, p=0.004) and having evidences of previous TB (OR= 6.185, 95% CI: 3.487-10.969, p<0.001). The prevalence of ATB was higher in patients with rheumatic diseases compared to the general population. Patients with SLE or BD, prolonged exposure to moderate to high dose of glucocorticoids and previous TB were independent risk factors for ATB.

IL-17 drives salivary gland dysfunction via inhibiting TRPC1-mediated calcium movement in Sjögren's syndrome.

OBJECTIVES: This study aims to determine a role of interleukin-17A (IL-17) in salivary gland (SG) dysfunction and therapeutic effects of targeting IL-17 in SG for treating autoimmune sialadenitis in primary Sjögren's syndrome (pSS). METHODS: Salivary IL-17 levels and IL-17-secreting cells in labial glands of pSS patients were examined. Kinetic changes of IL-17-producing cells in SG from mice with experimental Sjögren's syndrome (ESS) were analysed. To determine a role of IL-17 in salivary secretion, IL-17-deficient mice and constructed chimeric mice with IL-17 receptor C (IL-17RC) deficiency in non-hematopoietic and hematopoietic cells were examined for saliva flow rates during ESS development. Both human and murine primary SG epithelial cells were treated with IL-17 for measuring cholinergic activation-induced calcium movement. Moreover, SG functions were assessed in ESS mice with salivary retrograde cannulation of IL-17 neutralisation antibodies. RESULTS: Increased salivary IL-17 levels were negatively correlated with saliva flow rates in pSS patients. Both IL-17-deficient mice and chimeric mice with non-hematopoietic cell-restricted IL-17RC deficiency exhibited no obvious salivary reduction while chimeric mice with hematopoietic cell-restricted IL-17RC deficiency showed significantly decreased saliva secretion during ESS development. In SG epithelial cells, IL-17 inhibited acetylcholine-induced calcium movement and downregulated the expression of transient receptor potential canonical 1 via promoting Nfkbiz mRNA stabilisation. Moreover, local IL-17 neutralisation in SG markedly attenuated hyposalivation and ameliorated tissue inflammation in ESS mice. CONCLUSION: These findings identify a novel function of IL-17 in driving salivary dysfunction during pSS development and may provide a new therapeutic strategy for targeting SG dysfunction in pSS patients.

Novel autoantibodies identified in ACPA-negative rheumatoid arthritis.

OBJECTIVES: Lack of effective biomarkers in anti-citrullinated protein antibody (ACPA)-negative rheumatoid arthritis (RA) impedes early diagnosis and treatment. This study aimed to identify novel autoantibodies in RA and verify their diagnostic values in ACPA-negative RA based on protein microarray technology. METHODS: A total of 1011 sera from 559 RA (276 ACPA-positive and 283 ACPA-negative), 239 disease controls (DCs) and 213 healthy controls (HCs) were collected and sampled on two sequential microarrays and ELISA and western blot (WB) detection, for novel autoantibodies discovery, validation and verification, respectively. The high-density protein microarray printed with a broad spectrum of recombinant human proteins was first employed to screen candidate autoantibodies, then focused microarrays composed of candidate autoantigens were used for validation, followed by ELISA and WB to verify the presence of the most promising candidates in ACPA-negative RA. RESULTS: Nine novel autoantibodies were identified by two sequential microarrays with positivity 17.93%-27.59% and specificities >90% in ACPA-negative RA. Among these, anti-PTX3 and anti-DUSP11 autoantibodies presented with the highest sensitivity and were consistently verified by ELISA and WB. Pooling samples of all cohorts, the positivities of anti-PTX3 and anti-DUSP11 in ACPA-negative RA were 27.56% and 31.80%, respectively, similar to those in ACPA-positive RA, and significantly higher than in HCs (4.69% and 2.35%) and DCs (10.04% and 8.49%) (p<0.0001). Combination of anti-PTX3 with anti-DUSP11 significantly increased the diagnostic sensitivity (38.00%) with specificity of 88.72%, regardless of ACPA status. CONCLUSION: Anti-PTX3 and anti-DUSP11 autoantibodies are newly identified biomarkers for diagnosis of ACPA-negative RA.

The amino acid variants in HLA II molecules explain the major association with adult-onset Still's disease in the Han Chinese population.

Adult-onset Still's disease (AOSD) is a rare autoinflammatory disease with systemic involvement, and its pathophysiology remains unclear. Genome-wide association studies (GWAS) in the Chinese population have revealed an association between AOSD and the major histocompatibility complex (MHC) locus; however, causal variants in the MHC remain undetermined. In the present study, we identified independent amino-acid polymorphisms in human leukocyte antigen (HLA) molecules that are associated with Han Chinese patients with AOSD by fine-mapping the MHC locus. Through conditional analyses, we identified position 34 in HLA-DQα1 (p = 1.44 × 10-14) and Asn in HLA-DRß1 position 37 (p = 5.12 × 10-11) as the major determinants for AOSD. Moreover, we identified the associations for three main HLA class II alleles: HLA-DQB1*06:02 (OR = 2.70, p = 3.02 × 10-14), HLA-DRB1*15:01 (OR = 2.44, p = 3.66 × 10-13), and HLA-DQA1*01:02 (OR = 1.97, p = 1.09 × 10-9). This study reveals the relationship between functional variations in the class II HLA region and AOSD, implicating the MHC locus in the pathogenesis of AOSD.

Clinical features and current treatments of adult-onset Still's disease: a multicentre survey of 517 patients in China.

OBJECTIVES: As a rare systemic autoinflammatory disease, adult-onset Still's disease (AOSD) has heterogeneous clinical manifestations, response to treatment and outcome. This study tried to assess the clinical characteristics, laboratory tests, and treatments of Chinese AOSD patients, and make a retrospective analysis. METHODS: We collected from 7 hospitals in China a total of 517 Chinese patients with AOSD who satisfied the Yamaguchi criteria. We retrospectively evaluated their clinical features, laboratory tests, treatments and compared them with published data from different studies. All the data in this study were from medical records and further statistic analyses. RESULTS: We evaluated a total of 517 AOSD patients, 72% female, average age of onset was 37.7; spiking fever, rash and arthralgia occurred in 472 (91.3%), 413 (79.9%), 378 (73.1%) cases, respectively. There were 439/513 (85.6%) cases with leukocytosis and 456/476 (95.8%) cases with raised serum ferritin. The highest frequently used medications and regimens for remission were glucocorticoids (498/517, 96.3%), methotrexate (273/517, 52.8%) and hydroxychloroquine (174/517, 33.7%). 84.4%. 357/423 of AOSD cases were able to achieve initial remission with different regimens, mostly including glucocorticoids, methotrexate or hydroxychloroquine. 47.2% of them (244/517) received 30

Causes of death in hospitalized patients with systemic lupus erythematosus: a 10-year multicenter nationwide Chinese cohort.

To estimate the mortality and describe the causes of death in a large multicenter cohort of hospitalized patients with SLE in China. This was a retrospective study of a nationwide SLE cohort (10 centers, 29,510 hospitalized patients) from 2005 to 2014 in China. Standardized mortality ratios (SMRs) were calculated for all death and were stratified by sex and age. Chi-square test was used to determine whether the major causes of death vary in age, sex, duration of SLE, disease activity, or medications. Comparison between dead patients and survival controls was used to identify the risk factors for mortality. Logistic regression analysis was used to evaluate the risk factors for mortality. A total of 360 patients died during the study period, accounting for 1.22%. The overall SMR was 2.13 (95% CI 1.96, 2.30), with a particularly high SMR seen in subgroups characterized by younger age. Infection (65.8%) was the most common cause of death, followed by lupus nephritis (48.6%), hematological abnormality (18.1%), neuropsychiatric lupus/NPSLE (15.8%), and interstitial pneumonia (13.1%). Cardiovascular disease and malignancy contributed little to the causes of death. Infection, in particular severe pulmonary infection, emerged as the foremost risk factor for mortality, followed by lupus encephalopathy. However, lupus nephritis and hematological abnormalities occurred more frequently in survival patients. SLE patients at a younger age of diagnosis have a poorer prognosis. Infection dominated the causes of death in recent China. Ethnicity and medications might account for the differences in causes of death compared with western populations.

Increased Macroautophagy in Interferon-Gamma-Producing T Cells from Patients with Newly Diagnosed Systemic Lupus Erythematosus.

BACKGROUND: Imbalance of interferon-gamma (IFN-γ), interleukin (IL)-4, and IL-17 producing by T cells is confirmed to contribute to the pathogenesis of systemic lupus erythematosus (SLE). Autophagy is now emerging as a core player in the development and the function of the immune system. Therefore, we investigated the autophagic behavior in IFN-γ-, IL-4-, and IL-17-producing T cells from patients with SLE. METHODS: Thirty patients with SLE and 25 healthy controls matched for gender and age were recruited between September 2016 and May 2017. The autophagic levels in IFN-γ+ T cells, IL-4+ T cells, and IL-17+ T cells from patients with newly diagnosed SLE and healthy controls were measured using flow cytometry. The plasma levels of IFN-γ were determined by enzyme-linked immunosorbent assay in SLE patients and healthy controls. Unpaired t-tests and the nonparametric Mann-Whitney U-test were used to compare data from patients with SLE and controls. Spearman's rank correlation coefficient was applied for calculation of the correlation between parallel variables in single samples. RESULTS: Our results showed increased percentage of autophagy in IFN-γ+ T cells from patients with SLE and healthy controls ([8.07 ± 2.72]% vs. [3.76 ± 1.67]%, t = 5.184, P < 0.001), but not in IL-4+ T cells or IL-17+ T cells (P > 0.05) as compared to healthy donors. Moreover, the plasma levels of IFN-γ in SLE patients were significantly higher than those in healthy controls ([68.9 ± 29.1] pg/ml vs. [24.7 ± 17.6] pg/ml, t = 5.430, P < 0.001). Moreover, in SLE patients, the percentage of autophagy in IFN-γ+ T cells was positively correlated with the plasma levels of IFN-γ (r = 0.344, P = 0.046), as well as the disease activity of patients with SLE (r = 0.379, P = 0.039). CONCLUSION: The results indicate that autophagy in IFN-γ+ T cells from SLE patients is activated, which might contribute to the persistence of T cells producing IFN-γ, such as Th1 cells, and consequently result in the high plasma levels of IFN-γ, and then enhance the disease activity of SLE.

Investigation of C1-complex regions reveals new C1Q variants associated with protection from systemic lupus erythematosus, and affect its transcript abundance.

Although rare variant C1Q deficiency was identified as causative risk for systemic lupus erythematosus (SLE), there are limited and inconsistent reports regarding the common polymorphisms of C1Q genes in SLE susceptibility. Furthermore, there are no reports concerning polymorphisms of C1S, C1R, and C1RL and whether they confer susceptibility to SLE. We therefore evaluated 22 SNPs across six C1-complex genes in two independent case-control cohorts, and identified four novel SNPs that confer protection from SLE. The four SNPs are all located in C1Q. Particularly, the variant rs653286 displayed an independent reduced risk on SLE susceptibility (OR 0.75, P = 2.16 × 10-3) and anti-dsDNA antibodies (OR 0.68, P = 0.024). By bioinformatics analysis, SNPs rs653286 and rs291985 displayed striking cis-eQTL effects on C1Q genes expression. Individuals homozygous for the 'protective' allele at four SNPs had significantly higher levels of serum C1q (rs680123-rs682658: P = 0.0022; rs653286-rs291985: P = 0.0076). To our knowledge, this is the first study to demonstrate that only C1Q polymorphisms are associated with SLE. The C1Q SNP rs653286 confers an independent protective effect on SLE susceptibility and affects transcript abundance.

[The prevalence of hyperuricemia in Xinjiang Kazaks in Fuhaii].

OBJECTIVE: to investigate the prevalence of hyperuricemia (HUA) in Kazak population in Fuhai county, Xinjiang Aletai area. METHODS: A randomized cross-sectional cluster sampling was performed in Kazaks in Fuhai. A total of 2 006 inhabitants were investigated by household survey. The questionnaires were completed for general performance, past illness history and family history. Height, weight, body mass index (BMI) and blood pressure were measured and recorded. Fasting blood samples were collected for serum uric acid and other biochemical tests. RESULTS: (1) Finally a total of 1 921 Kazaks were enrolled.The serum uric acid level in Kazaks in Fuhai was (234.11 ± 82.56) µmol/L, which was higher in males (287.48 ± 80.27) µmol/L than that in females (201.03 ± 64.74) µmol/L (P<0.001). In HUA group, serum uric acid level also had significant difference between men and women [(498.93 ± 130.24) µmol/L vs (471.20 ± 167.71) µmol/L; P = 0.044]. (2) the prevalence of HUA in Kazak general population was 2.97% (57/1 921). After standardization according to the natural population survey in 2000, the standardized rate in general group was 3.34%, in which the prevalence in males (4.76%, standardized rate was 4.64%) was higher than that in females (1.85%, standardized rate was 1.88%) (P<0.001). There was no gout found in 1 921 Kazaks in Fuhan when the study was taken. (3) Cohorts of different dietary habit ( who preferred vegetable or meat or both of them) who had drunk alcohol had higher prevalence of HUA than those with vegetarian diet without alcohol group (7.69%, 5.66% and 5.62% vs 0.78%; all P<0.05). The cohort who preferred animal food with alcohol had higher prevalence of HUA than those who preferred both vegetable and meat without alcohol (5.66% vs 5.62%; P = 0.009). (4) In men, those who had stable career, higher degree of education, regular income, had higher HUA prevalence than other cohorts. (5) people with HUA had higher plasma TC, TG, LDL, urea nitrogen and creatinine levels, higher blood pressure and higher BMI. CONCLUSION: The prevalence of HUA was lower in Kazaks in Fuhai than that in other areas in China. Males were more susceptible to HUA. diet, occupation, educational background and economic status may partly affect the prevalence of HUA in Kazaks in Fuhai.

Impact of the leucocyte immunoglobulin-like receptor A3 (LILRA3) on susceptibility and subphenotypes of systemic lupus erythematosus and Sjögren's syndrome.

BACKGROUND: Recently, our research group identified the non-deleted (functional) leucocyte immunoglobulin-like receptor A3 (LILRA3) as a new genetic risk for rheumatoid arthritis. OBJECTIVES: To further investigate whether the functional LILRA3 is a new susceptibility factor for other autoimmune diseases-for example, systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS). METHODS: The LILRA3 deletion polymorphism and its tagging single nucleotide polymorphism rs103294 were genotyped for 1099 patients with SLE, 403 patients with pSS and 2169 healthy controls. Association analyses were performed in whole dataset or clinical/serological subsets. The impact of LILRA3 on SLE activity and LILRA3 expression was evaluated. RESULTS: The functional LILRA3 conferred high susceptibility to both SLE (p=3.51×10(-7), OR=2.03) and pSS (p=1.40×10(-3), OR=2.32). It was associated with almost all the clinical/serological features in SLE, especially with leucopenia (p=4.09×10(-7), OR=2.19) and thrombocytopenia (p=1.68×10(-5), OR=1.70). In pSS, functional LILRA3 was specifically associated with leucopenia (p=4.39×10(-4), OR=3.25), anti-Ro/SSA-positive subphenotypes (p=4.54×10(-3), OR=2.34) and anti-La/SSB-positive subphenotypes (p=0.012, OR=2.49). Functional LILRA3 conferred higher disease activity in patients with SLE (p=0.044) and higher LILRA3 expression in both SLE (p=5.57×10(-8)) and pSS (p=1.49×10(-7)) than in controls. CONCLUSIONS: Functional LILRA3 is a new susceptibility factor for SLE and pSS. It highly predisposes to certain phenotypes such as leucopenia and thrombocytopenia in SLE, and may confer increased disease activity in SLE and a higher risk of leucopenia and autoantibody-positive subphenotypes in pSS.