Phenome-wide association study identifies dsDNA as a driver of major organ involvement in systemic lupus erythematosus

[Update on lupus nephritis]

In addition to the butterfly rash, lupus nephritis is the most specific manifestation of systemic lupus erythematosus (SLE). The perspective on this organ manifestation has fundamentally changed as well as the manifestation of SLE itself 40 years after the first multicenter clinical study on lupus nephritis. Even if there is a faint glimpse of hope of a cure, there is still the fight against the problem of nonresponders and also the progressive loss of organ function. This update gives an overview of the current importance of lupus nephritis in the context of the whole SLE disease, of the special features and on the options provided by the new diagnostic and therapeutic developments.

Phenotype Risk Score but Not Genetic Risk Score Aids in Identifying Individuals With Systemic Lupus Erythematosus in the Electronic Health Record

OBJECTIVE

Systemic lupus erythematosus (SLE) poses diagnostic challenges. We undertook this study to evaluate the utility of a phenotype risk score (PheRS) and a genetic risk score (GRS) to identify SLE individuals in a real-world setting.

METHODS

Using a de-identified electronic health record (EHR) database with an associated DNA biobank, we identified 789 SLE cases and 2,261 controls with available MEGAEX genotyping. A PheRS for SLE was developed using billing codes that captured American College of Rheumatology SLE criteria. We developed a GRS with 58 SLE risk single-nucleotide polymorphisms (SNPs).

RESULTS

SLE cases had a significantly higher PheRS (mean ± SD 7.7 ± 8.0 versus 0.8 ± 2.0 in controls; P < 0.001) and GRS (mean ± SD 12.2 ± 2.3 versus 11.0 ± 2.0 in controls; P < 0.001). Black individuals with SLE had a higher PheRS compared to White individuals (mean ± SD 10.0 ± 10.1 versus 7.1 ± 7.2, respectively; P = 0.002) but a lower GRS (mean ± SD 9.0 ± 1.4 versus 12.3 ± 1.7, respectively; P < 0.001). Models predicting SLE that used only the PheRS had an area under the curve (AUC) of 0.87. Adding the GRS to the PheRS resulted in a minimal difference with an AUC of 0.89. On chart review, controls with the highest PheRS and GRS had undiagnosed SLE.

CONCLUSION

We developed a SLE PheRS to identify established and undiagnosed SLE individuals. A SLE GRS using known risk SNPs did not add value beyond the PheRS and was of limited utility in Black individuals with SLE. More work is needed to understand the genetic risks of SLE in diverse populations.

The diagnostic and prognostic value of D-dimer in different types of aortic dissection

Objective

To evaluate the serum D-dimer level and its diagnostic and prognostic predictive value in patients with different types of aortic dissection.

Methods

Eighty-four aortic dissection patients who were diagnosed clinically in our hospital from January 2017 to January 2021 were selected for the study. All patients were divided into Stanford type A (39 cases) and Stanford type B (45 cases) groups. The serum D-dimer level was detected at 1 h, 6 h, 12 h, 24 h, and 72 h after admission to the hospital, and its expression level with different types of aortic dissection was analyzed. The relationship between D-dimer and the prognosis of patients was also analyzed.

Results

The serum D-dimer levels of patients in group A were significantly higher than those in group B at 6 h, 12 h, 24 h, and 72 h after admission, and the differences were statistically significant. In group A, 16 patients died, and 23 patients survived, while in group B, 18 patients died, and 27 patients survived. The serum D-dimer level of the dead and surviving patients in group A was significantly higher than that of group B, and the serum D-dimer level of dead patients in groups A and B was significantly higher than that of surviving patients. For diagnostic value, the AUC was 0.89, sensitivity was 76.92%, specificity was 90.00% in group A, and the AUC was 0.82, sensitivity was 71.11%, and specificity was 85.00% in group B. For the prognostic predicted value, the AUC was 0.74 in group A, while the AUC was 0.69 in group B.

Conclusions

D-dimer has different serum levels in different types of aortic dissection patients, with higher levels in Stanford A. Serum D-dimer levels may be used as a better biomarker to diagnose the two types of aortic dissection and play an important role in patient prognostic prediction, especially Stanford type A.

Diagnostic, predictive and prognostic biomarkers in systemic lupus erythematosus: current insights

PURPOSE OF REVIEW

Biomarkers for diagnosis, monitoring and prognosis still constitute an unmet need for systemic lupus erythematosus (SLE). Focusing on recent findings, this review summarises the current landscape of biomarkers in lupus.

RECENT FINDINGS

Urine activated leukocyte cell adhesion molecule (ALCAM) exhibited good diagnostic ability in SLE and lupus nephritis (LN) whereas cerebrospinal fluid neutrophil gelatinase-associated lipocalin (NGAL) showed promise in neuropsychiatric SLE. Urine ALCAM, CD163 and vascular cell adhesion molecule 1 (VCAM-1) may be useful in surveillance of LN. Urine monocyte chemoattractant protein 1 was found to predict treatment response in SLE, and urine CD163 and NGAL treatment response in LN. Serum complement component 3 (C3) and urinary VCAM-1 have been reported to portend long-term renal prognosis in LN.

SUMMARY

NGAL holds promise as a versatile biomarker in SLE whereas urine ALCAM, CD163 and VCAM-1 displayed good performance as biomarkers in LN. The overall lack of concerted corroboration of leading candidates across multiple cohorts and diverse populations leaves the current biomarker landscape in SLE in an urgent need for further survey and systematic validation.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Characterizing the Clinical and Genetic Spectrum of Polycystic Ovary Syndrome in Electronic Health Records

CONTEXT

Polycystic ovary syndrome (PCOS) is one of the leading causes of infertility, yet current diagnostic criteria are ineffective at identifying patients whose symptoms reside outside strict diagnostic criteria. As a result, PCOS is underdiagnosed and its etiology is poorly understood.

OBJECTIVE

We aim to characterize the phenotypic spectrum of PCOS clinical features within and across racial and ethnic groups.

METHODS

We developed a strictly defined PCOS algorithm (PCOSkeyword-strict) using the International Classification of Diseases, ninth and tenth revisions and keywords mined from clinical notes in electronic health records (EHRs) data. We then systematically relaxed the inclusion criteria to evaluate the change in epidemiological and genetic associations resulting in 3 subsequent algorithms (PCOScoded-broad, PCOScoded-strict, and PCOSkeyword-broad). We evaluated the performance of each phenotyping approach and characterized prominent clinical features observed in racially and ethnically diverse PCOS patients.

RESULTS

The best performance came from the PCOScoded-strict algorithm, with a positive predictive value of 98%. Individuals classified as cases by this algorithm had significantly higher body mass index (BMI), insulin levels, free testosterone values, and genetic risk scores for PCOS, compared to controls. Median BMI was higher in African American females with PCOS compared to White and Hispanic females with PCOS.

CONCLUSIONS

PCOS symptoms are observed across a severity spectrum that parallels the continuous genetic liability to PCOS in the general population. Racial and ethnic group differences exist in PCOS symptomology and metabolic health across different phenotyping strategies.