Free Light Chains κ and λ as New Biomarkers of Selected Diseases

Coronary health index based on immunoglobulin light chains to assess coronary heart disease risk with machine learning: a diagnostic trial

BACKGROUND

Recent studies suggest a connection between immunoglobulin light chains (IgLCs) and coronary heart disease (CHD). However, current diagnostic methods using peripheral blood IgLCs levels or subtype ratios show limited accuracy for CHD, lacking comprehensive assessment and posing challenges in early detection and precise disease severity evaluation. We aim to develop and validate a Coronary Health Index (CHI) incorporating total IgLCs levels and their distribution. Additionally, we aim to evaluate its effectiveness by integrating patient data and using machine learning models through diagnostic trial.

METHODS

The CHI was developed and combined with other clinical data. Nine machine learning models were screened to identify optimal diagnostic performance, with the XGBoost model emerging as the top performer. Performance was assessed based on accuracy, sensitivity, and its ability to identify severe CHD cases characterized by complex lesions (SYNTAX score > 33).

RESULTS

The XGBoost model demonstrated high accuracy and sensitivity in diagnosing CHD, with an area under the curve (AUC) of 0.927. It also accurately identified patients with severe CHD, achieving an AUC of 0.991. An online web tool was introduced for broader external validation, confirming the model's effectiveness.

CONCLUSIONS

Combining the CHI with the XGBoost model offers significant advantages in diagnosing CHD and assessing disease severity. This approach can guide clinical interventions and improve large-scale CHD screening.

Network-based pharmacology and UHPLC-Q-Exactive-Orbitrap-MS reveal Jinhua Qinggan granule's mechanism in reducing cellular inflammation in COVID-19

Introduction

The outbreak of SARS-CoV-2, leading to COVID-19, poses a major global health threat. While specific treatments and vaccines are under development, Traditional Chinese Medicine (TCM) has historically been effective against pandemics, including viral pneumonias. Our study explores the efficacy and mechanisms of Jinhua Qinggan Granules (JHQG) in treating COVID-19.

Methods

We analyzed JHQG's components using UHPLC-Q-Exactive-Orbitrap-MS, identifying 73 compounds. Network pharmacology and single-cell RNA sequencing (scRNA-seq) were used to assess JHQG's effects on immune cells from peripheral blood mononuclear cells (PBMCs). Literature review supported the antiviral and anti-inflammatory effects of JHQG.

Results

JHQG targets were found to interact with immune cells, including neutrophils, monocytes, plasmablasts, and effector T cells, reducing their overactivation in severe COVID-19. JHQG's modulation of these cells' activity likely contributes to reduced inflammation and improved clinical outcomes.

Discussion

Our findings provide insights into JHQG's mechanism of action, highlighting its potential in controlling the inflammatory response in COVID-19 patients. The study supports the use of JHQG as a safe and effective treatment for COVID-19 and similar viral infections, leveraging its ability to modulate immune cell activity and reduce inflammation.

Effects of aerobic and resistance exercise for 9 months on serum free light chains in type 2 diabetes

Background and aims: Serum polyclonal free light chains (FLCs) levels are associated with overall survival in the general population, reflecting their utility as a biomarker of underlying immune activation and inflammation. Regular exercise is known to ameliorate low-grade inflammation in chronic diseases such as type 2 diabetes; however, the effects of different exercise training modalities on FLCs in adults with type 2 diabetes is unknown. This study investigated the effects of 9-month of aerobic, resistance or combined supervised exercise on serum FLCs in 164 patients with type 2 diabetes (age 58 ± 8 years; 63% female). Methods: 164 participants from the Health Benefits of Aerobic and Resistance Training in individuals with type 2 diabetes trial (HART-D) were randomly assigned to no exercise (n = 27), aerobic exercise alone (n = 41), resistance exercise alone (n = 49), or a combination of aerobic and resistance exercise (n = 47). Fasting serum samples were collected before and after completion of the intervention to quantify changes in kappa and lambda FLCs, and serum creatinine, using commercially-available ELISAs. Results: At baseline, combined kappa and lambda FLCs (FLC sum; calculated as kappa + lambda FLCs) were positively correlated with high-sensitive C-reactive protein (hs-CRP) (r = 0.237, p < 0.05) and fat mass (r = 0.162, p < 0.05), and negatively associated with aerobic fitness (r = -0.238, p < 0.05). While non-exercise controls exhibited an increase in FLCs over the 9-month study, exercise training blunted this increase (Δ FLC sum control arm: 3.25 ± 5.07 mg∙L-1 vs. all exercise arms: -0.252 ± 6.60 mg∙L-1, p < 0.05), regardless of exercise modality. Conclusion: Serum FLCs were associated with physical fitness and body composition in patients with type 2 diabetes. 9-month of exercise training prevented the accumulation of FLCs, regardless of exercise modality. Unlike hs-CRP-which did not change during the trial-serum FLCs may serve as a more sensitive biomarker of chronic low-grade inflammation in this population.

Serum Neurofilament and Free Light Chain Levels in Patients Undergoing Treatment for Chronic Inflammatory Demyelinating Polyneuropathy

Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is an immune-mediated disorder affecting the peripheral nervous system. Despite the established diagnostic criteria, monitoring disease activity and treatment remains challenging. To address this limitation, we investigated serum neurofilament light chain (sNfL) and serum free light chains (sFLCs) as potential biomarkers. A total of 32 CIDP patients undergoing immunoglobulin therapy and 32 healthy controls enrolled in the present study, and agreed to have their blood plasma sNfL and sFLCs analyzed, while CIDP severity was assessed through the modified Rankin Scale (mRS) and the Overall Neuropathy Limitations Scale (ONLS). In line with the immunoglobulin treatment aimed at limiting neuronal damage administered to the majority of patients, sNfL levels did not exhibit significant differences between the two groups. However, CIDP patients showed significantly elevated sFLC and sFLC ratios, while the marker levels did not correlate with the clinical scores. The study confirms the potential of sFLCs as a sensitive biomarker of inflammatory processes in CIDP. Additionally, the present study results regarding neurofilaments strengthen the role of sNfL in monitoring CIDP treatments, confirming the effectiveness of immunoglobulin therapy. Overall, our results demonstrate how combining these markers can lead to better patient characterization for improved treatment.