Biomarkers, kits and uses thereof for assessing immune dysfunction in hiv-infected individuals

Abstract

This invention relates to the biomedical field, specifically to biomarkers, kits, and their applications for assessing immune dysfunction in HIV-infected individuals. This invention discloses the application of the molecular marker SMPD1 and / or substances that detect said molecular marker in the assessment, auxiliary assessment, screening, and risk stratification of immune dysfunction in HIV-infected patients with hyperlipidemia. This invention is the first to identify an immunometabolic paralysis subtype in HIV-infected individuals with hyperlipidemia. SMPD1 expression was significantly upregulated in these subtype patients, and its high expression was significantly associated with the risk of immune reconstitution failure. A combined diagnostic model based on SMPD1 and triglycerides demonstrated excellent diagnostic efficacy. Therefore, substances that detect SMPD1 can be used for the assessment and risk stratification of immune dysfunction in HIV-infected patients with hyperlipidemia. This invention provides an effective tool for the early identification and risk stratification of these patients and has broad clinical application prospects.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, and more specifically to biomarkers, kits, and their applications for assessing immune dysfunction in HIV-infected individuals. Background Technology

[0002] The advent of combination antiretroviral therapy (cART) has fundamentally changed the course of HIV infection, transforming a once fatal diagnosis into a manageable chronic disease. However, the surge in non-AIDS-defined comorbidities has cast a shadow over this clinical success, with metabolic disorders—particularly HIV-associated hyperlipidemia (HLP)—becoming a widespread challenge affecting up to 50% of the treatment population. Despite sustained viral suppression, a significant proportion of HLP patients fail to achieve adequate immune reconstitution, remaining in a state of “immune non-response” characterized by persistent inflammation and functional T-cell depletion. Current treatment paradigms, primarily relying on statins or fibrates, while successfully lowering circulating lipids, often fail to salvage underlying immune capacity. This clinical disconnect suggests that HLP is not merely a biochemical imbalance but may be driving an unrecognized immune dysfunction.

[0003] Monocytes, as a key interface between systemic metabolism and immune surveillance, have impaired adaptability in the context of chronic dyslipidemia. While the concept of "metabolic inflammation" links lipid accumulation to systemic immune activation, the precise molecular mechanisms by which specific lipid types hijack monocyte signaling remains a critical knowledge gap. Furthermore, the clinical heterogeneity of HLP poses a significant obstacle; treating all hyperlipidemic patients as a homogeneous cohort masks the existence of different molecular subtypes, some of which may be driven by specific, targetable metabolic checkpoints. Currently, clinical assessment of the immune status of HIV-infected individuals mainly relies on CD4+ T cell count and viral load, but these indicators are insufficient to reveal underlying mechanisms of immune dysfunction. Traditional lipid tests such as total cholesterol (TC) and triglycerides (TG) reflect lipid levels but cannot directly correlate with immune status, let alone identify high-risk subtypes. Therefore, exploring diagnostic biomarkers that can identify and predict the risk of immune dysfunction in HIV-infected individuals at an early stage, especially targeting potentially high-risk subtypes in hyperlipidemic populations, is of significant clinical importance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies, especially for high-risk subtypes that may exist in people with hyperlipidemia, in the lack of diagnostic biomarkers that can identify and predict the risk of immune dysfunction in HIV-infected individuals at an early stage, and to provide a biomarker, kit and application for assessing immune dysfunction in HIV-infected individuals.

[0005] The aforementioned assessment of immune dysfunction in HIV-infected individuals refers to the identification and prediction of the risk of immune reconstitution failure in HIV-infected individuals, particularly a pathological state characterized by monocyte metabolic disorders and loss of T-cell support function in HIV-infected individuals with hyperlipidemia.

[0006] Through transcriptomic analysis of peripheral blood mononuclear cells from HIV-infected individuals, the inventors of this application have for the first time identified a unique molecular subtype in HIV-related hyperlipidemia. This subtype exhibits the aforementioned pathological features, with its core characteristic being "immunometabolic paralysis," and is hereinafter referred to as the immunometabolic paralysis subtype.

[0007] Patients with the immunometabolic paralysis subtype face a high risk of immune reconstitution failure despite antiretroviral therapy, manifested by difficulty in effectively restoring CD4+ T cell counts. Identifying this subtype has significant clinical value for accurate stratification and early intervention in HIV-positive patients with hyperlipidemia.

[0008] The inventors conducted in-depth analysis of transcriptome data from individuals with and without the immunometabolic paralysis subtype, and used various bioinformatics methods to screen for the core molecules most relevant to this subtype, ultimately identifying SMPD1 as the key gene for the immunometabolic paralysis subtype.

[0009] In an independent validation cohort, SMPD1 expression levels showed a strong positive correlation with serum triglycerides (R=0.72) and could significantly distinguish this subtype from non-SMPD1 individuals. Therefore, SMPD1 can serve as a biomarker for identifying immunometabolic paralysis subtypes. Based on this, the inventors completed the following invention.

[0010] A first aspect of the present invention is to provide a molecular marker and / or a substance for detecting the molecular marker in any of the following applications: A1) Application in assessing immune dysfunction in HIV-positive patients with hyperlipidemia or in preparing products for assessing immune dysfunction in HIV-positive patients with hyperlipidemia; A2) Application in the auxiliary assessment of immune dysfunction in HIV-positive patients with hyperlipidemia or in the preparation of products for the auxiliary assessment of immune dysfunction in HIV-positive patients with hyperlipidemia; A3) Application in screening for immune dysfunction in HIV-positive patients with hyperlipidemia or in the preparation of products for screening for immune dysfunction in HIV-positive patients with hyperlipidemia; A4) Application in risk stratification of immune dysfunction in HIV patients with hyperlipidemia or in the preparation of products for risk stratification of immune dysfunction in HIV patients with hyperlipidemia; A5) Application in assessing the risk of immune reconstitution failure in HIV patients with hyperlipidemia or in the preparation of products for assessing the risk of immune reconstitution failure in HIV patients with hyperlipidemia; The molecular marker is SMPD1 (acid sphingomyelinase).

[0011] Furthermore, the substances used to detect the molecular markers include reagents and / or instruments for detecting the molecular markers using reverse transcription-polymerase chain reaction, real-time quantitative PCR, transcriptome sequencing, Northern blot, in situ hybridization, gene chip technology, enzyme-linked immunosorbent assay, chemiluminescent immunoassay, immunoturbidimetry, or Western blotting.

[0012] Furthermore, the substance used to detect the molecular marker is a reagent and / or instrument for detecting SMPD1 expression levels. The reagent for detecting SMPD1 expression levels includes reagents for detecting the mRNA expression level of SMPD1 and / or reagents for detecting the protein expression level of SMPD1.

[0013] Furthermore, the reagents for detecting SMPD1 mRNA expression include oligonucleotide probes targeting the SMPD1 mRNA sequence and PCR primers targeting SMPD1 mRNA; the reagents for detecting SMPD1 protein expression include SMPD1-specific antibodies, SMPD1-specific nucleic acid aptamers, or SMPD1-targeting molecularly imprinted polymers.

[0014] The probe may be a DNA probe, RNA probe, cDNA probe, or oligonucleotide probe. The antibody may be a monoclonal antibody or a polyclonal antibody.

[0015] A second aspect of the present invention is to provide reagents for detecting SMPD1 expression levels and reagents for detecting triglyceride levels in the preparation of products for risk stratification of immune dysfunction in HIV patients with hyperlipidemia.

[0016] Furthermore, the risk stratification is based on a diagnostic model that integrates SMPD1 expression levels and serum triglyceride levels. The diagnostic model is a nomogram model with an AUC of 0.867 in the validation cohort.

[0017] A third aspect of the present invention is to provide a kit for assessing immune dysfunction in HIV patients with hyperlipidemia, the kit comprising reagents for detecting SMPD1 expression levels.

[0018] Furthermore, the kit also includes reagents for detecting triglyceride levels.

[0019] Furthermore, the kit also includes a computer-readable medium containing a risk stratification model that performs risk assessment based on SMPD1 expression levels and triglyceride levels.

[0020] The test samples for the kit can be blood samples (such as whole blood, plasma, serum), tissue samples, peripheral blood mononuclear cell samples, etc., but are not limited to these. The various reagent components of the kit can be present in separate containers, or can be pre-assembled into a reagent mixture, either wholly or partially.

[0021] A fourth aspect of the present invention is to provide a system for assessing immune dysfunction in HIV-positive patients with hyperlipidemia, the system comprising a detection device and an assessment device; the detection device is used to detect the expression level of SMPD1 in a sample; the assessment device is used to assess or risk-stratify the immune dysfunction in HIV-positive patients with hyperlipidemia based on the expression level of SMPD1, or based on the expression level of SMPD1 and serum triglyceride levels.

[0022] Furthermore, the detection device includes a device for running a PCR program, a high-throughput sequencing platform, an enzyme-linked immunosorbent assay (ELISA) reader, a chemiluminescence analyzer, or an immunoturbidimetric analyzer.

[0023] Furthermore, the assessment device includes a computer-readable storage medium storing a computer program that, when executed, performs a risk assessment based on SMPD1 expression levels or based on a diagnostic model integrating SMPD1 expression levels and serum triglyceride levels.

[0024] A fifth aspect of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements a method for assessing the risk of immune dysfunction in HIV-positive patients with hyperlipidemia based on SMPD1 expression levels or based on a diagnostic model integrating SMPD1 expression levels and serum triglyceride levels.

[0025] A sixth aspect of the present invention provides a method for assessing immune dysfunction in HIV patients with hyperlipidemia, the method comprising: obtaining a test sample from the subject, detecting the expression level of SMPD1 in the test sample, and assessing whether the subject has a risk of immune dysfunction based on the expression level of SMPD1.

[0026] Furthermore, the method also includes detecting triglyceride levels in the sample to be tested and performing risk stratification based on a diagnostic model that integrates SMPD1 expression levels and serum triglyceride levels.

[0027] In the above method, the subject may include an HIV patient with hyperlipidemia.

[0028] The products described in this article include, but are not limited to, reagents, kits, chips, test strips, test cards, high-throughput sequencing platforms, or biosensors.

[0029] The significant advantages of this invention are: This invention provides a novel biomarker, SMPD1, for the diagnosis of immune dysfunction in HIV patients with hyperlipidemia. By detecting the expression level of SMPD1 in subjects, or by detecting it in combination with triglycerides, HIV-infected individuals at risk of immune dysfunction can be identified. This invention can help guide the early intervention of immune dysfunction in HIV patients with hyperlipidemia and has broad clinical application prospects. Attached Figure Description

[0030] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0031] Figure 1 Principal component analysis plots of samples from the HIV group and the HIVHLP group; Figure 2 A consistency matrix diagram for HIVHLP group samples when the number of clusters k=2; Figure 3 Transcriptome expression heatmaps for two molecular subtypes (cluster 1 and cluster 2) of the HIVHLP group; Figure 4 Transcriptome switch heatmap for two molecular subtypes of the HIV HLP group (HLP_C1, HLP_C2); Figure 5 The expression differences of SMPD1, TRIM3, and PIK3R2 genes between the immunometabolic paralysis subtype (cluster 2) and the compensatory subtype (cluster 1) in the HIVHLP group; Figure 6 ssGSEA analysis of monocyte abundance in the immunometabolic paralysis subtype and compensatory subtype of the HIVHLP group. Figure 7 A graph showing the enrichment analysis of the GSEA pathway between two molecular subtypes of the HIVHLP group; Figure 8 The image shows the results of WGCNA soft threshold screening. Figure 9 Figure showing the construction results of the WGCNA co-expression gene module; Figure 10 A graph showing the association between the WGCNA module and clinical traits; Figure 11 A graph showing the importance of features in Elastic Net regression. Figure 12 This is a graph showing the feature weights of a Support Vector Machine (SVM). Figure 13 The diagram shows the validation results of the pivot gene for SMPD1. Figure 14 The intersection analysis diagram of WGCNA hub genes, Elastic Net features, and SVM-RFE features shows that SMPD1 was finally identified as the consensus key regulatory factor. Figure 15 The results of the correlation analysis between SMPD1 gene expression level and serum triglyceride (TG) level in the cohort were shown in the figure. Figure 16 The results of the correlation analysis between PIK3R2 gene expression level and CD4+ T cell count in the cohort were shown in the figure. Figure 17 A graph showing the expression differences of downstream effector molecules (BCL2, NFKB1) between the immunometabolic paralysis subtype and the compensatory subtype. Figure 18 A comparison of SMPD1 mRNA expression levels in independent validation cohorts; Figure 19 A comparison of SMPD1 protein expression levels in CD14+ monocytes in an independent validation cohort; Figure 20 A comparison of SMPD1 expression levels between patients with immunometabolic paralysis subtypes and healthy controls in an independent validation cohort; Figure 21 The ROC curve for SMPD1 single-index diagnosis of immunometabolic paralysis subtype in the independent validation cohort is shown, with AUC=0.8244. Figure 22 This is a nomogram model of a diagnosis constructed based on SMPD1 expression levels and serum triglyceride levels. Figure 23 The ROC curve for the diagnostic nomogram model shows AUC = 0.867; Figure 24 Decision curve analysis (DCA) plot for diagnostic nomograms. Detailed Implementation

[0032] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] The terms "first," "second," and "third" used in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include multiple such features. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific posture (as shown in the figures). If the specific posture changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to these processes, methods, products, or devices.

[0034] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in multiple embodiments of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0035] Discovery of the "Immune Metabolic Paralysis" Subtype To investigate whether different molecular subtypes exist in individuals with HIV-related hyperlipidemia, the inventors first performed transcriptomic analysis on peripheral blood mononuclear cells (PBMCs) from HIV-infected individuals. The discovery set cohort consisted of 16 HIV-infected individuals from the Department of Infectious Diseases and the Department of Cardiology at the 416 Hospital of the Nuclear Industry, including 8 individuals with hyperlipidemia (HIVHLP group) and 8 individuals without hyperlipidemia (HIV group). Hyperlipidemia was defined according to the NCEP ATP III guidelines as serum triglycerides ≥ 150 mg / dL. After collecting peripheral blood samples, peripheral blood mononuclear cells (PBMCs) were separated using Ficoll-Paque density gradient centrifugation for subsequent analysis.

[0036] Transcriptome sequencing was performed on PBMC samples from the study cohort to obtain gene expression profile data. For example... Figure 1 As shown, principal component analysis revealed a significant transcriptomic layer within the HLP population. Further analysis using consistent clustering (...) Figure 2(k=2) The samples were clearly divided into two main subgroups: one with transcriptomic characteristics similar to the HIV control group, called the "compensated" subgroup (cluster 1); the other showing a significant deviation in gene expression profile, called the "severe" subgroup (cluster 2). Hierarchical clustering heatmap ( Figure 3 This visually demonstrates the differentially expressed transcriptomic features between the two subtypes, and uses a "transcriptome switch" mode ( Figure 4 The biological robustness of the clustering results was verified. This invention names this "severe" subgroup the "immunometabolic paralysis" subtype.

[0037] Further comparative analysis of the two subtypes revealed that in the immunometabolic paralysis subtype (cluster 2), SMPD1 (acid sphingomyelinase) and E3 ubiquitin ligase TRIM3 were significantly upregulated, while PIK3R2 (a key regulatory subunit of the PI3K survival pathway) was significantly downregulated. Figure 5 Immune cell abundance analysis using ssGSEA showed that the abundance of monocytes in cluster 2 was not significantly reduced (P>0.05), indicating that the observed loss of PIK3R2 and survival signals represented a state of "functional paralysis" rather than cell exhaustion. Figure 6 Gene set enrichment analysis (GSEA) further confirmed that the "sphingolipid metabolism" and "necroptosis" pathways were significantly positively enriched in cluster 2 (NES>0), while the "PI3K-Akt signaling pathway" was significantly inhibited (NES<0). Figure 7 ).

[0038] In summary, these data redefine the HLP landscape, identifying a specific subtype driven by SMPD1-mediated metabolic stress and PI3K-dependent immune surveillance collapse. Identifying this subtype has significant clinical value for precise stratification and early intervention in HIV-positive patients with hyperlipidemia.

[0039] The following will provide a detailed explanation of the screening and diagnostic efficacy verification process of SMPD1 as a biomarker for the "immunometabolic paralysis" subtype through specific embodiments and in conjunction with the accompanying drawings.

[0040] Example 1: Screening of molecular markers for diagnosing or assisting in the diagnosis of immune dysfunction in HIV-infected individuals. 1.1 Data Source The data used in this embodiment came from the transcriptome sequencing data of the aforementioned discovery set research cohort. All participants met the corresponding inclusion and exclusion criteria and signed informed consent forms.

[0041] 1.2 Weighted Gene Co-expression Network Analysis (WGCNA) Scale-free co-expression networks were constructed using transcriptomic data from the discovery set cohort using the WGCNA R package. First, a soft threshold power β=3 was chosen to achieve a scale-free topology fit index >0.9.

[0042] Subsequently, a dynamic tree slicing algorithm was used to group genes into color-coded modules, with a minimum module size of 30. Module-trait relationships were evaluated by associating module feature genes with clinical traits (serum triglycerides and CD4+ T cell count).

[0043] 1.3 Machine Learning Feature Selection Two machine learning algorithms are combined to perform feature selection on the genes in the blue module: (1) Elastic network regression: using the glmnet package, setting α=0.5, and performing 10-fold cross-validation.

[0044] (2) SVM-RFE: Use the caret package to perform 5x cross-validation and iterate 100 times.

[0045] 1.4 Consensus on the Determination of Key Regulatory Factors Intersection analysis was performed between the pivot genes identified by WGCNA and the top features screened by two machine learning models.

[0046] 2. Results Analysis Figure 8 WGCNA soft thresholding results: Scale independence analysis shows that when the soft threshold β=3, the signed R² of the scale-free topology model reaches above 0.8 for the first time; Average connectivity analysis shows that when β=3, the average connectivity of the network remains at a reasonable level, meeting the requirements for building a scale-free network. Therefore, β=3 is selected as the optimal parameter for subsequent co-expression network analysis.

[0047] Figure 9-10 Results of WGCNA module construction and trait association analysis: Based on the optimal soft threshold, a total of 7 co-expressed gene modules were constructed ( Figure 9 ); among which the blue module (MEblue, Figure 10 It showed a significant positive correlation with serum triglycerides (TG) (R=0.77, P=5×10). -4 The count was significantly negatively correlated with CD4+ T cell count (R=-0.84, P=5×10⁻⁶). -5 This suggests that the module may be closely related to lipotoxicity-mediated immune metabolic disorders.

[0048] Figure 11 The results of the feature importance analysis for Elastic Net regression are as follows: SMPD1 is the second most important key feature in the Elastic Net model, with an importance score second only to DHODH.

[0049] Figure 12 The feature weight analysis results for Support Vector Machine (SVM) show that SMPD1 is at the top of the feature weights in the SVM classifier, demonstrating a stable and key regulatory role.

[0050] Figure 13 The results of the SMPD1 pivot gene validation show that SMPD1 has extremely high module membership (kME=0.82, p=3.4e-56) and gene significance (GS=0.88), and is the core pivot gene of the blue module, suggesting that it is the core driver gene of the "immunometabolic paralysis" subtype.

[0051] Figure 14 The results of the intersection analysis of the three methods are as follows: By integrating the screening results of WGCNA hub genes, Elastic Net features, and SVM-RFE features, SMPD1 was finally identified as the consensus key regulatory factor.

[0052] Example 2: Correlation analysis between SMPD1 expression and clinical indicators Based on the discovery set cohort data, correlation analysis was performed between the expression levels of SMPD1 and PIK3R2 and clinical indicators. Gene expression data (FPKM value) and corresponding clinical indicators, including serum triglyceride level (mg / dL) and CD4+ T cell count (cells / μL), were obtained for each subject in the discovery set cohort. The Pearson correlation coefficient was calculated using the cor.test function in R software to assess the linear correlation between gene expression levels and clinical indicators, and the significance P-value was calculated using a t-test.

[0053] Figure 15-16 The correlation analysis results between SMPD1 and PIK3R2 expression levels and clinical indicators are presented. Based on the discovery set cohort data, Pearson correlation analysis was used to assess the linear association between gene expression levels (FPKM value) and clinical indicators. The results showed that SMPD1 gene expression level was significantly and strongly positively correlated with serum triglyceride (TG) level (R=0.98, P<0.001), indicating that a high triglyceride lipotoxic environment may induce a significant upregulation of SMPD1. Figure 15 Meanwhile, PIK3R2 expression levels showed a strong positive correlation with CD4+ T cell counts (R=0.85, P=0.015), suggesting that downregulation of PIK3R2 may be one of the molecular characteristics of reduced CD4+ T cell numbers. Figure 16 ).

[0054] Figure 17Further analysis of downstream effector molecule expression revealed a significant downregulation of the signal transduction mechanism in this subtype. The results showed that the expression of the anti-apoptotic factor BCL2 and the key inflammatory regulator NFKB1 was significantly downregulated. These findings confirm that SMPD1, identified in the study, is not only a core gene selected by WGCNA but also a key regulatory factor highly correlated with the core clinical phenotypes of the "immunometabolic paralysis" subtype (hyperlipidemia and T-cell immunodeficiency), thus demonstrating its potential diagnostic biomarker value.

[0055] Example 3: Independent validation cohort to validate the diagnostic efficacy of molecular biomarkers 1) Research Subjects An independent validation cohort (N=45) was recruited at the Nuclear Industry 416 Hospital, which has no overlap with the discovery set cohort.

[0056] The participants were divided into three groups: Healthy blood donors (Healthy, n=15), HIV-infected individuals with mild lipid profile abnormalities (HIV-Mild, n=15), and HIV-infected individuals with severe hyperlipidemia (HIV-HLP, n=15).

[0057] Hyperlipidemia is defined according to the NCEP ATP III guidelines as serum triglycerides ≥ 150 mg / dL.

[0058] Inclusion criteria: (1) Age 18-65 years old; (2) Confirmed diagnosis of HIV infection; (3) HIV-Mild group: HIV-infected individuals with triglycerides <150 mg / dL; (4) HIV-HLP group: HIV-infected individuals with triglycerides ≥150 mg / dL; (5) Healthy control group: HIV negative, triglycerides <150 mg / dL.

[0059] Exclusion criteria: (1) Active co-infection (HBV, HCV); (2) Malignant tumors; (3) Currently undergoing lipid-lowering therapy.

[0060] 2) RT-qPCR detection of SMPD1 expression Total RNA was extracted from PBMCs using TRIzol reagent, and SMPD1 mRNA expression was quantified by RT-qPCR using the SYBR Green chemical method on an Applied Biosystems 7500 real-time PCR system. Relative expression levels were calculated using the comparative Ct method (2^-ΔΔCt), with GAPDH as an internal control.

[0061] 3) ELISA detection of PIK3R2 protein Serum PIK3R2 protein concentration was measured using a high-sensitivity sandwich ELISA kit, and optical density was measured at 450 nm using an enzyme-linked immunosorbent assay (ELISA) reader.

[0062] 4) Results Analysis Please see Figure 18 In the independent validation cohort, SMPD1 mRNA expression showed a stepwise upregulation, with the highest level observed in the HIV-HLP group (P<0.001). Please see [link to relevant documentation]. Figure 19 SMPD1 protein expression in CD14+ monocytes also showed a stepwise upregulation, reaching the highest level in the HIV-HLP group (P<0.001). These results, at both the mRNA and protein levels, confirmed the high expression of SMPD1 in HIV-positive individuals with hyperlipidemia in an independent validation cohort, consistent with the findings in the discovery set.

[0063] Example 4: SMPD1 Single-Indicator Diagnostic Efficacy Analysis 1) Experimental Methods: The diagnostic efficacy of SMPD1 was evaluated based on the RT-qPCR results of 45 samples in the validation cohort. Whether a sample belonged to an immunometabolic paralysis subtype was used as a binary state variable, and the relative expression level of SMPD1 mRNA was used as a test variable. Receiver operating characteristic (ROC) curves were plotted using R software, and the area under the curve (AUC) was calculated to assess the diagnostic accuracy of SMPD1 as a single indicator for immunometabolic paralysis subtypes. 2) Experimental results: See Figure 20-21 Compared with the healthy control group, the expression level of SMPD1 was significantly upregulated in patients with the immunometabolic paralysis subtype; the area under the ROC curve (AUC) was 0.8244, with a sensitivity of 73.33% and a specificity of 96.67%. The AUC value ranged from 0 to 1, with 0.7 being acceptable performance and 0.9 being excellent performance; indicating that using the expression level of the SMPD1 gene as a diagnostic indicator can effectively determine whether a subject has immunometabolic paralysis.

[0064] Example 5: Diagnostic Model Construction and Validation 1) Experimental Methods: Based on validation set cohort data, a diagnostic nomogram model was constructed to determine whether patients belong to the "immunometabolic paralysis" subtype, using SMPD1 expression level and serum triglyceride level as predictive variables. The model's discriminative ability was evaluated using ROC curves, and the area under the curve (AUC), sensitivity, and specificity were calculated. Simultaneously, decision curve analysis (DCA) was performed to quantify the model's net clinical benefit.

[0065] 2) Results Analysis: This embodiment established a diagnostic nomogram model based on SMPD1 expression levels and serum triglyceride levels, such as... Figure 22 As shown, this model is used to quantify and predict the probability of a patient developing the "immunometabolic paralysis" subtype. The nomogram model combines SMPD1 expression levels and serum triglyceride levels, calculating the likelihood of each patient belonging to the "immunometabolic paralysis" subtype by summing the scores of these two variables.

[0066] Please see Figure 23 Validated by ROC curve analysis, the diagnostic nomogram model achieved an AUC of 0.867, a sensitivity of 85.8%, and a specificity of 80.7%. Please refer to [link to relevant documentation]. Figure 24 Decision curve analysis showed that, within the clinically relevant risk threshold range, the net benefit of this model was higher than that of the "all treatments" or "no treatments" strategies, indicating that it has good clinical applicability.

[0067] The above results demonstrate that the diagnostic nomogram model based on SMPD1 expression levels and serum triglyceride levels can effectively identify the "immunometabolic paralysis" subtype, providing an intuitive and reliable tool for precise stratification and early intervention of immune dysfunction in HIV-positive patients with hyperlipidemia. The single SMPD1 biomarker exhibits extremely high specificity (96.67%), suitable for diagnostic purposes. The diagnostic model combining SMPD1 and triglycerides, while maintaining good specificity (80.7%), significantly improved sensitivity (85.8%), and increased the AUC to 0.867, demonstrating superior overall diagnostic efficacy and suitability for screening and risk stratification in high-risk populations.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. Use of a substance for detecting the expression level of a molecular marker in the manufacture of a product for assessing immune dysfunction in HIV combined with hyperlipidemia, characterized in that, The molecular marker is SMPD1.

2. Use according to claim 1, characterized in that, The immune dysfunction is referred to as immune metabolic paralysis.

3. Use according to claim 1, characterized in that, The substance used to detect the molecular marker is a reagent for detecting the expression level of SMPD1 mRNA or a reagent for detecting the expression level of SMPD1 protein.

4. The application according to claim 3, characterized in that, The reagents are RNA-seq analysis reagents and / or enzyme-linked immunosorbent assay reagents.

5. The application according to claim 3, characterized in that, The reagents include at least one of the following: oligonucleotide probes targeting the SMPD1 coding sequence, PCR primers targeting the SMPD1 coding sequence, SMPD1-specific nucleic acid aptamers, and SMPD1-targeting molecularly imprinted polymers.

6. The application according to claim 1, characterized in that, The product also includes a substance for detecting triglyceride levels, and the assessment includes risk stratification based on a diagnostic model that integrates SMPD1 expression levels and serum triglyceride levels.

7. The application according to claim 5 or 6, characterized in that, The product in question is a reagent kit.

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