Increased plasma levels of neuro-related proteins in patients with stress-related exhaustion: A longitudinal study.

Exhaustion disorder (ED) is a stress-related disorder characterized by physical and mental symptoms of exhaustion. Recent data suggest that pathophysiological processes in the central nervous system are involved in the biological mechanisms underlying ED. The aims of this study were to investigate if plasma levels of neuro-related proteins differ between patients with ED and healthy controls, and, if so, to investigate if these differences persist over time. Using the Olink Neuro Exploratory panel, we quantified the plasma levels of 92 neuro-related proteins in 163 ED patients at the time of diagnosis (baseline), 149 patients at long-term follow-up (7-12 years later, median follow-up time 9 years and 5 months), and 100 healthy controls. We found that the plasma levels of 40 proteins were significantly higher in the ED group at baseline compared with the control group. Out of these, the plasma levels of 36 proteins were significantly lower in the ED group at follow-up compared with the same group at baseline and the plasma levels of four proteins did not significantly differ between the groups. At follow-up, the plasma levels of two proteins were significantly lower in the ED group compared with the control group. These data support the hypothesis that pathophysiological processes in the central nervous system are involved in the biological mechanisms underlying ED.

Elevated plasma neurofilament light was associated with multi-modal neuroimaging features in Alzheimer's disease signature regions and predicted future tau deposition.

BACKGROUND: Neurofilament Light (NfL) is a biomarker for early neurodegeneration in Alzheimer's disease (AD). This study aims to examine the association between plasma NfL and multi-modal neuroimaging features across the AD spectrum and whether NfL predicts future tau deposition. METHODS: The present study recruited 517 participants comprising Aß negative cognitively normal (CN-) participants (n = 135), Aß positive cognitively normal (CN +) participants (n = 64), individuals with amnestic mild cognitive impairment (aMCI) (n = 212), and those diagnosed with AD dementia (n = 106). All the participants underwent multi-modal neuroimaging examinations. Cross-sectional and longitudinal associations between plasma NfL and multi-modal neuro-imaging features were evaluated using partial correlation analysis and linear mixed effects models. We also used linear regression analysis to investigate the association of baseline plasma NfL with future PET tau load. Mediation analysis was used to explore whether the effect of NfL on cognition was mediated by these imaging biomarkers. RESULTS: The results showed that baseline NfL levels and the rate of change were associated with Aß deposition, brain atrophy, brain connectome, glucose metabolism, and brain perfusion in AD signature regions (P<0.05). In both Aß positive CN and MCI participants, baseline NfL showed a significant predictive value of elevating tau burden in the left medial orbitofrontal cortex and para-hippocampus (ß = 0.336, P = 0.032; ß = 0.313, P = 0.047). Lastly, the multi-modal neuroimaging features mediated the association between plasma NfL and cognitive performance. CONCLUSIONS: The study supports the association between plasma NfL and multi-modal neuroimaging features in AD-vulnerable regions and its predictive value for future tau deposition.

Plasma alpha B crystallin as potential biomarker for predicting pre-operative seizures in glioma.

PURPOSE: Glioma-associated epilepsy affects a significant proportion of glioma patients, contributing to disease progression and diminished survival rates. However, the lack of a reliable preoperative seizure predictor hampers effective surgical planning. This study investigates the potential of Alpha B crystallin protein (CRYAB) plasma levels as a predictive biomarker for epilepsy seizures in glioma patients. METHODS: Plasma samples were obtained from 75 participants, including 21 glioma patients with pre-operative epilepsy, 14 glioma patients without pre-operative epilepsy, and 21 age- and sex-matched control subjects. Additionally, 11 idiopathic epilepsy patients and 8 intractable epilepsy patients served as positive disease control groups. The study utilized ELISA to accurately quantify the circulating levels of CRYAB in the plasma samples of all participants. RESULTS: The analysis revealed a significant reduction in plasma CRYAB levels in glioma patients with pre-operative epilepsy and idiopathic epilepsy. The receiver operating characteristic (ROC) curve analysis displayed an impressive performance, indicating an AUC of 0.863 (95% CI, 0.810-0.916) across the entire patient cohort. Furthermore, plasma CRYAB levels exhibited a robust diagnostic capability, with an AUC of 0.9135, a sensitivity of 100.0%, and a specificity of 73.68%, effectively distinguishing glioma patients with preoperative epilepsy from those without epilepsy. The Decision Curve Analysis (DCA) underscored the clinical relevance of plasma CRYAB levels in predicting pre-operative epilepsy in glioma. CONCLUSION: The findings imply that the reduced levels of CRYAB may assist in prediction of seizure occurrence in glioma patients, although future large-scale prospective studies are warranted.

The limited role of serum neurofilament light chain in predicting pain severity of patients with diabetic polyneuropathy.

Pain is one of many complaints expressed by patients with diabetic polyneuropathy. However, no objective measure for pain severity has been available. Neurofilament light chains have been widely used for assessing axonal damage in the neuronal system. Hence, we sought to investigate whether neurofilament light chains can serve as a marker reflecting pain severity in diabetic polyneuropathy. We enrolled the patients with diabetic polyneuropathy. Serum concentrations of neurofilament light chain were then measured using a single-molecule array. Pain severity was evaluated using painDETECT and the Brief Pain Inventory. Moreover, laboratory results including, serum creatinine, HbA1c, and glomerular filtration rate. A correlation test was used to analyze each variable. A total of 42 patients were enrolled. Neurofilament light chain levels were unable to reflect current neuropathic pain severity. However, high levels of neurofilament light chain were a significant predictor of poor diabetes control (r = 0.41; p = 0.02) and kidney damage (r = 0.45; p = 0.01). Serum levels of neurofilament light chain could not reflect current pain severity but was strongly associated with kidney dysfunction and poor diabetes control. Other biomarkers that could predict pain severity need to be uncovered.

The role of tobacco smoking in anti-neutrophil cytoplasmic antibody-associated vasculitis: a systematic review.

OBJECTIVES: Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitides (AAV) are a group of systemic pauci-immune necrotising vasculitides involving small vessels, characterised by the presence of specific ANCA autoantibodies directed to leukocyte proteinase 3 (PR3-ANCA) or myeloperoxidase (MPO-ANCA) and subdivided into three clinical entities: granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA) and eosinophilic granulomatosis with polyangiitis (EGPA). The aetiology of AAV is unknown and many genetic, epigenetic and environmental factors have been reported to be involved in pathogenesis. Smoking is widely recognised as a risk factor for the development of many autoimmune diseases, such as rheumatoid arthritis and systemic lupus erythematosus. This systematic review will analyse known data about the role of smoking in the development, clinical presentation and outcome of AAV. METHODS: Articles that examined interactions between tobacco smoking and AAV (GPA, MPA, EGPA) were included. All articles selected were in English. No limitation on publication date was established. Case reports were excluded. The systematic search was performed using PubMed/Medline and Cochrane Library databases. RESULTS: The search provided a total of 131 articles. Three studies were added, obtained from the review of the reference lists of articles. 70 were removed because they were duplicated or written in languages other than English. The title and abstract of 64 articles were screened. Of these, 30 were excluded as the title and/or abstract did not meet the inclusion criteria. Thus, 34 remained for full-text review, of which 8 were excluded. 26 articles were therefore included in this review. The role of smoking in AAV development is unclear. AAV patients current smoking appear appear to be younger and more frequently males, with a lower prevalence of EGPA and MPA than GPA. Ever smokers show higher relapse rate. Smoking seems to be associated with a higher risk of cardiovascular events during follow-up. Smokers incur an increased risk of infections. Finally, many data support smoking as a risk factor for end stage renal disease and mortality in AAV patients. CONCLUSIONS: Current data support the hypothesis that smoking influences prevalence, clinical phenotype and prognosis of ANCA-associated vasculitis. However, further studies are required to fully determine its role.

Linear isomer but not branched isomers of perfluorooctane sulfonate in plasma is associated with eicosapentaenoic acid, a seafood consumption biomarker.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are global contaminants. Seafood consumption is a possible PFAS exposure route to humans while the isomer specific analysis has not been conducted. METHODS: Perfluorooctane sulfonate (PFOS), perfluoroheptane sulfonate (PFHpS) and perfluorohexane sulfonate (PFHxS) were investigated in residents of Kyoto, Japan (n = 51). The relationship between plasma PFAS and seafood consumption biomarker, the ratio of eicosapentaenoic acid to arachidonic acid (EPA/AA) was examined by multiple regression analysis. RESULTS: Linear PFOS concentrations showed a significant positive correlation with the EPA/AA ratio in plasma samples (ß = 6.80, p = 0.0014). Linear PFHpS was marginally associated with EPA/AA ratio (ß = 0.178, p = 0.0874). Branched PFOS isomers and PFHxS had no associations with EPA/AA ratios. CONCLUSION: Seafood intake may be a significant exposure pathway for PFAS, such as PFOS but the isomers differ.

Prognostic value of serum immunoglobulin M levels in patients with acute coronary syndrome.

BACKGROUND AND AIMS: The immuno-inflammatory response is a crucial early step in the development of acute coronary syndrome (ACS). In this study, we investigated whether immunoglobulin M (IgM) in the body's initial immune response can predict the prognosis of patients with ACS. METHODS: This prospective cohort study enrolled 1556 ACS patients at Beijing Hospital between March 2017 and October 2020. All patients underwent coronary angiography (CAG). The serum IgM concentration and biochemical indicators were evaluated prior to CAG. The primary endpoint was the composite endpoint of major adverse cardiovascular and cerebrovascular events (MACCEs). Multivariate Cox proportional hazards models was used to explore the association between IgM levels and the endpoint. RESULTS: The average serum IgM levels of the population was 61.3 (42.6-88.4) mg/dL. During the median follow-up period of 55 months, 150 MACCEs occurred. Kaplan-Meier analysis showed that low serum IgM levels were associated with occurrence of MACCEs (log-rank p = 0.009). Univariate Cox proportional hazards models showed that low serum IgM (≤78.05 mg/dL) was associated with MACCEs (hazard ratio (HR) 1.648, 95 % confidence interval (CI): 1.129-2.406, p = 0.010). In patients with IgM ≤78.05 mg/dL, the HR for partially adjusted MACCEs events was 1.576 (95 % CI: 1.075-2.310) and 1.930 (95 % CI: 1.080-3.449) after adjusting for multiple covariates. The subgroup analysis showed that for patients in ≤24 BMI, never smoking and non-dyslipidemia subgroup, the lower serum IgM levels was significantly associated with the risk of MACCEs (pinteraction < 0.001, pinteraction = 0.037, pinteraction = 0.024, respectively). CONCLUSIONS: Low serum IgM levels was independently associated with MACCEs in ACS patients, especially for patients without obesity, smoking and dyslipidemia.

Label-Free and Ultrasensitive Detection of Cartilage Acidic Protein 1 in Osteoarthritis Using a Single-Walled Carbon Nanotube Field-Effect Transistor Biosensor.

Osteoarthritis (OA), a prevalent degenerative joint disease, significantly affects the well-being of afflicted individuals and compromises the standard functionality of human joints. The emerging biomarker, Cartilage acidic protein 1 (CRTAC1), intricately associates with OA initiation and serves as a prognostic indicator for the trajectory toward joint replacement. However, existing diagnostic methods for CRTAC1 are hampered by the limited abundance, thus restricting the precision and specificity. Herein, a novel approach utilizing a single-walled carbon nanotube field-effect transistor (SWCNTs FET) biosensor is reported for the direct label-free detection of CRTAC1. High-purity semiconducting carbon nanotube films, functionalized with antibodies of CRTAC1, provide excellent electrical and sensing properties. The SWCNTs FET biosensor exhibits high sensitivity, notable reproducibility, and a wide linear detection range (1 fg/mL to 100 ng/mL) for CRTAC1 with a theoretical limit of detection (LOD) of 0.2 fg/mL. Moreover, the SWCNTs FET biosensor is capable of directly detecting human serum samples, showing excellent sensing performance in differentiating clinical samples from OA patients and healthy populations. Comparative analysis with traditional enzyme-linked immunosorbent assay (ELISA) reveals that the proposed biosensor demonstrates faster detection speeds, higher sensitivity/accuracy, and lower errors, indicating high potential for the early OA diagnosis. Furthermore, the SWCNTs FET biosensor has good scalability for the combined diagnosis and measurement of multiple disease markers, thereby significantly expanding the application of SWCNTs FETs in biosensing and clinical diagnostics.

Verification of Outer Hair Cell Motor Protein, Prestin, as a Serological Biomarker for Mouse Cochlear Damage.

The motor protein prestin, found in the inner ear's outer hair cells (OHCs), is responsible for high sensitivity and sharp frequency selectivity in mammalian hearing. Some studies have suggested that prestin could be a serological biomarker for cochlear damage, as OHCs are highly vulnerable to damage from various sources. However, the reported data are inconsistent and lack appropriate negative controls. To investigate whether prestin can be used as a serological biomarker for cochlear damage or stress, we measured prestin quantities in the bloodstreams of mice using ELISA kits from different companies. Wildtype (WT) mice were exposed to different ototoxic treatments, including noise exposure and ototoxic reagents that rapidly kill OHCs. Prestin-knockout (KO) mice were used as a negative control. Our data show that some ELISA kits were not able to detect prestin specifically. The ELISA kit that could detect the prestin protein from cochlear homogenates failed to detect prestin in the bloodstream, despite there being significant damage to OHCs in the cochleae. Furthermore, the optical densities of the serum samples, which correlate to prestin quantities, were significantly influenced by hemolysis in the samples. In conclusion, Prestin from OHCs is not a sensitive and reliable serological biomarker for detecting cochlear damage in mice using ELISA.

Growth Differentiation Factor 15 and Matrix Metalloproteinase 3 in Plasma as Biomarkers for Neuropathy and Nephropathy in Type 1 Diabetes.

Diabetic neuropathy and nephropathy are common complications of type 1 diabetes (T1D). The symptoms are often elusive in the early stages, and available diagnostic methods can be improved using biomarkers. Matrix metalloproteinase 3 (MMP-3) has been identified in the kidneys and is thought to be involved in diabetic nephropathy. Growth differentiation factor 15 (GDF-15) has been suggested to have positive effects in diabetes, but is otherwise associated with adverse effects such as cardiovascular risk, declined kidney function, and neurodegeneration. This study aims to investigate plasma MMP-3 and GDF-15 as systemic biomarkers for diabetic neuropathy and nephropathy in T1D. The study involves patients with childhood-onset T1D (n = 48, age 38 ± 4 years) and a healthy control group (n = 30, age 38 ± 5 years). Neurophysiology tests, evaluations of albuminuria, and measurements of routine biochemical markers were conducted. The neuropathy impairment assessment (NIA) scoring system, where factors such as loss of sensation and weakened reflexes are evaluated, was used to screen for symptoms of neuropathy. MMP-3 and GDF-15 concentrations were determined in heparinized plasma using ELISA kits. In total, 9 patients (19%) had albuminuria, and 25 (52%) had diabetic neuropathy. No significant differences were found in MMP-3 concentrations between the groups. GDF-15 levels were higher in T1D, with median and interquartile range (IQR) of 358 (242) pg/mL in T1D and 295 (59) in controls (p < 0.001). In the merged patient group, a positive correlation was found between MMP-3 and plasma creatinine, a negative correlation was found between MMP-3 and estimated glomerular filtration rate (eGFR; rho = -0.358, p = 0.012), and there was a positive correlation between GDF-15 and NIA (rho = 0.723, p < 0.001) and high-sensitive C-reactive protein (rho = 0.395, p = 0.005). MMP-3 was increased in macroalbuminuria and correlated positively with NIA only in the nine T1D patients with albuminuria (rho = 0.836, p = 0.005). The present study indicates that high MMP-3 is associated with low eGFR, high plasma creatinine, and macroalbuminuria, and that GDF-15 can be a biomarker for diabetic neuropathy in T1D. MMP-3 may be useful as biomarker for neuropathy in T1D with albuminuria.

Associations of Circulating Biomarkers with Disease Risks: A Two-Sample Mendelian Randomization Study.

Circulating biomarkers play a pivotal role in personalized medicine, offering potential for disease screening, prevention, and treatment. Despite established associations between numerous biomarkers and diseases, elucidating their causal relationships is challenging. Mendelian Randomization (MR) can address this issue by employing genetic instruments to discern causal links. Additionally, using multiple MR methods with overlapping results enhances the reliability of discovered relationships. Here, we report an MR study using multiple methods, including inverse variance weighted, simple mode, weighted mode, weighted median, and MR-Egger. We use the MR-base resource (v0.5.6) from Hemani et al. 2018 to evaluate causal relationships between 212 circulating biomarkers (curated from UK Biobank analyses by Neale lab and from Shin et al. 2014, Roederer et al. 2015, and Kettunen et al. 2016 and 99 complex diseases (curated from several consortia by MRC IEU and Biobank Japan). We report novel causal relationships found by four or more MR methods between glucose and bipolar disorder (Mean Effect Size estimate across methods: 0.39) and between cystatin C and bipolar disorder (Mean Effect Size: -0.31). Based on agreement in four or more methods, we also identify previously known links between urate with gout and creatine with chronic kidney disease, as well as biomarkers that may be causal of cardiovascular conditions: apolipoprotein B, cholesterol, LDL, lipoprotein A, and triglycerides in coronary heart disease, as well as lipoprotein A, LDL, cholesterol, and apolipoprotein B in myocardial infarction. This Mendelian Randomization study not only corroborates known causal relationships between circulating biomarkers and diseases but also uncovers two novel biomarkers associated with bipolar disorder that warrant further investigation. Our findings provide insight into understanding how biological processes reflecting circulating biomarkers and their associated effects may contribute to disease etiology, which can eventually help improve precision diagnostics and intervention.

Optimized AF4 combined with density cushion ultracentrifugation enables profiling of high-purity human blood extracellular vesicles.

Extracellular vesicles (EVs) have emerged as a promising tool for clinical liquid biopsy. However, the identification of EVs derived from blood samples is hindered by the presence of abundant plasma proteins, which impairs the downstream biochemical analysis of EV-associated proteins and nucleic acids. Here, we employed optimized asymmetric flow field-flow fractionation (AF4) combined with density cushion ultracentrifugation (UC) to obtain high-purity and intact EVs with very low lipoprotein contamination from human plasma and serum. Further proteomic analysis revealed more than 1000 EV-associated proteins, a large proportion of which has not been previously reported. Specifically, we found that cell-line-derived EV markers are incompatible with the identification of plasma-EVs and proposed that the proteins MYCT1, TSPAN14, MPIG6B and MYADM, as well as the traditional EV markers CD63 and CD147, are plasma-EV markers. Benefiting from the high-purity of EVs, we conducted comprehensive miRNA profiling of plasma EVs and nanosized particles (NPs), as well as compared plasma- and serum-derived EVs, which provides a valuable resource for the EV research community. Overall, our findings provide a comprehensive assessment of human blood EVs as a basis for clinical biopsy applications.

Decoding depression: a comprehensive multi-cohort exploration of blood DNA methylation using machine learning and deep learning approaches.

The causes of depression are complex, and the current diagnosis methods rely solely on psychiatric evaluations with no incorporation of laboratory biomarkers in clinical practices. We investigated the stability of blood DNA methylation depression signatures in six different populations using six public and two domestic cohorts (n = 1942) conducting mega-analysis and meta-analysis of the individual studies. We evaluated 12 machine learning and deep learning strategies for depression classification both in cross-validation (CV) and in hold-out tests using merged data from 8 separate batches, constructing models with both biased and unbiased feature selection. We found 1987 CpG sites related to depression in both mega- and meta-analysis at the nominal level, and the associated genes were nominally related to axon guidance and immune pathways based on enrichment analysis and eQTM data. Random forest classifiers achieved the highest performance (AUC 0.73 and 0.76) in CV and hold-out tests respectively on the batch-level processed data. In contrast, the methylation showed low predictive power (all AUCs < 0.57) for all classifiers in CV and no predictive power in hold-out tests when used with harmonized data. All models achieved significantly better performance (>14% gain in AUCs) with pre-selected features (selection bias), with some of the models (joint autoencoder-classifier) reaching AUCs of up to 0.91 in the final testing regardless of data preparation. Different algorithmic feature selection approaches may outperform limma, however, random forest models perform well regardless of the strategy. The results provide an overview over potential future biomarkers for depression and highlight many important methodological aspects for DNA methylation-based depression profiling including the use of machine learning strategies.

Serum and salivary interleukin-1ß level in oral precancer: An observational study.

BACKGROUND AND AIM: Precancer biomarkers help in early detection and management of oral potentially malignant disorders (OPMDs). Interleukin-1ß (IL-1ß), a biomarker, is known to be altered in oral submucous fibrosis (OSMF) and oral leukoplakia (OL). Therefore, we evaluated and compared the serum and salivary IL-1ß levels in patients with OSMF/oral leukoplakia and in gender- and age-matched healthy individuals. MATERIALS AND METHODS: An in vivo, prospective, observational study was conducted on 40 subjects. Subjects were divided into two groups with 20 individuals in each group, that is, Group I: OSMF/oral leukoplakia and Group II: control group. Salivary and serum IL-1ß levels were quantitatively estimated using enzyme-linked immunosorbent assay (ELISA). The statistical tests used were unpaired t-test and Chi-square test. RESULTS: The serum IL-1ß levels were significantly (P 0.001) lesser in Group I in comparison to Group II. The salivary IL-1ß levels remained insignificant between both the groups. However, in both the groups, the salivary IL-1ß levels were significantly higher compared to the serum IL-1ß levels. CONCLUSION: We found that the serum IL-1ß level can be considered as a prospective biomarker for dysplasia, whereas salivary IL-1ß alone needs more elaborated studies to account for its application as a potential biomarker in OPMD.

Role of Claudin- 3 as a biomarker of gut-skin axis integrity in patients with psoriasis.

Increased intestinal permeability and gut dysbiosis are important factors in the pathophysiology of psoriasis and its associated conditions. Claudin-3 is a protein that is found in tight junctions and may be used to assess the integrity of the gut barrier. The aim of this study was to investigate serum concentration of Claudin- 3 (CLDN3) in patients with psoriasis. Exploring its possible relations with patients' demographic, clinical and laboratory findings was another objective. Fifty psoriatic patients and thirty-five age- and sex-matched healthy volunteers served as the study's control group in this case-control, hospital-based research. The amount of serum CLDN3 was determined by means of an enzyme-linked immunosorbent test (ELISA). Concentration of serum CLDN3 was found to be significantly higher in patients with psoriasis. (p = 0.002). There was no statistically significant correlation between CLDN3 and patient's clinical & laboratory variables. We demonstrated that gut permeability is dysfunctional in patients with psoriasis as indicated by reduction of serum CLDN3. Further investigations are needed to determine whether modulation of gut barrier may represent a new therapeutic approach for psoriasis.

Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients.

BACKGROUND: The immune response of critically ill patients, such as those with sepsis, severe trauma, or major surgery, is heterogeneous and dynamic, but its characterization and impact on outcomes are poorly understood. Until now, the primary challenge in advancing our understanding of the disease has been to concurrently address both multiparametric and temporal aspects. METHODS: We used a clustering method to identify distinct groups of patients, based on various immune marker trajectories during the first week after admission to ICU. In 339 severely injured patients, we initially longitudinally clustered common biomarkers (both soluble and cellular parameters), whose variations are well-established during the immunosuppressive phase of sepsis. We then applied this multi-trajectory clustering using markers composed of whole blood immune-related mRNA. RESULTS: We found that both sets of markers revealed two immunotypes, one of which was associated with worse outcomes, such as increased risk of hospital-acquired infection and mortality, and prolonged hospital stays. This immunotype showed signs of both hyperinflammation and immunosuppression, which persisted over time. CONCLUSION: Our study suggest that the immune system of critically ill patients can be characterized by two distinct longitudinal immunotypes, one of which included patients with a persistently dysregulated and impaired immune response. This work confirms the relevance of such methodology to stratify patients and pave the way for further studies using markers indicative of potential immunomodulatory drug targets.

Distinct plasma metabolomic signatures differentiate autoimmune encephalitis from drug-resistant epilepsy.

OBJECTIVE: Differentiating forms of autoimmune encephalitis (AE) from other causes of seizures helps expedite immunotherapies in AE patients and informs studies regarding their contrasting pathophysiology. We aimed to investigate whether and how Nuclear Magnetic Resonance (NMR)-based metabolomics could differentiate AE from drug-resistant epilepsy (DRE), and stratify AE subtypes. METHODS: This study recruited 238 patients: 162 with DRE and 76 AE, including 27 with contactin-associated protein-like 2 (CASPR2), 29 with leucine-rich glioma inactivated 1 (LGI1) and 20 with N-methyl-d-aspartate receptor (NMDAR) antibodies. Plasma samples across the groups were analyzed using NMR spectroscopy and compared with multivariate statistical techniques, such as orthogonal partial least squares discriminant analysis (OPLS-DA). RESULTS: The OPLS-DA model successfully distinguished AE from DRE patients with a high predictive accuracy of 87.0 ± 3.1% (87.9 ± 3.4% sensitivity and 86.3 ± 3.6% specificity). Further, pairwise OPLS-DA models were able to stratify the three AE subtypes. Plasma metabolomic signatures of AE included decreased high-density lipoprotein (HDL, -(CH2)n-, -CH3), phosphatidylcholine and albumin (lysyl moiety). AE subtype-specific metabolomic signatures were also observed, with increased lactate in CASPR2, increased lactate, glucose, and decreased unsaturated fatty acids (UFA, -CH2CH=) in LGI1, and increased glycoprotein A (GlycA) in NMDAR-antibody patients. INTERPRETATION: This study presents the first non-antibody-based biomarker for differentiating DRE, AE and AE subtypes. These metabolomics signatures underscore the potential relevance of lipid metabolism and glucose regulation in these neurological disorders, offering a promising adjunct to facilitate the diagnosis and therapeutics.

Surrogate measures of first-phase insulin secretion versus reference methods intravenous glucose tolerance test and hyperglycemic clamp: a systematic review and meta-analyses.

INTRODUCTION: In this systematic review, we investigated the diagnostic accuracy of surrogate measures of insulin secretion based on fasting samples and the oral glucose tolerance test (OGTT). The first phase of insulin secretion was calculated using two gold standard methods; the hyperglycemic clamp (HGC) test and intravenous glucose tolerance test (IVGTT). RESEARCH DESIGN AND METHODS: We conducted searches in the PubMed, Cochrane Central, and Web of Science databases, the last of which was conducted at the end of June 2021. Studies were included that measured first-phase insulin secretion in adults using both a gold-standard reference method (either HGC or IVGTT) and one or more surrogate measures from either fasting samples, OGTT or a meal-tolerance test. QUADAS-2, a revised tool for the quality assessment of diagnostic accuracy studies, was used for quality assessment. Random-effects meta-analyses were performed to examine the correlation between first-phase measured with gold standard and surrogate methods. RESULTS: A total of 33 articles, encompassing 5362 individuals with normal glucose tolerance, pre-diabetes or type 2 diabetes, were included in our systematic review. Homeostatic model assessment (HOMA)-beta and Insulinogenic Index 30 (IGI(30)) were the surrogate measures validated in the largest number of studies (17 and 13, respectively). HOMA-beta's pooled correlation to the reference methods was 0.48 (95% CI 0.40 to 0.56) The pooled correlation of IGI to the reference methods was 0.61 (95% CI 0.54 to 0.68). The surrogate measures with the highest correlation to the reference methods were Kadowaki (0.67 (95% CI 0.61 to 0.73)) and Stumvoll's first-phase secretion (0.65 (95% CI 0.58 to 0.71)), both calculated from an OGTT. CONCLUSIONS: Surrogate measures from the first 30 min of an OGTT capture the first phase of insulin secretion and are a good choice for epidemiological studies. HOMA-beta has a moderate correlation to the reference methods but is not a measure of the first phase specifically. PROSPERO REGISTRATION NUMBER: The meta-analysis was registered at PROSPERO (Id: CRD42020169064) before inclusion started.

Association between the atherogenic index of plasma and coronary collateral circulation in patients with chronic total occlusion.

BACKGROUND: The atherogenic index of plasma (AIP) is considered an independent risk factor for coronary artery disease (CAD). The present study investigated whether AIP correlates with the formation of coronary collateral circulation (CCC) in CAD patients with chronic total occlusion (CTO). METHODS: This retrospective study included 1093 CAD patients with CTO confirmed by coronary angiography from January 2020 to December 2020 at Beijing Anzhen Hospital. Based on the Rentrop scoring system, the patients were divided into the good CCC group and the poor CCC group. AIP was calculated by log (triglyceride/high-density lipoprotein cholesterol). Meanwhile, the study population was further divided into four groups according to the quartiles of AIP. RESULTS: Patients in the poor CCC group exhibited significantly higher AIP compared to those in the good CCC group (0.31 ± 0.27 vs. 0.14 ± 0.24, p < 0.001). Multivariate logistic regression analysis revealed an independent association between AIP and poor CCC, regardless of whether AIP was treated as a continuous or categorical variable (p < 0.001), after adjusting for confounding factors. Besides, this association remained consistent across most subgroups. The incorporation of AIP into the baseline model significantly enhanced the accuracy of identifying poor CCC [area under the curve (AUC): baseline model, 0.661 vs. baseline model + AIP, 0.721, p for comparison < 0.001]. CONCLUSIONS: Elevated AIP is independently associated with an increased risk of poor CCC in CAD patients with CTO, and AIP may improve the ability to identify poor CCC in clinical practice.