Healthy Control Individuals Research Articles

White matter microstructural abnormalities in individuals with attenuated positive symptom syndromes

White matter (WM) microstructural alterations have been extensively studied in patients with psychosis, but research on the microstructure of WM in individuals with attenuated positive symptom syndrome (APSS) is currently limited. To improve the understanding of the neuropathology in APSS, this study investigated the WM of individuals with APSS using diffusion tensor and T1-weighted imaging. Automated fiber quantification was used to calculate the diffusion index values along the trajectories of 20 major fiber tracts in 42 individuals with APSS and 51 age-and sex-matched healthy control (HC) individuals. The diffusion index values in each of fiber tracts were compared node-by-node between the 2 groups. Compared with the HC group, the APSS group showed differences in the diffusion index values in partial segments of the callosum forceps minor, left and right cingulum cingulate, inferior fronto-occipital fasciculus, right corticospinal tract, left superior longitudinal fasciculus, and arcuate fasciculus. Notably, in the APSS group positive associations were found between the axial diffusivity values of the partial nodes of the left and right cingulum cingulate and the current Global Assessment of Functioning scores, as well as between the axial diffusivity values of the partial nodes of the right corticospinal tract and negative symptoms scores and reasoning and problem-solving scores. These findings suggest that individuals with APSS exhibit reduced WM integrity or possible impaired myelin in certain segments of WM tracts involved in the frontal- and limbic-cortical connections. Additionally, abnormal WM tracts appear to be associated with impaired general function and neurocognitive function. This study provides important new insights into the neurobiology of APSS and highlights potential targets for future intervention and treatment.

Time-frequency analysis of speech signal using Chirplet transform for automatic diagnosis of Parkinson's disease.

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder in the world after Alzheimer's disease. Early diagnosing PD is challenging as it evolved slowly, and its symptoms eventuate gradually. Recent studies have demonstrated that changes in speech may be utilized as an excellent biomarker for the early diagnosis of PD. In this study, we have proposed a Chirplet transform (CT) based novel approach for diagnosing PD using speech signals. We employed CT to get the time-frequency matrix (TFM) of each speech recording, and we extracted time-frequency based entropy (TFE) features from the TFM. The statistical analysis demonstrates that the TFE features reflect the changes in speech that occurs in the speech due to PD, hence can be used for classifying the PD and healthy control (HC) individuals. The effectiveness of the proposed framework is validated using the vowels and words from the PC-GITA database. The genetic algorithm is utilized to select the optimum features subset, while a support vector machine (SVM), decision tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB) classifiers are employed for classification. The TFE features outperform the breathiness and Mel frequency cepstral coefficients (MFCC) features. The SVM classifier is most effective compared to other machine-learning classifiers. The highest classification accuracy rates of 98% and 99% are achieved using the vowel /a/ and word /atleta/, respectively. The results reveal that the proposed CT-based entropy features effectively diagnose PD using the speech of a person.

Association of single nucleotide polymorphisms with dyslipidemia and risk of metabolic disorders in the State of Qatar.

Dyslipidemia is recognized as one of the risk factors of cardiovascular diseases (CVDs), type 2 diabetes mellitus (T2DM), and non-alcoholic fatty liver disease (NAFLD). The study aimed to investigate the association between selected single nucleotide polymorphisms (SNPs) with dyslipidemia and increased susceptibility risks of CVD, NAFLD, and/or T2DM in dyslipidemia patients in comparison with healthy control individuals from the Qatar genome project. A community-based cross-sectional study was conducted among 2933 adults (859 dyslipidemia patients and 2074 healthy control individuals) from April to December 2021 to investigate the association between 331 selected SNPs with dyslipidemia and increased susceptibility risks of CVD, NAFLD and/or T2DM, and covariates. The genotypic frequencies of six SNPs were found to be significantly different in dyslipidemia patients subjects compared to the control group among males and females. In males, three SNPs were found to be significant, the rs11172113 in over-dominant model, the rs646776 in recessive and over-dominant models, and the rs1111875 in dominant model. On the other hand, two SNPs were found to be significant in females, including rs2954029 in recessive model, and rs1801251 in dominant and recessive models. The rs17514846 SNP was found for dominant and over-dominant models among males and only the dominant model for females. We found that the six SNPs linked to gender type had an influence in relation to disease susceptibility. When controlling for the four covariates (gender, obesity, hypertension, and diabetes), the difference between dyslipidemia and the control group remained significant for the six variants. Finally, males were three times more likely to have dyslipidemia in comparison with females, hypertension was two times more likely to be present in the dyslipidemia group, and diabetes was six times more likely to be in the dyslipidemia group. The current investigation provides evidence of association for a common SNP to coronary heart disease and suggests a sex-dependent effect and encourage potential therapeutic applications.

Induction of macrophages matriptase and activation of macrophage stimulating protein 1 in sickle cell anemia - related chronic kidney disease

BACKGROUND: More than 50% of sickle cell anemia (SCA) patients develop chronic kidney disease (CKD). The mechanism of CKD is multifactorial and is not yet fully understood. SCA is a vasculopathy associated with multiple mechanisms driven by cell-free mutated hemoglobin (HbS). We have demonstrated that activation of recepteur d'origine nantais (RON) kinase contributes to the renal endothelial injury in SCA mouse model. Inhibition of RON reduces endothelial injury and ameliorates renal disease. Macrophage-stimulating protein 1 (MSP1) is the only known ligand for RON. MSP1 is synthesized by the liver and released in the circulation as biologically inactive pro-MSP1 protein that is activated by proteolytic cleavage. Macrophage membrane matriptase (MT-SP1) cleaves MSP1, making it able to bind RON receptor. The levels of plasma MSP1 and macrophage MT-SP1 in SCA have not been investigated. OBJECTIVES: To evaluate MT-SP1 expression and serum MSP1 levels in SCA patients and mouse model. HYPOTHESIS: The level of circulating MSP1 is reduced in SCA patients and mice due to its accumulation in organs. METHODS: The study was approved by Howard University IRB.All subjects provided written informed consent. Whole blood samples were obtained from 44 SCA patients enrolled at HU CSCD Registry study and 6 healthy control individuals. SCA mice (Townes) were housed in Howard University; the animal protocol was approved by the IACUC. ELISA kits were used to test human and mouse MSP1. Human THP-1 promonocytic cells were differentiated into macrophages with 25 nM PMA for 72 hrs and treated with either purified HbS or HbA (5 μM). Total RNA was used for RNA Seq. Comparisons were conducted using Dragen differential expression software v. 3.6.3 (Illumina), and gene counts were normalized using DESeq2 package from Bioconductor. RESULTS: RNA Seq results demonstrated increased levels of MT-SP1 in THP-1-derived macrophages after treatment with HbS compared to treatment with normal hemoglobin (HbA) (p=0.036). The results were further confirmed by real-time RT-PCR. The levels of plasma MSP1 were about 7-fold lower in SCD patients compared to non-SCD controls (146.8±111.7 ng/ml, N=6 in control vs. 20.6 ng/ml, N=44 in SCD, p=3.97x10-10). Log2 (MSP1) slightly correlated with albuminuria log2 (ALB/CRE) (r=0.1243, R2=0.0154, p=0.4216, N=44). This correlation was more profound in females (r=0.3158, R2=0.0997, p=0.1632, N=21). No correlation was found in males. We found moderate inverted correlation between eGFR and log2 (MSP1) (r=-0.3313, R2=0.1094, p=0.0280, N=44). In SCA patients with renal disease, MSP1 levels positively correlated with stages of CKD (r=0.3990, R2=0.1592, p=0.0320, N=29). In mouse, serum MSP1 levels were about 2.3-fold reduced in SCA compared to control mice (441.0±97.98 ng/ml, N=5 in control vs. 192.2 ±27.02, N=7 in SCD, p=0.0173). Immunostaining demonstrated the high levels of MSP1 accumulation on the surface of endothelial cells in the ki This work was supported by NIH Research Grants SC1HL150685, P50HL118006, R01HL125005, and G12MD007597. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Epigenetic Signatures in Hypertension.

Clear epigenetic signatures were found in hypertensive and pre-hypertensive patients using DNA methylation data and neural networks in a classification algorithm. It is shown how by selecting an appropriate subset of CpGs it is possible to achieve a mean accuracy classification of 86% for distinguishing control and hypertensive (and pre-hypertensive) patients using only 2239 CpGs. Furthermore, it is also possible to obtain a statistically comparable model achieving an 83% mean accuracy using only 22 CpGs. Both of these approaches represent a substantial improvement over using the entire amount of available CpGs, which resulted in the neural network not generating accurate classifications. An optimization approach is followed to select the CpGs to be used as the base for a model distinguishing between hypertensive and pre-hypertensive individuals. It is shown that it is possible to find methylation signatures using machine learning techniques, which can be applied to distinguish between control (healthy) individuals, pre-hypertensive individuals and hypertensive individuals, illustrating an associated epigenetic impact. Identifying epigenetic signatures might lead to more targeted treatments for patients in the future.

Identification of Novel Immunosuppressive Drug Targets for Liver Transplantation Using Genome-Scale Metabolic Modeling

Abstract The global database on donation and transplantation reported 129,681 worldwide organ transplants in 2020, with kidneys and livers being the most commonly transplanted organs. Rejection of the transplanted organ by the host's immune system is a common complication and a major reason for graft failure and patient mortality. To address these limitations, we developed genome-scale metabolic models of liver and CD4+ T cells using the pipeline for MetAbolic Drug Repurposing IDentification-MADRID and integrating our data analysis results from RNA-seq, proteomics, and microarray human sample datasets (step 1). Drug targets obtained from the ConnectivityMap database are mapped to metabolic genes in the model where systematic knockouts are performed on each gene which leads to identifying differential fluxes (step 2). Our human-cell-specific models identified novel immunosuppressive drug targets by integrating differentially expressed genes in post-liver transplanted non-tolerant versus healthy control individuals and differential fluxes (step 3). We identified 193, 217, and 196 potential metabolic gene targets for T-helper 1, 2, and 17, respectively, against patients with a non-tolerant phenotype after liver transplantation. This genome-scale metabolic modeling approach for liver transplantation can assist in personalized treatment by using any individual’s sample data at step 1 and 2. It can also save time and resources in preclinical drug development studies by predicting more precise drug targets and drug repurposing based on patients’ omics data. By using this approach, we aim to improve the success rates of liver transplantations and reduce the complications associated with organ rejection. The work was supported by University of Nebraska collaboration initiatives.

Subclinical hip involvement in rheumatoid arthritis. An ultrasound assessment

IntroductionHip joint involvement in rheumatoid arthritis has been related to disability and lower quality of life. Currently, not enough studies provide information for subclinical hip involvement. ObjectivesTo determine the prevalence of subclinical hip joint involvement in rheumatoid arthritis, characterizing it by ultrasound. Materials and methodsA descriptive cross-sectional study in rheumatoid arthritis patients with an established diagnosis of rheumatoid arthritis according to EULAR/ACR 2010 classification criteria, without hip pain or abnormalities during a clinical examination (subclinical group), this group was compared with a group of symptomatic patients with rheumatoid arthritis of the hip (pathological control group), and with healthy control individuals matched by age and body mass index. The presence of joint capsule distension, osteophytes, joint remodelling process, erosions, iliopsoas bursitis, and power Doppler signal was assessed. The images were interpreted by an experienced rheumatologist blinded to clinical and laboratory variables. Results234 hips were studied; 37 (31.6%) symptomatic hip patients, 40 (34.1%) asymptomatic hip patients and 40 (34.1%) healthy individuals. The prevalence of joint capsule distension was 20%, while symptomatic hip patients and healthy individuals had a prevalence of 48.6% and 15%, respectively. The asymptomatic group presented higher values for osteophytes and joint remodelling. ConclusionsUltrasound is a sensitive tool to identify subclinical hip joint synovitis in rheumatoid arthritis patients, reaffirming the value of ultrasound in preclinical diagnosis. The presence of erosions and iliopsoas bursitis was low; we observed no power Doppler signals nor significant associations between ultrasound findings with disease activity and laboratory test results.

Evaluation of Brain-Body Health in Individuals With Common Neuropsychiatric Disorders

Physical health and chronic medical comorbidities are underestimated, inadequately treated, and often overlooked in psychiatry. A multiorgan, systemwide characterization of brain and body health in neuropsychiatric disorders may enable systematic evaluation of brain-body health status in patients and potentially identify new therapeutic targets. To evaluate the health status of the brain and 7 body systems across common neuropsychiatric disorders. Brain imaging phenotypes, physiological measures, and blood- and urine-based markers were harmonized across multiple population-based neuroimaging biobanks in the US, UK, and Australia, including UK Biobank; Australian Schizophrenia Research Bank; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer's Disease Neuroimaging Initiative; Prospective Imaging Study of Ageing; Human Connectome Project-Young Adult; and Human Connectome Project-Aging. Cross-sectional data acquired between March 2006 and December 2020 were used to study organ health. Data were analyzed from October 18, 2021, to July 21, 2022. Adults aged 18 to 95 years with a lifetime diagnosis of 1 or more common neuropsychiatric disorders, including schizophrenia, bipolar disorder, depression, generalized anxiety disorder, and a healthy comparison group were included. Deviations from normative reference ranges for composite health scores indexing the health and function of the brain and 7 body systems. Secondary outcomes included accuracy of classifying diagnoses (disease vs control) and differentiating between diagnoses (disease vs disease), measured using the area under the receiver operating characteristic curve (AUC). There were 85 748 participants with preselected neuropsychiatric disorders (36 324 male) and 87 420 healthy control individuals (40 560 male) included in this study. Body health, especially scores indexing metabolic, hepatic, and immune health, deviated from normative reference ranges for all 4 neuropsychiatric disorders studied. Poor body health was a more pronounced illness manifestation compared to brain changes in schizophrenia (AUC for body = 0.81 [95% CI, 0.79-0.82]; AUC for brain = 0.79 [95% CI, 0.79-0.79]), bipolar disorder (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.57-0.58]), depression (AUC for body = 0.67 [95% CI, 0.67-0.68]; AUC for brain = 0.58 [95% CI, 0.58-0.58]), and anxiety (AUC for body = 0.63 [95% CI, 0.63-0.63]; AUC for brain = 0.57 [95% CI, 0.57-0.58]). However, brain health enabled more accurate differentiation between distinct neuropsychiatric diagnoses than body health (schizophrenia-other: mean AUC for body = 0.70 [95% CI, 0.70-0.71] and mean AUC for brain = 0.79 [95% CI, 0.79-0.80]; bipolar disorder-other: mean AUC for body = 0.60 [95% CI, 0.59-0.60] and mean AUC for brain = 0.65 [95% CI, 0.65-0.65]; depression-other: mean AUC for body = 0.61 [95% CI, 0.60-0.63] and mean AUC for brain = 0.65 [95% CI, 0.65-0.66]; anxiety-other: mean AUC for body = 0.63 [95% CI, 0.62-0.63] and mean AUC for brain = 0.66 [95% CI, 0.65-0.66). In this cross-sectional study, neuropsychiatric disorders shared a substantial and largely overlapping imprint of poor body health. Routinely monitoring body health and integrated physical and mental health care may help reduce the adverse effect of physical comorbidity in people with mental illness.

Discovery of Biomarkers for Amyotrophic Lateral Sclerosis from Human Cerebrospinal Fluid Using Mass-Spectrometry-Based Proteomics.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by the loss of upper and lower motor neurons, which eventually may lead to death. Critical to the mission of developing effective therapies for ALS is the discovery of biomarkers that can illuminate mechanisms of neurodegeneration and have diagnostic, prognostic, or pharmacodynamic value. Here, we merged unbiased discovery-based approaches and targeted quantitative comparative analyses to identify proteins that are altered in cerebrospinal fluid (CSF) from patients with ALS. Mass spectrometry (MS)-based proteomic approaches employing tandem mass tag (TMT) quantification methods from 40 CSF samples comprising 20 patients with ALS and 20 healthy control (HC) individuals identified 53 proteins that are differential between the two groups after CSF fractionation. Notably, these proteins included both previously identified ones, validating our approach, and novel ones that have the potential for expanding biomarker repertoire. The identified proteins were subsequently examined using parallel reaction monitoring (PRM) MS methods on 61 unfractionated CSF samples comprising 30 patients with ALS and 31 HC individuals. Fifteen proteins (APOB, APP, CAMK2A, CHI3L1, CHIT1, CLSTN3, ERAP2, FSTL4, GPNMB, JCHAIN, L1CAM, NPTX2, SERPINA1, SERPINA3, and UCHL1) showed significant differences between ALS and the control. Taken together, this study identified multiple novel proteins that are altered in ALS, providing the foundation for developing new biomarkers for ALS.

Transcriptomics unravels molecular changes associated with cilia and COVID-19 in chronic rhinosinusitis with nasal polyps

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a common upper respiratory tract complication where the pathogenesis is largely unknown. Herein, we investigated the transcriptome profile in nasal mucosa biopsies of CRSwNP patients and healthy individuals. We further integrated the transcriptomics data with genes located in chromosomal regions containing genome-wide significant gene variants for COVID-19. Among the most significantly upregulated genes in polyp mucosa were CCL18, CLEC4G, CCL13 and SLC9A3. Pathways involving “Ciliated epithelial cells” were the most differentially expressed molecular pathways when polyp mucosa and non-polyp mucosa from the same patient was compared. Natural killer T-cell (NKT) and viral pathways were the most statistically significant pathways in the mucosa of CRSwNP patients compared with those of healthy control individuals. Upregulated genes in polyp mucosa, located within the genome-wide associated regions of COVID-19, included LZTFL1, CCR9, SLC6A20, IFNAR1, IFNAR2 and IL10RB. Interestingly, the second most over-expressed gene in our study, CLEC4G, has been shown to bind directly to SARS-CoV-2 spike's N-terminal domain and mediate its entry and infection. Our results on altered expression of genes related to cilia and viruses point to the de-regulation of viral defenses in CRSwNP patients, and may give clues to future intervention strategies.

Patterns of risk-Using machine learning and structural neuroimaging to identify pedophilic offenders.

Child sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA. To use machine learning and MRI data to identify PO individuals. From a single-center male cohort of 14 PO individuals and 15 matched healthy control (HC) individuals, we acquired diffusion tensor imaging data (anisotropy, diffusivity, and fiber tracking) in literature-based regions of interest (prefrontal cortex, anterior cingulate cortex, amygdala, and corpus callosum). We trained a linear support vector machine to discriminate between PO and HC individuals using these WM microstructure data. Post hoc, we investigated the PO model decision scores with respect to sociodemographic (age, education, and IQ) and forensic characteristics (psychopathy, sexual deviance, and future risk of sexual violence) in the PO subpopulation. We assessed model specificity in an external cohort of 53 HC individuals. The classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P 5000 = 0.018) and an out-of-sample specificity to correctly identify HC individuals of 94.3%. The predictive brain pattern contained bilateral fractional anisotropy in the anterior cingulate cortex, diffusivity in the left amygdala, and structural prefrontal cortex-amygdala connectivity in both hemispheres. This brain pattern was associated with the number of previous child victims, the current stance on sexuality, and the professionally assessed risk of future sexual violent reoffending. Aberrant white matter microstructure in the prefronto-temporo-limbic circuit could be a potential neurobiological correlate for PO individuals at high-risk of reoffending with CSA. Although preliminary and exploratory at this point, our findings highlight the general potential of MRI-based biomarkers and particularly WM microstructure patterns for future CSA risk assessment and preventive efforts.

Association analysis between the TLR9 gene polymorphism rs352140 and type 1 diabetes.

To a great extent, genetic factors contribute to the susceptibility to type 1 diabetes (T1D) development, and by triggering immune imbalance, Toll-like receptor (TLR) 9 is involved in the development of T1D. However, there is a lack of evidence supporting a genetic association between polymorphisms in the TLR9 gene and T1D. In total, 1513 individuals, including T1D patients (n=738) and healthy control individuals (n=775), from the Han Chinese population were recruited for an association analysis of the rs352140 polymorphism of the TLR9 gene and T1D. rs352140 was genotyped by MassARRAY. The allele and genotype distributions of rs352140 in the T1D and healthy groups and those in different T1D subgroups were analyzed by the chi-squared test and binary logistic regression model. The chi-square test and Kruskal-Wallis H test were performed to explore the association between genotype and phenotype in T1D patients. The allele and genotype distributions of rs352140 were significantly different in T1D patients and healthy control individuals (p=0.019, p=0.035). Specifically, the T allele and TT genotype of rs352140 conferred a higher risk of T1D (OR=1.194, 95% CI=1.029-1.385, p=0.019, OR=1.535, 95% CI=1.108-2.126, p=0.010). The allele and genotype distributions of rs352140 were not significantly different between childhood-onset and adult-onset T1D and between T1D with a single islet autoantibody and T1D with multiple islet autoantibodies (p=0.603, p=0.743). rs352140 was associated with T1D susceptibility according to the recessive and additive models (p=0.015, p=0.019) but was not associated with T1D susceptibility in the dominant and overdominant models (p=0.117, p=0.928). Moreover, genotype-phenotype association analysis showed that the TT genotype of rs352140 was associated with higher fasting C-peptide levels (p=0.017). In the Han Chinese population, the TLR9 polymorphism rs352140 is associated with T1D and is a risk factor for susceptibility to T1D.

Nonlinear Techniques and Ridge Regression as a Combined Approach: Carcinoma Identification Case Study

As more genetic information becomes available, such as DNA methylation levels, it becomes increasingly important to have techniques to analyze such data in the context of cancers such as anal and cervical carcinomas. In this paper, we present an algorithm that differentiates between healthy control patients and individuals with anal and cervical carcinoma, using as an input DNA methylation data. The algorithm used a combination of ridge regression and neural networks for the classification task, achieving high accuracy, sensitivity and specificity. The relationship between methylation levels and carcinoma could in principle be rather complex, particularly given that a large number of CpGs could be involved. Therefore, nonlinear techniques (machine learning) were used. Machine learning techniques (nonlinear) can be used to model linear processes, but the opposite (linear techniques simulating nonlinear models) would not likely generate accurate forecasts. The feature selection process is carried out using a combination of prefiltering, ridge regression and nonlinear modeling (artificial neural networks). The model selected 13 CpGs from a total of 450,000 CpGs available per patient with 171 patients in total. The model was also tested for robustness and compared to other more complex models that generated less precise classifications. The model obtained (testing dataset) an accuracy, sensitivity and specificity of 97.69%, 95.02% and 98.26%, respectively. The reduction of the dimensionality of the data, from 450,000 to 13 CpGs per patient, likely also reduced the likelihood of overfitting, which is a very substantial risk in this type of modelling. All 13 CpGs individually generated classification forecasts less accurate than the proposed model.

Ferritin as a potential disease marker in patients with bipolar disorder

BackgroundLow-grade inflammation and oxidative stress have been implicated as potential pathophysiological processes in bipolar disorder, but the underlying mechanism is unknown. Ferritin is a marker of iron stores and involved in redox processes and inflammation but its role in bipolar disorder is unclear. MethodsWe investigated the possible association of increased plasma ferritin levels and bipolar disorder. We pooled two studies using similar longitudinal repeated measures designs and included 330 blood- and urinary samples from 95 patients with bipolar disorder across all affective states and 84 samples from 84 healthy control individuals. Plasma ferritin was measured along with multiple blood inflammatory markers and urinary markers of oxidatively generated damage to DNA and RNA. ResultsPlasma ferritin levels, adjusting for multiple demographical- and lifestyle variables, did not differ between patients with bipolar disorder compared with healthy control individuals (b = 1.09, 95 % CI: 0.86 to 1.39, p = 0.49). Within patients with bipolar disorder ferritin levels were higher in a depressed state compared with euthymia (b = 1.12, 95 % CI: 1.01 to 1.24, p < 0.04), and ferritin levels were positively associated with Interleukin-18 blood levels and urinary levels of 8-oxodG. LimitationsPatients with bipolar disorder received medication which could potentially influence iron metabolism. ConclusionElevated ferritin levels in depressed patients with bipolar disorder may point to a role for iron metabolism in bipolar disorder pathophysiology, and potentially as a biomarker, linking low-grade inflammation with redox biology and the well-known increased risk of medical comorbidity and reduced life expectancy.

An Evaluation of Biomarkers as Determinants of Peripheral Arterial Disease in those with Diabetes Mellitus

AbstractPeripheral arterial disease (PAD) is characterized by reduced arterial perfusion in lower limb extremities due to atherosclerosis. The disease is mostly asymptomatic with poor diagnosis and prognosis, usually co‐existing with other diseases. Its burden is rising globally, especially among diabetics; consequently increasing the risk of vascular damage, lower limb amputation, and cardiovascular disorders. Therefore, it is essential to identify biomarkers for in‐time diagnosis and efficient prognosis of diabetes‐associated PAD. In this study, several markers were evaluated that can identify PAD development in diabetics. These biomarkers include blood urea nitrogen (BUN), serum creatinine, creatine kinase‐MB (CK‐MB), prothrombin time (PT), activated partial thrombopalstin time (aPTT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), glycated hemoglobin (HbA1c), total cholesterol, triglycerides, low‐density lipoproteins (LDL), high density lipoproteins (HDL) and C reactive protein (CRP). All these markers were analyzed in blood samples using standard methods and results were interpreted by comparing them with widely accepted cut‐off values. After all experimental procedures, it was found that levels of AST (35.04 U/L), ALT (37.21 U/L), BUN (37.93 mg/dL), CK‐MB (22.01 IU/L), HbA1c (8.79 %), triglycerides (297.65 mg/dL), total cholesterol (234.59 mg/dL) and CRP (5.48 mg/L) were significantly higher in diabetic patients having PAD while slight increase was observed in coagulation profile (PT (14.27 s), aPTT (35.15 s) and INR (1.15), in diabetics plus PAD as compared to diabetics alone. However, it has been observed that levels of HDL were reduced in both diabetics (41.14 mg/dL) and diabetics plus PAD (43.54) as compared to control (healthy individuals) results (53.67) while LDL levels were increased in diabetics (117.42 mg/dL) and diabetics plus PAD (122.22) as compared to healthy individuals (102.76). Therefore, it was concluded that AST, ALT, urea, cholesterol, triglycerides, HDL, LDL, CK‐MB and CRP can serve as diagnostic and prognostic biomarkers in diabetics having PAD to improve life of patients.

A longitudinal study on physiological stress in individuals at ultra high-risk of psychosis.

Individuals at ultra high-risk (UHR) of psychosis exhibit significantly higher stress levels than healthy controls (HC). This study investigates how physiological stress measures differ between HC and UHR individuals and how physiological stress is associated with attenuated psychotic symptoms and changes over time in UHR individuals. Additionally, it examines how the use of medication affects physiological levels of stress. The study included 72 UHR individuals and 36 HC. UHR were included according to the comprehensive assessment of at-risk mental state (CAARMS); a total-CAARMS score measured the attenuated psychotic symptoms and was calculated from the four psychosis subscales. HC and UHR were examined at baseline, and 47 UHR individuals were followed up after six months. Physiological stress measures were salivary cortisol, alpha-amylase (SAA) and heart-rate variability (HRV). Saliva was collected at four-time points during the day. There was no significant difference regarding cortisol (awakening response) or SAA measures between HC and UHR individuals. The use of antipsychotics and antidepressants was associated with low HRV in UHR individuals. In an exploratory analysis of 19 UHR individuals, we found an association between the change in total-CAARMS (six months total-CAARMS minus baseline total CAARMS) and the change in HRV during sleep (six months HRV minus baseline HRV). Our findings indicate that the use of antipsychotics and antidepressants could be associated with lower HRV in UHR individuals. There might be potential to investigate how HRV develops during the course of illness in UHR individuals.

A metabolomics approach to the validation of predictive metabolites and phenotypic expression in non-alcoholic fatty liver disease

AimsNonalcoholic fatty liver disease (NAFLD) is becoming more common and severe. Individuals with NAFLD have an altered composition of gut- microbial metabolites. We used metabolomics profiling to identify microbial metabolites that could indicate gut-liver metabolic severity. Noninvasive biomarkers are required for NAFLD, especially for patients at high risk of disease progression. Main methodsWe compared the stool metabolomes, untargeted metabolomics, and clinical data of 80 patients. Patients with nonalcoholic fatty liver (NAFL: n = 16), nonalcoholic steatohepatitis (NASH: n = 26), and cirrhosis (n = 19) and healthy control individuals (HC: n = 19) were enrolled. The identified metabolites in NAFLD were evaluated by multivariate statistical analysis and metabolic pathotypic expression. Gas chromatography–mass spectrometry (GC–MS) and liquid chromatography coupled to time-of-flight–mass spectrometry (LC–TOF-MS) were used to analyze metabolites. Key findingsUntargeted metabolomics was used to identify and quantify 103 metabolites. Principal component analysis (PCA) was used to assess the metabolic discrimination of NAFL, NASH, and cirrhosis. Short-chain fatty acids (SCFA) levels were significantly lower in NAFLD patients, including those of acetate (p = 0.03), butyrate (p = 0.0008), and propionate. The stool cholic acid (p = 0.001) level was significantly increased in NAFLD patients. Palmitoylcarnitine and l-carnitine levels were significantly increased in NASH and cirrhosis patients. The phenotypic expression of these metabolites was linked to β-oxidation. SignificanceWe demonstrated a distinct metabolome profile in NAFLD patients with NAFL, NASH, and cirrhosis. We also discovered that the expression of certain metabolites and metabolic pathways was linked to NAFLD.

Association Between the Fronto-Limbic Network and Cognitive and Emotional Functioning in Individuals With Bipolar Disorder

Individuals with bipolar disorder (BD) experience cognitive and emotional dysfunctions. Various brain circuits are implicated in BD but have not been investigated in a meta-analysis of functional magnetic resonance imaging (fMRI) studies. To investigate the brain functioning of individuals with BD compared with healthy control individuals in the domains of emotion processing, reward processing, and working memory. All fMRI experiments on BD published before March 2020, as identified in a literature search of PubMed, Embase, Web of Science, Cochrane Library, PsycInfo, Emcare, Academic Search Premier, and ScienceDirect. The literature search was conducted on February 21, 2017, and March 2, 2020, and data were analyzed from January 2021 to January 2022. fMRI experiments comparing adult individuals with BD and healthy control individuals were selected if they reported whole-brain results, including a task assessing at least 1 of the domains. In total, 2320 studies were screened, and 253 full-text articles were evaluated. A total of 49 studies were included after selection procedure. Coordinates reporting significant activation differences between individuals with BD and healthy control individuals were extracted. Differences in brain region activity were tested using the activation likelihood estimation method. A whole-brain meta-analysis evaluated whether reported differences in brain activation in response to stimuli in 3 cognitive domains between individuals with BD and healthy control individuals were different. The study population included 999 individuals with BD (551 [55.2%] female) and 1027 healthy control individuals (532 [51.8%] female). Compared with healthy control individuals, individuals with BD showed amygdala and hippocampal hyperactivity and hypoactivation in the inferior frontal gyrus during emotion processing (20 studies; 324 individuals with BD and 369 healthy control individuals), hyperactivation in the orbitofrontal cortex during reward processing (9 studies; 195 individuals with BD and 213 healthy control individuals), and hyperactivation in the ventromedial prefrontal cortex and subgenual anterior cingulate cortex during working memory (20 studies; 530 individuals with BD and 417 healthy control individuals). Limbic hyperactivation was only found during euthymia in the emotion and reward processing domains; abnormalities in frontal cortex activity were also found in individuals with BD with mania and depression. This systematic review and meta-analysis revealed evidence for activity disturbances in key brain areas involved in cognitive and emotion processing in individuals with BD. Most of the regions are part of the fronto-limbic network. The results suggest that aberrations in the fronto-limbic network, present in both euthymic and symptomatic individuals, may be underlying cognitive and emotional dysfunctions in BD.

Data-Independent Acquisition Phosphoproteomics of Urinary Extracellular Vesicles Enables Renal Cell Carcinoma Grade Differentiation

Translating the research capability and knowledge in cancer signaling into clinical settings has been slow and ineffective. Recently, extracellular vesicles (EVs) have emerged as a promising source for developing disease phosphoprotein markers to monitor disease status. This study focuses on the development of a robust data-independent acquisition (DIA) using mass spectrometry to profile urinary EV phosphoproteomics for renal cell cancer (RCC) grades differentiation. We examined gas-phase fractionated library, direct DIA (library-free), forbidden zones, and several different windowing schemes. After the development of a DIA mass spectrometry method for EV phosphoproteomics, we applied the strategy to identify and quantify urinary EV phosphoproteomes from 57 individuals representing low-grade clear cell RCC, high-grade clear cell RCC, chronic kidney disease, and healthy control individuals. Urinary EVs were efficiently isolated by functional magnetic beads, and EV phosphopeptides were subsequently enriched by PolyMAC. We quantified 2584 unique phosphosites and observed that multiple prominent cancer-related pathways, such as ErbB signaling, renal cell carcinoma, and regulation of actin cytoskeleton, were only upregulated in high-grade clear cell RCC. These results show that EV phosphoproteome analysis utilizing our optimized procedure of EV isolation, phosphopeptide enrichment, and DIA method provides a powerful tool for future clinical applications.

Decreased dorsal attention network homogeneity as a potential neuroimaging biomarker for major depressive disorder

BackgroundGaining insight into abnormal functional brain network homogeneity (NH) has the potential to aid efforts to target or otherwise study major depressive disorder (MDD). The NH of the dorsal attention network (DAN) in first-episode treatment-naive MDD patients, however, has yet to be studied. As such, the present study was developed to explore the NH of the DAN in order to determine the ability of this parameter to differentiate between MDD patients and healthy control (HC) individuals. MethodsThis study included 73 patients with first-episode treatment-naive MDD and 73 age-, gender-, and educational level-matched healthy controls. All participants completed the attentional network test (ANT), Hamilton Rating Scale for Depression (HRSD), and resting-state functional magnetic resonance imaging (rs-fMRI) analyses. A group independent component analysis (ICA) was used to identify the DAN and to compute the NH of the DAN in patients with MDD. Spearman's rank correlation analyses were used to explore relationships between significant NH abnormalities in MDD patients, clinical parameters, and executive control reaction time. ResultsRelative to HCs, patients exhibited reduced NH in the left supramarginal gyrus (SMG). Support vector machine (SVM) analyses and receiver operating characteristic curves indicated that the NH of the left SMG could be used to differentiate between HCs and MDD patients with respective accuracy, specificity, sensitivity, and AUC values of 92.47 %, 91.78 %, 93.15 %, and 65.39 %. A significant positive correlation was observed between the left SMG NH values and HRSD scores among MDD patients. ConclusionsThese results suggest that NH changes in the DAN may offer value as a neuroimaging biomarker capable of differentiating between MDD patients and healthy individuals.