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ABSTRACTAnemia is common in older adults, but often unexplained. Previously, we conducted a randomized, controlled trial of intravenous (IV) iron sucrose to study its impact on the 6-minute walk test and hemoglobin in older adults with unexplained anemia and ferritin levels of 20–200 ng/mL. In this report, we present for the first time the response of hemoglobin, as well as the dynamic response of biomarkers of erythropoiesis and iron indices, in a pooled analysis of the initially IV iron-treated group of 9 subjects and the subsequently IV iron treated 10 subjects from the delayed treatment group. We hypothesized that there would be a reproducible hemoglobin response from IV iron, and that iron indices and erythropoietic markers would reflect appropriate iron loading and reduced erythropoietic stress. To investigate the biochemical response of anemia to IV iron, we studied the dynamics of soluble transferrin receptor (STfR), hepcidin, erythropoietin (EPO), and iron indices over 12 weeks after treatment. In total, all 19 treated subjects were evaluable: 9 from initial treatment and 10 after cross-over. Hemoglobin rose from 11.0 to 11.7 g/dL, 12 weeks after initiating IV iron treatment of 1000 mg divided weekly over 5 weeks. We found early changes of iron loading after 1–2 IV iron dose: serum iron increased by 184 mcg/dL from a baseline of 66 mcg/dL, ferritin by 184 ng/mL from 68 ng/mL, and hepcidin by 7.49 ng/mL from 19.2 ng/mL, while STfR and serum EPO declined by 0.55 mg/L and 3.5 mU/mL from 19.2 ng/mL and 14 mU/mL, respectively. The erythroid response and evidence of enhanced iron trafficking are consistent with the hypothesis that IV iron overcomes iron deficient or iron-restricted erythropoiesis. These data provide new insight that iron-restricted erythropoiesis is a potential and targetable mechanism for patients diagnosed with unexplained anemia of the elderly and offers support for larger prospective trials of IV iron among anemic older adults of low to normal ferritin.

Abstract Congenital heart disease (CHD) represents a significant contributor to both morbidity and mortality in neonates and children. There’s currently no analogous dried blood spot (DBS) screening for CHD immediately after birth. This study was set to assess the feasibility of using DBS to identify reliable metabolite biomarkers with clinical relevance, with the aim to screen and classify CHD utilizing the DBS. We assembled a cohort of DBS datasets from the California Department of Public Health (CDPH) Biobank, encompassing both normal controls and three pre-defined CHD categories. A DBS-based quantitative metabolomics method was developed using liquid chromatography with tandem mass spectrometry (LC-MS/MS). We conducted a correlation analysis comparing the absolute quantitated metabolite concentration in DBS against the CDPH NBS records to verify the reliability of metabolic profiling. For hydrophilic and hydrophobic metabolites, we executed significant pathway and metabolite analyses respectively. Logistic and LightGBM models were established to aid in CHD discrimination and classification. Consistent and reliable quantification of metabolites were demonstrated in DBS samples stored for up to 15 years. We discerned dysregulated metabolic pathways in CHD patients, including deviations in lipid and energy metabolism, as well as oxidative stress pathways. Furthermore, we identified three metabolites and twelve metabolites as potential biomarkers for CHD assessment and subtypes classifying. This study is the first to confirm the feasibility of validating metabolite profiling results using long-term stored DBS samples. Our findings highlight the potential clinical applications of our DBS-based methods for CHD screening and subtype classification.

Abstract Motivation Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer. Methods A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13). Results Eleven functional modules were identified as significant differentiators (false discovery rate, FDR

Abstract Purpose Several studies have reported sleep disturbances during the COVID‐19 virus pandemic. Little data exist about the impact of the pandemic on sleep and mental health among older women with breast cancer. We sought to examine whether women with and without breast cancer who experienced new sleep problems during the pandemic had worsening depression and anxiety. Methods Breast cancer survivors aged ≥60 years with a history of nonmetastatic breast cancer (n = 242) and frequency‐matched noncancer controls (n = 158) active in a longitudinal cohort study completed a COVID‐19 virus pandemic survey from May to September 2020 (response rate 83%). Incident sleep disturbance was measured using the restless sleep item from the Center for Epidemiological Studies‐Depression Scale (CES‐D). CES‐D score (minus the sleep item) captured depressive symptoms; the State‐Anxiety subscale of the State Trait Anxiety Inventory measured anxiety symptoms. Multivariable linear regression models examined how the development of sleep disturbance affected changes in depressive or anxiety symptoms from the most recent prepandemic survey to the pandemic survey, controlling for covariates. Results The prevalence of sleep disturbance during the pandemic was 22.3%, with incident sleep disturbance in 10% and 13.5% of survivors and controls, respectively. Depressive and anxiety symptoms significantly increased during the pandemic among women with incident sleep disturbance (vs. no disturbance) (β = 8.16, p

Abstract Objective Numerous investigators have theorized that postoperative changes in Alzheimer's disease neuropathology may underlie postoperative neurocognitive disorders. Thus, we determined the relationship between postoperative changes in cognition and cerebrospinal (CSF) tau, p‐tau‐181p, or Aβ levels after non‐cardiac, non‐neurologic surgery in older adults. Methods Participants underwent cognitive testing before and 6 weeks after surgery, and lumbar punctures before, 24 h after, and 6 weeks after surgery. Cognitive scores were combined via factor analysis into an overall cognitive index. In total, 110 patients returned for 6‐week postoperative testing and were included in the analysis. Results There was no significant change from before to 24 h or 6 weeks following surgery in CSF tau (median [median absolute deviation] change before to 24 h: 0.00 [4.36] pg/mL, p = 0.853; change before to 6 weeks: −1.21 [3.98] pg/mL, p = 0.827). There were also no significant changes in CSF p‐tau‐181p or Aβ over this period. There was no change in cognitive index (mean [95% CI] 0.040 [−0.018, 0.098], p = 0.175) from before to 6 weeks after surgery, although there were postoperative declines in verbal memory (−0.346 [−0.523, −0.170], p = 0.003) and improvements in executive function (0.394, [0.310, 0.479], p 0.05 for each). Interpretation Neurocognitive changes after non‐cardiac, non‐neurologic surgery in the majority of cognitively healthy, community‐dwelling older adults are unlikely to be related to postoperative changes in AD neuropathology (as assessed by CSF Aβ, tau or p‐tau‐181p levels or the p‐tau‐181p/Aβ or tau/Aβ ratios). Trial Registration: clinicaltrials.gov (NCT01993836).

BackgroundKawasaki disease (KD) is the leading cause of acquired heart disease in children. The major challenge in KD diagnosis is that it shares clinical signs with other childhood febrile control (FC) subjects. We sought to determine if our algorithmic approach applied to a Taiwan cohort.MethodsA single center (Chang Gung Memorial Hospital in Taiwan) cohort of patients suspected with acute KD were prospectively enrolled by local KD specialists for KD analysis. Our previously single-center developed computer-based two-step algorithm was further tested by a five-center validation in US. This first blinded multi-center trial validated our approach, with sufficient sensitivity and positive predictive value, to identify most patients with KD diagnosed at centers across the US. This study involved 418 KDs and 259 FCs from the Chang Gung Memorial Hospital in Taiwan.FindingsOur diagnostic algorithm retained sensitivity (379 of 418; 90.7%), specificity (223 of 259; 86.1%), PPV (379 of 409; 92.7%), and NPV (223 of 247; 90.3%) comparable to previous US 2016 single center and US 2020 fiver center results. Only 4.7% (15 of 418) of KD and 2.3% (6 of 259) of FC patients were identified as indeterminate. The algorithm identified 18 of 50 (36%) KD patients who presented 2 or 3 principal criteria. Of 418 KD patients, 157 were infants younger than one year and 89.2% (140 of 157) were classified correctly. Of the 44 patients with KD who had coronary artery abnormalities, our diagnostic algorithm correctly identified 43 (97.7%) including all patients with dilated coronary artery but one who found to resolve in 8 weeks.InterpretationThis work demonstrates the applicability of our algorithmic approach and diagnostic portability in Taiwan.

Preeclampsia (PE) is a condition that poses a significant risk of maternal mortality and multiple organ failure during pregnancy. Early prediction of PE can enable timely surveillance and interventions, such as low-dose aspirin administration. In this study, conducted at Stanford Health Care, we examined a cohort of 60 pregnant women and collected 478 urine samples between gestational weeks 8 and 20 for comprehensive metabolomic profiling. By employing liquid chromatography mass spectrometry (LCMS/MS), we identified the structures of seven out of 26 metabolomics biomarkers detected. Utilizing the XGBoost algorithm, we developed a predictive model based on these seven metabolomics biomarkers to identify individuals at risk of developing PE. The performance of the model was evaluated using 10-fold cross-validation, yielding an area under the receiver operating characteristic curve of 0.856. Our findings suggest that measuring urinary metabolomics biomarkers offers a noninvasive approach to assess the risk of PE prior to its onset.

Background: Frail hospitalized older adults are at risk for adverse outcomes. Previous studies have suggested benefits for inpatient geriatric management (GEM). We sought to determine whether hospitalized patients with a history of heart failure (HF) benefitted from inpatient GEM or not. Methods: We studied 309 inpatients previously diagnosed with HF who were participants in a randomized trial of geriatric evaluation and management (GEM) versus usual care (UC). The intervention involved multidisciplinary teams that provided comprehensive geriatric assessment. We evaluated health-related quality of life (HRQOL), basic activities of daily living (ADLs), health service utilization, and survival at discharge, 6 months, and 1 year post randomization. Results: GEM patients had higher mean change scores for physical function (unadjusted means: 0.17 vs. –4.67, p = 0.046) and basic ADLs (1.25 vs. 0.67, p = 0.003) at hospital discharge, which remained significant after adjusting for baseline HRQOL scores and in-hospital days. Outcomes were not significantly different at 1 year. Length of stay for GEM was greater than UC (24 days vs. 17 days, p = 0.03), but total costs at 1 year were not different (p = 0.9). Mortality rates at 1 year were high and similar (GEM 29.0%, UC 27.3%, p = 0.73) in both the groups. Conclusion: Inpatient GEM was associated with better maintenance of physical function and basic ADLs at hospital discharge; however, no differences in HRQOL or survival were observed between GEM and UC at 1 year post randomization. Restructuring inpatient care models to incorporate inpatient GEM principles may be one method to optimize health-care delivery.

Survivors of childhood cancer have a relatively high risk of developing second cancers. The incidence of brain tumor in these patients approaches 1% at 10 years, over 80-fold that in the general population. This high incidence increases the likelihood that early detection of brain tumors in survivors of childhood cancer is feasible. By analogy with other epithelial cancers, detection and treatment of brain tumors at a pre-neoplastic or premalignant stage may render screening and treatment cost effective for certain high-risk populations. Our animal studies with a clinically appropriate model of this condition suggest that there is a pre-neoplastic, pre-malignant brain tumor lesion that is potentially both detectable and effectively treated. The possibility of detecting such a treatable antecedent to brain tumors provides the rationale for genomic and proteomic screening of tumor tissue, CSF, plasma and urine in this animal model, of tumor tissue and body fluids of patients with known brain tumors at various stages, and of body fluids of survivors of childhood cancer.

Objectives The aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision.Study design A retrospective cohort study.Setting Two medical centres from the USA.Participants Thirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms.Outcome measures Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.Results A model to determine gestational age was developed (R2=0.98) and validated (R2=0.81). 66.7% of the estimates fell within ±1 week of ultrasound results during model validation. Significant disruptions from full-term pregnancy metabolic patterns were observed in preterm pregnancies (R2=−0.68). A separate algorithm to predict preterm birth was developed using a set of 10 metabolic pathways that resulted in an area under the curve of 0.96 and 0.92, a sensitivity of 0.88 and 0.86, and a specificity of 0.96 and 0.92 during development and validation testing, respectively.Conclusions In this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.

Resistance to intravenous immunoglobulin (IVIG) occurs in 10-20% of patients with Kawasaki disease (KD). The risk of resistance is about two-fold higher in patients with elevated gamma glutamyl transferase (GGT) levels. We sought to understand the biological mechanisms underlying IVIG resistance in patients with elevated GGT levels.We explored the association between elevated GGT levels and IVIG-resistance with a cohort of 686 KD patients (Cohort I). Gene expression data from 130 children with acute KD (Cohort II) were analyzed using the R square statistic and false discovery analysis to identify genes that were differentially represented in patients with elevated GGT levels with regard to IVIG responsiveness. Two additional KD cohorts (Cohort III and IV) were used to test the hypothesis that sialylation and GGT may be involved in IVIG resistance through neutrophil apoptosis.Thirty-six genes were identified that significantly explained the variations of both GGT levels and IVIG responsiveness in KD patients. After Bonferroni correction, significant associations with IVIG resistance persisted for 12 out of 36 genes among patients with elevated GGT levels and none among patients with normal GGT levels. With the discovery of ST6GALNAC3, a sialyltransferase, as the most differentially expressed gene, we hypothesized that sialylation and GGT are involved in IVIG resistance through neutrophil apoptosis. We then confirmed that in Cohort III and IV there was significantly less reduction in neutrophil count in IVIG non-responders.Gene expression analyses combining molecular and clinical datasets support the hypotheses that: (1) neutrophil apoptosis induced by IVIG may be a mechanism of action of IVIG in KD; (2) changes in sialylation and GGT level in KD patients may contribute synergistically to IVIG resistance through blocking IVIG-induced neutrophil apoptosis. These findings have implications for understanding the mechanism of action in IVIG resistance, and possibly for development of novel therapeutics.

Kawasaki disease (KD) is an acute pediatric vasculitis of infants and young children with unknown etiology and no specific laboratory-based test to identify. A specific molecular diagnostic test is urgently needed to support the clinical decision of proper medical intervention, preventing subsequent complications of coronary artery aneurysms. We used a simple and low-cost colorimetric sensor array to address the lack of a specific diagnostic test to differentiate KD from febrile control (FC) patients with similar rash/fever illnesses.Demographic and clinical data were prospectively collected for subjects with KD and FCs under standard protocol. After screening using a genetic algorithm, eleven compounds including metalloporphyrins, pH indicators, redox indicators and solvatochromic dye categories, were selected from our chromatic compound library (n = 190) to construct a colorimetric sensor array for diagnosing KD. Quantitative color difference analysis led to a decision-tree-based KD diagnostic algorithm.This KD sensing array allowed the identification of 94% of KD subjects (receiver operating characteristic [ROC] area under the curve [AUC] 0.981) in the training set (33 KD, 33 FC) and 94% of KD subjects (ROC AUC: 0.873) in the testing set (16 KD, 17 FC). Color difference maps reconstructed from the digital images of the sensing compounds demonstrated distinctive patterns differentiating KD from FC patients.The colorimetric sensor array, composed of common used chemical compounds, is an easily accessible, low-cost method to realize the discrimination of subjects with KD from other febrile illness.

BACKGROUND:Kawasaki disease (KD) is an acute vasculitis in children that can cause coronary artery abnormalities. Its diagnosis is challenging, and many cytokines, chemokines, acute phase reactants, and growth factors have failed evaluation as specific biomarkers to distinguish KD from other febrile illnesses. We performed protein profiling, comparing plasma from children with KD with febrile control (FC) subjects to determine if there were specific proteins or peptides that could distinguish the two clinical states. MATERIALS AND METHODS:Plasma from three independent cohorts from the blood of 68 KD and 61 FC subjects was fractionated by anion exchange chromatography, followed by surface-enhanced laser desorption ionization (SELDI) mass spectrometry of the fractions. The mass spectra of KD and FC plasma samples were analyzed for peaks that were statistically significantly different. RESULTS:A mass spectrometry peak with a mass of 7,860 Da had high intensity in acute KD subjects compared to subacute KD (p = 0.0003) and FC (p = 7.9 x 10-10) subjects. We identified this peak as a novel truncated form of serum amyloid A with N-terminal at Lys-34 of the circulating form and validated its identity using a hybrid mass spectrum immunoassay technique. The truncated form of serum amyloid A was present in plasma of KD subjects when blood was collected in tubes containing protease inhibitors. This peak disappeared when the patients were examined after their symptoms resolved. Intensities of this peptide did not correlate with KD-associated laboratory values or with other mass spectrum peaks from the plasma of these KD subjects. CONCLUSIONS:Using SELDI mass spectrometry, we have discovered a novel truncated form of serum amyloid A that is elevated in the plasma of KD when compared with FC subjects. Future studies will evaluate its relevance as a diagnostic biomarker and its potential role in the pathophysiology of KD.

BACKGROUND: Understanding the early relationship between brain tumor cells and their environment could lead to more sensitive biomarkers and new therapeutic strategies. We have been using a rodent model of neurocarcinogenesis in which all animals develop brain tumors by six months of age to establish two early landmarks in glioma development: the appearance of a nestin(+) cell at thirty days of age and the appearance of cellular hyperplasia between 60 and 120 days of age. We now report an assessment of the CSF proteome to determine the changes in protein composition that occur during this period. MATERIALS AND METHODS: Nestin(+) cell clusters and microtumors were assessed in 63 ethylnitrosourea-exposed rats on 30, 60, and 90 days of age. CSF was obtained from the cisterna magna from 101 exposed and control rats at 30, 60, and 90 days and then analyzed using mass spectrometry. Differentially expressed peaks were isolated and identified. RESULTS: Nestin(+) cells were noted in all ethylnitrosourea-exposed rats assessed pathologically. Small microtumors were noted in 0%, 18%, and 67% of 30-, 60-, and 90-day old rats, respectively (p

Objective This study aimed to develop a blood test for the prediction of pre-eclampsia (PE) early in gestation. We hypothesised that the longitudinal measurements of circulating adipokines and sphingolipids in maternal serum over the course of pregnancy could identify novel prognostic biomarkers that are predictive of impending event of PE early in gestation.Study design Retrospective discovery and longitudinal confirmation.Setting Maternity units from two US hospitals.Participants Six previously published studies of placental tissue (78 PE and 95 non-PE) were compiled for genomic discovery, maternal sera from 15 women (7 non-PE and 8 PE) enrolled at ProMedDx were used for sphingolipidomic discovery, and maternal sera from 40 women (20 non-PE and 20 PE) enrolled at Stanford University were used for longitudinal observation.Outcome measures Biomarker candidates from discovery were longitudinally confirmed and compared in parallel to the ratio of placental growth factor (PlGF) and soluble fms-like tyrosine kinase (sFlt-1) using the same cohort. The datasets were generated by enzyme-linked immunosorbent and liquid chromatography-tandem mass spectrometric assays.Results Our discovery integrating genomic and sphingolipidomic analysis identified leptin (Lep) and ceramide (Cer) (d18:1/25:0) as novel biomarkers for early gestational assessment of PE. Our longitudinal observation revealed a marked elevation of Lep/Cer (d18:1/25:0) ratio in maternal serum at a median of 23 weeks’ gestation among women with impending PE as compared with women with uncomplicated pregnancy. The Lep/Cer (d18:1/25:0) ratio significantly outperformed the established sFlt-1/PlGF ratio in predicting impending event of PE with superior sensitivity (85% vs 20%) and area under curve (0.92 vs 0.52) from 5 to 25 weeks of gestation.Conclusions Our study demonstrated the longitudinal measurement of maternal Lep/Cer (d18:1/25:0) ratio allows the non-invasive assessment of PE to identify pregnancy at high risk in early gestation, outperforming the established sFlt-1/PlGF ratio test.

BACKGROUND:Placental protein expression plays a crucial role during pregnancy. We hypothesized that: (1) circulating levels of pregnancy-associated, placenta-related proteins throughout gestation reflect the temporal progression of the uncomplicated, full-term pregnancy, and can effectively estimate gestational ages (GAs); and (2) preeclampsia (PE) is associated with disruptions in these protein levels early in gestation; and can identify impending PE. We also compared gestational profiles of proteins in the human and mouse, using pregnant heme oxygenase-1 (HO-1) heterozygote (Het) mice, a mouse model reflecting PE-like symptoms. METHODS:Serum levels of placenta-related proteins-leptin (LEP), chorionic somatomammotropin hormone like 1 (CSHL1), elabela (ELA), activin A, soluble fms-like tyrosine kinase 1 (sFlt-1), and placental growth factor (PlGF)-were quantified by ELISA in blood serially collected throughout human pregnancies (20 normal subjects with 66 samples, and 20 subjects who developed PE with 61 samples). Multivariate analysis was performed to estimate the GA in normal pregnancy. Mean-squared errors of GA estimations were used to identify impending PE. The human protein profiles were then compared with those in the pregnant HO-1 Het mice. RESULTS:An elastic net-based gestational dating model was developed (R2 = 0.76) and validated (R2 = 0.61) using serum levels of the 6 proteins measured at various GAs from women with normal uncomplicated pregnancies. In women who developed PE, the model was not (R2 = -0.17) associated with GA. Deviations from the model estimations were observed in women who developed PE (P = 0.01). The model developed with 5 proteins (ELA excluded) performed similarly from sera from normal human (R2 = 0.68) and WT mouse (R2 = 0.85) pregnancies. Disruptions of this model were observed in both human PE-associated (R2 = 0.27) and mouse HO-1 Het (R2 = 0.30) pregnancies. LEP outperformed sFlt-1 and PlGF in differentiating impending PE at early human and late mouse GAs. CONCLUSIONS:Serum placenta-related protein profiles are temporally regulated throughout normal pregnancies and significantly disrupted in women who develop PE. LEP changes earlier than the well-established biomarkers (sFlt-1 and PlGF). There may be evidence of a causative action of HO-1 deficiency in LEP upregulation in a PE-like murine model.

Malignant gliomas remain incurable with a poor prognosis despite of aggressive treatment. We have been studying the development of brain tumors in a glioma rat model, where rats develop brain tumors after prenatal exposure to ethylnitrosourea (ENU), and there is a sizable interval between when the first pathological changes are noted and tumors become detectable with MRI. Our aim to define a molecular timeline through proteomic profiling of the cerebrospinal fluid (CSF) such that brain tumor commitment can be revealed earlier than at the presymptomatic stage. A comparative proteomic approach was applied to profile CSF collected serially either before, at and after the time MRI becomes positive. Elastic net (EN) based models were developed to infer the timeline of normal or tumor development respectively, mirroring a chronology of precisely timed, "clocked", adaptations. These CSF changes were later quantified by longitudinal entropy analyses of the EN predictive metric. False discovery rates (FDR) were computed to control the expected proportion of the EN models that are due to multiple hypothesis testing. Our ENU rat brain tumor dating EN model indicated that protein content in CSF is programmed even before tumor MRI detection. The findings of the precisely timed CSF tumor microenvironment changes at presymptomatic stages, deviation from the normal development timeline, may provide the groundwork for the understanding of adaptation of the brain environment in tumorigenesis to devise effective brain tumor management strategies.