A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers

Can serum NSE predict and evaluate sepsis-associated encephalopathy: A protocol for a systematic review and meta-analysis

INTRODUCTION

Brain dysfunction in sepsis is known as sepsis-associated encephalopathy (SAE), which often results in severe cognitive and neurological sequelae and increases the risk of death. Neuron specific enolase (NSE) may serve as an important neurocritical biomarker for detection and longitudinal monitoring in SAE patients. Our systematic review and meta-analysis will aim to explore the diagnostic and prognostic value of serum NSE in SAE patients. Currently, no systematic review and meta-analysis have been assessed that NSE as a biomarker of SAE.

METHODS AND ANALYSIS

We will conduct a systematic review and meta-analysis of serum NSE for the diagnostic and prognostic value of SAE patients. The primary objective is to evaluate the diagnostic accuracy of serum NSE as an independent biomarker for SAE. The secondary objective is to determine the prognostic strength of serum NSE as an independent biomarker of mortality in septic patients determine. We will perform a systematic search and descriptive review using the MEDLINE database and the PubMed interface. We will assign two independent reviewers to review all collected titles and associated abstracts, review full articles, and extract study data. We will use the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) assessment tool according to the recommendation by the Cochrane Collaboration to evaluate quality and risk of bias of the selected studies. Subgroup and sensitivity analyses will also be used to assess heterogeneity. Review Manager version 5.4 and Stata16.0. will be used for statistical analysis.

ETHICS AND DISSEMINATION

The meta-analysis will provide ICU physicians with the most current information to predict which patients are at risk of SAE and take corresponding intervention measures to reduce morbidity and ameliorate neurological outcomes. There is no need for ethics approval for this review. The findings will be disseminated in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

CRD42023398736.

Meta-analysis of evaluating neuron specific enolase as a serum biomarker for sepsis-associated encephalopathy

INTRODUCTION

Brain dysfunction in sepsis is known as Sepsis-associated encephalopathy (SAE), which often results in severe cognitive and neurological sequelae and increases the risk of death. Neuron specific enolase (NSE) may serve as an important neurocritical biomarker for detection and longitudinal monitoring in SAE patients. Our Meta-analysis aimed to explore the diagnostic and prognostic value of serum NSE in SAE patients. Currently, no systematic Review and Meta-analysis have been assessed that NSE as a biomarker of SAE.

METHODS

The study protocol was registered in the PROSPERO database (CRD42023398736) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We conducted a systematic review and Meta-analysis to evaluate the serum NSE's diagnostic accuracy for SAE and prognostic strength for probability of death of septic patients. We systematic searched electronic bibliographic databases from PubMed, MEDLINE, Web of Science, Embase, Cochrane databases, CNKI, CQVIP, and WFSD. QUADAS-2 assessment tool was used to evaluate quality and risk of bias of the selected studies. Subgroup analyses, funnel plots, sensitivity analyses were also carried out. Review Manager version 5.4 and Stata16.0. was used for statistical analysis.

RESULTS

This Meta-analysis included 22 studies with 1361 serum samples from SAE patients and 1580 serum samples from no-encephalopathy septic (NE) patients. The Meta-analysis showed that individuals with SAE had higher serum NSE level than NE controls (SMD 1.93 (95 % CI 1.51-2.35), P < 0.00001). In addition, there are 948 serum samples from survival septic patients and 446 serum samples from non-survival septic patients, septic patients with survival outcomes had lower serum NSE levels than those with death outcomes (SMD -1.87 (95 % CI -2.43 to -1.32), P < 0.00001).

CONCLUSION

Our Meta-analysis reveals a significant association between elevated NSE concentrations and the increased likelihood of concomitant SAE and mortality during septic patients. This comprehensive analysis will equip ICU physicians with up-to-date insights to accurately identify patients at risk of SAE and implement appropriate intervention strategies to mitigate morbidity and improve neurological outcomes. However, it is important to note that the presence of substantial heterogeneity among studies poses challenges in determining the most effective discrimination cutoff values and optimal sampling collection time.

Knowledgebase strategies to aid interpretation of clinical correlation research

OBJECTIVE

Knowledgebases are needed to clarify correlations observed in real-world electronic health record (EHR) data. We posit design principles, present a unifying framework, and report a test of concept.

MATERIALS AND METHODS

We structured a knowledge framework along 3 axes: condition of interest, knowledge source, and taxonomy. In our test of concept, we used hypertension as our condition of interest, literature and VanderbiltDDx knowledgebase as sources, and phecodes as our taxonomy. In a cohort of 832 566 deidentified EHRs, we modeled blood pressure and heart rate by sex and age, classified individuals by hypertensive status, and ran a Phenome-wide Association Study (PheWAS) for hypertension. We compared the correlations from PheWAS to the associations in our knowledgebase.

RESULTS

We produced PhecodeKbHtn: a knowledgebase comprising 167 hypertension-associated diseases, 15 of which were also negatively associated with blood pressure (pos+neg). Our hypertension PheWAS included 1914 phecodes, 129 of which were in the PhecodeKbHtn. Among the PheWAS association results, phecodes that were in PhecodeKbHtn had larger effect sizes compared with those phecodes not in the knowledgebase.

DISCUSSION

Each source contributed unique and additive associations. Models of blood pressure and heart rate by age and sex were consistent with prior cohort studies. All but 4 PheWAS positive and negative correlations for phecodes in PhecodeKbHtn may be explained by knowledgebase associations, hypertensive cardiac complications, or causes of hypertension independently associated with hypotension.

CONCLUSION

It is feasible to assemble a knowledgebase that is compatible with EHR data to aid interpretation of clinical correlation research.

Diagnostic and prognostic value of serum S100B in sepsis-associated encephalopathy: A systematic review and meta-analysis

Background

In sepsis, brain dysfunction is known as Sepsis-associated encephalopathy (SAE), which often results in severe cognitive and neurological sequelae and increases the risk of death. Our systematic review and meta-analysis aimed to explore the diagnostic and prognostic value of serum S100 calcium-binding protein B (S100B) in SAE patients.

Methods

We conducted a systematic search of the databases PubMed, Web of Science, Embase, Cochrane databases, CNKI, VIP, and WFSD from their inception dates until August 20, 2022. A Meta-analysis of the included studies was also performed using Review Manager version 5.4 and Stata16.0.

Results

This meta-analysis included 28 studies with 1401 serum samples from SAE patients and 1591 serum samples from no-encephalopathy septic (NE) patients. The Meta-Analysis showed that individuals with SAE had higher serum S100B level than NE controls (MD, 0.49 [95% CI (0.37)-(0.60), Z =8.29, P < 0.00001]), and the baseline level of serum S100B in septic patients with burn was significantly higher than average (1.96 [95% CI (0.92)-(2.99), Z =3.71, P < 0.0002]) In addition, septic patients with favorable outcomes had lower serum S100B levels than those with unfavorable outcomes (MD, -0.35 [95% CI (-0.50)-(-0.20), Z =4.60, P < 0.00001]).

Conclusion

Our Meta-Analysis indicates that higher serum S100B level in septic patients are moderately associated with SAE and unfavorable outcomes (The outcomes here mainly refer to the mortality). The serum S100B level may be a useful diagnostic and prognostic biomarker of SAE.

Development and Evaluation of a Peptide Heterodimeric Tracer Targeting CXCR4 and Integrin αvβ3 for Pancreatic Cancer Imaging

Nowadays, pancreatic cancer is still a formidable disease to diagnose. The CXC chemokine receptor 4 (CXCR4) and integrin αvβ3 play important roles in tumor development, progression, invasion, and metastasis, which are overexpressed in many types of human cancers. In this study, we developed a heterodimeric tracer 68Ga-yG5-RGD targeting both CXCR4 and integrin αvβ3, and evaluated its feasibility and utility in PET imaging of pancreatic cancer. The 68Ga-yG5-RGD could accumulate in CXCR4/integrin αvβ3 positive BxPC3 tumors in a high concentration and was much higher than that of 68Ga-yG5 (p < 0.001) and 68Ga-RGD (p < 0.001). No increased uptake of 68Ga-yG5-RGD was found in MX-1 tumors (CXCR4/integrin αvβ3, negative). In addition, the uptake of 68Ga-yG5-RGD in BxPC3 was significantly blocked by excess amounts of AMD3100 (an FDA-approved CXCR4 antagonist) and/or unlabeled RGD (p < 0.001), confirming its dual-receptor targeting properties. The ex vivo biodistribution and immunohistochemical results were consistent with the in vivo imaging results. The dual-receptor targeting strategy achieved improved tumor-targeting efficiency and prolonged tumor retention in BxPC3 tumors, suggesting 68Ga-yG5-RGD is a promising tracer for the noninvasive detection of tumors that express either CXCR4 or integrin αvβ3 or both, and therefore may have good prospects for clinical translation.

Analysis of risk factors progression of preterm delivery using electronic health records

Background

Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and reducing the frequency of occurrence of preterm deliveries. The purpose of this work is to identify preterm delivery risk factors and their progression throughout the pregnancy from a large collection of Electronic Health Records (EHR).

Results

The study cohort includes about 60,000 deliveries in the USA with the complete medical history from EHR for diagnoses, medications and procedures. We propose a temporal analysis of risk factors by estimating and comparing risk ratios and variable importance at different time points prior to the delivery event. We selected the following time points before delivery: 0, 12 and 24 week(s) of gestation. We did so by conducting a retrospective cohort study of patient history for a selected set of mothers who delivered preterm and a control group of mothers that delivered full-term. We analyzed the extracted data using logistic regression and random forests models. The results of our analyses showed that the highest risk ratio and variable importance corresponds to history of previous preterm delivery. Other risk factors were identified, some of which are consistent with those that are reported in the literature, others need further investigation.

Conclusions

The comparative analysis of the risk factors at different time points showed that risk factors in the early pregnancy related to patient history and chronic condition, while the risk factors in late pregnancy are specific to the current pregnancy. Our analysis unifies several previously reported studies on preterm risk factors. It also gives important insights on the changes of risk factors in the course of pregnancy. The code used for data analysis will be made available on github.

A framework for employing longitudinally collected multicenter electronic health records to stratify heterogeneous patient populations on disease history

OBJECTIVE

To facilitate patient disease subset and risk factor identification by constructing a pipeline which is generalizable, provides easily interpretable results, and allows replication by overcoming electronic health records (EHRs) batch effects.

MATERIAL AND METHODS

We used 1872 billing codes in EHRs of 102 880 patients from 12 healthcare systems. Using tools borrowed from single-cell omics, we mitigated center-specific batch effects and performed clustering to identify patients with highly similar medical history patterns across the various centers. Our visualization method (PheSpec) depicts the phenotypic profile of clusters, applies a novel filtering of noninformative codes (Ranked Scope Pervasion), and indicates the most distinguishing features.

RESULTS

We observed 114 clinically meaningful profiles, for example, linking prostate hyperplasia with cancer and diabetes with cardiovascular problems and grouping pediatric developmental disorders. Our framework identified disease subsets, exemplified by 6 "other headache" clusters, where phenotypic profiles suggested different underlying mechanisms: migraine, convulsion, injury, eye problems, joint pain, and pituitary gland disorders. Phenotypic patterns replicated well, with high correlations of ≥0.75 to an average of 6 (2-8) of the 12 different cohorts, demonstrating the consistency with which our method discovers disease history profiles.

DISCUSSION

Costly clinical research ventures should be based on solid hypotheses. We repurpose methods from single-cell omics to build these hypotheses from observational EHR data, distilling useful information from complex data.

CONCLUSION

We establish a generalizable pipeline for the identification and replication of clinically meaningful (sub)phenotypes from widely available high-dimensional billing codes. This approach overcomes datatype problems and produces comprehensive visualizations of validation-ready phenotypes.

The genetic architecture of plasma kynurenine includes cardiometabolic disease mechanisms associated with the SH2B3 gene

Inflammation increases the risk of cardiometabolic disease. Delineating specific inflammatory pathways and biomarkers of their activity could identify the mechanistic underpinnings of the increased risk. Plasma levels of kynurenine, a metabolite involved in inflammation, associates with cardiometabolic disease risk. We used genetic approaches to identify inflammatory mechanisms associated with kynurenine variability and their relationship to cardiometabolic disease. We identified single-nucleotide polymorphisms (SNPs) previously associated with plasma kynurenine, including a missense-variant (rs3184504) in the inflammatory gene SH2B3/LNK. We examined the association between rs3184504 and plasma kynurenine in independent human samples, and measured kynurenine levels in SH2B3-knock-out mice and during human LPS-evoked endotoxemia. We conducted phenome scanning to identify clinical phenotypes associated with each kynurenine-related SNP and with a kynurenine polygenic score using the UK-Biobank (n = 456,422), BioVU (n = 62,303), and Electronic Medical Records and Genetics (n = 32,324) databases. The SH2B3 missense variant associated with plasma kynurenine levels and SH2B3-/- mice had significant tissue-specific differences in kynurenine levels.LPS, an acute inflammatory stimulus, increased plasma kynurenine in humans. Mendelian randomization showed increased waist-circumference, a marker of central obesity, associated with increased kynurenine, and increased kynurenine associated with C-reactive protein (CRP). We found 30 diagnoses associated (FDR q < 0.05) with the SH2B3 variant, but not with SNPs mapping to genes known to regulate tryptophan-kynurenine metabolism. Plasma kynurenine may be a biomarker of acute and chronic inflammation involving the SH2B3 pathways. Its regulation lies upstream of CRP, suggesting that kynurenine may be a biomarker of one inflammatory mechanism contributing to increased cardiometabolic disease risk.

Generalizability of Polygenic Risk Scores for Breast Cancer Among Women With European, African, and Latinx Ancestry

IMPORTANCE

Multiple polygenic risk scores (PRSs) for breast cancer have been developed from large research consortia; however, their generalizability to diverse clinical settings is unknown.

OBJECTIVE

To examine the performance of previously developed breast cancer PRSs in a clinical setting for women of European, African, and Latinx ancestry.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study using the Electronic Medical Records and Genomics (eMERGE) network data set included 39 591 women from 9 contributing medical centers in the US that had electronic medical records (EMR) linked to genotype data. Breast cancer cases and controls were identified through a validated EMR phenotyping algorithm.

MAIN OUTCOMES AND MEASURES

Multivariable logistic regression was used to assess the association between breast cancer risk and 7 previously developed PRSs, adjusting for age, study site, breast cancer family history, and first 3 ancestry informative principal components.

RESULTS

This study included 39 591 women: 33 594 with European, 3801 with African, and 2196 with Latinx ancestry. The mean (SD) age at breast cancer diagnosis was 60.7 (13.0), 58.8 (12.5), and 60.1 (13.0) years for women with European, African, and Latinx ancestry, respectively. PRSs derived from women with European ancestry were associated with breast cancer risk in women with European ancestry (highest odds ratio [OR] per 1-SD increase, 1.46; 95% CI, 1.41-1.51), women with Latinx ancestry (highest OR, 1.31; 95% CI, 1.09-1.58), and women with African ancestry (OR, 1.19; 95% CI, 1.05-1.35). For women with European ancestry, this association with breast cancer risk was largest in the extremes of the PRS distribution, with ORs ranging from 2.19 (95% CI, 1.84-2.53) to 2.48 (95% CI, 1.89-3.25) for the 3 different PRSs examined for those in the highest 1% of the PRS compared with those in the middle quantile. Among women with Latinx and African ancestries at the extremes of the PRS distribution, there were no statistically significant associations.

CONCLUSIONS AND RELEVANCE

This cohort study found that PRS models derived from women with European ancestry for breast cancer risk generalized well for women with European, Latinx, and African ancestries across different clinical settings, although the effect sizes for women with African ancestry were smaller, likely because of differences in risk allele frequencies and linkage disequilibrium patterns. These results highlight the need to improve representation of diverse population groups, particularly women with African ancestry, in genomic research cohorts.

Personizing the prediction of future susceptibility to a specific disease

A traceable biomarker is a member of a disease's molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual's degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S' be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S'' ⊆{S-S'} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S'+S''}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual's degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.

The polygenic architecture of left ventricular mass mirrors the clinical epidemiology

Left ventricular (LV) mass is a prognostic biomarker for incident heart disease and all-cause mortality. Large-scale genome-wide association studies have identified few SNPs associated with LV mass. We hypothesized that a polygenic discovery approach using LV mass measurements made in a clinical population would identify risk factors and diseases associated with adverse LV remodeling. We developed a polygenic single nucleotide polymorphism-based predictor of LV mass in 7,601 individuals with LV mass measurements made during routine clinical care. We tested for associations between this predictor and 894 clinical diagnoses measured in 58,838 unrelated genotyped individuals. There were 29 clinical phenotypes associated with the LV mass genetic predictor at FDR q < 0.05. Genetically predicted higher LV mass was associated with modifiable cardiac risk factors, diagnoses related to organ dysfunction and conditions associated with abnormal cardiac structure including heart failure and atrial fibrillation. Secondary analyses using polygenic predictors confirmed a significant association between higher LV mass and body mass index and, in men, associations with coronary atherosclerosis and systolic blood pressure. In summary, these analyses show that LV mass-associated genetic variability associates with diagnoses of cardiac diseases and with modifiable risk factors which contribute to these diseases.

Association of Thyroid Function Genetic Predictors With Atrial Fibrillation: A Phenome-Wide Association Study and Inverse-Variance Weighted Average Meta-analysis

Importance

Thyroid hormone levels are tightly regulated through feedback inhibition by thyrotropin, produced by the pituitary gland. Hyperthyroidism is overwhelmingly due to thyroid disorders and is well recognized to contribute to a wide spectrum of cardiovascular morbidity, particularly the increasingly common arrhythmia atrial fibrillation (AF).

Objective

To determine the association between genetically determined thyrotropin levels and AF.

Design, Setting, and Participants

This phenome-wide association study scanned 1318 phenotypes associated with a polygenic predictor of thyrotropin levels identified by a previously published genome-wide association study that included participants of European ancestry. North American individuals of European ancestry with longitudinal electronic health records were analyzed from May 2008 to November 2016. Analysis began March 2018.

Main Outcomes and Measures

Clinical diagnoses associated with a polygenic predictor of thyrotropin levels.

Exposures

Genetically determined thyrotropin levels.

Results

Of 37 154 individuals, 19 330 (52%) were men. The thyrotropin polygenic predictor was positively associated with hypothyroidism (odds ratio [OR], 1.10; 95% CI, 1.07-1.14; P = 5 × 10-11) and inversely associated with diagnoses related to hyperthyroidism (OR, 0.64; 95% CI, 0.54-0.74; P = 2 × 10-8 for toxic multinodular goiter). Among nonthyroid associations, the top association was AF/flutter (OR, 0.93; 95% CI, 0.9-0.95; P = 9 × 10-7). When the analyses were repeated excluding 9801 individuals with any diagnoses of a thyroid-related disease, the AF association persisted (OR, 0.91; 95% CI, 0.88-0.95; P = 2.9 × 10-6). To replicate this association, we conducted an inverse-variance weighted average meta-analysis using AF single-nucleotide variant weights from a genome-wide association study of 17 931 AF cases and 115 142 controls. As in the discovery analyses, each SD increase in predicted thyrotropin was associated with a decreased risk of AF (OR, 0.86; 95% CI, 0.79-0.93; P = 4.7 × 10-4). In a set of AF cases (n = 745) and controls (n = 1680) older than 55 years, directly measured thyrotropin levels that fell within the normal range were inversely associated with AF risk (OR, 0.91; 95% CI, 0.83-0.99; P = .04).

Conclusions and Relevance

This study suggests a role for genetically determined variation in thyroid function within a physiologically accepted normal range as a risk factor for AF. The clinical decision to treat subclinical thyroid disease should incorporate the risk for AF as antithyroid medications to treat hyperthyroidism may reduce AF risk and thyroid hormone replacement for hypothyroidism may increase AF risk.