Objective
To evaluate the efficacy of three different carrier screening workflows designed to identify couples at risk for having offspring with autosomal recessive conditions.
Methods
Partner ...testing compliance, unnecessary testing, turnaround time, and ability to identify at‐risk couples (ARCs) were measured across all three screening strategies (sequential, tandem, or tandem reflex).
Results
A total of 314,100 individuals who underwent carrier screening were analyzed. Sequential, tandem, and tandem reflex screening yielded compliance frequencies of 25.8%, 100%, and 95.9%, respectively. Among 14,595 couples tested in tandem, 42.2% of females were screen‐negative, resulting in unnecessary testing of the male partner. In contrast, less than 1% of tandem reflex couples included unnecessary male testing. The median turnaround times were 29.2 days (sequential), 8 days (tandem), and 13.3 days (tandem reflex). The proportion of ARCs detected per total number of individual screens were 0.5% for sequential testing and 1.3% for both tandem and tandem reflex testing.
Conclusion
The tandem reflex strategy simplifies a potentially complex clinical scenario by providing a mechanism by which providers can maximize partner compliance and the detection of at‐risk couples while minimizing workflow burden and unnecessary testing and is more efficacious than both sequential and tandem screening strategies.
Highlights
What's already known about this topic?
Studies have explored barriers to carrier screening and follow up partner testing to identify at‐risk couples. However, to date, no one has explored the efficacy of different carrier screening workflows.
What does this study add?
This study highlights how providers could maximize the utility of carrier screening in identifying at‐risk couples based on the screening strategy utilized.
An extensive body of theory addresses the topic of pathogen virulence evolution, yet few studies have empirically demonstrated the presence of fitness trade-offs that would select for intermediate ...virulence. Here we show the presence of transmission-clearance trade-offs in dengue virus using viremia measurements. By fitting a within-host model to these data, we further find that the interaction between dengue and the host immune response can account for the observed trade-offs. Finally, we consider dengue virulence evolution when selection acts on the virus's production rate. By combining within-host model simulations with empirical findings on how host viral load affects human-to-mosquito transmission success, we show that the virus's transmission potential is maximized at production rates associated with intermediate virulence and that the optimal production rate critically depends on dengue's epidemiological context. These results indicate that long-term changes in dengue's global distribution impact the invasion and spread of virulent dengue virus genotypes.
Background
Disease severity is important when considering genes for inclusion on reproductive expanded carrier screening (ECS) panels. We applied a validated and previously published algorithm that ...classifies diseases into four severity categories (mild, moderate, severe, and profound) to 176 genes screened by ECS. Disease traits defining severity categories in the algorithm were then mapped to four severity‐related ECS panel design criteria cited by the American College of Obstetricians and Gynecologists (ACOG).
Methods
Eight genetic counselors (GCs) and four medical geneticists (MDs) applied the severity algorithm to subsets of 176 genes. MDs and GCs then determined by group consensus how each of these disease traits mapped to ACOG severity criteria, enabling determination of the number of ACOG severity criteria met by each gene.
Results
Upon consensus GC and MD application of the severity algorithm, 68 (39%) genes were classified as profound, 71 (40%) as severe, 36 (20%) as moderate, and one (1%) as mild. After mapping of disease traits to ACOG severity criteria, 170 out of 176 genes (96.6%) were found to meet at least one of the four criteria, 129 genes (73.3%) met at least two, 73 genes (41.5%) met at least three, and 17 genes (9.7%) met all four.
Conclusion
This study classified the severity of a large set of Mendelian genes by collaborative clinical expert application of a trait‐based algorithm. Further, it operationalized difficult to interpret ACOG severity criteria via mapping of disease traits, thereby promoting consistency of ACOG criteria interpretation.
Dengue is a vector-borne viral disease of humans that endemically circulates in many tropical and subtropical regions worldwide. Infection with dengue can result in a range of disease outcomes. A ...considerable amount of research has sought to improve our understanding of this variation in disease outcomes and to identify predictors of severe disease. Contributing to this research, patterns of viral load in dengue infected patients have been quantified, with analyses indicating that peak viral load levels, rates of viral load decline, and time to peak viremia are useful predictors of severe disease. Here, we take a complementary approach to understanding patterns of clinical manifestation and inter-individual variation in viral load dynamics. Specifically, we statistically fit mathematical within-host models of dengue to individual-level viral load data to test virological and immunological hypotheses explaining inter-individual variation in dengue viral load. We choose between alternative models using model selection criteria to determine which hypotheses are best supported by the data. We first show that the cellular immune response plays an important role in regulating viral load in secondary dengue infections. We then provide statistical support for the process of antibody-dependent enhancement (but not original antigenic sin) in the development of severe disease in secondary dengue infections. Finally, we show statistical support for serotype-specific differences in viral infectivity rates, with infectivity rates of dengue serotypes 2 and 3 exceeding those of serotype 1. These results contribute to our understanding of dengue viral load patterns and their relationship to the development of severe dengue disease. They further have implications for understanding how dengue transmissibility may depend on the immune status of infected individuals and the identity of the infecting serotype.
A multi-biomarker disease activity (MBDA)-based cardiovascular disease (CVD) risk score was developed and internally validated in a Medicare cohort to predict 3-year risk for myocardial infarction ...(MI), stroke or CVD death in patients with rheumatoid arthritis (RA). It combines the MBDA score, leptin, MMP-3, TNF-R1, age and four clinical variables. We are now externally validating it in a younger RA cohort.
Claims data from a private aggregator were linked to MBDA test data to create a cohort of RA patients ≥18 years old. A univariable Cox proportional hazards regression model was fit using the MBDA-based CVD risk score as sole predictor of time-to-a-CVD event (hospitalized MI or stroke). Hazard ratio (HR) estimate was determined for all patients and for clinically relevant subgroups. A multivariable Cox model evaluated whether the MBDA-based CVD risk score adds predictive information to clinical data.
49,028 RA patients (340 CVD events) were studied. Mean age was 52.3 years; 18.3% were male. HR for predicting 3-year risk of a CVD event by the MBDA-based CVD risk score in the full cohort was 3.99 (95% CI: 3.51-4.49, p = 5.0×10-95). HR were also significant for subgroups based on age, comorbidities, disease activity, and drug use. In a multivariable model, the MBDA-based CVD risk score added significant information to hypertension, diabetes, tobacco use, history of CVD, age, sex and CRP (HR = 2.27, p = 1.7×10-7).
The MBDA-based CVD risk score has been externally validated in an RA cohort that is younger than and independent of the Medicare cohort that was used for development and internal validation.
In recent years, the within-host viral dynamics of dengue infections have been increasingly characterized, and the relationship between aspects of these dynamics and the manifestation of severe ...disease has been increasingly probed. Despite this progress, there are few mathematical models of within-host dengue dynamics, and the ones that exist focus primarily on the general role of immune cells in the clearance of infected cells, while neglecting other components of the immune response in limiting viraemia. Here, by considering a suite of mathematical within-host dengue models of increasing complexity, we aim to isolate the critical components of the innate and the adaptive immune response that suffice in the reproduction of several well-characterized features of primary and secondary dengue infections. By building up from a simple target cell limited model, we show that only the innate immune response is needed to recover the characteristic features of a primary symptomatic dengue infection, while a higher rate of viral infectivity (indicative of antibody-dependent enhancement) and infected cell clearance by T cells are further needed to recover the characteristic features of a secondary dengue infection. We show that these minimal models can reproduce the increased risk of disease associated with secondary heterologous infections that arises as a result of a cytokine storm, and, further, that they are consistent with virological indicators that predict the onset of severe disease, such as the magnitude of peak viraemia, time to peak viral load, and viral clearance rate. Finally, we show that the effectiveness of these virological indicators to predict the onset of severe disease depends on the contribution of T cells in fuelling the cytokine storm.
Rheumatoid arthritis (RA) patients have increased risk for cardiovascular disease (CVD). Accurate CVD risk prediction could improve care for RA patients. Our goal is to develop and validate a ...biomarker-based model for predicting CVD risk in RA patients.
Medicare claims data were linked to multi-biomarker disease activity (MBDA) test results to create an RA patient cohort with age ≥ 40 years that was split 2:1 for training and internal validation. Clinical and RA-related variables, MBDA score, and its 12 biomarkers were evaluated as predictors of a composite CVD outcome: myocardial infarction (MI), stroke, or fatal CVD within 3 years. Model building used Cox proportional hazard regression with backward elimination. The final MBDA-based CVD risk score was internally validated and compared to four clinical CVD risk prediction models.
30,751 RA patients (904 CVD events) were analyzed. Covariates in the final MBDA-based CVD risk score were age, diabetes, hypertension, tobacco use, history of CVD (excluding MI/stroke), MBDA score, leptin, MMP-3 and TNF-R1. In internal validation, the MBDA-based CVD risk score was a strong predictor of 3-year risk for a CVD event, with hazard ratio (95% CI) of 2.89 (2.46-3.41). The predicted 3-year CVD risk was low for 9.4% of patients, borderline for 10.2%, intermediate for 52.2%, and high for 28.2%. Model fit was good, with mean predicted versus observed 3-year CVD risks of 4.5% versus 4.4%. The MBDA-based CVD risk score significantly improved risk discrimination by the likelihood ratio test, compared to four clinical models. The risk score also improved prediction, reclassifying 42% of patients versus the simplest clinical model (age + sex), with a net reclassification index (NRI) (95% CI) of 0.19 (0.10-0.27); and 28% of patients versus the most comprehensive clinical model (age + sex + diabetes + hypertension + tobacco use + history of CVD + CRP), with an NRI of 0.07 (0.001-0.13). C-index was 0.715 versus 0.661 to 0.696 for the four clinical models.
A prognostic score has been developed to predict 3-year CVD risk for RA patients by using clinical data, three serum biomarkers and the MBDA score. In internal validation, it had good accuracy and outperformed clinical models with and without CRP. The MBDA-based CVD risk prediction score may improve RA patient care by offering a risk stratification tool that incorporates the effect of RA inflammation.
The multi-biomarker disease activity (MBDA) test measures 12 serum protein biomarkers to quantify disease activity in RA patients. A newer version of the MBDA score, adjusted for age, sex, and ...adiposity, has been validated in two cohorts (OPERA and BRASS) for predicting risk for radiographic progression. We now extend these findings with additional cohorts to further validate the adjusted MBDA score as a predictor of radiographic progression risk and compare its performance with that of other risk factors.
Four cohorts were analyzed: the BRASS and Leiden registries and the OPERA and SWEFOT studies (total N = 953). Treatments included conventional DMARDs and anti-TNFs. Associations of radiographic progression (ΔTSS) per year with the adjusted MBDA score, seropositivity, and clinical measures were evaluated using linear and logistic regression. The adjusted MBDA score was (1) validated in Leiden and SWEFOT, (2) compared with other measures in all four cohorts, and (3) used to generate curves for predicting risk of radiographic progression.
Univariable and bivariable analyses validated the adjusted MBDA score and found it to be the strongest, independent predicator of radiographic progression (ΔTSS > 5) compared with seropositivity (rheumatoid factor and/or anti-CCP), baseline TSS, DAS28-CRP, CRP SJC, or CDAI. Neither DAS28-CRP, CDAI, SJC, nor CRP added significant information to the adjusted MBDA score as a predictor, and the frequency of radiographic progression agreed with the adjusted MBDA score when it was discordant with these measures. The rate of progression (ΔTSS > 5) increased from < 2% in the low (1-29) adjusted MBDA category to 16% in the high (45-100) category. A modeled risk curve indicated that risk increased continuously, exceeding 40% for the highest adjusted MBDA scores.
The adjusted MBDA score was validated as an RA disease activity measure that is prognostic for radiographic progression. The adjusted MBDA score was a stronger predictor of radiographic progression than conventional risk factors, including seropositivity, and its prognostic ability was not significantly improved by the addition of DAS28-CRP, CRP, SJC, or CDAI.
BackgroundEstablished biomarkers of immune checkpoint inhibitor (ICI) response in metastatic non-small cell lung cancer (mNSCLC), such as PD-L1 and tumor mutational burden (TMB), do not identify all ...patients with durable response. While biomarkers spanning multiple data modalities have been proposed to address this unmet need, additional evidence is required for use in the clinic. Here, we performed a comparative study assessing the association between previously described biomarkers of ICI response and outcomes in a real-world mNSCLC cohort.MethodsUsing the Tempus database, we analyzed de-identified records of non-squamous EGFR-negative and ALK fusion-negative mNSCLC patients treated with first-line ICI regimens and profiled with targeted-panel DNA-seq and whole-exome-capture RNA-seq. ICI-related biomarkers were calculated following published methods using the Tempus IO platform (table 1). Immune biomarkers included features describing tumor biology (including PD-L1 IHC, STK11 and KEAP1 mutations, APOBEC signature1), HLA (HLA-LOH, HLA A*03 genotype), neoantigens (TMB, neoantigen burden), and RNA-based signatures of immune activity such as IFNγ tumor inflammation signature,2 IMPRES,3 Cytotoxic score,4 NRS,5tertiary lymphoid structure (TLS),6 7 and others. Real-world time to progression (rwTTP) was defined as the interval from ICI start to the first progression event, censored on last known physician encounter. Cox proportional hazards (Cox PH) models were fitted to evaluate the relationship between each biomarker and rwTTP.ResultsIn this mNSCLC cohort (N=199), multiple RNA-based signatures related to immune infiltration, TLS, cytotoxic and cytolytic activity, and IFNγ tumor inflammation signature (TIS) were significantly associated with longer rwTTP (P < 0.001). HLA- and tumor-related biomarkers had no significant associations with longer rwTTP. As expected, TMB and PD-L1 IHC were significantly associated with longer rwTTP (P < 0.05). In multivariable Cox PH analyses controlling for PD-L1 IHC and TMB, several immune signatures remained significantly associated with rwTTP (table 1). Notably, many RNA-based signatures were highly correlated despite non-overlapping genes and functions, underscoring a broad profile of immune infiltration and activation is likely associated with ICI response.ConclusionsHere we demonstrate that RNA-based immune signatures are significantly associated with clinical benefit and may supplement well-established ICI biomarkers in therapy selection. These immune RNA-based signatures need to be prospectively validated in future studies.ReferencesWang S, Jia M, He Z, Liu XS. APOBEC3B and APOBEC mutational signature as potential predictive markers for immunotherapy response in non-small cell lung cancer. Oncogene. 2018;37(29):3924–3936.Ayers M, Lunceford J, Nebozhyn M, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017;127(8):2930–2940.Auslander N, Zhang G, Lee JS, et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma published correction appears in Nat Med. 2018 Dec;24(12):1942. Nat Med. 2018;24(10):1545–1549.Lau D, Khare S, Stein MM, et al. Integration of tumor extrinsic and intrinsic features associates with immunotherapy response in non-small cell lung cancer. Nat Commun. 2022;13(1):4053.Huang AC, Orlowski RJ, Xu X, et al. A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat Med. 2019;25(3):454–461.Cabrita R, Lauss M, Sanna A, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma published correction appears in Nature. 2020 Apr;580(7801):E1. Nature. 2020;577(7791):561–565.Andersson A, Larsson L, Stenbeck L, et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat Commun. 2021;12(1):6012.Abstract 181 Table 1ICI biomarker associations with rwTTP in ICI-treated mNSCLC cohort (n=199). Cox PH regression results are shown for each biomarker as a single predictor (Univariate Cox PH HR, P-value) and as a predictor with TMB and PD-Ll IHC included in a multivariable Cox PH model (Multivariable Cox PH HR, P-value).
Acetaminophen (N-acetyl-para-aminophenol) is the most widely used over-the-counter or prescription painkiller in the world. Acetaminophen is metabolized in the liver where a toxic byproduct is ...produced that can be removed by conjugation with glutathione. Acetaminophen overdoses, either accidental or intentional, are the leading cause of acute liver failure in the United States, accounting for 56,000 emergency room visits per year. The standard treatment for overdose is N-acetyl-cysteine (NAC), which is given to stimulate the production of glutathione.
We have created a mathematical model for acetaminophen transport and metabolism including the following compartments: gut, plasma, liver, tissue, urine. In the liver compartment the metabolism of acetaminophen includes sulfation, glucoronidation, conjugation with glutathione, production of the toxic metabolite, and liver damage, taking biochemical parameters from the literature whenever possible. This model is then connected to a previously constructed model of glutathione metabolism.
We show that our model accurately reproduces published clinical and experimental data on the dose-dependent time course of acetaminophen in the plasma, the accumulation of acetaminophen and its metabolites in the urine, and the depletion of glutathione caused by conjugation with the toxic product. We use the model to study the extent of liver damage caused by overdoses or by chronic use of therapeutic doses, and the effects of polymorphisms in glucoronidation enzymes. We use the model to study the depletion of glutathione and the effect of the size and timing of N-acetyl-cysteine doses given as an antidote. Our model accurately predicts patient death or recovery depending on size of APAP overdose and time of treatment.
The mathematical model provides a new tool for studying the effects of various doses of acetaminophen on the liver metabolism of acetaminophen and glutathione. It can be used to study how the metabolism of acetaminophen depends on the expression level of liver enzymes. Finally, it can be used to predict patient metabolic and physiological responses to APAP doses and different NAC dosing strategies.
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