Multi-tissue proteomics identifies molecular signatures for sporadic and genetically defined Alzheimer disease cases

2021 ◽  
Author(s):  
Carlos Cruchaga ◽  
Yun Ju Sung ◽  
Chengran Yang ◽  
Fengxian Wang ◽  
Adam Suhy ◽  
...  
Keyword(s):  
Alzheimer Disease ◽  
Autosomal Dominant ◽  
Prediction Models ◽  
Disease Risk ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Csf Proteins ◽  
Tissue Proteomics ◽  
Specific Prediction ◽  
Genetic Profiles

Abstract Alzheimer disease (AD) is a heterogeneous disease with many genes are associated with AD risk. Most proteomic studies, while instrumental in identifying AD pathways and genes, focus on single tissues and sporadic AD cases. Multi-tissue proteomic signatures for sporadic and genetically defined AD (e.g., pathogenic variant carriers in APP and PSEN1/2 and risk variant carriers in TREM2) will illuminate the biology of this heterogeneous disease.1,2 Here, we present one of the largest multi-tissue proteomic profiles, accessible through our web portal, based on 1,305 proteins in brain (n=360), cerebrospinal fluid (CSF; n=717), and plasma (n=490) from the Knight Alzheimer Disease Research Center (Knight ADRC) and Dominantly Inherited Alzheimer Network (DIAN) cohorts.3-5 We identified proteomic signatures in brain, CSF, and plasma for sporadic AD status and replicated these findings in multiple, independent datasets. The area under the curve (AUC) for CSF proteins was 0.89 in discovery and 0.90 in the replication dataset, which was significantly higher than the AUC for CSF p-tau181/Aβ42 (AUC = 0.81; P = 2.4×10-6). We also identified a specific proteomic signature for TREM2 variant carriers that differentiated TREM2 variant carriers from sporadic AD cases and controls with high sensitivity and specificity (AUC = 0.81 - 1). In addition, the proteins that showed differential levels in sporadic AD were also altered in autosomal dominant AD, but with greater effect size (1.4 times, P = 3.8×10-5), and proteins associated with autosomal dominant AD, in brain tissue also replicated on CSF (p=1.36×10-9). Enrichment analyses highlighted several pathways including AD (calcineurin, APOE, GRN), Parkinson disease (α-synuclein, LRRK2), and innate immune response (SHC1, MAPK3, SPP1) for the sporadic AD or TREM2 variant carriers. Our findings show the power of multi-tissue proteomics’ contribution to the understanding of AD biology and to the creation of tissue-specific prediction models for individuals with specific genetic profiles, ultimately supporting its utility in creating individualized disease risk evaluation and treatment.

scholarly journals Type 2 diabetes-related variants influence the risk of developing multiple myeloma: results from the IMMEnSE consortium

2015 ◽  
Vol 22 (4) ◽  
pp. 545-559 ◽  
Author(s):  
Rafael Ríos ◽  
Carmen Belén Lupiañez ◽  
Daniele Campa ◽  
Alessandro Martino ◽  
Joaquin Martínez-López ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Multiple Myeloma ◽  
Prediction Models ◽  
Disease Risk ◽  
Association Studies ◽  
Area Under The Curve ◽  
Genome Wide Association Studies ◽  
Increased Risk ◽  
Stratified Analysis

Type 2 diabetes (T2D) has been suggested to be a risk factor for multiple myeloma (MM), but the relationship between the two traits is still not well understood. The aims of this study were to evaluate whether 58 genome-wide-association-studies (GWAS)-identified common variants for T2D influence the risk of developing MM and to determine whether predictive models built with these variants might help to predict the disease risk. We conducted a case–control study including 1420 MM patients and 1858 controls ascertained through the International Multiple Myeloma (IMMEnSE) consortium. Subjects carrying the KCNQ1rs2237892T allele or the CDKN2A-2Brs2383208G/G, IGF1rs35767T/T and MADDrs7944584T/T genotypes had a significantly increased risk of MM (odds ratio (OR)=1.32–2.13) whereas those carrying the KCNJ11rs5215C, KCNJ11rs5219T and THADArs7578597C alleles or the FTOrs8050136A/A and LTArs1041981C/C genotypes showed a significantly decreased risk of developing the disease (OR=0.76–0.85). Interestingly, a prediction model including those T2D-related variants associated with the risk of MM showed a significantly improved discriminatory ability to predict the disease when compared to a model without genetic information (area under the curve (AUC)=0.645 vs AUC=0.629; P=4.05×10−06). A gender-stratified analysis also revealed a significant gender effect modification for ADAM30rs2641348 and NOTCH2rs10923931 variants (Pinteraction=0.001 and 0.0004, respectively). Men carrying the ADAM30rs2641348C and NOTCH2rs10923931T alleles had a significantly decreased risk of MM whereas an opposite but not significant effect was observed in women (ORM=0.71 and ORM=0.66 vs ORW=1.22 and ORW=1.15, respectively). These results suggest that TD2-related variants may influence the risk of developing MM and their genotyping might help to improve MM risk prediction models.

scholarly journals Assessing the Predictive Validity of Simple Dementia Risk Models in Harmonized Stroke Cohorts

Stroke ◽  
2020 ◽  
Vol 51 (7) ◽  
pp. 2095-2102
Author(s):  
Eugene Y.H. Tang ◽  
Christopher I. Price ◽  
Louise Robinson ◽  
Catherine Exley ◽  
David W. Desmond ◽  
...  
Keyword(s):  
Risk Factors ◽  
General Population ◽  
Risk Prediction ◽  
Prediction Models ◽  
Disease Risk ◽  
Risk Index ◽  
Area Under The Curve ◽  
Risk Prediction Models ◽  
C Statistic ◽  
Dementia Risk

Background and Purpose: Stroke is associated with an increased risk of dementia. To assist in the early identification of individuals at high risk of future dementia, numerous prediction models have been developed for use in the general population. However, it is not known whether such models also provide accurate predictions among stroke patients. Therefore, the aim of this study was to determine whether existing dementia risk prediction models that were developed for use in the general population can also be applied to individuals with a history of stroke to predict poststroke dementia with equivalent predictive validity. Methods: Data were harmonized from 4 stroke studies (follow-up range, ≈12–18 months poststroke) from Hong Kong, the United States, the Netherlands, and France. Regression analysis was used to test 3 risk prediction models: the Cardiovascular Risk Factors, Aging and Dementia score, the Australian National University Alzheimer Disease Risk Index, and the Brief Dementia Screening Indicator. Model performance or discrimination accuracy was assessed using the C statistic or area under the curve. Calibration was tested using the Grønnesby and Borgan and the goodness-of-fit tests. Results: The predictive accuracy of the models varied but was generally low compared with the original development cohorts, with the Australian National University Alzheimer Disease Risk Index (C-statistic, 0.66) and the Brief Dementia Screening Indicator (C-statistic, 0.61) both performing better than the Cardiovascular Risk Factors, Aging and Dementia score (area under the curve, 0.53). Conclusions: Dementia risk prediction models developed for the general population do not perform well in individuals with stroke. Their poor performance could have been due to the need for additional or different predictors related to stroke and vascular risk factors or methodological differences across studies (eg, length of follow-up, age distribution). Future work is needed to develop simple and cost-effective risk prediction models specific to poststroke dementia.

scholarly journals The Performance of Vascular Age in the Assessment of Cardiovascular Risk of Patients with Rheumatoid Arthritis

2020 ◽  
Vol 9 (12) ◽  
pp. 4065
Author(s):  
Iván Ferraz-Amaro ◽  
Alfonso Corrales ◽  
Juan Carlos Quevedo-Abeledo ◽  
Belén Atienza-Mateo ◽  
Diana Prieto-Peña ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Carotid Plaque ◽  
Prediction Models ◽  
Disease Risk ◽  
Absolute Risk ◽  
Subclinical Atherosclerosis ◽  
Area Under The Curve ◽  
Intima Media Thickness ◽  
Chronological Age ◽  
Vascular Age

Background. Cardiovascular (CV) disease risk prediction models developed for use in the general population have suboptimal performance in patients with rheumatoid arthritis (RA). Vascular age (VA) is a new concept that has been proposed as a measure of CV ‘relative’ risk instead of the ‘absolute’ risk that current prediction models provide. In the present study we aim to study the performance of vascular age (VA) in the assessment of CV risk in patients with RA. We additionally aimed to analyze its relation with subclinical atherosclerosis as measured through carotid plaque ultrasound. Methods. A total of 1173 non-diabetic RA patients without previous CV events were included. Disease characteristics, SCORE, VA determined on SCORE and on carotid intima media thickness (cIMT), and the presence of plaque through carotid ultrasound were assessed. The interrelations of VA with SCORE, and its associations with subclinical carotid atherosclerosis were studied. Results. On average, RA patients had both a SCORE determined VA (4.7 years) and a cIMT-based VA (2.4 years) significantly higher than the chronological age. When these differences were analyzed in different age intervals, while VA based on SCORE was significantly higher compared to chronological age in all age ranges, VA determined on cIMT was significantly elevated only in RA patients younger than 60 years. The area under the curve analysis for the association of SCORE and VA with the presence of carotid plaque disclosed no differences between both parameters. VA was associated with the presence of carotid plaque after multivariable regression analysis in patients younger than 60 years old. Conclusion. VA is significantly higher than chronological age in patients with RA. The performance of VA in its relation to carotid plaque is similar to that of the SCORE.

scholarly journals Statistical Evaluation of Prognostic versus Diagnostic Models: Beyond the ROC Curve

2008 ◽  
Vol 54 (1) ◽  
pp. 17-23 ◽  
Author(s):  
Nancy R Cook
Keyword(s):  
Cardiovascular Disease ◽  
Roc Curve ◽  
Prediction Models ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Clinical Impact ◽  
Prognostic Models ◽  
C Statistic ◽  
Diagnostic Models ◽  
Predicted Values

Abstract Background: Diagnostic and prognostic or predictive models serve different purposes. Whereas diagnostic models are usually used for classification, prognostic models incorporate the dimension of time, adding a stochastic element. Content: The ROC curve is typically used to evaluate clinical utility for both diagnostic and prognostic models. This curve assesses how well a test or model discriminates, or separates individuals into two classes, such as diseased and nondiseased. A strong risk predictor, such as lipids for cardiovascular disease, may have limited impact on the area under the curve, called the AUC or c-statistic, even if it alters predicted values. Calibration, measuring whether predicted probabilities agree with observed proportions, is another component of model accuracy important to assess. Reclassification can directly compare the clinical impact of two models by determining how many individuals would be reclassified into clinically relevant risk strata. For example, adding high-sensitivity C-reactive protein and family history to prediction models for cardiovascular disease using traditional risk factors moves approximately 30% of those at intermediate risk levels, such as 5%–10% or 10%–20% 10-year risk, into higher or lower risk categories, despite little change in the c-statistic. A calibration statistic can asses how well the new predicted values agree with those observed in the cross-classified data. Summary: Although it is useful for classification, evaluation of prognostic models should not rely solely on the ROC curve, but should assess both discrimination and calibration. Risk reclassification can aid in comparing the clinical impact of two models on risk for the individual, as well as the population.

scholarly journals Utility of recombinant human TSH stimulation test in the follow-up of patients with differentiated thyroid cancer depending on basal thyroglobulin results

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Amaia Sandúa ◽  
Monica Macias ◽  
Carolina Perdomo ◽  
Juan Carlos Galofre ◽  
Roser Ferrer ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Differentiated Thyroid Cancer ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
Thyroid Stimulating Hormone ◽  
Stimulation Test ◽  
Human Thyroid ◽  
Antithyroglobulin Antibodies ◽  
Structural Disease

AbstractBackgroundThyroglobulin (Tg) is fundamental for differentiated thyroid cancer (DTC) monitoring. Tg detection can be enhanced using recombinant human thyroid-stimulating hormone (TSH) (rhTSH). This study is aimed to evaluate the use of the rhTSH stimulation test when using a high-sensitivity Tg assay.MethodsWe retrospectively studied 181 rhTSH tests from 114 patients with DTC and negative for antithyroglobulin antibodies (anti-TgAb). Image studies were performed in all cases. Serum Tg and anti-TgAb were measured using specific immunoassays.ResultsrhTSH stimulation in patients with basal serum Tg (b-Tg) concentrations lower than 0.2 ng/mL always resulted in rhTSH-stimulated serum Tg (s-Tg) concentrations lower than 1.0 ng/mL and negative structural disease. In patients with b-Tg concentration between 0.2 and 1.0 ng/mL, s-Tg detected one patient (1/30) who showed biochemical incomplete response. Patients with negative images had lower s-Tg than those with nonspecific or abnormal findings (p<0.05). Receiver operating characteristic curve analysis of the s-Tg to detect altered images showed an area under the curve of 0.763 (p<0.05). With an s-Tg cutoff of 0.85 ng/mL, the sensitivity was 100%, decreasing to 96.15% with an s-Tg cutoff of 2 ng/mL.ConclusionsPatients with DTC with b-Tg concentrations equal or higher than 0.2 ng/mL can benefit from the rhTSH stimulation test.

scholarly journals The associations between whole grain and refined grain intakes and serum C-reactive protein

2021 ◽  
Author(s):  
Riikka E. Taskinen ◽  
Sari Hantunen ◽  
Tomi-Pekka Tuomainen ◽  
Jyrki K. Virtanen
Keyword(s):  
Disease Risk ◽  
High Sensitivity ◽  
Epidemiological Studies ◽  
Whole Grains ◽  
Dietary Factors ◽  
Low Grade ◽  
Dietary Intakes ◽  
Whole Grain ◽  
Refined Grains ◽  
Hs Crp

Abstract Background/objectives Epidemiological studies suggest that whole grain intake has inverse associations with low-grade inflammation, but findings regarding refined grains are inconclusive. Our objective was to investigate whether consumption of whole or refined grains is associated with serum high sensitivity CRP (hs-CRP). Subjects/methods The study included 756 generally healthy men and women aged 53–73 years from the Kuopio Ischaemic Heart Disease Risk Factory Study, examined in 1999–2001. Dietary intakes were assessed using 4-day food records. ANCOVA and linear regression were used for analyses. Results The mean intake of whole and refined grains was 136 g/day (SD 80) and 84 g/day (SD 46), respectively. Higher whole grain intake was associated with lower hs-CRP concentration and higher refined grain intake with higher concentration after adjustment for lifestyle and dietary factors. Each 50 g/d higher whole grain intake was associated with 0.12 mg/L (95% Cl 0.02–0.21 mg/L) lower hs-CRP concentration and each 50 g/d higher refined grain intake with 0.23 mg/L (95% Cl 0.08–0.38) higher concentration. Adjustment for fibre from grains attenuated the associations especially with whole grains. There were no statistically significant interactions according to gender or BMI (P for interactions >0.065). Conclusions The results of this study suggest that higher intake of whole grains is associated with lower concentrations of hs-CRP and higher intake of refined grains is associated with higher concentrations. However, especially the association with whole grain intake was attenuated after adjusting for fibre intake from grains, suggesting that cereal fibre may partly explain the association.

scholarly journals Validation and verification of predictive salivary biomarkers for oral health

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nagihan Bostanci ◽  
Konstantinos Mitsakakis ◽  
Beral Afacan ◽  
Kai Bao ◽  
Benita Johannsen ◽  
...  
Keyword(s):  
Oral Health ◽  
Predictive Accuracy ◽  
Statistical Tests ◽  
Area Under The Curve ◽  
Regression Tree ◽  
High Sensitivity ◽  
Oral Health Status ◽  
Classification And Regression Tree ◽  
Cart Analysis ◽  
Salivary Biomarkers

AbstractOral health is important not only due to the diseases emerging in the oral cavity but also due to the direct relation to systemic health. Thus, early and accurate characterization of the oral health status is of utmost importance. There are several salivary biomarkers as candidates for gingivitis and periodontitis, which are major oral health threats, affecting the gums. These need to be verified and validated for their potential use as differentiators of health, gingivitis and periodontitis status, before they are translated to chair-side for diagnostics and personalized monitoring. We aimed to measure 10 candidates using high sensitivity ELISAs in a well-controlled cohort of 127 individuals from three groups: periodontitis (60), gingivitis (31) and healthy (36). The statistical approaches included univariate statistical tests, receiver operating characteristic curves (ROC) with the corresponding Area Under the Curve (AUC) and Classification and Regression Tree (CART) analysis. The main outcomes were that the combination of multiple biomarker assays, rather than the use of single ones, can offer a predictive accuracy of > 90% for gingivitis versus health groups; and 100% for periodontitis versus health and periodontitis versus gingivitis groups. Furthermore, ratios of biomarkers MMP-8, MMP-9 and TIMP-1 were also proven to be powerful differentiating values compared to the single biomarkers.

scholarly journals Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044500
Author(s):  
Yauhen Statsenko ◽  
Fatmah Al Zahmi ◽  
Tetiana Habuza ◽  
Klaus Neidl-Van Gorkom ◽  
Nazar Zaki
Keyword(s):  
Lymphocyte Count ◽  
Prediction Models ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Supervised Machine Learning ◽  
Study Cohort ◽  
Threshold Values ◽  
Laboratory Findings ◽  
Study Results

BackgroundDespite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.ObjectivesTo identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).MethodsThe study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.ResultsWith the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×109/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.ConclusionThe performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

scholarly journals Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Espen Jimenez-Solem ◽  
Tonny S. Petersen ◽  
Casper Hansen ◽  
Christian Hansen ◽  
Christina Lioma ◽  
...  
Keyword(s):  
Prediction Models ◽  
Severe Disease ◽  
External Validation ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Mortality Prediction ◽  
Icu Admission ◽  
Risk Of Death ◽  
Online Risk ◽  
The United Kingdom

AbstractPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

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