ABSTRACT
Rapid diagnostic tests (RDTs) are critical for preparedness and response against an outbreak or pandemic and have been highlighted in the 100 Days Mission, a global initiative that aims to prepare the world for the next epidemic/pandemic by driving the development of diagnostics, vaccines and therapeutics within 100 days of recognition of a novel Disease X threat.RDTs play a pivotal role in early case identification, surveillance and case management, and are critical for initiating deployment of vaccine and monoclonal antibodies. Currently available RDTs, however, have limited clinical sensitivity and specificity and inadequate validation. The development, validation and implementation of RDTs require adequate and sustained financing from both public and private sources. While the World Health Assembly recently passed a resolution on diagnostic capacity strengthening that urges individual Member States to commit resources towards this, the resolution is not binding and implementation will likely be impeded by limited financial resources and other competing priorities, particularly in low-income countries. Meanwhile, the diagnostic industry has not sufficiently invested in RDT development for high priority pathogens.Currently, vaccine development projects are getting the largest funding support among medical countermeasures. Yet vaccines are insufficient tools in isolation, and pandemic preparedness will be incomplete without parallel investment in diagnostics and therapeutics.The Pandemic Fund, a global financing mechanism recently established for strengthening pandemic prevention, preparedness and response, may be a future avenue for supporting diagnostic development.In this paper, we discuss why RDTs are critical for preparedness and response. We also discuss RDT investment challenges and reflect on the way forward.
Subject(s)
Diagnostic Tests, Routine , Disease Outbreaks , Humans , Disease Outbreaks/prevention & control , COVID-19/prevention & control , COVID-19/diagnosis , Pandemics/prevention & control , Global Health , Rapid Diagnostic TestsABSTRACT
A whole genome screen was performed using oligonucleotide microarray analysis on blood from a large clinical cohort of Alzheimer's disease (AD) patients and control subjects as clinical sample. Blood samples for total RNA extraction were collected in PAXgene tubes, and gene expression analysis performed on the AB1700 Whole Genome Survey Microarrays. When comparing the gene expression of 94 AD patients and 94 cognitive healthy controls, a Jackknife gene selection based method and Partial Least Square Regression (PLSR) was used to develop a disease classifier algorithm, which gives a test score indicating the presence (positive) or absence (negative) of AD. This algorithm, based on 1239 probes, was validated in an independent test set of 63 subjects comprising 31 AD patients, 25 age-matched cognitively healthy controls, and 7 young controls. This algorithm correctly predicted the class of 55/63 (accuracy 87%), including 26/31 AD samples (sensitivity 84%) and 29/32 controls (specificity 91%). The positive likelihood ratio was 8.9 and the area under the receiver operating characteristic curve (ROC AUC) was 0.94. Furthermore, the algorithm also discriminated AD from Parkinson's disease in 24/27 patients (accuracy 89%). We have identified and validated a gene expression signature in blood that classifies AD patients and cognitively healthy controls with high accuracy and show that alterations specific for AD can be detected distant from the primary site of the disease.
Subject(s)
Alzheimer Disease/blood , Alzheimer Disease/diagnosis , Gene Expression/physiology , Adult , Age Factors , Aged , Aged, 80 and over , Alzheimer Disease/classification , Alzheimer Disease/complications , Case-Control Studies , Cognition Disorders/blood , Cognition Disorders/etiology , Early Diagnosis , Female , Gene Expression Profiling/methods , Humans , Male , Mental Status Schedule , Oligonucleotide Array Sequence Analysis/methods , Reproducibility of Results , Sensitivity and Specificity , Young AdultABSTRACT
Despite a variety of testing approaches, it is often difficult to make an accurate diagnosis of Alzheimer's disease (AD), especially at an early stage of the disease. Diagnosis is based on clinical criteria as well as exclusion of other causes of dementia but a definitive diagnosis can only be made at autopsy. We have investigated the diagnostic value of a 96-gene expression array for detection of early AD. Gene expression analysis was performed on blood RNA from a cohort of 203 probable AD and 209 cognitively healthy age matched controls. A disease classification algorithm was developed on samples from 208 individuals (AD = 103; controls = 105) and was validated in two steps using an independent initial test set (n = 74; AD = 32; controls = 42) and another second test set (n = 130; AD = 68; controls = 62). In the initial analysis, diagnostic accuracy was 71.6 ± 10.3%, with sensitivity 71.9 ± 15.6% and specificity 71.4 ± 13.7%. Essentially the same level of agreement was achieved in the two independent test sets. High agreement (24/30; 80%) between algorithm prediction and subjects with available cerebrospinal fluid biomarker was found. Assuming a clinical accuracy of 80%, calculations indicate that the agreement with underlying true pathology is in the range 85%-90%. These findings suggest that the gene expression blood test can aid in the diagnosis of mild to moderate AD, but further studies are needed to confirm these findings.
