79 results found
Wolters FJ, Chibnik LB, Waziry R, et al., 2020, Twenty-seven-year time trends in dementia incidence in Europe and the United States: The Alzheimer Cohorts Consortium, Neurology, Vol: 95, Pages: e519-e531, ISSN: 0028-3878
OBJECTIVE: To determine changes in the incidence of dementia between 1988 and 2015. METHODS: This analysis was performed in aggregated data from individuals >65 years of age in 7 population-based cohort studies in the United States and Europe from the Alzheimer Cohort Consortium. First, we calculated age- and sex-specific incidence rates for all-cause dementia, and then defined nonoverlapping 5-year epochs within each study to determine trends in incidence. Estimates of change per 10-year interval were pooled and results are presented combined and stratified by sex. RESULTS: Of 49,202 individuals, 4,253 (8.6%) developed dementia. The incidence rate of dementia increased with age, similarly for women and men, ranging from about 4 per 1,000 person-years in individuals aged 65-69 years to 65 per 1,000 person-years for those aged 85-89 years. The incidence rate of dementia declined by 13% per calendar decade (95% confidence interval [CI], 7%-19%), consistently across studies, and somewhat more pronouncedly in men than in women (24% [95% CI 14%-32%] vs 8% [0%-15%]). CONCLUSION: The incidence rate of dementia in Europe and North America has declined by 13% per decade over the past 25 years, consistently across studies. Incidence is similar for men and women, although declines were somewhat more profound in men. These observations call for sustained efforts to finding the causes for this decline, as well as determining their validity in geographically and ethnically diverse populations.
Hadjichrysanthou C, Evans S, Bajaj S, et al., 2020, The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease, Alzheimers Research & Therapy, Vol: 12, Pages: 1-16, ISSN: 1758-9193
BackgroundQuantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer’s disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical trial recruitment and treatment assessment. This study aims to characterise continuous changes of such markers and determine their rate of change and temporal order throughout the AD continuum.MethodsThe methodology is founded on the development of stochastic models to estimate the expected time to reach different clinical disease states, for different risk groups, and synchronise short-term individual biomarker data onto a disease progression timeline. Twenty-seven markers are considered, including a range of cognitive scores, cerebrospinal (CSF) and plasma fluid proteins, and brain structural and molecular imaging measures. Data from 2014 participants in the Alzheimer’s Disease Neuroimaging Initiative database is utilised.ResultsThe model suggests that detectable memory dysfunction could occur up to three decades prior to the onset of dementia due to AD (ADem). This is closely followed by changes in amyloid-β CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-β accumulation. Brain hypometabolism starts later, about 14 years before onset, along with changes in the levels of total and phosphorylated tau proteins. Loss of functional abilities occurs rapidly around ADem onset. Neurofilament light is the only protein with notable early changes in plasma levels. The rate of change varies, with CSF, memory, amyloid PET and brain structural measures exhibiting the highest rate before the onset of ADem, followed by a decline. The probability of progressing to a more severe clinical state increases almost exponentially with age. In accordance with previous stu
de Wolf F, Ghanbari M, Licher S, et al., 2020, Plasma tau, neurofilament light chain and amyloid-β levels and risk of dementia; a population-based cohort study., Brain, Vol: 143, Pages: 1220-1232
CSF biomarkers, including total-tau, neurofilament light chain (NfL) and amyloid-β, are increasingly being used to define and stage Alzheimer's disease. These biomarkers can be measured more quickly and less invasively in plasma and may provide important information for early diagnosis of Alzheimer's disease. We used stored plasma samples and clinical data obtained from 4444 non-demented participants in the Rotterdam study at baseline (between 2002 and 2005) and during follow-up until January 2016. Plasma concentrations of total-tau, NfL, amyloid-β40 and amyloid-β42 were measured using the Simoa NF-light® and N3PA assays. Associations between biomarker plasma levels and incident all-cause and Alzheimer's disease dementia during follow-up were assessed using Cox proportional-hazard regression models adjusted for age, sex, education, cardiovascular risk factors and APOE ε4 status. Moreover, biomarker plasma levels and rates of change over time of participants who developed Alzheimer's disease dementia during follow-up were compared with age and sex-matched dementia-free control subjects. During up to 14 years follow-up, 549 participants developed dementia, including 374 cases with Alzheimer's disease dementia. A log2 higher baseline amyloid-β42 plasma level was associated with a lower risk of developing all-cause or Alzheimer's disease dementia, adjusted hazard ratio (HR) 0.61 [95% confidence interval (CI), 0.47-0.78; P < 0.0001] and 0.59 (95% CI, 0.43-0.79; P = 0.0006), respectively. Conversely, a log2 higher baseline plasma NfL level was associated with a higher risk of all-cause dementia [adjusted HR 1.59 (95% CI, 1.38-1.83); P < 0.0001] or Alzheimer's disease [adjusted HR 1.50 (95% CI, 1.26-1.78); P < 0.0001]. Combining the lowest quartile group of amyloid-β42 with the highest of NfL resulted in a stronger association with all-cause dementia [adjusted HR 9.5 (95%
Evans S, McRae-McKee K, Hadjichrysanthou C, et al., 2019, Alzheimer's disease progression and risk factors: A standardized comparison between six large data sets., Alzheimers & Dementia, Vol: 5, Pages: 515-523, ISSN: 1552-5260
There exist a large number of cohort studies that have been used to identify genetic and biological risk factors for developing Alzheimer's disease (AD). However, there is a disagreement between studies as to how strongly these risk factors affect the rate of progression through diagnostic groups toward AD. We have calculated the probability of transitioning through diagnostic groups in six studies and considered how uncertainty around the strength of the effect of these risk factors affects estimates of the distribution of individuals in each diagnostic group in an AD clinical trial simulator. In this work, we identify the optimal choice of widely collected variables for comparing data sets and calculating probabilities of progression toward AD. We use the estimated transition probabilities to inform stochastic simulations of AD progression that are based on a Markov model and compare predicted incidence rates to those in a community-based study, the Cardiovascular Health Study.
McRae-McKee K, Chinedu T, Udeh-Momoh CT, et al., 2019, Perspective: Clinical relevance of the dichotomous classification of Alzheimer’s disease biomarkers: Should there be a “grey zone”?, Alzheimers & Dementia, Vol: 15, Pages: 1348-1356, ISSN: 1552-5260
The 2018 National Institute on Aging and the Alzheimer's Association (NIA-AA) research framework recently redefined Alzheimer's disease (AD) as a biological construct, based on in vivo biomarkers reflecting key neuropathologic features. Combinations of normal/abnormal levels of three biomarker categories, based on single thresholds, form the AD signature profile that defines the biological disease state as a continuum, independent of clinical symptomatology. While single thresholds may be useful in defining the biological signature profile, we provide evidence that their use in studies with cognitive outcomes merits further consideration. Using data from the Alzheimer's Disease Neuroimaging Initiative with a focus on cortical amyloid binding, we discuss the limitations of applying the biological definition of disease status as a tool to define the increased likelihood of the onset of the Alzheimer's clinical syndrome and the effects that this may have on trial study design. We also suggest potential research objectives going forward and what the related data requirements would be.
Waziry R, Gras L, Sedaghat S, et al., 2019, Quantification of biological age as a determinant of age-related diseases in the Rotterdam Study: a structural equation modeling approach., Eur J Epidemiol, Vol: 34, Pages: 793-799
Chronological age alone is not a sufficient measure of the true physiological state of the body. The aims of the present study were to: (1) quantify biological age based on a physiological biomarker composite model; (2) and evaluate its association with death and age-related disease onset in the setting of an elderly population. Using structural equation modeling we computed biological age for 1699 individuals recruited from the first and second waves of the Rotterdam study. The algorithm included nine physiological parameters (c-reactive protein, creatinine, albumin, total cholesterol, cytomegalovirus optical density, urea nitrogen, alkaline phosphatase, forced expiratory volume and systolic blood pressure). We assessed the association between biological age, all-cause mortality, all-cause morbidity and specific age-related diseases over a median follow-up of 11 years. Biological age, compared to chronological age or the traditional biomarkers of age-related diseases, showed a stronger association with all-cause mortality (HR 1.15 vs. 1.13 and 1.10), all-cause morbidity (HR 1.06 vs. 1.05 and 1.03), stroke (HR 1.17 vs. 1.08 and 1.04), cancer (HR 1.07 vs. 1.04 and 1.02) and diabetes mellitus (HR 1.12 vs. 1.01 and 0.98). Individuals who were biologically younger exhibited a healthier life-style as reflected in their lower BMI (P < 0.001) and lower incidence of stroke (P < 0.001), cancer (P < 0.01) and diabetes mellitus (P = 0.02). Collectively, our findings suggest that biological age based on the biomarker composite model of nine physiological parameters is a useful construct to assess individuals 65 years and older at increased risk for specific age-related diseases.
McRae-McKee K, Evans S, Hadjichrysanthou C, et al., 2019, Combining hippocampal volume metrics to better understand Alzheimer's disease progression in at-risk individuals, Scientific Reports, Vol: 9, ISSN: 2045-2322
To date nearly all clinical trials of Alzheimer’s disease (AD) therapies have failed. These failures are, at least in part, attributable to poor endpoint choice and to inadequate recruitment criteria. Recently, focus has shifted to targeting at-risk populations in the preclinical stages of AD thus improved predictive markers for identifying individuals likely to progress to AD are crucial to help inform the sample of individuals to be recruited into clinical trials. We focus on hippocampal volume (HV) and assess the added benefit of combining HV and rate of hippocampal atrophy over time in relation to disease progression. Following the cross-validation of previously published estimates of the predictive value of HV, we consider a series of combinations of HV metrics and show that a combination of HV and rate of hippocampal atrophy characterises disease progression better than either measure individually. Furthermore, we demonstrate that the risk of disease progression associated with HV metrics does not differ significantly between clinical states. HV and rate of hippocampal atrophy should therefore be used in tandem when describing AD progression in at-risk individuals. Analyses also suggest that the effects of HV metrics are constant across the continuum of the early stages of the disease.
Gras L, May M, Ryder LP, et al., 2019, Determinants of Restoration of CD4 and CD8 Cell Counts and Their Ratio in HIV-1-Positive Individuals With Sustained Virological Suppression on Antiretroviral Therapy, JAIDS-JOURNAL OF ACQUIRED IMMUNE DEFICIENCY SYNDROMES, Vol: 80, Pages: 292-300, ISSN: 1525-4135
Darweesh SKL, Wolters FJ, Ikram MA, et al., 2018, Inflammatory markers and the risk of dementia and Alzheimer's disease: A meta-analysis., Alzheimers Dement, Vol: 14, Pages: 1450-1459
INTRODUCTION: Inflammatory markers are often elevated in patients with dementia, including Alzheimer's disease (AD). However, it remains unclear whether inflammatory markers are associated with the risk of developing dementia. METHODS: We searched PubMed, Embase, and Cochrane library for prospective population-based studies reporting associations between inflammatory markers and all-cause dementia or AD. We used random effects meta-analyses to obtain pooled hazard ratios (HRs) and 95% confidence intervals of inflammatory markers (highest vs. lowest quantile) for all-cause dementia and AD. RESULTS: Fifteen articles from 13 studies in six countries reported data that could be meta-analyzed. C-reactive protein (HR = 1.37 [1.05; 1.78]), interleukin-6 (HR = 1.40 [1.13; 1.73]), α1-antichymotrypsin (HR = 1.54 [1.14; 2.80]), lipoprotein-associated phospholipase A2 activity (HR = 1.40 [1.03; 1.90]), and fibrinogen were each associated with all-cause dementia, but neither was significantly associated with AD. DISCUSSION: Several inflammatory markers are associated with an increased risk of all-cause dementia; however, these markers are not specific for AD. Whether inflammatory markers closely involved in AD pathology are associated with the risk of AD remains to be elucidated.
Bos D, Wolters FJ, Darweesh SKL, et al., 2018, Cerebral small vessel disease and the risk of dementia: A systematic review and meta-analysis of population-based evidence., Alzheimers Dement, Vol: 14, Pages: 1482-1492
INTRODUCTION: Cerebral small vessel disease is increasingly linked to dementia. METHODS: We systematically searched Medline, Embase, and Cochrane databases for prospective population-based studies addressing associations of white matter hyperintensities, covert brain infarcts (i.e., clinically silent infarcts), and cerebral microbleeds with risk of all-dementia or Alzheimer's disease and performed meta-analyses. RESULTS: We identified 11 studies on white matter hyperintensities, covert brain infarcts, or cerebral microbleeds with risk of all-dementia or Alzheimer's disease. Pooled analyses showed an association of white matter hyperintensity volume and a borderline association of covert brain infarcts with risk of all-dementia (hazard ratio: 1.39 [95% confidence interval: 1.00; 1.94], N = 3913, and 1.47 [95% confidence interval: 0.97; 2.22], N = 8296). Microbleeds were not statistically significantly associated with an increased risk of all-dementia (hazard ratio: 1.25 [95% confidence interval: 0.66; 2.38], N = 8739). DISCUSSION: White matter hyperintensities are associated with an increased risk of all-dementia and Alzheimer's disease in the general population. However, studies are warranted to further determine the role of markers of cerebral small vessel disease in dementia.
Hadjichrysanthou C, McRae-McKee K, Evans S, et al., 2018, Potential factors associated with cognitive improvement of individuals diagnosed with mild cognitive impairment or dementia in longitudinal studies, Journal of Alzheimer's Disease, Vol: 66, Pages: 587-600, ISSN: 1387-2877
Despite the progressive nature of Alzheimer’s disease and other dementias, it is observed that many individuals that are diagnosed with mild cognitive impairment (MCI) in one clinical assessment, may return back to normal cognition (CN) in a subsequent assessment. Less frequently, such ‘back-transitions’ are also observed in people that had already been diagnosed with later stages of dementia. In this study, an analysis was performed on two longitudinal cohort datasets provided by 1) the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 2) the National Alzheimer’s Coordinating Centre (NACC). The focus is on the observed improvement of individuals’ clinical condition recorded in these datasets to explore potential associations with different factors. It is shown that, in both datasets, transitions from MCI to CN are significantly associated with younger age, better cognitive function, and the absence of ApoE ɛ4 alleles. Better cognitive function and in some cases the absence of ApoE ɛ4 alleles are also significantly associated with transitions from types of dementia to less severe clinical states. The effect of gender and education is not clear-cut in these datasets, although highly educated people who reach MCI tend to be more likely to show an improvement in their clinical state. The potential effect of other factors such as changes in symptoms of depression is also discussed. Although improved clinical outcomes can be associated with many factors, better diagnostic tools are required to provide insight into whether such improvements are a result of misdiagnosis, and if they are not, whether they are linked to improvements in the underlying neuropathological condition.
Ower AK, Hadjichrysanthou C, Gras L, et al., 2018, Temporal association patterns and dynamics of amyloid-β and tau in Alzheimer's disease, European Journal of Epidemiology, Vol: 33, Pages: 657-666, ISSN: 0393-2990
The elusive relationship between underlying pathology and clinical disease hampers diagnosis of Alzheimer's disease (AD) and preventative intervention development. We seek to understand the relationship between two classical AD biomarkers, amyloid-β1-42 (Aβ1-42) and total-tau (t-tau), and define their trajectories across disease development, as defined by disease onset at diagnosis of mild cognitive impairment (MCI). Using longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a correlation analysis of biomarkers CSF Aβ1-42 and t-tau, and longitudinal quantile analysis. Using a mixed effects model, with MCI onset as an anchor, we develop linear trajectories to describe the rate of change across disease development. These trajectories were extended through the incorporation of data from cognitively normal, healthy adults (aged 20-62 years) from the literature, to fit sigmoid curves by means of non-linear least squares estimators, to create curves encompassing the 50 years prior to MCI onset. A strong right-angled relationship between the biomarkers Aβ1-42 and t-tau is detected, implying a highly non-linear relationship. The rate of change of Aβ1-42 is correlated with the baseline concentration per quantile, reflecting a reduction in the rate of loss across disease within subjects. Regression models reveal significant amyloid loss relative to MCI onset (- 2.35 pg/mL/year), compared to minimal loss relative to AD onset (- 0.97 pg/mL/year). Tau accumulates consistently relative to MCI and AD onset, (2.05 pg/mL/year) and (2.46 pg/mL/year), respectively. The fitted amyloid curve shows peak loss of amyloid 8.06 years prior to MCI diagnosis, while t-tau exhibits peak accumulation 14.17 years following MCI diagnosis, with the upper limit not yet reached 30 years post diagnosis. Biomarker trajectories aid unbiased, objective assessment of disease progression. Q
Evans S, McRae-McKee K, Wong MM, et al., 2018, The importance of endpoint selection: how effective does a drug need to be for success in a clinical trial of a possible Alzheimer's disease treatment?, European Journal of Epidemiology, Vol: 33, Pages: 635-644, ISSN: 0393-2990
To date, Alzheimer's disease (AD) clinical trials have been largely unsuccessful. Failures have been attributed to a number of factors including ineffective drugs, inadequate targets, and poor trial design, of which the choice of endpoint is crucial. Using data from the Alzheimer's Disease Neuroimaging Initiative, we have calculated the minimum detectable effect size (MDES) in change from baseline of a range of measures over time, and in different diagnostic groups along the AD development trajectory. The Functional Activities Questionnaire score had the smallest MDES for a single endpoint where an effect of 27% could be detected within 3 years in participants with Late Mild Cognitive Impairment (LMCI) at baseline, closely followed by the Clinical Dementia Rating Sum of Boxes (CDRSB) score at 28% after 2 years in the same group. Composite measures were even more successful than single endpoints with an MDES of 21% in 3 years. Using alternative cognitive, imaging, functional, or composite endpoints, and recruiting patients that have LMCI could improve the success rate of AD clinical trials.
Hadjichrysanthou C, Ower AK, de Wolf F, et al., 2018, The development of a stochastic mathematical model of Alzheimer's disease to help improve the design of clinical trials of potential treatments, PLOS ONE, Vol: 13, ISSN: 1932-6203
Alzheimer’sdisease(AD)is a neurodegenerative disordercharacterisedbya slowprogres-sivedeteriorationof cognitivecapacity.Drugsareurgentlyneededforthetreatmentof ADandunfortunatelyalmostallclinicaltrialsof ADdrugcandidateshavefailedor beendiscon-tinuedto date.Mathematical,computationalandstatisticaltoolscanbeemployedin theconstructionof clinicaltrialsimulatorsto assistin theimprovementof trialdesignandenhancethechancesof successof potentialnewtherapies.Basedontheanalysisof a setof clinicaldataprovidedbytheAlzheimer’sDiseaseNeuroimagingInitiative(ADNI)wedevelopeda simplestochasticmathematicalmodelto simulatethedevelopmentandpro-gressionof Alzheimer’sin a longitudinalcohortstudy.Weshowhowthismodellingframe-workcouldbeusedto assesstheeffectandthechancesof successof hypotheticaltreatmentsthatareadministeredat differentstagesanddelaydiseasedevelopment.Wedemonstratethatthedetectionof thetrueefficacyof anADtreatmentcanbeverychalleng-ing,evenif thetreatmentis highlyeffective.Animportantreasonbehindtheinabilitytodetectsignalsof efficacyin a clinicaltrialin thistherapyareacouldbethehighbetween-andwithin-individualvariabilityin themeasurement of diagnosticmarkersandendpoints,whichconsequentlyresultsin themisdiagnosisof anindividual’sdiseasestate.
Lawrence E, Vegvari C, Ower A, et al., 2017, A Systematic Review of Longitudinal Studies Which Measure Alzheimer's Disease Biomarkers, JOURNAL OF ALZHEIMERS DISEASE, Vol: 59, Pages: 1359-1379, ISSN: 1387-2877
Alzheimer’s disease (AD) is a progressive and fatal neurodegenerative disease, with no effective treatment orcure. A gold standard therapy would be treatment to slow or halt disease progression; however, knowledge of causationin the early stages of AD is very limited. In order to determine effective endpoints for possible therapies, a number ofquantitative surrogate markers of disease progression have been suggested, including biochemical and imaging biomarkers.The dynamics of these various surrogate markers over time, particularly in relation to disease development, are, however,not well characterized. We reviewed the literature for studies that measured cerebrospinal fluid or plasma amyloid-andtau, or took magnetic resonance image or fluorodeoxyglucose/Pittsburgh compound B-positron electron tomography scans,in longitudinal cohort studies. We summarized the properties of the major cohort studies in various countries, commonlyused diagnosis methods and study designs. We have concluded that additional studies with repeat measures over time in arepresentative population cohort are needed to address the gap in knowledge of AD progression. Based on our analysis, wesuggest directions in which research could move in order to advance our understanding of this complex disease, includingrepeat biomarker measurements, standardization and increased sample sizes.
Cornelissen M, Gall A, Vink M, et al., 2017, From clinical sample to complete genome: Comparing methods for the extraction of HIV-1 RNA for high-throughput deep sequencing, Virus Research, Vol: 239, Pages: 10-16, ISSN: 0168-1702
The BEEHIVE (Bridging the Evolution and Epidemiology of HIV in Europe) project aims to analyse nearly-complete viral genomes from >3000 HIV-1 infected Europeans using high-throughput deep sequencing techniques to investigate the virus genetic contribution to virulence. Following the development of a computational pipeline, including a new de novo assembler for RNA virus genomes, to generate larger contiguous sequences (contigs) from the abundance of short sequence reads that characterise the data, another area that determines genome sequencing success is the quality and quantity of the input RNA. A pilot experiment with 125 patient plasma samples was performed to investigate the optimal method for isolation of HIV-1 viral RNA for long amplicon genome sequencing. Manual isolation with the QIAamp Viral RNA Mini Kit (Qiagen) was superior over robotically extracted RNA using either the QIAcube robotic system, the mSample Preparation Systems RNA kit with automated extraction by the m2000sp system (Abbott Molecular), or the MagNA Pure 96 System in combination with the MagNA Pure 96 Instrument (Roche Diagnostics). We scored amplification of a set of four HIV-1 amplicons of ∼1.9, 3.6, 3.0 and 3.5 kb, and subsequent recovery of near-complete viral genomes.Subsequently, 616 BEEHIVE patient samples were analysed to determine factors that influence successful amplification of the genome in four overlapping amplicons using the QIAamp Viral RNA Kit for viral RNA isolation. Both low plasma viral load and high sample age (stored before 1999) negatively influenced the amplification of viral amplicons >3 kb. A plasma viral load of >100,000 copies/ml resulted in successful amplification of all four amplicons for 86% of the samples, this value dropped to only 46% for samples with viral loads of <20,000 copies/ml.
Vegvari C, Hadjichrysanthou C, Cauët E, et al., 2016, How Can Viral Dynamics Models Inform Endpoint Measures in Clinical Trials of Therapies for Acute Viral Infections?, PLOS One, Vol: 11, ISSN: 1932-6203
Acute viral infections pose many practical challenges for the accurate assessment of the impact of novel therapies on viral growth and decay. Using the example of influenza A, we illustrate how the measurement of infection-related quantities that determine the dynamics of viral load within the human host, can inform investigators on the course and severity of infection and the efficacy of a novel treatment. We estimated the values of key infection-related quantities that determine the course of natural infection from viral load data, using Markov Chain Monte Carlo methods. The data were placebo group viral load measurements collected during volunteer challenge studies, conducted by Roche, as part of the oseltamivir trials. We calculated the values of the quantities for each patient and the correlations between the quantities, symptom severity and body temperature. The greatest variation among individuals occurred in the viral load peak and area under the viral load curve. Total symptom severity correlated positively with the basic reproductive number. The most sensitive endpoint for therapeutic trials with the goal to cure patients is the duration of infection. We suggest laboratory experiments to obtain more precise estimates of virological quantities that can supplement clinical endpoint measurements.
Vegvari C, Cauët E, Hadjichrysanthou C, et al., 2016, Using Clinical Trial Simulators to Analyse the Sources of Variance in Clinical Trials of Novel Therapies for Acute Viral Infections., PLOS One, Vol: 11, ISSN: 1932-6203
BACKGROUND: About 90% of drugs fail in clinical development. The question is whether trials fail because of insufficient efficacy of the new treatment, or rather because of poor trial design that is unable to detect the true efficacy. The variance of the measured endpoints is a major, largely underestimated source of uncertainty in clinical trial design, particularly in acute viral infections. We use a clinical trial simulator to demonstrate how a thorough consideration of the variability inherent in clinical trials of novel therapies for acute viral infections can improve trial design. METHODS AND FINDINGS: We developed a clinical trial simulator to analyse the impact of three different types of variation on the outcome of a challenge study of influenza treatments for infected patients, including individual patient variability in the response to the drug, the variance of the measurement procedure, and the variance of the lower limit of quantification of endpoint measurements. In addition, we investigated the impact of protocol variation on clinical trial outcome. We found that the greatest source of variance was inter-individual variability in the natural course of infection. Running a larger phase II study can save up to $38 million, if an unlikely to succeed phase III trial is avoided. In addition, low-sensitivity viral load assays can lead to falsely negative trial outcomes. CONCLUSIONS: Due to high inter-individual variability in natural infection, the most important variable in clinical trial design for challenge studies of potential novel influenza treatments is the number of participants. 100 participants are preferable over 50. Using more sensitive viral load assays increases the probability of a positive trial outcome, but may in some circumstances lead to false positive outcomes. Clinical trial simulations are powerful tools to identify the most important sources of variance in clinical trials and thereby help improve trial design.
Hadjichrysanthou C, Cauët E, Lawrence E, et al., 2016, Understanding the within-host dynamics of influenza A virus: from theory to clinical implications, Journal of the Royal Society Interface, Vol: 13, ISSN: 1742-5689
Mathematical models have provided important insights into acute viral dynamics within individual patients. In this paper, we study the simplest target cell-limited models to investigate the within-host dynamics of influenza A virus infection in humans. Despite the biological simplicity of the models, we show how these can be used to understand the severity of the infection and the key attributes of possible immunotherapy and antiviral drugs for the treatment of infection at different times post infection. Through an analytic approach, we derive and estimate simple summary biological quantities that can provide novel insights into the infection dynamics and the definition of clinical endpoints. We focus on nine quantities, including the area under the viral load curve, peak viral load, the time to peak viral load and the level of cell death due to infection. Using Markov chain Monte Carlo methods, we fitted the models to data collected from 12 untreated volunteers who participated in two clinical studies that tested the antiviral drugs oseltamivir and zanamivir. Based on the results, we also discuss various difficulties in deriving precise estimates of the parameters, even in the very simple models considered, when experimental data are limited to viral load measures and/or there is a limited number of viral load measurements post infection.
Ratmann O, van Sighem A, Bezemer D, et al., 2016, Sources of HIV infection among men having sex with men and implications for prevention, Science Translational Medicine, Vol: 8, ISSN: 1946-6242
Bezemer D, Cori A, Ratmann O, et al., 2015, Dispersion of the HIV-1 Epidemic in Men Who Have Sex with Men in the Netherlands: A Combined Mathematical Model and Phylogenetic Analysis., PLOS Medicine, Vol: 12, Pages: e1001898-e1001898, ISSN: 1549-1277
BACKGROUND: The HIV-1 subtype B epidemic amongst men who have sex with men (MSM) is resurgent in many countries despite the widespread use of effective combination antiretroviral therapy (cART). In this combined mathematical and phylogenetic study of observational data, we aimed to find out the extent to which the resurgent epidemic is the result of newly introduced strains or of growth of already circulating strains. METHODS AND FINDINGS: As of November 2011, the ATHENA observational HIV cohort of all patients in care in the Netherlands since 1996 included HIV-1 subtype B polymerase sequences from 5,852 patients. Patients who were diagnosed between 1981 and 1995 were included in the cohort if they were still alive in 1996. The ten most similar sequences to each ATHENA sequence were selected from the Los Alamos HIV Sequence Database, and a phylogenetic tree was created of a total of 8,320 sequences. Large transmission clusters that included ≥10 ATHENA sequences were selected, with a local support value ≥ 0.9 and median pairwise patristic distance below the fifth percentile of distances in the whole tree. Time-varying reproduction numbers of the large MSM-majority clusters were estimated through mathematical modeling. We identified 106 large transmission clusters, including 3,061 (52%) ATHENA and 652 Los Alamos sequences. Half of the HIV sequences from MSM registered in the cohort in the Netherlands (2,128 of 4,288) were included in 91 large MSM-majority clusters. Strikingly, at least 54 (59%) of these 91 MSM-majority clusters were already circulating before 1996, when cART was introduced, and have persisted to the present. Overall, 1,226 (35%) of the 3,460 diagnoses among MSM since 1996 were found in these 54 long-standing clusters. The reproduction numbers of all large MSM-majority clusters were around the epidemic threshold value of one over the whole study period. A tendency towards higher numbers was visible in recent years, especially in the more recently
van Sighem A, Nakagawa F, De Angelis D, et al., 2015, Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data, EPIDEMIOLOGY, Vol: 26, Pages: 653-660, ISSN: 1044-3983
Smit M, Brinkman K, Geerlings S, et al., 2015, Future challenges for clinical care of an ageing population infected with HIV: a modelling study, Lancet Infectious Diseases, Vol: 15, Pages: 810-818, ISSN: 1473-3099
Background The population infected with HIV is getting older and these people will increasingly develop age-relatednon-communicable diseases (NCDs). We aimed to quantify the scale of the change and the implications for HIV carein the Netherlands in the future.Methods We constructed an individual-based model of the ageing HIV-infected population, which followed patientson HIV treatment as they age, develop NCDs—including cardiovascular disease (hypertension, hypercholesterolaemia,myocardial infarctions, and strokes), diabetes, chronic kidney disease, osteoporosis, and non-AIDS malignancies—and start co-medication for these diseases. The model was parameterised by use of data for 10 278 patients from thenational Dutch ATHENA cohort between 1996 and 2010. We made projections up to 2030.Findings Our model suggests that the median age of HIV-infected patients on combination antiretroviral therapy(ART) will increase from 43·9 years in 2010 to 56·6 in 2030, with the proportion of HIV-infected patients aged50 years or older increasing from 28% in 2010 to 73% in 2030. In 2030, we predict that 84% of HIV-infected patientswill have at least one NCD, up from 29% in 2010, with 28% of HIV-infected patients in 2030 having three or moreNCDs. 54% of HIV-infected patients will be prescribed co-medications in 2030, compared with 13% in 2010, with20% taking three or more co-medications. Most of this change will be driven by increasing prevalence ofcardiovascular disease and associated drugs. Because of contraindications and drug–drug interactions, in 2030, 40%of patients could have complications with the currently recommended fi rst-line HIV regimens.Interpretation The profi le of patients in the Netherlands infected with HIV is changing, with increasing numbers ofolder patients with multiple morbidities. These changes mean that, in the near future, HIV care will increasingly need todraw on a wide range of medical disciplines, in addition to evidence-bas
Zhang S, van Sighem A, Kesselring A, et al., 2015, Risk of non-AIDS-defining events among HIV-infected patients not yet on antiretroviral therapy, HIV MEDICINE, Vol: 16, Pages: 265-272, ISSN: 1464-2662
Gras L, de Wolf F, Smit C, et al., 2015, Changes in HIV RNA and CD4 cell count after acute HCV infection in chronically HIV-infected individuals., J Acquir Immune Defic Syndr, Vol: 68, Pages: 536-542
OBJECTIVE: Little is known about the impact of acute hepatitis C virus (HCV) co-infection on HIV-1 disease progression. We investigated CD4 cell count and HIV RNA concentration changes after HCV infection in individuals chronically infected with HIV-1. METHODS: We selected individuals that had the last negative and first positive HCV RNA test less than 1 year apart. Bivariate linear mixed-effects regression was used to model trends in HIV RNA level and CD4 cell count from 2 years before the last negative HCV RNA test until the first of the following dates: start of anti-HCV medication, change in combination antiretroviral therapy (cART) status, and end of follow-up. RESULTS: At the estimated time of HCV co-infection, of 89 individuals, 63 (71%) were cART-treated and 26 (29%) were not on cART. In persons on cART, median CD4 cell count declined from 587 to 508 cells per cubic millimeter (P < 0.0001) during the first 5 months after HCV infection and returned to 587 cells per cubic millimeter after 2.2 years. Also, the probability of an HIV RNA >50 copies per milliliter peaked to 18.6% at HCV co-infection, with lower probabilities 6 months before (3.5%, P = 0.006 compared with peak probability) and after (2.9%, P = 0.009). In persons not on cART, no significant impact of HCV co-infection on trends in the HIV RNA level or CD4 cell count was observed. CONCLUSIONS: Acute HCV infection in cART-treated, chronically HIV-infected patients was associated with a temporary decrease in CD4 cell counts and increased risk of HIV viremia >50 copies per milliliter. This may increase the risk of further HIV transmission.
Worm SW, Bower M, Reiss P, et al., 2013, Non-AIDS defining cancers in the D:A:D Study - time trends and predictors of survival: a cohort study, BMC INFECTIOUS DISEASES, Vol: 13
Smit M, Smit C, Geerlings S, et al., 2013, Changes in First-Line cART Regimens and Short-Term Clinical Outcome between 1996 and 2010 in The Netherlands, PLOS One, Vol: 8, ISSN: 1932-6203
Objectives: Document progress in HIV-treatment in the Netherlands since 1996 by reviewing changing patterns of cART useand relating those to trends in patients’ short-term clinical outcomes between 1996 and 2010.Design and Methods: 1996–2010 data from 10,278 patients in the Dutch ATHENA national observational cohort wereanalysed. The annual number of patients starting a type of regimen was quantified. Trends in the following outcomes weredescribed: i) recovery of 150 CD4 cells/mm3 within 12 months of starting cART; ii) achieving viral load (VL) suppression#1,000 copies/ml within 12 months of starting cART; iii) switching from first-line to second-line regimen within three yearsof starting treatment; and iv) all-cause mortality rate per 100 person-years within three years of starting treatment.Results: Between 1996 and 2010, first-line regimens changed from lamivudine/zidovudine-based or lamivudine/stavudinebasedregimens with unboosted-PIs to tenofovir with either emtricitabine or lamivudine with NNRTIs. Mortality rates did notchange significantly over time. VL suppression and CD4 recovery improved over time, and the incidence of switching due tovirological failure and toxicity more than halved between 1996 and 2010. These effects appear to be related to the use ofnew regimens rather than improvements in clinical care.Conclusion: The use of first-line cART in the Netherlands closely follows changes in guidelines, to the benefit of patients.While there was no significant improvement in mortality, newer drugs with better tolerability and simpler dosing resulted inimproved immunological and virological recovery and reduced incidences of switching due to toxicity and virologicalfailure.
Gras L, Geskus RB, Jurriaans S, et al., 2013, Has the Rate of CD4 Cell Count Decline before Initiation of Antiretroviral Therapy Changed over the Course of the Dutch HIV Epidemic among MSM?, PLOS ONE, Vol: 8, ISSN: 1932-6203
Gijsbers EF, van Sighem A, Harskamp AM, et al., 2013, The presence of CXCR4-using HIV-1 prior to start of antiretroviral therapy is an independent predictor of delayed viral suppression., PLoS One, Vol: 8
The emergence of CXCR4-using HIV variants (X4-HIV) is associated with accelerated disease progression in the absence of antiretroviral therapy. However, the effect of X4-HIV variants on the treatment response remains unclear. Here we determined whether the presence of X4-HIV variants influenced the time to undetectable viral load and CD4+ T cell reconstitution after initiation of cART in 732 patients. The presence of X4-HIV variants was determined by MT-2 assay prior to cART initiation and viral load and CD4+ T cell counts were analyzed every 3 to 6 months during a three year follow-up period. Kaplan-Meier and Cox proportional hazard analyses were performed to compare time to viral suppression and the absolute CD4+ T cell counts and increases in CD4+ T cell counts during follow-up were compared for patients with and without X4-HIV at start of cART. Patients harboring X4-HIV variants at baseline showed a delay in time to achieve viral suppression below the viral load detection limit. This delay in viral suppression was independently associated with high viral load and the presence of X4-HIV variants. Furthermore, the absolute CD4+ T cell counts were significantly lower in patients harboring X4-HIV variants at all time points during follow-up. However, no differences were observed in the increase in absolute CD4+ T cell numbers after treatment initiation, indicating that the reconstitution of CD4+ T cells is independent of the presence of X4-HIV variants. The emergence of X4-HIV has been associated with an accelerated CD4+ T cell decline during the natural course of infection and therefore, patients who develop X4-HIV variants may benefit from earlier treatment initiation in order to obtain faster reconstitution of the CD4+ T cell population to normal levels.
Grijsen M, Koster G, van Vonderen M, et al., 2012, Temporary antiretroviral treatment during primary HIV-1 infection has a positive impact on health-related quality of life: data from the Primo-SHM cohort study., HIV Med, Vol: 13, Pages: 630-635
OBJECTIVES: The aim of the study was to compare health-related quality of life (HRQL) over 96 weeks in patients receiving no treatment or 24 or 60 weeks of combination antiretroviral therapy (cART) during primary HIV-1 infection (PHI). METHODS: A multicentre prospective cohort study of PHI patients, with an embedded randomized trial, was carried out. HRQL was assessed with the Medical Outcomes Study Health Survey for HIV (MOS-HIV) and a symptom checklist administered at weeks 0, 8, 24, 36, 48, 60, 72, 84 and 96. Mixed linear models were used for the analysis of differences in HRQL among the three groups. RESULTS: A total of 112 patients were included in the study: 28 received no treatment, 45 received 24 weeks of cART and 39 received 60 weeks of cART. Over 96 weeks of follow-up, the groups receiving 24 and 60 weeks of cART had better cognitive functioning than the no-treatment group (P = 0.005). Patients receiving 60 weeks of cART had less pain (P = 0.004), better role functioning (P = 0.001), better physical functioning (P = 0.02) and a better physical health summary score (P = 0.006) than the groups receiving no treatment or 24 weeks of cART. Mental health was better in patients receiving 24 weeks of cART than in patients in the no-treatment group or the group receiving 60 weeks of cART (P = 0.02). At week 8, patients in the groups receiving 24 and 60 weeks of cART reported more nausea (P = 0.002), diarrhoea (P < 0.001), abdominal pain (P = 0.02), stomach pain (P = 0.049) and dizziness (P = 0.01) than those in the no-treatment group. These differences had disappeared by week 24. CONCLUSIONS: Temporary cART during PHI had a significant positive impact on patients' HRQL as compared with no treatment, despite the initial, short-term occurrence of more physical symptoms, probably related to drug toxicity.
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