SARS-CoV-2 elicits non-sterilizing immunity and evades vaccine-induced immunity: implications for future vaccination strategies.

Neither vaccination nor natural infection result in long-lasting protection against SARS-COV-2 infection and transmission, but both reduce the risk of severe COVID-19. To generate insights into optimal vaccination strategies for prevention of severe COVID-19 in the population, we extended a Susceptible-Exposed-Infectious-Removed (SEIR) mathematical model to compare the impact of vaccines that are highly protective against severe COVID-19 but not against infection and transmission, with those that block SARS-CoV-2 infection. Our analysis shows that vaccination strategies focusing on the prevention of severe COVID-19 are more effective than those focusing on creating of herd immunity. Key uncertainties that would affect the choice of vaccination strategies are: (1) the duration of protection against severe disease, (2) the protection against severe disease from variants that escape vaccine-induced immunity, (3) the incidence of long-COVID and level of protection provided by the vaccine, and (4) the rate of serious adverse events following vaccination, stratified by demographic variables.

Plasma tau, neurofilament light chain and amyloid-ß levels and risk of dementia; a population-based cohort study.

CSF biomarkers, including total-tau, neurofilament light chain (NfL) and amyloid-ß, 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-ß40 and amyloid-ß42 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-ß42 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-ß42 with the highest of NfL resulted in a stronger association with all-cause dementia [adjusted HR 9.5 (95% CI, 2.3-40.4); P < 0.002] and with Alzheimer's disease [adjusted HR 15.7 (95% CI, 2.1-117.4); P < 0.0001], compared to the highest quartile group of amyloid-ß42 and lowest of NfL. Total-tau and amyloid-ß40 levels were not associated with all-cause or Alzheimer's disease dementia risk. Trajectory analyses of biomarkers revealed that mean NfL plasma levels increased 3.4 times faster in participants who developed Alzheimer's disease compared to those who remained dementia-free (P < 0.0001), plasma values for cases diverged from controls 9.6 years before Alzheimer's disease diagnosis. Amyloid-ß42 levels began to decrease in Alzheimer's disease cases a few years before diagnosis, although the decline did not reach significance compared to dementia-free participants. In conclusion, our study shows that low amyloid-ß42 and high NfL plasma levels are each independently and in combination strongly associated with risk of all-cause and Alzheimer's disease dementia. These data indicate that plasma NfL and amyloid-ß42 levels can be used to assess the risk of developing dementia in a non-demented population. Plasma NfL levels, although not specific, may also be useful in monitoring progression of Alzheimer's disease dementia.

Quantification of biological age as a determinant of age-related diseases in the Rotterdam Study: a structural equation modeling approach.

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.

Perspective: Clinical relevance of the dichotomous classification of Alzheimer's disease biomarkers: Should there be a "gray zone"?

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.

Temporal association patterns and dynamics of amyloid-ß and tau in Alzheimer's disease.

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-ß1-42 (Aß1-42) 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 Aß1-42 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 Aß1-42 and t-tau is detected, implying a highly non-linear relationship. The rate of change of Aß1-42 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. Quantitative trajectories are likely to be of use in clinical trial design, as they allow for a more detailed insight into the effectiveness of treatments designed to delay development of biological disease.

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?

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.

Inflammatory markers and the risk of dementia and Alzheimer's disease: A meta-analysis.

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.

Cerebral small vessel disease and the risk of dementia: A systematic review and meta-analysis of population-based evidence.

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.

Dispersion of the HIV-1 Epidemic in Men Who Have Sex with Men in the Netherlands: A Combined Mathematical Model and Phylogenetic Analysis.

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 introduced clusters. The mean age of MSM at diagnosis increased by 0.45 years/year within clusters, but new clusters appeared with lower mean age. Major strengths of this study are the high proportion of HIV-positive MSM with a sequence in this study and the combined application of phylogenetic and modeling approaches. Main limitations are the assumption that the sampled population is representative of the overall HIV-positive population and the assumption that the diagnosis interval distribution is similar between clusters. CONCLUSIONS: The resurgent HIV epidemic amongst MSM in the Netherlands is driven by several large, persistent, self-sustaining, and, in many cases, growing sub-epidemics shifting towards new generations of MSM. Many of the sub-epidemics have been present since the early epidemic, to which new sub-epidemics are being added.

Estimating HIV Incidence, Time to Diagnosis, and the Undiagnosed HIV Epidemic Using Routine Surveillance Data.

BACKGROUND: Estimates of the size of the undiagnosed HIV-infected population are important to understand the HIV epidemic and to plan interventions, including "test-and-treat" strategies. METHODS: We developed a multi-state back-calculation model to estimate HIV incidence, time between infection and diagnosis, and the undiagnosed population by CD4 count strata, using surveillance data on new HIV and AIDS diagnoses. The HIV incidence curve was modelled using cubic splines. The model was tested on simulated data and applied to surveillance data on men who have sex with men in The Netherlands. RESULTS: The number of HIV infections could be estimated accurately using simulated data, with most values within the 95% confidence intervals of model predictions. When applying the model to Dutch surveillance data, 15,400 (95% confidence interval [CI] = 15,000, 16,000) men who have sex with men were estimated to have been infected between 1980 and 2011. HIV incidence showed a bimodal distribution, with peaks around 1985 and 2005 and a decline in recent years. Mean time to diagnosis was 6.1 (95% CI = 5.8, 6.4) years between 1984 and 1995 and decreased to 2.6 (2.3, 3.0) years in 2011. By the end of 2011, 11,500 (11,000, 12,000) men who have sex with men in The Netherlands were estimated to be living with HIV, of whom 1,750 (1,450, 2,200) were still undiagnosed. Of the undiagnosed men who have sex with men, 29% (22, 37) were infected for less than 1 year, and 16% (13, 20) for more than 5 years. CONCLUSIONS: This multi-state back-calculation model will be useful to estimate HIV incidence, time to diagnosis, and the undiagnosed HIV epidemic based on routine surveillance data.

No treatment versus 24 or 60 weeks of antiretroviral treatment during primary HIV infection: the randomized Primo-SHM trial.

BACKGROUND: The objective of this study was to assess the benefit of temporary combination antiretroviral therapy (cART) during primary HIV infection (PHI). METHODS AND FINDINGS: Adult patients with laboratory evidence of PHI were recruited in 13 HIV treatment centers in the Netherlands and randomly assigned to receive no treatment or 24 or 60 wk of cART (allocation in a 1∶1∶1 ratio); if therapy was clinically indicated, participants were randomized over the two treatment arms (allocation in a 1∶1 ratio). Primary end points were (1) viral set point, defined as the plasma viral load 36 wk after randomization in the no treatment arm and 36 wk after treatment interruption in the treatment arms, and (2) the total time that patients were off therapy, defined as the time between randomization and start of cART in the no treatment arm, and the time between treatment interruption and restart of cART in the treatment arms. cART was (re)started in case of confirmed CD4 cell count < 350 cells/mm(3) or symptomatic HIV disease. In total, 173 participants were randomized. The modified intention-to-treat analysis comprised 168 patients: 115 were randomized over the three study arms, and 53 randomized over the two treatment arms. Of the 115 patients randomized over the three study arms, mean viral set point was 4.8 (standard deviation 0.6) log(10) copies/ml in the no treatment arm, and 4.0 (1.0) and 4.3 (0.9) log(10) copies/ml in the 24- and 60-wk treatment arms (between groups: p < 0.001). The median total time off therapy in the no treatment arm was 0.7 (95% CI 0.0-1.8) y compared to 3.0 (1.9-4.2) and 1.8 (0.5-3.0) y in the 24- and 60-wk treatment arms (log rank test, p < 0.001). In the adjusted Cox analysis, both 24 wk (hazard ratio 0.42 [95% CI 0.25-0.73]) and 60 wk of early treatment (hazard ratio 0.55 [0.32-0.95]) were associated with time to (re)start of cART. CONCLUSIONS: In this trial, temporary cART during PHI was found to transiently lower the viral set point and defer the restart of cART during chronic HIV infection.

When to initiate combined antiretroviral therapy to reduce mortality and AIDS-defining illness in HIV-infected persons in developed countries: an observational study.

BACKGROUND: Most clinical guidelines recommend that AIDS-free, HIV-infected persons with CD4 cell counts below 0.350 × 10(9) cells/L initiate combined antiretroviral therapy (cART), but the optimal CD4 cell count at which cART should be initiated remains a matter of debate. OBJECTIVE: To identify the optimal CD4 cell count at which cART should be initiated. DESIGN: Prospective observational data from the HIV-CAUSAL Collaboration and dynamic marginal structural models were used to compare cART initiation strategies for CD4 thresholds between 0.200 and 0.500 × 10(9) cells/L. SETTING: HIV clinics in Europe and the Veterans Health Administration system in the United States. PATIENTS: 20, 971 HIV-infected, therapy-naive persons with baseline CD4 cell counts at or above 0.500 × 10(9) cells/L and no previous AIDS-defining illnesses, of whom 8392 had a CD4 cell count that decreased into the range of 0.200 to 0.499 × 10(9) cells/L and were included in the analysis. MEASUREMENTS: Hazard ratios and survival proportions for all-cause mortality and a combined end point of AIDS-defining illness or death. RESULTS: Compared with initiating cART at the CD4 cell count threshold of 0.500 × 10(9) cells/L, the mortality hazard ratio was 1.01 (95% CI, 0.84 to 1.22) for the 0.350 threshold and 1.20 (CI, 0.97 to 1.48) for the 0.200 threshold. The corresponding hazard ratios were 1.38 (CI, 1.23 to 1.56) and 1.90 (CI, 1.67 to 2.15), respectively, for the combined end point of AIDS-defining illness or death. LIMITATIONS: CD4 cell count at cART initiation was not randomized. Residual confounding may exist. CONCLUSION: Initiation of cART at a threshold CD4 count of 0.500 × 10(9) cells/L increases AIDS-free survival. However, mortality did not vary substantially with the use of CD4 thresholds between 0.300 and 0.500 × 10(9) cells/L.

Exploring the Role of Antiviral Nasal Sprays in the Control of Emerging Respiratory Infections in the Community.

INTRODUCTION: The COVID-19 pandemic has demonstrated that there is an unmet need for the development of novel prophylactic antiviral treatments to control the outbreak of emerging respiratory virus infections. Passive antibody-based immunisation approaches such as intranasal antibody prophylaxis have the potential to provide immediately accessible universal protection as they act directly at the most common route of viral entry, the upper respiratory tract. The need for such products is very apparent for SARS-CoV-2 at present, given the relatively low effectiveness of vaccines to prevent infection and block virus onward transmission. We explore the benefits and challenges of the use of antibody-based nasal sprays prior and post exposure to the virus. METHODS: The classic susceptible-exposed-infectious-removed (SEIR) mathematical model was extended to describe the potential population-level impact of intranasal antibody prophylaxis on controlling the spread of an emerging respiratory infection in the community. RESULTS: Intranasal administration of monoclonal antibodies provides only a short-term protection to the mucosal surface. Consequently, sustained intranasal antibody prophylaxis of a substantial proportion of the population would be needed to contain infections. Post-exposure prophylaxis against the development of severe disease would be essential for the overall reduction in hospital admissions. CONCLUSION: Antibody-based nasal sprays could provide protection against infection to individuals that are likely to be exposed to the virus. Large-scale administration for a long period of time would be challenging. Intranasal antibody prophylaxis alone cannot prevent community-wide transmission of the virus. It could be used along with other protective measures, such as non-pharmaceutical interventions, to bridge the time required to develop and produce effective vaccines, and complement active immunisation strategies.

Immunodeficiency as a risk factor for non-AIDS-defining malignancies in HIV-1-infected patients receiving combination antiretroviral therapy.

BACKGROUND: The aim of this study was to investigate the association between immunodeficiency, viremia, and non-AIDS-defining malignancies (NADM). METHODS: Patients starting combination antiretroviral therapy (cART) as of 1 January 1996 were selected from the AIDS Therapy Evaluation in the Netherlands (ATHENA) cohort. In Cox models, risk factors for NADM were investigated. These included age, sex, transmission route, smoking, alcohol abuse, prior AIDS diagnosis, duration of exposure to cART, and estimated duration of human immunodeficiency virus infection. CD4+ cell count and viral load (VL) were considered as time-updated variables and as measures of cumulative exposure to CD4+ cell counts of < 200, < 350, or < 500 cells/mm³ and detectable VL >50, >400, and >1000 copies/mL, respectively. RESULTS: In a cohort of 11,459 patients, 236 NADMs were diagnosed; 102 were caused by infection, and 134 were attributable to other causes. Median CD4+ cell count at NADM diagnosis was 340 cells/mm³ (range, 210-540 cells/mm³). Median time to first NADM after starting cART was 5.0 years (range, 2.2-8.2 years). In multivariate models, cumulative exposure to CD4+ cell counts < 200 cells/mm³ remained significant (hazard ratio [HR], 1.12; range, 1.03-1.22) for each additional year of exposure. In stratified analyses, cumulative exposure to CD4+ cell counts < 200 cells/mm³ was associated with malignancies possibly caused by infection (HR, 1.16; range, 1.03-1.31]) but was not associated with other types of cancers. No significant effect of viremia was seen in either type of cancer. CONCLUSIONS: Cumulative exposure to CD4+ cell counts < 200 cells/mm³ during cART was associated with an increased risk of infection-related non-AIDS-defining malignancies.

Estimating the risk of HIV transmission from homosexual men receiving treatment to their HIV-uninfected partners.

OBJECTIVE: To determine how the risk of HIV transmission from homosexual men receiving antiretroviral treatment is related to patterns of patient monitoring and condom use. METHODS: A stochastic mathematical simulation model was developed of cohorts of men in the Netherlands who have sex with men (MSM), defining the parameters of the model using observational cohort data. The model incorporates viral load trends during first-line treatment, patient monitoring and different scenarios for the way in which condom use may depend on recent viral load measurements. The model does not include the effect of sexually transmitted infections on HIV transmission. RESULTS: For MSM receiving treatment, the risk of transmitting HIV to their long-term partner is 22% (uncertainty interval: 9-37%) if condoms are never used. With incomplete use (in 30% of sex acts) the risk is reduced slightly, to 17% (7-29%). However, the risk is as low as 3% (0.2-8%) when men receiving treatment use condoms only 6 months beyond their last undetectable viral load measurement. The risk is further reduced when 3 months is the time period beyond which condoms are used. CONCLUSIONS: When condom use by HIV-infected men receiving combination treatment with antiretroviral agents is based on their last viral load measurement, the transmission risk is much lower than with incomplete condom use. The key message for patients is that although always using condoms during treatment is the best way to protect partners from the risk of HIV transmission, when such use cannot be achieved, the second best strategy is to use condoms whenever the last undetectable viral load was measured more than 3 months ago.

Adherence to HIV therapeutic drug monitoring guidelines in The Netherlands.

BACKGROUND: Therapeutic drug monitoring (TDM) is recommended in several international HIV treatment guidelines. The adherence of clinicians to these recommendations is unknown. The authors evaluated the adherence to the Dutch TDM guideline of 2005. METHODS: From the ATHENA cohort study, three scenarios were selected for which the guideline recommended TDM: 1) start of a combination of lopinavir/ritonavir + efavirenz or nevirapine (drug-drug interaction); 2) start of efavirenz (routine TDM); and 3) use of nelfinavir during pregnancy. For each scenario, we determined the proportion of patients for whom TDM was performed. Multivariable logistic regression modeling was used to identify determinants for the use of TDM. RESULTS: The adherence to the TDM guideline was 46.7% in patients who started lopinavir/ritonavir plus efavirenz or nevirapine; 9.5% for patients who started efavirenz; and 58.5% for patients who used nelfinavir during pregnancy. Patients treated in clinics that had a TDM assay available locally and patients treated in academic clinics were more likely to receive TDM. A higher baseline HIV viral load was another significant predictor for the performing TDM. CONCLUSION: The adherence of clinicians to the Dutch TDM guidelines varied from low to moderate for the three investigated TDM scenarios. This study identifies several determinants for the use of TDM, which may be useful information for those responsible for generating TDM guidelines.

The comparison of the performance of two screening strategies identifying newly-diagnosed HIV during pregnancy.

BACKGROUND: In the Netherlands, a non-selective opt-out instead of a selective opt-in antenatal HIV screening strategy was implemented in 2004. In case of infection, screening was followed by prevention of mother-to-child-transmission (PMTCT). We compared the performance of the two strategies in terms of detection of new cases of HIV and vertical transmission. METHODS: HIV-infected pregnant women were identified retrospectively from the Dutch HIV cohort ATHENA January 2000 to January 2008. Apart from demographic, virological and immunological data, the date of HIV infection in relation to the index pregnancy was established. Separately, all infants diagnosed with HIV born following implementation of the screening program were identified by a questionnaire via the paediatric HIV centres. RESULTS: 162/481 (33.7%) HIV-positive pregnant women were diagnosed with HIV before 2004 and 172/214 (80.3%) after January 2004. Multivariate analysis showed an 8-fold (95% confidence interval 5.47-11.87) increase in the odds of HIV detection during pregnancy after the national introduction of the opt-out strategy. Still, three children born during a 5-year period after July 2004 were infected due to de novo infection in pregnancy. CONCLUSIONS: Implementation of a nation-wide screening strategy based upon non-selective opt-out screening followed by effective PMTCT appeared to detect more HIV-infected women for the first time in pregnancy and to reduce vertical transmission of HIV substantially. Nonetheless, still few children are infected because of maternal infection after the first trimester. We propose the introduction of partner screening on HIV as part of the antenatal screening strategy.

Health-related quality of life and survival among HIV-infected patients receiving highly active antiretroviral therapy: a study of patients in the AIDS Therapy Evaluation in the Netherlands (ATHENA) Cohort.

BACKGROUND: Previous studies have shown that health-related quality of life (HRQL) predicts survival in patients infected with human immunodeficiency virus (HIV). However, these studies predated the highly active antiretroviral therapy (HAART) era, included only a few patients receiving HAART, or had a limited duration of follow-up. This study investigates whether HRQL predicts survival among HIV-infected patients receiving HAART. METHODS: HIV-infected patients participating in the focus group of the AIDS Therapy Evaluation in the Netherlands (ATHENA) study and starting or already receiving HAART completed the Medical Outcomes Study HIV Health Survey at study entry (1 May 1998 through 31 December 2000). The physical health summary (PHS) and mental health summary (MHS) scores were calculated. All-cause mortality was established at 31 March 2008. Kaplan-Meier analysis and Cox regression models were performed to predict survival. RESULTS: The median follow-up was 8.4 years. Sixty-six patients (11.8%) died during follow-up. We found a significant relation between quartiles of PHS and survival (P < .001, log-rank test). Of patients with a PHS, 26 (20%) died in quartile 1 (indicating worst HRQL), 17 (13%) died in quartile 2, 10 (8%) died in quartile 3, and 5 (4%) died in quartile 4 (indicating best HRQL) (P< .001). The prediction of PHS on survival was independent of other (clinical) parameters (P< .001). No relation was found between MHS and survival (P= .13). CONCLUSION: Patient-reported HRQL predicted survival among HIV-infected patients receiving HAART. This information could be highly useful for physicians in determining the prognosis of their patients.

Timing of initiation of antiretroviral therapy in AIDS-free HIV-1-infected patients: a collaborative analysis of 18 HIV cohort studies.

BACKGROUND: The CD4 cell count at which combination antiretroviral therapy should be started is a central, unresolved issue in the care of HIV-1-infected patients. In the absence of randomised trials, we examined this question in prospective cohort studies. METHODS: We analysed data from 18 cohort studies of patients with HIV. Antiretroviral-naive patients from 15 of these studies were eligible for inclusion if they had started combination antiretroviral therapy (while AIDS-free, with a CD4 cell count less than 550 cells per microL, and with no history of injecting drug use) on or after Jan 1, 1998. We used data from patients followed up in seven of the cohorts in the era before the introduction of combination therapy (1989-95) to estimate distributions of lead times (from the first CD4 cell count measurement in an upper range to the upper threshold of a lower range) and unseen AIDS and death events (occurring before the upper threshold of a lower CD4 cell count range is reached) in the absence of treatment. These estimations were used to impute completed datasets in which lead times and unseen AIDS and death events were added to data for treated patients in deferred therapy groups. We compared the effect of deferred initiation of combination therapy with immediate initiation on rates of AIDS and death, and on death alone, in adjacent CD4 cell count ranges of width 100 cells per microL. FINDINGS: Data were obtained for 21 247 patients who were followed up during the era before the introduction of combination therapy and 24 444 patients who were followed up from the start of treatment. Deferring combination therapy until a CD4 cell count of 251-350 cells per microL was associated with higher rates of AIDS and death than starting therapy in the range 351-450 cells per microL (hazard ratio [HR] 1.28, 95% CI 1.04-1.57). The adverse effect of deferring treatment increased with decreasing CD4 cell count threshold. Deferred initiation of combination therapy was also associated with higher mortality rates, although effects on mortality were less marked than effects on AIDS and death (HR 1.13, 0.80-1.60, for deferred initiation of treatment at CD4 cell count 251-350 cells per microL compared with initiation at 351-450 cells per microL). INTERPRETATION: Our results suggest that 350 cells per microL should be the minimum threshold for initiation of antiretroviral therapy, and should help to guide physicians and patients in deciding when to start treatment.

The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease.

BACKGROUND: Quantifying 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. METHODS: The 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. RESULTS: The 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-ß CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-ß 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 studies, the presence of apolipoprotein E4 alleles and amyloid-ß accumulation can be associated with an increased risk of developing the disease, but their influence depends on age and clinical state. CONCLUSIONS: Despite the limited longitudinal data at the individual level and the high variability observed in such data, the study elucidates the link between the long asynchronous pathophysiological processes and the preclinical and clinical stages of AD.