Data-Driven Thresholding Statistically Biases ATN Profiling across Cohort Datasets

BACKGROUND

While the amyloid/tau/neurodegeneration (ATN) framework has found wide application in Alzheimer's disease research, it is unclear if thresholds obtained using distinct thresholding methods are concordant within the same dataset and interchangeable across cohorts.

OBJECTIVES

To investigate the robustness of data-driven thresholding methods and ATN profiling across cohort datasets.

DESIGN AND SETTING

We evaluated the impact of thresholding methods on ATN profiles by applying five commonly-used methodologies across cohort datasets. We assessed the generalizability of disease patterns discovered within ATN profiles by clustering individuals from different cohorts who were assigned to the same ATN profile.

PARTICIPANTS AND MEASUREMENTS

Participants with available CSF amyloid-β 1-42, phosphorylated tau, and total tau measurements were included from eleven AD cohort studies.

RESULTS

We observed high variability among obtained ATN thresholds, both across methods and datasets that impacted the resulting profile assignments of participants significantly. Clustering participants from different cohorts within the same ATN category indicated that identified disease patterns were comparable across most cohorts and biases introduced through distinct thresholding and data representations remained insignificant in most ATN profiles.

CONLUSION

Thresholding method selection is a decision of statistical relevance that will inevitably bias the resulting profiling and affect its sensitivity and specificity. Thresholds are likely not directly interchangeable between independent cohorts. To apply the ATN framework as an actionable and robust profiling scheme, a comprehensive understanding of the impact of used thresholding methods, their statistical implications, and a validation of results is crucial.

Temporal Course of SARS-CoV-2 Antibody Positivity in Patients with COVID-19 following the First Clinical Presentation

Knowledge of the sensitivities of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody tests beyond 35 days after the clinical onset of COVID-19 is insufficient. We aimed to describe positivity rate of SARS-CoV-2 assays employing three different measurement principles over a prolonged period. Two hundred sixty-eight samples from 180 symptomatic patients with COVID-19 and a reverse transcription polymerase chain reaction (RT-PCR) test followed by serological investigation of SARS-CoV-2 antibodies were included. We conducted three chemiluminescence (including electrochemiluminescence assay (ECLIA)), four enzyme-linked immunosorbent assay (ELISA), and one lateral flow immunoassay (LFIA) test formats. Positivity rates, as well as positive (PPVs) and negative predictive values (NPVs), were calculated for each week after the first clinical presentation for COVID-19. Furthermore, combinations of tests were assessed within an orthogonal testing approach employing two independent assays and predictive values were calculated. Heat maps were constructed to graphically illustrate operational test characteristics. During a follow-up period of more than 9 weeks, chemiluminescence assays and one ELISA IgG test showed stable positivity rates after the third week. With the exception of ECLIA, the PPVs of the other chemiluminescence assays were ≥95% for COVID-19 only after the second week. ELISA and LFIA had somewhat lower PPVs. IgM exhibited insufficient predictive characteristics. An orthogonal testing approach provided PPVs ≥ 95% for patients with a moderate pretest probability (e.g., symptomatic patients), even for tests with a low single test performance. After the second week, NPVs of all but IgM assays were ≥95% for patients with low to moderate pretest probability. The confirmation of negative results using an orthogonal algorithm with another assay provided lower NPVs than the single assays. When interpreting results from SARS-CoV-2 tests, the pretest probability, time of blood draw, and assay characteristics must be carefully considered. An orthogonal testing approach increases the accuracy of positive, but not negative, predictions.

Socio-Economic Status and Prevalence of Self-Reported Osteoporosis in Tehran: Results from a Large Population-Based Cross-Sectional Study (Urban HEART-2)

Osteoporosis is a widespread disease among older peoples. The aim of this study is to estimate the prevalence of self-reported osteoporosis and assessing its association with socio-economic status. A population-based cross-sectional study was conducted in Tehran, Iran in 2011. Participants were 45,990 individuals aged above 20 years from 22 urban districts. Osteoporosis was measured by self-administrative questionnaire. Wealth index was constructed using principal component analysis based on household assets. Chi-square test, chi square test for trend, and crude odds ratio were used to assess associations in univariate analysis. Multiple logistic regression utilized to estimate adjusted associations between self-reported osteoporosis and socio-economic status.The overall estimated prevalence of self-reported osteoporosis was 4% (95% CI 3.88-4.13), 1.19% in men, and 6.84% in women (P < 0.001). The prevalence increased considerably as age increased (P for trend < 0.001). In multivariable analysis, education and wealth status were negative, and smoking was positively associated with the prevalence of self-reported osteoporosis. No association was found between participants' skill levels and Townsend deprivation index with the prevalence of self-reported osteoporosis.The findings of the present study have improved understanding of the association between socioeconomic status and osteoporosis in the Iranian population. It is important to consider socioeconomic status in screening and prevention programs.

Effect of socio-economic status, family smoking and mental health through social network on the substance use potential in adolescents: a mediation analysis

OBJECTIVES

Understanding pathways that influence substance use potential (SUP) can help with effective substance use prevention interventions among adolescents. The aim of the present study is to contribute to a better understanding of the SUP of adolescents by examining the mediating role of social network quality in the SUP of Iranian adolescents.

STUDY DESIGN

A cross-sectional study.

METHODS

Structural equation modeling was conducted to assess the hypothesized model that social network quality would mediate the association of family socio-economic status, a mental health disorder, and family smoking with addiction potential.

RESULTS

The model shows a good fit to the data. Social network quality mediated the effect of family smoking on the SUP for boys. A mental health disorder had a positive significant direct effect on addiction potential for both girls and boys.

CONCLUSIONS

Social network quality mediates the effect of family smoking on boys' addiction potential in the context of Iran. Educational programs based on local societal ways and cultural norms are recommended to change tobacco smoking behavior among family members. In addition, to prevent subsequent substance use among adolescents, more effort is needed to improve their mental health.