Cardiovascular Health Trajectories and Elevated C‐Reactive Protein: The CARDIA Study

Background The relationship between long‐term cardiovascular health (CVH) patterns and elevated CRP (C‐reactive protein) in late middle age has yet to be investigated. We aimed to assess this relationship. Methods and Results Individual CVH components were measured in 4405 Black and White men and women (aged 18–30 years at baseline) in the CARDIA (Coronary Artery Risk Development in Young Adults) study at 8 examinations over 25 years. CRP was measured at 4 examinations (years 7, 15, 20, and 25). Latent class modeling was used to identify individuals with similar trajectories in CVH from young adulthood to middle age. Multivariable Poisson regression models were used to assess the association between race‐specific CVH trajectories and prevalence of elevated CRP levels (>3.0 mg/L) after 25 years of follow‐up. Five distinct CVH trajectories were identified for each race. Lower and decreasing trajectories had higher prevalence of elevated CRP relative to the highest trajectory. Prevalence ratios for elevated CRP in lowest trajectory groups at year 25 were 2.58 (95% CI, 1.89–3.51) and 7.20 (95% CI, 5.09–10.18) among Black and White people, respectively. Prevalence ratios for chronically elevated CRP (elevated CRP at 3 or more of the examinations) in the lowest trajectory groups were 8.37 (95% CI, 4.37–16.00) and 15.89 (95% CI, 9.01–28.02) among Black and White people, respectively. Conclusions Lower and decreasing CVH trajectories are associated with higher prevalence of elevated CRP during the transition from young adulthood to middle age.

Performance of three molecular tests for SARS-CoV-2 on a university campus estimated jointly with Bayesian latent class modeling

Accurate tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been critical in efforts to control its spread. The accuracy of molecular tests for SARS-CoV-2 has been assessed numerous times, usually in reference to a gold standard diagnosis. One major disadvantage of that approach is the possibility of error due to inaccuracy of the gold standard, which is especially problematic for evaluating testing in a real-world surveillance context. We used an alternative approach known as Bayesian latent class modeling (BLCM), which circumvents the need to designate a gold standard by simultaneously estimating the accuracy of multiple tests. We applied this technique to a collection of 1,716 tests of three types applied to 853 individuals on a university campus during a one-week period in October 2020. We found that reverse transcriptase polymerase chain reaction (RT-PCR) testing of saliva samples performed at a campus facility had higher sensitivity (median: 0.923; 95% credible interval: 0.732-0.996) than RT-PCR testing of nasal samples performed at a commercial facility (median: 0.859; 95% CrI: 0.547-0.994). The reverse was true for specificity, although the specificity of saliva testing was still very high (median: 0.993; 95% CrI: 0.983-0.999). An antigen test was less sensitive and specific than both of the RT-PCR tests. These results suggest that RT-PCR testing of saliva samples at a campus facility can be an effective basis for surveillance screening to prevent SARS-CoV-2 transmission in a university setting.

Trajectories of frailty in aging: Prospective cohort study

Background Emerging evidence suggests that there is significant variability in the progression of frailty in aging. We aimed to identify latent subpopulations of frailty trajectories, and examine their clinical and biological correlates. Methods We characterized frailty using a 41-item cumulative deficit score at baseline and annual visits up to 12 years in 681 older adults (55% women, mean age 74·6 years). Clinical risk profile and walking while talking performance as a clinical marker of trajectories were examined. Mortality risk associated with trajectories was evaluated using Cox regression adjusted for established survival predictors, and reported as hazard ratios (HR). Proteome-wide analysis was done. Findings Latent class modeling identified 4 distinct frailty trajectories: relatively stable (34·4%) as well as mild (36·1%), moderate (24·1%) and severely frail (5·4%). Four distinct classes of frailty trajectories were also shown in an independent sample of 515 older adults (60% women, 68% White, 26% Black). The stable group took a median of 31 months to accumulate one additional deficit compared to 20 months in the severely frail group. The worst trajectories were associated with modifiable risk factors such as low education, living alone, obesity, and physical inactivity as well as slower walking while talking speed. In the pooled sample, mild (HR 2·33, 95% CI 1·30–4·18), moderate (HR 2·49, 95% CI 1·33–4·66), and severely frail trajectories (HR 5·28, 95% CI 2·68–10·41) had higher mortality compared to the stable group. Proteomic analysis showed 11 proteins in lipid metabolism and growth factor pathways associated with frailty trajectories. Conclusion Frailty shows both stable and accelerated patterns in aging, which can be distinguished clinically and biologically.

Regional Difference in the Association between the Trajectory of Selenium Intake and Hypertension: A 20-Year Cohort Study

The effect of selenium on hypertension is inconclusive. We aimed to study the relationship between selenium intake and incident hypertension. Adults (age ≥20 years) in the China Health and Nutrition Survey were followed up from 1991 to 2011 (N = 13,668). The latent class modeling method was used to identify trajectory groups of selenium intake. A total of 4039 respondents developed hypertension. The incidence of hypertension was 30.1, 30.5, 30.6, and 31.2 per 1000 person-years among participants with cumulative average selenium intake of 21.0 ± 5.1, 33.2 ± 2.8, 43.8 ± 3.6, and 68.3 ± 25.2 µg/day, respectively. Region and selenium intake interaction in relation to hypertension was significant. In the multivariable model, cumulative intake of selenium was only inversely associated with the incident hypertension in northern participants (low selenium zone), and not in southern participants. Compared to selenium intake trajectory Group 1 (stable low intake), all three trajectory groups had a low hazard ratio for hypertension among the northern participants. However, Group 4 (high intake and decreased) showed an increasing trend of hypertension risk in the south. In conclusion, the association between selenium intake and the incidence of hypertension varied according to regions in China. In the low soil selenium zone, high selenium intake might be beneficial for hypertension prevention.

Comparison of Simple Averaging and Latent Class Modeling to Estimate the Area of Land Cover in the Presence of Reference Data Variability

Estimates of the area or percent area of the land cover classes within a study region are often based on the reference land cover class labels assigned by analysts interpreting satellite imagery and other ancillary spatial data. Different analysts interpreting the same spatial unit will not always agree on the land cover class label that should be assigned. Two approaches for accommodating interpreter variability when estimating the area are simple averaging (SA) and latent class modeling (LCM). This study compares agreement between area estimates obtained from SA and LCM using reference data obtained by seven trained, professional interpreters who independently interpreted an annual time series of land cover reference class labels for 300 sampled Landsat pixels. We also compare the variability of the LCM and SA area estimates over different numbers of interpreters and different subsets of interpreters within each interpreter group size, and examine area estimates of three land cover classes (forest, developed, and wetland) and three change types (forest gain, forest loss, and developed gain). Differences between the area estimates obtained from SA and LCM are most pronounced for the estimates of wetland and the three change types. The percent area estimates of these rare classes were usually greater for LCM compared to SA, with the differences between LCM and SA increasing as the number of interpreters providing the reference data increased. The LCM area estimates generally had larger standard deviations and greater ranges over different subsets of interpreters, indicating greater sensitivity to the selection of the individual interpreters who carried out the reference class labeling.