Accumulation of non-traditional risk factors for coronary heart disease is associated with incident coronary heart disease hospitalization and death

Assessing multiple traditional risk factors improves prediction for late-life diseases, including coronary heart disease (CHD). It appears that non-traditional risk factors can also predict risk. The objective was to investigate contributions of non-traditional risk factors to coronary heart disease risk using a deficit accumulation approach.Community-dwelling adults with no known history of CHD (n = 2195, mean age 46.9±18.7 years, 51.8% women) participated in the 1995 Nova Scotia Health Survey. Three risk factor indices were constructed to quantify the proportion of deficits present in individuals: 1) a 17-item Non-Traditional Risk Factor Index (e.g. sinusitis, arthritis); 2) a 9-item Traditional Risk Factor Index (e.g. hypertension, diabetes); and 3) a frailty index (25 items combined from the other two index measures). Ten-year risks of CHD events (defined as CHD-related hospitalization and CHD-related mortality) were evaluated.The Non-Traditional Risk Factor Index, made up of health deficits unrelated to CHD, was independently associated with incident CHD events over 10 years after controlling for age, sex, and the Traditional Risk Factor Index [adjusted {adj.} Hazard Ratio {HR} = 1.31; Confidence Interval {CI} 1.14-1.51]. When all health deficits, both those related and unrelated to CHD, were included in a frailty index the corresponding adjusted hazard ratio was 1.61; CI 1.40-1.85.Both traditional and non-traditional risk factor indices are independently associated with incident CHD events. CHD risk assessment may benefit from consideration of general health information as well as from traditional risk factors.Lindsay M. K. Wallace, Olga Theou, Susan A. Kirkland, Michael R. H. Rockwood, Karina W. Davidson, Daichi Shimbo, Kenneth Rockwoo

Can We Reduce Prolonged Sitting? Feasibility of a Tactile Vibration Prompt To Initiate Movement

Please refer to the pdf version of the abstract located adjacent to the title

Unmasking masked hypertension: prevalence, clinical implications, diagnosis, correlates and future directions

‘Masked hypertension’ is defined as having non-elevated clinic blood pressure (BP) with elevated out-of-clinic average BP, typically determined by ambulatory BP monitoring. Approximately 15–30% of adults with non-elevated clinic BP have masked hypertension. Masked hypertension is associated with increased risks of cardiovascular morbidity and mortality compared to sustained normotension (non-elevated clinic and ambulatory BP), which is similar to or approaching the risk associated with sustained hypertension (elevated clinic and ambulatory BP). The confluence of increased cardiovascular risk and a failure to be diagnosed by the conventional approach of clinic BP measurement makes masked hypertension a significant public health concern. However, many important questions remain. First, the definition of masked hypertension varies across studies. Further, the best approach in the clinical setting to exclude masked hypertension also remains unknown. It is unclear whether home BP monitoring is an adequate substitute for ambulatory BP monitoring in identifying masked hypertension. Few studies have examined the mechanistic pathways that may explain masked hypertension. Finally, scarce data are available on the best approach to treating individuals with masked hypertension. Herein, we review the current literature on masked hypertension including definition, prevalence, clinical implications, special patient populations, correlates, issues related to diagnosis, treatment, and areas for future research

Association Between Annual Visit-to-Visit Blood Pressure Variability and Stroke in Postmenopausal Women: Data From the Women's Health Initiative

Accumulating evidence suggests that increased visit-to-visit variability (VVV) of blood pressure is associated with stroke. No study has examined the association between VVV of blood pressure and stroke in postmenopausal women, and scarce data exist as to whether this relation is independent of the temporal trend of blood pressure. We examined the association of VVV of blood pressure with stroke in 58228 postmenopausal women enrolled in the Women's Health Initiative. Duplicate blood pressure readings, which were averaged, were taken at baseline and at each annual visit. VVV was defined as the SD for the participant's mean systolic blood pressure (SBP) across visits (SD) and about the participant's regression line with SBP regressed across visits (SDreg). Over a median follow-up of 5.4 years, 997 strokes occurred. In an adjusted model including mean SBP over time, the hazard ratios (95% CI) of stroke for higher quartiles of SD of SBP compared with the lowest quartile (referent) were 1.39 (1.03–1.89) for quartile 2, 1.52 (1.13–2.03) for quartile 3, and 1.72 (1.28–2.32) for quartile 4 (P trend <0.001). The relation was similar for SDreg of SBP quartiles in a model that additionally adjusted for the temporal trend in SBP (P trend <0.001). The associations did not differ by stroke type (ischemic versus hemorrhagic). There was a significant interaction between mean SBP and SDreg on stroke with the strongest association seen below 120 mmHg. In postmenopausal women, greater VVV of SBP was associated with increased risk of stroke, particularly in the lowest range of mean SBP

A Pilot Study Identifying Statin Nonadherence With Visit-to-Visit Variability of Low-Density Lipoprotein Cholesterol

Nonadherence to cardiovascular medications such as statins is a common, important problem. Clinicians currently rely on intuition to identify medication nonadherence. The visit-to-visit variability (VVV) of low-density lipoprotein (LDL) cholesterol might represent an opportunity to identify statin nonadherence with greater accuracy. We examined the clinical and pharmacy data from 782 members of the Boston Medical Center Health Plan, seen at either the Boston Medical Center or its affiliated community health centers, who were taking statins and had ≥3 LDL cholesterol measurements from 2008 to 2011. The LDL cholesterol VVV (defined by the within-patient SD) was categorized into quintiles. Multivariate logistic regression models were generated with statin nonadherence (defined by the standard 80% pharmacy refill-based medication possession ratio threshold) as the dependent variable. The proportion of statin nonadherence increased across the quintiles of LDL cholesterol VVV (64.3%, 71.2%, 89.2%, 92.3%, 91.7%). Higher quintiles of LDL cholesterol VVV had a strong positive association with statin nonadherence, with an adjusted odds ratio of 3.4 (95% confidence interval 1.7 to 7.1) in the highest versus lowest quintile of LDL cholesterol VVV. The age- and gender-adjusted model had poor discrimination (C-statistic 0.62, 95% confidence interval 0.57 to 0.67), but the final adjusted model (age, gender, race, mean LDL cholesterol) demonstrated good discrimination (C-statistic 0.75, 95% confidence interval 0.71 to 0.79) between the adherent and nonadherent patients. In conclusion, the VVV of LDL cholesterol demonstrated a strong association with statin nonadherence in a clinic setting. Furthermore, a VVV of LDL cholesterol-based model had good discrimination characteristics for statin nonadherence. Research is needed to validate and generalize these findings to other populations and biomarkers

Low correlation between visit-to-visit variability and 24-h variability of blood pressure

Visit-to-visit variability (VVV) of clinic systolic blood pressure (SBP) has been associated with cardiovascular disease risk. Given the need for obtaining blood pressure (BP) at multiple visits to calculate VVV, substituting BP variability from ambulatory blood pressure monitoring (ABPM) may be a practical alternative. We assessed the correlation between VVV of BP and BP variability from ABPM using data from 146 untreated, mostly normotensive participants (mean age 47.9 years) in a substudy of the ongoing Masked Hypertension Study. VVV of SBP and diastolic blood pressure (DBP) was estimated by the standard deviation (SDvvv) and average real variability (ARVvvv) from 6 study visits over a median of 216 days. ABPM data were used to calculate the day-night SD (SDdn) and the ARV of SBP and DBP over 24 hours (ARV24). For SBP, the mean SDvvv and SDdn were 6.3 (SD=2.5) and 8.8 (SD=1.8) mmHg, respectively, and mean ARVvvv and ARV24 were 7.2 (SD=3.2) and 8.4 (SD=2.1) mmHg, respectively. The Spearman correlation coefficient between SDvvv and SDdn of SBP was rs=0.25 and between ARVvvv and ARV24 was rs=0.17. Participants in the highest quartile of SDdn of SBP were 1.66 (95% CI: 0.93 – 2.75) times more likely to be in the highest quartile of SDvvv of SBP. The observed-to-expected ratio between the highest quartiles of ARVvvv and ARV24 of SBP was 0.89 (95% CI: 0.41 – 1.69). The correlations for SDvvv and SDdn and ARVvvv and ARV24 of DBP were minimal. These data suggest VVV and 24-hour variability are weakly correlated and not interchangeable

Design of a multi-center immunophenotyping analysis of peripheral blood, sputum and bronchoalveolar lavage fluid in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Background Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS) is a multi-center longitudinal, observational study to identify novel phenotypes and biomarkers of chronic obstructive pulmonary disease (COPD). In a subset of 300 subjects enrolled at six clinical centers, we are performing flow cytometric analyses of leukocytes from induced sputum, bronchoalveolar lavage (BAL) and peripheral blood. To minimize several sources of variability, we use a “just-in-time” design that permits immediate staining without pre-fixation of samples, followed by centralized analysis on a single instrument. Methods The Immunophenotyping Core prepares 12-color antibody panels, which are shipped to the six Clinical Centers shortly before study visits. Sputum induction occurs at least two weeks before a bronchoscopy visit, at which time peripheral blood and bronchoalveolar lavage are collected. Immunostaining is performed at each clinical site on the day that the samples are collected. Samples are fixed and express shipped to the Immunophenotyping Core for data acquisition on a single modified LSR II flow cytometer. Results are analyzed using FACS Diva and FloJo software and cross-checked by Core scientists who are blinded to subject data. Results Thus far, a total of 152 sputum samples and 117 samples of blood and BAL have been returned to the Immunophenotyping Core. Initial quality checks indicate useable data from 126 sputum samples (83%), 106 blood samples (91%) and 91 BAL samples (78%). In all three sample types, we are able to identify and characterize the activation state or subset of multiple leukocyte cell populations (including CD4+ and CD8+ T cells, B cells, monocytes, macrophages, neutrophils and eosinophils), thereby demonstrating the validity of the antibody panel. Conclusions Our study design, which relies on bi-directional communication between clinical centers and the Core according to a pre-specified protocol, appears to reduce several sources of variability often seen in flow cytometric studies involving multiple clinical sites. Because leukocytes contribute to lung pathology in COPD, these analyses will help achieve SPIROMICS aims of identifying subgroups of patients with specific COPD phenotypes. Future analyses will correlate cell-surface markers on a given cell type with smoking history, spirometry, airway measurements, and other parameters. Trial registration This study was registered with ClinicalTrials.gov as NCT01969344