Coronary Heart Disease Events in Framingham Participants with Differing Exposure to Risk Factors During Young Adulthood.

<p>Coronary Heart Disease Events in Framingham Participants with Differing Exposure to Risk Factors During Young Adulthood.</p

Young Adult Exposure to Cardiovascular Risk Factors and Risk of Events Later in Life: The Framingham Offspring Study

<div><p>Background</p><p>It is unclear whether coronary heart disease (CHD) risk factor exposure during early adulthood contributes to CHD risk later in life. Our objective was to analyze whether extent of early adult exposures to systolic and diastolic blood pressure (SBP, DBP) and low-and high-density lipoprotein cholesterol (LDL, HDL) are independent predictors of CHD events later in life.</p><p>Methods and Findings</p><p>We used all available measurements of SBP, DBP, LDL, and HDL collected over 40 years in the Framingham Offspring Study to estimate risk factor trajectories, starting at age 20 years, for all participants. Average early adult (age 20–39) exposure to each risk factor was then estimated, and used to predict CHD events (myocardial infarction or CHD death) after age 40, with adjustment for risk factor exposures later in life (age 40+). 4860 participants contributed an average of 6.3 risk factor measurements from in-person examinations and 24.5 years of follow-up after age 40, and 510 had a first CHD event. Early adult exposures to high SBP, DBP, LDL or low HDL were associated with 8- to 30-fold increases in later life CHD event rates, but were also strongly correlated with risk factor levels later in life. After adjustment for later life levels and other risk factors, early adult DBP and LDL remained strongly associated with later life risk. Compared with DBP≤70 mmHg, adjusted hazard ratios (HRs) were 2.1 (95% confidence interval: 0.8–5.7) for DBP = 71–80, 2.6 (0.9–7.2) for DBP = 81–90, and 3.6 (1.2–11) for DBP>90 (p-trend = 0.019). Compared with LDL≤100 mg/dl, adjusted HRs were 1.5 (0.9–2.6) for LDL = 101–130, 2.2 (1.2–4.0) for LDL = 131–160, and 2.4 (1.2–4.7) for LDL>160 (p-trend = 0.009). While current levels of SBP and HDL were also associated with CHD events, we did not detect an independent association with early adult exposure to either of these risk factors.</p><p>Conclusions</p><p>Using a mixed modeling approach to estimation of young adult exposures with trajectory analysis, we detected independent associations between estimated early adult exposures to non-optimal DBP and LDL and CHD events later in life.</p></div

Framingham Offspring Study Sample Examinations and Observation Period.

<p>Framingham Offspring Study Sample Examinations and Observation Period.</p

Adjusted associations between SBP, DBP, LDL and HDL levels at different ages and CHD events.

<p>Hazard ratios (95% confidence intervals) are adjusted for age (via Cox model), sex, calendar year (via spline), body mass index, diabetes, years with diabetes, smoking status (current/past/never), pack-years of tobacco exposure (via spline), and use of blood pressure and lipid medications. The first column of results (for age 20–39) corresponds to the right-hand column of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0154288#pone.0154288.t002" target="_blank">Table 2</a>. Categories for systolic blood pressure (SBP) are ≤120 (reference), 121–140, 141–160 and >160 mmHg; for diastolic blood pressure (DBP) are ≤80, 81–90, 91–100, and >100; for low-density lipoprotein cholesterol (LDL) are ≤100 (reference), 101–130, 131–160 and >160 mg/dl; and for high-density lipoprotein cholesterol (HDL) are >65 (reference), 51–65, 36–50, and ≤35 mg/dl. “P Overall” refers to a test of the overall contribution of the risk factor (including early, later, and current exposures) to the model. No participants had an average SBP>160 mmHg from age 20–39. The * indicates a truncated confidence interval.</p

Characteristics of participants at the beginning of follow up, stratified by early life exposure to LDL.

<p>Characteristics of participants at the beginning of follow up, stratified by early life exposure to LDL.</p

Prevalence of e-cigarette only, cigarette only, and dual use in the past 30 days by demographic characteristics and lifestyle and well-being factors in the health eheart study, N = 39,747^*.

<p>Prevalence of e-cigarette only, cigarette only, and dual use in the past 30 days by demographic characteristics and lifestyle and well-being factors in the health eheart study, N = 39,747^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198681#t001fn001" target="_blank">*</a>}.</p

Percent of “yes” responses to past or current medical symptoms or conditions among cigarette only users and dual users.

<p>The only statistically significant difference between cigarette only users and dual users was arrhythmia (ADJ <i>p</i> = .02). Models adjusted for age, sex, race, education, cigarettes per day, and as needed, coronary artery disease, congestive heart failure, and COPD.</p

E-cigarette use dose measures among e-cigarette only users and dual users.

<p>(a) # days per month was defined as “number of days of e-cigarette use in the past 30 days” (b) # cartridges per day: “number of e-liquid cartridges/refills used per day” (c) # puffs per day: “number of puffs off an e-cigarette per day.” Error bars denote the interquartile range.</p

Median SF-36 general health scores, breathing difficulty “typically” scores, and breathing difficulty in the “past month” scores, among cigarette only users and dual users.

<p>Dual use was associated with lower (poorer) general health scores (ADJ <i>p</i> = .002) and higher (poorer) past month breathing difficulty scores (ADJ <i>p</i> = .001). Models adjusted for age, sex, race, education, cigarettes per day, coronary artery disease, congestive heart failure, and COPD. Error bars denote the interquartile range.</p

Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep

<div><p>Background</p><p>Smartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality.</p><p>Aims</p><p>The aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep.</p><p>Methods</p><p>We performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index–PSQI) were obtained by survey. Linear regression was used to obtain effect estimates.</p><p>Results</p><p>Total screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency.</p><p>Conclusions</p><p>These findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.</p></div