Abstract 015: Critical Periods in Cardiovascular Health Across the Life Course: A Pooled Cohort Analysis

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Norrina B Allen ◽  
Amy Krefman ◽  
Darwin Labarthe ◽  
Philip Greenland ◽  
Markus Juonala ◽  
...  
Keyword(s):  
Change Point ◽  
Mixed Model ◽  
Cardiovascular Health ◽  
Linear Mixed Model ◽  
Cohort Analysis ◽  
Change Points ◽  
Rapid Decline ◽  
Critical Periods ◽  
Ideal Cardiovascular Health ◽  
Inflection Points

Background: The prevalence of Ideal Cardiovascular Health (CVH) decreases with age, beginning in childhood. However, more precise estimates of trajectories of CVH across the lifespan are needed to guide intervention. The aims of this analysis are to describe trajectories in CVH from childhood through middle age and examine whether there are critical inflection points in the decline in CVH. Methods: We pooled data from five prospective childhood/early adulthood cohorts including Bogalusa, Young Finns, HB!, CARDIA, and STRIP. Clinical CVH factors—blood pressure, BMI, cholesterol, glucose—were categorized as poor, intermediate and ideal then summed to create a clinical CVH score, ranging from 0 to 8 (higher score= more ideal CVH). The association between clinical CVH score and age in years was modeled using a segmented linear mixed model, with a random participant intercept, fixed slopes, and fixed change points. Change points were estimated using an extension of the R package ‘segmented’ which utilizes a likelihood based approach to iteratively determine one or more change points. All models were adjusted for race, gender and cohort. Results: This study included 18,290 participants (51% female, 67% White, 46% between the ages of 8-11 at baseline). CVH scores decline with age from 8 through 55 years. We found two ages at which the slope of the CVH trajectories change significantly. CVH scores are generally stable from age 8 until the first change point at age 17 (95% CI 16.3-17.4), when they begin to decline more rapidly with a 0.08 CVH unit loss per year from age 17 to 30. The second change point occurs at age 30 (26.7-33.6) when the rate of decline increases by an additional 0.01 units per year. Conclusion: The clinical CVH score declines from favorable levels from childhood through adulthood, with a rapid decline starting at age 17 that becomes slightly steeper from age 30 to 55 years. These inflection points signal that there are critical periods in an individual’s clinical CVH trajectory during which prevention efforts may be targeted.

Change Point Detection For Clustered Expression Data In Short Time Series.

2021 ◽  
Author(s):  
Miriam Sieg ◽  
Lina Katrin Sciesielski ◽  
Karin Kirschner ◽  
Jochen Kruppa
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Change Point ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Change Point Detection ◽  
Change Points ◽  
Point Detection ◽  
Over Time ◽  
Time Point

Abstract Background: In longitudinal studies, observations are made over time. Hence, the single observations at each time point are dependent, making them a repeated measurement. In this work, we explore a different, counterintuitive setting: At each developmental time point, a lethal observation is performed on the pregnant or nursing mother. Therefore, the single time points are independent. Furthermore, the observation in the offspring at each time point is correlated with each other because each litter consists of several (genetically linked) littermates. In addition, the observed time series is short from a statistical perspective as animal ethics prevent killing more mother mice than absolutely necessary, and murine development is short anyway. We solve these challenges by using multiple contrast tests and visualizing the change point by the use of confidence intervals.Results: We used linear mixed models to model the variability of the mother. The estimates from the linear mixed model are then used in multiple contrast tests.There are a variety of contrasts and intuitively, we would use the Changepoint method. However, it does not deliver satisfying results. Interestingly, we found two other contrasts, both capable of answering different research questions in change point detection: i) Should a single point with change direction be found, or ii) Should the overall progression be determined? The Sequen contrast answers the first, the McDermott the second. Confidence intervals deliver effect estimates for the strength of the potential change point. Therefore, the scientist can define a biologically relevant limit of change depending on the research question.Conclusion: We present a solution with effect estimates for short independent time series with observations nested at a given time point. Multiple contrast tests produce confidence intervals, which allow determining the position of change points or to visualize the expression course over time. We suggest to use McDermott’s method to determine if there is an overall significant change within the time frame, while Sequen is better in determining specific change points. In addition, we offer a short formula for the estimation of the maximal length of the time series.

scholarly journals Change point detection for clustered expression data

2021 ◽  
Author(s):  
Miriam Sieg ◽  
Lina Katrin Sciesielski ◽  
Karin Michaela Kirschner ◽  
Jochen Kruppa
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Change Point ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Change Point Detection ◽  
Change Points ◽  
Point Detection ◽  
Over Time ◽  
Time Point

Abstract Background: In longitudinal studies, observations are made over time. Hence, the single observations at each time point are dependent, making them a repeated measurement. In this work, we explore a different, counterintuitive setting: At each developmental time point, a lethal observation is performed on the pregnant or nursing mother. Therefore, the single time points are independent. Furthermore, the observation in the offspring at each time point is correlated with each other because each litter consists of several (genetically linked) littermates. In addition, the observed time series is short from a statistical perspective as animal ethics prevent killing more mother mice than absolutely necessary, and murine development is short anyway. We solve these challenges by using multiple contrast tests and visualizing the change point by the use of confidence intervals.Results: We used linear mixed models to model the variability of the mother. The estimates from the linear mixed model are then used in multiple contrast tests.There are a variety of contrasts and intuitively, we would use the Changepoint method. However, it does not deliver satisfying results. Interestingly, we found two other contrasts, both capable of answering different research questions in change point detection: i) Should a single point with change direction be found, or ii) Should the overall progression be determined? The Sequen contrast answers the first, the McDermott the second. Confidence intervals deliver effect estimates for the strength of the potential change point. Therefore, the scientist can define a biologically relevant limit of change depending on the research question.Conclusion: We present a solution with effect estimates for short independent time series with observations nested at a given time point. Multiple contrast tests produce confidence intervals, which allow determining the position of change points or to visualize the expression course over time. We suggest to use McDermott’s method to determine if there is an overall significant change within the time frame, while Sequen is better in determining specific change points. In addition, we offer a short formula for the estimation of the maximal length of the time series.

scholarly journals Influencing cardiovascular health habits in the rural, deep south: results of a cluster randomized trial

2019 ◽  
Vol 34 (2) ◽  
pp. 200-208
Author(s):  
Laurie S Abbott ◽  
Elizabeth H Slate ◽  
Jennifer L Lemacks
Keyword(s):  
Health Program ◽  
Mixed Model ◽  
Cardiovascular Health ◽  
Linear Mixed Model ◽  
Intervention Group ◽  
The United States ◽  
Control Group ◽  
Cvd Risk ◽  
Health Habits ◽  
Cluster Randomized

Abstract Cardiovascular disease (CVD) is a major cause of death among people living in the United States. Populations, especially minorities, living in the rural South are disproportionately affected by CVD and have greater CVD risk, morbidity and mortality. Culturally relevant cardiovascular health programs implemented in rural community settings can potentially reduce CVD risk and facilitate health behavior modification. The purpose of this study was to examine the effects of a cardiovascular health promotion intervention on the health habits of a group of rural African American adults. The study had a cluster randomized controlled trial design involving 12 rural churches that served as statistical clusters. From the churches (n = 6) randomized to the intervention group, 115 participants were enrolled, received the 6-week health program and completed pretest–posttest measures. The 114 participants from the control group churches (n = 6) did not receive the health program and completed the same pretest–posttest measures. The linear mixed model was used to compare group differences from pretest to posttest. The educational health intervention positively influenced select dietary and confidence factors that may contribute toward CVD risk reduction.

scholarly journals Excess Cardiovascular Disease and Mortality in Diabetes: Can It Be Offset With Ideal Cardiovascular Health Trajectories?

2021 ◽  
Author(s):  
Bamba Gaye ◽  
Donald Lloyd-Jones ◽  
Eugenie Valentin ◽  
Joshua J Joseph ◽  
Xavier Jouven ◽  
...  
Keyword(s):  
Physical Activity ◽  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Mortality Risk ◽  
Cardiovascular Health ◽  
Proportional Hazards ◽  
Cohort Analysis ◽  
Cox Proportional Hazards ◽  
Ideal Cardiovascular Health ◽  
Incidence And Mortality

Abstract Objective: To describe patterns of change in cardiovascular health (CVH) measured by the Life’s Simple 7 (LS7) over 6 years in participants with diabetes, and assess the association of these patterns of change with subsequent cardiovascular disease (CVD) and all-cause mortality.Methods: We conducted a long-term prospective cohort analysis (median follow-up 19 years) using the ARIC study, a community-based, biracial cohort with participants aged 45-64 years at baseline in 1987–1989. The LS7 metrics (smoking, body mass index, physical activity, diet, total cholesterol, blood glucose, and blood pressure) were collected at baseline and 6 years later. At both time points, participants were classified as low (0-2 ideal metrics), moderate (3-4), and favorable (5-7) CVH, and 6-year CVH change categories were created. Cox proportional hazards regressions were used to estimate the association between change categories and CVD and mortality. Results: The majority (62.2%) of participants with diabetes had a stable-low CVH at both visits, whereas 12.1% had stable-moderate and 0.4% stable-favorable, 14.9% improved, and 10.5% worsened. Compared to participants with stable-low CVH, CVD and mortality risk was lower for those who improved CVH 6 years later, or stable-moderate, or even high- or moderate-to-low CVH. This was driven by favorable changes in blood pressure, cholesterol, physical activity and smoking. Compared to participants free of diabetes with a stable-favorable CVH change, participants with diabetes had higher CVD and mortality risk regardless of their CVH trajectory. However, those with a favorable CVH trajectory displayed the same excess risk as participants without diabetes with stable-low CVH. Conclusions: Among individuals with diabetes, maintaining or reaching ideal levels of LS7 metrics can help reduce the excess CVD incidence and mortality. A favorable CVH trajectory in people with diabetes carried approximately the same excess CVD and mortality risk than an unfavorable CVH trajectory in people free of diabetes.

Impact of Climatic Conditions on Physical Activity: A 2-Year Cohort Study in the Arabian Gulf Region

2016 ◽  
Vol 13 (9) ◽  
pp. 929-937 ◽  
Author(s):  
Abdulla S. Al-Mohannadi ◽  
Abdulaziz Farooq ◽  
Angus Burnett ◽  
Mercia Van Der Walt ◽  
Mohamed Ghaith Al-Kuwari
Keyword(s):  
Physical Activity ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Cohort Analysis ◽  
Arabian Gulf ◽  
Climatic Conditions ◽  
World Health ◽  
Gulf Region ◽  
Arabian Gulf Region ◽  
Temperature And Humidity

Background:Little is known about the effects that climatic conditions, especially extreme heat, have on physical activity (PA) levels. The aim of this 2-year cohort analysis undertaken in the Arabian Gulf region was to assess changes in objectively-assessed PA as a result of changes in climatic conditions.Methods:A total of 2088 adults (1390 men and 698 women) from many regions of the world were enrolled in a community health program in Qatar, were included in this study. For 2 years participants recorded daily step count using an Omron HJ-720 ITC pedometer. Daily climate parameters included temperature, relative humidity and other conditions.Results:From linear mixed model analysis it was evident that higher average temperature and humidity, and the Wet bulb Globe Temperature (WbGT), resulted in a reduction of the number of steps taken per day. Analysis of interaction effects also revealed that the effects of heat stress were equivalent across gender, age, and the region participants came from according to a World Health Organization classification scheme.Conclusions:Increased temperature and humidity are associated with a reduction in the number of steps taken per day. Community-based PA programs in hot hot/humid climates should consider novel approaches to increase PA levels.

scholarly journals Using Spot Urine Samples to Estimate Sodium and Potassium intake in Healthy vs CKD Patients: Results from a Controlled Feeding Study

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 1822-1822
Author(s):  
Andrea Lobene ◽  
Elizabeth Stremke ◽  
George McCabe ◽  
Sharon Moe ◽  
Ranjani Moorthi ◽  
...  
Keyword(s):  
Urine Sample ◽  
Mixed Model ◽  
Cardiovascular Health ◽  
Linear Mixed Model ◽  
Secondary Analysis ◽  
Urine Samples ◽  
Inpatient Setting ◽  
Spot Urine ◽  
Feeding Study ◽  
Funding Sources

Abstract Objectives Evaluating sodium (Na) and potassium (K) intake is important as both have implications for blood pressure and cardiovascular health, especially in individuals with chronic kidney disease (CKD). Spot urine samples may be used to estimate Na and K intake with several published equations, but the accuracy has not been thoroughly explored. Our objective was to compare estimated 24-hour urinary Na and K excretion (e24hUNa and e24hUK), calculated from a spot urine sample using published equations, to measured 24hUNa and 24hUK and Na and K intake, and to determine if there are differences between healthy and CKD participants. Methods This is a secondary analysis of a controlled feeding study in participants with moderate CKD matched to healthy adults (n = 16). Participants consumed a controlled diet for 9 days, providing ∼2400 mg Na/d and ∼3000 mg K/d. On days 7 and 8, participants collected all urine in an inpatient setting. Urine Na and K were analyzed by ICP and urine creatinine by the Jaffe reaction. The day 7 fasting urine sample was used to calculate e24hUNa using 2 equations and e24huK using 1 equation. Log-transformed Na intake, measured 24hUNa, and e24hUNa were compared using a general linear mixed model and partial correlations, and agreement between Na intake and e24hUNa was assessed using Bland-Altman plots. Similar analyses were run for K. Results Participants were aged 54.6 ± 13.0 y, n = 8 were female, n = 6 were black, and n = 10 were white. In CKD participants, eGFR was 40.7 ± 7.9 mL/min. Average Na intake on day 7 was 2138 ± 302 mg and K intake was 2528 ± 254 mg. Na intake, measured 24hUNa, and e24hUNa were not significantly different (P = 0.17) or correlated with each other (all P > 0.20) and there was no significant interaction with CKD status (P = 0.51). However, both e24hUNa were ∼500 mg higher than Na intake. For K, e24hUK was significantly different from K intake (P < 0.001) and measured 24hUK (P = 0.02), with no interaction with CKD (P = 0.53). K intake, measured 24hUK, and e24hUK were not correlated (all P > 0.05). Bland-Altman plots show poor agreement between both e24hUNa and Na intake and e24hUK and K intake. Conclusions Estimated 24hUNa and estimated 24hUK are poor predictors of Na and K intake, respectively, in both healthy and CKD participants. Results should be confirmed in a larger sample. Funding Sources Indiana CTSI (funded by NCATS CTSA program), NIDDK.

scholarly journals Change point detection for clustered expression data

2021 ◽  
Author(s):  
Miriam Sieg ◽  
Lina Katrin Sciesielski ◽  
Karin Michaela Kirschner ◽  
Jochen Kruppa
Keyword(s):  
Data Structure ◽  
Confidence Intervals ◽  
Change Point ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Research Question ◽  
Change Point Detection ◽  
Expression Data ◽  
Abrupt Changes ◽  
Point Detection

Abstract Background: To detect changes in biological processes samples are oftenmeasured at several time points. We observe expression data measured atdifferent developmental stages, or more broadly, historical data. Hence, the mainassumption of our proposed methodology is the independence between theobserved samples over time. In addition, the observations are clustered at eachpoint in time. The clustering is caused by measuring litter mates from relativelyfew mother mice at each development stage. The examination is lethal.Therefore, we have an independent data structure over the entire history, but adependent data structure at a particular point in time. Over the course of thehistorical data, we want to identify abrupt changes in the outcome - a changepoint. Results: In this paper, we demonstrate the application of generalized hypothesistesting using a linear mixed effects model as one possible method for detectingchange points. The coefficients from the linear mixed model are used in multiplecontrast tests. The effect estimates are then visualized with simultaneousconfidence intervals. The figure of the confidence intervals can be used for thedetermination of the change point. Multiple contrast tests depend on the choiceof the used contrast. A variety of possible usable contrasts exists. In smallsimulation studies, we model different courses with abrupt changes and illustratedifferent contrasts. We found two contrasts, both capable of answering differentresearch questions in change point detection. Sequen contrast to detectindividual points of change or McDermott contrast to illustrate overallprogression. In addition, we show the application on a clinical pilot study. Conclusion: Simultaneous confidence intervals estimated by multiple contrasttests using the model fit from a linear mixed model are usable to determinepossible change points in clustered expression data. The confidence intervalsdeliver direct interpretable effect estimates on the scale of the outcome for thestrength of the potential change point. Hence, scientists can define biologicallyrelevant limits of change depending on the research question. We found tworarely used contrast with the best properties to detect a possible change: theSequen and McDermott contrast. We provide R code for the direct applicationwith examples

Abstract P304: High Cardiovascular Health Metrics Score Predicts Slower Cognitive Performance Decline in Coronary Patients

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Uri Goldbourt ◽  
Miri Lutski ◽  
David Tanne
Keyword(s):  
Cognitive Function ◽  
Health Behaviors ◽  
Cognitive Performance ◽  
Cognitive Functions ◽  
Mixed Model ◽  
Cardiovascular Health ◽  
Linear Mixed Model ◽  
Inverse Association ◽  
Secondary Prevention Trial ◽  
Visual Spatial

Introduction: In 2010, the American Heart Association introduced a metric defining the concept of ideal cardiovascular health (CVH) metrics as part of its 2020 Impact Goals definition designed for the health general population. Several studies have demonstrated that a higher number of ideal CVH metrics were associated with a lower rate of cardiovascular events, stroke and all-cause mortality. Hypothesis: We hypothesized that a modified cardiovascular health (CVH) metrics score among patients with CHD may be associated with the change in cognitive functions two decades later in patients. Methods: CVH metrics were assessed in a subgroup of men, who had previously participated in a secondary prevention trial and two successive examinations of cognitive function (N=200, mean age at baseline 57.3±6.3 yrs.). A CVH metrics score at baseline was calculated including 3 health parameters, glucose, LDL-cholesterol, blood-pressure; and 4 health behaviors, smoking, obesity, physical-activity and adherence to Mediterranean diet. We scored each of these CVH metrics into best (2 points), intermediate (1 point), and poor levels (0 points). Cognitive performance was evaluated 14.7±1.9 and 19.9±1.0 years after entry to the trial. Cognitive function was assessed using the NeuroTrax Computerized Cognitive Battery. Linear mixed model was used to assess change in cognitive functions between T1 and T2 cognitive evaluations. Results: Among the 200 patients, 68 (34.0%) had less than 7 (bottom group), 85 (42.5%) had 8-9 (middle group) and 47 (23.5%) had at least 10 CVH metrics points (top group). After adjustments, the top group of CVH score vs. others was associated with slower decline in overall cognitive performance composite z-score [0.23±0.09; p=0.009] and on tests of executive and visual spatial functions [0.23±0.11; p=0.047 and 0.49±0.17; p=0.004, respectively]. A 3 point-Increment in the health behaviors component was related to a slower decline in visuospatial functions [0.325±0.12; p=0.01]. Conclusion: an inverse association was observed between the score of best CVH metrics and cognitive decline among men with pre-existing CHD.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

2020 ◽  
Vol 641 ◽  
pp. 159-175
Author(s):  
J Runnebaum ◽  
KR Tanaka ◽  
L Guan ◽  
J Cao ◽  
L O’Brien ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
Management Tool ◽  
American Lobster ◽  
Suitable Habitat ◽  
Climate Conditions ◽  
Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

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