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Low blood pressure BP is associated with frailty in older adults. Our aim was to explore how BP predicts transitions between frailty states. We used an illness-death discrete multi-state Markov model to estimate hazard ratios of forward and backward transitions between frailty states outcome in relation to BP predictor of interest with adjustment for sex, age and antihypertensive medication other predictors.
Over an average follow-up of 5. Diastolic BP was a weaker predictor of forward transitions. BP had no strong relationship with either forward transitions or backward transitions in frailty states. If our findings are confirmed with greater precision and assuming a causal relationship, they would suggest that there is no well-defined optimal BP level to prevent frailty among older adults.
Low blood pressure BP is associated with frailty in older adults in cross-sectional studies. Frailty is a dynamic condition: individuals transition forwards and backwards through several states of progressive severity in frailty. An illness-death model allowed us to explore transition rates between three frailty states, i.
We found that BP had no strong relationship with either forward transitions or backward transitions in frailty states. Our findings might suggest that there is no well-defined optimal BP level to prevent frailty among older adults. Several cross-sectional studies have shown that low blood pressure BP is associated with frailty in older adults. These findings contribute to the current uncertainties surrounding hypertension management in older adults.
Indeed, it is still debated whether intense hypertension management in older adults is beneficial, especially in those who are vulnerable and frail. Further, if BP predicts frailty, this could indicate a causal relationship, hence certain BP levels might be detrimental by causing frailty in older adults.
Currently, few studies have investigated the longitudinal relationship between BP and frailty. In these studies, BP did not predict or only weakly predicted incident frailty over time 2 ; the dynamic nature of frailty, i. Inthe Lancet published a two-paper series in which the authors called for appropriate statistical methods to capture and for the dynamics of frailty over time.
Multi-state models may help to better characterize the relationship between BP and the dynamic of frailty. In this study, our aim was to explore how BP predicts transitions between frailty states over time using a multi-state model applied to data from a cohort study of older adults. We hypothesized that compared with intermediate BP, low BP would predict more forward transitions in frailty states, i. The current study is, to our knowledge, the first to investigate how BP predicts transitions between frailty states, capturing the dynamic nature of both BP and frailty. Initially, three representative samples of older adults aged 65 to 70 years residing in the city of Lausanne, Switzerland, were invited by mail to participate inand Supplementary Figure S1available as Supplementary data at IJE online.2 PEOPLE DATE AFTER a ONE NIGHT STAND - ONS #1.2
These samples were drawn at random from the population registry of the city of Lausanne. Individuals living in institutions or not able to respond by themselves to questionnaires due to advanced dementia were excluded. Data were collected through questionnaires every year and through physical and cognitive measurements every 3 years.
For the statistical analyses, we used data from the three samples up toi. Data collection was done over the whole year, e. We excluded individuals who died or left the study between recruitment and baseline and those who had fewer than two measurements of frailty over the whole observation period and therefore had no observed transition among the frailty states Supplementary Figure S1. Frailty was defined using Fried's phenotype. In some cases when the walking test and the grip strength test could not be performed, slowness and weakness were imputed based on the judgment of the research assistant following pre-established decision algorithms 1 ; the decision algorithms are described in Supplementary Boxes S1 and S2, available as Supplementary data at IJE online.
Weight loss, exhaustion and low physical activity were assessed by self-report. The assessment of frailty was done following a standardized procedure identically applied across follow-ups in the three samples. BP was measured at the study centre by trained medical research assistants using a clinically validated oscillometric automated device and following a standardized protocol across years.
The detailed procedure is described elsewhere. The mean of the three BP measurements was used for our analyses. Date of birth and sex were taken from the residential registry and date of death was obtained through linkage with death certificates obtained through the canton of Vaud population registry. At baseline, information on self-reported hypertension, other cardiovascular risk factors [hypercholesterolaemia, diabetes, history of cardiovascular disease, smoking status, body mass index BMI category], of chronic conditions, polypharmacy and level of education were collected.
Hypertension was defined for participants who reported a diagnosis or use of BP-lowering medication; hypercholesterolaemia as diagnosis or use of cholesterol-lowering medication; diabetes as diagnosis or use of insulin; history of cardiovascular disease as diagnoses of stroke, coronary artery disease or another heart disease or use of medication for the heart; and smoking as current smoking former- and never-smokers were defined as non-smokers. BMI was defined based on measured weight and height. We defined four states that included three frailty states, i. The state of death is called absorbing because once a participant enters the state, he or she remains in that state.
In comparison, the frailty states are not absorbing but transient states, because when a participant is in one of the frailty states, he or she can still move to other states. Multi-state transition model; in rectangles are the states, with three frailty states and an absorbing state of death The arrows indicate permitted transitions. Over time, individuals move forward or backward in frailty states, remain in the same frailty state or die. An interval was defined by two successive visits with physical measurements, e. At the beginning of an interval, participants can be in either of the three frailty states.
They can stay in the same state, experience a forward or a backward transition to any consecutive frailty state, or die Figure 1. Transitions were defined when a participant moved from one frailty state to another over time. Transitions could be forwards, i.
Transitions were not observed per se, they occurred during an interval. We computed means and standard deviations SD for normally distributed continuous variables, median [minimum to maximum min—max ] for non-normally distributed continuous variables and counts percentages for categorical variables. We calculated the observed proportions of participants who were alive, dead or dropped out at baseline and at 3- 6- 9- and year follow-up. Further, among those alive, we calculated the proportion of non-frail, pre-frail and frail at baseline and at 3- 6- 9- and year follow-up.
To count the of consecutive pairs of states, including forward and backward transitions in frailty as well as transitions to death and censored states, we used the state. To model transitions in frailty states over time, we used a discrete multi-state Markov model, applying the msm package in R. The underlying Markov assumption is that future transitions in frailty only depend on the current state; the model is memoryless and is not influenced by the history of state transitions prior to the current state.
Censored individuals, i. One additional assumption is that participants cannot skip a state in the process. It is however possible, e. In these cases, the model implicitly considered that these participants transited through pre-frailty during the interval, even if it had not been recorded. Age and BP were integrated as time-varying covariates, which means that transitions over an interval, e.
We verified the assumption of constant transition rates through visual checking, comparing observed against predicted proportions of frailty states and death over time. Where the assumption did not hold i. To assess potential selection bias, we compared baseline characteristics of participants in the analytical sample with participants excluded because they had fewer than two measurements of frailty over the observation period Supplementary Table S1available as Supplementary data at IJE online. To address missing data, we computed the proportion of missing data in frailty assessments at each data collection time point and summarized them in Supplementary Table S2available as Supplementary data at IJE online.
For baseline characteristics, missing data in self-reported hypertension diagnosis, antihypertensive medication intake, variables defining other cardiovascular risk factors, variables defining the of chronic conditions and the variable defining polypharmacy were interpreted as absence of those characteristics. For the analyses, other missing data were left as they were. In the multi-state transition models, missing observations were censored. At baseline, the median min—max age was 69 66—71 years. At 3- 6- 9- and year follow-up, the proportions of individuals with frailty were 2. The participants who were excluded because they had fewer than two measurements of frailty were slightly less educated and were overall slightly less healthy than individuals in the analytical sample Supplementary Table S1.
Nof participants; SD, standard deviation; BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; polypharmacy, five of medications at least once a week. Over 24 person-years of follow-up, with a mean SD; min—max follow-up per participant of 5. Supplementary Table S3available as Supplementary data at IJE online, shows all observed pairs of consecutive states frailty states, deathincluding those who moved to a censored state.
Among non-frail, pre-frail and frail individuals,and 36 transitions to death were observed, respectively. These transitions represented 2. Among non-frail, pre-frail and frail individuals,and 20 participants, respectively, moved to a censored state. These transitions represent 5. Baseline 3 years yrs of follow-up f-u6 yrs f-u, 9 yrs f-u, 12 yrs f-u. Percentage was computed in relation to the total of forward and backward transitions, respectively. Through visual checking, 9 we compared observed and predicted proportions of frailty states and death over time for the adjusted time-homogeneous multi-state model model 1 and concluded that the assumption of constant transitions rates did not hold Supplementary Figure S4available as Supplementary data at IJE online.
Hence, we based our final estimates on a time-non-homogeneous model with piecewise constant transition rates for the periods from baseline to 9 years, and from 9 to 12 years model 3. Blood pressure category was ed for as a time-varying covariate and blood pressure values are in mmHg. Hazard ratios of backward transitions frail to pre-frail and pre-frail to non-frail by BP can be found in Supplementary Table S4available as Supplementary data at IJE online. Our study showed that among transitions in frailty states, about two-thirds were forward and one-third was backward.
Our findings are, however, subject to uncertainty as revealed by the wide confidence intervals around point estimates.Adult sex speed dating online
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