Interleukin 10 gene polymorphisms and frailty syndrome in elderly Mexican people: (Sadem study).
ABSTRACT: Frailty is a geriatric syndrome, characterized by a loss in functional reserve with an increase in morbidity and mortality. There are no reports that link the genetic polymorphisms between interleukin 10 (IL10) and frailty; for this reason, our objective was used to analyze the role of the polymorphisms of IL10 (rs1800896, rs1800871) in the susceptibility to frailty in a Mexican population. Our study included 984 participants divided into 368 nonfrail, 309 prefrail, and 307 frail. The models for the polymorphisms rs1800896 and rs1800871 were recessive models in association with frailty (OR = 2.3, CI 95% = 1.6-3.2; OR = 1.53, CI 95% = 1.0-2.6), respectively. Two risk haplotypes were identified: ACG and CCG (p < .0001), and three protective haplotypes were identified: ACA, ATG, and ATA (p < .05). This study evaluated the relationship between IL10 and the three subtypes of this geriatric syndrome (frail, prefrail, and nonfrail). These results support a greater susceptibility to frailty for the minor alleles of rs1800871 and rs1800896. In addition, we found two risk haplotypes supporting the participation of the IL10 in the susceptibility for frailty in the Mexican population.
Project description:PURPOSE:To investigate the effect of early frailty transitions on 15-year mortality risk. METHODS:Longitudinal data analysis of the Hispanic Established Populations for the Epidemiological Study of the Elderly involving 1171 community-dwelling Mexican Americans aged ≥67 years and older. Frailty was determined using the modified frailty phenotype, including unintentional weight loss, weakness, self-reported exhaustion, and slow walking speed. Participants were defined at baseline as nonfrail, prefrail, or frail and divided into nine transition groups, during a 3-year observation period. RESULTS:Mean age was 77.0 years (standard deviation [SD] = 5.3) and 59.1% were female. Participants who transitioned from prefrail to frail (hazard ratio [HR] = 1.68, 95% confidence interval [CI] = 1.23-2.28), frail to prefrail (HR = 1.54, 95% CI = 1.05-2.28); or who remained frail (HR = 1.72, 95% CI = 1.21-2.44), had significant higher 15-year mortality risk than those who remained nonfrail. Participants transitioning from frail to nonfrail had a similar 15-year mortality risk as those who remained nonfrail (HR = 0.96, 95% CI = 0.53-1.72). Weight loss and slow walking speed were associated with transitions to frailty. CONCLUSIONS:An early transition from frail to nonfrail in older Mexican Americans was associated with a 4% decrease in mortality compared with those who remained nonfrail, although this difference was not statistically significant. Additional longitudinal research is needed to understand positive transitions in frailty.
Project description:BACKGROUND:Disability in activities of daily living (ADLs) is a dynamic process and transitions among different disability states are common. However, little is known about factors affecting recovery from disability. We examined the association between frailty and recovery from disability among nondisabled community-dwelling elders. METHODS:We studied 1,023 adults from the Cardiovascular Health Study (CHS) and 685 adults from the Health and Retirement Study (HRS), who were ?65 years and had incident disability, defined as having difficulty in ?1 ADL (dressing, eating, toileting, bathing, transferring, walking across a room). Disability recovery was defined as having no difficulty in any ADLs. Frailty was assessed by slowness, weakness, exhaustion, inactivity, and shrinking. Persons were classified as "nonfrail" (0 criteria), "prefrail" (1-2 criteria), or "frail" (3-5 criteria). RESULTS:In total, 539 (52.7%) CHS participants recovered from disability within 1 year. Almost two-thirds of nonfrail persons recovered, while less than two-fifths of the frail recovered. In the HRS, 234 (34.2%) participants recovered from disability within 2 years. Approximately half of the nonfrail recovered, while less than one-fifth of the frail recovered. After adjustment, prefrail and frail CHS participants were 16% and 36% less likely to recover than the nonfrail, respectively. In the HRS, frail persons had a 41% lower likelihood of recovery than the nonfrail. CONCLUSIONS:Frailty is an independent predictor of poor recovery from disability among nondisabled older adults. These findings validate frailty as a marker of decreased resilience and may offer opportunities for individualized interventions and geriatric care based on frailty assessment.
Project description:BACKGROUND:Frailty is a geriatric syndrome thought to identify the most vulnerable older adults, and morbidity and mortality has been reported to be higher for frail patients after cardiac surgery compared to nonfrail patients. However, the cognitive consequences of frailty after cardiac surgery have not been well described. In this study, we examined the hypothesis that baseline frailty would be associated with postoperative delirium and cognitive change at 1 and 12 months after cardiac surgery. METHODS:This study was nested in 2 trials, each of which was conducted by the same research team with identical measurement of exposures and outcomes. Before surgery, patients were assessed with the validated "Fried" frailty scale, which evaluates 5 domains (shrinking, weakness, exhaustion, low physical activity, and slowed walking speed) and classifies patients as nonfrail, prefrail, and frail. The primary outcome was postoperative delirium during hospitalization, which was assessed using the Confusion Assessment Method, Confusion Assessment Method for the Intensive Care Unit, and validated chart review. Neuropsychological testing was a secondary outcome and was generally performed within 2 weeks of surgery and then 4-6 weeks and 1 year after surgery, and the outcome of interest was change in composite Z-score of the test battery. Associations were analyzed using logistic and linear regression models, with adjustment for variables considered a priori (age, gender, race, education, and logistic European System for Cardiac Operative Risk Evaluation). Multiple imputation was used to account for missing data at the 12-month follow-up. RESULTS:Data were available from 133 patients with baseline frailty assessments. Compared to nonfrail patients (13% delirium incidence), the incidence of delirium was higher in prefrail (48% delirium incidence; risk difference, 35%; 95% CI, 10%-51%) and frail patients (48% delirium incidence; risk difference, 35%; 95% CI, 7%-53%). In both univariable and multivariable models, the odds of delirium were significantly higher for prefrail (adjusted odds ratio, 6.43; 95% CI, 1.31-31.64; P = .02) and frail patients (adjusted odds ratio, 6.31; 95% CI, 1.18-33.74; P = .03) compared to nonfrail patients. The adjusted decline in composite cognitive Z-score was greater from baseline to 1 month only in frail patients compared to nonfrail patients. By 1 year after surgery, there were no differences in the association of baseline frailty with change in cognition. CONCLUSIONS:Compared to nonfrail patients, both prefrail and frail patients were at higher risk for the primary outcome of delirium after cardiac surgery. Frail patients were also at higher risk for the secondary outcome of greater decline in cognition from baseline to 1 month, but not baseline to 1 year, after surgery.
Project description:Frailty is a geriatric syndrome resulting from age-related cumulative decline across multiple physiologic systems, impaired homeostatic reserve, and reduced capacity to resist stress. Based on recent estimates, 10% of community-dwelling older individuals are frail and another 41.6% are prefrail. Frail elders account for the highest health care costs in industrialized nations. Impaired physical function is a major indicator of frailty, and functional performance tests are useful for the identification of frailty. Objective instrumented assessments of physical functioning that are feasible for home frailty screening have not been adequately developed.To examine the ability of wearable sensor-based in-home assessment of gait, balance, and physical activity (PA) to discriminate between frailty levels (nonfrail, prefrail, and frail).In an observational cross-sectional study, in-home visits were completed in 125 older adults (nonfrail: n=44, prefrail: n=60, frail: n=21) living in Tucson, Ariz., USA, between September 2012 and November 2013. Temporal-spatial gait parameters (speed, stride length, stride time, double support, and variability of stride velocity), postural balance (sway of hip, ankle, and center of mass), and PA (percentage of walking, standing, sitting, and lying; mean duration and variability of single walking, standing, sitting, and lying bouts) were measured in the participant's home using validated wearable sensor technology. Logistic regression was used to assess the most sensitive gait, balance, and PA variables for identifying prefrail participants (vs. nonfrail). Multinomial logistic regression was used to identify variables sensitive to discriminate between three frailty levels.Gait speed (area under the curve, AUC=0.802), hip sway (AUC=0.734), and steps/day (AUC=0.736) were the most sensitive parameters for the identification of prefrailty. Multinomial regression revealed that stride length (AUC=0.857) and double support (AUC=0.841) were the most sensitive gait parameters for discriminating between three frailty levels. Interestingly, walking bout duration variability was the most sensitive PA parameter for discriminating between three frailty levels (AUC=0.818). No balance parameter discriminated between three frailty levels.Our results indicate that unique parameters derived from objective assessment of gait, balance, and PA are sensitive for the identification of prefrailty and the classification of a subject's frailty level. The present findings highlight the potential of wearable sensor technology for in-home assessment of frailty status.
Project description:<h4>Background</h4>Electronic frailty indices (eFIs) are increasingly used to identify patients at risk for morbidity and mortality. Whether eFIs capture the spectrum of frailty change, including decline, stability, and improvement, is unknown.<h4>Methods</h4>In a nationwide retrospective birth cohort of U.S. Veterans, a validated eFI, including 31 health deficits, was calculated annually using medical record and insurance claims data (2002-2012). K-means clustering was used to assign patients into frailty trajectories measured 5 years prior to death.<h4>Results</h4>There were 214 250 veterans born between 1927 and 1934 (mean [SD] age at death = 79.4 [2.8] years, 99.2% male, 90.3% White) with an annual eFI in the 5 years before death. Nine frailty trajectories were identified. Those starting at nonfrail or prefrail had 2 stable trajectories (nonfrail to prefrail, n = 29 786 and stable prefrail, n = 28 499) and 2 rapidly increasing trajectories (prefrail to moderately frail, n = 28 244 and prefrail to severely frail, n = 22 596). Those who were mildly frail at baseline included 1 gradually increasing trajectory (mildly to moderately frail, n = 33 806) and 1 rapidly increasing trajectory (mildly to severely frail, n = 15 253). Trajectories that started at moderately or severely frail included 2 gradually increasing trajectories (moderately to severely frail, n = 27 662 and progressing severely frail, n = 14 478) and 1 recovering trajectory (moderately frail to mildly frail, n = 13 926).<h4>Conclusions</h4>Nine frailty trajectories, including 1 recovering trajectory, were identified in this cohort of older U.S. Veterans. Future work is needed to understand whether prevention and treatment strategies can improve frailty trajectories and contribute to compression of morbidity toward the end of life.
Project description:Physical frailty is an age-associated syndrome of decreased reserve leading to vulnerability to physiological stressors and associated with negative outcomes. The underlying structural brain abnormalities of physical frailty are unclear. We investigated the association between brain volume, cortical brain infarcts, and physical frailty. In this multicenter study, 214 nondemented participants were classified as frail (n = 32), prefrail (n = 107), or nonfrail (n = 75) based on the Fried frailty phenotype. The associations between frailty and brain volumes and cortical brain infarcts were investigated by linear or logistic regression analyses. Participants in the frail group showed a lower total brain volume (-19.67 mL [95% confidence interval -37.84 to -1.50]) and lower gray matter volume (-12.19 mL [95% confidence interval -23.84 to -0.54]) compared to nonfrail participants. Frailty was associated with cortical brain infarcts [frail 16% [n = 5], prefrail 11% [n = 12], and nonfrail 3% [n = 2]). Reduced total brain volume and gray matter volume and increased cortical brain infarcts seem therefore to be part of the structural substrate of the physical frailty phenotype.
Project description:Examine the relationship between frailty and falls.A total of 847 Mexican Americans from the Hispanic Established Population for the Epidemiological Study of the Elderly were evaluated. The outcome variable was fall occurrence. Some predictor variables included were frailty, sociodemographic variables, functional and health status, and prior falls.Those who fell were more likely to be women, not married, had prior falls, more functional problems and poorer health. The incidence rate ratio (IRR) for falls was 1.9 for nonfrail individuals and 3.2 for frail individuals. Prefrail individuals had 1.36 higher odds of falls (95% CI [1.11, 1.67]), individuals with prior falls had 1.26 higher odds of falls (95% CI [1.15, 1.37]), and those with poor balance had 1.49 higher odds of falls (95% CI [1.15, 1.95]) over the 2 years (p<.01).Frailty increases the odds of falls in older Mexican Americans. Interventions tailored to reduce fall incidence and improve health care quality for older Mexican Americans are needed.
Project description:BACKGROUND:Frailty is a risk factor for cardiovascular disease (CVD). Underlying mechanisms to explain the connection between frailty and CVD are unclear. We sought to examine the association between frailty and arterial stiffness, a precursor of hypertension and CVD. METHODS:We conducted a cross-sectional analysis of community-dwelling Framingham Heart Study Offspring and Omni participants ≥60 years of age examined in 2005-2008. Frailty was defined primarily according to the Fried physical phenotype definition, which identifies nonfrail, prefrail, and frail individuals. Arterial stiffness was assessed using carotid-femoral pulse wave velocity (CFPWV). Generalized linear regression was used to examine the association between frailty level and CFPWV (modeled as -1000/CFPWV in msec/m, then transformed back to the original scale, m/s), adjusted for age, sex, cohort, mean arterial pressure, heart rate, height, and smoking. RESULTS:Of 2,171 participants (55% women, 91% white), 45% were prefrail and 7% were frail. Mean ages were 67, 70, and 73 years, and adjusted CFPWV least squares means were 10.0 (95% CI, 9.9-10.1), 10.3 (10.2-10.5), and 10.5 m/s (10.1-11.0); p = .0002 for nonfrail, prefrail, and frail groups, respectively. Results were similar using the Rockwood cumulative deficit model of frailty, and in a sensitivity analysis adjusting for prevalent coronary heart disease and diabetes. CONCLUSIONS:Prefrailty and frailty were associated with higher arterial stiffness in a cohort of community-dwelling older adults. Arterial stiffness may help explain the relationship between frailty and CVD.
Project description:To quantify the prognostic importance of prefrailty and frailty in a population-based sample of cancer survivors.The Third National Health and Nutrition Examination Survey mortality-linked prospective cohort study.Eighty-nine survey locations across the United States.Population-based sample of older adults (average age 72.2) with a self-reported diagnosis of non-skin-related cancer (N = 416).The primary outcome was all-cause mortality. Frailty components included low weight for height, slow walking, weakness, exhaustion, and low physical activity. Participants with none of the five criteria were classified as nonfrail, those with one or two as prefrail, and those with three or more as frail.The prevalence of prefrailty was 37.3% and of frailty was 9.1%. During a median follow-up of 11.2 years, 319 (76.7%) participants died. Median survival was 13.9 years for participants classified as nonfrail, 9.5 years for those classified as prefrail, and 2.5 years for those classified as frail. Cancer survivors classified as prefrail (hazard ratio (HR) = 1.84, 95% confidence interval (CI) = 1.28-2.65, P = .001) or frail (HR = 2.79, 95% CI = 1.34-5.81, P = .006) had a higher risk of premature mortality than those classified as nonfrail.Prefrailty and frailty are prevalent clinical syndromes that may confer greater risk of premature mortality in older adult cancer survivors. Identifying frail cancer survivors and targeting interventions for them may be a strategy to improve survival after cancer.
Project description:<h4>Background</h4>Use of information and communication technologies (ICT) among seniors is increasing; however, studies on the use of ICT by seniors at the highest risk of health impairment are lacking. Frail and prefrail seniors are a group that would likely benefit from preventive nutrition and exercise interventions, both of which can take advantage of ICT.<h4>Objective</h4>The objective of the study was to quantify the differences in ICT use, attitudes, and reasons for nonuse among physically frail, prefrail, and nonfrail home-dwelling seniors.<h4>Methods</h4>This was a population-based questionnaire study on people aged 65-98 years living in Northern Finland. A total of 794 eligible individuals responded out of a contacted random sample of 1500.<h4>Results</h4>In this study, 29.8% (237/794) of the respondents were classified as frail or prefrail. The ICT use of frail persons was lower than that of the nonfrail ones. In multivariable logistic regression analysis, age and education level were associated with both the use of Internet and advanced mobile ICT such as smartphones or tablets. Controlling for age and education, frailty or prefrailty was independently related to the nonuse of advanced mobile ICT (odds ratio, OR=0.61, P=.01), and frailty with use of the Internet (OR=0.45, P=.03). The frail or prefrail ICT nonusers also held the most negative opinions on the usefulness or usability of mobile ICT. When opinion variables were included in the model, frailty status remained a significant predictor of ICT use.<h4>Conclusions</h4>Physical frailty status is associated with older peoples' ICT use independent of age, education, and opinions on ICT use. This should be taken into consideration when designing preventive and assistive technologies and interventions for older people at risk of health impairment.