Dataset Information

CLINICAL AND BIOLOGICAL PREDICTORS FOR COGNITIVE FRAILTY: A POPULATION PREDICTIVE MODEL

ABSTRACT: Abstract This study aims to create a population predictive model to gain a more in-depth understanding of the underlying biological mechanisms for cognitive frailty as currently defined by the International Consensus Group in 2013. Data were from the InCHIANTI study, collected at baseline from 1998–2000. This group is a representative sample (n=1,453) of a population of white European origin from two small towns in Tuscany, Italy. To build our model, we used biomarkers with implications for clinical research and practice; a total of 132 putative SNPs and 155 protein biomarkers were identified from a systematic review. We used a tree boosting model, Extreme Gradient Boosting (xgboost), a machine learning technique for supervised learning. We developed two predictive models with high accuracy, AUCs for Model I is 0.877 (95% CI 0.825–0.903) and 0.864 (95% CI 0.804–0.899) for Model II. Results provide clinical and biological evidence for the relationship between cognitive decline and physical frailty supporting findings of dysregulation across multiple systems, specifically depression, anticholinergic burden, inflammatory proteins, and elevated levels of circulating pro-inflammatory proteins (e.g., IL-6, TNF-alpha, IL-18, and IL-1-beta). Associated genetic variants that influenced the production of circulating proteins were also found to be predictive, specifically IL6 rs1800796, TNF rs1800629, IL-18 rs360722, and IL1-beta rs16944, thus suggesting that they are clinically relevant SNPs. The results from this study establish a foundation for an understanding of the underlying biological mechanisms for the relationship between cognitive decline and physical frailty.

SUBMITTER: Sargent L

PROVIDER: S-EPMC6230026 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundCognitive frailty (CF) is characterized by the simultaneous presence of physical frailty and cognitive impairment. Previous studies have investigated its prevalence and impact on different adverse health-related outcomes. Few studies have focused on the progression and reversibility of CF and their potential predictors.MethodsData were derived from the China Health and Retirement Longitudinal Study (CHARLS). A total of 4051 older adults with complete data on three waves of the survey (2011, 2013, and 2015) were included and categorized into four groups: normal state (NS), cognitive impairment (CI) only, physical frailty (PF) only and CF (with both PF and CI). A multi-state Markov model was constructed to explore the transitions and predicting factors of CF.ResultsThe incidence and improvement rates of CF were 1.70 and 11.90 per 100 person-years, respectively. The 1-year transition probability of progression to CF in those with CI was higher than that in the PF population (0.340 vs. 0.054), and those with CF were more likely to move to PF (0.208). Being female [hazard ratio (HR) = 1.46, 95%CI = 1.06, 2.02)], dissatisfied with life (HR = 4.94, 95%CI = 1.04, 23.61), had a history of falls (HR = 2.36, 95%CI = 1.02, 5.51), rural household registration (HR = 2.98, 95%CI = 1.61, 5.48), multimorbidity (HR = 2.17, 95%CI = 1.03, 4.59), and depression (HR = 1.75, 95%CI = 1.26, 2.45) increased the risk of progression to CF, whereas literacy (HR = 0.46, 95%CI = 0.33, 0.64) decreased such risk. Depression (HR = 0.43, 95%CI = 0.22, 0.84) reduced the likelihood of CF improvement, whereas literacy (HR = 2.23, 95%CI = 1.63, 3.07) increased such likelihood.ConclusionsCognitive frailty is a dynamically changing condition in older adults. Possible interventions aimed at preventing the onset and facilitating the recovery of cognitive frailty should focus on improving cognitive function in older adults.

Project description:ObjectivesDetermine the effects of missing data in frailty identification and risk prediction.DesignAnalysis of the National Health in Aging Trends Study.SettingCommunity.ParticipantsAbout 6206 older adults.MeasurementsA 41-variable frailty index (FI) was constructed with the following domains: comorbidities, activities of daily living (ADLs), instrumental activities of daily living, self-reported physical limitations, physical performance, and neuropsychiatric tests. We evaluated discrimination after removing single and multiple domains, comparing C-statistics for predicting 5-year risk of mortality and 1-year risks of disability and falls.ResultsThe full FI yielded a mean of .18 and C-statistics of .72 (95% confidence interval, .70-.74) for mortality, .80 (.77-.82) for disability, and .66 (.64-.68) for falls. Removal of any single domain shifted the FI distribution, resulting in a mean FI ranging from .13 (removing comorbidities) to .20 (removing ADLs) and frailty prevalence (FI ≥ .25) from 16.0% to 28.7%. Among robust participants models missing ADLs misclassified most often, (19% as pre-frail). Among pre-frail and frail participants missing comorbidities misclassified most often(69.2% from pre-frail to robust, 24% from frail to pre-frail, and 4.9% from frail to robust). Removal of any single domain minimally changed C-statistics: mortality, .71-.73; disability, .79-.80; and falls, .64-.66. Removing neuropsychiatric testing and physical performance yielded comparable C-statistics of .70, .78, and .66 for mortality, ADLs, and falls, respectively. However, removal of three or four domains based on likely availability decreased C-statistics for mortality (.69, .66),disability (.75, .70), and falls (.64, .63), respectively.ConclusionWhile FI discrimination is robust to missing information in any single domain, risk prediction is affected by absence of multiple domains. This work informs the application of FI as a clinical and research tool. J Am Geriatr Soc 68:1771-1777, 2020.

Project description:BackgroundCognitive frailty (CF) includes reversible and potentially reversible subtypes; the former is known as concurrent physical frailty (PF) and pre-mild cognitive impairment subjective cognitive decline (pre-MCI SCD), whereas the latter is known as concurrent PF and MCI. The diagnoses of pre-MCI SCD and MCI are based on clinical criteria and various subjective cognitive decline questionnaires. Heterogeneous assessment of cognitive impairment (CI) results in significant variability of CI, CF, and their subtype prevalence in various population-based studies.ObjectiveThis study aimed to compare the classification differences in CI and CF subtypes from PF and normal cognition by applying clinical and objective cognitive criteria. Clinical criteria comprised Fried PF and clinical MCI criteria combined with the SCD questionnaire, whereas objective criteria comprised Fried PF and objective cognitive criteria based on the norm-adjusted six neuropsychological test scores.MethodsOf the 335 volunteers (age ≥ 60 years) in this study, 191 were diagnosed with CI based on clinical cognitive diagnosis criteria, and 144 were identified as robust normal based on objective cognitive assessment from the community-dwelling older adult cohort. Individuals with clinical CI, including 94 with MCI and 97 with pre-MCI SCD, were reclassified into different z-score-derived MCI, pre-MCI SCD, and normal subgroups based on objective cognitive criteria. The classification diagnostic accuracy of normal cognition, PF, pre-MCI, MCI, CF, and CF subtypes based on clinical and objective criteria was compared before and after adjusting for age, sex, and education level.ResultsThe reclassification of objective assessments indicated better performance than that of clinical assessments in terms of discerning CI severity among different subgroups before adjusting for demographic factors. After covariate adjustment, clinical assessments significantly improved the ability to cognitively discriminate normal individuals from those with pre-MCI SCD and MCI but not the z-score-derived pre-MCI SCD and MCI groups from the robust normal group. Furthermore, the adjustment did not improve the ability to discriminate among individuals with reversible CF from those with potentially reversible CF and pre-MCI only SCD from MCI only SCD.ConclusionsObjective criteria showed better performance than clinical criteria in the diagnosis of individuals with CI or CF subtypes. Rapid clinical cognitive screening in combination with normative z-scores criteria is cost effective and sustainable in clinical practice.

Dataset Information

CLINICAL AND BIOLOGICAL PREDICTORS FOR COGNITIVE FRAILTY: A POPULATION PREDICTIVE MODEL

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets