Heart rate variability: a possible marker of resilience

Frailty Identification Using Heart Rate Dynamics: A Deep Learning Approach

Previous research showed that frailty can influence autonomic nervous system and consequently heart rate response to physical activities, which can ultimately influence the homeostatic state among older adults. While most studies have focused on resting state heart rate characteristics or heart rate monitoring without controlling for physical activities, the objective of the current study was to classify pre-frail/frail vs non-frail older adults using heart rate response to physical activity (heart rate dynamics). Eighty-eight older adults (≥65 years) were recruited and stratified into frailty groups based on the five-component Fried frailty phenotype. Groups consisted of 27 non-frail (age = 78.80±7.23) and 61 pre-frail/frail (age = 80.63±8.07) individuals. Participants performed a normal speed walking as the physical task, while heart rate was measured using a wearable electrocardiogram recorder. After creating heart rate time series, a long short-term memory model was used to classify participants into frailty groups. In 5-fold cross validation evaluation, the long short-term memory model could classify the two above-mentioned frailty classes with a sensitivity, specificity, F1-score, and accuracy of 83.0%, 80.0%, 87.0%, and 82.0%, respectively. These findings showed that heart rate dynamics classification using long short-term memory without any feature engineering may provide an accurate and objective marker for frailty screening.

Genetic Variants Associated With Resilience in Human and Animal Studies

Resilience is broadly defined as the ability to maintain or regain functioning in the face of adversity and is influenced by both environmental and genetic factors. The identification of specific genetic factors and their biological pathways underpinning resilient functioning can help in the identification of common key factors, but heterogeneities in the operationalisation of resilience have hampered advances. We conducted a systematic review of genetic variants associated with resilience to enable the identification of general resilience mechanisms. We adopted broad inclusion criteria for the definition of resilience to capture both human and animal model studies, which use a wide range of resilience definitions and measure very different outcomes. Analyzing 158 studies, we found 71 candidate genes associated with resilience. OPRM1 (Opioid receptor mu 1), NPY (neuropeptide Y), CACNA1C (calcium voltage-gated channel subunit alpha1 C), DCC (deleted in colorectal carcinoma), and FKBP5 (FKBP prolyl isomerase 5) had both animal and human variants associated with resilience, supporting the idea of shared biological pathways. Further, for OPRM1, OXTR (oxytocin receptor), CRHR1 (corticotropin-releasing hormone receptor 1), COMT (catechol-O-methyltransferase), BDNF (brain-derived neurotrophic factor), APOE (apolipoprotein E), and SLC6A4 (solute carrier family 6 member 4), the same allele was associated with resilience across divergent resilience definitions, which suggests these genes may therefore provide a starting point for further research examining commonality in resilience pathways.

A template for physical resilience research in older adults: Methods of the PRIME-KNEE study

BACKGROUND

Older adults with similar health conditions often experience widely divergent outcomes following health stressors. Variable recovery after a health stressor may be due in part to differences in biological mechanisms at the molecular, cellular, or system level, that are elicited in response to stressors. We describe the PRIME-KNEE study as an example of ongoing research to validate provocative clinical tests and biomarkers that predict resilience to specific health stressors.

METHODS

PRIME-KNEE is an ongoing, prospective cohort study that will enroll 250 adults ≥60 years undergoing total knee arthroplasty. Data are collected at baseline (pre-surgery), during surgery, daily for 7 days after surgery, and at 1, 2, 4, and 6 months post-surgery. Provocative tests include a cognition-motor dual-task walking test, cerebrovascular reactivity assessed by functional near-infrared spectroscopy, peripheral blood mononuclear cell reactivity ex vivo to lipopolysaccharide toxin and influenza vaccine, and heart rate variability during surgery. Cognitive, psychological, and physical performance batteries are collected at baseline to estimate prestressor reserve. Demographics, medications, comorbidities, and stressor characteristics are abstracted from the electronic medical record and via participant interview. Blood-based biomarkers are collected at baseline and postoperative day 1. Repeated measures after surgery include items from a delirium assessment tool and pain scales administered daily by telephone for 7 days and cognitive change index (participant and informant), lower extremity activities of daily living, pain scales, and step counts assessed by Garmin actigraphy at 1, 2, 4, and 6 months after surgery. Statistical models use these measures to characterize resilience phenotypes and evaluate prestressor clinical indicators associated with poststressor resilience.

CONCLUSION

If PRIME-KNEE validates feasible clinical tests and biomarkers that predict recovery trajectories in older surgical patients, these tools may inform surgical decision-making, guide pre-habilitation efforts, and elucidate mechanisms underlying resilience. This study design could motivate future geriatric research on resilience.