Frailty Index as a clinical measure of biological age in psychiatry

Abstract In mammals, the lack of accurate biomarkers for biological age is a current limitation to identifying novel aging interventions. Molecular biomarkers including DNA methylation hold promise but are invasive and currently expensive. The Frailty Index (FI) quantifies the accumulation of health-related deficits and is fast, cheap, and non-invasive. Studies have demonstrated that FI correlates with age and mortality risk in mice and humans. However, the FI has not been modelled to directly predict biological age or life expectancy. We tracked aging male C57BL/6 mice until their natural deaths, scoring them longitudinally with the FI. We find that FI score correlates with and is predictive of age and that some but not all parameters of the FI are individually well-correlated with age. To better predict chronological age, we performed an elastic net regression on the FI termed FRIGHT (Frailty Inferred Geriatric Health Timeline) Age. FRIGHT Age is a strong predictor of age (r2=0.73, median error=47.5 days), but is not superior to chronological age at predicting life expectancy. To better predict mortality, we built a random forest model termed the AFRAID (Analysis of Frailty and Death) score, which predicted survival at multiple ages (r2=0.375, median error = 46.4 days). The FRIGHT and AFRAID models were responsive to chronic treatment with enalapril (30mg/kg/day), an angiotensin converting enzyme inhibitor that extends healthspan, and methionine restriction, a dietary intervention that extends healthspan and lifespan. Our findings underscore the value of assessing non-invasive biomarkers for aging research and may help speed the identification of aging interventions.

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Aerobic exercise attenuates frailty in aging male and female C57Bl/6 mice and effects systemic cytokines differentially by sex

The Journals of Gerontology Series A ◽

10.1093/gerona/glab297 ◽

2021 ◽

Author(s):

Elise S Bisset ◽

Stefan Heinze-Milne ◽

Scott A Grandy ◽

Susan E Howlett

Keyword(s):

Life Expectancy ◽

Aerobic Exercise ◽

Systemic Inflammation ◽

Frailty Index ◽

Biological Age ◽

Beneficial Effects ◽

Serum Cytokines ◽

Cytokine Levels ◽

The Impact ◽

Pro Inflammatory Cytokines

Abstract Aerobic exercise is a promising intervention to attenuate frailty, but preclinical studies have used only male animals. We investigated the impact of voluntary aerobic exercise on frailty, biological age (FRIGHT clock), predicted life expectancy (AFRAID clock) and mortality in both sexes and determined whether exercise was associated with changes in inflammation. Older (21-23 months) male (n=12) and female (n=22) C57Bl/6 mice matched for baseline frailty scores were randomized into exercise (running wheel) and sedentary (no wheel) groups. Frailty index scores were measured biweekly (13 weeks), and 23 serum cytokines were measured at midpoint and endpoint. Exercise levels varied between mice but not between the sexes. Exercise had no effect on mortality, but it attenuated the development of frailty in both sexes (female=0.32±0.04 vs 0.21±0.01; p=0.005; male=0.30±0.02 vs. 0.22±0.02; p=0.042) and reduced frailty in older females after 10 weeks. FRIGHT scores were unaffected by exercise but increased with time in sedentary males indicating increased biological age. Exercise prevented the age-associated decline in AFRAID scores in older females such that exercised females had a longer life expectancy. We investigated whether aerobic exercise was associated with changes in systemic inflammation. Cytokine levels were not affected by exercise in males, but levels of pro-inflammatory cytokines were positively correlated with the frequency of exercise in females. Despite increases in systemic inflammation, exercise reduced frailty and increased lifespan in older females. Thus, voluntary aerobic exercise, even late in life, has beneficial effects on health in both sexes but may be especially helpful in older females.

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Telomere Length as a Marker of Biological Age: State-of-the-Art, Open Issues, and Future Perspectives

Frontiers in Genetics ◽

10.3389/fgene.2020.630186 ◽

2021 ◽

Vol 11 ◽

Author(s):

Alexander Vaiserman ◽

Dmytro Krasnienkov

Keyword(s):

Telomere Length ◽

State Of The Art ◽

Frailty Index ◽

Biological Age ◽

Epigenetic Clock ◽

Telomere Attrition ◽

Immune Parameters ◽

Biomarkers Of Aging ◽

Age Related ◽

Research Findings

Telomere shortening is a well-known hallmark of both cellular senescence and organismal aging. An accelerated rate of telomere attrition is also a common feature of age-related diseases. Therefore, telomere length (TL) has been recognized for a long time as one of the best biomarkers of aging. Recent research findings, however, indicate that TL per se can only allow a rough estimate of aging rate and can hardly be regarded as a clinically important risk marker for age-related pathologies and mortality. Evidence is obtained that other indicators such as certain immune parameters, indices of epigenetic age, etc., could be stronger predictors of the health status and the risk of chronic disease. However, despite these issues and limitations, TL remains to be very informative marker in accessing the biological age when used along with other markers such as indices of homeostatic dysregulation, frailty index, epigenetic clock, etc. This review article is aimed at describing the current state of the art in the field and at discussing recent research findings and divergent viewpoints regarding the usefulness of leukocyte TL for estimating the human biological age.

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Feature Selection Algorithms Enhance the Accuracy of Frailty Indexes as Measures of Biological Age

The Journals of Gerontology Series A ◽

10.1093/gerona/glab018 ◽

2021 ◽

Author(s):

Sangkyu Kim ◽

Jessica Fuselier ◽

David A Welsh ◽

Katie E Cherry ◽

Leann Myers ◽

...

Keyword(s):

Machine Learning ◽

Proportional Hazards ◽

Oldest Old ◽

Frailty Index ◽

Cox Proportional Hazards ◽

Independent Study ◽

Machine Learning Algorithms ◽

Biological Age ◽

Biological Aging ◽

Age Estimates

Abstract Biological age captures some of the variance in life expectancy for which chronological age is not accountable, and it quantifies the heterogeneity in the presentation of the aging phenotype in various individuals. Among the many quantitative measures of biological age, the mathematically uncomplicated frailty/deficit index is simply the proportion of the total health deficits in various health items surveyed in different individuals. We used 3 different statistical methods that are popular in machine learning to select 17–28 health items that together are highly predictive of survival/mortality, from independent study cohorts. From the selected sets, we calculated frailty indexes and Klemera–Doubal’s biological age estimates, and then compared their mortality prediction performance using Cox proportional hazards regression models. Our results indicate that the frailty index outperforms age and Klemera–Doubal’s biological age estimates, especially among the oldest old who are most prone to biological aging-caused mortality. We also showed that a DNA methylation index, which was generated by applying the frailty/deficit index calculation method to 38 CpG sites that were selected using the same machine learning algorithms, can predict mortality even better than the best performing frailty index constructed from health, function, and blood chemistry.

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BIOLOGICAL AGE ESTIMATION BASED ON HEART RATE VARIABILITY: A PILOT STUDY

Proceedings of the Shevchenko Scientific Society. Medical Sciences ◽

10.25040/ntsh2021.02.21 ◽

2021 ◽

Vol 65 (2) ◽

Author(s):

Oleksiy Bashkirtsev ◽

◽

Vitaliy Sagan ◽

Vira Gaevska ◽

Olena Zimba ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Age Estimation ◽

Statistical Tests ◽

Frailty Index ◽

Reference Method ◽

Machine Learning Algorithms ◽

Biological Age ◽

Distribution Width ◽

Bivariate Correlation

Introduction. Biomarkers of biological age (BA) are essential for anti-aging research and practice because of their prediction of life expectancy, detection of premature aging, and estimation of anti-ageing programs' effectiveness. The purpose of this study is a clinical validation of the method of biological age estimation based on the analysis of heart rate variability (HRV), artificial intelligence technologies, and biometric monitoring. Methods. In 51 patients who received wellness and rehabilitation services in the medical center "Edem Medical", biological age was determined based on the analysis of HRV and machine learning algorithms. A comparison was made between the proposed method and other known methods of biological age estimation. Biological age estimation by physicians which is based on the Frailty Index was chosen as a reference method. The second method was DNA methylation age (DNAm PhenoAge). This method predicts biological age based on nine parameters of blood (albumin, creatinine, glucose, C-reactive protein, lymphocytes [%], mean corpuscular volume [MCV], red cell distribution width [RDW], alkaline phosphatase, WBC count). Using the «leave one out» technique, an additional algorithm was created for approximating biological age in view of blood test parameters and ECG signals as input data. Morning HRV assessment was performed on empty stomach and after 10-minute rest in horizontal position. ECG was recorded using Mawi Vital multisensor device. The following statistical tests were used to reveal associations between different methods of biological age estimation: 1. bivariate correlation, 2. mean absolute error (MAE), 3. qualitative binary age estimation. Results. All tested methods of BA evaluation were strongly correlated with the reference method (physician-determined age). HRV based approach was superior in comparison with other methods. In 9 out of 10 cases, the qualitative binary age assessment using HRV coincided with the reference method. The HRV method was the most accurate for biological age estimation (3.62 vs 12.62) based on MAE. Conclusion. The method based on HRV is an affordable and convenient approach to biological age estimation. This method offers opportunities for early stratification of individuals at risk of accelerated aging. It combines well with the paradigm of 3 P medicine which is based on Prevention, Prediction, and Personalized approach to each patient

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THE FRAILTY INDEX CONSTRUCTED USING FEATURE SELECTION METHODS IS THE BEST MEASURE OF BIOLOGICAL AGE

Innovation in Aging ◽

10.1093/geroni/igz038.3261 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S891-S892

Author(s):

Sangkyu Kim ◽

Jessica Fuselier ◽

David Welsh ◽

Katie E Cherry ◽

Leann Myers ◽

...

Keyword(s):

Healthy Aging ◽

Frailty Index ◽

Composite Index ◽

Adverse Outcomes ◽

Biological Age ◽

Model Parameters ◽

Functional Abilities ◽

Machine Learning Methods ◽

Aging Study ◽

Multiple Biomarkers

Abstract No single biomarker can reliably represent the complexity of aging. One way to overcome this shortcoming is to aggregate multiple biomarkers into a composite index. The frailty index (FI), which is simply the proportion of accumulated deficits among a set of various health markers, reflects functional abilities and risks of adverse outcomes. Furthermore, the FI accounts for the variation in mortality among individuals of the same chronological age (CA). Thus, the FI is a reliable measure of biological age (BA). Unlike the FI, other popular BA-estimating algorithms use CA directly as a biomarker or indirectly to derive model parameters. However, genetic, pharmaceutical, and intervention studies have shown that aging is delayable or reversible, indicating that CA is not the direct cause of aging. The popular Klemera-Doubal (K-D) method proposes two equations for BA estimation: BE uses CA to derive equation parameters, and BEC directly incorporates CA as an additional biomarker. BA estimates by the K-D method, especially by BEC, have been shown to outperform CA. Using Louisiana Healthy Aging Study (LHAS) data, we constructed an FI from a battery of health items selected using machine learning methods for their ability to predict mortality. We compared the FI with CA and the two K-D BA estimates and found that the FI was the better predictor of mortality, especially among nonagenarians. The results were replicable with the FI calculated from different sets of selected health items using NHANES and HRS datasets. These results demonstrate the FI as the best-performing measure of BA.

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Frailty Index as a Measure of Biological Age in a Chinese Population

The Journals of Gerontology Series A ◽

10.1093/gerona/60.8.1046 ◽

2005 ◽

Vol 60 (8) ◽

pp. 1046-1051 ◽

Cited By ~ 159

Author(s):

W. B. Goggins ◽

J. Woo ◽

A. Sham ◽

S. C. Ho

Keyword(s):

Chinese Population ◽

Frailty Index ◽

Biological Age

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The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age

GeroScience ◽

10.1007/s11357-017-9960-3 ◽

2017 ◽

Vol 39 (1) ◽

pp. 83-92 ◽

Cited By ~ 47

Author(s):

Sangkyu Kim ◽

Leann Myers ◽

Jennifer Wyckoff ◽

Katie E. Cherry ◽

S. Michal Jazwinski

Keyword(s):

Dna Methylation ◽

Frailty Index ◽

Biological Age ◽

Dna Methylation Age

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“I Can See What You’re Saying”: Clinical Utility of Spectral Moment Analysis

Perspectives on Speech Science and Orofacial Disorders ◽

10.1044/ssod21.2.44 ◽

2011 ◽

Vol 21 (2) ◽

pp. 44-54

Author(s):

Kerry Callahan Mandulak

Keyword(s):

Speech Production ◽

Speech Signal ◽

Clinical Utility ◽

Acoustic Analysis ◽

Moment Analysis ◽

Analysis Tool ◽

Spectral Moment ◽

Clinical Measure ◽

Perceptual Analysis ◽

Disordered Speech

Spectral moment analysis (SMA) is an acoustic analysis tool that shows promise for enhancing our understanding of normal and disordered speech production. It can augment auditory-perceptual analysis used to investigate differences across speakers and groups and can provide unique information regarding specific aspects of the speech signal. The purpose of this paper is to illustrate the utility of SMA as a clinical measure for both clinical speech production assessment and research applications documenting speech outcome measurements. Although acoustic analysis has become more readily available and accessible, clinicians need training with, and exposure to, acoustic analysis methods in order to integrate them into traditional methods used to assess speech production.

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