What can we learn from physical capacity about biological age? A systematic review

Signs of aging in midlife: physical function and sex differences in microbiota

Microbiota composition has been linked to physical activity, health measures, and biological age, but a shared profile has yet to be shown. The aim of this study was to examine the associations between microbiota composition and measures of function, such as a composite measure of physical capacity, and biological age in midlife, prior to onset of age-related diseases. Seventy healthy midlife individuals (age 44.58 ± 0.18) were examined cross-sectionally, and their gut-microbiota profile was characterized from stool samples using 16SrRNA gene sequencing. Biological age was measured using the Klemera-Doubal method and a composition of blood and physiological biomarkers. Physical capacity was calculated based on sex-standardized functional tests. We demonstrate that the women had significantly richer microbiota, p = 0.025; however, microbiota diversity was not linked with chronological age, biological age, or physical capacity for either women or men. Men had slightly greater β-diversity; however, β-diversity was positively associated with biological age and with physical capacity for women only (p = 0.01 and p = 0.04; respectively). For women, an increase in abundance of Roseburia faecis and Collinsella aerofaciens, as well as genus Ruminococcus and Dorea, was significantly associated with higher biological age and lower physical capacity; an increase in abundance of Akkermansia muciniphila and genera Bacteroides and Alistipes was associated with younger biological age and increased physical capacity. Differentially abundant taxa were also associated with non-communicable diseases. These findings suggest that microbiota composition is a potential mechanism linking physical capacity and health status; personalized probiotics may serve as a new means to support health-promoting interventions in midlife. Investigating additional factors underlying this link may facilitate the development of a more accurate method to estimate the rate of aging.

Associations that Cardiorespiratory Fitness and Body Mass Index Loss Have with Deficit Accumulation Frailty

INTRODUCTION/PURPOSE

Lower cardiorespiratory fitness and obesity may accelerate aging processes. The degree to which changes in fitness and body mass index (BMI) may alter the rate of aging may be important for planning treatment. We assessed cross-sectional and longitudinal associations that cardiorespiratory fitness and BMI had with a deficit accumulation frailty index (FI).

METHODS

Fitness, based on standardized graded exercise tests, and weight to calculate BMI at baseline and year 4 were collected from 3944 participants aged 45-76 yr in the Action for Health in Diabetes (Look AHEAD) randomized controlled clinical trial. A validated 38-item deficit accumulation FI was used as a marker of aging. Associations between baseline and changes in fitness and BMI with changes in FI were assessed using linear models.

RESULTS

Both baseline and 4-yr changes in fitness and BMI were independently associated with 4-yr changes in frailty (all P < 0.001). Mean (95% confidence interval) changes in FI ranged from -0.019 (-0.024, -0.013) for participants in the group with the greatest fitness increase and BMI loss to 0.029 (0.024, 0.034) for participants in the group with the greatest fitness loss and BMI gain. Associations of 4-yr changes in fitness and BMI with FI changes were similar across subgroups based on age, sex, baseline BMI, diabetes duration, and cardiovascular disease history. Increased fitness across 4 yr was associated with less FI accumulation independent of baseline fitness.

CONCLUSIONS

Adults with type 2 diabetes and overweight or obesity may slow aging processes captured by an FI by increasing their cardiorespiratory fitness and losing weight.

CRP, but not fibrinogen, is associated with gait speed as early as middle age, in females but not males

Low grade systemic inflammation and age-related gait speed decline are known to be related in older adults, but their relations in the early stages of the aging process are yet to be fully described. The aim of this study was to examine the relationship between gait speed and two inflammation markers-c-reactive protein (CRP) and fibrinogen-in a cohort of middle-aged adults in Israel. 326 healthy, middle-aged, Muslim-Arabs from three villages in northern Israel participated in this cross-sectional study. Serum CRP and fibrinogen were measured via blood tests, and gait speed was assessed with the 6-min walk test (6MWT). After adjusting for sex, age, height, BMI, systolic blood pressure, fasting blood glucose and triglycerides, executive function, smoking status and aerobic physical activity, gait speed was negatively and significantly associated with CRP (b = - 0.01, p = 0.029). When stratifying by gender, this link remained significant only among females (b = - 0.012, p = 0.041), such as that an increase of one SD unit of CRP was associated with a 0.047 m/s decrease in gait speed. No significant link was found between fibrinogen levels and gait speed. Blood CRP levels are associated with a slower walking speed already in middle age, independent of age, executive function and cardio-metabolic factors, among female Arab-Muslims in Israel. Future studies should examine this relationship longitudinally and investigate a broader array of inflammation markers. Systemic inflammation may serve as an early marker for people at risk of decreased walking or accelerated aging; Early identification and intervention among at-risk individuals may help prevent or slow gait speed decline, and promote healthier aging.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Preoperative low absolute and relative handgrip strength as predictors of postoperative short-term outcomes: a prospective study based on patients aged 60 years and older with gastric cancer

PURPOSE

This study aimed to determine the association of absolute and relative HGS indicators with postoperative short-term outcomes in older patients with gastric cancer.

METHODS

The prospective, single-center, observational study involved 230 patients (age ≥ 60 years) who underwent radical gastrectomy. Absolute HGS was directly obtained from results measured by a hand dynamometer, while relative HGS was calculated as the value of absolute HGS divided by body mass index (BMI), appendicular skeletal muscle mass index (ASMI) and age, respectively. Low absolute HGS was defined according to the criterion in AWGS 2019 consensus and the first gender-specific quartile. Low relative handgrip strength was defined if it was below the first gender-specific quartile of the distribution. Postoperative outcomes included total complications, malnutrition and length of hospital stay. Univariate and multivariate regression models were performed to investigate and compare the predictive values of different HGS indicators.

RESULTS

For the five HGS indicators, multivariate analyses showed that low absolute HGS (AWGS consensus), low HGS/BMI and HGS/age were independent risk factors for both postoperative total complications (absolute HGS(AWGS consensus): OR  2.03, 95%CI:1.05-3.93; HGS/BMI: OR  2.12, 95%CI 1.05-4.28; HGS/age: OR  2.79, 95%CI 1.40-5.54) and malnutrition (absolute HGS(AWGS consensus): OR   2.01, 95%CI 1.10-3.67; HGS/BMI: OR  2.28, 95%CI 1.20-4.33; HGS/age: OR   2.70, 95%CI 1.42-5.14). Low absolute HGS (quartile) was an independent risk factor for malnutrition (OR   1.96, 95%CI 1.04-3.71). Moreover, Low HGS/age was associated with lengthened postoperative hospital stay (OR  2.07, 95%CI 1.12-3.85).

CONCLUSIONS

Except HGS/ASMI, both absolute and relative HGS indicators were associated with postoperative short-term outcomes. Particularly, HGS/age revealed relatively better predictive value for the studied outcomes.

A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model

Background

Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learning-based BA (ML-BA) on the Chinese population; Neglect of the influence of model overfitting degree on the stability of the association results.

Methods and results

Based on the medical examination data of the Chinese population (45–90 years), we first evaluated the most suitable missing interpolation method, then constructed 14 ML-BAs based on biomarkers, and finally explored the associations between ML-BAs and health statuses (healthy risk indicators and disease). We found that round-robin linear regression interpolation performed best, while AutoEncoder showed the highest interpolation stability. We further illustrated the potential overfitting problem in ML-BAs, which affected the stability of ML-Bas’ associations with health statuses. We then proposed a composite ML-BA based on the Stacking method with a simple meta-model (STK-BA), which overcame the overfitting problem, and associated more strongly with CA (r = 0.66, P < 0.001), healthy risk indicators, disease counts, and six types of disease.

Conclusion

We provided an improved aging measurement method for middle-aged and elderly groups in China, which can more stably capture aging characteristics other than CA, supporting the emerging application potential of machine learning in aging research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04966-7.