Gut Microbiota predicts Healthy Late-life Aging in Male Mice

Calorie restriction (CR) extends lifespan and retards age-related chronic diseases in most species. There is growing evidence that the gut microbiota has a pivotal role in host health and age-related pathological conditions. Yet, it is still unclear how CR and the gut microbiota are related to healthy aging. Here we report findings from a small longitudinal study of male C57BL/6 mice maintained on either ad libitum or mild (15%) CR diets from 21 months of age and tracked until natural death. We demonstrate that CR results in a significant reduction in frailty index (FI), a well-established indicator of aging. We observed significant alterations in bacterial load, diversity, and compositional patterns of the mouse gut microbiota during the aging process. Interrogating the FI-related microbial features using machine learning techniques, we show that gut microbial signatures from 21-month-old mice can predict the healthy aging of 30-month-old mice with reasonable accuracy. This study deepens our understanding of the links between CR, gut microbiota, and frailty in the aging process of mice.

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Gut Microbiota Predicts Healthy Late-Life Aging in Male Mice

Nutrients ◽

10.3390/nu13093290 ◽

2021 ◽

Vol 13 (9) ◽

pp. 3290

Author(s):

Shanlin Ke ◽

Sarah J. Mitchell ◽

Michael R. MacArthur ◽

Alice E. Kane ◽

David A. Sinclair ◽

...

Keyword(s):

Gut Microbiota ◽

Aging Process ◽

Healthy Aging ◽

Frailty Index ◽

Machine Learning Techniques ◽

Age Related ◽

Rate Of Increase ◽

Reduced Rate ◽

Compositional Patterns ◽

Old Mice

Calorie restriction (CR) extends lifespan and retards age-related chronic diseases in most species. There is growing evidence that the gut microbiota has a pivotal role in host health and age-related pathological conditions. Yet, it is still unclear how CR and the gut microbiota are related to healthy aging. Here, we report findings from a small longitudinal study of male C57BL/6 mice maintained on either ad libitum or mild (15%) CR diets from 21 months of age and tracked until natural death. We demonstrate that CR results in a significantly reduced rate of increase in the frailty index (FI), a well-established indicator of aging. We observed significant alterations in diversity, as well as compositional patterns of the mouse gut microbiota during the aging process. Interrogating the FI-related microbial features using machine learning techniques, we show that gut microbial signatures from 21-month-old mice can predict the healthy aging of 30-month-old mice with reasonable accuracy. This study deepens our understanding of the links between CR, gut microbiota, and frailty in the aging process of mice.

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A Cross-Sectional Study of Compositional and Functional Profiles of Gut Microbiota in Sardinian Centenarians

mSystems ◽

10.1128/msystems.00325-19 ◽

2019 ◽

Vol 4 (4) ◽

Cited By ~ 10

Author(s):

Lu Wu ◽

Tiansheng Zeng ◽

Angelo Zinellu ◽

Salvatore Rubino ◽

David J. Kelvin ◽

...

Keyword(s):

Gut Microbiota ◽

Healthy Aging ◽

High Capacity ◽

Functional Independence ◽

Metagenomic Sequencing ◽

Bifidobacterium Adolescentis ◽

Content Type ◽

Age Related ◽

Genes Encoding ◽

Functional Profiles

ABSTRACT Sardinia, Italy, has a high prevalence of residents who live more than 100 years. The reasons for longevity in this isolated region are currently unknown. Gut microbiota may hold a clue. To explore the role gut microbiota may play in healthy aging and longevity, we used metagenomic sequencing to determine the compositional and functional differences in gut microbiota associated with populations of different ages in Sardinia. Our data revealed that the gut microbiota of both young and elderly Sardinians shared similar taxonomic and functional profiles. A different pattern was found in centenarians. Within the centenarian group, the gut microbiota was correlated with the functional independence measurement of the host. Centenarians had a higher diversity of core microbiota species and microbial genes than those in the young and elderly. We found that the gut microbiota in Sardinian centenarians displayed a rearranged taxonomic pattern compared with those of the young and elderly, featured by depletion of Faecalibacterium prausnitzii and Eubacterium rectale and enriched for Methanobrevibacter smithii and Bifidobacterium adolescentis. Moreover, functional analysis revealed that the microbiota in centenarians had high capacity for central metabolism, especially glycolysis and fermentation to short-chain fatty acids (SCFAs), although the gut microbiota in centenarians was low in genes encoding enzymes involved in degradation of carbohydrates, including fibers and galactose. IMPORTANCE The gut microbiota has been proposed as a promising determinant for human health. Centenarians as a model for extreme aging may help us understand the correlation of gut microbiota with healthy aging and longevity. Here we confirmed that centenarians had microbiota elements usually associated with benefits to health. Our finding of a high capacity of glycolysis and related SCFA production represented a healthy microbiome and environment that is regarded as beneficial for host gut epithelium. The low abundance of genes encoding components of pathways involved in carbohydrate degradation was also found in the gut microbiota of Sardinian centenarians and is often associated with poor gut health. Overall, our study here represents an expansion of previous research investigating the age-related changes in gut microbiota. Furthermore, our study provides a new prospective for potential targets for gut microbiota intervention directed at limiting gut inflammation and pathology and enhancing a healthy gut barrier.

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Predicting Alzheimer’s disease progression trajectory and clinical subtypes using machine learning

10.1101/792432 ◽

2019 ◽

Author(s):

Vipul K. Satone ◽

Rachneet Kaur ◽

Anant Dadu ◽

Hampton Leonard ◽

Hirotaka Iwaki ◽

...

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Disease Progression ◽

Disease Onset ◽

Machine Learning Techniques ◽

Progression Rate ◽

Early Stages ◽

Age Related ◽

Learning Techniques

AbstractBackgroundAlzheimer’s disease (AD) is a common, age-related, neurodegenerative disease that impairs a person’s ability to perform day-to-day activities. Diagnosing AD is challenging, especially in the early stages. Many patients still go undiagnosed, partly due to the complex heterogeneity in disease progression. This highlights a need for early prediction of the disease course to assist its treatment and tailor therapy options to the disease progression rate. Recent developments in machine learning techniques provide the potential to not only predict disease progression and trajectory of AD but also to classify the disease into different etiological subtypes.Methods and findingsThe work shown here clusters participants in distinct and multifaceted progression subgroups of AD and discusses an approach to predict the progression rate from baseline diagnosis. We observed that the myriad of clinically reported symptoms summarized in the proposed AD progression space corresponds directly with memory and cognitive measures, which are routinely used to monitor disease onset and progression. Our analysis demonstrated accurate prediction of disease progression after four years from the first 12 months of post-diagnosis clinical data (Area Under the Curve of 0.96 (95% confidence interval (CI), 0.92-1.0), 0.81 (95% CI, 0.74-0.88) and 0.98 (95% CI, 0.96-1.0) for slow, moderate and fast progression rate patients respectively). Further, we explored the long short-term memory (LSTM) neural networks to predict the trajectory of an individual patient’s progression.ConclusionThe machine learning techniques presented in this study may assist providers in identifying different progression rates and trajectories in the early stages of the disease, hence allowing for more efficient and personalized healthcare deliveries. With additional information about the progression rate of AD at hand, providers may further individualize the treatment plans. The predictive tests discussed in this study not only allow for early AD diagnosis but also facilitate the characterization of distinct AD subtypes relating to trajectories of disease progression. These findings are a crucial step forward for early disease detection. These models can be used to design improved clinical trials for AD research.

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The Roles of the Gut Microbiota and Chronic Low-Grade Inflammation in Older Adults With Frailty

Frontiers in Cellular and Infection Microbiology ◽

10.3389/fcimb.2021.675414 ◽

2021 ◽

Vol 11 ◽

Author(s):

YuShuang Xu ◽

XiangJie Liu ◽

XiaoXia Liu ◽

Di Chen ◽

MengMeng Wang ◽

...

Keyword(s):

Older Adults ◽

Gut Microbiota ◽

Chronic Inflammation ◽

Healthy Aging ◽

Low Grade ◽

Age Related ◽

Composition Structure ◽

Low Grade Inflammation

Frailty is a major public issue that affects the physical health and quality of life of older adults, especially as the population ages. Chronic low-grade inflammation has been speculated to accelerate the aging process as well as the development of age-related diseases such as frailty. Intestinal homeostasis plays a crucial role in healthy aging. The interaction between the microbiome and the host regulates the inflammatory response. Emerging evidence indicates that in older adults with frailty, the diversity and composition structure of gut microbiota are altered. Age-associated changes in gut microbiota composition and in their metabolites contribute to increased gut permeability and imbalances in immune function. In this review, we aim to: identify gut microbiota changes in the aging and frail populations; summarize the role of chronic low-grade inflammation in the development of frailty; and outline how gut microbiota may be related to the pathogenesis of frailty, more specifically, in the regulation of gut-derived chronic inflammation. Although additional research is needed, the regulation of gut microbiota may represent a safe, easy, and inexpensive intervention to counteract the chronic inflammation leading to frailty.

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AGE-RELATED ELEVATED CD4+ T HELPER 17 CELL RESPONSE PROMOTES PROSTATE CANCER CELL GROWTH, MIGRATION, AND INVASION

Innovation in Aging ◽

10.1093/geroni/igz038.3221 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S879-S879

Author(s):

Qiuyang Zhang ◽

Sen Liu ◽

Bing Zhang ◽

Elizabeth Norton ◽

S Michal Jazwinski ◽

...

Keyword(s):

Prostate Cancer ◽

T Cells ◽

Th17 Cell ◽

Aging Process ◽

Conditioned Media ◽

Migration And Invasion ◽

T Helper ◽

T Helper 17 ◽

Age Related ◽

Old Mice

Abstract Age is the most important risk factor for prostate cancer (PCa). But, how age contributes to PCa remains unknown. Interleukin-17 (IL-17) -producing CD4+ T helper 17 (Th17) cells play a critical role in inflammatory diseases. It is often elevated in aging humans and mice, however, whether aging affects Th17 cell function and subsequent PCa risk increase is unclear. In this study, we investigated the role of CD4+ T cells in PCa cell growth during the aging process. Splenic T cells were isolated and purified into CD4+CD25- T cells from young and old mice, then cultured in the presence of plate-bound anti-CD3/anti-CD28. Four days later, the cells were re-stimulated with PMA and ionomycin in the presence of brefeldin A for 4 hours and then were collected and used for flow cytometry and/or qPCR. The supernatant (conditioned media) from young and old cultures was collected and used in subsequent experiments. Flow and qPCR results showed that 17-producing T cells and associated cytokines were significantly increased in old mice compared to young mice. When PCa cell lines (LNCaP, DU-145, and PC3) were treated by the conditioned media for 48 and 72 hours. The cell proliferation, migration, and invasion, as well as the activation of NF-B signaling in PCa cells, were significantly increased after exposure to the conditioned media from aged mice, compared to that from young mice. These results indicated that age-related CD4+ Th17 cell responses are elevated in mice in the aging process and play an important role in PCa growth.

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Individual DNA Methylation Profile is Correlated with Age and can be Targeted to Modulate Healthy Aging and Longevity

Current Pharmaceutical Design ◽

10.2174/1381612825666191112095655 ◽

2019 ◽

Vol 25 (39) ◽

pp. 4139-4149 ◽

Cited By ~ 3

Author(s):

Francesco Guarasci ◽

Patrizia D'Aquila ◽

Alberto Montesanto ◽

Andrea Corsonello ◽

Dina Bellizzi ◽

...

Keyword(s):

Dna Methylation ◽

Mitochondrial Dna ◽

Aging Process ◽

Healthy Aging ◽

Nuclear Dna ◽

Epigenetic Modification ◽

Biological Aging ◽

Biomarkers Of Aging ◽

Age Related

: Patterns of DNA methylation, the best characterized epigenetic modification, are modulated by aging. In humans, different studies at both site-specific and genome-wide levels have reported that modifications of DNA methylation are associated with the chronological aging process but also with the quality of aging (or biological aging), providing new perspectives for establishing powerful biomarkers of aging. : In this article, the role of DNA methylation in aging and longevity has been reviewed by analysing literature data about DNA methylation variations occurring during the lifetime in response to environmental factors and genetic background, and their association with the aging process and, in particular, with the quality of aging. Special attention has been devoted to the relationship between nuclear DNA methylation patterns, mitochondrial DNA epigenetic modifications, and longevity. Mitochondrial DNA has recently been reported to modulate global DNA methylation levels of the nuclear genome during the lifetime, and, in spite of the previous belief, it has been found to be the target of methylation modifications. : Analysis of DNA methylation profiles across lifetime shows that a remodeling of the methylome occurs with age and/or with age-related decline. Thus, it can be an excellent biomarker of aging and of the individual decline and frailty status. The knowledge about the mechanisms underlying these modifications is crucial since it might allow the opportunity for targeted treatment to modulate the rate of aging and longevity.

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Estimation of calendar age based on autonomic cardiovascular function by applying machine learning techniques

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2021-2177 ◽

2021 ◽

Vol 7 (2) ◽

pp. 696-699

Author(s):

Rassoul Sabeghi ◽

Karl-Jürgen Bär ◽

Andy Schumann

Keyword(s):

Machine Learning ◽

Age Estimation ◽

Vascular Function ◽

Healthy Subjects ◽

Cardiovascular Function ◽

Machine Learning Techniques ◽

Support Vector ◽

Modern Approach ◽

Age Related ◽

Learning Techniques

Abstract Aging is accompanied by changes in the cardiovascular physiology that promote the development of age-related diseases. This paper presents a modern approach to quantify the physiological effects of age on the cardiovascular system by applying modern machine learning techniques to several indicators of autonomic cardiovascular function. In 885 healthy subjects, 33 different indices were calculated on resting state electrocardiogram and continuous blood pressure recordings. Based on those parameters, five different approaches were applied in order to reconstruct the calendar age of healthy subjects, i.e., linear regression (LR), neural network (NN), Gaussian process regression (GPR), support vector regression (SVR), and relevance vector regression (RVR). Hyper parameters of machine learning methods were optimized via grid search. After 20 repetitions of a five-fold cross-validation, the mean absolute error (MAE) was computed between the calendar and estimated age to assess the accuracy of each method. The results show that the lowest error for age estimation was achieved using SVR with a MAE of 5.49 years. GPR performed comparably well to SVR with a MAE of 5.55 years, while NN led to a MAE of 5.72 years. RVR and LR revealed MAE of more than six years (6.21 and 6.34 years). The error of age estimation could be further reduced by applying outlier correction to the input data leading to a minimum MAE of 4.53 years with GPR. In conclusion, our results suggest that machine learning can be used to quantify the effects of healthy aging on cardio-vascular function.

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Physical performance is enhanced in old mice fed a short term diet medicated with rapamycin, acarbose, and phenylbutyrate

Aging Pathobiology and Therapeutics ◽

10.31491/apt.2021.03.051 ◽

2021 ◽

Vol 3 (1) ◽

pp. 12-13

Author(s):

Megan Ellis ◽

◽

Warren Ladiges ◽

Zhou Jiang

Keyword(s):

Grip Strength ◽

Physical Performance ◽

Healthy Aging ◽

Term Treatment ◽

Drug Cocktail ◽

Short Term ◽

Short Term Treatment ◽

Full Dose ◽

Age Related ◽

Old Mice

Loss of physical performance, as seen in humans by decreased grip strength and overall physical fitness, is generally accepted to be a consequence of aging. Treatments to delay or reduce these changes or increase resilience to them are generally not available. In this preliminary study, 20-month-old male and female C57BL/6 mice were given either a standard mouse diet or a formulated mouse diet containing rapamycin (14 ppm), acarbose (1000 ppm), and phenylbutyrate (1000 ppm), or a diet containing one half dose of each drug, for 3 months. At the end of the study, performance on a rotarod and grip strength test was compared. In general, mice fed the full dose drug cocktail diet performed better on these assays, with significant improvements in rotarod performance in females fed the full dose cocktail and in grip strength in males fed the full dose cocktail, and females fed the low dose cocktail. These observations provide support for the concept that short term treatment with a cocktail of drugs that targets multiple aging pathways can increase resilience to aging, and suggests that this prototype cocktail could be part of a clinical therapeutic strategy for delaying age-related loss of physical performance in people. Keywords: Healthy aging, physical performance, aging processes, anti-aging drug cocktail, rapamycin, acarbose, phenylbutyrate, aging mice

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Small extracellular vesicles from young mice prevent frailty and improve healthspan in old mice.

10.1101/2021.07.29.454302 ◽

2021 ◽

Author(s):

Jorge Sanz-Ros ◽

Cristina Mas-Bargues ◽

Daniel Monleon ◽

Mar Dromant ◽

Aksinya Derevyanko ◽

...

Keyword(s):

Extracellular Vesicles ◽

Healthy Aging ◽

Motor Coordination ◽

Frailty Index ◽

Functional Improvement ◽

Index Analysis ◽

Increased Risk ◽

Adipose Mesenchymal Stem Cells ◽

Old Mice ◽

Insight Into

Aging is associated with an increased risk of frailty, disability, comorbidities, institutionalization, falls, fractures, hospitalization, and mortality. Searching for strategies to delay the degenerative changes associated with aging and frailty is interesting. We treated old animals intravenously with small extracellular vesicles (sEVs) derived from adipose mesenchymal stem cells (ADSCs) of young animals, and we found an improvement of several functional parameters usually altered with aging, such as motor coordination, grip strength, fatigue resistance, fur regeneration, and renal function. Frailty index analysis showed that 40% of old control mice were frail, whereas none of the old ADSCs-sEVs treated mice were. Molecular and structural benefits in muscle and kidney accompanied this functional improvement. ADSCs-sEVs induced pro-regenerative effects and a decrease in oxidative stress, inflammation, and senescence markers. The metabolome of treated mice changed to a youth-like pattern. Finally, we gained some insight into the miRNAs contained in sEVs that might be, at least in part, responsible for the effects observed. We propose that young sEVs treatment can be beneficial against frailty and therefore can promote healthy aging.

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AGE-RELATED CHANGES IN THE MUSCLE SECRETOME

Innovation in Aging ◽

10.1093/geroni/igz038.378 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S101-S101

Author(s):

Ning Zhang ◽

Susan Weintraub ◽

Nicolas Musi

Keyword(s):

Mass Spectrometry ◽

Aging Process ◽

Young Group ◽

The Body ◽

Age Related ◽

Secretion Profile ◽

And Function ◽

Old Mice ◽

Body Support ◽

Age Related Changes

Abstract Skeletal muscle is one of the most abundant tissues in the body. In addition to its key roles in body support, movement and metabolic homeostasis, muscle also functions as an endocrine/secretory organ producing and releasing proteins into the circulation that modulate distant tissues (i.e. myokines). Considering that muscle mass and function changes with advancing age, here we tested the hypothesis that aging alters the muscle secretome profile. After euthanasia, soleus muscles from sedentary young and old mice were dissected, and incubated in oxygenated KRB buffer for 2 h. The buffer was subjected to in-gel trypsin-digestion and peptides analyzed by mass spectrometry. The concentration of 36 proteins were significantly (P<0.05) elevated in the young vs. the old group. In contrast, only 7 proteins were significantly elevated in the old group. Some notable differences include those in HSPA1B and HSPA5 that were detected only in the young group. HSPA8 also was significantly elevated by 1.8-fold (P<0.05) in the young versus the old group. Another prominent difference between groups involved translationally controlled tumor protein (TCTP), a critical regulator of apoptosis/carcinogenesis, that was elevated by 7-fold in the young vs. the old group (P<0.05). These results indicate that aging alters the muscle secretion profile. Identified differences in the muscle secretome could reflect intrinsic changes in muscle cells with age. Because these myokines are released into the circulation, it is also possible that myokine secretion is a regulated cellular process by which muscle communicates and modulates the aging process in distant tissues.

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