scholarly journals Human Skin, Oral, and Gut Microbiomes Predict Chronological Age

mSystems ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Shi Huang ◽  
Niina Haiminen ◽  
Anna-Paola Carrieri ◽  
Rebecca Hu ◽  
Lingjing Jiang ◽  
...  
Keyword(s):  
Gut Microbiome ◽  
Prediction Models ◽  
The Body ◽  
Oral Microbiome ◽  
Chronological Age ◽  
Future Research ◽  
Skin Microbiome ◽  
Age Related ◽  
Relative Value

ABSTRACT Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging. IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person’s age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site’s microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases.

scholarly journals Is the Oral Microbiome Important in HIV-Associated Inflammation?

mSphere ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Jennifer A. Fulcher
Keyword(s):  
Gastrointestinal Tract ◽  
Antiretroviral Therapy ◽  
Gut Microbiome ◽  
Inflammatory Markers ◽  
Oral Microbiome ◽  
Future Research ◽  
Soluble Cd14 ◽  
Microbiome Diversity ◽  
The Relationship

ABSTRACT Alterations in the gut microbiome during HIV infection have been implicated in chronic inflammation, but the role of the oral microbiome in this process is less clear. The article by M. K. Annavajhala, S. D. Khan, S. B. Sullivan, J. Shah, et al. (mSphere 5:e00798-19, 2020, https://doi.org/10.1128/mSphere.00798-19) investigated the relationship between oral and gut microbiome diversity and immune activation in patients with HIV on antiretroviral therapy. In this study, oral microbiome diversity was inversely associated with inflammatory markers such as soluble CD14 (sCD14), but surprisingly similar associations were not seen with gut microbiome diversity. Oral microbiome diversity was also associated with periodontitis in these patients. This study highlights the importance of continuing multisite examinations in studying the gastrointestinal tract microbiome and also stimulates important directions for future research defining the role of the oral-gut axis in HIV-associated inflammation.

scholarly journals The Skin and Gut Microbiome and Its Role in Common Dermatologic Conditions

2019 ◽  
Vol 7 (11) ◽  
pp. 550 ◽  
Author(s):  
Samantha R. Ellis ◽  
Mimi Nguyen ◽  
Alexandra R. Vaughn ◽  
Manisha Notay ◽  
Waqas A. Burney ◽  
...  
Keyword(s):  
Atopic Dermatitis ◽  
Environmental Factors ◽  
Gut Microbiome ◽  
The Body ◽  
Skin Microbiome ◽  
Disease States ◽  
Normal Microflora ◽  
The Impact ◽  
The Relationship

Microorganisms inhabit various areas of the body, including the gut and skin, and are important in maintaining homeostasis. Changes to the normal microflora due to genetic or environmental factors can contribute to the development of various disease states. In this review, we will discuss the relationship between the gut and skin microbiome and various dermatological diseases including acne, psoriasis, rosacea, and atopic dermatitis. In addition, we will discuss the impact of treatment on the microbiome and the role of probiotics.

scholarly journals Strategies and cognitive reserve to preserve lexical production in aging

GeroScience ◽  
2021 ◽  
Author(s):  
Monica Baciu ◽  
Sonja Banjac ◽  
Elise Roger ◽  
Célise Haldin ◽  
Marcela Perrone-Bertolotti ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Aging ◽  
Individual Variability ◽  
Future Research ◽  
Aging Effects ◽  
Main Hypothesis ◽  
Compensatory Strategies ◽  
Age Related ◽  
Complementary Domain

AbstractIn the absence of any neuropsychiatric condition, older adults may show declining performance in several cognitive processes and among them, in retrieving and producing words, reflected in slower responses and even reduced accuracy compared to younger adults. To overcome this difficulty, healthy older adults implement compensatory strategies, which are the focus of this paper. We provide a review of mainstream findings on deficient mechanisms and possible neurocognitive strategies used by older adults to overcome the deleterious effects of age on lexical production. Moreover, we present findings on genetic and lifestyle factors that might either be protective or risk factors of cognitive impairment in advanced age. We propose that “aging-modulating factors” (AMF) can be modified, offering prevention opportunities against aging effects. Based on our review and this proposition, we introduce an integrative neurocognitive model of mechanisms and compensatory strategies for lexical production in older adults (entitled Lexical Access and Retrieval in Aging, LARA). The main hypothesis defended in LARA is that cognitive aging evolves heterogeneously and involves complementary domain-general and domain-specific mechanisms, with substantial inter-individual variability, reflected at behavioral, cognitive, and brain levels. Furthermore, we argue that the ability to compensate for the effect of cognitive aging depends on the amount of reserve specific to each individual which is, in turn, modulated by the AMF. Our conclusion is that a variety of mechanisms and compensatory strategies coexist in the same individual to oppose the effect of age. The role of reserve is pivotal for a successful coping with age-related changes and future research should continue to explore the modulating role of AMF.

The putative effects of green tea on body fat: an evaluation of the evidence and a review of the potential mechanisms

2011 ◽  
Vol 106 (9) ◽  
pp. 1297-1309 ◽  
Author(s):  
Navamayooran Thavanesan
Keyword(s):  
Weight Loss ◽  
Green Tea ◽  
Body Fat ◽  
Fatty Acid Synthase ◽  
The Body ◽  
Alternative Methods ◽  
Future Research ◽  
Prevalence Of Obesity ◽  
Simultaneous Inhibition

The increase in the prevalence of obesity in recent years has prompted research into alternative methods of modulating body weight and body fat. The last decade has reflected this with a surge in studies investigating the potential of green tea as a natural agent of weight loss, with a view to confirming and elucidating the mechanisms underlying its effect on the body. Currently, it is widely believed that the polyphenolic components present in green tea have an anti-obesogenic effect on fat homeostasis, by increasing thermogenesis or reducing fat absorption among other ways. The data published to date, however, are inconsistent, with numerous putative modes of action suggested therein. While several unimodal mechanisms have been postulated, a more plausible explanation of the observed results might involve a multimodal approach. Such a mechanism is suggested here, involving simultaneous inhibition of the enzymes catechol-O-methyltransferase, acetyl-CoA carboxylase, fatty acid synthase and impeding absorption of fat via the gut. An evaluation of the available evidence supports a role of green tea in weight loss; however the extent of the effects obtained is still subject to debate, and requires more objective quantification in future research.

scholarly journals Cyber-Flirting: An Examination of Men's and Women's Flirting Behaviour Both Offline and on the Internet

2004 ◽  
Vol 21 (2) ◽  
pp. 115-126 ◽  
Author(s):  
Monica T. Whitty
Keyword(s):  
Physical Attractiveness ◽  
The Body ◽  
Nonverbal Cues ◽  
Future Research ◽  
The Internet ◽  
Research Needs ◽  
Men And Women ◽  
Face To Face ◽  
Online Interactions

AbstractWhile flirting is a relatively underresearched area within psychology, even less is known about how people cyber-flirt. This paper explores how often individuals flirt offline compared to online. Moreover, it attempts to examine how men and women flirt within these different spaces. Five thousand, six hundred and ninety-seven individuals, of which 3554 (62%) were women and 2143 (38%) were men, completed a survey about their flirting behaviour both in face-to-face interactions and in chatrooms. The first hypothesis, which stated that the body would be used to flirt with as frequently online as offline, was partly supported. However, it was found that individuals downplayed the importance of physical attractiveness online. Women flirted by displaying nonverbal signals (offline) or substitutes for nonverbal cues (online), to a greater extent than men. In chatrooms men were more likely than women to initiate contact. It is concluded that cyber-flirting is more than simply a meeting of minds and that future research needs to consider the role of the body in online interactions.

A metabolomic aging clock using human CSF

2021 ◽  
Author(s):  
Nathan Hwangbo ◽  
Xinyu Zhang ◽  
Daniel Raftery ◽  
Haiwei Gu ◽  
Shu-Ching Hu ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Healthy Aging ◽  
Prediction Models ◽  
Mass Spectrometry Analysis ◽  
Chronological Age ◽  
Healthy Human ◽  
Missing Data Imputation ◽  
Spectrometry Analysis ◽  
Residual Variation ◽  
Age Related

Abstract Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that in regression models using “-omic” platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these “omic clocks” provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid from healthy human subjects, and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer’s and Parkinson’s disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently over-predict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small dataset (n = 85), and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of cerebrospinal fluid.

scholarly journals The Gut Microbiota and Healthy Aging: A Mini-Review

Gerontology ◽  
2018 ◽  
Vol 64 (6) ◽  
pp. 513-520 ◽  
Author(s):  
Sangkyu Kim ◽  
S. Michal Jazwinski
Keyword(s):  
Gut Microbiota ◽  
Individual Variation ◽  
Gut Microbiome ◽  
Chronological Age ◽  
Low Grade ◽  
Beneficial Effects ◽  
Gut Dysbiosis ◽  
Age Related ◽  
Host Health ◽  
Nutrient Signaling

The gut microbiota shows a wide inter-individual variation, but its within-individual variation is relatively stable over time. A functional core microbiome, provided by abundant bacterial taxa, seems to be common to various human hosts regardless of their gender, geographic location, and age. With advancing chronological age, the gut microbiota becomes more diverse and variable. However, when measures of biological age are used with adjustment for chronological age, overall richness decreases, while a certain group of bacteria associated with frailty increases. This highlights the importance of considering biological or functional measures of aging. Studies using model organisms indicate that age-related gut dysbiosis may contribute to unhealthy aging and reduced longevity. The gut microbiome depends on the host nutrient signaling pathways for its beneficial effects on host health and lifespan, and gut dysbiosis disrupting the interdependence may diminish the beneficial effects or even have reverse effects. Gut dysbiosis can trigger the innate immune response and chronic low-grade inflammation, leading to many age-related degenerative pathologies and unhealthy aging. The gut microbiota communicates with the host through various biomolecules, nutrient signaling-independent pathways, and epigenetic mechanisms. Disturbance of these communications by age-related gut dysbiosis can affect the host health and lifespan. This may explain the impact of the gut microbiome on health and aging.

scholarly journals A Brief Taxonomy of Hybrid Intelligence

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 633-643
Author(s):  
Niccolo Pescetelli
Keyword(s):  
Prediction Models ◽  
Collective Intelligence ◽  
Machine Intelligence ◽  
Future Research ◽  
Human Errors ◽  
Wide Range ◽  
Hybrid Intelligence ◽  
Forecasting System ◽  
Problem Solvers

As artificial intelligence becomes ubiquitous in our lives, so do the opportunities to combine machine and human intelligence to obtain more accurate and more resilient prediction models across a wide range of domains. Hybrid intelligence can be designed in many ways, depending on the role of the human and the algorithm in the hybrid system. This paper offers a brief taxonomy of hybrid intelligence, which describes possible relationships between human and machine intelligence for robust forecasting. In this taxonomy, biological intelligence represents one axis of variation, going from individual intelligence (one individual in isolation) to collective intelligence (several connected individuals). The second axis of variation represents increasingly sophisticated algorithms that can take into account more aspects of the forecasting system, from information to task to human problem-solvers. The novelty of the paper lies in the interpretation of recent studies in hybrid intelligence as precursors of a set of algorithms that are expected to be more prominent in the future. These algorithms promise to increase hybrid system’s resilience across a wide range of human errors and biases thanks to greater human-machine understanding. This work ends with a short overview for future research in this field.

scholarly journals α-Secretase nonsense mutation (ADAM10 Tyr167*) in familial Alzheimer’s disease

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Pablo Agüero ◽  
María José Sainz ◽  
María-Salud García-Ayllón ◽  
Javier Sáez-Valero ◽  
Raquel Téllez ◽  
...  
Keyword(s):  
Nonsense Mutation ◽  
Genetic Model ◽  
Late Onset ◽  
Future Research ◽  
Clinical Genetic ◽  
Csf Analysis ◽  
Amyloidogenic Processing ◽  
Age Related ◽  
Spanish Cohort

Abstract Background The disintegrin metalloproteinase 10 (ADAM10) is the main α-secretase acting in the non-amyloidogenic processing of APP. Some ADAM10 gene variants have been associated with higher susceptibility to develop late-onset AD, though clear clinical-genetic correlates remain elusive. Methods Clinical-genetic and biomarker study of a first family with early- and late-onset AD associated with a nonsense ADAM10 mutation (p.Tyr167*). CSF analysis included AD core biomarkers, as well as Western blot of ADAM10 species and sAPPα and sAPPβ peptides. We evaluate variant’s pathogenicity, pattern of segregation, and further screened for the p.Tyr167* mutation in 197 familial AD cases from the same cohort, 200 controls from the same background, and 274 AD cases from an independent Spanish cohort. Results The mutation was absent from public databases and segregated with the disease. CSF Aβ42, total tau, and phosphorylated tau of affected siblings were consistent with AD. The predicted haploinsufficiency effect of the nonsense mutation was supported by (a) ADAM10 isoforms in CSF decreased around 50% and (b) 70% reduction of CSF sAPPα peptide, both compared to controls, while sAPPβ levels remained unchanged. Interestingly, sporadic AD cases had a similar decrease in CSF ADAM10 levels to that of mutants, though their sAPPα and sAPPβ levels resembled those of controls. Therefore, a decreased sAPPα/sAPPβ ratio was an exclusive feature of mutant ADAM10 siblings. The p.Tyr167* mutation was not found in any of the other AD cases or controls screened. Conclusions This family illustrates the role of ADAM10 in the amyloidogenic process and the clinical development of the disease. Similarities between clinical and biomarker findings suggest that this family could represent a genetic model for sporadic late-onset AD due to age-related downregulation of α-secretase. This report encourages future research on ADAM10 enhancers.

scholarly journals The Oral and Gut Bacterial Microbiomes: Similarities, Differences, and Connections

2020 ◽  
Vol 23 (1) ◽  
pp. 7-20
Author(s):  
Katherine A. Maki ◽  
Narjis Kazmi ◽  
Jennifer J. Barb ◽  
Nancy Ames
Keyword(s):  
Gut Microbiome ◽  
Alpha Diversity ◽  
Oral Microbiome ◽  
Taxonomic Resolution ◽  
Future Research ◽  
Sequencing Analysis ◽  
Systemic Diseases ◽  
Shotgun Metagenomics ◽  
Healthy Humans ◽  
Microbiome Research

Background: The oral cavity is associated with local and systemic diseases, although oral samples are not as commonly studied as fecal samples in microbiome research. There is a gap in understanding between the similarities and differences in oral and gut microbiomes and how they may influence each other. Methods: A scoping literature review was conducted comparing oral and gut microbiome communities in healthy humans. Results: Ten manuscripts met inclusion criteria and were examined. The oral microbiome sites demonstrated great variance in differential bacterial abundance and the oral microbiome had higher alpha diversity as compared to the gut microbiome. Studies using 16S rRNA sequencing analysis resulted in overall community differences between the oral and gut microbiomes when beta diversity was analyzed. Shotgun metagenomics sequencing increased taxonomic resolution to strain level (intraspecies) and demonstrated a greater percentage of shared taxonomy and oral bacterial translocation to the gut microbiome community. Discussion: The oral and gut microbiome bacterial communities may be more similar than earlier research has suggested, when species strain is analyzed through shotgun metagenomics sequencing. The association between oral health and systemic diseases has been widely reported but many mechanisms underlying this relationship are unknown. Although future research is needed, the oral microbiome may be a novel interventional target through its downstream effects on the gut microbiome. As nurse scientists are experts in symptom characterization and phenotyping of patients, they are also well posed to lead research on the connection of the oral microbiome to the gut microbiome in health and disease.

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