Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology.

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

GrimAge is elevated in older adults with mild COVID-19 an exploratory analysis.

COVID-19 has been contained; however, the side effects associated with its infection continue to be a challenge for public health, particularly for older adults. On the other hand, epigenetic status contributes to the inter-individual health status and is associated with COVID-19 severity. Nevertheless, current studies focus only on severe COVID-19. Considering that most of the worldwide population developed mild COVID-19 infection. In the present exploratory study, we aim to analyze the association of mild COVID-19 with epigenetic ages (HorvathAge, HannumAge, GrimAge, PhenoAge, SkinAge, and DNAmTL) and clinical variables obtained from a Mexican cohort of older adults. We found that all epigenetic ages significantly differ from the chronological age, but only GrimAge is elevated. Additionally, both the intrinsic epigenetic age acceleration (IEAA) and the extrinsic epigenetic age acceleration (EEAA) are accelerated in all patients. Moreover, we found that immunological estimators and DNA damage were associated with PhenoAge, SkinBloodHorvathAge, and HorvathAge, suggesting that the effects of mild COVID-19 on the epigenetic clocks are mainly associated with inflammation and immunology changes. In conclusion, our results show that the effects of mild COVID-19 on the epigenetic clock are mainly associated with the immune system and an increase in GrimAge, IEAA, and EEAA.

Unraveling the Neuroprotective Effect of Natural Bioactive Compounds Involved in the Modulation of Ischemic Stroke by Network Pharmacology.

Ischemic stroke (IS) is one of the leading causes of mortality worldwide. It is characterized by the partial or total occlusion of arteries that supply blood to the brain, leading to the death of brain cells. In recent years, natural bioactive compounds (NBCs) have shown properties that ameliorate the injury after IS and improve the patient's outcome, which has proven to be a potential therapeutic strategy due to their neuroprotective effects. Hence, in the present study, we use both systems pharmacology and chemoinformatic analyses to identify which NBCs have the most potential to be used against IS in clinics. Our results identify that flavonoids and terpenoids are the most studied NBCs, and, mainly, salidrosides, ginkgolides A, B, C, and K, cordycepin, curcumin, baicalin, resveratrol, fucose, and cannabidiol, target the main pathological processes occurring in IS. However, the medicinal chemistry properties of such compounds demonstrate that only six fulfill such criteria. However, only cordycepin and salidroside possess properties as leader molecules, suggesting that these compounds may be considered in developing novel drugs against IS.

Data-Driven Approaches Used for Compound Library Design for the Treatment of Parkinson's Disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease in older individuals worldwide. Pharmacological treatment for such a disease consists of drugs such as monoamine oxidase B (MAO-B) inhibitors to increase dopamine concentration in the brain. However, such drugs have adverse reactions that limit their use for extended periods; thus, the design of less toxic and more efficient compounds may be explored. In this context, cheminformatics and computational chemistry have recently contributed to developing new drugs and the search for new therapeutic targets. Therefore, through a data-driven approach, we used cheminformatic tools to find and optimize novel compounds with pharmacological activity against MAO-B for treating PD. First, we retrieved from the literature 3316 original articles published between 2015-2021 that experimentally tested 215 natural compounds against PD. From such compounds, we built a pharmacological network that showed rosmarinic acid, chrysin, naringenin, and cordycepin as the most connected nodes of the network. From such compounds, we performed fingerprinting analysis and developed evolutionary libraries to obtain novel derived structures. We filtered these compounds through a docking test against MAO-B and obtained five derived compounds with higher affinity and lead likeness potential. Then we evaluated its antioxidant and pharmacokinetic potential through a docking analysis (NADPH oxidase and CYP450) and physiologically-based pharmacokinetic (PBPK modeling). Interestingly, only one compound showed dual activity (antioxidant and MAO-B inhibitors) and pharmacokinetic potential to be considered a possible candidate for PD treatment and further experimental analysis.

A low-intensity lifelong exercise routine changes miRNA expression in aging and prevents osteosarcopenic obesity by modulating inflammation.

Osteosarcopenic obesity (OSO) has been associated with increase immobility, falls, fractures, and other dysfunctions, which could increase mortality risk during aging. However, its etiology remains unknown. Recent studies revealed that sedentarism, fat gain, and epigenetic regulators are critical in its development. One effective intervention to prevent and treat OSO is exercise. Therefore, in the present study, by keeping rats in conditions of sedentarism and others under a low-intensity exercise routine, we established an experimental model of OSO. We determined the degree of sarcopenia, obesity, and osteopenia at different ages and analyzed the miRNA expression during the lifespan using miRNA microarrays from gastrocnemius muscle. Interestingly microarrays results showed that there is a set of miRNAs that changed their expression with exercise. The pathway enrichment analysis showed that these miRNAs are strongly associated with immune regulation. Further inflammatory profiles with IL-6/IL-10 and TNF-α/IL-10 ratios showed that exercised rats presented a lower pro-inflammatory profile than sedentary rats. Also, the body fat gain in the sedentary group increased the inflammatory profile, ultimately leading to muscle dysfunction. Exercise prevented strength loss over time and maintained skeletal muscle functionality over time. Differential expression of miRNAs suggests that they might participate in this process by regulating the inflammatory response associated with aging, thus preventing the development of OSO.

Structural and Pharmacological Network Analysis of miRNAs Involved in Acute Ischemic Stroke: A Systematic Review.

Acute ischemic stroke (AIS) is among the main causes of mortality worldwide. A rapid and opportune diagnosis is crucial to improve a patient's outcomes; despite the current advanced image technologies for diagnosis, their implementation is challenging. MicroRNAs have been recognized as useful as biomarkers since they are specific and stable for characterization of AIS. However, there is still a lack of consensus over the primary miRNAs implicated in AIS. Here, we performed a systematic review of the literature covering from 2015-2021 regarding miRNAs expression during AIS and built structural networks to analyze and identify the most common miRNAs expressed during AIS and shared pathways, genes, and compounds that seem to influence their expression. We identified two sets of miRNAs: on one side, a set that was independent of geographical location and tissue (miR-124, miR-107, miR-221, miR-223, miR-140, miR-151a, miR-181a, miR-320b, and miR-484); and on the other side, a set that was connected (hubs) in biological networks (miR-27b-3p, miR-26b-5p, miR-124-3p, miR-570-3p, miR-19a-3p, miR-101-3p and miR-25-3p), which altered FOXO3, FOXO4, and EP300 genes. Interestingly, such genes are involved in cell death, FOXO-mediated transcription, and brain-derived neurotrophic factor signaling pathways. Finally, our pharmacological network analysis depicted a set of toxicants and drugs related to AIS for the first time.

A spatio-temporal study of state-wide case-fatality risks during the first wave of the COVID-19 pandemic in Mexico.

spatio-temporal analysis of the first wave of the coronavirus (COVID-19) pandemic in Mexico (April to September 2020) was performed by state. Descriptive analyses through diagrams, mapping, animations and time series representations were carried out. Greater risks were observed at certain times in specific regions. Various trends and clusters were observed and analysed by fitting linear mixed models and time series clustering. The association of co-morbidities and other variables were studied by fitting a spatial panel data linear model (SPLM). On average, the greatest risks were observed in Baja California Norte, Chiapas and Sonora, while some other densely populated states, e.g., Mexico City, had lower values. The trends varied by state and a four-order polynomial, including fixed and random effects, was necessary to model them. The most common risk development was observed in states belonging to two clusters and consisted of an initial increase followed by a decrease. Some states presented cluster configurations with a retarded risk increase before the decrease, while the risk increased throughout the time of study in others. A cyclic behaviour with a second increasing trend was also observed in some states. The SPLM approach revealed a positive significant association with respect to case fatality risk between certain groups, such as males and individuals aged 50 years and more, and the prevalence of chronic kidney disease, cardiovascular disease, asthma and hypertension. The analysis may provide valuable insight into COVID-19 dynamics applicable in future outbreaks, as well as identify determinants signifying certain trends at the state level. The combination of spatial and temporal information may provide a better understanding of the fatalities due to COVID-19.

Physical capability in a rural birth cohort at the age of 52: association with early environmental, nutritional, and developmental factors.

INTRODUCTION: Midlife physical capability (PC) is associated with developmental factors in the populations of economically developed countries. As far as we know, there is no information for rural populations of low- and middle-income countries. The aim of the study was to investigate the influence of pre- and postnatal factors on midlife objective measures of PC in a 1966-67 birth cohort from a Mexican rural community. The hypothesis was that adverse developmental conditions are associated with low midlife PC. METHODS: In 1966-67, a birth cohort of all children from a poor Mexican rural community was assembled. Data on family socioeconomic status (SES), parental health and nutritional status, birth weight, postnatal growth and feeding patterns were registered. In 2018, out of the 336 cohort members, 118 were living in the community, and eighty-two of them underwent a comprehensive clinical evaluation. The evaluation included grip strength, gait velocity and chair-stand PC tests. In multivariable linear models, PC tests were the dependent variables, and prenatal, birth and postnatal factors were the independent variables. Adjustment for confounding was made with adult anthropometric, body composition, clinical and ageing status variables. RESULTS: Independent of adult health status and other ageing indicators, lower PC was associated with family organization and SES, parental nutritional status, birth weight, infant postnatal growth velocity, and weaning time. These results indicate that adverse family and environmental conditions that are prevalent in poor rural communities are associated with low midlife PC.

Years of Schooling Could Reduce Epigenetic Aging: A Study of a Mexican Cohort.

Adverse conditions in early life, including environmental, biological and social influences, are risk factors for ill-health during aging and the onset of age-related disorders. In this context, the recent field of social epigenetics offers a valuable method for establishing the relationships among them However, current clinical studies on environmental changes and lifespan disorders are limited. In this sense, the Tlaltizapan (Mexico) cohort, who 52 years ago was exposed to infant malnutrition, low income and poor hygiene conditions, represents a vital source for exploring such factors. Therefore, in the present study, 52 years later, we aimed to explore differences in clinical/biochemical/anthropometric and epigenetic (DNA methylation) variables between individuals from such a cohort, in comparison with an urban-raised sample. Interestingly, only cholesterol levels showed significant differences between the cohorts. On the other hand, individuals from the Tlaltizapan cohort with more years of schooling had a lower epigenetic age in the Horvath (p-value = 0.0225) and PhenoAge (p-value = 0.0353) clocks, compared to those with lower-level schooling. Our analysis indicates 12 differentially methylated sites associated with the PI3-Akt signaling pathway and galactose metabolism in individuals with different durations of schooling. In conclusion, our results suggest that longer durations of schooling could promote DNA methylation changes that may reduce epigenetic age; nevertheless, further studies are needed.

Spatial epidemiological study of the distribution, clustering, and risk factors associated with early COVID-19 mortality in Mexico.

COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems worldwide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions, including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases, including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Thus, in the present study, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico. Firstly, to evaluate how mortality risk from COVID-19 among tested individuals (MRt) is geographically distributed and secondly, to analyze the association of spatial predictors of MRt across different states in Mexico, controlling for the severity of the disease. Among health-related variables, diabetes and obesity were positively associated with COVID-19 fatality. When analyzing Mexico as a whole, we identified that both the percentages of external and internal migration had positive associations with early COVID-19 mortality risk with external migration having the second-highest positive association. As an indirect measure of urbanicity, population density, and overcrowding in households, the physicians-to-population ratio has the highest positive association with MRt. In contrast, the percentage of individuals in the age group between 10 to 39 years had a negative association with MRt. Geographically, Quintana Roo, Baja California, Chihuahua, and Tabasco (until April 2020) had higher MRt and standardized mortality ratios, suggesting that risks in these states were above what was nationally expected. Additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risk varied by zone.

Chemoinformatic Screening for the Selection of Potential Senolytic Compounds from Natural Products.

Cellular senescence is a cellular condition that involves significant changes in gene expression and the arrest of cell proliferation. Recently, it has been suggested in experimental models that the elimination of senescent cells with pharmacological methods delays, prevents, and improves multiple adverse outcomes related to age. In this sense, the so-called senoylitic compounds are a class of drugs that selectively eliminates senescent cells (SCs) and that could be used in order to delay such adverse outcomes. Interestingly, the first senolytic drug (navitoclax) was discovered by using chemoinformatic and network analyses. Thus, in the present study, we searched for novel senolytic compounds through the use of chemoinformatic tools (fingerprinting and network pharmacology) over different chemical databases (InflamNat and BIOFACQUIM) coming from natural products (NPs) that have proven to be quite remarkable for drug development. As a result of screening, we obtained three molecules (hinokitiol, preussomerin C, and tanshinone I) that could be considered senolytic compound candidates since they share similarities in structure with senolytic leads (tunicamycin, ginsenoside Rb1, ABT 737, rapamycin, navitoclax, timosaponin A-III, digoxin, roxithromycin, and azithromycin) and targets involved in senescence pathways with potential use in the treatment of age-related diseases.

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level.

The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19th to March 18th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran's I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.

The Unexplored World of Human Virome, Mycobiome, and Archaeome in Aging.

In the last decades, improvements in different aspects of sanitation, medical care, and nutrition, among others, have permitted an increase in the average lifespan of human population around the world. These advances have stimulated an increased interest in the study of the aging process and age-sensitive characteristics, such as the microbial community that colonizes the human body (microbiome). The human microbiome is composed of bacteria (bacteriome), archaea (archaeome), fungi (mycobiome), and viruses (virome). To date, research has mainly been centered on the composition of the bacteriome, with other members remain poorly studied. Interestingly, changes in the composition of the microbiome have been implicated in aging and age-related diseases. Therefore, in the present perspective, we suggest expanding the scope to research to include the role and the possible associations that the other members of the microbiome could have in the aging organism. An expanded view of the microbiome would increase our knowledge of the physiology of aging and may be particularly valuable for the treatment and diagnosis of age-related diseases.

Network analysis of frailty and aging: Empirical data from the Mexican Health and Aging Study.

BACKGROUND: Frailty remains a challenge in the aging research area with a number of gaps in knowledge still to be filled. Frailty seems to behave as a network, and in silico evidence is available on this matter. Having in vivo evidence that frailty behaves as a complex network was the main purpose of our study. METHODS: Data from the Mexican Health and Aging Study (main data 2012, mortality 2015) was used. Frailty was operationalized with a 35-deficit frailty index (FI). Analyzed nodes were the deficits plus death. The edges, linking those nodes were obtained through structural learning, and an undirected graph associated with a discrete probabilistic graphical model (Markov network) was derived. Two algorithms, hill-climbing (hc) and Peter and Clark (PC), were used to derive the graph structure. Analyses were performed for the whole population and tertiles of the total FI score. RESULTS: From the total sample of 10,983 adults aged 50 or older, 43.8% were women, and the mean age was 64.6 years (SD = 9.3). The number of connections increased according to the tertile level of the FI score. As the FI score raised, groups of interconnected deficits increased and how the nodes are connected changed. CONCLUSIONS: Frailty phenomenon can be modeled using a Bayesian network. Using the full sample, the most central nodes were self-report of health (most connected node) and difficulty walking a block, and all deficits related to mobility were very interconnected. When frailty levels are considered, the most connected nodes differ, but are related with vitality, mainly at lower frailty levels. We derived that not all deficits are equally related since clusters of very related deficits and non-connected deficits were obtained, which might be considered in the construction of the FI score. Further research should aim to identify the nature of all observed interactions, which might allow the development of specific interventions to mitigate the consequences of frailty in older adults.

Network Pharmacology Uncovers Anticancer Activity of Mammea-Type Coumarins from Calophyllum brasiliense.

Mammea-type coumarins are a particular type of secondary metabolites biosynthesized by the tropical rainforest tree Calophyllum Brasiliense, which is distributed from South America to Mexico. Particularly, mammea A/BA and A/BB (alone or as a mixture) possess biological properties such as cytotoxic and antitumoral activities, however, most of its molecular targets remain unknown. In this context, novel bioinformatic approaches, such as network pharmacology analysis, have been successfully used in herbal medicine to accelerate research in this field, and the support of experimental validations has been shown to be quite robust. In the present study, we performed a network pharmacology analysis to assess the possible molecular biological networks that interact with mammea A/BA and A/BB. Moreover, we validated the most relevant networks experimentally in vitro on K562 cancer cells. The results of the network pharmacology analysis indicate that mammea A/BA and A/BB interacts with cell death, PI3K/AKT, MAPK, Ras, and cancer pathways. The in vitro model shows that mammea A/BA and A/BB induce apoptosis through the overexpression of the proapoptotic proteins Bax and Bak, disrupt the autophagic flux as seen by the cytosolic accumulation of LC3-II and p62, disrupting the mitochondria ultrastructure and concomitantly increase the intracellular calcium concentration. Additionally, docking analysis predicted a possible interaction with a rapamycin-binding domain of mTOR. In conclusion, we validated network pharmacology analysis and report, for the first time, that mammea A/BA and A/BB coumarins induce apoptosis through the inhibition of the autophagic flux, possibly interacting with mTOR.

Chemoinformatic Analysis of Selected Cacalolides from Psacalium decompositum (A. Gray) H. Rob. & Brettell and Psacalium peltatum (Kunth) Cass. and Their Effects on FcεRI-Dependent Degranulation in Mast Cells.

Cacalolides are a kind of sesquiterpenoids natural compounds synthesized by Psacalium decompositum (A. Gray) H. Rob. & Brettell or Psacalium peltatum (Kunth) Cass. Antioxidant and hypoglycemic effects have been found for cacalolides such as cacalol, cacalone or maturine, however, their effects on inflammatory processes are still largely unclear. The main aim of this study was to investigate the biological activities of secondary metabolites from P. decompositum and P. peltatum through two approaches: (1) chemoinformatic and toxicoinformatic analysis based on ethnopharmacologic background; and (2) the evaluation of their potential anti-inflammatory/anti-allergic effects in bone marrow-derived mast cells by IgE/antigen complexes. The bioinformatics properties of the compounds: cacalol; cacalone; cacalol acetate and maturin acetate were evaluated through Osiris DataWarrior software and Molinspiration and PROTOX server. In vitro studies were performed to test the ability of these four compounds to inhibit antigen-dependent degranulation and intracellular calcium mobilization, as well as the production of reactive oxygen species in bone marrow-derived mast cells. Our findings showed that cacalol displayed better bioinformatics properties, also exhibited a potent inhibitory activity on IgE/antigen-dependent degranulation and significantly reduced the intracellular calcium mobilization on mast cells. These data suggested that cacalol could reduce the negative effects of the mast cell-dependent inflammatory process.