Potential Benefits of Combined Statin and Metformin Therapy on Resistance Training Response in Older Individuals

Metformin and statins are currently the focus of large clinical trials testing their ability to counter age-associated declines in health, but recent reports suggest that both may negatively affect skeletal muscle response to exercise. However, it has also been suggested that metformin may act as a possible protectant of statin-related muscle symptoms. The potential impact of combined drug use on the hypertrophic response to resistance exercise in healthy older adults has not been described. We present secondary statin analyses of data from the MASTERS trial where metformin blunted the hypertrophy response in healthy participants (\u3e65 years) following 14 weeks of progressive resistance training (PRT) when compared to identical placebo treatment (n = 94). Approximately one-third of MASTERS participants were taking prescribed statins. Combined metformin and statin resulted in rescue of the metformin-mediated impaired growth response to PRT but did not significantly affect strength. Improved muscle fiber growth may be associated with medication-induced increased abundance of CD11b+/CD206+ M2-like macrophages. Sarcopenia is a significant problem with aging and this study identifies a potential interaction between these commonly used drugs which may help prevent metformin-related blunting of the beneficial effects of PRT

Seasonal variation in collective mood via Twitter content and medical purchases

The analysis of sentiment contained in vast amounts of Twitter messages has reliably shown seasonal patterns of variation in multiple studies, a finding that can have great importance in the understanding of seasonal affective disorders, particularly if related with known seasonal variations in certain hormones. An important question, however, is that of directly linking the signals coming from Twitter with other sources of evidence about average mood changes. Specifically we compare Twitter signals relative to anxiety, sadness, anger, and fatigue with purchase of items related to anxiety, stress and fatigue at a major UK Health and Beauty retailer. Results show that all of these signals are highly correlated and strongly seasonal, being under-expressed in the summer and over-expressed in the other seasons, with interesting differences and similarities across them. Anxiety signals, extracted from both Twitter and from Health product purchases, peak in spring and autumn, and correlate also with the purchase of stress remedies, while Twitter sadness has a peak in the Winter, along with Twitter anger and remedies for fatigue. Surprisingly, purchase of remedies for fatigue do not match the Twitter fatigue, suggesting that perhaps the names we give to these indicators are only approximate indications of what they actually measure. This study contributes both to the clarification of the mood signals contained in social media, and more generally to our understanding of seasonal cycles in collective mood

Gender detection of Twitter users based on multiple information sources

Twitter provides a simple way for users to express feelings, ideas and opinions, makes the user generated content and associated metadata, available to the community, and provides easy-to-use web and application programming interfaces to access data. The user profile information is important for many studies, but essential information, such as gender and age, is not provided when accessing a Twitter account. However, clues about the user profile, such as the age and gender, behaviors, and preferences, can be extracted from other content provided by the user. The main focus of this paper is to infer the gender of the user from unstructured information, including the username, screen name, description and picture, or by the user generated content. We have performed experiments using an English labelled dataset containing 6.5 M tweets from 65 K users, and a Portuguese labelled dataset containing 5.8 M tweets from 58 K users. We have created four distinct classifiers, trained using a supervised approach, each one considering a group of features extracted from four different sources: user name and screen name, user description, content of the tweets, and profile picture. Features related with the activity, such as number of following and number of followers, were discarded, since these features were found not indicative of gender. A final classifier that combines the prediction of each one of the four previous individual classifiers achieves the best performance, corresponding to 93.2% accuracy for English and 96.9% accuracy for Portuguese data.info:eu-repo/semantics/acceptedVersio

A Guide for Using NIH Image J for Single Slice Cross-Sectional Area and Composition Analysis of the Thigh from Computed Tomography

Reports using computed tomography (CT) to estimate thigh skeletal muscle cross-sectional area and mean muscle attenuation are often difficult to evaluate due to inconsistent methods of quantification and/or poorly described analysis methods. This CT tutorial provides step-by-step instructions in using free, NIH Image J software to quantify both muscle size and composition in the mid-thigh, which was validated against a robust commercially available software, SliceOmatic. CT scans of the mid-thigh were analyzed from 101 healthy individuals aged 65 and older. Mean cross-sectional area and mean attenuation values are presented across seven defined Hounsfield unit (HU) ranges along with the percent contribution of each region to the total mid-thigh area. Inter-software correlation coefficients ranged from R2 = 0.92–0.99 for all specific area comparisons measured using the Image J method compared to SliceOmatic. We recommend reporting individual HU ranges for all areas measured. Although HU range 0–100 includes the majority of skeletal muscle area, HU range -29 to 150 appears to be the most inclusive for quantifying total thigh muscle. Reporting all HU ranges is necessary to determine the relative contribution of each, as they may be differentially affected by age, obesity, disease, and exercise. This standardized operating procedure will facilitate consistency among investigators reporting computed tomography characteristics of the thigh on single slice images. Trial Registration: ClinicalTrials.gov NCT02308228

A Muscle Cell-Macrophage Axis Involving Matrix Metalloproteinase 14 Facilitates Extracellular Matrix Remodeling with Mechanical Loading

The extracellular matrix (ECM) in skeletal muscle plays an integral role in tissue development, structural support, and force transmission. For successful adaptation to mechanical loading, remodeling processes must occur. In a large cohort of older adults, transcriptomics revealed that genes involved in ECM remodeling, including matrix metalloproteinase 14 (MMP14), were the most upregulated following 14 weeks of progressive resistance exercise training (PRT). Using single-cell RNA-seq, we identified macrophages as a source of Mmp14 in muscle following a hypertrophic exercise stimulus in mice. In vitro contractile activity in myotubes revealed that the gene encoding cytokine leukemia inhibitory factor (LIF) is robustly upregulated and can stimulate Mmp14 expression in macrophages. Functional experiments confirmed that modulation of this muscle cell-macrophage axis facilitated Type I collagen turnover. Finally, changes in LIF expression were significantly correlated with MMP14 expression in humans following 14 weeks of PRT. Our experiments reveal a mechanism whereby muscle fibers influence macrophage behavior to promote ECM remodeling in response to mechanical loading

Immunohistochemical Identification of Human Skeletal Muscle Macrophages

Macrophages have well-characterized roles in skeletal muscle repair and regeneration. Relatively little is known regarding the role of resident macrophages in skeletal muscle homeostasis, extracellular matrix remodeling, growth, metabolism and adaptation to various stimuli including exercise and training. Despite speculation into macrophage contributions during these processes, studies characterizing macrophages in non-injured muscle are limited and methods used to identify macrophages vary. A standardized method for the identification of human resident skeletal muscle macrophages will aide in the characterization of these immune cells and allow for the comparison of results across studies. Here, we present an immunohistochemistry (IHC) protocol, validated by flow cytometry, to distinctly identify resident human skeletal muscle macrophage populations. We show that CD11b and CD206 double IHC effectively identifies macrophages in human skeletal muscle. Furthermore, the majority of macrophages in non-injured human skeletal muscle show a ‘mixed’ M1/M2 phenotype, expressing CD11b, CD14, CD68, CD86 and CD206. A relatively small population of CD11b+/CD206- macrophages are present in resting skeletal muscle. Changes in the relative abundance of this population may reflect important changes in the skeletal muscle environment. CD11b and CD206 IHC in muscle also reveals distinct morphological features of macrophages that may be related to the functional status of these cells

Effects of end-stage osteoarthritis on markers of skeletal muscle Long INterspersed Element-1 activity

Objective: Long INterspersed Element-1 (L1) is an autonomous transposable element in the genome. L1 transcripts that are not reverse transcribed back into the genome can accumulate in the cytoplasm and activate an inflammatory response via the cyclic GMP-AMP (cGAS)-STING pathway. We examined skeletal muscle L1 markers as well as STING protein levels in 10 older individuals (63 ± 11 y, BMI= 30.2 ± 6.8 kg/m2) with end-stage osteoarthritis (OA) undergoing total hip (THA, n= 4) or knee (TKA, n= 6) arthroplasty versus 10 young, healthy comparators (Y, 22 ± 2 y, BMI= 23.2 ± 2.5 kg/m2). For OA, muscle was collected from surgical (SX) and contralateral (CTL) sides whereas single vastus lateralis samples were collected from Y. Results: L1 mRNA was higher in CTL and SX compared to Y (p \u3c 0.001 and p= 0.001, respectively). Protein expression was higher in SX versus Y for ORF1p (p= 0.002) and STING (p= 0.022). While these data are preliminary due to limited n-sizes and the lack of a BMI-matched younger control group, higher L1 mRNA expression, ORF1p and STING protein are evident in older versus younger adults. More research is needed to determine whether cGAS-STING signaling contributes to heightened muscle inflammation during aging and/or OA

Metformin Blunts Muscle Hypertrophy in Response to Progressive Resistance Exercise Training in Older Adults: A Randomized, Double‐Blind, Placebo‐Controlled, Multicenter Trial: The MASTERS Trial

Progressive resistance exercise training (PRT) is the most effective known intervention for combating aging skeletal muscle atrophy. However, the hypertrophic response to PRT is variable, and this may be due to muscle inflammation susceptibility. Metformin reduces inflammation, so we hypothesized that metformin would augment the muscle response to PRT in healthy women and men aged 65 and older. In a randomized, double-blind trial, participants received 1,700 mg/day metformin (N = 46) or placebo (N = 48) throughout the study, and all subjects performed 14 weeks of supervised PRT. Although responses to PRT varied, placebo gained more lean body mass (p = .003) and thigh muscle mass (p \u3c .001) than metformin. CT scan showed that increases in thigh muscle area (p = .005) and density (p = .020) were greater in placebo versus metformin. There was a trend for blunted strength gains in metformin that did not reach statistical significance. Analyses of vastus lateralis muscle biopsies showed that metformin did not affect fiber hypertrophy, or increases in satellite cell or macrophage abundance with PRT. However, placebo had decreased type I fiber percentage while metformin did not (p = .007). Metformin led to an increase in AMPK signaling, and a trend for blunted increases in mTORC1 signaling in response to PRT. These results underscore the benefits of PRT in older adults, but metformin negatively impacts the hypertrophic response to resistance training in healthy older individuals. ClinicalTrials.gov Identifier: NCT02308228