Reallocating just 10 min to moderate-to-vigorous physical activity from other components of 24-hour movement behaviors improves cardiovascular health in adults.

BACKGROUND: As components of a 24-hour day, sedentary behavior (SB), physical activity (PA), and sleep are all independently linked to cardiovascular health (CVH). However, insufficient understanding of components' mutual exclusion limits the exploration of the associations between all movement behaviors and health outcomes. The aim of this study was to employ compositional data analysis (CoDA) approach to investigate the associations between 24-hour movement behaviors and overall CVH. METHODS: Data from 581 participants, including 230 women, were collected from the 2005-2006 wave of the US National Health and Nutrition Examination Survey (NHANES). This dataset included information on the duration of SB and PA, derived from ActiGraph accelerometers, as well as self-reported sleep duration. The assessment of CVH was conducted in accordance with the criteria outlined in Life's Simple 7, encompassing the evaluation of both health behaviors and health factors. Compositional linear regression was utilized to examine the cross-sectional associations of 24-hour movement behaviors and each component with CVH score. Furthermore, the study predicted the potential differences in CVH score that would occur by reallocating 10 to 60 min among different movement behaviors. RESULTS: A significant association was observed between 24-hour movement behaviors and overall CVH (p < 0.001) after adjusting for potential confounders. Substituting moderate-to-vigorous physical activity (MVPA) for other components was strongly associated with favorable differences in CVH score (p < 0.05), whether in one-for-one reallocations or one-for-remaining reallocations. Allocating time away from MVPA consistently resulted in larger negative differences in CVH score (p < 0.05). For instance, replacing 10 min of light physical activity (LPA) with MVPA was related to an increase of 0.21 in CVH score (95% confidence interval (95% CI) 0.11 to 0.31). Conversely, when the same duration of MVPA was replaced with LPA, CVH score decreased by 0.67 (95% CI -0.99 to -0.35). No such significance was discovered for all duration reallocations involving only LPA, SB, and sleep (p > 0.05). CONCLUSIONS: MVPA seems to be as a pivotal determinant for enhancing CVH among general adult population, relative to other movement behaviors. Consequently, optimization of MVPA duration is an essential element in promoting overall health and well-being.

Exploring Iberian ham triacylglycerols: Unraveling the influence of growing area, season, breed, and feeding patterns through innovative compositional data analysis.

This research examined the triacylglycerol composition of Iberian pig hams from Sevilla province, focusing on the influence of growing area, season, breed, age, montanera duration, and feeding types. Compositional data analysis (CoDA) tools and standard multivariate statistics were employed to analyse the original and CoDa-transformed data. ANOVA (ilr) and ANCOVA (log ratios) revealed significant effects of season, feeding type, and towns on triacylglycerol profiles, while montanera showed limited or no effect. Breeds and age were deemed irrelevant. Various discriminant analysis (DA) methods consistently distinguished samples from the 2004/2005 season and the cebo feeding type but struggled with other distinctions. PLS-R analysis indicated that bellota feeding was associated with triacylglycerols rich in oleic acid, while cebo was predominantly linked to those containing palmitic and stearic acids. The study challenges traditional assumptions about the effects of montanera, breeds, and age on Iberian pig hams and highlights the need for further investigation.

Evaluation of normalization methods for predicting quantitative phenotypes in metagenomic data analysis.

Genotype-to-phenotype mapping is an essential problem in the current genomic era. While qualitative case-control predictions have received significant attention, less emphasis has been placed on predicting quantitative phenotypes. This emerging field holds great promise in revealing intricate connections between microbial communities and host health. However, the presence of heterogeneity in microbiome datasets poses a substantial challenge to the accuracy of predictions and undermines the reproducibility of models. To tackle this challenge, we investigated 22 normalization methods that aimed at removing heterogeneity across multiple datasets, conducted a comprehensive review of them, and evaluated their effectiveness in predicting quantitative phenotypes in three simulation scenarios and 31 real datasets. The results indicate that none of these methods demonstrate significant superiority in predicting quantitative phenotypes or attain a noteworthy reduction in Root Mean Squared Error (RMSE) of the predictions. Given the frequent occurrence of batch effects and the satisfactory performance of batch correction methods in predicting datasets affected by these effects, we strongly recommend utilizing batch correction methods as the initial step in predicting quantitative phenotypes. In summary, the performance of normalization methods in predicting metagenomic data remains a dynamic and ongoing research area. Our study contributes to this field by undertaking a comprehensive evaluation of diverse methods and offering valuable insights into their effectiveness in predicting quantitative phenotypes.

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome.

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of next-generation sequencing data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic data sets. Using more than 6.76∙108 read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials (CIAs). Using these profiles, we found evidence of several ARGs conferring resistance for CIAs being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs. IMPORTANCE: Understanding the collateral effects happening in a resistome can reveal previously unknown links between antimicrobial resistance genes (ARGs). Through the analysis of pairwise ARG abundances in 214K metagenomic samples, we observed that the co-abundance is highly dependent on the environmental context and argue that these correlations can be used to show the risk of co-selection occurring in different settings.

Is patients' activities of daily living self-care score in Norwegian home care a proxy for workers standing at work?

BACKGROUND: Prolonged standing at work may contribute to increased risk of musculoskeletal pain in home care workers. Patients' activities of daily living (ADL) score may be a proxy for home care workers' standing time at work. The objective of the present study was to investigate the association between patients' ADL self-care score, and workers standing time. METHODS: This cross-sectional study measured time spent standing, sitting and in physical activity for seven days using thigh-worn accelerometers, among 14 home care workers. Patients' ADL self-care scores are routinely adjusted by home care nurses, and time intervals of home care visits are stored in home care services electronic patient journal. We collected ADL self-care scores and start and end time points of visits, and categorized ADL self-care scores as low (ADL ≤ 2.0), medium (ADL > 2.0 to 3.0) or high (ADL > 3.0). Physical behavior data were transformed to isometric log-ratios and a mixed-effect model was used to investigate differences in physical behavior between the three ADL self-care score categories. RESULTS: We analyzed 931 patient visits and found that high ADL self-care scores were associated with longer standing times relative to sitting and physical activity, compared to low ADL score (0.457, p = 0.001). However, no significant differences in time spent standing were found between high and medium ADL patient visits (0.259, p = 0.260), nor medium and low (0.204, p = 0.288). High ADL score patients made up 33.4% of the total care time, despite only making up 7.8% of the number of patients. CONCLUSION: Our findings suggest that caring for patients with high ADL self-care score requires workers to stand for longer durations and that this group of patients constitute a significant proportion of home care workers' total work time. The findings of this study can inform interventions to improve musculoskeletal health among home care workers by appropriate planning of patient visits.

Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index.

This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010-2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH4+-N, Cl-, and NO3--N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.

Dietary Amino Acid Composition and Glycemic Biomarkers in Japanese Adolescents.

Protein intake reportedly increases the risk of diabetes; however, the results have been inconsistent. Diabetes in adulthood may be attributed to early life dietary amino acid composition. This study aimed to investigate the association between amino acid composition and glycemic biomarkers in adolescents. Dietary intake was assessed using a food frequency questionnaire, and fasting glucose and insulin levels were measured in 1238 eighth graders. The homeostatic model assessment (HOMA) indices (insulin resistance and ß-cell function) were calculated. Anthropometrics were measured and other covariates were obtained from a questionnaire. Amino acid composition was isometric log transformed according to the compositional data analysis, which was used as explanatory variables in multivariate linear regression models for glucose, insulin, and HOMA indices. Only the association between glucose and leucine was significant. In replacement of other amino acids with leucine, an increase of 0.1% of total amino acids correlated with a lower glucose level (-1.02 mg/dL). One-to-one substitution of leucine for isoleucine or methionine decreased glucose (-2.98 and -2.28 mg/dL, respectively). Associations with other biomarkers were not observed. In conclusion, compositional data analysis of amino acids revealed an association only with glucose in adolescents; however, the results of this study should be verified in other populations.

A nose for trouble: ecotoxicological implications for climate change and disease in Saiga antelope (S. t. tatarica).

In recent decades, Saiga antelope (Saiga t. tatarica) mass die-offs have become more common. The mass die-off of 2015 in central Kazakhstan, recorded 140,000 individual deaths across multiple herds. Previously, research has shown atmospheric humidity, the bacterium Pasteurella multocida serotype B, and resultant haemorrhagic septicaemia, were the primary cause. However, other synergistic factors may have impacted this process. Here we use a multivariate compositional data analysis (CoDA) approach to assess what other factors may have been involved. We show a pollutant linkage mechanism where relative humidity and dewpoint temperature combine with environmental pollutants, potentially toxic elements (e.g., Hg, As), complex carbon compounds (e.g., Acetone, Toluene), and inorganic compounds (e.g., CHx, SO2) which affected the Saiga during the calving season (start and peak) and at the onset of the mass die-off. We suggest a mechanism for this process. Upon arrival at their carving grounds, the Saiga experienced a sudden precipitation event, a spike in temperatures, and resultant high humidity occurs. The infectious bacterium P. multocida serotype B then spreads. Further, environmental pollutants contained within steppe soils are released to the air, forming localised smog events, these synergistically combine, and mass die-off occurs.

Time use and dimensions of healthy sleep: A cross-sectional study of Australian children and adults.

BACKGROUND: Sleep is increasingly recognized as a multidimensional construct that occurs within the 24-hour day. Despite advances in our understanding, studies continue to consider the relationship between sleep, sedentary time and physical activity separately, and not as part of the 24-hour day. AIMS: To determine the association between the 24-hour activity composition and dimensions of healthy sleep. METHODS: This study examined data on 1168 children (mean age 12years; 49% female) and 1360 adults (mean age 44years; 87% female) collected as part of the Child Health CheckPoint study. Participants were asked to wear a GENEActiv monitor (Activinsights, Cambs, UK) on their nondominant wrist for eight consecutive days to measure 24-hour time-use. Compositional data analysis was used to examine the association between time use (actigraphy-derived sleep duration, sedentary time, light physical activity and moderate-vigorous physical activity) and dimensions of healthy sleep. Healthy sleep was conceptualized in terms of continuity/efficiency, timing, alertness/sleepiness, satisfaction/quality, and regularity. Time allocations were also examined. RESULTS: The 24-hour activity composition was significantly associated with all objectively measured and self-report dimensions of healthy sleep in both children and adults. Allocating more time to sleep was associated with earlier sleep onsets, later sleep offsets, less efficient and more consistent sleep patterns for both children and adults. CONCLUSION: This study highlights the integral relationship between daily activities and dimensions of sleep. Considering sleep within the 24-hour day activity composition framework may help inform lifestyle decisions to improve sleep health.

Advancing forensic research: An examination of compositional data analysis with an application on petrol fraud detection.

In recent years, numerous studies have examined the chemical compounds of petrol and petrol data for forensic research. Standard quantitative methods often assume that the variables or compounds do not have compositional constraints or are not part of a constrained whole, operating within an Euclidean vector space. However, chemical compounds are typically part of a whole, and the appropriate vector space for their analysis is the simplex. Biased and arbitrary results result when statistical analysis are applied on such data without proper pre-processing of such data. Compositional analysis of data has not yet been considered in forensic science. Therefore, we compare classical statistical analysis as applied in forensic research and the new proposed paradigm of compositional data analysis (CoDa). It is demonstrated how such analysis improves the analysis in petrol and forensic science. Our study shows how principal component analysis (PCA) and classification results are affected by the preprocessing steps performed on the raw data. Our results indicate that results from a log ratio analysis provides a better separation between subgroups of the data and leads to an easier interpretation of the results. In addition, with a compositional analysis a higher classification accuracy is obtained. Even a non-linear classification method - in our case a random forest - was shown to perform poorly when applied without using compositional methods. Moreover, normalization of samples due to laboratory/unit-of-measurement effects is no longer necessary, since the composition of an observation is in compositional thinking equivalent to a multiple of it, because the used (log) ratios on raw and log ratio transformed data are equal. Petrol data from different petrol stations in Brazil are used for the demonstration. This data is highly susceptible to counterfeit petrol. Forensic analysis of its chemical elements requires non-biased statistical analysis designed for compositional data to detect fraud. Based on these results, we recommend the use of compositional data methods for gasoline and petrol chemical element analysis and gasoline product characterization, authentication and fraud detection in forensic sciences.

Device-measured physical activity and cardiometabolic health: the Prospective Physical Activity, Sitting, and Sleep (ProPASS) consortium.

BACKGROUND AND AIMS: Physical inactivity, sedentary behaviour (SB), and inadequate sleep are key behavioural risk factors of cardiometabolic diseases. Each behaviour is mainly considered in isolation, despite clear behavioural and biological interdependencies. The aim of this study was to investigate associations of five-part movement compositions with adiposity and cardiometabolic biomarkers. METHODS: Cross-sectional data from six studies (n = 15 253 participants; five countries) from the Prospective Physical Activity, Sitting and Sleep consortium were analysed. Device-measured time spent in sleep, SB, standing, light-intensity physical activity (LIPA), and moderate-vigorous physical activity (MVPA) made up the composition. Outcomes included body mass index (BMI), waist circumference, HDL cholesterol, total:HDL cholesterol ratio, triglycerides, and glycated haemoglobin (HbA1c). Compositional linear regression examined associations between compositions and outcomes, including modelling time reallocation between behaviours. RESULTS: The average daily composition of the sample (age: 53.7 ± 9.7 years; 54.7% female) was 7.7 h sleeping, 10.4 h sedentary, 3.1 h standing, 1.5 h LIPA, and 1.3 h MVPA. A greater MVPA proportion and smaller SB proportion were associated with better outcomes. Reallocating time from SB, standing, LIPA, or sleep into MVPA resulted in better scores across all outcomes. For example, replacing 30 min of SB, sleep, standing, or LIPA with MVPA was associated with -0.63 (95% confidence interval -0.48, -0.79), -0.43 (-0.25, -0.59), -0.40 (-0.25, -0.56), and -0.15 (0.05, -0.34) kg/m2 lower BMI, respectively. Greater relative standing time was beneficial, whereas sleep had a detrimental association when replacing LIPA/MVPA and positive association when replacing SB. The minimal displacement of any behaviour into MVPA for improved cardiometabolic health ranged from 3.8 (HbA1c) to 12.7 (triglycerides) min/day. CONCLUSIONS: Compositional data analyses revealed a distinct hierarchy of behaviours. Moderate-vigorous physical activity demonstrated the strongest, most time-efficient protective associations with cardiometabolic outcomes. Theoretical benefits from reallocating SB into sleep, standing, or LIPA required substantial changes in daily activity.

Analysis of Psychological Well-Being from a Compositional Data Analysis Perspective: A New Approach.

Well-being is a widely studied construct in psychology. In 1989, Carol Ryff proposed the "Scale of Psychological Well-Being (SPWB)", which has been validated in multiple languages. The instrument assesses six dimensions of psychological well-being: Self-acceptance, Positive Relationships with Others, Autonomy, Environmental Mastery, Purpose in Life, and Personal Growth. In this article, we propose to enrich the traditional approach of directly interpreting the raw scores in each dimension by incorporating Compositional Data Analysis. This new approach aims to identify "what proportion" of each dimension constitutes well-being, which will allow us to analyze the interactions between the different dimensions of well-being and balance among them. To achieve this, we introduce two position ratios (PR1 and PR2) and a general adjustment indicator called the General Indicator of Subjective Psychological Well-Being (GISPW), which characterizes individuals in a compositional manner, providing a fresh perspective in the interpretation of psychological test results, specifically those related to PWB. The proposal is illustrated with three cases taken from a study involving 628 university students who completed the psychological well-being scale questionnaire. The results show that the GISPW, PR1, and PR2 obtained offer relevant information about the overall balance of each case in the different dimensions.

Do Sit-Stand Tables Affect Physical Behavior and Body Composition Similarly in Normal-Weight and Overweight Office Workers? A Pilot Study.

OCCUPATIONAL APPLICATIONSSedentary behavior is a significant health concern among office workers. We completed the same 6-month sit-stand table intervention at work for groups of normal-weight and overweight workers, and compared it to not having sit-stand tables. The intervention caused the intended decrease in sitting time in both groups and a corresponding increase in standing. We did not find compensation effects on physical behavior outside of work. Furthermore, the intervention did not change the composition of fat, lean, and bone mass in either group. Thus, strategies including initiatives to increase physical activity are likely needed to have effects on body composition; and an intervention needs to be sustained for longer than six months for any changes in body composition to be observed.

Background: A sit-stand table (SST) at work may help office workers shorten and break up sitting by periods of standing. However, it is not clear whether SST use influences sitting at and outside work to similar extents among normal-weight and overweight office workers, and whether it can change body composition. Purpose: To investigate if introducing SSTs affects physical behavior and body composition similarly in normal-weight and overweight office workers. Methods: In this pilot intervention study, physical behavior and body composition were evaluated and compared between normal-weight (n = 8) and overweight (n = 14) office workers, both before and after using an SST for 6 months. Time spent sitting, standing, and moving was recorded using a triaxial thigh accelerometer during work and leisure. Dual-energy X-ray absorptiometry was used to determine fat, lean, and bone mass. Physical behavior and body composition data were both processed using compositional data analysis. Results: The intervention was similarly effective for normal weight and overweight workers in changing physical behavior during work, mainly by reducing sitting time and increasing standing time, while no effects were seen during leisure. We found no effect of the intervention on body composition. Conclusion: A sit-stand table intervention for 6 months changed physical behaviors at work to a similar extent among normal weight and overweight office workers. No compensatory behaviors were observed during leisure, and the intervention had no significant effects on body composition.

Multivariate Independent Component Analysis Identifies Patients in Newborn Screening Equally to Adjusted Reference Ranges.

Newborn screening (NBS) of inborn errors of metabolism (IEMs) is based on the reference ranges established on a healthy newborn population using quantile statistics of molar concentrations of biomarkers and their ratios. The aim of this paper is to investigate whether multivariate independent component analysis (ICA) is a useful tool for the analysis of NBS data, and also to address the structure of the calculated ICA scores. NBS data were obtained from a routine NBS program performed between 2013 and 2022. ICA was tested on 10,213/150 free-diseased controls and 77/20 patients (9/3 different IEMs) in the discovery/validation phases, respectively. The same model computed during the discovery phase was used in the validation phase to confirm its validity. The plots of ICA scores were constructed, and the results were evaluated based on 5sd levels. Patient samples from 7/3 different diseases were clearly identified as 5sd-outlying from control groups in both phases of the study. Two IEMs containing only one patient each were separated at the 3sd level in the discovery phase. Moreover, in one latent variable, the effect of neonatal birth weight was evident. The results strongly suggest that ICA, together with an interpretation derived from values of the "average member of the score structure", is generally applicable and has the potential to be included in the decision process in the NBS program.

The association between reallocations of time and health using compositional data analysis: a systematic scoping review with an interactive data exploration interface.

BACKGROUND: How time is allocated influences health. However, any increase in time allocated to one behaviour must be offset by a decrease in others. Recently, studies have used compositional data analysis (CoDA) to estimate the associations with health when reallocating time between different behaviours. The aim of this scoping review was to provide an overview of studies that have used CoDA to model how reallocating time between different time-use components is associated with health. METHODS: A systematic search of four electronic databases (MEDLINE, Embase, Scopus, SPORTDiscus) was conducted in October 2022. Studies were eligible if they used CoDA to examine the associations of time reallocations and health. Reallocations were considered between movement behaviours (sedentary behaviour (SB), light physical activity (LPA), moderate-to-vigorous physical activity (MVPA)) or various activities of daily living (screen time, work, household chores etc.). The review considered all populations, including clinical populations, as well as all health-related outcomes. RESULTS: One hundred and three studies were included. Adiposity was the most commonly studied health outcome (n = 41). Most studies (n = 75) reported reallocations amongst daily sleep, SB, LPA and MVPA. While other studies reported reallocations amongst sub-compositions of these (work MVPA vs. leisure MVPA), activity types determined by recall (screen time, household chores, passive transport etc.) or bouted behaviours (short vs. long bouts of SB). In general, when considering cross-sectional results, reallocating time to MVPA from any behaviour(s) was favourably associated with health and reallocating time away from MVPA to any behaviour(s) was unfavourably associated with health. Some beneficial associations were seen when reallocating time from SB to both LPA and sleep; however, the strength of the association was much lower than for any reallocations involving MVPA. However, there were many null findings. Notably, most of the longitudinal studies found no associations between reallocations of time and health. Some evidence also suggested the context of behaviours was important, with reallocations of leisure time toward MVPA having a stronger favourable association for health than reallocating work time towards MVPA. CONCLUSIONS: Evidence suggests that reallocating time towards MVPA from any behaviour(s) has the strongest favourable association with health, and reallocating time away from MVPA toward any behaviour(s) has the strongest unfavourable association with health. Future studies should use longitudinal and experimental study designs, and for a wider range of outcomes.

Hybrid office work in women and men: do directly measured physical behaviors differ between days working from home and days working at the office?

OBJECTIVE: We investigated and compared temporal sitting patterns among male and female hybrid office workers when working at the office (WAO), working from home (WFH), and for non-working days (NWD). METHODS: We analyzed data collected in 2020 among 165 hybrid office workers, carrying thigh-worn accelerometers for 938 days in total. Day type (WAO, WFH, or NWD) and time in bed were identified using diaries. Time awake was exhaustively classified as non-sitting time and time sitting in short, moderate, and long bouts. Effects of day type and gender on the 24-h compositions of physical behaviors were analyzed using multilevel linear mixed models. RESULTS: During workdays (both WAO and WFH), workers spent less time in bed and more time sitting, particularly in moderate and long bouts, than during NWD. Time in bed was longer when working from home than when working at the office, and more of the awake time was spent sitting. Differences between WAO and WFH in ratios between short, moderate, and long bouts of sitting were small and inconsistent. Men spent more time sitting than women, and more time in moderate and long sitting bouts relative to short bouts. CONCLUSIONS: When working from home, hybrid office workers sat more during their hours awake compared to when working at the office. Sitting time was larger during working days than during non-working days and was higher in men than in women. These results may contribute to support organizational policies for hybrid work.

Longitudinal reallocations of time between 24-h movement behaviours and their associations with inflammation in children and adolescents: the UP&DOWN study.

BACKGROUND: While there is evidence that physical activity, sedentary behaviour (SB) and sleep may all be associated with modified levels of inflammatory markers in adolescents and children, associations with one movement behaviour have not always been adjusted for other movement behaviours, and few studies have considered all movement behaviours in the 24-hour day as an exposure. PURPOSE: The aim of the study was to explore how longitudinal reallocations of time between moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), SB and sleep are associated with changes in inflammatory markers in children and adolescents. METHODS: A total of 296 children/adolescents participated in a prospective cohort study with a 3-year follow-up. MVPA, LPA and SB were assessed by accelerometers. Sleep duration was assessed using the Health Behavior in School-aged Children questionnaire. Longitudinal compositional regression models were used to explore how reallocations of time between movement behaviours are associated with changes in inflammatory markers. RESULTS: Reallocations of time from SB to sleep were associated with increases in C3 levels (difference for 60 min/d reallocation [d60] = 5.29 mg/dl; 95% confidence interval [CI] = 0.28, 10.29) and TNF-α (d60 = 1.81 mg/dl; 95% CI = 0.79, 15.41) levels. Reallocations from LPA to sleep were also associated with increases in C3 levels (d60 = 8.10 mg/dl; 95% CI = 0.79, 15.41). Reallocations from LPA to any of the remaining time-use components were associated with increases in C4 levels (d60 ranging from 2.54 to 3.63 mg/dl; p < 0.05), while any reallocation of time away from MVPA was associated with unfavourable changes in leptin (d60 ranging from 3088.44 to 3448.07 pg/ml; p < 0.05). CONCLUSIONS: Reallocations of time between 24-h movement behaviours are prospectively associated with some inflammatory markers. Reallocating time away from LPA appears to be most consistently unfavourably associated with inflammatory markers. Given that higher levels of inflammation during childhood and adolescence are associated with an increased risk of chronic diseases in adulthood, children and adolescents should be encouraged to maintain or increase the level of LPA to preserve a healthy immune system.

Cross-sectional and longitudinal associations of domain-specific physical activity composition with health-related quality of life in childhood and adolescence in Australia.

BACKGROUND: Health benefits have been linked with physical activity (PA), as well as some domains of PA among youth (e.g. organized PA and active transport). However, less is known about whether some PA domains are more beneficial than others. There is also a lack of evidence about whether health outcomes are related to the composition of PA (i.e. the share of PA spent in different domains). This study aimed to identify: (1) how the absolute durations of organized PA, non-organized PA, active transport and active chores/work at 10-11y are individually associated with physical, psychosocial and total health-related quality of life (HRQOL) at 10-11y and 12-13y; and (2) how the domain-specific composition of PA at 10-11y is associated with HRQOL at 10-11y and 12-13y. METHODS: Data from the Longitudinal Study of Australian Children were used in cross-sectional (n ≥ 2730) and longitudinal analyses (n ≥ 2376). Measurement included the Pediatric Quality of Life Inventory (PedsQL™) for HRQOL domains and one-day time-use diaries (TUDs) for PA domains. Robust linear regression models were used, controlling for age, sex, pubertal status, socioeconomic position, body mass index and TUD context (season and school attendance). Compositional models additionally adjusted for total PA duration and longitudinal models controlled for baseline PedsQL™ scores. RESULTS: Non-compositional models indicated that the duration of organized PA, and to a lesser extent non-organized PA, were positively but weakly associated with some HRQOL outcomes at 10-11y. These trends were not reflected in longitudinal models, although a 30-min increase in non-organized PA per day did predict marginally better psychosocial HRQOL at 12-13y (+ 0.17%; 95%CI = + 0.03%, + 0.32%). Compositional models revealed that a 30-min increase in organized PA relative to other domains was positively but weakly associated with physical (+ 0.32%; 95%CI = + 0.01%, + 0.63%), psychosocial (+ 0.41%; 95%CI = + 0.11%, + 0.72%) and total HRQOL (+ 0.39%; 95%CI = + 0.12%, + 0.66%) at 10-11y. However, the overall PA composition at 10-11y was not related to HRQOL at 12-13y. CONCLUSIONS: Non-compositional and compositional models generally concurred on the direction of cross-sectional and longitudinal relationships (and lack thereof) between PA domains and HRQOL outcomes. The strongest associations were cross-sectional between organized PA and HRQOL at 10-11y. However, all associations between PA domains and HRQOL outcomes were weak and may not be clinically meaningful.

Evaluation of DNA extraction methods and direct PCR in metabarcoding of mock and marine bacterial communities.

Recent advances in new molecular biology methods and next-generation sequencing (NGS) technologies have revolutionized metabarcoding studies investigating complex microbial communities from various environments. The inevitable first step in sample preparation is DNA extraction which introduces its own set of biases and considerations. In this study, we assessed the influence of five DNA extraction methods [B1: phenol/chloroform/isoamyl extraction, B2 and B3: isopropanol and ethanol precipitations, respectively-both modifications of B1, K1: DNeasy PowerWater Kit (QIAGEN), K2: modified DNeasy PowerWater Kit (QIAGEN) and direct PCR approach (P) that completely circumvents this step on community composition and DNA yield of mock and marine sample communities from the Adriatic Sea]. B1-B3 methods generally produced higher DNA yields and more similar microbial communities, but with higher interindividual variability. Each method demonstrated significant differences in a specific community structure, where rare taxa seem to play a crucial role. There was not one superior method closest to the theoretically expected mock community composition, they all demonstrated skewed ratios, but in a similar way which might be attributed to other factors, such as primer bias or 16S rRNA gene count for specific taxa. Direct PCR represents an interesting approach when high throughput in sample processing is required. We emphasize the importance of making a cautious decision about the choice of the extraction method or direct PCR approach, but even more importantly its consistent application throughout the study.

Oral intake of Lactiplantibacillus pentosus LPG1 Produces a Beneficial Regulation of Gut Microbiota in Healthy Persons: A Randomised, Placebo-Controlled, Single-Blind Trial.

The search for vegetable-origin probiotic microorganisms is a recent area of interest. This study conducted a phase I clinical trial to assess the effects of oral administration of Lactiplantibacillus pentosus LPG1, a natural strain with probiotic potential isolated from table olive fermentations, on the gut microbiota. The trial was a randomised, placebo-controlled, single-blind study involving 39 healthy volunteers. Group A (n = 20) ingested one capsule/day of L. pentosus LPG1 containing 1 × 1010 UFC/capsule, while Group B (n = 19) received one capsule/day containing only dextrose (placebo). The capsules were taken during breakfast for 30 consecutive days. Human stool samples were collected from all volunteers at the beginning (baseline) and at the end of the study (post-intervention) and were subjected to 16S rRNA metataxonomic analysis using Illumina MiSeq. Sequencing data at the genus level were statistically analysed using traditional methods and compositional data analysis (CoDA). After treatment, the alpha diversity in Group B (placebo) decreased according to an increase in the Berger and Parker dominance index (p-value < 0.05); moreover, dominance D increased and Simpson 1-D index decreased (p-value < 0.10). The Lactobacillus genus in the faeces was included in the CoDA signature balances (selbal and coda4microbiome) and played a notable role in distinguishing samples from baseline and post-intervention in Group A (LPG1). Additionally, ingesting L. pentosus LPG1 modified the gut microbiota post-intervention, increasing the presence of Parabacteroides and Agathobacter, but reducing Prevotella. These findings suggest that L. pentosus LPG1 is a potentially beneficial gut microbiota modulator in healthy persons.