Quantifying resilience of humans and other animals

Intraindividual variability in post-stroke cognition and its relationship with activities of daily living and social functioning: an ecological momentary assessment approach

INTRODUCTION

Ecological momentary assessment (EMA) is a methodological approach to studying intraindividual variation over time. This study aimed to use EMA to determine the variability of cognition in individuals with chronic stroke, identify the latent classes of cognitive variability, and examine any differences in daily activities, social functioning, and neuropsychological performance between these latent classes.

METHODS

Participants (N = 202) with mild-to-moderate stroke and over 3-month post-stroke completed a study protocol, including smartphone-based EMA and two lab visits. Participants responded to five EMA surveys daily for 14 days to assess cognition. They completed patient-reported measures and neuropsychological assessments during lab visits. Using latent class analysis, we derived four indicators to quantify cognitive variability and identified latent classes among participants. We used ANOVA and Chi-square to test differences between these latent classes in daily activities, social functioning, and neuropsychological performance.

RESULTS

The latent class analysis converged on a three-class model. The moderate and high variability classes demonstrated significantly greater problems in daily activities and social functioning than the low class. They had significantly higher proportions of participants with problems in daily activities and social functioning than the low class. Neuropsychological performance was not statistically different between the three classes, although a trend approaching statistically significant difference was observed in working memory and executive function domains.

DISCUSSION

EMA could capture intraindividual cognitive variability in stroke survivors. It offers a new approach to understanding the impact and mechanism of post-stroke cognitive problems in daily life and identifying individuals benefiting from self-regulation interventions.

Monitoring resilience in bursts

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.

DAMP-ing IBD: Extinguish the Fire and Prevent Smoldering

In inflammatory bowel diseases (IBD), the most promising therapies targeting cytokines or immune cell trafficking demonstrate around 40% efficacy. As IBD is a multifactorial inflammation of the intestinal tract, a single-target approach is unlikely to solve this problem, necessitating an alternative strategy that addresses its variability. One approach often overlooked by the pharmaceutically driven therapeutic options is to address the impact of environmental factors. This is somewhat surprising considering that IBD is increasingly viewed as a condition heavily influenced by such factors, including diet, stress, and environmental pollution-often referred to as the "Western lifestyle". In IBD, intestinal responses result from a complex interplay among the genetic background of the patient, molecules, cells, and the local inflammatory microenvironment where danger- and microbe-associated molecular patterns (D/MAMPs) provide an adjuvant-rich environment. Through activating DAMP receptors, this array of pro-inflammatory factors can stimulate, for example, the NLRP3 inflammasome-a major amplifier of the inflammatory response in IBD, and various immune cells via non-specific bystander activation of myeloid cells (e.g., macrophages) and lymphocytes (e.g., tissue-resident memory T cells). Current single-target biological treatment approaches can dampen the immune response, but without reducing exposure to environmental factors of IBD, e.g., by changing diet (reducing ultra-processed foods), the adjuvant-rich landscape is never resolved and continues to drive intestinal mucosal dysregulation. Thus, such treatment approaches are not enough to put out the inflammatory fire. The resultant smoldering, low-grade inflammation diminishes physiological resilience of the intestinal (micro)environment, perpetuating the state of chronic disease. Therefore, our hypothesis posits that successful interventions for IBD must address the complexity of the disease by simultaneously targeting all modifiable aspects: innate immunity cytokines and microbiota, adaptive immunity cells and cytokines, and factors that relate to the (micro)environment. Thus the disease can be comprehensively treated across the nano-, meso-, and microscales, rather than with a focus on single targets. A broader perspective on IBD treatment that also includes options to adapt the DAMPing (micro)environment is warranted.

Breeding for resilience in finishing pigs can decrease tail biting, lameness and mortality

BACKGROUND

Previous research showed that deviations in longitudinal data are heritable and can be used as a proxy for pigs' general resilience. However, only a few studies investigated the relationship between these resilience traits and other traits related to resilience and welfare. Therefore, this study investigated the relationship between resilience traits derived from deviations in longitudinal data and traits related to animal resilience, health and welfare, such as tail and ear biting wounds, lameness and mortality.

RESULTS

In our experiment, 1919 finishing pigs with known pedigree (133 Piétrain sires and 266 crossbred dams) were weighed every 2 weeks and scored for physical abnormalities, such as lameness and ear and tail biting wounds (17,066 records). Resilience was assessed via deviations in body weight, deviations in weighing order and deviations in observed activity during weighing. The association between these resilience traits and physical abnormality traits was investigated and genetic parameters were estimated. Deviations in body weight had moderate heritability estimates (h2 = 25.2 to 36.3%), whereas deviations in weighing order (h2 = 4.2%) and deviations in activity during weighing (h2 = 12.0%) had low heritability estimates. Moreover, deviations in body weight were positively associated and genetically correlated with tail biting wounds (rg = 0.22 to 0.30), lameness (rg = 0.15 to 0.31) and mortality (rg = 0.19 to 0.33). These results indicate that events of tail biting, lameness and mortality are associated with deviations in pigs' body weight evolution. This relationship was not found for deviations in weighing order and activity during weighing. Furthermore, individual body weight deviations were positively correlated with uniformity at the pen level, providing evidence that breeding for these resilience traits might increase both pigs' resilience and within-family uniformity.

CONCLUSIONS

In summary, our findings show that breeding for resilience traits based on deviations in longitudinal weight data can decrease pigs' tail biting wounds, lameness and mortality while improving uniformity at the pen level. These findings are valuable for pig breeders, as they offer evidence that these resilience traits are an indication of animals' general health, welfare and resilience. Moreover, these results will stimulate the quantification of resilience via longitudinal body weights in other species.

Genetic parameters for novel climatic resilience indicators derived from automatically-recorded vaginal temperature in lactating sows under heat stress conditions

BACKGROUND

Longitudinal records of automatically-recorded vaginal temperature (TV) could be a key source of data for deriving novel indicators of climatic resilience (CR) for breeding more resilient pigs, especially during lactation when sows are at an increased risk of suffering from heat stress (HS). Therefore, we derived 15 CR indicators based on the variability in TV in lactating sows and estimated their genetic parameters. We also investigated their genetic relationship with sows' key reproductive traits.

RESULTS

The heritability estimates of the CR traits ranged from 0.000 ± 0.000 for slope for decreased rate of TV (SlopeDe) to 0.291 ± 0.047 for sum of TV values below the HS threshold (HSUB). Moderate to high genetic correlations (from 0.508 ± 0.056 to 0.998 ± 0.137) and Spearman rank correlations (from 0.431 to 1.000) between genomic estimated breeding values (GEBV) were observed for five CR indicators, i.e. HS duration (HSD), the normalized median multiplied by normalized variance (Nor_medvar), the highest TV value of each measurement day for each individual (MaxTv), and the sum of the TV values above (HSUA) and below (HSUB) the HS threshold. These five CR indicators were lowly to moderately genetically correlated with shoulder skin surface temperature (from 0.139 ± 0.008 to 0.478 ± 0.048) and respiration rate (from 0.079 ± 0.011 to 0.502 ± 0.098). The genetic correlations between these five selected CR indicators and sow reproductive performance traits ranged from - 0.733 to - 0.175 for total number of piglets born alive, from - 0.733 to - 0.175 for total number of piglets born, and from - 0.434 to - 0.169 for number of pigs weaned. The individuals with the highest GEBV (most climate-sensitive) had higher mean skin surface temperature, respiration rate (RR), panting score (PS), and hair density, but had lower mean body condition scores compared to those with the lowest GEBV (most climate-resilient).

CONCLUSIONS

Most of the CR indicators evaluated are heritable with substantial additive genetic variance. Five of them, i.e. HSD, MaxTv, HSUA, HSUB, and Nor_medvar share similar underlying genetic mechanisms. In addition, individuals with higher CR indicators are more likely to exhibit better HS-related physiological responses, higher body condition scores, and improved reproductive performance under hot conditions. These findings highlight the potential benefits of genetically selecting more heat-tolerant individuals based on CR indicators.

Review: Environmental enrichment builds functional capacity and improves resilience as an aspect of positive welfare in production animals

The success of the animal in coping with challenges, and in harnessing opportunities to thrive, is central to its welfare. Functional capacity describes the capacity of molecules, cells, organs, body systems, the whole animal, and its community to buffer against the impacts of environmental perturbations. This buffering capacity determines the ability of the animal to maintain or regain functions in the face of environmental perturbations, which is recognised as resilience. The accuracy of physiological regulation and the maintenance of homeostatic balance underwrite the dynamic stability of outcomes such as biorhythms, feed intake, growth, milk yield, and egg production justifying their assessment as indicators of resilience. This narrative review examines the influence of environmental enrichments, especially during developmental stages in young animals, in building functional capacity and in its subsequent expression as resilience. Experience of enriched environments can build skills and competencies across multiple functional domains including but not limited to behaviour, immunity, and metabolism thereby increasing functional capacity and facilitating resilience within the context of challenges such as husbandry practices, social change, and infection. A quantitative method for measuring the distributed property of functional capacity may improve its assessment. Methods for analysing embedded energy (emergy) in ecosystems may have utility for this goal. We suggest functional capacity provides the common thread that links environmental enrichments with an ability to express resilience and may provide a novel and useful framework for measuring and reporting resilience. We conclude that the development of functional capacity and its subsequent expression as resilience is an aspect of positive animal welfare. The emergence of resilience from system dynamics highlights a need to shift from the study of physical and mental states to the study of physical and mental dynamics to describe the positive dimension of animal welfare.

Potential of guar gum as a leaky gut model in broilers: Digestibility, performance, and microbiota responses

Diet is a major modulator of animal resilience and its three pillars: host's immune response, gut microbiota, and intestinal barrier. In the present study, we endeavour to delineate a challenging condition aimed to degrade these pillars and elucidate its impact on broiler performance and nutrient digestibility. To attain this objective, we opted to use guar gum (GG) as a source of galactomannan. A series of three in vivo experiments were conducted employing conventional or semi-purified diets, supplemented with or without GG during the grower phase (14-28 d). Our findings demonstrate a substantial decline in animal performance metrics such as body weight (reduced by 29%, P < 0.001), feed intake (decreased by 12%, P < 0.001), and feed conversion ratio (up to 58% increase, P < 0.001) in the presence of GG at 2%. The supplementation of a semi-purified diet with incremental doses of GG resulted in a linear reduction (P < 0.001) in the apparent total tract digestibility of dry matter and apparent metabolisable energy. Additionally, a marked reduction in ileal endogenous losses, as well as apparent and standardised digestibility of all amino acids with varying proportions (P < 0.05), was observed. These alterations were accompanied by disrupted gut integrity assessed by fluorescein isothiocyanate-dextran (FITC-d) (P < 0.001) as well as an inflammatory status characterised by elevated levels of acute-phase proteins, namely orosomucoid and serum amyloid A in the sera (P = 0.03), and increased mRNA expression levels of IL-1, IL-6, IL-8, Inos, and K203 genes in the ileum, along with a decrease in IgA levels in the gut lumen (P < 0.05). Microbial ecology and activity were characterised by reduced diversity and richness (Shannon index, P = 0.005) in the presence of GG. Consequently, our results revealed diminished levels of short-chain fatty acids (P = 0.01) and their producer genera, such as Clostridium_XIVa and Blautia, in the gut caeca, coupled with excessive accumulation of lactate (17-fold increase, P < 0.01) in the presence of GG at 2%. In addition to providing a more comprehensive characterisation of the GG supplementation as a leaky gut model, our results substantiate a thorough understanding of the intricate adjustments and interplay between the intestinal barrier, immune response, and microbiota. Furthermore, they underscore the significance of feed components in modulating these dynamics.

A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review

Importance

Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry.

Observations

Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions.

Conclusions and Relevance

Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

Transformation starts at the periphery of networks where pushback is less

Complex systems ranging from societies to ecological communities and power grids may be viewed as networks of connected elements. Such systems can go through critical transitions driven by an avalanche of contagious change. Here we ask, where in a complex network such a systemic shift is most likely to start. Intuitively, a central node seems the most likely source of such change. Indeed, topological studies suggest that central nodes can be the Achilles heel for attacks. We argue that the opposite is true for the class of networks in which all nodes tend to follow the state of their neighbors, a category we call two-way pull networks. In this case, a well-connected central node is an unlikely starting point of a systemic shift due to the buffering effect of connected neighbors. As a result, change is most likely to cascade through the network if it spreads first among relatively poorly connected nodes in the periphery. The probability of such initial spread is highest when the perturbation starts from intermediately connected nodes at the periphery, or more specifically, nodes with intermediate degree and relatively low closeness centrality. Our finding is consistent with empirical observations on social innovation, and may be relevant to topics as different as the sources of originality of art, collapse of financial and ecological networks and the onset of psychiatric disorders.

Is resilience a unifying concept for the biological sciences?

There is increasing interest in applying resilience concepts at different scales of biological organization to address major interdisciplinary challenges from cancer to climate change. It is unclear, however, whether resilience can be a unifying concept consistently applied across the breadth of the biological sciences, or whether there is limited capacity for integration. In this review, we draw on literature from molecular biology to community ecology to ascertain commonalities and shortcomings in how resilience is measured and interpreted. Resilience is studied at all levels of biological organization, although the term is often not used. There is a suite of resilience mechanisms conserved across biological scales, and there are tradeoffs that affect resilience. Resilience is conceptually useful to help diverse researchers think about how biological systems respond to perturbations, but we need a richer lexicon to describe the diversity of perturbations, and we lack widely applicable metrics of resilience.

Early warning signals observed in motor activity preceding mood state change in bipolar disorder

INTRODUCTION

Alterations in motor activity are well-established symptoms of bipolar disorder, and time series of motor activity can be considered complex dynamical systems. In such systems, early warning signals (EWS) occur in a critical transition period preceding a sudden shift (tipping point) in the system. EWS are statistical observations occurring due to a system's declining ability to maintain homeostasis when approaching a tipping point. The aim was to identify critical transition periods preceding bipolar mood state changes.

METHODS

Participants with a validated bipolar diagnosis were included to a one-year follow-up study, with repeated assessments of the participants' mood. Motor activity was recorded continuously by a wrist-worn actigraph. Participants assessed to have relapsed during follow-up were analyzed. Recognized EWS features were extracted from the motor activity data and analyzed by an unsupervised change point detection algorithm, capable of processing multi-dimensional data and developed to identify when the statistical property of a time series changes.

RESULTS

Of 49 participants, four depressive and four hypomanic/manic relapses among six individuals occurred, recording actigraphy for 23.8 ± 0.2 h/day, for 39.8 ± 4.6 days. The algorithm detected change points in the time series and identified critical transition periods spanning 13.5 ± 7.2 days. For depressions 11.4 ± 1.8, and hypomania/mania 15.6 ± 10.2 days.

CONCLUSION

The change point detection algorithm seems capable of recognizing impending mood episodes in continuous flowing data streams. Hence, we present an innovative method for forecasting approaching relapses to improve the clinical management of bipolar disorder.

Assessing dynamical resilience indicators in older adults: a prospective feasibility cohort study

PURPOSE

Measuring dynamical resilience indicators based on time series data may improve the prediction of health deterioration in older adults after hospital discharge. We examined the feasibility of an intensive prospective cohort study examining dynamical resilience indicators based on time series data of symptoms and physical activity in acutely ill older adults who visited the Emergency Department (ED).

METHODS

This is a prospective cohort study with time series data from symptom questionnaires and activity trackers. Thirty older adults (aged 75.9 ± 5.5 years, 37% female) who were discharged from the ED of a tertiary hospital in the Netherlands were included in the study. We monitored self-reported symptoms using a daily online questionnaire, and physical activity using an activity tracker for 30 days. Descriptive data on participant eligibility and adherence to and acceptability of the assessments were collected.

RESULTS

Of 134 older patients visiting the ED, 109/134 (81%) were eligible for inclusion and 30/109 (28%) were included. Twenty-eight (93%) of the included participants completed follow-up. Regarding the adherence rate, 78% of participants filled in the questionnaire and 80% wore the activity tracker. Three (10%) participants completed fewer than three questionnaires. Most participants rated the measurements as acceptable and seven (23%) participants experienced an adverse outcome in the 30 days after discharge.

CONCLUSION

Such an intensive prospective cohort study examining dynamical resilience indicators in older adults was feasible. The quality of the collected data was sufficient, some adjustments to the measurement protocol are recommended. This study is an important first step to study resilience in older adults.

Genetic analysis of lactation consistency in US Holsteins using temporal variation in daily milk weights

The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience. Consistency, defined as the quality of performing as expected each day of the lactation, could be highly associated with resilience, defined as animal's ability to maintain health and performance in the presence of environmental challenges, including pathogens, heat waves, and nutritional changes. A total of 51,415,022 daily milk weights collected from 2018 to 2023 were provided for 255,191 multiparous Holstein cows milked 3 times daily in conventional parlor systems on farms in 32 states. The temporal variance (TEMPVAR) of milk yield from 5 to 305 d postpartum was computed as the log-transformed variance of daily deviations between observed and expected individual milk weights. Lower values of TEMPVAR imply smaller day-to-day deviations from expectations, indicating consistent performance, whereas larger values indicate inconsistent performance. Expected daily milk weights were computed using 3 nonparametric and parametric regression models: (1) loceally estimated scatterplot smoothing regression with a 0.75 span; (2) polynomial quantile regression using the median (0.5 quantile), and (3) polynomial quantile regression using a 0.7 quantile. The univariate statistical model included age at first calving and herd-year-season as fixed effects and cow as a random effect. Heritability estimates (standard errors) of TEMPVAR phenotypes calculated over the entire lactation ranged between 0.227 (0.011) and 0.237 (0.011), demonstrating that cows are genetically predisposed to display consistent or inconsistent performance. Estimated genetic correlations calculated using a multiple-trait model between TEMPVAR traits and between lactations were high (>0.95), indicating TEMPVAR is repeatable across lactations and robust to the model used to compute expected daily milk yield. Higher TEMPVAR phenotypes reflect more variation in performance, hence greater inconsistency, which is undesirable. Therefore, correlations between predicted transmitting abilities (PTA) for TEMPVAR and milk yield of 0.57 indicate that high-producing cows exhibit more day-to-day variation in performance. Correlations with productive life and livability were -0.38 and -0.48, respectively. Correlations between PTA for TEMPVAR and those of postpartum health traits were also negative, ranging from -0.41 to -0.08. Given that health traits are derived from disease resistance measurements, and higher health trait PTA are preferred, our results indicate that more consistent cows tend to have fewer health problems and greater longevity. Overall, our findings suggest that temporal variation in daily milk weights can be used to identify consistent animals that maintain expected performance throughout the lactation, which will enable selection for greater resilience to management and environmental perturbations.

A first characterization of the microbiota-resilience link in swine

BACKGROUND

The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes.

RESULTS

This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience.

CONCLUSION

Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations. Video Abstract.

Emotion dynamics in current and remitted depression: an ecological momentary assessment study

BACKGROUND

Individuals in a depressive episode and healthy controls exhibit robust differences on affect dynamics captured with ecological momentary assessment (EMA). However, few studies have explored affect dynamics in individuals in remission from depression, and results have been mixed.

METHODS

A community sample of 18-year-olds (N = 345) completed diagnostic interviews and EMA probing emotions and low interest/motivation 5× daily for 2 weeks. Affect home base, variability, and inertia were compared across currently depressed, remitted, and never-depressed groups.

RESULTS

Both depression groups had a higher negative affect (NA) and low interest/motivation home base, lower positive affect (PA) home base, greater variability of NA, PA, and low interest/motivation, and greater NA and low interest/motivation inertia than never-depressed participants. Additionally, the currently depressed group had a higher sad home base specifically, greater variability across most negative emotions and low interest/motivation, and greater low interest/motivation inertia than the remitted group. The currently depressed and remitted groups did not differ in anxious, upset, or PA home base, anxious or PA variability, and inertia of all negative emotions and PA.

CONCLUSIONS

Findings suggest that a number of abnormalities in emotion and reward functioning persist after a depressive episode resolves, however, the tendency to experience higher levels of sadness, greater range of a variety of negative emotions, and more variable and persistent low interest/motivation are exacerbated during depressive episodes. Conversely, greater intensity and persistence of some negative emotions (anxiety, upset) and blunted positive emotions appear to equally characterize depression in both the symptomatic and remitted state.

Microphysiological systems for human aging research

Recent advances in microphysiological systems (MPS), also known as organs-on-a-chip (OoC), enable the recapitulation of more complex organ and tissue functions on a smaller scale in vitro. MPS therefore provide the potential to better understand human diseases and physiology. To date, numerous MPS platforms have been developed for various tissues and organs, including the heart, liver, kidney, blood vessels, muscle, and adipose tissue. However, only a few studies have explored using MPS platforms to unravel the effects of aging on human physiology and the pathogenesis of age-related diseases. Age is one of the risk factors for many diseases, and enormous interest has been devoted to aging research. As such, a human MPS aging model could provide a more predictive tool to understand the molecular and cellular mechanisms underlying human aging and age-related diseases. These models can also be used to evaluate preclinical drugs for age-related diseases and translate them into clinical settings. Here, we provide a review on the application of MPS in aging research. First, we offer an overview of the molecular, cellular, and physiological changes with age in several tissues or organs. Next, we discuss previous aging models and the current state of MPS for studying human aging and age-related conditions. Lastly, we address the limitations of current MPS and present future directions on the potential of MPS platforms for human aging research.

Anticipating regime shifts by mixing early warning signals from different nodes

Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.

Exploring genetic variation in potential indicators of resilience in sheep using fibre diameter measured along the wool staple

Production animals are increasingly exposed to a wide variety of disturbances that can compromise their productivity, health and well-being. As a result, there is a growing need to be able to select animals that are more resilient to environmental disturbances. Fibre diameter variation measured along a wool staple is expected to contain information about how resilient sheep are to the disturbances of their internal and external environment. This study aimed to develop potential resilience indicators from fibre diameter variation, estimate their genetic parameters and assess whether these traits are genetically correlated across three age stages. The study used 6 140 Merino sheep from the Sheep Cooperative Research Centre Information Nucleus Flocks recorded at yearling, 2 years old, and adult ages. Eight potential traits were defined based on theory, literature and exploratory analysis, which were suggested to capture the animal's ability to resist, respond and recover from potential disturbances. Genetic evaluation of the traits was conducted using pedigree-based animal models. The traits were shown to be low to moderately heritable (0.01-0.33) when examined at each of the three age stages. The potential indicators were generally well correlated with one another within age stages. Further, the genetic correlation between the same trait measured at different age stages was moderate to high between yearling and 2 years old (0.35-0.94) and between 2 years old and adults (0.18-0.70), while slightly lower between yearling and adult estimates (0.09-0.62). These results suggest that selection for resilience indicators from fibre diameter is possible; however, further studies are warranted to refine the trait definitions and validate these indicators against other measures of health, fitness and productive performance.

Consistency of dry matter intake in Holstein cows: Heritability estimates and associations with feed efficiency

Resilience can be defined as the capacity to maintain performance or bounce back to normal functioning after a perturbation, and studying fluctuations in daily feed intake may be an effective way to identify resilient dairy cows. Our goal was to develop new phenotypes based on daily dry matter intake (DMI) consistency in Holstein cows, estimate genetic parameters and genetic correlations with feed efficiency and milk yield consistency, and evaluate their relationships with production, longevity, health, and reproduction traits. Data consisted of 397,334 daily DMI records of 6,238 lactating Holstein cows collected from 2007 to 2022 at 6 research stations across the United States. Consistency phenotypes were calculated based on the deviations from expected daily DMI for individual cows during their respective feeding trials, which ranged from 27 to 151 d in duration. Expected values were derived from different models, including simple average, quadratic and cubic quantile regression with a 0.5 quantile, and locally estimated scatterplot smoothing (LOESS) regression with span parameters 0.5 and 0.7. We then calculated the log of variance (log-Var-DMI) of daily deviations for each model as the consistency phenotype. Consistency of milk yield was also calculated, as a reference, using the same methods (log-Var-Milk). Genetic parameters were estimated using an animal model, including lactation, days in milk and cohort as fixed effects, and animal as random effect. Relationships between log-Var-DMI and traits currently considered in the US national genetic evaluation were evaluated using Spearman's rank correlations between sires' breeding values. Heritability estimates for log-Var-DMI ranged from 0.11 ± 0.02 to 0.14 ± 0.02 across models. Different methods (simple average, quantile regressions, and LOESS regressions) used to calculate log-Var-DMI yielded very similar results, with genetic correlations ranging from 0.94 to 0.99. Estimated genetic correlations between log-Var-DMI and log-Var-Milk ranged from 0.51 to 0.62. Estimated genetic correlations between log-Var-DMI and feed efficiency ranged from 0.55 to 0.60 with secreted milk energy, from 0.59 to 0.63 with metabolic body weight, and from 0.26 to 0.31 with residual feed intake (RFI). Relationships between log-Var-DMI and the traits in the national genetic evaluation were moderate and positive correlations with milk yield (0.20 to 0.21), moderate and negative correlations with female fertility (-0.07 to -0.20), no significant correlations with health and longevity, and favorable correlations with feed efficiency (-0.23 to -0.25 with feed saved and 0.21 to 0.26 with RFI). We concluded that DMI consistency is heritable and may be an indicator of resilience. Cows with lower variation in the difference between actual and expected daily DMI (more consistency) may be more effective in maintaining performance in the face of challenges or perturbations, whereas cows with greater variation in observed versus expected daily DMI (less consistency) are less feed efficient and may be less resilient.

The network structure of psychopathological and resilient responses to the pandemic: A multicountry general population study of depression and anxiety

Commonly identified patterns of psychological distress in response to adverse events are characterized by resilience (i.e., little to no distress), delayed (i.e., distress that increases over time), recovery (i.e., distress followed by a gradual decrease over time), and sustained (i.e., distress remaining stable over time). This study aimed to examine these response patterns during the COVID-19 pandemic. Anxiety and depressive symptom data collected across four European countries over the first year of the pandemic were analyzed (N = 3,594). Participants were first categorized into groups based on the four described patterns. Network connectivity and symptom clustering were then estimated for each group and compared. Two thirds (63.6%) of the sample displayed a resilience pattern. The sustained distress network (16.3%) showed higher connectivity than the recovery network (10.0%) group, p = .031; however, the resilient network showed higher connectivity than the delayed network (10.1%) group, p = .016. Regarding symptom clustering, more clusters emerged in the recovery network (i.e., three) than the sustained network (i.e., two). These results replicate findings that resilience was the most common mental health pattern over the first pandemic year. Moreover, they suggest that high network connectivity may be indicative of a stable mental health response over time, whereas fewer clusters may be indicative of a sustained distress pattern. Although exploratory, the network perspective provides a useful tool for examining the complexity of psychological responses to adverse events and, if replicated, could be useful in identifying indicators of protection against or vulnerability to future psychological distress.

Stress-induced loss of social resilience in honeybee colonies and its implications on fitness

Stressors may lead to a shift in the timing of life-history events of species, causing a mismatch with optimal environmental conditions, potentially reducing fitness. In honeybees, the timing of brood rearing and nest emergence in late winter/early spring is critical as colonies need to grow fast after winter to prepare for reproduction. However, the effects of stress on these life-history events in late winter/early spring and the possible consequences are not well understood. Therefore, we tested whether (i) honeybee colonies shift timing of brood rearing and nest emergence as response to stressors, and (ii) if there is a consequent loss of social resilience, reflected in colony fitness (survival, growth and reproduction). We monitored stressed (high load of the parasitic mite Varroa destructor or nutrition restricted) colonies and presumably non-stressed colonies from the beginning of 2020 till spring of 2021. We found that honeybee colonies do not shift the timing of brood rearing and nest emergence in spring as a coping mechanism to stressors. However, we show that there is loss of social resilience in stressed colonies, leading to reduced growth and reproduction. Our study contributes to better understanding the effects of stressors on social resilience in eusocial organisms.

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs

Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (>0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3 d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).

Robust Responses of Female Caribou to Changes in Food Supply

AbstractUngulates can respond to changes in food supply by altering foraging behavior, digestive function, and metabolism. A multifaceted response to an environmental change is considered robust. Short seasons of plant growth make herbivores sensitive to changes in food supply because maintenance and production must be accomplished in less time with fewer options in a more fragile response. Caribou live at high latitudes where short summers constrain their response to changes in food supply. We measured the ability of female caribou to resist and tolerate changes in the quality and quantity of their food supply during winter and summer. Caribou resisted changes in food abundance and quality by changing food intake and physical activity with changes in daily temperature within each season. Peak food intake rose by 134% from winter pregnancy to summer lactation (98 vs. 229 g kg-0.75 d-1), as digestible requirements to maintain the body increased by 85% for energy (1,164 vs. 2,155 kJ kg-0.75 d-1) and by 266% for N (0.79 vs. 2.89 g N kg-0.75 d-1). Caribou required a diet with a digestible content of 12 kJ g-1 and 0.8% N in pregnancy, 18 kJ g-1 and 1.9% N in early lactation, and 11 kJ g-1 and 1.2% N in late lactation, which corresponds with the phenology of the wild diet. Female caribou tolerated restriction of ad lib. food intake to 58% of their energy requirement (680 vs. 1,164 kJ kg-0.75 d-1) during winter pregnancy and to 84% of their energy requirement (1,814 vs. 2,155 kJ kg-0.75 d-1) during summer lactation without a change in stress level, as indicated by fecal corticosterone concentration. Conversely, caribou can respond to increased availability of food with a spare capacity to process digestible energy and N at 123% (2,642 vs. 2,155 kJ kg-0.75 d-1) and 145% (4.20 vs. 2.89 g N kg-0.75 d-1) of those respective requirements during lactation. Robust responses to changes in food supply allow caribou to sustain reproduction, which would buffer demographic response. However, herds may decline when thresholds of behavioral resistance and physiological tolerance are frequently exceeded. Therefore, the challenge for managing declining populations of caribou and other robust species is to identify declines in robustness before their response becomes fragile.

Quantifying the Resilience of a Healthcare System: Entropy and Network Science Perspectives

In this study, we consider the human body and the healthcare system as two complex networks and use theories regarding entropy, requisite variety, and network centrality metrics with resilience to assess and quantify the strengths and weaknesses of healthcare systems. Entropy is used to quantify the uncertainty and variety regarding a patient's health state. The extent of the entropy defines the requisite variety a healthcare system should contain to be able to treat a patient safely and correctly. We use network centrality metrics to visualize and quantify the healthcare system as a network and assign the strengths and weaknesses of the network and of individual agents in the network. We apply organization design theories to formulate improvements and explain how a healthcare system should adjust to create a more robust and resilient healthcare system that is able to continuously deal with variations and uncertainties regarding a patient's health, despite possible stressors and disturbances at the healthcare system. In this article, these concepts and theories are explained and applied to a fictive and a real-life example. We conclude that entropy and network science can be used as tools to quantify the resilience of healthcare systems.

Potential for quantifying general environmental resilience of dairy cattle in sub-Saharan Africa using deviations in milk yield

Introduction: Genetic improvement of general resilience of dairy cattle is deemed as a part of the solution to low dairy productivity and poor cattle adaptability in sub-Saharan Africa (SSA). While indicators of general resilience have been proposed and evaluated in other regions, their applicability in SSA remains unexplored. This study sought to test the viability of utilizing log-transformed variance (LnVar), autocorrelation (rauto), and skewness (Skew) of deviations in milk yield as indicators of general resilience of dairy cows performing in the tropical environment of Kenya. Methods: Test-day milk yield records of 2,670 first-parity cows performing in three distinct agroecological zones of Kenya were used. To predict expected milk yield, quantile regression was used to model lactation curve for each cow. Subsequently, resilience indicators were defined based on actual and standardized deviations of observed milk yield from the expected milk yield. The genetic parameters of these indicators were estimated, and their associations with longevity and average test-day milk yield were examined. Results: All indicators were heritable except skewness of actual and standardized deviation. The log-transformed variance of actual (LnVar1) and standardized (LnVar2) deviations had the highest heritabilities of 0.19 ± 0.04 and 0.17 ± 0.04, respectively. Auto-correlation of actual (rauto1) and standardized (rauto2) deviations had heritabilities of 0.05 ± 0.03 and 0.07 ± 0.03, respectively. Weak to moderate genetic correlations were observed among resilience indicators. Both rauto and Skew indicators had negligible genetic correlations with both longevity and average test-day milk yield. LnVar1 and LnVar2 were genetically associated with better longevity (rg = -0.47 ± 0.26 and -0.49 ± 0.26, respectively). Whereas LnVar1 suggested that resilient animals produce lower average test-day milk yield, LnVar2 revealed a genetic association between resilience and higher average test-day milk yield. Discussion: Log transformed variance of deviations in milk yield holds a significant potential as a robust resilience indicator for dairy animals performing in SSA. Moreover, standardized as opposed to actual deviations should be employed in defining resilience indicators because the resultant indicator does not inaccurately infer that low-producing animals are inherently resilient. This study offers an opportunity for enhancing the productivity of dairy cattle performing in SSA through selective breeding for resilience to environmental stressors.

Temporal associations between salivary cortisol and emotions in clinically depressed individuals and matched controls: A dynamic time warp analysis

Depression can be understood as a complex dynamic system where depressive symptoms interact with one another. Cortisol is suggested to play a major role in the pathophysiology of depression, but knowledge on the temporal interplay between cortisol and depressive symptoms is scarce. We aimed to analyze the temporal connectivity between salivary cortisol and momentary affective states in depressed individuals and controls. Thirty pair-matched depressed and non-depressed participants completed questionnaires on momentary positive (PA) and negative (NA) affect and collected saliva three times a day for 30 days. The association between cortisol and affect was analyzed by dynamic time warp (DTW) analyses. These analyses involved lag-1 backward to lag-1 forward undirected analyses and lag-0 and lag-1 forward directed analyses. Large inter- and intra-individual variability in the networks were found. At the group level, with undirected analysis PA and NA were connected in the networks in depressed individuals and in controls. Directed analyses indicated that increases in cortisol preceded specific NA items in controls, but tended to follow upon specific affect items increase in depressed individuals. To conclude, at group level, changes in cortisol levels in individuals diagnosed with a depression may be a result of changes in affect, rather than a cause.

The vulnerability of aging states: A survival analysis across premodern societies

How states and great powers rise and fall is an intriguing enigma of human history. Are there any patterns? Do polities become more vulnerable over time as they age? We analyze longevity in hundreds of premodern states using survival analysis to help provide initial insights into these questions. This approach is commonly used to study the risk of death in biological organisms or failure in mechanical systems. The results reveal that the risk of state termination increased steeply over approximately the first two centuries after formation and stabilized thereafter. This provides the first quantitative support for the hypothesis that the resilience of political states decreases over time. Potential mechanisms that could drive such declining resilience include environmental degradation, increasing complexity, growing inequality, and extractive institutions. While the cases are from premodern times, such dynamics and drivers of vulnerability may remain relevant today.

Biological effects of electromagnetic fields on insects: a systematic review and meta-analysis

Worldwide, insects are declining at an alarming rate. Among other causes, the use of pesticides and modern agricultural practices play a major role in this. Cumulative effects of multiple low-dose toxins and the distribution of toxicants in nature have only started to be investigated in a methodical way. Existing research indicates another factor of anthropogenic origin that could have subtle harmful effects: the increasingly frequent use of electromagnetic fields (EMF) from man-made technologies. This systematic review summarizes the results of studies investigating the toxicity of electromagnetic fields in insects. The main objective of this review is to weigh the evidence regarding detrimental effects on insects from the increasing technological infrastructure, with a particular focus on power lines and the cellular network. The next generation of mobile communication technologies, 5G, is being deployed - without having been tested in respect of potential toxic effects. With humanity's quest for pervasiveness of technology, even modest effects of electromagnetic fields on organisms could eventually reach a saturation level that can no longer be ignored. An overview of reported effects and biological mechanisms of exposure to electromagnetic fields, which addresses new findings in cell biology, is included. Biological effects of non-thermal EMF on insects are clearly proven in the laboratory, but only partly in the field, thus the wider ecological implications are still unknown. There is a need for more field studies, but extrapolating from the laboratory, as is common practice in ecotoxicology, already warrants increasing the threat level of environmental EMF impact on insects.

Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs

BACKGROUND

There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR).

RESULTS

The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (- 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (- 0.76 ± 0.06) and residual feed intake (- 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm.

CONCLUSIONS

Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs.

Obesity-Induced Hyperglycemia and Heart Failure Preserved Ejection Fraction: Uncharted Territories to Remission

Until the end of World War II, food security was a global challenge. Consequently, in 1948, type 2 diabetes was relatively uncommon, with the majority of cases being type 1 diabetes requiring insulin therapy. Since then, food has become increasingly palatable and readily available, leading to a rise in obesity across all age groups. Understanding the impact of obesity on our health has become crucial for optimizing healthcare. In this context, we draw attention to two significant, yet relatively uncharted pathogenic effects associated with obesity: Hyperglycemia and Heart Failure with Preserved Ejection Fraction (HFpEF). Thorough pathophysiologic, hemodynamic, and echocardiographic characterization have revealed the existence of a distinct phenotype known as "obese HFpEF" within the broader HFpEF population, and "obesity-induced hyperglycemia" within the diabetes population. In these phenotypes, patients often present with higher Body Mass Index and experience clinical symptoms decades earlier. Recent insights have enhanced our understanding of the mechanisms underlying obesity-mediated heart failure preserved ejection fraction and hyperglycemia. Early detection offers the potential for reversibility of many pathologies associated with obesity through adequate weight reduction. The objective of this review is to provide a deeper insight into these uncharted territories and explore the potential for improved outcomes by reframing these two narratives toward achieving remission. Such a shift has the potential to positively impact individual engagement with healthier lifestyles.

Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes

In a context of growing interest in breeding more resilient animals, a noninvasive indicator of resilience would be very valuable. We hypothesized that the time-course of concentrations of several milk metabolites through a short-term underfeeding challenge could reflect the variation of resilience mechanisms to such a challenge. We submitted 138 one-year-old primiparous goats, selected for extreme functional longevity (i.e., productive longevity corrected for milk yield [60 low longevity line goats and 78 high longevity line goats]), to a 2-d underfeeding challenge during early lactation. We measured the concentration of 13 milk metabolites and the activity of 1 enzyme during prechallenge, challenge, and recovery periods. Functional principal component analysis summarized the trends of milk metabolite concentration over time efficiently without preliminary assumptions concerning the shapes of the curves. We first ran a supervised prediction of the longevity line of the goats based on the milk metabolite curves. The partial least square analysis could not predict the longevity line accurately. We thus decided to explore the large overall variability of milk metabolite curves with an unsupervised clustering. The large year × facility effect on the metabolite concentrations was precorrected for. This resulted in 3 clusters of goats defined by different metabolic responses to underfeeding. The cluster that showed higher β-hydroxybutyrate, cholesterol, and triacylglycerols increase during the underfeeding challenge was associated with poorer survival compared with the other 2 clusters. These results suggest that multivariate analysis of noninvasive milk measures show potential for deriving new resilience phenotypes.

Autonomic nervous system and postural control regulation during orthostatic test as putative markers of physical resilience among community-dwelling older adults

INTRODUCTION

We examined whether autonomic nervous system (ANS) and postural control regulation during orthostatic test reflect physical resilience by studying their associations with maximal walking speed and mortality.

METHODS

The participants were community-dwelling Finnish men (n = 303) and women (n = 386) aged 75, 80, and 85 years at baseline. Systolic and diastolic blood pressure (BP), heart rate, heart rate variability (HRV), respiratory rate, and postural sway were obtained using a digital sphygmomanometer, a single-channel ECG, and thigh- and chest-worn accelerometers. Linear and Cox regression models were used to estimate the associations of the physiological indices with maximal 10-m walking speed and 5-year mortality separately for sexes.

RESULTS

Better maintenance of BP under orthostatic stress was associated with faster walking speed in women and lower mortality hazard in men. Greater HRV in terms of low frequency power and lower respiration rate in supine position and smaller orthostatic changes in these were associated with faster walking speed especially in women. Less postural sway after standing up was associated with faster walking speed in women (-0.057, SE 0.022, p = 0.011) and more postural sway with increased mortality hazard in men (HR 1.71, 95 % CI 1.20-2.43) even after controlling for BP responses.

CONCLUSIONS

In addition to ANS regulation at rest and under stress, adaptation of postural control system to orthostasis may be used in quantifying older adults' physical resilience. Wearable sensors capturing stimulus-response patterns and natural fluctuations of body functions may provide opportunities to monitor and incorporate different subsystems' resilience also in free-living conditions.

A blend of selected botanicals maintains intestinal epithelial integrity and reduces susceptibility to Escherichia coli F4 infection by modulating acute and chronic inflammation in vitro

In the pig production cycle, the most delicate phase is weaning, a sudden and early change that requires a quick adaptation, at the cost of developing inflammation and oxidation, especially at the intestinal level. In this period, pathogens like enterotoxigenic Escherichia coli (ETEC) contribute to the establishment of diarrhea, with long-lasting detrimental effects. Botanicals and their single bioactive components represent sustainable well-recognized tools in animal nutrition thanks to their wide-ranging beneficial functions. The aim of this study was to investigate the in vitro mechanism of action of a blend of botanicals (BOT), composed of thymol, grapeseed extract, and capsicum oleoresin, in supporting intestinal cell health during inflammatory challenges and ETEC infections. To reach this, we performed inflammatory and ETEC challenges on Caco-2 cells treated with BOT, measuring epithelial integrity, cellular oxidative stress, bacterial translocation and adhesion, gene expression levels, and examining tight junction distribution. BOT protected enterocytes against acute inflammation: while the challenge reduced epithelial tightness by 40%, BOT significantly limited its drop to 30%, also allowing faster recovery rates. In the case of chronic inflammation, BOT systematically improved by an average of 25% the integrity of challenged cells (p < 0.05). Moreover, when cells were infected with ETEC, BOT maintained epithelial integrity at the same level as an effective antibiotic and significantly reduced bacterial translocation by 1 log average. The mode of action of BOT was strictly related to the modulation of the inflammatory response, protecting tight junctions' expression and structure. In addition, BOT influenced ETEC adhesion to intestinal cells (-4%, p < 0.05), also thanks to the reduction of enterocytes' susceptibility to pathogens. Finally, BOT effectively scavenged reactive oxygen species generated by inflammatory and H2O2 challenges, thus alleviating oxidative stress by 40% compared to challenge (p < 0.05). These results support the employment of BOT in piglets at weaning to help manage bacterial infections and relieve transient or prolonged stressful states thanks to the modulation of host-pathogen interaction and the fine-tuning activity on the inflammatory tone.

New Possibilities for Evaluating the Development of Age-Related Pathologies Using the Dynamical Network Biomarkers Theory

Aging is the slowest process in a living organism. During this process, mortality rate increases exponentially due to the accumulation of damage at the cellular level. Cellular senescence is a well-established hallmark of aging, as well as a promising target for preventing aging and age-related diseases. However, mapping the senescent cells in tissues is extremely challenging, as their low abundance, lack of specific markers, and variability arise from heterogeneity. Hence, methodologies for identifying or predicting the development of senescent cells are necessary for achieving healthy aging. A new wave of bioinformatic methodologies based on mathematics/physics theories have been proposed to be applied to aging biology, which is altering the way we approach our understand of aging. Here, we discuss the dynamical network biomarkers (DNB) theory, which allows for the prediction of state transition in complex systems such as living organisms, as well as usage of Raman spectroscopy that offers a non-invasive and label-free imaging, and provide a perspective on potential applications for the study of aging.

Behavioral patterns as indicators of resilience after parturition in dairy cows

During the transition phase, dairy cows are susceptible to develop postpartum diseases. Cows that stay healthy or recover rapidly can be considered to be more resilient in comparison to those that develop postpartum diseases. An indication of loss of resilience will allow for early intervention with preventive and supportive measures before the onset of disease. We investigated which quantitative behavioral characteristics during the dry period could be used as indicators of reduced resilience after calving, using noninvasive Smart Tag neck and Smart Tag leg sensors in dairy cows (Nedap N.V.). We followed 180 cows during 2 wk before until 6 wk after parturition at 4 farms in the Netherlands. Serving as proxy for loss of resilience, as defined by the duration and severity of disease, a clinical assessment was performed twice weekly and blood samples were taken in the first and fifth week after parturition. For each cow, clinical and serum value deviations were aggregated into a total deficit score (TDS total). We also calculated TDS values relating to inflammation, locomotion, or metabolic problems, which were further divided into macro-mineral and liver-related deviations. Smart Tag neck and leg sensors provided continuous behavioral activity signals of which we calculated the average, variance, and autocorrelation during the dry period. Diurnal patterns in the behavioral activity signals were derived by fast Fourier transformation and the calculation of the nonperiodicity. To select significant predictors of resilience, we first performed a univariate analysis with TDS as dependent variable and the behavioral characteristics that were measured during the dry period, as potential predictors with cow as experimental unit. We included parity group as fixed effect and farm as random effect. Next, we performed multivariable analysis with only significant predictors, followed by a variable selection procedure to obtain a final linear mixed model with an optimal subset of predictors with parity group as fixed effect and farm as random effect. The TDS total was best predicted by average inactive time, nonperiodicity ruminating, nonperiodicity of bouts standing up and fast Fourier transformation stand still. Average inactive time was negatively correlated with average eating time, and these 2 predictors could be exchanged with only little difference in model performance. Our best performing model predicted TDS total at a cutoff level of 60 points, with a sensitivity of 79.5% and a specificity of 73.2% with a positive predicted value of 0.69 and a negative predicted value of 0.83. The models to predict the other TDS categories showed a lower predictive performance as compared with the TDS total model, which could be related to the limited sample size and therefore, low occurrence of problems within a specific TDS category. Furthermore, more resilient dairy cows are characterized by high averages of eating time with high regularity in rumination and low averages of inactive time. They reveal high regularity in standing time and transitions from lying to standing, in the dry period. These behaviors can be used as indicators of resilience and allow for preventive intervention during the dry period in vulnerable dairy cattle. However, further examination is still required to find clues for adequate intervention strategies.

Animal board invited review: Quantification of resilience in farm animals

Resilience, when defined as the capacity of an animal to respond to short-term environmental challenges and to return to the prechallenge status, is a dynamic and complex trait. Resilient animals can reinforce the capacity of the herd to cope with often fluctuating and unpredictable environmental conditions. The ability of modern technologies to simultaneously record multiple performance measures of individual animals over time is a huge step forward to evaluate the resilience of farm animals. However, resilience is not directly measurable and requires mathematical models with biologically meaningful parameters to obtain quantitative resilience indicators. Furthermore, interpretive models may also be needed to determine the periods of perturbation as perceived by the animal. These applications do not require explicit knowledge of the origin of the perturbations and are developed based on real-time information obtained in the data during and outside the perturbation period. The main objective of this paper was to review and illustrate with examples, different modelling approaches applied to this new generation of data (i.e., with high-frequency recording) to detect and quantify animal responses to perturbations. Case studies were developed to illustrate alternative approaches to real-time and post-treatment of data. In addition, perspectives on the use of hybrid models for better understanding and predicting animal resilience are presented. Quantification of resilience at the individual level makes possible the inclusion of this trait into future breeding programmes. This would allow improvement of the capacity of animals to adapt to a changing environment, and therefore potentially reduce the impact of disease and other environmental stressors on animal welfare. Moreover, such quantification allows the farmer to tailor the management strategy to help individual animals to cope with the perturbation, hence reducing the use of pharmaceuticals, and decreasing the level of pain of the animal.

Building an Early Warning System for Depression: Rationale, Objectives, and Methods of the WARN-D Study

Background

Depression is common, debilitating, often chronic, and affects young people disproportionately. Given that only 50% of patients improve under initial treatment, experts agree that prevention is the most effective way to change depression's global disease burden. The biggest barrier to successful prevention is to identify individuals at risk for depression in the near future. To close this gap, this protocol paper introduces the WARN-D study, our effort to build a personalized early warning system for depression.

Method

To develop the system, we follow around 2,000 students over 2 years. Stage 1 comprises an extensive baseline assessment in which we collect a broad set of predictors for depression. Stage 2 lasts 3 months and zooms into participants' daily experiences that may predict depression; we use smartwatches to collect digital phenotype data such as sleep and activity, and we use a smartphone app to query participants about their experiences 4 times a day and once every Sunday. In Stage 3, we follow participants for 21 months, assessing transdiagnostic outcomes (including stress, functional impairment, anxiety, and depression) as well as additional predictors for future depression every 3 months. Collected data will be utilized to build a personalized prediction model for depression onset.

Discussion

Overall, WARN-D will function similarly to a weather forecast, with the core difference that one can only seek shelter from a thunderstorm and clean up afterwards, while depression may be successfully prevented before it occurs.

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals

BACKGROUND

The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables.

METHODS

This is a population-based cohort study of 48,944 Danish citizens 65 years of age and older suffering bereavement within 2013-2016. Individuals were followed from date of spousal loss until death from all causes or 31st of December 2016, whichever came first. Healthcare expenditures were available on weekly basis for each person during the follow-up and used as predictors for mortality risk in Extreme Gradient Boosting models. The extent to which medical spending trajectories improved mortality predictions compared to models with sociodemographics, was assessed with respect to discrimination (AUC), overall prediction error (Brier score), calibration, and clinical benefit (decision curve analysis).

RESULTS

The AUC of age and sex for mortality the year after spousal loss was 70.8% [95% CI 68.8, 72.8]. The addition of sociodemographic variables led to an increase of AUC ranging from 0.9% to 3.1% but did not significantly reduce the overall prediction error. The AUC of the model combining the variables above plus medical spending usage was 80.8% [79.3, 82.4] also exhibiting smaller Brier score and better calibration. Overall, patterns of healthcare expenditures improved mortality predictions the most, also exhibiting the highest clinical benefit among the rest of the models.

CONCLUSION

Temporal patterns of medical spending have the potential to significantly improve our assessment on who is at high risk of dying after suffering spousal loss. The proposed methodology can assist in a more efficient risk profiling and prognosis of bereaved individuals.

Predicting mortality risk after a fall in older adults using health care spending patterns: a population-based cohort study

OBJECTIVE

To develop a prognostic model of 1-year mortality for individuals aged 65+ presenting at the emergency department (ED) with a fall based on health care spending patterns to guide clinical decision-making.

DESIGN

Population-based cohort study (n = 35,997) included with a fall in 2013 and followed 1 year.

METHODS

Health care spending indicators (dynamical indicators of resilience, DIORs) 2 years before admission were evaluated as potential predictors, along with age, sex and other clinical and sociodemographic covariates. Multivariable logistic regression models were developed and internally validated (10-fold cross-validation). Performance was assessed via discrimination (area under the receiver operating characteristic curve, AUC), Brier scores, calibration and decision curve analysis.

RESULTS

The AUC of age and sex for mortality was 72.5% [95% confidence interval 71.8 to 73.2]. The best model included age, sex, number of medications and health care spending DIORs. It exhibited high discrimination (AUC: 81.1 [80.5 to 81.6]), good calibration and potential clinical benefit for various threshold probabilities. Overall, health care spending patterns improved predictive accuracy the most while also exhibiting superior performance and clinical benefit.

CONCLUSIONS

Patterns of health care spending have the potential to significantly improve assessments on who is at high risk of dying following admission to the ED with a fall. The proposed methodology can assist in predicting the prognosis of fallers, emphasising the added predictive value of longitudinal health-related information next to clinical and sociodemographic predictors.

A promising resilience parameter for breeding: the use of weight and feed trajectories in growing pigs

BACKGROUND

Increasing resilience is a priority in modern pig breeding. Recent research shows that general resilience can be quantified via variability in longitudinal data. The collection of such longitudinal data on weight, feed intake and feeding behaviour in pigs has been facilitated by the development of technologies such as automated feeding stations. The goal of this study was to investigate resilience traits, which were estimated as deviations from longitudinal weight, feed intake and feeding behaviour data during the finishing phase. A dataset with 324,207 records between the age of 95 and 155 days on 5,939 Piétrain pigs with known pedigree and genomic information was used. We provided guidelines for a rigid quality control of longitudinal body weight data, as we found that outliers can significantly affect results. Gompertz growth curve analysis, linear modelling and trajectory analyses were used for quantifying resilience traits.

RESULTS

To our knowledge, this is the first study comparing resilience traits from longitudinal body weight, feed intake and feeding behaviour data in pigs. We demonstrated that the resilience traits are lowly to moderately heritable for deviations in body weight (h² = 2.9%-20.2%), in feed intake (9.4%-23.3%) and in feeding behaviour (16.2%-28.3%). Additionally, these traits have good predictive abilities in cross-validation analyses. Deviations in individual body weight and feed intake trajectories are highly correlated (r_g = 0.78) with low to moderate favourable genetic correlations with feed conversion ratio (r_g = 0.39-0.49). Lastly, we showed that some resilience traits, such as the natural logarithm of variances of observed versus predicted body weights (lnvar_weight), are more robust to lower observation frequencies and are repeatable over three different time periods of the finishing phase.

CONCLUSIONS

Our results will help future studies investigating resilience traits and resilience-related traits. Moreover, our study provides first results on standardization of quality control and efficient data sampling from automated feeding station data. Our findings will be valuable for breeding organizations as they offer evidence that pigs' general resilience can be selected on with good accuracy. Moreover, this methodology might be extended to other species to quantify resilience based on longitudinal data.

An overview of the resilience world: Proceedings of the American Geriatrics Society and National Institute on Aging State of Resilience Science Conference

Resilience, which relates to one's ability to respond to stressors, typically declines with age and the development of comorbid conditions in older organisms. Although progress has been made to improve our understanding of resilience in older adults, disciplines have employed different frameworks and definitions to study various aspects of older adults' response to acute or chronic stressors. "Overview of the Resilience World: State of the Science," a bench-to-bedside conference on October 12-13, 2022, was sponsored by the American Geriatrics Society and National Institute on Aging. This conference, summarized in this report, explored commonalities and differences among the frameworks of resilience most commonly used in aging research in the three domains of resilience: physical, cognitive, and psychosocial. These three main domains are intertwined, and stressors in one domain can lead to effects in other domains. The themes of the conference sessions included underlying contributors to resilience, the dynamic nature of resilience throughout the life span, and the role of resilience in health equity. Although participants did not agree on a single definition of "resilience(s)," they identified common core elements of a definition that can be applied to all domains and noted unique features that are domain specific. The presentations and discussions led to recommendations for new longitudinal studies of the impact of exposures to stressors on resilience in older adults, the use of new and existing cohort study data, natural experiments (including the COVID-19 pandemic), and preclinical models for resilience research, as well as translational research to bring findings on resilience to patient care.

Review: Towards an integrated concept of animal welfare

Animal welfare is an important field of study due to animal sentience, yet there is to date no consensus on the definition of animal welfare. There have been four key developments in the field of animal welfare science since its birth: the theoretical and empirical study of affective states, and hence our understanding thereof, has increased; there has been a shift from a primary focus on unpleasant experiences towards an inclusion of pleasant experiences; there has been an increasing mention and investigation of the notion of cumulation of experiences in time, and with this, the importance of the time component of both affective states and animal welfare has come forward. Following others, we define welfare as a balance or cumulation of pleasant and unpleasant experiences over time. The time period of welfare depends on when welfare considerations are necessary, and may range from the duration of single and relatively short-term experiences to the entire life of an animal. We further propose that animal welfare conceptualised in this way can be assessed at three levels: level 1 represents the assessment of the environment and 'internal factors' such as health and personality, which interact in their impact on the affective experiences of animals; level 2 represents the assessment of affective states; and level 3 represents the assessment of the balance or cumulation of these affective states in time. The advancement of research necessitates studies to be more or less comparable, and this would be facilitated by researchers mentioning which concept of animal welfare they are basing their work on, at which level of assessment they are working, which assumptions they might be drawing from to infer welfare and which time period of interest they are focusing on, even if this is not mirrored by the timing of the assessment in practice. Assessment at levels 2 and 3 still needs much study, at both the theoretical and empirical levels, including agreements on validation tools.

Differences across herds with different dairy breeds in daily milk yield based proxies for resilience

Global sustainability issues such as climate change, biodiversity loss and food security require food systems to become more resource efficient and better embedded in the local environment. This needs a transition towards more diverse, circular and low-input dairy farming systems with animals best suited to the specific environmental conditions. When varying environmental challenges are posed to animals, cows need to become resilient to disturbances they face. This resilience of dairy cows for disturbances can be quantified using sensor features and resilience indicators derived from daily milk yield records. The aim of this study was to explore milk yield based sensor features and resilience indicators for different cattle groups according to their breeds and herds. To this end, we calculated 40 different features to describe the dynamics and variability in milk production of first parity dairy cows. After correction for milk production level, we found that various aspects of the milk yield dynamics, milk yield variability and perturbation characteristics indeed differed across herds and breeds. On farms with a lower breed proportion of Holstein Friesian across cows, there was more variability in the milk yield, but perturbations were less severe upon critical disturbances. Non-Holstein Friesian breeds had a more stable milk production with less (severe) perturbations. These differences can be attributed to differences in genetics, environments, or both. This study demonstrates the potential to use milk yield sensor features and resilience indicators as a tool to quantify how cows cope with more dynamic production conditions and select animals for features that best suit a farms' breeding goal and specific environment.

Reciprocal relationship between interpersonal communication and depressive symptoms and the mediating role of resilience across two years: Three-wave cross-lagged panel model

BACKGROUND

Depression among children has been a growing public health concern. It is generally recognized that individuals with depression are likely to have interpersonal malfunctioning. However, there remains a limited scientific understanding of the reciprocal relationship between interpersonal communication and depressive symptoms among rural Chinese children in a longitudinal approach.

METHODS

Therefore, guided by the interpersonal model of depression and the developmental cascade model, the present study conducted a cross-lagged panel analysis study to explore the bidirectional relationship between interpersonal communication and depressive symptoms across three waves among 2188 elementary school students in rural areas of one county of Gansu Province, China. We also examined the mediating effect of resilience and sex differences of the models.

RESULTS

Our results showed that depressive symptoms negatively predicted interpersonal communication from T1 to T2 and T2 to T3. Interpersonal communication negatively predicted depressive symptoms from T1 to T2, but not T2 to T3. Furthermore, resilience showed significant partial mediating effects in the reciprocal relationship between interpersonal communication and depressive symptoms. In terms of sex differences, the significant relationship between depressive symptoms at T1 and interpersonal communication at T2 was found to be significant among male students and marginally significant among female students. The full mediating effect of resilience at T1 was found only among male students, whereas resilience at T2 functioned as a full mediator between depressive symptoms at T2 and interpersonal communication at T3 only among female students.

LIMITATIONS

First, the present sample consisted of only third and fourth grade (i.e., in T1) students from one county in rural China. Second, the present study examined depressive symptoms instead of depression as a clinical diagnosis. Third, the third wave of the data was collected during COVID-19. The impact of the COVID-19 pandemic could unexpectedly pose on child mental health.

CONCLUSIONS

The finding underlined the importance of providing comprehensive depression prevention and intervention from fostering children's inner resilience and promoting their ability to navigate interpersonal resources.

Early Recognition of Overweight Hyperglycaemia May Improve Clinical Outcomes in Type 2 Diabetes

Diabetes is the ninth leading cause of death, directly accounting for 1.5 million deaths annually worldwide. Despite several breakthrough discoveries, little progress has been made in type 2 diabetes outcomes over the past 100 years. Younger age (below 60 years), a diet high in calories and processed food, and severe obesity (body mass index >35 kg/m²) may identify reversible beta cell dysfunction. Much of the clinical presentation pertains to flooding the body's adaptive limits with overnutrition. Recognizing this as a global societal trend brought about by lifestyle changes, sedentary work, mental stress and unlimited access to calorie-dense foods is crucial. Insulin resistance and genetic abnormalities cannot account for the dramatic increase in diabetes, from only 1% five decades ago to nearly 10% today. Obesity - and not insulin resistance - is at the core of the problem. As well as hyperglycaemia, end-organ damage can also be reversed with diet and weight loss in many affected individuals. We present the evolution of our understanding and compelling reasons to reframe diabetes in the severely obese to what it really is - overweight hyperglycaemia. This may shift societal perception, governmental funding, workplace reformations and individual engagement with healthy lifestyles. The objective of this review is to better understand global trends and the potential to improve outcomes by reframing the diabetes narrative towards remission. This may shift societal perception, governmental funding, workplace reformations and individual engagement with healthy lifestyles.

Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (r_auto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01-0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88-0.96) than between lactations 1 and 2 or 3 (0.34-0.88) for dPert, LnVar, and r_auto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and r_auto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.

Resilience characterized and quantified from physical activity data: A tutorial in R

Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.

Intrinsic capacity and resilience: Taking frailty to the next level

In addition to frailty, two novel concepts have been introduced in the field of geriatrics to capture the heterogeneous ageing process: the first is intrinsic capacity, which uses a community-based, holistic approach and is propagated by the World Health Organization (WHO); and the second is resilience, which provides a more dynamic perspective on the individual's reserves, injury and recovery. While both concepts are linked to frailty, with all three focusing on reserves in relation to ageing, each approaches this issue from a different point of view. In this paper, we will compare and contrast these three concepts - frailty, intrinsic capacity and resilience - and assess their relevance to future geriatric oncology research as well as daily clinical practice.

Effects of 10 add-on HF-rTMS treatment sessions on alcohol use and craving among detoxified inpatients with alcohol use disorder: a randomized sham-controlled clinical trial

BACKGROUND AND AIMS

Alcohol use disorder (AUD) is a chronic disorder with high relapse rates. There are currently few clinical trials of high frequency repetitive transcranial magnetic stimulation (HF-rTMS) to reduce alcohol use among AUD patients, and results are mixed. The current study tested the effect of 10 add-on sessions of HF-rTMS over the right dorsolateral pre-frontal cortex (DLPFC) on alcohol use and craving.

DESIGN

Single-center, single blind sham-controlled parallel-group RCT (n = 80), with 3 and 6 months follow-up.

SETTING

Clinical treatment center in Amsterdam, the Netherlands.

PARTICIPANTS

Eighty detoxified and abstinent AUD inpatients in clinical treatment (20 females, average age = 44.35 years).

INTERVENTION

Ten sessions of active or sham HF-rTMS (60 10 Hz trains of 5 sec at 110% motor threshold) over the right DLPFC on 10 consecutive work-days.

MEASUREMENTS

The primary outcome measure is the number of abstinent days over 6-month follow-up (FU). Secondary outcome measures are craving over 6-month FU (alcohol urge questionnaire and obsessive-compulsive drinking scale), time to first relapse over 6-month FU and grams of alcohol consumed over 6-month FU. Additional outcome measures: full abstinence over 6-month FU and treatment success over 12-month FU.

FINDINGS

HF-rTMS did not affect the number of abstinent days over 6 months FU [sham = 124 ± 65.9 days, active = 115 ± 69.8 days, difference: 9 days, 95% confidence interval (CI) = Poisson model: 0.578-3.547]. Moreover, HF-rTMS did not affect craving (AUQ/OCDS) (sham = 15.38/5.28, active = 17.48/4.75, differences = 2.1/-0.53, 95% CI mixed-effects model = -9.14 to 2.07/-1.44 to 2.40).

CONCLUSIONS

There was no clear evidence that high-frequency repetitive transcranial magnetic stimulation over the right dorsolateral pre-frontal cortex treatment has a long-term positive effect on alcohol use or craving as add-on treatment for alcohol use disorder. High treatment response at 6-month follow-up could have limited the possibility to find an effect.

Sex differences in health care expenditures and mortality after spousal bereavement: A register-based Danish cohort study

BACKGROUND

Spousal bereavement is a life event that affects older people differently. We investigated the impact of spousal bereavement on medical expenditures and mortality in the general population, emphasizing on age and sex.

METHODS

Data are from a population-based, retrospective cohort study following 924,958 Danish citizens over the age of 65 years, within 2011-2016. Changes in health care expenditures in those who suffer bereavement were compared with time matched changes among those who did not. Mortality hazards were analysed with time to event analysis.

RESULTS

A total of 77,722 (~8.4%) individuals experienced bereavement, 65.8% being females. Among males, bereavement was associated with increase of expenditures the year after, that was 42 Euros per week (95% CI, 36 to 48) larger than the non-bereaved group. The corresponding increase for females was 35 Euros per week (95% CI, 30 to 40). The increase of mortality hazards was highest in the first year after bereavement, higher in males than females, in young old and almost absent in the oldest old. Compared with the reference, mortality the year after spousal loss was 70% higher (HR 1.70 [95% CI 1.40 to 2.08]) for males aged 65-69 years and remained elevated for a period of six years. Mortality for females aged 65-69 years was 27% higher in the first year (HR 1.27, [1.07 to 1.52]), normalizing thereafter.

CONCLUSION

Bereavement affects older people differently with younger males being most frail with limited recovery potential.