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Autophagy is essential to maintaining cellular homeostasis in all eukaryotic cells and to tolerance of acute stressors such as starvation, heat, and recovery after exercise. Limited information exists regarding the exercise intensity-dependent autophagic response in humans, and it is unknown how environmental heat stress may modulate this response. Therefore, we evaluated autophagy and accompanying pathways of cellular stress [the heat-shock response (HSR), apoptosis, and acute inflammation] in peripheral blood mononuclear cells (PBMCs) from 10 young men (mean [SD]; 22 [2] years) before, immediately after and up to 6-h postexercise recovery from 30 min of low-, moderate-, and high-intensity semirecumbent cycling [40%, 55%, and 70% of maximal oxygen consumption (V̇o2max), respectively] in a temperate environment (25°C) and at 70% of V̇o2max in a hot environment (40°C). Changes in protein content were analyzed via Western blot. Each increase in exercise intensity was associated with elevations in mean body temperature. LC3-II increased after moderate-intensity exercise, with further increases after high-intensity exercise ( P < 0.05). However, an increase in beclin-2 and ULK1, with a decrease in p62 was only observed after high-intensity exercise, which was paralleled by elevated TNF-α and cleaved-caspase-3, with the HSR peaking at 6 h after exercise ( P < 0.05). When exercise was performed in the heat, greater LC3-II and cleaved-caspase-3 accumulation were observed; however, beclin-2 declined in recovery ( P < 0.05). Therefore, our findings indicate that autophagy in PBMCs during exercise may be associated with greater heat strain exhibited during increasing exercise intensities, which is modulated by exposure to heat.

Autophagy is a crucial cell survival mechanism that involves the degradation and recycling of old or damaged organelles and proteins to maintain cellular homeostasis. Impairments in autophagy are central to the pathogenesis of many conditions including metabolic and neurodegenerative disorders, cardiovascular and pulmonary diseases, diabetes, and aging. Although various pharmacological agents may be able to stimulate autophagic function, to our knowledge, few interventions exist that have been deemed safe and effective in humans. An emerging body of evidence suggests that targeting the autophagic pathway via passive heating (heat therapy) may stimulate autophagic function. Therefore, the primary focus of the present review is to analyze the mechanisms in which passive heating induces autophagy as defined by in vitro and in vivo (animal and human) models. Our secondary focus is to examine the implications of utilizing passive heating to restore dysfunctional autophagy in chronic disease and aging. Finally, we discuss potential therapeutic strategies to implement passive heating to stimulate autophagic function in humans.

Additional file 1: Fig. S1. Distribution of coefficients for covariates in the multiple linear regressions. Regression coefficients for the covariates type of patient (in- or out-patient), type of diagnosis (primary or non-primary diagnosis code), and sex (male or female). One observation per evaluated diagnosis and all diagnoses were evaluated in each plot. Fig. S2. Disease trajectory network pieced together from length two trajectories. Circles represent diagnosis codes (ICD-10 codes), and arrows represent a length two trajectory. IHD risk factors such as type 2-diabetes (E11) and hypertension (I10) appear in the left side of the graph. Angina pectoris (I20), acute myocardial infarction (I21), and chronic IHD (I25) have many incoming as well as outgoing edges consistent with the wide phenotypic spectrum they represent. ICD-10: International Statistical Classification of Diseases and Related Health Problems 10th Revision. IHD: Ischemic heart disease. For a full list of ICD-10 code definitions see Table S1. Fig. S3. Color key according to ICD-10 chapter. ICD-10: International Statistical Classification of Diseases and Related Health Problems 10th Revision chapter I through XIV. Table S1. ICD-10 codes and descriptions.

New findings What is the topic of this review? The use of proning for improving pulmonary gas exchange in critically ill patients. What advances does it highlight? Proning places the lung in its 'natural' posture, and thus optimises the ventilation-perfusion distribution, which enables lung protective ventilation and the alleviation of potentially life-threatening hypoxaemia in COVID-19 and other types of critical illness with respiratory failure. Abstract The survival benefit of proning patients with acute respiratory distress syndrome (ARDS) is well established and has recently been found to improve pulmonary gas exchange in patients with COVID-19-associated ARDS (CARDS). This review outlines the physiological implications of transitioning from supine to prone on alveolar ventilation-perfusion ( V A -- Q ) relationships during spontaneous breathing and during general anaesthesia in the healthy state, as well as during invasive mechanical ventilation in patients with ARDS and CARDS. Spontaneously breathing, awake healthy individuals maintain a small vertical (ventral-to-dorsal) V A / Q ratio gradient in the supine position, which is largely neutralised in the prone position, mainly through redistribution of perfusion. In anaesthetised and mechanically ventilated healthy individuals, a vertical V A / Q ratio gradient is present in both postures, but with better V A -- Q matching in the prone position. In ARDS and CARDS, the vertical V A / Q ratio gradient in the supine position becomes larger, with intrapulmonary shunting in gravitationally dependent lung regions due to compression atelectasis of the dorsal lung. This is counteracted by proning, mainly through a more homogeneous distribution of ventilation combined with a largely unaffected high perfusion dorsally, and a consequent substantial improvement in arterial oxygenation. The data regarding proning as a therapy in patients with CARDS is still limited and whether the associated improvement in arterial oxygenation translates to a survival benefit remains unknown. Proning is nonetheless an attractive and lung protective manoeuvre with the potential benefit of improving life-threatening hypoxaemia in patients with ARDS and CARDS.

Summary Background Intensive-care units (ICUs) treat the most critically ill patients, which is complicated by the heterogeneity of the diseases that they encounter. Severity scores based mainly on acute physiology measures collected at ICU admission are used to predict mortality, but are non-specific, and predictions for individual patients can be inaccurate. We investigated whether inclusion of long-term disease history before ICU admission improves mortality predictions. Methods Registry data for long-term disease histories for more than 230 000 Danish ICU patients were used in a neural network to develop an ICU mortality prediction model. Long-term disease histories and acute physiology measures were aggregated to predict mortality risk for patients for whom both registry and ICU electronic patient record data were available. We compared mortality predictions with admission scores on the Simplified Acute Physiology Score (SAPS) II, the Acute Physiologic Assessment and Chronic Health Evaluation (APACHE) II, and the best available multimorbidity score, the Multimorbidity Index. An external validation set from an additional hospital was acquired after model construction to confirm the validity of our model. During initial model development data were split into a training set (85%) and an independent test set (15%), and a five-fold cross-validation was done during training to avoid overfitting. Neural networks were trained for datasets with disease history of 1 month, 3 months, 6 months, 1 year, 2·5 years, 5 years, 7·5 years, 10 years, and 23 years before ICU admission. Findings Mortality predictions with a model based solely on disease history outperformed the Multimorbidity Index (Matthews correlation coefficient 0·265 vs 0·065), and performed similarly to SAPS II and APACHE II (Matthews correlation coefficient with disease history, age, and sex 0·326 vs 0·347 and 0·300 for SAPS II and APACHE II, respectively). Diagnoses up to 10 years before ICU admission affected current mortality prediction. Aggregation of previous disease history and acute physiology measures in a neural network yielded the most precise predictions of in-hospital mortality (Matthews correlation coefficient 0·391 for in-hospital mortality compared with 0·347 with SAPS II and 0·300 with APACHE II). These results for the aggregated model were validated in an external independent dataset of 1528 patients (Matthews correlation coefficient for prediction of in-hospital mortality 0·341). Interpretation Longitudinal disease-spectrum-wide data available before ICU admission are useful for mortality prediction. Disease history can be used to differentiate mortality risk between patients with similar vital signs with more precision than SAPS II and APACHE II scores. Machine learning models can be deconvoluted to generate novel understandings of how ICU patient features from long-term and short-term events interact with each other. Explainable machine learning models are key in clinical settings, and our results emphasise how to progress towards the transformation of advanced models into actionable, transparent, and trustworthy clinical tools. Funding Novo Nordisk Foundation and Innovation Fund Denmark.

It is unknown how sequential drug patterns convey information on a patient’s health status and treatment guidelines rarely account for this. Drug-agnostic longitudinal analyses of prescription trajectories in a population-wide setting are needed. In this cohort study, we used 24 years of data (1.1 billion prescriptions) from the Danish prescription registry to model the risk of sequentially redeeming a drug after another. Drug pairs were used to build multistep longitudinal prescription trajectories. These were subsequently used to stratify patients and calculate survival hazard ratios between the stratified groups. The similarity between prescription histories was used to determine individuals’ best treatment option. Over the course of 122 million person-years of observation, we identified 9 million common prescription trajectories and demonstrated their predictive power using hypertension as a case. Among patients treated with agents acting on the renin-angiotensin system we identified four groups: patients prescribed angiotensin converting enzyme (ACE) inhibitor without change, angiotensin receptor blockers (ARBs) without change, ACE with posterior change to ARB, and ARB posteriorly changed to ACE. In an adjusted time-to-event analysis, individuals treated with ACE compared to those treated with ARB had lower survival probability (hazard ratio, 0.73 [95% CI, 0.64–0.82]; P −16). Replication in UK Biobank data showed the same trends. Prescription trajectories can provide novel insights into how individuals’ drug use change over time, identify suboptimal or futile prescriptions and suggest initial treatments different from first line therapies. Observations of this kind may also be important when updating treatment guidelines.

Aims Appropriate analysis of big data is fundamental to precision medicine. While statistical analyses often uncover numerous associations, associations themselves do not convey predictive value. Confusion between association and prediction harms clinicians, scientists, and ultimately, the patients. We analyzed published papers in the field of diabetes that refer to “prediction” in their titles. We assessed whether these articles report metrics relevant to prediction. Methods A systematic search was undertaken using NCBI PubMed. Articles with the terms “diabetes” and “prediction” were selected. All abstracts of original research articles, within the field of diabetes epidemiology, were searched for metrics pertaining to predictive statistics. Simulated data was generated to visually convey the differences between association and prediction. Results The search-term yielded 2,182 results. After discarding non-relevant articles, 1,910 abstracts were evaluated. Of these, 39% (n = 745) reported metrics of predictive statistics, while 61% (n = 1,165) did not. The top reported metrics of prediction were ROC AUC, sensitivity and specificity. Using the simulated data, we demonstrated that biomarkers with large effect sizes and low P values can still offer poor discriminative utility. Conclusions We demonstrate a landscape of confused reporting within the field of diabetes epidemiology where the term “prediction” is often incorrectly used to refer to association statistics. We propose guidelines for future reporting, and two major routes forward in terms of main analytic procedures and research goals: the explanatory route, which contributes to precision medicine, and the prediction route which contributes to personalized medicine.