Identification of predictive models including polymorphisms in cytokines genes and clinical variables associated with post-transplant complications after identical HLA-allogeneic stem cell transplantation.

Backgrounds: Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative therapy for hematological malignancies, it can be associated with relevant post-transplant complications. Several reports have shown that polymorphisms in immune system genes are correlated with the development of post-transplant complications. Within this context, this work focuses on identifying novel polymorphisms in cytokine genes and developing predictive models to anticipate the risk of developing graft-versus-host disease (GVHD), transplantation-related mortality (TRM), relapse and overall survival (OS). Methods: Our group developed a 132-cytokine gene panel which was tested in 90 patients who underwent an HLA-identical sibling-donor allo-HSCT. Bayesian logistic regression (BLR) models were used to select the most relevant variables. Based on the cut-off points selected for each model, patients were classified as being at high or low-risk for each of the post-transplant complications (aGVHD II-IV, aGVHD III-IV, cGVHD, mod-sev cGVHD, TRM, relapse and OS). Results: A total of 737 polymorphisms were selected from the custom panel genes. Of these, 41 polymorphisms were included in the predictive models in 30 cytokine genes were selected (17 interleukins and 13 chemokines). Of these polymorphisms, 5 (12.2%) were located in coding regions, and 36 (87.8%) in non-coding regions. All models had a statistical significance of p<0.0001. Conclusion: Overall, genomic polymorphisms in cytokine genes make it possible to anticipate the development all complications studied following allo-HSCT and, consequently, to optimize the clinical management of patients.

Continuous Assessment of Function and Disability via Mobile Sensing: Real-World Data-Driven Feasibility Study.

BACKGROUND: Functional limitations are associated with poor clinical outcomes, higher mortality, and disability rates, especially in older adults. Continuous assessment of patients' functionality is important for clinical practice; however, traditional questionnaire-based assessment methods are very time-consuming and infrequently used. Mobile sensing offers a great range of sources that can assess function and disability daily. OBJECTIVE: This work aims to prove the feasibility of an interpretable machine learning pipeline for predicting function and disability based on the World Health Organization Disability Assessment Schedule (WHODAS) 2.0 outcomes of clinical outpatients, using passively collected digital biomarkers. METHODS: One-month-long behavioral time-series data consisting of physical and digital activity descriptor variables were summarized using statistical measures (minimum, maximum, mean, median, SD, and IQR), creating 64 features that were used for prediction. We then applied a sequential feature selection to each WHODAS 2.0 domain (cognition, mobility, self-care, getting along, life activities, and participation) in order to find the most descriptive features for each domain. Finally, we predicted the WHODAS 2.0 functional domain scores using linear regression using the best feature subsets. We reported the mean absolute errors and the mean absolute percentage errors over 4 folds as goodness-of-fit statistics to evaluate the model and allow for between-domain performance comparison. RESULTS: Our machine learning-based models for predicting patients' WHODAS functionality scores per domain achieved an average (across the 6 domains) mean absolute percentage error of 19.5%, varying between 14.86% (self-care domain) and 27.21% (life activities domain). We found that 5-19 features were sufficient for each domain, and the most relevant being the distance traveled, time spent at home, time spent walking, exercise time, and vehicle time. CONCLUSIONS: Our findings show the feasibility of using machine learning-based methods to assess functional health solely from passively sensed mobile data. The feature selection step provides a set of interpretable features for each domain, ensuring better explainability to the models' decisions-an important aspect in clinical practice.

A Quinoa Protein Hydrolysate Fractionated by Electrodialysis with Ultrafiltration Membranes Improves Maternal and Fetal Outcomes in a Mouse Model of Gestational Diabetes Mellitus.

SCOPE: Quinoa intake exerts hypoglycemic and hypolipidemic effects in animals and humans. Although peptides from quinoa inhibit key enzymes involved in glucose homeostasis in vitro, their in vivo antidiabetic properties have not been investigated. METHODS AND RESULTS: This study evaluated the effect of oral administration of a quinoa protein hydrolysate (QH) produced through enzymatic hydrolysis and fractionation by electrodialysis with ultrafiltration membrane (EDUF) (FQH) on the metabolic and pregnancy outcomes of Lepdb/+ pregnant mice, a preclinical model of gestational diabetes mellitus. The 4-week pregestational consumption of 2.5 mg mL-1 of QH in water prevented glucose intolerance and improves hepatic insulin signaling in dams, also reducing fetal weights. Sequencing and bioinformatic analyses of the defatted FQH (FQHD) identified 11 peptides 6-10 amino acids long that aligned with the quinoa proteome and exhibited putative anti-dipeptidyl peptidase-4 (DPP-IV) activity, confirmed in vitro in QH, FQH, and FDQH fractions. Peptides homologous to mouse and human proteins enriched for biological processes related to glucose metabolism are also identified. CONCLUSION: Processing of quinoa protein may be used to develop a safe and effective nutritional intervention to control glucose intolerance during pregnancy. Further studies are required to confirm if this nutritional intervention is applicable to pregnant women.

Automatic patient functionality assessment from multimodal data using deep learning techniques - Development and feasibility evaluation.

Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations. This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients. Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.

Handling Ill-Conditioned Omics Data With Deep Probabilistic Models.

The advent of high-throughput technologies has produced an increase in the dimensionality of omics datasets, which limits the application of machine learning methods due to the great unbalance between the number of observations and features. In this scenario, dimensionality reduction is essential to extract the relevant information within these datasets and project it in a low-dimensional space, and probabilistic latent space models are becoming popular given their capability to capture the underlying structure of the data as well as the uncertainty in the information. This article aims to provide a general classification and dimensionality reduction method based on deep latent space models that tackles two of the main problems that arise in omics datasets: the presence of missing data and the limited number of observations against the number of features. We propose a semi-supervised Bayesian latent space model that infers a low-dimensional embedding driven by the target label: the Deep Bayesian Logistic Regression (DBLR) model. During inference, the model also learns a global vector of weights that allows it to make predictions given the low-dimensional embedding of the observations. Since this kind of dataset is prone to overfitting, we introduce an additional probabilistic regularization method based on the semi-supervised nature of the model. We compared the performance of the DBLR against several state-of-the-art methods for dimensionality reduction, both in synthetic and real datasets with different data types. The proposed model provides more informative low-dimensional representations, outperforms the baseline methods in classification, and can naturally handle missing entries.

Regularizing transformers with deep probabilistic layers.

Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer. We study its advantages regarding the depth where it is placed and prove its effectiveness in several scenarios. Experimental result demonstrates that the inclusion of deep generative models within Transformer-based architectures such as BERT, RoBERTa, or XLM-R can bring more versatile models, able to generalize better and achieve improved imputation score in tasks such as SST-2 and TREC or even impute missing/noisy words with richer text.

Multi-task longitudinal forecasting with missing values on Alzheimer's disease.

BACKGROUND AND OBJECTIVE: Machine learning techniques typically used in dementia assessment are not able to learn multiple tasks jointly and deal with time-dependent heterogeneous data containing missing values. In this paper, we reformulate SSHIBA, a recently introduced Bayesian multi-view latent variable model, for jointly learning diagnosis, ventricle volume, and ADAS score in dementia on longitudinal data with missing values. METHODS: We propose a novel Bayesian Variational inference framework capable of simultaneously imputing missing values and combining information from several views. This way, we can combine different data views from different time-points in a common latent space and learn the relationships between each time-point, using the semi-supervised formulation to fully exploit the temporal structure of the data and handle missing values. In turn, the model can combine all the available information to simultaneously model and predict multiple output variables. RESULTS: We applied the proposed model to jointly predict diagnosis, ventricle volume, and ADAS score in dementia. The comparison of imputation strategies demonstrated the superior performance of the semi-supervised formulation of the model, improving the best baseline methods. Moreover, the performance in simultaneous prediction of diagnosis, ventricle volume, and ADAS score led to an improved prediction performance over the best baseline method. CONCLUSIONS: The results demonstrate that the proposed SSHIBA framework can learn an excellent imputation of the missing values and outperforming the baselines while simultaneously predicting three different tasks.

Medical Data Wrangling With Sequential Variational Autoencoders.

Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is that these patterns occur in bursts due to sensors that are off for some time or data collected in a misaligned uneven fashion, among other causes. This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs). In particular, we propose a new methodology, the Shi-VAE, which extends the capabilities of VAEs to sequential streams of data with missing observations. We compare our model against state-of-the-art solutions in an intensive care unit database (ICU) and a dataset of passive human monitoring. Furthermore, we find that standard error metrics such as RMSE are not conclusive enough to assess temporal models and include in our analysis the cross-correlation between the ground truth and the imputed signal. We show that Shi-VAE achieves the best performance in terms of using both metrics, with lower computational complexity than the GP-VAE model, which is the state-of-the-art method for medical records.

Similar Metabolic Health in Overweight/Obese Individuals With Contrasting Metabolic Flexibility to an Oral Glucose Tolerance Test.

Background: Low metabolic flexibility (MetF) may be an underlying factor for metabolic health impairment. Individuals with low MetF are thus expected to have worse metabolic health than subjects with high MetF. Therefore, we aimed to compare metabolic health in individuals with contrasting MetF to an oral glucose tolerance test (OGTT). Methods: In individuals with excess body weight, we measured MetF as the change in respiratory quotient (RQ) from fasting to 1 h after ingestion of a 75-g glucose load (i.e., OGTT). Individuals were then grouped into low and high MetF (Low-MetF n = 12; High-MetF n = 13). The groups had similar body mass index, body fat, sex, age, and maximum oxygen uptake. Metabolic health markers (clinical markers, insulin sensitivity/resistance, abdominal fat, and intrahepatic fat) were compared between groups. Results: Fasting glucose, triglycerides (TG), and high-density lipoprotein (HDL) were similar between groups. So were insulin sensitivity/resistance, visceral, and intrahepatic fat. Nevertheless, High-MetF individuals had higher diastolic blood pressure, a larger drop in TG concentration during the OGTT, and a borderline significant (P = 0.05) higher Subcutaneous Adipose Tissue (SAT). Further, compared to Low-MetF, High-MetF individuals had an about 2-fold steeper slope for the relationship between SAT and fat mass index. Conclusion: Individuals with contrasting MetF to an OGTT had similar metabolic health. Yet High-MetF appears related to enhanced circulating TG clearance and enlarged subcutaneous fat.

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach.

BACKGROUND: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a promising opportunity to construct behavioral markers of mental health. Combining such data with self-reported information about psychological symptoms may provide a more comprehensive and contextualized view of a patient's mental state than questionnaire data alone. However, mobile sensed data are usually noisy and incomplete, with significant amounts of missing observations. Therefore, recognizing the clinical potential of mHealth tools depends critically on developing methods to cope with such data issues. OBJECTIVE: This study aims to present a machine learning-based approach for emotional state prediction that uses passively collected data from mobile phones and wearable devices and self-reported emotions. The proposed methods must cope with high-dimensional and heterogeneous time-series data with a large percentage of missing observations. METHODS: Passively sensed behavior and self-reported emotional state data from a cohort of 943 individuals (outpatients recruited from community clinics) were available for analysis. All patients had at least 30 days' worth of naturally occurring behavior observations, including information about physical activity, geolocation, sleep, and smartphone app use. These regularly sampled but frequently missing and heterogeneous time series were analyzed with the following probabilistic latent variable models for data averaging and feature extraction: mixture model (MM) and hidden Markov model (HMM). The extracted features were then combined with a classifier to predict emotional state. A variety of classical machine learning methods and recurrent neural networks were compared. Finally, a personalized Bayesian model was proposed to improve performance by considering the individual differences in the data and applying a different classifier bias term for each patient. RESULTS: Probabilistic generative models proved to be good preprocessing and feature extractor tools for data with large percentages of missing observations. Models that took into account the posterior probabilities of the MM and HMM latent states outperformed those that did not by more than 20%, suggesting that the underlying behavioral patterns identified were meaningful for individuals' overall emotional state. The best performing generalized models achieved a 0.81 area under the curve of the receiver operating characteristic and 0.71 area under the precision-recall curve when predicting self-reported emotional valence from behavior in held-out test data. Moreover, the proposed personalized models demonstrated that accounting for individual differences through a simple hierarchical model can substantially improve emotional state prediction performance without relying on previous days' data. CONCLUSIONS: These findings demonstrate the feasibility of designing machine learning models for predicting emotional states from mobile sensing data capable of dealing with heterogeneous data with large numbers of missing observations. Such models may represent valuable tools for clinicians to monitor patients' mood states.

Basal Glucose on Tolerance Test During Pregnancy Predicts Impaired Fasting Glucose and Type 2 Diabetes Within 2 Months After Gestational Diabetes.

OBJECTIVES: Postpartum mothers with gestational diabetes may remain with either type 2 diabetes mellitus, impaired glucose tolerance or impaired fasting glucose. Our aim in this study was to identify maternal variables that could predict 1 or more of these conditions. METHODS: In 193 singleton pregnancies with gestational diabetes, we applied bivariate logistic regression and receiver-operating characteristic curves to data from the index glucose-challenge test that allowed the diagnosis of gestational diabetes. RESULTS: Receiver-operating characteristic curves of fasting glucose from the index glucose-challenge test predicted impaired fasting glucose and type 2 diabetes mellitus combined, with a sensitivity of 100%, false-positive rate of 40.5%, area under the curve of 0.849, p=0.004 and positive predictive value 45%, and with a cutoff point of 4.7 mmol/L. CONCLUSIONS: At the time of diagnosis of gestational diabetes during pregnancy, a basal glucose level of ≥4.7 mmol/L on index glucose-challenge test indicates a 45% probability of either type 2 diabetes mellitus or impending diabetes early postpartum.

Obesity and Gestational Diabetes in Pregnant Care and Clinical Practice.

Obesity and Gestational Diabetes Mellitus (GDM) are the most frequent pathologies affecting mothers and offspring during pregnancy. Both conditions have shown a sustained increase in their prevalence in recent years, and they worsen the outcome of pregnancy and the long-term health of mothers. Obesity increases the risk of GDM and pre-eclampsia during pregnancy and elevates the risk of developing metabolic syndrome in later life. Offspring of obese mothers have an increased risk of obstetric morbidity and mortality and, consistent with the developmental origins of health and disease, a long term risk of childhood obesity and metabolic dysfunction. On the other hand, GDM also increases the risk of pre-eclampsia, caesarean section, and up to 50% of women will develop type 2 diabetes later in life. From a fetal point of view, it increases the risk of macrosomia, large-for-gestational-age fetuses, shoulder dystocia and birth trauma. The insulin resistance and inflammatory mediators released by a hypoxic trophoblast are mainly responsible for the poor pregnancy outcome in obese or GDM patients. The adequate management of both pathologies includes modifications in the diet and physical activity. Drug therapy should be considered when medical nutrition therapy and moderate physical activity fail to achieve treatment goals. The antenatal prediction of macrosomia is a challenge for physicians. The timing and the route of delivery should consider adequate metabolic control, gestational age, and optimal conditions for a vaginal birth. The best management of these pathologies includes pre-conception planning to reduce the risks during pregnancy and improve the quality of life of these patients.

Direct Relationship Between Metabolic Flexibility Measured During Glucose Clamp and Prolonged Fast in Men.

OBJECTIVE: This study aimed to determine the relationship between metabolic flexibility (MetFlex) measured during a euglycemic-hyperinsulinemic clamp and a prolonged fast. This study also analyzed the association between MetFlex and metabolic health. METHODS: Eighteen healthy men (mean [SD]: 22 [2] years old; BMI: 22 [1] kg/m2 ) performed two sessions: (1) euglycemic-hyperinsulinemic clamp (2 mIU/kg of insulin per minute) and (2) ~20-hour fast. Clamp MetFlex corresponded to the change in (Δ) respiratory quotient (RQ) (ΔRQ = postchallenge RQ - prechallenge RQ) adjusted for M value and prechallenge RQ. Prolonged fast MetFlex corresponded to the ΔRQ adjusted for the Δß-hydroxybutyrate and prechallenge RQ. RESULTS: MetFlex during the clamp related directly with MetFlex during prolonged fast (r = 0.59, P = 0.014). Using the median of MetFlex for each challenge, this study split participants into high or low MetFlex. Participants with high or low MetFlex to both challenges were identified. Participants with high MetFlex had 3% lower serum low-density lipoprotein cholesterol than participants with low MetFlex (P = 0.021). CONCLUSIONS: Measuring MetFlex during a clamp or a prolonged fast produces similar results, despite challenging the oxidation of different substrates. An impaired MetFlex in response to these challenges may be an early event in the development of abnormal lipid metabolism.

Platelet Serotonin Levels Are Associated with Plasma Soluble Leptin Receptor Concentrations in Normoglycemic Women.

Most peripheral serotonin (5-hydroxytryptamine (5HT)) is synthetized in the gut with platelets being its main circulating reservoir. 5HT is acting as a hormone in key organs to regulate glucose and lipid metabolism. However, the relation between platelet 5HT levels and traits related to glucose homeostasis and lipid metabolism in humans remains poorly explored. The objectives of this study were (a) to assess the association between platelet 5HT levels and plasma concentration of nonesterified fatty acids (NEFAs) and some adipokines including leptin and its soluble leptin receptor (sOb-R), (b) to assess the association between platelet 5HT levels and anthropometric traits and indexes of insulin secretion/sensitivity derived from oral glucose tolerance test (OGTT), and (c) to evaluate changes in platelet 5HT levels in response to OGTT. In a cross-sectional study, 59 normoglycemic women underwent a standard 2-hour OGTT. Plasma leptin, sOb-R, total and high molecular weight adiponectin, TNFα, and MCP1 were determined by immunoassays. Platelet 5HT levels and NEFAs were measured before and after OGTT. The free leptin index was calculated from leptin and sOb-R measurements. Insulin sensitivity indexes derived from OGTT (HOMA-S and Matsuda ISICOMP) and plasma NEFAs (Adipose-IR, Revised QUICKI) were also calculated. Our data show that among metabolic traits, platelet 5HT levels were associated with plasma sOb-R (r = 0.39, p = 0.003, corrected p = 0.018). Platelet 5HT levels were reduced in response to OGTT (779 ± 237 vs.731 ± 217 ng/109 platelets, p = 0.005). In conclusion, platelet 5HT levels are positively associated with plasma sOb-R concentrations and reduced in response to glucose intake possibly indicating a role of peripheral 5HT in leptin-mediated appetite regulation.

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly sampled data sequences. In our method, we model each of them with a recurrent neural network, and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent pre-trained schemes to cope with very short sequences. Using a database of 1023 patients, our experimental results show that the addition of EMA records boosts the system recall to predict the suicidal ideation diagnosis from 48.13% obtained exclusively from EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides interpretability through the t-distributed stochastic neighbor embedding (t-SNE) representation of the latent space. Furthermore, the most relevant input features are identified and interpreted medically.

Gestational Diabetes: Glycemic Control in the Last Two Weeks Before Delivery Contributes to Newborn Insulinemia.

INTRODUCTION: Fetal hyperinsulinemia in gestational diabetes mellitus (GDM) not only is important during intrauterine life, a time when it can result in macrosomia, but also at delivery, since it can result in neonatal hypoglycemia and hyperbilirubinemia. The question is, how long before delivery does maternal glycemic control contribute to newborn insulinemia in GDM? METHODS: In 72 women with GDM, we calculated Spearman's rank (rs) correlations between umbilical cord blood C-peptide at birth (a biomarker of insulin secretion), and both maternal glycosylated hemoglobin (HbA1c) and mean blood glucose (MBG) recorded in the last two visits prior to delivery. Iterative correlations were done between umbilical cord blood C-peptide at birth, and maternal glucose control, at 0, 1, 2, 3, 4, and 5 weeks before delivery. RESULTS: At an early visit (32.95 ± 1.8 weeks), rs = 0.353 (P = 0.07) between HbA1c and C-peptide, whereas rs = 0.244 (P = 0.186) between MBG and C-peptide. At the latest visit (35.04 ± 1.6 weeks), rs = 0.456 (P = 0.004) between HbA1c versus C-peptide, and rs = 0.359 (P = 0.023) between MBG versus C-peptide. Iterative correlations between MBG and C-peptide became significant at 2 weeks before delivery. CONCLUSION: To further reduce the risk of hypoglycemia and hyperbilirubinemia in infants born to women with GDM, besides applying a strict in-patient glucose control protocol at delivery, it is necessary to improve even more the quality of maternal glucose control during the last 2 weeks prior to delivery.

Elastin degradation products in acute lung injury induced by gastric contents aspiration.

BACKGROUND: Gastric contents aspiration is a high-risk condition for acute lung injury (ALI). Consequences range from subclinical pneumonitis to respiratory failure, depending on the volume of aspirate. A large increment in inflammatory cells, an important source of elastase, potentially capable of damaging lung tissue, has been described in experimental models of aspiration. We hypothesized that in early stages of aspiration-induced ALI, there is proteolytic degradation of elastin, preceding collagen deposition. Our aim was to evaluate whether after a single orotracheal instillation of gastric fluid, there is evidence of elastin degradation. METHODS: Anesthesized Sprague-Dawley rats received a single orotracheal instillation of gastric fluid and were euthanized 4, 12 and 24 h and at day 4 after instillation (n = 6/group). We used immunodetection of soluble elastin in lung tissue and BALF and correlated BALF levels of elastin degradation products with markers of ALI. We investigated possible factors involved in elastin degradation and evaluated whether a similar pattern of elastin degradation can be found in BALF samples of patients with interstitial lung diseases known to have aspirated. Non-parametric ANOVA (Kruskall-Wallis) and linear regression analysis were used. RESULTS: We found evidence of early proteolytic degradation of lung elastin. Elastin degradation products are detected both in lung tissue and BALF in the first 24 h and are significantly reduced at day 4. They correlate significantly with ALI markers, particularly PMN cell count, are independent of acidity and have a similar molecular weight as those obtained using pancreatic elastase. Evaluation of BALF from patients revealed the presence of elastin degradation products not present in controls that are similar to those found in BALF of rats treated with gastric fluid. CONCLUSIONS: A single instillation of gastric fluid into the lungs induces early proteolytic degradation of elastin, in relation to the magnitude of alveolar-capillary barrier derangement. PMN-derived proteases released during ALI are mostly responsible for this damage. BALF from patients showed elastin degradation products similar to those found in rats treated with gastric fluid. Long-lasting effects on lung elastic properties could be expected under conditions of repeated instillations of gastric fluid in experimental animals or repeated aspiration events in humans.

47-Fold rise of diabetes in childbearing age Chilean women: Markov model and cost-effectiveness of prevention of birth defects.

AIM: Prevalence of type 2 diabetes mellitus (T2DM) during childbearing age in Chile had a 47-fold rise in 7 years, reaching 120 844 women, half of which are unaware of their condition. We aimed to project pregnancies and births among Chilean women of childbearing age (WCBA) with T2DM and report the incidence of birth defects and the associated years of life lost and lifetime costs. METHODS: Markov model of cohort of WCBA with T2DM (WCBA-DM) with a 20-year time horizon (2018-2037), using data from previous studies. Two scenarios were assessed: scenario A: no universal detection of T2DM and scenario B: universal screening of T2DM using glycosylated hemoglobin levels. Both lifetime costs and disability-adjusted life years (DALY) were calculated with a 5% discount rate (US$ of 2017). RESULTS: In scenario A, 12 163 infants with birth defects could be born among the analyzed cohort, resulting in 243 260 years of life lost, 296 652 DALY and in lifetime costs of US$ 1 957 657 966. In scenario B, the first three figures could be reduced by 70.4% to 3599 infants with birth defects, 71 980 years of life lost and 87 794 DALY. Due to the addition of diabetes screening and new patient costs to scenario B, there would be a lesser reduction (67.3%) in total lifetime costs, to US$ 640 669 296. CONCLUSION: Screening of diabetes in WCBA would yield a 20-year reduction of 70.4% in the number of infants with birth defects, years of life lost and DALY. Total lifetime costs could be reduced by 67.3%.

Changes in the pattern of fibrosis in the rat lung with repetitive orotracheal instillations of gastric contents: evidence of persistent collagen accumulation.

Recurrent aspiration of gastric contents has been associated with several interstitial lung diseases. Despite this association, the pathogenic role of aspiration in these diseases has been poorly studied and little is known about extracellular matrix (ECM) changes in animal models of repetitive events of aspiration. Our aim was to study the repair phase of lung injury induced by each of several instillations of gastric fluid in Sprague-Dawley rats to evaluate changes in ECM and their reversibility. Anesthetized animals received weekly orotracheal instillations of gastric fluid for 1, 2, 3, and 4 wk and were euthanized at day 7 after last instillation. For reversibility studies, another group received 7 weekly instillations and was euthanized at day 7 or 60 after last instillation. Biochemical and histological measurements were used to evaluate ECM changes. Lung hydroxyproline content increased progressively and hematoxylin and eosin, Masson's trichrome, and alpha-SMA stains showed that after a single instillation, intra-alveolar fibrosis predominated, whereas with repetitive instillations this fibrosis pattern became less prominent and interstitial fibrosis progressively became evident. Both type I and III collagen increased in intra-alveolar and interstitial fibrosis. Imbalance between matrix metalloproteinase-2 (MMP-2) activity and tissue inhibitor of metalloproteinase-2 (TIMP-2) expression was observed, favoring either collagen degradation or accumulation depending on the number of instillations. Caspase-3 activation was also dose dependent. ECM changes were partially reversible at long-term evaluation, since Masson bodies, granulomas, and foreign body giant cells disappeared, whereas interstitial collagen accumulated. In conclusion, repetitive lung instillations of gastric fluid induce progressive fibrotic changes in rat lung ECM that persist at long-term evaluation.

Effectiveness on maternal and offspring metabolic control of a home-based dietary counseling intervention and DHA supplementation in obese/overweight pregnant women (MIGHT study): A randomized controlled trial-Study protocol.

BACKGROUND: Lifestyle interventions are the primary prevention strategy for gestational diabetes (GDM) in obese/overweight women; however, these interventions have shown limited effectiveness. Omega-3 polyunsaturated fatty acids (PUFAs) intake has shown beneficial effects on glucose metabolism, lipid fractions and inflammatory factors in women who already have GDM. Combining PUFAs supplementation with a lifestyle intervention could achieve lower increase of glucose levels by improving insulin sensitivity. Our aim is to assess two prenatal nutritional interventions (home-based dietary counseling and/or docosahexaenoic acid (DHA) supplementation) delivered to obese/overweight women during pregnancy for them and their offspring to achieve better metabolic control. METHODS/DESIGN: Randomized controlled trial, 2 × 2 factorial design. Eligible pregnant women will be randomly allocated to one of the four parallel arms: 1) Home-based dietary counseling +800 mg/day DHA supplementation (n = 250); 2) 800 mg/day DHA (n = 250); 3) Home-based dietary counseling +200 mg/day DHA (n = 250); 4) 200 mg/day DHA (n = 250). Primary outcomes are: GDM; macrosomia; and neonatal insulin resistance. Data analyses will be done on an intention-to-treat basis. DISCUSSION: We expect the present study to contribute to the understanding of the potential effectiveness of an omega-3 supplementation on the risk of developing GDM in overweight/obese pregnant women. We will also test if the combination of having better dietary habits alongside with omega 3 supplementation will improve insulin sensitivity and as consequence, a lower elevation of glucose levels could be achieved. TRIAL REGISTRATION: NCT02574767.