Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis

Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring, and computed tomography data and have ignored abdominal ultrasound, despite its noninvasive nature and widespread availability. In this work, we present interpretable machine learning models for predicting the diagnosis, management and severity of suspected appendicitis using ultrasound images. Our approach utilizes concept bottleneck models (CBM) that facilitate interpretation and interaction with high-level concepts understandable to clinicians. Furthermore, we extend CBMs to prediction problems with multiple views and incomplete concept sets. Our models were trained on a dataset comprising 579 pediatric patients with 1709 ultrasound images accompanied by clinical and laboratory data. Results show that our proposed method enables clinicians to utilize a human-understandable and intervenable predictive model without compromising performance or requiring time-consuming image annotation when deployed. For predicting the diagnosis, the extended multiview CBM attained an AUROC of 0.80 and an AUPR of 0.92, performing comparably to similar black-box neural networks trained and tested on the same dataset.

Blood glucose forecasting from temporal and static information in children with T1D

Background

The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level.

Materials and Methods

In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data-(dilated) recurrent neural networks and a transformer-on our dataset for short-term (30  min) and long-term (2  h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group.

Results

Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of 30  min (RMSE of 1.66  mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of 1.50  mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data.

Conclusion

We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons.

Validating the early phototherapy prediction tool across cohorts

Background

Hyperbilirubinemia of the newborn infant is a common disease worldwide. However, recognized early and treated appropriately, it typically remains innocuous. We recently developed an early phototherapy prediction tool (EPPT) by means of machine learning (ML) utilizing just one bilirubin measurement and few clinical variables. The aim of this study is to test applicability and performance of the EPPT on a new patient cohort from a different population.

Materials and methods

This work is a retrospective study of prospectively recorded neonatal data from infants born in 2018 in an academic hospital, Regensburg, Germany, meeting the following inclusion criteria: born with 34 completed weeks of gestation or more, at least two total serum bilirubin (TSB) measurement prior to phototherapy. First, the original EPPT-an ensemble of a logistic regression and a random forest-was used in its freely accessible version and evaluated in terms of the area under the receiver operating characteristic curve (AUROC). Second, a new version of the EPPT model was re-trained on the data from the new cohort. Third, the predictive performance, variable importance, sensitivity and specificity were analyzed and compared across the original and re-trained models.

Results

In total, 1,109 neonates were included with a median (IQR) gestational age of 38.4 (36.6-39.9) and a total of 3,940 bilirubin measurements prior to any phototherapy treatment, which was required in 154 neonates (13.9%). For the phototherapy treatment prediction, the original EPPT achieved a predictive performance of 84.6% AUROC on the new cohort. After re-training the model on a subset of the new dataset, 88.8% AUROC was achieved as evaluated by cross validation. The same five variables as for the original model were found to be most important for the prediction on the new cohort, namely gestational age at birth, birth weight, bilirubin to weight ratio, hours since birth, bilirubin value.

Discussion

The individual risk for treatment requirement in neonatal hyperbilirubinemia is robustly predictable in different patient cohorts with a previously developed ML tool (EPPT) demanding just one TSB value and only four clinical parameters. Further prospective validation studies are needed to develop an effective and safe clinical decision support system.

Machine learning analysis of humoral and cellular responses to SARS-CoV-2 infection in young adults

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) induces B and T cell responses, contributing to virus neutralization. In a cohort of 2,911 young adults, we identified 65 individuals who had an asymptomatic or mildly symptomatic SARS-CoV-2 infection and characterized their humoral and T cell responses to the Spike (S), Nucleocapsid (N) and Membrane (M) proteins. We found that previous infection induced CD4 T cells that vigorously responded to pools of peptides derived from the S and N proteins. By using statistical and machine learning models, we observed that the T cell response highly correlated with a compound titer of antibodies against the Receptor Binding Domain (RBD), S and N. However, while serum antibodies decayed over time, the cellular phenotype of these individuals remained stable over four months. Our computational analysis demonstrates that in young adults, asymptomatic and paucisymptomatic SARS-CoV-2 infections can induce robust and long-lasting CD4 T cell responses that exhibit slower decays than antibody titers. These observations imply that next-generation COVID-19 vaccines should be designed to induce stronger cellular responses to sustain the generation of potent neutralizing antibodies.

Multimodal Remote Home Monitoring of Lung Transplant Recipients during COVID-19 Vaccinations: Usability Pilot Study of the COVIDA Desk Incorporating Wearable Devices

Background and Objectives: Remote patient monitoring (RPM) of vital signs and symptoms for lung transplant recipients (LTRs) has become increasingly relevant in many situations. Nevertheless, RPM research integrating multisensory home monitoring in LTRs is scarce. We developed a novel multisensory home monitoring device and tested it in the context of COVID-19 vaccinations. We hypothesize that multisensory RPM and smartphone-based questionnaire feedback on signs and symptoms will be well accepted among LTRs. To assess the usability and acceptability of a remote monitoring system consisting of wearable devices, including home spirometry and a smartphone-based questionnaire application for symptom and vital sign monitoring using wearable devices, during the first and second SARS-CoV-2 vaccination. Materials and Methods: Observational usability pilot study for six weeks of home monitoring with the COVIDA Desk for LTRs. During the first week after the vaccination, intensive monitoring was performed by recording data on physical activity, spirometry, temperature, pulse oximetry and self-reported symptoms, signs and additional measurements. During the subsequent days, the number of monitoring assessments was reduced. LTRs reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. Results: Ten LTRs planning to receive the first COVID-19 vaccinations were recruited. For the intensive monitoring study phase, LTRs recorded symptoms, signs and additional measurements. The most frequent adverse events reported were local pain, fatigue, sleep disturbance and headache. The duration of these symptoms was 5-8 days post-vaccination. Adherence to the main monitoring devices was high. LTRs rated usability as high. The majority were willing to continue monitoring. Conclusions: The COVIDA Desk showed favorable technical performance and was well accepted by the LTRs during the vaccination phase of the pandemic. The feasibility of the RPM system deployment was proven by the rapid recruitment uptake, technical performance (i.e., low number of errors), favorable user experience questionnaires and detailed individual user feedback.

Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis

BACKGROUND

Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs.

STUDY DESIGN

Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier.

RESULTS

One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random.

CONCLUSIONS

Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics.

Re-focusing explainability in medicine

Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma and how much explanation is required to use artificial intelligence safely in healthcare. A key issue is the lack of consensus on the definition of explainability by experts, regulators, and healthcare professionals, resulting in a wide variety of terminology and expectations. This paper aims to fill the gap by defining minimal explainability standards to serve the views and needs of essential stakeholders in healthcare. In that sense, we propose to define minimal explainability criteria that can support doctors’ understanding, meet patients’ needs, and fulfill legal requirements. Therefore, explainability need not to be exhaustive but sufficient for doctors and patients to comprehend the artificial intelligence models’ clinical implications and be integrated safely into clinical practice. Thus, minimally acceptable standards for explainability are context-dependent and should respond to the specific need and potential risks of each clinical scenario for a responsible and ethical implementation of artificial intelligence.

A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions

Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on patient populations with certain diseases. However, it is unclear whether these specialized ML models-trained on patient subpopulations with certain diseases or defined by other clinical characteristics-are more accurate than a general ML model trained on an unrestricted hospital cohort. In this study based on an electronic health record cohort of consecutive inpatient cases of a single tertiary care center, we demonstrate that accurate prediction of hospital readmissions may be obtained by general, disease-independent, ML models. This general approach may substantially decrease the cost of development and deployment of respective ML models in daily clinical routine, as all predictions are obtained by the use of a single model.

Vaginal Delivery and Low Gestational Age are Key Risk Factors for Hypernatremia in Neonates<32 Weeks

Background Preterm neonates frequently experience hypernatremia (plasma sodium concentrations >145 mmol/l), which is associated with clinical complications, such as intraventricular hemorrhage.

Study design In this single center retrospective observational study, the following 7 risk factors for hypernatremia were analyzed in very low gestational age (VLGA, below 32 weeks) neonates: gestational age (GA), delivery mode (DM; vaginal or caesarian section), sex, birth weight, small for GA, multiple birth, and antenatal corticosteroids. Machine learning (ML) approaches were applied to obtain probabilities for hypernatremia.

Results 824 VLGA neonates were included (median GA 29.4 weeks, median birth weight 1170 g, caesarean section 83%). 38% of neonates experienced hypernatremia. Maximal sodium concentration of 144 mmol/l (interquartile range 142–147) was observed 52 hours (41–65) after birth. ML identified vaginal delivery and GA as key risk factors for hypernatremia. The risk of hypernatremia increased with lower GA from 22% for GA ≥ 31–32 weeks to 46% for GA < 31 weeks and 60% for GA < 27 weeks. A linear relationship between maximal sodium concentrations and GA was found, showing decreases of 0.29 mmol/l per increasing week GA in neonates with vaginal delivery and 0.49 mmol/l/week after cesarean section. Sex, multiple birth and antenatal corticosteroids were not associated hypernatremia.

Conclusion VLGA neonates with vaginal delivery and low GA have the highest risk for hypernatremia. Early identification of neonates at risk and early intervention may prevent extreme sodium excursions and associated clinical complications.

Identifying key predictors of mortality in young patients on chronic haemodialysis-a machine learning approach

BACKGROUND

The mortality risk remains significant in paediatric and adult patients on chronic haemodialysis (HD) treatment. We aimed to identify factors associated with mortality in patients who started HD as children and continued HD as adults.

METHODS

The data originated from a cohort of patients <30 years of age who started HD in childhood (≤19 years) on thrice-weekly HD in outpatient DaVita dialysis centres between 2004 and 2016. Patients with at least 5 years of follow-up since the initiation of HD or death within 5 years were included; 105 variables relating to demographics, HD treatment and laboratory measurements were evaluated as predictors of 5-year mortality utilizing a machine learning approach (random forest).

RESULTS

A total of 363 patients were included in the analysis, with 84 patients having started HD at <12 years of age. Low albumin and elevated lactate dehydrogenase (LDH) were the two most important predictors of 5-year mortality. Other predictors included elevated red blood cell distribution width or blood pressure and decreased red blood cell count, haemoglobin, albumin:globulin ratio, ultrafiltration rate, z-score weight for age or single-pool Kt/V (below target). Mortality was predicted with an accuracy of 81%.

CONCLUSIONS

Mortality in paediatric and young adult patients on chronic HD is associated with multifactorial markers of nutrition, inflammation, anaemia and dialysis dose. This highlights the importance of multimodal intervention strategies besides adequate HD treatment as determined by Kt/V alone. The association with elevated LDH was not previously reported and may indicate the relevance of blood-membrane interactions, organ malperfusion or haematologic and metabolic changes during maintenance HD in this population.

Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis

Rationale

Tuberculosis diagnosis in children remains challenging. Microbiological confirmation of tuberculosis disease is often lacking, and standard immunodiagnostic including the tuberculin skin test and interferon-γ release assay for tuberculosis infection has limited sensitivity. Recent research suggests that inclusion of novel Mycobacterium tuberculosis antigens has the potential to improve standard immunodiagnostic tests for tuberculosis.

Objective

To identify optimal antigen–cytokine combinations using novel Mycobacterium tuberculosis antigens and cytokine read-outs by machine learning algorithms to improve immunodiagnostic assays for tuberculosis.

Methods

A total of 80 children undergoing investigation of tuberculosis were included (15 confirmed tuberculosis disease, five unconfirmed tuberculosis disease, 28 tuberculosis infection and 32 unlikely tuberculosis). Whole blood was stimulated with 10 novel Mycobacterium tuberculosis antigens and a fusion protein of early secretory antigenic target (ESAT)-6 and culture filtrate protein (CFP) 10. Cytokines were measured using xMAP multiplex assays. Machine learning algorithms defined a discriminative classifier with performance measured using area under the receiver operating characteristics.

Measurements and main results

We found the following four antigen–cytokine pairs had a higher weight in the discriminative classifier compared to the standard ESAT-6/CFP-10-induced interferon-γ: Rv2346/47c- and Rv3614/15c-induced interferon-gamma inducible protein-10; Rv2031c-induced granulocyte-macrophage colony-stimulating factor and ESAT-6/CFP-10-induced tumor necrosis factor-α. A combination of the 10 best antigen–cytokine pairs resulted in area under the curve of 0.92 ± 0.04.

Conclusion

We exploited the use of machine learning algorithms as a key tool to evaluate large immunological datasets. This identified several antigen–cytokine pairs with the potential to improve immunodiagnostic tests for tuberculosis in children.

Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis

Background: Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children. Materials and Methods: Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables. Results: A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis. Discussion: This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool. Conclusion: ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system.

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable and may demand the use of artificial intelligence and machine learning to allow the data to be analyzed for decision making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric end points and biomarkers, the prediction of treatment nonresponders, and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.

Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning

BACKGROUND

Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital.

METHODS

We conducted a longitudinal study on preterm and term born neonates with serial measurements of total serum bilirubin in the first two weeks of life. An ensemble, that combines a logistic regression with a random forest classifier, was trained to discriminate between the two classes phototherapy treatment vs. no treatment.

RESULTS

Of 362 neonates included in this study, 98 had a phototherapy treatment, which our model was able to predict up to 48 h in advance with an area under the ROC-curve of 95.20%. From a set of 44 variables, including potential laboratory and clinical confounders, a subset of just four (bilirubin, weight, gestational age, hours since birth) suffices for a strong predictive performance. The resulting early phototherapy prediction tool (EPPT) is provided as an open web application.

CONCLUSION

Early detection of clinically relevant hyperbilirubinemia can be enhanced by the application of machine learning. Existing guidelines can be further improved to optimize timing of bilirubin measurements to avoid toxic hyperbilirubinemia in high-risk patients while minimizing unneeded measurements in neonates who are at low risk.

Gene expression analysis of biopsy samples reveals critical limitations of transcriptome-based molecular classifications of hepatocellular carcinoma

Molecular classification of hepatocellular carcinomas (HCC) could guide patient stratification for personalized therapies targeting subclass‐specific cancer ‘driver pathways’. Currently, there are several transcriptome‐based molecular classifications of HCC with different subclass numbers, ranging from two to six. They were established using resected tumours that introduce a selection bias towards patients without liver cirrhosis and with early stage HCCs. We generated and analyzed gene expression data from paired HCC and non‐cancerous liver tissue biopsies from 60 patients as well as five normal liver samples. Unbiased consensus clustering of HCC biopsy profiles identified 3 robust classes. Class membership correlated with survival, tumour size and with Edmondson and Barcelona Clinical Liver Cancer (BCLC) stage. When focusing only on the gene expression of the HCC biopsies, we could validate previously reported classifications of HCC based on expression patterns of signature genes. However, the subclass‐specific gene expression patterns were no longer preserved when the fold‐change relative to the normal tissue was used. The majority of genes believed to be subclass‐specific turned out to be cancer‐related genes differentially regulated in all HCC patients, with quantitative rather than qualitative differences between the molecular subclasses. With the exception of a subset of samples with a definitive β‐catenin gene signature, biological pathway analysis could not identify class‐specific pathways reflecting the activation of distinct oncogenic programs. In conclusion, we have found that gene expression profiling of HCC biopsies has limited potential to direct therapies that target specific driver pathways, but can identify subgroups of patients with different prognosis.

Unsupervised Structure Detection in Biomedical Data

A major challenge in computational biology is to find simple representations of high-dimensional data that best reveal the underlying structure. In this work, we present an intuitive and easy-to-implement method based on ranked neighborhood comparisons that detects structure in unsupervised data. The method is based on ordering objects in terms of similarity and on the mutual overlap of nearest neighbors. This basic framework was originally introduced in the field of social network analysis to detect actor communities. We demonstrate that the same ideas can successfully be applied to biomedical data sets in order to reveal complex underlying structure. The algorithm is very efficient and works on distance data directly without requiring a vectorial embedding of data. Comprehensive experiments demonstrate the validity of this approach. Comparisons with state-of-the-art clustering methods show that the presented method outperforms hierarchical methods as well as density based clustering methods and model-based clustering. A further advantage of the method is that it simultaneously provides a visualization of the data. Especially in biomedical applications, the visualization of data can be used as a first pre-processing step when analyzing real world data sets to get an intuition of the underlying data structure. We apply this model to synthetic data as well as to various biomedical data sets which demonstrate the high quality and usefulness of the inferred structure.

Pegylated IFN-α regulates hepatic gene expression through transient Jak/STAT activation

The use of pegylated interferon-α (pegIFN-α) has replaced unmodified recombinant IFN-α for the treatment of chronic viral hepatitis. While the superior antiviral efficacy of pegIFN-α is generally attributed to improved pharmacokinetic properties, the pharmacodynamic effects of pegIFN-α in the liver have not been studied. Here, we analyzed pegIFN-α-induced signaling and gene regulation in paired liver biopsies obtained prior to treatment and during the first week following pegIFN-α injection in 18 patients with chronic hepatitis C. Despite sustained high concentrations of pegIFN-α in serum, the Jak/STAT pathway was activated in hepatocytes only on the first day after pegIFN-α administration. Evaluation of liver biopsies revealed that pegIFN-α induces hundreds of genes that can be classified into four clusters based on different temporal expression profiles. In all clusters, gene transcription was mainly driven by IFN-stimulated gene factor 3 (ISGF3). Compared with conventional IFN-α therapy, pegIFN-α induced a broader spectrum of gene expression, including many genes involved in cellular immunity. IFN-induced secondary transcription factors did not result in additional waves of gene expression. Our data indicate that the superior antiviral efficacy of pegIFN-α is not the result of prolonged Jak/STAT pathway activation in hepatocytes, but rather is due to induction of additional genes that are involved in cellular immune responses.

Malaria haplotype frequency estimation

We present a Bayesian approach for estimating the relative frequencies of multi-single nucleotide polymorphism (SNP) haplotypes in populations of the malaria parasite Plasmodium falciparum by using microarray SNP data from human blood samples. Each sample comes from a malaria patient and contains one or several parasite clones that may genetically differ. Samples containing multiple parasite clones with different genetic markers pose a special challenge. The situation is comparable with a polyploid organism. The data from each blood sample indicates whether the parasites in the blood carry a mutant or a wildtype allele at various selected genomic positions. If both mutant and wildtype alleles are detected at a given position in a multiply infected sample, the data indicates the presence of both alleles, but the ratio is unknown. Thus, the data only partially reveals which specific combinations of genetic markers (i.e. haplotypes across the examined SNPs) occur in distinct parasite clones. In addition, SNP data may contain errors at non-negligible rates. We use a multinomial mixture model with partially missing observations to represent this data and a Markov chain Monte Carlo method to estimate the haplotype frequencies in a population. Our approach addresses both challenges, multiple infections and data errors.

Interferon-induced gene expression is a stronger predictor of treatment response than IL28B genotype in patients with hepatitis C

BACKGROUND & AIMS

The host immune response during the chronic phase of hepatitis C virus infection varies among individuals; some patients have a no interferon (IFN) response in the liver, whereas others have full activation of IFN-stimulated genes (ISGs). Preactivation of this endogenous IFN system is associated with nonresponse to pegylated IFN-α (pegIFN-α) and ribavirin. Genome-wide association studies have associated allelic variants near the IL28B (IFNλ3) gene with treatment response. We investigated whether IL28B genotype determines the constitutive expression of ISGs in the liver and compared the abilities of ISG levels and IL28B genotype to predict treatment outcome.

METHODS

We genotyped 109 patients with chronic hepatitis C for IL28B allelic variants and quantified the hepatic expression of ISGs and of IL28B. Decision tree ensembles, in the form of a random forest classifier, were used to calculate the relative predictive power of these different variables in a multivariate analysis.

RESULTS

The minor IL28B allele was significantly associated with increased expression of ISG. However, stratification of the patients according to treatment response revealed increased ISG expression in nonresponders, irrespective of IL28B genotype. Multivariate analysis of ISG expression, IL28B genotype, and several other factors associated with response to therapy identified ISG expression as the best predictor of treatment response.

CONCLUSIONS

IL28B genotype and hepatic expression of ISGs are independent predictors of response to treatment with pegIFN-α and ribavirin in patients with chronic hepatitis C. The most accurate prediction of response was obtained with a 4-gene classifier comprising IFI27, ISG15, RSAD2, and HTATIP2.

Energy metabolism of overweight women 1 mo and 1 y after an 8-wk slimming period

Twenty-four hour energy expenditure (24EE) and spontaneous physical activity were measured in 13 overweight women before and at the end of an 8-wk slimming period. These measurements were repeated in 10 women 1 mo after the slimming period (1-mo follow-up) and in eight women 1 y after slimming (1-y follow-up). The weight loss achieved after 8 wk of slimming (8.7-9.9 kg) was maintained throughout the follow-up periods; 24EE decreased during slimming from 9572 +/- 703 (means +/- SD) to 8060 +/- 471 kJ/d and increased after refeeding to 8379 +/- 739 kJ/d after 1 mo and to 8285 +/- 454 kJ/d after 1 y. On the basis of body weight, energy requirement (approximately 126 kJ.kg-1.d-1) did not change throughout the slimming and follow-up programs. Spontaneous physical activity, which had been lowered during slimming, tended to increase afterwards. No changes in metabolic efficiency seemed to occur or persist.

Adaptation of energy metabolism of overweight women to alternating and continuous low energy intake

To investigate whether a slimming diet based on alternating (low with normal) energy intakes could counteract a decrease in energy requirement, 24-h energy expenditure (24EE), sleeping energy expenditure (sleeping EE), and physical activity were determined in a respiration chamber in 27 overweight women: before weight reduction and after 4 and 8 wk of slimming. Daily alternating and continuous slimming diets were supplied. Average weight losses over 8 wk of slimming were 6.9-9.0 kg. After 8 wk at low energy intake, 24EE had declined by 12-16% (from 2328 +/- 219 to 1987 +/- 204 kcal, mean +/- SD). Sleeping EE had declined by 7-13% (from 64 +/- 6 to 57 +/- 6 kcal/h). Measurements of physical activity indicated a reduction of spontaneous physical activity during slimming. Alternating low energy intake did not prevent 24EE from declining. The reduction in 24EE was determined by a decrease of body weight, dietary induced thermogenesis (in proportion to caloric restriction), and physical activity. There seems little reason to consider other adaptive mechanisms.

Energy metabolism and requirements in different ethnic groups

Some studies on energy metabolism of men and women in Third World countries suggested that their basal metabolic rate (BMR) is lower compared to BMRs of people in Northern European and American countries. It is, however, not clear whether this results from ethnic factors, climate or adaptation to, for instance, a low energy intake. A study on energy requirements of people from Third World countries has therefore been performed. People with different ethnic backgrounds participated; they were divided into four ethnic groups: 8 African males, 7 Asian males of Mongolian origin (Asian-M), 8 Asian males of Caucasian origin (Asian-C) and 7 European males, who formed the control group. The participants from outside Europe had spent at least 3 months in the Netherlands. All participants consumed a diet (12 per cent of energy from protein, 22 per cent from fat and 66 per cent from carbohydrate) during 8 d. The dietary energy given to each individual was estimated to maintain energy equilibrium during the experiment. The last 3 nights and 2 days were spent in an indirect whole-body calorimeter. Two 24-h energy expenditure (24hEE) measurements were performed on each subject. The environmental temperature inside the calorimeter was 22.0-24.5 degrees C. Physical activity was light, mainly sedentary, with 75 min bicycling at 15 W. The Asian subjects had a significantly lower body weight and fat-free mass than the Europeans. Energy requirement (ER), 24hEE and EE during the night (8 h sleep) was lower in the Asian and African subjects compared to the Europeans, but the difference only reached significance for the Asian-C and African males. When ER, 24hEE and EE-night were expressed in relation to body weight and fat-free mass the Asian groups showed a higher ER and higher EE than the Europeans. This result is contrary to findings of others and may be caused eg, by a higher body weight and fat-free mass of the European controls. Comparison of EE-night with BMR estimated from FAO/WHO/UNU equations showed that the EE-night was consistently lower by about 9 per cent. This suggests that EE during the night may not be predicted by the BMR estimated by widely used equations. This study does not give conclusive evidence that an ethnic factor is involved in energy metabolism in humans.

Reproducibility of 24 h energy expenditure measurements using a human whole body indirect calorimeter

Ten female subjects completed two similar experimental procedures (periods 1 and 2) to obtain values of reproducibility of energy intake and 24 h energy expenditure (24hEE) measurements in a whole body indirect calorimeter. The periods consisted of consumption of a provided weight-maintenance diet for 6-8 d, faeces and urine collection during the last 4 d and occupation of the calorimeter during the last 3 d. The daily routine inside the calorimeter simulated a sedentary day in normal life with some physical activity: 8 h sleep, 75 min bicycling and the remaining time spent on sedentary activities. The metabolizable energy (ME) content of the diet (14% energy as protein, 46% energy as carbohydrate, 40% energy as fat) was calculated using food tables. The actual ME intake as well as digestibility and metabolizability of the diet were obtained later by analyses of food, faeces and urine for energy. Three consecutive 24hEE measurements were performed during the stay in the calorimeter in each period. The time interval between the two periods varied from 2 to 24 months. Reproducibility was assessed at group and individual level. Mean digestibility and metabolizability of the diet showed no significant difference between periods. The within-subject coefficient of variation of metabolizability between periods was 1.7%. Mean 24hEE (MJ) over 3 d did not differ between period 1 (8.78 (SD 0.63)) and period 2 (8.73 (SD 0.66)). The within-subject coefficient of variation in mean 24hEE over three successive days between periods was 3.1% but decreased, after deletion of values for subjects who were less adapted to the calorimeter, to 1.9%.(ABSTRACT TRUNCATED AT 250 WORDS)

Energy balances of eight volunteers fed on diets supplemented with either lactitol or saccharose

1. Complete 24 h energy and nitrogen balances were measured for eight subjects both while consuming a basal diet supplemented with 49 g saccharose/d (diet S) and while consuming the same basal diet but supplemented with 50 g lactitol monohydrate/d (diet L). 2. The subjects ate the two diets for 8 d. Faeces and urine were collected for the final 4 d. Exchange of respiratory gases (oxygen, carbon dioxide, hydrogen and methane) was measured during the final 72 h while the subjects stayed in an open-circuit respiration chamber, 11 m3, and simulated office work. Before eating diet L, subjects ate 50 g lactitol daily for 10 d. 3. On diets L and S, faecal moisture content averaged 0.787 and 0.753 g/g respectively, the difference being significant (P less than 0.05). On diet L, energy and nitrogen digestibilities and energy metabolizability averaged 0.922, 0.836 and 0.881 respectively, and on diet S 0.935, 0.869 and 0.896 respectively; the differences were also significant (P less than 0.05). Urinary energy losses and N balances were not significantly different for the two diets. 4. In all subjects only traces of methane were produced but hydrogen production differed significantly (P less than 0.05) for diets L and S, being 2.3 and 0.4 litres (normal temperature and pressure)/d respectively. 5. Intakes of metabolizable energy (ME) were corrected, within subjects, to energy equilibrium and equal metabolic body-weight. The corrected ME intakes did not show differences between diets. However, when on diet L the subjects were probably less active than when on diet S because differences within subjects of ankle actometer counts between diets showed a high correlation with the corresponding differences in corrected ME intakes (r 0.92). Further correction of ME intake toward equal actometer activity showed a significant (P less than 0.05) difference between diets: for maintaining energy equilibrium 5.6 (SE 0.8; P less than 0.05)% more ME from diet L was needed than from diet S. The reliability of this 5.6% difference depends on whether or not one ankle actometer gives an accurate picture of the subject's physical activity. 6. The energy contribution to the body is clearly smaller from lactitol than from saccharose, certainly due to the effect of lactitol on digestion, and probably also due to the effect on the utilization of ME.

Human energy metabolism below, near and above energy equilibrium

Complete 24 h energy and nitrogen balances were measured for fifteen subjects at three levels of energy intake and for two other subjects at two levels of intake. At each level, the fifteen subjects ate diets consisting of fifteen to twenty separate foods for 7 or 8 d. Faeces and urine were collected for the final 4 d. Respiratory gas exchange was measured during the final 72 h while the subjects stayed in an 11 m3 open-circuit respiration chamber, and simulated office or light household work. The energy balance of the other two subjects was determined initially in a similar way when they consumed a diet which was sufficient for energy equilibrium. Subsequently, the measurements were repeated twice at the same high level of metabolizable energy (ME) intake after 4 and 18 d on that diet. Neither energy nor N digestibilities were significantly affected by intake level or subject. Due to relatively small urinary energy losses the ME content of the gross energy increased slightly at the higher intake. Respiratory quotient increased with intake level from 0.78 to 0.87. The efficiencies of utilization of ME were approximately 1.0 for maintenance (from the low to the intermediate intake level) and decreased to about 0.9 for maintenance and energy deposition (from the intermediate to the high intake level). Estimates of daily ME requirements at energy equilibrium were 149 (SD 13) kJ ME/kg body-weight, 432 (SD 33) kJ ME/kg body-weight 0.75 and 204 (SD 22) kJ/kg lean body mass. The former two values were negatively correlated with percentage body fat although not significantly so. ME utilization and heat production of the other two subjects were nearly equal after 6 and 20 d on a diet supplying 1.5-1.7 times the ME needed for energy equilibrium.