The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits

Identifying nonalcoholic fatty liver disease patients with active fibrosis by measuring extracellular matrix remodeling rates in tissue and blood.

Excess collagen synthesis (fibrogenesis) in the liver plays a causal role in the progression of nonalcoholic fatty liver disease (NAFLD). Methods are needed to identify patients with more rapidly progressing disease and to demonstrate early response to treatment. We describe here a novel method to quantify hepatic fibrogenesis flux rates both directly in liver tissue and noninvasively in blood. Twenty-one patients with suspected NAFLD ingested heavy water (2 H2 O, 50-mL aliquots) two to three times daily for 3-5 weeks prior to a clinically indicated liver biopsy. Liver collagen fractional synthesis rate (FSR) and plasma lumican FSR were measured based on 2 H labeling using tandem mass spectrometry. Patients were classified by histology for fibrosis stage (F0-F4) and as having nonalcoholic fatty liver or nonalcoholic steatohepatitis (NASH). Magnetic resonance elastography measurements of liver stiffness were also performed. Hepatic collagen FSR in NAFLD increased with advancing disease stage (e.g., higher in NASH than nonalcoholic fatty liver, positive correlation with fibrosis score and liver stiffness) and correlated with hemoglobin A1C. In addition, plasma lumican FSR demonstrated a significant correlation with hepatic collagen FSR.ConclusionUsing a well-characterized cohort of patients with biopsy-proven NAFLD, this study demonstrates that hepatic scar in NASH is actively remodeled even in advanced fibrosis, a disease that is generally regarded as static and slowly progressive. Moreover, hepatic collagen FSR correlates with established risks for fibrotic disease progression in NASH, and plasma lumican FSR correlates with hepatic collagen FSR, suggesting applications as direct or surrogate markers, respectively, of hepatic fibrogenesis in humans. (Hepatology 2017;65:78-88)

Daily Vitamin D3 Supplementation as a Treatment for Health Disparities

There is discordance in the vitamin D literature between correlational studies, which show an association between higher vitamin D exposure and good health, and randomized controlled trials of vitamin D supplementation, which are inconclusive. I test the theory that this discordance is due to false assumptions about how vitamin D affects human health. I use the method of systematic review and meta-analysis—accepting only experimental studies that supplement with the animal version of vitamin D, D3, not D2 or vitamin D metabolites or analogues, as well as accepting only studies with daily rather than less-frequent but larger doses—to show that daily vitamin D3 supplementation has a statistically-significant beneficial effect on blood pressure and markers of diabetes. Using nationally-representative correlational data, I also demonstrate that the health disparities in blood pressure, if not diabetes, will be eliminated only with new health policies dedicated to health education on the vast nutritional difference in vitamin D status between black and white Americans. As a part of this dissertation, I also developed an online multi-user web application to facilitate systematic reviews and meta-analyses

Peripheral and central arterial pressure and its relationship to vascular target organ damage in carotid artery, retina and arterial stiffness. Development and validation of a tool. The Vaso risk study

<p>Abstract</p> <p>Background</p> <p>Ambulatory blood pressure monitoring (ABPM) shows a better correlation to target organ damage and cardiovascular morbidity-mortality than office blood pressure. A loss of arterial elasticity and an increase in carotid artery intima-media thickness (IMT) has been associated with increased cardiovascular morbidity-mortality. Tools have been developed that allow estimation of the retinal arteriovenous index but not all studies coincide and there are contradictory results in relation to the evolution of the arteriosclerotic lesions and the caliber of the retinal vessels. The purpose of this study is to analyze the relationship between peripheral and central arterial pressure (clinic and ambulatory) and vascular structure and function as evaluated by the carotid artery intima-media thickness, retina arteriovenous index, pulse wave velocity (PWV) and ankle-brachial index in patients with and without type 2 diabetes. In turn, software is developed and validated for measuring retinal vessel thickness and automatically estimating the arteriovenous index.</p> <p>Methods/Design</p> <p>A cross-sectional study involving a control group will be made, with a posterior 4-year follow-up period in primary care. The study patients will be type 2 diabetics, with a control group of non-diabetic individuals. Consecutive sampling will be used to include 300 patients between 34-75 years of age and no previous cardiovascular disease, one-half being assigned to each group. Main measurements: age, gender, height, weight and abdominal circumference. Lipids, creatinine, microalbuminuria, blood glucose, HbA1c, blood insulin, high sensitivity C-reactive protein and endothelial dysfunction markers. Clinic and ambulatory blood pressure monitoring. Carotid ultrasound to evaluate IMT, and retinography to evaluate the arteriovenous index. ECG to assess left ventricle hypertrophy, ankle-brachial index, and pulse wave analysis (PWA) and pulse wave velocity (PWV) with the Sphigmocor System.</p> <p>Discussion</p> <p>We hope to obtain information on the correlation of different ABPM-derived parameters and PWA to organ target damage - particularly vascular structure and function evaluated from the IMT and PWV - and endothelial dysfunction in patients with and without type 2 diabetes. We also hope to demonstrate the usefulness of the instrument developed for the automated evaluation of retinal vascularization in the early detection of alterations in vascular structure and function and in the prognosis of middle-term cardiovascular morbidity.</p> <p>Trial Registration</p> <p>Clinical Trials.gov Identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01325064">NCT01325064</a></p

Acute pancreatitis - severity classification, complications and outcome

Acute pancreatitis, with an annual incidence of approximately 35 per 100 000 inhabitants in Sweden, is in most cases mild and self-limiting. Severe acute pancreatitis, affecting 10-15% of the cases is, however, associated with severe complications and even death. The optimal management of acute pancreatitis includes accurate early prediction of the disease severity. The aims of this thesis were to investigate early severity classification, complications and outcome in acute pancreatitis patients, with special regard to patients developing the severe form of the disease. The results of the studies were: I) Two early risk factors for death were identified: increasing age and hypotension at admission. Deaths were to a high extent related to multiple organ dysfunction. Early recurrence after biliary acute pancreatitis was common. II) A model for early prediction of severity in acute pancreatitis with artificial neural networks was developed, identifying 6 risk factors. The ROC area for the model was 0.92, and it performed significantly better than the APACHE II score. III) Patients with pancreatic pseudocysts were found to be resource demanding in regard to recurrences and repeated hospital visits. Even larger pancreatic pseudocysts could be managed successfully with conservative treatment. IV) In a national Swedish survey, the treatment of patients with pancreatic pseudocysts appeared to be heterogeneous, with different treatment options available and varying local traditions. V) In long-term follow-up after acute pancreatitis, impairment was mainly seen in the endocrine pancreatic function, and especially after severe disease. The time to rehabilitation and return to work and normal life was long, and the costs for the society high. The quality of life years after the disease was, however, as good as in the normal population. VI) A survey of patients dying in acute pancreatitis without reaching the hospital showed that this group represents a substantial part of all deaths from the disease. The dominating aetiology was alcohol. Pulmonary injury was the most common organ manifestation outside the pancreas. To reduce mortality due to acute pancreatitis it is important to target also the patients that never reach hospital

Expression data dnalysis and regulatory network inference by means of correlation patterns

With the advance of high-throughput techniques, the amount of available data in the bio-molecular field is rapidly growing. It is now possible to measure genome-wide aspects of an entire biological system as a whole. Correlations that emerge due to internal dependency structures of these systems entail the formation of characteristic patterns in the corresponding data. The extraction of these patterns has become an integral part of computational biology. By triggering perturbations and interventions it is possible to induce an alteration of patterns, which may help to derive the dependency structures present in the system. In particular, differential expression experiments may yield alternate patterns that we can use to approximate the actual interplay of regulatory proteins and genetic elements, namely, the regulatory network of a cell. In this work, we examine the detection of correlation patterns from bio-molecular data and we evaluate their applicability in terms of protein contact prediction, experimental artifact removal, the discovery of unexpected expression patterns and genome-scale inference of regulatory networks. Correlation patterns are not limited to expression data. Their analysis in the context of conserved interfaces among proteins is useful to estimate whether these may have co-evolved. Patterns that hint on correlated mutations would then occur in the associated protein sequences as well. We employ a conceptually simple sampling strategy to decide whether or not two pathway elements share a conserved interface and are thus likely to be in physical contact. We successfully apply our method to a system of ABC-transporters and two-component systems from the phylum of Firmicute bacteria. For spatially resolved gene expression data like microarrays, the detection of artifacts, as opposed to noise, corresponds to the extraction of localized patterns that resemble outliers in a given region. We develop a method to detect and remove such artifacts using a sliding-window approach. Our method is very accurate and it is shown to adapt to other platforms like custom arrays as well. Further, we developed Padesco as a way to reveal unexpected expression patterns. We extract frequent and recurring patterns that are conserved across many experiments. For a specific experiment, we predict whether a gene deviates from its expected behaviour. We show that Padesco is an effective approach for selecting promising candidates from differential expression experiments. In Chapter 5, we then focus on the inference of genome-scale regulatory networks from expression data. Here, correlation patterns have proven useful for the data-driven estimation of regulatory interactions. We show that, for reliable eukaryotic network inference, the integration of prior networks is essential. We reveal that this integration leads to an over-estimate of network-wide quality estimates and suggest a corrective procedure, CoRe, to counterbalance this effect. CoRe drastically improves the false discovery rate of the originally predicted networks. We further suggest a consensus approach in combination with an extended set of topological features to obtain a more accurate estimate of the eukaryotic regulatory network for yeast. In the course of this work we show how correlation patterns can be detected and how they can be applied for various problem settings in computational molecular biology. We develop and discuss competitive approaches for the prediction of protein contacts, artifact repair, differential expression analysis, and network inference and show their applicability in practical setups.Mit der Weiterentwicklung von Hochdurchsatztechniken steigt die Anzahl verfügbarer Daten im Bereich der Molekularbiologie rapide an. Es ist heute möglich, genomweite Aspekte eines ganzen biologischen Systems komplett zu erfassen. Korrelationen, die aufgrund der internen Abhängigkeits-Strukturen dieser Systeme enstehen, führen zu charakteristischen Mustern in gemessenen Daten. Die Extraktion dieser Muster ist zum integralen Bestandteil der Bioinformatik geworden. Durch geplante Eingriffe in das System ist es möglich Muster-Änderungen auszulösen, die helfen, die Abhängigkeits-Strukturen des Systems abzuleiten. Speziell differentielle Expressions-Experimente können Muster-Wechsel bedingen, die wir verwenden können, um uns dem tatsächlichen Wechselspiel von regulatorischen Proteinen und genetischen Elementen anzunähern, also dem regulatorischen Netzwerk einer Zelle. In der vorliegenden Arbeit beschäftigen wir uns mit der Erkennung von Korrelations-Mustern in molekularbiologischen Daten und schätzen ihre praktische Nutzbarkeit ab, speziell im Kontext der Kontakt-Vorhersage von Proteinen, der Entfernung von experimentellen Artefakten, der Aufdeckung unerwarteter Expressions-Muster und der genomweiten Vorhersage regulatorischer Netzwerke. Korrelations-Muster sind nicht auf Expressions-Daten beschränkt. Ihre Analyse im Kontext konservierter Schnittstellen zwischen Proteinen liefert nützliche Hinweise auf deren Ko-Evolution. Muster die auf korrelierte Mutationen hinweisen, würden in diesem Fall auch in den entsprechenden Proteinsequenzen auftauchen. Wir nutzen eine einfache Sampling-Strategie, um zu entscheiden, ob zwei Elemente eines Pathways eine gemeinsame Schnittstelle teilen, berechnen also die Wahrscheinlichkeit für deren physikalischen Kontakt. Wir wenden unsere Methode mit Erfolg auf ein System von ABC-Transportern und Zwei-Komponenten-Systemen aus dem Firmicutes Bakterien-Stamm an. Für räumlich aufgelöste Expressions-Daten wie Microarrays enspricht die Detektion von Artefakten der Extraktion lokal begrenzter Muster. Im Gegensatz zur Erkennung von Rauschen stellen diese innerhalb einer definierten Region Ausreißer dar. Wir entwickeln eine Methodik, um mit Hilfe eines Sliding-Window-Verfahrens, solche Artefakte zu erkennen und zu entfernen. Das Verfahren erkennt diese sehr zuverlässig. Zudem kann es auf Daten diverser Plattformen, wie Custom-Arrays, eingesetzt werden. Als weitere Möglichkeit unerwartete Korrelations-Muster aufzudecken, entwickeln wir Padesco. Wir extrahieren häufige und wiederkehrende Muster, die über Experimente hinweg konserviert sind. Für ein bestimmtes Experiment sagen wir vorher, ob ein Gen von seinem erwarteten Verhalten abweicht. Wir zeigen, dass Padesco ein effektives Vorgehen ist, um vielversprechende Kandidaten eines differentiellen Expressions-Experiments auszuwählen. Wir konzentrieren uns in Kapitel 5 auf die Vorhersage genomweiter regulatorischer Netzwerke aus Expressions-Daten. Hierbei haben sich Korrelations-Muster als nützlich für die datenbasierte Abschätzung regulatorischer Interaktionen erwiesen. Wir zeigen, dass für die Inferenz eukaryotischer Systeme eine Integration zuvor bekannter Regulationen essentiell ist. Unsere Ergebnisse ergeben, dass diese Integration zur Überschätzung netzwerkübergreifender Qualitätsmaße führt und wir schlagen eine Prozedur - CoRe - zur Verbesserung vor, um diesen Effekt auszugleichen. CoRe verbessert die False Discovery Rate der ursprünglich vorhergesagten Netzwerke drastisch. Weiterhin schlagen wir einen Konsensus-Ansatz in Kombination mit einem erweiterten Satz topologischer Features vor, um eine präzisere Vorhersage für das eukaryotische Hefe-Netzwerk zu erhalten. Im Rahmen dieser Arbeit zeigen wir, wie Korrelations-Muster erkannt und wie sie auf verschiedene Problemstellungen der Bioinformatik angewandt werden können. Wir entwickeln und diskutieren Ansätze zur Vorhersage von Proteinkontakten, Behebung von Artefakten, differentiellen Analyse von Expressionsdaten und zur Vorhersage von Netzwerken und zeigen ihre Eignung im praktischen Einsatz

Workplace screening programs for chronic disease prevention: a rapid review

This review examined the effectiveness of workplace screening programs for chronic disease prevention based on evidence retrieved from the main databases of biomedical and health economic literature published to March 2012, supplemented with relevant reports. The review found: 1. Strong evidence of effectiveness of HRAs (when used in combination with other interventions) in relation to tobacco use, alcohol use, dietary fat intake, blood pressure and cholesterol 2. Sufficient evidence for effectiveness of worksite programs to control overweight and obesity 3. Sufficient evidence of effectiveness for workplace HRAs in combination with additional interventions to have favourable impact on the use of healthcare services (such as reductions in emergency department visits, outpatient visits, and inpatient hospital days over the longer term) 4. Sufficient evidence for effectiveness of benefits-linked financial incentives in increasing HRA and program participation 5. Sufficient evidence that for every dollar invested in these programs an annual gain of $3.20 (range$1.40 to $4.60) can be achieved 6. Promising evidence that even higher returns on investment can be achieved in programs incorporating newer technologies such as telephone coaching of high risk individuals and benefits-linked financial incentive

Stratified medicine: an exploration of the utility of non-invasive serum markers for the management of chronic liver diseases

Chronic liver disease (CLD), the 3rd commonest cause of premature death in the UK, is detected late when interventions are often ineffective. Non-alcoholic fatty liver disease (NAFLD) and chronic hepatitis C (CHC) account for a significant proportion of CLD in the UK. Numerous direct (molecules involved in matrix biology) and indirect biomarkers (standard laboratory tests) have been successfully developed to detect advanced liver fibrosis. Less success, however, has been achieved in the detection of alternative diagnostic targets such as early stage fibrosis, non-alcoholic steatohepatitis (NASH) and fibrosis evolution. In a study of 17 candidate biomarkers amongst patients with NAFLD, terminal peptide of procollagen 3 was identified as the only biomarker demonstrating good performance for the detection of NASH in both a derivation and validation cohort. Thereafter, these results were further validated in another NAFLD cohort. In a study of 9 biomarkers (indirect and direct) in the detection of fibrosis in NAFLD, direct biomarkers demonstrated better diagnostic performance overall and for early stage fibrosis although some indirect biomarkers identified advanced fibrosis and cirrhosis with good effect. Thereafter, parallel and serial combinations of 3 biomarkers of advanced fibrosis were proposed and successfully employed in a cohort of patients with NAFLD to improve diagnostic performance. In a study of 10 biomarkers in CHC, fibrosis detection was enhanced using complex biomarker panels that incorporated direct tests. Of note, the use an alternative assay for a constituent component significantly affected biomarker panel performance both overall and at diagnostic thresholds. The ability of the biomarkers to monitor fibrosis evolution arising due to putative antifibrotic was then studied in CHC. In the first study, changes in direct biomarker, ELF, could predict fibrosis evolution. In the second study, an improvement of indirect biomarker scores in patients with CHC cirrhosis during treatment was found to denote an improved prognosis