Assessing food allergy risks from residual peanut protein in highly refined vegetable oil.

Refined vegetable oils including refined peanut oil are widely used in foods. Due to shared production processes, refined non-peanut vegetable oils can contain residual peanut proteins. We estimated the predicted number of allergic reactions to residual peanut proteins using probabilistic risk assessment applied to several scenarios involving food products made with vegetable oils. Variables considered were: a) the estimated production scale of refined peanut oil, b) estimated cross-contact between refined vegetable oils during production, c) the proportion of fat in representative food products and d) the peanut protein concentration in refined peanut oil. For all products examined the predicted risk of objective allergic reactions in peanut-allergic users of the food products was extremely low. The number of predicted reactions ranged depending on the model from a high of 3 per 1000 eating occasions (Weibull) to no reactions (LogNormal). Significantly, all reactions were predicted for allergen intakes well below the amounts reported for the most sensitive individual described in the clinical literature. We conclude that the health risk from cross-contact between vegetable oils and refined peanut oil is negligible. None of the food products would warrant precautionary labelling for peanut according to the VITAL® programme of the Allergen Bureau.

Food Consumption and Nutrient Intake by Children Aged 10 to 48 Months Attending Day Care in The Netherlands.

The diet of young children is an important determinant of long-term health effects, such as overweight and obesity. We analyzed two-day food consumption records from 1526 young children (10-48 months old) attending 199 daycare centers across The Netherlands. Data were observed and recorded in diaries by caregivers at the day nursery and by parents at home on days that the children attended the daycare center. According to national and European reference values, the children had an adequate nutrient intake with exception of low intakes of total fat, n-3 fatty acids from fish and possibly iron. Intakes of energy and protein were substantially higher than recommended and part of the population exceeded the tolerable upper intake levels for sodium, zinc and retinol. Consumption of fruit, fats, fish, and fluids was substantially less than recommended. The children used mostly (semi-)skimmed milk products and non-refined bread and cereals, as recommended. Two thirds of the consumed beverages, however, contained sugar and contributed substantially to energy intake. In young children, low intakes of n-3 fatty acids and iron are a potential matter of concern, as are the high intakes of energy, protein, sugared beverages, and milk, since these may increase the risk of becoming overweight.

Metabolomic biomarkers for personalised glucose lowering drugs treatment in type 2 diabetes.

We aimed to identify metabolites to predict patients' response to glucose lowering treatment during the first 5 years after detection of type 2 diabetes. Metabolites were measured by GC-MS in baseline samples from 346 screen-detected type 2 diabetes patients in the ADDITION-NL study. The response to treatment with metformin and/or sulphonylurea (SU) was analysed to identify metabolites predictive of 5 year HbA1c change by multiple regression analysis. Baseline glucose and 1,5 anhydro-glucitol were associated with HbA1c decrease in all medication groups. In patients on SU no other metabolite was associated with HbA1c decrease. A larger set of metabolites was associated with HbA1c change in the metformin and the combination therapy (metformin + SU) groups. These metabolites included metabolites related to liver metabolism, such as 2-hydroxybutanoic acid, 3-hydroxybutanoic acid, 2-hydroxypiperidine and 4-oxoproline). Metabolites involved in oxidative stress and insulin resistance were higher when the HbA1c decrease was larger in the metformin/sulphonylurea group. The associations between baseline metabolites and responsiveness to medication are in line with its mode of action. If these results could be replicated in other populations, the most promising predictive candidates might be tested to assess whether they could enhance personalised treatment.

Dietary medium chain fatty acid supplementation leads to reduced VLDL lipolysis and uptake rates in comparison to linoleic acid supplementation.

Dietary medium chain fatty acids (MCFA) and linoleic acid follow different metabolic routes, and linoleic acid activates PPAR receptors. Both these mechanisms may modify lipoprotein and fatty acid metabolism after dietary intervention. Our objective was to investigate how dietary MCFA and linoleic acid supplementation and body fat distribution affect the fasting lipoprotein subclass profile, lipoprotein kinetics, and postprandial fatty acid kinetics. In a randomized double blind cross-over trial, 12 male subjects (age 51±7 years; BMI 28.5±0.8 kg/m2), were divided into 2 groups according to waist-hip ratio. They were supplemented with 60 grams/day MCFA (mainly C8:0, C10:0) or linoleic acid for three weeks, with a wash-out period of six weeks in between. Lipoprotein subclasses were measured using HPLC. Lipoprotein and fatty acid metabolism were studied using a combination of several stable isotope tracers. Lipoprotein and tracer data were analyzed using computational modeling. Lipoprotein subclass concentrations in the VLDL and LDL range were significantly higher after MCFA than after linoleic acid intervention. In addition, LDL subclass concentrations were higher in lower body obese individuals. Differences in VLDL metabolism were found to occur in lipoprotein lipolysis and uptake, not production; MCFAs were elongated intensively, in contrast to linoleic acid. Dietary MCFA supplementation led to a less favorable lipoprotein profile than linoleic acid supplementation. These differences were not due to elevated VLDL production, but rather to lower lipolysis and uptake rates.

Food allergy population thresholds: an evaluation of the number of oral food challenges and dosing schemes on the accuracy of threshold dose distribution modeling.

For most allergenic foods, limited availability of threshold dose information within the population restricts the advice on action levels of unintended allergenic foods which should trigger advisory labeling on packaged foods. The objective of this paper is to provide guidance for selecting an optimal sample size for threshold dosing studies for major allergenic foods and to identify factors influencing the accuracy of estimation. A simulation study was performed to evaluate the effects of sample size and dosing schemes on the accuracy of the threshold distribution curve. The relationships between sample size, dosing scheme and the employed statistical distribution on the one hand and accuracy of estimation on the other hand were obtained. It showed that the largest relative gains in accuracy are obtained when sample size increases from N=20 to N=60. Moreover, it showed that the EuroPrevall dosing scheme is a useful start, but that it may need revision for a specific allergen as more data become available, because a proper allocation of the dosing steps is important. The results may guide risk assessors in minimum sample sizes for new studies and in the allocation of proper dosing schemes for allergens in provocation studies.

Controlling false discovery rates in factorial experiments with between-subjects and within-subjects tests.

BACKGROUND: The False Discovery Rate (FDR) controls the expected number of false positives among the positive test results. It is not straightforward how to conduct a FDR controlling procedure in experiments with a factorial structure, while at the same time there are between-subjects and within-subjects factors. This is because there are P-values for different tests in one and the same response along with P-values for the same test and different responses. FINDINGS: We propose a procedure resulting in a single P-value per response, calculated over the tests of all the factorial effects. FDR control can then be based on the set of single P-values. CONCLUSIONS: The proposed procedure is very easy to apply and is recommended for all designs with factors applied at different levels of the randomization, such as cross-over designs with added between-subjects factors. TRIAL REGISTRATION: NCT00959790.

Fish oil and inflammatory status alter the n-3 to n-6 balance of the endocannabinoid and oxylipin metabolomes in mouse plasma and tissues.

It is well established that dietary intake of n-3 fatty acids is associated with anti-inflammatory effects, and this has been linked to modulation of the oxylipin and endocannabinoid metabolomes. However, the amount of data on specific tissue effects is limited, and it is not known how inflammation affects this relation. In the present study we systematically explored the combined effects of n-3 fatty acid diets and inflammation on the in vivo endocannabinoid and oxylipin metabolomes using a multicompartment, detailed targeted lipidomics approach. Male C57BL/6 mice received diets containing 0, 1, or 3 % w/w fish oil (FO) for 6 weeks, after which 2 mg/kg LPS or saline was administered i.p. Levels of endocannabinoids/N-acylethanolamines (NAEs) and oxylipins, covering n-3 and n-6 fatty acid derived compounds, were determined in plasma, liver, ileum and adipose tissue using LC-MS/MS. FO generally increased 'n-3' NAEs and oxylipins at the expense of compounds derived from other fatty acids, affecting all branches of the oxylipin metabolome. LPS generally increased levels of endocannabinoids/NAEs and oxylipins, with opposing effects across plasma and tissues. Multivariate data analysis revealed that separation between diet groups in the saline treated groups was primarily explained by decreases in other than n-3 derived compounds. In the LPS treated groups, the separation was primarily explained by increases in n-3 derived compounds. In conclusion, FO caused marked changes in the n-3 to n-6 balance of the endocannabinoid and oxylipin metabolomes, with specific effects depending on inflammatory status. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-012-0421-9) contains supplementary material, which is available to authorized users.

The Stokes number approach to support scale-up and technology transfer of a mixing process.

Transferring processes between different scales and types of mixers is a common operation in industry. Challenges within this operation include the existence of considerable differences in blending conditions between mixer scales and types. Obtaining the correct blending conditions is crucial for the ability to break up agglomerates in order to achieve the desired blend uniformity. Agglomerate break up is often an abrasion process. In this study, the abrasion rate potential of agglomerates is described by the Stokes abrasion (St(Abr)) number of the system. The St(Abr) number equals the ratio between the kinetic energy density of the moving powder bed and the work of fracture of the agglomerate. In this study, the St(Abr) approach demonstrates to be a useful tool to predict the abrasion of agglomerates during blending when technology is transferred between mixer scales/types. Applying the St(Abr) approach revealed a transition point between parameters that determined agglomerate abrasion. This study gave evidence that (1) below this transition point, agglomerate abrasion is determined by a combination of impeller effects and by the kinetic energy density of the powder blend, whereas (2) above this transition point, agglomerate abrasion is mainly determined by the kinetic energy density of the powder blend.

Time-dependent effect of in vivo inflammation on eicosanoid and endocannabinoid levels in plasma, liver, ileum and adipose tissue in C57BL/6 mice fed a fish-oil diet.

Eicosanoids and endocannabinoids/N-acylethanolamines (NAEs) are fatty acid derived compounds with a regulatory role in inflammation. Considering their complex metabolism, it is likely that inflammation affects multiple compounds at the same time, but how lipid profiles change in plasma and other tissues after an inflammatory stimulus has not been described in detail. In addition, dietary fish oil increases levels of several n-3 fatty acid derived eicosanoids and endocannabinoids, and this may lead to a broader change in the profiles of bioactive lipids. In the present study mice were fed a diet containing 3% w/w fish oil for 6 weeks before receiving i.p. saline or 3 mg/kg lipopolysaccharide (LPS) to induce an inflammatory response. Eicosanoid and endocannabinoid/NAE levels (in total 61 metabolites) in plasma, liver, ileum, and adipose tissue were quantified using targeted lipidomics after 2, 4, 8, and 24 h, respectively. Tissue- and time-dependent effects of LPS on bioactive lipid profiles were observed. For example, levels of CYP derived eicosanoids in the ileum were markedly affected by LPS, whereas this was less pronounced in the plasma and adipose tissue. For some compounds, such as 9,10-DiHOME, opposing effects of LPS were seen in the plasma compared to the other tissues, suggesting differential regulation of bioactive lipid levels after an inflammatory stimulus. Taken together, our results show that plasma levels do not always correlate with the effects found in the tissues, which underlines the need to measure profiles and pathways of mediators involved in inflammation, including endocannabinoid-like structures, in both plasma and tissues.

Plasma metabolomics and proteomics profiling after a postprandial challenge reveal subtle diet effects on human metabolic status.

We introduce the metabolomics and proteomics based Postprandial Challenge Test (PCT) to quantify the postprandial response of multiple metabolic processes in humans in a standardized manner. The PCT comprised consumption of a standardized 500 ml dairy shake containing respectively 59, 30 and 12 energy percent lipids, carbohydrates and protein. During a 6 h time course after PCT 145 plasma metabolites, 79 proteins and 7 clinical chemistry parameters were quantified. Multiple processes related to metabolism, oxidation and inflammation reacted to the PCT, as demonstrated by changes of 106 metabolites, 31 proteins and 5 clinical chemistry parameters. The PCT was applied in a dietary intervention study to evaluate if the PCT would reveal additional metabolic changes compared to non-perturbed conditions. The study consisted of a 5-week intervention with a supplement mix of anti-inflammatory compounds in a crossover design with 36 overweight subjects. Of the 231 quantified parameters, 31 had different responses over time between treated and control groups, revealing differences in amino acid metabolism, oxidative stress, inflammation and endocrine metabolism. The results showed that the acute, short term metabolic responses to the PCT were different in subjects on the supplement mix compared to the controls. The PCT provided additional metabolic changes related to the dietary intervention not observed in non-perturbed conditions. Thus, a metabolomics based quantification of a standardized perturbation of metabolic homeostasis is more informative on metabolic status and subtle health effects induced by (dietary) interventions than quantification of the homeostatic situation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0320-5) contains supplementary material, which is available to authorized users.

Visualization and identification of health space, based on personalized molecular phenotype and treatment response to relevant underlying biological processes.

BACKGROUND: Being able to visualize multivariate biological treatment effects can be insightful. However the axes in visualizations are often solely defined by variation and thus have no biological meaning. This makes the effects of treatment difficult to interpret. METHODS: A statistical visualization method is presented, which analyses and visualizes the effects of treatment in individual subjects. The visualization is based on predefined biological processes as determined by systems-biological datasets (metabolomics proteomics and transcriptomics). This allows one to evaluate biological effects depending on shifts of either groups or subjects in the space predefined by the axes, which illustrate specific biological processes. We built validated multivariate models for each axis to represent several biological processes. In this space each subject has his or her own score on each axis/process, indicating to which extent the treatment affects the related process. RESULTS: The health space model was applied to visualize the effects of a nutritional intervention, with the goal of applying diet to improve health. The model was therefore named the 'health space' model. The 36 study subjects received a 5-week dietary intervention containing several anti-inflammatory ingredients. Plasma concentrations of 79 proteins and 145 metabolites were quantified prior to and after treatment. The principal processes modulated by the intervention were oxidative stress, inflammation, and metabolism. These processes formed the axes of the 'health space'. The approach distinguished the treated and untreated groups, as well as two different response subgroups. One subgroup reacted mainly by modulating its metabolic stress profile, while a second subgroup showed a specific inflammatory and oxidative response to treatment. CONCLUSIONS: The 'health space' model allows visualization of multiple results and to interpret them. The model presents treatment group effects, subgroups and individual responses.

Kinetic energy density and agglomerate abrasion rate during blending of agglomerates into powders.

Problems related to the blending of a cohesive powder with a free flowing bulk powder are frequently encountered in the pharmaceutical industry. The cohesive powder often forms lumps or agglomerates which are not dispersed during the mixing process and are therefore detrimental to blend uniformity. Achieving sufficient blend uniformity requires that the blending conditions are able to break up agglomerates, which is often an abrasion process. This study was based on the assumption that the abrasion rate of agglomerates determines the required blending time. It is shown that the kinetic energy density of the moving powder bed is a relevant parameter which correlates with the abrasion rate of agglomerates. However, aspects related to the strength of agglomerates should also be considered. For this reason the Stokes abrasion number (St(Abr)) has been defined. This parameter describes the ratio between the kinetic energy density of the moving powder bed and the work of fracture of the agglomerate. The St(Abr) number is shown to predict the abrasion potential of agglomerates in the dry-mixing process. It appeared possible to include effects of filler particle size and impeller rotational rate into this concept. A clear relationship between abrasion rate of agglomerates and the value of St(Abr) was demonstrated.

An antiinflammatory dietary mix modulates inflammation and oxidative and metabolic stress in overweight men: a nutrigenomics approach.

BACKGROUND: Low-grade chronic inflammation in overweight subjects is thought to play an important role in disease development. OBJECTIVE: It was hypothesized that specific dietary components are able to reduce low-grade inflammation as well as metabolic and oxidative stress. DESIGN: Dietary products [resveratrol, green tea extract, alpha-tocopherol, vitamin C, n-3 (omega-3) polyunsaturated fatty acids, and tomato extract] selected for their evidence-based antiinflammatory properties were combined and given as supplements to 36 healthy overweight men with mildly elevated plasma C-reactive protein concentrations in a double-blind, placebo-controlled, crossover study with treatment periods of 5 wk. Inflammatory and oxidative stress defense markers were quantified in plasma and urine. Furthermore, 120 plasma proteins, 274 plasma metabolites (lipids, free fatty acids, and polar compounds), and the transcriptomes of peripheral blood mononuclear cells and adipose tissue were quantified. RESULTS: Plasma adiponectin concentrations increased by 7%, whereas C-reactive protein (principal inflammation marker) was unchanged. However, a multitude of subtle changes were detected by an integrated analysis of the "omics" data, which indicated modulated inflammation of adipose tissue, improved endothelial function, affected oxidative stress, and increased liver fatty acid oxidation. CONCLUSION: An intervention with selected dietary products affected inflammatory processes, oxidative stress, and metabolism in humans, as shown by large-scale profiling of genes, proteins, and metabolites in plasma, urine, and adipose tissue. This trial was registered at clinical trials.gov as NCT00655798.

Metabolomics data exploration guided by prior knowledge.

In metabolomics research, it is often important to focus the data analysis to specific areas of interest within the metabolome. In this paper, we describe the application of consensus principal component analysis (CPCA) and canonical correlation analysis (CCA) as a means to explore the relation between metabolome data and (i) biochemically related metabolites and (ii) an amino acid biosynthesis pathway. CPCA searches for major trends in the behavior of metabolite concentrations that are in common for the metabolites of interest and the remainder of the metabolome. CCA identifies the strongest correlations between the metabolites of interest and the remainder of the metabolome. CPCA and CCA were applied to two different microbial metabolomics data sets. The first data set, derived from Pseudomonas putida S12, was relatively simple as it contained metabolomes obtained under four environmental conditions only. The second data set, obtained from Escherichia coli, was much more complex as it consisted of metabolomes obtained under 28 different environmental conditions. In case of the simple and coherent P. putida S12 data set, CCA and CPCA gave similar results as the variation in the subset of the selected metabolites and the remainder of the metabolome was similar. In contrast, CCA and CPCA yielded different results in case of the E. coli data set. With CPCA the trends in the selected subset--the phenylalanine biosynthesis pathway--dominated the results. The main trends were related to high and low phenylalanine productivity, and the metabolites showing a similar behavior in concentration were metabolites regulating the phenylalanine biosynthesis route in the subset and metabolites related to general amino acid metabolism in the remainder of the metabolome. With CCA, neither subset truly dominated the data analysis. CCA described the differences between the wild type and the overproducing strain and the differences between the succinate and glucose grown cells. For the difference between the wild type and the overproducing strain, metabolites from the beginning and the end of aromatic amino acid pathways like erythrose-4-phosphate, tryptophan, and phenylalanine were important for the selected metabolites. CCA and CPCA proved to be complementary data analysis tools that enable the focusing of the data analysis on groups of metabolites that are of specific interest in relation to the remainder of the metabolome. Compared to an ordinary PCA, focusing the data analysis on biologically relevant metabolites lead especially for the complex E. coli data to a better biological interpretation of the data.

Analyzing longitudinal microbial metabolomics data.

A longitudinal experimental design in combination with metabolomics and multiway data analysis is a powerful approach in the identification of metabolites whose correlation with bioproduct formation shows a shift in time. In this paper, a strategy is presented for the analysis of longitudinal microbial metabolomics data, which was performed in order to identify metabolites that are likely inducers of phenylalanine production by Escherichia coli. The variation in phenylalanine production as a function of differences in metabolism induced by the different environmental conditions in time was described by a validated multiway statistical model. Notably, most of the metabolites showing the strongest relations with phenylalanine production seemed to hardly change in time. Apparently, potential bottlenecks in phenylalanine seem to hardly change in the course of a batch fermentation. The approach described in this study is not limited to longitudinal microbial studies but can also be applied to other (biological) studies in which similar longitudinal data need to be analyzed.

Metabolic profiling of the response to an oral glucose tolerance test detects subtle metabolic changes.

BACKGROUND: The prevalence of overweight is increasing globally and has become a serious health problem. Low-grade chronic inflammation in overweight subjects is thought to play an important role in disease development. Novel tools to understand these processes are needed. Metabolic profiling is one such tool that can provide novel insights into the impact of treatments on metabolism. METHODOLOGY: To study the metabolic changes induced by a mild anti-inflammatory drug intervention, plasma metabolic profiling was applied in overweight human volunteers with elevated levels of the inflammatory plasma marker C-reactive protein. Liquid and gas chromatography mass spectrometric methods were used to detect high and low abundant plasma metabolites both in fasted conditions and during an oral glucose tolerance test. This is based on the concept that the resilience of the system can be assessed after perturbing a homeostatic situation. CONCLUSIONS: Metabolic changes were subtle and were only detected using metabolic profiling in combination with an oral glucose tolerance test. The repeated measurements during the oral glucose tolerance test increased statistical power, but the metabolic perturbation also revealed metabolites that respond differentially to the oral glucose tolerance test. Specifically, multiple metabolic intermediates of the glutathione synthesis pathway showed time-dependent suppression in response to the glucose challenge test. The fact that this is an insulin sensitive pathway suggests that inflammatory modulation may alter insulin signaling in overweight men.

The effect of Korean pine nut oil on in vitro CCK release, on appetite sensations and on gut hormones in post-menopausal overweight women.

Appetite suppressants may be one strategy in the fight against obesity. This study evaluated whether Korean pine nut free fatty acids (FFA) and triglycerides (TG) work as an appetite suppressant. Korean pine nut FFA were evaluated in STC-1 cell culture for their ability to increase cholecystokinin (CCK-8) secretion vs. several other dietary fatty acids from Italian stone pine nut fatty acids, oleic acid, linoleic acid, alpha-linolenic acid, and capric acid used as a control. At 50 muM concentration, Korean pine nut FFA produced the greatest amount of CCK-8 release (493 pg/ml) relative to the other fatty acids and control (46 pg/ml). A randomized, placebo-controlled, double-blind cross-over trial including 18 overweight post-menopausal women was performed. Subjects received capsules with 3 g Korean pine (Pinus koraiensis) nut FFA, 3 g pine nut TG or 3 g placebo (olive oil) in combination with a light breakfast. At 0, 30, 60, 90, 120, 180 and 240 minutes the gut hormones cholecystokinin (CCK-8), glucagon like peptide-1 (GLP-1), peptide YY (PYY) and ghrelin, and appetite sensations were measured. A wash-out period of one week separated each intervention day.CCK-8 was higher 30 min after pine nut FFA and 60 min after pine nut TG when compared to placebo (p < 0.01). GLP-1 was higher 60 min after pine nut FFA compared to placebo (p < 0.01). Over a period of 4 hours the total amount of plasma CCK-8 was 60% higher after pine nut FFA and 22% higher after pine nut TG than after placebo (p < 0.01). For GLP-1 this difference was 25% after pine nut FFA (P < 0.05). Ghrelin and PYY levels were not different between groups. The appetite sensation "prospective food intake" was 36% lower after pine nut FFA relative to placebo (P < 0.05). This study suggests that Korean pine nut may work as an appetite suppressant through an increasing effect on satiety hormones and a reduced prospective food intake.

How to distinguish healthy from diseased? Classification strategy for mass spectrometry-based clinical proteomics.

SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X-omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.

Risk estimation for carcinogens based on epidemiological data: a structured approach, illustrated by an example on chromium.

It is generally recognized that human, epidemiological data, if available, are preferred as the starting point for quantitative risk analysis above the use of data from animal studies. Although methods to obtain proper risk estimates from epidemiological data are available, several impediments prevent their widespread application. These impediments include unfamiliarity with epidemiological methods and the lack of a structured and transparent approach. We described a framework to conduct quantitative cancer risk assessment based on epidemiological studies in a structured, transparent, and reproducible manner. Important features of the process include a weight-of-the-evidence approach, estimation of the optimal exposure-risk function by fitting a regression model to the epidemiological data, estimation of uncertainty introduced by potential biases and missing information in the epidemiological studies, and calculation of excess lifetime risk through a life table to take into account competing risks. Sensitivity analyses are a useful tool to obtain insight into the impact of assumptions made and the variability of the underlying data. The framework is sufficiently flexible to allow many types of data, ranging from published, sometimes incomplete data to detailed individual data, while maintaining an optimal result, i.e., a state-of-the-art risk estimate with confidence intervals, based on all available evidence of sufficient quality.