Empirically derived phenotypic subgroups - qualitative and quantitative trait analyses

Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study

The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions.

Paving the Way to Precision Nutrition Through Metabolomics

Nutrition is an interdisciplinary science that studies the interactions of nutrients with the body in relation to maintenance of health and well-being. Nutrition is highly complex due to the underlying various internal and external factors that could model it. Thus, hacking this complexity requires more holistic and network-based strategies that could unveil these dynamic system interactions at both time and space scales. The ongoing omics era with its high-throughput molecular data generation is paving the way to embrace this complexity and is deeply reshaping the whole field of nutrition. Understanding the future paths of nutrition science is of importance from both translational and clinical perspectives. Basic nutrients which might include metabolites are important in nutrition science. Moreover, metabolites are key biological communication channels and represent an appealing functional readout at the interface of different major influential factors that define health and disease. Metabolomics is the technology that enables holistic and systematic analyses of metabolites in a biological system. Hence, given its intrinsic functionality, its tight connection to metabolism and its high clinical actionability potential, metabolomics is a very appealing technology for nutrition science. The ultimate goal is to deliver a tailored and clinically relevant nutritional recommendations and interventions to achieve precision nutrition. This work intends to present an update on the applications of metabolomics to personalize nutrition in translational and clinical settings. It also discusses the current conceptual shifts that are remodeling clinical nutrition practices in this Precision Medicine era. Finally, perspectives of clinical nutrition in the ever-growing, data-driven healthcare landscape are presented.

Identification of Comprehensive Metabotypes Associated with Cardiometabolic Diseases in the Population-Based KORA Study

SCOPE

"Metabotyping" describes the grouping of metabolically similar individuals. We aimed to identify valid metabotypes in a large cohort for targeted dietary intervention, for example, for disease prevention.

METHODS AND RESULTS

We grouped 1729 adults aged 32-77 years of the German population-based KORA F4 study (2006-2008) using k-means cluster analysis based on 34 biochemical and anthropometric parameters. We identified three metabolically distinct clusters showing significantly different biochemical parameter concentrations. Cardiometabolic disease status was determined at baseline in the F4 study and at the 7 year follow-up termed FF4 (2013/2014) to compare disease prevalence and incidence between clusters. Cluster 3 showed the most unfavorable marker profile with the highest prevalence of cardiometabolic diseases. Also, disease incidence was higher in cluster 3 compared to clusters 2 and 1, respectively, for hypertension (41.2%/25.3%/18.2%), type 2 diabetes (28.3%/5.1%/2.0%), hyperuricemia/gout (10.8%/2.3%/0.7%), dyslipidemia (19.2%/18.3%/5.6%), all metabolic (54.5%/36.8%/19.7%), and all cardiovascular (6.3%/5.5%/2.3%) diseases together.

CONCLUSION

Cluster analysis based on an extensive set of biochemical and anthropometric parameters allows the identification of comprehensive metabotypes that were distinctly different in cardiometabolic disease occurrence. As a next step, targeted dietary strategies should be developed with the goal of preventing diseases, especially in cluster 3.

Metabotyping and its application in targeted nutrition: an overview

Metabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping – that is, grouping metabolically similar individuals – tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term ‘metabotype’ has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.

Genetic polymorphisms underlying the skeletal Class III phenotype

INTRODUCTION

Our goal was to verify the association between candidate polymorphisms and skeletal Class III malocclusion in a well-characterized homogeneous sample set.

METHODS

Thirty-five single-nucleotide polymorphisms were studied from 10 candidate loci in 54 Class III subjects and 120 controls. Skeletal Class III characteristics included ANB angle less than 0°, SNB angle greater than 83° (mandibular prognathism), SNA angle less than 79° (maxillary deficiency), Class III molar relationship, and negative overjet. Inclusion criteria for the controls were ANB angle between 0° and 4°, Class I molar relationship, and normal overjet. Chi-square and Fisher exact tests and principal component (PC) analysis were used to determine overrepresentation of marker alleles with alpha of 0.05. Odds ratios and 95% confidence intervals were calculated.

RESULTS

MYO1H (rs10850110 A<G) (P <0.01; odds ratio, 7.44 [4.02-13.77]) was associated with an increased risk for the mandibular prognathism phenotype. These results were confirmed by PC analysis, which showed 4 PCs representing the sample variations (PC1, 37.24%; PC2, 20.02%; PC3, 12.18%; and PC4, 11.40%), and PC1 was associated with MYO1H (P <0.001). We also found by PC analysis associations between MYO1H (P <0.001) and GHR (rs2973015 A>G) (P = 0.001) with PC2 and between FGF10 (rs593307 A<G) (P = 0.001) with PC4.

CONCLUSIONS

Polymorphism in MYO1H could be used as a marker for genetic susceptibility to Class III malocclusion with mandibular prognathism, and polymorphisms in GHR and FGF were associated with maxillomandibular discrepancies. This study may contribute to improved diagnosis and further research assessing possible differences in treatment responses based on genetic polymorphisms.

Phenotypic diversity in white adults with moderate to severe Class II malocclusion

INTRODUCTION

Class II malocclusion affects about 15% of the population in the United States and is characterized by a convex profile and occlusal disharmonies. The specific etiologic mechanisms resulting in the range of Class II dentoskeletal combinations observed are not yet understood. Most studies describing Class II phenotypic diversity have used moderate sample sizes or focused on younger patients who later in life might outgrow their Class II discrepancies; such a focus might also preclude the visualization of adult Class II features. The majority have used simple correlation methods resulting in phenotypes that might not be generalizable to different samples and thus might not be suitable for studies of malocclusion etiology. The purpose of this study was to address these knowledge gaps by capturing the maximum phenotypic variations in a large sample of white Class II subjects selected with strict eligibility criteria and rigorously standardized multivariate reduction analyses.

METHODS

Sixty-three lateral cephalometric variables were measured from the pretreatment records of 309 white Class II adults (82 male, 227 female; ages, 16-60 years). Principal component analysis and cluster analysis were used to generate comprehensive phenotypes to identify the most homogeneous groups of subjects, reducing heterogeneity and improving the power of future malocclusion etiology studies.

RESULTS

Principal component analysis resulted in 7 principal components that accounted for 81% of the variation. The first 3 components represented variation on mandibular rotation, maxillary incisor angulation, and mandibular length. The cluster analysis identified 5 distinct Class II phenotypes.

CONCLUSIONS

A comprehensive spectrum of Class II phenotypic definitions was obtained that can be generalized to other samples to advance our efforts for identifying the etiologic factors underlying Class II malocclusion.

Phenotypic diversity in white adults with moderate to severe Class III malocclusion

INTRODUCTION

Class III malocclusion is characterized by a composite of dentoskeletal patterns that lead to the forward positioning of the mandibular teeth in relation to the maxillary teeth and a concave profile. Environmental and genetic factors are associated with this condition, which affects 1% of the population in the United States and imposes significant esthetic and functional burdens on affected persons. The purpose of this study was to capture the phenotypic variation in a large sample of white adults with Class III malocclusion using multivariate reduction methods.

METHODS

Sixty-three lateral cephalometric variables were measured from the pretreatment records of 292 white subjects with Class II malocclusion (126 male, 166 female; ages, 16-57 years). Principal component analysis and cluster analysis were used to capture the phenotypic variation and identify the most homogeneous groups of subjects to reduce genetic heterogeneity.

RESULTS

Principal component analysis resulted in 6 principal components that accounted for 81.2% of the variation. The first 3 components represented variation in mandibular horizontal and vertical positions, maxillary horizontal position, and mandibular incisor angulation. The cluster model identified 5 distinct subphenotypes of Class III malocclusion.

CONCLUSIONS

A spectrum of phenotypic definitions was obtained replicating results of previous studies and supporting the validity of these phenotypic measures in future research of the genetic and environmental etiologies of Class III malocclusion.

Clinical heterogeneity of duchenne muscular dystrophy (DMD): definition of sub-phenotypes and predictive criteria by long-term follow-up

Background

To explore clinical heterogeneity of Duchenne muscular dystrophy (DMD), viewed as a major obstacle to the interpretation of therapeutic trials

Methodology/Principal Findings

A retrospective single institution long-term follow-up study was carried out in DMD patients with both complete lack of muscle dystrophin and genotyping. An exploratory series (series 1) was used to assess phenotypic heterogeneity and to identify early criteria predicting future outcome; it included 75 consecutive steroid-free patients, longitudinally evaluated for motor, respiratory, cardiac and cognitive functions (median follow-up: 10.5 yrs). A validation series (series 2) was used to test robustness of the selected predictive criteria; it included 34 more routinely evaluated patients (age>12 yrs). Multivariate analysis of series 1 classified 70/75 patients into 4 clusters with distinctive intellectual and motor outcomes: A (early infantile DMD, 20%): severe intellectual and motor outcomes; B (classical DMD, 28%): intermediate intellectual and poor motor outcome; C (moderate pure motor DMD, 22%): normal intelligence and delayed motor impairment; and D (severe pure motor DMD, 30%): normal intelligence and poor motor outcome. Group A patients had the most severe respiratory and cardiac involvement. Frequency of mutations upstream to exon 30 increased from group A to D, but genotype/phenotype correlations were restricted to cognition (IQ>71: OR 7.7, 95%CI 1.6–20.4, p<0.003). Diagnostic accuracy tests showed that combination of “clinical onset <2 yrs” with “mental retardation” reliably assigned patients to group A (sensitivity 0.93, specificity 0.98). Combination of “lower limb MMT score>6 at 8 yrs” with “normal or borderline mental status” reliably assigned patients to group C (sensitivity: 1, specificity: 0.94). These criteria were also predictive of “early infantile DMD” and “moderate pure motor DMD” in series 2.

Conclusions/Significance

DMD can be divided into 4 sub-phenotypes differing by severity of muscle and brain dysfunction. Simple early criteria can be used to include patients with similar outcomes in future therapeutic trials.

A survey of data mining methods for linkage disequilibrium mapping

Data mining methods are gaining more interest as potential tools in mapping and identification of complex disease loci. The methods are well suited to large numbers of genetic marker loci produced by high-throughput laboratory analyses, but also might be useful for clarifying the phenotype definitions prior to more traditional mapping analyses. Here, the current data mining-based methods for linkage disequilibrium mapping and phenotype analyses are reviewed.

Phenotype definition in epilepsy

Phenotype definition consists of the use of epidemiologic, biological, molecular, or computational methods to systematically select features of a disorder that might result from distinct genetic influences. By carefully defining the target phenotype, or dividing the sample by phenotypic characteristics, we can hope to narrow the range of genes that influence risk for the trait in the study population, thereby increasing the likelihood of finding them. In this article, fundamental issues that arise in phenotyping in epilepsy and other disorders are reviewed, and factors complicating genotype-phenotype correlation are discussed. Methods of data collection, analysis, and interpretation are addressed, focusing on epidemiologic studies. With this foundation in place, the epilepsy subtypes and clinical features that appear to have a genetic basis are described, and the epidemiologic studies that have provided evidence for the heritability of these phenotypic characteristics, supporting their use in future genetic investigations, are reviewed. Finally, several molecular approaches to phenotype definition are discussed, in which the molecular defect, rather than the clinical phenotype, is used as a starting point.

Defining the Phenotype in Human Genetic Studies: Forward Genetics and Reverse Phenotyping

The definition of phenotypes for genetic study is a challenging endeavor. Just as we apply strict quality standards to genotype data, we should expect that phenotypes meet consistently high standards of reproducibility and validity. The methods for achieving accurate phenotype assignment in the research setting--the 'research diagnosis'--are different from the methods used in clinical diagnosis in the patient care setting. We evaluate some of the main challenges of phenotype definition in human genetics, and begin to outline a set of standards to which phenotypes used in genetics studies may aspire with the goal of increasing the quality and reproducibility of linkage and association studies. Revisiting the traditional phenotype definitions through a focus on familial components and heritable endophenotypes is a time-honored approach. Reverse phenotyping, where phenotypes are refined based on genetic marker data, may be a promising new approach. The stakes are high, since the success of gene mapping in genetically complex disorders hinges on the ability to delineate the target phenotype with accuracy and precision.

Quantitative trait linkage analysis of longitudinal change in body weight

One of the great strengths of the Framingham Heart Study data, provided for the Genetic Analysis Workshop 13, is the long-term survey of phenotypic data. We used this unique data to create new phenotypes representing the pattern of longitudinal change of the provided phenotypes, especially systolic blood pressure and body weight. We performed a linear regression of body weight and systolic blood pressure on age and took the slopes as new phenotypes for quantitative trait linkage analysis using the SOLAR package. There was no evidence for heritability of systolic blood pressure change. Heritability was estimated as 0.15 for adult life "body weight change", measured as the regression slope, and "body weight gain" (including only individuals with a positive regression slope), and as 0.22 for body weight "change up to 50" (regression slope of weight on age up to an age of 50). With multipoint analysis, two regions on the long arm of chromosome 8 showed the highest LOD scores of 1.6 at 152 cM for "body weight change" and of >1.9 around location 102 cM for "body weight gain" and "change up to 50". The latter two LOD scores almost reach the threshold for suggestive linkage. We conclude that the chromosome 8 region may harbor a gene acting on long-term body weight regulation, thereby contributing to the development of the metabolic syndrome.

Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13

The participants of Presentation Group 1 used the GAW13 data to derive new phenotypes, which were then analyzed for linkage and, in one case, for association to the genetic markers. Since the trait measurements ranged over longer time periods, the participants looked at the time dependence of particular traits in addition to the trait itself. The phenotypes analyzed with the Framingham data can be roughly divided into 1) body weight-related traits, which also include a type 2 diabetes progression trait, and 2) traits related to systolic blood pressure. Both trait classes are associated with metabolic syndrome. For traits related to body weight, linkage was consistently identified by at least two participating groups to genetic regions on chromosomes 4, 8, 11, and 18. For systolic blood pressure, or its derivatives, at least two groups obtained linkage for regions on chromosomes 4, 6, 8, 11, 14, 16, and 19. Five of the 13 participating groups focused on the simulated data. Due to the rather sparse grid of microsatellite markers, an association analysis for several traits was not successful. Linkage analysis of hypertension and body mass index using LODs and heterogeneity LODs (HLODs) had low power. For the glucose phenotype, a combination of random coefficient regression models and variance component linkage analysis turned out to be strikingly powerful in the identification of a trait locus simulated on chromosome 5. Haseman-Elston regression methods, applied to the same phenotype, had low power, but the above-mentioned chromosome 5 locus was not included in this analysis.