A clustering analysis of lipoprotein diameters in the metabolic syndrome

The Nutri-Score nutrition label: Associations between the underlying nutritional profile of foods and lipoprotein particle subclass profiles in adults

BACKGROUND AND AIMS

Lipoprotein particle concentrations and size are associated with increased risk for atherosclerosis and premature cardiovascular disease. Certain dietary behaviours may be cardioprotective and public health strategies are needed to guide consumers' dietary choices and help prevent diet-related disease. The Food Standards Agency nutrient profiling system (FSAm-NPS) constitutes the basis of the five-colour front-of-pack Nutri-Score labelling system. No study has examined FSAm-NPS index associations with a wide range of lipoprotein particle subclasses.

METHODS

This was a cross-sectional study of 2006 middle-to older-aged men and women randomly selected from a large primary care centre. Individual participant FSAm-NPS dietary scores were derived from validated food frequency questionnaires. Lipoprotein particle subclass concentrations and size were determined using nuclear magnetic resonance spectroscopy. Multivariate-adjusted linear regression analyses were performed to examine FSAm-NPS relationships with lipoprotein particle subclasses.

RESULTS

In fully adjusted models which accounted for multiple testing, higher FSAm-NPS scores, indicating poorer dietary quality, were positively associated with intermediate-density lipoprotein (β = 0.096, p = 0.005) and small high-density lipoprotein (HDL) (β = 0.492, p = 0.006) concentrations, a lipoprotein insulin resistance score (β = 0.063, p = 0.02), reflecting greater lipoprotein-related insulin resistance, and inversely associated with HDL size (β = -0.030, p = 0.045).

CONCLUSIONS

A higher FSAm-NPS score is associated with a less favourable lipoprotein particle subclass profile in middle-to older-aged adults which may be a potential mechanism underlying reported health benefits of a healthy diet according to Nutri-Score rating.

Plant-based diet indices and lipoprotein particle subclass profiles: A cross-sectional analysis of middle- to older-aged adults

BACKGROUND AND AIMS

Plant-based diets (PBDs) are associated with favourable lipid profiles and cardiometabolic outcomes. However, limited data regarding PBD indices (PDIs) and lipoprotein subclasses exist. We examined overall PDI, healthful PDI (hPDI) and unhealthful PDI (uPDI) associations with lipid and lipoprotein profiles.

METHODS

This cross-sectional analysis includes 1,986 middle- to older-aged adults from the Mitchelstown Cohort. The PDI, hPDI and uPDI scores were calculated from validated food frequency questionnaires. Higher PDI, hPDI and uPDI scores indicate a more PBD, healthful PBD and unhealthful PBD, respectively. Lipoprotein particle size and subclass concentrations were measured using nuclear magnetic resonance spectroscopy. Relationships between PDIs and lipid and lipoprotein profiles were examined via correlation and regression analyses adjusted for covariates.

RESULTS

In fully adjusted regression analyses, higher PDI scores were associated with lower high-density lipoprotein (HDL) cholesterol concentrations and more triglyceride-rich lipoprotein and small very low-density lipoprotein (VLDL) particles. Higher hPDI scores were negatively associated with non-HDL cholesterol concentrations, large VLDL and small HDL particles, the Lipoprotein Insulin Resistance Index (LP-IR) score and VLDL particle size. Higher uPDI scores were associated with lower HDL cholesterol and greater triglyceride concentrations and more medium and large VLDL, total LDL, small LDL and total non-HDL particles, less large LDL and large HDL particles, a greater LP-IR score, greater VLDL particle size and smaller LDL and HDL particle size.

CONCLUSIONS

This study provides novel evidence regarding associations between PBD quality and lipoprotein subclasses. A more unhealthful PBD was robustly associated with a more pro-atherogenic lipoprotein profile.

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.

Cluster Analysis of Cardiovascular Phenotypes in Patients With Type 2 Diabetes and Established Atherosclerotic Cardiovascular Disease: A Potential Approach to Precision Medicine

OBJECTIVE

Phenotypic heterogeneity among patients with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD) is ill defined. We used cluster analysis machine-learning algorithms to identify phenotypes among trial participants with T2DM and ASCVD.

RESEARCH DESIGN AND METHODS

We used data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS) study (n = 14,671), a cardiovascular outcome safety trial comparing sitagliptin with placebo in patients with T2DM and ASCVD (median follow-up 3.0 years). Cluster analysis using 40 baseline variables was conducted, with associations between clusters and the primary composite outcome (cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina) assessed by Cox proportional hazards models. We replicated the results using the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial.

RESULTS

Four distinct phenotypes were identified: cluster I included Caucasian men with a high prevalence of coronary artery disease; cluster II included Asian patients with a low BMI; cluster III included women with noncoronary ASCVD disease; and cluster IV included patients with heart failure and kidney dysfunction. The primary outcome occurred, respectively, in 11.6%, 8.6%, 10.3%, and 16.8% of patients in clusters I to IV. The crude difference in cardiovascular risk for the highest versus lowest risk cluster (cluster IV vs. II) was statistically significant (hazard ratio 2.74 [95% CI 2.29-3.29]). Similar phenotypes and outcomes were identified in EXSCEL.

CONCLUSIONS

In patients with T2DM and ASCVD, cluster analysis identified four clinically distinct groups. Further cardiovascular phenotyping is warranted to inform patient care and optimize clinical trial designs.

Range of SHH signaling in adrenal gland is limited by membrane contact to cells with primary cilia

The signaling protein Sonic Hedgehog (SHH) is crucial for the development and function of many vertebrate tissues. It remains largely unclear, however, what defines the range and specificity of pathway activation. The adrenal gland represents a useful model to address this question, where the SHH pathway is activated in a very specific subset of cells lying near the SHH-producing cells, even though there is an abundance of lipoproteins that would allow SHH to travel and signal long-range. We determine that, whereas adrenal cells can secrete SHH on lipoproteins, this form of SHH is inactive due to the presence of cosecreted inhibitors, potentially explaining the absence of long-range signaling. Instead, we find that SHH-producing cells signal at short range via membrane-bound SHH, only to receiving cells with primary cilia. Finally, our data from NCI-H295R adrenocortical carcinoma cells suggest that adrenocortical tumors may evade these regulatory control mechanisms by acquiring the ability to activate SHH target genes in response to TGF-β.

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.

Genetics of non-conventional lipoprotein fractions

Lipoprotein subclass measures associate with cardiometabolic disease risk. Currently the information that lipoproteins convey on disease risk over that of traditional demographic and lipid measures is minimal, and so their use is clinics is limited. However, lipoprotein subclass perturbations represent some of the earliest manifestations of metabolic dysfunction, and their etiology is partially distinct from lipids, so information on the genetic etiology of lipoproteins offers promise for improved risk prediction, and unique mechanistic insights into IR and atherosclerosis. Here, I review the genetic variants validated as associating with lipoprotein measures to date, and show that the majority of identified variants have functionality that is best understood as related to lipid measures. Until we focus on the genes as they relate to lipoprotein subclass production, we are limiting our understanding of biological mechanisms underlying cardiometabolic disease.

Genetic risk scores associated with baseline lipoprotein subfraction concentrations do not associate with their responses to fenofibrate

Lipoprotein subclass concentrations are modifiable markers of cardiovascular disease risk. Fenofibrate is known to show beneficial effects on lipoprotein subclasses, but little is known about the role of genetics in mediating the responses of lipoprotein subclasses to fenofibrate. A recent genomewide association study (GWAS) associated several single nucleotide polymorphisms (SNPs) with lipoprotein measures, and validated these associations in two independent populations. We used this information to construct genetic risk scores (GRSs) for fasting lipoprotein measures at baseline (pre-fenofibrate), and aimed to examine whether these GRSs also associated with the responses of lipoproteins to fenofibrate. Fourteen lipoprotein subclass measures were assayed in 817 men and women before and after a three week fenofibrate trial. We set significance at a Bonferroni corrected alpha <0.05 (p < 0.004). Twelve subclass measures changed with fenofibrate administration (each p = 0.003 to <0.0001). Mixed linear models which controlled for age, sex, body mass index (BMI), smoking status, pedigree and study-center, revealed that GRSs were associated with eight baseline lipoprotein measures (p < 0.004), however no GRS was associated with fenofibrate response. These results suggest that the mechanisms for changes in lipoprotein subclass concentrations with fenofibrate treatment are not mediated by the genetic risk for fasting levels.

Methylation at CPT1A locus is associated with lipoprotein subfraction profiles

Lipoprotein subfractions help discriminate cardiometabolic disease risk. Genetic loci validated as associating with lipoprotein measures do not account for a large proportion of the individual variation in lipoprotein measures. We hypothesized that DNA methylation levels across the genome contribute to interindividual variation in lipoprotein measures. Using data from participants of the Genetics of Lipid Lowering Drugs and Diet Network (n = 663 for discovery and n = 331 for replication stages, respectively), we conducted the first systematic screen of the genome to determine associations between methylation status at ∼470,000 cytosine-guanine dinucleotide (CpG) sites in CD4(+) T cells and 14 lipoprotein subfraction measures. We modeled associations between methylation at each CpG site and each lipoprotein measure separately using linear mixed models, adjusted for age, sex, study site, cell purity, and family structure. We identified two CpGs, both in the carnitine palmitoyltransferase-1A (CPT1A) gene, which reached significant levels of association with VLDL and LDL subfraction parameters in both discovery and replication phases (P < 1.1 × 10(-7) in the discovery phase, P < .004 in the replication phase, and P < 1.1 × 10(-12) in the full sample). CPT1A is regulated by PPARα, a ligand for drugs used to reduce CVD. Our associations between methylation in CPT1A and lipoprotein measures highlight the epigenetic role of this gene in metabolic dysfunction.

Sex-specific associations between screen time and lipoprotein subfractions

BACKGROUND

Time spent in sedentary activities (such as watching television) has previously been associated with several risk factors for cardiovascular disease (CVD) such as increased low-density lipoprotein cholesterol (LDL-C). Little is known about associations with lipoprotein subfractions. Using television and computer screen time in hours per day as a measure of sedentary time, we examined the association of screen time with lipoprotein subfractions.

METHODS

Data were used from men and women forming the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study population. Mixed linear models specified lipoprotein measures as the outcome, and screen time as the predictor for fourteen lipoprotein subfraction measures, and included age, smoking status, pedigree, and fat, carbohydrate daily alcohol and energy intake as covariates. Analyses were run separately for men (n = 623) and women (n = 671). A step-down Bonferroni correction was applied to results. The analysis was repeated for significant results (p < .05), additionally controlling for body mass index (BMI) and moderate and vigorous physical activity.

RESULTS

Linear models indicated that screen time was associated with five lipoprotein parameters in women: the concentration of large VLDL particles (p = .01), LDL particle number (p = .01), concentration of small LDL particles (p = .04), the concentration of large HDL particles (p = .04), and HDL diameter (p = .02). All associations remained after controlling for moderate or vigorous physical activity and BMI.

CONCLUSIONS

We show that sedentary time is associated with lipoprotein measures, markers of cardiometabolic disease, independently of physical activity and BMI, in women but not men.

Opportunities for using lipoprotein subclass profile by nuclear magnetic resonance spectroscopy in assessing insulin resistance and diabetes prediction

The incidence of type 2 diabetes mellitus (T2DM) has reached epidemic levels, and current trends indicate that its prevalence will continue to rise. The development of T2DM can be delayed by several years, and may even be prevented, by identifying individuals at risk for T2DM and treating them with lifestyle modification and/or pharmacological therapies. There are a number of methods available for assessing the insulin resistance (IR) that characterizes, and is the precursor to, T2DM. However, current clinical methods for assessing IR, based on measures of plasma glucose and/or insulin are either laborious and time-consuming or show a low specificity. IR manifests its earliest measurable abnormalities through changes in lipoproteins, and thus we propose that by examining lipoprotein subclass profile, it may be possible to alert physicians and patients to a heightened risk of developing diabetes. This will allow us to institute appropriate lifestyle changes and treatment potentially to delay the onset or possibly prevent the progression to diabetes.

Weight loss is still an essential intervention in obesity and its complications: a review

The prevalence of obesity is more than 20% in many developed countries and it increases in developing countries. Obesity is associated with metabolic disorders, cardiovascular diseases, pulmonary diseases, digestive diseases, and cancers. Although other specific treatments for these complications exist, weight loss is still an essential intervention in obesity and its complications. Therapeutic life change, behavior modification, pharmacotherapy, and surgery are major approaches to weight loss. In addition, medicine used in diabetes such as Glucagon-like peptide-1 analogues may be a new type of medicine for obesity, at least for those obese patients with diabetes.