Genetic modulation of lipid profiles following lifestyle modification or metformin treatment: the Diabetes Prevention Program

A genetic sum score of effect alleles associated with serum lipid concentrations interacts with educational attainment

High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) levels are influenced by both genes and the environment. The aim was to investigate whether education and income as indicators of socioeconomic position (SEP) interact with lipid-increasing genetic effect allele scores (GES) in a population-based cohort. Using baseline data of 4516 study participants, age- and sex-adjusted linear regression models were fitted to investigate associations between GES and lipids stratified by SEP as well as including GES×SEP interaction terms. In the highest education group compared to the lowest stronger effects per GES standard deviation were observed for HDL-C (2.96 mg/dl [95%-CI: 2.19, 3.83] vs. 2.45 mg/dl [95%-CI: 1.12, 3.72]), LDL-C (6.57 mg/dl [95%-CI: 4.73, 8.37] vs. 2.66 mg/dl [95%-CI: −0.50, 5.76]) and TC (8.06 mg/dl [95%-CI: 6.14, 9.98] vs. 4.37 mg/dl [95%-CI: 0.94, 7.80]). Using the highest education group as reference, interaction terms showed indication of GES by low education interaction for LDL-C (ß_{GES×Education}: −3.87; 95%-CI: −7.47, −0.32), which was slightly attenuated after controlling for GES_LDL-C×Diabetes interaction (ß_{GES×Education}: −3.42; 95%-CI: −6.98, 0.18). The present study showed stronger genetic effects on LDL-C in higher SEP groups and gave indication for a GES_LDL-C×Education interaction, demonstrating the relevance of SEP for the expression of genetic health risks.

Polygenic approaches to detect gene-environment interactions when external information is unavailable

The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.

Precision behavioral medicine: Implications of genetic and genomic discoveries for behavioral weight loss treatment

This article reviews the concept of precision behavioral medicine and the progress toward applying genetics and genomics as tools to optimize weight management intervention. We discuss genetic, epigenetic, and genomic markers, as well as interactions between genetics and the environment as they relate to obesity and behavioral weight loss to date. Recommendations for the conditions under which genetics and genomics could be incorporated to support clinical decision-making in behavioral weight loss are outlined and illustrative scenarios of how this approach could improve clinical outcomes are provided. It is concluded that there is not yet sufficient evidence to leverage genetics or genomics to aid the treatment of obesity but the foundations are being laid. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

On the utilization of polygenic risk scores for therapeutic targeting

The promise of personalized genomic medicine is that knowledge of a person's gene sequences and activity will facilitate more appropriate medical interventions, particularly drug prescriptions, to reduce the burden of disease. Early successes in oncology and pediatrics have affirmed the power of positive diagnosis and are mostly based on detection of one or a few mutations that drive the specific pathology. However, genetically more complex diseases require the development of polygenic risk scores (PRSs) that have variable accuracy. The rarity of events often means that they have necessarily low precision: many called positives are actually not at risk, and only a fraction of cases are prevented by targeted therapy. In some situations, negative prediction may better define the population at low risk. Here, I review five conditions across a broad spectrum of chronic disease (opioid pain medication, hypertension, type 2 diabetes, major depression, and osteoporotic bone fracture), considering in each case how genetic prediction might be used to target drug prescription. This leads to a call for more research designed to evaluate genetic likelihood of response to therapy and a call for evaluation of PRS, not just in terms of sensitivity and specificity but also with respect to potential clinical efficacy.

Genotype-Based Recall Studies in Complex Cardiometabolic Traits

In genotype-based recall (GBR) studies, people (or their biological samples) who carry genotypes of special interest for a given hypothesis test are recalled from a larger cohort (or biobank) for more detailed investigations. There are several GBR study designs that offer a range of powerful options to elucidate (1) genotype-phenotype associations (by increasing the efficiency of genetic association studies, thereby allowing bespoke phenotyping in relatively small cohorts), (2) the effects of environmental exposures (within the Mendelian randomization framework), and (3) gene-treatment interactions (within the setting of GBR interventional trials). In this review, we overview the literature on GBR studies as applied to cardiometabolic health outcomes. We also review the GBR approaches used to date and outline new methods and study designs that might enhance the utility of GBR-focused studies. Specifically, we highlight how GBR methods have the potential to augment randomized controlled trials, providing an alternative application for the now increasingly accepted Mendelian randomization methods usually applied to large-scale population-based data sets. Further to this, we consider how functional and basic science approaches alongside GBR designs offer intellectually intriguing and potentially powerful ways to explore the implications of alterations to specific (and potentially druggable) biological pathways.

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function

Background

Smoking is the strongest environmental risk factor for reduced pulmonary function. The genetic component of various pulmonary traits has also been demonstrated, and at least 26 loci have been reproducibly associated with either FEV 1 (forced expiratory volume in 1 second) or FEV 1 /FVC (FEV 1 /forced vital capacity). Although the main effects of smoking and genetic loci are well established, the question of potential gene-by-smoking interaction effect remains unanswered. The aim of the present study was to assess, using a genetic risk score approach, whether the effect of these 26 loci on pulmonary function is influenced by smoking.

Methods

We evaluated the interaction between smoking exposure, considered as either ever vs never or pack-years, and a 26-single nucleotide polymorphisms (SNPs) genetic risk score in relation to FEV 1 or FEV 1 /FVC in 50 047 participants of European ancestry from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and SpiroMeta consortia.

Results

We identified an interaction ( βint  = -0.036, 95% confidence interval, -0.040 to -0.032, P  = 0.00057) between an unweighted 26 SNP genetic risk score and smoking status (ever/never) on the FEV 1 /FVC ratio. In interpreting this interaction, we showed that the genetic risk of falling below the FEV /FVC threshold used to diagnose chronic obstructive pulmonary disease is higher among ever smokers than among never smokers. A replication analysis in two independent datasets, although not statistically significant, showed a similar trend in the interaction effect.

Conclusions

This study highlights the benefit of using genetic risk scores for identifying interactions missed when studying individual SNPs and shows, for the first time, that persons with the highest genetic risk for low FEV 1 /FVC may be more susceptible to the deleterious effects of smoking.

Increasing insulin resistance accentuates the effect of triglyceride-associated loci on serum triglycerides during 5 years

Blood concentrations of triglycerides are influenced by genetic factors as well as a number of environmental factors, including adiposity and glucose homeostasis. The aim was to investigate the association between a serum triglyceride weighted genetic risk score (wGRS) and changes in fasting serum triglyceride level over 5 years and to test whether the effect of the wGRS was modified by 5 year changes of adiposity, insulin resistance, and lifestyle factors. A total of 3,474 nondiabetic individuals from the Danish Inter99 cohort participated in both the baseline and 5 year follow-up physical examinations and had information on the wGRS comprising 39 genetic variants. In a linear regression model adjusted for age, sex, and baseline serum triglyceride, the wGRS was associated with increased serum triglyceride levels over 5 years [per allele effect = 1.3% (1.0-1.6%); P = 1.0 × 10-17]. This triglyceride-increasing effect of the wGRS interacted with changes in insulin resistance (Pinteraction = 1.5 × 10-6). This interaction indicated that the effect of the wGRS was stronger in individuals who became more insulin resistant over 5 years. In conclusion, our findings suggest that increased genetic risk load is associated with a larger increase in fasting serum triglyceride levels in nondiabetic individuals during 5 years of follow-up. This effect of the wGRS is accentuated by increasing insulin resistance.

Statistical power considerations in genotype-based recall randomized controlled trials

Randomized controlled trials (RCT) are often underpowered for validating gene-treatment interactions. Using published data from the Diabetes Prevention Program (DPP), we examined power in conventional and genotype-based recall (GBR) trials. We calculated sample size and statistical power for gene-metformin interactions (vs. placebo) using incidence rates, gene-drug interaction effect estimates and allele frequencies reported in the DPP for the rs8065082 SLC47A1 variant, a metformin transported encoding locus. We then calculated statistical power for interactions between genetic risk scores (GRS), metformin treatment and intensive lifestyle intervention (ILI) given a range of sampling frames, clinical trial sample sizes, interaction effect estimates, and allele frequencies; outcomes were type 2 diabetes incidence (time-to-event) and change in small LDL particles (continuous outcome). Thereafter, we compared two recruitment frameworks: GBR (participants recruited from the extremes of a GRS distribution) and conventional sampling (participants recruited without explicit emphasis on genetic characteristics). We further examined the influence of outcome measurement error on statistical power. Under most simulated scenarios, GBR trials have substantially higher power to observe gene-drug and gene-lifestyle interactions than same-sized conventional RCTs. GBR trials are becoming popular for validation of gene-treatment interactions; our analyses illustrate the strengths and weaknesses of this design.

Comprehensive Analysis of Established Dyslipidemia-Associated Loci in the Diabetes Prevention Program

BACKGROUND

We assessed whether 234 established dyslipidemia-associated loci modify the effects of metformin treatment and lifestyle intervention (versus placebo control) on lipid and lipid subfraction levels in the Diabetes Prevention Program randomized controlled trial.

METHODS AND RESULTS

We tested gene treatment interactions in relation to baseline-adjusted follow-up blood lipid concentrations (high-density lipoprotein [HDL] and low-density lipoprotein-cholesterol, total cholesterol, and triglycerides) and lipoprotein subfraction particle concentrations and size in 2993 participants with pre-diabetes. Of the previously reported single-nucleotide polymorphism associations, 32.5% replicated at P<0.05 with baseline lipid traits. Trait-specific genetic risk scores were robustly associated (3×10^-4>P>1.1×10^-16) with their respective baseline traits for all but 2 traits. Lifestyle modified the effect of the genetic risk score for large HDL particle numbers, such that each risk allele of the genetic risk scores was associated with lower concentrations of large HDL particles at follow-up in the lifestyle arm (β=-0.11 µmol/L per genetic risk scores risk allele; 95% confidence interval, -0.188 to -0.033; P=5×10^-3; P_interaction=1×10^-3 for lifestyle versus placebo), but not in the metformin or placebo arms (P>0.05). In the lifestyle arm, participants with high genetic risk had more favorable or similar trait levels at 1-year compared with participants at lower genetic risk at baseline for 17 of the 20 traits.

CONCLUSIONS

Improvements in large HDL particle concentrations conferred by lifestyle may be diminished by genetic factors. Lifestyle intervention, however, was successful in offsetting unfavorable genetic loading for most lipid traits.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov. Unique Identifier: NCT00004992.

A perspective on interaction effects in genetic association studies

The identification of gene–gene and gene–environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression‐based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypothetical interaction patterns. Linking this components it appears first that the simplest biological interaction models—in which the magnitude of a genetic effect depends on a common exposure—are among the most difficult to identify. Second, I highlight the demerit of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome this issue. Finally, I explore the advantages and limitations of multivariate interaction models, when testing for interaction between multiple SNPs and/or multiple exposures, over univariate approaches. Together, these new insights can be leveraged for future method development and to improve our understanding of the genetic architecture of multifactorial traits.

Leveraging Genetics to Advance Type 2 Diabetes Prevention

In this Perspective, Jose Florez discusses how information from genetics and genomics may be able to contribute to prevention of type 2 diabetes and predicting individual responses to behavioral and other interventions.

Diet Quality and Change in Blood Lipids during 16 Years of Follow-up and Their Interaction with Genetic Risk for Dyslipidemia

A high diet quality according to the Swedish nutrition recommendations is associated with a reduced risk of cardiovascular disease in the population-based Malmö Diet and Cancer cohort. To further clarify this protective association, we examined the association between high diet quality and change in triglycerides, high density lipoprotein-cholesterol (HDL-C), and low density lipoprotein-cholesterol (LDL-C) after 16 years of follow-up in 3152 individuals (61% women; 46–68 years at baseline). In addition, we examined if genetic risk scores composed of 80 lipid-associated genetic variants modify these associations. A diet quality index based on intakes of saturated fat, polyunsaturated fat, sucrose, fiber, fruit and vegetables, and fish was constructed. A high diet quality was associated with lower risk of developing high triglycerides (p = 0.02) and high LDL-C (p = 0.03) during follow-up compared with a low diet quality. We found an association between diet quality and long-term change in HDL-C only among those with lower genetic risk for low HDL-C as opposed to those with higher genetic risk (p-interaction = 0.04). Among those with lower genetic risk for low HDL-C, low diet quality was associated with decreased HDL-C during follow-up (p = 0.05). In conclusion, individuals with high adherence to the Swedish nutrition recommendation had lower risk of developing high triglycerides and LDL-C during 16 years of follow-up.

Do Genetic Factors Modify the Relationship Between Obesity and Hypertriglyceridemia?

Background—

Obesity is a major risk factor for dyslipidemia, but this relationship is highly variable. Recently published data from 2 Danish cohorts suggest that genetic factors may underlie some of this variability.

Methods and Results—

We tested whether established triglyceride-associated loci modify the relationship of body mass index (BMI) and triglyceride concentrations in 2 Swedish cohorts (the Gene–Lifestyle Interactions and Complex Traits Involved in Elevated Disease Risk [GLACIER Study; N=4312] and the Malmö Diet and Cancer Study [N=5352]). The genetic loci were amalgamated into a weighted genetic risk score (WGRS TG ) by summing the triglyceride-elevating alleles (weighted by their established marginal effects) for all loci. Both BMI and the WGRS TG were strongly associated with triglyceride concentrations in GLACIER, with each additional BMI unit (kg/m 2 ) associated with 2.8% ( P =8.4×10 –84 ) higher triglyceride concentration and each additional WGRS TG unit with 2% ( P =7.6×10 –48 ) higher triglyceride concentration. Each unit of the WGRS TG was associated with 1.5% higher triglyceride concentrations in normal weight and 2.4% higher concentrations in overweight/obese participants ( P interaction =0.056). Meta-analyses of results from the Swedish cohorts yielded a statistically significant WGRS TG ×BMI interaction effect ( P interaction =6.0×10 –4 ), which was strengthened by including data from the Danish cohorts ( P interaction =6.5×10 –7 ). In the meta-analysis of the Swedish cohorts, nominal evidence of a 3-way interaction (WGRS TG ×BMI×sex) was observed ( P interaction =0.03), where the WGRS TG ×BMI interaction was only statistically significant in females. Using protein–protein interaction network analyses, we identified molecular interactions and pathways elucidating the metabolic relationships between BMI and triglyceride-associated loci.

Conclusions—

Our findings provide evidence that body fatness accentuates the effects of genetic susceptibility variants in hypertriglyceridemia, effects that are most evident in females.

Effects of lifestyle intervention on weight and metabolic parameters in patients with impaired glucose tolerance related to beta-3 adrenergic receptor gene polymorphism Trp64Arg(C/T): Results from the Japan Diabetes Prevention Program

The beta‐3 adrenergic receptor (ADRB3), primarily expressed in adipose tissue, is involved in the regulation of energy metabolism. The present study hypothesized that ADRB3 (Trp64Arg, rs4994) polymorphisms modulate the effects of lifestyle intervention on weight and metabolic parameters in patients with impaired glucose tolerance. Data were analyzed from 112 patients with impaired glucose tolerance in the Japan Diabetes Prevention Program, a lifestyle intervention trial, randomized to either an intensive lifestyle intervention group or usual care group. Changes in weight and metabolic parameters were measured after the 6‐month intervention. The ADRB3 polymorphisms were determined using the polymerase chain reaction restriction fragment length polymorphism method. Non‐carriers showed a greater weight reduction compared with the carriers in both the lifestyle intervention group and usual care group, and a greater increase of high‐density lipoprotein cholesterol levels than the carriers only in the lifestyle intervention group. ADRB3 polymorphisms could influence the effects of lifestyle interventions on weight and lipid parameters in impaired glucose tolerance patients.

Therapeutic Use of Metformin in Prediabetes and Diabetes Prevention

People with elevated, non-diabetic, levels of blood glucose are at risk of progressing to clinical type 2 diabetes and are commonly termed 'prediabetic'. The term prediabetes usually refers to high-normal fasting plasma glucose (impaired fasting glucose) and/or plasma glucose 2 h following a 75 g oral glucose tolerance test (impaired glucose tolerance). Current US guidelines consider high-normal HbA1c to also represent a prediabetic state. Individuals with prediabetic levels of dysglycaemia are already at elevated risk of damage to the microvasculature and macrovasculature, resembling the long-term complications of diabetes. Halting or reversing the progressive decline in insulin sensitivity and β-cell function holds the key to achieving prevention of type 2 diabetes in at-risk subjects. Lifestyle interventions aimed at inducing weight loss, pharmacologic treatments (metformin, thiazolidinediones, acarbose, basal insulin and drugs for weight loss) and bariatric surgery have all been shown to reduce the risk of progression to type 2 diabetes in prediabetic subjects. However, lifestyle interventions are difficult for patients to maintain and the weight loss achieved tends to be regained over time. Metformin enhances the action of insulin in liver and skeletal muscle, and its efficacy for delaying or preventing the onset of diabetes has been proven in large, well-designed, randomised trials, such as the Diabetes Prevention Program and other studies. Decades of clinical use have demonstrated that metformin is generally well-tolerated and safe. We have reviewed in detail the evidence base supporting the therapeutic use of metformin for diabetes prevention.

Interactions of Lipid Genetic Risk Scores With Estimates of Metabolic Health in a Danish Population

Background—

There are several well-established lifestyle factors influencing dyslipidemia and currently; 157 genetic susceptibility loci have been reported to be associated with serum lipid levels at genome-wide statistical significance. However, the interplay between lifestyle risk factors and these susceptibility loci has not been fully elucidated. We tested whether genetic risk scores (GRS) of lipid-associated single nucleotide polymorphisms associate with fasting serum lipid traits and whether the effects are modulated by lifestyle factors or estimates of metabolic health.

Methods and Results—

The single nucleotide polymorphisms were genotyped in 2 Danish cohorts: inter99 (n=5961) for discovery analyses and Health2006 (n=2565) for replication. On the basis of published effect sizes of single nucleotide polymorphisms associated with circulating fasting levels of total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, or triglyceride, 4 weighted GRS were constructed. In a cross-sectional design, we investigated whether the effect of these weighted GRSs on lipid levels were modulated by diet, alcohol consumption, physical activity, and smoking or the individual metabolic health status as estimated from body mass index, waist circumference, and insulin resistance assessed using homeostasis model assessment of insulin resistance. All 4 lipid weighted GRSs associated strongly with their respective trait (from P =3.3×10 –69 to P =1.1×10 –123 ). We found interactions between the triglyceride weighted GRS and body mass index and waist circumference on fasting triglyceride levels in Inter99 and replicated these findings in Health2006 ( P interaction =9.8×10 –5 and 2.0×10 –5 , respectively, in combined analysis).

Conclusions—

Our findings suggest that individuals who are obese may be more susceptible to the cumulative genetic burden of triglyceride single nucleotide polymorphisms. Therefore, it is suggested that especially these genetically at-risk individuals may benefit more from targeted interventions aiming at obesity prevention.

Promoting health and longevity through diet: from model organisms to humans

Reduced food intake, avoiding malnutrition, can ameliorate aging and aging-associated diseases in invertebrate model organisms, rodents, primates, and humans. Recent findings indicate that meal timing is crucial, with both intermittent fasting and adjusted diurnal rhythm of feeding improving health and function, in the absence of changes in overall intake. Lowered intake of particular nutrients rather than of overall calories is also key, with protein and specific amino acids playing prominent roles. Nutritional modulation of the microbiome can also be important, and there are long-term, including inter-generational, effects of diet. The metabolic, molecular, and cellular mechanisms that mediate both improvement in health during aging to diet and genetic variation in the response to diet are being identified. These new findings are opening the way to specific dietary and pharmacological interventions to recapture the full potential benefits of dietary restriction, which humans can find difficult to maintain voluntarily.

High genetic risk individuals benefit less from resistance exercise intervention

Background/Objectives

Genetic factors play an important role in body mass index (BMI) variation, and also likely play a role in the weight-loss and body composition response to physical activity/exercise. With the recent identification of BMI–associated genetic variants, it is possible to investigate the interaction of these genetic factors with exercise on body composition outcomes.

Subjects/Methods

In a block-randomized clinical trial of resistance exercise among women (n=148), we examined whether the putative effect of exercise on weight and DXA-derived body composition measurements differs according to genetic risk for obesity. Approximately one-half of the sample was randomized to an intervention consisting of a supervised, intensive, resistance exercise program, lasting one year. Genetic risk for obesity was defined as a genetic risk score (GRS) comprised of 21 SNPs known to be associated with normal BMI variation. We examined the interaction of exercise intervention and the GRS on anthropometric and body composition measurements after one year of the exercise intervention.

Results

We found statistically significant interactions for body weight (p=0.01), body fat (p=0.01), body fat % (p=0.02), and abdominal fat (p=0.02), whereby the putative effect of exercise is greater among those with a lower level of genetic risk for obesity. No single SNP appears to be a major driver of these interactions.

Conclusions

The weight-loss response to resistance exercise, including changes in body composition, differs according to an individual’s genetic risk for obesity.

The association between carbohydrate-rich foods and risk of cardiovascular disease is not modified by genetic susceptibility to dyslipidemia as determined by 80 validated variants

Background

It is still unclear whether carbohydrate consumption is associated with cardiovascular disease (CVD) risk. Genetic susceptibility might modify the associations between dietary intakes and disease risk.

Objectives

The aim was to examine the association between the consumption of carbohydrate-rich foods (vegetables, fruits and berries, juice, potatoes, whole grains, refined grains, cookies and cakes, sugar and sweets, and sugar-sweetened beverages) and the risk of incident ischemic CVD (iCVD; coronary events and ischemic stroke), and whether these associations differ depending on genetic susceptibility to dyslipidemia.

Methods

Among 26,445 individuals (44–74 years; 62% females) from the Malmö Diet and Cancer Study cohort, 2,921 experienced an iCVD event during a mean follow-up time of 14 years. At baseline, dietary data were collected using a modified diet history method, and clinical risk factors were measured in 4,535 subjects. We combined 80 validated genetic variants associated with triglycerides and HDL-C or LDL-C, into genetic risk scores and examined the interactions between dietary intakes and genetic risk scores on the incidence of iCVD.

Results

Subjects in the highest intake quintile for whole grains had a 13% (95% CI: 3–23%; p-trend: 0.002) lower risk for iCVD compared to the lowest quintile. A higher consumption of foods rich in added sugar (sugar and sweets, and sugar-sweetened beverages) had a significant cross-sectional association with higher triglyceride concentrations and lower HDL-C concentrations. A stronger positive association between a high consumption of sugar and sweets on iCVD risk was observed among those with low genetic risk score for triglycerides (p-interaction=0.05).

Conclusion

In this prospective cohort study that examined food sources of carbohydrates, individuals with a high consumption of whole grains had a decreased risk of iCVD. No convincing evidence of an interaction between genetic susceptibility for dyslipidemia, measured as genetic risk scores of dyslipidemia-associated variants, and the consumption of carbohydrate-rich foods on iCVD risk was observed.

Advances in exercise, fitness, and performance genomics in 2013

The most significant and scientifically sound articles in exercise genomics that were published in 2013 are reviewed in this report. No article on the genetic basis of sedentary behavior or physical activity level was identified. A calcineurin- and alpha actinin-2-based mechanism has been identified as the potential molecular basis for the observed lower muscular strength and power in alpha actinin-3-deficient individuals. Although baseline muscle transcriptomic signatures were found to be associated with strength training-induced muscle hypertrophy, no predictive genomic variants could be identified as of yet. One study found no clear evidence that the inverse relation between physical activity level and incident CHD events was influenced by 58 genomic variants clustered into four genetic scores. Lower physical activity level in North American populations may be driving the apparent risk of obesity in fat mass- and obesity-associated gene (FTO)-susceptible individuals compared with more active populations. Two large studies revealed that common genetic variants associated with baseline levels of plasma HDL cholesterol and triglycerides are not clear predictors of changes induced by interventions focused on weight loss, diet, and physical activity behavior. One large study from Japan reported that a higher fitness level attenuated the arterial stiffness-promoting effect of the Ala54 allele at the fatty acid binding protein 2 locus, which is a controversial finding because previous studies have suggested that Thr54 was the risk allele. Using transcriptomics to generate genomic targets in an unbiased manner for subsequent DNA sequence variants studies appears to be a growing trend. Moreover, exercise genomics is rapidly embracing gene and pathway analysis to better define the underlying biology and provide a foundation for the study of human variation.

What We Know About Diet, Genes, and Dyslipidemia: Is There Potential for Translation?

Cardiovascular disease, particularly coronary artery disease (CAD), is the leading cause of death in the United States. Dyslipidemia, including elevated low density lipoprotein cholesterol (LDL-C) and triglyceride (TG) levels and low high density lipoprotein cholesterol (HDL-C), is a well-established risk factor for CAD and is influenced by both genetic and lifestyle factors, including the diet and dietary fat in particular. Major strides in elucidating the genetic basis for dyslipidemia have been made in recent years, and the quest to clarify how genetic differences influence lipid response to dietary intervention continues. Some monogenic conditions such as famililal hypercholesterolemia and sitosterolemia already have customized dietary recommendations. Some promising associations have emerged for more polygenic dyslipidemia, but further studies are needed in large dietary intervention studies capturing increasing amounts of explainable genetic variation before recommendations can be made for clinical translation.

Impact of the LDL subfraction phenotype on Lp-PLA2 distribution, LDL modification and HDL composition in type 2 diabetes

Background

Qualitative alterations of lipoproteins underlie the high incidence of atherosclerosis in diabetes. The objective of this study was to assess the impact of low-density lipoprotein (LDL) subfraction phenotype on the qualitative characteristics of LDL and high-density lipoprotein (HDL) in patients with type 2 diabetes.

Methods

One hundred twenty two patients with type 2 diabetes in poor glycemic control and 54 healthy subjects were included in the study. Patients were classified according to their LDL subfraction phenotype. Seventy-seven patients presented phenotype A whereas 45 had phenotype B. All control subjects showed phenotype A. Several forms of modified LDL, HDL composition and the activity and distribution of lipoprotein-associated phospholipase A2 (Lp-PLA2) were analyzed.

Results

Oxidized LDL, glycated LDL and electronegative LDL were increased in both groups of patients compared with the control group. Patients with phenotype B had increased oxidized LDL and glycated LDL concentration than patients with phenotype A. HDL composition was abnormal in patients with diabetes, being these abnormalities more marked in patients with phenotype B. Total Lp-PLA2 activity was higher in phenotype B than in phenotype A or in control subjects. The distribution of Lp-PLA2 between HDL and apoB-containing lipoproteins differed in patients with phenotype A and phenotype B, with higher activity associated to apoB-containing lipoproteins in the latter.

Conclusions

The presence of LDL subfraction phenotype B is associated with increased oxidized LDL, glycated LDL and Lp-PLA2 activity associated to apoB-containing lipoproteins, as well as with abnormal HDL composition.

Do genetic modifiers of high-density lipoprotein cholesterol and triglyceride levels also modify their response to a lifestyle intervention in the setting of obesity and type-2 diabetes mellitus?: The Action for Health in Diabetes (Look AHEAD) study

BACKGROUND

High-density lipoprotein cholesterol (HDL-C) and triglycerides are cardiovascular risk factors susceptible to lifestyle behavior modification and genetics. We hypothesized that genetic variants identified by genome-wide association studies as associated with HDL-C or triglyceride levels modify 1-year treatment response to an intensive lifestyle intervention, relative to a usual care of diabetes mellitus support and education.

METHODS AND RESULTS

We evaluated 82 single-nucleotide polymorphisms, which represent 31 loci demonstrated by genome-wide association studies to be associated with HDL-C and triglycerides, in 3561 participants who consented for genetic studies and met eligibility criteria. Variants associated with higher baseline HDL-C levels, cholesterol ester transfer protein (CETP) rs3764261 and hepatic lipase (LIPC) rs8034802, were found to be associated with HDL-C increases with intensive lifestyle intervention (P=0.0038 and 0.013, respectively) and had nominally significant treatment interactions (P=0.047 and 0.046, respectively). The fatty acid desaturase-2 rs1535 variant, associated with low baseline HDL-C (P=0.017), was associated with HDL-C increases with intensive lifestyle intervention (0.0037) and had a nominal treatment interaction (P=0.035). Apolipoprotein B (rs693) and LIPC (rs8034802) single-nucleotide polymorphisms showed nominally significant associations with HDL-C and triglyceride changes with intensive lifestyle intervention and a treatment interaction (P<0.05). Phosphatidylglycerophosphate synthase-1 single-nucleotide polymorphisms (rs4082919) showed the most significant triglyceride treatment interaction in the full cohort (P=0.0009).

CONCLUSIONS

This is the first study to identify genetic variants modifying lipid responses to a randomized lifestyle behavior intervention in overweight or obese individuals with diabetes mellitus. The effects of genetic factors on lipid changes may differ from the effects on baseline lipids and are modifiable by behavioral intervention.