Akram Alyass

From big data analysis to personalized medicine for all: challenges and opportunities

Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine.

Validity of Self-Report of Cardiovascular Risk Factors in a Population at High Risk for Stroke

BACKGROUND Screening for vascular risk factors is commonly assessed through self-report, despite reports of low sensitivity using this approach in healthy populations. The validity of self-reported vascular risk factors in a population at high risk for stroke has yet to be explored. AIMS This study investigated the validity of self-reported cardiovascular risk factors (e.g., hypertension, hypercholesterolemia, and type II diabetes mellitus) in a population of patients with a recent history of high-risk transient ischemic attack or minor stroke. METHODS Data were extracted from patient questionnaire responses and medical records (n = 101). Agreement between self-report and clinical measures (blood pressure, fasting blood glucose, lipid profile, and active medications) was assessed using estimates of sensitivity, specificity, and positive and negative predictive values for each vascular risk factor. RESULTS Forty-nine percent of the study population inaccurately self-reported at least 1 vascular risk factor. Sensitivities of self-report for hypertension, hypercholesterolemia, and diabetes were 84.5% (confidence interval [CI]: 72.1-92.2), 57.5% (CI: 44.1-69.7), and 77.8% (CI: 57.3-90.6), respectively, while specificities were 76.7% (CI: 61.0-87.7), 83.3% (CI: 67.3-93.2), and 95.4% (CI: 87.8-98.9), respectively. Accuracy of self-report for hypercholesterolemia was significantly lower than that for diabetes (P < .001) and hypertension (P < .05), with 42.6% of those with high cholesterol under-reporting their diagnosis. Logistic regression revealed that odds of accurate self-report were greater among younger adults and males. CONCLUSIONS These results highlight the need for clinicians, scientists, and epidemiologists to be cautious when screening for vascular risk factors using self-report measures as cross validation against objectives measures reveals poor sensitivity. Our results also highlight a lack of public education concerning these significant conditions.

Ethnic and population differences in the genetic predisposition to human obesity.

Obesity rates have escalated to the point of a global pandemic with varying prevalence across ethnic groups. These differences are partially explained by lifestyle factors in addition to genetic predisposition to obesity. This review provides a comprehensive examination of the ethnic differences in the genetic architecture of obesity. Using examples from evolution, heritability, admixture, monogenic and polygenic studies of obesity, we provide explanations for ethnic differences in the prevalence of obesity. The debate over definitions of race and ethnicity, the advantages and limitations of multi‐ethnic studies and future directions of research are also discussed. Multi‐ethnic studies have great potential to provide a better understanding of ethnic differences in the prevalence of obesity that may result in more targeted and personalized obesity treatments.

A systematic survey of the methods literature on the reporting quality and optimal methods of handling participants with missing outcome data for continuous outcomes in randomized controlled trials

OBJECTIVE To conduct (1) a systematic survey of the reporting quality of simulation studies dealing with how to handle missing participant data (MPD) in randomized control trials and (2) summarize the findings of these studies. STUDY DESIGN AND SETTING We included simulation studies comparing statistical methods dealing with continuous MPD in randomized controlled trials addressing bias, precision, coverage, accuracy, power, type-I error, and overall ranking. For the reporting of simulation studies, we adapted previously developed criteria for reporting quality and applied them to eligible studies. RESULTS Of 16,446 identified citations, the 60 eligible generally had important limitations in reporting, particularly in reporting simulation procedures. Of the 60 studies, 47 addressed ignorable and 32 addressed nonignorable data. For ignorable missing data, mixed model was most frequently the best on overall ranking (9 times best, 34.6% of times tested) and bias (10, 55.6%). Multiple imputation was also performed well. For nonignorable data, mixed model was most frequently the best on overall ranking (7, 46.7%) and bias (8, 57.1%). Mixed model performance varied on other criteria. Last observation carried forward (LOCF) was very seldom the best performing, and for nonignorable MPD frequently the worst. CONCLUSION Simulation studies addressing methods to deal with MPD suffered from serious limitations. The mixed model approach was superior to other methods in terms of overall performance and bias. LOCF performed worst.Please cite this article as: Zhang Y, Alyass A, Vanniyasingam T, Sadeghirad B, Flórez ID, Pichika SC, Kennedy SA, Abdulkarimova U, Zhang Y, Iljon T, Morgano GP, Colunga Lozano LE, Aloweni FAB, Lopes LC, Yepes-Nuñez JJ, Fei Y, Wang L, Kahale LA, Meyre D, Akl EA, Thabane L, Guyatt G, Reporting quality and optimal methods of handling participants with missing outcome data for continuous outcomes in randomized controlled trials: a systematic survey of the methods literature, Journal of Clinical Epidemiology (2017), doi: 10.1016/j.jclinepi.2017.05.016.

Association between PPAR-γ2 Pro12Ala genotype and insulin resistance is modified by circulating lipids in Mexican children.

The Pro12Ala (rs1801282) polymorphism in peroxisome proliferator-activated receptor-γ2 (PPAR-γ2) has been convincingly associated with insulin resistance (IR) and type 2 diabetes (T2D) among Europeans, in interaction with a high-fat diet. Mexico is disproportionally affected by obesity and T2D however, whether the Pro12Ala polymorphism is associated with early metabolic complications in this population is unknown. We assessed the association of PPAR-γ2 Pro12Ala with metabolic traits in 1457 Mexican children using linear regression models. Interactions between PPAR-γ2 Pro12Ala and circulating lipids on metabolic traits were determined by adding an interaction term to regression models. We observed a high prevalence of overweight/obesity (49.2%), dyslipidemia (34.9%) and IR (11.1%). We detected nominally significant/significant interactions between lipids (total cholesterol, HDL-cholesterol, LDL-cholesterol), the PPAR-γ2 Pro12Ala genotype and waist-to-hip ratio, fasting insulin, HOMA-IR and IR (9.30 × 10−4  ≤ Pinteraction ≤ 0.04). Post-hoc subgroup analyses evidenced that the association between the PPAR-γ2 Pro12Ala genotype and fasting insulin, HOMA-IR and IR was restricted to children with total cholesterol or LDL-cholesterol values higher than the median (0.02 ≤ P ≤ 0.03). Our data support an association of the Pro12Ala polymorphism with IR in Mexican children and suggest that this relationship is modified by dyslipidemia.

Evaluating the transferability of 15 European-derived fasting plasma glucose SNPs in Mexican children and adolescents

Genome wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) that are associated with fasting plasma glucose (FPG) in adult European populations. The contribution of these SNPs to FPG in non-Europeans and children is unclear. We studied the association of 15 GWAS SNPs and a genotype score (GS) with FPG and 7 metabolic traits in 1,421 Mexican children and adolescents from Mexico City. Genotyping of the 15 SNPs was performed using TaqMan Open Array. We used multivariate linear regression models adjusted for age, sex, body mass index standard deviation score, and recruitment center. We identified significant associations between 3 SNPs (G6PC2 (rs560887), GCKR (rs1260326), MTNR1B (rs10830963)), the GS and FPG level. The FPG risk alleles of 11 out of the 15 SNPs (73.3%) displayed significant or non-significant beta values for FPG directionally consistent with those reported in adult European GWAS. The risk allele frequencies for 11 of 15 (73.3%) SNPs differed significantly in Mexican children and adolescents compared to European adults from the 1000G Project, but no significant enrichment in FPG risk alleles was observed in the Mexican population. Our data support a partial transferability of European GWAS FPG association signals in children and adolescents from the admixed Mexican population.

Empirical evaluation of the Q-Genie tool: a protocol for assessment of effectiveness

Introduction Meta-analyses of genetic association studies are affected by biases and quality shortcomings of the individual studies. We previously developed and validated a risk of bias tool for use in systematic reviews of genetic association studies. The present study describes a larger empirical evaluation of the Q-Genie tool. Methods and analysis MEDLINE, Embase, Global Health and the Human Genome Epidemiology Network will be searched for published meta-analyses of genetic association studies. Twelve reviewers in pairs will apply the Q-Genie tool to all studies in included meta-analyses. The Q-Genie will then be evaluated on its ability to (i) increase precision after exclusion of low quality studies, (ii) decrease heterogeneity after exclusion of low quality studies and (iii) good agreement with experts on quality rating by Q-Genie. A qualitative assessment of the tool will also be conducted using structured questionnaires. Discussion This systematic review will quantitatively and qualitatively assess the Q-Genies ability to identify poor quality genetic association studies. This information will inform the selection of studies for inclusion in meta-analyses, conduct sensitivity analyses and perform metaregression. Results of this study will strengthen our confidence in estimates of the effect of a gene on an outcome from meta-analyses, ultimately bringing us closer to deliver on the promise of personalised medicine. Ethics and dissemination An updated Q-Genie tool will be made available from the Population Genomics Program website and the results will be submitted for a peer-reviewed publication.

Fine-mapping of 98 obesity loci in Mexican children

Background/objectivesMexico has one of the highest prevalence of childhood obesity in the world. Genome-wide association studies (GWAS) for obesity have identified multiple single-nucleotide polymorphisms (SNPs) in populations of European, East Asian, and African descent. The contribution of these loci to obesity in Mexican children is unclear. We assessed the transferability of 98 obesity loci in Mexican children and fine-mapped the association signals.Subjects/methodsThe study included 405 and 390 Mexican children with normal weight and obesity. Participants were genotyped with a genome-wide dense SNP array designed for Latino populations, allowing for the analysis of GWAS index SNPs as well as fine-mapping SNPs, totaling 750 SNPs covering 98 loci. Two genetic risk scores (GRS) were constructed: a “discovery GRS” and a “best-associated GRS”, representing the number of effect alleles at the GWAS index SNPs and at the best-associated SNPs after fine-mapping for each subject.ResultsSeventeen obesity loci were significantly associated with obesity, and five had fine-mapping SNPs significantly better associated with obesity than their corresponding GWAS index SNPs in Mexican children. Six obesity-associated SNPs significantly departed from additive to dominant (N = 5) or recessive (N = 1) models, and a significant interaction was found between rs274609 (TNNI3K) and rs1010553 (ITIH4) on childhood obesity risk. The best-associated GRS was significantly more associated with childhood obesity (OR = 1.21 per additional risk allele [95%CI:1.17–1.25], P = 4.8 × 10−25) than the discovery GRS (OR = 1.05 per additional risk allele [95%CI:1.02–1.08], P = 8.0 × 10−4), and was also associated with waist-to-hip ratio, fasting glucose, fasting insulin and triglyceride levels, the association being mediated by obesity. An overall depletion of obesity risk alleles was observed in Mexican children with normal weight when compared to GWAS discovery populations.ConclusionsOur study indicates a partial transferability of GWAS obesity loci in Mexican children, and supports the pertinence of post-GWAS fine-mapping experiments in the admixed Mexican population.

Penetrance of polygenic obesity susceptibility loci across the body mass index distribution: an update on scaling effects.

A growing number of single nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effect of these obesity susceptibility loci is uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI/obesity-associated SNPs in 75,230 adults of European ancestry along BMI percentiles using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of 9 SNPs (24%) increased significantly across the sample BMI distribution including, FTO (rs1421085, p=8.69×10−15), PCSK1 (rs6235, p=7.11×10−06), TCF7L2 (rs7903146, p=9.60×10−06), MC4R (rs11873305, p=5.08×10−05), FANCL (rs12617233, p=5.30×10−05), GIPR (rs11672660, p=1.64×−04), MAP2K5 (rs997295, p=3.25×10−04), FTO (rs6499653, p=6.23×10−04) and NT5C2 (rs3824755, p=7.90×10−04). We showed that such increases stem from unadjusted gene interactions that enhanced the effects of SNPs in persons with high BMI. When 125 height-associated were analyzed for comparison, only one (<1%), IGF1 (rs6219, p=1.80×10−04), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-Height, respectively) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p=7.03×10−37, Height: p=0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method to detect such interactions using only the sample outcome distribution.

Gain-of-function variants in the melanocortin 4 receptor gene confer susceptibility to binge eating disorder in subjects with obesity: a systematic review and meta-analysis: MC4R coding variants and BED

The association between coding variants in the melanocortin 4 receptor gene (MC4R) and binge eating disorder (BED) in patients with obesity is controversial. Two independent reviewers systematically searched MEDLINE, Embase, PsycINFO, BIOSIS Previews, Web of Science Core Collection and Google Scholar up to February 2018, using terms describing the MC4R gene and BED. Six of 103 identified references were included. Studies examined associations between at least one coding variant/mutation in MC4R and BED and screened for BED as per the Diagnostic and Statistical Manual of Mental Disorders. Risk of bias was assessed using a modified version of the Q‐Genie tool, and overall quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation guidance. Meta‐analysis was conducted via logistic regression models. A positive association between gain‐of‐function (GOF) variants in the MC4R and BED was observed (odds ratio [OR] = 3.05; 95% confidence interval [CI]: 1.82, 5.04; p = 1.7 × 10−5), while no association was detected between loss‐of‐function (LOF) mutations and BED (OR = 1.50; 95% CI: 0.73, 2.96; p = 0.25). Similar results were found after accounting for study quality (GOF variants: OR = 3.15; 95% CI: 1.76, 5.66; p = 1.1 × 10−4; LOF mutations: OR = 1.50; 95% CI: 0.73, 2.97; p = 0.25). Our systematic review and meta‐analysis provides evidence that GOF variants as opposed to LOF mutations in MC4R are associated with BED in subjects with obesity.