Genetics of Type 2 Diabetes in U.S. Hispanic/Latino Individuals: Results From the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Validation of GenProb-T1D and its clinical utility for differentiating types of diabetes in a biobank from a US healthcare system

Atypical diabetes with overlapping clinical features of type 1 (T1D) and type 2 (T2D) is common and challenging diagnostically and for implementing effective treatment. Here, we validate a recently reported genetic probability of type 1 diabetes (GenProb-T1D) from the UK Biobank (UKB) for differentiating type 1 diabetes and type 2 diabetes in a diabetes patient cohort from a healthcare system-based biobank in the USA. Among 3,363 diabetes patients, we confirmed the performance of GenProb-T1D in differentiating typical type 1 diabetes vs type 2 diabetes. Furthermore, for 359 atypical diabetes patients, those with GenProb-T1D higher than the pre-defined cutoff derived from the UKB had clinical presentations more consistent with that of typical type 1 diabetes. Similar findings were found in participants of European and non-European ancestries. This study provides necessary validation to translate GenProb-T1D into genetic testing in a multi-ancestry cohort. Measuring underlying genetic susceptibility of type 1 diabetes and type 2 diabetes can supplement current clinical tools for earlier and more accurate diagnoses of diabetes.

Blood metabolomic shift links diet and gut microbiota to multiple health outcomes among Hispanic/Latino immigrants in the U.S

Immigrants from less industrialized countries who are living in the U.S. often bear an elevated risk of multiple disease due to the adoption of a U.S. lifestyle. Blood metabolome holds valuable information on environmental exposure and the pathogenesis of chronic diseases, offering insights into the link between environmental factors and disease burden. Analyzing 634 serum metabolites from 7,114 Hispanics (1,141 U.S.-born, 5,973 foreign-born) in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), we identified profound blood metabolic shift during acculturation. Machine learning highlighted the prominent role of non-genetic factors, especially food and gut microbiota, in these changes. Immigration-related metabolites correlated with plant-based foods and beneficial gut bacteria for foreign-born Hispanics, and with meat-based or processed food and unfavorable gut bacteria for U.S.-born Hispanics. Cardiometabolic traits, liver, and kidney function exhibited a link with immigration-related metabolic changes, which were also linked to increased risk of diabetes, severe obesity, chronic kidney disease, and asthma.

Graphical abstract

Highlights

A substantial proportion of identified blood metabolites differ between U.S.-born and foreign-born Hispanics/Latinos in the U.S.Food and gut microbiota are the major modifiable contributors to blood metabolomic difference between U.S.-born and foreign-born Hispanics/Latinos.U.S. nativity related metabolites collectively correlate with a spectrum of clinical traits and chronic diseases.

Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population

Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.

Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes

CONTEXT

Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations.

OBJECTIVE

This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D.

METHODS

Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients.

RESULTS

The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D.

CONCLUSION

PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.

Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions

Diabetes is a serious health issue that causes a progressive dysregulation of carbohydrate metabolism due to insufficient insulin hormone, leading to consistently high blood glucose levels. According to the epidemiological data, the prevalence of diabetes has been increasing globally, affecting millions of individuals. It is a long-term condition that increases the risk of various diseases caused by damage to small and large blood vessels. There are two main subtypes of diabetes: type 1 and type 2, with type 2 being the most prevalent. Genetic and molecular studies have identified several genetic variants and metabolic pathways that contribute to the development and progression of diabetes. Current treatments include gene therapy, stem cell therapy, statin therapy, and other drugs. Moreover, recent advancements in therapeutics have also focused on developing novel drugs targeting these pathways, including incretin mimetics, SGLT2 inhibitors, and GLP-1 receptor agonists, which have shown promising results in improving glycemic control and reducing the risk of complications. However, these treatments are often expensive, inaccessible to patients in underdeveloped countries, and can have severe side effects. Peptides, such as glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), are being explored as a potential therapy for diabetes. These peptides are postprandial glucose-dependent pancreatic beta-cell insulin secretagogues and have received much attention as a possible treatment option. Despite these advances, diabetes remains a major health challenge, and further research is needed to develop effective treatments and prevent its complications. This review covers various aspects of diabetes, including epidemiology, genetic and molecular basis, and recent advancements in therapeutics including herbal and synthetic peptides.

Genetic risk prediction in Hispanics/Latinos: milestones, challenges, and social-ethical considerations

Genome-wide association studies (GWAS) have allowed the identification of disease-associated variants, which can be leveraged to build polygenic scores (PGSs). Even though PGSs can be a valuable tool in personalized medicine, their predictive power is limited in populations of non-European ancestry, particularly in admixed populations. Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. Given the recent increase of genomic studies that include Hispanics/Latinos, we review the milestones and efforts that focus on genetic risk prediction, summarize the potential for improving PGS transferability, and highlight the challenges yet to be addressed. Additionally, we summarize social-ethical considerations and provide ideas to promote genetic risk prediction models that can be implemented equitably.

Favorable outcomes in Hispanic recipients receiving simultaneous pancreas kidney transplantation

The objective of this study was to compare the long-term outcomes of Hispanic versus white recipients who underwent simultaneous pancreas kidney transplantation (SPKT). This single-center study, conducted from 2003 to 2022, had a median follow-up of 7.5 years. The study included 91 Hispanic and 202 white SPKT recipients. The mean age (44 vs. 46 years), percentage of males (67% vs. 58%), and body mass index (BMI) (25.6 vs. 25.3 kg/m2 ) were similar between the Hispanic and white groups. The Hispanic group had more recipients with type 2 diabetes (38%) compared to the white group (5%, p < .001). The duration of dialysis was longer in Hispanics (640 vs. 473 days, p = .02), and fewer patients received preemptive transplants (10% vs. 29%, p < .01) compared to whites. Hospital length of stay, rates of BK Viremia, and acute rejection episodes within 1 year were similar between the groups. The estimated 5-year kidney, pancreas, and patient survival rates were also similar between the groups, 94%, 81%, and 95% in Hispanics, compared to 90%, 79%, and 90% in whites. Increasing age and longer duration of dialysis were risk factors for death. Although Hispanic recipients had a longer duration on dialysis and fewer preemptive transplants, the survival rates were similar to those of white recipients. However, referring providers and many transplant centers continue to overlook pancreas transplants for appropriately selected patients with type 2 diabetes, particularly among minority populations. As a transplant community, it is crucial that we make efforts to comprehend and tackle these obstacles to transplantation.

A polygenic risk score for Alzheimer's disease constructed using APOE-region variants has stronger association than APOE alleles with mild cognitive impairment in Hispanic/Latino adults in the U.S

INTRODUCTION

Polygenic Risk Scores (PRSs) are summaries of genetic risk alleles for an outcome.

METHODS

We used summary statistics from five GWASs of AD to construct PRSs in 4,189 diverse Hispanics/Latinos (mean age 63 years) from the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA). We assessed the PRS associations with MCI in the combined set of people and in diverse subgroups, and when including and excluding the APOE gene region. We also assessed PRS associations with MCI in an independent dataset from the Mass General Brigham Biobank.

RESULTS

A simple sum of 5 PRSs ("PRSsum"), each constructed based on a different AD GWAS, was associated with MCI (OR = 1.28, 95% CI [1.14, 1.41]) in a model adjusted for counts of the APOE-[Formula: see text] and APOE-[Formula: see text] alleles. Associations of single-GWAS PRSs were weaker. When removing SNPs from the APOE region from the PRSs, the association of PRSsum with MCI was weaker (OR = 1.17, 95% CI [1.04,1.31] with adjustment for APOE alleles). In all association analyses, APOE-[Formula: see text] and APOE-[Formula: see text] alleles were not associated with MCI.

DISCUSSION

A sum of AD PRSs is associated with MCI in Hispanic/Latino older adults. Despite no association of APOE-[Formula: see text] and APOE-[Formula: see text] alleles with MCI, the association of the AD PRS with MCI is stronger when including the APOE region. Thus, APOE variants different than the classic APOE alleles may be important predictors of MCI in Hispanic/Latino adults.

Evolving Science on Cardiovascular Disease Among Hispanic/Latino Adults: JACC International

The landmark, multicenter HCHS/SOL (Hispanic Community Health Study/Study of Latinos) is the largest, most comprehensive, longitudinal community-based cohort study to date of diverse Hispanic/Latino persons in the United States. The HCHS/SOL aimed to address the dearth of comprehensive data on risk factors for cardiovascular disease (CVD) and other chronic diseases in this population and has expanded considerably in scope since its inception. This paper describes the aims/objectives and data collection of the HCHS/SOL and its ancillary studies to date and highlights the critical and sizable contributions made by the study to understanding the prevalence of and changes in CVD risk/protective factors and the burden of CVD and related chronic conditions among adults of diverse Hispanic/Latino backgrounds. The continued follow-up of this cohort will allow in-depth investigations on cardiovascular and pulmonary outcomes in this population, and data from the ongoing ancillary studies will facilitate generation of new hypotheses and study questions.

Integrating Common Risk Factors with Polygenic Scores Improves the Prediction of Type 2 Diabetes

We tested associations between 13 established genetic variants and type 2 diabetes (T2D) in 1371 study participants from the Volga-Ural region of the Eurasian continent, and evaluated the predictive ability of the model containing polygenic scores for the variants associated with T2D in our dataset, alone and in combination with other risk factors such as age and sex. Using logistic regression analysis, we found associations with T2D for the CCL20 rs6749704 (OR = 1.68, P_FDR = 3.40 × 10^-5), CCR5 rs333 (OR = 1.99, P_FDR = 0.033), ADIPOQ rs17366743 (OR = 3.17, P_FDR = 2.64 × 10^-4), TCF7L2 rs114758349 (OR = 1.77, P_FDR = 9.37 × 10^-5), and CCL2 rs1024611 (OR = 1.38, P_FDR = 0.033) polymorphisms. We showed that the most informative prognostic model included weighted polygenic scores for these five loci, and non-genetic factors such as age and sex (AUC 85.8%, 95%CI 83.7-87.8%). Compared to the model containing only non-genetic parameters, adding the polygenic score for the five T2D-associated loci showed improved net reclassification (NRI = 37.62%, 1.39 × 10^-6). Inclusion of all 13 tested SNPs to the model with age and sex did not improve the predictive ability compared to the model containing five T2D-associated variants (NRI = -17.86, p = 0.093). The five variants associated with T2D in people from the Volga-Ural region are linked to inflammation (CCR5, CCL2, CCL20) and glucose metabolism regulation (TCF7L, ADIPOQ2). Further studies in independent groups of T2D patients should validate the prognostic value of the model and elucidate the molecular mechanisms of the disease development.

Insight into genetic, biological, and environmental determinants of sexual-dimorphism in type 2 diabetes and glucose-related traits

There is growing evidence that sex and gender differences play an important role in risk and pathophysiology of type 2 diabetes (T2D). Men develop T2D earlier than women, even though there is more obesity in young women than men. This difference in T2D prevalence is attenuated after the menopause. However, not all women are equally protected against T2D before the menopause, and gestational diabetes represents an important risk factor for future T2D. Biological mechanisms underlying sex and gender differences on T2D physiopathology are not yet fully understood. Sex hormones affect behavior and biological changes, and can have implications on lifestyle; thus, both sex-specific environmental and biological risk factors interact within a complex network to explain the differences in T2D risk and physiopathology in men and women. In addition, lifetime hormone fluctuations and body changes due to reproductive factors are generally more dramatic in women than men (ovarian cycle, pregnancy, and menopause). Progress in genetic studies and rodent models have significantly advanced our understanding of the biological pathways involved in the physiopathology of T2D. However, evidence of the sex-specific effects on genetic factors involved in T2D is still limited, and this gap of knowledge is even more important when investigating sex-specific differences during the life course. In this narrative review, we will focus on the current state of knowledge on the sex-specific effects of genetic factors associated with T2D over a lifetime, as well as the biological effects of these different hormonal stages on T2D risk. We will also discuss how biological insights from rodent models complement the genetic insights into the sex-dimorphism effects on T2D. Finally, we will suggest future directions to cover the knowledge gaps.

TrustGWAS: A full-process workflow for encrypted GWAS using multi-key homomorphic encryption and pseudorandom number perturbation

The statistical power of genome-wide association studies (GWASs) is affected by the effective sample size. However, the privacy and security concerns associated with individual-level genotype data pose great challenges for cross-institutional cooperation. The full-process cryptographic solutions are in demand but have not been covered, especially the essential principal-component analysis (PCA). Here, we present TrustGWAS, a complete solution for secure, large-scale GWAS, recapitulating gold standard results against PLINK without compromising privacy and supporting basic PLINK steps including quality control, linkage disequilibrium pruning, PCA, chi-square test, Cochran-Armitage trend test, covariate-supported logistic regression and linear regression, and their sequential combinations. TrustGWAS leverages pseudorandom number perturbations for PCA and multiparty scheme of multi-key homomorphic encryption for all other modules. TrustGWAS can evaluate 100,000 individuals with 1 million variants and complete QC-LD-PCA-regression workflow within 50 h. We further successfully discover gene loci associated with fasting blood glucose, consistent with the findings of the ChinaMAP project.

Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

INTRODUCTION

A major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension.

METHODS

We collected genotyped data from a novel DTC platform where participants upload their genotype data files and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation, and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case-control setting using the BASIL algorithm.

RESULTS

We collected data on N = 4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D and N = 4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC = 0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers.

DISCUSSION

DTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly.

CONCLUSIONS

The genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.

Multiple polygenic scores improve bone mineral density prediction in an independent sample of Caucasian women

PURPOSE OF THE STUDY

To determine if multiple Genetic Risk Scores (GRSs) improve bone mineral density (BMD) prediction over single GRS in an independent sample of Caucasian women.

STUDY DESIGN

Based on summary statistics of four genome-wide association studies related to two osteoporosis-associated traits, namely BMD and heel quantitative ultrasound derived estimated BMD (eBMD), four GRSs were derived for 1205 individuals in the Genome-Wide Scan for Female Osteoporosis Gene Study. The effect of each GRS on BMD variation was assessed using multivariable linear regression, with conventional risk factors adjusted for. Next, the eBMD-related GRS that explained the most variance in BMD was selected to be entered into a multi-score model, along with the BMD-related GRS. Elastic net regularised regression was used to develop the multiscore model, which estimated the joint effect of two GRSs (GRS_BMD and GRS_eBMD) on BMD variation, after being adjusted for conventional risk factors.

RESULTS

With the same clinical risk factors having been adjusted for, the model that included GRS_BMD performed best by explaining 32.53% of the variance in BMD; the single-score model that included GRS_eBMD explained 34.03% of BMD variance. The model that includes both GRS_BMD and GRS_ eBMD, as well as the clinical risk factors, aggregately explained 35.05% in BMD variation. Compared with the single GRS models, the multiscore model explained significantly more variance in BMD.

CONCLUSIONS

The multipolygenic score model explained a considerable amount of BMD variation. Compared with single score models, multipolygenic score model provided significant improvement in explaining BMD variation.

Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts

Here, we seek to identify metabolite predictors of dysglycemia in youth. In the discovery analysis among 391 youth in the Exploring Perinatal Outcomes among CHildren (EPOCH) cohort, we used reduced rank regression (RRR) to identify sex-specific metabolite predictors of impaired fasting glucose (IFG) and elevated fasting glucose (EFG: Q4 vs. Q1 fasting glucose) 6 years later and compared the predictive capacity of four models: Model 1: ethnicity, parental diabetes, in utero exposure to diabetes, and body mass index (BMI); Model 2: Model 1 covariates + baseline waist circumference, insulin, lipids, and Tanner stage; Model 3: Model 2 + baseline fasting glucose; Model 4: Model 3 + baseline metabolite concentrations. RRR identified 19 metabolite predictors of fasting glucose in boys and 14 metabolite predictors in girls. Most compounds were on lipid, amino acid, and carbohydrate metabolism pathways. In boys, no improvement in aurea under the receiver operating characteristics curve AUC occurred until the inclusion of metabolites in Model 4, which increased the AUC for prediction of IFG (7.1%) from 0.81 to 0.97 (p = 0.002). In girls, %IFG was too low for regression analysis (3.1%), but we found similar results for EFG. We replicated the results among 265 youth in the Project Viva cohort, focusing on EFG due to low %IFG, suggesting that the metabolite profiles identified herein have the potential to improve the prediction of glycemia in youth.

Genome-wide risk prediction of common diseases across ancestries in one million people

Polygenic risk scores (PRS) measure genetic disease susceptibility by combining risk effects across the genome. For coronary artery disease (CAD), type 2 diabetes (T2D), and breast and prostate cancer, we performed cross-ancestry evaluation of genome-wide PRSs in six biobanks in Europe, the United States, and Asia. We studied transferability of these highly polygenic, genome-wide PRSs across global ancestries, within European populations with different health-care systems, and local population substructures in a population isolate. All four PRSs had similar accuracy across European and Asian populations, with poorer transferability in the smaller group of individuals of African ancestry. The PRSs had highly similar effect sizes in different populations of European ancestry, and in early- and late-settlement regions with different recent population bottlenecks in Finland. Comparing genome-wide PRSs to PRSs containing a smaller number of variants, the highly polygenic, genome-wide PRSs generally displayed higher effect sizes and better transferability across global ancestries. Our findings indicate that in the populations investigated, the current genome-wide polygenic scores for common diseases have potential for clinical utility within different health-care settings for individuals of European ancestry, but that the utility in individuals of African ancestry is currently much lower.

Identification of genetic effects underlying type 2 diabetes in South Asian and European populations

South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.

Type 2 Diabetes Mellitus in Latinx Populations in the United States: A Culturally Relevant Literature Review

Type 2 diabetes mellitus (T2DM) affects a large number of the American population. When compared to their representation in the general American population, a disproportionate number of Latinx individuals are affected. Within the Latinx American population, T2DM prevalence rates vary among individuals based on their country of origin. Deaths from T2DM among Latinx American population are also more compared to other ethnicities. This disparity underlines the importance of understanding the cultural considerations of T2DM disease presentation and management in Latinx communities, including risk factors, socioeconomic variables, and other social determinants of health such as access to care. There are various modifiable and non-modifiable risk factors for the development of T2DM, regardless of race. Staple foods in the diet of Latinx American communities, such as tortillas, rice, and beans, can cause spikes in blood sugar levels and can lead to obesity, which predisposes patients to develop T2DM. Latinx American populations suffer from lower access to healthcare than the general population due to many reasons, including language proficiency, immigration status, socioeconomic status, and level of acculturation. This study utilized the format of a commentary, while incorporating elements of a scoping review for data collection, to further explore these disparities and their impact on these populations. Understanding the cultural beliefs of Latinx individuals and how these beliefs contribute to the perceived development of T2DM is essential to properly treat these unique populations. Despite high rates of T2DM affecting Latinx individuals, non-adherence to prescribed diabetes medications is high among these populations. Interventions in the form of culturally tailored preventative education, in addition to active T2DM management, are necessary to combat the toll of this disease on Latinx Americans. Generic interventional techniques and methods should be replaced entirely by those that acknowledge, highlight, and utilize the sociocultural characteristics of Latinx Americans.

Celebrities in the heart, strangers in the pancreatic beta cell: Voltage-gated potassium channels Kv 7.1 and Kv 11.1 bridge long QT syndrome with hyperinsulinaemia as well as type 2 diabetes

Voltage‐gated potassium (K_v) channels play an important role in the repolarization of a variety of excitable tissues, including in the cardiomyocyte and the pancreatic beta cell. Recently, individuals carrying loss‐of‐function (LoF) mutations in KCNQ1, encoding K_v7.1, and KCNH2 (hERG), encoding K_v11.1, were found to exhibit post‐prandial hyperinsulinaemia and episodes of hypoglycaemia. These LoF mutations also cause the cardiac disorder long QT syndrome (LQTS), which can be aggravated by hypoglycaemia. Interestingly, patients with LQTS also have a higher burden of diabetes compared to the background population, an apparent paradox in relation to the hyperinsulinaemic phenotype, and KCNQ1 has been identified as a type 2 diabetes risk gene. This review article summarizes the involvement of delayed rectifier K⁺ channels in pancreatic beta cell function, with emphasis on K_v7.1 and K_v11.1, using the cardiomyocyte for context. The functional and clinical consequences of LoF mutations and polymorphisms in these channels on blood glucose homeostasis are explored using evidence from pre‐clinical, clinical and genome‐wide association studies, thereby evaluating the link between LQTS, hyperinsulinaemia and type 2 diabetes.

Ancestry-Matched and Cross-Ancestry Genetic Risk Scores of Type 2 Diabetes in Pregnant Women and Fetal Growth: A Study in an Ancestrally Diverse Cohort

Maternal genetic variants associated with offspring birth weight and adult type 2 diabetes (T2D) risk loci show some overlap. Whether T2D genetic risk influences longitudinal fetal weight and the gestational timing when these relationships begin is unknown. We investigated the associations of T2D genetic risk scores (GRS) with longitudinal fetal weight and birth weight among 1,513 pregnant women from four ancestral groups. Women had up to five ultrasonography examinations. Ancestry-matched GRS were constructed separately using 380 European- (GRSeur), 104 African- (GRSafr), and 189 East Asian- (GRSeas) related T2D loci discovered in different population groups. Among European Americans, the highest quartile GRSeur was significantly associated with 53.8 g higher fetal weight (95% CI 19.2-88.5) over the pregnancy. The associations began at gestational week 24 and continued through week 40, with a 106.8 g (95% CI 6.5-207.1) increase in birth weight. The findings were similar in analysis further adjusted for maternal glucose challenge test results. No consistent association was found using ancestry-matched or cross-ancestry GRS in non-Europeans. In conclusion, T2D genetic susceptibility may influence fetal growth starting at midsecond trimester among Europeans. Absence of similar associations in non-Europeans urges the need for further genetic T2D studies in diverse ancestries.

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

The Association between Fasting Glucose and Sugar Sweetened Beverages Intake Is Greater in Latin Americans with a High Polygenic Risk Score for Type 2 Diabetes Mellitus

Chile is one of the largest consumers of sugar-sweetened beverages (SSB) world-wide. However, it is unknown whether the effects from this highly industrialized food will mimic those reported in industrialized countries or whether they will be modified by local lifestyle or population genetics. Our goal is to evaluate the interaction effect between SSB intake and T2D susceptibility on fasting glucose. We calculated a weighted genetic risk score (GRSw) based on 16 T2D risk SNPs in 2828 non-diabetic participants of the MAUCO cohort. SSB intake was categorized in four levels using a food frequency questionnaire. Log-fasting glucose was regressed on SSB and GRSw tertiles while accounting for socio-demography, lifestyle, obesity, and Amerindian ancestry. Fasting glucose increased systematically per unit of GRSw (β = 0.02 ± 0.006, p = 0.00002) and by SSB intake (β[cat4] = 0.04 ± 0.01, p = 0.0001), showing a significant interaction, where the strongest effect was observed in the highest GRSw-tertile and in the highest SSB consumption category (β = 0.05 ± 0.02, p = 0.02). SNP-wise, SSB interacted with additive effects of rs7903146 (TCF7L2) (β = 0.05 ± 0.01, p = 0.002) and with the G/G genotype of rs10830963 (MTNRB1B) (β = 0.19 ± 0.05, p = 0.001). Conclusions: The association between SSB intake and fasting glucose in the Chilean population without diabetes is modified by T2D genetic susceptibility.

Efficacy of Mobile Health for Self-management of Cardiometabolic Risk Factors: A Theory-Guided Systematic Review

BACKGROUND

Although mobile health (mHealth) technologies are burgeoning in the research arena, there is a lack of mHealth interventions focused on improving self-management of individuals with cardiometabolic risk factors (CMRFs).

OBJECTIVE

The purpose of this article was to critically and systematically review the efficacy of mHealth interventions for self-management of CMRF while evaluating quality, limitations, and issues with disparities using the technology acceptance model as a guiding framework.

METHODS

PubMed, CINAHL, EMBASE, and Lilacs were searched to identify research articles published between January 2008 and November 2018. Articles were included if they were published in English, included adults, were conducted in the United States, and used mHealth to promote self-care or self-management of CMRFs. A total of 28 articles were included in this review.

RESULTS

Studies incorporating mHealth have been linked to positive outcomes in self-management of diabetes, physical activity, diet, and weight loss. Most mHealth interventions included modalities such as text messaging, mobile applications, and wearable technologies. There was a lack of studies that are (1) in resource-poor settings, (2) theoretically driven, (3) community-engaged research, (4) measuring digital/health literacy, (5) measuring and evaluating engagement, (6) measuring outcomes related to disease self-management, and (7) focused on vulnerable populations, especially immigrants.

CONCLUSION

There is still a lack of mHealth interventions created specifically for immigrant populations, especially within the Latino community-the largest growing minority group in the United States. In an effort to meet this challenge, more culturally tailored mHealth interventions are needed.

Associations of network-derived metabolite clusters with prevalent type 2 diabetes among adults of Puerto Rican descent

INTRODUCTION

We investigated whether network analysis revealed clusters of coregulated metabolites associated with prevalent type 2 diabetes (T2D) among Puerto Rican adults.

RESEARCH DESIGN AND METHODS

We used liquid chromatography-mass spectrometry to measure fasting plasma metabolites (>600) among participants aged 40-75 years in the Boston Puerto Rican Health Study (BPRHS; discovery) and San Juan Overweight Adult Longitudinal Study (SOALS; replication), with (n=357; n=77) and without (n=322; n=934) T2D, respectively. Among BPRHS participants, we used unsupervised partial correlation network-based methods to identify and calculate metabolite cluster scores. Logistic regression was used to assess cross-sectional associations between metabolite clusters and prevalent T2D at the baseline blood draw in the BPRHS, and significant associations were replicated in SOALS. Inverse-variance weighted random-effect meta-analysis was used to combine cohort-specific estimates.

RESULTS

Six metabolite clusters were significantly associated with prevalent T2D in the BPRHS and replicated in SOALS (false discovery rate (FDR) <0.05). In a meta-analysis of the two cohorts, the OR and 95% CI (per 1 SD increase in cluster score) for prevalent T2D were as follows for clusters characterized primarily by glucose transport (0.21 (0.16 to 0.30); FDR <0.0001), sphingolipids (0.40 (0.29 to 0.53); FDR <0.0001), acyl cholines (0.35 (0.22 to 0.56); FDR <0.0001), sugar metabolism (2.28 (1.68 to 3.09); FDR <0.0001), branched-chain and aromatic amino acids (2.22 (1.60 to 3.08); FDR <0.0001), and fatty acid biosynthesis (1.54 (1.29 to 1.85); FDR <0.0001). Three additional clusters characterized by amino acid metabolism, cell membrane components, and aromatic amino acid metabolism displayed significant associations with prevalent T2D in the BPRHS, but these associations were not replicated in SOALS.

CONCLUSIONS

Among Puerto Rican adults, we identified several known and novel metabolite clusters that associated with prevalent T2D.

Inappropriately sweet: Environmental endocrine-disrupting chemicals and the diabetes pandemic

Afflicting hundreds of millions of individuals globally, diabetes mellitus is a chronic disorder of energy metabolism characterized by hyperglycemia and other metabolic derangements that result in significant individual morbidity and mortality as well as substantial healthcare costs. Importantly, the impact of diabetes in the United States is not uniform across the population; rather, communities of color and those with low income are disproportionately affected. While excessive caloric intake, physical inactivity, and genetic susceptibility are undoubted contributors to diabetes risk, these factors alone fail to fully explain the rapid global rise in diabetes rates. Recently, environmental contaminants acting as endocrine-disrupting chemicals (EDCs) have been implicated in the pathogenesis of diabetes. Indeed, burgeoning data from cell-based, animal, population, and even clinical studies now indicate that a variety of structurally distinct EDCs of both natural and synthetic origin have the capacity to alter insulin secretion and action as well as global glucose homeostasis. This chapter reviews the evidence linking EDCs to diabetes risk across this spectrum of evidence. It is hoped that improving our understanding of the environmental drivers of diabetes development will illuminate novel individual-level and policy interventions to mitigate the impact of this devastating condition on vulnerable communities and the population at large.

Promoting Culturally Tailored mHealth: A Scoping Review of Mobile Health Interventions in Latinx Communities

This scoping review of mHealth research focuses on intervention studies that utilize mobile technologies to promote behavior change and improve health outcomes in U.S. Latinx communities. 342 mHealth articles were reviewed using PRIMSA protocols; most did not include a majority Latinx study population or did not report on an intervention. The final sample resulted in 23 articles published between 2012 and 2020. Reviewed interventions focused on conditions such as: diabetes, depression, substance abuse, obesity, hypertension, maternal health, and farmworker safety. About one-third of mHealth interventions included mobile applications, the rest were limited to texting programs. Text message reminders can help improve medication adherence and care access, especially when coupled with support from community health workers. Bi-directional text message interventions with feedback loops and personalized treatment options can build user agency. Additionally, multi-modal applications that combine texting with self-guided interactive content show promise for culturally tailored mHealth.

Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.

A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors

Background

Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D.

Objective

To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans.

Methods

This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC).

Results

The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01).

Conclusion

The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.

Polymorphic genetic markers and how they are associated with clinical and metabolic indicators of type 2 diabetes mellitus in the Kazakh population

Background

Type 2 diabetes mellitus is a serious public health problem worldwide. The aim of the study was to analyze the relationship of eight polymorphic gene variants with the development of clinical-metabolic rates of type 2 diabetes mellitus inside Kazakh population.

Materials and methods

139 patients with type 2 diabetes mellitus and 100 patients in the control group were examined. Genotyping of polymorphisms of candidate genes was carried out on a next generation QuantStudio 12 K Flex unit.

Results

Gene TCF7L2 locus rs7901695 and rs7903146, gene KCNQ1 locus rs2237892, rs7756992, and gene CDKAL1 locus rs7754840 demonstrated statistically significant associations with glucose metabolism, lipid profile and body mass index (BMI) in type 2 DM inside the population. Statistically significant difference in anthropometric and biochemical measures of rs17584499, rs4712523 and rs163184 has not been revealed.

Conclusions

Genetic polymorphisms that influence pancreatic gland beta-cells insulin release and secretion associate with metabolic and anthropometric measures definitive for type 2 DM in Kazakh population.

Response heterogeneity to lifestyle intervention among Latino adolescents

OBJECTIVE

To characterize the heterogeneity in response to lifestyle intervention among Latino adolescents with obesity.

METHODS

We conducted secondary data analysis of 90 Latino adolescents (age 15.4 ± 0.9 y, female 56.7%) with obesity (BMI% 98.1 ± 1.5%) that were enrolled in a 3 month lifestyle intervention and were followed for a year. Covariance pattern mixture models identified response phenotypes defined by changes in insulin sensitivity as measured using a 2 hour oral glucose tolerance test. Baseline characteristics were compared across response phenotypes using one-way ANOVA and chi-square test.

RESULTS

Three distinct response phenotypes (PH1, PH2, PH3) were identified. PH1 exhibited the most robust response defined by the greatest increase in insulin sensitivity over time (β ± SE, linear 0.52 ± 0.17, P < .001; quadratic -0.03 ± 0.01, P = .001). PH2 showed non-significant changes, while PH3 demonstrated modest short-term increases in insulin sensitivity which were not sustained over time (linear 0.08 ± 0.03, P = .002; quadratic -0.01 ± 0.002, P = .003). At baseline, PH3 (1.1 ± 0.4) was the most insulin resistant phenotype and exhibited the highest BMI% (98.5 ± 1.1%), 2 hours glucose concentrations (144.0 ± 27.5 mg/dL), and lowest beta-cell function as estimated by the oral disposition index (4.5 ± 2.8).

CONCLUSION

Response to lifestyle intervention varies among Latino youth with obesity and suggests that precision approaches are warranted to meet the prevention needs of high risk youth.

Incidence and risk factors of vascular complications in people with impaired fasting glucose: a national cohort study in Korea

This study aimed to evaluate the risk of vascular complications of impaired fasting glucose (IFG). This population-based study included 425,608 participants from the National Health Screening Cohort in Korea in 2003 and 2004 who were followed-up until 2015. The participants were classified into normal, IFG, and diabetes groups based on fasting plasma glucose levels. Incidence rate (per 1000 person-year) was evaluated for the following vascular complications: cardiovascular (ischemic heart disease, cerebrovascular disease, arterial and capillary disease), renal, and retinal diseases. Hazard ratios (HR) of IFG for diabetes were estimated after adjusting for patient characteristics. Among the 88,330 IFG participants, the incidence of cardiovascular, chronic renal and retinal diseases were 11.52, 0.47, and 1.08 per 1000 person-years, respectively. Furthermore, IFG patients with a family history of diabetes, past history of hypertension, and high body mass index had significantly increased risk of vascular complications [adjusted HR, cardiovascular: 1.39 (95% CI 1.33–1.46); renal: 2.17 (95% CI 1.66–2.83); and retinal: 1.14 (95% CI 0.98–1.32)]. IFG patients have a substantial risk of cardiovascular, chronic renal and retinal diseases. Therefore, early preventative interventions are beneficial, especially for those with high-risk factors, in whom should emphasize on maintaining a healthy lifestyle, early screening and continuous follow-up.

Ethnic differences in adiposity and diabetes risk - insights from genetic studies

Type 2 diabetes is more common in non-Europeans and starts at a younger age and at lower BMI cut-offs. This review discusses the insights from genetic studies about pathophysiological mechanisms which determine risk of disease with a focus on the role of adiposity and body fat distribution in ethnic disparity in risk of type 2 diabetes. During the past decade, genome-wide association studies (GWAS) have identified more than 400 genetic variants associated with the risk of type 2 diabetes. The Eurocentric nature of these genetic studies has made them less effective in identifying mechanisms that make non-Europeans more susceptible to higher risk of disease. One possible mechanism suggested by epidemiological studies is the role of ethnic difference in body fat distribution. Using genetic variants associated with an ability to store extra fat in a safe place, which is subcutaneous adipose tissue, we discuss how different ethnic groups could be genetically less susceptible to type 2 diabetes by developing a more favourable fat distribution.

Genetic Risk Score in Diabetes Associated With Chronic Pancreatitis Versus Type 2 Diabetes Mellitus

Diabetes mellitus (DM) is a complication of chronic pancreatitis (CP). Whether pancreatogenic diabetes associated with CP-DM represents a discrete pathophysiologic entity from type 2 DM (T2DM) remains uncertain. Addressing this question is needed for development of specific measures to manage CP-DM. We approached this question from a unique standpoint, hypothesizing that if CP-DM and T2DM are separate disorders, they should be genetically distinct. To test this hypothesis, we sought to determine whether a genetic risk score (GRS) based on validated single nucleotide polymorphisms for T2DM could distinguish between groups with CP-DM and T2DM.

An Exploratory Association Analysis of the Insulin Gene Region With Diabetes Mellitus in Two Dog Breeds

Samoyeds and Australian Terriers are the 2 dog breeds at highest risk (>10-fold) for diabetes mellitus in the United States. It is unknown if the insulin (INS) gene is involved in the pathophysiology of diabetes in Samoyeds and Australian Terriers. It was hypothesized that the INS gene region provides a common genetic causality for diabetes in Samoyeds and Australian Terriers. We conducted a 2-stage genetic association study involving both breeds. In the discovery stage (Stage 1), Samoyeds with and without diabetes were compared in the frequencies of 447 tagging single-nucleotide polymorphisms (SNPs) within 2.5 megabases (Mb) up- and downstream of the INS gene on the Illumina CanineHD BeadChip. SNPs yielding a P-value < 0.005 were selected for further follow-up. In the validation stage (Stage 2), Australian Terriers with and without diabetes were compared in the SNPs genotyped by the Affymetrix GeneChip Canine Genome 2.0 Array and within 1 Mb up- and downstream of the selected SNPs from Stage 1. Two SNPs that were in high linkage disequilibrium (LD, r2 = 0.7) were selected from Stage 1. In Stage 2, among the 76 SNPs examined, 5 were significantly associated with diabetes after Bonferroni's correction for multiple comparisons. Three of these 5 SNPs were in complete LD (r2 = 1 for all associations) and the 2 remaining SNPs were in moderate LD (r2 = 0.4). In conclusion, an association between the INS gene region and diabetes was suggested in 2 dog breeds of different clades. This region could have importance in diabetes in other breeds or in canine diabetes at large.

Genetics of diabetes mellitus and diabetes complications

Diabetes is one of the fastest growing diseases worldwide, projected to affect 693 million adults by 2045. Devastating macrovascular complications (cardiovascular disease) and microvascular complications (such as diabetic kidney disease, diabetic retinopathy and neuropathy) lead to increased mortality, blindness, kidney failure and an overall decreased quality of life in individuals with diabetes. Clinical risk factors and glycaemic control alone cannot predict the development of vascular complications; numerous genetic studies have demonstrated a clear genetic component to both diabetes and its complications. Early research aimed at identifying genetic determinants of diabetes complications relied on familial linkage analysis suited to strong-effect loci, candidate gene studies prone to false positives, and underpowered genome-wide association studies limited by sample size. The explosion of new genomic datasets, both in terms of biobanks and aggregation of worldwide cohorts, has more than doubled the number of genetic discoveries for both diabetes and diabetes complications. We focus herein on genetic discoveries for diabetes and diabetes complications, empowered primarily through genome-wide association studies, and emphasize the gaps in research for taking genomic discovery to the next level.

Classification of Type 2 Diabetes Genetic Variants and a Novel Genetic Risk Score Association With Insulin Clearance

CONTEXT

Genome-wide association studies have identified more than 450 single nucleotide polymorphisms (SNPs) for type 2 diabetes (T2D).

OBJECTIVE

To facilitate use of these SNPs in future genetic risk score (GRS)-based analyses, we aimed to classify the SNPs based on physiology. We also sought to validate GRS associations with insulin-related traits in deeply phenotyped Mexican Americans.

DESIGN, SETTING, AND PARTICIPANTS

A total of 457 T2D SNPs from the literature were assigned physiologic function based on association studies and cluster analyses. All SNPs (All-GRS), beta-cell (BC-GRS), insulin resistance (IR-GRS), lipodystrophy (Lipo-GRS), and body mass index plus lipids (B + L-GRS) were evaluated for association with diabetes and indices of insulin secretion (from oral glucose tolerance test), insulin sensitivity and insulin clearance (from euglycemic clamp), and adiposity and lipid markers in 1587 Mexican Americans.

RESULTS

Of the 457 SNPs, 52 were classified as BC, 30 as IR, 12 as Lipo, 12 as B + L, whereas physiologic function of 351 was undefined. All-GRS was strongly associated with T2D. Among nondiabetic Mexican Americans, BC-GRS was associated with reduced insulinogenic index, IR-GRS was associated with reduced insulin sensitivity, and Lipo-GRS was associated with reduced adiposity. B + L-GRS was associated with increased insulin clearance. The latter did not replicate in an independent cohort wherein insulin clearance was assessed by a different method.

CONCLUSIONS

Supporting their utility, BC-GRS, IR-GRS, and Lipo-GRS, based on SNPs discovered largely in Europeans, exhibited expected associations in Mexican Americans. The novel association of B + L-GRS with insulin clearance suggests that impaired ability to reduce insulin clearance in compensation for IR may play a role in the pathogenesis of T2D. Whether this applies to other ethnic groups remains to be determined.

Mediterranean Diet Adherence Modulates Anthropometric Measures by TCF7L2 Genotypes among Puerto Rican Adults

BACKGROUND

Transcription factor 7-like 2 (TCF7L2) genetic variants that predispose individuals to type 2 diabetes (T2D) show inconsistent associations with anthropometric traits. Interaction between TCF7L2 genotypes and dietary factors may help explain these observations.

OBJECTIVE

We aimed to examine the potential modulation of TCF7L2-rs7903146 and rs12255372 on anthropometric markers by a Mediterranean diet (MedDiet).

METHODS

Cross-sectional analysis was conducted in 1120 participants (aged 45-75 y) of the Boston Puerto Rican Health Study. Anthropometric variables were measured, and polymorphisms were genotyped using standardized protocols. Diet was assessed using a validated FFQ. The MedDiet was defined based on adherence to 9 food and nutrient components using sex-specific population-based median cut-offs; high adherence was defined as meeting ≥4 components. Haplotypes were tested for association with obesity traits, independently and via interaction with the MedDiet.

RESULTS

TCF7L2-rs7903146 showed significant interaction with the MedDiet influencing BMI, weight, and waist circumference. The T risk-allele carriers (CT + TT) with a high MedDiet score had lower weight (77.3 ± 1.0 compared with CC 80.9 ± 1.0 kg; P = 0.013) and waist circumference (99.2 ± 0.9 compared with CC 102.2 ± 0.9 cm; P = 0.021), when compared with CC participants. A low MedDiet score resulted in no significant differences between genotypes. For TCF7L2-rs12255372, we found significant interactions with the MedDiet for weight (P-interaction = 0.034) and BMI (P-interaction = 0.036). The T allele carriers with a higher MedDiet score showed a trend of lower but no significant differences when compared with CC participants for BMI (P = 0.19), weight (P = 0.09), and waist circumference (P = 0.11). We found significant interactions between the 2 risk-carrying haplotypes and the MedDiet compared with the common haplotype (GC), with lower BMI (β ± SE, TT: -1.53 ± 0.68; P-interaction = 0.024), weight (TT: -4.16 ± 1.77; P-interaction = 0.019), and waist circumference (GT: -5.07 ± 2.50; P-interaction = 0.042) at a high MedDiet score.

CONCLUSION

Puerto Ricans with the TCF7L2-rs7903146 and rs12255372 T2D risk genotypes, although still high, had better anthropometric profiles when adhering to a MedDiet, suggesting that this diet may offset unfavorable genetic predisposition.

Genetic factors and risk of type 2 diabetes among women with a history of gestational diabetes: findings from two independent populations

OBJECTIVE

Women with a history of gestational diabetes mellitus (GDM) have an exceptionally high risk for type 2 diabetes (T2D). Yet, little is known about genetic determinants for T2D in this population. We examined the association of a genetic risk score (GRS) with risk of T2D in two independent populations of women with a history of GDM and how this association might be modified by non-genetic determinants for T2D.

RESEARCH DESIGN AND METHODS

This cohort study included 2434 white women with a history of GDM from the Nurses' Health Study II (NHSII, n=1884) and the Danish National Birth Cohort (DNBC, n=550). A GRS for T2D was calculated using 59 candidate single nucleotide polymorphisms for T2D identified from genome-wide association studies in European populations. An alternate healthy eating index (AHEI) score was derived to reflect dietary quality after the pregnancy affected by GDM.

RESULTS

Women on average were followed for 21 years in NHSII and 13 years in DNBC, during which 446 (23.7%) and 155 (28.2%) developed T2D, respectively. The GRS was generally positively associated with T2D risk in both cohorts. In the pooled analysis, the relative risks (RRs) for increasing quartiles of GRS were 1.00, 0.97, 1.25 and 1.19 (p trend=0.02). In both cohorts, the association appeared to be stronger among women with poorer (AHEI <median) than better dietary quality (AHEI ≥median), although the interaction was not significant. For example, in NHSII, the RRs across increasing quartiles of GRS were 1.00, 0.99, 1.51 and 1.29 (p trend=0.06) among women with poorer dietary quality and 1.00, 0.83, 0.81 and 0.94 (p trend=0.79) among women with better dietary quality (p interaction=0.11).

CONCLUSIONS

Among white women with a history of GDM, higher GRS for T2D was associated with an increased risk of T2D.

GlobAl Distribution of GEnetic Traits (GADGET) web server: polygenic trait scores worldwide

Human populations from around the world show striking phenotypic variation across a wide variety of traits. Genome-wide association studies (GWAS) are used to uncover genetic variants that influence the expression of heritable human traits; accordingly, population-specific distributions of GWAS-implicated variants may shed light on the genetic basis of human phenotypic diversity. With this in mind, we developed the GlobAl Distribution of GEnetic Traits web server (GADGET http://gadget.biosci.gatech.edu). The GADGET web server provides users with a dynamic visual platform for exploring the relationship between worldwide genetic diversity and the genetic architecture underlying numerous human phenotypes. GADGET integrates trait-implicated single nucleotide polymorphisms (SNPs) from GWAS, with population genetic data from the 1000 Genomes Project, to calculate genome-wide polygenic trait scores (PTS) for 818 phenotypes in 2504 individual genomes. Population-specific distributions of PTS are shown for 26 human populations across 5 continental population groups, with traits ordered based on the extent of variation observed among populations. Users of GADGET can also upload custom trait SNP sets to visualize global PTS distributions for their own traits of interest.

Analysis of polygenic risk score usage and performance in diverse human populations

A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10⁻⁶), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.

Predominant participation of European-ancestry individuals in genetic studies has hindered the better understanding of genetic risk in non-European ancestry individuals. Here, Duncan et al. quantify polygenic risk score use and performance in worldwide populations.

Genome-wide association study of type 2 diabetes in Africa

AIMS/HYPOTHESIS

Genome-wide association studies (GWAS) for type 2 diabetes have uncovered >400 risk loci, primarily in populations of European and Asian ancestry. Here, we aimed to discover additional type 2 diabetes risk loci (including African-specific variants) and fine-map association signals by performing genetic analysis in African populations.

METHODS

We conducted two type 2 diabetes genome-wide association studies in 4347 Africans from South Africa, Nigeria, Ghana and Kenya and meta-analysed both studies together. Likely causal variants were identified using fine-mapping approaches.

RESULTS

The most significantly associated variants mapped to the widely replicated type 2 diabetes risk locus near TCF7L2 (p = 5.3 × 10^-13). Fine-mapping of the TCF7L2 locus suggested one type 2 diabetes association signal shared between Europeans and Africans (indexed by rs7903146) and a distinct African-specific signal (indexed by rs17746147). We also detected one novel signal, rs73284431, near AGMO (p = 5.2 × 10^-9, minor allele frequency [MAF] = 0.095; monomorphic in most non-African populations), distinct from previously reported signals in the region. In analyses focused on 100 published type 2 diabetes risk loci, we identified 21 with shared causal variants in African and non-African populations.

CONCLUSIONS/INTERPRETATION

These results demonstrate the value of performing GWAS in Africans, provide a resource to larger consortia for further discovery and fine-mapping and indicate that additional large-scale efforts in Africa are warranted to gain further insight in to the genetic architecture of type 2 diabetes.

Understanding the growing epidemic of type 2 diabetes in the Hispanic population living in the United States

The prevalence and incidence of type 2 diabetes (T2D) among the Hispanic population in the United States are higher than the national average. This is partly due to sociocultural factors, such as lower income and decreased access to education and health care, as well as a genetic susceptibility to obesity and higher insulin resistance. This review focuses on understanding the Hispanic population living in the United States from a multidisciplinary approach and underlines the importance of cultural, social, and biological factors in determining the increased risk of T2D in this population. An overview of the acute and chronic complications of T2D upon this population is included, which is of paramount importance to understand the toll that diabetes has upon this population, the health system, and society as a whole. Specific interventions directed to the Hispanic populations are needed to prevent and alleviate some of the burdens of T2D. Different prevention strategies based on medications, lifestyle modifications, and educational programmes are discussed herein. Diabetes self-management education (DSME) is a critical element of care of all people with diabetes and is considered necessary to improve patient outcomes. To be more effective, programmes should take into consideration cultural factors that influence the development and progression of diabetes. These interventions aim to enhance long-term effects by reducing the incidence, morbidity, and mortality of T2D in the Hispanic population of the United States.

Generalizing polygenic risk scores from Europeans to Hispanics/Latinos

Polygenic risk scores (PRSs) are weighted sums of risk allele counts of single-nucleotide polymorphisms (SNPs) associated with a disease or trait. PRSs are typically constructed based on published results from Genome-Wide Association Studies (GWASs), and the majority of which has been performed in large populations of European ancestry (EA) individuals. Although many genotype-trait associations have generalized across populations, the optimal choice of SNPs and weights for PRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns. We compare various approaches for PRS construction, using GWAS results from both large EA studies and a smaller study in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL, n = 12 , 803 ). We consider multiple approaches for selecting SNPs and for computing SNP weights. We study the performance of the resulting PRSs in an independent study of Hispanics/Latinos from the Women's Health Initiative (WHI, n = 3 , 582 ). We support our investigation with simulation studies of potential genetic architectures in a single locus. We observed that selecting variants based on EA GWASs generally performs well, except for blood pressure trait. However, the use of EA GWASs for weight estimation was suboptimal. Using non-EA GWAS results to estimate weights improved results.

Associations between SLC16A11 variants and diabetes in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Five sequence variants in SLC16A11 (rs117767867, rs13342692, rs13342232, rs75418188, and rs75493593), which occur in two non-reference haplotypes, were recently shown to be associated with diabetes in Mexicans from the SIGMA consortium. We aimed to determine whether these previous findings would replicate in the HCHS/SOL Mexican origin group and whether genotypic effects were similar in other HCHS/SOL groups. We analyzed these five variants in 2492 diabetes cases and 5236 controls from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), which includes U.S. participants from six diverse background groups (Mainland groups: Mexican, Central American, and South American; and Caribbean groups: Puerto Rican, Cuban, and Dominican). We estimated the SNP-diabetes association in the six groups and in the combined sample. We found that the risk alleles occur in two non-reference haplotypes in HCHS/SOL, as in the SIGMA Mexicans. The haplotype frequencies were very similar between SIGMA Mexicans and the HCHS/SOL Mainland groups, but different in the Caribbean groups. The SLC16A11 sequence variants were significantly associated with risk for diabetes in the Mexican origin group (P = 0.025), replicating the SIGMA findings. However, these variants were not significantly associated with diabetes in a combined analysis of all groups, although the power to detect such effects was 85% (assuming homogeneity of effects among the groups). Additional analyses performed separately in each of the five non-Mexican origin groups were not significant. We also analyzed (1) exclusion of young controls and, (2) SNP by BMI interactions, but neither was significant in the HCHS/SOL data. The previously reported effects of SLC16A11 variants on diabetes in Mexican samples was replicated in a large Mexican-American sample, but these effects were not significant in five non-Mexican Hispanic/Latino groups sampled from U.S. populations. Lack of replication in the HCHS/SOL non-Mexicans, and in the entire HCHS/SOL sample combined may represent underlying genetic heterogeneity. These results indicate a need for future genetic research to consider heterogeneity of the Hispanic/Latino population in the assessment of disease risk, but add to the evidence suggesting SLC16A11 as a potential therapeutic target for type 2 diabetes.

Complex patterns of direct and indirect association between the transcription Factor-7 like 2 gene, body mass index and type 2 diabetes diagnosis in adulthood in the Hispanic Community Health Study/Study of Latinos

Background

Genome-wide association studies have implicated the transcription factor 7-like 2 (TCF7L2) gene in type 2 diabetes risk, and more recently, in decreased body mass index. Given the contrary direction of genetic effects on these two traits, it has been suggested that the observed association with body mass index may reflect either selection bias or a complex underlying biology at TCF7L2.

Methods

Using 9031 Hispanic/Latino adults (21–76 years) with complete weight history and genetic data from the community-based Hispanic Community Health Study/Study of Latinos (HCHS/SOL, Baseline 2008–2011), we estimated the multivariable association between the additive number of type 2 diabetes increasing-alleles at TCF7L2 (rs7903146-T) and body mass index. We then used structural equation models to simultaneously model the genetic association on changes in body mass index across the life course and estimate the odds of type 2 diabetes per TCF7L2 risk allele.

Results

We observed both significant increases in type 2 diabetes prevalence at examination (independent of body mass index) and decreases in mean body mass index and waist circumference across genotypes at rs7903146. We observed a significant multivariable association between the additive number of type 2 diabetes-risk alleles and lower body mass index at examination. In our structured modeling, we observed non-significant inverse direct associations between rs7903146-T and body mass index at ages 21 and 45 years, and a significant positive association between rs7903146-T and type 2 diabetes onset in both middle and late adulthood.

Conclusions

Herein, we replicated the protective effect of rs7930146-T on body mass index at multiple time points in the life course, and observed that these effects were not explained by past type 2 diabetes status in our structured modeling. The robust replication of the negative effects of TCF7L2 on body mass index in multiple samples, including in our diverse Hispanic/Latino community-based sample, supports a growing body of literature on the complex biologic mechanism underlying the functional consequences of TCF7L2 on obesity and type 2 diabetes across the life course.

Electronic supplementary material

The online version of this article (10.1186/s40608-018-0200-x) contains supplementary material, which is available to authorized users.

Genetic variants of gestational diabetes mellitus: a study of 112 SNPs among 8722 women in two independent populations

AIMS/HYPOTHESIS

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that has substantial short- and long-term adverse health implications for women and their children. However, large-scale studies on genetic risk loci for GDM remain sparse.

METHODS

We conducted a case-control study among 2636 women with GDM and 6086 non-GDM control women from the Nurses' Health Study II and the Danish National Birth Cohort. A total of 112 susceptibility genetic variants confirmed by genome-wide association studies for type 2 diabetes were selected and measured. A weighted genetic risk score (GRS) was created based on variants that were significantly associated with risk of GDM after correcting for the false discovery rate.

RESULTS

For the first time, we identified eight variants associated with GDM, namely rs7957197 (HNF1A), rs10814916 (GLIS3), rs3802177 (SLC30A8), rs9379084 (RREB1), rs34872471 (TCF7L2), rs7903146 (TCF7L2), rs11787792 (GPSM1) and rs7041847 (GLIS3). In addition, we confirmed three variants, rs10830963 (MTNR1B), rs1387153 (MTNR1B) and rs4506565 (TCF7L2), that had previously been significantly associated with GDM risk. Furthermore, compared with participants in the first (lowest) quartile of weighted GRS based on these 11 SNPs, the ORs for GDM were 1.07 (95% CI 0.93, 1.22), 1.23 (95% CI 1.07, 1.41) and 1.53 (95% CI 1.34, 1.74) for participants in the second, third and fourth (highest) quartiles, respectively. The significant positive associations between the weighted GRS and risk of GDM persisted across most of the strata of major risk factors for GDM, including family history of type 2 diabetes, smoking status, BMI and age.

CONCLUSIONS/INTERPRETATION

In this large-scale case-control study with women from two independent populations, eight novel GDM SNPs were identified. These findings offer the potential to improve our understanding of the aetiology of GDM, and particularly of biological mechanisms related to beta cell function.

Genome-wide association study of depressive symptoms in the Hispanic Community Health Study/Study of Latinos

Although genome-wide association studies (GWAS) have identified several variants linked to depression, few GWAS of non-European populations have been performed. We conducted a genome-wide analysis of depression in a large, population-based sample of Hispanics/Latinos. Data came from 12,310 adults in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Past-week depressive symptoms were assessed using the 10-item Center for Epidemiological Studies of Depression Scale. Three phenotypes were examined: a total depression score, a total score modified to account for psychiatric medication use, and a score excluding anti-depressant medication users. We estimated heritability due to common variants (h2SNP), and performed a GWAS of the three phenotypes. Replication was attempted in three independent Hispanic/Latino cohorts. We also performed sex-stratified analyses, analyzed a binary trait indicating probable depression, and conducted three trans-ethnic analyses. The three phenotypes exhibited significant heritability (h2SNP = 6.3-6.9%; p = .002) in the total sample. No SNPs were genome-wide significant in analyses of the three phenotypes or the binary indicator of probable depression. In sex-stratified analyses, seven genome-wide significant SNPs (one in females; six in males) were identified, though none were supported through replication. Four out of 24 loci identified in prior GWAS were nominally associated in HCHS/SOL. There was no evidence of overlap in genetic risk factors across ancestry groups, though this may have been due to low power. We conducted the largest GWAS of depression-related phenotypes in Hispanic/Latino adults. Results underscore the genetic complexity of depressive symptoms as a phenotype in this population and suggest the need for much larger samples.

The Genetic Basis of Type 2 Diabetes in Hispanics and Latin Americans: Challenges and Opportunities

Type 2 diabetes (T2D) affects 415 million people worldwide, and has a much higher prevalence in Hispanics (16.9%), compared to non-Hispanic whites (10.2%). Genome-wide association studies and whole-genome and whole-exome sequencing studies have discovered more than 100 genetic regions associated with modified risk for T2D. However, the identified genetic factors explain a very small fraction of the estimated heritability. Until recently, little attention has been put in studying other non European populations that suffer from a higher burden of T2D, such as Hispanics/Latinos. In the past few years, genetic studies in Hispanic populations have started to provide new insights into the genetic architecture of T2D in this ancestry group. Of note, several genetic variants that are absent or very rare in non-Hispanic populations but more common in Hispanics have shown from moderate to strong association with T2D and have provided new insights into the biology of T2D, which may be ultimately useful for developing novel therapeutic strategies applicable to all populations. Studying diverse populations can also improve the ability to find the causal variants in known T2D loci by a multi-ancestry fine-mapping approach, which leverages the different patterns of linkage disequilibrium between the causal and the ascertained genetic variants. In this mini-review, we summarize the main genetic findings discovered in Hispanics and discuss the limitations and challenges of performing genetic studies in these populations. Finally, we present possible next steps to make studies in Latino populations more valuable in providing a deeper understanding of T2D and anticipate their future application to the development of predictive and preventive medicine and personalized therapies.