Sequence variants in SLC16A11 are a common risk factor for type 2 diabetes in Mexico

The association between breakfast frequency and diabetes incidence in middle-aged women: Results from the MTC study

BACKGROUND AND AIMS

Breakfast consumption could have a synchronizer role in chronobiological functions. Across observational studies, the assessment of breakfast frequency consumption is heterogeneous, therefore consensus on the relation between of weekly frequency of breakfast consumption and the risk of diabetes is unclear. We examined the relation between weekly breakfast frequency consumption and the incidence of diabetes in middle-age women.

METHODS AND RESULTS

Since baseline (2006-2008) we prospectively followed 71,373 women from the Mexican Teachers' Cohort. Participants were classified according to breakfast consumption frequency of 0, 1-3, 4-6, or 7 days/week. Diabetes was identified by self-report and clinical-administrative databases. We used Cox proportional hazards multivariable models to estimate hazard ratios (HR) and 95% confidence intervals (CI) for breakfast frequency and diabetes adjusting for covariates. Stratified analyses were performed for age, birth weight, ethnicity, and physical activity. We identified 3613 new diabetes cases between baseline and 2014. The prevalence of daily breakfast consumers was 25%. The median follow-up was 2.2 years, interquartile range 1.8-3.8 years. Relative to women who skipped breakfast, those who consumed breakfast every day had a 12% lower risk of diabetes (multivariable HR = 0.88; 95% CI 0.78, 0.99; p-trend = 0.0018). One additional day per week of breakfast was associated with a lower risk of diabetes (HR = 0.98; 95% CI 0.97, 0.99). In stratified analysis, the observed inverse relation appeared to be stronger in women aged ≥40 years and in indigenous women.

CONCLUSIONS

Breakfast frequency was inversely associated with the incidence of diabetes independently of lifestyle factors. Regular breakfast consumption may be a potential component of diabetes prevention.

Genetic associations with disease in populations with Indigenous American ancestries

The genetic architecture of complex diseases affecting populations with Indigenous American ancestries is poorly understood due to their underrepresentation in genomics studies. While most of the genetic diversity associated with disease trait variation is shared among worldwide populations, a fraction of this component is expected to be unique to each continental group, including Indigenous Americans. Here, I describe the current state of knowledge from genome-wide association studies on Indigenous populations, as well as non-Indigenous populations with partial Indigenous ancestries from the American continent, focusing on disease susceptibility and anthropometric traits. While some studies identified risk alleles unique to Indigenous populations, their effects on trait variation are mostly small. I suggest that the associations rendered by many inter-population studies are probably inflated due to the absence of socio-cultural-economic covariates in the association models. I encourage the inclusion of admixed individuals in future GWAS studies to control for inter-ancestry differences in environmental factors. I suggest that some complex diseases might have arisen as trade-off costs of adaptations to past evolutionary selective pressures. Finally, I discuss how expanding panels with Indigenous ancestries in GWAS studies is key to accurately assess genetic risk in populations from the American continent, thus decreasing global health disparities.

Ethnicity-related differences in mitochondrial regulation by insulin stimulation in diabetes

Mitochondrial dysfunction has long been implicated in the development of insulin resistance, which is a hallmark of type 2 diabetes. However, recent studies reveal ethnicity-related differences in mitochondrial processes, underscoring the need for nuance in studying mitochondrial dysfunction and insulin sensitivity. Furthermore, the higher prevalence of type 2 diabetes among African Americans and individuals of African descent has brought attention to the role of ethnicity in disease susceptibility. In this review, which covers existing literature, genetic studies, and clinical data, we aim to elucidate the complex relationship between mitochondrial alterations and insulin stimulation by considering how mitochondrial dynamics, contact sites, pathways, and metabolomics may be differentially regulated across ethnicities, through mechanisms such as single nucleotide polymorphisms (SNPs). In addition to achieving a better understanding of insulin stimulation, future studies identifying novel regulators of mitochondrial structure and function could provide valuable insights into ethnicity-dependent insulin signaling and personalized care.

Variations in the LINGO2 and GLIS3 Genes and Gene-Environment Interactions Increase Gestational Diabetes Mellitus Risk in Chinese Women

Gestational diabetes mellitus (GDM) has been found to be a common complication in pregnant women, known to escalate the risk of negative obstetric outcomes. In our study, we genotyped 1,566 Chinese pregnant women for two single nucleotide polymorphisms (SNPs) in the LINGO2 gene and one SNP in the GLIS3 gene, utilizing targeted next-generation sequencing. The impact of two interacting genes, and the interaction of genes with the environment─including exposure to particulate matter (PM2.5), ozone (O3), and variations in prepregnancy body mass index (BMI)─on the incidence of GDM were analyzed using logistic regression. Our findings identify the variants LINGO2 rs10968576 (P = 0.022, OR = 1.224) and rs1412239 (P = 0.018, OR = 1.231), as well as GLIS3 rs10814916 (P = 0.028, OR = 1.172), as risk mutations significantly linked to increased susceptibility to GDM. Further analysis underscores the crucial role of gene-gene and gene-environment interactions in the development of GDM among Chinese women (P < 0.05). Particularly, the individuals carrying the rs10968576 G-rs1412239 G-rs10814916 C haplotype exhibit increased susceptibility to GDM during the prepregnancy period when interacting with PM2.5, O3, and BMI (P = 8.004 × 10-7, OR = 1.206; P = 6.3264 × 10-11, OR = 1.280; P = 9.928 × 10-7, OR = 1.334, respectively). In conclusion, our research emphasizes the importance of the interaction between specific gene variations─LINGO2 and GLIS3─and environmental factors in influencing GDM risk. Notably, we found significant associations between these gene variations and GDM risk across various environmental exposure periods.

Epistasis Between HLA-DRB1*16:02:01 and SLC16A11 T-C-G-T-T Reduces Odds for Type 2 Diabetes in Southwest American Indians

We sought to identify genetic/immunologic contributors of type 2 diabetes (T2D) in an indigenous American community by genotyping all study participants for both high-resolution HLA-DRB1 alleles and SLC16A11 to test their risk and/or protection for T2D. These genes were selected based on independent reports that HLA-DRB1*16:02:01 is protective for T2D and that SLC16A11 associates with T2D in individuals with BMI <35 kg/m2. Here, we test the interaction of the two loci with a more complete data set and perform a BMI sensitivity test. We defined the risk protection haplotype of SLC16A11, T-C-G-T-T, as allele 2 of a diallelic genetic model with three genotypes, SLC16A11*11, *12, and *22, where allele 1 is the wild type. Both earlier findings were confirmed. Together in the same logistic model with BMI ≥35 kg/m2, DRB1*16:02:01 remains protective (odds ratio [OR] 0.73), while SLC16A11 switches from risk to protection (OR 0.57 [*22] and 0.78 [*12]); an added interaction term was statistically significant (OR 0.49 [*12]). Bootstrapped (b = 10,000) statistical power of interaction, 0.4801, yielded a mean OR of 0.43. Sensitivity analysis demonstrated that the interaction is significant in the BMI range of 30-41 kg/m2. To investigate the epistasis, we used the primary function of the HLA-DRB1 molecule, peptide binding and presentation, to search the entire array of 15-mer peptides for both the wild-type and ancient human SLC16A11 molecules for a pattern of strong binding that was associated with risk and protection for T2D. Applying computer binding algorithms suggested that the core peptide at SLC16A11 D127G, FSAFASGLL, might be key for moderating risk for T2D with potential implications for type 1 diabetes.

ARTICLE HIGHLIGHTS

PNPLA3 fatty liver allele was fixed in Neanderthals and segregates neutrally in humans

OBJECTIVE

Fat deposition is modulated by environmental factors and genetic predisposition. Genome-wide association studies identified PNPLA3 p.I148M (rs738409) as a common variant that increases risk of developing liver steatosis. When and how this variant evolved in humans has not been studied to date.

DESIGN

Here we analyse ancient DNA to track the history of this allele throughout human history. In total, 6444 published ancient (modern humans, Neanderthal, Denisovan) and 3943 published present day genomes were used for analysis after extracting genotype calls for PNPLA3 p.I148M. To quantify changes through time, logistic and, by grouping individuals according to geography and age, linear regression analyses were performed.

RESULTS

We find that archaic human individuals (Neanderthal, Denisovan) exclusively carried a fixed PNPLA3 risk allele, whereas allele frequencies in modern human populations range from very low in Africa to >50% in Mesoamerica. Over the last 15 000 years, distributions of ancestral and derived alleles roughly match the present day distribution. Logistic regression analyses did not yield signals of natural selection during the last 10 000 years.

CONCLUSION

Archaic human individuals exclusively carried a fixed PNPLA3 allele associated with fatty liver, whereas allele frequencies in modern human populations are variable even in the oldest samples. Our observation might underscore the advantage of fat storage in cold climate and particularly for Neanderthal under ice age conditions. The absent signals of natural selection during modern human history does not support the thrifty gene hypothesis in case of PNPLA3 p.I148M.

Diabetes subgroups and sociodemographic inequalities in Mexico: a cross-sectional analysis of nationally representative surveys from 2016 to 2022

Background

Differences in the prevalence of four diabetes subgroups have been reported in Mexico compared to other populations, but factors that may contribute to these differences are poorly understood. Here, we estimate the prevalence of diabetes subgroups in Mexico and evaluate their correlates with indicators of social disadvantage using data from national representative surveys.

Methods

We analyzed serial, cross-sectional Mexican National Health and Nutrition Surveys spanning 2016, 2018, 2020, 2021, and 2022, including 23,354 adults (>20 years). Diabetes subgroups (obesity-related [MOD], severe insulin-deficient [SIDD], severe insulin-resistant [SIRD], and age-related [MARD]) were classified using self-normalizing neural networks based on a previously validated algorithm. We used the density-independent social lag index (DISLI) as a proxy of state-level social disadvantage.

Findings

We identified 4204 adults (median age: 57, IQR: 47-66, women: 64%) living with diabetes, yielding a pooled prevalence of 16.04% [95% CI: 14.92-17.17]. When stratified by diabetes subgroup, prevalence was 6.62% (5.69-7.55) for SIDD, 5.25% (4.52-5.97) for MOD, 2.39% (1.95-2.83) for MARD, and 1.27% (1.00-1.54) for SIRD. SIDD and MOD clustered in Southern Mexico, whereas MARD and SIRD clustered in Northern Mexico and Mexico City. Each standard deviation increase in DISLI was associated with higher odds of SIDD (OR: 1.12, 95% CI: 1.06-1.12) and lower odds of MOD (OR: 0.93, 0.88-0.99). Speaking an indigenous language was associated with higher odds of SIDD (OR: 1.35, 1.16-1.57) and lower odds of MARD (OR 0.58, 0.45-0.74).

Interpretation

Diabetes prevalence in Mexico is rising in the context of regional and sociodemographic inequalities across distinct diabetes subgroups. SIDD is a subgroup of concern that may be associated with inadequate diabetes management, mainly in marginalized states.

Funding

This research was supported by Instituto Nacional de Geriatría in Mexico.

Prediabetes as a risk factor for all-cause and cause-specific mortality: a prospective analysis of 115,919 adults without diabetes in Mexico City

BACKGROUND

Prediabetes has been associated with increased all-cause and cardiovascular mortality. However, no large-scale studies have been conducted in Mexico or Latin America examining these associations.

METHODS

We analyzed data from 115,919 adults without diabetes (diagnosed or undiagnosed) aged 35-84 years who participated in the Mexico City Prospective Study between 1998 and 2004. Participants were followed until January 1 st , 2021 for cause-specific mortality. We defined prediabetes according to the American Diabetes Association (ADA, HbA 1c 5.7% to 6.4%) and the International Expert Committee (IEC, HbA 1c 6.0-6.4%) definitions. Cox regression adjusted for confounders was used to estimate all-cause and cause-specific mortality rate ratios (RR) at ages 35-74 years associated with prediabetes.

FINDINGS

During 2,085,392 person-years of follow-up (median in survivors 19 years), there were 6,810 deaths at ages 35-74, including 1,742 from cardiovascular disease, 892 from renal disease and 108 from acute diabetic crises. Of 110,405 participants aged 35-74 years at recruitment, 28,852 (26%) had ADA-defined prediabetes and 7,203 (7%) had IEC-defined prediabetes. Compared with those without prediabetes, individuals with prediabetes had higher risk of all-cause mortality at ages 35-74 years (RR 1.13, 95% CI 1.07-1.19 for ADA-defined prediabetes and RR 1.28, 1.18-1.39 for IEC-defined prediabetes), as well as increased risk of cardiovascular mortality (RR 1.22 [1.10-1.35] and 1.42 [1.22-1.65], respectively), renal mortality (RR 1.35 [1.08-1.68] and 1.69 [1.24-2.31], respectively), and death from an acute diabetic crisis (RR 2.63 [1.76-3.94] and 3.43 [2.09-5.62], respectively). RRs were larger at younger than at older ages, and similar for men compared to women. The absolute excess risk associated with ADA and IEC-defined prediabetes at ages 35-74 accounted for6% and 3% of cardiovascular deaths respectively, 10% and 5% of renal deaths respectively, and 31% and 14% of acute diabetic deaths respectively.

INTERPRETATION

Prediabetes is a significant risk factor for all-cause, cardiovascular, renal, and acute diabetic deaths in Mexican adults. Identification and timely management of individuals with prediabetes for targeted risk reduction could contribute to reducing premature mortality from cardiometabolic causes in this population.

FUNDING

Wellcome Trust, the Mexican Health Ministry, the National Council of Science and Technology for Mexico, Cancer Research UK, British Heart Foundation, UK Medical Research Council. Instituto Nacional de Geriatría (Mexico City).

RESEARCH IN CONTEXT

Evidence before this study: We conducted a literature search in PubMed to identify articles published in English before February 27 th , 2024 that reported on prospective studies examining the association between prediabetes with all-cause or cause-specific mortality or progression to diabetes in a Mexican or Latin American population, using the terms ("prediabetes" OR "impaired fasting glucose" OR "impaired glucose tolerance") AND ("mortality" OR "death") AND ("Mexico" OR "Mexican" OR "Latin America" OR "Latin American"). There were no studies examining risk associated with prediabetes definitions and mortality among adults in Mexico. We identified one study from Peru that included 988 participants and investigated only all-cause mortality for impaired fasting glucose and HbA 1c -based definitions of prediabetes from ADA and IEC; this study reported increased mortality risk related to ADA-defined prediabetes based on HbA 1c measures. Generalizability of these findings to other Latin American countries and regions with distinct cardiometabolic profiles in unclear. Added value of this study: Our study included 115,919 participants without diabetes from Mexico City, of whom (26%) had ADA-defined prediabetes and 7,203 (7%) had IEC-defined prediabetes. We found that prediabetes is associated with higher risks of all-cause and cause-specific mortality (cardiovascular, renal, and acute diabetic causes) than among participants without prediabetes. We found RRs to be larger at younger than at older ages, and largely similar for men compared to women. Among those without previously diagnosed diabetes, we found that the excess risk associated with ADA- and IEC-defined prediabetes at ages 35-74 years accounted for 6% and 3% of cardiovascular deaths, 10% and 5% of renal deaths, and 31% and 14% of acute diabetic deaths, respectively. .Implications of all the available evidence: Our results show that prediabetes is a significant risk factor for cardiovascular, kidney, and acute diabetic deaths among Mexican adults and accounts for a notable fraction of such deaths. Identification of individuals with prediabetes should be prioritized for optimized management to improve cardiometabolic outcomes in Mexican adults.

Comparing the effect of imputation reference panel composition in four distinct Latin American cohorts

Genome-wide association studies have been useful in identifying genetic risk factors for various phenotypes. These studies rely on imputation and many existing panels are largely composed of individuals of European ancestry, resulting in lower levels of imputation quality in underrepresented populations. We aim to analyze how the composition of imputation reference panels affects imputation quality in four target Latin American cohorts. We compared imputation quality for chromosomes 7 and X when altering the imputation reference panel by: 1) increasing the number of Latin American individuals; 2) excluding either Latin American, African, or European individuals, or 3) increasing the Indigenous American (IA) admixture proportions of included Latin Americans. We found that increasing the number of Latin Americans in the reference panel improved imputation quality in the four populations; however, there were differences between chromosomes 7 and X in some cohorts. Excluding Latin Americans from analysis resulted in worse imputation quality in every cohort, while differential effects were seen when excluding Europeans and Africans between and within cohorts and between chromosomes 7 and X. Finally, increasing IA-like admixture proportions in the reference panel increased imputation quality at different levels in different populations. The difference in results between populations and chromosomes suggests that existing and future reference panels containing Latin American individuals are likely to perform differently in different Latin American populations.

Perspectives on genetic studies of type 2 diabetes from the genome-wide association studies era to precision medicine

Genome-wide association studies (GWAS) have facilitated a substantial and rapid increase in the number of confirmed genetic susceptibility variants for complex diseases. Approximately 700 variants predisposing individuals to the risk for type 2 diabetes have been identified through GWAS until 2023. From 2018 to 2022, hundreds of type 2 diabetes susceptibility loci with smaller effect sizes were identified through large-scale GWAS with sample sizes of 200,000 to >1 million. The clinical translation of genetic information for type 2 diabetes includes the development of novel therapeutics and risk predictions. Although drug discovery based on loci identified in GWAS remains challenging owing to the difficulty of functional annotation, global efforts have been made to identify novel biological mechanisms and therapeutic targets by applying multi-omics approaches or searching for disease-associated coding variants in isolated founder populations. Polygenic risk scores (PRSs), comprising up to millions of associated variants, can identify individuals with higher disease risk than those in the general population. In populations of European descent, PRSs constructed from base GWAS data with a sample size of approximately 450,000 have predicted the onset of diseases well. However, European GWAS-derived PRSs have limited predictive performance in non-European populations. The predictive accuracy of a PRS largely depends on the sample size of the base GWAS data. The results of GWAS meta-analyses for multi-ethnic groups as base GWAS data and cross-population polygenic prediction methodology have been applied to establish a universal PRS applicable to small isolated ethnic populations.

Association of tyrosine hydroxylase 01 (TH01) microsatellite and insulin gene (INS) variable number of tandem repeat (VNTR) with type 2 diabetes and fasting insulin secretion in Mexican population

PURPOSE

A variable number of tandem repeats (VNTR) in the insulin gene (INS) control region may be involved in type 2 diabetes (T2D). The TH01 microsatellite is near INS and may regulate it. We investigated whether the TH01 microsatellite and INS VNTR, assessed via the surrogate marker single nucleotide polymorphism rs689, are associated with T2D and serum insulin levels in a Mexican population.

METHODS

We analyzed a main case-control study (n = 1986) that used univariate and multivariate logistic regression models to calculate the risk conferred by TH01 and rs689 loci for T2D development; rs689 results were replicated in other case-control (n = 1188) and cross-sectional (n = 1914) studies.

RESULTS

TH01 alleles 6, 8, 9, and 9.3 and allele A of rs689 were independently associated with T2D, with differences between sex and age at diagnosis. TH01 alleles with ≥ 8 repeats conferred an increased risk for T2D in males compared with ≤ 7 repeats (odds ratio, ≥ 1.46; 95% confidence interval, 1.1-1.95). In females, larger alleles conferred a 1.5-fold higher risk for T2D when diagnosed ≥ 46 years but conferred protection when diagnosed ≤ 45 years. Similarly, rs689 allele A was associated with T2D in these groups. In males, larger TH01 alleles and the rs689 A allele were associated with a significant decrease in median fasting plasma insulin concentration with age in T2D cases; the reverse occurred in controls.

CONCLUSION

Larger TH01 alleles and rs689 A allele may potentiate insulin synthesis in males without T2D, a process disabled in those with T2D.

An indel introduced by Neanderthal introgression, rs3835124:ATTTATT > ATT, might contribute to prostate cancer risk by regulating PDK1 expression

INTRODUCTION

Prostate cancer is one of the most common cancer types in males and rs12621278:A > G has been suggested to be associated with this disease by previous genome-wide association studies. One thousand genomes project data analysis indicated that rs12621278:A > G is within two long-core haplotypes. However, the origin, causal variant(s), and molecular function of these haplotypes were remaining unclear.

MATERIALS AND METHODS

Population genetics analysis and functional genomics work was performed for this locus.

RESULTS

Phylogeny analysis verified that the rare haplotype is derived from Neanderthal introgression. Genome annotation suggested that three genetic variants in the core haplotypes, rs116108611:G > A, rs139972066:AAAAAAAA > AAAAAAAAA, and rs3835124:ATTTATT > ATT, are located in functional regions. Luciferase assay indicated that rs139972066:AAAAAAAA > AAAAAAAAA and rs116108611:G > A are not able to alter ITGA6 (integrin alpha 6) and ITGA6 antisense RNA 1 expression, respectively. In contrast, rs3835124:ATTTATT > ATT can significantly influence PDK1 (pyruvate dehydrogenase kinase 1) expression, which was verified by expression quantitative trait locus analysis. This genetic variant can alter transcription factor cut like homeobox 1 interaction efficiency. The introgressed haplotype was observed to be subject to positive selection in East Asian populations. The molecular function of the haplotype suggested that Neanderthal should be with lower PDK1 expression and further different energy homeostasis from modern human.

CONCLUSION

This study provided new insight into the contribution of Neanderthal introgression to human phenotypes.

The genetic changes that shaped Neandertals, Denisovans, and modern humans

Modern human ancestors diverged from the ancestors of Neandertals and Denisovans about 600,000 years ago. Until about 40,000 years ago, these three groups existed in parallel, occasionally met, and exchanged genes. A critical question is why modern humans, and not the other two groups, survived, became numerous, and developed complex cultures. Here, we discuss genetic differences among the groups and some of their functional consequences. As more present-day genome sequences become available from diverse groups, we predict that very few, if any, differences will distinguish all modern humans from all Neandertals and Denisovans. We propose that the genetic basis of what constitutes a modern human is best thought of as a combination of genetic features, where perhaps none of them is present in each and every present-day individual.

Role of PERK-Mediated Endoplasmic Reticulum Stress in Ferroptosis Caused by Hexavalent Chromium in Chicken Hepatocytes

This study aimed to investigate whether Cr(VI) can induce ferroptosis in chicken hepatocytes and determine the role of PERK-mediated endoplasmic reticulum stress (ERS). First, a model of Cr(VI) poisoning was established by exposing chicken hepatocytes to Cr(VI). The levels of ferroptosis-related proteins, meanwhile, GSH, SOD, MDA, and lipid ROS, were measured. Furthermore, the expression of GRP78 and PERK proteins was examined. Changes in ERS and ferroptosis were evaluated by silencing the PERK gene. Results showed that Cr(VI) led to the accumulation of lipid ROS, decreased expression of GPX4 and HSP27, increased expression of COX2, and induced ferroptosis in chicken hepatocytes. Exposure to Cr(VI) increased the protein expression of GRP78 and PERK, and silencing of PERK worsened Cr(VI)-induced ferroptosis. In conclusion, Cr(VI) can induce ferroptosis in chicken hepatocytes, and PERK plays an important role as a negative regulator.

ChREBP is activated by reductive stress and mediates GCKR-associated metabolic traits

Common genetic variants in glucokinase regulator (GCKR), which encodes GKRP, a regulator of hepatic glucokinase (GCK), influence multiple metabolic traits in genome-wide association studies (GWASs), making GCKR one of the most pleiotropic GWAS loci in the genome. It is unclear why. Prior work has demonstrated that GCKR influences the hepatic cytosolic NADH/NAD+ ratio, also referred to as reductive stress. Here, we demonstrate that reductive stress is sufficient to activate the transcription factor ChREBP and necessary for its activation by the GKRP-GCK interaction, glucose, and ethanol. We show that hepatic reductive stress induces GCKR GWAS traits such as increased hepatic fat, circulating FGF21, and circulating acylglycerol species, which are also influenced by ChREBP. We define the transcriptional signature of hepatic reductive stress and show its upregulation in fatty liver disease and downregulation after bariatric surgery in humans. These findings highlight how a GCKR-reductive stress-ChREBP axis influences multiple human metabolic traits.

Applications of genomic research in pediatric endocrine diseases

Recent advances in molecular genetics have advanced our understanding of the molecular mechanisms involved in pediatric endocrine disorders and now play a major role in mainstream medical practice. The spectrum of endocrine genetic disorders has 2 extremes: Mendelian and polygenic. Mendelian or monogenic diseases are caused by rare variants of a single gene, each of which exerts a strong effect on disease risk. Polygenic diseases or common traits are caused by the combined effects of multiple genetic variants in conjunction with environmental and lifestyle factors. Testing for a single gene is preferable if the disease is phenotypically and/or geneically homogeneous. Next-generation sequencing (NGS) can be applied to phenotypically and genetically heterogeneous conditions. Genome-wide association studies (GWASs) have examined genetic variants across the entire genome in a large number of individuals who have been matched for population ancestry and assessed for a disease or trait of interest. Common endocrine diseases or traits, such as type 2 diabetes mellitus, obesity, height, and pubertal timing, result from the combined effects of multiple variants in various genes that are frequently found in the general population, each of which contributes a small individual effect. Isolated founder mutations can result from a true founder effect or an extreme reduction in population size. Studies of founder mutations offer powerful advantages for efficiently localizing the genes that underlie Mendelian disorders. The Korean population has settled in the Korean peninsula for thousands of years, and several recurrent mutations have been identified as founder mutations. The application of molecular technology has increased our understanding of endocrine diseases, which have impacted on the practice of pediatric endocrinology related to diagnosis and genetic counseling. This review focuses on the application of genomic research to pediatric endocrine diseases using GWASs and NGS technology for diagnosis and treatment.

Prevalence of prediabetes in Mexico: a retrospective analysis of nationally representative surveys spanning 2016-2022

Background

Characterizing prediabetes phenotypes may be useful in guiding diabetes prevention efforts; however, heterogeneous criteria to define prediabetes have led to inconsistent prevalence estimates, particularly in low- and middle-income countries. Here, we estimated trends in prediabetes prevalence in Mexico across different prediabetes definitions and their association with prevalent cardiometabolic conditions.

Methods

We conducted a serial cross-sectional analysis of National Health and Nutrition Surveys in Mexico (2016-2022), totalling 22 081 Mexican adults. After excluding individuals with diagnosed or undiagnosed diabetes, we defined prediabetes using ADA (impaired fasting glucose [IFG] 100-125 mg/dL and/or HbA1c 5.7-6.4%), WHO (IFG 110-125 mg/dL), and IEC criteria (HbA1c 6.0-6.4%). Prevalence trends of prediabetes over time were evaluated using weighted Poisson regression and its association with prevalent cardiometabolic conditions with weighted logistic regression.

Findings

The prevalence of prediabetes (either IFG or high HbA1c [ADA]) in Mexico was 20.9% in 2022. Despite an overall downward trend in prediabetes (RR 0.973, 95% CI 0.957-0.988), this was primarily driven by decreases in prediabetes by ADA-IFG (RR 0.898, 95% CI 0.880-0.917) and WHO-IFG criteria (RR 0.919, 95% CI 0.886-0.953), while prediabetes by ADA-HbA1c (RR 1.055, 95% CI 1.033-1.077) and IEC-HbA1C criteria (RR 1.085, 95% CI 1.045-1.126) increased over time. Prediabetes prevalence increased over time in adults >40 years, with central obesity, self-identified as indigenous or living in urban areas. For all definitions, prediabetes was associated with an increased risk of cardiometabolic conditions.

Interpretation

Prediabetes rates in Mexico from 2016 to 2022 varied based on defining criteria but consistently increased for HbA1c-based definitions and high-risk subgroups.

Funding

This research was supported by Instituto Nacional de Geriatría in Mexico. JAS was supported by NIH/NIDDK Grant# K23DK135798.

A deep catalog of protein-coding variation in 985,830 individuals

Coding variants that have significant impact on function can provide insights into the biology of a gene but are typically rare in the population. Identifying and ascertaining the frequency of such rare variants requires very large sample sizes. Here, we present the largest catalog of human protein-coding variation to date, derived from exome sequencing of 985,830 individuals of diverse ancestry to serve as a rich resource for studying rare coding variants. Individuals of African, Admixed American, East Asian, Middle Eastern, and South Asian ancestry account for 20% of this Exome dataset. Our catalog of variants includes approximately 10.5 million missense (54% novel) and 1.1 million predicted loss-of-function (pLOF) variants (65% novel, 53% observed only once). We identified individuals with rare homozygous pLOF variants in 4,874 genes, and for 1,838 of these this work is the first to document at least one pLOF homozygote. Additional insights from the RGC-ME dataset include 1) improved estimates of selection against heterozygous loss-of-function and identification of 3,459 genes intolerant to loss-of-function, 83 of which were previously assessed as tolerant to loss-of-function and 1,241 that lack disease annotations; 2) identification of regions depleted of missense variation in 457 genes that are tolerant to loss-of-function; 3) functional interpretation for 10,708 variants of unknown or conflicting significance reported in ClinVar as cryptic splice sites using splicing score thresholds based on empirical variant deleteriousness scores derived from RGC-ME; and 4) an observation that approximately 3% of sequenced individuals carry a clinically actionable genetic variant in the ACMG SF 3.1 list of genes. We make this important resource of coding variation available to the public through a variant allele frequency browser. We anticipate that this report and the RGC-ME dataset will serve as a valuable reference for understanding rare coding variation and help advance precision medicine efforts.

Improving imputation quality in Samoans through the integration of population-specific sequences into existing reference panels

Genotype imputation is fundamental to association studies, and yet even gold standard panels like TOPMed are limited in the populations for which they yield good imputation. Specifically, Pacific Islanders are poorly represented in extant panels. To address this, we constructed an imputation reference panel using 1,285 Samoan individuals with whole-genome sequencing, combined with 1000 Genomes (1000G) samples, to create a reference panel that better represents Pacific Islander, specifically Samoan, genetic variation. We compared this panel to 1000G and TOPMed panels based on imputed variants using genotyping array data for 1,834 Samoan participants who were not part of the panels. The 1000G + 1285 Samoan panel yielded up to 2.25-2.76 times more well-imputed (r 2 ≥ 0.80) variants than TOPMed and 1000G. There was improved imputation accuracy across the minor allele frequency (MAF) spectrum, although it was more pronounced for variants with 0.01 ≤ MAF ≤ 0.05. Imputation accuracy (r 2 ) was greater for population-specific variants (high fixation index, F ST ) and those from larger haplotypes (high LD score). The gain in imputation accuracy over TOPMed was largest for small haplotypes (low LD score), reflecting the Samoan panel's ability to capture population-specific variation not well tagged by other panels. We also augmented the 1000G reference panel with varying numbers of Samoan samples and found that panels with 48 or more Samoans included outperformed TOPMed for all variants with MAF ≥ 0.001. This study identifies variants with improved imputation using population-specific reference panels and provides a framework for constructing other population-specific reference panels.

Neanderthal introgression in SCN9A impacts mechanical pain sensitivity

The Nav1.7 voltage-gated sodium channel plays a key role in nociception. Three functional variants in the SCN9A gene (encoding M932L, V991L, and D1908G in Nav1.7), have recently been identified as stemming from Neanderthal introgression and to associate with pain symptomatology in UK BioBank data. In 1000 genomes data, these variants are absent in Europeans but common in Latin Americans. Analysing high-density genotype data from 7594 Latin Americans, we characterized Neanderthal introgression in SCN9A. We find that tracts of introgression occur on a Native American genomic background, have an average length of ~123 kb and overlap the M932L, V991L, and D1908G coding positions. Furthermore, we measured experimentally six pain thresholds in 1623 healthy Colombians. We found that Neanderthal ancestry in SCN9A is significantly associated with a lower mechanical pain threshold after sensitization with mustard oil and evidence of additivity of effects across Nav1.7 variants. Our findings support the reported association of Neanderthal Nav1.7 variants with clinical pain, define a specific sensory modality affected by archaic introgression in SCN9A and are consistent with independent effects of the Neanderthal variants on Nav1.7 function.

Genotyping, sequencing and analysis of 140,000 adults from Mexico City

The Mexico City Prospective Study is a prospective cohort of more than 150,000 adults recruited two decades ago from the urban districts of Coyoacán and Iztapalapa in Mexico City1. Here we generated genotype and exome-sequencing data for all individuals and whole-genome sequencing data for 9,950 selected individuals. We describe high levels of relatedness and substantial heterogeneity in ancestry composition across individuals. Most sequenced individuals had admixed Indigenous American, European and African ancestry, with extensive admixture from Indigenous populations in central, southern and southeastern Mexico. Indigenous Mexican segments of the genome had lower levels of coding variation but an excess of homozygous loss-of-function variants compared with segments of African and European origin. We estimated ancestry-specific allele frequencies at 142 million genomic variants, with an effective sample size of 91,856 for Indigenous Mexican ancestry at exome variants, all available through a public browser. Using whole-genome sequencing, we developed an imputation reference panel that outperforms existing panels at common variants in individuals with high proportions of central, southern and southeastern Indigenous Mexican ancestry. Our work illustrates the value of genetic studies in diverse populations and provides foundational imputation and allele frequency resources for future genetic studies in Mexico and in the United States, where the Hispanic/Latino population is predominantly of Mexican descent.

Human-specific genetics: new tools to explore the molecular and cellular basis of human evolution

Our ancestors acquired morphological, cognitive and metabolic modifications that enabled humans to colonize diverse habitats, develop extraordinary technologies and reshape the biosphere. Understanding the genetic, developmental and molecular bases for these changes will provide insights into how we became human. Connecting human-specific genetic changes to species differences has been challenging owing to an abundance of low-effect size genetic changes, limited descriptions of phenotypic differences across development at the level of cell types and lack of experimental models. Emerging approaches for single-cell sequencing, genetic manipulation and stem cell culture now support descriptive and functional studies in defined cell types with a human or ape genetic background. In this Review, we describe how the sequencing of genomes from modern and archaic hominins, great apes and other primates is revealing human-specific genetic changes and how new molecular and cellular approaches - including cell atlases and organoids - are enabling exploration of the candidate causal factors that underlie human-specific traits.

The associations between dietary flavonoid intake and the prevalence of diabetes mellitus: Data from the National Health and Nutrition Examination Survey 2007-2010 and 2017-2018

Background

Diabetes mellitus (DM) is a prominent health concern worldwide, leading to the high incidence of disability and mortality and bringing in heavy healthcare and social burden. Plant-based diets are reported associated with a reduction of DM risk. Plant-based diets are rich in flavonoids, which possess properties such as scavenging free radicals and exerting both anti-inflammatory and antioxidant effects.

Purpose

However, whether dietary flavonoids are associated with the prevalence of DM remains controversial. The potential reasons for contradictory epidemiological outcomes on the association between dietary flavonoids and DM prevalence have not been determined.

Methods

To address these limitations, we employed data from 22,481 participants in the National Health and Nutrition Examination Survey to explore the association between the intake of flavonoids and DM prevalence by weighted Logistic regression and weighted restricted cubic splines.

Results

We found that the prevalence of DM was inversely associated with the intake of total flavonoids in the second quartile [Odds Ratio (OR) 0.78 95% confidence interval (CI) (0.63, 0.97), p = 0.028], in the third quartile [0.76 (0.60, 0.97), p = 0.031], and in the fourth quartile [0.80 (0.65, 0.97), p = 0.027]. However, the p for trend was not significant [0.94 (0.88, 1.01), p = 0.096]. Moreover, the association between DM prevalence and the intake of total flavonoids was significantly influenced by race (p for interaction = 0.006). In Mexican Americans, there was a significant positive association between DM prevalence and total flavonoid intake within the third quartile [1.04 (1.02, 1.07), p = 0.003]. Total flavan-3-ol and subtotal catechin intake exhibited a non-linear U-shaped association with DM prevalence (p for non-linearity < 0.0001 and p for non-linearity < 0.0001, respectively). Compared to the first quartile of corresponding intakes, consumption within the third quartile of subtotal catechins [0.70 (0.55, 0.89), p = 0.005] and total flavan-3-ols [0.65 (0.50, 0.84), p = 0.002] was associated with a lower prevalence of DM.

Conclusion

Taken together, our study may provide preliminary research evidence for personalized improvement of dietary habits to reduce the prevalence of diabetes.

Ancestry-driven metabolite variation provides insights into disease states in admixed populations

BACKGROUND

Metabolic pathways are related to physiological functions and disease states and are influenced by genetic variation and environmental factors. Hispanics/Latino individuals have ancestry-derived genomic regions (local ancestry) from their recent admixture that have been less characterized for associations with metabolite abundance and disease risk.

METHODS

We performed admixture mapping of 640 circulating metabolites in 3887 Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Metabolites were quantified in fasting serum through non-targeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Replication was performed in 1856 nonoverlapping HCHS/SOL participants with metabolomic data.

RESULTS

By leveraging local ancestry, this study identified significant ancestry-enriched associations for 78 circulating metabolites at 484 independent regions, including 116 novel metabolite-genomic region associations that replicated in an independent sample. Among the main findings, we identified Native American enriched genomic regions at chromosomes 11 and 15, mapping to FADS1/FADS2 and LIPC, respectively, associated with reduced long-chain polyunsaturated fatty acid metabolites implicated in metabolic and inflammatory pathways. An African-derived genomic region at chromosome 2 was associated with N-acetylated amino acid metabolites. This region, mapped to ALMS1, is associated with chronic kidney disease, a disease that disproportionately burdens individuals of African descent.

CONCLUSIONS

Our findings provide important insights into differences in metabolite quantities related to ancestry in admixed populations including metabolites related to regulation of lipid polyunsaturated fatty acids and N-acetylated amino acids, which may have implications for common diseases in populations.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes

AIMS/HYPOTHESIS

The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population.

METHODS

We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort.

RESULTS

Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.

CONCLUSIONS/INTERPRETATION

Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.

DATA AVAILABILITY

Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

Dysbiosis signatures of gut microbiota and the progression of type 2 diabetes: a machine learning approach in a Mexican cohort

Introduction

The gut microbiota (GM) dysbiosis is one of the causal factors for the progression of different chronic metabolic diseases, including type 2 diabetes mellitus (T2D). Understanding the basis that laid this association may lead to developing new therapeutic strategies for preventing and treating T2D, such as probiotics, prebiotics, and fecal microbiota transplants. It may also help identify potential early detection biomarkers and develop personalized interventions based on an individual's gut microbiota profile. Here, we explore how supervised Machine Learning (ML) methods help to distinguish taxa for individuals with prediabetes (prediabetes) or T2D.

Methods

To this aim, we analyzed the GM profile (16s rRNA gene sequencing) in a cohort of 410 Mexican naïve patients stratified into normoglycemic, prediabetes, and T2D individuals. Then, we compared six different ML algorithms and found that Random Forest had the highest predictive performance in classifying T2D and prediabetes patients versus controls.

Results

We identified a set of taxa for predicting patients with T2D compared to normoglycemic individuals, including Allisonella, Slackia, Ruminococus_2, Megaspgaera, Escherichia/Shigella, and Prevotella, among them. Besides, we concluded that Anaerostipes, Intestinibacter, Prevotella_9, Blautia, Granulicatella, and Veillonella were the relevant genus in patients with prediabetes compared to normoglycemic subjects.

Discussion

These findings allow us to postulate that GM is a distinctive signature in prediabetes and T2D patients during the development and progression of the disease. Our study highlights the role of GM and opens a window toward the rational design of new preventive and personalized strategies against the control of this disease.

Sex differences in the influence of type 2 diabetes (T2D)-related genes, parental history of T2D, and obesity on T2D development: a case-control study

BACKGROUND

This study investigated the effect of sex and age at type 2 diabetes (T2D) diagnosis on the influence of T2D-related genes, parental history of T2D, and obesity on T2D development.

METHODS

In this case-control study, 1012 T2D cases and 1008 healthy subjects were selected from the Diabetes in Mexico Study database. Participants were stratified by sex and age at T2D diagnosis (early, ≤ 45 years; late, ≥ 46 years). Sixty-nine T2D-associated single nucleotide polymorphisms were explored and the percentage contribution (R²) of T2D-related genes, parental history of T2D, and obesity (body mass index [BMI] and waist-hip ratio [WHR]) on T2D development was calculated using univariate and multivariate logistic regression models.

RESULTS

T2D-related genes influenced T2D development most in males who were diagnosed early (R² = 23.5%; females, R² = 13.5%; males and females diagnosed late, R² = 11.9% and R² = 7.3%, respectively). With an early diagnosis, insulin production-related genes were more influential in males (76.0% of R²) while peripheral insulin resistance-associated genes were more influential in females (52.3% of R²). With a late diagnosis, insulin production-related genes from chromosome region 11p15.5 notably influenced males while peripheral insulin resistance and genes associated with inflammation and other processes notably influenced females. Influence of parental history was higher among those diagnosed early (males, 19.9%; females, 17.5%) versus late (males, 6.4%; females, 5,3%). Unilateral maternal T2D history was more influential than paternal T2D history. BMI influenced T2D development for all, while WHR exclusively influenced males.

CONCLUSIONS

The influence of T2D-related genes, maternal T2D history, and fat distribution on T2D development was greater in males than females.

Rare and Common Variants in GALNT3 May Affect Bone Mass Independently of Phosphate Metabolism

Anabolic treatment options for osteoporosis remain limited. One approach to discovering novel anabolic drug targets is to identify genetic causes of extreme high bone mass (HBM). We investigated a pedigree with unexplained HBM within the UK HBM study, a national cohort of probands with HBM and their relatives. Whole exome sequencing (WES) in a family with HBM identified a rare heterozygous missense variant (NM_004482.4:c.1657C > T, p.Arg553Trp) in GALNT3, segregating appropriately. Interrogation of data from the UK HBM study and the Anglo-Australasian Osteoporosis Genetics Consortium (AOGC) revealed an unrelated individual with HBM with another rare heterozygous variant (NM_004482.4:c.831 T > A, p.Asp277Glu) within the same gene. In silico protein modeling predicted that p.Arg553Trp would disrupt salt-bridge interactions, causing instability of GALNT3, and that p.Asp277Glu would disrupt manganese binding and consequently GALNT3 catalytic function. Bi-allelic loss-of-function GALNT3 mutations alter FGF23 metabolism, resulting in hyperphosphatemia and causing familial tumoral calcinosis (FTC). However, bone mineral density (BMD) in FTC cases, when reported, has been either normal or low. Common variants in the GALNT3 locus show genome-wide significant associations with lumbar, femoral neck, and total body BMD. However, no significant associations with BMD are observed at loci coding for FGF23, its receptor FGFR1, or coreceptor klotho. Mendelian randomization analysis, using expression quantitative trait loci (eQTL) data from primary human osteoblasts and genome-wide association studies data from UK Biobank, suggested increased expression of GALNT3 reduces total body, lumbar spine, and femoral neck BMD but has no effect on phosphate concentrations. In conclusion, rare heterozygous loss-of-function variants in GALNT3 may cause HBM without altering phosphate concentration. These findings suggest that GALNT3 may affect BMD through pathways other than FGF23 regulation, the identification of which may yield novel anabolic drug targets for osteoporosis. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Progress in genetics of type 2 diabetes and diabetic complications

Type 2 diabetes results from a complex interaction between genetic and environmental factors. Precision medicine for type 2 diabetes using genetic data is expected to predict the risk of developing diabetes and complications and to predict the effects of medications and life-style intervention more accurately for individuals. Genome-wide association studies (GWAS) have been conducted in European and Asian populations and new genetic loci have been identified that modulate the risk of developing type 2 diabetes. Novel loci were discovered by GWAS in diabetic complications with increasing sample sizes. Large-scale genome-wide association analysis and polygenic risk scores using biobank information is making it possible to predict the development of type 2 diabetes. In the ADVANCE clinical trial of type 2 diabetes, a multi-polygenic risk score was useful to predict diabetic complications and their response to treatment. Proteomics and metabolomics studies have been conducted and have revealed the associations between type 2 diabetes and inflammatory signals and amino acid synthesis. Using multi-omics analysis, comprehensive molecular mechanisms have been elucidated to guide the development of targeted therapy for type 2 diabetes and diabetic complications.

Development and validation of a multivariable genotype-informed gestational diabetes prediction algorithm for clinical use in the Mexican population: insights into susceptibility mechanisms

INTRODUCTION

Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables.

RESEARCH DESIGN AND METHODS

Data from pregnant Mexican women enrolled in the 'Cuido mi Embarazo' (CME) cohort were used for development (107 cases, 469 controls) and data from the 'Mónica Pretelini Sáenz' Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24-28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort.

RESULTS

Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively.

CONCLUSIONS

We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations.

Haptools: a toolkit for admixture and haplotype analysis

SUMMARY

Leveraging local ancestry and haplotype information in genome-wide association studies and downstream analyses can improve the utility of genomics for individuals from diverse and recently admixed ancestries. However, most existing simulation, visualization and variant analysis frameworks are based on variant-level analysis and do not automatically handle these features. We present haptools, an open-source toolkit for performing local ancestry aware and haplotype-based analysis of complex traits. Haptools supports fast simulation of admixed genomes, visualization of admixture tracks, simulation of haplotype- and local ancestry-specific phenotype effects and a variety of file operations and statistics computed in a haplotype-aware manner.

AVAILABILITY AND IMPLEMENTATION

Haptools is freely available at https://github.com/cast-genomics/haptools.

DOCUMENTATION

Detailed documentation is available at https://haptools.readthedocs.io.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Diagnostic criteria and etiopathogenesis of type 2 diabetes and its complications: Lessons from the Pima Indians

The Phoenix Epidemiology and Clinical Research Branch of the National Institute of Diabetes and Digestive and Kidney Diseases has conducted prospective studies of diabetes and its complications in the Pima Indians living in Arizona, USA for over 50 years. In this review we highlight areas in which these studies provided vital insights into the criteria used to diagnose type 2 diabetes, the pathophysiologic changes that accompany the development of type 2 diabetes, and the course and determinants of diabetes complications-focusing specifically on diabetic kidney disease. We include data from our longitudinal population-based study of diabetes and its complications, studies on the role of insulin resistance and insulin secretion in the pathophysiology of type 2 diabetes, and in-depth studies of diabetic kidney disease that include measures of glomerular function and research kidney biopsies. We also focus on the emerging health threat posed by youth-onset type 2 diabetes, which was first seen in the Pima Indians in the 1960s and is becoming an increasing issue worldwide.

Variants in the Control Region of Mitochondrial Genome Associated with type 2 Diabetes in a Cohort of Mexican Mestizos

BACKGROUND

According to the International Diabetes Federation, Mexico is seventh place in the prevalence of type 2 diabetes (T2D) worldwide. Mitochondrial DNA variant association studies in multifactorial diseases like T2D are scarce in Mexican populations.

AIM OF THE STUDY

The objective of this study was to analyze the association between 18 variants in the mtDNA control region and T2D and related metabolic traits in a Mexican mestizo population from Mexico City.

METHODS

This study included 1001 participants divided into 477 cases with T2D and 524 healthy controls aged between 42 and 62 years and 18 mtDNA variants with frequencies >15%.

RESULTS

Association analyses matched by age and sex showed differences in the distribution between cases and controls for variants m.315_316insC (p = 1.18 × 10^-6), m.489T>C (p = 0.009), m.16362T>C (p = 0.001), and m.16519T>C (p = 0.004). The associations between T2D and variants m.315_316ins (OR = 6.13, CI = 3.42-10.97, p = 1.97 × 10^-6), m.489T>C (OR = 1.45, CI = 1.00-2.11, p = 0.006), m.16362T>C (OR = 2.17, CI = 1.57-3.00, p = 0.001), and m.16519T>C (OR = 1.69, CI = 1.23-2.33, p = 0.006) were significant after performing logistic regression models adjusted for age, sex, and diastolic blood pressure. Metabolic traits in the control group through linear regressions, adjusted for age, sex and BMI, and corrected for multiple comparisons showed nominal association between glucose and variants m.263A>G (p <0.050), m.16183A>C (p <0.010), m.16189T>C (p <0.020), and m.16223C>T (p <0.024); triglycerides, and cholesterol and variant m.309_310insC (p <0.010 and p <0.050 respectively); urea, and creatinine, and variant m.315_316insC (p <0.007, and p <0.004 respectively); diastolic blood pressure and variants m.235A>G (p <0.016), m.263A>G (p <0.013), m.315_316insC (p <0.043), and m.16111C>T (p <0.022).

CONCLUSION

These results demonstrate a strong association between variant m.315_316insC and T2D and a nominal association with T2D traits.

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.

Consensus document of the management of type 2 diabetes and heart failure: Consejo Interamericano de Falla Cardíaca e Hipertensión Pulmonar (CIFACAH) and Inter-American Society of Cardiology (IASC)

Heart failure (HF) syndrome is a global public health issue. On the other hand, type 2 diabetes is a risk factor associated with overweight/obesity and a sedentary lifestyle. This consensus aims to compile information available on the relationship between HF and type 2 diabetes and present, in a summarized and practical way, the management recommendations based on scientific evidence. The document includes the description of the epidemiology of HF and type 2 diabetes; pathophysiology of HF and type 2 diabetes; cardiovascular complications of type 2 diabetes; stages of HF; management of type 2 diabetes in patients with HF; and management of HF in patients with type 2 diabetes. Lastly, in the conclusions section, the growing trend of both events and the need to start preventive activities is presented, as well as the favorable role of antidiabetic drugs in the treatment of patients with HF.

The role of Neanderthal introgression in liver cancer

BACKGROUND

Neanderthal introgressed DNA has been linked to different normal and disease traits including immunity and metabolism-two important functions that are altered in liver cancer. However, there is limited understanding of the relationship between Neanderthal introgression and liver cancer risk. The aim of this study was to investigate the relationship between Neanderthal introgression and liver cancer risk.

METHODS

Using germline and somatic DNA and tumor RNA from liver cancer patients from The Cancer Genome Atlas, along with ancestry-match germline DNA from unaffected individuals from the 1000 Genomes Resource, and allele specific expression data from normal liver tissue from The Genotype-Tissue Expression project we investigated whether Neanderthal introgression impacts cancer etiology. Using a previously generated set of Neanderthal alleles, we identified Neanderthal introgressed haplotypes. We then tested whether somatic mutations are enriched or depleted on Neanderthal introgressed haplotypes compared to modern haplotypes. We also computationally assessed whether somatic mutations have a functional effect or show evidence of regulating expression of Neanderthal haplotypes. Finally, we compared patterns of Neanderthal introgression in liver cancer patients and the general population.

RESULTS

We find Neanderthal introgressed haplotypes exhibit an excess of somatic mutations compared to modern haplotypes. Variant Effect Predictor analysis revealed that most of the somatic mutations on these Neanderthal introgressed haplotypes are not functional. We did observe expression differences of Neanderthal alleles between tumor and normal for four genes that also showed a pattern of enrichment of somatic mutations on Neanderthal haplotypes. However, gene expression was similar between liver cancer patients with modern ancestry and liver cancer patients with Neanderthal ancestry at these genes. Provocatively, when analyzing all genes, we find evidence of Neanderthal introgression regulating expression in tumor from liver cancer patients in two genes, ARK1C4 and OAS1. Finally, we find that most genes do not show a difference in the proportion of Neanderthal introgression between liver cancer patients and the general population.

CONCLUSION

Our results suggest that Neanderthal introgression provides opportunity for somatic mutations to accumulate, and that some Neanderthal introgression may impact liver cancer risk.

Human genetics uncovers MAP3K15 as an obesity-independent therapeutic target for diabetes

We performed collapsing analyses on 454,796 UK Biobank (UKB) exomes to detect gene-level associations with diabetes. Recessive carriers of nonsynonymous variants in MAP3K15 were 30% less likely to develop diabetes (P = 5.7 × 10-10) and had lower glycosylated hemoglobin (β = -0.14 SD units, P = 1.1 × 10-24). These associations were independent of body mass index, suggesting protection against insulin resistance even in the setting of obesity. We replicated these findings in 96,811 Admixed Americans in the Mexico City Prospective Study (P < 0.05)Moreover, the protective effect of MAP3K15 variants was stronger in individuals who did not carry the Latino-enriched SLC16A11 risk haplotype (P = 6.0 × 10-4). Separately, we identified a Finnish-enriched MAP3K15 protein-truncating variant associated with decreased odds of both type 1 and type 2 diabetes (P < 0.05) in FinnGen. No adverse phenotypes were associated with protein-truncating MAP3K15 variants in the UKB, supporting this gene as a therapeutic target for diabetes.

A novel splice-affecting HNF1A variant with large population impact on diabetes in Greenland

Background

The genetic disease architecture of Inuit includes a large number of common high-impact variants. Identification of such variants contributes to our understanding of the genetic aetiology of diseases and improves global equity in genomic personalised medicine. We aimed to identify and characterise novel variants in genes associated with Maturity Onset Diabetes of the Young (MODY) in the Greenlandic population.

Methods

Using combined data from Greenlandic population cohorts of 4497 individuals, including 448 whole genome sequenced individuals, we screened 14 known MODY genes for previously identified and novel variants. We functionally characterised an identified novel variant and assessed its association with diabetes prevalence and cardiometabolic traits and population impact.

Findings

We identified a novel variant in the known MODY gene HNF1A with an allele frequency of 1.9% in the Greenlandic Inuit and absent elsewhere. Functional assays indicate that it prevents normal splicing of the gene. The variant caused lower 30-min insulin (β = -232 pmol/L, β_SD = -0.695, P = 4.43 × 10^-4) and higher 30-min glucose (β = 1.20 mmol/L, β_SD = 0.441, P = 0.0271) during an oral glucose tolerance test. Furthermore, the variant was associated with type 2 diabetes (OR 4.35, P = 7.24 × 10^-6) and HbA1c (β = 0.113 HbA1c%, β_SD = 0.205, P = 7.84 × 10^-3). The variant explained 2.5% of diabetes variance in Greenland.

Interpretation

The reported variant has the largest population impact of any previously reported variant within a MODY gene. Together with the recessive TBC1D4 variant, we show that close to 1 in 5 cases of diabetes (18%) in Greenland are associated with high-impact genetic variants compared to 1-3% in large populations.

Funding

Novo Nordisk Foundation, Independent Research Fund Denmark, and Karen Elise Jensen's Foundation.

A combined polygenic score of 21,293 rare and 22 common variants improves diabetes diagnosis based on hemoglobin A1C levels

Polygenic scores (PGSs) combine the effects of common genetic variants1,2 to predict risk or treatment strategies for complex diseases3-7. Adding rare variation to PGSs has largely unknown benefits and is methodically challenging. Here, we developed a method for constructing rare variant PGSs and applied it to calculate genetically modified hemoglobin A1C thresholds for type 2 diabetes (T2D) diagnosis7-10. The resultant rare variant PGS is highly polygenic (21,293 variants across 154 genes), depends on ultra-rare variants (72.7% observed in fewer than three people) and identifies significantly more undiagnosed T2D cases than expected by chance (odds ratio = 2.71; P = 1.51 × 10-6). A PGS combining common and rare variants is expected to identify 4.9 million misdiagnosed T2D cases in the United States-nearly 1.5-fold more than the common variant PGS alone. These results provide a method for constructing complex trait PGSs from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.

Precision nutrition in diabetes: when population-based dietary advice gets personal

Diet plays a fundamental role in maintaining long-term health, with healthful diets being endorsed by current dietary guidelines for the prevention and management of type 2 diabetes. However, the response to dietary interventions varies widely, highlighting the need for refinement and personalisation beyond population-based 'one size fits all'. This article reviews the clinical evidence supporting precision nutrition as a fundamental approach for dietary advice in diabetes. Further, it proposes a framework for the eventual implementation of precision nutrition and discusses key challenges for the application of this approach in the prevention of diabetes. One implication of this approach is that precision nutrition would not exclude the parallel goal of population-based healthy dietary advice. Nevertheless, the shift in prioritising precision nutrition is needed to reflect the dynamic nature of responses to dietary interventions that vary among individuals and change over the life course.

The contribution of Neanderthal introgression to modern human traits

Neanderthals, our closest extinct relatives, lived in western Eurasia from 400,000 years ago until they went extinct around 40,000 years ago. DNA retrieved from ancient specimens revealed that Neanderthals mated with modern human contemporaries. As a consequence, introgressed Neanderthal DNA survives scattered across the human genome such that 1-4% of the genome of present-day people outside Africa are inherited from Neanderthal ancestors. Patterns of Neanderthal introgressed genomic sequences suggest that Neanderthal alleles had distinct fates in the modern human genetic background. Some Neanderthal alleles facilitated human adaptation to new environments such as novel climate conditions, UV exposure levels and pathogens, while others had deleterious consequences. Here, we review the body of work on Neanderthal introgression over the past decade. We describe how evolutionary forces shaped the genomic landscape of Neanderthal introgression and highlight the impact of introgressed alleles on human biology and phenotypic variation.

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.

On the Verge of Precision Medicine in Diabetes

The epidemic of type 2 diabetes (T2D) is a significant global public health challenge and a major cause of morbidity and mortality. Despite the recent proliferation of pharmacological agents for the treatment of T2D, current therapies simply treat the symptom, i.e. hyperglycemia, and do not directly address the underlying disease process or modify the disease course. This article summarizes how genomic discovery has contributed to unraveling the heterogeneity in T2D, reviews relevant discoveries in the pharmacogenetics of five commonly prescribed glucose-lowering agents, presents evidence supporting how pharmacogenetics can be leveraged to advance precision medicine, and calls attention to important research gaps to its implementation to guide treatment choices.

New and Emerging Research on Solute Carrier and ATP Binding Cassette Transporters in Drug Discovery and Development: Outlook From the International Transporter Consortium

Enabled by a plethora of new technologies, research in membrane transporters has exploded in the past decade. The goal of this state-of-the-art article is to describe recent advances in research on membrane transporters that are particularly relevant to drug discovery and development. This review covers advances in basic, translational, and clinical research that has led to an increased understanding of membrane transporters at all levels. At the basic level, we describe the available crystal structures of membrane transporters in both the solute carrier (SLC) and ATP binding cassette superfamilies, which has been enabled by the development of cryogenic electron microscopy methods. Next, we describe new research on lysosomal and mitochondrial transporters as well as recently deorphaned transporters in the SLC superfamily. The translational section includes a summary of proteomic research, which has led to a quantitative understanding of transporter levels in various cell types and tissues and new methods to modulate transporter function, such as allosteric modulators and targeted protein degraders of transporters. The section ends with a review of the effect of the gut microbiome on modulation of transporter function followed by a presentation of 3D cell cultures, which may enable in vivo predictions of transporter function. In the clinical section, we describe new genomic and pharmacogenomic research, highlighting important polymorphisms in transporters that are clinically relevant to many drugs. Finally, we describe new clinical tools, which are becoming increasingly available to enable precision medicine, with the application of tissue-derived small extracellular vesicles and real-world biomarkers.

Genome-wide DNA methylation status of Mongolians exhibits signs of cellular stress response related to their nomadic lifestyle

Background

Epigenetics is crucial for connecting environmental stresses with physiological responses in humans. Mongolia, where nomadic livestock pastoralism has been the primal livelihood, has a higher prevalence of various chronic diseases than the surrounding East Asian regions, which are more suitable for crop farming. The genes related to dietary stress and pathogenesis of related disorders may have varying epigenetic statuses among the human populations with diverse dietary cultures. Hence, to understand such epigenetic differences, we conducted a comparative analysis of genome-wide DNA methylation of Mongolians and crop-farming East Asians.

Methods

Genome-wide DNA methylation status of peripheral blood cells (PBCs) from 23 Mongolian adults and 24 Thai adults was determined using the Infinium Human Methylation 450K arrays and analyzed in combination with previously published 450K data of 20 Japanese and 8 Chinese adults. CpG sites/regions differentially methylated between Mongolians and crop-farming East Asians were detected using a linear model adjusted for sex, age, ethnicity, and immune cell heterogeneity on RnBeads software.

Results

Of the quality-controlled 389,454 autosomal CpG sites, 223 CpG sites were significantly differentially methylated among Mongolians and the four crop farming East Asian populations (false discovery rate < 0.05). Analyses focused on gene promoter regions revealed that PM20D1 (peptidase M20 domain containing 1), which is involved in mitochondrial uncoupling and various processes, including cellular protection from reactive oxygen species (ROS) and thermogenesis, was the top differentially methylated gene. Moreover, gene ontology enrichment analysis revealed that biological processes related to ROS metabolism were overrepresented among the top 1% differentially methylated genes. The promoter regions of these genes were generally hypermethylated in Mongolians, suggesting that the metabolic pathway detoxifying ROS might be globally suppressed in Mongolians, resulting in the high susceptibility of this population to various chronic diseases.

Conclusions

This study showed a significantly diverse DNA methylation status among Mongolians and crop-farming East Asians. Further, we found an association between the differentially methylated genes and various metabolic and neurodegenerative diseases. Knowledge of the epigenetic regulators might help in proper understanding, treatment, and control of such disorders, and physiological adaptation in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40101-022-00305-0.

Rare loss of function variants in the hepatokine gene INHBE protect from abdominal obesity

Identifying genetic variants associated with lower waist-to-hip ratio can reveal new therapeutic targets for abdominal obesity. We use exome sequences from 362,679 individuals to identify genes associated with waist-to-hip ratio adjusted for BMI (WHRadjBMI), a surrogate for abdominal fat that is causally linked to type 2 diabetes and coronary heart disease. Predicted loss of function (pLOF) variants in INHBE associate with lower WHRadjBMI and this association replicates in data from AMP-T2D-GENES. INHBE encodes a secreted protein, the hepatokine activin E. In vitro characterization of the most common INHBE pLOF variant in our study, indicates an in-frame deletion resulting in a 90% reduction in secreted protein levels. We detect associations with lower WHRadjBMI for variants in ACVR1C, encoding an activin receptor, further highlighting the involvement of activins in regulating fat distribution. These findings highlight activin E as a potential therapeutic target for abdominal obesity, a phenotype linked to cardiometabolic disease.

Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs

Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.

Downregulation of SLC16A11 is Present in Offspring of Mothers with Gestational Diabetes

BACKGROUND

Studies have identified that diseases in pregnancy affect fetal growth and development of the newborn. In Mexican population, the gene SLC16A11 has been identified as a factor that increases the risk of developing type 2 diabetes mellitus. To date, information is scarce about its expression in gestational diabetes mellitus (GDM); epigenetic modifications due to maternal hyperglycemic state could be identified early in fetal development.

PURPOSE

This study aimed to determine the SLC16A11 expression and methylation status in umbilical cord blood of newborns offspring of mothers with or without GDM.

METHODS

Cross-sectional, analytic study. Pregnant patients undergoing caesarean delivery with and without GDM in the Unidad Medica de Alta Especialidad Hospital de Gineco-obstetricia #4 Luis Castelazo Ayala, Instituto Mexicano del Seguro Social, were invited to participate. DNA was extracted from the mothers' blood cells, or umbilical cord blood cells of their newborns, and subjected to methylation status. Total RNA was used to evaluate the SLC16A11 expression by endpoint RT-PCR. Variables were analyzed with Student t. Values of p <0.05 were considered statistically significant.

RESULTS

A SLC16A11 downregulation was observed for newborns, while methylation status was found in only 1 of 68 mother-child pairs. Somatometry of newborns showed no differences between groups. Differences were found in total cholesterol, triglycerides, ALT, glucose, and HbA1c.

CONCLUSIONS

For the first time, a differential expression for SLC16A11 was observed in offspring. Downregulation in this gene expression could characterize the offspring from GDM. No difference was found in somatometry of newborns of mothers with and without GDM.