Gene-environment and gene-treatment interactions in type 2 diabetes: progress, pitfalls, and prospects

CRISPR Screening Uncovers a Long-Range Enhancer for ONECUT1 in Pancreatic Differentiation and Links a Diabetes Risk Variant

Functional enhancer annotation is a valuable first step for understanding tissue-specific transcriptional regulation and prioritizing disease-associated non-coding variants for investigation. However, unbiased enhancer discovery in physiologically relevant contexts remains a major challenge. To discover regulatory elements pertinent to diabetes, we conducted a CRISPR interference screen in the human pluripotent stem cell (hPSC) pancreatic differentiation system. Among the enhancers uncovered, we focused on a long-range enhancer ∼664 kb from the ONECUT1 promoter, since coding mutations in ONECUT1 cause pancreatic hypoplasia and neonatal diabetes. Homozygous enhancer deletion in hPSCs was associated with a near-complete loss of ONECUT1 gene expression and compromised pancreatic differentiation. This enhancer contains a confidently fine-mapped type 2 diabetes associated variant (rs528350911) which disrupts a GATA motif. Introduction of the risk variant into hPSCs revealed substantially reduced binding of key pancreatic transcription factors (GATA4, GATA6 and FOXA2) on the edited allele, accompanied by a slight reduction of ONECUT1 transcription, supporting a causal role for this risk variant in metabolic disease. This work expands our knowledge about transcriptional regulation in pancreatic development through the characterization of a long-range enhancer and highlights the utility of enhancer discovery in disease-relevant settings for understanding monogenic and complex disease.

Haplotype analysis on association between C-reactive protein gene and susceptibility to type 2 diabetes mellitus in Chinese Han population : CRP gene and type 2 diabetes mellitus

AIMS

We aimed to evaluate the impact of C-reactive protein (CRP) gene polymorphism, additional gene-gene interaction, and haplotypes on susceptibility to type 2 diabetes mellitus (T2DM).

METHODS

SNPstats online software ( https://www.snpstats.net/start.htm ) was employed to evaluate the association between CRP gene and T2DM risk. High-order interactions among SNPs was tested using generalized multifactor dimensionality reduction, and the testing balanced accuracy, training balanced accuracy and cross-validation consistency were calculated. The SHEsisPlus ( http://shesisplus.bio-x.cn/SHEsis.html ) online software was used for haplotype analysis.

RESULTS

A total of 730 T2DM patients and 765 controls were enrolled. The T allele of rs1205 is associated with increased susceptibility to T2DM, OR (95% CI) were 1.51 (1.13-2.01), 1.44 (1.10-1.89) and 1.25 (1.01-1.54) for codominant, dominant and over-dominant models, respectively. We also found that minor allele of rs2794521 is associated with decreased susceptibility to T2DM under codominant and recessive models, OR (95%CI) were 0.38 (0.18-0.79) and 0.37 (0.16-0.80) for codominant and recessive models, respectively. No significant gene-gene interaction existed among CRP gene SNPs, all interaction p- values were more than 0.05. Haplotype analyses suggested the CGCA haplotype containing rs1205-C, rs1130864-G, rs2794521- C and rs3093059- A allele was associated with decreased risk of T2DM, OR (95% CI) = 0.83 (0.68-0.98), P = 0.047.

CONCLUSIONS

Minor allele of rs1205 was associated with increased T2DM risk. Minor allele of rs2794521 and the CGCA haplotype were associated with decreased T2DM risk.

Examination of the Complex Molecular Landscape in Obesity and Type 2 Diabetes

The escalating prevalence of metabolic disorders, notably type 2 diabetes (T2D) and obesity, presents a critical global health challenge, necessitating deeper insights into their molecular underpinnings. Our study integrates proteomics and metabolomics analyses to delineate the complex molecular landscapes associated with T2D and obesity. Leveraging data from 130 subjects, including individuals with T2D and obesity as well as healthy controls, we elucidate distinct molecular signatures and identify novel biomarkers indicative of disease progression. Our comprehensive characterization of cardiometabolic proteins and serum metabolites unveils intricate networks of biomolecular interactions and highlights differential protein expression patterns between T2D and obesity cohorts. Pathway enrichment analyses reveal unique mechanisms underlying disease development and progression, while correlation analyses elucidate the interplay between proteomics, metabolomics, and clinical parameters. Furthermore, network analyses underscore the interconnectedness of cardiometabolic proteins and provide insights into their roles in disease pathogenesis. Our findings may help to refine diagnostic strategies and inform the development of personalized interventions, heralding a new era in precision medicine and healthcare innovation. Through the integration of multi-omics approaches and advanced analytics, our study offers a crucial framework for deciphering the intricate molecular underpinnings of metabolic disorders and paving the way for transformative therapeutic strategies.

Green space, genetic susceptibility, and risk of osteoporosis:a cohort study from the UK Biobank

OBJECTIVE

This study aimed to investigate the effect of residential exposure to green space on the incident osteoporosis and further explore the modification effect of genetic susceptibility.

METHODS

Participants from the UK Biobank were followed from 2006 to 2010 (baseline) to December 31st, 2022. Using land use coverage, we evaluated exposure to residential surrounding green space, natural environment, and domestic gardens. We used the Cox regression to examine the association between the residential environment and incident osteoporosis. The interactive effects between polygenic risk score (PRS) of osteoporosis and residential environments on incident osteoporosis were investigated.

RESULTS

This study included 292,662 participants. Over a median follow-up period of 13.65 years, we documented 9177 incidents of osteoporosis. Per interquartile (IQR) increase in greenness and natural environment at a 300 m buffer was associated with a 4% lower risk of incident osteoporosis [HR = 0.96 (95% CI: 0.93, 0.99)] and [HR = 0.96 (95% CI: 0.93, 0.98)], respectively. We did not identify any interactive effects between genetic risk and residential environment on incident osteoporosis.

CONCLUSIONS

This study found that public greenness and natural environments could reduce the risk of incident osteoporosis regardless of genetic predisposition. Developing sustainable and publicly accessible natural environments might benefit populations' bone health.

Ancestry-attenuated effects of socioeconomic deprivation on type 2 diabetes disparities in the All of Us cohort

Background

Diabetes is a common disease with a major burden on morbidity, mortality, and productivity. Type 2 diabetes (T2D) accounts for roughly 90% of all diabetes cases in the USA and has a greater observed prevalence among those who identify as Black or Hispanic.

Methods

This study aimed to assess T2D racial and ethnic disparities using the All of Us Research Program data and to measure associations between genetic ancestry (GA), socioeconomic deprivation, and T2D. We used the All of Us Researcher Workbench to analyze T2D prevalence and model its associations with GA, individual-level (iSDI), and zip code-based (zSDI) socioeconomic deprivation indices among participant self-identified race and ethnicity (SIRE) groups.

Results

The study cohort of 86,488 participants from the four largest SIRE groups in All of Us: Asian (n = 2311), Black (n = 16,282), Hispanic (n = 16,966), and White (n = 50,292). SIRE groups show characteristic genetic ancestry patterns, consistent with their diverse origins, together with a continuum of ancestry fractions within and between groups. The Black and Hispanic groups show the highest levels of socioeconomic deprivation, followed by the Asian and White groups. Black participants show the highest age- and sex-adjusted T2D prevalence (21.9%), followed by the Hispanic (19.9%), Asian (15.1%), and White (14.8%) groups. Minority SIRE groups and socioeconomic deprivation, both iSDI and zSDI, are positively associated with T2D, when the entire cohort is analyzed together. However, SIRE and GA both show negative interaction effects with iSDI and zSDI on T2D. Higher levels of iSDI and zSDI are negatively associated with T2D in the Black and Hispanic groups, and higher levels of iSDI and zSDI are negatively associated with T2D at high levels of African and Native American ancestry.

Conclusions

Socioeconomic deprivation is associated with a higher prevalence of T2D in Black and Hispanic minority groups, compared to the majority White group. Nonetheless, socioeconomic deprivation is associated with reduced T2D risk within the Black and Hispanic groups. These results are paradoxical and have not been reported elsewhere, with possible explanations related to the nature of the All of Us data along with SIRE group differences in access to healthcare, diet, and lifestyle.

The Interaction of Dietary Patterns and Genetic Variants on the Risk of Cardiovascular Diseases in Chinese Patients with Type 2 Diabetes

SCOPE

Diabetes is an important risk factor for cardiovascular disease (CVD), which in turn is the most common and serious complication of diabetes. This study analyzes dietary patterns and single nucleotide polymorphisms (SNPs) in 543 diabetes patients with new-onset cardiovascular events and 461 diabetic patients without.

METHODS AND RESULTS

SNPs are determined and analyzed using real time PCR and gene chip method. Factor analysis and logistic regression are used to determine dietary patterns and evaluate the level of associations and interaction effects, respectively. The legumes and edible fungi pattern and vegetable pattern show a significant negative correlation with complication risk. ADIPOQ rs37563 and legumes and edible fungi pattern have a significant interactive effect on disease, and patients with a high score of C polymorphism genotype (GC + CC) have a lower risk of disease. 5-10-Methylenetetrahydrofolate reductase (MTHFR) rs1801131 and vegetable pattern have a borderline interaction effect on disease, and those patients with TT genotype have a lower risk of disease.

CONCLUSION

These findings provide new insights into the role of the interactive protection of dietary patterns and SNPs. And participants with specific alleles show a lower risk of cardiovascular complications.

Ancestry-attenuated effects of socioeconomic deprivation on type 2 diabetes disparities in the All of Us cohort

Background

Diabetes is a common disease with a major burden on morbidity, mortality, and productivity. Type 2 diabetes (T2D) accounts for roughly 90% of all diabetes cases in the United States and has greater observed prevalence among those who identify as Black or Hispanic.

Methods

The aims of this study were to determine whether T2D racial and ethnic disparities can be observed in data from the All of Us Research Program and to measure associations of genetic ancestry (GA) and socioeconomic deprivation with T2D. The All of Us Researcher Workbench was used to calculate T2D prevalence and to model T2D associations with GA, individual-level (iSDI) and zip code-based (zSDI) socioeconomic deprivation indices within and between participant self-identified race and ethnicity (SIRE) groups.

Results

The study cohort of 86,488 participants from the four largest SIRE groups in All of Us: Asian (n=2,311), Black (n=16,282), Hispanic (n=16,966), and White (n=50,292). SIRE groups show characteristic genetic ancestry patterns, consistent with their diverse origins, together with a continuum of ancestry fractions within and between groups. The Black and Hispanic groups show the highest median SDI values, followed by the Asian and White groups. Black participants show the highest age- and sex-adjusted T2D prevalence (21.9%), followed by the Hispanic (19.9%), Asian (15.1%), and White (14.8%) groups. Minority SIRE groups and socioeconomic deprivation are positively associated with T2D, when the entire cohort is analyzed together. However, SIRE and GA both show negative interaction effects with SDI on T2D. Higher levels of SDI are negatively associated with T2D in the Black and Hispanic groups, and higher levels of SDI are negatively associated with T2D at high levels of African and Native American ancestry.

Conclusion

Socioeconomic deprivation is positively associated with the SIRE group T2D disparities observed here but negatively associated with T2D within the Black and Hispanic groups that show the highest T2D prevalence. These results are paradoxical and have not been reported elsewhere. We discuss possible explanations for this paradox related to the nature of the All of Us data along with SIRE group differences in access to healthcare, diet, and lifestyle.

Gene-diet interaction analysis using novel weighted food scores discovers the adipocytokine signaling pathway associated with the development of type 2 diabetes

Introduction

The influence of dietary patterns measured using Recommended Food Score (RFS) with foods with high amounts of antioxidant nutrients for Type 2 diabetes (T2D) was analyzed. Our analysis aims to find associations between dietary patterns and T2D and conduct a gene-diet interaction analysis related to T2D.

Methods

Data analyzed in the current study were obtained from the Korean Genome and Epidemiology Study Cohort. The dietary patterns of 46 food items were assessed using a validated food frequency questionnaire. To maximize the predictive power of the RFS, we propose two weighted food scores, namely HisCoM-RFS calculated using the novel Hierarchical Structural Component model (HisCoM) and PLSDA-RFS calculated using Partial Least Squares-Discriminant Analysis (PLS-DA) method.

Results

Both RFS (OR: 1.11; 95% CI: 1.03- 1.20; P = 0.009) and PLSDA-RFS (OR: 1.10; 95% CI: 1.02-1.19, P = 0.011) were positively associated with T2D. Mapping of SNPs (P < 0.05) from the interaction analysis between SNPs and the food scores to genes and pathways yielded some 12 genes (CACNA2D3, RELN, DOCK2, SLIT3, CTNNA2, etc.) and pathways associated with T2D. The strongest association was observed with the adipocytokine signalling pathway, highlighting 32 genes (STAT3, MAPK10, MAPK8, IRS1, AKT1-3, ADIPOR2, etc.) most likely associated with T2D. Finally, the group of the subjects in low, intermediate and high using both the food scores and a polygenic risk score found an association between diet quality groups with issues at high genetic risk of T2D.

Conclusion

A dietary pattern of poor amounts of antioxidant nutrients is associated with the risk of T2D, and diet affects pathway mechanisms involved in developing T2D.

Changes of the acute myocardial infarction-related resident deaths in a transitioning region: a real-world study involving 3.17 million people

Background

The impact of acute myocardial infarction (AMI) on the life span of residents in a transitioning region has not been studied in depth. Therefore, we aimed to evaluate the changes in AMI-related resident deaths in a transitioning region in China.

Methods

A longitudinal, population-based study was performed to analyze the deaths with/of AMI in Pudong New Area (PNA), Shanghai from 2005 to 2021. The average annual percentage change (AAPC) of AMI in crude mortality rates (CMR), age-standardized mortality rates worldwide (ASMRW), and rates of years of life lost (YLLr) were calculated by the joinpoint regression. The impact of demographic and non-demographic factors on the mortality of residents who died with/of AMI was quantitatively analyzed by the decomposition method.

Results

In 7,353 residents who died with AMI, 91.74% (6,746) of them were died of AMI from 2005 to 2021. In this period, the CMR and ASMRW of residents died with/of AMI were 15.23/105 and 5.17/105 person-years, the AAPC of CMR was 0.01% (95% CI: -0.71,0.72, p = 0.989) and 0.06% (95% CI: -0.71,0.84, p = 0.868), and the ASMRW decreased by 2.83% (95% CI: -3.66,-2.00, p < 0.001) and 2.76% (95% CI: -3.56,-1.95, p < 0.001), respectively. The CMR of people died of AMI showed a downward trend (all p < 0.05) in people ≥60 years but an upward trend [AAPC = 2.47% (95% CI: 0.07,4.94, p = 0.045)] in people of 45-59 years. The change in CMR of people died with/of AMI caused by demographic factors was 28.70% (95% CI: 12.99,46.60, p = 0.001) and 28.07% (95% CI: 12.71,45.52, p = 0.001) per year, respectively.

Conclusion

Preventative strategies for AMI should be applied to enhance the health management of residents aged 45-59 years or with comorbidities in the transitioning region.

Genotype-by-environment interactions in chronic back pain

BACKGROUND CONTEXT

Chronic back pain (CBP) is a common debilitating condition with substantial societal impact. While understanding genotype-by-environment (GxE) interactions may be crucial to achieving the goals of personalized medicine, there are few large-scale studies investigating this topic for CBP. None of them systematically explore multiple CBP risk factors.

PURPOSE

To estimate the extent to which genetic effects on CBP are modified by known demographic and clinical risk factors.

RESEARCH DESIGN

Case-control study, genome-wide GxE interaction study.

PATIENT SAMPLE

Data on up to 331,610 unrelated participants (57,881 CBP cases and 273,729 controls) from the UK Biobank cohort were used. UK Biobank is a prospective cohort with collected deep genetic and phenotypic data on approximately 500,000 individuals across the UK.

OUTCOME MEASURES

Self-reported chronic back pain.

METHODS

We applied a whole-genome approach to estimate the proportion of phenotypic variance explained by interactions between genotype and 12 known risk factors. We also analyzed if effects of common single-nucleotide polymorphisms on CBP are changed in presence of known risk factors.

RESULTS

The results indicate a modest, if any, modification of genetic effects by examined risk factors in CBP. Our estimates suggest that detecting such weak effects would require a sample size of millions of individuals.

CONCLUSIONS

The GxE interactions with examined common risk factors for CBP are either weak or absent. Interactions of such magnitude are unlikely to have the potential to inform and influence treatment strategies. Risk estimation models may use common genetic variation and the considered risk factors as independent predictors, without accounting for GxE.

Optimal strategies for learning multi-ancestry polygenic scores vary across traits

Polygenic scores (PGSs) are individual-level measures that aggregate the genome-wide genetic predisposition to a given trait. As PGS have predominantly been developed using European-ancestry samples, trait prediction using such European ancestry-derived PGS is less accurate in non-European ancestry individuals. Although there has been recent progress in combining multiple PGS trained on distinct populations, the problem of how to maximize performance given a multiple-ancestry cohort is largely unexplored. Here, we investigate the effect of sample size and ancestry composition on PGS performance for fifteen traits in UK Biobank. For some traits, PGS estimated using a relatively small African-ancestry training set outperformed, on an African-ancestry test set, PGS estimated using a much larger European-ancestry only training set. We observe similar, but not identical, results when considering other minority-ancestry groups within UK Biobank. Our results emphasise the importance of targeted data collection from underrepresented groups in order to address existing disparities in PGS performance.

Early detection system of risk factors for diabetes mellitus type 2 utilization of machine learning-random forest

BACKGROUND

The prevalence of morbidity and mortality for type 2 diabetes mellitus (DM) is still increasing because of changing lifestyles. There needs to be a means of controlling the rise in the incidence of the disease. Many researchers have utilized technological advances such as machine learning for disease prevention and control, especially in noncommunicable conditions. Researchers are, therefore, interested in creating an early detection system for risk factors of type 2 diabetes.

MATERIALS AND METHODS

The study was conducted in February 2022, utilizing secondary surveillance data from Puskesmas Johar Baru, Jakarta, in 2019, 2020, and 2021. Data was analyzed utilizing various bivariate and multivariate statistical methods at 5% significance level and machine learning methods (random forest algorithm) with an accuracy rate of >80%. The data for the three years was cleaned, normalized, and merged.

RESULTS

The final population was 65,533 visits out of the initial data of 196,949, and the final number of DM 2 population was 2766 out of the initial data of 9903. Age, gender, family history of DM, family history of hypertension, hypertension, high blood sugar levels, obesity, and central obesity were significantly associated with type 2 DM. Family history was the strongest risk factor of all independent variables, odds ratio of 15.101. The classification results of feature importance, with an accuracy rate of 84%, obtained in order were age, blood sugar level, and body mass index.

CONCLUSION

Blood sugar level is the most influential factor in the incidence of DM in Puskesmas Johar Baru. In other words, a person with a family history of type 2 diabetes, at unproductive age, of female gender, and of excessive weight can avoid type 2 diabetes if they can regularly maintain their blood sugar levels.

Maternal androgen excess increases the risk of pre-diabetes mellitus in male offspring in later life: a long-term population-based follow-up study

PURPOSE

Prenatal androgen exposure could be a source of early programming, leading to the development of cardiometabolic diseases in later life. In this study, we aimed to examine cardiometabolic disturbances in males exposed to maternal androgen excess during their prenatal life.

METHODS

In this prospective population-based study, 409 male offspring with maternal hyperandrogenism (MHA), and 954 male offspring without MHA, as controls, were included. Both groups of male offspring were followed from the baseline to the date of the incidence of events, censoring, or end of the study period, whichever came first. Age-scaled unadjusted and adjusted Cox regression models were applied to assess the hazard ratios (HR) and 95% confidence intervals (CIs) for the association between MHA with pre-diabetes mellitus (Pre-DM), type 2 diabetes mellitus (T2DM), pre-hypertension (Pre-HTN), hypertension (HTN), dyslipidemia, overweight, and obesity in the offspring of both groups. Statistical analysis was performed using the STATA software package; the significance level was set at P < 0.05.

RESULTS

A higher risk of Pre-DM (adjusted HR: 1.46 (1.20, 1.78)) was observed in male offspring with MHA after adjustment for potential confounders, including body mass index, education, and physical activity. However, no significant differences were observed in the risk of T2DM, Pre-HTN, HTN, dyslipidemia, overweight, and obesity in males with MHA compared to controls in both the unadjusted and adjusted models.

CONCLUSION

Maternal androgen excess increases the risk of Pre-DM in male offspring in later life. More longitudinal studies with long enough follow-up are needed to clarify the effects of MHA on the cardiometabolic risk factors of male offspring in later life.

The association study between changes in HbA1C with rs2250486 and rs67238751 genetic variants for SLC47A1 in newly diagnosed Iranian patients with type 2 diabetes mellitus: 6 months follow-up study

OBJECTIVES

One of the most well-known oral medications for the treatment of T2DM is metformin. Variants have been found in studies to be useful in detecting new genes connected to T2DM aetiology and affecting metformin's mechanism of action. In this research, we aimed to study two variations of the SLC47A1 gene; rs2250486 and rs67238751, in T2DM patients who had been taking metformin for the first 6 months after the diagnosis in the Iranian population for the first time.

DESIGN AND METHODS

A total of 200 individuals were recruited for the study. According to their glycosylated haemoglobin (HbA1c) levels, the patients were divided into two groups: responders (HbA1c levels were reduced by at least 1% after 6 months of metformin treatment.) and non-responders. DNA was extracted from whole blood and genotyped by Tetra ARMS PCR. High-performance liquid chromatography (HPLC) was used to measure HbA1c levels at the start of the treatment and again 6 months later.

RESULTS

rs2250486 variant in the dominant model reduces the HbA1C levels after 6 months of metformin treatment. In fact, when compared to the T/C + C/C genotypes, the T/T genotype improves HbA1C levels (p-value = .014). Furthermore, in the allelic model, the T allele improves HbA1C levels in comparison to the C allele (p-value = .008). After 6 months of metformin treatment, serum levels of HbA1C in responders were reduced significantly in both groups (T/T and T/C + C/C), (p-value = <.0001). However, the rs67238751 variant did not reveal a meaningful relationship with lower HbA1C levels in any of the models.

CONCLUSIONS

This study found that the rs2250486 variant could be associated with reducing HbA1C levels while the rs67238751 variant, had no relationship.

Interactions between genetic and lifestyle factors on cardiometabolic disease-related outcomes in Latin American and Caribbean populations: A systematic review

Introduction

The prevalence of cardiometabolic diseases has increased in Latin American and the Caribbean populations (LACP). To identify gene-lifestyle interactions that modify the risk of cardiometabolic diseases in LACP, a systematic search using 11 search engines was conducted up to May 2022.

Methods

Eligible studies were observational and interventional studies in either English, Spanish, or Portuguese. A total of 26,171 publications were screened for title and abstract; of these, 101 potential studies were evaluated for eligibility, and 74 articles were included in this study following full-text screening and risk of bias assessment. The Appraisal tool for Cross-Sectional Studies (AXIS) and the Risk Of Bias In Non-Randomized Studies-of Interventions (ROBINS-I) assessment tool were used to assess the methodological quality and risk of bias of the included studies.

Results

We identified 122 significant interactions between genetic and lifestyle factors on cardiometabolic traits and the vast majority of studies come from Brazil (29), Mexico (15) and Costa Rica (12) with FTO, APOE, and TCF7L2 being the most studied genes. The results of the gene-lifestyle interactions suggest effects which are population-, gender-, and ethnic-specific. Most of the gene-lifestyle interactions were conducted once, necessitating replication to reinforce these results.

Discussion

The findings of this review indicate that 27 out of 33 LACP have not conducted gene-lifestyle interaction studies and only five studies have been undertaken in low-socioeconomic settings. Most of the studies were cross-sectional, indicating a need for longitudinal/prospective studies. Future gene-lifestyle interaction studies will need to replicate primary research of already studied genetic variants to enable comparison, and to explore the interactions between genetic and other lifestyle factors such as those conditioned by socioeconomic factors and the built environment. The protocol has been registered on PROSPERO, number CRD42022308488.

Systematic review registration

https://clinicaltrials.gov, identifier CRD420223 08488.

DNA methylation trajectories and accelerated epigenetic aging in incident type 2 diabetes

DNA methylation (DNAm) patterns across the genome changes during aging and development of complex diseases including type 2 diabetes (T2D). Our study aimed to estimate DNAm trajectories of CpG sites associated with T2D, epigenetic age (DNAmAge), and age acceleration based on four epigenetic clocks (GrimAge, Hannum, Horvath, phenoAge) in the period 10 years prior to and up to T2D onset. In this nested case-control study within Doetinchem Cohort Study, we included 132 incident T2D cases and 132 age- and sex-matched controls. DNAm was measured in blood using the Illumina Infinium Methylation EPIC array. From 107 CpG sites associated with T2D, 10 CpG sites (9%) showed different slopes of DNAm trajectories over time (p < 0.05) and an additional 8 CpG sites (8%) showed significant differences in DNAm levels (at least 1%, p-value per time point < 0.05) at all three time points with nearly parallel trajectories between incident T2D cases and controls. In controls, age acceleration levels were negative (slower epigenetic aging), while in incident T2D cases, levels were positive, suggesting accelerated aging in the case group. We showed that DNAm levels at specific CpG sites, up to 10 years before T2D onset, are different between incident T2D cases and healthy controls and distinct patterns of clinical traits over time may have an impact on those DNAm profiles. Up to 10 years before T2D diagnosis, cases manifested accelerated epigenetic aging. Markers of biological aging including age acceleration estimates based on Horvath need further investigation to assess their utility for predicting age-related diseases including T2D.

Importance of Heparan Sulfate Proteoglycans in Pancreatic Islets and β-Cells

β-cells in the islets of Langerhans of the pancreas secrete insulin in response to the glucose concentration in the blood. When these pancreatic β-cells are damaged, diabetes develops through glucose intolerance caused by insufficient insulin secretion. High molecular weight polysaccharides, such as heparin and heparan sulfate (HS) proteoglycans, and HS-degrading enzymes, such as heparinase, participate in the protection, maintenance, and enhancement of the functions of pancreatic islets and β-cells, and the demand for studies on glycobiology within the field of diabetes research has increased. This review introduces the roles of complex glycoconjugates containing high molecular weight polysaccharides and their degrading enzymes in pancreatic islets and β-cells, including those obtained in studies conducted by us earlier. In addition, from the perspective of glycobiology, this study proposes the possibility of application to diabetes medicine.

Effect of TCF7L2 on the relationship between lifestyle factors and glycemic parameters: a systematic review

Background

Among candidate genes related to type 2 diabetes (T2DM), one of the strongest genes is Transcription factor 7 like 2 (TCF7L2), regarding the Genome-Wide Association Studies. We aimed to conduct a systematic review of the literature on the modification effect of TCF7L2 on the relation between glycemic parameters and lifestyle factors.

Methods

A systematic literature search was done for relevant publications using electronic databases, including PubMed, EMBASE, Scopus, and Web of Science, from January 1, 2000, to November 2, 2021.

Results

Thirty-eight studies (16 observational studies, six meal test trials, and 16 randomized controlled trials (RCTs)) were included. Most observational studies had been conducted on participants with non-diabetes showing that TCF7L2 modified the association between diet (fatty acids and fiber) and insulin resistance. In addition, findings from meal test trials showed that, compared to non-risk-allele carriers, consumption of meals with different percentages of total dietary fat in healthy risk-allele carriers increased glucose concentrations and impaired insulin sensitivity. However, ten RCTs, with intervention periods of less than ten weeks and more than one year, showed that TCF7L2 did not modify glycemic parameters in response to a dietary intervention involving different macronutrients. However, two weight loss dietary RCTs with more than 1-year duration showed that serum glucose and insulin levels decreased and insulin resistance improved in non-risk allele subjects with overweight/obesity. Regarding artichoke extract supplementation (ALE), two RCTs observed that ALE supplementation significantly decreased insulin concentration and improved insulin resistance in the TT genotype of the rs7903146 variant of TCF7L2. In addition, four studies suggested that physical activity levels and smoking status modified the association between TCF7L2 and glycemic parameters. However, three studies observed no effect of TCF7L2 on glycemic parameters in participants with different levels of physical activity and smoking status.

Conclusion

The modification effects of TCF7L2 on the relation between the lifestyle factors (diet, physical activity, and smoking status) and glycemic parameters were contradictory.

PROSPERO registration number

CRD42020196327

Supplementary Information

The online version contains supplementary material available at 10.1186/s12937-022-00813-w.

IPMK modulates hepatic glucose production and insulin signaling

Hepatic glucose production (HGP) is crucial for the maintenance of normal glucose homeostasis. Although hepatic insulin resistance contributes to excessive glucose production, its mechanism is not well understood. Here, we show that inositol polyphosphate multikinase (IPMK), a key enzyme in inositol polyphosphate biosynthesis, plays a role in regulating hepatic insulin signaling and gluconeogenesis both in vitro and in vivo. IPMK-deficient hepatocytes exhibit decreased insulin-induced activation of Akt-FoxO1 signaling. The expression of messenger RNA levels of phosphoenolpyruvate carboxykinase 1 (Pck1) and glucose 6-phosphatase (G6pc), key enzymes mediating gluconeogenesis, are increased in IPMK-deficient hepatocytes compared to wild type hepatocytes. Importantly, re-expressing IPMK restores insulin sensitivity and alleviates glucose production in IPMK-deficient hepatocytes. Moreover, hepatocyte-specific IPMK deletion exacerbates hyperglycemia and insulin sensitivity in mice fed a high-fat diet, accompanied by an increase in HGP during pyruvate tolerance test and reduction in Akt phosphorylation in IPMK deficient liver. Our results demonstrate that IPMK mediates insulin signaling and gluconeogenesis and may be potentially targeted for treatment of diabetes.

Relative contributions of the host genome, microbiome, and environment to the metabolic profile

BACKGROUND

Metabolic syndrome is as a well-known risk factor for cardiovascular disease, which is associated with both genetic and environmental factors. Recently, the microbiome composition has been shown to affect the development of metabolic syndrome. Thus, it is expected that the complex interplay among host genetics, the microbiome, and environmental factors could affect metabolic syndrome.

OBJECTIVE

To evaluate the relative contributions of genetic, microbiome, and environmental factors to metabolic syndrome using statistical approaches.

METHODS

Data from the prospective Korean Association REsource project cohort (N = 8476) were used in this study, including single-nucleotide polymorphisms, phenotypes and lifestyle factors, and the urine-derived microbial composition. The effect of each data source on metabolic phenotypes was evaluated using a heritability estimation approach and a prediction model separately. We further experimented with various types of metagenomic relationship matrices to estimate the phenotypic variance explained by the microbiome.

RESULTS

With the heritability estimation, five of the 11 metabolic phenotypes were significantly associated with metagenome-wide similarity. We found significant heritability for fasting glucose (4.8%), high-density lipoprotein cholesterol (4.9%), waist-hip ratio (7.7%), and waist circumference (5.6%). Microbiome compositions provided more accurate estimations than genetic factors for the same sample size. In the prediction model, the contribution of each source to the prediction accuracy varied for each phenotype.

CONCLUSION

The effects of host genetics, the metagenome, and environmental factors on metabolic syndrome were minimal. Our statistical analysis suffers from a small sample size, and the measurement error is expected to be substantial. Further analysis is necessary to quantify the effects with better accuracy.

A Personalized Diet Approach Study: Interaction between PPAR-γ Pro12Ala and Dietary Insulin Indices on Metabolic Markers in Diabetic Patients

BACKGROUND

The objectives were to investigate the effect of the interaction between peroxisome proliferator-activated receptor gamma (PPAR-γ) Pro12Ala polymorphisms and dietary insulin load and insulin index (DIL and DII) on Cardio-metabolic Markers among diabetic patients.

METHODS

This cross-sectional study was conducted on 393 diabetic patients. Food-frequency questionnaire (FFQ) was used for DIL and DII calculation. PPAR-γ Pro12Ala was genotyped by the PCR-RFLP method. Biochemical markers including TC, LDL, HDL, TG, SOD, CRP, TAC, PTX3, PGF2α. IL18, leptin and ghrelin were measured by standard protocol.

RESULT

Risk-allele carriers (CG, GG) had higher obesity indices WC (P _interaction =0.04), BMI (P _interaction =0.006) and, WC (P _interaction =0.04) compared with individuals with the CC genotype when they consumed a diet with higher DIL and DII respectively. Besides, carriers of the G allele who were in the highest tertile of DIL, had lower HDL (P _interaction =0.04) and higher PGF2α (P _interaction =0.03) and PTX3 (P _interaction =0.03). Moreover, the highest tertile of the DII, showed an increase in IL18 (P _interaction =0.01) and lower SOD (P _interaction =0.03) for risk allele carriers compared to those with CC homozygotes.

CONCLUSION

We revealed PPAR-γ Pro12Ala polymorphism was able to intensify the effect of DIL and DII on CVD risk factors; risk-allele carriers who consumed a diet with high DIL and DII score have more likely to be obese and have higher inflammatory markers. Also, protective factor against CVD risk factors were reduced significantly in this group compared to CC homozygotes. This article is protected by copyright. All rights reserved.

Gene-environment interaction in type 2 diabetes in Korean cohorts: Interaction of a type 2 diabetes polygenic risk score with triglyceride and cholesterol on fasting glucose levels

Type 2 diabetes (T2D) is caused by genetic and environmental factors as well as gene-environment interactions. However, these interactions have not been systematically investigated. We analyzed these interactions for T2D and fasting glucose levels in three Korean cohorts, HEXA, KARE, and CAVAS, using the baseline data with a multiple regression model. Two polygenic risk scores for T2D (PRS_T2D ) and fasting glucose (PRS_FG ) were calculated using 488 and 82 single nucleotide polymorphisms (SNP) for T2D and fasting glucose, respectively, which were extracted from large-scaled genome-wide association studies with multiethnic data. Both lifestyle risk factors and T2D-related biochemical measurements were assessed. The effect of interactions between PRS_T2D -triglyceride (TG) and PRS_T2D -total cholesterol (TC) on fasting glucose levels was observed as follows: β ± SE = 0.0005 ± 0.0001, p = 1.06 × 10^-19 in HEXA, β ± SE = 0.0008 ± 0.0001, p = 2.08 × 10^-8 in KARE for TG; β ± SE = 0.0006 ± 0.0001, p = 2.00 × 10^-6 in HEXA, β ± SE = 0.0020 ± 0.0004, p = 2.11 × 10^-6 in KARE, β ± SE = 0.0007 ± 0.0004, p = 0.045 in CAVAS for TC. PRS_T2D -based classification of the participants into four groups showed that the fasting glucose levels in groups with higher PRS_T2D were more adversely affected by both the TG and TC. In conclusion, blood TG and TC levels may affect the fasting glucose level through interaction with T2D genetic factors, suggesting the importance of consideration of gene-environment interaction in the preventive medicine of T2D.

MC4R Gene Polymorphisms Interact With the Urbanized Living Environment on Obesity: Results From the Yi Migrant Study

Objectives: This study aimed to determine the association of MC4R gene polymorphisms (rs17782313 and rs12970134) and urbanized living environment and the gene–environment interaction with obesity in Yi people in China.

Methods: A 1:2 frequency-matched case–control study based on the cross-sectional data was designed. Those with BMI ≥28 kg/m² were included as the case group. The age- and sex-matched controls were selected from those with BMI <24 kg/m². Unconditional logistic models were used to determine the association of SNPs with obesity. Additive interaction was evaluated by calculating relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI).

Results: A total of 322 cases and 643 controls were included. After adjustment, allele C of rs17782313 was significantly associated with obesity (additive model, OR = 1.52, 95%CI: 1.18–1.96) in Yi people. A similar association was found in allele A of rs12970134 (additive model, OR = 1.45, 95%CI: 1.13–1.89). Yi rural-to-urban migrants were found at 2.59-fold (95%CI: 1.70–3.95) higher odds of obesity than Yi farmers. Additive interactions were found between the two SNPs and rural-to-urban migration (rs17782313: AP = 0.65, 95%CI: 0.22–1.09; rs12970134: AP = 0.59, 95%CI: 0.02–1.17).

Conclusion:MC4R gene polymorphisms positively interacted with the urbanized living environment on obesity in Yi people. The effect of the MC4R gene on obesity was modified by the living environment.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Associations between serum total bilirubin, obesity and type 2 diabetes

BACKGROUND

This study aims to examine the cross-sectional association between serum total bilirubin (STB) and type 2 diabetes (T2D) risk in the general population, and whether obesity could moderate this association.

METHODS

We used data from the 1999-2018 National Health and Nutrition Examination Surveys (NHANES), including a total of 38,641 US adult participants who were 18 years or older. The STB was classified as the low, moderate, and high groups according to tertiles.

RESULTS

We found that participants with lower STB had a significantly higher risk of T2D than those with moderate (OR = 0.81; 95% CI 0.74, 0.89; P < 0.001) and high (OR = 0.65; 95% CI 0.59, 0.73; P < 0.001) STB. Also, a significant interaction between body mass index (BMI) and STB on T2D was observed (P < 0.001). Stratified analysis showed that low STB was associated with a 20% and 27% decrease of T2D risk for moderate and high STB groups in obese patients, however, these effect estimates were smaller in the population with lower BMI (< 30 kg/m²). Similar associations of STB with glycohemoglobin and insulin resistance were observed.

CONCLUSION

This study suggests that STB is associated with an elevated risk of T2D. More importantly, we reported for the first time that BMI may moderate the association between bilirubin and T2D.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Heterogeneity of Diabetes: β-Cells, Phenotypes, and Precision Medicine: Proceedings of an International Symposium of the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases

One hundred years have passed since the discovery of insulin-an achievement that transformed diabetes from a fatal illness into a manageable chronic condition. The decades since that momentous achievement have brought ever more rapid innovation and advancement in diabetes research and clinical care. To celebrate the important work of the past century and help to chart a course for its continuation into the next, the Canadian Institutes of Health Research's Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health's National Institute of Diabetes and Digestive and Kidney Diseases recently held a joint international symposium, bringing together a cohort of researchers with diverse interests and backgrounds from both countries and beyond to discuss their collective quest to better understand the heterogeneity of diabetes and thus gain insights to inform new directions in diabetes treatment and prevention. This article summarizes the proceedings of that symposium, which spanned cutting-edge research into various aspects of islet biology, the heterogeneity of diabetic phenotypes, and the current state of and future prospects for precision medicine in diabetes.

Scientific Advances in Diabetes: The Impact of the Innovative Medicines Initiative

Diabetes Mellitus is one of the World Health Organization's priority diseases under research by the first and second programmes of Innovative Medicines Initiative, with the acronyms IMI1 and IMI2, respectively. Up to October of 2019, 13 projects were funded by IMI for Diabetes & Metabolic disorders, namely SUMMIT, IMIDIA, DIRECT, StemBANCC, EMIF, EBiSC, INNODIA, RHAPSODY, BEAT-DKD, LITMUS, Hypo-RESOLVE, IM2PACT, and CARDIATEAM. In general, a total of €447 249 438 was spent by IMI in the area of Diabetes. In order to prompt a better integration of achievements between the different projects, we perform a literature review and used three data sources, namely the official project's websites, the contact with the project's coordinators and co-coordinator, and the CORDIS database. From the 662 citations identified, 185 were included. The data collected were integrated into the objectives proposed for the four IMI2 program research axes: (1) target and biomarker identification, (2) innovative clinical trials paradigms, (3) innovative medicines, and (4) patient-tailored adherence programmes. The IMI funded projects identified new biomarkers, medical and research tools, determinants of inter-individual variability, relevant pathways, clinical trial designs, clinical endpoints, therapeutic targets and concepts, pharmacologic agents, large-scale production strategies, and patient-centered predictive models for diabetes and its complications. Taking into account the scientific data produced, we provided a joint vision with strategies for integrating personalized medicine into healthcare practice. The major limitations of this article were the large gap of data in the libraries on the official project websites and even the Cordis database was not complete and up to date.

Effect of interaction between obesity-promoting genetic variants and behavioral factors on the risk of obese phenotypes

The studies investigating gene-gene and gene-environment (or gene-behavior) interactions provide valuable insight into the pathomechanisms underlying obese phenotypes. The Pakistani population due to its unique characteristics offers numerous advantages for conducting such studies. In this view, the current study was undertaken to examine the effects of gene-gene and gene-environment/behavior interactions on the risk of obesity in a sample of Pakistani population. A total of 578 adult participants including 290 overweight/obese cases and 288 normal-weight controls were involved. The five key obesity-associated genetic variants namely MC4R rs17782313, BDNF rs6265, FTO rs1421085, TMEM18 rs7561317, and NEGR1 rs2815752 were genotyped using the TaqMan allelic discrimination assays. The data related to behavioral factors, such as eating pattern, diet consciousness, the tendency toward fat-dense food (TFDF), sleep duration, sleep-wake cycle (SWC), shift work (SW), and physical activity levels were collected via a questionnaire. Gene-gene and gene-behavior interactions were analyzed by multifactor dimensionality reduction and linear regression, respectively. In our study, only TMEM18 rs7561317 was found to be significantly associated with anthropometric traits with no significant effect of gene-gene interactions were observed on obesity-related phenotypes. However, the genetic variants were found to interact with the behavioral factors to significantly influence various obesity-related anthropometric traits including BMI, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio, and percentage of body fat. In conclusion, the interaction between genetic architecture and behavior/environment determines the outcome of obesity-related anthropometric phenotypes. Thus, gene-environment/behavior interaction studies should be promoted to explore the risk of complex and multifactorial disorders, such as obesity.

Interaction Between Physical Activity and Polygenic Score on Type 2 Diabetes Mellitus in Older Black and White Participants From the Health and Retirement Study

This study investigated the association of lifestyle factors and polygenic risk scores (PGS), and their interaction, on type 2 diabetes mellitus (T2D). We examined data from the U.S. Health and Retirement Study, a prospective longitudinal cohort of adults aged 50 years and older, containing nationally representative samples of Black and White Americans with precalculated PGS for T2D (N = 14 001). Predicted prevalence and incidence of T2D were calculated with logistic regression models. We calculated differences in T2D prevalence and incidence by PGS percentiles and for interaction variables using nonparametric bootstrap method. Black participants had approximately twice the prevalence of Whites (26.2% vs 14.2%), with a larger difference between the 90th and 10th PGS percentile from age 50 to 80 years. Significant interaction (pinteraction = .0096) was detected between PGS and physical activity among Whites. Among Whites in the 90th PGS percentile, T2D prevalence for moderate physical activity was 17.0% (95% CI: 14.8, 19.6), 6.8% lower compared to no/some physical activity (23.8%; 95% CI: 20.4, 27.5). T2D prevalence was similar (~10%) for both groups in the 10th PGS percentile. Incident T2D in Whites followed a similar pattern (pinteraction = .0325). No significant interactions with PGS were detected among Black participants. Interaction of different genetic risk profiles with lifestyle factors may inform understanding of varying inventions' efficacy for different groups of people, potentially improving clinical and prevention interventions.

Genome Editing Human Pluripotent Stem Cells to Model β-Cell Disease and Unmask Novel Genetic Modifiers

A mechanistic understanding of the genetic basis of complex diseases such as diabetes mellitus remain elusive due in large part to the activity of genetic disease modifiers that impact the penetrance and/or presentation of disease phenotypes. In the face of such complexity, rare forms of diabetes that result from single-gene mutations (monogenic diabetes) can be used to model the contribution of individual genetic factors to pancreatic β-cell dysfunction and the breakdown of glucose homeostasis. Here we review the contribution of protein coding and non-protein coding genetic disease modifiers to the pathogenesis of diabetes subtypes, as well as how recent technological advances in the generation, differentiation, and genome editing of human pluripotent stem cells (hPSC) enable the development of cell-based disease models. Finally, we describe a disease modifier discovery platform that utilizes these technologies to identify novel genetic modifiers using induced pluripotent stem cells (iPSC) derived from patients with monogenic diabetes caused by heterozygous mutations.

The Emerging Role of HDACs: Pathology and Therapeutic Targets in Diabetes Mellitus

Diabetes mellitus (DM) is one of the principal manifestations of metabolic syndrome and its prevalence with modern lifestyle is increasing incessantly. Chronic hyperglycemia can induce several vascular complications that were referred to be the major cause of morbidity and mortality in DM. Although several therapeutic targets have been identified and accessed clinically, the imminent risk of DM and its prevalence are still ascending. Substantial pieces of evidence revealed that histone deacetylase (HDAC) isoforms can regulate various molecular activities in DM via epigenetic and post-translational regulation of several transcription factors. To date, 18 HDAC isoforms have been identified in mammals that were categorized into four different classes. Classes I, II, and IV are regarded as classical HDACs, which operate through a Zn-based mechanism. In contrast, class III HDACs or Sirtuins depend on nicotinamide adenine dinucleotide (NAD+) for their molecular activity. Functionally, most of the HDAC isoforms can regulate β cell fate, insulin release, insulin expression and signaling, and glucose metabolism. Moreover, the roles of HDAC members have been implicated in the regulation of oxidative stress, inflammation, apoptosis, fibrosis, and other pathological events, which substantially contribute to diabetes-related vascular dysfunctions. Therefore, HDACs could serve as the potential therapeutic target in DM towards developing novel intervention strategies. This review sheds light on the emerging role of HDACs/isoforms in diabetic pathophysiology and emphasized the scope of their targeting in DM for constituting novel interventional strategies for metabolic disorders/complications.

Glycaemic control and outcomes in children with type 2 diabetes diagnosed at or before 10 years of age

Background

Type 2 diabetes (T2DM) in children is considered rare before puberty.

Objective

Describe the characteristics and outcomes of children with T2DM diagnosed at or before 10 years of age.

Methods

Retrospective electronic medical record review of children diagnosed with T2DM at a University Children's Hospital over 12 years was conducted. Patient characteristics at diagnosis, 2-3-year follow-up, and 4-5-year follow-up were analysed as a whole and by age groups, 5-8 and 9-10 years.

Results

There were 42 children ≤ 10 years with T2DM (5-8-year age group, n = 8 and 9-10-year age group, n = 34). There were 88.1% African American, 11.9% Caucasian, and 88.1% females. Body mass index (BMI) was ≥95th percentile in 95.2%. Average BMI z score was 2.5 ± 0.4 and higher in the 5-8-year age group (2.7 ± 0.5 vs 2.4 ± 0.4, P = .02). Average haemoglobin A1C at diagnosis was 10.5 ± 2.4%, and improvement was seen at 2-3 years, but subsequent worsening was noted at 4-5 years in both age groups. At 4-5 years after diagnosis, 93.9% required insulin for management of their hyperglycaemia, 21.2% had hypertension requiring treatment, 28.6% had low-density lipoprotein ≥130 mg/dL, and 28.6% had high-density lipoprotein <40 mg/dL.

Conclusions

T2DM at or below 10 years of age disproportionately affected females and ethnic minorities and was associated with morbid obesity. The majority of these children did not achieve glycaemic control and required insulin for management of their hyperglycaemia after 4-5 years, indicating the need for increased awareness of T2DM and intensive treatment in this special group.

Mortality in first- and second-generation immigrants to Sweden diagnosed with type 2 diabetes: a 10 year nationwide cohort study

AIMS/HYPOTHESIS

Non-Western immigrants to Europe are at high risk for type 2 diabetes. In this nationwide study including incident cases of type 2 diabetes, the aim was to compare all-cause mortality (ACM) and cause-specific mortality (CSM) rates in first- and second-generation immigrants with native Swedes.

METHODS

People living in Sweden diagnosed with new-onset pharmacologically treated type 2 diabetes between 2006 and 2012 were identified through the Swedish Prescribed Drug Register. They were followed until 31 December 2016 for ACM and until 31 December 2012 for CSM. Analyses were adjusted for age at diagnosis, sex, socioeconomic status, education, treatment and region. Associations were assessed using Cox regression analysis.

RESULTS

In total, 138,085 individuals were diagnosed with type 2 diabetes between 2006 and 2012 and fulfilled inclusion criteria. Of these, 102,163 (74.0%) were native Swedes, 28,819 (20.9%) were first-generation immigrants and 7103 (5.1%) were second-generation immigrants with either one or both parents born outside Sweden. First-generation immigrants had lower ACM rate (HR 0.80 [95% CI 0.76, 0.84]) compared with native Swedes. The mortality rates were particularly low in people born in non-Western regions (0.46 [0.42, 0.50]; the Middle East, 0.41 [0.36, 0.47]; Asia, 0.53 [0.43, 0.66]; Africa, 0.47 [0.38, 0.59]; and Latin America, 0.53 [0.42, 0.68]). ACM rates decreased with older age at migration and shorter stay in Sweden. Compared with native Swedes, first-generation immigrants with ≤ 24 years in Sweden (0.55 [0.51, 0.60]) displayed lower ACM rates than those spending >24 years in Sweden (0.92 [0.87, 0.97]). Second-generation immigrants did not have better survival rates than native Swedes but rather displayed higher ACM rates for people with both parents born abroad (1.28 [1.05, 1.56]).

CONCLUSIONS/INTERPRETATION

In people with type 2 diabetes, the lower mortality rate in first-generation non-Western immigrants compared with native Swedes was reduced over time and was equalised in second-generation immigrants. These findings suggest that acculturation to Western culture may impact ACM and CSM in immigrants with type 2 diabetes but further investigation is needed. Graphical abstract.

Diabetes and cardiovascular disease: inter-relation of risk factors and treatment

Background

The diabetes mellitus prevalence is still advancing and increasingly becoming one of the globally most severe and expensive chronic illnesses. The strong correlation between diabetes as well as the most prominent reason for diabetes and death in diabetic patients is cardiovascular disorders. Health conditions like dyslipidemia, hypertension, obesity, and other factors of risk like the risk of cardiovascular are frequent in diabetic persons and raise the likelihood of heart attacks.

Main text

In particular, several researchers have found diabetes mellitus-related biochemical pathways that raise the likelihood of cardiovascular disorder in people with diabetes individually. This review describes diabetes-cardiovascular disorder relationships, explores potential therapeutic mechanisms, addresses existing treatment, care, and describes the directions for the future for study.

Conclusion

Thus, in individuals with diabetes, it is important to concentrate on cardiovascular threat variables to reduce the illness’s lasting cardiovascular complications. Further work to enhance knowledge of the disease state and its impact on cardiovascular function is required to boost medical treatment and cardiovascular disorders result in people with diabetes.

The Need for Precision Medicine to be Applied to Diabetes

Precision medicine refers to the tailoring of medical treatment for an individual based on large amounts of biologic and extrinsic data. The fast advancing fields of molecular biology, gene sequencing, machine learning, and other technologies enable precision medicine to utilize this detailed information to enhance clinical management decision-making for an individual in the real time of the disease course. Traditional clinical decision making is based on reacting to a relatively limited number of phenotypes that are determined by history, physical examination, and conventional lab tests. Precision medicine depends on highly detailed profiling of the patient's genetic, morphologic, and metabolic makeup. The precision medicine approach can be applied to individuals with diabetes to select treatments most likely to offer benefit and least likely to cause side effects, offering prospects of improved clinical outcomes and economic costs savings over current empiric practices. As genetic, metabolomic, immunologic, and other sophisticated testing becomes less expensive and more widespread in the medical record, it is expected that precision medicine will become increasingly applied to diabetes care.

Whole exome sequencing identifies the novel putative gene variants related with type 2 diabetes in Mizo population, northeast India

The contribution of genes towards T2D development varies among different population groups across the world. It has been reported that a number of loci involved in T2D susceptibility are common across certain population groups, but ethnicity specific variants are also observed. The population of Mizoram has an independent ethnic identity and there are no scientific records about the history of the Mizo people; which makes this ethnic group unique and interesting to study. The aim of the study focuses on the identification of the gene variants which may contribute to T2D susceptibility in Mizo-Mongloid ethnic tribe of North east India through whole exome sequencing. The variants like 328G > C (KRT18), 997G > T (CYP4A11), 2368 T > C (SLC4A3), 508G > A (SLC26A5), 1659C > T (KCNS1), 650C > A (ABCD1) 821A > T (YTHDC2), 931G > T (PINX1), 3280C > A (TNRC6A), 48C > A(TACO1), 6035A > T(LAMA1), 805C > A(ACP7) and 806A > G(ACP7) variants were not reported for any disease in the database and were found to be pathogenic in different insilico analysis softwares. The changes in protein stability upon mutation has been predicted where 35.71% increases the stability of the protein, while 64.28% of the variants decrease the stability of the protein. These findings present the population specific variants which might involve in the susceptibility to T2D in Mizo population. Further, in this study some gene variants have contribution as a possible diagnostic or prognostic marker for other diseases as well, which suggests the need for performing association analysis for different disease manifestations in Mizo population in the near future.

Pathophysiology of Type 2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM), one of the most common metabolic disorders, is caused by a combination of two primary factors: defective insulin secretion by pancreatic β-cells and the inability of insulin-sensitive tissues to respond appropriately to insulin. Because insulin release and activity are essential processes for glucose homeostasis, the molecular mechanisms involved in the synthesis and release of insulin, as well as in its detection are tightly regulated. Defects in any of the mechanisms involved in these processes can lead to a metabolic imbalance responsible for the development of the disease. This review analyzes the key aspects of T2DM, as well as the molecular mechanisms and pathways implicated in insulin metabolism leading to T2DM and insulin resistance. For that purpose, we summarize the data gathered up until now, focusing especially on insulin synthesis, insulin release, insulin sensing and on the downstream effects on individual insulin-sensitive organs. The review also covers the pathological conditions perpetuating T2DM such as nutritional factors, physical activity, gut dysbiosis and metabolic memory. Additionally, because T2DM is associated with accelerated atherosclerosis development, we review here some of the molecular mechanisms that link T2DM and insulin resistance (IR) as well as cardiovascular risk as one of the most important complications in T2DM.

Management of Type 2 Diabetes: Current Strategies, Unfocussed Aspects, Challenges, and Alternatives

Type 2 diabetes mellitus (T2DM) accounts for >90% of the cases of diabetes in adults. Resistance to insulin action is the major cause that leads to chronic hyperglycemia in diabetic patients. T2DM is the consequence of activation of multiple pathways and factors involved in insulin resistance and β-cell dysfunction. Also, the etiology of T2DM involves the complex interplay between genetics and environmental factors. This interplay can be governed efficiently by lifestyle modifications to achieve better management of diabetes. The present review aims at discussing the major factors involved in the development of T2DM that remain unfocussed during the anti-diabetic therapy. The review also focuses on lifestyle modifications that are warranted for the successful management of T2DM. In addition, it attempts to explain flaws in current strategies to combat diabetes. The employability of phytoconstituents as multitargeting molecules and their potential use as effective therapeutic adjuvants to first line hypoglycemic agents to prevent side effects caused by the synthetic drugs are also discussed.

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

Assessing the host genetic background effects on type 2 diabetes and obesity development in response to mixed-oral bacteria and high-fat diet using the collaborative cross mouse model

BACKGROUND

Host genetic background and sex, play central roles in defining the pathogenesis of type 2 diabetes (T2D), obesity and infectious diseases. Our previous studies demonstrated the utilization of genetically highly diverse inbred mouse lines, namely collaborative cross (CC), for dissecting host susceptibility for the development of T2D and obesity, showing significant variations following high-fat (42% fat) diet (HFD). Here, we aimed to assessing the host genetic background and sex effects on T2D and obesity development in response to oral-mixed bacterial infection and HFD using the CC lines.

MATERIALS AND METHODS

Study cohort consists of 97 mice from 2 CC lines (both sexes), maintained on either HFD or Standard diet (CHD) for 12 weeks. At week 5 a group of mice from each diet were infected with Porphyromonas gingivalis (Pg) and Fusobacterium nucleatum (Fn) bacteria (control groups without infection). Body weight (BW) and glucose tolerance ability were assessed at the end time point of the experiment.

RESULTS

The CC lines varied (P < .05) at their BW gain and glucose tolerance ability (with sex effect) in response to diets and/or infection, showing opposite responses despite sharing the same environmental conditions. The combination of diet and infection enhances BW accumulation for IL1912, while restraints it for IL72. As for glucose tolerance ability, only females (both lines) were deteriorated in response to infection.

CONCLUSIONS

This study emphasizes the power of the CC mouse population for the characterization of host genetic makeup for defining the susceptibility of the individual to development of obesity and/or impaired glucose tolerance.

Developmental programming of insulin resistance: are androgens the culprits?

Insulin resistance is a common feature of many metabolic disorders. The dramatic rise in the incidence of insulin resistance over the past decade has enhanced focus on its developmental origins. Since various developmental insults ranging from maternal disease, stress, over/undernutrition, and exposure to environmental chemicals can all program the development of insulin resistance, common mechanisms may be involved. This review discusses the possibility that increases in maternal androgens associated with these various insults are key mediators in programming insulin resistance. Additionally, the intermediaries through which androgens misprogram tissue insulin sensitivity, such as changes in inflammatory, oxidative, and lipotoxic states, epigenetic, gut microbiome and insulin, as well as data gaps to be filled are also discussed.

The Importance of Precision Medicine in Type 2 Diabetes Mellitus (T2DM): From Pharmacogenetic and Pharmacoepigenetic Aspects

BACKGROUND

Type 2 Diabetes Mellitus (T2DM) is a worldwide disorder as the most important challenges of health-care systems. Controlling the normal glycaemia greatly profit long-term prognosis and gives explanation for early, effective, constant, and safe intervention.

MATERIAL AND METHODS

Finding the main genetic and epigenetic profile of T2DM and the exact molecular targets of T2DM medications can shed light on its personalized management. The comprehensive information of T2DM was earned through the genome-wide association study (GWAS) studies. In the current review, we represent the most important candidate genes of T2DM like CAPN10, TCF7L2, PPAR-γ, IRSs, KCNJ11, WFS1, and HNF homeoboxes. Different genetic variations of a candidate gene can predict the efficacy of T2DM personalized strategy medication.

RESULTS

SLCs and AMPK variations are considered for metformin, CYP2C9, KATP channel, CDKAL1, CDKN2A/2B and KCNQ1 for sulphonylureas, OATP1B, and KCNQ1 for repaglinide and the last but not the least ADIPOQ, PPAR-γ, SLC, CYP2C8, and SLCO1B1 for thiazolidinediones response prediction.

CONCLUSION

Taken everything into consideration, there is an extreme need to determine the genetic status of T2DM patients in some known genetic region before planning the medication strategies.

Pathophysiology of Type 2 Diabetes in Children and Adolescents

BACKGROUND

The prevalence of type 2 diabetes (DM) in children is disturbingly increasing in parallel with the increasing childhood obesity. Better knowledge regarding the pathophysiology of type 2 DM in children is paramount to devise an effective management plan.

OBJECTIVE

Discuss the pathophysiology of type 2 DM in children and adolescents.

METHODS AND RESULTS

This is a comprehensive review of the literature on this topic. Type 2 DM in childhood is viewed as a continuum of insulin resistance (IR) which is determined by an underlying genetic predisposition, intrauterine environment, excessive food consumption, continued rapid weight gain, and poor lifestyle. Besides IR, this is compounded by multiple metabolic defects including β-cell dysfunction and inadequate insulin secretion, α-cell dysfunction, hyperglucagonemia and increased hepatic glucose production, lipotoxicity, inflammation, deficiencies in incretin production and action, and increased renal glucose reabsorption. The confluence of genetic and environmental factors underscores the complexity in disease progression.

CONCLUSION

A consistent single risk factor for type 2 DM is obesity and related IR and therefore it is essential to curtail the progression of obesity. It is important to investigate the role of stringent dietary and nutritional approaches, medications that enhance β-cell function and insulin sensitivity.

Associations of Residential Greenness with Diabetes Mellitus in Chinese Uyghur Adults

Greenness exposure is nominated as a potential beneficial factor for health, but evidence is limited on its diabetes effects. We conducted a cross-sectional study between May and September 2016 in rural areas of northwestern China, including 4670 Uyghur adults, to explore the associations between residential greenness and fasting glucose levels and diabetes prevalence. Fasting glucose levels were determined, and information on covariates was collected by questionnaire. Normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) were calculated to assess greenness levels. Generalized linear mixed models were applied to evaluate the associations of greenness with fasting glucose levels and diabetes prevalence. The prevalence of diabetes was 11.6%. We found that living in rural areas characterized by increased amounts of greenness was associated with reduced diabetes prevalence (e.g., NDVI_1000m: OR, 0.92; 95% CI, 0.86, 0.99). Stratified analyses showed that the protective effects of greenness on diabetes prevalence were found only in women (NDVI_1000m: OR, 0.90; 95% CI, 0.82, 0.99). However, none of the interaction was statistically significant. Our study suggests that greater residential greenness levels were associated with a lower odds ratio of diabetes prevalence in Xinjiang Uyghur adults. Further well-designed longitudinal studies are needed to confirm our findings.

Dietary sodium and potassium and risk of diabetes: A prospective study using data from the China Health and Nutrition Survey

AIMS

Dietary sodium and potassium intakes are well-known risk factors for cardiovascular outcomes. However, the associations between dietary sodium and potassium and diabetes are still controversial. Our study aimed to examine whether dietary sodium, potassium and the sodium-potassium ratio are associated with the risk of diabetes, based on a large sample of Chinese adults.

METHODS

The study data were from the 2004-2009 China Health and Nutrition Survey (CHNS), and 5867 participants were eligible for analysis. Sodium and potassium intakes were estimated based on three consecutive 24-h recalls at an individual level combined with a food inventory at a household level performed over the same 3-day period. Diabetes was defined as fasting glucose ≥7.0mmol/L (≥126mg/dL), HbA_1c ≥6.5% or use of antidiabetic drugs.

RESULTS

Over a mean follow-up of 4.7 years, there were 611 (10.4%) incident cases of diabetes. Participants in the higher quartiles (Q3 and Q4) of sodium intake had significantly higher risks of diabetes than those with the lowest sodium intake [Q3, RR: 1.41, 95% CI: 1.06-1.86 and Q4, RR: 1.35, 95% CI: 1.02-1.80; P<0.001 for trend]. In addition, high sodium intakes were significantly associated with levels of fasting glucose and HbA_1c (P<0.05 for trend), with similar associations also found with sodium-potassium ratios (P<0.05 for trend), but not for potassium intakes.

CONCLUSION

This study found that higher sodium intakes and sodium-potassium ratios were significantly associated with a higher risk of diabetes. Further clinical research is now necessary to confirm these results.

Dietary Fat and the Genetic Risk of Type 2 Diabetes

PURPOSE OF REVIEW

We review recent evidence of the relationship between dietary fat intake and risk of type 2 diabetes (T2D), the role of epigenetic alterations as a mediator of this relationship, and the impact of gene-dietary fat interactions in the development of the disease. Based on the observations made, we will discuss whether there is evidence to support genetic personalization of fat intake recommendations in T2D prevention.

RECENT FINDINGS

Strong evidence suggests that polyunsaturated fatty acids (PUFA) have a protective effect on T2D risk, whereas the roles of saturated and monounsaturated fatty acids (SFA and MUFA) remain unclear. Diets enriched with PUFA vs SFA lead to distinct epigenetic alterations that may mediate their effects on T2D risk by changing gene function. However, it is not currently known which of the epigenetic alterations, if any, are causal for T2D. The current literature shows no replicated evidence of genetic variants modifying the effect of dietary fat intake on T2D risk. There is consistent evidence of a protective role of PUFA in T2D prevention. No evidence supports genetic personalization of dietary recommendations in T2D prevention.

Gene-lifestyle interaction on risk of type 2 diabetes: A systematic review

The pathophysiological influence of gene-lifestyle interactions on the risk to develop type 2 diabetes (T2D) is currently under intensive research. This systematic review summarizes the evidence for gene-lifestyle interactions regarding T2D incidence. MEDLINE, EMBASE, and Web of Science were systematically searched until 31 January 2019 to identify publication with (a) prospective study design; (b) T2D incidence; (c) gene-diet, gene-physical activity, and gene-weight loss intervention interaction; and (d) population who are healthy or prediabetic. Of 66 eligible publications, 28 reported significant interactions. A variety of different genetic variants and dietary factors were studied. Variants at TCF7L2 were most frequently investigated and showed interactions with fiber and whole grain on T2D incidence. Further gene-diet interactions were reported for, eg, a western dietary pattern with a T2D-GRS, fat and carbohydrate with IRS1 rs2943641, and heme iron with variants of HFE. Physical activity showed interaction with HNF1B, IRS1, PPARγ, ADRA2B, SLC2A2, and ABCC8 variants and weight loss interventions with ENPP1, PPARγ, ADIPOR2, ADRA2B, TNFα, and LIPC variants. However, most findings represent single study findings obtained in European ethnicities. Although some interactions have been reported, their conclusiveness is still low, as most findings were not yet replicated across multiple study populations.