Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance--a population-based twin study

Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan

Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We refine the estimates of genetic heritability and quantify community-level environmental contributions. We also use wind speed and direction as instrumental variables to assess the causal effects of air pollution. In total, we find PM2.5 or NO2 have statistically significant causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders, where PM2.5 and NO2 tend to affect biologically distinct disease categories. These analyses showcase several robust strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets and will benefit upcoming biobank studies in the era of precision medicine.

Variant level heritability estimates of type 2 diabetes in African Americans

Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Omics analyses collectively refer to the possibility of profiling genetic variants, RNA, epigenetic markers, proteins, lipids, and metabolites. The most common analytical approaches used for detecting molecules present within biofluids related to metabolism are vibrational spectroscopy techniques, represented by infrared, Raman, and nuclear magnetic resonance (NMR) spectroscopies and mass spectrometry (MS). Omics-based assessments utilizing MS are rapidly expanding and being applied to various scientific disciplines and clinical settings. Most of the omics instruments are operated by specialists in dedicated laboratories; however, the development of miniature portable omics has made the technology more available to users for field applications. Variations in molecular information gained from omics approaches are useful for evaluating human health following environmental exposure and the development and progression of numerous diseases. As MS technology develops so do statistical and machine learning methods for the detection of molecular deviations from personalized metabolism, which are correlated to altered health conditions, and they are intended to provide a multi-disciplinary overview for researchers interested in adding multiomic analysis to their current efforts. This includes an introduction to mass spectrometry-based omics technologies, current state-of-the-art capabilities and their respective strengths and limitations for surveying molecular information. Furthermore, we describe how knowledge gained from these assessments can be applied to personalized medicine and diagnostic strategies.

A, Sarma DK, Verma V, Nagpal R, Mohania D, Tiwari R, Kumar M. Deciphering the complex interplay of risk factors in type 2 diabetes mellitus: A comprehensive review

The complex and multidimensional landscape of type 2 diabetes mellitus (T2D) is a major global concern. Despite several years of extensive research, the precise underlying causes of T2D remain elusive, but evidence suggests that it is influenced by a myriad of interconnected risk factors such as epigenetics, genetics, gut microbiome, environmental factors, organelle stress, and dietary habits. The number of factors influencing the pathogenesis is increasing day by day which worsens the scenario; meanwhile, the interconnections shoot up the frame. By gaining deeper insights into the contributing factors, we may pave the way for the development of personalized medicine, which could unlock more precise and impactful treatment pathways for individuals with T2D. This review summarizes the state of knowledge about T2D pathogenesis, focusing on the interplay between various risk factors and their implications for future therapeutic strategies. Understanding these factors could lead to tailored treatments targeting specific risk factors and inform prevention efforts on a population level, ultimately improving outcomes for individuals with T2D and reducing its burden globally.

Additive interaction of family medical history of diabetes with hypertension on the diagnosis of diabetes among older adults in India: longitudinal ageing study in India

BACKGROUND

The present study aimed to estimate the additive interaction of family history of diabetes and hypertension on the diagnosis of diabetes among individuals aged 45 years and above in India. The coexistence of these two exposures may act synergistically on the risk of diabetes, leading to adverse health outcomes.

METHODS

The study utilized the data from the Longitudinal Ageing Study in India (LASI) Wave 1 (2017-2018). The total sample size for the current study was 58,612 individuals aged 45 years and above. Multivariable logistic regression models were employed to determine the individual and joint effect of a family history of diabetes with hypertension on diabetes. An additive model was applied to assess the interaction effect of the family medical history of diabetes with hypertension on the diagnosis of diabetes by calculating three different measures of additive interaction such as the relative excess risk ratio (RERI), attribution proportion due to interaction (AP), and synergy index (S).

RESULTS

The prevalence of diabetes was three times higher among individuals with family history of diabetes (27.8% vs. 9.2%) than those without family history. Individuals with family history of diabetes (AOR: 2.47, CI: 2.11 2.89) had 2.47 times higher odds of having diabetes than those without family history. The prevalence of diabetes was significantly higher among individuals with hypertension and family history of diabetes (46.6%, 95% CI: 39.7-53.6) than those without the coexistence of family history of diabetes and hypertension (9.9%, 95% CI: 9.5-10.4), individuals with hypertension and without a family history of diabetes (22.7%, 95% CI: 21.2-24.2), and individuals with family history of diabetes and without hypertension (16.5%, 95% CI: 14.5-18.7). Moreover, the adjusted odds ratio (AOR) of the joint effect between family medical history of diabetes and hypertension on diabetes was 9.28 (95% CI: 7.51-11.46). In the adjusted model, the RERI, AP, and S for diabetes were 3.5 (95% CI: 1.52-5.47), 37% (0.37; 95% CI: 0.22-0.51), and 1.69 (95% CI: 1.31-2.18) respectively, which indicates that there is a significant positive interaction between family history of diabetes and hypertension on the diagnosis of diabetes. The study findings on interaction effects further demonstrate consistent results for two models of hypertension (self-reported hypertension and hypertensive individuals receiving medication) even after adjustment with potential confounding factors on diabetes (self-reported diabetes and individuals with diabetes receiving medication).

CONCLUSIONS

The study findings strongly suggest that the interaction of family history of diabetes with hypertension has a positive and significant effect on the risk of diabetes even after adjustment with potential confounding factors. Furthermore, the findings indicate a synergistic effect, emphasizing the importance of considering both family medical history of diabetes and hypertension when assessing diabetes risk and designing preventive strategies or interventions.

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.

2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association

BACKGROUND

The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Longitudinal machine learning uncouples healthy aging factors from chronic disease risks

To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.

Genetic overlap for ten cardiovascular diseases: A comprehensive gene-centric pleiotropic association analysis and Mendelian randomization study

Recent studies suggest that pleiotropic effects may explain the genetic architecture of cardiovascular diseases (CVDs). We conducted a comprehensive gene-centric pleiotropic association analysis for ten CVDs using genome-wide association study (GWAS) summary statistics to identify pleiotropic genes and pathways that may underlie multiple CVDs. We found shared genetic mechanisms underlying the pathophysiology of CVDs, with over two-thirds of the diseases exhibiting common genes and single-nucleotide polymorphisms (SNPs). Significant positive genetic correlations were observed in more than half of paired CVDs. Additionally, we investigated the pleiotropic genes shared between different CVDs, as well as their functional pathways and distribution in different tissues. Moreover, six hub genes, including ALDH2, XPO1, HSPA1L, ESR2, WDR12, and RAB1A, as well as 26 targeted potential drugs, were identified. Our study provides further evidence for the pleiotropic effects of genetic variants on CVDs and highlights the importance of considering pleiotropy in genetic association studies.

Partners in diabetes epidemic: A global perspective

There is a recent increase in the worldwide prevalence of both obesity and diabetes. In this review we assessed insulin signaling, genetics, environment, lipid metabolism dysfunction and mitochondria as the major determinants in diabetes and to identify the potential mechanism of gut microbiota in diabetes diseases. We searched relevant articles, which have key information from laboratory experiments, epidemiological evidence, clinical trials, experimental models, meta-analysis and review articles, in PubMed, MEDLINE, EMBASE, Google scholars and Cochrane Controlled Trial Database. We selected 144 full-length articles that met our inclusion and exclusion criteria for complete assessment. We have briefly discussed these associations, challenges, and the need for further research to manage and treat diabetes more efficiently. Diabetes involves the complex network of physiological dysfunction that can be attributed to insulin signaling, genetics, environment, obesity, mitochondria and stress. In recent years, there are intriguing findings regarding gut microbiome as the important regulator of diabetes. Valid approaches are necessary for speeding medical advances but we should find a solution sooner given the burden of the metabolic disorder - What we need is a collaborative venture that may involve laboratories both in academia and industries for the scientific progress and its application for the diabetes control.

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Genetic associations of TMEM154, PRC1 and ZFAND6 loci with type 2 diabetes in an endogamous business community of North India

BACKGROUND

More than 250 loci have been identified by genome-wide scans for type 2 diabetes in different populations. South Asians have a very different manifestation of the diseases and hence role of these loci need to be investigated among Indians with huge burden of cardio-metabolic disorders. Thus the present study aims to validate the recently identified GWAS loci in an endogamous caste population in North India.

METHODS

219 T2D cases and 184 controls were recruited from hospitals and genotyped for 15 GWAS loci of T2D. Regression models adjusted for covariates were run to examine the association for T2D and fasting glucose levels.

RESULTS

We validated three variants for T2D namely, rs11634397 at ZFAND6 (OR = 3.05, 95%CI = 1.02-9.19, p = 0.047) and rs8042680 at PRC1 (OR = 3.67, 95%CI = 1.13-11.93, p = 0.031) showing higher risk and rs6813195 at TMEM154 (OR = 0.28, 95%CI = 0.09-0.90, p = 0.033) showing protective effect. The combined risk of 9 directionally consistent variants was also found to be significantly associated with T2D (OR = 1.91, 95%CI = 1.18-3.08, p = 0.008). One variant rs10842994 at KLHDC5 was validated for 9.15mg/dl decreased fasting glucose levels (SE = -17.25-1.05, p = 0.027).

CONCLUSION

We confirm the role of ZFAND6, PRC1 and TMEM154 in the pathophysiology of type 2 diabetes among Indians. More efforts are needed with larger sample sizes to validate the diabetes GWAS loci in South Asian populations for wider applicability.

Could Glycated Hemoglobin be Leakage Predictor in Sleeve Gastrectomy? A Retrospective Observational Study on 4233 Patients

PURPOSE

Diabetes increases the risks related to surgery. At the same time, bariatric surgery improves diabetes. Glycated hemoglobin (A1C) is an index of diabetes severity. The purpose of this study is to evaluate A1C as a possible predictor of postoperative complications after Sleeve Gastrectomy (SG), focusing on leakage.

MATERIALS AND METHODS

Monocentric retrospective study considering all consecutive patients with obesity, with or without diabetes, who underwent bariatric surgical procedures, from January 2018 to December 2021. All patients had preoperative A1C values.

RESULTS

4233 patients were considered. 522 patients (12.33%) were diabetics (A1C ≥ 6.5%). Of these, 260 patients (6.14%) had A1C ≥ 7% and 59 (1.39%) A1C ≥ 8%. 1718 patients (40.58%) were in a pre-diabetic range (A1C 5.7%-6.5%). Higher A1C values were associated with older age, male gender, higher BMI and increased rate of comorbidities. A longer operative time was observed for patients with A1C ≥ 7%, p = 0.027 (53 ± 20 vs 51 ± 18 min). The frequency of leakage was significantly higher when A1C ≥ 7% (3.8% vs 2.0%, p = 0.026). The frequency of leakage further increased when A1C ≥ 8% (5.1%), although this difference did not reach statistical significance.

CONCLUSION

Patients with obesity and A1C ≥ 7% need to be referred to a diabetologist to treat diabetes before surgery and consequently decrease the risk of leakage.

Associations between eating speed and food temperature and type 2 diabetes mellitus: a cross-sectional study

OBJECTIVE

This study aimed to evaluate the relationship between eating speed and food temperature and type 2 diabetes mellitus (T2DM) in the Chinese population.

METHODS

A cross-sectional survey was conducted between December 2020 to March 2022 from the department of Endocrinology at the Shandong Provincial Hospital. All recruited participants were asked to complete structured questionnaires on their eating behaviors at the time of recruitment. Clinical demographic data such as gender, age, height, weight, familial history of T2DM, prevalence of T2DM and various eating behaviors were collected. Univariate and multivariate logistic regression analyses were used to analyze the associations between eating behaviors and T2DM.

RESULTS

A total of 1,040 Chinese adults were included in the study, including 344 people with T2DM and 696 people without T2DM. Multivariate logistic regression analysis of the general population showed that gender (OR = 2.255, 95% CI: 1.559-3.260, p < 0.001), age (OR = 1.091, 95% CI: 1.075-1.107, p < 0.001), BMI (OR = 1.238, 95% CI: 1.034-1.483, p = 0.020), familial history of T2DM (OR = 5.709, 95% CI: 3.963-8.224, p < 0.001), consumption of hot food (OR = 4.132, 95% CI: 2.899-5.888, p < 0.001), consumption of snacks (OR = 1.745, 95% CI: 1.222-2.492, p = 0.002), and eating speed (OR = 1.292, 95% CI:1.048-1.591, p = 0.016) were risk factors for T2DM.

CONCLUSION

In addition to traditional risk factors such as gender, age, BMI, familial history of T2DM, eating behaviors associated with Chinese culture, including consumption of hot food, consumption of snacks, and fast eating have shown to be probable risk factors for T2DM.

Self-Care Practices and Their Role in the Control of Diabetes: A Narrative Review

Diabetes mellitus (DM) is a long-standing, continuously growing metabolic ailment in which levels of glucose in the blood increase due to a total (DM of type 1) or incomplete (DM of type 2) decrease in the level of the hormone insulin. Diabetes mellitus affects a large number of individuals worldwide, and as more people develop the disease, the burden will double from what it is now. The requirements of people suffering from diabetes are not only confined to the control of blood glucose; there is also a need to prevent disabilities, side effects, and difficulties in rehabilitation. Studies suggest that seven self-care practices for individuals suffering from this disease have shown good outcomes. Those practices include assessment of sugar levels in the blood, consuming healthy foods, remaining physically active, taking medications regularly and on time, maintaining healthy behavior, and decreasing risk factors. All of these practices collectively have shown good results in maintaining blood glucose levels, decreasing side effects, and increasing life expectancy in people with diabetes mellitus. Those who have DM and practice self-care have shown positive results by reducing the complications of DM, decreasing its progression, and leading to a huge reduction in the burden due to DM. Despite these positive changes, people sticking to these self-care practices are very few, specifically when we see broad and chronic changes. There are many positive contributing factors, such as social factors, demographic factors, and various socio-economic factors, but the role of physicians in increasing the practices associated with personal care for people with this disease is crucial and most important for the desired outcome. Keeping in mind the burden and multidimensional nature of the disorder, proper systematic and combined efforts are needed to increase these self-care practices in patients with diabetes to reduce any chronic side effects and complications.

Gene-environment interaction in long-term effects of polychlorinated biphenyls exposure on glucose homeostasis and type 2 diabetes: The modifying effects of genetic risk and lifestyle

The longitudinal relationships of polychlorinated biphenyls (PCBs) exposure with glucose homeostasis and type 2 diabetes (T2D) risk among Chinese population have not been assessed, and interactions of PCB exposure with genetic susceptibility and lifestyle are unclear. In this prospective cohort study, fasting plasma glucose (FPG) and insulin (FPI) and seven serum indicator-PCBs were measured for each participant. We constructed polygenic risk score (PRS) of T2D and healthy lifestyle score. Each 1-unit increment of ln-transformed PCB-118 was related with a 0.141 mmol/L, 11.410 pmol/L, 0.661, and 74.5% increase in FPG, FPI, homeostasis model assessment of insulin resistance, and incident T2D risk over 6 years, respectively. Each 1-unit increment in T2D-PRS was related with a 0.169 mmol/L elevation of FPG and 65.5% elevation of incident T2D risk during 6 years. Compared with participants who had low T2D-PRS and low PCB-118, participants with high T2D-PRS and high PCB-118 showed a significant increase in FPG (0.162 mmol/L; P for interaction <0.001) and incident T2D risk [hazard ratio (HR)= 2.222]. Participants with low PCB-118, low PRS, and healthy lifestyle had the lowest incident T2D risk (HR=0.232). Our findings highlighted the significance of reducing PCB exposure and improvement in lifestyle for T2D prevention and management, especially for individuals with higher genetic risk of T2D.

A polygenic and family risk score are both independently associated with risk of type 2 diabetes in a population-based study

The availability of polygenic scores for type 2 diabetes (T2D) raises the question, whether assessing family history might become redundant. However, family history not only involves shared genetics, but also shared environment. It was the aim of this study to assess the independent and combined effects of one family risk score (FamRS) and a polygenic score (PGS) on prevalent and incident T2D risk in a population-based study from Germany (n = 3071). The study was conducted in 2004/2005 with up to 12 years of follow-up. The FamRS takes into account not only the number of diseased first grade relatives, but also age at onset of the relatives and age of participants. 256 prevalent and additional 163 incident T2D cases were recorded. Prevalence of T2D increased sharply for those within the top quantile of the PGS distribution resulting in an OR of 19.16 (p < 2 × 10^-16) for the top 20% compared to the remainder of the population, independent of age, sex, BMI, physical activity and FamRS. On the other hand, having a very strong family risk compared to average was still associated with an OR of 2.78 (p = 0.001), independent of the aforementioned factors and the PGS. The PGS and FamRS were only slightly correlated (r²_Spearman = 0.018). The combined contribution of both factors varied with varying age-groups, though, with decreasing influence of the PGS with increasing age. To conclude, both, genetic information and family history are relevant for the prediction of T2D risk and might be used for identification of high risk groups to personalize prevention measures.

Machine Learning Model Based on Insulin Resistance Metagenes Underpins Genetic Basis of Type 2 Diabetes

Insulin resistance (IR) is considered the precursor and the key pathophysiological mechanism of type 2 diabetes (T2D) and metabolic syndrome (MetS). However, the pathways that IR shares with T2D are not clearly understood. Meta-analysis of multiple DNA microarray datasets could provide a robust set of metagenes identified across multiple studies. These metagenes would likely include a subset of genes (key metagenes) shared by both IR and T2D, and possibly responsible for the transition between them. In this study, we attempted to find these key metagenes using a feature selection method, LASSO, and then used the expression profiles of these genes to train five machine learning models: LASSO, SVM, XGBoost, Random Forest, and ANN. Among them, ANN performed well, with an area under the curve (AUC) > 95%. It also demonstrated fairly good performance in differentiating diabetics from normal glucose tolerant (NGT) persons in the test dataset, with 73% accuracy across 64 human adipose tissue samples. Furthermore, these core metagenes were also enriched in diabetes-associated terms and were found in previous genome-wide association studies of T2D and its associated glycemic traits HOMA-IR and HOMA-B. Therefore, this metagenome deserves further investigation with regard to the cardinal molecular pathological defects/pathways underlying both IR and T2D.

Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Circulating spexin levels are influenced by the glycemic status and correlated with pancreatic β-cell function in Chinese subjects

AIMS

Spexin plays a role in regulating glucose metabolism. This study investigated the spexin levels in different glycemic status and its association with insulin secretion in humans.

METHODS

A total of 462 subjects were recruited in this study, including 52 healthy subjects, 106 first-degree relatives (FDRs) of type 2 diabetes mellitus (T2DM), 115 impaired glucose regulation (IGR), 80 newly diagnosed T2DM, and 106 established T2DM. Serum spexin was measured using ELISA. The homeostasis model assessment of insulin resistance (HOMA2-IR) and β-cell function (HOMA2-β), and Stumvoll index estimating first- and second-phase insulin secretion were calculated.

RESULTS

Spexin levels were higher in FDRs [235.53 pg/ml (185.28, 293.95)] and IGR [239.79 pg/ml (191.52, 301.69)], comparable in newly diagnosed T2DM [224.68 pg/ml (187.37, 279.74)], and lower in established T2DM [100.11 pg/ml (78.50, 137.34)], compared with healthy subjects [200.23 pg/ml (160.32, 275.65)]. Spexin levels were negatively correlated with fasting plasma glucose (FPG) (r =  - 0.355, P < 0.001), hemoglobin A1C (HbA1c) (r =  - 0.379, P < 0.001), and HOMA2-IR (r =  -  0.225, P < 0.001), and positively correlated with HOMA2-β (r = 0.245, P < 0.001) after adjusting for age, sex, and BMI. Multivariate linear regression analysis showed that established T2DM and HOMA2-β were independently associated with serum spexin levels.

CONCLUSIONS

Serum spexin levels represented as a bell-shaped curve along the glycemic continuum and is closely related with insulin secretion in humans.

Investigation of risk factors associated with impaired glucose regulation: Using the momentum equation to assess the impact of risk factors on community residents

OBJECTIVE

To identify risk factors for impaired glucose regulation (IGR) and assess their impact on community residents, this study used a questionnaire to conduct cross-sectional surveys and analysis.

METHODS

Overall, 774 residents of an urban community in northern China (Jian city) participated in this study. Trained investigators conducted surveys using questionnaires. Based on their medical history, respondents were divided into three glucose status groups as follows: normal (NGT), IGR, and diabetes mellitus (DM). Statistical analysis of survey data was performed using SPSS v. 22.0.

RESULTS

Age, hypertension, family history of diabetes (FHD), dyslipidemia, obesity, and cardiovascular and cerebral disease (CVD) were positively correlated with IGR in men and women. IGR was negatively correlated with a sedentary lifestyle in men and positively correlated with being overweight in women. The number of type 2 diabetes mellitus (T2D) risk factors per subject was positively correlated with age in the NGT group. Glucose status deteriorated with increasing age and the number of risk factors. FHD was the strongest risk factor in both men and women.

CONCLUSIONS

Prevention of IGR includes weight control, physical activity, and prevention of hypertension and dyslipidemia, especially in subjects with FHD.

Reassessing the evidence of a survival advantage in Type 2 diabetes treated with metformin compared with controls without diabetes: a retrospective cohort study

BACKGROUND

Previous research has suggested that individuals with Type 2 diabetes and initiated on metformin monotherapy present with a survival advantage compared with the general population without diabetes. This finding has generated considerable interest in the prophylactic use of metformin against age-related morbidity.

METHODS

Utilizing Danish National Health Registers, we assessed differences in survival associated with metformin monotherapy for Type 2 diabetes compared with no diagnosis of diabetes in both singleton and discordant twin populations between 1996 and 2012. Data were analysed in both nested case-control and matched cohort study designs, with incidence rate ratios (IRRs) and hazard ratios estimated using conditional logistic regression and Cox proportional hazards regression, respectively.

RESULTS

In case-control pairs matched on birth year and sex or co-twin (sex, birth year and familial factors), incident Type 2 diabetes with treatment by metformin monotherapy initiation compared with no diagnosis of diabetes was associated with increased mortality in both singletons (IRR = 1.52, 95% CI: 1.37, 1.68) and discordant twin pairs (IRR = 1.90, 95% CI: 1.35, 2.67). After adjusting for co-morbidities and social indicators, these associations were attenuated to 1.32 (95% CI: 1.16, 1.50) and 1.64 (95% CI: 1.10, 2.46), respectively. Increased mortality was observed across all levels of cumulative use and invariant to a range of study designs and sensitivity analyses.

CONCLUSIONS

Treatment initiation by metformin monotherapy in Type 2 diabetes was not associated with survival equal or superior to that of the general population without diabetes. Our contrasting findings compared with previous research are unlikely to be the result of differences in epidemiological or methodological parameters.

Does crime trigger genetic risk for type 2 diabetes in young adults? A G x E interaction study using national data

BACKGROUND

Living in neighborhoods perceived as disordered exacerbates genetic risk for type 2 diabetes (T2D) among older adults. It is unknown whether this gene-neighborhood interaction extends to younger adults. The present study aims to investigate whether crime, an objectively measured indicator of neighborhood disorder, triggers genetic risk for T2D among younger adults, and whether this hypothesized triggering occurs through exposure to obesity.

METHODS

Data were from the Wave I (2008) National Longitudinal Study of Adolescent to Adult Health. A standardized T2D polygenic score was created using 2014 GWAS meta-analysis results. Weighted mediation analyses using generalized structural equation models were conducted in a final sample of 7606 adults (age range: 25-34) to test the overall association of T2D polygenic scores with T2D, and the mediating path through obesity exposure in low, moderate, and high county crime-rate groups. Age, sex, ancestry, educational degree, household income, five genetic principal components, and county-level concentrated advantage and population density were adjusted.

RESULTS

The overall association between T2D polygenic score and T2D was not significant in low-crime areas (p = 0.453), marginally significant in moderate-crime areas (p = 0.064), and statistically significant in high-crime areas (p = 0.007). The mediating path through obesity was not significant in low or moderate crime areas (ps = 0.560 and 0.261, respectively), but was statistically significant in high-crime areas (p = 0.023). The indirect path through obesity accounted for 12% of the overall association in high-crime area.

CONCLUSION

A gene-crime interaction in T2D was observed among younger adults, and this association was partially explained by exposure to obesity.

The genetics of bipolar disorder with obesity and type 2 diabetes

BACKGROUND

Bipolar disorder (BD) presents with high obesity and type 2 diabetes (T2D) and pathophysiological and phenomenological abnormalities shared with cardiometabolic disorders. Genomic studies may help define if they share genetic liability. This selective review of BD with obesity and T2D will focus on genomic studies, stress their current limitations and guide future steps in developing the field.

METHODS

We searched electronic databases (PubMed, Scopus) until December 2021 to identify genome-wide association studies, polygenic risk score analyses, and functional genomics of BD accounting for body mass index (BMI), obesity, or T2D.

RESULTS

The first genome-wide association studies (GWAS) of BD accounting for obesity found a promising genome-wide association in an intronic gene variant of TCF7L2 that was further replicated. Polygenic risk scores of obesity and T2D have also been associated with BD, yet, no genetic correlations have been demonstrated. Finally, human-induced stem cell studies of the intronic variant in TCF7L2 show a potential biological impact of the products of this genetic variant in BD risk.

LIMITATIONS

The narrative nature of this review.

CONCLUSIONS

Findings from BD GWAS accounting for obesity and their functional testing, have prompted potential biological insights. Yet, BD, obesity, and T2D display high phenotypic, genetic, and population-related heterogeneity, limiting our ability to detect genetic associations. Further studies should refine cardiometabolic phenotypes, test gene-environmental interactions and add population diversity.

Prevalence and clinical correlates of impaired glucose tolerance in first-episode versus chronic patients with schizophrenia

AIM

Studies using oral glucose tolerance tests (OGTT) have shown that impaired glucose metabolism presents in the early stages of schizophrenia (SCZ). However, there is a lack of studies on changes in glucose metabolism with the stage of the disease. We first explored the features of glucose metabolic pattern at different phases of male SCZ.

METHODS

We recruited 83 male first episode drug-naïve patients with SCZ (FEDN-SCZ) and 64 male chronic patients with SCZ (CH-SCZ), as well as 14 male healthy controls. The Positive and Negative Syndrome Scale (PANSS) was used to assess the psychopathology of patients. OGTT, fasting plasma glucose and lipid profiles of all participants were examined.

RESULTS

While the impaired glucose tolerance (IGT) rate of male SCZ patients was higher than that of HC (P < .05), there was no difference in IGT prevalence between FEDN-SCZ and CH-SCZ. In male FEDN-SCZ, LDL (OR = 2.64, 95% CI = 1.11-6.29, P = .028) and PANSS total score (OR = 1.03, 95% CI = 1.00-1.06, P = .046) were positively correlated with IGT; in male CH-SCZ, BMI (OR = 1.7, 95% CI = 1.08-2.67, P = .023), PANSS total score (OR = 0.82, 95% CI = 0.70-0.96, P = .015) and positive symptoms (OR = 0.45, 95% CI = 0.20-0.99, P = .046) were significantly correlated with IGT.

CONCLUSIONS

Our findings reflect different glucose metabolism patterns in different stages of SCZ.

Polygenic Risk Scores for Cardiovascular Disease: A Scientific Statement From the American Heart Association

Cardiovascular disease is the leading contributor to years lost due to disability or premature death among adults. Current efforts focus on risk prediction and risk factor mitigation' which have been recognized for the past half-century. However, despite advances, risk prediction remains imprecise with persistently high rates of incident cardiovascular disease. Genetic characterization has been proposed as an approach to enable earlier and potentially tailored prevention. Rare mendelian pathogenic variants predisposing to cardiometabolic conditions have long been known to contribute to disease risk in some families. However, twin and familial aggregation studies imply that diverse cardiovascular conditions are heritable in the general population. Significant technological and methodological advances since the Human Genome Project are facilitating population-based comprehensive genetic profiling at decreasing costs. Genome-wide association studies from such endeavors continue to elucidate causal mechanisms for cardiovascular diseases. Systematic cataloging for cardiovascular risk alleles also enabled the development of polygenic risk scores. Genetic profiling is becoming widespread in large-scale research, including in health care-associated biobanks, randomized controlled trials, and direct-to-consumer profiling in tens of millions of people. Thus, individuals and their physicians are increasingly presented with polygenic risk scores for cardiovascular conditions in clinical encounters. In this scientific statement, we review the contemporary science, clinical considerations, and future challenges for polygenic risk scores for cardiovascular diseases. We selected 5 cardiometabolic diseases (coronary artery disease, hypercholesterolemia, type 2 diabetes, atrial fibrillation, and venous thromboembolic disease) and response to drug therapy and offer provisional guidance to health care professionals, researchers, policymakers, and patients.

Genetics and Epigenetics in Personalized Nutrition: Evidence, Expectations and Experiences

With the presentation of the blueprint of the first human genome in 2001 and the advent of technologies for high-throughput genetic analysis, personalized nutrition (PN) became a new scientific field and the first commercial offerings of genotype-based nutrition advice emerged at the same time. Here, we summarize the state of evidence for the effect of genetic and epigenetic factors in the development of obesity, the metabolic syndrome and resulting illnesses such as non-insulin-dependent diabetes mellitus and cardiovascular diseases. We also critically value the concepts of PN that were built around the new genetic avenue from both the academic and a commercial perspective and their effectiveness in causing sustained changes in diet, lifestyle and for improving health. Despite almost 20 years of research and commercial direct-to-consumer offerings, evidence for the success of gene-based dietary recommendations is still generally lacking. This calls for new concepts of future PN solutions that incorporate more phenotypic measures and provide a panel of instruments (e.g., self- and bio-monitoring tools, feedback systems, algorithms based on artificial intelligence) that increases compliance based on the individual´s physical and social environment and value system. This article is protected by copyright. All rights reserved.

The effect of TCF7L2 polymorphisms on inflammatory markers after 16 weeks of legume-based dietary approach to stop hypertension (DASH) diet versus a standard DASH diet: a randomised controlled trial

Backgrounds

This randomized controlled trial aimed to investigate the effects of replacing red meat with legumes in the dietary approach to stop hypertension (DASH) diet on inflammatory markers over 16 weeks in overweight and obese individuals with type 2 diabetes. Also, the modulatory effects of TCF7L2 rs7903146 variant on this effect were assessed.

Methods

In this trial, 300 participants with type 2 diabetes, aged 30–65 years with an identified TCF7L2 rs7903146 genotype, were studied. The participants were randomly assigned to the DASH diet or the legume-based DASH diet over 16 weeks. In the DASH diet group, the participants were instructed to follow the standard DASH diet. The legume-based DASH diet was similar to the standard DASH diet, with the exception that one serving of red meat was replaced with one serving of legumes at least five days a week. At the beginning of the study and 16-week follow-up, venous blood samples were collected from all participants who fasted for 12–14 h overnight. The serum concentration of High-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) was measured using an enzyme-linked immunosorbent assay (ELISA) kit. Also, the serum malondialdehyde (MDA) concentration was assessed based on a colorimetric method using a commercial kit. The primary outcome was the difference in hs-CRP changes between the diets. A secondary outcomes was the difference in IL-6, TNF-α, and MDA between the groups among total population and based on TCF7L2 rs7903146 risk allele (CT + TT) and non-risk allele (CC) separately.

Results

The hs-CRP level reduced in the legume-based DASH diet group as compared to the DASH diet group in the 16-week follow-up group. The levels of TNF-α, IL-6, and MDA reduced after the legume-based DASH diet relative to the DASH diet. Reduction of inflammatory markers was observed in both carriers of rs7903146 risk allele and non-risk allele.

Conclusions

Substituting one serving of red meat with one serving of legumes in DASH diet, at least five days a week, could improve the hs-CRP, TNF-α, IL-6, and MDA in participants with type 2 diabetes regardless of having rs7903146 risk or non-risk allele.

Trial registration IRCT, IRCT20090203001640N17.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-022-00671-7.

Interaction between a diabetes-related methylation site (TXNIP cg19693031) and variant (GLUT1 rs841853) on fasting blood glucose levels among non-diabetics

Background

Type 2 diabetes mellitus (T2DM) is caused by a combination of environmental, genetic, and epigenetic factors including, fasting blood glucose (FBG), genetic variant rs841853, and cg19693031 methylation. We evaluated the interaction between rs841853 and cg19693031 on the FBG levels of non-diabetic Taiwanese adults.

Methods

We used Taiwan Biobank (TWB) data collected between 2008 and 2016. The TWB data source contains information on basic demographics, personal lifestyles, medical history, methylation, and genotype. The study participants included 1300 people with DNA methylation data. The association of cg19693031 methylation (stratified into quartiles) with rs841853 and FBG was determined using multiple linear regression analysis. The beta-coefficients (β) and p-values were estimated.

Results

The mean ± standard deviation (SD) of FBG in rs841853-CC individuals (92.07 ± 7.78) did not differ significantly from that in the CA + AA individuals (91.62 ± 7.14). However, the cg19693031 methylation levels were significantly different in the two groups (0.7716 ± 0.05 in CC individuals and 0.7631 ± 0.05 in CA + AA individuals (p = 0.002). The cg19693031 methylation levels according to quartiles were β < 0.738592 (< Q1), 0.738592 ≤ 0.769992 (Q1–Q2), 0.769992 ≤ 0.800918 (Q2–Q3), and β ≥ 0.800918 (≥ Q3). FBG increased with decreasing cg19693031 methylation levels in a dose–response manner (p_trend = 0.005). The β-coefficient was − 0.0236 (p = 0.965) for Q2–Q3, 1.0317 (p = 0.058) for Q1–Q2, and 1.3336 (p = 0.019 for < Q1 compared to the reference quartile (≥ Q3). The genetic variant rs841853 was not significantly associated with FBG. However, its interaction with cg19693031 methylation was significant (p-value = 0.036). Based on stratification by rs841853 genotypes, only the CC group retained the inverse and dose–response association between FBG and cg19693031 methylation. The β (p-value) was 0.8082 (0.255) for Q2–Q3, 1.6930 (0.022) for Q1–Q2, and 2.2190 (0.004) for < Q1 compared to the reference quartile (≥ Q3). The p_trend was 0.002.

Conclusion

Summarily, methylation at cg19693031 was inversely associated with fasting blood glucose in a dose-dependent manner. The inverse association was more prominent in rs841853-CC individuals, suggesting that rs841853 could modulate the association between cg19693031 methylation and FBG. Our results suggest that genetic variants may be involved in epigenetic mechanisms associated with FBG, a hallmark of diabetes. Therefore, integrating genetic and epigenetic data may provide more insight into the early-onset of diabetes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03269-y.

The hyperfunction theory: An emerging paradigm for the biology of aging

The process of senescence (aging) is predominantly determined by the action of wild-type genes. For most organisms, this does not reflect any adaptive function that senescence serves, but rather evolutionary effects of declining selection against genes with deleterious effects later in life. To understand aging requires an account of how evolutionary mechanisms give rise to pathogenic gene action and late-life disease, that integrates evolutionary (ultimate) and mechanistic (proximate) causes into a single explanation. A well-supported evolutionary explanation by G.C. Williams argues that senescence can evolve due to pleiotropic effects of alleles with antagonistic effects on fitness and late-life health (antagonistic pleiotropy, AP). What has remained unclear is how gene action gives rise to late-life disease pathophysiology. One ultimate-proximate account is T.B.L. Kirkwood's disposable soma theory. Based on the hypothesis that stochastic molecular damage causes senescence, this reasons that aging is coupled to reproductive fitness due to preferential investment of resources into reproduction, rather than somatic maintenance. An alternative and more recent ultimate-proximate theory argues that aging is largely caused by programmatic, developmental-type mechanisms. Here ideas about AP and programmatic aging are reviewed, particularly those of M.V. Blagosklonny (the hyperfunction theory) and J.P. de Magalhães (the developmental theory), and their capacity to make sense of diverse experimental findings is assessed.

Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.

RESULTS

Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Stem Cell-Derived β Cells: A Versatile Research Platform to Interrogate the Genetic Basis of β Cell Dysfunction

Pancreatic β cell dysfunction is a central component of diabetes progression. During the last decades, the genetic basis of several monogenic forms of diabetes has been recognized. Genome-wide association studies (GWAS) have also facilitated the identification of common genetic variants associated with an increased risk of diabetes. These studies highlight the importance of impaired β cell function in all forms of diabetes. However, how most of these risk variants confer disease risk, remains unanswered. Understanding the specific contribution of genetic variants and the precise role of their molecular effectors is the next step toward developing treatments that target β cell dysfunction in the era of personalized medicine. Protocols that allow derivation of β cells from pluripotent stem cells, represent a powerful research tool that allows modeling of human development and versatile experimental designs that can be used to shed some light on diabetes pathophysiology. This article reviews different models to study the genetic basis of β cell dysfunction, focusing on the recent advances made possible by stem cell applications in the field of diabetes research.

OUP accepted manuscript

Type 2 diabetes is a complex, systemic disease affected by both genetic and environmental factors. Previous research has identified genetic variants associated with type 2 diabetes risk; however, gene regulatory changes underlying progression to metabolic dysfunction are still largely unknown. We investigated RNA expression changes that occur during diabetes progression using a two-stage approach. In our discovery stage, we compared changes in gene expression using two longitudinally collected blood samples from subjects whose fasting blood glucose transitioned to a level consistent with type 2 diabetes diagnosis between the time points against those who did not with a novel analytical network approach. Our network methodology identified 17 networks, one of which was significantly associated with transition status. This 822-gene network harbors many genes novel to the type 2 diabetes literature but is also significantly enriched for genes previously associated with type 2 diabetes. In the validation stage, we queried associations of genetically determined expression with diabetes-related traits in a large biobank with linked electronic health records. We observed a significant enrichment of genes in our identified network whose genetically determined expression is associated with type 2 diabetes and other metabolic traits and validated 31 genes that are not near previously reported type 2 diabetes loci. Finally, we provide additional functional support, which suggests that the genes in this network are regulated by enhancers that operate in human pancreatic islet cells. We present an innovative and systematic approach that identified and validated key gene expression changes associated with type 2 diabetes transition status and demonstrated their translational relevance in a large clinical resource.

Association between Transcription Factor 7-like-2 Polymorphisms and Type 2 Diabetes Mellitus in a Ghanaian Population

Type 2 diabetes mellitus (T2DM) has been strongly associated with single nucleotide polymorphisms (SNPs) in the TCF7L2 gene. This study investigated the association between rs12255372, rs7903146 in the TCF7L2 gene and T2DM in a Ghanaian population. A case-control study design was used for this study. A total of 106 T2DM patients and 110 control participants were selected. Basic data collected included body mass index, blood pressure and socio-demographics. Fasting blood samples were collected and processed for: serum lipid analysis, plasma glucose estimation and plasma HbA1c estimation. Parts of the whole blood samples were used for DNA extraction using a modified salting-out method. Common and allele-specific primers were designed for genotyping using the Modified Tetra-Primer Amplification assay. Associations were evaluated using logistic regression models. The rs7903146 risk variant was significantly associated with 2.16 vs. 4.06 increased odds for T2DM in patients <60 years vs. ≥60 years. Both rs7903146 and rs12255372 were significantly associated with increased odds of T2DM in women, overweight/obese, T2DM negative family history (T2DM-NFH) and low-HDL-C. In a multivariate model, rs7903146 but not rs12255372 was significantly associated with 2.18, 5.01 and 2.25 increased odds of T2DM, under the codominant, recessive and additive model, respectively (p < 0.05). The association between rs7903146 and rs12255372 with T2DM is more highly associated in a subgroup—women and those with T2DM-NFH, yet who have cardiometabolic risk.

DNA Methylation and Type 2 Diabetes: Novel Biomarkers for Risk Assessment?

Diabetes is a severe threat to global health. Almost 500 million people live with diabetes worldwide. Most of them have type 2 diabetes (T2D). T2D patients are at risk of developing severe and life-threatening complications, leading to an increased need for medical care and reduced quality of life. Improved care for people with T2D is essential. Actions aiming at identifying undiagnosed diabetes and at preventing diabetes in those at high risk are needed as well. To this end, biomarker discovery and validation of risk assessment for T2D are critical. Alterations of DNA methylation have recently helped to better understand T2D pathophysiology by explaining differences among endophenotypes of diabetic patients in tissues. Recent evidence further suggests that variations of DNA methylation might contribute to the risk of T2D even more significantly than genetic variability and might represent a valuable tool to predict T2D risk. In this review, we focus on recent information on the contribution of DNA methylation to the risk and the pathogenesis of T2D. We discuss the limitations of these studies and provide evidence supporting the potential for clinical application of DNA methylation marks to predict the risk and progression of T2D.

The Genetics of Diabetes: What We Can Learn from Drosophila

Diabetes mellitus is a heterogeneous disease characterized by hyperglycemia due to impaired insulin secretion and/or action. All diabetes types have a strong genetic component. The most frequent forms, type 1 diabetes (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus (GDM), are multifactorial syndromes associated with several genes’ effects together with environmental factors. Conversely, rare forms, neonatal diabetes mellitus (NDM) and maturity onset diabetes of the young (MODY), are caused by mutations in single genes. Large scale genome screenings led to the identification of hundreds of putative causative genes for multigenic diabetes, but all the loci identified so far explain only a small proportion of heritability. Nevertheless, several recent studies allowed not only the identification of some genes as causative, but also as putative targets of new drugs. Although monogenic forms of diabetes are the most suited to perform a precision approach and allow an accurate diagnosis, at least 80% of all monogenic cases remain still undiagnosed. The knowledge acquired so far addresses the future work towards a study more focused on the identification of diabetes causal variants; this aim will be reached only by combining expertise from different areas. In this perspective, model organism research is crucial. This review traces an overview of the genetics of diabetes and mainly focuses on Drosophila as a model system, describing how flies can contribute to diabetes knowledge advancement.

Multi-Trait Genomic Risk Stratification for Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40-70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50-94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.

Multi-omics profiling: the way towards precision medicine in metabolic diseases

Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.

Heritability Estimation and Environmental Risk Assessment for Type 2 Diabetes Mellitus in a Rural Region in Henan, China: Family-Based and Case-Control Studies

Objective: The prevalence of type 2 diabetes mellitus (T2DM) varies greatly in different regions and populations. This study aims to assess the heritability and environmental risk factors of T2DM among rural Chinese adults.

Methods: Thousand five hundred thirty three participants from 499 extended families, which included 24 nuclear families, were recruited in the family-based study to assess the heritable risk of T2DM. Heritability of T2DM was estimated by the Falconer method. Using conditional logistic regression model, couple case-control study involving 127 couples were applied to assess the environmental risk factors of T2DM.

Results: Compared with the Henan Rural Cohort, T2DM was significantly clustered in the nuclear families (OR: 8.389, 95% CI: 5.537–12.711, P < 0.001) and heritability was 0.74. No association between the heredity of T2DM and sex was observed between the extended families and the Henan Rural Cohort. Besides, results from the couple case-control study showed that physical activity (OR: 0.482, 95% CI: 0.261–0.893, P = 0.020) and fat intake (OR: 3.036, 95% CI: 1.070–8.610, P = 0.037) was associated with T2DM, and the proportion of offspring engaged in medium and high physical activity was higher than that of mothers in mother-offspring pairs.

Conclusion: People with a family history of T2DM may have a higher risk of developing T2DM, however, there was no difference in genetic risk between males and females. Adherence to active physical activity and low fat intake can reduce the risk of T2DM.

Association of APOE genotype with lipid profiles and type 2 diabetes mellitus in a Korean population

BACKGROUND

Type 2 diabetes mellitus (T2DM) is associated with chronic hyperglycemia and lipid metabolism. A previous genome-wide association study revealed the TOMM40-APOE region as novel locus for T2DM susceptibility.

OBJECTIVE

This association study was conducted to determine the genetic effects of APOE single nucleotide polymorphisms (SNPs) on T2DM susceptibility and lipid profiles in a Korean population.

METHODS

A total of 6 tagging SNPs, including rs7412 and rs429358, were selected for ε genotype analysis and genotyped in 1436 subjects, consisting of 352 T2DM patients and 1084 unaffected controls.

RESULTS

Logistic regression analyses were conducted and there were no significant associations among the APOE 6 tagging SNPs, ε genotypes, and haplotypes with T2DM susceptibility. To investigate the association of the APOE tagging SNPs with the lipid profiles, a regression analysis was conducted. As a result, rs7412 was significantly associated with the total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) levels (P_corr = 2.30 × 10^-5 and 3.39 × 10^-13, respectively) in the unaffected controls. The ε2 allele and ε3 allele were significantly associated with the TC (P_corr = 4.46 × 10^-6 and 0.02, respectively) and LDL levels (P_corr = 3.54 × 10^-14 and 0.0006, respectively) in the unaffected controls. Further analysis of only the unaffected controls was conducted. As a result, the APOE alleles ε2 and ε3 showed a significant association with the TC and LDL levels (P < 0.05).

CONCLUSION

The results of this study may help in understanding APOE polymorphisms and ε alleles and lipid profiles, which have been highly linked to T2DM, in a Korean population.

Analysis of Evolution and Ethnic Diversity at Glucose-Associated SNPs of Circadian Clock-Related Loci with Cryptochrome 1, Cryptochrome 2, and Melatonin receptor 1B

Diabetes shows high heritability and, worldwide, causes significant health problems including cardiovascular disease and stroke. There is significant variation in the frequency of diabetes between different populations. Both Cryptochromes and Melatonin have a major role to regulate the circadian clock. Circadian clock failure causes metabolic dysfunctions including diabetes and obesity. Variations in the Cryptochrome 1, the Cryptochrome 2, and the Melatonin receptor 1B (MTNR1B) genes show associations with fasting glucose, and are also related to circadian clock. Here, we analyzed evidence for genetic selection and ethnic diversity at circadian clock- and glucose-related gene loci associated with Cryptochrome 1, Cryptochrome 2, and MTNR1B. We carried out a 3-step genetic method to investigate genetic selection at the Cryptochrome 1, Cryptochrome 2, and MTNR1B on four populations from the 1000 Genomes Project and HapMap. First we used F-statistics to quantify genetic population differences and find ethnic diversity. Then we applied a long-range haplotype test to detect significant extreme long haplotypes, and then the integrated haplotype score (iHS) to find genetic selection at Cryptochrome 1, Cryptochrome 2, and MTNR1B. We observed genetic population differences and ethnic diversity at one glucose-associated Cryptochrome 1 single-nucleotide polymorphism (SNP) (rs8192440), one glucose-associated Cryptochrome 2 SNP (rs11605924), and one glucose-associated MTNR1B SNP (rs10830963) by F-statistics. Both Cryptochrome 1 and MTNR1B also showed selection by the iHS. These observations show new evidence for evolution at Cryptochrome 1, Cryptochrome 2 and MTNR1B. Further investigation should continue to examine the evolution of circadian clock- and glucose-related genes.

Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association

BACKGROUND

The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).

METHODS

The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease.

RESULTS

Each of the 27 chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.

CONCLUSIONS

The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Insulin's Discovery: New Insights on Its Hundredth Birthday: From Insulin Action and Clearance to Sweet Networks

In 2021, the 100th anniversary of the isolation of insulin and the rescue of a child with type 1 diabetes from death will be marked. In this review, we highlight advances since the ingenious work of the four discoverers, Frederick Grant Banting, John James Rickard Macleod, James Bertram Collip and Charles Herbert Best. Macleoad closed his Nobel Lecture speech by raising the question of the mechanism of insulin action in the body. This challenge attracted many investigators, and the question remained unanswered until the third part of the 20th century. We summarize what has been learned, from the discovery of cell surface receptors, insulin action, and clearance, to network and precision medicine.

Novel susceptibility loci identified in a genome-wide association study of type 2 diabetes complications in population of Latvia

BACKGROUND

Type 2 diabetes complications cause a serious emotional and economical burden to patients and healthcare systems globally. Management of both acute and chronic complications of diabetes, which dramatically impair the quality of patients' life, is still an unsolved issue in diabetes care, suggesting a need for early identification of individuals with high risk for developing diabetes complications.

METHODS

We performed a genome-wide association study in 601 type 2 diabetes patients after stratifying them according to the presence or absence of four types of diabetes complications: diabetic neuropathy, diabetic nephropathy, macrovascular complications, and ophthalmic complications.

RESULTS

The analysis revealed ten novel associations showing genome-wide significance, including rs1132787 (GYPA, OR = 2.71; 95% CI = 2.02-3.64) and diabetic neuropathy, rs2477088 (PDE4DIP, OR = 2.50; 95% CI = 1.87-3.34), rs4852954 (NAT8, OR = 2.27; 95% CI = 2.71-3.01), rs6032 (F5, OR = 2.12; 95% CI = 1.63-2.77), rs6935464 (RPS6KA2, OR = 2.25; 95% CI = 6.69-3.01) and macrovascular complications, rs3095447 (CCDC146, OR = 2.18; 95% CI = 1.66-2.87) and ophthalmic complications. By applying the targeted approach of previously reported susceptibility loci we managed to replicate three associations: MAPK14 (rs3761980, rs80028505) and diabetic neuropathy, APOL1 (rs136161) and diabetic nephropathy.

CONCLUSIONS

Together these results provide further evidence for the implication of genetic factors in the development of type 2 diabetes complications and highlight several potential key loci, able to modify the risk of developing these conditions. Moreover, the candidate variant approach proves a strong and consistent effect for multiple variants across different populations.

Overexpression of Gjb4 impairs cell proliferation and insulin secretion in primary islet cells

OBJECTIVE

Altered gene expression contributes to the development of type 2 diabetes (T2D); thus, the analysis of differentially expressed genes between diabetes-susceptible and diabetes-resistant mouse models is an important tool for the determination of candidate genes that participate in the pathology. Based on RNA-seq and array data comparing pancreatic gene expression of diabetes-prone New Zealand Obese (NZO) mice and diabetes-resistant B6.V-ob/ob (B6-ob/ob) mice, the gap junction protein beta 4 (Gjb4) was identified as a putative novel T2D candidate gene.

METHODS

Gjb4 was overexpressed in primary islet cells derived from C57BL/6 (B6) mice and INS-1 cells via adenoviral-mediated infection. The proliferation rate of cells was assessed by BrdU incorporation, and insulin secretion was measured under low (2.8 mM) and high (20 mM) glucose concentration. INS-1 cell apoptosis rate was determined by Western blotting assessing cleaved caspase 3 levels.

RESULTS

Overexpression of Gjb4 in primary islet cells significantly inhibited the proliferation by 47%, reduced insulin secretion of primary islets (46%) and INS-1 cells (51%), and enhanced the rate of apoptosis by 63% in INS-1 cells. Moreover, an altered expression of the miR-341-3p contributes to the Gjb4 expression difference between diabetes-prone and diabetes-resistant mice.

CONCLUSIONS

The gap junction protein Gjb4 is highly expressed in islets of diabetes-prone NZO mice and may play a role in the development of T2D by altering islet cell function, inducing apoptosis and inhibiting proliferation.

Evaluation of the Associations of GC and CYP2R1 Genes and Gene-Obesity Interactions with Type 2 Diabetes Risk in a Chinese Rural Population

INTRODUCTION

Group-specific component (GC) and cytochrome P450 family 2 subfamily R member 1 (CYP2R1) gene polymorphisms and obesity have been associated with an increased risk for development of type 2 diabetes mellitus (T2DM) in Asian populations.

OBJECTIVE

This study assessed the associations of interactions between GC gene variants and CYP2R1 gene variants and between genes and obesity with T2DM risk.

METHODS

A study that included 2,271 subjects was performed. Eight single nucleotide polymorphisms in the GC and CYP2R1 genes were genotyped. Interaction analysis was performed using rs7041 in the GC gene and rs1993116 in the CYP2R1 gene. The effects of multiplicative and additive gene-gene and gene-environment interactions on T2DM risk were assessed.

RESULTS

The T2DM risk was significantly associated with being overweight/obese, abdominal obesity, rs7041, and rs1993116. A significant additive interaction between rs1993116 and rs7041 was associated with T2DM. In addition, there was a significant multiplicative interaction between rs7041 and body mass index (BMI) associated with elevated blood glucose levels, and at a higher BMI (>28.47), the G allele carrier showed a stronger effect than the TT genotype.

CONCLUSIONS

The interactions between GC rs7041-CYP2R1 rs1993116 and GC rs7041-BMI may explain the mechanisms by which these factors increase the risk of T2DM development.

Role of Leptin in Inflammation and Vice Versa

Inflammation is an essential immune response for the maintenance of tissue homeostasis. In a general sense, acute and chronic inflammation are different types of adaptive response that are called into action when other homeostatic mechanisms are insufficient. Although considerable progress has been made in understanding the cellular and molecular events that are involved in the acute inflammatory response to infection and tissue injury, the causes and mechanisms of systemic chronic inflammation are much less known. The pathogenic capacity of this type of inflammation is puzzling and represents a common link of the multifactorial diseases, such as cardiovascular diseases and type 2 diabetes. In recent years, interest has been raised by the discovery of novel mediators of inflammation, such as microRNAs and adipokines, with different effects on target tissues. In the present review, we discuss the data emerged from research of leptin in obesity as an inflammatory mediator sustaining multifactorial diseases and how this knowledge could be instrumental in the design of leptin-based manipulation strategies to help restoration of abnormal immune responses. On the other direction, chronic inflammation, either from autoimmune or infectious diseases, or impaired microbiota (dysbiosis) may impair the leptin response inducing resistance to the weight control, and therefore it may be a cause of obesity. Thus, we are reviewing the published data regarding the role of leptin in inflammation, and the other way around, the role of inflammation on the development of leptin resistance and obesity.

Dietary Protein and Fat Intake Affects Diabetes Risk with CDKAL1 Genetic Variants in Korean Adults

Cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) is one of the strongest diabetes loci identified to date; evidence suggests that it plays an important role in insulin secretion. Dietary factors that affect insulin demand might enhance the risk of diabetes associated with CDKAL1 variants. Our aim was to examine the interactions between dietary protein and fat intake and CDKAL1 genetic variants in relation to the risk of diabetes in Korean adults. Single nucleotide polymorphisms (SNPs) were selected with a genome-wide association study (GWAS) for diabetes after adjustment for age, gender, and examination site. Using data from the Health Examinees (HEXA) Study of the Korean Genome and Epidemiology Study (KoGES), 3988 middle-aged Korean adults between 40–76 years of age (2034 men and 1954 women) were included in the study. Finally, rs7756992 located within the CDKAL1 gene region was selected from GWAS (p-value < 5 × 10⁻⁸). Multivariable logistic regression models were used to evaluate the interactions between genotypes and dietary protein and fat intake in relation to diabetes risk after adjustment for age, gender, BMI, waist circumference, physical activity, smoking status, drinking habits, and examination site. Significant interactions between CDKAL1 rs7756992 and dietary protein and fat intake for the risk of diabetes were observed in men (p-value < 0.05). In women, significant interactions between dietary protein and fat intake and CDKAL1 variants (rs7756992) were associated with increased risk of diabetes (p-value < 0.05). Dietary protein and fat intake interacted differently with CDKAL1 variants in relation to the risk of diabetes in Korean adults of both genders. These findings indicate that CDKAL1 variants play a significant role in diabetes and that dietary protein and fat intake could affect these associations.

Inflammation: major denominator of obesity, Type 2 diabetes and Alzheimer's disease-like pathology?

Adipose tissue is an active metabolic organ that contributes to processes such as energy storage and utilization and to the production of a number of metabolic agents, such as adipokines, which play a role in inflammation. In this review, we try to elucidate the connections between peripheral inflammation at obesity and Type 2 diabetes and the central inflammatory process. Multiple lines of evidence highlight the importance of peripheral inflammation and its link to neuroinflammation, which can lead to neurodegenerative diseases such as dementia, Alzheimer's disease (AD) and Parkinson's disease. In addition to the accumulation of misfolded amyloid beta (Aβ) peptide and the formation of the neurofibrillary tangles of hyperphosphorylated tau protein in the brain, activated microglia and reactive astrocytes are the main indicators of AD progression. They were found close to Aβ plaques in the brains of both AD patients and rodent models of Alzheimer's disease-like pathology. Cytokines are key players in pro- and anti-inflammatory processes and are also produced by microglia and astrocytes. The interplay of seemingly unrelated pathways between the periphery and the brain could, in fact, have a common denominator, with inflammation in general being a key factor affecting neuronal processes in the brain. An increased amount of white adipose tissue throughout the body seems to be an important player in pro-inflammatory processes. Nevertheless, other important factors should be studied to elucidate the pathological processes of and the relationship among obesity, Type 2 diabetes and neurodegenerative diseases.