Active Cigarette Smoking Is Associated With an Exacerbation of Genetic Susceptibility to Diabetes

Clustering-based risk stratification of prediabetes populations: Insights from the Taiwan and UK Biobanks

AIMS/INTRODUCTION

This study aimed to identify low- and high-risk diabetes groups within prediabetes populations using data from the Taiwan Biobank (TWB) and UK Biobank (UKB) through a clustering-based Unsupervised Learning (UL) approach, to inform targeted type 2 diabetes (T2D) interventions.

MATERIALS AND METHODS

Data from TWB and UKB, comprising clinical and genetic information, were analyzed. Prediabetes was defined by glucose thresholds, and incident T2D was identified through follow-up data. K-means clustering was performed on prediabetes participants using significant features determined through logistic regression and LASSO. Cluster stability was assessed using mean Jaccard similarity, silhouette score, and the elbow method.

RESULTS

We identified two stable clusters representing high- and low-risk diabetes groups in both biobanks. The high-risk clusters showed higher diabetes incidence, with 15.7% in TWB and 13.0% in UKB, compared to 7.3% and 9.1% in the low-risk clusters, respectively. Notably, males were predominant in the high-risk groups, constituting 76.6% in TWB and 52.7% in UKB. In TWB, the high-risk group also exhibited significantly higher BMI, fasting glucose, and triglycerides, while UKB showed marginal significance in BMI and other metabolic indicators. Current smoking was significantly associated with increased diabetes risk in the TWB high-risk group (P < 0.001). Kaplan-Meier curves indicated significant differences in diabetes complication incidences between clusters.

CONCLUSIONS

UL effectively identified risk-specific groups within prediabetes populations, with high-risk groups strongly associated male gender, higher BMI, smoking, and metabolic markers. Tailored preventive strategies, particularly for young males in Taiwan, are crucial to reducing T2D risk.

Gene-Environment Interactions and Gene-Gene Interactions on Two Biological Age Measures: Evidence from Taiwan Biobank Participants

PhenoAge and BioAge are two commonly used biological age (BA) measures. The author here searched for gene-environment interactions (GxE) and gene-gene interactions (GxG) on PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 111,996 Taiwan Biobank (TWB) participants, including a discovery set of 86,536 TWB2 individuals and a replication set of 25,460 TWB1 individuals. Searching for variance quantitative trait loci (vQTLs) provides a convenient way to evaluate GxE and GxG. A total of 4 nearly independent (linkage disequilibrium measure r2 < 0.01) PhenoAgeAccel-vQTLs are identified from 5,303,039 autosomal TWB2 SNPs (p < 5E-8), whereas no vQTLs are found from BioAgeAccel. These 4 PhenoAgeAccel-vQTLs (rs35276921, rs141927875, rs10903013, and rs76038336) are further replicated by TWB1 (p < 5E-8). They are located in the OR51B5, FAM234A, and AXIN1 genes. All 4 PhenoAgeAccel-vQTLs are significantly associated with PhenoAgeAccel (p < 5E-8). A phylogenetic heat map of the GxE analyses showed that smoking exacerbated the PhenoAgeAccel-vQTLs' aging effects, while higher educational attainment attenuated the PhenoAgeAccel-vQTLs' aging effects. Body mass index, chronological age, alcohol consumption, and sex do not prominently modulate PhenoAgeAccel-vQTLs' aging effects. Based on these vQTL results, rs141927875-rs35276921 interaction (p = 4.7E-61) and rs76038336-rs10903013 interaction (p = 3.3E-116) on PhenoAgeAccel are detected.

Metabolic profiling of smoking, associations with type 2 diabetes and interaction with genetic susceptibility

BACKGROUND

Smokers are at increased risk of type 2 diabetes (T2D), but the underlying mechanisms are unclear. We investigated if the smoking-T2D association is mediated by alterations in the metabolome and assessed potential interaction with genetic susceptibility to diabetes or insulin resistance.

METHODS

In UK Biobank (n = 93,722), cross-sectional analyses identified 208 metabolites associated with smoking, of which 131 were confirmed in Mendelian Randomization analyses, including glycoprotein acetyls, fatty acids, and lipids. Elastic net regression was applied to create a smoking-related metabolic signature. We estimated hazard ratios (HR) of incident T2D in relation to baseline smoking/metabolic signature and calculated the proportion of the smoking-T2D association mediated by the signature. Additive interaction between the signature and genetic risk scores for T2D (GRS-T2D) and insulin resistance (GRS-IR) on incidence of T2D was assessed as relative excess risk due to interaction (RERI).

FINDINGS

The HR of T2D was 1·73 (95% confidence interval (CI) 1·54 - 1·94) for current versus never smoking, and 38·3% of the excess risk was mediated by the metabolic signature. The metabolic signature and its mediation role were replicated in TwinGene. The metabolic signature was associated with T2D (HR: 1·61, CI 1·46 - 1·77 for values above vs. below median), with evidence of interaction with GRS-T2D (RERI: 0·81, CI: 0·23 - 1·38) and GRS-IR (RERI 0·47, CI: 0·02 - 0·92).

INTERPRETATION

The increased risk of T2D in smokers may be mediated through effects on the metabolome, and the influence of such metabolic alterations on diabetes risk may be amplified in individuals with genetic susceptibility to T2D or insulin resistance.

Blood lipid profile, HbA1c, fasting glucose, and diabetes: a cohort study and a two-sample Mendelian randomization analysis

PURPOSE

The prevalence of diabetes is increasing worldwide. The associations between the lipid profile and glycated hemoglobin (HbA1c), fasting glucose, and diabetes remain unclear, so we aimed to perform a cohort study and a two-sample Mendelian randomization (MR) study to investigate the causality between blood lipid profile and HbA1c, fasting glucose, and diabetes.

METHODS

A total of 25,171 participants from the Taiwan Biobank were enrolled. We applied a cohort study and an MR study to assess the association between blood lipid profile and HbA1c, fasting glucose, and diabetes. The summary statistics were obtained from the Asian Genetic Epidemiology Network (AGEN), and the estimates between the instrumental variables (IVs) and outcomes were calculated using the inverse-variance weighted (IVW) method. A series of sensitivity analyses were performed.

RESULTS

In the cohort study, high-density lipoprotein cholesterol (HDL-C) was negatively associated with HbA1c, fasting glucose, and diabetes, while the causal associations between HDL-C and HbA1c (βIVW = - 0.098, p = 0.003) and diabetes (βIVW = - 0.594, p < 0.001) were also observed. Furthermore, there was no pleiotropy effect in this study using the MR-Egger intercept test and MR-PRESSO global test.

CONCLUSIONS

Our results support the hypothesis that a genetically determined increase in HDL-C is causally related to a reduction in HbA1c and a lower risk of diabetes.

Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes

Introduction: After the era of genome-wide association studies (GWAS), thousands of genetic variants have been identified to exhibit main effects on human phenotypes. The next critical issue would be to explore the interplay between genes, the so-called "gene-gene interactions" (GxG) or epistasis. An exhaustive search for all single-nucleotide polymorphism (SNP) pairs is not recommended because this will induce a harsh penalty of multiple testing. Limiting the search of epistasis on SNPs reported by previous GWAS may miss essential interactions between SNPs without significant marginal effects. Moreover, most methods are computationally intensive and can be challenging to implement genome-wide. Methods: I here searched for GxG through variance quantitative trait loci (vQTLs) of 29 continuous Taiwan Biobank (TWB) phenotypes. A discovery cohort of 86,536 and a replication cohort of 25,460 TWB individuals were analyzed, respectively. Results: A total of 18 nearly independent vQTLs with linkage disequilibrium measure r 2 < 0.01 were identified and replicated from nine phenotypes. 15 significant GxG were found with p-values <1.1E-5 (in the discovery cohort) and false discovery rates <2% (in the replication cohort). Among these 15 GxG, 11 were detected for blood traits including red blood cells, hemoglobin, and hematocrit; 2 for total bilirubin; 1 for fasting glucose; and 1 for total cholesterol (TCHO). All GxG were observed for gene pairs on the same chromosome, except for the APOA5 (chromosome 11)-TOMM40 (chromosome 19) interaction for TCHO. Discussion: This study provided a computationally feasible way to search for GxG genome-wide and applied this approach to 29 phenotypes.

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.

Association of long-term exposure to PM2.5 in workplace with fasting plasma glucose among asymptomatic adults: A multicenter study in North China

BACKGROUND

The impacts of long-term high exposure to PM2.5 in workplace on glucose metabolism in asymptomatic working adults (AWAs) have rarely been explored.

OBJECTIVES

To assess the relationship between long-term exposure to workplace PM2.5 and glucose metabolism in asymptomatic general working adults in heavily polluted regions.

METHODS

We used the baseline data of the asymptomatic working participants from the Beijing-Tianjin-Hebei Medical Examination Cohort, which recruited adults undergoing medical examinations. A machine learning-based spatial-temporal model was used to estimate daily average PM2.5 concentrations in the participants' workplaces. We assessed the association of long-term PM2.5 concentrations (three years prior to the interview) and fasting plasma glucose (FPG) using generalized linear mixed-effects models (GLMM) with inclusion of potential confounders. Stratified analyses by sex, age, BMI and smoking status, and two pollutant models were further performed.

RESULTS

A total of 37,619 individuals were interviewed and 28,865 were included in the analyses. The mean FPG was 5.20 (0.96) mmol/L, and the estimated three-year average concentration of PM2.5 exposure was 69.51 (6.92) μg/m3. We detected a significant association of long-term exposure to workplace PM2.5 and FPG, a 10 µg/m3 increase in the long-term workplace PM2.5 exposure was associated with 0.075 (95%CI: 0.050-0.100) mmol/L elevated FPG and 25% (OR = 1.25, 95%CI: 1.05-1.50) elevated odds of abnormal fasting glucose metabolism with control of the potential confounding. The detected association between workplace PM2.5 and FPG metabolism remained significant in males, individuals aged > 44 years, overweight and/or obese people, both smokers and non-smokers, and when NO2, SO2, O3, and CO were included in the model.

CONCLUSIONS

Long-term exposure to workplace PM2.5 was associated with elevated FPG and/or odds of abnormal glucose metabolism among AWAs. Male, middle-aged, overweight and/or obese AWAs were more susceptible to workplace PM2.5 regardless of smoking status.

Risk Factors and Lifestyle Interventions

There are many nonmodifiable and modifiable risk factors for type 2 diabetes. Nonmodifiable risk factors include age, genetics, epigenetics, and social determinants of health (including education level, socioeconomic status, and noise and arsenic exposure). Modifiable risk factors include obesity, the microbiome, diet, cigarette smoking, sleep duration, sleep quality, and sedentary behavior. Major lifestyle interventions to prevent and treat diabetes relate to these risk factors. Weight loss is the lifestyle intervention with the largest benefit for both preventing and treating diabetes. Exercise, even without weight loss, significantly reduces the incidence of type 2 diabetes.

Associations of five obesity metrics with epigenetic age acceleration: Evidence from 2,474 Taiwan Biobank participants

OBJECTIVE

Obesity is associated with epigenetic age acceleration (EAA), resulting in an increased risk of many age-related disorders. However, most studies have focused on the relationship of EAA with BMI. Whether any other obesity metric is more relevant to EAA remains unknown.

METHODS

Here, the methylation ages of 2,474 Taiwan Biobank (TWB) participants were calculated according to Levine's phenotypic age (PhenoAge) and Lu's GrimAge. Residuals from regressing methylation age on chronological age were used to quantify PhenoEAA and GrimEAA. Five obesity metrics were evaluated, namely BMI, body fat percentage, waist circumference, hip circumference, and waist-hip ratio. Sex-stratified EAA was regressed on each of the five obesity metrics.

RESULTS

For male individuals, an increase of one SD in waist-hip ratio (0.06) was associated with a 0.602-year PhenoEAA (p = 6.3E-6) and a 0.481-year GrimEAA (p = 1.2E-8). For female individuals, every SD increase in BMI (3.7 kg/m² ) was associated with a 0.600-year PhenoEAA (p = 3.3E-5) and a 0.305-year GrimEAA (p = 3.1E-5).

CONCLUSIONS

"Abdominal obesity" and "general obesity" are significantly associated with male and female EAA, respectively. The prevention of abdominal obesity and general obesity is associated with a lower risk of EAA in men and women, respectively.

Lifestyle factors and genetic variants on two biological age measures: evidence from 94,443 Taiwan Biobank participants

BACKGROUND

Biological age (BA) can be estimated by phenotypes and is useful for predicting lifespan and healthspan. Levine et al. proposed a PhenoAge and a BioAge to measure BA. Although there have been studies investigating the genetic predisposition to BA acceleration in Europeans, little has been known regarding this topic in Asians.

METHODS

I here estimated PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 94,443 Taiwan Biobank (TWB) participants, wherein 25,460 TWB1 subjects formed a discovery cohort and 68,983 TWB2 individuals constructed a replication cohort. Lifestyle factors and genetic variants associated with PhenoAgeAccel and BioAgeAccel were investigated through regression analysis and a genome-wide association study (GWAS).

RESULTS

A unit (kg/m 2) increase of BMI was associated with a 0.177-year PhenoAgeAccel (95% C.I. = 0.163~0.191, p = 6.0×) and 0.171-year BioAgeAccel (95% C.I. = 0.165~0.177, p = 0). Smokers on average had a 1.134-year PhenoAgeAccel (95% C.I. = 0.966~1.303, p = 1.3×) compared with non-smokers. Drinkers on average had a 0.640-year PhenoAgeAccel (95% C.I. = 0.433~0.847, p = 1.3×) and 0.193-year BioAgeAccel (95% C.I. = 0.107~0.279, p = 1.1×) relative to non-drinkers. A total of 11 and 4 single-nucleotide polymorphisms (SNPs) were associated with PhenoAgeAccel and BioAgeAccel (p<5× in both TWB1 and TWB2), respectively.

CONCLUSIONS

A PhenoAgeAccel-associated SNP (rs1260326 in GCKR) and two BioAgeAccel-associated SNPs (rs7412 in APOE; rs16998073 near FGF5) were consistent with the finding from the UK Biobank. The lifestyle analysis shows that prevention from obesity, cigarette smoking, and alcohol consumption is associated with a slower rate of biological aging.

Interactive Association Between Intronic Polymorphism (rs10506151) of the LRRK2 Gene and Type 2 Diabetes on Neurodegenerative Diseases

Purpose

We investigated the interactive effect of rs10506151 polymorphism of the Leucine-rich repeat kinase 2 (LRRK2) gene and type 2 diabetes (T2D) on neurodegenerative disease (ND) risk.

Materials and Methods

Data of 17, 927 participants in the Taiwan Biobank (TWB) assessed between 2008 and 2015 were linked to healthcare records in the National Health Insurance Research Database (NHIRD). The odd ratios (ORs) and 95% confidence intervals (CIs) for NDs were determined using logistic regression analysis.

Results

There were 145 cases with NDs, and 28.28% (n = 41) of these individuals had T2D. Associations of neurodegenerative disorders with LRRK2 rs10506151 variant and T2D were not significant. The corresponding ORs (95% CI) for NDs were 1.06 (0.75–1.49) in CA/AA compared to CC individuals and 0.93 (0.63–1.39) in those with T2D compared to non-diabetic participants. However, we found evidence of a significant interaction between rs10506151 and T2D (p = 0.0073). After stratification by genotypes of rs10506151, the OR for NDs was 0.37 (CI, 0.17–0.82) in CA/AA individuals with T2D and 1.41 (0.88–2.27) in their CC counterparts. When CA/AA individuals with T2D represented the reference group, the OR (95% CI) was 1.74 (0.81–3.73) in CC individuals with no T2D, 2.47 (CI, 1.14–5.38) in CA/AA individuals with no T2D, and 2.34 (CI, 1.07–5.11) in CC individuals with T2D.

Conclusion

Our data indicated that the risk of NDs was significantly lower among diabetic individuals with combined CA/AA of the LRRK2 rs10506151 variant in Taiwan.

A large-scale observational study linking various kinds of physical exercise to lipoprotein-lipid profile

Background

Being a major cardiovascular risk factor, dyslipidemia is a critical problem in public health. Recommendations in performing regular physical exercise are important to prevent dyslipidemia.

Methods

Based on a discovery cohort with 27,735 subjects and a replication cohort with 67,512 subjects, we evaluated the associations of regularly performing 23 exercises with 4 dyslipidemia indices measured from serum, including triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and TG/HDL-C ratio. Regular exercise was defined as performing 30 min of “exercise” three times a week. “Exercise” includes leisure-time activities such as jogging, swimming, etc. Sex, age, body mass index, alcohol drinking, cigarette smoking, and education level were adjusted in all statistical analyses.

Results

Among the 23 exercises, only jogging was associated with a decreased level of TG (95% confidence interval [C.I.] = 5.9–14.5 mg/dL) and TG/HDL-C ratio (95% C.I. = 0.22–0.49). A total of 5 exercises were associated with an increased level of HDL-C, including jogging (95% C.I. = 2.1–3.3 mg/dL), swimming (95% C.I. = 1.6–3.3 mg/dL), dance dance revolution (95% C.I. = 1.5–3.4 mg/dL), international standard dancing (95% C.I. = 1.0–2.7 mg/dL), and cycling (95% C.I. = 0.6–1.8 mg/dL). These significant findings were further well replicated in the cohort of 67,512 subjects.

Conclusion

Regular jogging was not only associated with an increased level of HDL-C, but also the only one exercise associated with a decreased level of TG and TG/HDL-C ratio. Nonetheless, jogging may be difficult to engage in for subjects with limited exercise capacity. We here found that swimming, dancing, and cycling are also significantly associated with an increased level of HDL-C. People who are seeking exercise to improve their lipoprotein-lipid profiles can have other choices now.

Interactive Effect of IGF2BP2 rs4402960 Variant, Smoking and Type 2 Diabetes

PURPOSE

Genetic and environmental factors are related to type 2 diabetes (T2D). Genetic modifiers of T2D have not been widely determined among smoking individuals. In this population-based study, we investigated the interactive association between rs4402960 polymorphism of the insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) gene and smoking with T2D among Taiwanese adults.

MATERIALS AND METHODS

We obtained genetic data collected between 2008 and 2018 for 22,039 participants (aged 30-70 years) from the Taiwan Biobank (TWB) database. These data were analyzed using the t-test, Chi-square (χ ²) test, and multiple logistic regression.

RESULTS

The mean ages for participants with and without diabetes were 58.11±8.75 and 48.58±11, respectively. Compared with the rs4402960 GG genotype, the odds ratio (OR) for T2D was 1.261 among GT and 1.545 among TT genotype individuals (p<0.05). Current smokers compared to nonsmokers were associated with a higher risk of T2D (OR=1.266, p=0.0404). There was a significant interaction between the IGF2BP2 rs4402960 variant and smoking on T2D (p = 0.0497). After stratification by rs4402960 genotypes and smoking status, the OR was substantial only in current smokers with GG genotype (OR, 1.663, p = 0.0008).

CONCLUSION

This population-based study indicated that the risk for T2D was stronger among current smoking rs4402960 GG individuals recruited between 2008 and 2019 in Taiwan.