The Causal Relationship of Circulating Triglyceride and Glycated Hemoglobin: A Mendelian Randomization Study

Impact of polygenic risk score for triglyceride trajectory and diabetic complications in subjects with type 2 diabetes based on large electronic medical record data from Taiwan: a case control study

BACKGROUND

The prevalence of diabetic dyslipidemia has gradually increased worldwide and individuals with hypertriglyceridemia often have a high polygenic burden of triglyceride (TG)-increasing variants. However, the contribution of genetic variants to dyslipidemia in patients with type 2 diabetes (T2D) remains limited. Therefore, in this study, we aimed to investigate the genetic characteristics of longitudinal changes in TG levels among patients with T2D and summarize the genetic effects of polygenic risk score (PRS) on TG trajectory and risk of diabetic complications.

METHODS

We conducted a case-control study. A total of 11,312 patients with T2D with longitudinal TG and genetic data were identified from a large hospital database in Taiwan. We then performed a genome-wide association study and calculated the relative PRS.

RESULTS

In total, 21 single-nucleotide polymorphisms (SNPs) related to TG trajectory were identified and yielded an area under the receiver operating characteristic curve (ROC) of 0.712 for high TG trajectory risk among Taiwanese patients with T2D. A cumulative genetic effect was observed for high TG trajectory, even when considering the adherence of a lipid-lowering agent in stratified analysis. An increased PRS increases high TG trajectory risk in a logistic regression model (odds ratio = 1.55; 95% confidence interval [CI] = 1.31-1.83 in the validation cohort). The TG-specific PRS was associated with the risk of diabetic microvascular complications, including diabetic retinopathy and nephropathy (with hazard ratios of 1.11 [95% CI = 1.01-1.21, P = 0.027] and 1.05 [95% CI = 1.01-1.1, P = 0.018], respectively).

CONCLUSIONS

This study may contribute to the identification of patients with T2D who are at risk of abnormal TG levels and diabetic microvascular complications using polygenic information.

Effect of sacubitril/valsartan on lipid metabolism in patients with chronic kidney disease combined with chronic heart failure: a retrospective study

BACKGROUND AND OBJECTIVE

Dyslipidemia is significantly more common in those with concurrent chronic kidney disease (CKD) and chronic heart failure (CHF). Sacubitril/valsartan has showcased its influence on both cardiac and renal functions, extending its influence to the modulation of lipid metabolism pathways. This study aimed to examine how sacubitril/valsartan affects lipid metabolism within the context of CKD and CHF.

METHODS

This study adopted a retrospective design, focusing on a single center and involving participants who were subjected to treatment with sacubitril/valsartan and valsartan. The investigation assessed the treatment duration, with a particular emphasis on recording blood lipid indicators, including triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A (ApoA), and apolipoprotein B (ApoB). Furthermore, cardiac and renal functions, blood pressure, potassium levels, and other factors influencing the blood lipids were analyzed in both groups at identical time points.

RESULTS

After 16 weeks of observation, the sacubitril/valsartan group exhibited lower TG levels compared to the valsartan group. Noteworthy was the fact that individuals undergoing sacubitril/valsartan treatment experienced an average reduction of 0.84 mmol/L in TG levels, in stark contrast to the valsartan group, which registered a decline of 0.27 mmol/L (P < 0.001). The sacubitril/valsartan group exhibited elevated levels of HDL-C and ApoA in comparison to the valsartan group (PHDL-C = 0.023, PApoA = 0.030). While TC, LDL-C, and ApoB decreased compared to baseline, the differences between groups were not statistical significance. Regarding cardiac indicators, there was an observed enhancement in the left ventricular ejection fraction (LVEF) within the sacubitril/valsartan group when compared to the baseline, and it was noticeably higher than that of the valsartan group. Spearman correlation analysis and multiple linear regression analysis revealed that medication, body mass index(BMI), and hemoglobin A1c (HbA1c) had a direct influencing effect on TG levels.

CONCLUSION

Sacubitril/valsartan demonstrated improvements in lipid metabolism and cardiac indicators in patients with CKD and CHF. Specifically, it presented promising benefits in reducing TG levels. In addition, both BMI and HbA1c emerged as influential factors contributing to alterations in TG levels, independent of the administration of sacubitril/valsartan.

A study of factors influencing long-term glycemic variability in patients with type 2 diabetes: a structural equation modeling approach

Aim

The present study aims to utilize structural equation modeling (SEM) to investigate the factors impacting long-term glycemic variability among patients afflicted with type 2 diabetes.

Method

The present investigation is a retrospective cohort study that involved the collection of data on patients with type 2 diabetes mellitus who received care at a hospital located in Chengdu, Sichuan Province, over a period spanning from January 1, 2013, to October 30, 2022. Inclusion criteria required patients to have had at least three laboratory test results available. Pertinent patient-related information encompassing general demographic characteristics and biochemical indicators was gathered. Variability in the dataset was defined by standard deviation (SD) and coefficient of variation (CV), with glycosylated hemoglobin variation also considering variability score (HVS). Linear regression analysis was employed to establish the structural equation models for statistically significant influences on long-term glycemic variability. Structural equation modeling was employed to analyze effects and pathways.

Results

Diabetes outpatient special disease management, uric acid variability, mean triglyceride levels, mean total cholesterol levels, total cholesterol variability, LDL variability, baseline glycated hemoglobin, and recent glycated hemoglobin were identified as significant factors influencing long-term glycemic variability. The overall fit of the structural equation model was found to be satisfactory and it was able to capture the relationship between outpatient special disease management, biochemical indicators, and glycated hemoglobin variability. According to the total effect statistics, baseline glycated hemoglobin and total cholesterol levels exhibited the strongest impact on glycated hemoglobin variability.

Conclusion

The factors that have a significant impact on the variation of glycosylated hemoglobin include glycosylated hemoglobin itself, lipids, uric acid, and outpatient special disease management for diabetes. The identification and management of these associated factors can potentially mitigate long-term glycemic variability, thereby delaying the onset of complications and enhancing patients' quality of life.

The indirect impact of the COVID-19 pandemic on people with type 2 diabetes mellitus and without COVID-19 infection: Systematic review and meta-analysis

BACKGROUND

The effect directly from the coronavirus disease 2019 (COVID-19) infection on health and fatality has received considerable attention, particularly among people with type 2 diabetes mellitus (T2DM). However, evidence on the indirect impact of disrupted healthcare services during the pandemic on people with T2DM is limited. This systematic review aims to assess the indirect impact of the pandemic on the metabolic management of T2DM people without a history of COVID-19 infection.

METHODS

PubMed, Web of Science, and Scopus were systematically searched for studies that compared diabetes-related health outcomes between pre-pandemic and during-pandemic periods in people with T2DM and without the COVID-19 infection and published from January 1, 2020, to July 13, 2022. A meta-analysis was performed to estimate the overall effect on the diabetes indicators, including hemoglobin A1c (HbA1c), lipid profiles, and weight control, with different effect models according to the heterogeneity.

RESULTS

Eleven observational studies were included in the final review. No significant changes in HbA1c levels [weighted mean difference (WMD), 0.06 (95% CI -0.12 to 0.24)] and body weight index (BMI) [0.15 (95% CI -0.24 to 0.53)] between the pre-pandemic and during-pandemic were found in the meta-analysis. Four studies reported lipid indicators; most reported insignificant changes in low-density lipoprotein (LDL, n = 2) and high-density lipoprotein (HDL, n = 3); two studies reported an increase in total cholesterol and triglyceride.

CONCLUSIONS

This review did not find significant changes in HbA1c and BMI among people with T2DM after data pooling, but a possible worsening in lipids parameters during the COVID-19 pandemic. There were limited data on long-term outcomes and healthcare utilization, which warrants further research.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022360433.

A Guide for Selection of Genetic Instruments in Mendelian Randomization Studies of Type 2 Diabetes and HbA1c: Toward an Integrated Approach

In this study we examine the instrument selection strategies currently used throughout the type 2 diabetes and HbA1c Mendelian randomization (MR) literature. We then argue for a more integrated and thorough approach, providing a framework to do this in the context of HbA1c and diabetes. We conducted a literature search for MR studies that have instrumented diabetes and/or HbA1c. We also used data from the UK Biobank (UKB) (N = 349,326) to calculate instrument strength metrics that are key in MR studies (the F statistic for average strength and R2 for total strength) with two different methods ("individual-level data regression" and Cragg-Donald formula). We used a 157-single nucleotide polymorphism (SNP) instrument for diabetes and a 51-SNP instrument (with partition into glycemic and erythrocytic as well) for HbA1c. Our literature search yielded 48 studies for diabetes and 22 for HbA1c. Our UKB empirical examples showed that irrespective of the method used to calculate metrics of strength and whether the instrument was the main one or included partition by function, the HbA1c genetic instrument is strong in terms of both average and total strength. For diabetes, a 157-SNP instrument was shown to have good average strength and total strength, but these were both substantially lesser than those of the HbA1c instrument. We provide a careful set of five recommendations to researchers who wish to genetically instrument type 2 diabetes and/or HbA1c. In MR studies of glycemia, investigators should take a more integrated approach when selecting genetic instruments, and we give specific guidance on how to do this.

Causal Graph Among Serum Lipids and Glycemic Traits: A Mendelian Randomization Study

We systematically investigated the bidirectional causality among HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides (TGs), fasting insulin (FI), and glycated hemoglobin A1c (HbA1c) based on genome-wide association summary statistics of Europeans (n = 1,320,016 for lipids, 151,013 for FI, and 344,182 for HbA1c). We applied multivariable Mendelian randomization (MR) to account for the correlation among different traits and constructed a causal graph with 13 significant causal effects after adjusting for multiple testing (P < 0.0025). Remarkably, we found that the effects of lipids on glycemic traits were through FI from TGs (β = 0.06 [95% CI 0.03, 0.08] in units of 1 SD for each trait) and HDL-C (β = -0.02 [-0.03, -0.01]). On the other hand, FI had a strong negative effect on HDL-C (β = -0.15 [-0.21, -0.09]) and positive effects on TGs (β = 0.22 [0.14, 0.31]) and HbA1c (β = 0.15 [0.12, 0.19]), while HbA1c could raise LDL-C (β = 0.06 [0.03, 0.08]) and TGs (β = 0.08 [0.06, 0.10]). These estimates derived from inverse-variance weighting were robust when using different MR methods. Our results suggest that elevated FI was a strong causal factor of high TGs and low HDL-C, which in turn would further increase FI. Therefore, early control of insulin resistance is critical to reduce the risk of type 2 diabetes, dyslipidemia, and cardiovascular complications.

Roles of Cardiometabolic Factors in Mediating the Causal Effect of Type 2 Diabetes on Cardiovascular Diseases: A Two-Step, Two-Sample Multivariable Mendelian Randomization Study

Objective

The objective of this study is to investigate the roles of cardiometabolic factors (including blood pressure, blood lipids, thyroid function, body mass, and insulin sensitivity) in mediating the causal effect of type 2 diabetes (T2DM) on cardiovascular disease (CVD) outcomes.

Design

Two-step, two-sample multivariable Mendelian randomization (MVMR) study.

Setting

International genome-wide association study (GWAS) consortia data.

Exposure

Type 2 diabetes, blood pressure: systolic blood pressure (SBP), diastolic blood pressure (DBP); blood lipids: low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), triglycerides (TG); thyroid function: hyperthyroidism, hypothyroidism; body mass index (BMI), waist-hip-ratio (WHR), and insulin sensitivity.

Main Outcomes

Cardiovascular disease includes coronary heart disease (CHD), myocardial infarction (MI), and stroke.

Methods

Summary-level data for exposures and main outcomes were extracted from GWAS consortia. We used two-sample MR to illustrate the causal effect of T2DM on CVD subtypes and regression-based MVMR to quantify the possible mediation effects of cardiometabolic factors on CVD.

Results

Each additional unit of log odds of T2DM increased 16% risk of CHD [odds ratio (OR): 1.16, 95% CI: 1.12–1.21], 15% risk of myocardial infarction (MI) (OR: 1.15, 95% CI: 1.10–1.20), and 10% risk of stroke (OR: 1.10, 95% CI: 1.06–1.13). In mediation analysis, SBP, DBP, and TG were found as main mediators, while the mediation effects of other cardiometabolic factors were not significant. The proportion of total effect of T2DM on CHD mediated by SBP, DBP, and TG was 16% (95% CI: 8–24%), 7% (95% CI: 1–13%) and 10% (95% CI: 2–18%), respectively. Mediation effect of SBP and DBP on MI and stroke, TG on MI was also prominent, while mediation effect of TG on stroke was not significant. The combined mediation effect of all the three mediators accounted for 29%, 26%, and 13% of the total effect of T2DM on CHD, MI, and stroke, respectively.

Conclusion

Systolic blood pressure, DBP, and TG mediate a substantial proportion of the causal effect of T2DM on CVD and thus interventions on these factors might reduce the considerable excess risk of CVD among patients with T2DM.

A common variant in 11q23.3 associated with hyperlipidemia is mediated by the binding and regulation of GATA4

Large-scale genome-wide associations comprising multiple studies have identified hundreds of genetic loci commonly associated with hyperlipidemia-related phenotypes. However, single large cohort remains necessary in aiming to investigate ethnicity-specific genetic risks and mechanical insights. A community-based cohort comprising 23,988 samples that included both genotype and biochemical information was assembled for the genome-wide association analysis (GWAS) of hyperlipidemia. The analysis identified fifty genetic variants (P < 5 × 10⁻⁸) on five different chromosomes, and a subsequent validation analysis confirmed the significance of the lead variants. Integrated analysis combined with cell-based experiments of the most statistically significant locus in 11q23.3 revealed rs651821 (P = 4.52 × 10⁻⁷⁶) as the functional variant. We showed transcription factor GATA4 preferentially binds the T allele of rs651821, the protective allele for hyperlipidemia, which promoted APOA5 expression in liver cells and individuals with the TT genotype of rs651821. As GATA4-APOA5 axis maintains triglyceride homeostasis, GATA4 activation by phenylephrine implies synergism for lowering triglyceride levels in hyperlipidemia patients. Our study demonstrates that rs651821 mediates APOA5 activation via allele-specific regulation by GATA4. We suggest elevating GATA4 activity could provide a therapeutic potential for treating the development of hyperlipidemia.

Redox index of Cys-thiol residues of serum apolipoprotein E and its diagnostic potential

Background: The redox modulation of Cys-thiol participates in various pathophysiological processes. We explored the proper index for estimating the redox status of Cys-thiol of serum apolipoprotein E (apoE), named “redox-IDX-apoE,” which is necessary to understand the redox biology of age-related diseases.

Methods: The fractions of the reduced form (red-), reversible oxidized form (roxi-), and irreversibly oxidized form (oxi-) apoE in serum, obtained from the patients with no apparent disease (controls, n=192) and with atherosclerosis and type 2 diabetes (patients, n=16), were measured by a band-shift assay using a maleimide compound. Redox-IDX-apoE candidates were determined by calculating the values of these fractions and the total apoE concentration.

Results: Cys number of apoE significantly increased for the ratio of roxi-apoE to total-apoE (roxi/total) (E2/E3>E3/E3>E3/E4) but decreased for the ratios of red-apoE to roxi-apoE (red/roxi) and [red-apoE + oxi-apoE] to roxi-apoE ([red + oxi]/roxi) (E2/E3<E3/E3<E3/E4). Considering the subjects with apoE3/E3, these ratios were independent of age and sex. Roxi/total showed negative correlations with serum triglyceride (TG) and HbA1c levels, while both red/roxi and [red + oxi]/roxi showed significant positive correlations with them. However, red/roxi and [red + oxi]/roxi in patients were significantly lower than those in controls, although serum TG and HbA1c levels in the patients were significantly higher than those in controls.

Conclusion: The redox status of serum apoE-Cys-thiol is closely involved in the metabolism of TG-rich lipoproteins and glucose. The appropriate use of redox-IDX-apoE could be helpful in the diagnosis and prognosis of age-related diseases and in understanding the underlying mechanisms.

The transcriptome-wide association search for genes and genetic variants which associate with BMI and gestational weight gain in women with type 1 diabetes

BACKGROUND

Clinical data suggest that BMI and gestational weight gain (GWG) are strongly interconnected phenotypes; however, the genetic basis of the latter is rather unclear. Here we aim to find genes and genetic variants which influence BMI and/or GWG.

METHODS

We have genotyped 316 type 1 diabetics using Illumina Infinium Omni Express Exome-8 v1.4 arrays. The GIANT, ARIC and T2D-GENES summary statistics were used for TWAS (performed with PrediXcan) in adipose tissue. Next, the analysis of association of imputed expression with BMI in the general and diabetic cohorts (Analysis 1 and 2) or GWG (Analysis 3 and 4) was performed, followed by variant association analysis (1 Mb around identified loci) with the mentioned phenotypes.

RESULTS

In Analysis 1 we have found 175 BMI associated genes and 19 variants (p < 10-4) which influenced GWG, with the strongest association for rs11465293 in CCL24 (p = 3.18E-06). Analysis 2, with diabetes included in the model, led to discovery of 1812 BMI associated loci and 207 variants (p < 10-4) influencing GWG, with the strongest association for rs9690213 in PODXL (p = 9.86E-07). In Analysis 3, among 648 GWG associated loci, 2091 variants were associated with BMI (FDR < 0.05). In Analysis 4, 7 variants in GWG associated loci influenced BMI in the ARIC cohort.

CONCLUSIONS

Here, we have shown that loci influencing BMI might have an impact on GWG and GWG associated loci might influence BMI, both in the general and T1DM cohorts. The results suggest that both phenotypes are related to insulin signaling, glucose homeostasis, mitochondrial metabolism, ubiquitinoylation and inflammatory responses.