Obesity Partially Mediates the Diabetogenic Effect of Lowering LDL Cholesterol

The Rotterdam Study. Design update and major findings between 2020 and 2024

The Rotterdam Study is a population-based cohort study, started in 1990 in the district of Ommoord in the city of Rotterdam, the Netherlands, with the aim to describe the prevalence and incidence, unravel the etiology, and identify targets for prediction, prevention or intervention of multifactorial diseases in mid-life and elderly. The study currently includes 17,931 participants (overall response rate 65%), aged 40 years and over, who are examined in-person every 3 to 5 years in a dedicated research facility, and who are followed-up continuously through automated linkage with health care providers, both regionally and nationally. Research within the Rotterdam Study is carried out along two axes. First, research lines are oriented around diseases and clinical conditions, which are reflective of medical specializations. Second, cross-cutting research lines transverse these clinical demarcations allowing for inter- and multidisciplinary research. These research lines generally reflect subdomains within epidemiology. This paper describes recent methodological updates and main findings from each of these research lines. Also, future perspective for coming years highlighted.

Impact of PCSK9 inhibitors in glycaemic control and new-onset diabetes

BACKGROUND

The diabetogenic effect of statins has been well established by clinical trials, Mendelian randomisation studies and meta-analyses. According to large clinical trials, PCSK9 inhibitors (PCSK9i) have no deleterious impact on glucose metabolism. However, few real-life studies have yet evaluated the long-term effects of these drugs on glucose homeostasis and their impact on new-onset diabetes (NODM).

METHODS

We studied 218 patients treated with either alirocumab or evolocumab (70% with familial hypercholesterolemia) for at least three years (PCSK9iG). We studied the NODM rate in the nondiabetic group at baseline (168) and overall glucose metabolism control in the whole group. Incidental DM was compared with two groups. The first was a propensity score matching (PSM)-selected group (n = 168) from the database of patients attending the Reus lipid unit (Metbank, n = 745) who were not on PCSK9i (PSMG). The second was a subgroup with a similar age range (n = 563) of the Di@bet.es study (Spanish prospective study on diabetes development n = 5072) (D@G). The incidence was reported as the percentage of NODM cases per year.

RESULTS

The fasting glucose (FG) level of the subjects with normoglycaemia at baseline increased from 91 (86-95.5) to 93 (87-101) mg/dL (p = 0.014). There were 14 NODM cases in the PCSK9i group (2.6%/y), all among people with prediabetes at baseline. The incidence of NODM in PSMG and D@G was 1.8%/y (p = 0.69 compared with the PCSK9iG). The incidence among the subjects with prediabetes was 5.1%/y in the PCSK9iG, 4.8%/y in the PSMG and 3.9%/y in the D@G (p = 0.922 and p = 0.682, respectively). In the multivariate analysis, only the FG level was associated with the development of NODM in the PCSK9iG (OR 1.1; 95% CI: 1.0-1.3; p = 0.027). Neither FG nor A1c levels changed significantly in patients with DM at baseline.

CONCLUSION

A nonsignificant increase in NODM occurred in the PCSK9iG, particularly in patients with prediabetes, compared with the PSMG and D@G groups. Baseline FG levels were the main variable associated with the development of DM. In the subjects who had DM at baseline, glucose control did not change. The impact of PCSK9i on glucose metabolism should not be of concern when prescribing these therapies.

Application of glycemic qualification rate based on fingerstick glucose monitoring in women with gestational diabetes mellitus

OBJECTIVE

To explore the appropriate application of glycemic qualification rate (GQR) calculated by fingerstick blood glucose (BG) monitoring for patients with gestational diabetes mellitus (GDM) by analyzing the relationship between BG control and adverse pregnancy outcomes.

METHODS

Fingerstick Blood Glucose data during the second and third trimester of singleton pregnant women diagnosed with GDM were collected. GQR which is defined as the percentage of fingerstick BG values reaching the targets of BG control in a period of time was calculated. Patients were divided into three groups according to tertiles (tertile 1, GQR <56.25%; tertile 2, GQR 56.25-75%; and tertile 3, GQR ≥75%). Pregnant outcomes were compared among the three groups. Univariate analysis and logistic regression were performed to analyze the potential relationship between GQR and pregnancy outcomes. Receiver operating characteristic (ROC) curves were calculated to determine the cutoff values. We also explored that whether twice or three times monitoring per day would be adequate for GQR calculation, so we brought in two or three glucose measuring times per day to explore the relationship between new GQR and adverse outcomes.

RESULTS

A total of 311 patients diagnosed with GDM were analyzed. In univariate analysis, the incidences of cesarean section of tertile 1-3 groups were 61.4%, 58.7%, and 44.9%, respectively (p < .05). The incidences of neonatal hypoglycemia of tertiles 1-3 groups were 19.8%, 18.6%, and 8.7% (p < .05). The difference of composite outcomes was statistically significant (p = .001). After adjustment, the patients with worse BG control (lower GQR) had higher risk of cesarean section (tertile 1 - aOR = 2.029, 1.128-3.648), neonatal hypoglycemia (tertile 1: aOR = 2.498, 1.082-5.766) as well as composite outcomes. The ROC curve of GQR indicated the predictive value for neonatal hypoglycemia (area under the ROC curve (AUC) 0.612 (0.532-0.692)) and neonatal composite outcomes (AUC 0.593 (0.528-0.657)) with optimal cutoff values of 81.1% and 73.5%, respectively. We also explored that whether twice or three times monitoring per day would be adequate for GQR calculation. The result showed that GQR only calculated by FBG + 2hPG after lunch (2h AL) per day also had well relationship with cesarean section (tertile 1: OR = 2.412, 1.322-4.398), neonatal hypoglycemia (tertile 1: aOR = 4.497, 1.607-12.586), and neonatal composite outcomes (tertile 1: aOR = 1.959, 95% confidence interval (CI): 1.114-3.444, p = .020).

CONCLUSIONS

The GQR calculated by the easily applicable fingerstick BG is related to occurrence of cesarean section and neonatal hypoglycemia in GDM women. GQR ≥ 80% is recommended for better pregnancy outcomes. As for the number of points monitoring per day, GQR calculated by FBG + 2h AL was an optimal option for better pregnancy outcomes if mothers needed to simplify the process of monitoring.

Genetically mimicked effects of ASGR1 inhibitors on all-cause mortality and health outcomes: a drug-target Mendelian randomization study and a phenome-wide association study

BACKGROUND

Asialoglycoprotein receptor 1 (ASGR1) is emerging as a potential drug target to reduce low-density lipoprotein (LDL)-cholesterol and coronary artery disease (CAD) risk. Here, we investigated genetically mimicked ASGR1 inhibitors on all-cause mortality and any possible adverse effects.

METHODS

We conducted a drug-target Mendelian randomization study to assess genetically mimicked effects of ASGR1 inhibitors on all-cause mortality and 25 a priori outcomes relevant to lipid traits, CAD, and possible adverse effects, i.e. liver function, cholelithiasis, adiposity and type 2 diabetes. We also performed a phenome-wide association study of 1951 health-related phenotypes to identify any novel effects. Associations found were compared with those for currently used lipid modifiers, assessed using colocalization, and replicated where possible.

RESULTS

Genetically mimicked ASGR1 inhibitors were associated with a longer lifespan (3.31 years per standard deviation reduction in LDL-cholesterol, 95% confidence interval 1.01 to 5.62). Genetically mimicked ASGR1 inhibitors were inversely associated with apolipoprotein B (apoB), triglycerides (TG) and CAD risk. Genetically mimicked ASGR1 inhibitors were positively associated with alkaline phosphatase, gamma glutamyltransferase, erythrocyte traits, insulin-like growth factor 1 (IGF-1) and C-reactive protein (CRP), but were inversely associated with albumin and calcium. Genetically mimicked ASGR1 inhibitors were not associated with cholelithiasis, adiposity or type 2 diabetes. Associations with apoB and TG were stronger for ASGR1 inhibitors compared with currently used lipid modifiers, and most non-lipid effects were specific to ASGR1 inhibitors. The probabilities for colocalization were > 0.80 for most of these associations, but were 0.42 for lifespan and 0.30 for CAD. These associations were replicated using alternative genetic instruments and other publicly available genetic summary statistics.

CONCLUSIONS

Genetically mimicked ASGR1 inhibitors reduced all-cause mortality. Beyond lipid-lowering, genetically mimicked ASGR1 inhibitors increased liver enzymes, erythrocyte traits, IGF-1 and CRP, but decreased albumin and calcium.

Causal factors underlying diabetes risk informed by Mendelian randomisation analysis: evidence, opportunities and challenges

Diabetes and its complications cause a heavy disease burden globally. Identifying exposures, risk factors and molecular processes causally associated with the development of diabetes can provide important evidence bases for disease prevention and spur novel therapeutic strategies. Mendelian randomisation (MR), an epidemiological approach that uses genetic instruments to infer causal associations between an exposure and an outcome, can be leveraged to complement evidence from observational and clinical studies. This narrative review aims to summarise the evidence on potential causal risk factors for diabetes by integrating published MR studies on type 1 and 2 diabetes, and to reflect on future perspectives of MR studies on diabetes. Despite the genetic influence on type 1 diabetes, few MR studies have been conducted to identify causal exposures or molecular processes leading to increased disease risk. In type 2 diabetes, MR analyses support causal associations of somatic, mental and lifestyle factors with development of the disease. These studies have also identified biomarkers, some of them derived from the gut microbiota, and molecular processes leading to increased disease risk. These studies provide valuable data to better understand disease pathophysiology and explore potential therapeutic targets. Because genetic association studies have mostly been restricted to participants of European descent, multi-ancestry cohorts are needed to examine the role of different types of physical activity, dietary components, metabolites, protein biomarkers and gut microbiome in diabetes development.

Statins, Type 2 Diabetes, and Body Mass Index: A Univariable and Multivariable Mendelian Randomization Study

CONTEXT

Statins and possibly other lipid modifiers increase type 2 diabetes risk and body mass index (BMI). However, to what extent BMI mediates the diabetogenic effects of lipid modifiers remains unclear.

OBJECTIVE

We used Mendelian randomization (MR) to investigate the effects of commonly used lipid modifiers on type 2 diabetes risk and glycemic traits, and any mediation by BMI.

METHODS

Using established genetic variants to mimic commonly used lipid modifiers (ie, statins, PCSK9 inhibitors, and ezetimibe), we assessed their associations with type 2 diabetes risk, glycated hemoglobin (HbA1c), fasting insulin, fasting glucose, and BMI in the largest relevant genome-wide association studies (GWAS) in people of European ancestry, and where possible, in East Asians. We used multivariable MR to examine the role of lipid modifiers independent of BMI.

RESULTS

Genetically mimicked effects of statins and ezetimibe, but not PCSK9 inhibitors were associated with higher risk of type 2 diabetes (odds ratio [OR] 1.74 [95% CI, 1.49 to 2.03]; 1.92 [1.22 to 3.02]; 1.06 [0.87 to 1.29] per SD reduction in low-density lipoprotein (LDL)-cholesterol). Of these lipid modifiers, only genetic mimics of statins were associated with higher BMI (0.33 SD [0.29 to 0.38] per SD reduction in LDL-cholesterol), which explained 54% of the total effect of statins on type 2 diabetes risk.

CONCLUSION

Higher BMI mediated more than half of the diabetogenic effects of statins, which did not extend to other commonly used lipid modifiers. Further investigations are needed to clarify drug-specific mechanisms underlying the effects of lipid modifiers on type 2 diabetes.

Linear and nonlinear analyses of the association between low-density lipoprotein cholesterol and diabetes: The spurious U-curve in observational study

OBJECTIVE

Hyperlipidemia is traditionally considered a risk factor for diabetes. The effect of low-density lipoprotein cholesterol (LDL-C) is counterintuitive to diabetes. We sought to investigate the relationship between LDL-C and diabetes for better lipid management.

METHODS

We tested the shape of association between LDL-C and diabetes and created polygenic risk scores of LDL-C and generated linear Mendelian randomization (MR) estimates for the effect of LDL-C and diabetes. We evaluated for nonlinearity in the observational and genetic relationship between LDL-C and diabetes.

RESULTS

Traditional observational analysis suggested a complex non-linear association between LDL-C and diabetes while nonlinear MR analyses found no evidence for a non-linear association. Under the assumption of linear association, we found a consistently protective effect of LDL-C against diabetes among the females without lipid-lowering drugs use. The ORs were 0.84 (95% CI, 0.72-0.97, P=0.0168) in an observational analysis which was more prominent in MR analysis and suggested increasing the overall distribution of LDL-C in females led to an overall decrease in the risk of diabetes (P=0.0258).

CONCLUSIONS

We verified the liner protective effect of LDL-C against diabetes among the females without lipid-lowering drug use. Non-linear associations between LDL-C against diabetes in observational analysis are not causal.