Low-Density Lipoprotein Cholesterol Is Associated With Insulin Secretion

Corinna Dannecker; Robert Wagner; Andreas Peter; Julia Hummel; Andreas Vosseler; Hans-Ulrich Häring; Andreas Fritsche; Andreas L. Birkenfeld; Norbert Stefan; Martin Heni

Disclosures

J Clin Endocrinol Metab. 2021;106(6):1576-1584. 

In This Article

Materials and Methods

Participants

Data from 3039 White individuals from the southern part of Germany who had participated in the Tübingen Family Study were analyzed. The individuals participated in different studies on the pathogenesis of type 2 diabetes between 1993 and 2017. All participants underwent metabolic characterization including a detailed medical history and physical examination, routine blood tests (fasting state) and a 5-point oral glucose tolerance test (OGTT).[17] Selection of the present study cohort was based on the absence of treatment with cholesterol-lowering drugs and the availability of complete clinical data. The participant characteristics are presented in Table 1.

Oral Glucose Tolerance Test

A 5-point 75-g OGTT was performed after an overnight fasting period of at least 8 hours, and venous blood samples were drawn at time points 0, 30, 60, 90, and 120 minutes for the determination of plasma glucose, insulin, and C-peptide. A total of 1833 participants showed normal glucose regulation, 1055 prediabetes, and 151 individuals fulfilled the diagnostic criteria for diabetes. In a subset of 595 participants, fasting glucagon was measured; in 387 participants glucagon was assessed at all time points.

Laboratory Measurements

Plasma glucose was measured using a YSI 2300 glucose analyzer (YSI). Serum insulin and C-peptide were determined by immunoassay with the ADVIA Centaur XP Immunoassay System (Siemens Healthcare Diagnostics). Glucagon was measured as previously described for the Tübingen Family Study cohort.[18] Total, HDL, LDL cholesterol, and triglycerides were measured on the ADVIA XPT Clinical Chemistry System (Siemens Healthcare Diagnostics). Glycated hemoglobin measurements were performed in the central laboratory of the University Hospital of Tübingen using the Tosoh A1c analyzer HLC-723G8 (Tosoh Bioscience GmbH).

Calculations

Insulin secretion indices were derived from the OGTT with insulin and C-peptide concentrations given in picomole per liter (pmol/L), and glucose concentration given in millimole per liter (mmol/L). Areas under the curve (AUCs) of insulin, C-peptide, glucose, and glucagon concentrations during the entire 120 minutes of the OGTT were calculated according to the trapezoid method as 0.5 × (0.5 × c0min + c30min + c60min + c90min + 0.5 × c120min) with c equal to concentration. AUCC-Peptide(0–30min)/AUCGlucose(0–30min) was calculated as (C-Pep0min + C-Pep30min)/(Glc0min + Glc30min).[19] Insulinogenic index (IGI0–30min) was calculated as (Ins30min – Ins0min)/(Glc30min – Glc0min).[19] Insulin sensitivity derived from the OGTT was estimated as proposed by Matsuda and DeFronzo (Matsuda ISI)[20] as 10 000/√ (Glc0min × Ins0min × Glcmean × Insmean). Disposition index (DI) was calculated as IGI × ISI.[19] Fasting insulin clearance was calculated as C-Pep0min/Ins0min with C-Pep0 min equal to fasting C-peptide; insulin clearance during the OGTT was calculated as AUCC-Peptide(0–120min)/AUCInsulin(0–120min).

Single-cell Data

Recently published single cell expression profiles[21] were analyzed for expression of LDLR. A t-distributed stochastic neighbor embedding (tSNE) plot from 2544 pancreatic single-cell data sets was generated. For this, we used the top 250 expressed genes with a perplexity parameter of 30 and a θ of 0.4. Then, we assigned each cell to a probable cell type based on the highest expression of the cell type–specific marker genes. Next, we plotted the log-transformed LDL receptor (LDLR) expression on these cells.

Statistical Analyses

Prior to statistical evaluation, variables with skewed distribution were log-transformed. For multiple linear regression analysis, the standard least-squares method was applied and the variable of interest was used as dependent variable, LDL cholesterol concentration as independent variable, and sex, age, body mass index (BMI), and Matsuda ISI (for all insulin and C-peptide–based secretion parameters except DI and glucagon secretion during the OGTT) as confounding variables. Data are presented as means ± SEM. For illustrative purposes we divided the cohort into LDL cholesterol quartiles and compared them by analysis of variance (Figure 1). A P value less than or equal to .05 was considered statistically significant. The statistical software package JMP 13.0.0 (SAS Institute Inc) was used. Single-cell data were analyzed using R (version 3.6.1) and the Rtsne library (version 0.15).

Figure 1.

Low-density lipoprotein (LDL) cholesterol levels and insulin secretion. Displayed are C-peptide–based insulin secretion parameter A, area under the curve (AUC)C-Peptide(0–30min)/AUCGlucose(0–30min); B, AUCC-Peptide(0–120min)/AUCGlucose(0–120min) and insulin-based secretion parameter; C, insulinogenic index; and D, disposition index after stratification of participants in LDL cholesterol quartiles. Bars represent means + SEM. P values are for comparison of LDL quartiles by analysis of variance for illustrative purpose, while P values from continuous models are reported in the text.

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