Associations of Insulin Resistance and Beta Cell Function with Abnormal Lipid Profile in Newly Diagnosed Diabetes: A Cross-sectional Study

doi:10.21203/rs.3.rs-130639/v1

Download PDF

Original investigation

Associations of Insulin Resistance and Beta Cell Function with Abnormal Lipid Profile in Newly Diagnosed Diabetes: A Cross-sectional Study

https://doi.org/10.21203/rs.3.rs-130639/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Abnormal lipids are strong predictive factors of cardiovascular disease (CVD) in both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). However, the potential associations of insulin resistance (IR) and beta cell function (BCF) in diabetes and abnormal lipids, i.e. high triglyceride (TG), low high-density lipoprotein cholesterol (HDL-C) and high low-density lipoprotein cholesterol (LDL-C) are not fully understood. In this study, we aim to explore whether decreased BCF and increased IR in newly diagnosed T1DM or T2DM are associated with abnormal lipids.

Methods: Clinical and laboratory data were collected from 16334 adults with newly diagnosed diabetes in this cross-sectional study. Types of diabetes were diagnosed based on clinical characteristics and diabetes-related biochemical measurement results. Homeostasis model assessment were used to estimate IR and BCF. Restricted cubic spline and binary logistic regression were used to examine the associations of IR or BCF and abnormal lipids in T1DM and T2DM, respectively.

Results: High TG, low HDL-C and high LDL-C accounted for 49.7%, 47.7% and 59.2%, respectively. In multivariable analysis, high IR was associated with increased risk of high TG (Odds ratios (ORs) of homeostasis model assessment of insulin resistance (HOMA2-IR) ≥2, ≥1-<2 vs <1: 4.77, 95% CI 2.69-8.57; 2.31, 95% CI 1.54-3.47, p for trend < 0.001) in T1DM and was associated with elevated risk of high TG, low HDL-C and high LDL-C (all p for trend <0.01) in T2DM. Low BCF, i.e., homeostasis model assessment of beta-cell function (HOMA2-B) <30 versus ≥30, was associated with marginally increased risk of high TG (OR 1.42, 95% CI 0.98-2.10, p = 0.07) in T1DM and associated with increased risk of high TG (OR 1.21, 95% CI 1.09-1.34, p <0.001) and high LDL-C (OR 1.23, 95% CI 1.12-1.36, p <0.001) in T2DM.

Conclusions: In patients with newly diagnosed diabetes, high IR and low BCF had different associations with risk of dyslipidemia in T1DM and T2DM, suggesting that early treatment that improve IR or BCF may have a benefit for abnormal lipid metabolism.

Cardiac & Cardiovascular Systems

Type 1 diabetes mellitus

type 2 diabetes mellitus

lipid abnormalities

insulin resistance

beta cell dysfunction

Cardiovascular disease (CVD), as one of the most severe complications, is the leading cause of death in both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) [1, 2]. It is established that some lipid components such as high triglyceride (TG) and low high-density lipoprotein cholesterol (HDL-C), are risk factors for diabetes. On the other hand, abnormal lipids are highly predictive of CVD in patients with diabetes [3, 4]. Disorders in carbohydrate metabolism in diabetes can cause or worsen abnormal lipid metabolism in numbers of ways. In both T1DM and T2DM, poor glycemic control can increase serum levels of TG and low-density lipoprotein cholesterol (LDL-C), and decreases levels of HDL-C [5]. Indeed, it is essential to understand biological links between diabetes and lipid abnormalities for further reduction in increased burden of CVD in patients with diabetes.

Pathophysiologically, insulin resistance (IR) and decreased beta cell dysfunction are two major contributors to diabetes. Interactions between IR and pancreatic beta cell function (BCF) play a key role in the pathogenesis of both T1DM and T2DM [6]. T1DM mainly arises from beta cell function impairment while T2DM results from insulin resistance along with inadequate beta cell function [7]. The decreased beta cell function and insulin resistance in diabetes may also play a key role in worsened lipid metabolism. In this connection, insulin deficiency or insulin resistance can increase TG by reducing the suppression of TG lipolysis and increasing fatty acids in the liver, and decrease HDL-C by reducing the inhibition of ApoA-I expression needed for HDL formation [8]. However, it remains unknown which of decreased BCF or increased IR contributes most to abnormal metabolism in different lipid components, i.e., high TG, low HDL-C and high LDL-C.

Statin treatment was associated with a 37% reduction in major CVD events in individuals with T2DM [9], but the residual risk of CVD remains substantially high. It is needed to understand the associations of the two fundamental features of diabetes, decreased BCF and increased IR, for different abnormal lipid components so as better control of CVD risk factors. Therefore, we conducted a cross-sectional study in China and aimed to explore whether decreased BCF and increased IR in newly diagnosed T1DM or T2DM are associated with abnormal lipids, i.e., high TG, low HDL-C and high LDL-C, with use of restricted cubic spline (RCS) to detect these potential non-linear associations.

Study design and population

From April 2015 to October 2017, we conducted a nationwide, multi-center, cross-sectional survey of 18, 976 participants with newly diagnosed diabetes in China. In this survey, we invited forty-six tertiary care hospitals across all seven geographic regions of China from twenty provinces and four municipalities to participate in this cross sectional survey. The Ethics Committees of the Second Xiangya hospitals, Central South University, China, approved this study, and all patients provided written informed consent before data collection.

The inclusion criteria were set as: 1) diagnosis of diabetes based on the World Health Organization 1999 criteria; 2) age 18 years and older; 3) diabetes duration less than one year; 4) outpatients attending clinics in the department of endocrinology. Individuals were excluded if pregnant at the time of diabetes diagnosis, if they had gestational diabetes mellitus, or if they had co-existing acute diseases such as infection or acute myocardial infarction that could affect glucose metabolism. In addition, we excluded 62 cases with insufficient data for disease classification, 1635 cases missing lipid data and 945 cases missing homeostasis model assessment of insulin resistance (HOMA2-IR) or beta-cell function (HOMA2-B) data. The remaining 16,334 patients were included in this analysis (Fig. 1).

Data collection procedures

Demographic characteristics (i.e., age and sex), clinical features, and lifestyle risk factors (i.e., exercise habits, diet, smoking, and alcohol consumption) were collected using a standard questionnaire by research nurses at each of the 46 participating hospitals. The nurses used standardized procedures to measure height, weight, waist circumference, hip circumference, systolic blood pressure (SBP) and diastolic blood pressure (DBP). Drug use information was retrieved from case notes.

Laboratory assays

Fasting plasma glucose (FPG), total cholesterol, triglycerides, HDL-C, LDL-C, plasma hemoglobin A1c (HbA1c) and fasting C-peptide were assayed by standard methods at the study sites. Postprandial blood samples were tested for 2-h postprandial plasma glucose (PPG) and C-peptide. The core laboratory (Central South University) performed serum glutamic acid decarboxylase antibodies (GADA) assays by a standardized radioligand assay as reported [10]. Serum samples for GADA assays from other hospitals were shipped on ice within one day and stored at -80 °C in the core laboratory. The assay was assessed in the 2016 islet autoantibody standardization program (IASP 2016).

Classification of diabetes

The classification of T1DM and T2DM were based on clinical characteristics and diabetes-related biochemical measurement results, including fasting and 2-hour postprandial plasma glucose and C-peptide, lipids levels, HbA1c and GADA serum levels. Type 1 diabetes was diagnosed based on insulin-dependent diabetes, prone to ketoacidosis or presence of GADA positivity. Type 2 diabetes was diagnosed as GADA-negative and insulin-independent patients.

Evaluation of insulin resistance and beta cell function

HOMA2-IR and HOMA2-B are estimated based on C peptide levels and plasma glucose using the HOMA calculator [11].

Definition of dyslipidemia

As recommended by the American Diabetes Association (ADA) [12], high TG was defined as TG ≥ 1.7 mmol/L, low HDL-C was defined as HDL-C < 1.0 mmol/L (males) and < 1.3 mmol/L (females), and high LDL-C was defined as LDL-C ≥ 2.6 mmol/L.

Statistical analysis

Continuous variables were expressed as mean ± (standard deviation, SD) or median (interquartile range) based on evaluation of normal distribution; categorical variable were given as number (percent). For analysis of continuous variables, Student t test or Mann-Whitney test were performed to compare differences between groups where appropriate. Frequency differences were compared using Chi-square test. R (version 4.0.2) and SPSS (version 26) were used to perform all the analysis. p < 0.05 was considered statistically significant.

Because there are no data to suggest that HOMA-B and HOMA-IR were linearly associated with abnormal lipids in diabetes, RCS analyses nested in the multivariable logistic regression analyses were used to check full-range associations of HOMA2-IR and HOMA2-B with different dyslipidemia in T1DM and T2DM, respectively. We first performed univariable analysis and then multivariable analyses with adjustment for age, sex, current smoking, current drinking, body mass index (BMI, calculated as kg/m²), HbA1c, SBP and current use of drugs including lipid lowering treatment, oral anti-diabetic treatment and insulin treatment to obtain full-range associations of HOMA2-IR and HOMA2-B with different dyslipidemia in T1DM and T2DM. In the RCS analysis, four knots were chosen because four knots were able to offer an adequate fit of the model and represents a good compromise between flexibility and the loss of precision caused by overfitting a small sample [13]. We identified threshold points of HOMA2-IR and HOMA2-B at the points, if any, where dyslipidemia risk started to rise or fall sharply, as in the previous investigations [14].We further stratified HOMA2-IR and HOMA2-B at the identified cutoff points and repeated the univariable and multivariable analysis to obtain odds ratios (OR) and 95% confidence intervals (CI) of HOMA2-IR and HOMA2-B in categorical variables as stratified at the these threshold points for high TG, low HDL-C and high LDL-C among patients with T1DM and T2DM, respectively.

Characteristics of the study participants

The mean age of the patients was 50.3 ± 13.3 years. Patients with T1DM were significantly younger, leaner and they had lower blood pressure, better lipid metabolic parameters but higher FPG and HbA1c levels than those with T2DM. Patients with T1DM were less insulin-resistant and less likely to have diet treatment, to be engaged in exercise and to have lipid lowering treatment than those with T2DM (Table 1).

Table 1

Characteristics of the study participants
Characteristics	T1DM	T2DM	p
n	1187	15147
Age (years)	42.9 ± 14.8	50.9 ± 13.0	< 0.001
Male (n,%)	697 (58.7%)	9078 (59.9%)	0.412
BMI (kg/m²)	21.8 ± 3.7	24.8 ± 3.6	< 0.001
FPG (mmol/L)	9.4 ± 4.2	9.1 ± 3.5	0.002
HbA1c (%)	10.7± 3.2	9.4 ± 2.7	< 0.001
Systolic BP (mmHg)	121.0 ± 15.8	127.8 ± 16.3	< 0.001
Diastolic BP (mmHg)	76.5 ± 10.7	80.2 ± 10.5	< 0.001
Waist circumference (cm)	81.3 ± 10.5	88.4 ± 10.6	< 0.001
PPG (mmol/L)	16.7 ± 6.6	15.3 ± 3.7	< 0.001
Triglyceride (mmol/L)	1.2 (0.8–1.8)	1.7 (1.2–2.7)	< 0.001
Triglyceride > 1.7 mmol/L (n,%)	344 (29.0%)	7770 (51.3%)	< 0.001
Total Cholesterol (mmol/L)	4.5 ± 1.3	4.8 ± 1.3	< 0.001
LDL-C (mmol/L)	2.7 ± 1.0	2.9 ± 1.0	< 0.001
LDL-C > 2.6 mmol/L (n,%)	599 (50.5%)	9078 (59.9%)	< 0.001
HDL-C (mmol/L)	1.2 (1.0-1.5)	1.1 (0.9–1.3)	< 0.001
HDL-C < 1.0 mmol/L, male, < 1.3 mmol/L female (n,%)	466 (39.3%)	7318 (48.3%)	< 0.001
Family history of diabetes (n,%)	280 (24.0%)	4310 (29.1%)	< 0.001
Fasting C-peptide (nmol/L)	200 (83.3–410.0)	569.4 (366.3–810.0)	< 0.001
Postprandial C-peptide (nmol/L)	423.5 (160.0-965.7)	1435.2 (879.9-2243.4)	< 0.001
HOMA2-IR	0.57 (0.22–1.17)	1.5 (1.0-2.2)	< 0.001
HOMA2-B (%)	20.2 (8.1–39.9)	43.9 (24.5–70.1)	< 0.001
Current smoking (n,%)	359 (30.7%)	4482 (30.0%)	0.611
Current drinking (n,%)	160 (13.8%)	2651 (17.9%)	< 0.001
Diet treatment (n,%)	551 (53.8%)	6929 (61.8%)	< 0.001
Physical activity (n,%)	453 (44.2%)	5914 (52.8%)	< 0.001
Lipid lowering drugs (n,%)	66 (6.1%)	1645 (12.3%)	< 0.001
Insulin treatment (n,%)	298 (25.5%)	1187 (8.0%)	< 0.001
Note: Data are presented as mean± SD, median (interquartile range), or n (%).
Abbreviation: BMI, body mass index; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; BP, blood pressure; PPG, postprandial plasma glucose; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol.
Comparisons between groups were performed with Mann-Whitney test or t test for continuous variables depending on normal distribution and chi-square test for categorical variables.

S- or U-shaped associations between HOMA2-IR or HOMA2-B and the risk of dyslipidemia in T1DM and T2DM

We modeled the associations of HOMA2-IR and HOMA2-B with the risk of dyslipidemia using RCS models in T1DM and T2DM after adjustment for age, sex, HOMA2-IR (or HOMA-B where appropriate), current smoking, current drinking, BMI, HbA1c, SBP, lipid lowering treatment, oral anti-diabetic treatment and insulin treatment (Fig. 2, Fig. 3).

The associations of HOMA2-IR and the risks of dyslipidemia (high TG, low HDL-C and high LDL-C) were S-shaped in T1DM with a positively linear association when HOMA2-IR was between 0.5 to 1.5-2.0 (Fig. 2a-c). However, in patients with T2DM, HOMA2-IR was positively associated with the risk of dyslipidemia when HOMA2-IR < 3.0 and levelled off for the risk of high TG and low HDL-C but showed a negative association with the risk of high LDL-C when HOMA2-IR > 3.0 (Fig. 2d-f).

The associations of HOMA2-B and the risk of dyslipidemia were different in T1DM as compared to those in T2DM. In patients with T1DM, there was a U-shaped association between HOMA2-B and the risk of high TG and high LDL-C with the lowest risk related to HOMA2-B of about 40.0 to 60.0 (Fig. 3a,3c). HOMA2-B was linearly positively associated with the risk of low HDL-C in T1DM (Fig. 3b). In patients with T2DM, however, the risk was stable at first and started to fall (for high TG) or rise (for low HDL-C) after HOMA2-B > 30 (Fig. 3d,3e). HOMA2-B was negatively associated with the risk of high LDL-C when less than 80 and then stable afterwards (Fig. 3f).

From above, the risk of abnormal lipid started to change steeply mainly from 1 to 2 of HOMA2-IR and at 30.0 and downwards of HOMA2-B, therefore, we selected these values as threshold points for further logistic regression analysis.

Associations between HOMA2-IR and the risk of dyslipidemia in T1DM and T2DM

The risks of dyslipidemia (high TG, low HDL-C and high LDL-C) associated with HOMA2-IR were estimated by both univariate logistic regression and multivariable logistic regression with adjustment for age, sex, beta cell function categories, current smoking, current drinking, BMI, HbA1c, SBP, lipid lowering treatment, oral anti-diabetic treatment and insulin treatment in T1DM and T2DM (Table 2). In patients with T1DM, high HOMA2-IR was associated with an elevated risk of high TG (p for trend < 0.001), low HDL-C (p for trend = 0.012) and high LDL-C (p for trend = 0.003) in univariable regression models but was only associated with increased risk of high TG (ORs of HOMA2-IR ≥ 2, ≥1-<2 vs < 1: 4.77, 95% CI 2.69–8.57; 2.31, 95% CI 1.54–3.47, p for trend < 0.001) after adjustment for potential confounders (Table 2). In patients with T2DM, high HOMA2-IR was associated with an elevated risk of high TG, low HDL-C and high LDL-C (all p for trend < 0.001) in univariable regression models and remained significant for high TG (ORs of HOMA2-IR ≥ 2, ≥1-<2 vs < 1: 2.80, 95% CI: 2.49–3.15; 1.71, 95% CI 1.54–1.89, p for trend < 0.001), low HDL-C (ORs of HOMA2-IR ≥ 2, ≥1-<2 vs < 1: 1.54, 95% CI 1.38–1.74; 1.21, 95% CI 1.09–1.34, p for trend < 0.001) and high LDL-C (ORs of HOMA2-IR ≥ 2, ≥1-<2 vs < 1: 1.19, 95% CI 1.06–1.33; 1.10, 95% CI 0.99–1.21, p for trend = 0.004) after adjustment for potential confounders (Table 2).

Table 2

Odds ratio of insulin resistance for abnormal lipid profile in T1DM and T2DM
Insulin resistance	High TG			Low HDL-C			High LDL-C
Insulin resistance	n (%)	OR (95% CI)	p	n (%)	OR (95% CI)	p	n (%)	OR (95% CI)	p
Univariable
T1DM			< 0.001ζ			0.012 ζ			0.003 ζ
High(≥ 2)	67 (59.8%)	6.11 (3.92–9.61)	< 0.001	55 (49.1%	1.60 (1.05–2.45)	0.03	69 (61.6%	1.85 (1.21–2.85)	0.005
Median(≥ 1-<2)	95 (37.8%	2.36 (1.71–3.26)	< 0.001	110 (43.8%)	1.32 (0.97–1.78)	0.07	134 (53.4%)	1.29 (0.96–1.74)	0.09
Low(< 1)	182 (22.1%)	Ref		301 (36.5%	Ref		396 (48.1%	Ref
T2DM			< 0.001 ζ			< 0.001 ζ			< 0.001 ζ
High(≥ 2)	3024 (65.3%)	3.47 (3.17–3.81)	< 0.001	2599 (56.2%)	1.76 (1.61–1.92)	< 0.001	2855 (61.7%)	1.29 (1.18–1.41)	< 0.001
Median(≥ 1-<2)	3284 (49.9%)	1.79 (1.65–1.95)	< 0.001	3066 (46.6%)	1.20 (1.10–1.30)	< 0.001	3930 (59.7%)	1.16 (1.06–1.25)	< 0.001
Low(< 1)	1462 (37.1%)	Ref		1653 (42.0%)	Ref		2293 (58.3%)	Ref
Multivariable
T1DM			< 0.001 ζ			0.232 ζ			0.470 ζ
High(≥ 2)	67 (59.8%)	4.77 (2.69–8.57)	< 0.001	55 (49.1%	1.24 (0.72–2.14)	0.44	69 (61.6%	1.11 (0.65–1.90)	0.71
Median(≥ 1-<2)	95 (37.8%	2.31(1.54–3.47)	< 0.001	110 (43.8%)	1.31 (0.90–1.91)	0.16	134 (53.4%)	1.20 (0.83–1.74	0.32
Low(< 1)	182 (22.1%)	Ref		301 (36.5%	Ref		396 (48.1%	Ref
T2DM			< 0.001 ζ			< 0.001 ζ			0.004 ζ
High(≥ 2)	3024 (65.3%)	2.80 (2.49–3.15)	< 0.001	2599 (56.2%)	1.54 (1.38–1.74)	< 0.001	2855 (61.7%)	1.19 (1.06–1.33)	0.004
Median(≥ 1-<2)	3284 (49.9%)	1.71 (1.54–1.89)	< 0.001	3066 (46.6%)	1.21 (1.09–1.34)	< 0.001	3930 (59.7%)	1.10 (0.99–1.21)	0.08
Low(< 1)	1462 (37.1%)	Ref		1653 (42.0%)	Ref		2293 (58.3%)	Ref
Note: Univariate model was adjusted for beta cell function categories; Multivariate model was adjusted for age, sex, beta cell function categories, current smoking status, current drinking status, BMI, HbA1c, systolic blood pressure, use of lipid lower drugs, use of oral antidiabetic drugs and insulin treatment.
ζ p for trend

Associations between HOMA2-B and risk of dyslipidemia in T1DM and T2DM

The risks of dyslipidemia (high TG, low HDL-C and high LDL-C) associated with HOMA2-B were estimated by both univariate logistic regression and multivariable logistic regression with adjustment for age, sex, insulin resistance categories, current smoking, current drinking, BMI, HbA1c, SBP, lipid lowering treatment, oral anti-diabetic treatment and insulin treatment in T1DM and T2DM (Table 3). In patients with T1DM, low HOMA2-B, i.e., < 30.0 versus ≥ 30.0, was associated with marginally increased risk of high TG in univariable analysis (OR: 1.32, 95% CI 0.98–1.79, p = 0.08) and multivariable analysis (OR: 1.42, 95% CI 0.98–2.10, p = 0.07) but not associated with low HDL-C and high LDL-C. While in patients with T2DM, HOMA2-B (< 30.0) was associated with an increased risk of high TG and high LDL-C (all p value < 0.001) in univariable regression models and remained associated with an increased risk of high TG (OR: 1.21, 95% CI 1.09–1.34, p < 0.001) and high LDL-C (OR:1.23, 95% CI 1.12–1.36, p < 0.001) in multivariable regression models (Table 3).

Table 3

Odds ratio of beta cell function for abnormal lipid profile in T1DM and T2DM
Beta cell function	High TG			Low HDL-C			High LDL-C
Beta cell function	n (%)	OR (95% CI)	p	n (%)	OR (95% CI)	p	n (%)	OR (95% CI)	p
Univariable
T1DM
High (≥ 30)	212 (27.2%)	Ref		292 (37.4%)	Ref		391 (50.1%)	Ref
Low(< 30)	132 (32.4%)	1.32 (0.98–1.79)	0.08	174 (42.8%)	0.92 (0.70–1.20)	0.54	208 (51.1%)	1.12 (0.86–1.46)	0.38
T2DM
High (≥ 30)	5195 (50.9%)	Ref		2285 (46.3%)	Ref		3233 (65.5%)	Ref
Low(< 30)	2575 (52.1%)	1.32 (1.23–1.42)	< 0.001	5033 (49.3%)	0.97 (0.91–1.04)	0.44	5845 (57.3%)	1.49 (1.38–1.60)	< 0.001
Multivariable
T1DM
High (≥ 30)	212 (27.2%)	Ref		292 (37.4%)	Ref		391 (50.1%)	Ref
Low(< 30)	132 (32.4%)	1.42 (0.98–2.10)	0.07	174 (42.8%)	0.94 (0.68–1.32)	0.74	208 (51.1%)	1.08 (0.79–1.49)	0.62
T2DM
High (≥ 30)	5195 (50.9%)	Ref		2285 (46.3%)	Ref		3233 (65.5%)	Ref
Low(< 30)	2575 (52.1%)	1.21 (1.09–1.34)	< 0.001	5033 (49.3%)	0.93 (0.84–1.03)	0.16	5845 (57.3%)	1.23 (1.12–1.36)	< 0.001
Note: Univariate model was adjusted for insulin resistance categories; Multivariate model was adjusted for age, sex, insulin resistance categories, current smoking status, current drinking status, BMI, HbA1c, systolic blood pressure, use of lipid lower drugs, use of oral antidiabetic drugs and insulin treatment.

In this study, we found that high IR and low BCF as estimated by the updated homeostasis model assessment had different associations with risk of dyslipidemia in patients with newly-onset T1DM and T2DM. In T1DM, high IR was associated with the risk of high TG while in T2DM, high IR was associated with increased risks of high TG, low HDL-C and high LDL-C and low BCF was associated with increased risk of high TG.

Studies in different populations reported consistent findings regarding the associations between IR and dyslipidemia. High TG, low HDL-C (p for trend = 0.052) and high LDL-C were associated with increased IR in Chinese elderly patients with newly-onset diabetes [15] and in Japanese nonobese patients with T2DM [16]. The Coronary Artery Calcification in Type 1 Diabetes Study found that high TG but not HDL-C or LDL-C was inversely associated with glucose infusion in American adult patients with T1DM [17]. The first study only included elderly patients and the latter two cohorts had an 8- and 23-year duration of diabetes, respectively. Using a large representative sample of patients with newly diagnosed diabetes, our study confirmed the above findings.

The associations of IR with high TG, low HDL-C and high LDL-C are biologically plausible. Lipid abnormalities associated with IR are very likely to be initiated by the resistance of adipocytes to insulin. Insulin-resistant fat cells lead to increased hydrolysis of TGs and release of fatty acids to the liver [18]. This can increase the synthesis of TG in the liver and stimulate the assembly and secretion of very low-density lipoprotein (VLDL), the main transporter of fasting TG and as a major contributor to the hypertriglyceridemia [19]. Decreased degradation of apolipoprotein B, the predominant surface protein of VLDL, was seen in IR states resulting from increased free fatty acids, thus causing an overproduction of VLDL [20]. An increased in TG-rich lipoproteins is often associated with an increase in small dense LDL and a decrease in HDL levels. Hypertriglyceridemia stimulates the transfer of TG-rich lipoproteins to HDL and LDL in exchange for cholesteryl esters [21], leading to increased TG content of HDL and LDL. Furthermore, the TG content is then converted to small dense LDL and small HDL. The expression of Apo-I, which can dissociate from TG-rich HDL, is decreased in patients with diabetes or with IR states, leading a reduction in HDL levels [18].

In this study, we found that low BCF associated with an increased risk of high TG and high LDL-C in T2DM. Previous studies also showed that TG/HDL-C or log(TG)/HDL-C was associated with BCF in patients with T2DM [22, 23]. However, we did not detect the association between HDL-C and BCF in T2DM. Dullaart et al also found that BCF was not significantly correlated with HDL-C in patients with well-controlled T2DM but were significantly correlated with HDL functional markers [24]. Furthermore, we observed an association of LDL-C with BCF in T2DM, suggesting a potential role of LDL-C lowering treatment in beta cell protection.

A biological link between low BCF and abnormal lipid metabolism is also plausible. Excess exposure of beta cells to free fatty acids can decrease beta cell secretory function and cause cell death [25]. Besides, insulin plays a central role in the regulation of lipid metabolism. Insulin inhibits lipolysis in the adipose tissue resulting in reduced free fatty acids into the circulation. Besides, insulin inhibits VLDL production and promotes the catabolism of TG-rich lipoproteins by activating lipoprotein lipase [26]. Thus, a relative insulin deficiency could increase VLDL production resulting in hypertriglyceridemia [27]. Insulin also stimulates the clearance of LDL by increasing LDL receptor expression and activity [28]. These effects might explain the associations between low beta cell function and an increased risk of high TG and LDL-C in those with T2DM. However, we did not observe the significant associations between BCF and risk of dyslipidemia in those with T1DM. Possible explanations could be: 1) The prevalence of dyslipidemia in T1DM is relative low. Lipid abnormalities in T1DM are more frequent in those with poor glycemic control [29], which is observed in several studies [30–32]. The lipid profile is similar in T1DM patients with good glycemic control and general population [3]. 2) An earlier study showed that insulin therapy may resolve lipid abnormalities in 24 hours in T1DM patients with diabetic ketoacidosis, by increasing TG-rich lipoprotein catabolism [33]. This finding may suggest that dyslipidemia affected by insulin insufficiency can be rapidly resolved by insulin treatment in T1DM.

Our study has clinical significances. First, we found that high IR was associated with increased risk of high TG in T1DM. Evidence also showed that anti-diabetic drugs like glucagon-like peptide-1 receptor (GLP-1R) agonists (exenatide), sodium glucose cotransporter 2 (SGLT2) inhibitors and metformin combined with insulin treatment have some beneficial effects in T1DM, such as contributing to weight loss or reducing insulin requirement [34–37]. These findings suggest that such anti-diabetic drugs combined with insulin treatment may have a potential beneficial effect on lipid metabolism by increasing insulin sensitivity and improve CVD outcomes in patients with T1DM, especially for those with obesity and insulin resistance. Additionally, it is novel and interesting to have detected significant associations of low BCF with lipid abnormalities (high TG and high LDL-C) in those with T2DM. T2DM in East Asians is characterized primarily by decreased beta-cell dysfunction [38]. Thus, our findings support early use of insulin in those T2DM patients with low BCF, which may have potential benefits for lipid metabolism and thus reduced risk of CVD in the future.

Our study has some limitations. First, our study was a cross sectional survey and causality cannot be established. It is also possible that some of the associations between low BCF and high IR with high TG, low HDL-C and high LDL-C had reverse causal relationships. Second, use of drugs, especially, lipid lowering drugs may have major confounding effects on our conclusions. Although information regarding use of these drugs was documented and we had made careful adjustment for use of those drugs, the adjustment cannot completely remove all of their confounding effects and residual confounding effects may be substantial. Third, newly-onset diagnosed patients may have beta cell function inhibition due to high glucose level, resulting in lower HOMA2-B and HOMA2-IR in those with poor glycemic control, leading to inaccurate estimation of these associations in the study.

In summary, in patients with newly diagnosed diabetes, IR and BCF had different associations with risk of dyslipidemia in T1DM and T2DM, supporting early use of anti-diabetes treatment that improve IR or BCF because it may have beneficial effects for lipid metabolism and therefore, reduced risk of CVD in the future.

CVD: Cardiovascular disease

T1DM: Type 1 diabetes mellitus

T2DM: Type 2 diabetes mellitus

IR: Insulin resistance

BCF: Beta cell function

TG: Triglyceride

HDL-C: High-density lipoprotein cholesterol

LDL-C: Low-density lipoprotein cholesterol

OR: Odds ratios

CI: Confidence intervals

HOMA2-IR: Homeostasis model assessment of insulin resistance

HOMA2-B: Homeostasis model assessment of beta-cell function

RCS: Restricted cubic spline

SBP: Systolic blood pressure

DBP: Diastolic blood pressure

FPG: Fasting plasma glucose

HbA1c: Hemoglobin A1c

PPG: Postprandial plasma glucose

GADA: Glutamic acid decarboxylase antibodies

IASP 2016: 2016 islet autoantibody standardization program

ADA: American Diabetes Association

SD: Standard deviation

BMI: Body mass index

VLDL: Very low-density lipoprotein

GLP-1R: Glucagon-like peptide-1 receptor

SGLT2: Sodium glucose cotransporter 2

Ethics approval and consent to participate

The Study was approved by Ethics Committees of the Second Xiangya hospitals, Central South University in China (NO. 2014032). All individual participants provided written informed consent before data collection.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Science and Technology Infrastructure Program (2013BAI09B12, 2015BAI12B13) and the National Key R&D Program of China (2016YFC1305000, 2017YFC1309604).

Authors' contributions

XT drafted the manuscript and performed data analysis. X Yan, XL, ZZ conceived of and designed the study. X Yan, HZ, GH, XN, HJ, XL, ZZ contributed to the data collection and conducting the study. X Yang, XL, ZZ contributed to the data interpretation and discussion. All authors reviewed and approved the manuscript in its original, revised and final form. All authors read and approved the final manuscript.

Acknowledgements

We would like to thank all of the patients and investigators involved involved at the 46 participating centers of National Clinical Research Center for Metabolic Diseases. The members of National Clinical Research Center for Metabolic Diseases (investigators and hospitals); Linong Ji, Xueyao Han, Ling Chen, Xiaoling Chen, Peking University People’s Hospital; Lixin Guo, Xiaofan Jia, Shan Ding, Beijing Hospital; Xinhua Xiao, Cuijuan Qi, Xiaojing Wang, Peking Union Medical College Hospital; Zhongyan Shan, Yaxin Lai, Zhuo Zhang, The First Hospital of China Medical University; Yu Liu, Yan Cheng, Hanqing Cai, The Second Hospital of Jilin University; Yadong Sun, Yan Ma, Haiying Wang, People’s Hospital of Jilin Province; Yiming Li, Chaoyun Zhang, Shuo Zhang, Hua Shan Hospital, Fudan University; Tao Yang, Hao Dai, Mei Zhang, The First Affiliated Hospital with Nanjing Medical University; Liyong Yang, Peiwen Wu, Xiaofang Yan, The First Affiliated Hospital of Fujian Medical University; Yangang Wang, Fang Wang, Hong Chen, The Affiliated Hospital of Qingdao University; Qifu Li, Rong Li, The First Affiliated Hospital of Chongqing Medical University; Qiuhe Ji, Li Wang, Xiangyang Liu, Xijing Hospital, Fourth Military Medical University; Jing Liu, Suhong Wei, Gansu Provincial Hospital; Yun Zhu, Rui Ma, The First Affiliated Hospital of Xinjiang Medical University; Gebo Wen, Xinhua Xiao, Jianping Qin, The First Affiliated Hospital of University of South China; Jian Kuang, Yan Lin, Guangdong General Hospital; Shaoda Lin, Kun Lin, the First Affiliated Hospital of Shantou University Medical College; Li Li, Heji Hospital Affiliated to Changzhi Medical College; Gan Huang, Shuoming Luo, The Second Xiangya Hospital of Central South University; Huibiao Quan, Leweihua Lin, Hainan General Hospital; Hongyu Kuang, Weihua Wu, The First Affiliated Hospital of Harbin Medical University; Yuling He, The First Affiliated Hospital of Guangxi Medical University; Xiaoyan Chen, Yuyu Tan, The First Affiliated Hospital of Guangzhou Medical University; Ling He, Guangzhou First People’s Hospital; Chao Zheng, The Second Affiliated Hospital of Wenzhou Medical University; Jianying Liu, Zhifang Yang, The First Affiliated Hospital of Nanchang University; Xiaoyang Lai, The Second Affiliated Hospital of Nanchang University; Ling Hu, Yan Zhu, Ying Hu, The Third Affiliated Hospital of Nanchang University; Xuqing Li, Henan Provincial People’s Hospital; Hong Li, Yushan Xu, The First Affiliated Hospital of Kunming Medical University; Yang Ou, The First People’s Hospital of Yunnan Province; Jianping Wang, The Second Hospital University of South China; Changqing Luo, Xiaoyue Wang, The First People’s Hospital of YueYang; Zhiming Deng, Shenglian Gan, The first People’s Hospital of Changde City; Zhaohui Mo, Ping Jin, Honghui He, The Third Xiangya Hospital of Central South University; Qiuxia Huang, Dongguan People’s Hospital; Fang Wang, Heping Hospital Affiliated to Changzhi Medical College; Yi Zhang, Zhenzhen Hong, First Hospital of Quanzhou Affiliated to Fujian Medical University; Yuezhong Ren, Pengfei Shan, The Second Affiliated Hospital of Zhejiang University School of Medicine; Caifeng Yan, Hui Zhang, Northern Jiangsu People’s Hospital; Zhiwen Liu, Shanghai Xuhui District Central Hospital; Meibiao Zhang, The First People’s Hospital of Huaihua; Ming Liu, Heting Wang, Tianjin Medical University General Hospital; Liujun Fu, The First Affiliated Hospital of the Henan University of Science and Technology; Hui Fang, Tangshan Gongren Hospital; Hui Sun, The Affiliated Hospital of Inner Mongolia Medical University.

Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H. Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes. Diabetologia. 2001;44 Suppl 2:S14-21. https://doi.org/10.1007/pl00002934.
Soedamah-Muthu SS, Fuller JH, Mulnier HE, Raleigh VS, Lawrenson RA, Colhoun HM. All-cause mortality rates in patients with type 1 diabetes mellitus compared with a non-diabetic population from the UK general practice research database, 1992-1999. Diabetologia. 2006;49(4):660-6. https://doi.org/10.1007/s00125-005-0120-4.
de Ferranti SD, de Boer IH, Fonseca V, Fox CS, Golden SH, Lavie CJ, et al. Type 1 diabetes mellitus and cardiovascular disease: a scientific statement from the American Heart Association and American Diabetes Association. Circulation. 2014;130(13):1110-30. https://doi.org/10.1161/CIR.0000000000000034.
Martin-Timon I, Sevillano-Collantes C, Segura-Galindo A, Del Canizo-Gomez FJ. Type 2 diabetes and cardiovascular disease: Have all risk factors the same strength? World J Diabetes. 2014;5(4):444-70. https://doi.org/10.4239/wjd.v5.i4.444.
Kahn SE. Clinical review 135: The importance of beta-cell failure in the development and progression of type 2 diabetes. J Clin Endocrinol Metab. 2001;86(9):4047-58. https://doi.org/10.1210/jcem.86.9.7713.
Vladu M, Clenciu D, Efrem IC, Fortofoiu MC, Amzolini A, Micu ST, et al. Insulin Resistance and Chronic Kidney Disease in Patients with Type 1 Diabetes Mellitus. J Nutr Metab. 2017;2017:6425359. https://doi.org/10.1155/2017/6425359.
Goldstein BJ. Insulin resistance as the core defect in type 2 diabetes mellitus. Am J Cardiol. 2002;90(5A):3G-10G. https://doi.org/10.1016/s0002-9149(02)02553-5.
Feingold KR. Dyslipidemia in Diabetes. In: Feingold KR, Anawalt B, Boyce A, Chrousos G, de Herder WW, Dungan K, et al., editors. Endotext. South Dartmouth (MA)2000.
Colhoun HM, Betteridge DJ, Durrington PN, Hitman GA, Neil HA, Livingstone SJ, et al. Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial. Lancet. 2004;364(9435):685-96. https://doi.org/10.1016/S0140-6736(04)16895-5.
Zhou Z, Xiang Y, Ji L, Jia W, Ning G, Huang G, et al. Frequency, immunogenetics, and clinical characteristics of latent autoimmune diabetes in China (LADA China study): a nationwide, multicenter, clinic-based cross-sectional study. Diabetes. 2013;62(2):543-50. https://doi.org/10.2337/db12-0207.
Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care. 1998;21(12):2191-2. https://doi.org/10.2337/diacare.21.12.2191.
American Diabetes Association. Standards of medical care in diabetes--2013. Diabetes Care. 2013;36 Suppl 1:S11-66. https://doi.org/10.2337/dc13-S011.
Harrell FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis: Springer International Publishing; 2015.
Li J, Huo X, Cao YF, Li SN, Du Z, Shao P, et al. Bile acid metabolites in early pregnancy and risk of gestational diabetes in Chinese women: A nested case-control study. EBioMedicine. 2018;35:317-24. https://doi.org/10.1016/j.ebiom.2018.08.015.
Pang SJ, Man QQ, Song S, Song PK, Liu Z, Li YQ, et al. [Association of lipid parameters with insulin resistance in different glycemic among the elderly population]. Zhonghua Yu Fang Yi Xue Za Zhi. 2018;52(6):629-35. https://doi.org/10.3760/cma.j.issn.0253-9624.2018.06.012.
Taniguchi A, Nakai Y, Sakai M, Yoshii S, Hamanaka D, Hatae Y, et al. Relationship of regional adiposity to insulin resistance and serum triglyceride levels in nonobese Japanese type 2 diabetic patients. Metabolism. 2002;51(5):544-8. https://doi.org/10.1053/meta.2002.31984.
Maahs DM, Nadeau K, Snell-Bergeon JK, Schauer I, Bergman B, West NA, et al. Association of insulin sensitivity to lipids across the lifespan in people with Type 1 diabetes. Diabet Med. 2011;28(2):148-55. https://doi.org/10.1111/j.1464-5491.2010.03143.x.
Ginsberg HN. Insulin resistance and cardiovascular disease. J Clin Invest. 2000;106(4):453-8. https://doi.org/10.1172/JCI10762.
Adiels M, Olofsson SO, Taskinen MR, Boren J. Overproduction of very low-density lipoproteins is the hallmark of the dyslipidemia in the metabolic syndrome. Arterioscler Thromb Vasc Biol. 2008;28(7):1225-36. https://doi.org/10.1161/ATVBAHA.107.160192.
Arca M, Pigna G, Favoccia C. Mechanisms of diabetic dyslipidemia: relevance for atherogenesis. Curr Vasc Pharmacol. 2012;10(6):684-6. https://doi.org/10.2174/157016112803520864.
Guerin M, Le Goff W, Lassel TS, Van Tol A, Steiner G, Chapman MJ. Atherogenic role of elevated CE transfer from HDL to VLDL(1) and dense LDL in type 2 diabetes : impact of the degree of triglyceridemia. Arterioscler Thromb Vasc Biol. 2001;21(2):282-8. https://doi.org/10.1161/01.atv.21.2.282.
Hermans MP, Ahn SA, Rousseau MF. log(TG)/HDL-C is related to both residual cardiometabolic risk and beta-cell function loss in type 2 diabetes males. Cardiovasc Diabetol. 2010;9:88. https://doi.org/10.1186/1475-2840-9-88.
Young KA, Maturu A, Lorenzo C, Langefeld CD, Wagenknecht LE, Chen YI, et al. The triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio as a predictor of insulin resistance, beta-cell function, and diabetes in Hispanics and African Americans. J Diabetes Complications. 2019;33(2):118-22. https://doi.org/10.1016/j.jdiacomp.2018.10.018.
Dullaart RP, Annema W, de Boer JF, Tietge UJ. Pancreatic beta-cell function relates positively to HDL functionality in well-controlled type 2 diabetes mellitus. Atherosclerosis. 2012;222(2):567-73. https://doi.org/10.1016/j.atherosclerosis.2012.03.037.
Poitout V, Robertson RP. Glucolipotoxicity: fuel excess and beta-cell dysfunction. Endocr Rev. 2008;29(3):351-66. https://doi.org/10.1210/er.2007-0023.
Verges B. New insight into the pathophysiology of lipid abnormalities in type 2 diabetes. Diabetes Metab. 2005;31(5):429-39. https://doi.org/10.1016/s1262-3636(07)70213-6.
Nikkila EA, Kekki M. Plasma triglyceride transport kinetics in diabetes mellitus. Metabolism. 1973;22(1):1-22. https://doi.org/10.1016/0026-0495(73)90024-3.
Chait A, Bierman EL, Albers JJ. Low-density lipoprotein receptor activity in cultured human skin fibroblasts. Mechanism of insulin-induced stimulation. J Clin Invest. 1979;64(5):1309-19. https://doi.org/10.1172/JCI109587.
Dullaart RP. Plasma lipoprotein abnormalities in type 1 (insulin-dependent) diabetes mellitus. Neth J Med. 1995;46(1):44-54. https://doi.org/10.1016/0300-2977(94)00048-e.
Lipid and lipoprotein levels in patients with IDDM diabetes control and complication. Trial experience. The DCCT Research Group. Diabetes Care. 1992;15(7):886-94. https://doi.org/10.2337/diacare.15.7.886.
Marcovecchio ML, Dalton RN, Prevost AT, Acerini CL, Barrett TG, Cooper JD, et al. Prevalence of abnormal lipid profiles and the relationship with the development of microalbuminuria in adolescents with type 1 diabetes. Diabetes Care. 2009;32(4):658-63. https://doi.org/10.2337/dc08-1641.
Guy J, Ogden L, Wadwa RP, Hamman RF, Mayer-Davis EJ, Liese AD, et al. Lipid and lipoprotein profiles in youth with and without type 1 diabetes: the SEARCH for Diabetes in Youth case-control study. Diabetes Care. 2009;32(3):416-20. https://doi.org/10.2337/dc08-1775.
Weidman SW, Ragland JB, Fisher JN, Jr., Kitabchi AE, Sabesin SM. Effects of insulin on plasma lipoproteins in diabetic ketoacidosis: evidence for a change in high density lipoprotein composition during treatment. J Lipid Res. 1982;23(1):171-82.
Guyton J, Jeon M, Brooks A. Glucagon-like peptide 1 receptor agonists in type 1 diabetes mellitus. Am J Health Syst Pharm. 2019;76(21):1739-48. https://doi.org/10.1093/ajhp/zxz179.
Buse JB, Garg SK, Rosenstock J, Bailey TS, Banks P, Bode BW, et al. Sotagliflozin in Combination With Optimized Insulin Therapy in Adults With Type 1 Diabetes: The North American inTandem1 Study. Diabetes Care. 2018;41(9):1970-80. https://doi.org/10.2337/dc18-0343.
Danne T, Cariou B, Banks P, Brandle M, Brath H, Franek E, et al. HbA1c and Hypoglycemia Reductions at 24 and 52 Weeks With Sotagliflozin in Combination With Insulin in Adults With Type 1 Diabetes: The European inTandem2 Study. Diabetes Care. 2018;41(9):1981-90. https://doi.org/10.2337/dc18-0342.
Petrie JR, Chaturvedi N, Ford I, Brouwers M, Greenlaw N, Tillin T, et al. Cardiovascular and metabolic effects of metformin in patients with type 1 diabetes (REMOVAL): a double-blind, randomised, placebo-controlled trial. Lancet Diabetes Endocrinol. 2017;5(8):597-609. https://doi.org/10.1016/S2213-8587(17)30194-8.
Moller JB, Dalla Man C, Overgaard RV, Ingwersen SH, Tornoe CW, Pedersen M, et al. Ethnic differences in insulin sensitivity, beta-cell function, and hepatic extraction between Japanese and Caucasians: a minimal model analysis. J Clin Endocrinol Metab. 2014;99(11):4273-80. https://doi.org/10.1210/jc.2014-1724.

Download PDF

Version 1

posted

You are reading this latest preprint version

Associations of Insulin Resistance and Beta Cell Function with Abnormal Lipid Profile in Newly Diagnosed Diabetes: A Cross-sectional Study

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study design and population

Data collection procedures

Laboratory assays

Classification of diabetes

Evaluation of insulin resistance and beta cell function

Definition of dyslipidemia

Statistical analysis

Results

Characteristics of the study participants

S- or U-shaped associations between HOMA2-IR or HOMA2-B and the risk of dyslipidemia in T1DM and T2DM

Associations between HOMA2-IR and the risk of dyslipidemia in T1DM and T2DM

Associations between HOMA2-B and risk of dyslipidemia in T1DM and T2DM

Discussion

Conclusions

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

References

Status:

Version 1