Lipidomics Analysis of Impaired Glucose Tolerance and Type 2 Diabetes Mellitus in Overweight or Obese Elderly Adults

DOI: https://doi.org/10.21203/rs.3.rs-2419491/v1

Abstract

Background

Aging, obesity, and type 2 diabetes mellitus (T2DM) form a metabolic disease continuum that has a continuously increasing prevalence. Lipidomics explains the complex interactions between lipid metabolism and metabolic diseases. We aimed to systematically investigate the plasma lipidome changes induced by impaired glucose tolerance (IGT) and T2DM in overweight/obese elderly individuals and to identify potential biomarkers to differentiate between the IGT, T2DM, and control groups.

Methods

In this cross-sectional study, plasma samples from 148 overweight/obese elderly individuals, including 52 patients with IGT, 47 patients with T2DM, and 49 euglycemic controls, were analyzed using a high-coverage non-targeted absolute quantitative lipidomics approach.

Results

We quantified 1,840 lipids from thirty-eight classes and seven lipid categories. Among overweight/obese elderly individuals, the lipidomic profiles of IGT and T2DM patients were significantly different from those of controls, while they were similar in the IGT and T2DM groups. The concentrations of diglycerides, triglycerides, phosphatidylcholines, and ceramides were altered in the IGT and T2DM groups. IGT and T2DM induced the accumulation of triglycerides with longer chain lengths and phosphatidylcholines with longer even-chains and regulated the saturation of triglyceride- and phosphatidylcholine-associated fatty acids. Finally, 17 lipids that could be used to differentiate between the IGT, T2DM, and control groups were successfully identified.

Conclusion

Lipidomics revealed apparent lipidome-wide changes in overweight/obese elderly patients with IGT and T2DM. This study’s results help to explain the complex dysfunctional lipid metabolism in aging, obesity, and diabetes.

Introduction

The growing aging population and the increasing prevalence of type 2 diabetes mellitus (T2DM) has become a substantial concern for healthcare systems worldwide. A study reported that the prevalence of diabetes peaks at the age of 65–79 years; furthermore, in 2017, there were 122 million adults aged ≥ 65 years with diabetes mellitus worldwide, and this number is projected to reach 253 million in 2045 [1]. China has the largest number of elderly people diagnosed with diabetes (35.5 million, aged ≥ 60 years) [2]. Impaired glucose tolerance (IGT) is a marked risk factor for T2DM, with an estimated 374 million people aged 18–99 years having IGT in 2017, and approximately half of these adults were above the age of 50 years [1]. Obesity plays a critical role in the development and progression of both diabetes and prediabetes, and the body mass index (BMI) is a powerful and modifiable risk factor for T2DM [3]. Hence, both aging and obesity are crucial contributing factors that lead to an imbalance between increased insulin resistance and deterioration of insulin secretory function, which results in the development and progressive worsening of T2DM [4, 5]. Aging, obesity, and T2DM have formed a metabolic disease continuum with a continuously increasing prevalence [6]. Overweight and obese older adults (> 65 years) with T2DM present particularly difficult challenges as they have increased exposure to various comorbidities, especially cardiovascular disease, hypertension, and dyslipidemia, and the complexity of disease management in these patients is also a challenge [7–10].

Lipids perform many physiological functions, such as energy storage, cell membrane composition, and cell signaling, as well as in many diseases, such as cardiovascular diseases, neurodegenerative diseases, and cancer [11–13].

Using lipidomics, researchers have identified significant biomarkers and investigated the relationship between disorders of lipid metabolism and the pathogenesis of T2DM. In a prospective study, five lipids, lysophosphatidylcholine (LPC) (18:2), triglyceride (TG) (50:1,54:5,56:4), and phosphatidylcholine (PC) (42:6e), were selected to predict T2DM combined with traditional risk factors [14]. Even-chain saturated fatty acids (SFAs) (14:0, 16:0, and 18:0) are positively associated with incident T2D, whereas odd-chain SFAs (15:0 and 17:0) are negatively correlated with diabetes [15]. Furthermore, a strong association between obesity, aging, and dysregulation of lipid metabolism has been well established. Obesity was observed to lead to increased levels of long-chain acyl-carnitines, and several lipids have been selected as biomarkers of aging, especially sphingolipids, ether-linked phospholipids, and ester-linked phospholipids [16–18]. It seems complex and of great significance to explore the relationship between lipid metabolism and IGT and T2DM in obese and elderly populations.

However, complexity and lack of comprehensive coverage of the lipids are major challenges for lipidomics technologies. Ultra-high-performance liquid chromatography (UHPLC) ranks among the most flexible and efficient separation techniques coupled with high-sensitivity detection via orbitrap mass spectrometry (UHPLC-orbitrap-MS), allowing for the detection and identification of a broad range of lipids [19].

In the current study, we used a high-coverage non-targeted absolute quantitative lipidomics approach constructed using UHPLC-orbitrap-MS system to perform lipidomics in a cohort of 148 overweight/obese elderly participants, including 49 individuals with normal glucose tolerance (NGT), 52 individuals with IGT, and 47 patients with T2DM. We systematically defined the lipidomic profiles of the three groups and further investigated the IGT/T2DM-induced alteration in PCs and TGs compositions. In addition, we successfully identified potential biomarkers to discriminate between overweight/obese elderly IGT and T2DM patients and NGT subjects. The result of this study could help explain the complex dysfunctional lipid metabolism in aging, obesity, and diabetes populations.

Methods

Study population

The participants in this study were from a community-based cohort in Chengguan District, Lanzhou, China. Permanent residents aged 65–70 years with a BMI ≥ 24 kg/m2 were included. The exclusion criteria were as follows: use of drugs that interfered with blood glucose levels, such as glucocorticoids, metformin, and glibenclamide; current or previous history of severe hepatic disease, cancer, autoimmune diseases, or blood diseases. 47 patients with T2DM, 52 patients with IGT, and 49 individuals with NGT were enrolled. Each participant was asked to complete a questionnaire to collect demographic information, such as age, sex, and medical history. Informed consent was obtained from all the participants. The study was approved by the ethics committee of the Gansu Provincial Hospital (No. 2018-076).

Measurements

Clinical measurements, such as body height, weight, and blood pressure, were measured by specially trained doctors and nurses using standardized methods. Plasma samples were collected from participants after an overnight fast of at least 8 h. All participants underwent a 75-g oral glucose tolerance test, and plasma glucose was obtained at 0 and 2 h during the test. T2DM was defined as FPG ≥ 7.0 mmol/L, or 2-h post-load plasma glucose (2hPG) ≥ 11.1 mmol/L. IGT was defined as FPG < 7.0 mmol/L, and 2hPG levels between 7.8 and 11.1 mmol/L [20].

Biochemical measurements, such as alanine transaminase (ALT), creatinine (CREA) and total cholesterol (TC), were measured using standard methods (Ci 1620 Automatic biochemical immune analysis system, Abbott Laboratories, USA).

Lipid profiling

Plasma collection and preparation

Plasma was immediately separated from the blood samples by centrifugation for 5 min at 3000 rpm and stored at − 80°C until analysis. Water (200 µL) was added to the thawed plasma samples at 4°C and vortexed for 5s. Subsequently, 240 µL of precooled methanol was added to 800 µL of methyl tert-butyl ether (MTBE), vortexed again, and sonicated for 20 min. The mixture was stored at room temperature for 30 min before centrifugation at 14 000 × g for 15 min. The upper layer was collected and dried under a nitrogen atmosphere. Quality control (QC) samples were prepared by mixing equal volumes of each sample.

Instrumental analysis and data preprocessing

Reverse phase chromatography (UHPLC Nexera LC-30A system) was used for sample separation using a CSH C18 column (1.7 µm, 2.1 mm× 100 mm, Waters). Mass spectra were acquired using Q-Exactive Plus in the positive and negative modes. The LipidSearch software (version 4.1, Thermo Scientific, USA) was used for peak identification, lipid identification (secondary identification), peak extraction, peak alignment, and quantitative processing. Plasma with lipid analyses conducted and data preprocessed as previously described[21]. Samples were analyzed continuously in a random order, and sampling of the queue was performed after every 10 samples by setting one of the QC samples.

Statistical analysis

Clinical and laboratory measurements of the control, IGT, and T2DM groups are expressed as the mean ± standard deviation, and the proportions were analyzed using the chi-square test. The analyses were conducted using SPSS Statistics 23. Unidimensional statistical analysis was performed using the Student’s t-test, and multidimensional statistical analysis and R software map were employed for principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), bubble chart, hierarchical cluster analysis, and correlation analysis. The ensemble feature selection algorithm and three machine learning algorithms were applied to identify and validate the potential lipidomic biomarkers. A P value of < 0.05 was considered statistically significant.

Results

Basic characteristics of the enrolled participants

The clinical and biochemical characteristics of the patients are summarized in Table 1. The participants aged 65–70 years, and all participants were classified as overweight (BMI ≥ 24 kg/m2 and BMI < 28 kg/m2) or obesity (BMI ≥ 28 kg/m2) according to the Chinese expert consensus criteria for T2DM combined with obesity [22, 23]. The levels of FPG and 2h-PG were significantly higher in T2DM and IGT patients than in NGT participants, while patients with T2DM exhibited higher values than those individuals with IGT.  

Table 1

Clinical and demographic characteristics of study participants

Variables

Control

(n = 49)

IGT

(n = 52)

T2DM

(n = 47)

P value

Females, n (%)

25 (51.02)

33 (63.46)

23 (48.94)

0.202

Age (years)

67.5 ± 1.14

67.3 ± 1.21

67.0 ± 1.26

0.165

Current or previous smoking, n (%)

14 (28.6)

13 (25.0)

16 (34.0)

0.610

Current or previous drinking, n (%)

14 (28.6)

11 (21.2)

16 (34.0)

0.354

history of hypertension, n (%)

30 (61.2)

34 (65.4)

33 (70.2)

0.651

Family history of diabetes, n (%)

6 (12.2)

17 (32.7) *

14 (29.8) *

0.039

WC (cm)

94.41 ± 6.56

94.47 ± 7.93

95.94 ± 8.70

0.441

WHR

0.91 ± 0.47

0.91 ± 0.58

0.90 ± 0.70

0.943

WHtR

0.58 ± 0.36

0.59 ± 0.52

0.58 ± 0.53

0.724

BMI (kg/m2)

26.82 ± 1.75

27.56 ± 2.39

27.28 ± 2.40

0.474

SBP (mmHg)

138.82 ± 16.20

135.71 ± 18.12

140.52 ± 13.44

0.250

DBP (mmHg)

84.82 ± 11.31

82.12 ± 10.55

83.38 ± 9.38

0.593

FPG (mmol/L)

5.18 ± 0.42

5.83 ± 0.58 *

7.05 ± 1.16 *#

< 0.001

2hPG (mmol/L)

6.18 ± 1.03

9.12 ± 0.97 *

13.67 ± 3.36 *#

< 0.001

ALT (U/L)

21.72 ± 9.01

22.21 ± 6.22

23.85 ± 8.29

0.371

AST (U/L)

23.02 ± 5.71

22.61 ± 6.32

23.95 ± 9.58

0.767

TBil (umol/L)

14.56 ± 5.89

15.93 ± 5.17

16.28 ± 6.90

0.242

CREA (umol/L)

75.09 ± 42.81

69.32 ± 17.16

66.51 ± 18.80

0.901

BUN (mmol/L)

5.46 ± 2.40

5.28 ± 1.49

5.05 ± 1.34

0.724

TG (mmol/L)

1.73 ± 0.96

1.93 ± 0.81

1.74 ± 0.79

0.353

TC (mmol/L)

4.99 ± 1.00

5.13 ± 0.99

4.85 ± 0.89

0.369

HDL-C (mmol/L)

1.33 ± 0.50

1.31 ± 0.47

1.43 ± 0.66

0.277

LDL-C (mmol/L)

2.63 ± 0.75

2.62 ± 0.81

2.54 ± 0.15

0.857

Data presented as mean (SD) for continuous variables or number (%) for categorical variables. IGT, impaired glucose tolerance; T2DM, type 2 diabetes mellitus; WC, waist circumference; WHR, waist to hip ratio; WHtR, waist to height ratio; BMI, body mass index; FPG, fasting plasma glucose; 2hPG, 2-h postload plasma glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine transaminase ; AST, aspartate transaminase; TBil, total bilirubin; CREA, creatinine; BUN, urea nitrogen; TG,  total triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. *：compared to control group, p＜0.05；#：compared to IGT group, p＜0.05.

Plasma lipidomic profiles of NGT, IGT, and T2DM in overweight/obese elderly individuals

In this high-coverage non-targeted absolute quantitative lipidomics anlysis, 1840 lipids (1274 lipids in positive modes and 566 lipids in negative modes) from thirty-eight classes and seven lipid categories were detected. (Fig. 1a). We compared concentrations of some of the main lipid classes in plasma from IGT and T2DM patients to those from euglycemic controls using a t-test analysis (Fig. 1b). The concentrations of dihexosyl N-acetylhexosyl ceramide (CerG3GNAc1) and trihexosyl di-N-acetylhexosyl ceramide (CerG3GNAc2) classes were significantly decreased, while those of diglyceride (DG), phosphatidic acid (PA), PC, and phosphatidylethanolamine (PE) classes were significantly increased in subjects with ITG and T2DM. Lipids with a fold change (FC) of > 1.5 or < 0.67 and a false discovery rate (FDR) of < 0.05, were selected, and the differential lipids were displayed using a Venn diagram (Fig. 1c, Supplementary Table 1–3). In the IGT, T2DM, and control groups, 19 lipids were found to be the common differential lipids of the three groups, and most of them were glycerophospholipids. Moreover, 78% of the common differential lipids induced by IGT and T2DM were glycerolipids (DGs and TGs).

Lipid alterations in the plasma of overweight/obese elderly patients with IGT

OPLS-DA was performed to achieve maximum separation between groups and mine the differential lipid molecules with biological significance. In OPLS-DA, the two groups showed obvious classified aggregation on the scatter plot without overfitting, indicating that there was a significant difference in lipidomic patterns between the IGT and control groups (Fig. 2a, Supplementary Fig. 1a). A total of 423 lipids with significant differences were screened according to a P-value of < 0.05 and variable importance in the projection (VIP) > 1, which are generally considered to contribute significantly to model interpretation.

Figure 2b shows significant IGT-induced changes in the abundance of lipid species in most of the analyzed lipid classes. Compared to the control group, the levels of most lipid species (especially lipids belonging to TG, PC, sphingomyelin (SM), and DG classes) in the IGT group increased, while the levels of lipids belonging to ceramide (Cer) and CerG3GNAc1 classes decreased. Correlation analysis could be useful in measuring the metabolic proximities of significantly changed lipids, which would help to further the current understanding of the interregulatory relationship between lipids in the process of biological state change [24]. Hierarchical clustering of the lipid-lipid correlation matrix based on the significantly different lipids was performed, as shown in Fig. 2c. Four distinct lipid clusters have been identified. The lipids within clusters B and C demonstrated diverse compositions, suggesting that clustered lipids might be co-regulated or functionally related.

We assessed the IGT-related PC and TG compositions by analyzing the saturation and chain length of the PC and TG species (Fig. 2d-i). IGT induced elevated total concentrations of PCs, and further analysis showed that the saturation and chain length of PC-associated fatty acids changed significantly. The concentrations of PCs with shorter carbon atom numbers (C17-30) and longer even chains (C32, C34, C36, C38, C40, C42, C50, C52, and C54) were elevated in the IGT group (Fig. 2e). The total TG content was similar in the control and IGT groups, but significantly increased levels of TGs containing longer chain fatty acids were observed in the IGT group (Fig. 2h); furthermore, patients in the IGT group had significantly more TGs with two or fewer double bonds (Fig. 2i).

Lipid alterations in the plasma of overweight/obese elderly patients with T2DM

The OPLS-DA analysis separated the T2DM and control groups with acceptable values (R2Y:0.9323) (Fig. 3a, Supplementary Fig. 1b). Using this approach, 535 lipids were identified, with significant differences between the two groups. The bubble chart (Fig. 3b) revealed T2DM-induced obvious changes in lipid species, especially lipids belonging to the TG, SM, phosphatidylserine (PS), PC, DG, and Cer classes. Consistent with the observed changes in patients with IGT, patients with T2DM had reduced plasma levels of lipids in the Cer and CerG3GNAc1 classes. Hierarchical clustering of the lipid-lipid correlation matrix indicated strong positive correlations within 535 significantly different lipid species. Five distinct lipid clusters were identified, and the lipids within clusters B and C were also identified (Fig. 3c). The overall abundance of TGs and PCs, which are the most and second most abundant lipid classes in the plasma, respectively, were both affected by T2DM (Fig. 3d, 3g). T2DM, which was similar to IGT, induced increased concentrations of longer even-chain (C32, C34, C36, C38, C40, and C52) PCs and elevated levels of longer length (C44-52, C56, and C58) TGs (Fig. 3e, 3h). Surprisingly, the levels of PCs with shorter chain lengths (C21, C24, C27, C29, and C30) decreased in the T2DM group, which was not consistent with those of the IGT group. We found that saturation profiles differed in TGs, with patients with T2DM having a significant increase in TGs with two or fewer double bonds (Fig. 3i).

Comparison of the plasma lipidomic profiles of the IGT and T2DM groups

The OPLS-DA analysis showed a separation of lipid metabolites in the IGT and T2DM groups without overfitting (Fig. 4a, supplementary Fig. 1c). Among the 255 lipid species with VIP > 1 and P < 0.05, 17%, 10%, and 18% were TGs, PCs, and Cers, respectively. Figure 4b and 4c show the lipid class distributions of these significantly different lipid species And lipid–lipid correlation matrix. The overall abundance of CerG3GNAc1 in patients with IGT were higher than those in patients with T2DM, and the saturation and chain length of CerG3GNAc1s changed significantly (Fig. 4d-f).

Identification of lipidomic biomarkers for IGT and T2DM in overweight/obese elderly individuals

After systematically defining the lipidomic profiles associated with IGT and T2DM, we developed prediction models by selecting lipidomic biomarkers that can be used to differentiate individuals with IGT and patients with T2DM from euglycemic controls. We identified the 10 most important differential lipidomic small molecules (LPE (20:4), Cer (m22:0/18:0), PA (18:2/10:4), PS (14:0/14:0), CerG3GNAc1 (d34:1), CerG3GNAc1 (t37:6), Cer (m32:0), TG (20:4e/12:2/12:2), Cer (m35:0), ChE (20:5)) between control group and IGT group, the top three important differential lipidomic small molecules (PA (18:2/10:4), Cer (m32:0), PG (30:3)) between control and T2DM groups, and the four most important differential lipidomic small molecules (Cer (m32:0), CerG3GNAc1 (d34:1), CerG3GNAc1 (t37:6), PC (10:0/11:4)) between the IGT and T2DM groups. The importance of the selected biomarkers in random forest (RF) is shown in supplementary Fig. 2a-c. In addition, receiver operating characteristic (ROC) analysis was performed to evaluate the effect of each substance on the area under the curve (AUC) of the models. The AUC demonstrated that the selected biomarkers contributed significantly to the classification ability (supplementary Fig. 2d-f), and the box plots show the abundance of these lipidomic biomarkers in the prediction models (Fig. 5a, 5c, 5e).

Finally, three commonly used machine learning algorithms, logistic regression (LR), RF, and support vector machine (SVM), were used to verify the screening results. LR, RF, and SVM yielded good AUCs (0.84–0.91) in differentiating patients with IGT from controls, although RF appeared slightly better (Fig. 5b). Potential biomarkers, PA (18:2/10:4), Cer (m32:0), and PG (30:3) achieved nearly excellent AUC values from 0.89 to 0.98 (Fig. 5d). Additionally, the prediction model differentiating IGT from T2DM yielded good AUCs of 0.84–0.86 using the three machine learning algorithms (Fig. 5f).

Discussion

Aging and obesity are crucial contributing factors to the development and progressive worsening of T2DM. The large population of obese elderly people with diabetes has caused a tremendous health crisis worldwide. Using lipidomics, many lipid metabolites have been detected and implicated as critical components that can be used to explain the complex interactions between aging, obesity, insulin resistance (IR), and T2DM [6].

To identify associations between lipids and T2DM incidence, several large-scale lipidomic cohort studies have been performed in adults [25–28]. Given the large number of obese older adults with T2DM, this study only included overweight/obese elderly adults with NGT, IGT, and T2DM and presented a comprehensive lipidomic evaluation.

In this study, a high-coverage non-targeted absolute quantitative lipidomics approach was used to investigate the plasma lipidome profiles of 148 overweight/obese elderly participants who with NGT, IGT or T2DM. Our findings showed that patients with IGT and T2D had increased concentrations of DG, TG, PC, sphingomyelins (SM), and Cer species. These results are in agreement with the findings of cross-sectional studies, which revealed that IGT and T2D induced significantly elevated levels of PCs, DGs, and TGs [29, 30]. Our results are also in agreement with data from prospective studies, which showed that PCs, DGs, and TGs were positively associated with the progression to dysglycemia and T2DM [14, 25, 26, 31].

TGs and DGs showed the most significant association with T2D and IGT. Our analysis is in line with those of previous studies, which showed that TG(46:1), TG(48:1), TG(48:2), TG(51:1), and TG (52:1) were positively associated with T2DM and prediabetes in an American Indian cohort [27], and positively associated with T2DM in Chinese[26] and Caucasian cohorts [32].

Chain length and desaturation of fatty acid moieties in lipid molecules complicate the assignment of biological roles to lipid classes [33]. In the PREDIMED trial [34], odd-chain TGs (C53, C55) were negatively associated with diabetes risk, and consistent results were discovered in a large-scale prospective lipidomics study [26]. In the same study, Lu et al. described TGs with 48–50 carbon atom numbers and 2–3 double bonds as risk factors for the development of T2DM [26]. Additionally, a cluster of TG species with saturated and monounsaturated acyl chains were identified to be associated with the prevalence and incidence of DM [32, 35]. Increased levels of saturated long-chain fatty acids are known to contribute to insulin resistance and T2DM [36]. We systematically examined IGT- and T2DM-associated alterations in the number of carbon atoms and double bonds in the various lipid classes that were investigated. Significant changes in the number of carbon atoms and the degree of unsaturation were observed in the PCs and TGs. Patients with IGT and T2DM tend to have increased levels of TGs with longer carbon atom numbers (C44–50) and low double bond numbers (n [C = C] = 0–2).

TG- and DG-mediated insulin resistance is the unifying molecular mechanism that explains the most common forms of IR associated with obesity and aging, as well as T2DM [33, 37, 38]. Different views were put forward when researchers discovered that inhibiting PE production and the subsequent accumulation of DG and TG retained insulin sensitivity and increased mitochondrial biogenesis and muscle oxidative capacity in knockout mouse muscles [39]. They believe that phospholipids, rather than DGs or TGs, are probable modulators of IR in muscles [6].

We found that the levels of multiple glycerophospholipids, including PC (10:0/11:4), PC (14:0/10:1), PS (11:0/16:0), PS (11:0/18:0), PI (16:0/16:0), and PI (16:0/16:1), changed significantly in individuals with IGT and T2DM. Although previous studies have reported associations of PC, PE, PS, and PI species with IR, T2D, and related traits [26, 27, 35, 40], some studies have shown inconsistent results as PCs and PEs were increased in some studies [41, 42] but were decreased in others [30, 31]. In young adults (aged 18–34 years), independent of age and BMI, PI (16:0/16:0) and PI (16:0/16:1) were positively associated with insulin AUC in men and homeostatic model assessment of insulin resistance (HOMA-IR) in both women and men [40]. PI (16:0/16:1) was also found to be positively associated with prediabetes over a 5-year follow-up period [27].

Furthermore, for PCs, we observed that IGT and T2DM induced increased concentrations of even-chain (C32, C34, C36, C38, C40, and C52) PCs. In contrast to the IGT group, the levels of PCs with shorter chain lengths (C21, C24, C27, C29, and C30) decreased in the T2DM group. A relationship between diabetes risk and the carbon number and double bond content among PCs was also identified by Rhee et al. They depicted a downsloping pattern in which PCs with relatively lower carbon number and double bond content were most significantly elevated in patients with T2DM compared to the levels in controls [32]. LPCs, such as LPC (18:0), LPC (18:1), and LPC (18:2), were negatively correlated with T2DM, while no significant change in LPCs was found in the present study.

Cer is the precursor of ganglioside and SM, and lipidomic profiling has revealed relationships between their levels, aging, obesity, and diabetes [6, 18]. In the present study, we found that the levels of most Cers in the IGT group were reduced and the levels of gangliosides GM3 and SMs were elevated (Fig. 2b). Compared with IGT, T2DM induced a higher proportion of Cers, less increased SMs, and slightly decreased ganglioside GM3 (Fig. 3b). These results suggest a shift in the balance of sphingolipid metabolism as diabetes progresses. Further, saturated SMs (C34:0, C36:0, C38:0, C40:0) and unsaturated sphingomyelins (C34:1, C36:1, C42:3) were reported to be risk factors for IR and incident T2D among 1974 ethnically Chinese individuals [43]. Our study showed that the levels of SM(d36:0), SM(d38:0), SM(d39:0), SM(d42:7), and SM(d44:4) were higher in patients with T2DM but not in patients with IGT.

Machine learning algorithms have been increasingly recognized as enabling techniques for selecting biomarkers for various human diseases [44]. Finally, we successfully selected potential biomarkers to distinguish the NGT, IGT, and T2DM groups in overweight/obese elderly individuals using three machine learning algorithms. Interestingly, PA (18:2/10:4) and Cer (m32:0) were the common biomarkers of IGT and T2DM. The prediction models differentiate the three groups yielding good AUCs.

The current study had several limitations. First, although a high-coverage and the most flexible and efficient system we used, we carried out only non-targeted lipidomics analysis. The sample size of this study was also relatively small. Second, to ensure the stability and reliability of the lipidomics analysis, we carried out a strict quality control evaluation of the results, while the cross-sectional study that lacked prospective lipidomics data weakened the credibility of the results.

In summary, our high-coverage non-targeted absolute quantitative lipidomic analysis revealed novel lipidomic patterns in overweight/obese elderly individuals with IGT and T2DM. A panel of differential lipids was successfully identified as a potential biomarker in patients with IGT and T2DM. The lipidomic profile may improve our understanding of the extent and complexity of lipid dysregulation in obesity, aging, and diabetes and provide new insights into the underlying molecular mechanisms of diabetes.

Declarations

Acknowledgments

We are grateful to all participants for their dedication to data collection and laboratory measurements.

Funding

This work was funded by Natural Key R&D Program of China (No. 2018YFC1311502).

Disclosure

None declared.

Ethical approval and consent to participants

This study was approved by the Ethics Committee of Gansu Provincial Hospital (No. 2018-076). All participants provided written informed consent prior to their inclusion in the study.

Author contributions

Limin Tian conceived the study, and reviewed/edited the manuscript. Feifei Shao analyzed the data, and wrote the manuscript. Xinxin Hu, Jiayu Li, Bona Bai contributed to the collection of samples and discussion the manuscript. All authors contributed to the article and approved the submitted version. 

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