Obesity and hyperlipidemia aggravate serum amino acid metabolism in patients with type 2 diabetes

doi:10.21203/rs.3.rs-2097946/v1

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Obesity and hyperlipidemia aggravate serum amino acid metabolism in patients with type 2 diabetes

https://doi.org/10.21203/rs.3.rs-2097946/v1

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Aims: Obesity and dyslipidemia are risk factors for insulin resistance and T2D development. The potential mechanism of progression of diabetes by the metabolomics approach is still unclear. This cross-sectional study aims to identify the metabolites related to T2D and T2D combined with obesity or hyperlipidemia.

Materials and methods: 58 T2D patients were allocated to 3 groups (T2D (n=20), T2D + obesity (n=12), T2D + hyperlipidemia groups (n=26)). An age-matched healthy subjects were recruited as the control group (n=20). The fasting serum was obtained for cytokine detection and metabolomics analysis.

Results: The highest levels of serum growth/differentiation factor 15 (GDF15) were found in the patients with T2D and obesity. Finally, 20 metabolites between the T2D + obesity and Healthy control groups, 32 metabolites between the T2D + hyperlipidemia and Healthy control groups, 11 metabolites between the T2D + obesity and the T2D groups, and 13 metabolites between the T2D + hyperlipidemia and the T2D groups were found significantly distinct. Amino acid metabolism was disturbed for patients with T2D with/without obesity or hyperlipidemia mainly including D-glutamine and D-glutamate, taurine and hypotaurine, beta-alanine, alanine, aspartate and glutamate, arginine and proline, glyoxylate and dicarboxylate and glycine, serine and threonine metabolism. In addition, beta-alanine, glycine, serine and threonine, arginine and proline, and pyruvate metabolism may be involved in the patients with T2D with obesity or hyperlipidemia compared with patients with T2D.

Conclusions: Overall, obesity and hyperlipidemia may aggravate the progression of T2D by disruption of amino acid metabolism.

type 2 diabetes

serum metabolomics

obesity

hyperlipidemia

According to the International Diabetes Federation in the year of 2017, 425 million people were living with type 2 diabetes (T2D) with high prevalence of overweight or obese or hyperlipidemia in the patients with T2D. The serum amino acid metabolism in T2D with obesity or hyperlipidemia were disturbed with metabolomics.

According to the International Diabetes Federation in the year of 2017, 425 million people were living with diabetes, and another 352 million people were estimated to be at high risk of developing type 2 diabetes (T2D) [1]. Overweight or obese in patients with T2D were estimated to be around 86% in the United Kingdom within 3637 patients [2]. Obesity with T2D [3] and hyperlipidemia [4] contributes to worse cardiovascular risk factor without weight maintenance management.

Metabolome is defined as all small-molecule metabolites found or produced in an organism, comprises the smallest set of biomarkers [5], contributing to the understanding of the mechanism, diagnosis, and personalized medicine for diabetes due to the environmental and biological changes [6]. In recent decades, plenty of studies of metabolomics have provided the potential physical of diabetes, which may be related to the regulation of leucine, alanine, oleic acid, lysophophatidylcholine and creatinine [7]. However, the results are inconsistent. One metabolomics study also showed that non-esterified fatty acids and lysophosphatidylinositol levels increased in T2D [8]. A recent study found that serum levels of alanine, arginine, isoleucine, proline, tyrosine, valine, hexose were positively associated with T2D, whereas lysophophatidylcholine and glycerophospholipids were negatively associated with T2D risk [9]. In additionally, metabolite profiling identified 24 metabolites specifically associated with diabetes but not with obesity [10]. Therefore, the potential mechanism of diabetes with metabolomics was explored compared to non-diabetics with inconsistent results. Furthermore, the metabolic profiling change during the progression of diabetes is still unclear. Therefore, the present study investigated the adipokines and inflammatory cytokines, and aims to identify the metabolites related to T2D as well as T2D combined with obesity or hyperlipidemia.

Ethics

The study was approved by the ethical committee of Southeast University affiliated Zhongda hospital, Nanjing, China, which also has been registered on Chinese clinical trial registry (NO. 2012ZD11KY17.0). Written informed consent was obtained from all participants.

Detailed protocol and study population

A survey was conducted among 75 patients who was clinical diagnosed as T2D in the hospital. We included patients with, fasting blood glucose (FBG) levels greater than 7.0 mmol/L and/or postprandial blood glucose levels greater than 11.1 mmol/L. The exclusion criteria were as follows, insulin therapy, constant alcohol or substance abuse, complications of diabetes, and history of heart disease, central nervous system disease, hematologic disease. 58 patients were qualified with the inclusion and exclusion criteria. They were divided into 3 groups, T2D group (n=20), T2D+obesity group (n=12), and T2D+hyperlipidemia group (n=26) according to the criteria for obesity with BMI > 28 kg/m², and the criteria for hyperlipidemia with serum triglycerides (TG) concentration > 1.7 mmol/L or serum total cholesterol (TC) concentration > 5.72 mmol/L, respectively. Then the age-matched healthy participants were recruited as the control group (n=20).

Clinical and biochemical assessment

First of all, demographic variables were recorded including gender, age, weight, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), duration, the usage of antidiabetic and anti-hyperlipidemia agents, and smoking. The second part of visit included a morning fasting blood draw, and physical examination. After overnight fasting, blood samples were taken by venipuncture to evaluate the levels of liver function indexes (alanine aminotransferase (ALT), aspartate aminotransferase (AST)), urine acid (UA), creatinine (Cr), FBG, insulin, TG, TC, high density lipoprotein (HDL) and apolipoprotein B (ApoB). All the parameters were measured in a centralized laboratory of the hospital.

Serum biomarkers measurement

The serum levels of apelin, C1q/TNF-related Protein-13 (CTRP13), growth/differentiation factor 15 (GDF15), glucagon-like peptide 1 (GLP1), insulin like growth factor 1 (IGF1), insulin-like growth factor binding protein (IGFBP), interleukin-18 (IL-18), lipocalin (LCN), nesfatin-1, resistin-1, and regenerating islet‐derived 1 alpha (REG1A) were assessed using ELISA kit (Nanjing Jin Yibai Biological Science and Technology Co., Ltd, Nanjing, China).

Serum metabolomics measurement

1 μL aliquot of the analyte was injected insplitless mode into an Agilent 7890 gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer, which was used to perform Gas Chromatography-Time of Flight-Mass Spectrometry (GC-TOF-MS). The Rxi-5Sil MS column (30 m × 250 μm inner diameter, 0.25 μm film thickness; Restek, USA). The temperature was at 50 °C for 1 minute, then raised to 330 °C at a rate of 10 °C/min, and kept at 330 °C for 5 minutes. The front inlet purge flow was 3 mL/min and the gas flow rate through the column was 20 mL/min with the helium used as the carrier gas. The mass spectrometry data were acquired in full-scan mode with the m/z range of 30-600 at a rate of 20 spectra per second after a solvent delay of 366 s.

Statistical Analysis

All data were presented as means ± SEM. Normality of distribution was assessed using the Shapiro-Wilk tests. The statistical analyses were performed with one-way ANOVA for continuous variables and the χ²test for non-continuous variables. A P value < 0.05 was considered statistically significant. The resulting data were imported into Statistical Package for Social Sciences Software Version 11.0 (SPSS). Multivariate analysis including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS- DA) were performed to analyze the metabolomics data by SIMCA software (V14.1, MKS Data Analytics Solutions, Umea, Sweden).

General description of included patients and controls

A total of 78 subjects (40 females, 38 males, average age 58.83 ± 0.94 y) were enrolled and divided into four study populations: healthy control, T2D, T2D+obesity and T2D+hyperlipidemia groups. There were no significant among four groups including the indexes of age, SBP, DBP, ALT, AST, UA levels and the duration of T2D and antidiabetic agents’ intake. Interestingly, no patient with hyperlipidemia accepted the anti-hyperlipidemics. Furthermore, as expected, the close association of weight, BMI, FBG, insulin, HOMA-IR, TG, TC levels was found between patients and T2D vs. controls. The Cr levels in T2D groups were significantly higher than in the control group. Patients with T2D and hyperlipidemia had the highest levels of serum HDL and ApoB. The main clinical and biochemical characteristics are summarized in Table 1.

Changes of serum biomarkers among T2D, T2D+obesity, T2D+hyperlipidemia groups and controls

The patients with T2D and obesity had the highest levels of serum GDF15, which is statistically significant when comparing with the control group and T2D group. However, no significant difference was observed in four groups including serum apelin, CTRP13, GLP1, IGF1, IGFBP, IL-18, LCN, Nesfatin-1, REG1A, and Resistin 1 levels (Table 2).

Alteration of serum metabolic profiles among T2D, T2D+obesity, T2D+hyperlipidemia groups and controls

Using untargeted GC-MS metabolomics approach, the fingerprinting of human serum was obtained. The PCA and OPLS-DA plots shown in Figure 1 were used to determine the different metabolic profiles among T2D, T2D+obesity, T2D+hyperlipidemia groups and controls. The total PCA plots showed clear differentiations in the serum metabolomics between the T2D groups and Healthy controls (Figure 1.A). As expected, further PCA plots between two groups (T2D vs. Healthy control, T2D + obesity vs. Healthy control, and T2D + hyperlipidemia vs. Healthy control) were separate shown in Figure 1.B, Figure 1.C, and Figure 1.D. However, no clear differentiation was observed between T2D + obesity, T2D + hyperlipidemia and T2D groups with PCA plots (Figure E, F). Therefore, the adjusted analysis, OPLS-DA was applied and showed the clear differentiations (Figure E.1, F.1). The stable cumulative R² and Q² were observed for comparing the T2D + obesity group and the T2D + hyperlipidemia group to the T2D group (data not shown).

Multivariate analysis was used to screen the significantly different metabolites between all T2D groups and the Healthy control group, and T2D combined with obesity and hyperlipidemia and T2D groups. P value < 0.05 and Variable importance in the Projection (VIP) of OPLS-DA model > 1 were considered as statistically significance. The metabolites between groups were visualized using the volcano plots. Each point in the volcano plot stands as one different metabolite between groups. The X axis stands for the change folds and the Y axis stands for the P value of Student’s T test between two groups. The size of the point stands for the VIP value, the bigger size and the higher VIP value. The red color of each point stands for the up-regulation of the metabolite and the blue color of each point stands for the down-regulation of the metabolite significantly. The gray color of each point stands for non-significant change of metabolites between two groups. In our present study, we listed the comparison of the T2D vs. Healthy control groups (Figure 2.A), the T2D + obesity vs. the Healthy control groups (Figure 2.B), the T2D + hyperlipidemia vs. the Healthy control groups (Figure 2.C), the T2D + obesity vs. T2D groups (Figure 2.D) and the T2D + hyperlipidemia vs. the T2D groups (Figure 2.E). Finally, 20 metabolites (9 increased, 11 decreased) between the T2D and Healthy control groups, 20 metabolites (11 increased, 9 decreased) between the T2D + obesity and Healthy control groups, 32 metabolites (14 increased, 18 decreased) between the T2D + hyperlipidemia and Healthy control groups, 11 metabolites (7 increased, 4 decreased) between the T2D + obesity and the T2D groups, and 13 metabolites (4 increased, 9 decreased) between the T2D + hyperlipidemia and the T2D groups were found significantly distinct shown in Table 3. The common 2 metabolites (lactose 1, myristic acid) increased and 5 metabolites (xylose 2, 3-hydroxypropionic acid 1, allose 1, N-ethylglycine 1, and farnesal 2) decreased when comparing all T2D groups with the Healthy control group. The common 1 metabolite (bis (2-hydroxypropyl) amine 2) increased and 2 metabolites (urea and 3-hydroxypropionic acid 1) decreased when comparing T2D combined with obesity or hyperlipidemia group with the T2D group. Differential metabolites among all groups were displayed with the heat-map to visualize the change of metabolites, which showed the similar trends for comparisons of two groups (shown as the supplementary data, Figure S1).

Metabolic pathway analysis among T2D, T2D+obesity, T2D+hyperlipidemia groups and controls

With the analysis of online database KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.kegg.jp/kegg/pathway.html) pathway [11], the related pathways were found for differential metabolites between two groups. The recognized KEGG metabolic pathways as the backend database was furtherly analyzed to screen and score all related pathways and they were ranked as the bubble plots shown in Figure 3. In the plot, each bubble represents for one pathway. The bigger value of X-axis and the bigger size of the bubble showed the higher impact factor for the pathway. The P value was represented by the location of Y-axis and the color of the bubble. The deeper color of the bubble was; the more significant enrichment would be. In the present study, D-glutamine and D-glutamate, taurine and hypotaurine, and beta-alanine metabolism were analyzed that with higher impact factor between the T2D and Healthy control groups (Figure 3.A). Alanine, aspartate and glutamate, arginine and proline, glyoxylate and dicarboxylate and glycine, serine and threonine metabolism were recognized as the most related pathways between the T2D + obesity and Healthy control groups (Figure 3.B). Glycine, serine and threonine and taurine and hypotaurine metabolism were identified as the potential pathways between the T2D + hyperlipidemia and Healthy control groups (Figure 3.C). Additionally, the potential pathways were analyzed between T2D patients combined with obesity or hyperlipidemia and T2D groups, which exhibited that beta-alanine, glycine, serine and threonine and arginine and proline metabolism were the potential pathways between patients with T2D combined with obesity and patients with T2D (Figure 3.D), and purine and glycine, serine and threonine metabolism were potential pathways between patients with T2D combined with hyperlipidemia and patients with T2D (Figure 3.E).

T2D is a chronic progress with the abnormal metabolism of glucose and following dysfunction due to the complications and the precise pathophysiology is difficult to determine in individual patients with diabetes [12]. In the present study, we investigated the serum metabolomics in patients with T2D with/without lipid dysfunction and healthy subjects. Some common metabolic profiles were found between T2D with/without obesity or hyperlipidemia and healthy controls. The results showed that serum lactose and myristic acid levels decreased and xylose 2, 3-hydroxypropionc acid 1, N-ethylglycine 1 and farnesal 2 levels increased in all patients with T2D, compared to the healthy subjects. Furthermore, serum urea and 3-hydroxypropionic acid 1 levels decreased and bis-(2-hydroxypropyl)-amine 2 level increased for patients with obesity or hyperlipidemia, compared to the T2D. Interestingly, we observed the different serum metabolic profiles between T2D with obesity and hyperlipidemia when compared with patients with T2D, which indicated the different underlying mechanism between obesity and hyperlipidemia. Serum phenylacetic acid and 2-monopalmitin levels decreased and aspartic acid 1, D-glyceric acid, tyrosine 1, threitol, glutamic acid and piceatannol 2 levels increased in patients with T2D and obesity, compared to patients with T2D. However, the metabolic profiles changed between patients with T2D and hyperlipidemia and T2D patients, and serum glycine 2, cysteine, 2-hydroxypyridine, pyruvic acid, 3-hydroxypyruvate, tartronic acid and N-ethylmaleamic acid 3 levels decreased and 2-keto-isovaleric acid 1, glucose-6-phosphate 2, 2’-deoxyadenosine 5’-momophosphate levels increased in patients with T2D and hyperlipidemia. Further pathway analysis was conducted to explore the potential mechanism. Amino acid metabolism was disturbed for T2D with/without obesity or hyperlipidemia mainly including D-glutamine and D-glutamate metabolism, taurine and hypotaurine metabolism, beta-alanine metabolism, alanine, aspartate and glutamate metabolism, arginine and proline metabolism, glyoxylate and dicarboxylate metabolism and glycine, serine and threonine metabolism, and glycine, serine and threonine metabolism. In addition, the potential mechanism for obesity and hyperlipidemia in T2D was explored as well, which indicates that beta-alanine metabolism, glycine, serine and threonine, arginine and proline, and pyruvate metabolism may be involved in T2D with obesity or hyperlipidemia compared with T2D.

Park et al reported that both amino acids and fatty acids may increase the risk of T2D in fourteen cohort studies [13]. In the present study, we found that the consistent results that amino acid metabolism and branch-chain amino acid metabolism was closely related to T2D development. Patients with T2D showed serum amino acids changed mainly including decrease of taurine, methionine, citrulline, N-ethylglycine, glycine, threonine and lysine. Taurine, as a β-amino acid, was reported to function as reversing impaired glucose metabolism and insulin resistance via activating PPARγ-mTORC2 signaling [14]. Methionine can be metalized into methionine metabolic enzymes in the liver and catalyzed into adenosylmethionine, engaged in glucose metabolism and insulin resistance [15]. Decreased serum citrulline level may be involved in iron induced impairment of glucose metabolism in T2D and metabolic syndrome [16]. Glycine and lysine were suggested as the closely inverse association with diabetes [7, 17]. All these researches showed the importance of amino acids in the prevention of diabetes and the lack of those amino acid may increase the risk of diabetes. Besides, we also found that serum xylose and allose levels decreased and serum lactose levels increased in the diabetic patients, compared to the healthy subjects. It was reported increased xylose can regulate blood glucose levels through regeneration of damaged pancreas and regulation of phosphoenolpyruvate carboxylase in the diabetic rats [18] and allose improved insulin secretion of islets in vitro study through anti-oxidation activity [19], which indicates that the dysfunction of saccharides may result in T2D as well. Above all, it furtherly supported that amino acid plays positive role in prevention of diabetes and diabetes progression, which is consistent with our findings in this study.

Asparagine and the tryptophan/large neutral amino acid ratio decreased in T2D and metabolic syndrome [17]. However, the relationship between T2D and T2D combined with obesity and lipid dysfunction is still not clear and whether obesity or lipid dysfunction can worsen the progression in metabolomics is unknown so far. The present study showed that serum aspartic acid, d-glyceric acid, tyrosine, threitol, and glutamic acid levels increased, and urea, phenylacetic acid, 3-hydroxypropionic acid 1 and 2-monopalmitin decreased in T2D combined with obesity. It has been reported that aspartic acid participates in the gluconeogenesis for production of glucose [20]. Lower gluconeogenesis may decrease the risk for fasting hyperlipidemia [21]. Patients with obesity and metabolic syndrome exhibited higher fasting plasma tyrosine levels significantly increased [17], which is consistent with the present study. Besides, the case control study showed plasma tyrosine level increased in patients with T2D compared to the non-diabetes significantly [22], which suggests that obesity may aggravate the development of T2D. The elevated systemic glutamic acid levels in diabetic mice induced beta cell apoptosis [23]. The fingerprint for obesity and related non-alcoholic fatty liver disease in urinary metabolomics displayed the decreased phenylacetic acid levels compared to normal weight children [24]. Sn-2 monopalmitin contributed to a decrease in triglyceride hydrolysis via a gastro-intestinal system [25]. As subsequently, decreased of monopalmitin levels in serum may cause the problem of triglyceride hydrolysis increase and absorption in the present study for obesity patients. Overall, obesity may result in aggravated metabolite metabolism mainly including beta-alanine, D-glutamine and D-glutamate, and alanine, aspartate and glutamate metabolism.

Furthermore, we explored whether hyperlipidemia could change the serum metabolite profiles in patients with T2D as well. The results showed that serum glucose-6-phosphate 2, 2-keto-isovaleric acid 1, bis(2-hydroxypropyl) amine 2, and 2'-deoxyadenosine 5'-monophosphate levels increased, and serum glycine 2, cystine, 2-hydroxypyridine, urea, pyruvic acid, 3-hydroxypropionic acid 1, 3-hydroxypyruvate, tartronic acid, and N-ethylmaleamic acid 3 levels decreased in diabetic patients combined with obesity compared to diabetic patients. In several observational studies plasma or serum glycine levels were reported to be decreased in patients with T2D compared to non-diabetes [26-28]. Low serum glycine levels were shown to have a positive relationship with red meat intake and T2D risk in a cohort study and the potential mechanism may be due to its relationship with glutathione synthesis by inducing oxidative stress and insulin resistance [29]. Cystine deficient diet led to hepatic steatosis by the elevation of lipid peroxidation [30]. Based on the studies the decrease of cystine may be engaged in the dysfunction of lipid metabolism. The decreased of pyruvic acid in the present study may promote the dysfunction of citrate cycle and caused metabolism abnormality. Taurine acid functions as attenuating insulin resistance [14], and the lack of it in our results showed the hyperlipidemia may lead to the worse insulin resistance. Above all, hyperlipidemia could promote the development of T2D and the potential pathways include glycine, serine and threonine metabolism and pyruvate metabolism from the prediction of KEGG online database.

Interestingly, in the present study, the KEGG pathway results showed that glycine, serine and threonine metabolism was influenced in both T2D + obesity and T2D + hyperlipidemia patients when compared with T2D, which indicates that obesity and hyperlipidemia may have the same metabolism by promoting T2D development. One of our limitations in the study was due to the small sample size, which may decrease the reliability. Furthermore, single ethnicity was included in the present study, therefore, more considerations should be paid to extrapolate results. Further studies are warranted in future.

type 2 diabetes, T2D; body mass index, BMI; triglycerides, TG; total cholesterol, TC; systolic blood pressure, SBP; diastolic blood pressure, DBP; alanine aminotransferase, ALT; aspartate aminotransferase, AST; urine acid, UA; creatinine, Cr; fasting blood glucose, FBG; high density lipoprotein, HDL; apolipoprotein B, ApoB; C1q/TNF-related Protein-13, CTRP13; growth/differentiation factor 15, GDF15; glucagon-like peptide 1, GLP1; insulin like growth factor 1, IGF1; insulin-like growth factor binding protein, IGFBP; interleukin-18, IL-18; lipocalin, LCN; regenerating islet‐derived 1 alpha, REG1A; Gas Chromatography-Time of Flight-Mass Spectrometry, GC-TOF-MS; principal component analysis, PCA; orthogonal partial least squares discriminant analysis, OPLS- DA; variable importance in the projection, VIP.

Ethical Approval

Competing interests

None.

Authors' contributions

Hui Xia:Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing-Original Draft;

Ying Wang:Data Curation,Writing-Original Draft;

Junhui Yu:Visualization, Investigation;

Da Pan and Yifei Lu:Resources, Supervision;

Dengfeng Xu:Software, Validation;

Shaokang Wang and Ligang Yang: Visualization, Writing - Review & Editing;

Guiju Sun:Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review & Editing.

Funding

We appreciate that the support from the National Natural Science Foundation of China (NO. 82003457), Jiangsu Province Science Foundation for Youths (NO. BK20200366), the Fundamental Research Funds for the Central Universities and “Zhishan” Scholars Programs of Southeast University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

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Table 1. Basic information for T2D with or without obesity and hyperlipidemia

	Healthy control, n=20	T2D, n=20	T2D + obesity, n=12	T2D + hyperlipidemia, n=26
Gender (F/M)	8/12	9/11	10/2	13/13
Age (y)	58.7±1.21	58.35±1.97	56.67±3.20	58.92±1.73
Weight (kg)	60.38±1.76	62.68 ±2.25	77.72±1.60^a	64.38±1.62
BMI (kg/m²)	22.63±2.33	23.24±1.90	29.83±1.92^a	24.24±1.88^a
SBP (mmHg)	119.30±3.80	118.75±2.57	127.50±4.20	118.12±2.86
DBP (mmHg)	79.35±2.11	75.60±1.71	78.33±3.66	79.65±1.61
Duration (y)	0	5.00±1.34^a	5.42±1.51^a	5.88±1.02^a
Oral antidiabetic drugs (n)	0	17	9	19
Antihyperlipidemics (n)	0	0	0	0
Smoking (n)	5	6	2	10
ALT (U/L)	17.44±1.52	23.43±2.54	21.96±2.84	27.00±2.99
AST (U/L)	21.07±0.94	28.31±7.63	24.21±3.05	24.63±1.88
UA (mmol/L)	264.73±91.44	256.74±75.35	279.35±76.74	309.62±88.45
Cr (μmmol/L)	76.52±3.94^b	66.54±4.23^a	62.91±3.16^a	64.88±2.59^a
FBG (Fasting blood glucose) (mmol/L)	5.01±0.05^b	9.52±0.59^a	8.10±1.65^a	9.01±0.76^a
Insulin (mU/L)	5.61±0.98^b	8.69±1.89^a	20.12±3.98^ab	16.57±2.74^ab
HOMA-IR	1.24±0.22^b	3.53±0.57^a	6.30±0.79^a	6.71±1.24^a
TG (mmol/L)	0.78±0.10	1.03±0.14	1.57±0.19	2.07±0.13^a
TC (mmol/L)	4.74±0.20	4.74±0.57	5.25±0.29	5.42±0.40^a
HDL (mmol/L)	1.52±0.10	1.28±0.24	1.13±0.08	1.10±0.14^a
ApoB (mmol/L)	0.78±0.04	0.73±0.05	0.83±0.04	0.91±0.06^b

^a, P < 0.05, compared with the control group.^b, P < 0.05, compared with the T2D group. All results were expressed with mean ± SEM. T2D, type 2 diabetes; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; ALT, alanine aminotransferase; AST, aspartate aminotransferase; UA, urine acid; Cr, creatinine; FBG, fasting blood glucose; HOMA-IR, homeostasis model assessment for insulin resistance; TC, total cholesterol; TG, triglycerides; HDL, high density lipoprotein; ApoB, apolipoprotein B.

Table 2 Changes of T2D-related risk biomarkers in T2D with or without obesity and hyperlipidemia

	Healthy control, n=20	T2D, n=20	T2D + obesity, n=12	T2D + hyperlipidemia, n=26
Apelin (ng/L)	1258.92±147.59	1646.38±121.16	1926.13±377.15	1669.46±265.44
CTRP13 (pg/mL)	414.15±35.27	339.92±21.07	427.70±85.63	368.98±53.29
GDF15 (ng/L)	36.77±4.84	39.13±3.10	62.34±7.51^ab	47.36±7.28
GLP1 (pmol/L)	6.59±1.12	7.76±1.01	5.28±1.13	7.56±1.08
IGF1 (μg/L)	153.87±16.68	195.72±26.67	248.76±45.27	220.27±33.75
IGFBP (μg/L)	120.39±8.14	105.18±5.90	123.65±15.33	104.71±8.36
IL-18 (ng/L)	64.41±7.08	76.81±7.22	87.61±10.15	74.97±7.14
LCN (μg/L)	4.99±0.52	4.00±0.31	5.60±0.81	4.57±0.52
Nesfatin-1 (pg/mL)	416.49±45.32	458.69±53.18	578.94±78.06	469.31±46.43
REG1A (pg/mL)	117.54±17.48	95.20±10.71	122.51±29.62	92.88±14.61
Resistin1 (μg/L)	12.90±1.44	14.29±1.47	15.96±3.36	15.00±1.73

^a, P < 0.05, compared with the control group.^b, P < 0.05, compared with the T2D group. ^*, P = 0.089, compared with the control group. T2D, type 2 diabetes; CTRP13, C1q/TNF-related Protein-13; GDF15, growth/differentiation factor 15; GLP1, glucagon-like peptide 1; IGF1, insulin like growth factor 1; IGFBP, insulin-like growth factor binding protein; AGEs, advanced glycationend products; IL-18, interleukin-18; LCN, lipocalin; REG1A, regenerating islet‐derived 1 alpha.

Table 3 Metabolite detection in serum samples among T2D and controls.

ID	Similarity	Mass	Relative abundance of peak area		VIP	P
T2D vs. Healthy control			T2D	Healthy control
Taurine	883	326	0.0465	0.0627	1.45	0.0455
Methionine 1	768	176	0.0362	0.0470	1.14	0.0159
Xylose 2	757	103	<0.0001	0.0030	3.03	0.0002
Monostearin	674	73	0.0234	0.0178	1.93	0.0104
Lactose 1	672	204	0.0007	0.0004	1.60	0.0398
Citrulline 1	664	256	0.0061	0.0074	1.22	0.0269
Myristic Acid	653	285	0.0061	0.0033	2.09	0.0120
d-Glucoheptose 2	646	117	0.0058	0.0042	1.53	0.0182
Maltitol	546	166	0.0076	0.0047	2.58	<0.0001
3-Hydroxypropionic acid 1	518	177	0.0020	0.0050	1.83	0.0041
Allose 1	504	204	0.0003	0.0025	2.48	0.0002
2-Monopalmitin	442	149	0.0020	0.0011	2.58	<0.0001
N-Ethylglycine 1	366	229	<0.0001	0.0007	2.75	<0.0001
Bis-(2-hydroxypropyl)-amine 2	364	209	0.0003	0.0029	2.02	<0.0001
beta-Glutamic acid 1	316	188	0.0009	0.0017	1.32	0.0463
O-Phosphoserine 2	309	284	0.0007	<0.0001	2.03	0.0096
Galactinol 1	302	215	0.0002	0.0001	1.49	0.0062
Tartronic acid	293	264	0.0037	0.0025	1.45	0.0062
Farnesal 2	244	393	<0.0001	0.0004	3.24	0.0001
2'-Deoxyadenosine 5'-monophosphate	234	223	0.0004	0.0007	1.28	0.0138
T2D + obesity vs. Healthy control			T2D + obesity	Healthy control
Aspartic acid 1	920	232	0.0736	0.0564	1.74	0.0170
Urea	831	189	<0.0001	0.0233	2.73	<0.0001
Pyruvic acid	826	174	0.0020	0.0205	3.02	0.0003
Galactose 2	825	103	1.6999	1.4861	2.35	0.0007
Mannose 2	813	119	0.3547	0.2710	2.38	0.0052
Succinic acid	772	129	0.0114	0.0094	1.52	0.0390
Xylose 2	757	103	<0.0001	0.0030	2.40	0.0002
Monostearin	674	73	0.0257	0.0178	1.93	0.0079
Lactose 1	672	204	0.0008	0.0004	1.35	0.0327
Citrulline 1	664	256	0.0059	0.0074	1.35	0.0327
Myristic Acid	653	285	0.0061	0.0033	1.46	0.0278
Conduritol b epoxide 2	642	361	0.0302	0.0233	2.13	0.0088
3-Hydroxypropionic acid 1	518	177	<0.0001	0.0050	2.71	<0.0001
Allose 1	504	204	<0.0001	0.0025	2.40	<0.0001
N-Ethylglycine 1	366	229	<0.0001	0.0007	2.50	<0.0001
3-Hydroxypyruvate	360	205	0.0006	0.0014	2.24	0.0018
Digitoxose 1	346	204	0.0025	0.0019	2.31	0.0010
Galactinol 1	302	215	0.0002	0.0001	2.03	0.0021
Tartronic acid	293	264	0.0035	0.0025	1.04	0.0318
Farnesal 2	244	393	<0.0001	0.0004	2.71	0.0001
T2D + hyperlipidemia vs. Healthy control			T2D + hyperlipidemia	Healthy control
Glycine 2	949	174	0.5304	0.6387	1.36	0.0252
Oleic acid	939	117	0.7736	0.5253	1.01	0.0019
Threonine 1	934	219	0.1425	0.1837	2.21	0.0003
Phosphate	905	211	0.0984	0.1323	1.10	0.0063
Lysine	891	174	0.2641	0.3096	1.45	0.0083
Taurine	883	326	0.0396	0.0627	1.93	0.0011
2-Hydroxypyridine	881	152	0.2249	0.2849	1.02	0.0134
Arachidonic acid	838	80	0.0070	0.0052	1.81	0.0376
Urea	831	189	<0.0001	0.0233	3.14	<0.0001
Pyruvic acid	826	174	0.0002	0.0205	3.59	0.0001
Mannose 2	813	119	0.3757	0.2710	2.47	<0.0001
Xylose 2	757	103	<0.0001	0.0030	2.85	0.0002
2-Keto-isovaleric acid 1	680	89	0.0079	0.0060	1.09	0.0209
Lactose 1	672	204	0.0007	0.0004	1.94	0.0127
Myristic Acid	653	285	0.0064	00.0033	1.55	0.0034
d-Glucoheptose 2	646	117	0.0062	0.0042	1.35	0.0017
Conduritol b epoxide 2	642	361	0.0340	0.0233	2.20	0.0003
Androsterone 1	589	91	0.0028	0.0019	1.66	0.0080
3-Hydroxypropionic acid 1	518	177	<0.0001	0.0050	3.12	<0.0001
Allose 1	504	204	<0.0001	0.0025	2.94	<0.0001
D-erythronolactone 2	398	424	0.0017	0.0021	1.53	0.0104
Glucose-6-phosphate 2	398	217	0.0094	0.0070	1.19	0.0107
N-Ethylglycine 1	366	229	0.0001	0.0007	2.58	<0.0001
3-Hydroxypyruvate	360	205	0.0002	0.0014	3.19	<0.0001
4-Aminobutyric acid 3	338	142	0.0209	0.0266	1.12	0.0217
Neohesperidin	325	73	0.0024	0.0008	2.17	0.0003
O-Phosphoserine 2	309	284	0.0006	<0.0001	1.57	0.0063
Galactinol 1	302	215	0.0002	0.0001	1.36	0.0157
N-(2-hydroxyethyl)-iminodiacetic acid 1	261	248	0.0031	0.0041	1.55	0.0165
Farnesal 2	244	393	<0.0001	0.0004	2.94	0.0001
T2D + obesity vs. T2D			T2D + obesity	T2D
Aspartic acid 1	920	232	0.0736	0.0578	2.17	0.0200
D-Glyceric acid	908	189	0.0172	0.0111	1.30	0.0059
Tyrosine 1	899	218	0.3378	0.2829	1.93	0.0385
Threitol	868	217	0.0093	0.0067	1.76	0.0123
Urea	831	189	<0.0001	0.0151	2.25	0.0023
Glutamic acid	819	246	0.0280	0.0210	1.27	0.0356
Phenylacetic acid	532	164	0.0001	0.0004	1.68	0.0214
3-Hydroxypropionic acid 1	518	177	<0.0001	0.0020	2.11	0.0038
2-Monopalmitin	442	149	0.0013	0.0020	2.15	0.0038
Bis-(2-hydroxypropyl)-amine 2	364	209	0.0046	0.0003	3.70	<0.0001
Piceatannol 2	334	221	0.0003	0.0002	1.83	0.0178
T2D + hyperlipidemia vs. T2D			T2D + hyperlipidemia	T2D
Glycine 2	949	174	0.5304	0.6355	1.82	0.0304
Cystine	889	218	0.0067	0.0106	1.36	0.0062
2-Hydroxypyridine	881	152	0.2249	0.2781	1.32	0.0299
Urea	831	189	<0.0001	0.0151	3.39	0.0023
Pyruvic acid	826	174	0.0002	0.0127	2.48	0.0062
2-Keto-isovaleric acid 1	680	89	0.0079	0.0059	1.57	0.0464
3-Hydroxypropionic acid 1	518	177	<0.0001	0.0020	3.01	0.0110
Glucose-6-phosphate 2	398	217	0.0094	0.0074	1.69	0.0229
Bis-(2-hydroxypropyl)-amine 2	364	209	0.0040	0.0003	3.50	<0.0001
3-Hydroxypyruvate	360	205	0.0002	0.0011	3.00	<0.0001
Tartronic acid	293	264	0.0028	0.0037	1.44	0.0334
N-ethylmaleamic acid 3	241	156	0.0236	0.0534	2.64	<0.0001
2'-Deoxyadenosine 5'-monophosphate	234	223	0.0006	0.0004	1.85	0.0393

VIP, variable importance in the Projection; T2D, type 2 diabetes.

No competing interests reported.

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Obesity and hyperlipidemia aggravate serum amino acid metabolism in patients with type 2 diabetes

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