Omics era in type 2 diabetes: From childhood to adulthood

Table 1 Main findings of the omics studies in animal models

Ref.	Experimental design	Main findings
Eid et al[33]	Examination of changes in glucose and lipid metabolism in the kidney, eye and nerve of leptin receptor KO BKS db/db mouse model	Glycolytic genes were uniformly upregulated in kidney and peripheral nerves; glycolytic metabolites were increased in kidney and retina but decreased in the peripheral nerve. Kidney and nerves showed an overall trend towards increased levels of different lipid species, while in the retina lipid content was decreased
Chen et al[32]	Evaluation of the characteristic of lipid species in serum and tissues in a diabetic mouse model fed a high fat diet and treated with streptozocin by using LC/HRMS and MS/MS	Brain and heart showed the largest reduction in cardiolipin levels, while the kidney had more alteration in triacylglycerol levels. Cardiolipin with highly polyunsaturated fatty acyls decreased only in the atrium but not in the ventricle; similarly, renal cortex showed longer fatty acyl chains both for increased and decreased triacylglycerol species than renal medulla
Guitton et al[25]	Systematic review about the role S1P in the development of T2D and obesity	SphK1 KO in rat pancreatic β-cells and in INS-1 cells resulted in both lowered glucose-stimulated insulin secretion and insulin content associated with decreased insulin gene expression. Conversely, SphK1 overexpression restored both insulin synthesis and secretion. HFD-fed SphK1 ko mice also showed a reduction of β-cells size, number and mass due to lipotoxic condition

T2D: Type 2 diabetes; HFD: High fat diet; LC/HRMS: Liquid chromatography high-resolution tandem; MS: Mass spectrometry.

Table 2 Main findings of the omics studies in adults

Ref.	Study design	Population (n)	Main findings
Ge et al[34]	Community-based case-control study	511 healthy adults, mean age 47.9 yr	76 patients had increased FPG and 435 had decreased or fluctuant FPG. Nine SNPs in five genes were significantly associated with increased FPG. Among the 24 glycan peaks identified, GPs 3, 8 and 11 had a positive trend with increased FPG levels, while opposite findings were found for GPs 4 and 14
Peterson et al[36]	Double-blind, randomized, placebo-controlled, parallel design study	65 adults aged 30-65 yr	Fenofibrate treatment lowered C24:0/C16:0 plasma ratio and minimally altered oxidative stress markers and correlated with worse diastolic function. Plasma TG lowering correlated with improvement in diastolic function
Yun et al[35]	Prospective study	1974 adults, aged 50-70 yr	During the 6 yr follow-up, 529 participants developed T2D. 14 sphingolipids (3 reported and 11 novel) were positively associated with incident T2D. WGCNA analysis generated 5 modules, containing different species of sphingolipids; of these, 2 modules containing saturated sphingomyelins showed the strongest association with increased T2D risk
Sun et al[38]	Systematic review	33 studies on the application of metabolomics to disease related-risk. 5 studies on the applications of metabolomics for disease prediction. 5 studies on the applications of metabolomics biomarkers for disease intervention. 8 studies about the integration of genomic and metabolomics data	The first 33 studies find out different metabolites associated with T2D, heart failure, IR and MetS. Studies about the disease prediction demonstrated that some metabolites (amino acids and lipids) were predictive for T2D. Studies about the applications of biomarkers investigated the effect of diet in reducing some risk factors. Studies on the integration of genomic and metabolomics data reported some allele positively associated with high levels of risk metabolites
Misra and Misra[2]	Systematic review	18 studies about heavy metals. 14 studies about persistent organic pollutants and pesticides. 7 studies about drugs and pharmaceuticals. 11 studies about atmospheric pollution	Heavy metals (e.g., arsenic, lead, selenium and mercury) were positively associated with increased T2D risk. Some pollutants of the POPs and pesticides’ family were directly associated with increased risk of developing T2D. Drugs such as antibiotics, antidepressant or antipsychotics were positively associated with increased T2D risk. Long exposure to atmospheric pollutants such as NO2 and PM2.5 were directly associated with T2D
Zhang et al[39]	Cohort study	694 patients (491 HIV-infected and 203 HIV-uninfected) aged 35-55 yr	11 lipids species were identified and associated with T2D risk. No association of HIV status with higher T2D risk was found, while ART use was associated with 8 risk lipids (3 decreased-risk lipids and 5 higher-risk lipids)
Wang et al[40]	Systematic review complication	1 study about application of proteomics in T2D. 1 study about the application of metabolomics in T2D. 1 study about the application of metagenomics in T2D	Proteomics analyses on 62 Mexican T2D patients showed 113 proteins related to T2D risk; in particular, 3 of these have been associated with obesity and T2D while 1 was associated with anti-inflammatory pathways. Metabolomics analyses found 33 metabolites strongly related to T2D. Metagenomics analyses reported different gut microbiota profiles between fecal sample of T2D patients and control subjects
Gudmundsdottir et al[41]	Prospective cohort study	2916 European patients (789 diabetic patients and 2127 non diabetic patients at high T2D risk development)	55 modules of coexpressed genes in the whole blood of the nondiabetic cohort were found. These modules were associated with inflammation, fat tissues, glucose tolerance, insulin sensitivity, and C-reactive protein levels, and were also preserved between non-diabetic and newly diagnosed T2D cohort
Gu et al[42]	Observational study	72 patients (30 normal weight, 26 obese and 16 newly T2D diagnosed)	Obese patients showed upregulation of 78 metabolites and downregulation of 111 metabolites than lean subjects. T2D patients showed upregulation of 459 metabolites and downregulation of 166 metabolites compared to obese subjects. Several metabolites, including amino acids and amino acids metabolites, were identified as IR potential biomarkers
Diamanti et al[43]	Cohort study	42 subjects (12 healthy controls, 16 with prediabetes and 14 T2D subjects)	Plasma metabolomics profiling revealed a positive association of hepatic fat content with tyrosine and a negative relationship with lysophosphatidylcholine. Visceral and subcutaneous adipose tissue insulin sensitivity was positively associated with several lysophospholipids, while the opposite was found for branched-chain amino acids. Several metabolites were significantly higher in T2D subjects than normal/prediabetes subjects
Salihovic et al[44]		1424 adult subjects	Three out of 62 identified metabolites were associated with prevalent T2D (mainly lower urine levels of 3-hydroxyundecanoyl-carnitine). In participants without T2D at baseline, 6 metabolites improving T2D prediction were identified

T2D: Type 2 diabetes; FPG: Fasting plasma glucose; SNP: Single nucleotide polymorphism; TG: Triglycerides; IR: Insulin resistance; WGCNA: Weighted gene coexpression network analysis.

Table 3 Main findings of the omics studies in children

Ref.	Study design	Population (n)	Main findings
Concepcion et al[45]	Cross-sectional study	90 children (30 healthy children, 30 obese children without T2D and 30 obese children with T2D) aged 13-19 yr	In urine samples of T2D patients, 22 metabolites (including succinylaminoimidazole carboxamide riboside (SAICA-riboside), betaine metabolites (betaine and dimethylglycine), branched chain amino acids (valine and leucine) and their direct catabolic derivatives (2-oxoisovaleric acid, 3-methyl-2-oxovaleric acid, 3-hydroxyisobutyrate) and aromatic amino acids (phenylalanine, tyrosine and tryptophan) were significantly associated in obese children. The metabolite pattern in OB and T2D groups differed between urine and plasma, suggesting that urinary BCAAs and their intermediates behaved as a more specific biomarker for T2D, while plasma BCAAs associated with obesity and IR independently of T2D
Perng et al[46]	Cross-sectional study	524 adolescents aged approximately 13 yr, grouped according to both obesity and glucose tolerance status	Five metabolite patterns differed with respect to phenotype: Factor 1 comprised long-chain fatty acids and was lower among non-OW/OB and high MetRisk vs non-OW/OB and low MetRisk. Factors 5 (branched chain amino acids; BCAAs), 8 (diacylglycerols) and 9 (steroid hormones) were highest among OW/OB and high MetRisk. Factor 7 (long-chain acylcarnitines) was higher among non-OWOB and high MetRisk and lower among OW/OB and low MetRisk
Gawlik et al[47]	Observational study	87 obese children divided in 2 groups (IR and Non-IR children) aged 8.5-17.9 yr old	31 steroid metabolites were quantified by GC-MS. IR was diagnosed in 20 (23%) of the examined patients. The steroidal IR signature was characterized by high adrenal androgens, glucocorticoids, and mineralocorticoid metabolites, higher 5a-reductase and 21-hydroxylase activity, and lower 11bHSD1 activity
Müllner et al[48]	Cross sectional study	81 adolescents aged > 10 yr, stratified into four groups based on BMI (lean vs obese), insulin responses (normal)	Two groups of metabolites were identified: (1) Metabolites associated with insulin response level: adolescents with HI (groups 3-4) had higher concentrations of BCAAs and tyrosine, and lower concentrations of serine, glycine, myo-inositol and dimethylsulfone, than adolescents with NI (groups 1-2); and (2) Metabolites associated with obesity status: obese adolescents (groups 2-4) had higher concentrations of acetylcarnitine, alanine, pyruvate and glutamate, and lower concentrations of acetate, than lean adolescents (group 1)
Mastrangelo et al[49]	Observational study	60 prepubertal obese children (30 girls/30 boys, 50% IR and 50% non-IR in each group, but with similar BMI)	47 metabolites out of 818 compounds were found to differ significantly between obese children with and without IR. Bile acids exhibit the greatest changes. The majority of metabolites differing between groups were lysophospholipids (15) and amino acids (17), indicating inflammation and central carbon metabolism as the most altered processes in impaired insulin signaling. Multivariate analysis (OPLS-DA models) showed subtle differences between groups that were magnified when females were analyzed alone
Martos-Moreno et al[50]	Observational study	100 prepubertal obese children (50 girls/50 boys, 50% IR and 50% non-IR in each group)	Twenty-three metabolite sets were enriched in the serum metabolome of IR obese children. The urea cycle, alanine metabolism and glucose-alanine cycle were the most significantly enriched pathways. The high correlation between metabolites related to fatty acid oxidation and amino acids (mainly branched chain and aromatic amino acids) pointed to the possible contribution of mitochondrial dysfunction in IR. The degree of BMI-standard deviation score excess did not correlate with any of the studied metabolomics components. Combination of leptin and alanine showed a high IR discrimination value in the whole cohort in both sexes. However, the specific metabolite/adipokine combinations with highest sensitivity were different between the sexes
Lopez et al[51]	Cross sectional study	28 children (14 obese female subjects with T2D and 14 lean healthy controls) aged 10-17 yr	Children with T2D had higher concentrations of C22:0 and C20:0 ceramides, with a 2-fold increase in C18:0 ceramide and C24:1 dihydroceramide. C22:0, C20:0 and C18:0 ceramide correlated with decreased adiponectin concentrations, increased HOMA-IR, BMI-SDS, triglyceride and fasting blood glucose concentrations. Plasma levels of C18:0, C20:0 and C22:0 ceramide, as well as C24:1 dihydroceramide were elevated in T2D obese female children and adolescents, probably due to tissue IR and low adiponectin levels

T2D: Type 2 diabetes; OB: Obesity; BMI: Body mass index; IR: Insulin resistance; GC-MS: Gas chromatography-mass spectrometry; HOMA-IR: Homeostatic model assessment for insulin resistance; MetRisk: Metabolic risk; 11bHSD1: 11β-Hydroxysteroid dehydrogenase type 1; OPLS-DA: Orthogonal partial least squares discriminant analysis.