1 INTRODUCTION

Diabetes mellitus (DM) is a global health crisis and is associated with increased risk of heart disease and stroke; 90%–95% are type 2 diabetes mellitus (T2DM), which is characterized by increasing beta-cell loss and insulin resistance, predominantly caused by obesity.¹ By 2045, 700 million individuals are expected to develop diabetes, with 463 million currently living with the disease worldwide.² The disease is caused by multiple factors, including genetics, immune responses, oxidative stress, and endoplasmic reticulum stress.³ Persistently elevated blood glucose levels damage organs and tissues, leading to the development of diabetic complications, such as microvascular disease, diabetic retinopathy (DR), and diabetic nephropathy (DN).^{4, 5} DR is the most common manifestation of diabetic microangiopathy worldwide. Approximately 191 million people are expected to have DR by 2030, which potentially results in blindness and has a significant impact on the quality of life of those who are affected.⁶ DN is another common microvascular complication of diabetes, affecting approximately 3% of the population.⁷ The diagnosis and treatment of T2DM and its complications have made tremendous strides in recent years owing to the rapid improvement of healthcare systems; yet, the treatment outcomes for this chronic disease remain unsatisfactory. Thus, identifying preventive measures and new biomarkers for the early detection of disease through noninvasive tests is essential to prevent deterioration of beta-cell function and preserve remaining beta-cell mass.

Non-protein-coding RNA molecules with a length longer than 200 nucleotides are known as long noncoding RNAs (lncRNAs).⁸ With the rapid development in microarray technologies and high-throughput sequencing, the biological properties and functions of lncRNAs are gradually being recognized. The regulation of various cellular responses and diseases is significantly affected by lncRNAs. Nowadays, noncoding RNAs have emerged as a diagnostic and prognostic biomarker for various diseases, such as cancer, infectious diseases, and autoimmune disorders.^9-11 According to early studies, diabetes development is linked to lncRNAs with distinct expression patterns that also play a role in other endocrine functions and diseases.¹² For example, Wang et al¹³ found that a characteristic feature of T2DM is increased expression of HOX antisense intergenic RNA, a type of lncRNA. High serum HOX antisense intergenic RNA expression is a promising noninvasive diagnostic marker and an independent predictor of T2DM. Moreover, many lncRNAs are aberrantly expressed with T2DM complications, such as DN and DR, and are also closely related to their pathogenesis.^{14, 15}

Because of their differential regulation, methylation, and other mechanisms,¹⁶ lncRNAs are anticipated to contribute to the early detection and treatment of T2DM and are attracting research attention. Thus, we aimed to assess lncRNAs as potential biomarkers for T2DM and associated comorbidities by conducting a systematic review and meta-analysis.

2 METHODS

3 RESULTS

3.1 Literature search and study characteristics

The methodology for selecting the studies is shown in Figure 1. A total of 2387 potentially relevant studies were identified in our online database, 807 of which were excluded owing to duplicate titles and abstract assessments or irrelevance to lncRNA, T2DM, or its complications. The remaining 373 articles were thoroughly evaluated. In addition to the 103 review articles, a total of 246 studies were removed for the following reasons: 120 were animal studies, 108 did not report AUC values, and 18 did not report numerical sensitivity or specificity results. In total, 24 studies ultimately met our eligibility criteria, of which T2DM, DN, and DR were analyzed in 17,^21-34 5,^{5, 30, 35-37} and 7 studies,^{4, 28, 29, 32, 37-39} respectively (some articles included more than one of these outcomes). Table 1 provides the specifics of the survey characteristics and summarizes the quality assessment results. The detail scores of Newcastle-Ottawa Scale for quality of each included article are shown in Supplementary S4.

TABLE 1. Main characteristics of the included studies.

Study (ref)	Year	Country	LncRNA	Disease type	Sample size		Specimen	Method	Regulation	Diagnostic power			NOS score
					Case	Control				Sen	Spe	AUC
Omidvar et al	2018	Iran	lncRNA VIM-AS1	T2DM	100	100	PBMCs	qRT-PCR	Down	0.56	0.68	0.63	7
Omidvar et al	2018	Iran	lncRNA CTBP1-AS2	T2DM	100	100	PBMCs	qRT-PCR	Down	0.59	0.75	0.68	7
Saeidi et al	2018	Iran	lncRNA LY86-AS1	T2DM	100	98	PBMCs	qRT-PCR	Down	0.65	0.80	0.747	7
Saeidi et al	2018	Iran	lncRNA HCG27_201	T2DM	100	98	PBMCs	qRT-PCR	Down	0.56	0.75	0.65	7
Mansoori et al	2018	Iran	lncRNA LINC00523	T2DM	100	98	PBMCs	qRT-PCR	Down	0.81	0.61	0.74	7
Mansoori et al	2018	Iran	lncRNA LINC00994	T2DM	100	98	PBMCs	qRT-PCR	Down	0.81	0.53	0.67	7
Wang et al	2018	China	lncRNA CASC2	T2DM	296	56	Blood	qRT-PCR	Down	0.66	0.48	0.63	6
Yin et al	2017	China	lncRNA GAS5	T2DM	10	30	Plasma	qRT-PCR	Down	0.7	0.6	0.74	7
Carter et al	2015	United States	lncRNA GAS5	T2DM	47	49	Blood	qRT-PCR	Down	0.85	0.67	0.81	7
Li et al	2017	China	lncRNA ENST00000550337.1	T2DM	64	60	Blood	qRT-PCR	Up	0.75	0.65	0.73	6
Li et al	2017	China	lncRNA ENST 00000550337.1	Prediabetes	63	60	Blood	qRT-PCR	Up	0.75	0.65	0.71	6
Li et al	2017	China	lncRNA TCONS_00007244	T2DM	20	20	Blood	qRT-PCR	Up	0.75	0.65	0.71	6
Li et al	2017	China	lncRNA TCONS_00007244	Prediabetes	20	20	Blood	qRT-PCR	Up	0.65	0.75	0.69	6
Li et al	2017	China	lncRNA TCONS_00000886	T2DM	20	20	Blood	qRT-PCR	Up	0.85	0.6	0.7	6
Li et al	2017	China	lncRNA TCONS_00000886	Prediabetes	20	20	Blood	qRT-PCR	Up	0.9	0.5	0.69	6
Shaker et al	2018	Egypt	LncRNA HOTAIR	T2DM	30	81	Serum	qRT-PCR	Up	0.93	0.65	0.72	7
Shaker et al	2018	Egypt	LncRNA MALAT1	T2DM	30	81	Serum	qRT-PCR	Up	0.57	0.69	0.64	7
Toraih et al	2019	Egypt	lncRNA CDKN2B-AS1	T2DM	55	108	Plasma	qRT-PCR	Up	0.70	0.60	0.73	6
Toraih et al	2019	Egypt	lncRNA PVT1	T2DM	55	108	Plasma	qRT-PCR	Up	0.83	0.60	0.74	6
Cai et al	2021	China	lncRNA SNHG5	T2DM	62	60	Serum	qRT-PCR	Up	0.81	0.83	0.90	7
Cao et al	2020	China	lncRNA LINC-P21	T2DM	108	68	Serum	qRT-PCR	Up	0.83	0.94	0.92	6
Ji et al	2022	China	lncRNA NR2F1-AS1	T2DM	111	106	Blood	qRT-PCR	Up	0.7664	0.6667	0.789	7
Su et al	2022	China	lncRNA TUG1	T2DM	105	105	Serum	qRT-PCR	Up	0.63	0.97	0.64	7
Su et al	2022	China	lncRNA MEG3	T2DM	105	105	Serum	qRT-PCR	Up	0.58	0.94	0.59	7
Ma et al	2021	China	ENST00000588707.1	T2DM	127	130	PBMCs	qRT-PCR	Down	0.72	0.80	0.82	7
Ma et al	2021	China	TCONS_00004187	T2DM	127	130	PBMCs	qRT-PCR	Down	0.82	0.61	0.83	7
Wang et al	2021	China	lncRNA HOTAIR	T2DM	96	82	Serum	qRT-PCR	Up	0.87	0.84	0.90	8
Saleh et al	2020	Egypt	lncRNA HCG27_201	T2DM	70	70	Plasma	qRT-PCR	Down	0.96	0.83	0.96	7
Saleh et al	2020	Egypt	lncRNA GAS5	T2DM	70	70	Plasma	qRT-PCR	Down	0.96	0.76	0.94	7
Saleh et al	2020	Egypt	lncRNA LY86-AS1	T2DM	70	70	Plasma	qRT-PCR	Down	0.93	0.69	0.83	7
Saleh et al	2020	Egypt	lncRNA HCG27_201	Prediabetics	65	70	Plasma	qRT-PCR	Down	0.91	0.64	0.756	7
Saleh et al	2020	Egypt	lncRNA GAS5	Prediabetics	65	70	Plasma	qRT-PCR	Down	0.85	0.64	0.699	7
Saleh et al	2020	Egypt	lncRNA LY86-AS1	Prediabetics	65	70	Plasma	qRT-PCR	Down	0.78	0.53	0.602	7
Anbari et al	2020	Arabia	lncRNA-GHRL-3:2	T2DM	71	32	Blood	qRT-PCR	Down	0.93	0.84	0.93	7
Anbari et al	2020	Arabia	lncRNA GHRL-3:3	T2DM	71	32	Blood	qRT-PCR	Down	0.88	0.91	0.90	7
Zhou et al	2020	China	lncRNA MALAT1	DN	27	20	PBMCs	qRT-PCR	Up	0.80	0.68	0.79	6
Cai et al	2021	China	lncRNA SNHG5	DN	58	62	Serum	qRT-PCR	Up	0.81	0.84	0.88	7
Rajabinejad et al	2022	Iran	lncRNA MALAT1	DN	20	20	Blood	qRT-PCR	Up	1.00	0.71	0.92	7
Rajabinejad et al	2022	Iran	lncRNA H19	DN	20	20	Blood	qRT-PCR	Up	0.84	0.88	0.92	7
Zhu et al	2022	China	lncRNA ANRIL	DN	66	20	Blood	qRT-PCR	Up	0.83	0.91	0.92	7
Alfaifi et al	2021	Britain	lncRNA NKILA	DN	200	200	Serum	qRT-PCR	Up	0.54	0.4	0.48	8
Alfaifi et al	2021	Britain	lncRNA NEAT1	DN	200	200	Serum	qRT-PCR	Up	0.52	0.48	0.52	8
Alfaifi et al	2021	Britain	lncRNA MALAT1	DN	200	200	Serum	qRT-PCR	Up	0.53	0.4	0.48	8
Alfaifi et al	2021	Britain	lncRNA MIAT	DN	200	200	Serum	qRT-PCR	Up	0.55	0.5	0.53	8
Shaker et al	2018	Egypt	lncRNA HOTAIR	DR	30	30	Serum	qRT-PCR	Up	0.76	0.8	0.78	7
Shaker et al	2018	Egypt	lncRNA MALAT1	DR	30	30	Serum	qRT-PCR	Up	0.74	0.73	0.74	7
Liu et al	2021	China	lncRNA ENST00000505731	PDR	30	30	Blood	qRT-PCR	Up	0.8	0.87	0.88	7
Liu et al	2021	China	lncRNA NR-126161	PDR	30	30	Blood	qRT-PCR	up	0.50	0.97	0.79	7
Atef et al	2022	Egypt	lncRNA HIF1A-AS2	NPDR	15	15	PBMCs	qRT-PCR	Up	0.89	0.92	0.94	7
Atef et al	2022	Egypt	lncRNA HIF1A-AS2	PDR	15	15	PBMCs	qRT-PCR	Up	0.96	0.93	0.97	7
Toraih et al	2019	Egypt	lncRNA RNCR2	DR	108	55	Plasma	qRT-PCR	Down	0.70	0.70	0.74	6
Toraih et al	2019	Egypt	lncRNA NEAT2	DR	108	55	Plasma	qRT-PCR	Down	0.62	0.56	0.62	6
Ji et al	2022	China	lncRNA NR2F1-AS1	DR	158	106	Serum	qRT-PCR	Up	0.83	0.82	0.90	7
Alfaifi et al	2021	Britain	lncRNA NKILA	DR	200	200	Serum	qRT-PCR	Up	0.59	0.41	0.56	8
Alfaifi et al	2021	Britain	lncRNA NEAT1	DR	200	200	Serum	qRT-PCR	Up	0.61	0.51	0.57	8
Alfaifi et al	2021	Britain	lncRNA MALAT1	DR	200	200	Serum	qRT-PCR	Up	0.61	0.5	0.6	8
Alfaifi et al	2021	Britain	lncRNA MIAT	DR	200	200	Serum	qRT-PCR	Up	0.68	0.58	0.66	8
Li et al	2022	China	lncRNA HIF1A-AS1	PDR	80	100	Serum	qRT-PCR	Up	0.66	0.79	0.77	7

• Abbreviations: AUC, area under the curve; DN, diabetic nephropathy; DR, diabetic retinopathy; NOS, Newcastle-Ottawa Scale; NPDR, nonproliferative diabetic retinopathy; PBMCs, peripheral blood mononuclear cells; PDR, proliferative diabetic retinopathy; qRT-PCR, quantitative reverse transcription polymerase chain reaction; Sen, sensitivity; Spe, specificity; T2DM, type 2 diabetes mellitus.

3.2 Diagnostic value of lncRNAs for type 2 diabetes mellitus

We identified 17 studies that compared patients with T2DM and healthy controls, involving a total of 4685 participants. We performed meta-analyses on the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) of lncRNAs in diagnosing T2DM. We also compiled the summary ROC (SROC) data from these analyses. The following results were obtained by using a random-effects model for the pooled lncRNA estimations: the AUC was 0.84 (95% CI: 0.80–0.87), and the sensitivity and specificity were 0.79 and 0.75, respectively; PLR was 3.1 (95% CI: 2.5–3.9), NLR was 0.28 (95% CI: 0.22–0.36), and DOR was 11 (95% CI: 7–16). In conclusion, lncRNA has an excellent diagnostic value for T2DM (Figure 2A,B).

4 DISCUSSION

We conducted a systematic review and meta-analysis of case–control and cohort studies on the association between lncRNA expression and diagnostic outcomes in patients with T2DM or its associated complications (see the schema in Figure 3).

Our study revealed a significant correlation between the expression of abnormal lncRNAs and the presence of T2DM, which is consistent with the findings of previous studies.⁴² In our meta-analysis, we included 10 studies more than the seven included in a previous study.⁴² We discovered that lncRNAs are highly accurate in diagnosing T2DM or prediabetes. In addition, we analyzed five and seven lncRNA studies on DN and DR, respectively, and our findings revealed that lncRNAs have some diagnostic power for the detection of DN and DR. We performed a regression analysis to investigate the causes of heterogeneity and did not identify any outliers. Using subgroup analysis, we found that lncRNAs had a unique diagnostic predictive value for T2DM across sample sources and ethnic differences, with high diagnostic value in serum samples and in samples from Asian populations.

Inflammation and dysregulated metabolism affect multiple cells and organs, such as beta cells, and liver, skeletal muscle, kidney, brain, and adipose tissue, gradually resulting in T2DM.⁴³ Cao et al³¹ suggested that serum expression of LINC-P21 was elevated in patients with T2DM, related to its targeting of miR-766-3p to upregulate NR3C2, resulting in insulin secretion and proliferation in pancreatic beta cells. Elevated serum LINC-P21 and decreased serum miR-766-3p levels are candidate diagnostic biomarkers in patients with T2DM. Studies investigating the upregulation or inhibition of lncRNA in T2DM revealed an association with functional impairment of INS-1 cells or increased hepatic glycogen synthesis.⁴⁴ In general, these studies suggest that lncRNAs have the potential as direct targets for therapeutic interventions in diabetes. Thus, lncRNAs have emerged as important regulators of glucose and lipid metabolism.

Moreover, the role of lncRNAs in diabetes-related complications should not be overlooked. One of the most serious microvascular complications of DM is DN due to mesangial cell proliferation and changes in the renal microenvironment.⁴⁵ In an animal study, the lncRNA SOX2OT was significantly downregulated in the mesangial cells by different pathways, whereas overexpression thereof significantly inhibited fibrosis of the mesangial cells.⁴⁶ In a study by Cai et al, SNHG5 expression was substantially elevated in DN, confirming its potential as a novel biomarker for the diagnosis of DN that may interact with miR-26a-5p. DN is characterized by an accumulation of extracellular matrix, hypertrophy, and fibrosis in the glomeruli and renal tubular cells. Additionally, growing evidence suggests that these DN features are closely linked to ncRNA regulation.⁴⁷

Ogurtsova et al⁴⁸ reported that lncRNAs have unique expression profiles and play an important role in the development of DR. One study found that lncRNA H19 blocked endothelial-mesenchymal transition (EndMT) in DR.⁴⁹ It inhibited transforming growth factor-1 and its signaling pathway by blocking the MAPK-ERK1/2 signaling pathway that controlled EndMT when glucose levels are elevated. A similar mechanistic study of lncRNAs in DR was reported, in which overexpression of the lncRNA SNHG7 inhibited EndMT and tube formation.⁵⁰

Our study demonstrated the applicability of lncRNAs as diagnostic biomarkers not only for T2DM but also for its complications, such as DN and DR.

We conducted a meta-regression and subgroup analysis based on ethnic origin, sample origin, and lncRNA levels. To improve the search for biomarkers for early diagnosis of type 2 diabetes, future studies should include subgroup analyses based on specific types of lncRNA, sex, and age. Fortunately, obtaining patient blood samples and determining the levels of lncRNA expression make this investigation easy and practical. Furthermore, this diagnostic analysis showed no asymmetry in the Deeks' funnel plot, indicating no publication bias. The high incidence and prevalence of T2DM poses challenges to the effective treatment of patients. Therefore, early detection of diabetes and prevention of its complications are particularly important for high-risk groups.

A large number of lncRNAs have been tested in animal models but not in human tissues. This may be attributed to the lower expense of animal experiments compared with that of clinical studies. Owing to the limited resources available for clinical studies, we aimed to validate lncRNA biomarkers that are prevalent in animals and humans by using Venn diagram statistics. As shown in Figure 4, different biomarkers have been detected in humans and animals or in blood, tissues, and cells. Our Venn diagrams show that MALAT1 and TUG1 are examples of the lncRNAs validated in animals and humans, whereas MALAT1 and NEAT1 are examples of those validated in diabetic cells, tissues, and blood. These findings may serve as a solid starting point for future studies. Table 2 provides specific information on the characteristics of the overlapping lncRNAs. To the best of our knowledge, this is the first meta-analysis to explore the diagnostic value of lncRNAs in T2DM, DN, and DR.

5 LIMITATIONS AND FUTURE RECOMMENDATIONS

Our meta-analysis has some limitations. First, some heterogeneity was detected, and subgroup analysis based on the lncRNA type could not fully explore the main sources of heterogeneity owing to the limited number of studies. Second, the sample size of studies on the diagnostic value of lncRNAs in DN and DR was relatively small, which may have led to insufficient statistical power. Third, the sample size of the lncRNA studies on other diabetic complications was insufficient for a meta-analysis, allowing only systematic assessment; thus, their associated diagnostic value requires further study. Fourth, although a large number of lncRNAs have been tested in humans, the experimental results did not provide specific AUCs, sensitivities, or specificities. Despite our attempts to obtain these data by email, the results were not available; therefore, we were unable to obtain additional data for the meta-analysis.

Recent studies have suggested that lncRNAs, circular RNAs, and mRNAs compete with each other through miRNA response elements to modulate the progression of T2DM and may function as miRNA sponges.⁵¹ Thus, future research may focus on constructing a competing endogenous RNA network to further understand the biological effects of lncRNAs in patients with T2DM and its complications. Furthermore, lncRNAs may compete for shared miRNAs to regulate other RNA transcripts, thereby influencing the pathogenesis of T2DM and its complications. Finally, with a large number of lncRNAs studies in the diagnosis of diabetes, we may focus on the diagnostic value of a specific LncRNA for diabetes and related complications.

6 CONCLUSION

Our findings suggest that lncRNAs are promising biomarkers and therapeutic targets for T2DM, DN, and DR. They may be extremely beneficial in the early detection and diagnosis of these conditions. The biological functions of these markers require further investigation to help determine the molecular mechanisms underlying the pathogenesis of T2DM.

AUTHORS CONTRIBUTIONS

Xuee Su, Huibin Huang, Yinqiong Huang, and Shu Lin were involved in the conception of the article. Xuee Su and Jinqing Lai independently examined studies that might be eligible for inclusion, and any disagreements were resolved by a third review author (Yinqiong Huang), if necessary. Xuee Su and Jinqing Lai obtained datasets for research based on a standardized form. After the consensus was reached, the data were entered into STATA statistical software. and any uncertainties were discussed and highlighted (Yinqiong Huang and Shu Lin). Xuee Su and Huibin Huang drafted the manuscript. Yinqiong Huang and Shu Lin contribute to project supervision, and study design. All authors have read and approved the final manuscript. Xuee Su and Huibin Huang contributed equally to this work.

FUNDING INFORMATION

This work was supported by the Science and Technology Bureau of Quanzhou (grant number 2020CT003), the Nursery Fund Project of the Second Affiliated Hospital of Fujian Medical University (2021MP25), Key Young Talents Health Training Project of Fujian Province (2020GGA057), Natural Science Foundation of Fujian Province (2020J01221), and National Natural Science Foundation of China (82200871). The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

DISCLOSURE

The authors declare no competing fnancial interests.