Abstract
Background
Obesity-related adipose tissue dysfunction leads to a chronic inflammatory state affecting distant organs and tissues. This metabolic inflammation has a detrimental impact on the expression of genes related to glucose metabolism, leading to systemic insulin resistance, which also affects the central nervous system and contributes to cognitive decline. Adipose tissue-derived microRNAs (miRNAs) have been implicated in this phenomenon. This study aimed to investigate whether the expression of genes critical for both insulin action and neuronal metabolism (APP, SOCS3, PTPN1, PTPN2) is altered in the adipose tissue of patients with obesity due to miRNA interference, predisposing them directly to the development of insulin resistance and metabolic inflammation, while indirectly leading to a decline in cognitive function.
Methods
The expression of mRNAs of the above-mentioned genes, selected adipokines (interleukins 1β, 6, 8, 15, tumor necrosis factor-alpha, resistin, adiponectin) and miRNAs was measured by real-time PCR in adipose tissue of 75 patients with obesity, 19 patients who successfully reduced body mass after metabolic surgery and 25 normal weight subjects, stratified by insulin sensitivity and diabetic status. The results were correlated with patients’ clinical and biochemical parameters.
Results
mRNA levels of genes promoting insulin sensitivity, APP and PTPN2, were significantly (p < 0.05) decreased in adipose tissue from patients with obesity, in both visceral (VAT) and subcutaneous (SAT) depots. After stratification by the triglyceride/high-density lipoprotein ratio (an indirect marker of insulin sensitivity), we found that individuals diagnosed with insulin resistance had lower VAT and SAT APP and PTPN2 mRNA levels, whereas APP expression was significantly decreased in VAT from patients with both obesity and type 2 diabetes compared to normoglycemic individuals with obesity, too. We also observed significant positive correlations between the mRNA levels of these genes and the expression of the above-mentioned adipokines. Finally, we analysed the levels of miRNAs targeting the mRNAs of the genes studied and observed significant negative correlations between APP mRNA levels and hsa-miR-579-5p and hsa-miR-142-3p, and between PTPN2 mRNA levels and hsa-miR-142-3p.
Conclusion
Adipose tissues of patients with obesity are characterised by altered expression of genes that are key for both insulin action and neuronal metabolism, and miRNAs may be involved in this phenomenon. However, due to the descriptive nature of our experiments, these results require verification in functional studies.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12986-026-01113-4.
Keywords: Obesity, Adipose tissue, Insulin sensitivity, Insulin resistance, Metabolic inflammation, Adipokines, MicroRNA
Background
Obesity is a major health problem worldwide, associated with numerous chronic complications resulting from mechanical stress, but first of all, from adipose tissue dysfunction. Indeed, in hypertrophic adipocytes overloaded with triglycerides, oxidative stress and mitochondrial dysfunction occur, impairing their lipid storage capacity and altering their secretome [1]. Among genes overexpressed in dysfunctional adipose tissue are those encoding pro-inflammatory cytokines and adipokines (e.g., interleukins 1β and 6, tumour necrosis factor-alpha, resistin) [2, 3]. Conversely, levels of adipokines with anti-inflammatory and insulin-sensitising properties (e.g., adiponectin) are often reduced in obesity [4]. The obesity-induced inflammatory process spreads throughout the body, contributing to insulin resistance in organs crucial for glucose metabolism, such as the liver and muscle, and impairing insulin secretion from pancreatic islets, thereby predisposing to the development of type 2 diabetes mellitus (T2DM) [5].
However, there is growing evidence linking metabolic inflammation to an increased risk of neurological disorders such as Alzheimer’s disease (AD), too [6–8]. The “type 3 diabetes” hypothesis posits that AD represents a form of diabetes affecting the brain, and obesity is considered a risk factor for cognitive impairment [9, 10]. Among the genes involved, both insulin action and neuronal metabolism are those encoding amyloid precursor protein (APP), suppressor of cytokine signalling 3 (SOCS3), protein tyrosine phosphatase, non-receptor type 1 (PTPN1), and protein tyrosine phosphatase, non-receptor type 2 (PTPN2) [11–14].
In recent years, microRNAs (miRNAs) have been pointed to as important regulators of gene expression in adipocytes. Indeed, these small non-coding RNAs are crucial for proper adipocyte differentiation, proliferation, and function, and are often responsible for the dysregulation of gene expression that leads to adipose tissue dysfunction [15]. Moreover, miRNAs generated in adipose tissue can be secreted into the circulation via exosomes and thus impact distant tissues and organs, including the nervous system [16]. Therefore, miRNAs may act as crucial molecular bridges that link obesity to neurodegenerative diseases by regulating gene expression associated with systemic inflammation, brain insulin resistance, and microglial activation [16, 17]. In parallel, cytokines from adipose tissue may reach the brain, deregulating local neural transcription. This altered miRNA landscape subsequently promotes oxidative stress, shifts microglia toward a destructive phenotype, and drives the hallmark pathologies of neurodegeneration, including amyloid-beta plaque formation [18].
Our research hypothesis is that the expression of genes critical for both insulin sensitivity and neuronal metabolism is altered in the adipose tissue of patients with obesity due to miRNA interference, predisposing them directly to the development of insulin resistance and metabolic inflammation, while indirectly leading to a decline in cognitive function.
Methods
The aim, design, and setting of the study
The expression of four genes involved in the pathogenesis of neurological disorders associated with insulin action: APP, SOCS3, PTPN1, and PTPN2 was measured in adipose tissue of patients with obesity (stratified by insulin sensitivity and diabetic status) before and after bariatric surgery, as well as in normal-weight individuals. Next, we searched for potential correlations between the expression of these genes and the mRNAs of the selected cytokines and adipokines involved in metabolic inflammation. Finally, we performed in silico analysis to identify miRNAs potentially targeting the mRNAs of APP, SOCS3, PTPN1, and PTPN2, and subsequently searched for correlations between gene and miRNA expression in the investigated tissues. Unfortunately, such a descriptive research model based on correlations does not explain the pathomechanisms of the observed phenomena. Therefore, the results obtained require verification in functional studies.
Study participants
Gene and miRNA expression analyses were performed on adipose tissue samples obtained from 75 individuals with obesity. Based on the World Health Organization (WHO) classification, all patients were diagnosed with class III obesity: mean body mass index (BMI) was ≥ 40.0 kg/m2. In this group, 25 individuals (20 females and 5 males) were diagnosed with T2DM or pre-diabetes (impaired glucose tolerance, abnormal fasting glucose). 19 patients who successfully lost weight after bariatric surgery (and achieved T2DM/pre-diabetes remission), 24 months after the procedure, underwent abdominoplasty.
The control group consisted of 25 age-matched individuals whose BMI was within the normal range. Although body composition was not assessed in the control group, based on normal results of basic blood tests and a negative history of chronic disease (including components of the metabolic syndrome), all individuals in the control group were considered to be metabolically healthy.
Since insulin serum levels were unavailable in the studied group, an indirect parameter – the triglyceride/high-density lipoprotein cholesterol ratio (TG/HDL) was used to screen for insulin sensitivity (participants taking lipid-lowering medication were excluded from this assessment). A ratio value above 3.75 for men and 3.00 for women was considered the cut-off point for diagnosing insulin resistance [19]. TG/HDL ratio was calculated in 58 patients with obesity, and based on its value, 39 of them (33 women and 6 men) were found to be insulin resistant (IR). Importantly, TG/HDL ratio values in the control group were within the normal range, which suggests a metabolically healthy phenotype.
The basic clinical and biochemical characteristics of study participants are summarised in Table 1. A comparison of patients’ parameters before and after surgery, with and without diabetes, is included in Supplementary Tables 1 and 3 (Additional file 1).
Table 1.
Basic clinical characteristics of the studied groups
| Patients with obesity before weight loss (N = 75) |
Patients after weight loss (N = 19) |
Normal weight individuals (N = 25) |
||||
|---|---|---|---|---|---|---|
| Males/Females | 13/62 | 4/15 | 6/19 | |||
| Mean ± SD | Min–Max | Mean ± SD | Min–Max | Mean ± SD | Min–Max | |
| Age (years) | 41.4 ± 10.2 | 20–62 | 41.5 ± 10.3 | 28–67 | 47.7 ± 13.5 | 23–62 |
| BMI (kg/m2) | 46.3 ± 5.5 | 40.1–59.5 | 27.2 ± 2.3 | 24.3–29.5 | 23.3 ± 1.6 | 20.1–24.9 |
| Adipose tissue (% body mass) | 47.1 ± 5.2 | 32.6–59.5 | 30.5 ± 3.3 | 24.8–34.0 | – | – |
| Waist circumference (m) | 1.2 ± 0.2 | 1.0–1.7 | 0.9 ± 0.1 | 0.8–1.1 | – | – |
| Weight loss (kg) | – | – | 47.8 ± 10.4 | 35.2–65.6 | – | – |
| TSH (mIU/l) | 1.8 ± 1.3 | 0.3–5.6 | 1.6 ± 0.2 | 1.1–1.3 | 1.2 ± 0.2 | 1.1–1.3 |
| Glucose (mmol/L) | 6.0 ± 1.4 | 3.5–11.1 | 4.8 ± 0.5 | 4.1–5.7 | 5.2 ± 0.2 | 4.2–5.4 |
| Total cholesterol (mmol/L) | 4.9 ± 1.1 | 2.4–7.9 | 4.6 ± 0.9 | 3.5–5.9 | 4.8 ± 0.2 | 3.8–4.9 |
| HDL cholesterol (mmol/L) | 0.97 ± 0.3 | 0.75–1.3 | 1.12 ± 0.2 | 0.9–1.29 | 1.24 ± 0.2 | 1.03–1.47 |
| Triglycerides (mmol/L) | 2.9 ± 0.3 | 2.6–3.2 | 1.9 ± 0.4 | 1.5–2.4 | 1.5 ± 0.3 | 1.1–1.8 |
| Triglycerides/HDL ratio* | 5.4 ± 1.8 | 3.6–7.4 | 4.2 ± 0.7 | 3.4–4.8 | 2.6 ± 0.4 | 2.2–2.9 |
| Obesity-related co-morbidities | ||||||
| N | % | N | % | N | % | |
| Type 2 diabetes/prediabetes | 25 | 33.3 | 3 | 15.8 | none | none |
| Hypertension | 42 | 56.0 | 6 | 31.6 | none | none |
| Hyperlipidemia | 46 | 61.3 | 6 | 31.6 | none | none |
| Metabolic syndrome** | 40 | 53.3 | none | none | none | none |
*The triglycerides/HDL ratio was calculated in 58 subjects not taking hypolipemic drugs
**Diagnosis of the metabolic syndrome was based on the criteria proposed by the International Diabetes Federation for the European population (waist circumference greater than 80 cm in women and 94 cm in men, and the presence of at least 2 of 3 disorders: dyslipidemia, hypertension, carbohydrate intolerance)
BMI – body mass index, HDL – high-density lipoprotein, N – number of individuals, SD – standard deviation, TSH – thyroid-stimulating hormone
Table 3.
Primers used for microRNA expression analysis
| microRNA | Primers | |
|---|---|---|
| hsa-miR-103a-3p | F | 5’ GCAGAGCAGCATTGTACAG 3’ |
| R | 5’ GGTCCAGTTTTTTTTTTTTTTTCATAG 3’ | |
| hsa-miR-142-3p | F | 5’ CGCAGTGTAGTGTTTCCT 3’ |
| R | 5’ GGTCCAGTTTTTTTTTTTTTTTCCA 3’ | |
| hsa-miR-561-3p | F | 5’ GCAGCAAAGTTTAAGATCCTTG 3’ |
| R | 5’ GGTCCAGTTTTTTTTTTTTTTTACTTC 3’ | |
| hsa-miR-561-5p | F | 5’ CGCAGATCAAGGATCTTAAACTT 3’ |
| R | 5’ TCCAGTTTTTTTTTTTTTTTGGCA 3’ | |
| hsa-miR-579-3p | F | 5’CGCAGTTCATTTGGTATAAACC 3’ |
| R | 5’ GGTCCAGTTTTTTTTTTTTTTTAATCG 3’ | |
| hsa-miR-579-5p | F | 5’ GCGGTTTGTGCCAGATG 3’ |
| R | 5’ GGTCCAGTTTTTTTTTTTTTTTCGT 3’ |
bp – base pairs, F – forward, R – reverse
Tissues
Samples of visceral (VAT) and subcutaneous (SAT) adipose tissue from 75 patients with obesity were obtained during bariatric surgery (37 patients underwent sleeve gastrectomy, 25 – mini gastric by-pass, and 13 – Roux-en-Y gastric bypass). In 19 patients who underwent abdominoplasty 24 months after the first surgical procedure, SAT samples were taken from the lower abdomen. Since the abdominal cavity was not opened during abdominoplasty, VAT samples could not be collected from these patients. The control adipose tissue samples were collected from normal-weight patients during elective cholecystectomy (VAT) or inguinal hernia surgery (SAT). After collection, all tissues were frozen in liquid nitrogen and stored at −80 °C until testing.
Gene and microRNA expression analysis
Isolation of total RNA from adipose tissue, reverse transcription (separately for mRNA and miRNA), and real-time PCR were performed as described previously [20]. ACTB, which encodes β-actin, was used as a reference gene. Primers used to analyse the expression of APP, SOCS3, PTPN1, PTPN2, and ACTB are listed in Table 2.
Table 2.
Primers used for gene expression analysis
| Gene | Primers | |
|---|---|---|
| APP | F | 5’ CCTTCTCGTTCCTGACAAGTGC 3’ |
| R | 5’ GGCAGCAACATGCCGTAGTCAT 3’ | |
| SOCS3 | F | 5’ CAAGGACGGAGACTTCGATT 3’ |
| R | 5’ GGAGCCAGCGTGGATCTG 3’ | |
| PTPN1 | F | 5’ TGTCTGGCTGATACCTGCCTCT 3’ |
| R | 5’ ATCAGCCCCATCCGAAACTTC 3’ | |
| PTPN2 | F | 5’ GCAGTCTATGCTGTTCAGGTGC 3’ |
| R | 5’ CCAAAATTCAGGTCCTCTCATAGG 3’ | |
| ACTB | F | 5’ CAGCCTGGATAGCAACGTAC 3’ |
| R | 5’ TTCTACAATGAGCTGCGTGTG 3’ |
bp – base pairs, F – forward, R – reverse
Bioinformatics resources (miRTarBase, miRBase, TargetScan, and miRD) were used to evaluate and select 5 miRNAs that may directly or indirectly regulate the expression of APP, SOCS3, PTPN1, and PTPN2 (Table 3). Next, the miRprimer2 program was used to design miRNA primers. The designed primers (Table 3) were verified in the miRanda software. The expression results of the miRNAs studied were related to the expression of hsa-miR-103a-3p, which is the recommended reference miRNA for adipose tissue [21].
Statistical analysis
Normality of distribution and homogeneity of variance for selected parameters were checked using the Shapiro–Wilk and Levene’s tests. Differences in mRNA and miRNA expression levels were analysed using a multi-step approach to account for the study design. A paired t-test was used to compare expression between VAT and SAT within the control group, accounting for within-subject correlations. For the group of patients with obesity, a mixed-effects model was employed to capture the repeated-measures structure (SAT vs. VAT) and the longitudinal aspect (SAT-O vs. SAT-PO), while accommodating the missing post-operative VAT data. Baseline differences between the groups were assessed using unpaired t-tests. Correlations between the studied parameters were analysed using Spearman’s correlation test. A value of p < 0.05 was used as the threshold for statistical significance. All statistical analyses were performed using Statistica v.10 (StatSoft, Tulsa, OK, USA) and GraphPad Prism v.7 (GraphPad Software, San Diego, CA, USA).
Results
Obesity is associated with reduced expression of genes related to insulin sensitivity in adipose tissue
The expression results of the four genes studied in adipose tissue are shown in Fig. 1. The levels of mRNA for the gene encoding APP (APP) varied significantly across adipose tissue types and patient groups (Fig. 1A). Its expression was decreased in subcutaneous adipose tissue (SAT) from patients with obesity (SAT-O) compared to those who were metabolically healthy (SAT-N) (p < 0.0001). Interestingly, in the SAT of patients who underwent bariatric surgery (SAT-PO), APP expression was significantly higher than in the SAT-O group (p = 0.0019). These results suggest that weight loss might be associated with a restoration of APP mRNA levels in the SAT. Additionally, we observed significant differences in APP expression between visceral tissues from patients with obesity (VAT-O) and normal-weight (VAT-N) patients (p = 0.0454).
Fig. 1.
Expression of genes related to insulin sensitivity: APP (a), SOCS3 (b), PTPN1 (c), and PTPN2 (d) in visceral adipose tissue (VAT) and subcutaneous (SAT) adipose tissue of patients with obesity before (O), after weight loss (PO) and of normal weight subjects (N). Results normalised to β-actin (ACTB) mRNA concentration are presented as the median with the interquartile range. “a” p < 0.0001; “b” p < 0.001; “c” p < 0.01
SOCS3 mRNA levels were lower (not significantly) in SAT-O compared to SAT-N, but in contrast to APP, they decreased after weight loss (SAT-O vs. SAT-PO p = 0.0001) – Fig. 1B.
In the case of PTPN1 (Fig. 1C), its expression was higher in the SAT tissues of patients with obesity than in normal-weight subjects, and these differences were statistically significant (p = 0.0144). Moreover, we observed a significant increase in PTPN1 mRNA levels in patients’ post-weight-loss (PO) tissues compared to normal-weight tissues (p = 0.0085).
In some respects, the expression profile of PTPN2 in the tissues studied resembled that observed for APP (Fig. 1D). Its mRNA levels were lower in patients with obesity than in healthy controls (p < 0.0001 for VAT and p < 0.0001 for SAT). However, we observed that weight loss was associated with a non-significant increase in PTPN2 expression (p = 0.67). Furthermore, in subjects with obesity, we observed a significant difference in PTPN2 expression between visceral and subcutaneous tissue (p < 0.0001), whereas in normal-weight individuals, PTPN2 mRNA levels did not differ significantly between these two depots.
Insulin resistance and type 2 diabetes are associated with altered expression of genes related to insulin sensitivity in adipose tissue
Given the importance of the genes studied for insulin action, the next step in our work was to investigate whether their expression differed in patients with obesity, stratified by the TG/HDL ratio (an indirect measure of insulin resistance) and by the presence of T2DM.
The results illustrating the differential expression of four genes in adipose tissue from patients with obesity and a TG/HDL ratio indicating insulin resistance (IR) compared to patients with a TG/HDL value indicating normal insulin sensitivity (IS) are shown in Fig. 2. We found that mRNA levels of APP (Fig. 2A) and PTPN2 (Fig. 2D) were significantly lower in visceral adipose tissue from patients with obesity and IR (p = 0.03 and p = 0.003, respectively) than in patients with TG/HDL ratios indicating normal insulin sensitivity.
Fig. 2.
mRNA levels of genes encoding amyloid precursor protein (APP), (A); suppressor of cytokine signaling 3 (SOCS3), (B); tyrosine-protein phosphatase non-receptor type 1 (PTPN1), (C); and protein tyrosine phosphatase, non-receptor type 2 (PTPN2), (D) in the visceral (VAT) and subcutaneous (SAT) adipose tissues of patients with obesity (O) diagnosed with insulin resistance (IR) and with normal insulin sensitivity (NS). Results normalized to β-actin (ACTB) mRNA concentration are presented as the median with the interquartile range. “a” p < 0.0001; “b” p < 0.001; “c” p < 0.01
Next, we divided patients with obesity into diabetic and non-diabetic groups. We found that APP expression was significantly decreased in visceral adipose tissue from patients with both obesity and T2DM (D) compared to normoglycemic (ND) patients with obesity (VAT-D vs. VAT-ND, p < 0.0001, Fig. 3A). Although a similar pattern was observed in subcutaneous adipose tissue, the difference did not reach statistical significance. In addition, visceral adipose tissue showed significantly lower APP expression than subcutaneous adipose tissue in diabetic patients (VAT-D vs. SAT-D, p = 0.0003, Fig. 3A).
Fig. 3.
mRNA levels of genes encoding amyloid precursor protein (APP), (A); suppressor of cytokine signaling 3 (SOCS3), (B); tyrosine-protein phosphatase non-receptor type 1 (PTPN1), (C); and protein tyrosine phosphatase, non-receptor type 2 (PTPN2), (D) in the visceral (VAT) and subcutaneous (SAT) adipose tissues of patients with obesity (O) diagnosed with type 2 diabetes (D) and without diabetes (ND). Results normalized to β-actin (ACTB) mRNA concentration are presented as the median with the interquartile range. “a” p < 0.0001; “b” p < 0.001; “c” p < 0.01
In contrast, SOCS3 mRNA levels did not differ significantly between diabetic and non-diabetic patients (Fig. 3B). However, in both groups, SOCS3 expression was higher in subcutaneous compared to visceral adipose tissue (p < 0.0001 for both). While PTPN1 expression did not differ between non-diabetic patients and those with T2DM (Fig. 3C), we found that PTPN2 mRNA levels were significantly lower in visceral depots in both diabetic and non-diabetic groups (p < 0.0001 and p = 0.0079, respectively; Fig. 3D).
Expression of genes associated with insulin sensitivity correlates with adipokine mRNA levels.
The altered expression of the genes studied in patients with obesity may itself contribute to impaired insulin sensitivity of adipose tissue. However, the ongoing inflammatory process in dysfunctional adipocytes and their milieu also contributes to IR. Therefore, our next step was to assess the correlation between the expression levels of the genes studied and the mRNAs of several adipokines (namely interleukins 1β, 6, 8, 15, tumour necrosis factor-alpha (TNFα), and resistin, and adiponectin) whose expression had been studied in our previous works [22, 23].
In the case of the gene encoding APP (APP), all observed correlations were related to subcutaneous tissue. We observed a positive correlation between mRNA levels of APP and ADIPOQ (encoding adiponectin) in SAT-O (p = 0.0145, rs = 0.319, Fig. 4A), suggesting that higher APP expression may be associated with a beneficial adipokine profile in subcutaneous adipose tissue. However, we also observed a significant positive correlation between APP and RETN (encoding pro-inflammatory resistin) mRNA levels (p = 0.0144, rs = 0.331, Fig. 4B).
Fig. 4.
Correlation of APP mRNA levels with mRNA levels of ADIPOQ (a) and RETN (b) in subcutaneous adipose tissue of patients with obesity (SAT-O). Black dots represent particular participants. Lines represent linear regression analysis with a 95% CI interval
Furthermore, we found positive correlations between SOCS3 mRNA concentrations and the mRNA levels of the selected adipokines. In the visceral adipose tissue of patients with obesity, SOCS3 expression correlated significantly with the mRNA levels of the gene encoding interleukin 1β (IL-1β, p = 0.0001, rs = 0.5056) (Fig. 5A). We also observed a positive correlation between SOCS3 expression and the expression of the gene encoding resistin (RETN) in the subcutaneous tissue (SAT-O) of these patients (p = 0.0014, r = 0.4215; Fig. 5B).
Fig. 5.
Correlation of SOCS3 mRNA levels with mRNA levels of IL1B (A) and RETN (B) in visceral (VAT) and subcutaneous (SAT) adipose tissue of patients with obesity. Black dots represent particular participants. Lines represent linear regression analysis with a 95% CI interval
Significant correlations with the expression of pro-inflammatory adipokines were also observed for PTPN1. We found a positive correlation between PTPN1 and TNFA (encoding TNFα) mRNA levels in VAT-O (p = 0.0257, rs=0.3324, Fig. 6A). In turn, in the subcutaneous adipose tissue from patients with obesity (SAT-O), there was a significant positive correlation between PTPN1 and IL6 (encoding interleukin 6, p = 0.0264, rs = 0.2994, Fig. 6B) and IL15 (encoding interleukin 15) mRNA levels (p = 0.0012, rs = 0.4691, Fig. 6C).
Fig. 6.
Correlation of PTPN1 mRNA levels with mRNA levels of TNFA (A), IL6 (B), and IL15 (C) in visceral (VAT) and subcutaneous (SAT) adipose tissue of patients with obesity. Black dots represent particular participants. Lines represent linear regression analysis with a 95% CI interval
In the case of PTPN2, we observed only one positive correlation – with the mRNA for the gene encoding resistin (RETN) in VAT-O (p = 0.0312, r = 0.2963, Fig. 7).
Fig. 7.
Correlation of PTPN2 mRNA levels with mRNA levels of RETN in visceral adipose tissue of patients with obesity (VAT-O). Black dots represent particular participants. Lines represent linear regression analysis with a 95% CI interval
MicroRNAs correlate with insulin sensitivity-related gene expression in adipose tissue.
To explore the mechanisms that might account for the observed variations in gene expression within adipose tissue, we examined the potential role of microRNAs (miRNAs). Utilising bioinformatics resources such as miRTarBase, miRBase, TargetScan, and miRDB, we identified miRNAs that could potentially interact with the mRNAs of the studied genes. Next, based on our previous miRNOME analysis using next-generation sequencing in adipose tissue from obese patients before and after weight loss and from normal-weight individuals, we selected from among the in silico-identified miRNAs those whose expression differed significantly between the study groups [24]. We then analysed their expression in the investigated tissues (supplementary Figure S1 in additional file 2) and correlated it with the mRNA levels of APP, SOCS3, PTPN1, and PTPN2.
We identified significant negative correlations between APP mRNA levels and hsa-miR-579-5p (p = 0.001, rs = −0.418) and hsa-miR-142-3p (p = 0.002, rs = −0.377) in visceral adipose tissue (VAT-O) of subjects with obesity (Fig. 8A and B).
Fig. 8.
Correlation of APP mRNA levels with levels of hsa-miR-579-5p (A) and hsa-miR-142-3p (B) in visceral adipose tissue of patients with obesity (VAT-O). Black dots represent particular participants. Lines represent linear regression analysis with a 95% CI interval
In the case of SOCS3, we observed a statistically significant negative correlation between its mRNA levels and hsa-miR-579-3p (p = 0.0397, rs = −0.301) in the visceral adipose tissue of patients with obesity, as shown in Fig. 9.
Fig. 9.
Correlation of SOCS3 mRNA levels with levels of hsa-miR-579-3p in visceral adipose tissue of patients with obesity (VAT-O). Black dots represent particular participants. Lines represent linear regression analysis with a 95% CI interval
We also observed significant negative correlations between PTPN1 mRNA levels and four investigated miRNAs in visceral adipose tissue of patients with obesity (VAT-O): hsa-miR-579-3p (p = 0.007, rs = −0. 412, Fig. 10A), hsa-miR-579-5p (p = 0.005, rs = −0.403, Fig. 10B), hsa-miR-561-3p (p = 0.047, rs = −0.282, Fig. 10C) and hsa-miR-561-5p (p = 0.004, rs = −0.403, Fig. 10D). In addition, we observed a negative correlation between PTPN2 mRNA levels and hsa-miR-142-3p in the same tissue (p = 0.003, r = −0.386; Fig. 11).
Fig. 10.
Correlation of APP mRNA levels with levels of hsa-miR-579-3p (A), hsa-miR-579-3p (B), hsa-miR-561-3p (C), and hsa-miR-561-5p (D) in visceral adipose tissue of patients with obesity (VAT-O). Black dots represent particular participants. Lines represent linear regression analysis with a 95% CI interval
Fig. 11.
Correlation of PTPN2 mRNA levels with levels of hsa-miR-142-3p in visceral adipose tissue of patients with obesity (VAT-O). Black dots represent particular participants. Lines represent linear regression analysis with a 95% CI interval Table 1. Basic clinical characteristics of the studied groups
Notably, no significant correlations were found between mRNA expression of the investigated genes and the selected miRNAs in the other studied tissues (VAT-N, SAT-O, SAT-N & SAT-PO).
Discussion
In our study, we performed an analysis of the expression profile of four genes crucial for both insulin sensitivity and neuronal metabolism – APP, SOCS3, PTPN1, and PTPN2, in adipose tissue from patients with obesity, both pre-and post-bariatric surgery, and compared them to the adipose tissue of normal-weight individuals. We found that obesity is associated with the altered expression of these genes in adipose tissue. However, after surgery-induced weight loss, expression profiles of APP and PTPN2 resembled those of individuals with normal body weight. Moreover, in the studied tissues, expression of APP, SOCS3, PTPN1, and PTPN2 correlated with mRNA levels of pro-inflammatory cytokines and adipokines. To identify the potential molecular mechanism underlying the observed phenomena, we identified several miRNAs whose levels correlated negatively with the mRNAs of the studied genes. However, our descriptive research model based on correlations does not elucidate the pathomechanisms underlying the observed phenomena, and the results must be confirmed in functional studies.
The selection of genes analysed in this study may require clarification. The first selection criterion was confirmed involvement in both nervous system function and insulin sensitivity regulation. The second criterion was data indicating a link between impaired expression of a given gene and the risk of developing neurodegenerative diseases and insulin resistance.
Amyloid precursor protein (APP) is known for its crucial role in the pathogenesis of Alzheimer’s disease [25]. However, evidence accumulated in recent years points to the indispensability of APP and its metabolites for normal brain physiology [26]. APP contributes to the regulation of synaptic transmission, plasticity, and calcium homeostasis and exerts neuroprotective effects [27]. However, recent studies have pointed to the pleiotropic actions of APP, including, e.g., downregulation of insulin-degrading enzyme levels and activity in the brain and peripheral tissues [28]. Our finding that APP mRNA levels are decreased in adipose tissue of patients with obesity may support a role for APP in metabolic regulation. Furthermore, when the studied group was stratified by insulin sensitivity and diabetic status, we found that both patients with IR and T2DM are characterised by lower APP expression than those who were insulin sensitive and normoglycemic. In turn, weight loss was associated with a significant increase in APP mRNA level. This phenomenon may contribute to improved metabolic health following significant weight reduction; however, its clinical significance requires further research. The positive correlation between APP and ADIPQ expression observed in SAT-O suggests a possible interplay between APP and adiponectin in improving insulin sensitivity. Meanwhile, within the same adipose tissue depot, APP mRNA levels correlated positively with RETN expression, encoding the pro-inflammatory adipokine resistin. This unexpected finding may, of course, be coincidental, but it may also result from complex interactions among the components of the adipose tissue milieu during obesity. These findings should be verified at the protein level and supported by functional studies. However, it may support the hypothesis on a dual role for APP in metabolic and neurological health [9, 29].
The choice of the suppressor of cytokine signalling 3 (SOCS3) gene for studying the impact of obesity on insulin sensitivity in adipose tissue is more evident. SOCS3 provides a crucial protective effect against excessive neuroinflammation by restricting the activation of myeloid cells, such as microglia and macrophages, into the pro-inflammatory M1 phenotype, thereby maintaining cellular quiescence in the brain [30]. Preclinical studies have shown that SOCS3 acts as a critical regulator of cytokine signalling and, in addition to its role in neuron regeneration, has been implicated in the pathogenesis of IR and obesity-related inflammation [31, 32]. Surprisingly, in our study, SOCS3 expression was consistently lower in patients with obesity compared to normal-weight controls, regardless of adipose tissue depot. Moreover, SOCS3 mRNA levels differed significantly between tissues of diabetic and non-diabetic study participants, as well as between patients stratified by insulin resistance/sensitivity status. Of interest: despite the higher expression in adipose tissues from the normal-weight subjects, SOCS3 mRNA levels correlated positively with the expression of the proinflammatory and diabetogenic adipokines (interleukin 1β and resistin) only in the tissues collected from patients with obesity. However, reduced SOCS3 expression, considered a marker of insulin resistance in tissues from patients with obesity, is a surprising finding that warrants verification in a replication study assessing gene expression at the protein level as well.
The protein tyrosine phosphatase 1B (PTP1B) gene expression showed an opposite pattern across the investigated tissues: it was non-significantly elevated in subjects with obesity and remained elevated after weight loss. PTP1B is known to be a negative regulator of several receptor and receptor-associated tyrosine kinases [2]. It plays a critical role in the brain by acting as a primary negative regulator of central insulin and leptin signalling [33]. In the central nervous system, PTP1B controls systemic metabolic homeostasis via the hypothalamus and also modulates synaptic plasticity, cognitive function, and neuroinflammatory responses in regions such as the hippocampus [34]. Therefore, PTP1B is considered a potential common therapeutic target for the treatment of diabetes and neurodegenerative diseases [35, 36]. A positive correlation between PTPN1 (a gene encoding PTP1B) mRNA levels and pro-inflammatory cytokines such as TNFα, interleukins 6 and15 in adipose tissue of patients with obesity in our study may support PTPN1’s role in sustaining obesity-induced inflammation [2]. The persistent elevation of PTPN1 after weight loss may suggest that obesity-related adipose tissue dysfunction may be chronic and only partially improved by weight reduction.
Protein tyrosine phosphatase non-receptor type 2 (PTPN2) has an established dual role in modulating inflammatory responses and metabolic processes [37]. In the hypothalamus, PTPN2 limits central insulin and leptin signalling by dephosphorylating key proteins, such as JAK2 and STAT3, in neurons critical for appetite regulation. Elevated PTPN2 activity dampens the brain’s sensitivity to these hormones, exacerbating diet-induced obesity and disrupting glucose homeostasis [38]. In addition, PTPN2 actively manages neuroinflammation by regulating the microglia [39]. Our study found reduced PTPN2 expression in adipose tissues of patients with obesity, with partial recovery following weight loss. This finding suggests that while weight reduction can ameliorate some metabolic disturbances, complete normalisation of gene expression may require additional interventions. Regardless of insulin sensitivity and glycemic status, the expression of this gene in patients with obesity was lowest in visceral adipose tissue. The positive correlation between PTPN2 and resistin mRNA levels in VAT-O is not obvious and, as with APP, indicates a complex interaction between PTPN2 and inflammatory mediators in obesity [40]. These interactions may influence both metabolic and immune responses, highlighting the multifaceted role of PTPN2 in obesity-related pathophysiology.
In the search for mechanisms underlying the observed changes in gene expression associated with insulin sensitivity in patients with obesity, we investigated whether selected miRNAs might play a role. For our study, we selected miRNAs that, in in silico analysis, showed interaction with the mRNA of the genes under study and, in a large-scale analysis, exhibited differential expression between adipose tissues from patients with obesity and normal-weight individuals. We observed significant negative correlations between specific miRNAs and the expression of APP, SOCS3, PTPN1, and PTPN2, particularly in the VAT of patients with obesity. These findings suggest that miRNAs regulate metabolic and inflammatory pathways in obesity [15, 41, 42]. For example, hsa-miR-579-5p and hsa-miR-142-3p were negatively correlated with APP expression, suggesting their involvement in modulating APP’s dual role in metabolic regulation and neurodegeneration. Interestingly, the latter miRNA has been identified as a serological marker of Alzheimer’s disease [43]. Similarly, miRNAs targeting SOCS3, PTPN1, and PTPN2 highlight their regulatory influence on insulin sensitivity and inflammatory responses. Given that adipose tissue-derived miRNAs released into the circulation in exosomes can regulate gene expression in other tissues, it cannot be excluded that they represent a link between obesity, insulin resistance, and cognitive decline, as well as neurodegenerative diseases [16]. A change in the profile of circulating miRNAs may be one of the mechanisms by which bariatric surgery benefits by reducing systemic inflammation, enhancing mitochondrial function, and ultimately improving neurocognitive outcomes and resilience against neurodegeneration [44].
At the end, we acknowledge the potential limitations of our work, including gene expression studies performed exclusively at the mRNA level (without verification at the protein level). Moreover, although we selected the miRNAs analysed based on in silico analyses of several databases, without performing functional studies, the biological significance of the observed correlations cannot be considered certain. Functional research could confirm or refute our observations and help explain the observed contradictions. Next, we did not assess the insulin sensitivity of the study participants using precise tools such as the hyperinsulinaemic euglycaemic clamp. We relied on the indirect indicator of insulin sensitivity – the TG/HDL ratio, which, of course, can be misleading. In turn, the lack of body composition assessment in the control group of healthy controls creates a potential risk that it included individuals with preclinical [45]. Nevertheless, a normal metabolic profile in this group suggests that such a situation is unlikely. In addition, neuropsychological studies to assess participants’ cognitive functions before and after surgical intervention would help put our findings in a clinical context. Without these data, it is impossible to assess the extent to which our molecular observations translate into changes in patients’ functioning. Finally, due to the observational and descriptive nature of our study, our results can serve to formulate hypotheses and suggest certain phenomena rather than explain the underlying mechanisms and processes. However, we hope that our work may draw attention to the role of miRNAs as potential mediators regulating gene expression related to insulin sensitivity and neurodegeneration.
Conclusions
In conclusion, our study might suggest that miRNAs regulate the expression of genes critical to both insulin action and neuronal metabolism in adipose tissue. However, due to the descriptive nature of our work and its several limitations, these findings need to be confirmed in functional studies.
Supplementary Information
Acknowledgements
None.
Abbreviations
- ACTB
Gene encoding β-actin
- ADIPOQ
Gene encoding adiponectin
- APP
Amyloid precursor protein
- APP
Gene encoding amyloid precursor protein)
- BMI
Body mass index
- D
Diabetic
- HDL
High-density lipoprotein cholesterol
- ILB1
Gene encoding interleukin 1β
- IL6
Gene encoding interleukin 6
- IL15
Gene encoding interleukin 15
- IR
Insulin resistance
- IS
Insulin sensitivity
- mRNA
Messenger ribonucleic acid
- miRNA
Micro ribonucleic acid
- ND
Nondiabetic
- NS
Nonsignificant
- PTPN1
Protein tyrosine phosphatase, non-receptor type 1
- PTPN1
Gene encoding protein tyrosine phosphatase, non-receptor type 1
- PTPN2
Protein tyrosine phosphatase, non-receptor type 2
- PTPN2
Gene encoding protein tyrosine phosphatase, non-receptor type 2
- RETN
Gene encoding resistin
- SAT
Subcutaneous adipose tissue
- SAT-D
Subcutaneous adipose tissue from study participants with obesity and diabetes
- SAT-IR
Subcutaneous adipose tissue from study participants with obesity and insulin resistance
- SAT-N
Subcutaneous adipose tissue from normal weight study participants
- SAT-ND
Subcutaneous adipose tissue from normoglycemic study participants with obesity
- SAT-O
Subcutaneous adipose tissue from study participants with obesity
- SAT-PO
Subcutaneous adipose tissue from study participants after weight loss
- SOCS3
Suppressor of cytokine signaling 3
- SOCS3
Gene encoding suppressor of cytokine signaling 3
- T2DM
Type 2 diabetes mellitus
- TG
Triglycerides
- TG/HDL
Triglycerides high-density lipoprotein cholesterol ratio
- TNFα
Tumor necrosis factor-alpha
- TNFA
Gene encoding tumor necrosis factor-alpha
- VAT
Visceral adipose tissue
- VAT-D
Visceral adipose tissue from study participants with obesity and diabetes
- VAT-IR
Visceral adipose tissue from study participants with obesity and insulin resistance
- VAT-IS
Visceral adipose tissue from study participants with obesity and normal insulin sensitivity
- VAT-N
Visceral adipose tissue from normal weight study participants
- VAT-ND
Visceral adipose tissue from normoglycemic study participants with obesity
- VAT-O
Visceral adipose tissue from study participants with obesity
- WHO
World Health Organization
Author contributions
Conceptualization, J.P.; methodology, J.P. and A.K.; investigation, J.P., M.W., M.I.J., W.L., M.J., A.B., P.J., W.T., B.N., and A.K.; data acquisition, M.W., M.I.J., W.L., M.J., A.B., P.J., W.T., B.N., and A.K.; data curation, J.P., and A.K.; writing—original draft preparation, J.P., and A.K.; writing—review and editing, M.P.-K. and A.K.; supervision, A.K.; project administration, A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the National Science Centre, Poland, grant 2018/31/B/NZ5/01556.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
All described procedures were approved by the Bioethics Committee of the Medical University of Warsaw, and written informed consent for participation in the study was obtained from all study participants. (decision no. KB 147/2009 issued on July 28, 2009, KB 91/A/2010 issued on July 19, 2010, KB 117/A/2011 issued on November 14, 2011, and KB 38/A/2022 issued on May 16, 2022. All studies were conducted following the Helsinki Declaration.
Consent for publication
Written informed consent for anonymised data publication was obtained from all study participants.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.