Graphical abstract
Keywords: MOTS-c, Obesity, Metabolic dysregulation
Highlights
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Circulating MOTS-c levels are increased in obese adults compared to lean controls.
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BMI and HOMA-IR are primary independent determinants of circulating MOTS-c in obesity.
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Circulating MOTS-c exhibits a nonlinear relationship with insulin resistance suggesting compensatory response.
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MOTS-c levels and HOMA-IR remain unchanged despite weight loss 6 months after bariatric surgery.
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Adipose tissue MOTS-c expression does not correlate with its circulating levels.
Abstract
Introduction
MOTS-c (mitochondrial open reading frame of the 12S rRNA type-c) is a mitochondrial-derived peptide and regulator of metabolic homeostasis. Although its role in glucose and lipid metabolism is emerging, changes in circulating MOTS-c with obesity remain unclear. We hypothesized that circulating MOTS-c concentrations would be altered in obese vs. lean adults in associations with altered metabolic and inflammatory markers.
Methods
Circulating MOTS-c levels, metabolic parameters, and inflammatory markers were compared between 22 lean controls and 32 obese participants scheduled for bariatric surgery. Longitudinal changes in weight, MOTS-c levels, and metabolic markers were also analyzed in 10 of the obese patients before and 6 months after bariatric surgery. Additionally, adipose tissue MOTS-c expression was assessed by immunofluorescence in lean kidney donors (n = 6) and obese (n = 14) subjects.
Results
Circulating MOTS-c levels were significantly higher in obese compared to lean individuals (273 ± 56 vs. 223 ± 50 pg/mL; P < 0.01). BMI and HOMA-IR independently predicted elevated MOTS-c levels (P = 0.035 and P = 0.032, respectively). MOTS-c showed a biphasic relationship with HOMA-IR, rising sharply above HOMA-IR of ∼ 6.6 mmol/L×µU/mL. Adipose tissue MOTS-c did not differ between the groups or correlate with circulating MOTS-c. Despite significant BMI improvements post-surgery (P < 0.001), circulating MOTS-c levels remained unchanged (P = 0.913).
Conclusion
Circulating MOTS-c levels are elevated in obesity, exhibiting a nonlinear relationship with BMI and insulin resistance. MOTS-c may represent a compensatory metabolic response in obesity and insulin-resistant states, highlighting its potential as a clinical biomarker. This preliminary exploratory study warrants validation in larger and independent cohorts.
Introduction
Obesity is a global health concern characterized by excess adiposity and profound metabolic disturbances [1], and increases the risk of insulin resistance [2], type-2 diabetes mellitus (T2DM) [3], dyslipidemia [4], and cardiovascular disease [5], [6]. These metabolic derangements are often accompanied by a state of chronic low-grade inflammation [7], [8], and growing evidence implicates mitochondrial dysfunction in obesity-related complications [9]. Mitochondria not only govern cellular energy metabolism but also communicate with the rest of the body through signaling molecules, including a newly recognized class of peptides encoded by the mitochondrial genome [10], [11].
MOTS-c (mitochondrial open-reading-frame of the 12S rRNA type-c) is a 16-amino acid mitochondrial-derived peptide (MDP) discovered in 2015 as a key player in metabolic regulation [12]. MOTS-c is encoded by the 12S mtDNA rRNA and is expressed in various tissues from which it can be released into the circulation to act as a hormone-like signaling molecule [13]. Experimental studies have shown that MOTS-c activates AMP-activated protein kinase (AMPK) and modulates the insulin signaling pathway, thereby improving insulin sensitivity and promoting metabolic homeostasis [10], [14]. In mice, administration of MOTS-c prevents diet-induced obesity and insulin resistance, attenuating weight gain and hyperinsulinemia on a high-fat diet [10]. These findings position MOTS-c as a potential protective factor against metabolic stress.
However, data on circulating MOTS-c levels in human obesity and related metabolic disorders remain limited and conflicting. Initial studies reported significantly lower MOTS-c concentrations in obese adolescents compared to their lean counterparts, with an inverse correlation between MOTS-c levels and markers of obesity and insulin resistance such as BMI and HOMA-IR [15], [16]. Conversely, studies in adults have yielded inconsistent findings: some reported no significant difference in MOTS-c levels between obese and lean subjects [17], whereas others observed elevated MOTS-c concentrations associated with insulin resistance or early metabolic syndrome [18], suggesting a potential compensatory response. Moreover, physiological contexts like pregnancy have also demonstrated elevated MOTS-c levels in obese mothers compared to lean controls [19], whereas subjects with endothelial dysfunction showed reduced levels [20] further complicating the interpretation of circulating MOTS-c regulation. These contrasting findings may reflect differences in study populations, age, sex, metabolic conditions, and stages of metabolic dysfunction. Consequently, whether circulating MOTS-c levels show an increase in obesity or decrease with progressive metabolic impairment remains uncertain.
To investigate the role of MOTS-c in adult obesity, we compared circulating MOTS-c levels between adults with severe obesity scheduled for bariatric surgery and age-matched lean controls. We examined associations of MOTS-c with metabolic and inflammatory biomarkers and assessed changes in MOTS-c concentrations following significant weight loss after bariatric surgery. We hypothesized that MOTS-c levels would be elevated in obesity and decrease following metabolic improvements after weight loss.
Methods
Study design and participants
This prospective study consisted of a cross-sectional comparison among lean (n = 22) and obese (n = 32) individuals at baseline, as well as a longitudinal follow-up of 10 of the obese individuals after bariatric surgery. As this study was exploratory and observational in nature, no formal sample size calculation was conducted. No participants were lost to follow-up during the 6-month postoperative period. Obese subjects were adults (age 18–80 years) with body mass index (BMI) ≥ 35 kg/m2 who were scheduled to undergo bariatric surgery for weight management. Lean control subjects were adults aged 18–80 with BMI < 30 kg/m2, recruited among healthy living kidney donors. The study protocol was approved by the Mayo Clinic Institutional Review Board (IRB #18–005076), and all participants provided written informed consent prior to inclusion.
All participants were free of active illness. Exclusion criteria for both groups included pregnancy; any chronic inflammatory or autoimmune disease (e.g. rheumatoid arthritis); active malignancy; recent major cardiovascular events (stroke or myocardial infarction within the past 3 months); history of solid organ transplantation; and use of immunosuppressive medications (including high-dose corticosteroids > 10 mg/day prednisone or equivalent).
Clinical assessments and sample collection
Demographic data (age, sex) and medical history were obtained for all participants. Blood pressure was measured prior to surgery using a standardized sphygmomanometer after a brief rest; the average of two readings was recorded for systolic and diastolic blood pressure. In 10 obese patients, clinical assessments were repeated approximately 6 months after surgery. The type of bariatric procedures (either Roux-en-Y gastric bypass or sleeve gastrectomy) was determined by clinical indications.
Fasting blood samples were collected from all participants at each visit after an overnight fast (8–12 h). Plasma and serum were separated by centrifugation and stored at –80 °C until analysis. Additionally, a first-morning urine sample was collected from each participant at each visit for measurement of renal injury and inflammatory markers.
Laboratory measurements of metabolic and biochemical markers
Fasting glucose, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides were measured by standard automated clinical chemistry analyzers (Cobas c311 analyzer, Roche Diagnostics, Mannheim, Germany). Plasma insulin was measured using an automated immunoassay (Cobas e411 analyzer, Roche Diagnostics, Mannheim, Germany), and HbA1c by high-performance liquid chromatography (D-100, Bio-Rad Laboratories, Hercules, CA). All assays were performed at the Mayo Clinic core Laboratory (Rochester, MN, USA). The estimated glomerular filtration rate (eGFR) was calculated from serum creatinine using the CKD-EPI equation [21].
Insulin resistance was evaluated using several indices according to the following published formulas.
HOMA-IR (mmol/L×µU/mL) = [fasting glucose (mmol/L) × fasting insulin (µU/mL)]/22.5 [22].
The triglyceride-glucose (TyG) index = ln[fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2] [23].
The metabolic score for insulin resistance (METS-IR) = ln[2 × Glucose (mg/dL) + Triglyceride (mg/dL)] × BMI (kg/m2)/ln[HDL (mg/dL)] [24].
Mots-c assessment
Circulating plasma MOTS-c concentrations were measured using an in-house MOTS-c specific sandwich ELISA, developed and validated by the Cohen laboratory (University of Southern California) as previously described [10], [20], [25], [26].
Adipose tissue samples from lean individuals undergoing kidney donation (n = 6) and obese individuals (n = 14) undergoing bariatric surgery were collected intraoperatively from abdominal subcutaneous fat. Immunofluorescence staining was performed to assess MOTS-c expression. Briefly, adipose tissue samples were embedded in paraffin and sectioned at a thickness of 5 µm. Sections were deparaffinized, rehydrated, and subjected to antigen retrieval using citrate buffer (pH 6.0) at 95 °C for 20 min. Subsequently, sections were blocked with 5 % bovine serum albumin for 1 h at room temperature to reduce nonspecific binding. Immunofluorescence staining was performed using MOTS-c antibody (ThermoFisher Cat#MOTSC-101AP) incubated overnight at 4 °C followed by Alexa Fluor 594-conjugated secondary antibody (Thermofisher; 1:500, 1hr, room temperature). Nuclear counterstaining was performed using 4′,6-diamidino-2-phenylindole (DAPI; ThermoFisher Cat#P36935). Fluorescence images were captured using a Nikon Eclipse Ci microscope (Nikon instruments, Melville, NY) at 400 × magnification. MOTS-c expression intensity was quantitatively assessed and normalized to DAPI staining using ImageJ (NIH, Bethesda, MD). Quantification was performed across 10 random microscopic fields, and results were averaged to determine of MOTS-c tissue expression in each patient.
Measurement of inflammatory markers
Plasma inflammatory markers were measured using validated commercial assays for interleukin (IL)-18, IL-1α, and IL-1β using Millipore Luminex Multiplex kit (Cat#HCYTA-60 K-03) and ELISA for vascular endothelial growth factor (VEGF) (ThermoFisher, Cat#KHG0111) and angiopoietin-2 (ThermoFisher, Cat#KHC1641). Urinary inflammatory and kidney injury markers were quantified using ELISA kit for kidney injury molecule 1 (KIM-1): Human KIM-1 (R&D, Cat# DKM100), monocyte chemoattractant protein-1 (MCP-1): (R&D, Cat# DCP00), tumor necrosis factor- α (TNF-α): (ThermoFisher, Cat# KHC3011), and neutrophil gelatinase-associated lipocalin (NGAL) (ThermoFisher, Cat# KIT036). Baseline assessments included all inflammatory markers, while follow-up measurements post-bariatric surgery focused on a selected subset of markers expected to change significantly with weight loss.
For the analysis of local inflammation, adipose tissue samples from lean (n = 8) and obese (n = 9) individuals underwent immunofluorescence staining using an anti-CD68 antibody (Abnova; Cat#MAB1715, Taiwan, 1:25).
Statistical analysis
Analyses were conducted using R version 4.2 and SPSS version 28.0 (IBM corp., Armonk, NY, USA). Continuous variables are reported as mean ± SD or median (IQR); categorical data are presented as counts (%). Comparisons between the lean and obese groups utilized Student’s t-test for normally distributed variables and the Mann-Whitney U test for variables not meeting normality assumptions. Categorical data were analyzed using Fisher’s exact test. Univariate and multivariate linear regressions identified predictors of MOTS-c, reporting regression estimates (β), 95 % CI, and p-values. Paired t-tests assessed changes pre- and post-bariatric surgery. LOWESS curves were employed to visually explore non-linear relationships between MOTS-c and key variables (BMI, HOMA-IR). Piecewise regression identified the inflection/deflection points in relationships between MOTS-c and BMI or HOMA-IR. Missing data were retained and handled as missing values in both SPSS and R analyses. Sensitivity analyses were not performed, as the study was exploratory, and sample size was limited. Statistical significance was defined as p < 0.05, with two-tailed tests throughout.
Results
Baseline characteristics of lean and obese groups
The baseline characteristics of the study participants stratified into normal weight (n = 22) and obese (n = 32) groups is shown in Table1. The mean age and gender distribution were similar between the two groups, but the obese group exhibited significantly higher BMI compared to the control group. Furthermore, systolic blood pressure, mean arterial pressure, HOMA-IR, fasting glucose and triglyceride levels were significantly elevated in the obese group.
Table 1.
Baseline characteristics of lean and obese patients.
| Characteristic | Lean (n = 22) | Obese(n = 32) | P-value |
|---|---|---|---|
| Age (year) | 52.09 ± 12.82 | 50.91 ± 14.91 | 0.763 |
| Male n (%) | 8 (36.4) | 11 (34.4) | 1.000 |
| BMI (kg/m2) | 25.53 (2.28) | 40.22 (4.30) | <0.001 |
| Systolic BP (mmHg) | 111.50 (17.00) | 123.00 (20.00) | 0.006 |
| Diastolic BP (mmHg) | 71.23 ± 8.74 | 74.28 ± 10.43 | 0.265 |
| MAP (mmHg) | 83.33 (12.00) | 91.83 (11.83) | 0.027 |
| Hemoglobin (g/L) | 138.6 ± 11.5 | 141.0 ± 13.4 | 0.569 |
| Glucose (mmol/L) | 5.38 ± 0.53 | 6.14 ± 1.17 | 0.007 |
| Albumin (g/L) | 44.5 ± 2.5 | 44.0 ± 2.8 | 0.608 |
| creatinine (µmol/L) | 79.6 ± 16.8 | 74.3 ± 17.7 | 0.264 |
| GFR (mL/min/1.73 m2) | 83.45 ± 10.58 | 80.68 ± 10.14 | 0.380 |
| Hba1c (%) | 5.20 (0.63) | 5.60 (1.25) | 0.086 |
| Total Cholesterol (mmol/L) | 5.09 ± 0.58 | 5.71 ± 2.49 | 0.680 |
| LDL Cholesterol (mmol/L) | 3.21 ± 0.63 | 3.18 ± 1.18 | 0.967 |
| HLD Cholesterol (mmol/L) | 1.52 ± 0.09 | 1.26 ± 0.27 | 0.133 |
| Triglyceride (mmol/L) | 1.00 (0.91) | 1.52 (1.55) | 0.004 |
| Urine protein (mg/L) | 124.9 ± 125.6 | 313.9 ± 488.3 | 0.084 |
| HOMA-IR (mmol/dL×µU/mL) | 2.28 ± 2.56 | 5.13 ± 4.27 | 0.013 |
| Medications −n (%) | |||
| Anti-hypertensive (%) | 0 (0.0) | 4 (12.5) | 0.232 |
| Anti-diabetic (%) | 0 (0.0) | 3 (9.4) | 0.383 |
| Anti-lipidemic (%) | 0 (0.0) | 0 (0.0) | NA |
Values expressed as mean ± standard deviation or median (interquartile range) for continuous variables, and number (percentage) for categorical variables. BMI, body mass index; BP, blood pressure; MAP, mean arterial pressure; GFR, estimated glomerular filtration rate; LDL, low-density lipoprotein; HDL, high-density lipoprotein; HOMA-IR, Homeostatic model assessment for insulin resistance; NA, not applicable.
P-values calculated using Student’s t-test for normally distributed variables, Mann-Whitney U test for variables not normally distributed, and Fisher’s exact test for categorical variables. Significance considered at P < 0.05 (bold font).
No statistically significant differences were observed between the groups in terms of diastolic blood pressure, hemoglobin, albumin, creatinine, estimated glomerular filtration rate (eGFR), HbA1c, total, LDL, HDL cholesterol, and urine protein levels. Urine protein and HbA1c levels tended to be higher in the obese group; however, these differences did not reach statistical significance. No differences in medication use between groups were statistically significant. Urine MCP1, urine TNFα, and plasma angiopoietin-2 levels were higher in obese patients (Supplementary Table 1), consistent with systemic inflammation.
Association of MOTS-c with clinical, metabolic and inflammatory markers
Circulating MOTS-c levels were significantly higher in obese subjects (263.6 ± 53.1 pg/mL) than in lean controls (213 ± 53.5 pg/mL, P = 0.001). We subsequently performed correlation analyses between MOTS-c and clinical, metabolic, and inflammatory parameters. Among clinical variables (Supplementary Table 2), in univariable analysis obesity (Estimate: 50.59, 95 % CI: 20.99 to 80.20, P = 0.0012) and BMI (Estimate: 2.30, 95 % CI: 0.50 to 4.10, P = 0.0133) were both significantly associated with higher MOTS-c levels (263.6 ± 53.1 vs 213.0 ± 53.5 pg/mL, P = 0.001 vs. lean) (Fig. 1A).
Fig. 1.
Circulating MOTS-c levels and relationships with BMI and HOMA-IR. A) Plasma MOTS-c levels in lean (BMI < 30 kg/m2) vs. obese (BMI ≥ 35 kg/m2) subjects. B) Nonlinear (LOWESS) relationships of MOTS-c with BMI, which achieved significance only for C) HOMA-IR, showing a biphasic pattern with an inflection point at HOMA-IR of approximately 6.6 mg/dL×µU/mL. D) Plasma MOTS-c levels remained unchanged post-bariatric surgery.
Metabolic marker univariate analysis (Table 2) revealed significant positive associations between MOTS-c and insulin, HOMA-IR, TyG index, METS-IR, total cholesterol, and triglyceride levels, but not with levels of glucose, HbA1c, hemoglobin, LDL-cholesterol, or HDL-cholesterol.
Table 2.
Univariate analyses of metabolic markers associated with MOTS-c.
| Variable | Estimate (95 % CI) | P-value |
|---|---|---|
| Insulin (mU/L) | 1.54 (0.30 to 2.78) |
0.0165 |
| Glucose (mmol/L) | 2.5 (−12.83 to 17.84) |
0.7442 |
| HOMA-IR (mmol/L×µU/mL) | 6.16 (1.92 to 10.41) |
0.0056 |
| HbA1c (%) | 3.52 (−13.01 to 20.05) |
0.6700 |
| TyG index | 35.33 (1.81 to 68.86) |
0.0395 |
| METS-IR | 1.68 (0.48 to 2.88) |
0.0076 |
| Hb (g/L) | 3.48 (−12.97 to 19.93) |
0.6714 |
| LDL cholesterol (mmol/L) | 10.90 (−9.23 to 31.04) |
0.2787 |
| HDL cholesterol (mmol/L) | −17.58 (−65.37 to 30.21) |
0.4600 |
| Total Cholesterol (mmol/L) | 15.27 (4.08 to 26.47) |
0.0089 |
| Triglyceride (mmol/L) | 7.78 (1.85 to 13.71) |
0.0117 |
Values are shown as regression estimates and 95% confidence intervals (CI).
HOMA-IR, Homeostatic model assessment for insulin resistance; HbA1c, Hemoglobin A1c; TyG index, Triglyceride-glucose index; METS-IR, Metabolic score for insulin resistance; Hb, Hemoglobin; HDL, High-density lipoprotein; LDL, Low-density lipoprotein.
P-value calculated using linear regression analysis.
Significance considered at P < 0.05 (bold font).
Inflammatory markers (Supplementary Table 3), including urinary levels of KIM1, MCP1, NGAL, TNFα, plasma ILα, ILβ, IL18, VEGF, and angiopoietin, did not show any significant association with MOTS-c levels.
To further clarify the relationship of MOTS-c with metabolic variables, we conducted separate univariate analyses within the lean and obese groups. In the lean group, only plasma IL-18 showed a significant negative correlation with MOTS-c levels, but no significant relationships with insulin resistance markers (HOMA-IR, insulin) or lipid metabolism indicators (Supplementary Table 4–6). Contrarily, in the obese group, MOTS-c showed significant positive correlations with levels of insulin, HOMA-IR, total cholesterol, and triglycerides, but a significant negative correlation with angiopoietin-2. In multivariate regression analysis including HOMA-IR and total cholesterol within the obese group alone, HOMA-IR remained significantly associated with MOTS-c, demonstrating a curvilinear correlation (Supplementary Table 7–10, Supplementary Fig.1).
Independent predictors of circulating MOTS-c levels
To identify independent determinants of circulating MOTS-c, we constructed multiple linear regression models incorporating variables significantly associated with MOTS-c in the univariate analysis (Table 3). Given that HOMA-IR, TyG index, and METS-IR each reflect aspects of glucose intolerance, they were analyzed separately across different models. In Model 1, BMI (P = 0.035) and HOMA-IR (P = 0.032) emerged as significant independent predictors of MOTS-c, together explaining approximately 27 % of MOTS-c variance (adjusted R2 = 0.2682), whereas total cholesterol was not a significant predictor. In Model 2, when categorical obesity status (lean vs. obese) replaced continuous BMI, obesity status (P = 0.064) and HOMA-IR (P = 0.055) showed only trends toward statistical significance, suggesting that their associations with MOTS-c were attenuated when obesity cut-off values were implements. The overall model explained approximately 24 % of MOTS-c variance (adjusted R2 = 0.2421). In Model 3, BMI (P = 0.0347) remained significant, while the TyG index did not (P = 0.2471). This model explained about 19 % of the MOTS-c variance (adjusted R2 = 0.1877). In Model 4, none of the potential predictors (BMI, METS-IR, cholesterol) reached statistical significance. This model explained approximately 17 % of the variance (adjusted R2 = 0.1658). Thus, multivariate analyses identified BMI and HOMA-IR as primary independent determinants of circulating MOTS-c.
Table 3.
Multivariate regression models for variables associated with MOTS-c levels.
| Model | Variable | Estimate | Std. Error | P-value | VIF |
|---|---|---|---|---|---|
| Model 1 | BMI (kg/m2) | 2.3110 | 1.043 | 0.035 | 1.080 |
| HOMA-IR (mmol/L×µU/mL) | 5.3470 | 2.377 | 0.032 | 1.083 | |
| Cholesterol (mmol/L) | 0.1960 | 0.198 | 0.33 | 1.004 | |
| Adjusted R2 | 0.2682 | ||||
| AIC | 365.9100 | ||||
| Model 2 | Group (Obese) | 35.4860 | 18.443 | 0.064 | 1.159 |
| HOMA-IR (mmol/L×µU/mL) | 5.0060 | 2.503 | 0.055 | 1.159 | |
| Cholesterol (mmol/L) | 0.1870 | 0.201 | 0.36 | 1.005 | |
| Adjusted R2 | 0.2421 | ||||
| AIC | 367.1000 | ||||
| Model 3 | BMI (kg/m2) | 2.5350 | 1.1491 | 0.0347 | 1.166 |
| TyG index | 19.9364 | 16.9108 | 0.2471 | 1.173 | |
| Cholesterol (mmol/L) | 0.2459 | 0.2180 | 0.2678 | 1.008 | |
| Adjusted R2 | 0.1877 | ||||
| AIC | 394.0419 | ||||
| Model 4 | BMI (kg/m2) | 0.8118 | 3.2967 | 0.807 | 9.344 |
| METS-IR | 1.2994 | 1.8120 | 0.479 | 9.370 | |
| Cholesterol (mmol/L) | 0.2925 | 0.2225 | 0.198 | 1.023 | |
| Adjusted R2 | 0.1658 | ||||
| AIC | 394.9985 |
BMI, body mass index; HOMA-IR, homeostasis model assessment of insulin resistance; TyG index, triglyceride-glucose index; METS-IR, metabolic score for insulin resistance; VIF, variance inflation factor; AIC, Akaike Information Criterion. Notes: Model 1 includes BMI, HOMA-IR, and cholesterol as independent variables. Model 2 replaces BMI with obesity status (obese vs non-obese). Model 3 includes BMI, TyG index, and cholesterol. Model 4 includes BMI, METS-IR, and cholesterol. Adjusted R2 indicates the proportion of variance explained by the respective model. A VIF value < 10 indicates absence of significant multicollinearity. P-values < 0.05 indicate statistical significance. Significance considered at P < 0.05 (bold font).
Further analysis suggested nonlinear, biphasic relationships of MOTS-c with HOMA-IR but not BMI. Specifically, while MOTS-c levels visually seemed to slightly increase with BMI up to approximately 34.6 kg/m2 (95 % CI: 29.8–39.4; β = 6.52, P = 0.21) and slightly decline thereafter (β = 0.57, P = 0.81; Fig. 1B), neither of these relationships were statistically significant. In contrast, MOTS-c concentrations remained relatively stable at HOMA-IR values below 6.6 mg/dL×µU/mL (95 % CI: 0.5–12.7; β = -116.3, P = 0.35) but increased significantly beyond this threshold (ß=8.43, P < 0.001; Fig. 1C), which may be consistent with threshold-dependent regulatory mechanisms.
Effects of bariatric surgery on circulating MOTS-c and metabolic parameters
Ten obese patients underwent bariatric surgery, and clinical and laboratory variables were reassessed approximately 6 months postoperatively (Table 4). Patients experienced significant weight loss, reflected by a substantial reduction in BMI (from 42.3 ± 6.1 to 34.8 ± 5.3 kg/m2, P < 0.001) 6 months after bariatric surgery, although on average they remained within the obesity range and higher than in lean subjects (Supplementary Table 11). Despite small falls in mean fasting glucose, triglycerides, total cholesterol, LDL-cholesterol, and HDL-cholesterol, these changes did not reach statistical significance, possibly due to the small sample size. While some indices directly reflecting insulin resistance significantly improved (both the TyG index and the METS-IR score dropped), whereas HOMA-IR did not significantly change after bariatric surgery.
Table 4.
Clinical and laboratory variables (Mean ± SD) measured in patients with obesity (n = 10) before and after bariatric surgery.
| Marker | Pre-surgery | Post-surgery | P-value |
|---|---|---|---|
| BMI (kg/m2) | 43.94 ± 3.77 | 31.76 ± 4.90 | <0.001 |
| MOTS-c (pg/mL) | 273.33 ± 56.02 | 276.00 ± 33.65 | 0.913 |
| Glucose (mmol/L) | 5.99 ± 1.44 | 5.24 ± 0.27 | 0.195 |
| Total Cholesterol (mmol/L) | 4.78 ± 1.35 | 4.44 ± 1.36 | 0.45 |
| Triglycerides (mmol/L) | 1.71 ± 0.80 | 1.00 ± 0.30 | 0.059 |
| HDL Cholesterol (mmol/L) | 1.27 ± 0.37 | 1.83 ± 1.07 | 0.18 |
| LDL Cholesterol (mmol/L) | 2.81 ± 1.12 | 2.61 ± 0.92 | 0.626 |
| TyG index | 8.92 ± 0.57 | 8.29 ± 0.35 | 0.039 |
| METS-IR | 66.59 ± 5.83 | 42.35 ± 4.57 | <0.001 |
| HOMA-IR (mg/dL×µU/mL) | 4.12 ± 2.81 | 3.63 ± 3.75 | 0.759 |
BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TyG index, triglyceride-glucose index; METS-IR, metabolic score for insulin resistance. P-values < 0.05 indicate statistical significance (bold font).
Circulating MOTS-c concentrations did not significantly change post-surgery (Tabel 4, Fig. 1D) and remained significantly higher than in lean subjects. TyG and METS-IR improved postoperatively, but only TyG normalized to lean levels. HOMA-IR did not significantly change after bariatric surgery (p = 0.484), although postoperative HOMA-IR values no longer significantly different from that in Lean patients either (p = 0.54), suggesting that surgery might have achieved a minor improvement in insulin resistance.
Mots-c expression in adipose tissue from lean and obese individuals
Adipose tissue samples demonstrated no significant differences in adipose tissue MOTS-c staining intensity (MOTS-c/DAPI ratio) between lean and obese subjects (Fig. 2A, P = 0.66). These findings suggest that local adipose tissue MOTS-c expression does not correspond to circulating MOTS-c levels or obesity status. Moreover, Pearson correlation analysis revealed no significant correlation between circulating plasma MOTS-c levels and adipose tissue MOTS-c expression (r = -0.07, P = 0.81), further arguing against direct association between local adipose tissue and systemic MOTS-c levels.
Fig. 2.
MOTS-c and CD68 immunofluorescence staining in adipose tissue. Representative immunofluorescence images of A) MOTS-c (red) and B) CD68 (green), both counterstained with DAPI (blue), in adipose tissue from lean and obese individuals. The quantitative analyses showed no significant difference in MOTS-c expression but markedly increased CD68 expression in obese adipose tissue (Data are mean ± SD).
On the other hand, in immunofluorescence-stained slides the CD68-positive area was significantly higher in obese compared with lean samples (Fig. 2B, P < 0.001), confirming enhanced local inflammation in obesity. Nevertheless, circulating MOTS-c levels did not correlate with CD68 expression (P = 0.832).
Discussion
In this study, we characterized circulating MOTS-c levels in lean and obese adults, their relationship with metabolic parameters, and their response to weight loss following bariatric surgery. Our key findings are as follows: first, plasma MOTS-c concentrations were significantly elevated in obese compared to lean individuals, positively correlated with BMI and insulin resistance (HOMA-IR), and exhibited a curvilinear relationship with HOMA-IR. Second, circulating MOTS-c levels were not correlated with inflammatory markers measured in this study. Third, despite metabolic improvements six months after bariatric surgery, circulating MOTS-c levels remained unchanged.
Our results shed light on the complex relationship between MOTS-c, obesity, and insulin resistance. Du et al. observed lower MOTS-c levels in obese compared to lean boys, inversely related to insulin resistance [15]. In contrast, results in adults with obesity have been mixed. Cataldo et al. reported no difference in MOTS-c levels between obese and lean adults; however, MOTS-c correlated positively with insulin resistance in lean subjects only [17]. Recent studies further support a rise in MOTS-c in patients with early or moderate metabolic dysfunction, but reduced levels in more advanced conditions such as type-2 diabetes [14], [19], [27]. Notably, divergence among studies may partly arise from methodological differences, as commercial ELISA kits for MOTS-c detection have not been compared to the in-house assay used in our study.
Our findings show a nonlinear relationship of MOTS, with a biphasic pattern observed when HOMA-IR exceeded approximately 6.6 mmol/L×µU/mL. The biphasic association between circulating MOTS-c and HOMA-IR may reflect a physiological compensatory mechanism to mitochondrial or oxidative stress. Beyond a certain threshold, sustained mitochondrial dysfunction and translational impairment may trigger MOTS-c synthesis or release, which may explain the observed inflection pattern that should be further investigated through mechanistic studies. Moreover, further analyses revealed distinct group-dependent correlations. While no correlations between MOTS-c and metabolic parameters were observed in lean subjects, obese subjects exhibited clear positive correlations with insulin resistance and lipid metabolism parameters. This novel observation underscores differential metabolic adaptations in response to varying degrees of obesity and insulin resistance. However, these findings should be interpreted with caution, especially given that MOTS-c continued rising with increasing HOMA-IR but not with BMI. Notably, our multiple regression analyses suggest BMI and HOMA-IR as stronger determinants of MOTS-c levels than alternative surrogate markers such as the TyG index or METS-IR. Although the TyG index and METS-IR were significantly correlated with circulating MOTS-c in univariate analysis, these associations disappeared in multivariate model, indicating that BMI and HOMA-IR better represent the metabolic condition underlying MOTS-c regulation. Possibly, circulating MOTS-c may predominantly reflect adiposity and insulin resistance status rather than generalized metabolic disturbances. Yet, these findings should be considered preliminary due to our limited simple size, and warrant larger prospective studies to confirm these relationships.
We observed no correlation between circulating MOTS-c and systemic inflammatory markers, indicating that MOTS-c regulation is likely driven by metabolic cues more than inflammatory signals. This aligns with experimental studies showing MOTS-c production to be primarily responsive to energy status and metabolic stress rather than inflammation [10], [28], [29]. MOTS-c levels also correlated with coronary endothelial function in early atherosclerosis and protected endothelial function in-vitro [20], supporting its vasculoprotective properties. On the other hand, they correlated inversely with plasma IL-18 in lean subjects and with angiopoietin-2 in obese patients. Given that IL-18 protects against obesity and metabolic dysfunction [30] and angiopoieitn-2 is involved in inflammation and endothelial dysfunction [31], these findings suggest contex-dependent effects of MOTS-c related to metabolic status. Congruently, we found increased CD68 + macrophage infiltration in adipose tissue of obese compared with lean individuals confirming local fat inflammation, but they did not correlate with circulating MOTS-c levels, dissociating MOTS-c from local immune cell infiltration.
Adipose tissue staining in our study revealed no significant changes in MOTS-c expression in obese individuals, nor correlation with circulating MOTS-c levels. While this suggests that the adipose tissue is unlikely to be the primary source of circulating MOTS-c in our study, we cannot rule out the possibility that it contributes to MOTS-c levels by virtue of the substantially greater fat mass in obese individuals. In fact, adipose tissue may be influenced by circulating MOTS-c rather than acting as its primary producer [32]. However, further studies are required to determine whether adipose tissue is a direct functional target of MOTS-c, and by what mechanisms. Pertinently, prior evidence suggested that that skeletal muscle predominantly contributes to circulating MOTS-c levels, identifying it as an exercise-induced myokine produced in response to metabolic demand [13], [33], [34]. Nevertheless, the exact relationship between MOTS-c secretion and skeletal muscle-mediated insulin resistance remains unclear, and further studies are necessary to elucidate whether and how MOTS-c influences insulin signaling in skeletal muscles.
We also took the opportunity to assess changes in circulating MOTS-c levels within the same individuals after weight-loss induced by bariatric surgery. Interestingly, despite improvements in BMI and metabolic parameters, MOTS-c levels remained unchanged six months post-surgery. Moreover, compared to lean subjects, post-surgical patients showed persistently elevated MOTS-c concentrations as well as higher METS-IR scores. Interestingly, HOMA-IR did not significantly improve after surgery despite substantial weight reduction, although its post-operative values were not different than those of lean subjects. This suggests that early post-operative metabolic adaptation may induce modest changes in insulin resistance, requiring long follow-up to detect meaningful improvement. These observations support the notion of continuous metabolic stress or residual insulin resistance, possibly reflecting long-term metabolic adaptation imposed by chronic obesity. Additionally, rapid metabolic changes post-surgery, including hormonal shifts and oxidative stress, may have contributed to sustained MOTS-c elevation. Our findings highlight the complexity of metabolic adaptation following significant weight-loss and suggest that normalization of circulating MOTS-c might require longer periods of metabolic stabilization or more profound weight reduction. These possibilities warrant further investigation over a longer follow-up period and across varying degrees of weight-loss.
Our study's strengths include clearly defined cohorts with comprehensive metabolic profiling and longitudinal measurements following weight-loss intervention. However, limitations include relatively small sample sizes, particularly for longitudinal analyses. Our sample sizes beyond the inflection/deflection points were particularly too small for a definitive conclusion, and larger studies are needed to elucidate the relationships of MOTS-c with BMI and insulin resistance. Lastly, the lean control group consisted of healthy kidney donors, which may not fully represent the general lean population due to some selection bias. Overall, this work should be regarded as a preliminary exploratory study that needs to be validated in larger independent cohorts.
In conclusion, circulating MOTS-c levels are significantly elevated in obese adults and demonstrate a biphasic association with insulin resistance. Nevertheless, circulating MOTS-c are unlikely to be released by the adipose tissue. Bariatric surgery-induced weight loss did not reduce circulating MOTS-c levels within a 6-month timeframe, suggesting sustained mitochondrial or metabolic adaptations. These novel insights into MOTS-c dynamics could guide future research exploring its potential as a biomarker for identifying critical thresholds of metabolic dysfunction and as a therapeutic target to manage insulin resistance and obesity-related complications. Longitudinal and mechanistic studies are needed to clarify regulation of the MOTS-c and optimize its clinical application in metabolic diseases.
Data availability
The data supporting this study are available from the corresponding author upon reasonable request.
CRediT authorship contribution statement
Se-Hee Yoon: Writing – review & editing, Writing – original draft, Visualization, Formal analysis. Fei Yuan: Writing – review & editing, Investigation. Xiangyang Zhu: Writing – review & editing, Formal analysis. Hui Tang: Writing – review & editing, Investigation. Dilbar Abdurakhimoova: Investigation, Writing – review & editing. James Krier: Investigation, Writing – review & editing. Alfonso Eirin: Writing – review & editing, Supervision. Amir Lerman: Writing – review & editing, Supervision. Pinchas Cohen: Writing – review & editing, Supervision, Investigation. Lilach O Lerman: Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization.
Funding
This study was partly supported by NIH grant numbers DK120292, DK122734, HL158691, HL85307, and DK100081.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Lerman is an advisor to CureSpec, Cellergy, LiveKidney Bio, and Ribocure Pharmaceuticals. The authors declare no conflict.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jcte.2025.100429.
Appendix A. Supplementary data
The following are the Supplementary data to this article:
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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 data supporting this study are available from the corresponding author upon reasonable request.