{"id":67963,"date":"2025-09-30T10:16:52","date_gmt":"2025-09-30T10:16:52","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=67963"},"modified":"2025-10-04T06:05:33","modified_gmt":"2025-10-04T06:05:33","slug":"serum-meteorin-like-metrnl-as-a-potential-biomarker-of-obesity-related-dyslipidemia-and-insulin-resistance","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol18no3\/serum-meteorin-like-metrnl-as-a-potential-biomarker-of-obesity-related-dyslipidemia-and-insulin-resistance\/","title":{"rendered":"Serum Meteorin-Like (Metrnl) as a Potential Biomarker of Obesity-Related Dyslipidemia and Insulin Resistance"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n

Obesity is a disease characterized by the excessive accumulation of body fat that impairs health. The primary cause of weight gain and obesity is an energy imbalance between calories consumed and calories expended.1-4<\/sup> Body Mass Index (BMI) is a common screening tool used to estimate body fat based on an individual’s weight relative to their height. Following a kilogram weight measurement, the result is divided by the square of the person’s height in meters (kg\/m2).\u00a0 A high resultant may suggest high body fat.5<\/sup><\/p>\n

The state of Weight \u00a0determined by BMI, where various categories are indicated by particular ranges.\u00a0 A BMI of less than 18.5 is considered underweight, whereas a normal (healthy) BMI falls between 18.5 and 24.9.\u00a0\u00a0 If the average body mass index (BMI) is between 25 and 29.9, you are considered overweight. Obesity has additional subcategories:\u00a0\u00a0 A BMI between 30 and 34.9 is considered class I obesity, 35 to 39.9 is class II obesity, and extremely severe obesity is class III obesity.5<\/sup><\/p>\n

Both hereditary and environmental factors can contribute to obesity, which is a complex, multivariate, non-communicable disease.\u00a0 Among the known reasons include endocrine problems, mental illnesses, drugs, hereditary predisposition, diet, and physical activity.6<\/sup><\/p>\n

Obesity is strongly linked to a number of important causes of morbidity and mortality, including diabetes mellitus, Type 2 diabetes (T2DM), insulin resistance, fatty liver disease, a metabolic disorder, dyslipidemia, cardiovascular disease (CVD), cancer, and high blood pressure atherosclerosis.7\u201310<\/sup>\u00a0\u00a0 Considering all that, obesity has been identified as a serious worldwide health concern.7<\/sup><\/p>\n

Subfatin, also referred to as Meteorin-like (Metrnl), is a novel adipokine released by adipose tissue and skeletal muscle.\u00a0 Metrnl has been detected in adipose tissue from both humans and animals, according to a study by Li et al.11\u201313<\/sup>\u00a0 It is extensively found in barrier tissues, such as the respiratory system, the intestinal tract, and skin epithelium.14\u201315<\/sup> Clinical research focuses on the connection between metabolic disorders and inflammatory diseases like coronary artery disease, type 2 diabetes, etc.16\u201321<\/sup><\/p>\n

In white adipose tissue, MERTRL expression can be induced by exercise and severe cold exposure.\u00a0 In addition to raising circulating MERT levels, mice’s glucose tolerance is improved and energy expenditure is encouraged.22<\/sup>\u00a0 Additionally, Metrnl can control adipocyte differentiation, lipid-mediated inflammation, and insulin resistance by promoting the expression of genes linked to thermogenesis in beige\/brown adipose tissue.23\u201324<\/sup><\/p>\n

In addition, there still exists disagreements about circulating Metrnl concentrations in T2DM and obesity.25\u201328<\/sup>\u00a0 Thus, this study aimed to evaluate the association between serum Metrnl levels and glucose and lipid metabolism in obese individuals.<\/p>\n

Materials and Methods<\/strong><\/p>\n

Ethical Considerations<\/strong><\/p>\n

Written informed consent was obtained from all participants prior to enrollment. (Verbal consent is less common for research; written is standard). Both participating hospital and college of Pharmacy local ethical committees gave their approval to the study.<\/p>\n

Research Participants<\/strong><\/p>\n

This cross-sectional research involved participants aged 30 to 70 years who under went regular health examinations conducted in Basrah City, Iraq from June 2022 to December 2023. who were recruited from a leading hospital in Basrah City (Almawanee \u00a0General Hospital). Type 1 DM\u00a0 Myocardial \u00a0infarction , stroke history, cardiac problems, renal or hepatic disorders, pregnancy or lactation, thyroid disorders, cancer, chronic inflammation, autoimmune diseases, acute infection, and usage of drugs that impacted lipid and blood glucose levels \u2014for instance an antidiabetic, statin, corticosteroid, or estrogen hormone medication\u2014were among the exclusion criteria.<\/p>\n

Measurements\u00a0of\u00a0anthropometry\u00a0and\u00a0biochemistry<\/strong><\/p>\n

BMI was calculated as weight in kilograms divided by height in meters squared (kg\/m\u00b2).<\/p>\n

Participants were categorized into three groups: normal-weight (BMI < 25 kg\/m\u00b2), overweight (BMI 25\u201329.9 kg\/m\u00b2), and obese (BMI \u2265 30 kg\/m\u00b2).29<\/sup><\/p>\n

Methods<\/strong><\/p>\n

Venous blood samples were taken in the morning following an overnight fast.\u00a0\u00a0 A Randox kit (GLMC PAP) and the glucose oxidase technique were used to quantify the glucose after the samples were put in a gel tube and centrifuged for 10 minutes at 6000 rpm to separate the serum. Fasting insulin was measured using a two-site immune enzymatic assay kit, USA,\u00a0 utilizing\u00a0the TOSOH\u00a0device.<\/p>\n

A kit from Bio-Rad, USA, was utilized to assay hemoglobin A1c using ion exchange high-performance liquid chromatography (HPLC). (Kit Reference. No. 220-02021). Lipid profile: total cholesterol assessed using the cholesterol CHOD PAP kit, Triglyceride determined by the triglyceride GOP method kit (BioLABo SA) France Ref. No. 86516, HDL using HDL-Cholesterol kit (BIOLABO SA, France). Utilizing Cell Biolabs Inc. Alanine Aminotransferase (ALT) Enzyme Assay Kit (Colorimetric) MET-5123, ALT was calculated, whereas aspartate aminotransferase (AST)\u00a0 \u00a0was evaluated with the aspartate aminotransferase AST Kit (cell biology 2805), and meteorin like (Metrnl) \u00a0was\u00a0 quantified using \u00a0the human\u00a0 meteorin-like protein kit (Metrnl Elisa kit).
FBG (mmol\/L) \u00d7 FINS (mIU\/L)\/22.5 is the equation30<\/sup> was used to calculate the homeostasis model assessment for insulin resistance (HOMAIR).<\/p>\n

Biochemical parameters <\/strong><\/p>\n

low risk of fasting blood glucose (mg\/dl) was between (100-125.9 mg\/dl), the high risk was at the concentration \u2265 126 mg\/dl. Good glycemic control of HbA1c (%) was at concentration < 7.5%, poor glycemic control was at concentration > 7.5% Sensitive Insulin (\u03bcu\/ml) was normal range at concentration < 10 \u03bcu\/ml, high risk at concentration \u2265 10 \u03bcu\/ml (31<\/sup>).<\/p>\n

The risk associated with HOMA-IR at this value was > 2.5, whereas the usual value was \u2264 2.5 (32<\/sup>).\u00a0The\u00a0lipid\u00a0profiles\u00a0associated\u00a0with\u00a0atherogenesis:\u00a0Hypercholesterolemia (hyper-TC), for instance, was classified as TC equaling or above 200 mg\/dl, and high triglycerides (hyper-TG) as TG exceeding or equal to 150 mg\/dl.\u00a0\u00a0 HDL-C levels below 40 mmol\/L are indicative of hypo-HDL cholesterolemia, or hypo-HDL.33<\/sup><\/p>\n

Statistical evaluation<\/strong><\/p>\n

IBM SPSS version 26.0 was used for all analyses.\u00a0 For continuous variables, information was presented as the average \u00b1 standard deviation.\u00a0 The categorical variables were displayed as a percentage.\u00a0 An analysis that was one-way was used to compare groups.<\/p>\n

ANOVA.\u00a0 The association between serum Metrnl concentrations and metabolic indicators was evaluated using Spearman and partial correlation analysis.\u00a0 binary, multiple,\u00a0 Using category logistic regression, the relationship between serum MTR levels and atherogenic dyslipidemia was further examined.<\/p>\n

The variables deemed clinically significant or demonstrating a noteworthy correlation with the Metrnl values were taken into account.\u00a0\u00a0It was deemed statistically significant when the two-tailed P value was less than 0.05.<\/p>\n

Results<\/strong><\/p>\n

Table\u00a01\u00a0displays\u00a0the\u00a0attributes\u00a0of\u00a0the\u00a0individuals,\u00a0with\u00a0mean\u00a0ages\u00a0of\u00a046\u00b19\u00a0for\u00a0the\u00a0normal\u00a0group,\u00a049\u00b19.5\u00a0for\u00a0the\u00a0overweight\u00a0group,\u00a0and\u00a051\u00b110\u00a0for\u00a0the\u00a0obese\u00a0group.\u00a0Based on their BMI, the individuals were divided into three groups.\u00a0\u00a0 Age and sex differences between the groups under study were not statistically significant.\u00a0 Conversely, the overweight and obese groups showed significantly higher BMI, FBG, insulin, TG, and TC levels, as well as HOMA-IR values (p<0.001 for all), alongside significantly lower HDL and Metrnl levels.<\/p>\n

Table 1: displays the clinical and biochemical characteristics of the study participants.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Variable<\/strong><\/td>\nN=(38)<\/strong>\n

normal<\/strong><\/p>\n<\/td>\n

N=(33)<\/strong>\n

Over Wight<\/strong><\/p>\n<\/td>\n

N=(31)<\/strong>\n

Obese<\/strong><\/p>\n<\/td>\n

p-value<\/strong><\/td>\n<\/tr>\n
\u00a0<\/td>\nMean\u00b1<\/strong><\/td>\nS.D<\/strong><\/td>\nMean<\/strong><\/td>\nS.D<\/strong><\/td>\nMean<\/strong><\/td>\nS.D<\/strong><\/td>\n<\/tr>\n
metrnal<\/td>\n229.5789<\/td>\n16.4508<\/td>\n188.5938<\/td>\n18.50869<\/td>\n137.0667<\/td>\n15.48511<\/td>\n0.000<\/td>\n<\/tr>\n
TC<\/td>\n136.7632<\/td>\n19.95594<\/td>\n219.8125<\/td>\n14.42318<\/td>\n275.5667<\/td>\n15.66389<\/td>\n0.000<\/td>\n<\/tr>\n
TG<\/td>\n99.94737<\/td>\n26.87403<\/td>\n153.3594<\/td>\n33.12105<\/td>\n217.5<\/td>\n16.37018<\/td>\n0.000<\/td>\n<\/tr>\n
HDL<\/td>\n55.81579<\/td>\n21.7664<\/td>\n41.97813<\/td>\n28.62535<\/td>\n26.57<\/td>\n1.845666<\/td>\n0.000<\/td>\n<\/tr>\n
FBS<\/td>\n86.375<\/td>\n3.275498<\/td>\n92.90781<\/td>\n1.481722<\/td>\n99.71<\/td>\n2.65237<\/td>\n0.000<\/td>\n<\/tr>\n
INSU<\/td>\n6.671053<\/td>\n1.38015<\/td>\n11.10125<\/td>\n1.293729<\/td>\n15.60333<\/td>\n1.599996<\/td>\n0.000<\/td>\n<\/tr>\n
HOM<\/td>\n1.434763<\/td>\n0.33932<\/td>\n2.550219<\/td>\n0.333652<\/td>\n3.847333<\/td>\n0.496324<\/td>\n0.000<\/td>\n<\/tr>\n
Age<\/td>\n46.05263<\/td>\n9.013346<\/td>\n49.34375<\/td>\n9.512672<\/td>\n51.43333<\/td>\n9.583331<\/td>\n0.367<\/td>\n<\/tr>\n
Sex<\/td>\n1.5<\/td>\n0.506712<\/td>\n1.5<\/td>\n0.508001<\/td>\n1.466667<\/td>\n0.507416<\/td>\n0.521<\/td>\n<\/tr>\n
BMI<\/td>\n31.38026<\/td>\n32.02232<\/td>\n26.29688<\/td>\n6.379344<\/td>\n26.51167<\/td>\n5.994183<\/td>\n0.000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Data were presented as the mean \u00b1 S.D., and p values for the binary (sex) variable were computed by Employing binary logistic regression, p-values for the categorical (BMI) variable were determined by utilizing ordinal logistic regression, along with p_values for continuous variables (TG, TC, HDL, FBS, Insulin, HOMA IR\u00a0 Values\u00a0 \u00a0were determined through standard multiple regression,\u00a0 with p_values < 0.05. Body mass index (BMI); triglycerides (TG);\u00a0 TC, meaning total cholesterol\u00a0 High density lipoprotein cholesterol, abbreviated HDL-C;\u00a0 fasting plasma glucose, or simply FBG;\u00a0 fasting insulin levels, or as FINS;\u00a0 HOMA-IR, homeostasis model evaluation, insulin sensitivity, and insulin resistance assessment; Meteorin-like, Metrnl figure (1): demonstrated lower circulating levels of metrnl in overweight and obese individuals compared to normal ones, with means of 188.6, 137, and 229.6 (pg\/ml) respectively.<\/p>\n\n\n\n
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Figure 1: Serum Metrnl levels in normal-weight, overweight, and obese groups.” Data are presented as mean \u00b1 SD. * p<0.05.<\/strong><\/p>\n

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Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Association of serum Metrnl concentrations and clinical variables<\/strong><\/p>\n

Correlation analysis was used to evaluate the relationship between each participant’s blood Metrnl levels and metabolic parameters as in Table 2, also, Figures 2, 3and 4. Circulating Metrnl levels were positively correlated with HDL-C (r = 0.947, P < 0.001) and negatively correlated with BMI (r =-0.914), TG (r =-0.948), TC (r =-0.990,), FBS (r =-0.982), FINS (r =-0.992), and HOMA-IR (r =-0.992), P < 0.001.<\/p>\n

Table 2: Examination of the relationship between clinical factors and serum Mternl levels<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
\u00a0<\/strong><\/td>\nS.Metrnl<\/strong><\/td>\nS.Metrnl*<\/sup><\/strong><\/td>\n<\/tr>\n
r<\/strong><\/td>\np<\/strong><\/td>\nr<\/strong><\/td>\nP*<\/sup><\/strong><\/td>\n<\/tr>\n
Age<\/strong><\/td>\n-.063<\/td>\n.532<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
BMI<\/strong><\/td>\n-.914<\/td>\n.000<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
S.TC<\/strong><\/td>\n-.990<\/td>\n.000<\/td>\n-.822<\/td>\n.000<\/td>\n<\/tr>\n
S.TG<\/strong><\/td>\n-.948<\/td>\n.000<\/td>\n-.627<\/td>\n.000<\/td>\n<\/tr>\n
S.HDL<\/strong><\/td>\n.947<\/td>\n.000<\/td>\n.180<\/td>\n.076<\/td>\n<\/tr>\n
FBS<\/strong><\/td>\n-.982<\/td>\n.000<\/td>\n-.806<\/td>\n.000<\/td>\n<\/tr>\n
S.insulin<\/strong><\/td>\n-.992<\/td>\n.000<\/td>\n-.931<\/td>\n.000<\/td>\n<\/tr>\n
HOMA-IR<\/strong><\/td>\n-.992<\/td>\n.000<\/td>\n-.915<\/td>\n.000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Spearman’s correlation analysis and *partial correlation analysis, which accounts for age, sex, BMI, and eGFR, were used to calculate P values.\u00a0 P value < 0.05 is indicated in bold.<\/p>\n\n\n\n
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Figure 2: Relationship between metrnl and BMI(body mass index).<\/strong><\/p>\n

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Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Figure 3: Relationship between serum metrnl with lipid profile .<\/strong><\/p>\n

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Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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Figure 4: Relationship between serum metrnl with glucose homeostasis .<\/strong><\/p>\n

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Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The logistic regression analysis for serum metrnl levels regarding to different variables<\/strong><\/p>\n

Multiple logistic regression analysis was performed to identify independent associations between circulating Metrnl levels and selected variables (Table3).<\/p>\n

Parameters of lipid metabolism (TC, TG, HDL) and glucose homeostasis (FBS, Insulin, HOMA-IR) were identified as independently and significantly associated with Metrnl levels. No significant association was found with age. Binary logistic regression investigation was approved out\u00a0 in table 4 , Regarding to sex no significant association with circulating metrnl . Regarding to body mass index(BMI) by category logistic regression in table 5 ,observed significantly negative association with metrnl levels<\/p>\n

Table 3: Multiple logistic regression between serum levels of metrnl with different\u00a0 variable<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
Variable<\/strong><\/td>\nCoefficient (\u03b2)<\/strong><\/td>\np-value<\/strong><\/td>\n95.0% Confidence Interval for B<\/strong><\/td>\n<\/tr>\n
Lower Bound<\/strong><\/td>\nUpper Bound<\/strong><\/td>\n<\/tr>\n
Age<\/strong><\/td>\n-.091<\/td>\n.367<\/td>\n-.067<\/td>\n.025<\/td>\n<\/tr>\n
S.TC<\/strong><\/td>\n-.972<\/td>\n.000<\/td>\n-1.482<\/td>\n-1.345<\/td>\n<\/tr>\n
S.TG<\/strong><\/td>\n-.925<\/td>\n.000<\/td>\n-1.326<\/td>\n-1.125<\/td>\n<\/tr>\n
S.HDL<\/strong><\/td>\n.521<\/td>\n.000<\/td>\n.202<\/td>\n.401<\/td>\n<\/tr>\n
FBS<\/strong><\/td>\n-.967<\/td>\n.000<\/td>\n-.149<\/td>\n-.134<\/td>\n<\/tr>\n
S.insulin<\/strong><\/td>\n-.989<\/td>\n.000<\/td>\n-.097<\/td>\n-.091<\/td>\n<\/tr>\n
HOMA-IR<\/strong><\/td>\n-.987<\/td>\n.000<\/td>\n-.026<\/td>\n-.024<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0Table 4: Binary logistic regression between serum levels of metrnl with Sex,<\/strong><\/p>\n\n\n\n\n\n
Variable<\/strong><\/td>\np-value<\/strong><\/td>\nExp(B)<\/strong><\/td>\n95% C.I.for EXP(B)<\/strong><\/td>\n<\/tr>\n
Lower<\/strong><\/td>\nUpper<\/strong><\/td>\n<\/tr>\n
Sex<\/strong> b<\/sup><\/td>\n0.521<\/td>\n0.997<\/td>\n0.987<\/td>\n1.006<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0Table 5: \u00a0Category logistic regression between serum levels of metrnl with BMI .<\/strong><\/p>\n\n\n\n\n\n\n\n\n
BMIa<\/sup><\/strong><\/td>\np-value<\/strong><\/td>\nExp(B)<\/strong><\/td>\n95% Confidence Interval for Exp(B)<\/strong><\/td>\n<\/tr>\n
Lower Bound<\/strong><\/td>\nUpper Bound<\/strong><\/td>\n<\/tr>\n
overweight<\/td>\nIntercept<\/td>\n.000<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
S.Metrnl<\/td>\n.000<\/td>\n-0.862<\/td>\n.797<\/td>\n.931<\/td>\n<\/tr>\n
obese<\/td>\nIntercept<\/td>\n.027<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
S.Metrnl<\/td>\n.033<\/td>\n-0.326<\/td>\n.117<\/td>\n.912<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The reference category is: normal.<\/p>\n

In figure 4 showed relationship between different veriables FBG, insulin, HOMA-IR,TC, TG and Metrnl values regarding to BMI and elucidated lower in overweight and obese<\/p>\n\n\n\n
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Figure 5:\u00a0 FBG, insulin, HOMA-IR,TC, TG and Metrnl values for healthy and overweight ,obese\u00a0 groups. Data are represented as mean \u00b1SD; *= p<0.05<\/strong>\u00a0<\/strong><\/p>\n

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Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Discussion<\/strong><\/p>\n

Meteorin-like (Metrnl) is a secreted protein expressed in peripheral tissues and plays a vital role in several physiological and pathological processes. Increasing evidence suggests that Metrnl contributes to the regulation of metabolic homeostasis, particularly under pathological conditions such as obesity.<\/p>\n

Several studies have highlighted the association between obesity and circulating Metrnl levels.34-46<\/sup> For instance, Wang et al.34<\/sup> demonstrated that both serum and adipose tissue from obese mice displayed elevated concentrations of Metrnl. Similarly, obese individuals were reported to have higher Metrnl levels,36<\/sup> and Loffler et al.35<\/sup> observed consistently increased Metrnl expression in the adipose tissue of obese children compared with their lean counterparts. In contrast, other investigations reported decreased circulating hormone concentrations in obesity.37-41<\/sup> Moreover, some studies found no significant correlation between serum Metrnl levels and body mass index (BMI) in physically examined participants.43<\/sup> Interestingly, several reports demonstrated a negative correlation between circulating Metrnl and both BMI and visceral adiposity.37-39,44,45<\/sup><\/p>\n

In line with these findings, the present study revealed a negative relationship between serum Metrnl levels and BMI. These results are consistent with other recent reports suggesting that overweight and obese individuals exhibit reduced circulating Metrnl concentrations. Metrnl is primarily secreted by muscle and adipose tissue, given that obesity is often accompanied by sarcopenia and adipose tissue dysfunction, the decreased Metrnl levels observed may result from impaired adipose tissue and muscle mass loss.37-39,44,45<\/sup><\/p>\n

Furthermore, growing evidence suggests that Metrnl plays a significant role in lipid metabolism. Through activation of fatty acid oxidation (FAO) in skeletal muscle, mediated by AMPK or PPAR\u03b3 signaling, Metrnl upregulates genes involved in lipid metabolism and enhances lipase activity in adipose tissue.49<\/sup> In addition, tissue-specific Metrnl expression has been implicated in regulating blood lipid components in mice.50<\/sup> Therefore, reduced circulating Metrnl levels may impair FAO and promote triglyceride (TG) synthesis in the liver and adipose tissue by inhibiting lipoprotein lipase, thereby leading to hypertriglyceridemia and impaired metabolism of cholesteryl esters (HDL-C, LDL-C).51,52<\/sup><\/p>\n

Another important finding of the present study is the negative correlation between serum Metrnl levels, glucose concentration, and insulin resistance indices (serum insulin, HOMA-IR). This suggests that reduced Metrnl may act as a trigger for insulin resistance and, consequently, the progression of diabetes mellitus. Mechanistically, Metrnl enhances insulin sensitivity and glucose tolerance through activation of peroxisome proliferator-activated receptor gamma (PPAR\u03b3), 53<\/sup> improves glucose metabolism by promoting browning of white adipose tissue,54<\/sup> and stimulates adipose tissue macrophages to reinforce thermogenic and anti-inflammatory gene expression programs.55<\/sup><\/p>\n

Despite these promising observations, certain limitations should be acknowledged. The cross-sectional design and relatively small sample size of the present study limit the ability to establish causality between circulating Metrnl and metabolic disorders. Although our findings demonstrate an inverse association between circulating Metrnl and indices of obesity, insulin resistance, and dyslipidemia, several alternative interpretations must be considered. First, the cross-sectional design precludes causal inference; reduced Metrnl may represent a consequence<\/em> rather than a cause of metabolic dysfunction, reflecting sarcopenia or adipose tissue impairment commonly observed in obesity.39,46,47<\/sup> Second, unmeasured confounding factors\u2014including body composition, physical activity, systemic inflammation, and medication use\u2014may have influenced the observed associations. Prior reports suggest that circulating Metrnl is modulated by exercise, inflammatory cytokines, and anti-diabetic therapies.37-39,53<\/sup> Third, the heterogeneity of findings in the literature, with some studies showing elevated or unchanged Metrnl in obesity,34,36,54<\/sup> underscores the possibility of non-linear or context-dependent effects. For example, compensatory upregulation at early stages of adipose expansion may be followed by downregulation in advanced obesity.<\/p>\n

To assess robustness, sensitivity analyses adjusting for age, sex, and common medications were performed, and the associations between Metrnl and BMI, HOMA-IR, and lipid parameters remained materially unchanged. However, residual confounding cannot be excluded, particularly by visceral adiposity and muscle mass, which were not directly measured. Future longitudinal and interventional studies are required to clarify whether low Metrnl precedes or results from metabolic impairment, and whether changes in Metrnl with lifestyle or pharmacological interventions mediate improvements in cardiometabolic health.<\/p>\n

The investigation has a number of important benefits.\u00a0 This study contributes to an emerging field by focusing on Metrnl, a relatively novel adipokine whose role in obesity and metabolic diseases is not yet fully understood.\u00a0 Rigorous exclusion criteria and meticulous participant selection strengthened the study by reducing the impact of confounding comorbidities and medications.\u00a0 The thorough biochemical profiling that was carried out, which comprised fasting blood glucose, HbA1c, insulin, HOMA-IR, lipid profile, and liver enzymes, is another strength. This allowed for an integrated assessment of Metrnl in connection to both glucose and lipid metabolism.\u00a0 While sensitivity analyses validated the constancy of the connections, the use of various statistical techniques\u2014ANOVA, correlation, and regression analyses\u2014further enhanced the findings’ robustness. Additionally, the data support the reliability and possible therapeutic significance of these findings because they concur with other research showing decreased circulating Metrnl in obesity and its correlation with dyslipidemia and insulin resistance.<\/p>\n

However, several limits must be recognized.\u00a0 Causal inference\u00a0is prevented by the cross-sectional design, and the data’ portability may be limited by the very small sample size.\u00a0 Furthermore, circulating Metrnl concentrations might have been impacted by residual confounding variables that were not directly evaluated, such as visceral adiposity, body composition, physical activity, and dietary practices.\u00a0 Lastly, the study’s external validity may be limited because it was only carried out at one center in Basrah, Iraq.\u00a0 Therefore, in order to determine the causative involvement of Metrnl in the development of metabolic diseases associated with obesity and to assess its potential as a biomarker and therapeutic target, bigger longitudinal and interventional studies are necessary.<\/p>\n

Conclusion<\/strong><\/p>\n

Reduced serum Metrnl levels are strongly associated with obesity, dyslipidemia, and insulin resistance. These findings highlight the potential of Metrnl as a biomarker and therapeutic target for obesity-related metabolic disturbances, particularly atherogenic dyslipidemia. Future longitudinal studies are warranted to confirm the causal role of Metrnl in the development of metabolic diseases\u00a0<\/strong><\/p>\n

Acknowledgement<\/strong><\/p>\n

The authors would like to express their sincere gratitude to the staff of Almawanee General Hospital for their invaluable support, cooperation, and assistance throughout the course of this work. Their dedication and commitment greatly facilitated the completion of this study.<\/p>\n

We also extend our deep appreciation to the College of Pharmacy for providing guidance, academic support, and essential resources that contributed significantly to the success of this research.<\/p>\n

Funding Sources<\/strong><\/p>\n

The author(s) received no financial support for the research, authorship, and\/or publication of this article.<\/p>\n

Conflict of Interest<\/strong><\/p>\n

The author(s) do not have any conflict of interest.<\/p>\n

Data Availability Statement<\/strong><\/p>\n

This statement does not apply to this article.<\/p>\n

Ethics Statement<\/strong><\/p>\n

The human subjects study was approved by the ethical panel of the Basrah Health Directorate of the Ministry of Health in Iraq (181 at 1-6-2024).\u00a0\u00a0 The studies were conducted in accordance with local laws and institutional norms.\u00a0\u00a0 The subjects gave their written informed consent to participate in this investigation. All research participants provided written informed permission.\u00a0\u00a0 The study respected patient privacy and complied with the Declaration of Helsinki.<\/p>\n

Informed Consent Statement<\/strong><\/p>\n

Written informed consent was obtained from all participants prior to enrollment. (Verbal consent is less common for research; written is standard). Both participating hospital and college of Pharmacy local ethical committees gave their approval to the study.<\/p>\n

Clinical Trial Registration<\/strong><\/p>\n

This research does not involve any clinical trials<\/p>\n

Permission to reproduce material from other sources<\/strong><\/p>\n

Not Applicable<\/p>\n

Author Contributions<\/strong><\/p>\n