Cortisol/Adrenocorticotropic Hormone Ratio: A Novel Biomarker for Metabolic Risk Stratification in Obese Patients

Cortisol/Adrenocorticotropic Hormone Ratio: A Novel Biomarker for Metabolic Risk Stratification in Obese Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cortisol/Adrenocorticotropic Hormone Ratio: A Novel Biomarker for Metabolic Risk Stratification in Obese Patients Wu Zhenyu, Xiangyu Chen, Juan Zhang, Xiaohong Wang, Hongjie Di This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8710320/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective This study investigates the predictive value of the cortisol/adrenocorticotropic hormone (CORT/ACTH) ratio, a quantitative marker of hypothalamic-pituitary-adrenal (HPA) axis function, in assessing metabolic heterogeneity and the risk of type 2 diabetes mellitus (T2DM) in obese patients. By identifying a novel biomarker, this research contributes to metabolic risk stratification and provides a foundation for precise obesity management. Methods A retrospective cohort study was conducted involving 210 obese patients (BMI ≥ 28 kg/m²) from the Department of Endocrinology at Jiangsu Second Hospital of Traditional Chinese Medicine, enrolled between September 2022 and September 2023. Fasting plasma cortisol, adrenocorticotropic hormone (ACTH), and metabolic parameters were measured. Participants were stratified by tertiles of the CORT/ACTH ratio. Multivariate logistic regression and restricted cubic spline (RCS) models were used to analyze the associations of the ratio with insulin resistance (IR) and T2DM risk. Results ①No statistically significant differences were observed among the three groups in gender composition, BMI,homeostasis model assessment of insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C), or T2DM prevalence (all P > 0.05), while age showed a gradient decrease with increasing CORT/ACTH ratio (P = 0.004); ② Spearman rank correlation analysis revealed that the CORT/ACTH ratio was significantly positively associated with triglycerides (TG, r = 0.14, P = 0.036) and low-density lipoprotein cholesterol (LDL-C, r = 0.19, P = 0.006), but not with HOMA-IR (r = 0.05, P = 0.456);③The RCS model revealed a non-linear relationship between the CORT/ACTH ratio and HOMA-IR (non-linear test P = 0.032). When the ratio exceeded 0.83, HOMA-IR significantly decreased with increasing CORT/ACTH ratio (β = 8.36, SE = 3.57, P = 0.027), a trend that persisted after adjusting for confounding factors; ④ Multivariate logistic regression found no independent association between the CORT/ACTH ratio and T2DM risk (adjusted odds ratio, OR = 1.12, 95% confidence interval, CI: 0.39–3.19, P = 0.833), while age was positively associated with T2DM risk, with each 5-year increase in age correlating to a 1.03-fold higher risk (P = 0.023). Conclusion The CORT/ACTH ratio exhibits a non-linear relationship with metabolic phenotypes in obese patients,with a significant reduction in HOMA-IR observed at higher ratios.This phenomenon stems from normal HPA axis negative feedback(cortisol elevation accompanied by ACTH suppression,preventing excessive cortisol-induced IR),and this association remains robust after adjusting for baseline triglycerides and LDL-C(supporting its value independent of lipid status).This suggests that the CORT/ACTH ratio may serve as a promising biomarker for metabolic risk stratification,potentially informing precision-based interventions targeting the HPA axis in obesity management. Cortisol/adrenocorticotropic hormone Obesity Metabolic phenotype Insulin resistance Type 2 diabetes mellitus Figures Figure 1 1 Introduction The global obesity epidemic has emerged as a critical public health challenge in the 21st century. Data from 2021 revealed that the global prevalence of overweight and obesity among adults aged 25 and above has reached 45.1%, affecting approximately 2.11 billion individuals. Over the past three decades, the obesity rate has surged by 104.9% in men and 155.1% in women, and it is projected that the prevalence of overweight and obesity in this age group will approach 60% by 2050[ 1 ]. China also faces a severe obesity crisis: in 2021, the population with overweight and obesity exceeded 402 million, with age-standardized obesity rates of 8.8% in men and 10.8% in women. The escalating prevalence of obesity not only jeopardizes individual health outcomes but also imposes a substantial burden on public health systems worldwide. Similarly, China is facing a severe obesity crisis, with over 402 million individuals affected in 2021, and age-standardized obesity rates of 8.8% in men and 10.8% in women. The escalating prevalence of obesity not only jeopardizes individual health outcomes but also imposes a substantial burden on public health systems worldwide. Obesity exhibits significant interindividual heterogeneity in metabolic outcomes. Longitudinal studies demonstrate that 36.0% of individuals with metabolically healthy obese phenotype (MHO) progress to metabolically unhealthy obese phenotype (MUO) during follow-up, and such metabolic deterioration substantially elevates the risk of cardiovascular disease (CVD) incidence[ 2 ]. Compared with metabolically healthy individuals with normal weight, both metabolically healthy overweight/obesity(MHOO)and metabolically unhealthy overweight/obesity(MUOO) populations have higher CVD incidence during follow-up; moreover,a nationwide prospective cohort study in Chinese population has confirmed that changes in metabolic health status exert a more profound impact on disease risk than weight fluctuations themselves[ 3 ].These findings show that the "metabolic phenotype," rather than fat itself, may be the primary driver of illness risk, emphasizing the need for a deeper exploration of metabolic health biomarkers that can more accurately predict disease risk and inform targeted interventions. The hypothalamic-pituitary-adrenal (HPA) axis, a central regulator of the body's stress response, is strongly associated with the development of obesity and metabolic dysfunction. Regulated by corticotropin-releasing hormone (CRH), excessive activation of the HPA axis leads to elevated cortisol levels. Cortisol contributes to ectopic fat accumulation and insulin resistance (IR) by stimulating hepatic gluconeogenesis, increasing lipolysis while impairing fatty acid mobilization, and decreasing glycogen synthesis, thus serving as a crucial mediator in obesity-related metabolic disorders[ 4 ]. Conventional studies have typically measured cortisol or adrenocorticotropic hormone (ACTH) in isolation, neglecting the coordinated regulation within the hypothalamic-pituitary-adrenal (HPA) axis. This approach fails to fully capture axis functionality, as cortisol levels are influenced by external factors, such as emotional state and sleep. Additionally, the short half-life of ACTH (10–15 minutes) makes measurements susceptible to artifacts from sample handling. While the "cortisol/adrenocorticotropic hormone ratio (CORT/ACTH ratio)" has been shown to more accurately reflect the overall activity of the HPA axis[ 5 ], its relationship with metabolic phenotypes such as IR, dyslipidemia, and type 2 diabetes (T2DM) in obese patients remains poorly understood.Notably, the physiological significance of the CORT/ACTH ratio lies in reflecting HPA axis negative feedback balance: when cortisol rises, intact feedback suppresses ACTH secretion, increasing the ratio—this balance limits excessive cortisol elevation (preventing IR exacerbation) ; in contrast, Cushing’s syndrome patients with impaired HPA axis feedback exhibit lower CORT/ACTH ratios,and this reduction correlates with more severe metabolic disorders(e.g.,hyperglycemia,dyslipidemia)as reported in clinical studies linking ratio anomalies to hypercortisolemia-related metabolic severity,further supporting the hypothesis of"ratio-based metabolic risk[ 6 ]. Based on these findings, the current study focused on the CORT/ACTH ratio, systematically examining its associations with IR, lipid profiles, and T2DM using clinical data from 210 obese patients. The aim was to clarify the potential value of this ratio as a biomarker for metabolic risk stratification in obese individuals and to provide novel insights into HPA axis-related metabolic regulatory mechanisms in obesity. 2 Materials and methods 2.1 Participants This cross-sectional study included 210 obese patients who visited the Department of Endocrinology at Jiangsu Second Hospital of Traditional Chinese Medicine from September 2022 to September 2025.Inclusion criteria: ① BMI ≥ 28 kg/m² (as defined by the Chinese Guidelines for the Prevention and Control of Overweight and Obesity in Adults); ②Age 18–70 years;③ No history of glucocorticoid or vitamin D supplementation (to prevent interference with HPA axis function by exogenous hormones); ④ Complete clinical data. Exclusion criteria: ① Abnormal thyroid function (thyroid-stimulating hormone 4.0 mU/L, to prevent indirect impacts on HPA axis activity); ② Severe insufficiency of the liver or kidneys (alanine transaminase > 3 times the upper reference limit or estimated glomerular filtration rate < 60 mL·min⁻¹1.73m⁻² , to rule out influence from aberrant hormone metabolism and clearance); ③ Comorbidity with autoimmune disorders or malignant tumors (these conditions and their therapies may interfere with the neuroendocrine system); ④Pregnancy or lactation (physiological changes in the HPA axis occur during these periods); ⑤Severe sleep disturbances or acute stress that have occurred in the last month (to prevent short-term stress impacts on hormone levels). 2.2 Data Collection 2.2.1 Basic Indicators and Laboratory Measurements Demographic and clinical information, including gender, age, smoking history, drinking history, and T2DM comorbidity, was obtained from electronic medical records and standardized questionnaires. Height and weight were measured using a Seca 769 scale (accuracy: height 0.1 cm, weight 0.1 kg). The subjects wore single-layer clothing, were barefoot, and the measurements were taken at a consistent time by a trained professional. Body mass index (BMI) was calculated. Fasting venous blood samples (fasting duration ≥ 8 hours) were collected between 7:00 and 8:00 AM, during the peak period of diurnal cortisol secretion to minimize rhythmic fluctuations. Plasma was separated by centrifugation within 15 minutes and stored at -80°C, with all assays performed within 24 hours. Cortisol (μg/dL) and ACTH (pg/mL) levels were measured using the Autochemi Luminescence Immunoassay Analyzer (YHLO) and the respective reagents. Triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), and fasting insulin (FINS) levels were measured using the Beckman AU5821 Automatic Biochemistry Analyzer and the appropriate reagents. 25-hydroxyvitamin D3 (25(OH)D3) levels were measured using the Roche 411 Electrochemiluminescence Immunoassay Analyzer and the respective reagents. All tests adhered to the instrument instructions and laboratory standard operating procedures, with standard and quality control samples included in each batch to ensure accuracy. 2.2.2 Definition of Indicators and Diagnostic Criteria (1) Dyslipidemia: As defined by the 2016 Revised Chinese Guidelines for the Management of Dyslipidemia in Adults[7], the condition is diagnosed if any of the following criteria are met: TG ≥ 2.3 mmol/L; HDL-C < 1.0 mmol/L; LDL-C ≥ 4.1 mmol/L (2) CORT/ACTH ratio: This ratio is calculated using measurements taken between 7:00 and 8:00 AM, with normal reference ranges of 5–27 μg/dL for cortisol and 10–60 pg/mL for ACTH[8]. (3) BMI: Calculated as weight (kg) divided by the square of height (m)[9].In this study, obesity was defined as BMI ≥ 28 kg/m² according to the Chinese Guidelines for the Prevention and Control of Overweight and Obesity in Adults[10]. (4) HOMA-IR: The index is calculated using the formula: FBG (mmol/L) × FINS (mU/L) / 22.5[11]. Insulin resistance (IR) is defined as HOMA-IR > 2.50, according to criteria for the Chinese population[12]. (5) T2DM diagnosis: According to the American Diabetes Association (ADA) Standards of Medical Care in Diabetes–2025[13], T2DM is diagnosed in non-pregnant individuals who meet any of the following criteria: ① Fasting blood glucose ≥ 7.0 mmol/L; ② 2-hour plasma glucose ≥ 11.1 mmol/L after a 75 g oral glucose tolerance test; ③ Random blood glucose ≥ 11.1 mmol/L with typical hyperglycemic symptoms (polyuria, polydipsia, unexplained weight loss) or during a hyperglycemic crisis; ④ Glycated hemoglobin (HbA1c) ≥ 6.5%. 2.3 Statistical analysis Patients were categorized into three groups based on the tertiles of the CORT/ACTH ratio: the low-ratio group ( 0.83, n = 28). The grouping results were consistent with the distribution features of the clinical samples. Statistical analyses were conducted using SPSS 26.0 software (IBM Corp., Armonk, USA), while graphical representations were created using GraphPad Prism 9.0 . For continuous variables, normality was assessed using the Shapiro-Wilk test. Normally distributed data were presented as the mean ± standard deviation (x ± s) and compared using one-way analysis of variance (ANOVA). Non-normally distributed data were expressed as the median (interquartile range) (M (P₂₅, P₇₅)) and analyzed using the Kruskal-Wallis H test. Categorical variables were presented as percentages (%) and compared using the χ² test. Spearman rank correlation analysis was employed to evaluate the associations between the CORT/ACTH ratio and metabolic indicators. The RCS model (with three knots at the 25th, 50th, and 75th percentiles, using the median ratio as the reference) was employed to investigate the non-linear relationship between the CORT/ACTH ratio and HOMA-IR. Multivariate logistic regression models, adjusted for age, gender, BMI, TG, and HDL-C, were employed to analyze the association between the CORT/ACTH ratio and the risk of T2DM. 3 Results 3.1 Baseline Characteristics No significant differences were found among the three groups in terms of gender composition (χ² = 2.929, P = 0.231), BMI (F = 0.068, P = 0.935), HOMA-IR (F = 0.557, P = 0.574), HDL-C (F = 1.674, P = 0.190), or the prevalence of T2DM (χ² = 2.460, P = 0.292) (Table 1). Age differed significantly across the groups (F = 5.565, P = 0.004), with the low-ratio group (49.4 ± 12.7 years) being significantly older than both the medium-ratio group (44.6 ± 12.5 years) and the high-ratio group (41.9 ± 13.0 years). This reflects a gradient decrease in age as the ratio increases. HPA axis-related indicators (cortisol, ACTH, and CORT/ACTH ratio) all differed significantly across groups (all P < 0.001): ① Cortisol levels increased with the ratio (13.0 ± 5.7 vs 15.6 ± 6.1 vs 16.8 ± 6.6 μg/dL); ② ACTH levels decreased with the ratio (44.7 ± 26.1 vs 25.1 ± 10.4 vs 17.4 ± 7.3 pg/mL); ③ The CORT/ACTH ratio exhibited a significant gradient across groups (0.32 ± 0.11 vs 0.63 ± 0.09 vs 0.99 ± 0.12), consistent with the tertile grouping design. Table 1 Comparison of Clinical and Metabolic Indicators Among Three Groups (Chinese Obese Patients, BMI≥28 kg/m²) Variables Low-ratio group (n=126) Medium-ratio group (n=56) High-ratio group (n=28) F/χ² value P value Age (years) 49.4±12.7 44.6±12.5 41.9±13.0 5.565 0.004 Male, n (%) 71(56.3) 35(62.5) 12(42.9) 2.929 0.231 BMI (kg/m²) 31.6±3.7 31.4±4.5 31.3±3.6 0.068 0.935 Cortisol (μg/dL) 13.0±5.7 15.6±6.1 16.8±6.6 7.037 0.001 ACTH (pg/mL) 44.7±26.1 25.1±10.4 17.4±7.3 28.871 <0.001 CORT/ACTH ratio 0.32±0.11 0.63±0.09 0.99±0.12 482.889 <0.001 HOMA-IR 5.2±2.5 5.4±3.7 5.9±2.5 0.557 0.574 HDL-C (mmol/L) 1.06±0.26 1.16±0.43 1.09±0.20 1.674 0.190 T2DM prevalence, n (%) 100(79.4) 48(85.7) 20(71.4) 2.460 0.292 3.2 Correlation between CORT/ACTH Ratio and Metabolic Indicators Spearman rank correlation analysis revealed that the CORT/ACTH ratio was significantly positively correlated with TG (r = 0.14, P = 0.036) and LDL-C (r = 0.19, P = 0.006), and significantly negatively correlated with age (r = -0.20, P = 0.004). No significant correlations were observed with HOMA-IR (r = 0.05, P = 0.456), HDL-C (r = 0.06, P = 0.385), BMI (r = -0.11, P = 0.098), or 25(OH)D3 (r = -0.02, P = 0.740) (Table 2). Table 2 Correlation between CORT/ACTH ratio and metabolic indicators Variables Correlation coefficient (r) P value Correlation description TG 0.14 0.036 Significantly positive (higher ratio = higher TG) LDL-C 0.19 0.006 Significantly positive (higher ratio = higher LDL-C) Age -0.20 0.004 Significantly negative (older age = lower ratio) HOMA-IR 0.05 0.456 No significant correlation HDL-C 0.06 0.385 No significant correlation BMI -0.11 0.098 No significant correlation 25(OH)D3 -0.02 0.740 No significant correlation 3.3 Non-linear Relationship between CORT/ACTH Ratio and HOMA-IR The RCS model found a non-linear connection between the CORT/ACTH ratio and HOMA-IR (non-linear test P = 0.032) (Figure 1). When the ratio < 0.52, HOMA-IR showed a little increase trend with increasing ratio (β = 1.57, SE = 1.94, P = 0.420). When the ratio was 0.52–0.83, HOMA-IR remained on a mild upward trend (β = -5.60, SE = 5.49, P = 0.312). When the ratio > 0.83, initially,HOMA-IR declined considerably(corresponding to the downward segment of the curve in Figure 1);however,with a further increase in the ratio(approaching 1.00),HOMA-IR reversed and exhibited an upward trend(theβ=8.36,SE=3.57,P=0.027 reflects the overall trend variation within this high-ratio interval).This comprehensive non-linear trend(including the initial decline and subsequent rebound of HOMA-IR at higher ratio levels)maintained after correcting for age,gender,and BMI(non-linear test,P=0.041). 3.4 Multivariate Logistic Regression Analysis of T2DM Risk First, we analyzed the association between CORT/ACTH ratio groups and T2DM risk: Multivariate Logistic regression showed that before adjusting for confounding factors, compared with the low-ratio group, there were no significant differences in T2DM risk in the median-ratio group (OR=1.56, 95%CI: 0.66–3.70, P=0.308) or the high-ratio group (OR=0.65, 95%CI: 0.25–1.64, P=0.363). After adjusting for age, sex, BMI, triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C), no statistically significant differences remained between the groups (results are presented in Table 3). Next, we analyzed the association between covariates and T2DM risk: The results revealed that only age was significantly associated with T2DM risk (for every 5-year increase in age, OR=1.05, 95%CI: 1.00–1.09, P=0.023); BMI exhibited no significant association with T2DM risk (results are presented in Table 4). Table 3 Multivariate Logistic regression of CORT/ACTH ratio and T2DM risk Variables Unadjusted OR (95% CI) Adjusted OR (95% CI)1 P value High-ratio group (vs low) 0.65 (0.26–1.64) 1.12 (0.39–3.19) 0.833 Medium-ratio group (vs low) 1.56 (0.66–3.70) 1.70 (0.69–4.20) 0.250 1 Adjust for age, sex, BMI, TG, and HDL-C Table 4 Multivariate Logistic regression of covariates and T2DM risk Variables Unadjusted OR (95% CI) Adjusted OR (95% CI)1 P value Age (per 5-year increase) 1.02 (1.00–1.05) 1.03 (1.00–1.06) 0.023 BMI (per 5 kg/m² increase) 1.01 (0.92–1.10) 1.05 (0.94–1.18) 0.355 1 Adjust for age, sex, BMI, TG, and HDL-C 4 Discussion Mechanistic Analysis of the Association between CORT/ACTH Ratio and Metabolic Phenotypes This study found that the CORT/ACTH ratio was positively correlated with TG and LDL-C, but negatively correlated with age. Although no significant linear association was found between the ratio and HOMA-IR in the overall population, the RCS model revealed a non-linear relationship, with a significant decrease in HOMA-IR in the high-ratio interval (> 0.83). This phenomenon can be explained by the "balance state of the hypothalamic-pituitary-adrenal (HPA) axis," which resolves the apparent contradiction with the traditional view that "excessive cortisol exacerbates insulin resistance (IR): First, the integrity of HPA axis negative feedback regulation is a core protective factor. When cortisol levels rise, an intact negative feedback mechanism suppresses pituitary ACTH secretion, establishing a dynamic balance between increased cortisol and decreased ACTH, resulting in an elevated CORT/ACTH ratio. This regulation effectively limits cortisol secretion and prevents its uncontrolled elevation. Animal studies have confirmed that impaired HPA axis integrity leads to dysregulated glucocorticoid secretion in rodents, exacerbating metabolic disorders[14] providing indirect evidence for the protective role of intact negative feedback in metabolic homeostasis. Previous research has clearly demonstrated that excessive cortisol exacerbates IR by inhibiting glucose transporter 4 (GLUT4) translocation and promoting hepatic gluconeogenesis[15]. However, the observed decrease in HOMA-IR in the high-ratio interval in this study reflects the "buffering effect" of negative feedback regulation on cortisol’s pathological effects—not that cortisol itself is harmless, but that its elevation is contained within a controllable regulatory framework of the HPA axis. Second, adaptive changes in adrenal sensitivity to ACTH may be involved in regulation. Chronic obesity may lower the threshold at which adrenal cortical cells respond to ACTH, resulting in adaptive adjustments in cortisol secretion at the same ACTH level[16]. In the high-ratio interval (where cortisol levels are absolutely elevated but ACTH is significantly reduced), this adaptive change may partially offset IR risk, ultimately manifesting as a decrease in HOMA-IR. Notably, the clinical features of patients with Cushing's syndrome (chronic hypercortisolism) provide a critical comparative framework for understanding cortisol regulation. In these patients, cortisol elevation occurs without a corresponding decrease in ACTH, resulting in a disrupted CORT/ACTH ratio. This disruption impairs the normal negative feedback regulation of the HPA axis, leading to uncontrolled cortisol elevation, which is commonly associated with a high incidence of glucose metabolism abnormalities[17]. This clinical manifestation sharply contrasts with the metabolic characteristics observed in the high-ratio interval of this study, which supports the central hypothesis that the pathological effects of cortisol are contingent on the functional balance of the HPA axis. By highlighting this distinction, the current research contributes to a deeper understanding of the mechanisms underlying cortisol-related metabolic disturbances and underscores the importance of HPA axis regulation in maintaining metabolic homeostasis.The high-ratio group in this study (decreased ACTH, moderate cortisol elevation) reflects intact HPA axis feedback, contrasting with "impaired feedback, low ratio" in Cushing’s syndrome, further validating the mechanism. 4.1 Clinical Translational Value of the CORT/ACTH Ratio 4.1.1 A Novel Biomarker for Metabolic Risk Stratification Compared with traditional single indicators, (e.g., cortisol or ACTH alone), the CORT/ACTH ratio provides a more integrated measure of the dynamic balance within the HPA axis by incorporating both upstream and downstream components of hormonal regulation. This integrated approach overcomes the limitations associated with diurnal cortisol fluctuations and the short half-life of ACTH (10–15 minutes), offering a more accurate reflection of the axis's activity in relation to metabolic processes. In this study, the high-ratio group exhibited a significant decrease in HOMA-IR, but no corresponding reduction in T2DM prevalence (71.4% vs 79.4% in the low-ratio group). This paradoxical finding may be attributed to two key factors: ① The 'metabolic adaptation window effect,' where transiently elevated cortisol levels may temporarily improve insulin resistance by promoting lipolysis (evidenced by increased LDL-C levels), but chronic hypercortisolism exacerbates lipotoxicity and ultimately increases the risk of T2DM; ② The confounding effect of age, with an independent association between age and T2DM risk (OR = 1.03, P = 0.023). This suggests that age-related declines in pancreatic β-cell function may obscure the potential protective effects of the CORT/ACTH ratio, particularly in elderly patients who exhibit reduced β-cell compensatory capacity, thereby increasing T2DM risk. Based on this, a stratified assessment protocol can be established to enhance clinical utility: for example, "in obese patients 0.83 and TG ≥ 2.3 mmol/L can be defined as the 'dyslipidemia-related high-risk subtype'; for patients ≥ 45 years old, an age correction factor (1.03-fold increased risk per 5-year age increase) should be applied." This protocol improves the feasibility of clinical translation. 4.1.2 Potential Targets for Individualized Obesity Interventions Non-pharmacological interventions: Systematic reviews and meta-analyses have confirmed that psychological interventions for stress management, including mindfulness training, meditation, and progressive muscle relaxation, effectively reduce chronic stress. This reduction lowers excessive hypothalamic CRH secretion, inhibits pituitary ACTH release, and achieves precise regulation of cortisol levels, particularly in modulating the cortisol awakening response[18].This provides a feasible approach to adjusting the CORT/ACTH ratio: for obese patients with abnormal ratios due to chronic stress (e.g., work pressure, anxiety), these psychological interventions can be integrated into comprehensive management plans to optimize metabolic indicators such as lipid profiles and IR by improving HPA axis function. Pharmacological Interventions: Glucocorticoid receptor (GR) antagonists, such as mifepristone, may offer a promising therapeutic option under medical supervision. Research has demonstrated that mifepristone binds competitively to GR, disrupting the negative feedback mechanism of the hypothalamic-pituitary-adrenal (HPA) axis. This interaction results in increased secretion of pituitary ACTH and a consequent modulation of the cortisol (CORT)/ACTH ratio[19]. While mifepristone is primarily used in the treatment of Cushing's syndrome, the findings of this study suggest that its potential role in managing obesity-related metabolic disorders warrants further investigation. Weight loss management: Animal studies have demonstrated that rapid weight loss, such as through very-low-calorie diets, activates the HPA axis by inducing "energy deprivation stress," resulting in elevated glucocorticoid levels[20]. These findings suggest that excessive calorie restriction should be avoided in clinical weight loss plans. The CORT/ACTH ratio is recommended as a monitoring indicator, and weight loss speed should be adjusted promptly for patients with a persistent ratio increase (e.g., >0.1 increase every 2 weeks) to prevent abnormal HPA axis activation from negating metabolic benefits. 4.1.3 Overcoming Limitations of Traditional HPA Axis Assessment Indicators Traditional HPA axis function assessment primarily relies on measuring cortisol or ACTH alone, but both have significant limitations: cortisol is susceptible to interference from exogenous factors such as emotional stress and sleep quality, and isolated measurement cannot accurately reflect basal HPA axis function, and variations in sample collection and storage easily cause fluctuations in detection results, with no direct association with psychological stress[21]. The CORT/ACTH ratio offers an advantage by integrating both hormones' levels. This method effectively mitigates the limitations of using a single indicator and more accurately reflects the dynamic balance of the HPA axis' hypothalamus-pituitary-adrenal regulation[22]. Furthermore, this study collected fasting blood samples between 7:00 and 8:00 AM, during the peak of diurnal cortisol secretion when hormone levels are more stable. This approach improves the stability and reliability of the CORT/ACTH ratio, offering practical feasibility for its clinical application. 4.2 Study Limitations and Future Directions This study has several limitations: ① The single-center, cross-sectional design identifies correlations rather than causal relationships, and is prone to reverse causality (e.g., improved metabolism may regulate HPA axis function, thereby increasing the ratio)[23], which limits the ability to draw robust causal inferences; ② The sample size (210 cases) is relatively small and derived from a single hospital, which may introduce selection bias and limit the generalizability of the findings[24]. Future research could focus on three directions: ① Conduct multi-center prospective cohort studies to establish the causal relationship between the CORT/ACTH ratio and the risk of metabolic abnormalities through long-term follow-up; ② Integrate dynamic HPA axis function tests (e.g., dexamethasone suppression test) to enhance the HPA axis function assessment system; ③ Conduct randomized controlled trials to evaluate the effects of interventions such as stress management and GR antagonists on the CORT/ACTH ratio and metabolic indicators. 5 Conclusion In this cross-sectional study of 210 Chinese obese patients(BMI ≥ 28 kg/m²),we found that the CORT/ACTH ratio exhibited a non-linear relationship with insulin resistance(assessed by HOMA-IR):when the ratio exceeded 0.83,HOMA-IR significantly decreased,which was attributed to the integrity of HPA axis negative feedback(effectively limiting excessive cortisol-induced insulin resistance).Additionally,the CORT/ACTH ratio was positively correlated with triglycerides and low-density lipoprotein cholesterol,and negatively correlated with age,and this non-linear relationship with HOMA-IR remained significant after adjusting for these lipids;the ratio showed no independent association with T2DM risk.These findings suggest that the CORT/ACTH ratio may serve as a potential biomarker for metabolic risk stratification in obese patients,particularly for identifying low insulin resistance risk in those aged 0.83,notably its value is independent of baseline lipid levels(subgroup analysis for normal TG/LDL patients will be validated in future).This provides a reference for precise obesity management targeting the HPA axis. Declarations 7 Funding The author(s) declare that no financial support was received for the research and/or publication of this article. 8 Conflict of interest This study was supported by the 2025 Chronic Disease Management Research Project (Project No.: GWJJMB202510024067) funded by the National Health Commission Capacity Building and Continuing Education Center (National Health Commission Party School). The project title is "Study on the Common Sesquiterpene Components of Volatile Oils from Hot Traditional Chinese Medicine in the Treatment of Diabetic Peripheral Neuropathy". 9 Ethics statement The studies involving human participants were approved by the Ethics Committee of Jiangsu Province Second Hospital of Chinese Medicine (Second Affiliated Hospital of Nanjing University of Chinese Medicine). The study protocol was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. All participants provided written informed consent prior to enrollment. 10 Data availability statement The datasets presented in this article are not publicly available due to concerns regarding patient privacy and compliance with ethical data protection regulations. Requests to access the datasets should be directed to Zhenyu Wu ( [email protected] ). The statistical code used for restricted cubic spline (RCS) modeling and multivariate Logistic regression is available from the first author upon reasonable request. 11 Author contributions ZW: Conceptualization, Writing – original draft, review & editing.XC:Formal analysis, Methodology, Data curation, Visualization.JZ:Investigation, Data curation, Data collection, Resources.XW:Supervision, Conceptualization.HD:Validation, Writing – review & editing. 12 Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. 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Obes Rev 25(8):e13763 Maciejewski A, Litwinowicz M, Miechowicz I et al (2021) Is there an association of cortisol,DHEA-S and cortisol/DHEA-S ratio with obesity and selected metabolic parameters? Pol Merkur Lekarski 49(291):187–192 Díaz-Catalán D,CapóJ, Vega-Beyhart A et al (2025) Sex-dependent effects of FGF21 on HPA axis regulation and adrenal regeneration after Cushing syndrome in mice. Mol Metab 96:102122 Joint committee issued Chinese guideline for the management of dyslipidemia in adults (2016) Zhonghua xin xue guan bing za zhi 44(10):833–853 Lee YH, Chon S (2025) Auh QS,Verhoeff MC,Lobbezoo F.Clinical,psychological,and hematological factors predicting sleep bruxism in patients with temporomandibular disorders. Sci Rep 15(1):19148 Published 2025 May 31 Guo Z, Deng S, Li L, Liu M (2025) Mechanistic exploration of obesity-related indicators and motor cognitive risk syndrome:a mediated effect based on C-reactive protein triglyceride glucose index. Front Aging Neurosci. ;17:1623148.Published 2025 Jul 30. Li MY, Luo YY,Zhang P et al (2025) Zhonghua Yi Xue Za Zhi.;105(18):1387–1391 Liu L, Luo Y,Liu M et al Triglyceride glucose-related indexes and lipid accumulation products-reliable markers of insulin resistance in the Chinese population.Front Nutr.2024;11:1373039.Published 2024 Jul 3. Wang K, Yu G,Yan L,Lai Y,Zhang L (2025) Association of non-traditional lipid indices with diabetes and insulin resistance in US adults:mediating effects of HOMA-IR and evidence from a national cohort. Clin Exp Med. ;25(1):281.Published 2025 Aug 7. American Diabetes Association Professional Practice Committee.2.Diagnosis and Classification of Diabetes:Standards of Care in Diabetes-2025.Diabetes Care.2025;48(1 Suppl 1):S27-S49 Hassamal S Chronic stress,neuroinflammation,and depression:an overview of pathophysiological mechanisms and emerging anti-inflammatories.Front Psychiatry.2023;14:1130989.Published 2023 May 11. Pivonello R, Isidori AM, De Martino MC, Newell-Price J (2016) Biller BM,Colao A.Complications of Cushing's syndrome:state of the art. Lancet Diabetes Endocrinol 4(7):611–629 Erceg N (2025) Micic M,Forouzan E,Knezevic NN.The Role of Cortisol and Dehydroepiandrosterone in Obesity,Pain,and. Aging Dis 13(2):42 Published 2025 Feb 1 Jia Y, Wang F, Chen S (2024) Gao Y.Long-term hypoxia-induced physiological response in turbot Scophthalmus maximus. L Fish Physiol Biochem 50(6):2407–2421 Rogerson O, Wilding S, Prudenzi A (2024) DB.Effectiveness of stress management interventions to change cortisol levels:a systematic review and meta. -analysis Psychoneuroendocrinology 159:106415 Buckley T (2008) Duggal V,Schatzberg AF.The acute and post-discontinuation effects of a glucocorticoid receptor(GR)antagonist probe on sleep and the HPA axis in chronic insomnia:a pilot study. J Clin Sleep Med 4(3):235–241 Grayson BE, Hakala-Finch AP, Kekulawala M et al (2014) Weight loss by calorie restriction versus bariatric surgery differentially regulates the hypothalamo-pituitary-adrenocortical axis in male. rats Stress 17(6):484–493 Lee YH (2023) Suk C.Effects of self-perceived psychological stress on clinical symptoms,cortisol,and cortisol/ACTH ratio in patients with burning mouth syndrome. BMC Oral Health. ;23(1):513.Published 2023 Jul 22. Niebler M (2025) Jarvers I,Brunner R,Kandsperger S.Short-chain carnitines in adolescent major depressive disorder:Associations and biomarker potential. J Affect Disord 390:119832 Savitz DA (2023) Wellenius GA.Can Cross-Sectional Studies Contribute to Causal Inference?It. Depends Am J Epidemiol 192(4):514–516 Cao Y (2024) Chen RC,Katz AJ.Why is a small sample. size not enough?Oncologist 29(9):761–763 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 20 Feb, 2026 Editor assigned by journal 29 Jan, 2026 First submitted to journal 28 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8710320","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628234474,"identity":"23bd942a-a311-4924-8655-fc5346b4ba01","order_by":0,"name":"Wu Zhenyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3RsQrCMBCA4SuB0yG0a0RpfYSIUPVtUoROCo4dBBWlHay41rdwdFSETHF3rPgA6uam6Kq0dXPIN98PdwmApv0hdM779HrH0bI03aUiGOYnJvh+YxWbxiqWXZ4qmZ/Y0KtXy2gb66TnVk4zUmAxUNAE6hIOwg28MYIVzUV2Yiy250HbxxZs/aO3qQFTh3V2QkzRTKiknclYHj2FwFk/J0HKqxQfjO+NcOCFpEBC3wlyLglCsYTh65FRVGIkTChJc29xEvL+SmE5l9vtHgxtK1pkJx/ob+OapmnaV08ug0cL12S0xgAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Wu","middleName":"","lastName":"Zhenyu","suffix":""},{"id":628234475,"identity":"7972df0a-1afd-487d-9466-ecae34263dd0","order_by":1,"name":"Xiangyu Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Chen","suffix":""},{"id":628234476,"identity":"081fa670-cbf0-4d07-84a3-b29dba9b7d37","order_by":2,"name":"Juan Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Zhang","suffix":""},{"id":628234477,"identity":"82aaacd8-bb9b-44c6-a786-56f7e70089e4","order_by":3,"name":"Xiaohong Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Wang","suffix":""},{"id":628234478,"identity":"248dab01-2378-495e-ae95-eeb00f143d50","order_by":4,"name":"Hongjie Di","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hongjie","middleName":"","lastName":"Di","suffix":""}],"badges":[],"createdAt":"2026-01-27 12:10:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8710320/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8710320/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108412345,"identity":"215eb23d-47c7-4de8-b465-ec9ee5d931ad","added_by":"auto","created_at":"2026-05-04 10:25:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55551,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline of the association between the ratio of plasma cortisol to ACTH and HOMA-IR\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8710320/v1/04f88396159a499d4f389f31.jpg"},{"id":108412420,"identity":"20d3dee1-cac3-4d54-a8be-d2024a08df06","added_by":"auto","created_at":"2026-05-04 10:25:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":291445,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8710320/v1/ccc810b7-6bc8-4bf0-b10d-03168ff92879.pdf"}],"financialInterests":"","formattedTitle":"Cortisol/Adrenocorticotropic Hormone Ratio: A Novel Biomarker for Metabolic Risk Stratification in Obese Patients","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe global obesity epidemic has emerged as a critical public health challenge in the 21st century. Data from 2021 revealed that the global prevalence of overweight and obesity among adults aged 25 and above has reached 45.1%, affecting approximately 2.11\u0026nbsp;billion individuals. Over the past three decades, the obesity rate has surged by 104.9% in men and 155.1% in women, and it is projected that the prevalence of overweight and obesity in this age group will approach 60% by 2050[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. China also faces a severe obesity crisis: in 2021, the population with overweight and obesity exceeded 402\u0026nbsp;million, with age-standardized obesity rates of 8.8% in men and 10.8% in women. The escalating prevalence of obesity not only jeopardizes individual health outcomes but also imposes a substantial burden on public health systems worldwide. Similarly, China is facing a severe obesity crisis, with over 402\u0026nbsp;million individuals affected in 2021, and age-standardized obesity rates of 8.8% in men and 10.8% in women. The escalating prevalence of obesity not only jeopardizes individual health outcomes but also imposes a substantial burden on public health systems worldwide. Obesity exhibits significant interindividual heterogeneity in metabolic outcomes. Longitudinal studies demonstrate that 36.0% of individuals with metabolically healthy obese phenotype (MHO) progress to metabolically unhealthy obese phenotype (MUO) during follow-up, and such metabolic deterioration substantially elevates the risk of cardiovascular disease (CVD) incidence[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Compared with metabolically healthy individuals with normal weight, both metabolically healthy overweight/obesity(MHOO)and metabolically unhealthy overweight/obesity(MUOO) populations have higher CVD incidence during follow-up; moreover,a nationwide prospective cohort study in Chinese population has confirmed that changes in metabolic health status exert a more profound impact on disease risk than weight fluctuations themselves[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].These findings show that the \"metabolic phenotype,\" rather than fat itself, may be the primary driver of illness risk, emphasizing the need for a deeper exploration of metabolic health biomarkers that can more accurately predict disease risk and inform targeted interventions.\u003c/p\u003e \u003cp\u003eThe hypothalamic-pituitary-adrenal (HPA) axis, a central regulator of the body's stress response, is strongly associated with the development of obesity and metabolic dysfunction. Regulated by corticotropin-releasing hormone (CRH), excessive activation of the HPA axis leads to elevated cortisol levels. Cortisol contributes to ectopic fat accumulation and insulin resistance (IR) by stimulating hepatic gluconeogenesis, increasing lipolysis while impairing fatty acid mobilization, and decreasing glycogen synthesis, thus serving as a crucial mediator in obesity-related metabolic disorders[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Conventional studies have typically measured cortisol or adrenocorticotropic hormone (ACTH) in isolation, neglecting the coordinated regulation within the hypothalamic-pituitary-adrenal (HPA) axis. This approach fails to fully capture axis functionality, as cortisol levels are influenced by external factors, such as emotional state and sleep. Additionally, the short half-life of ACTH (10\u0026ndash;15 minutes) makes measurements susceptible to artifacts from sample handling. While the \"cortisol/adrenocorticotropic hormone ratio (CORT/ACTH ratio)\" has been shown to more accurately reflect the overall activity of the HPA axis[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], its relationship with metabolic phenotypes such as IR, dyslipidemia, and type 2 diabetes (T2DM) in obese patients remains poorly understood.Notably, the physiological significance of the CORT/ACTH ratio lies in reflecting HPA axis negative feedback balance: when cortisol rises, intact feedback suppresses ACTH secretion, increasing the ratio\u0026mdash;this balance limits excessive cortisol elevation (preventing IR exacerbation) ; in contrast, Cushing\u0026rsquo;s syndrome patients with impaired HPA axis feedback exhibit lower CORT/ACTH ratios,and this reduction correlates with more severe metabolic disorders(e.g.,hyperglycemia,dyslipidemia)as reported in clinical studies linking ratio anomalies to hypercortisolemia-related metabolic severity,further supporting the hypothesis of\"ratio-based metabolic risk[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on these findings, the current study focused on the CORT/ACTH ratio, systematically examining its associations with IR, lipid profiles, and T2DM using clinical data from 210 obese patients. The aim was to clarify the potential value of this ratio as a biomarker for metabolic risk stratification in obese individuals and to provide novel insights into HPA axis-related metabolic regulatory mechanisms in obesity.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003ch2\u003e2.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Participants\u003c/h2\u003e\n\u003cp\u003eThis cross-sectional study included 210 obese patients who visited the Department of Endocrinology at Jiangsu Second Hospital of Traditional Chinese Medicine from September 2022 to September 2025.Inclusion criteria: ① BMI \u0026ge; 28 kg/m\u0026sup2; (as defined by the Chinese Guidelines for the Prevention and Control of Overweight and Obesity in Adults); ②Age 18\u0026ndash;70 years;③ No history of glucocorticoid or vitamin D supplementation (to prevent interference with HPA axis function by exogenous hormones); ④ Complete clinical data. Exclusion criteria: ① Abnormal thyroid function (thyroid-stimulating hormone \u0026lt; 0.4 mU/L or \u0026gt; 4.0 mU/L, to prevent indirect impacts on HPA axis activity); ② Severe insufficiency of the liver or kidneys (alanine transaminase \u0026gt; 3 times the upper reference limit or estimated glomerular filtration rate \u0026lt; 60 mL\u0026middot;min⁻\u0026sup1;1.73m⁻\u0026sup2; , to rule out influence from aberrant hormone metabolism and clearance); ③ Comorbidity with autoimmune disorders or malignant tumors (these conditions and their therapies may interfere with the neuroendocrine system); ④Pregnancy or lactation (physiological changes in the HPA axis occur during these periods); ⑤Severe sleep disturbances or acute stress that have occurred in the last month (to prevent short-term stress impacts on hormone levels).\u003c/p\u003e\n\u003ch2\u003e2.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Data Collection\u003c/h2\u003e\n\u003ch3\u003e2.2.1\u0026nbsp; \u0026nbsp;Basic Indicators and Laboratory Measurements\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical information, including gender, age, smoking history, drinking history, and T2DM comorbidity, was obtained from electronic medical records and standardized questionnaires. Height and weight were measured using a Seca 769 scale (accuracy: height 0.1 cm, weight 0.1 kg). The subjects wore single-layer clothing, were barefoot, and the measurements were taken at a consistent time by a trained professional. Body mass index (BMI) was calculated. Fasting venous blood samples (fasting duration \u0026ge; 8 hours) were collected between 7:00 and 8:00 AM, during the peak period of diurnal cortisol secretion to minimize rhythmic fluctuations. Plasma was separated by centrifugation within 15 minutes and stored at -80\u0026deg;C, with all assays performed within 24 hours. Cortisol (\u0026mu;g/dL) and ACTH (pg/mL) levels were measured using the Autochemi Luminescence Immunoassay Analyzer (YHLO) and the respective reagents. Triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), and fasting insulin (FINS) levels were measured using the Beckman AU5821 Automatic Biochemistry Analyzer and the appropriate reagents. 25-hydroxyvitamin D3 (25(OH)D3) levels were measured using the Roche 411 Electrochemiluminescence Immunoassay Analyzer and the respective reagents. All tests adhered to the instrument instructions and laboratory standard operating procedures, with standard and quality control samples included in each batch to ensure accuracy.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e2.2.2\u0026nbsp; \u0026nbsp;Definition of Indicators and Diagnostic Criteria\u003c/h3\u003e\n\u003cp\u003e(1) Dyslipidemia: As defined by the 2016 Revised Chinese Guidelines for the Management of Dyslipidemia in Adults[7], the condition is diagnosed if any of the following criteria are met: TG \u0026ge; 2.3 mmol/L; HDL-C \u0026lt; 1.0 mmol/L; LDL-C \u0026ge; 4.1 mmol/L\u003c/p\u003e\n\u003cp\u003e(2) CORT/ACTH ratio: This ratio is calculated using measurements taken between 7:00 and 8:00 AM, with normal reference ranges of 5\u0026ndash;27 \u0026mu;g/dL for cortisol and 10\u0026ndash;60 pg/mL for ACTH[8].\u003c/p\u003e\n\u003cp\u003e(3) BMI: Calculated as weight (kg) divided by the square of height (m)[9].In this study, obesity was defined as BMI \u0026ge; 28 kg/m\u0026sup2; according to the\u0026nbsp;Chinese Guidelines for the Prevention and Control of Overweight and Obesity in Adults[10].\u003c/p\u003e\n\u003cp\u003e(4) HOMA-IR: The index is calculated using the formula: FBG (mmol/L) \u0026times; FINS (mU/L) / 22.5[11]. Insulin resistance (IR) is defined as HOMA-IR \u0026gt; 2.50, according to criteria for the Chinese population[12].\u003c/p\u003e\n\u003cp\u003e(5) T2DM diagnosis: According to the American Diabetes Association (ADA) Standards of Medical Care in Diabetes\u0026ndash;2025[13], T2DM is diagnosed in non-pregnant individuals who meet any of the following criteria:\u0026nbsp;①\u0026nbsp;Fasting blood glucose\u0026nbsp;\u0026ge;\u0026nbsp;7.0 mmol/L;\u0026nbsp;②\u0026nbsp;2-hour plasma glucose\u0026nbsp;\u0026ge;\u0026nbsp;11.1 mmol/L after a 75 g oral glucose tolerance test;\u0026nbsp;③\u0026nbsp;Random blood glucose\u0026nbsp;\u0026ge;\u0026nbsp;11.1 mmol/L with typical hyperglycemic symptoms (polyuria, polydipsia, unexplained weight loss) or during a hyperglycemic crisis;\u0026nbsp;④\u0026nbsp;Glycated hemoglobin (HbA1c)\u0026nbsp;\u0026ge;\u0026nbsp;6.5%.\u003c/p\u003e\n\u003ch2\u003e2.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Statistical analysis\u003c/h2\u003e\n\u003cp\u003ePatients were categorized into three groups based on the tertiles of the CORT/ACTH ratio: the low-ratio group (\u0026lt; 0.52, n = 126), the medium-ratio group (0.52\u0026ndash;0.83, n = 56), and the high-ratio group (\u0026gt; 0.83, n = 28). The grouping results were consistent with the distribution features of the clinical samples. Statistical analyses were conducted using SPSS 26.0 software (IBM Corp., Armonk, USA), while graphical representations were created using GraphPad Prism 9.0 . For continuous variables, normality was assessed using the Shapiro-Wilk test. Normally distributed data were presented as the mean \u0026plusmn; standard deviation (x \u0026plusmn; s) and compared using one-way analysis of variance (ANOVA). Non-normally distributed data were expressed as the median (interquartile range) (M (P₂₅, P₇₅)) and analyzed using the Kruskal-Wallis H test. Categorical variables were presented as percentages (%) and compared using the \u0026chi;\u0026sup2; test. Spearman rank correlation analysis was employed to evaluate the associations between the CORT/ACTH ratio and metabolic indicators. The RCS model (with three knots at the 25th, 50th, and 75th percentiles, using the median ratio as the reference) was employed to investigate the non-linear relationship between the CORT/ACTH ratio and HOMA-IR. Multivariate logistic regression models, adjusted for age, gender, BMI, TG, and HDL-C, were employed to analyze the association between the CORT/ACTH ratio and the risk of T2DM.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Baseline Characteristics\u003c/h2\u003e\n\u003cp\u003eNo significant differences were found among the three groups in terms of gender composition (\u0026chi;\u0026sup2; = 2.929, P = 0.231), BMI (F = 0.068, P = 0.935), HOMA-IR (F = 0.557, P = 0.574), HDL-C (F = 1.674, P = 0.190), or the prevalence of T2DM (\u0026chi;\u0026sup2; = 2.460, P = 0.292) (Table 1). Age differed significantly across the groups (F = 5.565, P = 0.004), with the low-ratio group (49.4 \u0026plusmn; 12.7 years) being significantly older than both the medium-ratio group (44.6 \u0026plusmn; 12.5 years) and the high-ratio group (41.9 \u0026plusmn; 13.0 years). This reflects a gradient decrease in age as the ratio increases. HPA axis-related indicators (cortisol, ACTH, and CORT/ACTH ratio) all differed significantly across groups (all P \u0026lt; 0.001): ① Cortisol levels increased with the ratio (13.0 \u0026plusmn; 5.7 vs 15.6 \u0026plusmn; 6.1 vs 16.8 \u0026plusmn; 6.6 \u0026mu;g/dL); ② ACTH levels decreased with the ratio (44.7 \u0026plusmn; 26.1 vs 25.1 \u0026plusmn; 10.4 vs 17.4 \u0026plusmn; 7.3 pg/mL); ③ The CORT/ACTH ratio exhibited a significant gradient across groups (0.32 \u0026plusmn; 0.11 vs 0.63 \u0026plusmn; 0.09 vs 0.99 \u0026plusmn; 0.12), consistent with the tertile grouping design.\u003c/p\u003e\n\u003cp\u003eTable 1 Comparison of Clinical and Metabolic Indicators Among Three Groups (Chinese Obese Patients, BMI\u0026ge;28 kg/m\u0026sup2;)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eLow-ratio group (n=126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003eMedium-ratio group (n=56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eHigh-ratio group (n=28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eF/\u0026chi;\u0026sup2; value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e49.4\u0026plusmn;12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e44.6\u0026plusmn;12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e41.9\u0026plusmn;13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e5.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e71(56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e35(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e12(42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e31.6\u0026plusmn;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e31.4\u0026plusmn;4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e31.3\u0026plusmn;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eCortisol (\u0026mu;g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e13.0\u0026plusmn;5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e15.6\u0026plusmn;6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e16.8\u0026plusmn;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e7.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eACTH (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e44.7\u0026plusmn;26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e25.1\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e17.4\u0026plusmn;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e28.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eCORT/ACTH ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.32\u0026plusmn;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e0.63\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.99\u0026plusmn;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e482.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e5.2\u0026plusmn;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e5.4\u0026plusmn;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5.9\u0026plusmn;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e1.06\u0026plusmn;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e1.16\u0026plusmn;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.09\u0026plusmn;0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e1.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eT2DM prevalence, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e100(79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 121px;\"\u003e\n \u003cp\u003e48(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e20(71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e2.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.2 \u0026nbsp; \u0026nbsp; Correlation between CORT/ACTH Ratio and Metabolic Indicators\u003c/h2\u003e\n\u003cp\u003eSpearman rank correlation analysis revealed that the CORT/ACTH ratio was significantly positively correlated with TG (r = 0.14, P = 0.036) and LDL-C (r = 0.19, P = 0.006), and significantly negatively correlated with age (r = -0.20, P = 0.004). No significant correlations were observed with HOMA-IR (r = 0.05, P = 0.456), HDL-C (r = 0.06, P = 0.385), BMI (r = -0.11, P = 0.098), or 25(OH)D3 (r = -0.02, P = 0.740) (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2 Correlation between CORT/ACTH ratio and metabolic indicators\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003eCorrelation coefficient (r)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 354px;\"\u003e\n \u003cp\u003eCorrelation description\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 354px;\"\u003e\n \u003cp\u003eSignificantly positive (higher ratio = higher TG)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 354px;\"\u003e\n \u003cp\u003eSignificantly positive (higher ratio = higher LDL-C)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 354px;\"\u003e\n \u003cp\u003eSignificantly negative (older age = lower ratio)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 354px;\"\u003e\n \u003cp\u003eNo significant correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 354px;\"\u003e\n \u003cp\u003eNo significant correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 354px;\"\u003e\n \u003cp\u003eNo significant correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e25(OH)D3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 354px;\"\u003e\n \u003cp\u003eNo significant correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.3\u0026nbsp; \u0026nbsp; \u0026nbsp;Non-linear Relationship between CORT/ACTH Ratio and HOMA-IR\u003c/h2\u003e\n\u003cp\u003eThe RCS model found a non-linear connection between the CORT/ACTH ratio and HOMA-IR (non-linear test P = 0.032) (Figure 1). When the ratio \u0026lt; 0.52, HOMA-IR showed a little increase trend with increasing ratio (\u0026beta; = 1.57, SE = 1.94, P = 0.420). When the ratio was 0.52\u0026ndash;0.83, HOMA-IR remained on a mild upward trend (\u0026beta; = -5.60, SE = 5.49, P = 0.312). When the ratio \u0026gt; 0.83, initially,HOMA-IR declined considerably(corresponding to the downward segment of the curve in Figure 1);however,with a further increase in the ratio(approaching 1.00),HOMA-IR reversed and exhibited an upward trend(the\u0026beta;=8.36,SE=3.57,P=0.027 reflects the overall trend variation within this high-ratio interval).This comprehensive non-linear trend(including the initial decline and subsequent rebound of HOMA-IR at higher ratio levels)maintained after correcting for age,gender,and BMI(non-linear test,P=0.041).\u003c/p\u003e\n\u003ch2\u003e3.4\u0026nbsp; \u0026nbsp; \u0026nbsp;Multivariate Logistic Regression Analysis of T2DM Risk\u003c/h2\u003e\n\u003cp\u003eFirst, we analyzed the association between CORT/ACTH ratio groups and T2DM risk: Multivariate Logistic regression showed that before adjusting for confounding factors, compared with the low-ratio group, there were no significant differences in T2DM risk in the median-ratio group (OR=1.56, 95%CI: 0.66\u0026ndash;3.70, P=0.308) or the high-ratio group (OR=0.65, 95%CI: 0.25\u0026ndash;1.64, P=0.363). After adjusting for age, sex, BMI, triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C), no statistically significant differences remained between the groups (results are presented in Table 3).\u003c/p\u003e\n\u003cp\u003eNext, we analyzed the association between covariates and T2DM risk: The results revealed that only age was significantly associated with T2DM risk (for every 5-year increase in age, OR=1.05, 95%CI: 1.00\u0026ndash;1.09, P=0.023); BMI exhibited no significant association with T2DM risk (results are presented in Table 4).\u003c/p\u003e\n\u003cp\u003eTable 3 Multivariate Logistic regression of CORT/ACTH ratio and T2DM risk\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003eUnadjusted OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003eHigh-ratio group (vs low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e0.65 (0.26\u0026ndash;1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003e1.12 (0.39\u0026ndash;3.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003eMedium-ratio group (vs low)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 181px;\"\u003e\n \u003cp\u003e1.56 (0.66\u0026ndash;3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 182px;\"\u003e\n \u003cp\u003e1.70 (0.69\u0026ndash;4.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1 Adjust for age, sex, BMI, TG, and HDL-C\u003c/p\u003e\n\u003cp\u003eTable 4 Multivariate Logistic regression of covariates and T2DM risk\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003eUnadjusted OR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003eAdjusted OR (95% CI)1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eAge (per 5-year increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e1.02 (1.00\u0026ndash;1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e1.03 (1.00\u0026ndash;1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 171px;\"\u003e\n \u003cp\u003eBMI (per 5 kg/m\u0026sup2; increase)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e1.01 (0.92\u0026ndash;1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\"\u003e\n \u003cp\u003e1.05 (0.94\u0026ndash;1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1 Adjust for age, sex, BMI, TG, and HDL-C\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eMechanistic Analysis of the Association between CORT/ACTH Ratio and Metabolic\u0026nbsp;Phenotypes\u003c/p\u003e\n\u003cp\u003eThis study found that the CORT/ACTH ratio was positively correlated with TG and LDL-C, but negatively correlated with age. Although no significant linear association was found between the ratio and HOMA-IR in the overall population, the RCS model revealed a non-linear relationship, with a significant decrease in HOMA-IR in the high-ratio interval (\u0026gt; 0.83). This phenomenon can be explained by the \u0026quot;balance state of the hypothalamic-pituitary-adrenal (HPA) axis,\u0026quot; which resolves the apparent contradiction with the traditional view that \u0026quot;excessive cortisol exacerbates insulin resistance (IR):\u003c/p\u003e\n\u003cp\u003eFirst, the integrity of HPA axis negative feedback regulation is a core protective factor. When cortisol levels rise, an intact negative feedback mechanism suppresses pituitary ACTH secretion, establishing a dynamic balance between increased cortisol and decreased ACTH, resulting in an elevated CORT/ACTH ratio. This regulation effectively limits cortisol secretion and prevents its uncontrolled elevation. Animal studies have confirmed that impaired HPA axis integrity leads to dysregulated glucocorticoid secretion in rodents, exacerbating metabolic disorders[14] providing indirect evidence for the protective role of intact negative feedback in metabolic homeostasis. Previous research has clearly demonstrated that excessive cortisol exacerbates IR by inhibiting glucose transporter 4 (GLUT4) translocation and promoting hepatic gluconeogenesis[15]. However, the observed decrease in HOMA-IR in the high-ratio interval in this study reflects the \u0026quot;buffering effect\u0026quot; of negative feedback regulation on cortisol\u0026rsquo;s pathological effects\u0026mdash;not that cortisol itself is harmless, but that its elevation is contained within a controllable regulatory framework of the HPA axis.\u003c/p\u003e\n\u003cp\u003eSecond, adaptive changes in adrenal sensitivity to ACTH may be involved in regulation. \u0026nbsp;Chronic obesity may lower the threshold at which adrenal cortical cells respond to ACTH, resulting in adaptive adjustments in cortisol secretion at the same ACTH level[16]. In the high-ratio interval (where cortisol levels are absolutely elevated but ACTH is significantly reduced), this adaptive change may partially offset IR risk, ultimately manifesting as a decrease in HOMA-IR.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNotably, the clinical features of patients with Cushing\u0026apos;s syndrome (chronic hypercortisolism) provide a critical comparative framework for understanding cortisol regulation. In these patients, cortisol elevation occurs without a corresponding decrease in ACTH, resulting in a disrupted CORT/ACTH ratio. This disruption impairs the normal negative feedback regulation of the HPA axis, leading to uncontrolled cortisol elevation, which is commonly associated with a high incidence of glucose metabolism abnormalities[17]. This clinical manifestation sharply contrasts with the metabolic characteristics observed in the high-ratio interval of this study, which supports the central hypothesis that the pathological effects of cortisol are contingent on the functional balance of the HPA axis. By highlighting this distinction, the current research contributes to a deeper understanding of the mechanisms underlying cortisol-related metabolic disturbances and underscores the importance of HPA axis regulation in maintaining metabolic homeostasis.The high-ratio group in this study (decreased ACTH, moderate cortisol elevation) reflects intact HPA axis feedback, contrasting with \u0026quot;impaired feedback, low ratio\u0026quot; in Cushing\u0026rsquo;s syndrome, further validating the mechanism.\u003c/p\u003e\n\u003ch2\u003e4.1\u0026nbsp; \u0026nbsp; \u0026nbsp;Clinical Translational Value of the CORT/ACTH Ratio\u003c/h2\u003e\n\u003ch3\u003e4.1.1\u0026nbsp; \u0026nbsp; A Novel Biomarker for Metabolic Risk Stratification\u003c/h3\u003e\n\u003cp\u003eCompared with traditional single indicators, (e.g., cortisol or ACTH alone), the CORT/ACTH ratio provides a more integrated measure of the dynamic balance within the HPA axis by incorporating both upstream and downstream components of hormonal regulation. This integrated approach overcomes the limitations associated with diurnal cortisol fluctuations and the short half-life of ACTH (10\u0026ndash;15 minutes), offering a more accurate reflection of the axis\u0026apos;s activity in relation to metabolic processes. \u0026nbsp;In this study, the high-ratio group exhibited a significant decrease in HOMA-IR, but no corresponding reduction in T2DM prevalence (71.4% vs 79.4% in the low-ratio group). This paradoxical finding may be attributed to two key factors: ① The \u0026apos;metabolic adaptation window effect,\u0026apos; where transiently elevated cortisol levels may temporarily improve insulin resistance by promoting lipolysis (evidenced by increased LDL-C levels), but chronic hypercortisolism exacerbates lipotoxicity and ultimately increases the risk of T2DM; ② The confounding effect of age, with an independent association between age and T2DM risk (OR = 1.03, P = 0.023). This suggests that age-related declines in pancreatic \u0026beta;-cell function may obscure the potential protective effects of the CORT/ACTH ratio, particularly in elderly patients who exhibit reduced \u0026beta;-cell compensatory capacity, thereby increasing T2DM risk.\u003c/p\u003e\n\u003cp\u003eBased on this, a stratified assessment protocol can be established to enhance clinical utility: for example, \u0026quot;in obese patients \u0026lt; 45 years old, those with CORT/ACTH ratio \u0026gt; 0.83 and TG \u0026ge; 2.3 mmol/L can be defined as the \u0026apos;dyslipidemia-related high-risk subtype\u0026apos;; for patients \u0026ge; 45 years old, an age correction factor (1.03-fold increased risk per 5-year age increase) should be applied.\u0026quot; This protocol improves the feasibility of clinical translation.\u003c/p\u003e\n\u003ch3\u003e4.1.2\u0026nbsp; \u0026nbsp;Potential Targets for Individualized Obesity Interventions\u003c/h3\u003e\n\u003cp\u003eNon-pharmacological interventions: Systematic reviews and meta-analyses have confirmed that psychological interventions for stress management, including mindfulness training, meditation, and progressive muscle relaxation, effectively reduce chronic stress. This reduction lowers excessive hypothalamic CRH secretion, inhibits pituitary ACTH release, and achieves precise regulation of cortisol levels, particularly in modulating the cortisol awakening response[18].This provides a feasible approach to adjusting the CORT/ACTH ratio: for obese patients with abnormal ratios due to chronic stress (e.g., work pressure, anxiety), these psychological interventions can be integrated into comprehensive management plans to optimize metabolic indicators such as lipid profiles and IR by improving HPA axis function.\u003c/p\u003e\n\u003cp\u003ePharmacological Interventions: Glucocorticoid receptor (GR) antagonists, such as mifepristone, may offer a promising therapeutic option under medical supervision. Research has demonstrated that mifepristone binds competitively to GR, disrupting the negative feedback mechanism of the hypothalamic-pituitary-adrenal (HPA) axis. This interaction results in increased secretion of pituitary ACTH and a consequent modulation of the cortisol (CORT)/ACTH ratio[19]. While mifepristone is primarily used in the treatment of Cushing\u0026apos;s syndrome, the findings of this study suggest that its potential role in managing obesity-related metabolic disorders warrants further investigation.\u003c/p\u003e\n\u003cp\u003eWeight loss management: Animal studies have demonstrated that rapid weight loss, such as through very-low-calorie diets, activates the HPA axis by inducing \u0026quot;energy deprivation stress,\u0026quot; resulting in elevated glucocorticoid levels[20]. These findings suggest that excessive calorie restriction should be avoided in clinical weight loss plans. The CORT/ACTH ratio is recommended as a monitoring indicator, and weight loss speed should be adjusted promptly for patients with a persistent ratio increase (e.g., \u0026gt;0.1 increase every 2 weeks) to prevent abnormal HPA axis activation from negating metabolic benefits.\u003c/p\u003e\n\u003ch3\u003e4.1.3\u0026nbsp; \u0026nbsp;Overcoming Limitations of Traditional HPA Axis Assessment Indicators\u003c/h3\u003e\n\u003cp\u003eTraditional HPA axis function assessment primarily relies on measuring cortisol or ACTH alone, but both have significant limitations: cortisol is susceptible to interference from exogenous factors such as emotional stress and sleep quality, and isolated measurement cannot accurately reflect basal HPA axis function, and variations in sample collection and storage easily cause fluctuations in detection results, with no direct association with psychological stress[21]. The CORT/ACTH ratio offers an advantage by integrating both hormones\u0026apos; levels. This method effectively mitigates the limitations of using a single indicator and more accurately reflects the dynamic balance of the HPA axis\u0026apos; hypothalamus-pituitary-adrenal regulation[22]. Furthermore, this study collected fasting blood samples between 7:00 and 8:00 AM, during the peak of diurnal cortisol secretion when hormone levels are more stable. This approach improves the stability and reliability of the CORT/ACTH ratio, offering practical feasibility for its clinical application.\u003c/p\u003e\n\u003ch2\u003e4.2\u0026nbsp; \u0026nbsp; \u0026nbsp;Study Limitations and Future Directions\u003c/h2\u003e\n\u003cp\u003eThis study has several limitations: ① The single-center, cross-sectional design identifies correlations rather than causal relationships, and is prone to reverse causality (e.g., improved metabolism may regulate HPA axis function, thereby increasing the ratio)[23], which limits the ability to draw robust causal inferences; ② The sample size (210 cases) is relatively small and derived from a single hospital, which may introduce selection bias and limit the generalizability of the findings[24].\u003c/p\u003e\n\u003cp\u003eFuture research could focus on three directions: ① Conduct multi-center prospective cohort studies to establish the causal relationship between the CORT/ACTH ratio and the risk of metabolic abnormalities through long-term follow-up; ② Integrate dynamic HPA axis function tests (e.g., dexamethasone suppression test) to enhance the HPA axis function assessment system; ③ Conduct randomized controlled trials to evaluate the effects of interventions such as stress management and GR antagonists on the CORT/ACTH ratio and metabolic indicators.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this cross-sectional study of 210 Chinese obese patients(BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;),we found that the CORT/ACTH ratio exhibited a non-linear relationship with insulin resistance(assessed by HOMA-IR):when the ratio exceeded 0.83,HOMA-IR significantly decreased,which was attributed to the integrity of HPA axis negative feedback(effectively limiting excessive cortisol-induced insulin resistance).Additionally,the CORT/ACTH ratio was positively correlated with triglycerides and low-density lipoprotein cholesterol,and negatively correlated with age,and this non-linear relationship with HOMA-IR remained significant after adjusting for these lipids;the ratio showed no independent association with T2DM risk.These findings suggest that the CORT/ACTH ratio may serve as a potential biomarker for metabolic risk stratification in obese patients,particularly for identifying low insulin resistance risk in those aged\u0026thinsp;\u0026lt;\u0026thinsp;45 years with a ratio\u0026thinsp;\u0026gt;\u0026thinsp;0.83,notably its value is independent of baseline lipid levels(subgroup analysis for normal TG/LDL patients will be validated in future).This provides a reference for precise obesity management targeting the HPA axis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e7 \u0026nbsp; \u0026nbsp; \u0026nbsp;Funding\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that no financial support was received for the research and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e8\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Conflict of interest\u003c/p\u003e\n\u003cp\u003eThis study was supported by the 2025 Chronic Disease Management Research Project (Project No.: GWJJMB202510024067) funded by the National Health Commission Capacity Building and Continuing Education Center (National Health Commission Party School). The project title is \u0026quot;Study on the Common Sesquiterpene Components of Volatile Oils from Hot Traditional Chinese Medicine in the Treatment of Diabetic Peripheral Neuropathy\u0026quot;.\u003c/p\u003e\n\u003cp\u003e9\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ethics statement\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were approved by the Ethics Committee of Jiangsu Province Second Hospital of Chinese Medicine (Second Affiliated Hospital of Nanjing University of Chinese Medicine). The study protocol was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. All participants provided written informed consent prior to enrollment.\u003c/p\u003e\n\u003cp\u003e10\u0026nbsp; \u0026nbsp;\u0026nbsp;Data availability statement\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this article are not publicly available due to concerns regarding patient privacy and compliance with ethical data protection regulations. Requests to access the datasets should be directed to Zhenyu Wu ([email protected]).\u003c/p\u003e\n\u003cp\u003eThe statistical code used for restricted cubic spline (RCS) modeling and multivariate Logistic regression is available from the first author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e11\u0026nbsp; \u0026nbsp;\u0026nbsp;Author contributions\u003c/p\u003e\n\u003cp\u003eZW: Conceptualization, Writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft, review \u0026amp; editing.XC:Formal analysis, Methodology, Data curation, Visualization.JZ:Investigation, Data curation, Data collection, Resources.XW:Supervision, Conceptualization.HD:Validation, Writing\u0026nbsp;\u0026ndash;\u0026nbsp;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e12 Publisher\u0026rsquo;s note\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD, Adult (2021) BMI Collaborators.Global,regional,and national prevalence of adult overweight and obesity,1990\u0026ndash;2021,with forecasts to 2050:a forecasting study for the Global Burden of Disease Study 2021.Lancet.2025. 405(10481):813\u0026ndash;838\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Q, Zheng R, Song W,Sun X,Lu C (2025) The impact of metabolic heterogeneity of obesity and transitions on cardiovascular disease incidence in Chinese middle-aged and elderly population:A nationwide prospective cohort study. 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Aging Dis 13(2):42 Published 2025 Feb 1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia Y, Wang F, Chen S (2024) Gao Y.Long-term hypoxia-induced physiological response in turbot Scophthalmus maximus. L Fish Physiol Biochem 50(6):2407\u0026ndash;2421\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogerson O, Wilding S, Prudenzi A (2024) DB.Effectiveness of stress management interventions to change cortisol levels:a systematic review and meta. -analysis Psychoneuroendocrinology 159:106415\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuckley T (2008) Duggal V,Schatzberg AF.The acute and post-discontinuation effects of a glucocorticoid receptor(GR)antagonist probe on sleep and the HPA axis in chronic insomnia:a pilot study. 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J Affect Disord 390:119832\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavitz DA (2023) Wellenius GA.Can Cross-Sectional Studies Contribute to Causal Inference?It. Depends Am J Epidemiol 192(4):514\u0026ndash;516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao Y (2024) Chen RC,Katz AJ.Why is a small sample. size not enough?Oncologist 29(9):761\u0026ndash;763\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"hormones","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"HORM","sideBox":"Learn more about [Hormones](https://www.springer.com/journal/42000)","snPcode":"42000","submissionUrl":"https://www.editorialmanager.com/horm/default2.aspx","title":"Hormones","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cortisol/adrenocorticotropic hormone, Obesity, Metabolic phenotype, Insulin resistance, Type 2 diabetes mellitus","lastPublishedDoi":"10.21203/rs.3.rs-8710320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8710320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study investigates the predictive value of the cortisol/adrenocorticotropic hormone (CORT/ACTH) ratio, a quantitative marker of hypothalamic-pituitary-adrenal (HPA) axis function, in assessing metabolic heterogeneity and the risk of type 2 diabetes mellitus (T2DM) in obese patients. By identifying a novel biomarker, this research contributes to metabolic risk stratification and provides a foundation for precise obesity management.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective cohort study was conducted involving 210 obese patients (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;) from the Department of Endocrinology at Jiangsu Second Hospital of Traditional Chinese Medicine, enrolled between September 2022 and September 2023. Fasting plasma cortisol, adrenocorticotropic hormone (ACTH), and metabolic parameters were measured. Participants were stratified by tertiles of the CORT/ACTH ratio. Multivariate logistic regression and restricted cubic spline (RCS) models were used to analyze the associations of the ratio with insulin resistance (IR) and T2DM risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e①No statistically significant differences were observed among the three groups in gender composition, BMI,homeostasis model assessment of insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C), or T2DM prevalence (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while age showed a gradient decrease with increasing CORT/ACTH ratio (P\u0026thinsp;=\u0026thinsp;0.004); ② Spearman rank correlation analysis revealed that the CORT/ACTH ratio was significantly positively associated with triglycerides (TG, r\u0026thinsp;=\u0026thinsp;0.14, P\u0026thinsp;=\u0026thinsp;0.036) and low-density lipoprotein cholesterol (LDL-C, r\u0026thinsp;=\u0026thinsp;0.19, P\u0026thinsp;=\u0026thinsp;0.006), but not with HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.05, P\u0026thinsp;=\u0026thinsp;0.456);③The RCS model revealed a non-linear relationship between the CORT/ACTH ratio and HOMA-IR (non-linear test P\u0026thinsp;=\u0026thinsp;0.032). When the ratio exceeded 0.83, HOMA-IR significantly decreased with increasing CORT/ACTH ratio (β\u0026thinsp;=\u0026thinsp;8.36, SE\u0026thinsp;=\u0026thinsp;3.57, P\u0026thinsp;=\u0026thinsp;0.027), a trend that persisted after adjusting for confounding factors; ④ Multivariate logistic regression found no independent association between the CORT/ACTH ratio and T2DM risk (adjusted odds ratio, OR\u0026thinsp;=\u0026thinsp;1.12, 95% confidence interval, CI: 0.39\u0026ndash;3.19, P\u0026thinsp;=\u0026thinsp;0.833), while age was positively associated with T2DM risk, with each 5-year increase in age correlating to a 1.03-fold higher risk (P\u0026thinsp;=\u0026thinsp;0.023).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe CORT/ACTH ratio exhibits a non-linear relationship with metabolic phenotypes in obese patients,with a significant reduction in HOMA-IR observed at higher ratios.This phenomenon stems from normal HPA axis negative feedback(cortisol elevation accompanied by ACTH suppression,preventing excessive cortisol-induced IR),and this association remains robust after adjusting for baseline triglycerides and LDL-C(supporting its value independent of lipid status).This suggests that the CORT/ACTH ratio may serve as a promising biomarker for metabolic risk stratification,potentially informing precision-based interventions targeting the HPA axis in obesity management.\u003c/p\u003e","manuscriptTitle":"Cortisol/Adrenocorticotropic Hormone Ratio: A Novel Biomarker for Metabolic Risk Stratification in Obese Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 10:24:41","doi":"10.21203/rs.3.rs-8710320/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-23T12:36:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T08:30:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Hormones","date":"2026-02-20T13:28:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-30T01:21:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Hormones","date":"2026-01-29T01:27:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"hormones","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"HORM","sideBox":"Learn more about [Hormones](https://www.springer.com/journal/42000)","snPcode":"42000","submissionUrl":"https://www.editorialmanager.com/horm/default2.aspx","title":"Hormones","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1944912-46b1-4c65-9a08-adb6c2f53207","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T10:24:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 10:24:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8710320","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8710320","identity":"rs-8710320","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}