Association of Triglyceride-Glucose Index and Its Obesity-Adjusted Derivatives with Psoriasis: Findings from a Cross-Sectional Chinese study

Association of Triglyceride-Glucose Index and Its Obesity-Adjusted Derivatives with Psoriasis: Findings from a Cross-Sectional Chinese study | 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 Article Association of Triglyceride-Glucose Index and Its Obesity-Adjusted Derivatives with Psoriasis: Findings from a Cross-Sectional Chinese study Shue Tian, Man Yu, Jingjing Liu, Xin Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7988743/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background: The triglyceride-glucose (TyG) index and its anthropometric-adjusted derivatives, including TyG-body mass index (TyG-BMI) and TyG-waist circumference (TyG-WC), are surrogate markers of insulin resistance. Although metabolic dysfunction is implicated in psoriasis, evidence on these indices and psoriasis risk in Asian populations is scarce. Methods: We conducted a cross-sectional study of 3,314 Chinese adults recruited from Deyang Hospital, Chengdu University of Traditional Chinese Medicine (January 2022–June 2023). Demographic, lifestyle, anthropometric, and biochemical data were collected using standardized protocols. TyG, TyG-BMI, and TyG-WC were calculated and categorized into quartiles. Logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for age, sex, body mass index, blood pressure, lipids, high-sensitivity C-reactive protein, smoking, and alcohol consumption. Results: Among 3,314 participants, 1,560 had psoriasis and 1,754 did not. Patients with psoriasis were older, more frequently male, and exhibited higher rates of smoking, alcohol intake, obesity, hypertension, dyslipidemia, and systemic inflammation (P < 0.05 for all). The TyG index and its derivatives were significantly elevated in the psoriasis group. In fully adjusted models, higher quartiles of TyG, TyG-BMI, and TyG-WC were strongly associated with psoriasis risk. Compared with quartile 1, adjusted ORs (95% CIs) for quartile 4 were 8.91 (3.42–23.19) for TyG, 4.64 (2.18–9.90) for TyG-BMI, and 18.35 (4.80–70.22) for TyG-WC (all P for trend < 0.001). Each standard deviation increase in TyG, TyG-BMI, and TyG-WC corresponded to 58%, 43%, and 94% higher risks of psoriasis, respectively. Conclusions: Elevated TyG, TyG-BMI, and TyG-WC are independently associated with psoriasis in Chinese adults. These simple, cost-effective indices may aid in identifying individuals at high risk for psoriasis, with potential utility in preventive screening. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Triglyceride-glucose index TyG-BMI TyG-waist circumference insulin resistance psoriasis metabolic biomarkers Figures Figure 1 Introduction Psoriasis [ 1 ] is a chronic, immune-mediated inflammatory skin disorder affecting approximately 2–3% of the global population, with an increasing prevalence in Asian countries [ 2 ]. Beyond its primary skin manifestations, psoriasis is increasingly recognized as a systemic disease, often coexisting with cardiometabolic abnormalities [ 3 – 6 ], such as obesity, dyslipidemia, insulin resistance, and metabolic syndrome [ 4 ]. The inflammatory environment and metabolic dysregulation associated with psoriasis significantly contribute to its high morbidity, emphasizing the importance of early identification of individuals at elevated risk. Insulin resistance is a central feature of multiple metabolic disorders [ 7 – 10 ] and is implicated in the pathophysiology of psoriasis through shared inflammatory pathways, endothelial dysfunction, and altered lipid metabolism. The hyperinsulinemic state exacerbates systemic inflammation by upregulating pro-inflammatory cytokines, which may trigger or aggravate psoriatic lesions. Although the hyperinsulinemic-euglycemic clamp remains the gold standard for assessing insulin sensitivity, its invasive, time-consuming, and costly nature limits its clinical applicability. Therefore, increasing attention has been given to simpler, reproducible surrogate indices for assessing insulin resistance in epidemiological and clinical settings. The triglyceride-glucose (TyG) [ 11 – 13 ] index, calculated from fasting triglyceride and fasting glucose concentrations, has emerged as a reliable and validated surrogate marker of insulin resistance in diverse populations. Additionally, anthropometric-adjusted derivatives, such as TyG-body mass index (TyG-BMI) and TyG-waist circumference (TyG-WC), may better capture the combined effects of dysglycemia, dyslipidemia, and adiposity, potentially offering stronger predictive value for metabolic and inflammatory diseases. While previous studies have linked TyG and its derivatives to cardiovascular disease, type 2 diabetes, and non-alcoholic fatty liver disease, evidence regarding their role in psoriasis risk is limited. Existing research is often restricted to Western populations, and has not comprehensively evaluated TyG-BMI and TyG-WC Therefore, we conducted a large-scale, cross-section study in a Chinese adult population to examine the associations of TyG index, TyG-BMI, and TyG-WC with psoriasis. Materials and Methods Study Design and Population This cross-sectional study was conducted among Chinese adults participating in Deyang Hospital Affiliated to Chengdu University of Traditional Chinese Medicine from Jan 2022-Jun 2023. All participants underwent standardized interviews, anthropometric measurements, and laboratory testing. Individuals with Severe renal or hepatic dysfunction, previous malignant tumors, missing key variables information (missing BMI, n = 20; missing WC, n = 74; missing TG or FPG, n = 53) or Autoimmune diseases were excluded. From an initial pool of 3814 patients, 3314 met the eligibility requirements and were incorporated into the final analysis (see Fig. 1 for participant flow). The study was conducted in accordance with the Institutional Review Board IRB of Deyang Hospital, The Affiliated Hospital of Chengdu University of Traditional Chinese Medicine (Approval No. [2025-05-024-KDD]). Informed consents were waived because we used de-identified anonymous data. Data Collection and Measurements Trained staff collected demographic and lifestyle data, including age, sex, smoking status (never, former, current), alcohol consumption (yes/no), through structured questionnaires. Anthropometric measurements included weight, height, and waist circumference (WC) following standardized protocols, with body mass index (BMI) calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest. Blood pressure was measured in a seated position after at least five minutes of rest, with the average of two readings recorded for both systolic blood pressure (SBP) and diastolic blood pressure (DBP). Laboratory Measurements Fasting blood samples (≥ 8 hours) were collected to measure fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), high-sensitivity C-reactive protein (hs-CRP), and serum creatinine. Biochemical assays were performed using standardized enzymatic methods on automated analyzers. Fasting blood samples (≥ 8 hours) were collected to measure fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), high-sensitivity C-reactive protein (hs-CRP), and serum creatinine. Biochemical assays were performed using standardized enzymatic methods on automated analyzers. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Definition of TyG and Derived Indices The triglyceride-glucose (TyG) index was calculated by taking the natural logarithm of the product of fasting triglycerides (in mmol/L) and fasting plasma glucose (in mmol/L), divided by two. TyG-BMI was calculated by multiplying the TyG index by the body mass index (in kg/m²).TyG-WC was calculated by multiplying the TyG index by the waist circumference (in centimetres).Each index was categorized into quartiles based on baseline distribution, with quartile 1 as the reference category. Diagnose of Psoriasis Psoriasis was diagnosed based on established clinical and/or histopathological criteria, in accordance with the International Classification of Diseases, 10th Revision (ICD-10) code L40. Specifically, cases were confirmed by either:(a) Clinical diagnosis by a board-certified dermatologist, characterized by typical psoriatic lesions such as well-demarcated erythematous plaques with silvery scales, commonly located on extensor surfaces, scalp, lower back, or intertriginous areas;(b) Histopathological confirmation via skin biopsy demonstrating features including epidermal hyperplasia, parakeratosis, diminished granular layer, Munro’s microabscesses, and dilated capillaries in the dermal papillae. Statistical Analysis Baseline characteristics were summarized as means ± standard deviations (SDs) for continuous variables and counts (percentages) for categorical variables, with between-group differences assessed using t -tests or chi-square tests as appropriate. Logistic regression models were employed to estimate odd ratio (OR) and 95% confidence intervals (CIs) for the association between TyG indices and psoriasis. Three models were constructed: Model 0 (M0): unadjusted, Model 1 (M1): adjusted for age and sex, Model 2 (M2): further adjusted for BMI, SBP, HDL-C, LDL-C, hs-CRP, smoking status, alcohol consumption. Quartiles of TyG, TyG-BMI, and TyG-WC were modeled using quartile 1 as the reference. Linear trends were tested by modeling the median value of each quartile as a continuous variable. The linearity of the dose-response relationships was assessed using restricted cubic splines. Per standard deviation (SD) increases were also analyzed. All statistical analyses were performed using R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria), with two-sided p-values < 0.05 considered statistically significant. Results Baseline Characteristics A total of 3,314 participants were included, comprising 1,754 individuals without psoriasis and 1,560 with psoriasis. Compared with those without psoriasis, patients with psoriasis were older (54.3 ± 8.2 vs. 45.8 ± 7.7 years, P < 0.05) and more likely to be male (58.8% vs. 45.3%, P < 0.05). Lifestyle differences were also evident, as current smoking (48.5% vs. 8.3%) and alcohol consumption (62.2% vs. 34.0%) were significantly more prevalent in the psoriasis group (all P < 0.05). Metabolic and clinical parameters were consistently higher in individuals with psoriasis, including BMI, waist circumference, systolic blood pressure, and diastolic blood pressure (BMI: 27.0 ± 2.4 vs. 23.5 ± 2.3 kg/m²; WC: 92.1 ± 5.8 vs. 82.9 ± 5.9 cm; SBP: 136.2 ± 9.8 vs. 122.1 ± 10.1 mmHg; DBP: 81.8 ± 7.5 vs. 75.8 ± 6.9 mmHg; all P < 0.05). Similarly, fasting plasma glucose, triglycerides, LDL-c, and hs-CRP levels were significantly elevated in the psoriasis group, while HDL-c levels were lower (all P < 0.05). Moreover, renal function was reduced, with a lower mean eGFR observed among psoriasis patients (96.1 ± 10.2 vs. 104.4 ± 11.3 mL/min/1.73 m², P < 0.05). Of particular note, the TyG index and its derived parameters were substantially higher in individuals with psoriasis (TyG: 9.2 ± 0.2 vs. 8.8 ± 0.2; TyG-BMI: 248.4 ± 21.5 vs. 206.6 ± 20.5; TyG-WC: 847.2 ± 57.1 vs. 728.4 ± 54.6; all P < 0.05). Logistic regression on TyG Index and Psoriasis Logistic regeression showed that higher TyG index quartiles were strongly associated with psoriasis (Table 2 ). In the fully adjusted model(Model 2), compared with quartile 1, the adjusted odd ratio (aOR) (95% CIs) was 3.12 (1.21–8.04) for quartile 2, 7.46 (2.88–19.27) for quartile 3, and 8.91 (3.42–23.19) for quartile 4 ( P for trend < 0.001). Each standard deviation (SD) increases in TyG index corresponded to a 58% higher risk (aOR = 1.58, 95% CI: 1.44–1.73). Table 1 Baseline characteristics of participants according to the absence of Psoriasis Variable Overall (n = 3314) No Psoriasis (n = 1754) Psoriasis (n = 1560) Mean difference (95%CI) # Age (years) 49.2 ± 8.9 45.8 ± 7.7 54.3 ± 8.2* 8.5 (8.0, 9.0) Sex Male 1712 (51.7%) 795 (45.3%) 917 (58.8%) * - Female 1602 (48.3%) 959 (54.7%) 643 (34.0%) * - Smoking status Never 1894(57.2%) 1277(72.8%) 617 (39.6%) * - Former 518 (15.6%) 332 (18.9%) 186 (11.9%) * - Current 902 (27.2%) 145 (8.3%) 757 (48.5%) * - Alcohol consumption Yes 1543 (45.3%) 596 (34.0%) 947 (62.2%) * - No 1771(54.7%) 1158 (66.0%) 613 (37.8%) * - BMI (kg/m²) 24.9 ± 2.9 23.5 ± 2.3 27.0 ± 2.4 * 3.5 (3.3, 3.7) WC (cm) 86.6 ± 7.4 82.9 ± 5.9 92.1 ± 5.8 * 9.2 (8.8, 9.6) SBP (mmHg) 127.7 ± 12.1 122.1 ± 10.1 136.2 ± 9.8 * 14.1 (13.4, 14.8) DBP (mmHg) 78.2 ± 7.7 75.8 ± 6.9 81.8 ± 7.5 * 6.0 (5.5, 6.5) FPG (mmol/L) 5.5 ± 0.7 5.1 ± 0.6 6.0 ± 0.5 * 0.9 (0.8, 1.0) TG (mmol/LL) 1.81 ± 0.34 1.64 ± 0.26 2.07 ± 0.28 * 0.43 (0.41, 0.45) LDL-c (mmol/L) 3.0 ± 0.4 2.8 ± 0.3 3.4 ± 0.4 * 0.6 (0.5, 0.7) HDL-c(mmol/L) 1.36 ± 0.26 1.46 ± 0.23 1.21 ± 0.23 * -0.25 (-0.27, -0.23) Hs-CRP (mg/L) 2.1 ± 1.4 1.2 ± 0.8 3.4 ± 0.9 * 2.2 (2.1, 2.3) eGFR (mL/min/1.73 m²) 101.1 ± 11.6 104.4 ± 11.3 96.1 ± 10.2 * -8.3 (-9.2, -7.4) TyG index 8.9 ± 0.3 8.8 ± 0.2 9.2 ± 0.2 * 0.4 (0.38, 0.42) TyG-BMI 223.3 ± 29.3 206.6 ± 20.5 248.4 ± 21.5 * 41.8 (40.2, 43.4) TyG-WC 775.8 ± 80.4 728.4 ± 54.6 847.2 ± 57.1 * 118.8 (115.2, 122.4) Continuous variables are presented as mean ± standard deviation (SD) and categorical variables as number (percentage). *Compared to those without Psoriasis, P < 0.05 # Mean difference (95% Confidence Interval) is presented for continuous variables only, calculated as Psoriasis group minus No Psoriasis group. Table 2 Odd ratios of psoriasis by different levels of TyG TyG Per SD increase Q1 Q2 Q3 Q4 Psoriasis Crude model 1.00 5.11 (1.78–14.65) 14.65 (5.27–45.58) 23.82 (16.60–115.69) 2.72 (2.48–2.99) Model 1 1.00 4.90 (1.73–13.85) 10.91 (5.87–45.59) 19.60 (12.28–86.53) 2.45 (2.23–2.68) Model 2 1.00 3.12 (1.21–8.04) 7.46 (2.88–19.27) 8.91 (3.42–23.19) 1.58 (1.44–1.73) Model 1 was adjusted for age and sex and model 2 was further adjusted for BMI, BP, HDL-C, LDL-C, hs-CRP, smoking status, alcohol consumption. TyG-BMI, TyG-WC and Psoriasis Risk Table 3 presented the associations between TyG-BMI and psoriasis. Fully adjusted OR (95% CIs) for quartiles 2–4 versus quartile 1 were 2.03 (1.03–3.97), 4.21 (2.11–8.38), and 4.64 (2.18–9.90), respectively ( P for trend < 0.001). Each SD increase in TyG-BMI was associated with a 43% higher risk (aOR = 1.43, 95% CI: 1.32–1.54). Table 3 Logistic regression on different levels of TyG-BMI and TyG-WC and Psoriasis Crude model Model 1 Model 2 TyG-BMI Q1 Ref Ref Ref Q2 2.73 (1.25–5.98) 2.58 (1.20–5.55) 2.03 (1.03–3.97) Q3 9.61 (5.31–25.17) 8.12 (5.05–20.26) 4.21 (2.11–8.38) Q4 10.17 (3.21–39.85) 7.88 (3.49–19.56) 4.64 (2.18–9.90) Per SD increase 2.30 (2.14–2.48) 2.05 (1.90–2.22) 1.43 (1.32–1.54) TyG-WC Q1 Ref Ref Ref Q2 10.09 (2.40–42.36) 9.85 (2.39–40.62) 7.48 (1.99–28.13) Q3 53.67 (13.50–213.38) 43.88 (11.08–173.84) 16.17 (4.25–61.60) Q4 81.55 (20.61–322.73) 62.42 (15.77–247.10) 18.35 (4.80–70.22) Per SD increase 2.24 (2.05–2.44) 2.03 (1.87–2.21) 1.94 (1.39–2.21) Model 1 was adjusted for age and sex and model 2 was further adjusted for BP, HDL-C, LDL-C, hs-CRP, smoking status, alcohol consumption. TyG-WC exhibited the strongest association with psoriasis. In fully adjusted models, OR (95% CIs) for quartiles 2–4 compared with quartile 1 were 7.48 (1.99–28.13), 16.17 (4.25–61.60), and 18.35 (4.80–70.22), respectively ( P for trend < 0.001). Per SD increase in TyG-WC, the risk of psoriasis increased nearly fivefold (aOR = 1.94, 95% CI: 1.39–2.21). Discussion In this large-scale cross-sectional cohort study of Chinese adults, we found that higher levels of the triglyceride-glucose (TyG) index and its obesity-adjusted derivatives [ 14 ], TyG-BMI and TyG-WC, were significantly and independently associated with psoriasis. Previous studies have identified the TyG index and its derivatives as reliable surrogate markers of insulin resistance [ 15 , 16 ] with predictive value for various metabolic disorders. Although earlier research has suggested a possible link between insulin resistance and psoriasis [ 17 – 19 ]. Our findings extend current knowledge by demonstrating that elevated TyG, TyG-BMI, and TyG-WC had an independent association with psoriasis risk beyond traditional risk factors, supporting a role for metabolic dysfunction in psoriasis development. We observed that the associations of TyG-BMI, and especially TyG-WC, with psoriasis were stronger, consistent with the notion that central fat accumulation exacerbates insulin resistance and systemic inflammation [ 20 ], thereby increasing susceptibility to psoriasis. Waist circumference, as a measure of visceral adiposity [ 21 ], may better capture metabolically active fat depots that contribute to pro-inflammatory processes and immune dysregulation. Multiple mechanisms may underline these associations. Insulin resistance promotes a chronic low-grade inflammatory state [ 22 ], characterized by elevated pro-inflammatory cytokines that are closely linked to psoriasis onset. Dyslipidemia and hyperinsulinemia may impair endothelial function and alter keratinocyte proliferation and differentiation, facilitating the development of psoriatic lesions[ 23 ]. Additionally, adipokines secreted by visceral adipose tissue in individuals with higher TyG-WC can influence immune cell activity and inflammatory pathways implicated in psoriasis pathogenesis. These findings suggested that simple and routinely available measures, fasting glucose, triglycerides, BMI, and waist circumference, can be combined into composite indices to identify individuals at high risk for psoriasis. Such indices could be incorporated into routine health assessments to enable early identification, including weight management, dietary modification, and metabolic risk control, potentially preventing or delaying psoriasis onset. However, several limitations should be noted. Psoriasis diagnosis was based on EHRs, which may be subject to misclassification, although such misclassification is likely to be non-differential. Residual confounding from unmeasured factors such as dietary patterns, genetic predisposition, or psychosocial stress cannot be excluded. Secondly, despite adjusting for major confounders, residual confounding remains possible from unmeasured factors such as detailed medication history including systemic treatments for psoriasis, dietary patterns, genetic predisposition, or psychosocial stress. These findings are based on a Chinese population and may not be directly generalizable to other populations. Finally, the cross-sectional design precludes causal inference, and large-scale prospective study should be conducted to further explore the relations between TyG and psoriasis. Conclusions Elevated TyG index, TyG-BMI, and TyG-WC were independently associated with psoriasis in Chinese adults, with TyG-WC showing the strongest association. These indices, derived from simple anthropometric and biochemical measures, may serve as practical tools for early risk stratification in clinical and public health settings. Further research should confirm these associations in other populations with prospective design. Statement Declarations Ethics Approval and Consent to Participate The study was conducted in accordance with the Institutional Review Board IRB of Deyang Hospital, The Affiliated Hospital of Chengdu University of Traditional Chinese Medicine (Approval No. [2025-05-024-KDD]). Conflicts of Interest The authors declare that there were no conflicts of interest with respect to the authorship or the publication of this article. Availability of Data and Materials The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgments We gratefully acknowledge all participants for their contributions to this study. Funding This research received no external funding. Authors’ Contributions Study design and supervision: ST, XP., Data analysis and visualization: ST., Manuscript writing: ST, MY, JL., Conceptualization: XP. All authors critically revised the manuscript and approved the final version. References Griffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. Lancet. 2021;397(10281):1301-1315. doi:10.1016/S0140-6736(20)32549-6 Kodali N, Blanchard I, Kunamneni S, Lebwohl MG. Current management of generalized pustular psoriasis. Exp Dermatol. 2023;32(8):1204-1218. doi:10.1111/exd.14765 Piaserico S, Orlando G, Messina F. Psoriasis and Cardiometabolic Diseases: Shared Genetic and Molecular Pathways. Int J Mol Sci. 2022;23(16):9063. Published 2022 Aug 13. doi:10.3390/ijms23169063 Vata D, Tarcau BM, Popescu IA, et al. Update on Obesity in Psoriasis Patients. Life (Basel). 2023;13(10):1947. Published 2023 Sep 22. doi:10.3390/life13101947 Mirghani H, Altemani AT, Altemani ST, et al. The Cross Talk Between Psoriasis, Obesity, and Dyslipidemia: A Meta-Analysis. Cureus. 2023;15(11):e49253. Published 2023 Nov 22. doi:10.7759/cureus.49253 Parraga SP, Feldman SR. Insulin resistance and psoriasis. Br J Dermatol. 2024;191(4):486-487. doi:10.1093/bjd/ljae199 Wu JJ, Kavanaugh A, Lebwohl MG, Gniadecki R, Merola JF. Psoriasis and metabolic syndrome: implications for the management and treatment of psoriasis. J Eur Acad Dermatol Venereol. 2022;36(6):797-806. doi:10.1111/jdv.18044 Hao Y, Zhu YJ, Zou S, et al. Metabolic Syndrome and Psoriasis: Mechanisms and Future Directions. Front Immunol. 2021;12:711060. Published 2021 Jul 23. doi:10.3389/fimmu.2021.711060 Bellinato F, Maurelli M, Geat D, Girolomoni G, Gisondi P. Managing the Patient with Psoriasis and Metabolic Comorbidities. Am J Clin Dermatol. 2024;25(4):527-540. doi:10.1007/s40257-024-00857-0 Garbicz J, Całyniuk B, Górski M, et al. Nutritional Therapy in Persons Suffering from Psoriasis. Nutrients. 2021;14(1):119. Published 2021 Dec 28. doi:10.3390/nu14010119 Sun Y, Ji H, Sun W, An X, Lian F. Triglyceride glucose (TyG) index: A promising biomarker for diagnosis and treatment of different diseases. Eur J Intern Med. 2025;131:3-14. doi:10.1016/j.ejim.2024.08.026 Dang K, Wang X, Hu J, et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003-2018. Cardiovasc Diabetol. 2024;23(1):8. Published 2024 Jan 6. doi:10.1186/s12933-023-02115-9 Yang Z, Gong H, Kan F, Ji N. Association between the triglyceride glucose (TyG) index and the risk of acute kidney injury in critically ill patients with heart failure: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):232. Published 2023 Aug 31. doi:10.1186/s12933-023-01971-9 Duan M, Zhao X, Li S, et al. Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001-2018. Cardiovasc Diabetol. 2024;23(1):243. Published 2024 Jul 10. doi:10.1186/s12933-024-02334-8 Tian X, Chen S, Wang P, et al. Insulin resistance mediates obesity-related risk of cardiovascular disease: a prospective cohort study. Cardiovasc Diabetol. 2022;21(1):289. Published 2022 Dec 23. doi:10.1186/s12933-022-01729-9 Tahapary DL, Pratisthita LB, Fitri NA, et al. Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr. 2022;16(8):102581. doi:10.1016/j.dsx.2022.102581 Huang D, Zhong X, Jiang Y, et al. Insulin resistance impairs biologic agent response in moderate-to-severe plaque psoriasis: insights from a prospective cohort study in China. Br J Dermatol. 2024;191(4):616-623. doi:10.1093/bjd/ljae147 Caroppo F, Galderisi A, Ventura L, Belloni Fortina A. Metabolic syndrome and insulin resistance in pre-pubertal children with psoriasis. Eur J Pediatr. 2021;180(6):1739-1745. doi:10.1007/s00431-020-03924-w Zhong X, Huang D, Chen R, et al. Positive association between insulin resistance and fatty liver disease in psoriasis: evidence from a cross-sectional study. Front Immunol. 2024;15:1388967. Published 2024 Apr 23. doi:10.3389/fimmu.2024.1388967 Sakurai Y, Kubota N, Yamauchi T, Kadowaki T. Role of Insulin Resistance in MAFLD. Int J Mol Sci. 2021;22(8):4156. Published 2021 Apr 16. doi:10.3390/ijms22084156 Williams JC, Hum RM, Rogers K, Maglio C, Alam U, Zhao SS. Metabolic syndrome and psoriatic arthritis: the role of weight loss as a disease-modifying therapy. Ther Adv Musculoskelet Dis. 2024;16:1759720X241271886. Published 2024 Aug 19. doi:10.1177/1759720X241271886 Cruz KJC, de Oliveira ARS, Morais JBS, Severo JS, Marreiro PhD DDN. Role of microRNAs on adipogenesis, chronic low-grade inflammation, and insulin resistance in obesity. Nutrition. 2017;35:28-35. doi:10.1016/j.nut.2016.10.003 Secchiero P, Rimondi E, Marcuzzi A, et al. Metabolic Syndrome and Psoriasis: Pivotal Roles of Chronic Inflammation and Gut Microbiota. Int J Mol Sci. 2024;25(15):8098. Published 2024 Jul 25. doi:10.3390/ijms25158098 Additional Declarations No competing interests reported. Supplementary Files supplmentarytable1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers invited by journal 04 Nov, 2025 Editor invited by journal 04 Nov, 2025 Editor assigned by journal 31 Oct, 2025 Submission checks completed at journal 31 Oct, 2025 First submitted to journal 30 Oct, 2025 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-7988743","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":542332850,"identity":"40b0a352-4a42-4706-9c6b-458e0c996260","order_by":0,"name":"Shue Tian","email":"","orcid":"","institution":"Affiliated Deyang Hospital of Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shue","middleName":"","lastName":"Tian","suffix":""},{"id":542332851,"identity":"980b86ff-4201-454b-871d-7da7b317b01e","order_by":1,"name":"Man Yu","email":"","orcid":"","institution":"Hospital of Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Yu","suffix":""},{"id":542332852,"identity":"5aac19f9-99d7-4fc8-854b-27b56eaa2494","order_by":2,"name":"Jingjing Liu","email":"","orcid":"","institution":"Sichuan Academy of Chinese Medicine Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Liu","suffix":""},{"id":542332853,"identity":"fa6be491-f31c-4c51-8d25-39a11fe96f1c","order_by":3,"name":"Xin Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYNACAwkeNvYGBgbGBqKUMwNxgY0cH88BqBY2orR8SDOWk0ggUou5RP7BTzcMDie2Sb4xfPBzB0MevzwB11n2HGaWzgFpkc4xNuw9w1As2UbAFoPjzWzMUC1mErxtDIkbjhHScpgZqkXyjPnPv0At+wlqgdiSZswmwWPGDLaFkPeBfjEG+sVGjo0nrVhatk0iccaxBPxazCUSH37O+SPBI99+eOPHt202if3NBwg4DMHkALElCLgKVQv7A8LKR8EoGAWjYEQCALuhP1IVti2YAAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan Academy of Chinese Medicine Sciences","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2025-10-30 11:23:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7988743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7988743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95932164,"identity":"0d877598-82c0-4a21-8b29-30042144d91e","added_by":"auto","created_at":"2025-11-14 14:44:47","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":33111,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/6b5a8e884a420c95477bcb24.docx"},{"id":96244091,"identity":"835349a0-e89b-4c6d-b690-1c54c7c4d47e","added_by":"auto","created_at":"2025-11-19 07:17:41","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49550,"visible":true,"origin":"","legend":"","description":"","filename":"Figuer1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/e979c2b3ad2c9c204acac7f5.jpg"},{"id":95932165,"identity":"5992c9d1-7c14-4b3e-b633-2b182af1fa98","added_by":"auto","created_at":"2025-11-14 14:44:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26069,"visible":true,"origin":"","legend":"","description":"","filename":"tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/879253bf471df0a74b939fbd.docx"},{"id":95932167,"identity":"b6fbc090-0e3a-443b-8668-dd3809d7cb5b","added_by":"auto","created_at":"2025-11-14 14:44:47","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5229,"visible":true,"origin":"","legend":"","description":"","filename":"3ee49a7412e64c4b9b373d9b6f8c0ef9.json","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/7a758892635ef79225f8f95d.json"},{"id":96244409,"identity":"81d44983-5f37-4951-b070-0abc60f98bcb","added_by":"auto","created_at":"2025-11-19 07:18:23","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21525,"visible":true,"origin":"","legend":"","description":"","filename":"supplmentarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/bb30fe7ab575f6d271d65560.docx"},{"id":95932170,"identity":"c8f85d9b-de8c-4f8c-a6bc-896129cf5703","added_by":"auto","created_at":"2025-11-14 14:44:47","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79616,"visible":true,"origin":"","legend":"","description":"","filename":"3ee49a7412e64c4b9b373d9b6f8c0ef91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/65b4df3e40cc40c1ca90175e.xml"},{"id":96243691,"identity":"983829cf-a814-45a3-80c5-3a7242cfd5e7","added_by":"auto","created_at":"2025-11-19 07:16:51","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49550,"visible":true,"origin":"","legend":"","description":"","filename":"Figuer1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/7450cdeedc64fa832b562f0c.jpg"},{"id":95932168,"identity":"21ecec64-fc9a-41dd-b8a3-144cf12ffdbc","added_by":"auto","created_at":"2025-11-14 14:44:47","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11909,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFiguer1.png","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/f62a32cf4f16534bb64b4d3b.png"},{"id":95932172,"identity":"c8654549-1923-45f1-bdaf-b7cc10c0f938","added_by":"auto","created_at":"2025-11-14 14:44:47","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78816,"visible":true,"origin":"","legend":"","description":"","filename":"3ee49a7412e64c4b9b373d9b6f8c0ef91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/78591bfcdccaf71916c63bed.xml"},{"id":95932173,"identity":"4a7697be-5505-4c7f-8741-2901f542a108","added_by":"auto","created_at":"2025-11-14 14:44:47","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88111,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/ec4698a29966b45b0fbadfd9.html"},{"id":95932161,"identity":"0692a2e5-b7fe-4729-9693-d102d4c818e6","added_by":"auto","created_at":"2025-11-14 14:44:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49550,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of participant selection for the cohort study.\u003c/p\u003e","description":"","filename":"Figuer1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/45cfef9ca1ad79b0d5d97528.jpg"},{"id":96362885,"identity":"58ec3e08-f8f5-4ec9-b79b-f9251b5e89ed","added_by":"auto","created_at":"2025-11-20 10:02:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":817292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/be977e7a-0607-4a82-8e46-e441c6fa36bd.pdf"},{"id":96243753,"identity":"2cb076ba-0993-4959-8873-14be78730d4c","added_by":"auto","created_at":"2025-11-19 07:16:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21525,"visible":true,"origin":"","legend":"","description":"","filename":"supplmentarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7988743/v1/df8f441e9892d1d77632d1e7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Triglyceride-Glucose Index and Its Obesity-Adjusted Derivatives with Psoriasis: Findings from a Cross-Sectional Chinese study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsoriasis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] is a chronic, immune-mediated inflammatory skin disorder affecting approximately 2\u0026ndash;3% of the global population, with an increasing prevalence in Asian countries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Beyond its primary skin manifestations, psoriasis is increasingly recognized as a systemic disease, often coexisting with cardiometabolic abnormalities [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], such as obesity, dyslipidemia, insulin resistance, and metabolic syndrome [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The inflammatory environment and metabolic dysregulation associated with psoriasis significantly contribute to its high morbidity, emphasizing the importance of early identification of individuals at elevated risk.\u003c/p\u003e\u003cp\u003eInsulin resistance is a central feature of multiple metabolic disorders [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and is implicated in the pathophysiology of psoriasis through shared inflammatory pathways, endothelial dysfunction, and altered lipid metabolism. The hyperinsulinemic state exacerbates systemic inflammation by upregulating pro-inflammatory cytokines, which may trigger or aggravate psoriatic lesions. Although the hyperinsulinemic-euglycemic clamp remains the gold standard for assessing insulin sensitivity, its invasive, time-consuming, and costly nature limits its clinical applicability. Therefore, increasing attention has been given to simpler, reproducible surrogate indices for assessing insulin resistance in epidemiological and clinical settings.\u003c/p\u003e\u003cp\u003eThe triglyceride-glucose (TyG) [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] index, calculated from fasting triglyceride and fasting glucose concentrations, has emerged as a reliable and validated surrogate marker of insulin resistance in diverse populations. Additionally, anthropometric-adjusted derivatives, such as TyG-body mass index (TyG-BMI) and TyG-waist circumference (TyG-WC), may better capture the combined effects of dysglycemia, dyslipidemia, and adiposity, potentially offering stronger predictive value for metabolic and inflammatory diseases. While previous studies have linked TyG and its derivatives to cardiovascular disease, type 2 diabetes, and non-alcoholic fatty liver disease, evidence regarding their role in psoriasis risk is limited. Existing research is often restricted to Western populations, and has not comprehensively evaluated TyG-BMI and TyG-WC\u003c/p\u003e\u003cp\u003eTherefore, we conducted a large-scale, cross-section study in a Chinese adult population to examine the associations of TyG index, TyG-BMI, and TyG-WC with psoriasis.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Population\u003c/h2\u003e\u003cp\u003e This cross-sectional study was conducted among Chinese adults participating in Deyang Hospital Affiliated to Chengdu University of Traditional Chinese Medicine from Jan 2022-Jun 2023. All participants underwent standardized interviews, anthropometric measurements, and laboratory testing. Individuals with Severe renal or hepatic dysfunction, previous malignant tumors, missing key variables information (missing BMI, n\u0026thinsp;=\u0026thinsp;20; missing WC, n\u0026thinsp;=\u0026thinsp;74; missing TG or FPG, n\u0026thinsp;=\u0026thinsp;53) or Autoimmune diseases were excluded. From an initial pool of 3814 patients, 3314 met the eligibility requirements and were incorporated into the final analysis (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for participant flow). The study was conducted in accordance with the Institutional Review Board IRB of Deyang Hospital, The Affiliated Hospital of Chengdu University of Traditional Chinese Medicine (Approval No. [2025-05-024-KDD]). Informed consents were waived because we used de-identified anonymous data.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData Collection and Measurements\u003c/h3\u003e\n\u003cp\u003eTrained staff collected demographic and lifestyle data, including age, sex, smoking status (never, former, current), alcohol consumption (yes/no), through structured questionnaires. Anthropometric measurements included weight, height, and waist circumference (WC) following standardized protocols, with body mass index (BMI) calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest. Blood pressure was measured in a seated position after at least five minutes of rest, with the average of two readings recorded for both systolic blood pressure (SBP) and diastolic blood pressure (DBP).\u003c/p\u003e\n\u003ch3\u003eLaboratory Measurements\u003c/h3\u003e\n\u003cp\u003eFasting blood samples (\u0026ge;\u0026thinsp;8 hours) were collected to measure fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), high-sensitivity C-reactive protein (hs-CRP), and serum creatinine. Biochemical assays were performed using standardized enzymatic methods on automated analyzers. Fasting blood samples (\u0026ge;\u0026thinsp;8 hours) were collected to measure fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), high-sensitivity C-reactive protein (hs-CRP), and serum creatinine. Biochemical assays were performed using standardized enzymatic methods on automated analyzers. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation.\u003c/p\u003e\n\u003ch3\u003eDefinition of TyG and Derived Indices\u003c/h3\u003e\n\u003cp\u003eThe triglyceride-glucose (TyG) index was calculated by taking the natural logarithm of the product of fasting triglycerides (in mmol/L) and fasting plasma glucose (in mmol/L), divided by two.\u003c/p\u003e\u003cp\u003eTyG-BMI was calculated by multiplying the TyG index by the body mass index (in kg/m\u0026sup2;).TyG-WC was calculated by multiplying the TyG index by the waist circumference (in centimetres).Each index was categorized into quartiles based on baseline distribution, with quartile 1 as the reference category.\u003c/p\u003e\n\u003ch3\u003eDiagnose of Psoriasis\u003c/h3\u003e\n\u003cp\u003ePsoriasis was diagnosed based on established clinical and/or histopathological criteria, in accordance with the International Classification of Diseases, 10th Revision (ICD-10) code L40. Specifically, cases were confirmed by either:(a) Clinical diagnosis by a board-certified dermatologist, characterized by typical psoriatic lesions such as well-demarcated erythematous plaques with silvery scales, commonly located on extensor surfaces, scalp, lower back, or intertriginous areas;(b) Histopathological confirmation via skin biopsy demonstrating features including epidermal hyperplasia, parakeratosis, diminished granular layer, Munro\u0026rsquo;s microabscesses, and dilated capillaries in the dermal papillae.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eBaseline characteristics were summarized as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) for continuous variables and counts (percentages) for categorical variables, with between-group differences assessed using \u003cem\u003et\u003c/em\u003e-tests or chi-square tests as appropriate. Logistic regression models were employed to estimate odd ratio (OR) and 95% confidence intervals (CIs) for the association between TyG indices and psoriasis. Three models were constructed: Model 0 (M0): unadjusted, Model 1 (M1): adjusted for age and sex, Model 2 (M2): further adjusted for BMI, SBP, HDL-C, LDL-C, hs-CRP, smoking status, alcohol consumption.\u003c/p\u003e\u003cp\u003eQuartiles of TyG, TyG-BMI, and TyG-WC were modeled using quartile 1 as the reference. Linear trends were tested by modeling the median value of each quartile as a continuous variable. The linearity of the dose-response relationships was assessed using restricted cubic splines. Per standard deviation (SD) increases were also analyzed. All statistical analyses were performed using R version 4.0.3 (R Foundation for Statistical Computing, Vienna, Austria), with two-sided p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eA total of 3,314 participants were included, comprising 1,754 individuals without psoriasis and 1,560 with psoriasis. Compared with those without psoriasis, patients with psoriasis were older (54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2 vs. 45.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and more likely to be male (58.8% vs. 45.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Lifestyle differences were also evident, as current smoking (48.5% vs. 8.3%) and alcohol consumption (62.2% vs. 34.0%) were significantly more prevalent in the psoriasis group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eMetabolic and clinical parameters were consistently higher in individuals with psoriasis, including BMI, waist circumference, systolic blood pressure, and diastolic blood pressure (BMI: 27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 vs. 23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 kg/m\u0026sup2;; WC: 92.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 vs. 82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 cm; SBP: 136.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 vs. 122.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1 mmHg; DBP: 81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5 vs. 75.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9 mmHg; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similarly, fasting plasma glucose, triglycerides, LDL-c, and hs-CRP levels were significantly elevated in the psoriasis group, while HDL-c levels were lower (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, renal function was reduced, with a lower mean eGFR observed among psoriasis patients (96.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 vs. 104.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3 mL/min/1.73 m\u0026sup2;, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eOf particular note, the TyG index and its derived parameters were substantially higher in individuals with psoriasis (TyG: 9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 vs. 8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2; TyG-BMI: 248.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.5 vs. 206.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5; TyG-WC: 847.2\u0026thinsp;\u0026plusmn;\u0026thinsp;57.1 vs. 728.4\u0026thinsp;\u0026plusmn;\u0026thinsp;54.6; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLogistic regression on TyG Index and Psoriasis\u003c/h2\u003e\u003cp\u003eLogistic regeression showed that higher TyG index quartiles were strongly associated with psoriasis (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the fully adjusted model(Model 2), compared with quartile 1, the adjusted odd ratio (aOR) (95% CIs) was 3.12 (1.21\u0026ndash;8.04) for quartile 2, 7.46 (2.88\u0026ndash;19.27) for quartile 3, and 8.91 (3.42\u0026ndash;23.19) for quartile 4 (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Each standard deviation (SD) increases in TyG index corresponded to a 58% higher risk (aOR\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 1.44\u0026ndash;1.73).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of participants according to the absence of Psoriasis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;3314)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eNo Psoriasis (n\u0026thinsp;=\u0026thinsp;1754)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePsoriasis (n\u0026thinsp;=\u0026thinsp;1560)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean difference (95%CI) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.5 (8.0, 9.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1712 (51.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e795 (45.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e917 (58.8%) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1602 (48.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e959 (54.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e643 (34.0%) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1894(57.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1277(72.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e617 (39.6%) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e518 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e332 (18.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e186 (11.9%) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e902 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e145 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e757 (48.5%) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1543 (45.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e596 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e947 (62.2%) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1771(54.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1158 (66.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e613 (37.8%) *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5 (3.3, 3.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWC (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e86.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e92.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.2 (8.8, 9.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e127.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e136.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.1 (13.4, 14.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e78.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.0 (5.5, 6.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9 (0.8, 1.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mmol/LL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.43 (0.41, 0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-c (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6 (0.5, 0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-c(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.25 (-0.27, -0.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHs-CRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2 (2.1, 2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eeGFR (mL/min/1.73 m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e101.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.3 (-9.2, -7.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4 (0.38, 0.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e223.3\u0026thinsp;\u0026plusmn;\u0026thinsp;29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e248.4\u0026thinsp;\u0026plusmn;\u0026thinsp;21.5 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.8 (40.2, 43.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e775.8\u0026thinsp;\u0026plusmn;\u0026thinsp;80.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e728.4\u0026thinsp;\u0026plusmn;\u0026thinsp;54.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e847.2\u0026thinsp;\u0026plusmn;\u0026thinsp;57.1 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e118.8 (115.2, 122.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and categorical variables as number (percentage).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Compared to those without Psoriasis, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e#\u003c/sup\u003eMean difference (95% Confidence Interval) is presented for continuous variables only, calculated as Psoriasis group minus No Psoriasis group.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOdd ratios of psoriasis by different levels of TyG\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePer SD increase\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePsoriasis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.11 (1.78\u0026ndash;14.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.65 (5.27\u0026ndash;45.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.82 (16.60\u0026ndash;115.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.72 (2.48\u0026ndash;2.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.90 (1.73\u0026ndash;13.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.91 (5.87\u0026ndash;45.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.60 (12.28\u0026ndash;86.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.45 (2.23\u0026ndash;2.68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.12 (1.21\u0026ndash;8.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.46 (2.88\u0026ndash;19.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.91 (3.42\u0026ndash;23.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.58 (1.44\u0026ndash;1.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1 was adjusted for age and sex and model 2 was further adjusted for BMI, BP, HDL-C, LDL-C, hs-CRP, smoking status, alcohol consumption.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTyG-BMI, TyG-WC and Psoriasis Risk\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presented the associations between TyG-BMI and psoriasis. Fully adjusted OR (95% CIs) for quartiles 2\u0026ndash;4 versus quartile 1 were 2.03 (1.03\u0026ndash;3.97), 4.21 (2.11\u0026ndash;8.38), and 4.64 (2.18\u0026ndash;9.90), respectively (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Each SD increase in TyG-BMI was associated with a 43% higher risk (aOR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.32\u0026ndash;1.54).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression on different levels of TyG-BMI and TyG-WC and Psoriasis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCrude model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.73 (1.25\u0026ndash;5.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.58 (1.20\u0026ndash;5.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.03 (1.03\u0026ndash;3.97)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.61 (5.31\u0026ndash;25.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.12 (5.05\u0026ndash;20.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.21 (2.11\u0026ndash;8.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.17 (3.21\u0026ndash;39.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.88 (3.49\u0026ndash;19.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.64 (2.18\u0026ndash;9.90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30 (2.14\u0026ndash;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.05 (1.90\u0026ndash;2.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43 (1.32\u0026ndash;1.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-WC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.09 (2.40\u0026ndash;42.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.85 (2.39\u0026ndash;40.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.48 (1.99\u0026ndash;28.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.67 (13.50\u0026ndash;213.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.88 (11.08\u0026ndash;173.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.17 (4.25\u0026ndash;61.60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.55 (20.61\u0026ndash;322.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.42 (15.77\u0026ndash;247.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.35 (4.80\u0026ndash;70.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePer SD increase\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.24 (2.05\u0026ndash;2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.03 (1.87\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.94 (1.39\u0026ndash;2.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1 was adjusted for age and sex and model 2 was further adjusted for BP, HDL-C, LDL-C, hs-CRP, smoking status, alcohol consumption.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTyG-WC exhibited the strongest association with psoriasis. In fully adjusted models, OR (95% CIs) for quartiles 2\u0026ndash;4 compared with quartile 1 were 7.48 (1.99\u0026ndash;28.13), 16.17 (4.25\u0026ndash;61.60), and 18.35 (4.80\u0026ndash;70.22), respectively (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Per SD increase in TyG-WC, the risk of psoriasis increased nearly fivefold (aOR\u0026thinsp;=\u0026thinsp;1.94, 95% CI: 1.39\u0026ndash;2.21).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large-scale cross-sectional cohort study of Chinese adults, we found that higher levels of the triglyceride-glucose (TyG) index and its obesity-adjusted derivatives [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], TyG-BMI and TyG-WC, were significantly and independently associated with psoriasis.\u003c/p\u003e\u003cp\u003ePrevious studies have identified the TyG index and its derivatives as reliable surrogate markers of insulin resistance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] with predictive value for various metabolic disorders. Although earlier research has suggested a possible link between insulin resistance and psoriasis [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our findings extend current knowledge by demonstrating that elevated TyG, TyG-BMI, and TyG-WC had an independent association with psoriasis risk beyond traditional risk factors, supporting a role for metabolic dysfunction in psoriasis development.\u003c/p\u003e\u003cp\u003eWe observed that the associations of TyG-BMI, and especially TyG-WC, with psoriasis were stronger, consistent with the notion that central fat accumulation exacerbates insulin resistance and systemic inflammation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], thereby increasing susceptibility to psoriasis. Waist circumference, as a measure of visceral adiposity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], may better capture metabolically active fat depots that contribute to pro-inflammatory processes and immune dysregulation.\u003c/p\u003e\u003cp\u003eMultiple mechanisms may underline these associations. Insulin resistance promotes a chronic low-grade inflammatory state [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], characterized by elevated pro-inflammatory cytokines that are closely linked to psoriasis onset. Dyslipidemia and hyperinsulinemia may impair endothelial function and alter keratinocyte proliferation and differentiation, facilitating the development of psoriatic lesions[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, adipokines secreted by visceral adipose tissue in individuals with higher TyG-WC can influence immune cell activity and inflammatory pathways implicated in psoriasis pathogenesis.\u003c/p\u003e\u003cp\u003eThese findings suggested that simple and routinely available measures, fasting glucose, triglycerides, BMI, and waist circumference, can be combined into composite indices to identify individuals at high risk for psoriasis. Such indices could be incorporated into routine health assessments to enable early identification, including weight management, dietary modification, and metabolic risk control, potentially preventing or delaying psoriasis onset.\u003c/p\u003e\u003cp\u003eHowever, several limitations should be noted. Psoriasis diagnosis was based on EHRs, which may be subject to misclassification, although such misclassification is likely to be non-differential. Residual confounding from unmeasured factors such as dietary patterns, genetic predisposition, or psychosocial stress cannot be excluded. Secondly, despite adjusting for major confounders, residual confounding remains possible from unmeasured factors such as detailed medication history including systemic treatments for psoriasis, dietary patterns, genetic predisposition, or psychosocial stress. These findings are based on a Chinese population and may not be directly generalizable to other populations. Finally, the cross-sectional design precludes causal inference, and large-scale prospective study should be conducted to further explore the relations between TyG and psoriasis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eElevated TyG index, TyG-BMI, and TyG-WC were independently associated with psoriasis in Chinese adults, with TyG-WC showing the strongest association. These indices, derived from simple anthropometric and biochemical measures, may serve as practical tools for early risk stratification in clinical and public health settings. Further research should confirm these associations in other populations with prospective design.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eStatement\u003c/h2\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Institutional Review Board IRB of Deyang Hospital, The Affiliated Hospital of Chengdu University of Traditional Chinese Medicine (Approval No. [2025-05-024-KDD]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there were no conflicts of interest with respect to the authorship or the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge all participants for their contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy design and supervision: ST, XP.,\u003c/p\u003e\n\u003cp\u003eData analysis and visualization: ST.,\u003c/p\u003e\n\u003cp\u003eManuscript writing: ST, MY, JL.,\u003c/p\u003e\n\u003cp\u003eConceptualization: XP.\u003c/p\u003e\n\u003cp\u003eAll authors critically revised the manuscript and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGriffiths CEM, Armstrong AW, Gudjonsson JE, Barker JNWN. Psoriasis. Lancet. 2021;397(10281):1301-1315. doi:10.1016/S0140-6736(20)32549-6\u003c/li\u003e\n \u003cli\u003eKodali N, Blanchard I, Kunamneni S, Lebwohl MG. Current management of generalized pustular psoriasis. Exp Dermatol. 2023;32(8):1204-1218. doi:10.1111/exd.14765\u003c/li\u003e\n \u003cli\u003ePiaserico S, Orlando G, Messina F. Psoriasis and Cardiometabolic Diseases: Shared Genetic and Molecular Pathways. Int J Mol Sci. 2022;23(16):9063. Published 2022 Aug 13. doi:10.3390/ijms23169063\u003c/li\u003e\n \u003cli\u003eVata D, Tarcau BM, Popescu IA, et al. Update on Obesity in Psoriasis Patients. Life (Basel). 2023;13(10):1947. Published 2023 Sep 22. doi:10.3390/life13101947\u003c/li\u003e\n \u003cli\u003eMirghani H, Altemani AT, Altemani ST, et al. The Cross Talk Between Psoriasis, Obesity, and Dyslipidemia: A Meta-Analysis. Cureus. 2023;15(11):e49253. Published 2023 Nov 22. doi:10.7759/cureus.49253\u003c/li\u003e\n \u003cli\u003eParraga SP, Feldman SR. Insulin resistance and psoriasis. Br J Dermatol. 2024;191(4):486-487. doi:10.1093/bjd/ljae199\u003c/li\u003e\n \u003cli\u003eWu JJ, Kavanaugh A, Lebwohl MG, Gniadecki R, Merola JF. Psoriasis and metabolic syndrome: implications for the management and treatment of psoriasis. J Eur Acad Dermatol Venereol. 2022;36(6):797-806. doi:10.1111/jdv.18044\u003c/li\u003e\n \u003cli\u003eHao Y, Zhu YJ, Zou S, et al. Metabolic Syndrome and Psoriasis: Mechanisms and Future Directions. Front Immunol. 2021;12:711060. Published 2021 Jul 23. doi:10.3389/fimmu.2021.711060\u003c/li\u003e\n \u003cli\u003eBellinato F, Maurelli M, Geat D, Girolomoni G, Gisondi P. Managing the Patient with Psoriasis and Metabolic Comorbidities. Am J Clin Dermatol. 2024;25(4):527-540. doi:10.1007/s40257-024-00857-0\u003c/li\u003e\n \u003cli\u003eGarbicz J, Całyniuk B, G\u0026oacute;rski M, et al. Nutritional Therapy in Persons Suffering from Psoriasis. Nutrients. 2021;14(1):119. Published 2021 Dec 28. doi:10.3390/nu14010119\u003c/li\u003e\n \u003cli\u003eSun Y, Ji H, Sun W, An X, Lian F. Triglyceride glucose (TyG) index: A promising biomarker for diagnosis and treatment of different diseases. Eur J Intern Med. 2025;131:3-14. doi:10.1016/j.ejim.2024.08.026\u003c/li\u003e\n \u003cli\u003eDang K, Wang X, Hu J, et al. The association between triglyceride-glucose index and its combination with obesity indicators and cardiovascular disease: NHANES 2003-2018. Cardiovasc Diabetol. 2024;23(1):8. Published 2024 Jan 6. doi:10.1186/s12933-023-02115-9\u003c/li\u003e\n \u003cli\u003eYang Z, Gong H, Kan F, Ji N. Association between the triglyceride glucose (TyG) index and the risk of acute kidney injury in critically ill patients with heart failure: analysis of the MIMIC-IV database. Cardiovasc Diabetol. 2023;22(1):232. Published 2023 Aug 31. doi:10.1186/s12933-023-01971-9\u003c/li\u003e\n \u003cli\u003eDuan M, Zhao X, Li S, et al. Metabolic score for insulin resistance (METS-IR) predicts all-cause and cardiovascular mortality in the general population: evidence from NHANES 2001-2018. Cardiovasc Diabetol. 2024;23(1):243. Published 2024 Jul 10. doi:10.1186/s12933-024-02334-8\u003c/li\u003e\n \u003cli\u003eTian X, Chen S, Wang P, et al. Insulin resistance mediates obesity-related risk of cardiovascular disease: a prospective cohort study. Cardiovasc Diabetol. 2022;21(1):289. Published 2022 Dec 23. doi:10.1186/s12933-022-01729-9\u003c/li\u003e\n \u003cli\u003eTahapary DL, Pratisthita LB, Fitri NA, et al. Challenges in the diagnosis of insulin resistance: Focusing on the role of HOMA-IR and Tryglyceride/glucose index. Diabetes Metab Syndr. 2022;16(8):102581. doi:10.1016/j.dsx.2022.102581\u003c/li\u003e\n \u003cli\u003eHuang D, Zhong X, Jiang Y, et al. Insulin resistance impairs biologic agent response in moderate-to-severe plaque psoriasis: insights from a prospective cohort study in China. Br J Dermatol. 2024;191(4):616-623. doi:10.1093/bjd/ljae147\u003c/li\u003e\n \u003cli\u003eCaroppo F, Galderisi A, Ventura L, Belloni Fortina A. Metabolic syndrome and insulin resistance in pre-pubertal children with psoriasis. Eur J Pediatr. 2021;180(6):1739-1745. doi:10.1007/s00431-020-03924-w\u003c/li\u003e\n \u003cli\u003eZhong X, Huang D, Chen R, et al. Positive association between insulin resistance and fatty liver disease in psoriasis: evidence from a cross-sectional study. Front Immunol. 2024;15:1388967. Published 2024 Apr 23. doi:10.3389/fimmu.2024.1388967\u003c/li\u003e\n \u003cli\u003eSakurai Y, Kubota N, Yamauchi T, Kadowaki T. Role of Insulin Resistance in MAFLD. Int J Mol Sci. 2021;22(8):4156. Published 2021 Apr 16. doi:10.3390/ijms22084156\u003c/li\u003e\n \u003cli\u003eWilliams JC, Hum RM, Rogers K, Maglio C, Alam U, Zhao SS. Metabolic syndrome and psoriatic arthritis: the role of weight loss as a disease-modifying therapy. Ther Adv Musculoskelet Dis. 2024;16:1759720X241271886. Published 2024 Aug 19. doi:10.1177/1759720X241271886\u003c/li\u003e\n \u003cli\u003eCruz KJC, de Oliveira ARS, Morais JBS, Severo JS, Marreiro PhD DDN. Role of microRNAs on adipogenesis, chronic low-grade inflammation, and insulin resistance in obesity. Nutrition. 2017;35:28-35. doi:10.1016/j.nut.2016.10.003\u003c/li\u003e\n \u003cli\u003eSecchiero P, Rimondi E, Marcuzzi A, et al. Metabolic Syndrome and Psoriasis: Pivotal Roles of Chronic Inflammation and Gut Microbiota. Int J Mol Sci. 2024;25(15):8098. Published 2024 Jul 25. doi:10.3390/ijms25158098\u003c/li\u003e\n\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triglyceride-glucose index, TyG-BMI, TyG-waist circumference, insulin resistance, psoriasis, metabolic biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-7988743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7988743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eThe triglyceride-glucose (TyG) index and its anthropometric-adjusted derivatives, including TyG-body mass index (TyG-BMI) and TyG-waist circumference (TyG-WC), are surrogate markers of insulin resistance. Although metabolic dysfunction is implicated in psoriasis, evidence on these indices and psoriasis risk in Asian populations is scarce.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe conducted a cross-sectional study of 3,314 Chinese adults recruited from Deyang Hospital, Chengdu University of Traditional Chinese Medicine (January 2022\u0026ndash;June 2023). Demographic, lifestyle, anthropometric, and biochemical data were collected using standardized protocols. TyG, TyG-BMI, and TyG-WC were calculated and categorized into quartiles. Logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for age, sex, body mass index, blood pressure, lipids, high-sensitivity C-reactive protein, smoking, and alcohol consumption.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eAmong 3,314 participants, 1,560 had psoriasis and 1,754 did not. Patients with psoriasis were older, more frequently male, and exhibited higher rates of smoking, alcohol intake, obesity, hypertension, dyslipidemia, and systemic inflammation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). The TyG index and its derivatives were significantly elevated in the psoriasis group. In fully adjusted models, higher quartiles of TyG, TyG-BMI, and TyG-WC were strongly associated with psoriasis risk. Compared with quartile 1, adjusted ORs (95% CIs) for quartile 4 were 8.91 (3.42\u0026ndash;23.19) for TyG, 4.64 (2.18\u0026ndash;9.90) for TyG-BMI, and 18.35 (4.80\u0026ndash;70.22) for TyG-WC (all P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Each standard deviation increase in TyG, TyG-BMI, and TyG-WC corresponded to 58%, 43%, and 94% higher risks of psoriasis, respectively.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eElevated TyG, TyG-BMI, and TyG-WC are independently associated with psoriasis in Chinese adults. These simple, cost-effective indices may aid in identifying individuals at high risk for psoriasis, with potential utility in preventive screening.\u003c/p\u003e","manuscriptTitle":"Association of Triglyceride-Glucose Index and Its Obesity-Adjusted Derivatives with Psoriasis: Findings from a Cross-Sectional Chinese study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 14:44:42","doi":"10.21203/rs.3.rs-7988743/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-05T09:34:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-04T08:39:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"156025733765311940994005435845253909221","date":"2025-11-25T08:13:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T08:16:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184856178692851619120005763334239217259","date":"2025-11-10T01:51:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218178486093001259255216562373044077587","date":"2025-11-09T14:32:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-04T13:41:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-04T12:30:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-31T06:17:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-31T06:15:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-30T11:10:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa17dadf-d30e-4cea-932f-dd418fd5af9c","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":57689913,"name":"Health sciences/Biomarkers"},{"id":57689914,"name":"Health sciences/Diseases"},{"id":57689915,"name":"Health sciences/Endocrinology"},{"id":57689916,"name":"Health sciences/Health care"},{"id":57689917,"name":"Health sciences/Medical research"},{"id":57689918,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-12-05T09:39:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 14:44:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7988743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7988743","identity":"rs-7988743","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}