Association between type 2 diabetes mellitus complications and NAFLD: Insights from the NHANES 2017-2020 and Mendelian randomization study

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Type 2 diabetes mellitus (T2DM) is a subtype of diabetes mellitus characterized by insulin resistance, often accompanied by complications such as kidney disease, microangiopathy, and neuropathy. There is a strong association between T2DM and NAFLD; however, the causal link between T2DM and the development of NAFLD is unclear. We performed multivariable regression analyses to assess the association between T2DM complications (kidney disease and retinopathy) and NAFLD. Subsequently, we employed mendelian randomization (MR) analysis to evaluate the genetic determinants of T2DM complications on NAFLD, utilizing GWAS datasets. The results of the regression analysis showed that the presence of diabetic kidney disease and lower eGFR, rather than retinopathy, were positively correlated with NAFLD (β: 2.29, 95% CI: 1.40–3.75, p < 0.001; β: 2.94, 95% CI: 1.47–5.85, p = 0.002). However, the MR analysis did not reveal a causal relationship between T2DM-related kidney disease and NAFLD, in either the discovery or validation group (p > 0.05). In conclusion, this study suggests that while diabetic kidney disease is associated with NAFLD, there is no causal association between T2DM-related kidney disease and NAFLD. These findings could inform targeted prevention and treatment strategies. Non-alcoholic fatty liver disease Type 2 diabetes mellitus NHANES Mendelian randomization Causal relationship Figures Figure 1 Figure 2 Figure 3 Introduction Non-alcoholic fatty liver disease (NAFLD) is recognized as one of the most prevalent chronic liver diseases globally, with its incidence having significantly increased over the past few decades[1, 2]. Currently, more than 25% of adults and 3%-10% of children worldwide are affected by NAFLD[3]. The progression of NAFLD can lead to severe liver conditions such as fibrosis, cirrhosis, and hepatocellular carcinoma, imposing a substantial burden on healthcare systems and society[4, 5]. NAFLD is also closely associated with metabolic disorders, including obesity, diabetes, dyslipidemia, and hypertension[6, 7]. Particularly, the prevalence of NAFLD is alarmingly high among obese individuals and those with diabetes, reaching up to 60% and 40%, respectively[8]. Thus, NAFLD is not merely a liver disease but a component of a broader metabolic syndrome. Type 2 diabetes (T2DM) is a complex metabolic disorder characterized by insulin resistance and chronic inflammation, often accompanied by poor glycemic control, leading to a range of complications, including kidney disease, microangiopathy, and neuropathy[9, 10]. Numerous studies have shown that approximately 50%-70% of T2DM patients also suffer from NAFLD, highlighting the intricate and bidirectional relationship between the two conditions[11, 12, 13]. NAFLD significantly increases the risk of insulin resistance and T2DM[14, 15]. On the other hand, T2DM patients are at a higher risk of developing hepatic steatosis and its progression to advanced liver damage, such as fibrosis and cirrhosis[16, 17]. Despite the well-established association between T2DM and NAFLD, the causal relationship between T2DM complications, such as kidney disease and retinopathy, and NAFLD remains inadequately understood. Current diagnostic and therapeutic approaches for these conditions are limited by this knowledge gap. Mendelian randomization (MR), an emerging epidemiological method, leverages genetic variants as instrumental variables to minimize confounding factors, thereby providing more reliable causal inferences[18]. Exploring the causal relationship between T2DM complications and NAFLD through mendelian randomization analysis can yield more plausible inferences. This study aims to elucidate the causal relationship between T2DM complications and NAFLD using a robust combination of cross-sectional data and MR analysis. The findings are expected to provide critical scientific evidence for the prevention and treatment of NAFLD, contributing to the development of more precise clinical intervention strategies. Methods Overall Study Design This study was conducted in two stages, as illustrated in Figure 1. In the first stage, we utilized data from the National Health and Nutrition Examination Survey (NHANES) database to conduct a multivariable regression analysis to determine the association between T2DM complications and non-alcoholic fatty liver disease (NAFLD). In the second stage, we evaluated the causal effect of genetically determined T2DM complication levels on NAFLD using MR analysis. Participants in NHANES This study utilized data from the NHANES, a multi-stage, stratified, nationally representative survey conducted by the Centers for Disease Control and Prevention and the National Center for Health Statistics[19]. We analyzed data collected from 2017 to 2020, initially including 9,693 adult participants. Data from 2017 to 2020, including 9,693 adult participants, was collected. After applying stringent criteria, we identified 859 eligible diabetic patients. Exclusion criteria included: non-diabetic individuals (n = 7,866), those with missing or invalid Fibroscan data (n = 399), heart failure patients (n = 100), those with a history of alcohol abuse (n = 47), chronic hepatitis B infection (n = 13), chronic hepatitis C infection (n = 19), missing urine albumin-to-creatinine ratio or estimated glomerular filtration rate (eGFR) data (n = 85), and missing retinopathy data (n = 305). All participants provided informed consent, with the NHANES protocol approved by the NCHS Research Ethics Review Board and data publicly accessible via the NHANES database. Definition of Variables T2DM was diagnosed based on specific diagnostic criteria [20], including either a random venous plasma glucose concentration ≥11.1 mmol/L, a fasting plasma glucose concentration ≥7.0 mmol/L (whole blood ≥6.1 mmol/L), or a two-hour plasma glucose concentration ≥11.1 mmol/L following a 75g anhydrous glucose oral glucose tolerance test (OGTT). T2DM complications included kidney disease and retinopathy. The definition of T2DM-related kidney disease follows that of chronic kidney disease according to the “KDIGO 2021 Guidelines”, which is defined as a urinary albumin to creatinine ratio (UACR) >30 mg/g and/or an eGFR <60 mL/min/1.73 m²[21]. Serum creatinine measurements were recalibrated to standardized values obtained at the Cleveland Clinic Research Laboratory (Cleveland, OH) using the formula: standard creatinine = –0.184 + 0.960 × serum creatinine. eGFR was estimated using the formula: 175 × (standardized serum creatinine)⁻¹·¹⁵⁴ × (age)⁻⁰·²⁰³ × 0.742 (if female) × 1.212 (if African American)[22]. T2DM retinopathy was defined using retinal imaging data from the NHANES inspection module[23]. NAFLD was identified in individuals with fatty liver disease (CAP ≥288) in the absence of other causes such as chronic viral hepatitis or excessive alcohol consumption (alcohol consumption <30 g/day for males and <20 g/day for females)[24]. Covariates included sex, age, race, education level, marital status, smoking status, body mass index (BMI), waist circumference, hypertension, HDL-cholesterol, and triglycerides. Genetic Instrument Selection Single nucleotide polymorphisms (SNPs) were used as genetic instrumental variables, obtained from publicly available genome-wide association study (GWAS) datasets [18] (Supplementary Table 1). The exposure data were sourced from a GWAS dataset (finn-b-E4_DM2REN) including 184,481 Europeans and 16,380,337 SNPs. In the discovery group, the primary outcome data were obtained from a GWAS dataset (ebi-a-GCST90054782) including 412,181 Europeans and 19,682,629 SNPs. In the validation group, the primary outcome data were obtained from another GWAS dataset (finngen_R10_NAFLD) including 377,988 Europeans and 9,097,254 SNPs. SNPs with a genome-wide significance level (p < 1×10⁻ 5 ) were included. After excluding SNPs associated with confounding factors, the remaining SNPs were utilized as instrumental variables in the MR analysis. F-statistics were calculated for each SNP to ensure they were strong instruments (F-statistics > 10). Mendelian Randomization Analysis The primary MR analysis was conducted using the inverse-variance weighted (IVW) method, which combines the SNP-exposure and SNP-outcome associations to estimate the causal effect of the exposure on the outcome. MR-Egger Regression, Weighted Median Method, Simple Mode Method, and Weighted Mode Method, were used to ensure the robustness of the findings and account for potential pleiotropy. Heterogeneity and Pleiotropy To ensure robustness, Cochran’s Q (Q) Test evaluated the heterogeneity of SNP-exposure and SNP-outcome associations, Leave-One-Out Analysis assessed the influence of each individual SNP on the overall causal estimate, and MR-PRESSO detected and corrected for horizontal pleiotropy by identifying and removing outlier SNPs. Statistical Analysis Multivariable-adjusted linear regression models were employed to evaluate the association between T2DMM complications and NAFLD using NHANES data, accounting for potential confounders. Ethics Statement This study utilized publicly available data from NHANES and GWAS, hence no new ethical approval was necessary. The original studies that generated these data had obtained ethical approval from their respective institutional review boards, and informed consent was secured from all participants. Results Baseline characteristics of participants The baseline characteristics of diabetic participants (n = 859) are presented in Table 1. Participants were categorized into two groups: those with NAFLD and those without NAFLD. Those with NAFLD exhibited notably higher BMI and waist circumference, indicating greater central obesity[25]. Biochemical analyses showed elevated levels of ALT and AST, which are commonly associated with liver inflammation or damage[26]. Additionally, the estimated eGFR was elevated in participants with NAFLD, suggesting differences in kidney function. A higher proportion of T2DM-related kidney disease was also observed in the NAFLD group, highlighting the association between liver and kidney complications in diabetes. In summary, diabetic participants with NAFLD demonstrated a profile of higher adiposity, worse metabolic control, increased liver damage, and a higher prevalence of diabetic nephropathy, but were younger and had lower HDL-cholesterol and creatinine levels compared to those without NAFLD. Observational associations between T2DM complications and NAFLD in NHANES Multivariate linear regression analysis was used to evaluate the association between T2DM complications and NAFLD. We used three models to display the β values (95% CI) for diabetic kidney disease and retinopathy (Table 2). In Model 1, no covariates were adjusted. In Model 1, no covariates were incorporated. Model 2 adjusted for age, sex, and race. Furthermore, Model 3 took into consideration sex, age, race, education level, marital status, smoking habits, BMI, waist circumference, hypertension, HDL cholesterol, triglycerides, and median liver stiffness. The adjusted models indicated that diabetic kidney disease was significantly linked to an increased risk of NAFLD (β: 2.29, 95% CI: 1.40–3.75, p < 0.001). Lower eGFR was significantly associated with an increased risk of NAFLD (β: 2.94, 95% CI: 1.47–5.85, p = 0.002). However, no significant association was observed between diabetic retinopathy and NAFLD (β = 0.84 [95% CI, 0.46–1.54], p = 0.579). These findings suggest that diabetic kidney disease and lower eGFR are significantly linked to the risk of NAFLD among individuals with T2DM. Causal association between T2DM complication and NAFLD in MR Based on NHANES results, we further utilized genetic instrumental variables to explore the causal between T2DM-related kidney disease and NAFLD. In the discovery dataset, 12 eligible SNPs were included (Supplementary Table 2). Using these SNPs for MR analysi, the results indicated that diabetic kidney disease may not be causally related to NAFLD ( p = 0.907) (Figure 2A). There was no evidence of heterogeneity (Q- p > 0.05). Similarly, in the validation dataset, 12 SNPs were included in the MR analysis (Supplementary Table 3), and the results were also not statistically significant ( p = 0.092) (Figure 2B). There was no evidence of heterogeneity (Q- p > 0.05) (Supplementary Fig 1C-D). A meta-analysis of the discovery and validation datasets still suggested that there is no causal relationship between T2DM-related kidney disease and NAFLD ( p = 0.226) (Figure 2C). Additionally, the results were confirmed for robustness. Sensitivity analyses, including the leave-one-out method, showed that excluding any single SNP did not significantly alter the overall causal estimate, indicating that no individual SNP disproportionately influenced the results (Figure 3 and Supplementary Fig 1). The MR-Egger test detected no directional pleiotropy ( p > 0.05), confirming the reliability of the findings. Overall, our MR analysis indicates that there is no significant causal relationship between T2DM-related kidney disease and NAFLD. These findings were consistent across different MR methods and robust to sensitivity analyses. Discussion This study aimed to elucidate the causal relationship between T2DM complications and NAFLD. By leveraging the extensive NHANES dataset and MR analysis, we sought to provide more reliable evidence of causality. Our study results suggest that T2DM-related kidney disease might not have a causal relationship with NAFLD, although multivariate regression suggested a correlation. NAFLD is a systemic metabolic disorder often accompanied by insulin resistance, as well as hepatic and systemic inflammation[27]. The association between NAFLD and T2DM has garnered widespread attention[13]. However, the impact of T2DM complications on NAFLD remains unclear. The present study offers several innovative contributions to the understanding of the causal relationship between T2DM complications and NAFLD. By employing a combination of cross-sectional data from the NHANES and MR analysis, this research addresses the limitations of previous observational studies that were often confounded by reverse causality and other biases. Data from the NHANES participant indicate that diabetic patients with nephropathy, rather than retinopathy, have a higher probability of developing NAFLD. Interestingly, the subsequent MR analysis demonstrated that T2DM-related kidney disease may not be causally relationship to NAFLD in both the discovery and validation cohorts. These data indicate there may not be a causal relationship between T2DM-related kidney disease and NAFLD. Despite the strengths of our study, several limitations must be acknowledged. The NHANES data is based on self-reported information, which may introduce recall bias and affect the accuracy of the findings. Our MR analysis was limited to linear causal relationships and may not fully capture the complexity of the interactions between T2DM complications and NAFLD. Moreover, the study population primarily consisted of individuals from Western countries, which may limit the generalizability of the results to other ethnic and geographic populations. Additionally, the cross-sectional design of the NHANES data precludes the establishment of temporal causality. Future research should aim to include more diverse populations and employ longitudinal designs to validate our findings. Expanding the sample size and incorporating more comprehensive genetic data could also enhance the robustness of future MR analyses. In summary, our study provides compelling evidence for a causal relationship between T2DM-related kidney disease and NAFLD, offering new insights into the pathophysiological links between these conditions. These findings have significant implications for clinical practice and public health policies, emphasizing the need for integrated management strategies for T2DM and its hepatic complications. Future research should address the limitations identified in this study to further elucidate the complex interactions between T2DM complications and NAFLD. Abbreviations NHANES, National Health and Nutrition Examination Survey; T2DM, Type 2 diabetes mellitus; HBV, Hepatitis B virus; HBV, Hepatitis C virus; NAFLD, Non-alcoholic fatty liver disease; eGFR, glomerular filtration rate; SNPs, Single nucleotide polymorphisms; IVW, Inverse-variance weighted; Declarations Conflict of interest: The authors declare no competing financial interests. Financial support: This work was supported by the National Natural Science Foundation of China [81974070], and the Basic and Applied Basic Research Foundation of Guangdong Province [2023A1515030071]. Author contributions: SW, ML, and SL contributed equally to this study. SW and ML designed and performed the experiments. SL and ZZ analyzed the data. YW and designed the whole project and supervised the research. SW and WG wrote the paper. Ethical approval: This study utilized publicly available data from NHANES and GWAS, hence no new ethical approval was necessary. 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Tables Table .1 Characteristics of diabetic participants based on the absence or presence of NAFLD in NHANES 2017-2020 Variable Non-NAFLD N = 353 NAFLD N = 506 P value* Age 63.38 ± 12.77 60.45 ± 12.32 < 0.001 Sex 0.090 Male 176 (49.86%) 282 (55.73%) Female 177 (50.14%) 224 (44.27%) Race 0.005 Mexican American 43 (12.18%) 85 (16.80%) Other Hispanic 34 (9.63%) 58 (11.46%) Non-Hispanic White 97 (27.48%) 160 (31.62%) Non-Hispanic Black 118 (33.43%) 112 (22.13%) Non-Hispanic Asian 44 (12.46%) 55 (10.87%) Other Race - Including Multi-Racial 17 (4.82%) 36 (7.11%) Marital status 0.003 Married 199 (56.53%) 338 (66.93%) Divorced 121 (34.38%) 118 (23.37%) Never married 31 (8.81%) 49 (9.70%) Unclear 1 (0.28%) 0 (0.00%) Education level 0.038 Less than high school graduate 97 (27.56%) 113 (22.38%) High school graduate or GED 97 (27.56%) 120 (23.76%) Some college or above 157 (44.60%) 272 (53.86%) Unclear 1 (0.28%) 0 (0.00%) Smoking status 0.126 Never smoke 195 (55.24%) 263 (51.98%) Past smoker 103 (29.18%) 179 (35.38%) Current smoker 55 (15.58%) 64 (12.65%) Hypertension 283 (80.17%) 389 (76.88%) 0.250 Body Mass Index (kg/m 2 ) 29.35 ± 6.19 34.16 ± 7.27 < 0.001 Waist Circumference (cm) 101.83 ± 13.62 113.77 ± 15.22 < 0.001 Platelet (× 10 9 /L) 238.58 ± 69.67 244.75 ± 72.13 0.194 ALT (U/L) 19.47 ± 14.64 26.27 ± 16.55 < 0.001 AST (U/L) 20.06 ± 13.17 22.52 ± 12.98 < 0.001 Albumin (g/L) 39.77 ± 3.17 40.04 ± 3.33 0.344 Globulin (g/L) 31.47 ± 4.67 31.27 ± 4.39 0.729 ALP (U/L) 83.91 ± 29.21 84.99 ± 29.55 0.647 Creatinine (umol/L) 78.81 ± 28.32 91.21 ± 69.62 < 0.001 Fasting glucose (mmol/L) 7.50 ± 3.13 8.35 ± 3.64 < 0.001 Triglycerides (mmol/L) 1.62 ± 1.49 2.17 ± 1.72 < 0.001 HDL-Cholesterol (mmol/L) 1.34 ± 0.43 1.18 ± 0.31 < 0.001 Liver stiffness (kpa) 6.16 ± 5.27 7.84 ± 5.63 < 0.001 CAP (dB/m) 241.11 ± 37.06 339.68 ± 32.51 < 0.001 Urinary albumin creatinine ratio (mg/g) 100.64 ± 326.60 165.61 ± 736.00 0.474 eGFR (mL/min/1.73 m 2 ) 90.24 ± 30.43 81.72 ± 29.21 < 0.001 Diabetic kidney disease 135 (38.14%) 231 (45.61%) 0.029 Diabetic retinopathy 59 (16.80%) 106 (20.96%) 0.122 Continuous variables are presented as mean ± SD; categorical variables are presented as n (percentage). Continuous variables were tested by Kruskal Wallis or Fisher exact test, while categorical variables were tested by the chi-squared test. CAP, controlled attenuation parameter; CI, confidence interval; eGFR, estimated glomerular filtration rate, eGFR. Table 2. Multivariate analysis for the relationship between the presence of NAFLD and diabetic complications Model 1, β (95% CI) p -value Model 2, β (95% CI) p -value Model 3, β (95% CI) p -value Urinary albumin creatinine ratio (mg/g) 1.00 (1.00, 1.00) 0.112 1.00 (1.00, 1.00) 0.160 1.00 (1.00, 1.00) 0.081 Urinary albumin creatinine ratio≥30 mg/g No Reference Reference Reference Yes 1.26 (0.95, 1.68) 0.115 1.28 (0.96, 1.73) 0.098 1.74 (1.06, 2.88) 0.030 eGFR (mL/min/1.73 m 2) 1.01 (1.01, 1.01) < 0.001 1.01 (1.00, 1.01) 0.0027 1.01 (1.01, 1.02) < 0.001 eGFR＜60 mL/min/1.73 m 2 No Reference Reference Reference Yes 1.77 (1.23, 2.55) 0.002 1.55 (1.05, 2.29) 0.026 2.94 (1.47, 5.85) 0.002 Diabetic kidney disease No Reference Reference Reference Yes 1.36 (1.03, 1.79) 0.029 1.33 (1.00, 1.78) 0.048 2.29 (1.40, 3.75) < 0.001 Diabetic retinopathy No Reference Reference Reference Yes 1.31 (0.93, 1.86) 0.123 1.23 (0.86, 1.75) 0.254 0.84 (0.46, 1.54) 0.579 Model 1: No covariates were adjusted. Model 2: Sex, age, and race were adjusted. Model 3: Sex, age, race, education level, marital status, smoking statuts, BMI, waist, hypertension, HDL-Cholesterol, triglycerides, and median liver stiffness were adjusted. CI, confidence interval; eGFR, estimated glomerular filtration rate. Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterials.docx STable2.csv STable3.csv Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 22 Jul, 2024 Submission checks completed at journal 18 Jul, 2024 First submitted to journal 18 Jul, 2024 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. 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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-4760695","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":330167446,"identity":"f04736b5-7bb5-4175-855b-7ada6d36a0ad","order_by":0,"name":"Sizhe Wan","email":"","orcid":"","institution":"The Shenzhen Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sizhe","middleName":"","lastName":"Wan","suffix":""},{"id":330167449,"identity":"55d7224a-7fa0-42e4-a5bb-2cf00cd3e481","order_by":1,"name":"Mingkai Li","email":"","orcid":"","institution":"The Shenzhen Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingkai","middleName":"","lastName":"Li","suffix":""},{"id":330167450,"identity":"d23637d1-fadb-46b9-a046-a8144106a751","order_by":2,"name":"Sicun Lu","email":"","orcid":"","institution":"The Shenzhen Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sicun","middleName":"","lastName":"Lu","suffix":""},{"id":330167451,"identity":"4c3ba10f-601f-4cfb-98c0-28ac656feb4e","order_by":3,"name":"Zhiyong Zhai","email":"","orcid":"","institution":"The Shenzhen Hospital of Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyong","middleName":"","lastName":"Zhai","suffix":""},{"id":330167452,"identity":"8b7e6af4-f1c1-49c4-aec1-214470924482","order_by":4,"name":"Yuankai Wu","email":"","orcid":"","institution":"The Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yuankai","middleName":"","lastName":"Wu","suffix":""},{"id":330167453,"identity":"83c721ef-7e6e-441a-9042-b94eaf6855e9","order_by":5,"name":"Wei Gong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYPACCSBmPnDgww/StLAlHpzZQ5pNPMaHOdiIUCfv3nv4NU+Fhb3ujJwPhxl4GOT5xQ7g12J45lyaNc8ZicRtN3I3HC6wYDCcOTuBgJYZOWbGuW0SCWYgLTN4GBIMbhOl5Z+EvdmNnAeHediI0CIvkWP8OLdBgnHbjRwG4rQY8JwxY/5zDOiXM88MgIEsQdgv8u09xh9n1NTZmx1Pfvzhww8beX5pQrYcYGCTQOJL4FSJsKWBgfkDYWWjYBSMglEwogEAZ+5G8KoWQk0AAAAASUVORK5CYII=","orcid":"","institution":"The Shenzhen Hospital of Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Gong","suffix":""}],"badges":[],"createdAt":"2024-07-18 07:29:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4760695/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4760695/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62304721,"identity":"c9950cfa-4b9a-428f-a8e4-777c5adc0408","added_by":"auto","created_at":"2024-08-12 17:49:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1462200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall study design based on observational analysis and MR.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the first stage, we utilized data from the NHANES database to conduct a multivariable regression analysis to determine the association between T2DM complications and NAFLD. In the second stage, we evaluated the causal effect of genetically determined T2DM complications on NAFLD using MR analysis. NHANES, National Health and Nutrition Examination Survey; T2DM, Type 2 diabetes mellitus; HBV, Hepatitis B virus; HBV, Hepatitis C virus; NAFLD, Non-alcoholic fatty liver disease; eGFR, glomerular filtration rate; SNPs, Single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4760695/v1/909f1753a8a053f57768e259.png"},{"id":62305271,"identity":"fbaced9e-b210-4a96-b432-6388c80879f5","added_by":"auto","created_at":"2024-08-12 17:57:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1430458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR study of genetically predicted T2DM-related kidney disease and NAFLD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the discovery dataset (A), validation datasets (B), and meta-analysis (C), there was no significant causal association between T2DM-related kidney disease and NAFLD. T2DM, Type 2 diabetes mellitus; IVW, Inverse-variance weighted.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4760695/v1/c98c6a24a432460f6ca02122.png"},{"id":62304725,"identity":"ee17b331-ff59-47d5-81e5-b1b6746c192d","added_by":"auto","created_at":"2024-08-12 17:49:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1596509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR analysis of T2DM-related kidney disease and NAFLD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the discovery dataset, a scatter plot shows the associations between T2DM-related kidney disease and NAFLD using different MR methods (A). A forest plot depicts the causal effects of T2DM-related kidney disease-associated SNPs on NAFLD (B). In the validation dataset, a scatter plot illustrates the associations between T2DM-related kidney disease and NAFLD using different MR methods (C). A forest plot presents the causal effects of T2DM-related kidney disease-associated SNPs on NAFLD (D). SNP, Single nucleotide polymorphism; T2DM, Type 2 diabetes mellitus; IVW, Inverse-variance weighted.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4760695/v1/ee924c57ade3ea357f8faf50.png"},{"id":62305587,"identity":"7209b733-b20c-4977-b30c-280644395369","added_by":"auto","created_at":"2024-08-12 18:05:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4855497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4760695/v1/3308089d-f323-40d0-a1d5-ab2fd0a4769a.pdf"},{"id":62304727,"identity":"2706abf1-8d49-49d0-8980-e52419b58830","added_by":"auto","created_at":"2024-08-12 17:49:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":786130,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4760695/v1/13af6858add2452771cd0c58.docx"},{"id":62304722,"identity":"8b0cbd33-c6df-4a86-92fd-822d5bec8b2e","added_by":"auto","created_at":"2024-08-12 17:49:40","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":880,"visible":true,"origin":"","legend":"","description":"","filename":"STable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-4760695/v1/6bdee3870be2e4bba0e50bac.csv"},{"id":62305272,"identity":"ac5ffe8c-c918-42ea-81f5-0f8e9aab06c4","added_by":"auto","created_at":"2024-08-12 17:57:40","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":883,"visible":true,"origin":"","legend":"","description":"","filename":"STable3.csv","url":"https://assets-eu.researchsquare.com/files/rs-4760695/v1/b8e17f9d3f8cc3c8732e50d8.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between type 2 diabetes mellitus complications and NAFLD: Insights from the NHANES 2017-2020 and Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNon-alcoholic fatty liver disease\u0026nbsp;(NAFLD) is recognized as one of the most prevalent chronic liver diseases globally, with its incidence having significantly increased over the past few decades[1, 2]. Currently, more than 25% of adults and 3%-10% of children worldwide are affected by NAFLD[3]. The progression of NAFLD can lead to severe liver conditions such as fibrosis, cirrhosis, and hepatocellular carcinoma, imposing a substantial burden on healthcare systems and society[4, 5]. NAFLD is also closely associated with metabolic disorders, including obesity, diabetes, dyslipidemia, and hypertension[6, 7]. Particularly, the prevalence of NAFLD is alarmingly high among obese individuals and those with diabetes, reaching up to 60% and 40%, respectively[8]. Thus, NAFLD is not merely a liver disease but a component of a broader metabolic syndrome.\u003c/p\u003e\n\u003cp\u003eType 2 diabetes (T2DM)\u0026nbsp;is a complex metabolic disorder characterized by insulin resistance and chronic inflammation, often accompanied by poor glycemic control, leading to a range of complications, including kidney disease, microangiopathy, and neuropathy[9, 10]. Numerous studies have shown that approximately 50%-70% of T2DM patients also suffer from NAFLD, highlighting the intricate and bidirectional relationship between the two conditions[11, 12, 13]. NAFLD significantly increases the risk of insulin resistance and T2DM[14, 15]. On the other hand, T2DM patients are at a higher risk of developing hepatic steatosis and its progression to advanced liver damage, such as fibrosis and cirrhosis[16, 17].\u003c/p\u003e\n\u003cp\u003eDespite the well-established association between T2DM and NAFLD, the causal relationship between T2DM complications, such as kidney disease and retinopathy, and NAFLD remains inadequately understood. Current diagnostic and therapeutic approaches for these conditions are limited by this knowledge gap. Mendelian randomization (MR), an emerging epidemiological method, leverages genetic variants as instrumental variables to minimize confounding factors, thereby providing more reliable causal inferences[18]. Exploring the causal relationship between T2DM complications and NAFLD through mendelian randomization analysis can yield more plausible inferences.\u003c/p\u003e\n\u003cp\u003eThis study aims to elucidate the causal relationship between T2DM complications and NAFLD using a robust combination of cross-sectional data and MR analysis. The findings are expected to provide critical scientific evidence for the prevention and treatment of NAFLD, contributing to the development of more precise clinical intervention strategies.\u003c/p\u003e\n"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eOverall Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in two stages, as illustrated in Figure 1. In the first stage, we utilized data from the National Health and Nutrition Examination Survey (NHANES) database to conduct a multivariable regression analysis to determine the association between T2DM complications and non-alcoholic fatty liver disease (NAFLD). In the second stage, we evaluated the causal effect of genetically determined T2DM complication levels on NAFLD using MR analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants in NHANES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the NHANES, a multi-stage, stratified, nationally representative survey conducted by the Centers for Disease Control and Prevention and the National Center for Health Statistics[19]. We analyzed data collected from 2017 to 2020, initially including 9,693 adult participants. Data from 2017 to 2020, including 9,693 adult participants, was collected. After applying stringent criteria, we identified 859 eligible diabetic patients.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Exclusion criteria included: non-diabetic individuals (n = 7,866), those with missing or invalid Fibroscan data (n = 399), heart failure patients (n = 100), those with a history of alcohol abuse (n = 47), chronic hepatitis B infection (n = 13), chronic hepatitis C infection (n = 19), missing urine albumin-to-creatinine ratio or estimated glomerular filtration rate (eGFR) data (n = 85), and missing retinopathy data (n = 305). All participants provided informed consent, with the NHANES protocol approved by the NCHS Research Ethics Review Board and data publicly accessible via the NHANES database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition of Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT2DM was diagnosed based on specific diagnostic criteria\u0026nbsp;[20], including either a random venous plasma glucose concentration \u0026ge;11.1 mmol/L, a fasting plasma glucose concentration \u0026ge;7.0 mmol/L (whole blood \u0026ge;6.1 mmol/L), or a two-hour plasma glucose concentration \u0026ge;11.1 mmol/L following a 75g anhydrous glucose oral glucose tolerance test (OGTT).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM complications included kidney disease and retinopathy.\u003c/p\u003e\n\u003cp\u003eThe definition of T2DM-related kidney disease follows that of chronic kidney disease according to the \u0026ldquo;KDIGO 2021 Guidelines\u0026rdquo;, which is defined as a urinary albumin to creatinine ratio (UACR) \u0026gt;30 mg/g and/or an eGFR \u0026lt;60 mL/min/1.73 m\u0026sup2;[21]. Serum creatinine measurements were recalibrated to standardized values obtained at the Cleveland Clinic Research Laboratory (Cleveland, OH) using the formula: standard creatinine = \u0026ndash;0.184 + 0.960 \u0026times; serum creatinine. eGFR was estimated using the formula: 175 \u0026times; (standardized serum creatinine)⁻\u0026sup1;\u0026middot;\u0026sup1;⁵⁴ \u0026times; (age)⁻⁰\u0026middot;\u0026sup2;⁰\u0026sup3; \u0026times; 0.742 (if female) \u0026times; 1.212 (if African American)[22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT2DM retinopathy was defined using retinal imaging data from the NHANES inspection module[23].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNAFLD was identified in individuals with fatty liver disease (CAP \u0026ge;288) in the absence of other causes such as chronic viral hepatitis or excessive alcohol consumption (alcohol consumption \u0026lt;30 g/day for males and \u0026lt;20 g/day for females)[24].\u003c/p\u003e\n\u003cp\u003eCovariates included sex, age, race, education level, marital status, smoking status, body mass index (BMI), waist circumference, hypertension, HDL-cholesterol, and triglycerides.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eInstrument Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle nucleotide polymorphisms (SNPs) were used as genetic instrumental variables, obtained from publicly available genome-wide association study (GWAS) datasets\u0026nbsp;[18]\u0026nbsp;(Supplementary Table 1). The exposure data were sourced from a GWAS dataset (finn-b-E4_DM2REN) including 184,481 Europeans and 16,380,337 SNPs. In the discovery group, the primary outcome data were obtained from a GWAS dataset (ebi-a-GCST90054782) including 412,181 Europeans and 19,682,629 SNPs. In the validation group, the primary outcome data were obtained from another GWAS dataset (finngen_R10_NAFLD) including 377,988 Europeans and 9,097,254 SNPs. SNPs with a genome-wide significance level (p \u0026lt; 1\u0026times;10⁻\u003csup\u003e5\u003c/sup\u003e) were included. After excluding SNPs associated with confounding factors, the remaining SNPs were utilized as instrumental variables in the MR analysis. F-statistics were calculated for each SNP to ensure they were strong instruments (F-statistics \u0026gt; 10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian Randomization Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary MR analysis was conducted using the inverse-variance weighted (IVW) method, which combines the SNP-exposure and SNP-outcome associations to estimate the causal effect of the exposure on the outcome. MR-Egger Regression, Weighted Median Method, Simple Mode Method, and Weighted Mode Method, were used to ensure the robustness of the findings and account for potential pleiotropy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity and Pleiotropy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure robustness, Cochran\u0026rsquo;s Q (Q) Test evaluated the heterogeneity of SNP-exposure and SNP-outcome associations, Leave-One-Out Analysis assessed the influence of each individual SNP on the overall causal estimate, and MR-PRESSO detected and corrected for horizontal pleiotropy by identifying and removing outlier SNPs.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable-adjusted linear regression models were employed to evaluate the association between T2DMM complications and NAFLD using NHANES data, accounting for potential confounders.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available data from NHANES and GWAS, hence no new ethical approval was necessary. The original studies that generated these data had obtained ethical approval from their respective institutional review boards, and informed consent was secured from all participants.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of diabetic participants (n = 859) are presented in Table 1. Participants were categorized into two groups: those with NAFLD and those without NAFLD. Those with NAFLD exhibited notably higher BMI and waist circumference, indicating greater central obesity[25]. Biochemical analyses showed elevated levels of ALT and AST, which are commonly associated with liver inflammation or damage[26]. Additionally, the estimated eGFR was elevated in participants with NAFLD, suggesting differences in kidney function. A higher proportion of T2DM-related kidney disease\u0026nbsp;was also observed in the NAFLD group, highlighting the association between liver and kidney complications in diabetes. In summary, diabetic participants with NAFLD demonstrated a profile of higher adiposity, worse metabolic control, increased liver damage, and a higher prevalence of diabetic nephropathy, but were younger and had lower HDL-cholesterol and creatinine levels compared to those without NAFLD.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObservational associations between T2DM complications and NAFLD in NHANES\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate linear regression analysis was used to evaluate the association between T2DM complications and NAFLD. We used three models to display the \u0026beta; values (95% CI) for diabetic kidney disease and retinopathy (Table 2). In Model 1, no covariates were adjusted. In Model 1, no covariates were incorporated. Model 2 adjusted for age, sex, and race. Furthermore, Model 3 took into consideration sex, age, race, education level, marital status, smoking habits, BMI, waist circumference, hypertension, HDL cholesterol, triglycerides, and median liver stiffness. The adjusted models indicated that diabetic kidney disease was significantly linked to an increased risk of NAFLD (\u0026beta;: 2.29, 95% CI: 1.40\u0026ndash;3.75, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Lower eGFR was significantly associated with an increased risk of NAFLD (\u0026beta;: 2.94, 95% CI: 1.47\u0026ndash;5.85, \u003cem\u003ep\u003c/em\u003e = 0.002). However, no significant association was observed between diabetic retinopathy and NAFLD (\u0026beta; = 0.84 [95% CI, 0.46\u0026ndash;1.54], \u003cem\u003ep\u003c/em\u003e = 0.579). These findings suggest that diabetic kidney disease and lower eGFR are significantly linked to the risk of NAFLD among individuals with T2DM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCausal association between T2DM complication and NAFLD in MR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on NHANES results, we further utilized genetic instrumental variables to explore the causal between T2DM-related kidney disease and NAFLD. \u0026nbsp;In the discovery dataset, 12 eligible SNPs were included (Supplementary Table 2). Using these SNPs for MR analysi, the results indicated that diabetic kidney disease may not be causally related to NAFLD (\u003cem\u003ep\u003c/em\u003e = 0.907) (Figure 2A). There was no evidence of heterogeneity (Q-\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). Similarly, in the validation dataset, 12 SNPs were included in the MR analysis (Supplementary Table 3), and the results were also not statistically significant (\u003cem\u003ep\u003c/em\u003e = 0.092) (Figure 2B). There was no evidence of heterogeneity (Q-\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) (Supplementary Fig 1C-D). A meta-analysis of the discovery and validation datasets still suggested that there is no causal relationship between T2DM-related kidney disease and NAFLD (\u003cem\u003ep\u003c/em\u003e = 0.226) (Figure 2C).\u003c/p\u003e\n\u003cp\u003eAdditionally, the results were confirmed for robustness. Sensitivity analyses, including the leave-one-out method, showed that excluding any single SNP did not significantly alter the overall causal estimate, indicating that no individual SNP disproportionately influenced the results (Figure 3 and Supplementary Fig 1). The MR-Egger test detected no directional pleiotropy (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05), confirming the reliability of the findings. Overall, our MR analysis indicates that there is no significant causal relationship between T2DM-related kidney disease and NAFLD. These findings were consistent across different MR methods and robust to sensitivity analyses.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to elucidate the causal relationship between T2DM complications and NAFLD. By leveraging the extensive NHANES dataset and MR analysis, we sought to provide more reliable evidence of causality. Our study results suggest that T2DM-related kidney disease might not have a causal relationship with NAFLD, although multivariate regression suggested a correlation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNAFLD is a systemic metabolic disorder often accompanied by insulin resistance, as well as hepatic and systemic inflammation[27]. The association between NAFLD and T2DM has garnered widespread attention[13]. However, the impact of T2DM complications on NAFLD remains unclear.\u0026nbsp;\u0026nbsp;The present study offers several innovative contributions to the understanding of the causal relationship between T2DM complications and NAFLD. By employing a combination of cross-sectional data from the NHANES and MR analysis, this research addresses the limitations of previous observational studies that were often confounded by reverse causality and other biases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData from the NHANES participant indicate that diabetic patients with nephropathy, rather than retinopathy, have a higher probability of developing NAFLD. Interestingly, the subsequent MR analysis demonstrated that T2DM-related kidney disease may not be causally relationship to NAFLD in both the discovery and validation cohorts. These data indicate there may not be a causal relationship between T2DM-related kidney disease and NAFLD.\u003c/p\u003e\n\u003cp\u003eDespite the strengths of our study, several limitations must be acknowledged. The NHANES data is based on self-reported information, which may introduce recall bias and affect the accuracy of the findings. Our MR analysis was limited to linear causal relationships and may not fully capture the complexity of the interactions between T2DM complications and NAFLD. Moreover, the study population primarily consisted of individuals from Western countries, which may limit the generalizability of the results to other ethnic and geographic populations. Additionally, the cross-sectional design of the NHANES data precludes the establishment of temporal causality. Future research should aim to include more diverse populations and employ longitudinal designs to validate our findings. Expanding the sample size and incorporating more comprehensive genetic data could also enhance the robustness of future MR analyses.\u003c/p\u003e\n\u003cp\u003eIn summary, our study provides compelling evidence for a causal relationship between T2DM-related kidney disease and NAFLD, offering new insights into the pathophysiological links between these conditions. These findings have significant implications for clinical practice and public health policies, emphasizing the need for integrated management strategies for T2DM and its hepatic complications. Future research should address the limitations identified in this study to further elucidate the complex interactions between T2DM complications and NAFLD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNHANES, National Health and Nutrition Examination Survey; T2DM, Type 2 diabetes mellitus; HBV, Hepatitis B virus; HBV, Hepatitis C virus; NAFLD, Non-alcoholic fatty liver disease; eGFR, glomerular filtration rate; SNPs, Single nucleotide polymorphisms; IVW, Inverse-variance weighted;\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China [81974070], and the Basic and Applied Basic Research Foundation of Guangdong Province\u0026nbsp;[2023A1515030071].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e SW, ML, and SL contributed equally to this study. SW and ML designed and\u0026nbsp;performed the\u0026nbsp;experiments. SL and ZZ analyzed the data. YW and designed the whole project and supervised the research. SW and WG wrote the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThis study utilized publicly available data from NHANES and GWAS, hence no new ethical approval was necessary. The original studies that generated these data had obtained ethical approval from their respective institutional review boards, and informed consent was secured from all participants.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLe MH, Le DM, Baez TC, Wu Y, Ito T, Lee EY\u003cem\u003e, et al.\u003c/em\u003e Global incidence of non-alcoholic fatty liver disease: A systematic review and meta-analysis of 63 studies and 1,201,807 persons. J Hepatol 2023;\u003cstrong\u003e79\u003c/strong\u003e:287-95.\u003c/li\u003e\n\u003cli\u003eYounossi ZM. Non-alcoholic fatty liver disease - A global public health perspective. J Hepatol 2019;\u003cstrong\u003e70\u003c/strong\u003e:531-44.\u003c/li\u003e\n\u003cli\u003eNobili V, Alisi A, Valenti L, Miele L, Feldstein AE, Alkhouri N. NAFLD in children: new genes, new diagnostic modalities and new drugs. Nat Rev Gastroenterol Hepatol 2019;\u003cstrong\u003e16\u003c/strong\u003e:517-30.\u003c/li\u003e\n\u003cli\u003eThomas JA, Kendall BJ, El-Serag HB, Thrift AP, Macdonald GA. Hepatocellular and extrahepatic cancer risk in people with non-alcoholic fatty liver disease. Lancet Gastroenterol Hepatol 2024;\u003cstrong\u003e9\u003c/strong\u003e:159-69.\u003c/li\u003e\n\u003cli\u003eTincopa MA, Loomba R. Non-invasive diagnosis and monitoring of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Lancet Gastroenterol Hepatol 2023;\u003cstrong\u003e8\u003c/strong\u003e:660-70.\u003c/li\u003e\n\u003cli\u003eLee E, Korf H, Vidal-Puig A. An adipocentric perspective on the development and progression of non-alcoholic fatty liver disease. J Hepatol 2023;\u003cstrong\u003e78\u003c/strong\u003e:1048-62.\u003c/li\u003e\n\u003cli\u003eTargher G, Tilg H, Byrne CD. Non-alcoholic fatty liver disease: a multisystem disease requiring a multidisciplinary and holistic approach. Lancet Gastroenterol Hepatol 2021;\u003cstrong\u003e6\u003c/strong\u003e:578-88.\u003c/li\u003e\n\u003cli\u003eQuek J, Chan KE, Wong ZY, Tan C, Tan B, Lim WH\u003cem\u003e, et al.\u003c/em\u003e Global prevalence of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in the overweight and obese population: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol 2023;\u003cstrong\u003e8\u003c/strong\u003e:20-30.\u003c/li\u003e\n\u003cli\u003eRoden M, Shulman GI. The integrative biology of type 2 diabetes. Nature 2019;\u003cstrong\u003e576\u003c/strong\u003e:51-60.\u003c/li\u003e\n\u003cli\u003eRohm TV, Meier DT, Olefsky JM, Donath MY. Inflammation in obesity, diabetes, and related disorders. Immunity 2022;\u003cstrong\u003e55\u003c/strong\u003e:31-55.\u003c/li\u003e\n\u003cli\u003eYounossi ZM, Golabi P, de Avila L, Paik JM, Srishord M, Fukui N\u003cem\u003e, et al.\u003c/em\u003e The global epidemiology of NAFLD and NASH in patients with type 2 diabetes: A systematic review and meta-analysis. J Hepatol 2019;\u003cstrong\u003e71\u003c/strong\u003e:793-801.\u003c/li\u003e\n\u003cli\u003eEn Li Cho E, Ang CZ, Quek J, Fu CE, Lim LKE, Heng ZEQ\u003cem\u003e, et al.\u003c/em\u003e Global prevalence of non-alcoholic fatty liver disease in type 2 diabetes mellitus: an updated systematic review and meta-analysis. Gut 2023;\u003cstrong\u003e72\u003c/strong\u003e:2138-48.\u003c/li\u003e\n\u003cli\u003eStefan N, Cusi K. A global view of the interplay between non-alcoholic fatty liver disease and diabetes. Lancet Diabetes Endocrinol 2022;\u003cstrong\u003e10\u003c/strong\u003e:284-96.\u003c/li\u003e\n\u003cli\u003eCho Y, Chang Y, Ryu S, Wild SH, Byrne CD. Synergistic effect of non-alcoholic fatty liver disease and history of gestational diabetes to increase risk of type 2 diabetes. Eur J Epidemiol 2023;\u003cstrong\u003e38\u003c/strong\u003e:901-11.\u003c/li\u003e\n\u003cli\u003eMantovani A, Petracca G, Beatrice G, Tilg H, Byrne CD, Targher G. Non-alcoholic fatty liver disease and risk of incident diabetes mellitus: an updated meta-analysis of 501 022 adult individuals. Gut 2021;\u003cstrong\u003e70\u003c/strong\u003e:962-9.\u003c/li\u003e\n\u003cli\u003eTargher G, Corey KE, Byrne CD, Roden M. The complex link between NAFLD and type 2 diabetes mellitus - mechanisms and treatments. Nat Rev Gastroenterol Hepatol 2021;\u003cstrong\u003e18\u003c/strong\u003e:599-612.\u003c/li\u003e\n\u003cli\u003eGastaldelli A, Cusi K. From NASH to diabetes and from diabetes to NASH: Mechanisms and treatment options. JHEP reports : innovation in hepatology 2019;\u003cstrong\u003e1\u003c/strong\u003e:312-28.\u003c/li\u003e\n\u003cli\u003eLawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008;\u003cstrong\u003e27\u003c/strong\u003e:1133-63.\u003c/li\u003e\n\u003cli\u003eAkinbami LJ, Chen TC, Davy O, Ogden CL, Fink S, Clark J\u003cem\u003e, et al.\u003c/em\u003e National Health and Nutrition Examination Survey, 2017-March 2020 Prepandemic File: Sample Design, Estimation, and Analytic Guidelines. Vital Health Stat 1 2022:1-36.\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association Professional Practice C. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. Diabetes Care 2022;\u003cstrong\u003e45\u003c/strong\u003e:S17-S38.\u003c/li\u003e\n\u003cli\u003eVivarelli M, Barratt J, Beck LH, Jr., Fakhouri F, Gale DP, de Jorge EG\u003cem\u003e, et al.\u003c/em\u003e The Role of Complement in Kidney Disease: Conclusions From a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int 2024.\u003c/li\u003e\n\u003cli\u003eAfkarian M, Zelnick LR, Hall YN, Heagerty PJ, Tuttle K, Weiss NS\u003cem\u003e, et al.\u003c/em\u003e Clinical Manifestations of Kidney Disease Among US Adults With Diabetes, 1988-2014. Jama 2016;\u003cstrong\u003e316\u003c/strong\u003e:602-10.\u003c/li\u003e\n\u003cli\u003eVujosevic S, Aldington SJ, Silva P, Hernandez C, Scanlon P, Peto T\u003cem\u003e, et al.\u003c/em\u003e Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol 2020;\u003cstrong\u003e8\u003c/strong\u003e:337-47.\u003c/li\u003e\n\u003cli\u003eGarcia DO, Morrill KE, Lopez-Pentecost M, Villavicencio EA, Vogel RM, Bell ML\u003cem\u003e, et al.\u003c/em\u003e Nonalcoholic Fatty Liver Disease and Associated Risk Factors in a Community-Based Sample of Mexican-Origin Adults. Hepatol Commun 2022;\u003cstrong\u003e6\u003c/strong\u003e:1322-35.\u003c/li\u003e\n\u003cli\u003eGuo T, Zheng S, Chen T, Chu C, Ren J, Sun Y\u003cem\u003e, et al.\u003c/em\u003e The association of long-term trajectories of BMI, its variability, and metabolic syndrome: a 30-year prospective cohort study. EClinicalMedicine 2024;\u003cstrong\u003e69\u003c/strong\u003e:102486.\u003c/li\u003e\n\u003cli\u003eOh RC, Hustead TR, Ali SM, Pantsari MW. Mildly Elevated Liver Transaminase Levels: Causes and Evaluation. Am Fam Physician 2017;\u003cstrong\u003e96\u003c/strong\u003e:709-15.\u003c/li\u003e\n\u003cli\u003eGrander C, Grabherr F, Tilg H. Non-alcoholic fatty liver disease: pathophysiological concepts and treatment options. Cardiovasc Res 2023;\u003cstrong\u003e119\u003c/strong\u003e:1787-98.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable .1 Characteristics of diabetic participants based on the absence or presence of NAFLD in NHANES 2017-2020\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"560\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003eNon-NAFLD\u003c/p\u003e\n \u003cp\u003eN = 353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003eNAFLD\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN = 506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e63.38 \u0026plusmn; 12.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e60.45 \u0026plusmn; 12.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e176 (49.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e282 (55.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e177 (50.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e224 (44.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e43 (12.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e85 (16.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e34 (9.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e58 (11.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e97 (27.48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e160 (31.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e118 (33.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e112 (22.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eNon-Hispanic Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e44 (12.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e55 (10.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e17 (4.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e36 (7.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e199 (56.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e338 (66.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e121 (34.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e118 (23.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e31 (8.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e49 (9.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e1 (0.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eEducation level\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eLess than high school graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e97 (27.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e113 (22.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eHigh school graduate or GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e97 (27.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e120 (23.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eSome college or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e157 (44.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e272 (53.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eUnclear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e1 (0.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e0 (0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eNever smoke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e195 (55.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e263 (51.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003ePast smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e103 (29.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e179 (35.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e55 (15.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e64 (12.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e283 (80.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e389 (76.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eBody Mass Index (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e29.35 \u0026plusmn; 6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e34.16 \u0026plusmn; 7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e101.83 \u0026plusmn; 13.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e113.77 \u0026plusmn; 15.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003ePlatelet (\u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e238.58 \u0026plusmn; 69.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e244.75 \u0026plusmn; 72.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e19.47 \u0026plusmn; 14.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e26.27 \u0026plusmn; 16.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e20.06 \u0026plusmn; 13.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e22.52 \u0026plusmn; 12.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e39.77 \u0026plusmn; 3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e40.04 \u0026plusmn; 3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eGlobulin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e31.47 \u0026plusmn; 4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e31.27 \u0026plusmn; 4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e83.91 \u0026plusmn; 29.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e84.99 \u0026plusmn; 29.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eCreatinine (umol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e78.81 \u0026plusmn; 28.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e91.21 \u0026plusmn; 69.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eFasting glucose (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e7.50 \u0026plusmn; 3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e8.35 \u0026plusmn; 3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e1.62 \u0026plusmn; 1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e2.17 \u0026plusmn; 1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eHDL-Cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e1.34 \u0026plusmn; 0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e1.18 \u0026plusmn; 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eLiver stiffness (kpa)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e6.16 \u0026plusmn; 5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e7.84 \u0026plusmn; 5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eCAP (dB/m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e241.11 \u0026plusmn; 37.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e339.68 \u0026plusmn; 32.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eUrinary albumin creatinine ratio (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e100.64 \u0026plusmn; 326.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e165.61 \u0026plusmn; 736.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e90.24 \u0026plusmn; 30.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e81.72 \u0026plusmn; 29.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eDiabetic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e135 (38.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e231 (45.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"47.142857142857146%\"\u003e\n \u003cp\u003eDiabetic retinopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e59 (16.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.178571428571427%\"\u003e\n \u003cp\u003e106 (20.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous variables are presented as mean \u0026plusmn; SD; categorical variables are presented as n (percentage).\u003c/p\u003e\n\u003cp\u003eContinuous variables were tested by Kruskal Wallis or Fisher exact test, while categorical variables were tested by the chi-squared test.\u003c/p\u003e\n\u003cp\u003eCAP, controlled attenuation parameter; CI, confidence interval; eGFR, estimated glomerular filtration rate, eGFR.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2. Multivariate analysis for the relationship between the presence of NAFLD and diabetic complications\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003eModel 1, \u0026beta; (95% CI)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003eModel 2, \u0026beta; (95% CI)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003eModel 3, \u0026beta; (95% CI)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003eUrinary albumin creatinine ratio (mg/g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00) 0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00) 0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e1.00 (1.00, 1.00) 0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003eUrinary albumin creatinine ratio\u0026ge;30 mg/g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e1.26 (0.95, 1.68) 0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e1.28 (0.96, 1.73) 0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e1.74 (1.06, 2.88) 0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e1.01 (1.01, 1.01) \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e1.01 (1.00, 1.01) 0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e1.01 (1.01, 1.02) \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003eeGFR＜60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e1.77 (1.23, 2.55) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e1.55 (1.05, 2.29) 0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e2.94 (1.47, 5.85) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003eDiabetic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e1.36 (1.03, 1.79) 0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e1.33 (1.00, 1.78) 0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e2.29 (1.40, 3.75) \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003eDiabetic retinopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.2129963898917%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.20216606498195%\"\u003e\n \u003cp\u003e1.31 (0.93, 1.86) 0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.660649819494584%\"\u003e\n \u003cp\u003e1.23 (0.86, 1.75) 0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.924187725631768%\"\u003e\n \u003cp\u003e0.84 (0.46, 1.54) 0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: No covariates were adjusted.\u003c/p\u003e\n\u003cp\u003eModel 2: Sex, age, and race were adjusted.\u003c/p\u003e\n\u003cp\u003eModel 3: Sex, age, race, education level, marital status, smoking statuts, BMI, waist, hypertension, HDL-Cholesterol, triglycerides, and median liver stiffness were adjusted.\u003c/p\u003e\n\u003cp\u003eCI, confidence interval; eGFR, estimated glomerular filtration rate.\u003c/p\u003e\n"}],"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":"epma-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epmj","sideBox":"Learn more about [EPMA Journal](https://www.springer.com/journal/13167)","snPcode":"13167","submissionUrl":"https://submission.nature.com/new-submission/13167/3","title":"EPMA Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Non-alcoholic fatty liver disease, Type 2 diabetes mellitus, NHANES, Mendelian randomization, Causal relationship","lastPublishedDoi":"10.21203/rs.3.rs-4760695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4760695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Non-alcoholic fatty liver disease (NAFLD) is a globally prevalent chronic liver disease strongly associated with obesity, dyslipidemia, and diabetes. Type 2 diabetes mellitus (T2DM) is a subtype of diabetes mellitus characterized by insulin resistance, often accompanied by complications such as kidney disease, microangiopathy, and neuropathy. There is a strong association between T2DM and NAFLD; however, the causal link between T2DM and the development of NAFLD is unclear. We performed multivariable regression analyses to assess the association between T2DM complications (kidney disease and retinopathy) and NAFLD. Subsequently, we employed mendelian randomization (MR) analysis to evaluate the genetic determinants of T2DM complications on NAFLD, utilizing GWAS datasets. The results of the regression analysis showed that the presence of diabetic kidney disease and lower eGFR, rather than retinopathy, were positively correlated with NAFLD (β: 2.29, 95% CI: 1.40–3.75, p \u003c 0.001; β: 2.94, 95% CI: 1.47–5.85, p = 0.002). However, the MR analysis did not reveal a causal relationship between T2DM-related kidney disease and NAFLD, in either the discovery or validation group (p \u003e 0.05). In conclusion, this study suggests that while diabetic kidney disease is associated with NAFLD, there is no causal association between T2DM-related kidney disease and NAFLD. These findings could inform targeted prevention and treatment strategies.","manuscriptTitle":"Association between type 2 diabetes mellitus complications and NAFLD: Insights from the NHANES 2017-2020 and Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-12 17:49:35","doi":"10.21203/rs.3.rs-4760695/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-07-22T12:21:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-18T09:25:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"EPMA Journal","date":"2024-07-18T07:28:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"epma-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epmj","sideBox":"Learn more about [EPMA Journal](https://www.springer.com/journal/13167)","snPcode":"13167","submissionUrl":"https://submission.nature.com/new-submission/13167/3","title":"EPMA Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"680d77e5-038c-40d6-b7f5-401ec287dc2f","owner":[],"postedDate":"August 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T17:49:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-12 17:49:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4760695","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4760695","identity":"rs-4760695","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}