Relationship between remnant cholesterol and the risk of diabetic microvascular complications in type 2 diabetes: An observational study

Relationship between remnant cholesterol and the risk of diabetic microvascular complications in type 2 diabetes: An observational 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 Research Article Relationship between remnant cholesterol and the risk of diabetic microvascular complications in type 2 diabetes: An observational study Jinyan Liu, Rui Ren, Yi Li, Jilong Bai, Zhang Yao, Xingyue Ye, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6676665/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Diabetic microvascular complications (DMCs) are a major contributor to morbidity in type 2 diabetes (T2D), yet the role of remnant cholesterol (RC) in their development remains unclear. This study aimed to investigate the association between RC levels and DMC risk in patients with T2D. Methods This prospective cohort study was conducted using Mendelian randomization analysis and data from the United Kingdom Biobank, including 22,270 older adults with T2D at baseline (median age: 61 y; 40% women). Participants were grouped according to low, medium, and high RC levels. The main outcome measures were prevalence of diabetic nephropathy, neuropathy, and retinopathy based on hospital admissions and mortality data. Results Increased RC levels were significantly associated with an increased risk of composite microvascular complications (hazard ratio [HR] for highest vs. lowest tertiles = 1.13, 95% confidence interval [CI]: 1.04–1.23), particularly diabetic nephropathy (HR = 1.19, 95% CI: 1.08–1.31) and neuropathy (HR = 1.22, 95% CI: 1.04–1.43). Mendelian randomization revealed statistically significant causal relationships between genetically predicted RC levels and diabetic nephropathy ( p = 0.035) and retinopathy ( p = 0.044). Restricted cubic spline analysis revealed a significant nonlinear dose–response relationship between RC levels and the risk of composite microvascular complications and diabetic retinopathy. Conclusions Elevated RC levels were independently associated with the risk of DMCs, particularly nephropathy and neuropathy. The Mendelian randomization results support a causal role of RC in nephropathy. These results suggest the potential role of RC as a biomarker and that it can aid in the early screening and prediction of diabetic microvascular damage. Remnant cholesterol Diabetic microvascular complications Diabetic nephropathy Diabetic neuropathy Diabetic retinopathy Mendelian randomization Type 2 diabetes mellitus Causal inference Restricted cubic spline Prospective cohort study Figures Figure 1 Figure 2 Background Diabetes mellitus has become a predominant public health concern, with its prevalence projected to exceed over 783 million cases by 2045. [ 1 ] With the escalating incidence of diabetes mellitus, that of diabetic microvascular complications (DMCs) has also increased. DMCs, including diabetic nephropathy, retinopathy, and neuropathy, considerably contribute to the reduced quality of life and increased healthcare burden among individuals with type 2 diabetes mellitus (T2D). [ 2 , 3 ] The occurrence of DMCs is high among individuals with diabetes, affecting nearly half of those diagnosed with T2D. [ 4 ] Approximately 23% of patients with diabetes are prone to kidney ailments, [ 5 ] 34.6% experience vision impairments from diabetic retinopathy, [ 6 ] and 50% develop diabetic neuropathy. [ 7 ] These complications are the primary causes of functional impairment and disability in patients with diabetes. Therefore, the prevention and management of DMCs are crucial for improving patient quality of life. Remnant cholesterol (RC), the cholesterol remaining after the metabolism of triglyceride (TG)-rich lipoproteins (TRLs), has been widely studied and plays a key role in atherosclerotic cardiovascular disease. [ 8 , 9 ] RC promotes the development of atherosclerosis by damaging the vascular endothelium, promoting inflammation, and inducing oxidative stress, thereby increasing the risk of developing cardiovascular disease. [ 10 ] Numerous studies have corroborated the involvement of RC in atherosclerosis; however, its relationship with DMCs remains underexplored. Existing studies have primarily focused on the association between RC and large-vessel diseases; the connection between microvascular complications remains largely unexplored. Although few studies have demonstrated an association between RC levels and diabetic nephropathy, they used small sample sizes and limited outcomes and did not comprehensively evaluate the association of RC with other DMCs. [ 11 ] Furthermore, most existing studies have relied on cross-sectional designs and lack longitudinal data or clear evidence of causal relationships. [ 12 ] Additionally, studies have not effectively addressed confounding factors and reverse causality, limiting a deeper understanding of RC as a potential causal factor in DMCs. Therefore, comprehensive research on the relationship of RC with DMCs is urgently required. In this study, we used large-scale cohort data and Mendelian randomization (MR) analysis to evaluate the relationship between RC levels and multiple DMCs, including diabetic nephropathy, retinopathy, and neuropathy. The novelty of this study lies in its large sample size, long-term follow-up, and consideration of multiple related outcomes using a longitudinal analysis to capture the long-term effects of RC on DMCs. Additionally, MR analysis was used to explore the causal relationship between RC levels and DMCs, providing reliable evidence for DMC prevention and clinical interventions. Methods Cohort analysis Research design The United Kingdom Biobank study is a large-scale biomedical database containing data from a population study enrolling over 500,000 individuals aged 40–69 y between 2006 and 2010 at 22 sites across England, Scotland, and Wales. [ 13 ] The UK Biobank obtained approval from the Northwest Multicenter Research Ethics Committee (National Health Service National Research Ethics Service, Ref 11/NW/0382). Upon enrollment, participants completed an online survey detailing their sociodemographics, lifestyle risk factors, and overall health status; underwent a thorough medical evaluation conducted by a qualified health care provider; and submitted blood, urine, and saliva samples. [ 13 ] All participants provided written informed consent before participation, and the study adhered to the guidelines in the Declaration of Helsinki. The present study was performed under the UK Biobank application number 147427. Study sample Eastwood et al. used the UK Biobank algorithm to determine the prevalence of T2D at baseline from hospital inpatient records, self-reported medical histories, and medication data, achieving an accuracy of 96%. [ 14 ] In addition to using this algorithm, we also considered glycated hemoglobin (HbA1c) test results. [ 15 ] In this study, the diagnostic criteria for T2D included the self-reported age of onset, physician diagnosis, use of antidiabetic medications and insulin, non-cancer disease codes, baseline HbA1c levels, and International Classification of Disease (ICD)-9 and ICD-10 codes. These criteria ensured comprehensive and accurate identification of T2D cases, providing a reliable data foundation for exploring the relationship between RC levels and DMCs. Furthermore, we analyzed the onset of diabetes relative to the enrollment of the participants in the study. The participants who self-reported a diagnosis or were diagnosed with diabetes before or on the day of enrollment were categorized as having existing diabetes. Those without diabetes at the study’s start (n = 471,240), those with gaps or incomplete exposure information (n = 4,708), those with diabetes mellitus complications at baseline (n = 3,626), and those with unaccounted-for variables (n = 366) were excluded. Ultimately, our final analysis included 22,270 patients diagnosed with T2D ( see Additional file 1 ). Exposure assessment Low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels were directly measured, and all other analytes were assessed using standard laboratory methods (see UK Biobank Methods online: https://biobank.ctsu.ox.ac.uk ). RC levels were calculated using the formula: RC = total cholesterol (TC) – (HDL-C − LDL-C). [ 16 ] Outcome ascertainment The primary outcome was the prevalence of DMCs—a comprehensive indicator of the initial appearances of diabetic nephropathy, neuropathy, and retinopathy. Secondary outcomes included the prevalence of individual subtypes. DMC cases were identified using cumulative hospital inpatient records, mortality data, and connections to the National Death Registry. These were categorized based on the ICD-9 and ICD-10 as well as on self-reported data, with codes tailored to selection, disease, or procedure specifics. To distinguish between pre-existing and newly diagnosed DMCs, we cross-referenced the date of the first DMC diagnosis with the initial assessment date. The participants who were diagnosed with a DMC following the initial follow-up period were classified as having a new-onset DMC. Covariates Data on socioeconomic status, lifestyle habits, and prior health issues were collected at baseline using a digital questionnaire. Participants were divided into two groups—smokers and non-smokers. Participants reported their alcohol intake according to the dietary guidelines from the USA and UK. A moderate drinking limit was defined as ≤ 1 drink per day for women and ≤ 2 drinks per day for men, where one drink is considered to contain 14 g of ethanol in the USA and 8 g in the UK. During the first visit to the assessment center, trained experts measured the height, weight, and blood pressure of the participants. The body mass index (BMI) was calculated as weight (kg) divided by the square of the height (m 2 ). Blood samples collected during enrollment were used to measure the levels of HbA1c, lipids (TC, LDL-C, HDL-C, and TG), and serum creatinine. Subsequently, the estimated glomerular filtration rate (eGFR) was calculated based on serum creatinine levels and the sex of the participants. [ 17 ] Considering the established association between long-term chronic diseases and RC, [ 18 ] we included multiple long-term diseases [ 19 ] and corresponding medications as covariates. Statistical analysis To analyze the effects of RC levels on DMCs, we employed the Cox proportional hazards regression model, which allows for the adjustment of multiple potential confounders (such as age, sex, and BMI), [ 20 ] providing more accurate results. The results are presented herein as hazard ratios (HRs) and 95% confidence intervals (CIs). The time-to-event was defined as the period from baseline recruitment to the date of first DMC diagnosis, loss to follow-up, death, or censoring. RC levels were categorized into three groups (low, medium, and high) based on tertiles, with the “low” group serving as the reference in each model. Model 1 was the crude model with no adjustments; Model 2 was adjusted for age (continuous), sex (male or female), ethnicity (White or Other), education level (university or above, high school or equivalent, or less than high school), smoking status (smoker or non-smoker), drinking status (drinker or non-drinker), BMI (< 25.0, 25.0–29.9, or ≥ 30.0 kg/m²), and LDL-C level; Model 3 was further adjusted for eGFR, hypertension history, T2D medication use, and lipid-lowering medication use, in addition to the adjustments in Model 2. In subgroup analyses, RC levels were treated as a continuous variable to assess whether significant interactions existed between RC levels and each covariate. Restricted cubic spline (RCS) analysis was performed to explore the nonlinear relationship between RC levels and DMCs. Two types of sensitivity analyses were performed in this study. First, we excluded cases occurring within 2 y of follow-up and re-performed the Cox analysis for patients with T2D, which helped reduce the effects of reverse causality and confounding factors ( see Additional file 2 ). Second, we added C-reactive protein (CRP) levels to Model 3 and repeated the Cox analysis to allow for a more comprehensive confounder control, improve the explanatory power of the model, assess the moderating effects of CRP levels, and verify the robustness of the results ( see Additional file 3 ). The Kaplan–Meier method was used to calculate cumulative incidence rates for composite DMCs and diabetic nephropathy, retinopathy, and neuropathy in each RC level group ( see Additional files 2–5 ). These analyses were used to compare the effects of RC levels on survival rates in patients with DMCs. The threshold for statistical significance (two-sided) was set at p < 0.05. Statistical analyses were performed using R 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). Mendelian randomization analysis Research design We performed MR analyses with RC levels as the “exposure” and DMCs as the “outcome.” MR studies adhere to the following three key assumptions: (1) instrumental variables (IVs) must be strongly associated with exposure factors; (2) IVs must be independent of confounding factors that are related to both exposure and outcome; and (3) IVs should influence outcomes only through exposure and not directly. Genetic instruments for exposure and outcomes Genome-wide association study (GWAS) summary statistics for RC were extracted online from the IEU Open GWAS database, whereas DMC-associated GWAS data were derived from the FinnGen database. All participants included in the abovementioned datasets were predominantly of European ancestry. To meet the relevance assumption, we selected single nucleotide polymorphisms (SNPs) significantly associated with RC levels at a threshold of p < 5 × 10⁻⁸. SNPs were preserved without linkage disequilibrium using the criteria of R² 10 Mb, based on the European 1000 Genomes reference panel. We identified 54 independent SNPs each for composite microvascular complications and diabetic nephropathy, neuropathy, and retinopathy ( see Additional file 6 ). Employing a two-sample MR approach, we initially ascertained the causal association between RC levels and four DMC-related outcomes. Statistical analysis The genetically predicted effects of RC levels on four DMC-related outcomes were estimated using the TwoSampleMR R package. Causal associations were evaluated using the inverse variance-weighted (IVW), MR-Egger, weighted mode, simple mode, and weighted median methods. IVW was the primary method owing to its ability to combine Wald estimates of genetic associations for each SNP to evaluate the effect of exposure on the outcome. It assumes that all selected SNPs are reliable IVs and is considered the gold standard for estimating causal effects owing to its precision. [ 21 ] Cochran’s Q statistic was used to assess the heterogeneity (I 2 ) among IVs, [ 21 ] calculated as I 2 = [Q − (K − 1)]/Q, where K represents the number of SNPs and Q represents the Q-statistic. Horizontal pleiotropy was assessed by analyzing the MR-Egger intercept values. [ 22 ] All statistical analyses were performed using the TwoSampleMR (version 0.6.8) package in R software. The threshold for statistical significance was set at p < 0.05. Results Baseline characteristics The baseline characteristics of the study participants are presented in Table 1 , categorized by RC level tertiles. The median age of the 22,270 participants at baseline was 61 y. Compared with participants with low RC levels, those with high levels were more likely to be male, have a higher BMI, smoke tobacco, and have a higher eGFR and TG and LDL-C levels. At baseline, 14,565 (66%) participants were using lipid-lowering medications, primarily statins. Notably, this proportion decreased as RC levels increased, from 78% in the lowest tertile to 50% in the highest tertile ( p < 0.001). Similarly, 11,325 (51%) participants were using T2D medications, with usage decreasing with increasing RC levels, from 62% in the lowest tertile to 38% in the highest tertile ( p < 0.001). A total of 5,364 DMC cases, including those of 4,251, 2,294, and 1,438 of diabetic nephropathy, retinopathy, and neuropathy, respectively, were recorded at baseline ( see Additional file 7 ). Table 1 Baseline characteristics of the study population Variable Overall, N = 22,270 Low, N = 7,426 Medium, N = 7,428 High, N = 7,416 p -value Age (years) 61 (55–65) 61 (55–66) 61 (55–65) 60 (53–64) < 0.001 * Sex 0.153 Female 8,895 (40) 2,902 (39) 2,980 (40) 3,013 (41) Male 13,375 (60) 4,524 (61) 4,448 (60) 4,403 (59) Ethnic < 0.001 ** Others 2,782 (12) 1,040 (14) 906 (12) 836 (11) White 19,488 (88) 6,386 (86) 6,522 (88) 6,580 (89) Education < 0.001 ** Less than high school 6,267 (28) 2,070 (28) 2,162 (29) 2,035 (27) High school or equivalent 10,633 (48) 3,443 (46) 3,489 (47) 3,701 (50) College or above 6,267 (28) 2,070 (28) 2,162 (29) 2,035 (27) BMI < 0.001 ** < 25 2,534 (11) 1,300 (18) 756 (10) 478 (6.4) 25-29.9 7,724 (35) 2,719 (37) 2,525 (34) 2,480 (33) ≥ 30 12,012 (54) 3,407 (46) 4,147 (56) 4,458 (60) Smoking 0.002 ** Non-smoking 10,020 (45) 3,458 (47) 3,318 (45) 3,244 (44) Smoking 12,250 (55) 3,968 (53) 4,110 (55) 4,172 (56) Alcohol 0.920 ** Non-drinking 14,364 (64) 4,803 (65) 4,787 (64) 4,774 (64) Drinking 7,906 (36) 2,623 (35) 2,641 (36) 2,642 (36) Hypertension 14,423 (65) 5,006 (67) 5,041 (68) 4,376 (59) < 0.001 ** eGFR 95.4 (85.4–102.5) 94.7 (84.5–101.8) 95.0 (84.9–102.0) 96.5 (86.9–103.4) < 0.001 * Triglycerides 1.9 (1.3–2.7) 1.2 (0.9–1.6) 1.9 (1.5–2.4) 2.9 (2.3–3.9) < 0.00 * LDL-C 2.7 (2.3–3.4) 2.2 (1.9–2.6) 2.7 (2.4–3.1) 3.5 (2.9–4.1) < 0.001 1 HDL-C 1.2 (1.0–1.4) 1.2 (1.0– 1.5) 1.1 (1.0–1.3) 1.1 (0.9–1.3) < 0.001 1 Use of diabetes medication 14,602 (66) 5,790 (78) 5,106 (69) 3,706 (50) < 0.001 2 Use of lipid-lowing medication 14,565 (66) 5,781 (78) 5,097 (69) 3,687 (50) < 0.001 2 *Kruskal–Wallis test **Pearson's Chi-squared test BMI, body mass index; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol RC levels and outcomes The results of Cox regression analysis are presented in Table 2 . In the fully adjusted model (Model 3), when RC was treated as a categorical variable, increased RC levels were significantly associated with an increased risk of developing composite microvascular complications, diabetic nephropathy, and diabetic neuropathy. Compared with patients in the low RC group, those in the high RC group had 13%, 19%, and 22% increased likelihood of developing composite microvascular complications, diabetic nephropathy, and diabetic neuropathy, respectively (Table 2 ). Diabetic retinopathy was not significantly associated with high RC levels (HR: 0.99; 95% CI: 0.87–1.12). When RC level was treated as a continuous variable after adjusting for age, sex, ethnicity, education level, BMI, tobacco use, alcohol consumption, and LDL-C levels, each one-standard deviation increase in RC was associated with an HR of 1.40 (95% CI: 1.26–1.55) for microvascular complications. After adjusting for eGFR, history of hypertension, T2D, and use of lipid-lowering medications, each one-standard deviation increase in RC levels was associated with an HR of 1.38 (95% CI: 1.24–1.54). Additionally, RC levels were significantly associated with the risk of diabetic retinopathy (HR: 1.06; 95% CI: 1.00–1.12). In summary, high RC levels were significantly associated with an increased risk of developing microvascular complications. Table 2 HRs (95% CL) of microvascular complications according to the RC among individuals with T2D Outcomes Events/n Per 1000 person-years HR (95% CI) Model 1 Model 2 Model 3 Composite microvascular complications Per 1-SD measure increase 0.91 (0.83–0.99) 1.40 (1.26–1.55) 1.38 (1.24–1.54) Tertile 1 (low) 1884/7426 18.8 1.00 1.00 1.00 Tertile 2 (middle) 1760/7428 17.3 0.92 (0.86–0.99) 1.00 (0.93–1.07) 0.96 (0.90–1.03) Tertile 3 (high) 1720/7416 16.8 0.90 (0.84–0.96) 1.20 (1.10–1.31) 1.13 (1.04–1.23) Diabetic nephropathy Per 1-SD measure increase 0.99 (0.96–1.02) 1.14 (1.10–1.19) 1.12 (1.08–1.17) Tertile 1 (low) 1412/8533 11.7 1.00 1.00 1.00 Tertile 2 (middle) 1454/8539 12.0 1.03 (0.96–1.11) 1.12 (1.03–1.21) 1.07 (0.99–1.16) Tertile 3 (high) 1385/8528 11.4 0.98 (0.91–1.05) 1.34 (1.22–1.48) 1.19 (1.08–1.31) Diabetic retinopathy Per 1-SD measure increase 0.93 (0.89–0.97) 1.06 (1.01–1.12) 1.06 (1.00–1.12) Tertile 1 (low) 879/7543 8.0 1.00 1.00 1.00 Tertile 2 (middle) 712/7545 6.4 0.80 (0.72–0.88) 0.87 (0.78–0.96) 0.85 (0.76–0.94) Tertile 3 (high) 703/7531 6.3 0.79 (0.71–0.87) 1.00 (0.88–1.14) 0.99 (0.87–1.12) Diabetic neuropathy Per 1-SD measure increase 1.03 (0.98–1.09) 1.15 (1.08–1.22) 1.14 (1.07–1.21) Tertile 1 (low) 486/8490 3.9 1.00 1.00 1.00 Tertile 2 (middle) 457/8498 3.6 0.94 (0.82–1.06) 1.00 (0.87–1.14) 0.97 (0.85–1.11) Tertile 3 (high) 495/8498 3.9 1.02 (0.90–1.15) 1.26 (1.07–1.48) 1.22 (1.04–1.43) SD, standard deviation; HR, hazard ratio; CI, confidence interval Dose–response analysis The results of RCS analysis are shown in Fig. 1 . The RCS analysis was used to examine the dose–response relationship between RC levels and the risk of microvascular complication development. Increased RC levels were significantly associated with an elevated risk of microvascular complication, including composite microvascular complications and diabetic nephropathy, retinopathy, and neuropathy ( p -overall < 0.001). Specifically, for composite microvascular complications and diabetic retinopathy, the relationship was nonlinear ( p -nonlinear = 0.031 and 0.001, respectively), whereas for diabetic nephropathy and neuropathy, it was linear ( p -nonlinear = 0.862 and 0.683, respectively). Sensitivity analysis Subgroup analysis showed that the effect of RC level (as a continuous variable) on outcome events was generally consistent across all subgroups. No significant interactions were observed between RC levels and sex, ethnicity, BMI, tobacco use, alcohol consumption, baseline blood pressure, baseline diabetes medication use, LDL-C levels, TG levels, or lipid-lowering medication use (Fig. 2 ). This suggests that the risk effects of the RC level are broadly applicable across different populations, although different diabetic complications may be more sensitive to specific influencing factors. Diabetic nephropathy was more affected by tobacco use status and medication use ( see Additional file 8 ); diabetic retinopathy was significantly associated with LDL-C levels and T2D medication use ( see Additional file 9 ); diabetic neuropathy was closely related to ethnicity ( see Additional file 10 ). To reduce the possibility of finding reverse causation, we excluded participants diagnosed with microvascular complications within 2 y of follow-up; the findings remained consistent ( see Additional file 2 ). The addition of CRP level as a new covariate did not significantly alter the results ( see Additional file 3 ). MR analysis results The MR analysis demonstrated a significant causal relationship between genetically predicted RC levels and diabetic nephropathy (OR = 1.05, 95% CI 1.01–1.09, p = 0.035) as well as diabetic retinopathy (OR = 1.10, 95% CI 1.02–1.18, p = 0.044; Table 3 ). Genetically predicted RC levels may be associated with a reduced risk of diabetic nephropathy and retinopathy. Additional details are provided in Table 3 . Additional file 11 shows the results of the MR-Egger analysis, indicating the absence of horizontal pleiotropy across all analyses. The Cochran’s Q test results are presented in Additional file 11. Table 3 Mendelian randomization for remnant cholesterol and diabetic microvascular complications Exposure Outcome Method SNP (n) Beta Se OR (95% CI) p- value Remnant cholesterol Composite microvascular complications MR-Egger 54 -0.107 0.084 0.90 (0.76–1.06) 0.211 Remnant cholesterol Composite microvascular complications Weighted median 54 -0.057 0.040 0.94 (0.87–1.02) 0.153 Remnant cholesterol Composite microvascular complications IVW 54 -0.083 0.054 0.92 (0.83–1.02) 0.129 Remnant cholesterol Composite microvascular complications Simple mode 54 -0.245 0.079 0.78 (0.67–0.91) 0.003 Remnant cholesterol Composite microvascular complications Weighted mode 54 -0.083 0.036 0.92 (0.86–0.99) 0.026 Remnant cholesterol Diabetic nephropathy MR-Egger 54 -0.085 0.125 0.92 (0.72–1.17) 0.501 Remnant cholesterol Diabetic nephropathy Weighted median 54 -0.164 0.103 0.85 (0.69–1.04) 0.110 Remnant cholesterol Diabetic nephropathy IVW 54 0.171 0.081 1.05 (1.01–1.09) 0.035 Remnant cholesterol Diabetic nephropathy Simple mode 54 0.450 0.178 1.03 (1.02–1.04) 0.014 Remnant cholesterol Diabetic nephropathy Weighted mode 54 -0.195 0.105 0.82 (0.67–1.01) 0.069 Remnant cholesterol Diabetic neuropathy MR-Egger 54 -0.128 0.136 0.88 (0.67–1.15) 0.350 Remnant cholesterol Diabetic neuropathy Weighted median 54 -0.123 0.122 0.88 (0.70–1.12) 0.314 Remnant cholesterol Diabetic neuropathy IVW 54 -0.147 0.088 0.86 (0.73–1.03) 0.094 Remnant cholesterol Diabetic neuropathy Simple mode 54 -0.181 0.216 0.83 (0.55–1.27) 0.406 Remnant cholesterol Diabetic neuropathy Weighted mode 54 -0.132 0.134 0.88 (0.67–1.14) 0.326 Remnant cholesterol Diabetic retinopathy MR-Egger 54 -0.076 0.120 1.06 (0.99–1.13) 0.062 Remnant cholesterol Diabetic retinopathy Weighted median 54 -0.027 0.073 0.97 (0.84–1.12) 0.715 Remnant cholesterol Diabetic retinopathy IVW 54 0.157 0.078 1.10 (1.02–1.18) 0.044 Remnant cholesterol Diabetic retinopathy Simple mode 54 -0.035 0.145 0.97 (0.73–1.28) 0.808 Remnant cholesterol Diabetic retinopathy Weighted mode 54 -0.020 0.068 0.98 (0.86–1.12) 0.771 MR-Egger, Mendelian randomization-Egger; IVW, inverse variance-weighted; SNP, single-nucleotide polymorphism; OR, odds ratio Discussion This study investigated the associations between RC levels and DMCs, confirming their connection through observational and MR analyses. RC levels were independently associated with increased risk of renal impairment and peripheral nerve damage in individuals with T2D. MR analysis further supported the causal link between genetically elevated RC levels and nephropathy progression, emphasizing the pivotal contribution of RC to microvascular pathogenesis. Through the analysis of the data of 22,270 patients with T2D, we found that high RC levels were significantly associated with diabetic nephropathy and neuropathy. This result aligns with the findings of previous studies indicating that lipid metabolism abnormalities in patients with diabetes, especially high TG and HDL-C levels, are often accompanied by elevated RC levels.[9] These lipid particles left behind during the metabolism of TRLs, including chylomicrons and very low-density lipoproteins, enter vessel walls and exacerbate microvascular damage by promoting atherosclerosis. [ 23 ] Furthermore, hyperglycemia and insulin resistance in patients with diabetes worsen these lipid metabolism disorders, further driving increased in RC levels, [ 24 ] and these disorders increase the risk of microvascular complications and exacerbate microvascular damage by promoting advanced glycation end-product (AGE) formation, particularly during diabetic nephropathy development. [ 25 ] RC exacerbates the development of DMCs via multiple physiological mechanisms. First, RC particles affect vascular health by promoting atherosclerosis, wherein these lipid deposits cause vessel wall damage, [ 26 ] making them more fragile and promoting microvascular damage. Second, RC accelerates AGE formation, which also enhances microvascular damage by increasing oxidative stress and vascular inflammation. [ 27 ] AGEs not only alter the physical properties of vessel walls but also trigger multiple inflammatory responses by binding to endothelial cell receptors, thereby accelerating the progression of microvascular complications. [ 28 ] AGEs also interfere with lipoprotein metabolism, leading to abnormal cholesterol and lipid deposition in microvessels, particularly in the kidneys, which accelerates the progression of diabetic nephropathy. [ 29 ] These physiological mechanisms explain how RC promotes DMC development via multiple pathways. Observational studies have demonstrated a significant association between RC levels and DMCs; however, confounding factors and reverse causality may have influenced these results. Therefore, we used MR analysis to rigorously confirm whether a causal relationship exists between RC levels and DMCs. The MR analysis used RC-associated genetic variations as IVs, eliminating reverse causality and potential environmental confounders. The results confirmed the causal relationship between high RC levels and the risk of diabetic nephropathy and retinopathy. This provides strong causal evidence supporting the associations observed in observational studies and highlights an independent role of RC in DMC development. Further subgroup analyses revealed that tobacco use, LDL-C levels, and ethnicity significantly influenced the relationship between RC levels and DMCs. People who smoked tobacco showed significantly higher risk of developing diabetic nephropathy and retinopathy, which exacerbated microvascular damage through biological mechanisms likely caused by increased inflammatory responses, oxidative stress, and vascular dysfunction. [ 24 ] Elevated LDL-C levels were also associated with a high risk of microvascular complications, particularly those of diabetic retinopathy and neuropathy. LDL-C may amplify the harmful effects of RC on microvessels by promoting atherosclerosis and inflammation. [ 30 ] Additionally, ethnic differences, particularly among Caucasians, exhibited significant interactions in the subgroup with diabetic neuropathy, which may be related to genetic susceptibility, metabolic characteristics, and environmental factors between different ethnicities, making Caucasians more sensitive to the effects of RC. [ 31 ] These findings suggest individualized risks for DMC development, with RC having a more significant impact on the microvascular damage in high-risk groups, including the individuals who smoke tobacco and those with high LDL-C levels. These results further support the close relationship between RC levels and DMCs, suggesting that RC, as a biomarker, can play an important role in the early screening, prediction, and clinical intervention of diabetic microvascular damage. Moreover, ethnic differences emphasize the need to consider genetic and environmental factors in managing diabetic complications. This study has certain limitations. First, the sample source data were obtained from the UK Biobank, and the single-country data source may limit the generalizability of our findings. Second, many health-related variables (such as tobacco and alcohol use and physical activity) were self-reported, introducing reporting bias, as individuals may not accurately recall or disclose their behavioral habits. Third, although the UK Biobank provides follow-up data, some study designs were not prospective and may not have accurately captured the temporal sequence between exposure and outcomes, leading to inaccurate causal inferences in studies of chronic diseases that require long-term risk accumulation. Fourth, there was a lack of multicenter validation. The study sample was large; however, it lacked validation from large-scale multicenter studies from other countries or regions. Finally, although MR analysis helps control for genetic bias, some environmental factors such as dietary habits, air pollution, and lifestyle remain unaccounted for, potentially impacting the occurrence of diabetic complications in real-life settings. Additionally, more research is required to investigate the varying effects of RC levels on distinct categories of microvascular complications. Future studies should adopt a multicenter, large-sample, prospective design to further validate RC’s role in patients with diabetes. The generalizability of this research could be increased by including participants of different geographic and ethnic backgrounds. Additionally, future research should assess the effectiveness of interventions targeting RC levels in preventing diabetic complications, providing stronger clinical evidence to support the personalized management of diabetes. Conclusions Within this extensive, forward-looking cohort encompassing 22,270 patients with diabetes, we observed that high RC levels were significantly associated with increased risk of diabetic nephropathy and neuropathy. MR analysis further confirmed the causal relationship between high RC levels and diabetic nephropathy. Therefore, implementing measures to control and reduce RC levels may be a viable approach for the prevention and management of microvascular complications among patients with diabetes, warranting consideration in clinical settings. Abbreviations AGE, advanced glycation end-product; BMI, body mass index; CI, confidence interval; CRP, C-reactive protein; DMCs, diabetic microvascular complications; eGFR, estimated glomerular filtration rate; GWAS, genome-wide association study; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; ICD, International Classification of Diseases; IVs, instrumental variables; IVW, inverse variance-weighted; LD, linkage disequilibrium; LDL-C, low-density lipoprotein cholesterol; MR, Mendelian randomization; RC, remnant cholesterol; RCS, restricted cubic spline; SNP, single nucleotide polymorphism; T2D, type 2 diabetes mellitus; TG, triglyceride; TRL, TG-rich lipoprotein Declarations Ethics approval and consent to participate UK Biobank received ethical approval from the North West Multicenter Research Ethics Committee (06/MRE08/65). All participants provided written informed consent for original data collection, analysis, and recording linkage. Availability of data and materials Data will be made available on request. Competing interests The authors declare that they have no known competing financial interests or personal relationships that may have influenced the work reported in this study. Funding This study was funded by the National Natural Science Foundation of China (grant number 82073668 to Lixin Tao). The funder had no role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript. Authors’ contributions All authors contributed to the conception and design of this study. JL conceptualized the study, developed the methodology, performed data analysis, and drafted the original manuscript. RR contributed to methodology and data analysis. YL, JB, ZY, XY, RZ, and LW contributed to data visualization. DW supervised and contributed to writing, reviewing, and editing the manuscript. Acknowledgments Not applicable References Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. https://doi.org/10.1016/j.diabres.2021.109119 Litwak L, Goh SY, Hussein Z, Malek R, Prusty V, Khamseh ME. Prevalence of diabetes complications in people with type 2 diabetes mellitus and its association with baseline characteristics in the multinational A1chieve study. Diabetol Metab Syndr. 2013;5:57. https://doi.org/10.1186/1758-5996-5-57 Kähm K, Laxy M, Schneider U, Rogowski WH, Lhachimi SK, Holle R. Health care costs associated with incident complications in patients with type 2 diabetes in Germany. Diabetes Care. 2018;41:971-8. https://doi.org/10.2337/dc17-1763 Chen HY, Kuo S, Su PF, Wu JS, Ou HT. Health care costs associated with macrovascular, microvascular, and metabolic complications of type 2 diabetes across time: estimates from a population-based cohort of more than 0.8 million individuals with up to 15 years of follow-up. Diabetes Care. 2020;43:1732-40. https://doi.org/10.2337/dc20-0072 Zelnick LR, Weiss NS, Kestenbaum BR, Robinson-Cohen C, Heagerty PJ, Tuttle K, et al. Diabetes and CKD in the United States population, 2009–2014. Clin J Am Soc Nephrol. 2017;12:1984-90. https://doi.org/10.2215/CJN.03700417 Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556-64. https://doi.org/10.2337/dc11-1909 Feldman EL, Callaghan BC, Pop-Busui R, Zochodne DW, Wright DE, Bennett DL, et al. Diabetic neuropathy. Nat Rev Dis Primers. 2019;5:42. https://doi.org/10.1038/s41572-019-0097-9 Castañer O, Pintó X, Subirana I, Amor AJ, Ros E, Hernáez Á, et al. Remnant cholesterol, not LDL cholesterol, is associated with incident cardiovascular disease. J Am Coll Cardiol. 2020;76:2712-24. https://doi.org/10.1016/j.jacc.2020.10.008 Sandesara PB, Virani SS, Fazio S, Shapiro MD. The forgotten lipids: triglycerides, remnant cholesterol, and atherosclerotic cardiovascular disease risk. Endocr Rev. 2019;40:537-57. https://doi.org/10.1210/er.2018-00184 Nègre-Salvayre A, Salvayre R. Reactive carbonyl species and protein lipoxidation in atherogenesis. Antioxidants (Basel). 2024;13:232. https://doi.org/10.3390/antiox13020232 Wu Z, Yu S, Zhu Q, Li Z, Zhang H, Kang X, et al. Association of baseline and cumulative remnant cholesterol with incidence of diabetic nephropathy: a longitudinal cohort study. Diabetes Res Clin Pract. 2022;191:110079. https://doi.org/10.1016/j.diabres.2022.110079 Zhu W, Liu Q, Liu F, Jiao C, Zhang L, Xie H. High remnant cholesterol as a risk factor for developing chronic kidney disease in patients with prediabetes and type 2 diabetes: a cross-sectional study of a US population. Acta Diabetol. 2024;61:735-43. https://doi.org/10.1007/s00592-024-02249-6 Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med. 2015;12:e1001779. https://doi.org/10.1371/journal.pmed.1001779 Eastwood SV, Mathur R, Atkinson M, Brophy S, Sudlow C, Flaig R, et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLOS One. 2016;11:e0162388. https://doi.org/10.1371/journal.pone.0162388 Wu Y, Xiong T, Tan X, Chen L. Frailty and risk of microvascular complications in patients with type 2 diabetes: a population-based cohort study. BMC Med. 2022;20:473. https://doi.org/10.1186/s12916-022-02675-9 Chen J, Kuang J, Tang X, Mao L, Guo X, Luo Q, et al. Comparison of calculated remnant lipoprotein cholesterol levels with levels directly measured by nuclear magnetic resonance. Lipids Health Dis. 2020;19:132. https://doi.org/10.1186/s12944-020-01311-w Inker LA, Schmid CH, Tighiouart H, Eckfeldt Jh, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:20-9. https://doi.org/10.1056/NEJMoa1114248 Wu Y, Wei Q, Li H, Yang H, Wu Y, Yu Y, et al. Association of remnant cholesterol with hypertension, type 2 diabetes, and their coexistence: the mediating role of inflammation-related indicators. Lipids Health Dis. 2023;22:158. https://doi.org/10.1186/s12944-023-01915-y Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380:37-43. https://doi.org/10.1016/S0140-6736(12)60240-2 Penno G, Solini A, Orsi E, Bonora E, Fondelli C, Trevisan R, et al. Insulin resistance, diabetic kidney disease, and all-cause mortality in individuals with type 2 diabetes: a prospective cohort study. BMC Med. 2021;19:66. https://doi.org/10.1186/s12916-021-01936-3 Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016;35:1880-906. https://doi.org/10.1002/sim.6835 Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377-89. https://doi.org/10.1007/s10654-017-0255-x Sascău R, Clement A, Radu R, Prisacariu C, Stătescu C. Triglyceride-rich lipoproteins and their remnants as silent promoters of atherosclerotic cardiovascular disease and other metabolic disorders: a review. Nutrients. 2021;13:1774. https://doi.org/10.3390/nu13061774 Ménégaut L, Laubriet A, Crespy V, Leleu D, Pilot T, Van Dongen K, et al. Inflammation and oxidative stress markers in type 2 diabetes patients with advanced carotid atherosclerosis. Cardiovasc Diabetol. 2023;22:248. https://doi.org/10.1186/s12933-023-01979-1 Mengstie MA, Chekol Abebe E, Behaile Teklemariam A, Tilahun Mulu A, Agidew MM, Teshome Azezew M, et al. Endogenous advanced glycation end products in the pathogenesis of chronic diabetic complications. Front Mol Biosci. 2022;9:1002710. https://doi.org/10.3389/fmolb.2022.1002710 Zheng XY, Liu L. Remnant-like lipoprotein particles impair endothelial function: direct and indirect effects on nitric oxide synthase. J Lipid Res. 2007;48:1673-80. https://doi.org/10.1194/jlr.R700001-JLR200 Adamska A, Araszkiewicz A, Pilacinski S, Gandecka A, Grzelka A, Kowalska K, et al. Dermal microvessel density and maturity is closely associated with atherogenic dyslipidemia and accumulation of advanced glycation end products in adult patients with type 1 diabetes. Microvasc Res. 2019;121:46-51. https://doi.org/10.1016/j.mvr.2018.10.002 Soro-Paavonen A, Forbes JM. Novel therapeutics for diabetic micro- and macrovascular complications. Curr Med Chem. 2006;13:1777-88. https://doi.org/10.2174/092986706777452515 Yuan Y, Sun H, Sun Z. Advanced glycation end products (AGEs) increase renal lipid accumulation: a pathogenic factor of diabetic nephropathy (DN). Lipids Health Dis. 2017;16:126. https://doi.org/10.1186/s12944-017-0522-6 Femlak M, Gluba-Brzózka A, Ciałkowska-Rysz A, Rysz J. The role and function of HDL in patients with diabetes mellitus and the related cardiovascular risk. Lipids Health Dis. 2017;16:207. https://doi.org/10.1186/s12944-017-0594-3 Spanakis EK, Golden SH. Race/ethnic difference in diabetes and diabetic complications. Curr Diab Rep. 2013;13:814-23. https://doi.org/10.1007/s11892-013-0421-9 Additional Declarations No competing interests reported. Supplementary Files Supplementmaterial.docx Cite Share Download PDF Status: Posted Version 1 posted 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-6676665","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459787557,"identity":"b4664066-0a6f-46a4-9987-467efe2a57fa","order_by":0,"name":"Jinyan Liu","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinyan","middleName":"","lastName":"Liu","suffix":""},{"id":459787558,"identity":"cb68cde0-822f-446c-aae0-5a1bc519d92e","order_by":1,"name":"Rui Ren","email":"","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Ren","suffix":""},{"id":459787559,"identity":"0c488d1a-46a6-48fd-b0e1-3e6f306a5660","order_by":2,"name":"Yi Li","email":"","orcid":"","institution":"Gansu Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Li","suffix":""},{"id":459787560,"identity":"91ea49d8-64b8-413f-bf3d-96b51df6de34","order_by":3,"name":"Jilong Bai","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jilong","middleName":"","lastName":"Bai","suffix":""},{"id":459787561,"identity":"52eec3fc-590a-40cc-9ffa-ca6eec905817","order_by":4,"name":"Zhang Yao","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Yao","suffix":""},{"id":459787562,"identity":"61c7dae9-5e51-487c-a738-ba5a14889f2b","order_by":5,"name":"Xingyue Ye","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingyue","middleName":"","lastName":"Ye","suffix":""},{"id":459787563,"identity":"e803c99f-ca06-4d69-9791-b48066cf69aa","order_by":6,"name":"Ru Zhang","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ru","middleName":"","lastName":"Zhang","suffix":""},{"id":459787564,"identity":"57b29444-6a28-4d8b-a4cc-4a176b1f7803","order_by":7,"name":"Liying Wang","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liying","middleName":"","lastName":"Wang","suffix":""},{"id":459787565,"identity":"a3e5db26-b90f-43ac-bd86-98371e64ff77","order_by":8,"name":"Difei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYDCCwwdApA2Ew0OUlmMJIDKNdC2HSdDCd4zH8HPBr/PyujMSGB+8bWOQNyekRfIYj7H0zL7bhttuJDAbzm1jMNzZQECLwf3eDdK8PbcTzG4ksEnztjEkGBwgpOUY7+bfvD3nQFrYfxOrZZs0z48DYFuYidIieYz/mzVvQ7LhtjMPmyXnnJMw3EBIC98xtuTbPH/s5M2OJx/88KbMRp6gLWDA2AYmG4CEBDHqQeAPsQpHwSgYBaNgRAIAFVlCIplcnl8AAAAASUVORK5CYII=","orcid":"","institution":"Shengjing Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Difei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-16 03:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6676665/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6676665/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83327756,"identity":"bc951cef-7cc7-4991-84cd-9d7e795de666","added_by":"auto","created_at":"2025-05-23 07:03:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170222,"visible":true,"origin":"","legend":"\u003cp\u003eDose–response relationship between RC levels and the risk of microvascular complication development among individuals with T2D. (a) The relationship between RC levels and the risk of diabetic nephropathy; the red line represents the estimate, the shaded area represents the 95% CI, the dashed line represents the reference line with a risk ratio of 1, and the \u003cem\u003ep\u003c/em\u003e-value represents the significance of the overall association and potential non-linearity. (b) Relationship between RC levels and the risk of diabetic nephropathy. (c) Relationship between RC levels and the risk of diabetic neuropathy. (d) Relationship between RC levels and the risk of diabetic retinopathy.\u003c/p\u003e\n\u003cp\u003eAbbreviations: T2D, type 2 diabetes mellitus; HR, hazard ratio; 95% CI, 95% confidence interval; RC, remnant cholesterol\u003c/p\u003e","description":"","filename":"Figure1a.png","url":"https://assets-eu.researchsquare.com/files/rs-6676665/v1/ff0155b157a16b835394c887.png"},{"id":83328867,"identity":"af7191dd-3e96-4d1e-afcb-58f8b69bd76a","added_by":"auto","created_at":"2025-05-23 07:11:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":427905,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analyses of associations of RC with composite microvascular complications, and diabetic nephropathy forest plots. The HR of different variables for diabetic nephropathy with 95% CIs.\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; CI, confidence interval; RC, remnant cholesterol\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6676665/v1/ed608328e06ebec39fd910e4.png"},{"id":85707671,"identity":"049c6557-472a-4f9d-9035-701c17b27c21","added_by":"auto","created_at":"2025-07-01 01:31:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1987125,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6676665/v1/29c4a4b0-40a0-4982-aca6-ebf115455dda.pdf"},{"id":83327773,"identity":"43e02439-71d8-4fb0-9730-c8840f697efd","added_by":"auto","created_at":"2025-05-23 07:03:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":566578,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementmaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6676665/v1/a01f39555103c23f971e591c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between remnant cholesterol and the risk of diabetic microvascular complications in type 2 diabetes: An observational study","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes mellitus has become a predominant public health concern, with its prevalence projected to exceed over 783\u0026nbsp;million cases by 2045.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e With the escalating incidence of diabetes mellitus, that of diabetic microvascular complications (DMCs) has also increased. DMCs, including diabetic nephropathy, retinopathy, and neuropathy, considerably contribute to the reduced quality of life and increased healthcare burden among individuals with type 2 diabetes mellitus (T2D).\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e The occurrence of DMCs is high among individuals with diabetes, affecting nearly half of those diagnosed with T2D.\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e Approximately 23% of patients with diabetes are prone to kidney ailments,\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e 34.6% experience vision impairments from diabetic retinopathy,\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e and 50% develop diabetic neuropathy.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e These complications are the primary causes of functional impairment and disability in patients with diabetes. Therefore, the prevention and management of DMCs are crucial for improving patient quality of life.\u003c/p\u003e \u003cp\u003eRemnant cholesterol (RC), the cholesterol remaining after the metabolism of triglyceride (TG)-rich lipoproteins (TRLs), has been widely studied and plays a key role in atherosclerotic cardiovascular disease.\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e RC promotes the development of atherosclerosis by damaging the vascular endothelium, promoting inflammation, and inducing oxidative stress, thereby increasing the risk of developing cardiovascular disease.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e Numerous studies have corroborated the involvement of RC in atherosclerosis; however, its relationship with DMCs remains underexplored. Existing studies have primarily focused on the association between RC and large-vessel diseases; the connection between microvascular complications remains largely unexplored.\u003c/p\u003e \u003cp\u003eAlthough few studies have demonstrated an association between RC levels and diabetic nephropathy, they used small sample sizes and limited outcomes and did not comprehensively evaluate the association of RC with other DMCs.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e Furthermore, most existing studies have relied on cross-sectional designs and lack longitudinal data or clear evidence of causal relationships.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e Additionally, studies have not effectively addressed confounding factors and reverse causality, limiting a deeper understanding of RC as a potential causal factor in DMCs. Therefore, comprehensive research on the relationship of RC with DMCs is urgently required.\u003c/p\u003e \u003cp\u003eIn this study, we used large-scale cohort data and Mendelian randomization (MR) analysis to evaluate the relationship between RC levels and multiple DMCs, including diabetic nephropathy, retinopathy, and neuropathy. The novelty of this study lies in its large sample size, long-term follow-up, and consideration of multiple related outcomes using a longitudinal analysis to capture the long-term effects of RC on DMCs. Additionally, MR analysis was used to explore the causal relationship between RC levels and DMCs, providing reliable evidence for DMC prevention and clinical interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCohort analysis\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003eThe United Kingdom Biobank study is a large-scale biomedical database containing data from a population study enrolling over 500,000 individuals aged 40\u0026ndash;69 y between 2006 and 2010 at 22 sites across England, Scotland, and Wales. \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e The UK Biobank obtained approval from the Northwest Multicenter Research Ethics Committee (National Health Service National Research Ethics Service, Ref 11/NW/0382). Upon enrollment, participants completed an online survey detailing their sociodemographics, lifestyle risk factors, and overall health status; underwent a thorough medical evaluation conducted by a qualified health care provider; and submitted blood, urine, and saliva samples.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e All participants provided written informed consent before participation, and the study adhered to the guidelines in the Declaration of Helsinki. The present study was performed under the UK Biobank application number 147427.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eStudy sample\u003c/h3\u003e\n\u003cp\u003eEastwood et al. used the UK Biobank algorithm to determine the prevalence of T2D at baseline from hospital inpatient records, self-reported medical histories, and medication data, achieving an accuracy of 96%.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e In addition to using this algorithm, we also considered glycated hemoglobin (HbA1c) test results.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e In this study, the diagnostic criteria for T2D included the self-reported age of onset, physician diagnosis, use of antidiabetic medications and insulin, non-cancer disease codes, baseline HbA1c levels, and International Classification of Disease (ICD)-9 and ICD-10 codes. These criteria ensured comprehensive and accurate identification of T2D cases, providing a reliable data foundation for exploring the relationship between RC levels and DMCs. Furthermore, we analyzed the onset of diabetes relative to the enrollment of the participants in the study. The participants who self-reported a diagnosis or were diagnosed with diabetes before or on the day of enrollment were categorized as having existing diabetes. Those without diabetes at the study\u0026rsquo;s start (n\u0026thinsp;=\u0026thinsp;471,240), those with gaps or incomplete exposure information (n\u0026thinsp;=\u0026thinsp;4,708), those with diabetes mellitus complications at baseline (n\u0026thinsp;=\u0026thinsp;3,626), and those with unaccounted-for variables (n\u0026thinsp;=\u0026thinsp;366) were excluded. Ultimately, our final analysis included 22,270 patients diagnosed with T2D (\u003cb\u003esee Additional file 1\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eExposure assessment\u003c/h3\u003e\n\u003cp\u003eLow-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels were directly measured, and all other analytes were assessed using standard laboratory methods (see UK Biobank Methods online: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ctsu.ox.ac.uk\u003c/span\u003e\u003cspan address=\"https://biobank.ctsu.ox.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). RC levels were calculated using the formula: RC\u0026thinsp;=\u0026thinsp;total cholesterol (TC) \u0026ndash; (HDL-C\u0026thinsp;\u0026minus;\u0026thinsp;LDL-C).\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eOutcome ascertainment\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the prevalence of DMCs\u0026mdash;a comprehensive indicator of the initial appearances of diabetic nephropathy, neuropathy, and retinopathy. Secondary outcomes included the prevalence of individual subtypes. DMC cases were identified using cumulative hospital inpatient records, mortality data, and connections to the National Death Registry. These were categorized based on the ICD-9 and ICD-10 as well as on self-reported data, with codes tailored to selection, disease, or procedure specifics. To distinguish between pre-existing and newly diagnosed DMCs, we cross-referenced the date of the first DMC diagnosis with the initial assessment date. The participants who were diagnosed with a DMC following the initial follow-up period were classified as having a new-onset DMC.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eData on socioeconomic status, lifestyle habits, and prior health issues were collected at baseline using a digital questionnaire. Participants were divided into two groups\u0026mdash;smokers and non-smokers. Participants reported their alcohol intake according to the dietary guidelines from the USA and UK. A moderate drinking limit was defined as \u0026le;\u0026thinsp;1 drink per day for women and \u0026le;\u0026thinsp;2 drinks per day for men, where one drink is considered to contain 14 g of ethanol in the USA and 8 g in the UK. During the first visit to the assessment center, trained experts measured the height, weight, and blood pressure of the participants. The body mass index (BMI) was calculated as weight (kg) divided by the square of the height (m\u003csup\u003e2\u003c/sup\u003e). Blood samples collected during enrollment were used to measure the levels of HbA1c, lipids (TC, LDL-C, HDL-C, and TG), and serum creatinine. Subsequently, the estimated glomerular filtration rate (eGFR) was calculated based on serum creatinine levels and the sex of the participants.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e Considering the established association between long-term chronic diseases and RC,\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e we included multiple long-term diseases\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e and corresponding medications as covariates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo analyze the effects of RC levels on DMCs, we employed the Cox proportional hazards regression model, which allows for the adjustment of multiple potential confounders (such as age, sex, and BMI),\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e providing more accurate results. The results are presented herein as hazard ratios (HRs) and 95% confidence intervals (CIs). The time-to-event was defined as the period from baseline recruitment to the date of first DMC diagnosis, loss to follow-up, death, or censoring. RC levels were categorized into three groups (low, medium, and high) based on tertiles, with the \u0026ldquo;low\u0026rdquo; group serving as the reference in each model. Model 1 was the crude model with no adjustments; Model 2 was adjusted for age (continuous), sex (male or female), ethnicity (White or Other), education level (university or above, high school or equivalent, or less than high school), smoking status (smoker or non-smoker), drinking status (drinker or non-drinker), BMI (\u0026lt;\u0026thinsp;25.0, 25.0\u0026ndash;29.9, or \u0026ge;\u0026thinsp;30.0 kg/m\u0026sup2;), and LDL-C level; Model 3 was further adjusted for eGFR, hypertension history, T2D medication use, and lipid-lowering medication use, in addition to the adjustments in Model 2. In subgroup analyses, RC levels were treated as a continuous variable to assess whether significant interactions existed between RC levels and each covariate. Restricted cubic spline (RCS) analysis was performed to explore the nonlinear relationship between RC levels and DMCs.\u003c/p\u003e \u003cp\u003eTwo types of sensitivity analyses were performed in this study. First, we excluded cases occurring within 2 y of follow-up and re-performed the Cox analysis for patients with T2D, which helped reduce the effects of reverse causality and confounding factors (\u003cb\u003esee Additional file 2\u003c/b\u003e). Second, we added C-reactive protein (CRP) levels to Model 3 and repeated the Cox analysis to allow for a more comprehensive confounder control, improve the explanatory power of the model, assess the moderating effects of CRP levels, and verify the robustness of the results (\u003cb\u003esee Additional file 3\u003c/b\u003e). The Kaplan\u0026ndash;Meier method was used to calculate cumulative incidence rates for composite DMCs and diabetic nephropathy, retinopathy, and neuropathy in each RC level group (\u003cb\u003esee Additional files 2\u0026ndash;5\u003c/b\u003e). These analyses were used to compare the effects of RC levels on survival rates in patients with DMCs. The threshold for statistical significance (two-sided) was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analyses were performed using R 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMendelian randomization analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003eWe performed MR analyses with RC levels as the \u0026ldquo;exposure\u0026rdquo; and DMCs as the \u0026ldquo;outcome.\u0026rdquo; MR studies adhere to the following three key assumptions: (1) instrumental variables (IVs) must be strongly associated with exposure factors; (2) IVs must be independent of confounding factors that are related to both exposure and outcome; and (3) IVs should influence outcomes only through exposure and not directly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenetic instruments for exposure and outcomes\u003c/h2\u003e \u003cp\u003eGenome-wide association study (GWAS) summary statistics for RC were extracted online from the IEU Open GWAS database, whereas DMC-associated GWAS data were derived from the FinnGen database. All participants included in the abovementioned datasets were predominantly of European ancestry.\u003c/p\u003e \u003cp\u003eTo meet the relevance assumption, we selected single nucleotide polymorphisms (SNPs) significantly associated with RC levels at a threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸. SNPs were preserved without linkage disequilibrium using the criteria of R\u0026sup2; \u0026lt; 0.001 and a clumping distance\u0026thinsp;\u0026gt;\u0026thinsp;10 Mb, based on the European 1000 Genomes reference panel. We identified 54 independent SNPs each for composite microvascular complications and diabetic nephropathy, neuropathy, and retinopathy (\u003cb\u003esee Additional file 6\u003c/b\u003e). Employing a two-sample MR approach, we initially ascertained the causal association between RC levels and four DMC-related outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe genetically predicted effects of RC levels on four DMC-related outcomes were estimated using the TwoSampleMR R package. Causal associations were evaluated using the inverse variance-weighted (IVW), MR-Egger, weighted mode, simple mode, and weighted median methods. IVW was the primary method owing to its ability to combine Wald estimates of genetic associations for each SNP to evaluate the effect of exposure on the outcome. It assumes that all selected SNPs are reliable IVs and is considered the gold standard for estimating causal effects owing to its precision.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e Cochran\u0026rsquo;s Q statistic was used to assess the heterogeneity (I\u003csup\u003e2\u003c/sup\u003e) among IVs,\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e calculated as I\u003csup\u003e2\u003c/sup\u003e = [Q \u0026minus; (K\u0026thinsp;\u0026minus;\u0026thinsp;1)]/Q, where \u003cem\u003eK\u003c/em\u003e represents the number of SNPs and \u003cem\u003eQ\u003c/em\u003e represents the Q-statistic. Horizontal pleiotropy was assessed by analyzing the MR-Egger intercept values.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e All statistical analyses were performed using the TwoSampleMR (version 0.6.8) package in R software. The threshold for statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the study participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, categorized by RC level tertiles. The median age of the 22,270 participants at baseline was 61 y. Compared with participants with low RC levels, those with high levels were more likely to be male, have a higher BMI, smoke tobacco, and have a higher eGFR and TG and LDL-C levels. At baseline, 14,565 (66%) participants were using lipid-lowering medications, primarily statins. Notably, this proportion decreased as RC levels increased, from 78% in the lowest tertile to 50% in the highest tertile (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, 11,325 (51%) participants were using T2D medications, with usage decreasing with increasing RC levels, from 62% in the lowest tertile to 38% in the highest tertile (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A total of 5,364 DMC cases, including those of 4,251, 2,294, and 1,438 of diabetic nephropathy, retinopathy, and neuropathy, respectively, were recorded at baseline (\u003cb\u003esee Additional file 7\u003c/b\u003e).\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 the study population\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=\"char\" char=\".\" 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;22,270\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow, N\u0026thinsp;=\u0026thinsp;7,426\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium, N\u0026thinsp;=\u0026thinsp;7,428\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh, N\u0026thinsp;=\u0026thinsp;7,416\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\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\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (55\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (55\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (55\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (53\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.153\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\u003e8,895 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,902 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,980 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,013 (41)\u003c/p\u003e \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\" colname=\"c2\"\u003e \u003cp\u003e13,375 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,524 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,448 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,403 (59)\u003c/p\u003e \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\u003e\u003cb\u003eEthnic\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,782 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,040 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e906 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e836 (11)\u003c/p\u003e \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\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,488 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,386 (86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6,522 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,580 (89)\u003c/p\u003e \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\u003e\u003cb\u003eEducation\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,267 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,070 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,162 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,035 (27)\u003c/p\u003e \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\u003eHigh school or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,633 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,443 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,489 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,701 (50)\u003c/p\u003e \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\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,267 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,070 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,162 (29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,035 (27)\u003c/p\u003e \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\u003e\u003cb\u003eBMI\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,534 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,300 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e756 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e478 (6.4)\u003c/p\u003e \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\u003e25-29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,724 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,719 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,525 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,480 (33)\u003c/p\u003e \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\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,012 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,407 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,147 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,458 (60)\u003c/p\u003e \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\u003e\u003cb\u003eSmoking\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,020 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,458 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,318 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,244 (44)\u003c/p\u003e \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\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,250 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,968 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,110 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,172 (56)\u003c/p\u003e \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\u003e\u003cb\u003eAlcohol\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.920\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,364 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,803 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,787 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,774 (64)\u003c/p\u003e \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\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,906 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,623 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,641 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,642 (36)\u003c/p\u003e \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\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,423 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,006 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,041 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,376 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.4 (85.4\u0026ndash;102.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.7 (84.5\u0026ndash;101.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.0 (84.9\u0026ndash;102.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.5 (86.9\u0026ndash;103.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9 (1.3\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (0.9\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 (1.5\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9 (2.3\u0026ndash;3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.00\u003c/b\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7 (2.3\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (1.9\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 (2.4\u0026ndash;3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5 (2.9\u0026ndash;4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (1.0\u0026ndash;1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2 (1.0\u0026ndash; 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1 (1.0\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.9\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUse of diabetes medication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,602 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,790 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,106 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,706 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUse of lipid-lowing medication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,565 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,781 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,097 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,687 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Kruskal\u0026ndash;Wallis test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e**Pearson's Chi-squared test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI, body mass index; eGFR, estimated glomerular filtration rate; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRC levels and outcomes\u003c/h2\u003e \u003cp\u003eThe results of Cox regression analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the fully adjusted model (Model 3), when RC was treated as a categorical variable, increased RC levels were significantly associated with an increased risk of developing composite microvascular complications, diabetic nephropathy, and diabetic neuropathy. Compared with patients in the low RC group, those in the high RC group had 13%, 19%, and 22% increased likelihood of developing composite microvascular complications, diabetic nephropathy, and diabetic neuropathy, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Diabetic retinopathy was not significantly associated with high RC levels (HR: 0.99; 95% CI: 0.87\u0026ndash;1.12). When RC level was treated as a continuous variable after adjusting for age, sex, ethnicity, education level, BMI, tobacco use, alcohol consumption, and LDL-C levels, each one-standard deviation increase in RC was associated with an HR of 1.40 (95% CI: 1.26\u0026ndash;1.55) for microvascular complications. After adjusting for eGFR, history of hypertension, T2D, and use of lipid-lowering medications, each one-standard deviation increase in RC levels was associated with an HR of 1.38 (95% CI: 1.24\u0026ndash;1.54). Additionally, RC levels were significantly associated with the risk of diabetic retinopathy (HR: 1.06; 95% CI: 1.00\u0026ndash;1.12). In summary, high RC levels were significantly associated with an increased risk of developing microvascular complications.\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\u003eHRs (95% CL) of microvascular complications according to the RC among individuals with T2D\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEvents/n\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePer 1000 person-years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eComposite microvascular complications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePer 1-SD measure increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.83\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40 (1.26\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.38 (1.24\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1 (low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1884/7426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 2 (middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1760/7428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.86\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.93\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96 (0.90\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3 (high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1720/7416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.84\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20 (1.10\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.13 (1.04\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetic nephropathy\u003c/b\u003e\u003c/p\u003e \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\u003ePer 1-SD measure increase\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.14 (1.10\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.12 (1.08\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1 (low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1412/8533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 2 (middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1454/8539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.96\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12 (1.03\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (0.99\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3 (high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1385/8528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.91\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34 (1.22\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19 (1.08\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetic retinopathy\u003c/b\u003e\u003c/p\u003e \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\u003ePer 1-SD measure increase\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.89\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.06 (1.01\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06 (1.00\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1 (low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e879/7543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 2 (middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e712/7545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.72\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87 (0.78\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85 (0.76\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3 (high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e703/7531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.71\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.88\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99 (0.87\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetic neuropathy\u003c/b\u003e\u003c/p\u003e \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\u003ePer 1-SD measure increase\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.98\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15 (1.08\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14 (1.07\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 1 (low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e486/8490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 2 (middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e457/8498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.82\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.87\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97 (0.85\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertile 3 (high)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495/8498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.90\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.26 (1.07\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22 (1.04\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSD, standard deviation; HR, hazard ratio; CI, confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDose\u0026ndash;response analysis\u003c/h2\u003e \u003cp\u003eThe results of RCS analysis are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The RCS analysis was used to examine the dose\u0026ndash;response relationship between RC levels and the risk of microvascular complication development. Increased RC levels were significantly associated with an elevated risk of microvascular complication, including composite microvascular complications and diabetic nephropathy, retinopathy, and neuropathy (\u003cem\u003ep\u003c/em\u003e-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, for composite microvascular complications and diabetic retinopathy, the relationship was nonlinear (\u003cem\u003ep\u003c/em\u003e-nonlinear\u0026thinsp;=\u0026thinsp;0.031 and 0.001, respectively), whereas for diabetic nephropathy and neuropathy, it was linear (\u003cem\u003ep\u003c/em\u003e-nonlinear\u0026thinsp;=\u0026thinsp;0.862 and 0.683, respectively).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eSubgroup analysis showed that the effect of RC level (as a continuous variable) on outcome events was generally consistent across all subgroups. No significant interactions were observed between RC levels and sex, ethnicity, BMI, tobacco use, alcohol consumption, baseline blood pressure, baseline diabetes medication use, LDL-C levels, TG levels, or lipid-lowering medication use (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests that the risk effects of the RC level are broadly applicable across different populations, although different diabetic complications may be more sensitive to specific influencing factors. Diabetic nephropathy was more affected by tobacco use status and medication use (\u003cb\u003esee Additional file 8\u003c/b\u003e); diabetic retinopathy was significantly associated with LDL-C levels and T2D medication use (\u003cb\u003esee Additional file 9\u003c/b\u003e); diabetic neuropathy was closely related to ethnicity (\u003cb\u003esee Additional file 10\u003c/b\u003e). To reduce the possibility of finding reverse causation, we excluded participants diagnosed with microvascular complications within 2 y of follow-up; the findings remained consistent (\u003cb\u003esee Additional file 2\u003c/b\u003e). The addition of CRP level as a new covariate did not significantly alter the results (\u003cb\u003esee Additional file 3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis results\u003c/h2\u003e \u003cp\u003eThe MR analysis demonstrated a significant causal relationship between genetically predicted RC levels and diabetic nephropathy (OR\u0026thinsp;=\u0026thinsp;1.05, 95% CI 1.01\u0026ndash;1.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) as well as diabetic retinopathy (OR\u0026thinsp;=\u0026thinsp;1.10, 95% CI 1.02\u0026ndash;1.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Genetically predicted RC levels may be associated with a reduced risk of diabetic nephropathy and retinopathy. Additional details are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cb\u003eAdditional file 11\u003c/b\u003e shows the results of the MR-Egger analysis, indicating the absence of horizontal pleiotropy across all analyses. The Cochran\u0026rsquo;s Q test results are presented in \u003cb\u003eAdditional file 11.\u003c/b\u003e\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\u003eMendelian randomization for remnant cholesterol and diabetic microvascular complications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSNP (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposite microvascular complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.90 (0.76\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposite microvascular complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94 (0.87\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposite microvascular complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92 (0.83\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposite microvascular complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.78 (0.67\u0026ndash;0.91)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComposite microvascular complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.92 (0.86\u0026ndash;0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92 (0.72\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85 (0.69\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.05 (1.01\u0026ndash;1.09)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.03 (1.02\u0026ndash;1.04)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82 (0.67\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88 (0.67\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88 (0.70\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86 (0.73\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83 (0.55\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic neuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88 (0.67\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.06 (0.99\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.84\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.10 (1.02\u0026ndash;1.18)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.73\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemnant cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic retinopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98 (0.86\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eMR-Egger, Mendelian randomization-Egger; IVW, inverse variance-weighted; SNP, single-nucleotide polymorphism; OR, odds ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the associations between RC levels and DMCs, confirming their connection through observational and MR analyses. RC levels were independently associated with increased risk of renal impairment and peripheral nerve damage in individuals with T2D. MR analysis further supported the causal link between genetically elevated RC levels and nephropathy progression, emphasizing the pivotal contribution of RC to microvascular pathogenesis.\u003c/p\u003e \u003cp\u003eThrough the analysis of the data of 22,270 patients with T2D, we found that high RC levels were significantly associated with diabetic nephropathy and neuropathy. This result aligns with the findings of previous studies indicating that lipid metabolism abnormalities in patients with diabetes, especially high TG and HDL-C levels, are often accompanied by elevated RC levels.[9] These lipid particles left behind during the metabolism of TRLs, including chylomicrons and very low-density lipoproteins, enter vessel walls and exacerbate microvascular damage by promoting atherosclerosis.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e Furthermore, hyperglycemia and insulin resistance in patients with diabetes worsen these lipid metabolism disorders, further driving increased in RC levels,\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e and these disorders increase the risk of microvascular complications and exacerbate microvascular damage by promoting advanced glycation end-product (AGE) formation, particularly during diabetic nephropathy development.\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRC exacerbates the development of DMCs via multiple physiological mechanisms. First, RC particles affect vascular health by promoting atherosclerosis, wherein these lipid deposits cause vessel wall damage,\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e making them more fragile and promoting microvascular damage. Second, RC accelerates AGE formation, which also enhances microvascular damage by increasing oxidative stress and vascular inflammation.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e AGEs not only alter the physical properties of vessel walls but also trigger multiple inflammatory responses by binding to endothelial cell receptors, thereby accelerating the progression of microvascular complications.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e AGEs also interfere with lipoprotein metabolism, leading to abnormal cholesterol and lipid deposition in microvessels, particularly in the kidneys, which accelerates the progression of diabetic nephropathy.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e These physiological mechanisms explain how RC promotes DMC development via multiple pathways.\u003c/p\u003e \u003cp\u003eObservational studies have demonstrated a significant association between RC levels and DMCs; however, confounding factors and reverse causality may have influenced these results. Therefore, we used MR analysis to rigorously confirm whether a causal relationship exists between RC levels and DMCs. The MR analysis used RC-associated genetic variations as IVs, eliminating reverse causality and potential environmental confounders. The results confirmed the causal relationship between high RC levels and the risk of diabetic nephropathy and retinopathy. This provides strong causal evidence supporting the associations observed in observational studies and highlights an independent role of RC in DMC development.\u003c/p\u003e \u003cp\u003eFurther subgroup analyses revealed that tobacco use, LDL-C levels, and ethnicity significantly influenced the relationship between RC levels and DMCs. People who smoked tobacco showed significantly higher risk of developing diabetic nephropathy and retinopathy, which exacerbated microvascular damage through biological mechanisms likely caused by increased inflammatory responses, oxidative stress, and vascular dysfunction.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e Elevated LDL-C levels were also associated with a high risk of microvascular complications, particularly those of diabetic retinopathy and neuropathy. LDL-C may amplify the harmful effects of RC on microvessels by promoting atherosclerosis and inflammation.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e Additionally, ethnic differences, particularly among Caucasians, exhibited significant interactions in the subgroup with diabetic neuropathy, which may be related to genetic susceptibility, metabolic characteristics, and environmental factors between different ethnicities, making Caucasians more sensitive to the effects of RC.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e These findings suggest individualized risks for DMC development, with RC having a more significant impact on the microvascular damage in high-risk groups, including the individuals who smoke tobacco and those with high LDL-C levels. These results further support the close relationship between RC levels and DMCs, suggesting that RC, as a biomarker, can play an important role in the early screening, prediction, and clinical intervention of diabetic microvascular damage. Moreover, ethnic differences emphasize the need to consider genetic and environmental factors in managing diabetic complications.\u003c/p\u003e \u003cp\u003eThis study has certain limitations. First, the sample source data were obtained from the UK Biobank, and the single-country data source may limit the generalizability of our findings. Second, many health-related variables (such as tobacco and alcohol use and physical activity) were self-reported, introducing reporting bias, as individuals may not accurately recall or disclose their behavioral habits. Third, although the UK Biobank provides follow-up data, some study designs were not prospective and may not have accurately captured the temporal sequence between exposure and outcomes, leading to inaccurate causal inferences in studies of chronic diseases that require long-term risk accumulation. Fourth, there was a lack of multicenter validation. The study sample was large; however, it lacked validation from large-scale multicenter studies from other countries or regions. Finally, although MR analysis helps control for genetic bias, some environmental factors such as dietary habits, air pollution, and lifestyle remain unaccounted for, potentially impacting the occurrence of diabetic complications in real-life settings. Additionally, more research is required to investigate the varying effects of RC levels on distinct categories of microvascular complications.\u003c/p\u003e \u003cp\u003eFuture studies should adopt a multicenter, large-sample, prospective design to further validate RC\u0026rsquo;s role in patients with diabetes. The generalizability of this research could be increased by including participants of different geographic and ethnic backgrounds. Additionally, future research should assess the effectiveness of interventions targeting RC levels in preventing diabetic complications, providing stronger clinical evidence to support the personalized management of diabetes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWithin this extensive, forward-looking cohort encompassing 22,270 patients with diabetes, we observed that high RC levels were significantly associated with increased risk of diabetic nephropathy and neuropathy. MR analysis further confirmed the causal relationship between high RC levels and diabetic nephropathy. Therefore, implementing measures to control and reduce RC levels may be a viable approach for the prevention and management of microvascular complications among patients with diabetes, warranting consideration in clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAGE, advanced glycation end-product; BMI, body mass index; CI, confidence interval; CRP, C-reactive protein; DMCs, diabetic microvascular complications; eGFR, estimated glomerular filtration rate; GWAS, genome-wide association study; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; HR, hazard ratio; ICD, International Classification of Diseases; IVs, instrumental variables; IVW, inverse variance-weighted; LD, linkage disequilibrium; LDL-C, low-density lipoprotein cholesterol; MR, Mendelian randomization; RC, remnant cholesterol; RCS, restricted cubic spline; SNP, single nucleotide polymorphism; T2D, type 2 diabetes mellitus; TG, triglyceride; TRL, TG-rich lipoprotein\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUK Biobank received ethical approval from the North West Multicenter Research Ethics Committee (06/MRE08/65). All participants provided written informed consent for original data collection, analysis, and recording linkage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003einterests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that may have influenced the work reported in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (grant number 82073668 to Lixin Tao). The funder had no role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the conception and design of this study. JL conceptualized the study, developed the methodology, performed data analysis, and drafted the original manuscript. RR contributed to methodology and data analysis. YL, JB, ZY, XY, RZ, and LW contributed to data visualization. DW supervised and contributed to writing, reviewing, and editing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. https://doi.org/10.1016/j.diabres.2021.109119\u003c/li\u003e\n\u003cli\u003eLitwak L, Goh SY, Hussein Z, Malek R, Prusty V, Khamseh ME. Prevalence of diabetes complications in people with type 2 diabetes mellitus and its association with baseline characteristics in the multinational A1chieve study. Diabetol Metab Syndr. 2013;5:57. https://doi.org/10.1186/1758-5996-5-57\u003c/li\u003e\n\u003cli\u003eK\u0026auml;hm K, Laxy M, Schneider U, Rogowski WH, Lhachimi SK, Holle R. Health care costs associated with incident complications in patients with type 2 diabetes in Germany. Diabetes Care. 2018;41:971-8. https://doi.org/10.2337/dc17-1763\u003c/li\u003e\n\u003cli\u003eChen HY, Kuo S, Su PF, Wu JS, Ou HT. Health care costs associated with macrovascular, microvascular, and metabolic complications of type 2 diabetes across time: estimates from a population-based cohort of more than 0.8 million individuals with up to 15 years of follow-up. Diabetes Care. 2020;43:1732-40. https://doi.org/10.2337/dc20-0072\u003c/li\u003e\n\u003cli\u003eZelnick LR, Weiss NS, Kestenbaum BR, Robinson-Cohen C, Heagerty PJ, Tuttle K, et al. Diabetes and CKD in the United States population, 2009\u0026ndash;2014. Clin J Am Soc Nephrol. 2017;12:1984-90. https://doi.org/10.2215/CJN.03700417\u003c/li\u003e\n\u003cli\u003eYau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care. 2012;35:556-64. https://doi.org/10.2337/dc11-1909\u003c/li\u003e\n\u003cli\u003eFeldman EL, Callaghan BC, Pop-Busui R, Zochodne DW, Wright DE, Bennett DL, et al. Diabetic neuropathy. Nat Rev Dis Primers. 2019;5:42. https://doi.org/10.1038/s41572-019-0097-9\u003c/li\u003e\n\u003cli\u003eCasta\u0026ntilde;er O, Pint\u0026oacute; X, Subirana I, Amor AJ, Ros E, Hern\u0026aacute;ez \u0026Aacute;, et al. Remnant cholesterol, not LDL cholesterol, is associated with incident cardiovascular disease. J Am Coll Cardiol. 2020;76:2712-24. https://doi.org/10.1016/j.jacc.2020.10.008\u003c/li\u003e\n\u003cli\u003eSandesara PB, Virani SS, Fazio S, Shapiro MD. The forgotten lipids: triglycerides, remnant cholesterol, and atherosclerotic cardiovascular disease risk. Endocr Rev. 2019;40:537-57. https://doi.org/10.1210/er.2018-00184\u003c/li\u003e\n\u003cli\u003eN\u0026egrave;gre-Salvayre A, Salvayre R. Reactive carbonyl species and protein lipoxidation in atherogenesis. Antioxidants (Basel). 2024;13:232. https://doi.org/10.3390/antiox13020232\u003c/li\u003e\n\u003cli\u003eWu Z, Yu S, Zhu Q, Li Z, Zhang H, Kang X, et al. Association of baseline and cumulative remnant cholesterol with incidence of diabetic nephropathy: a longitudinal cohort study. Diabetes Res Clin Pract. 2022;191:110079. https://doi.org/10.1016/j.diabres.2022.110079\u003c/li\u003e\n\u003cli\u003eZhu W, Liu Q, Liu F, Jiao C, Zhang L, Xie H. High remnant cholesterol as a risk factor for developing chronic kidney disease in patients with prediabetes and type 2 diabetes: a cross-sectional study of a US population. Acta Diabetol. 2024;61:735-43. https://doi.org/10.1007/s00592-024-02249-6\u003c/li\u003e\n\u003cli\u003eSudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Med. 2015;12:e1001779. https://doi.org/10.1371/journal.pmed.1001779\u003c/li\u003e\n\u003cli\u003eEastwood SV, Mathur R, Atkinson M, Brophy S, Sudlow C, Flaig R, et al. Algorithms for the capture and adjudication of prevalent and incident diabetes in UK Biobank. PLOS One. 2016;11:e0162388. https://doi.org/10.1371/journal.pone.0162388\u003c/li\u003e\n\u003cli\u003eWu Y, Xiong T, Tan X, Chen L. Frailty and risk of microvascular complications in patients with type 2 diabetes: a population-based cohort study. BMC Med. 2022;20:473. https://doi.org/10.1186/s12916-022-02675-9\u003c/li\u003e\n\u003cli\u003eChen J, Kuang J, Tang X, Mao L, Guo X, Luo Q, et al. Comparison of calculated remnant lipoprotein cholesterol levels with levels directly measured by nuclear magnetic resonance. Lipids Health Dis. 2020;19:132. https://doi.org/10.1186/s12944-020-01311-w\u003c/li\u003e\n\u003cli\u003eInker LA, Schmid CH, Tighiouart H, Eckfeldt Jh, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:20-9. https://doi.org/10.1056/NEJMoa1114248\u003c/li\u003e\n\u003cli\u003eWu Y, Wei Q, Li H, Yang H, Wu Y, Yu Y, et al. Association of remnant cholesterol with hypertension, type 2 diabetes, and their coexistence: the mediating role of inflammation-related indicators. Lipids Health Dis. 2023;22:158. https://doi.org/10.1186/s12944-023-01915-y\u003c/li\u003e\n\u003cli\u003eBarnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380:37-43. https://doi.org/10.1016/S0140-6736(12)60240-2\u003c/li\u003e\n\u003cli\u003ePenno G, Solini A, Orsi E, Bonora E, Fondelli C, Trevisan R, et al. Insulin resistance, diabetic kidney disease, and all-cause mortality in individuals with type 2 diabetes: a prospective cohort study. BMC Med. 2021;19:66. https://doi.org/10.1186/s12916-021-01936-3\u003c/li\u003e\n\u003cli\u003eBurgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016;35:1880-906. https://doi.org/10.1002/sim.6835\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377-89. https://doi.org/10.1007/s10654-017-0255-x\u003c/li\u003e\n\u003cli\u003eSascău R, Clement A, Radu R, Prisacariu C, Stătescu C. Triglyceride-rich lipoproteins and their remnants as silent promoters of atherosclerotic cardiovascular disease and other metabolic disorders: a review. Nutrients. 2021;13:1774. https://doi.org/10.3390/nu13061774\u003c/li\u003e\n\u003cli\u003eM\u0026eacute;n\u0026eacute;gaut L, Laubriet A, Crespy V, Leleu D, Pilot T, Van Dongen K, et al. Inflammation and oxidative stress markers in type 2 diabetes patients with advanced carotid atherosclerosis. Cardiovasc Diabetol. 2023;22:248. https://doi.org/10.1186/s12933-023-01979-1\u003c/li\u003e\n\u003cli\u003eMengstie MA, Chekol Abebe E, Behaile Teklemariam A, Tilahun Mulu A, Agidew MM, Teshome Azezew M, et al. Endogenous advanced glycation end products in the pathogenesis of chronic diabetic complications. Front Mol Biosci. 2022;9:1002710. https://doi.org/10.3389/fmolb.2022.1002710\u003c/li\u003e\n\u003cli\u003eZheng XY, Liu L. Remnant-like lipoprotein particles impair endothelial function: direct and indirect effects on nitric oxide synthase. J Lipid Res. 2007;48:1673-80. https://doi.org/10.1194/jlr.R700001-JLR200\u003c/li\u003e\n\u003cli\u003eAdamska A, Araszkiewicz A, Pilacinski S, Gandecka A, Grzelka A, Kowalska K, et al. Dermal microvessel density and maturity is closely associated with atherogenic dyslipidemia and accumulation of advanced glycation end products in adult patients with type 1 diabetes. Microvasc Res. 2019;121:46-51. https://doi.org/10.1016/j.mvr.2018.10.002\u003c/li\u003e\n\u003cli\u003eSoro-Paavonen A, Forbes JM. Novel therapeutics for diabetic micro- and macrovascular complications. Curr Med Chem. 2006;13:1777-88. https://doi.org/10.2174/092986706777452515\u003c/li\u003e\n\u003cli\u003eYuan Y, Sun H, Sun Z. Advanced glycation end products (AGEs) increase renal lipid accumulation: a pathogenic factor of diabetic nephropathy (DN). Lipids Health Dis. 2017;16:126. https://doi.org/10.1186/s12944-017-0522-6\u003c/li\u003e\n\u003cli\u003eFemlak M, Gluba-Brz\u0026oacute;zka A, Ciałkowska-Rysz A, Rysz J. The role and function of HDL in patients with diabetes mellitus and the related cardiovascular risk. Lipids Health Dis. 2017;16:207. https://doi.org/10.1186/s12944-017-0594-3\u003c/li\u003e\n\u003cli\u003eSpanakis EK, Golden SH. Race/ethnic difference in diabetes and diabetic complications. Curr Diab Rep. 2013;13:814-23. https://doi.org/10.1007/s11892-013-0421-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Remnant cholesterol, Diabetic microvascular complications, Diabetic nephropathy, Diabetic neuropathy, Diabetic retinopathy, Mendelian randomization, Type 2 diabetes mellitus, Causal inference, Restricted cubic spline, Prospective cohort study","lastPublishedDoi":"10.21203/rs.3.rs-6676665/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6676665/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiabetic microvascular complications (DMCs) are a major contributor to morbidity in type 2 diabetes (T2D), yet the role of remnant cholesterol (RC) in their development remains unclear. This study aimed to investigate the association between RC levels and DMC risk in patients with T2D.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis prospective cohort study was conducted using Mendelian randomization analysis and data from the United Kingdom Biobank, including 22,270 older adults with T2D at baseline (median age: 61 y; 40% women). Participants were grouped according to low, medium, and high RC levels. The main outcome measures were prevalence of diabetic nephropathy, neuropathy, and retinopathy based on hospital admissions and mortality data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIncreased RC levels were significantly associated with an increased risk of composite microvascular complications (hazard ratio [HR] for highest vs. lowest tertiles\u0026thinsp;=\u0026thinsp;1.13, 95% confidence interval [CI]: 1.04\u0026ndash;1.23), particularly diabetic nephropathy (HR\u0026thinsp;=\u0026thinsp;1.19, 95% CI: 1.08\u0026ndash;1.31) and neuropathy (HR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.04\u0026ndash;1.43). Mendelian randomization revealed statistically significant causal relationships between genetically predicted RC levels and diabetic nephropathy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035) and retinopathy (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044). Restricted cubic spline analysis revealed a significant nonlinear dose\u0026ndash;response relationship between RC levels and the risk of composite microvascular complications and diabetic retinopathy.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated RC levels were independently associated with the risk of DMCs, particularly nephropathy and neuropathy. The Mendelian randomization results support a causal role of RC in nephropathy. These results suggest the potential role of RC as a biomarker and that it can aid in the early screening and prediction of diabetic microvascular damage.\u003c/p\u003e","manuscriptTitle":"Relationship between remnant cholesterol and the risk of diabetic microvascular complications in type 2 diabetes: An observational study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 07:03:10","doi":"10.21203/rs.3.rs-6676665/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03321dec-a6a2-47a7-9b8e-44b529d932b1","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-01T01:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-23 07:03:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6676665","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6676665","identity":"rs-6676665","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}