Dietary mineral intake was correlated with seral HDL-C in patients with type 2 diabetes

Dietary mineral intake was correlated with seral HDL-C in patients with type 2 diabetes | 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 Dietary mineral intake was correlated with seral HDL-C in patients with type 2 diabetes Wenmin Li, Yingying Shi, Deyi Xu, Haofan Yang, Wenhao Zheng, Liang Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6019693/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 While dietary interventions are critical for managing diabetes, there is limited research on the role of specific minerals in regulating lipid metabolism. This study aims to examine the correlation between dietary mineral intake and serum lipid profiles in patients with type 2 diabetes. Methods Daily mineral intake was accessed using a validated dietary questionnaire administered to 149 subjects. Partial correlation and multivariable linear regression analysis were conducted to examine the relationship between daily mineral intake and serum lipid profiles. Results According to the Dietary Nutrient Reference Intakes (DRI) for Chinese Residents, daily intake of calcium, zinc, potassium, and dietary fiber was significantly lower in both men and women (all P < 0.001). In contrast, sodium, iron and iodine intake were elevated. Partial correlation analysis indicated that daily intake of calcium, iron, iodine, zinc and selenium was positively associated with serum high-density lipoprotein cholesterol (HDL-C) ( P < 0.05), whereas dietary iodine intake was negatively related to HDL-C (r= -0.181, P = 0.049). Multivariable linear regression analysis showed that dietary intake of calcium, iron, iodine, zinc and selenium was significantly associated with HDL-C after adjusting for covariates (all P < 0.05). However, there existed not significant correlation of dietary mineral intake with total cholesterol, low-density lipoprotein cholesterol, or triglyceride. Conclusions The dietary mineral intake of patients with type 2 diabetes was largely suboptimal. Dietary calcium, iron, zinc and selenium intake were positively associated with serum HDL-C, suggesting a potential benefit for lipid homeostasis in this population. dietary mineral intake nutrient HDL-C Type 2 diabetes dyslipidemia Background Diabetes has become a global epidemic, with the prevalence reaching 12.8% among adults in China ( 1 ). Dietary interventions have been widely shown to reduce the incidence of diabetes and all-cause mortality ( 2 , 3 ). In patients with type 2 diabetes mellitus (T2DM), the quality of diet has been closely linked to metabolic disorders such as hyperglycemia, dyslipidemia and excessive weight gain ( 4 , 5 ). As a result, lifestyle intervention that incorporates both diet and exercise have become key strategies in diabetes management ( 6 ). Despite this, deficiencies in dietary mineral intake are prevalent worldwide ( 7 , 8 ). For instance, a study in Italy revealed that only about 20% older adults with T2DM met their recommended dietary intake for minerals like calcium, magnesium and selenium ( 7 ). However, few studies have examined the status of dietary mineral intake among diabetic patients in China. Recently, researchers pointed out that mineral deficiency was associated with dyslipidemia ( 9 – 14 ). Specifically, serum zinc concentration was positively associated with serum levels of triglycerides (TG), total cholesterol (TC) and low - density lipoprotein cholesterol (LDL-C), but negatively correlated with high - density lipoprotein cholesterol (HDL-C) ( 15 – 18 ). Similarly, serum ferritin concentrations have been correlated with dyslipidemia, though the relationship between dietary iron intake and lipid profiles remains controversial ( 19 , 20 ). Some studies suggested that iron supplementation has no significant effect on plasma HDL-C level ( 20 – 22 ), while others reported a positive association with triglycerides ( 19 , 23 , 24 ). In regions with excessive iodine intake, dietary iodine levels have been observed to decrease serum HDL-C level ( 25 , 26 ). However, limited data existed on the relationship between dietary mineral intake and lipid profiles among patients with T2DM in China, particularly in coastal cities like Shanghai. Methods Study design and patients Patients aged 20 years and older were recruited from the Department of Endocrinology and Metabolism at Putuo Hospital, Shanghai University of Traditional Chinese Medicine, between February 1st and April 25th, 2024. T2DM was diagnosed according to the criteria defined by the World Health Organization (WHO) in 1999 ( 27 ). Patients were excluded based on the following criteria: 1) severe hepatic or renal insufficiency; 2) other forms of diabetes: such as type 1 diabetes mellitus or gestational diabetes; 3) malignancy; 4) cognitive impairment or psychiatric disorders; 5) conditions that impeded the completion of questionnaires. A total of 149 patients with T2DM, who provided complete clinical and dietary data, were included in this cross-sectional study. Informed written consent was obtained from all participants. This study protocol was approved by the Ethics Committee of Putuo Hospital, Shanghai University of Traditional Chinese Medicine (Approval No. PTEC-A-2024-40(S)-1). Questionnaires and evaluation of mineral intake The participants’ intake of 35 major food groups and beverages over the previous 72 hours was assessed using a previously validated food frequency questionnaire. Daily intake of energy and minerals was calculated using a food composition database published in 2002 ( 28 ). The Chinese Dietary Guideline (CDGs, 2013) was used to evaluate whether the participants’ daily mineral intake met the recommended levels outlined by the Dietary Reference Intakes (DRI) ( 29 ). Anthropometric and biochemical measurements Height and weight were measured with patients barefoot and wearing light clothing. Body mass index (BMI, kg/m 2 ) was calculated by dividing weight in kilograms by height in meter squared ( 30 ). Venous blood samples were collected after an overnight fast of at least 10 hours. Lipid profiles, including triglycerides, total cholesterol, LDL-C, and HDL-C were measured using Roche cobas 8000 fully automated biochemical analyzer. Statistical Analysis Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables were presented as number (percentage). Differences in means were examined using t test (Table 1 , 2 ), and differences in proportions were analyzed with the chi-square test. Differences between patients’ daily mineral intake and DRI were calculated using Medcalc statistical software (version 20.0). Partial correlation analysis was conducted to evaluate the association between dietary mineral intake and serum lipid profiles adjusting for covariates including gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake, daily energy intake, metformin usage and lipid-lowing treatment. Multivariable linear regression models were used to examine the association between HDL-C and level of each daily mineral after adjusting for gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake and daily energy intake (Model 2), and further adjusted for HbA1c, proportion of energy from fat, metformin usage and lipid-lowing treatment on the basis of model 2 (Model 3). All the analyses were performed using SPSS version 26.0 IBM SPSS Statistics for Windows, Version 26.0. Armonk, N Y: IBM Corp.). Table 1 Clinical characteristics of the patients Variables Total (N = 149) Men (n = 79) Women (n = 70) P value Current smoking (%) 15.4 27.8 1.4 < 0.001 Current drinking (%) 8.7 15.2 1.4 0.002 Age(years) 68.0 ± 11.1 67.2 ± 10.5 68.9 ± 11.8 0.348 Duration of diabetes (year) 10.9 ± 9.1 9.2 ± 8.15 13.0 ± 10.0 0.015 Body weight (kg) 70.4 ± 10.9 71.2 ± 11.4 69.4 ± 10.4 0.315 BMI(kg/m 2 ) 25.7 ± 3.8 25.3 ± 3.8 26.2 ± 3.7 0.118 ALT(U/L) 20.7 ± 16.2 19.8 ± 15.8 21.7 ± 16.7 0.501 AST(U/L) 20.9 ± 23.0 18.3 ± 7.4 24.0 ± 32.9 0.197 γ-GT(U/L) 28.0 ± 24.4 28.8 ± 27.3 27.2 ± 20.9 0.716 FPG(mmol/L) 8.0 ± 2.7 8.1 ± 2.9 7.8 ± 2.3 0.592 PPG(mmol/L) 13.4 ± 5.3 14.0 ± 5.9 12.8 ± 4.4 0.189 HbA1c (%) 9.9 ± 2.6 10.4 ± 3.1 9.3 ± 2.0 0.013 TC(mmol/L) 4.50 ± 1.20 4.30 ± 1.20 4.70 ± 1.10 0.056 TG(mmol/L) 1.70 ± 1.20 1.60 ± 1.20 1.80 ± 1.30 0.332 LDL-C(mmol/L) 2.7 ± 1.0 2.5 ± 0.9 2.8 ± 1.1 0.051 HDL-C(mmol/L) 1.10 ± 0.40 1.00 ± 0.30 1.10 ± 0.40 0.076 BUN(mg/dL) 7.8 ± 3.9 7.9 ± 3.1 7.7 ± 4.6 0.773 SCR(µmol/L) 77.9 ± 43.5 87.4 ± 44.2 67.5 ± 40.7 0.010 UA(µmol/L) 336 ± 113 344 ± 122 326 ± 103 0.388 GFR(mL/min) 94.8 ± 34.8 95.8 ± 34.2 93.7 ± 35.8 0.739 Medication Lipid-lowering treatment (%) 22.8 27.8 17.1 0.087 Statin (%) 22.1 26.6 17.1 0.117 Fibrates (%) 0.7 1.3 0 0.530 Metformin (%) 43.6 41.8 45.7 0.375 BMI, body mass index; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; γ-GT, γ-Glutamyl transpeptidase; FPG, fasting plasma glucose; PPG, postprandial plasma glucose; HbA1c, Glycosylated hemoglobin; TC, Total cholesterol; TG, Triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen, SCR, serum creatinine; UA, uric acid; GFR, glomerular filtration rate Table 2 Nutrient intake of patients included. Variables Total (n = 149) Men (n = 79) Women (n = 70) P value DRIs Energy intake Energy (kcal/d) 1626 ± 346 1697 ± 323 1546 ± 356 0.007 2050 # , 1700 & Energy intake from Carbohydrates (%/) 43 ± 10.2 43.4 ± 10.3 44.5 ± 10.1 0.493 50–65 Energy intake from Fat (%) 39 ± 11.3 40.6 ± 11.6 39.0 ± 10.9 0.377 20–30 #, & Energy intake from protein (%/) 17.2 ± 4.7 16.9 ± 4.2 17.5 ± 5.1 0.484 10–15 Daily mineral intake Sodium(mg/d) 2315 ± 1135 2411 ± 1179 2206 ± 1082 0.553 1400 #, & Calcium (mg/d) 451 ± 310 435 ± 294 468 ± 369 0.274 1000 #, & Iron (mg/d) 15.7 ± 7.8 15.5 ± 6.0 15.9 ± 9.4 0.510 12 #, & Potassium (mg/d) 1773 ± 611 1828 ± 595 1711 ± 628 0.783 2000 *, & Iodine (mg/d) 215 ± 267 231 ± 279 196 ± 252 0.243 120 #, § Zinc (mg/d) 9.39 ± 3.50 9.61 ± 3.20 9.15 ± 3.82 0.436 12.5 # , 7.5 & Selenium (mg/d) 45.3 ± 44.2 43.3 ± 35.0 47.5 ± 52.7 0.424 60 # Dietary Fiber intake (g/d) 9.3 ± 3.8 9.4 ± 3.6 9.1 ± 4.0 0.575 20–25 #, & Levels of daily macronutrient and mineral intake were compared with data of the 2023 revised edition of the Dietary Reference Intakes (DRIs) for Chinese Residents. # P<0.001 in men, & P<0.001 in women, *P<0.05 in men, § P<0.05 in women. Table 3. Correlation between mineral intake and levels of lipid profiles Total cholesterol Triglycerides LDL-C HDL-C r P value r P value r P value r P value Sodium(mg/d) -0.056 0.543 -0.109 0.238 0.021 0.822 -0.038 0.683 Calcium (mg/d) 0.000 0.998 -0.149 0.107 0.018 0.848 0.247 0.007 Iron (mg/d) 0.018 0.847 0.019 0.836 -0.091 0.326 0.329 <0.001 Potassium (mg/d) 0.029 0.757 -0.041 0.662 0.005 0.960 0.121 0.189 Iodine (mg/d) 0.008 0.930 -0.039 0.674 0.077 0.407 -0.181 0.049 Zinc (mg/d) 0.047 0.614 -0.010 0.911 -0.077 0.405 0.309 0.001 Selenium (mg/d) -0.004 0.963 -0.059 0.525 -0.048 0.607 0.229 0.012 Data were analyzed using partial correlation analysis, and adjusted for gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake, daily energy intake, metformin usage and lipid-lowing treatment. Table 4. Linear regression analysis of the correlation HDL-C with dietary mineral intake. Variables Model 1 Model 2 Model 3 t P value t P value t P value Sodium -0.442 0.659 -0.426 0.671 0.055 0.956 Calcium 0.792 0.430 2.891 0.005 2.450 0.016 Iron 3.444 0.001 3.783 <0.001 4.050 <0.001 Potassium 0.791 0.430 1.298 0.197 0.953 0.343 Iodine -0.265 0.791 -2.028 0.045 -2.194 0.030 Zinc 2.221 0.028 3.372 0.001 2.901 0.005 Selenium 0.822 0.413 2.690 0.008 2.277 0.025 Model 1. Univariable linear regression. Model 2. Data were adjusted by gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake and daily energy intake. Moder 3. Further adjusted for HbA1c, proportion of energy from fat, metformin usage and lipid-lowing treatment on the basis of model 2. Results Clinical characteristics of the patients Table 1 presents the clinical characteristics of the 149 patients included in the study, comprising 79 males and 70 females. The mean age of participants was 68 years. The duration of diabetes was shorter in men compared to women, while levels of glycosylated hemoglobin A1c (HbA1c) and serum creatinine were significantly higher in men (P < 0.05). Other parameters, including BMI, alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transpeptidase, fasting blood glucose, postprandial blood glucose, TC, TG, LDL-C, HDL-C, serum urea nitrogen, uric acid, glomerular filtration rate, proportion of lipid lowing treatment and metformin usage were comparable between men and women. Status of dietary mineral intake in the participants As shown in Table 2 , the total energy intake was higher in men than in women (P < 0.05), while the proportion of energy derived from carbohydrates, fats and proteins was similar between men and women. There were no significant sex differences in dietary mineral intake. However, compared to the DRIs, both men and women had significantly increased intakes of sodium, iron and iodine (all P < 0.05). Specifically, sodium intake was 65.4% (2315 mg/d VS 1400 mg/d) higher than the DRI, iron intake was 30.8%(15.7 mg/d VS. 12 mg/d)higher, and iodine intake was 79.2% (215 mg/d VS. 120 mg/d) higher. In contrast, the intake of calcium, potassium, zinc, and dietary fiber was significantly lower than the DRI in both groups (all P < 0.05), with calcium intake reduced by 54.9%༈451 mg/d VS 1000 mg/d༉, potassium by 11.35% (1773 mg/d VS. 2000 mg/d༉, zinc by 24.88%༈9.39 mg/d VS 12.5 mg/d༉, and dietary fiber by 53.5% (9.3 mg/d VS 20 mg/d). The daily intake of selenium was similar to the DRI. Correlation between mineral intake and lipid profiles Table 3 displayed the results of partial correlation analysis, showing that the daily intake of calcium, iron, iodine, zinc and selenium was significantly associated with HDL-C levels (all P <0.05). Specifically, dietary calcium, iron, zinc and selenium intake showed a positive correlation with HDL-C ( P <0.05), while iodine intake was negatively associated with HDL-C (r = -181, P =0.049). No significant associations were observed between daily mineral intake and TC, TG, and LDL-C. Additionally, no significant correlations were found between HDL-C and the intake of sodium and potassium. Univariable and multivariable linear regression analysis indicated that dietary iron and zinc intake was significant related to serum HDL-C ( P <0.05, Table 4). However, the association of dietary calcium, iodine and selenium with HDL-C became significant after adjusting for gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake and daily energy intake, and further adjustment HbA1c, proportion of energy from fat, metformin usage and lipid-lowing treatment (all P <0.05, Model 2-3 in Table 4). Discussion This study offers critical insights into the relationship between dietary mineral intake and lipid profiles in patients with T2DM. First, compared to the DRI for Chinese residents, sodium, iron and iodine intake were significantly higher in both men and women, while calcium, potassium, zinc, and dietary fiber intake were markedly lower. Second, we found significant association of daily intake of calcium, iron, iodine, zinc and selenium with serum HDL-C levels. The findings of elevated sodium and reduced potassium intake are consistent with dietary shifts seen in rapidly urbanizing regions of China, where diets have moved towards higher consumption of fats, proteins, and sodium, but lower potassium intake ( 11 , 31 ). Similarly, studies in Europe have reported that a very low proportion of participants met the recommended intake for calcium and selenium among population adhering to the Mediterranean diet ( 7 ). In our study, the intake of calcium and selenium was also insufficient, confirming the global prevalence of inadequate mineral intake in populations with diabetes. The longstanding policy of universal salt iodization in China has successfully increased iodine sufficiency ( 32 , 33 ); however, it has led to excessive iodine intake in coastal regions ( 34 ), which our data also reflected. The association between mineral intake and lipid metabolism has been explored in previous research. Firstly, Zinc deficiency has been linked to dyslipidemia ( 14 ). Clinical trials have demonstrated that zine supplementation significantly reduces serum TG, TC, and LDL-C, while raising HDL-C level in patients with T2DM ( 15 , 16 , 35 ).Our findings align with this, showing a positive association between dietary zinc intake and HDL-C after adjusting for confounders. Notably, zinc’s role in lipid homeostasis may be enhanced when combined with selenium supplementation, as seen in other studies ( 17 ). Secondly, the relationship between iron intake and lipid profiles is more contentious. While some studies suggested that dietary iron intake is associated with adverse lipid profiles (e.g., high TG and low HDL-C), others have found no significant effect of iron supplementation on HDL-C levels ( 19 – 23 ). Our study observed a positive association between iron intake and HDL-C, warranting further investigation to confirm this link. Thirdly, meta-analysis showed that elevated dietary calcium intake was associated with lower blood TG, LDL-C and higher HDL-C concentrations ( 36 ), while a positive relationship between dietary calcium intake and HDL-C was also observed. Finally, the role of iodine in lipid metabolism is complex and has yielded inconsistent findings across different studies. Research from Korea suggests that dietary iodine is inversely associated with triglyceride and positively correlated with HDL-C in postmenopausal women ( 37 ). In contrast, excessive iodine intake has been shown to negatively impact HDL-C and apoA1 levels in certain populations ( 38 ). In our study, we observed that excessive iodine intake was negatively associated with HDL-C, supporting previous findings of an inverted U-shaped relationship between iodine exposure and lipid abnormalities ( 38 , 39 ). Further studies are needed to better understand the mechanisms underlying these associations. A key strength of this study is its detailed assessment of dietary mineral intake and its comprehensive analysis of associations with serum lipid profiles in a sample of patients with T2DM. The use of a validated food frequency questionnaire ensured reliable data collection, and the application of multivariable linear regression models allowed us to control for potential confounders, such as age, gender, BMI, fiber intake and energy intake. However, several limitations must be acknowledged. First, the cross-sectional nature of the study precludes any causal inference between dietary mineral intake and lipid profiles. Additionally, the reliance on self-reported dietary intake data may introduce recall bias, as participants may not accurately remember or report their dietary consumption. Moreover, the study was conducted in a single hospital setting, which may limit the generalizability of the findings to broader populations, particularly those outside urban areas or in different regions of China. Multi-center longitudinal studies should be warranted to validate the causal relationships between dietary mineral balance and lipid homeostasis. Conclusions This study highlights significant disparities in dietary mineral intake among patients with T2DM in China, with a clear divergence from DRI. Importantly, this study demonstrated significant associations between dietary mineral intake and serum HDL-C, indicating that these minerals may play a key role in lipid homeostasis. Abbreviations BMI, body mass index; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; γ-GT, γ-Glutamyl transpeptidase; FPG, fasting plasma glucose; PPG, postprandial plasma glucose; HbA1c, Glycosylated hemoglobin; TC, Total cholesterol; T2DM, type 2 diabetes mellitus; TG, Triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen, SCR, serum creatinine; UA, uric acid; GFR, glomerular filtration rate Declarations Ethics approval and consent to participate Statement Written informed consents were collected from all participants, and this study was approved by the Ethics Committee of Putuo Hospital, Shanghai University of Traditional Chinese Medicine (Number of approval form of ethics committee: PTEC-A-2024-40(S)-1). Consent for publication All authors have read and approved the final manuscript. Availability of date The datasets used and/or analyzed during the current study were available from the corresponding author on reasonable request. Competing interests The authors have no conflicts of interests. Funding This work was supported by Clinical Characteristic of Health System in Putuo District, Shanghai (2024tszk02), Research Project of Shanghai Municipal Health Care Commission (202240309), Technology Innovation Project of Putuo District Health System (ptkwws202003, ptkwws202302). Authors 'contributions W L, Y S, H Y and J L performed the statistical analysis and wrote the manuscript, D X, W Z and L X contributed to data collection, L W and T L participated in the design of this study and reviewed the manuscript. Acknowledgements Thanks to W L, Y S, H Y and J L for conducting statistical analysis and writing the manuscript, D X, W Z and L X for assisting in data collection, L W and T L for participating in the design of this study and reviewing the manuscript. 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Obesity reviews : an official journal of the International Association for the Study of Obesity (2014) 15 Suppl 1:16-26. doi: 10.1111/obr.12124 Shan Z, Li Y, Li Y, Wang H, Teng D, Teng X, et al. Changing Iodine Status and the Incidence of Thyroid Disease in Mainland China: A Prospective 20-Year Follow-Up Study. Thyroid (2023) 33:858-66. doi: 10.1089/thy.2022.0505 Li Y, Teng D, Ba J, Chen B, Du J, He L, et al. Efficacy and Safety of Long-Term Universal Salt Iodization on Thyroid Disorders: Epidemiological Evidence from 31 Provinces of Mainland China. Thyroid (2020) 30:568-79. doi: 10.1089/thy.2019.0067 Lin Y, Chen D, Wu J, Chen Z. Iodine status five years after the adjustment of universal salt iodization: a cross-sectional study in Fujian Province, China. Nutr J (2021) 20:17. doi: 10.1186/s12937-021-00676-7 Heidari Seyedmahalleh M, Montazer M, Ebrahimpour-Koujan S, Azadbakht L. The Effect of Zinc Supplementation on Lipid Profiles in Patients with Type 2 Diabetes Mellitus: A Systematic Review and Dose-Response Meta-Analysis of Randomized Clinical Trials. Adv Nutr (2023) 14:1374-88. doi: 10.1016/j.advnut.2023.08.006 Hajhashemy Z, Rouhani P, Saneei P. Dietary calcium intake in relation to blood lipids and lipoproteins profiles: A systematic review and meta-analysis of epidemiologic studies. Nutr Metab Cardiovasc Dis (2022) 32:1609-26. doi: 10.1016/j.numecd.2022.03.018 Park JK, Woo HW, Kim MK, Shin J, Lee YH, Shin DH, et al. Dietary iodine, seaweed consumption, and incidence risk of metabolic syndrome among postmenopausal women: a prospective analysis of the Korean Multi-Rural Communities Cohort Study (MRCohort). European journal of nutrition (2021) 60:135-46. doi: 10.1007/s00394-020-02225-0 Liu M, Li SM, Li XW, Wang PH, Liang P, Li SH. [Exploratory study on the association between high iodine intake and lipid]. Zhonghua Liu Xing Bing Xue Za Zhi (2009) 30:699-701. Wang D, Wan S, Liu P, Meng F, Ren B, Qu M, et al. Associations between water iodine concentration and the prevalence of dyslipidemia in Chinese adults: A cross-sectional study. Ecotoxicol Environ Saf (2021) 208:111682. doi: 10.1016/j.ecoenv.2020.111682 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6019693","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416941126,"identity":"2ed5334e-7e69-462e-9b6c-bf3755cf1a00","order_by":0,"name":"Wenmin Li","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wenmin","middleName":"","lastName":"Li","suffix":""},{"id":416941129,"identity":"113f368b-3c61-4622-8b9a-b5d3db12a251","order_by":1,"name":"Yingying Shi","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Shi","suffix":""},{"id":416941130,"identity":"c0db5f7a-2ffd-4754-9b91-84c25362b6df","order_by":2,"name":"Deyi Xu","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Deyi","middleName":"","lastName":"Xu","suffix":""},{"id":416941131,"identity":"7d4a1473-5c2f-474d-b693-38c9adca7777","order_by":3,"name":"Haofan Yang","email":"","orcid":"","institution":"New York University Grossman School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Haofan","middleName":"","lastName":"Yang","suffix":""},{"id":416941132,"identity":"23f29cfe-745d-48fb-adc6-15b0580f551d","order_by":4,"name":"Wenhao Zheng","email":"","orcid":"","institution":"Baylor University","correspondingAuthor":false,"prefix":"","firstName":"Wenhao","middleName":"","lastName":"Zheng","suffix":""},{"id":416941136,"identity":"16217318-5a04-4544-b9c1-48883b242b99","order_by":5,"name":"Liang Wang","email":"","orcid":"","institution":"Marshall University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Wang","suffix":""},{"id":416941138,"identity":"543acbaf-f818-4e40-94d6-8101d2806bdf","order_by":6,"name":"Lin Xu","email":"","orcid":"","institution":"Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xu","suffix":""},{"id":416941139,"identity":"1701c5ec-0d59-4262-83e6-530fee934132","order_by":7,"name":"Tao Lei","email":"","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Lei","suffix":""},{"id":416941141,"identity":"f107fd03-10d1-42df-8a1d-6ff0b6a5549b","order_by":8,"name":"Jun Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACPgaGBAYGNjCb8UFCRQ1hLWwwLTwMDMwGD84cI0oLA0wLm+TDFmYitEgkPJMuKLORt2fvfVaR2MDGwN/enUBIS5r0jHNphj08x81uJO6QYZA4c3YDYS28bYcTeCTS2G4knmFjMJDIJUFLQWIbM4laGIjTwvMg2ZoH5Jczx5glEs4c4yHoF372nMTbPMAQY29vY/z4o6JGjr+9F78WYIQkoHIJKAcB9gNEKBoFo2AUjIIRDQCk5D1hOosu8QAAAABJRU5ErkJggg==","orcid":"","institution":"Shanghai University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-02-13 05:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6019693/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6019693/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77514868,"identity":"0177b649-4302-4cc4-a6ba-c7e39eff870f","added_by":"auto","created_at":"2025-03-02 08:01:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":921105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6019693/v1/dde03ca9-8209-414e-9697-9ca7e1ee8013.pdf"},{"id":76629756,"identity":"83a66af4-9e40-4e9c-b7b1-1c0fceb262d5","added_by":"auto","created_at":"2025-02-19 06:24:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19494,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6019693/v1/6653427722e03d9fab318ed0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dietary mineral intake was correlated with seral HDL-C in patients with type 2 diabetes","fulltext":[{"header":"Background","content":"\u003cp\u003eDiabetes has become a global epidemic, with the prevalence reaching 12.8% among adults in China (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Dietary interventions have been widely shown to reduce the incidence of diabetes and all-cause mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In patients with type 2 diabetes mellitus (T2DM), the quality of diet has been closely linked to metabolic disorders such as hyperglycemia, dyslipidemia and excessive weight gain (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). As a result, lifestyle intervention that incorporates both diet and exercise have become key strategies in diabetes management (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Despite this, deficiencies in dietary mineral intake are prevalent worldwide (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). For instance, a study in Italy revealed that only about 20% older adults with T2DM met their recommended dietary intake for minerals like calcium, magnesium and selenium (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, few studies have examined the status of dietary mineral intake among diabetic patients in China.\u003c/p\u003e \u003cp\u003eRecently, researchers pointed out that mineral deficiency was associated with dyslipidemia (\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Specifically, serum zinc concentration was positively associated with serum levels of triglycerides (TG), total cholesterol (TC) and low - density lipoprotein cholesterol (LDL-C), but negatively correlated with high - density lipoprotein cholesterol (HDL-C) (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Similarly, serum ferritin concentrations have been correlated with dyslipidemia, though the relationship between dietary iron intake and lipid profiles remains controversial (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Some studies suggested that iron supplementation has no significant effect on plasma HDL-C level (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), while others reported a positive association with triglycerides (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In regions with excessive iodine intake, dietary iodine levels have been observed to decrease serum HDL-C level (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, limited data existed on the relationship between dietary mineral intake and lipid profiles among patients with T2DM in China, particularly in coastal cities like Shanghai.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003ePatients aged 20 years and older were recruited from the Department of Endocrinology and Metabolism at Putuo Hospital, Shanghai University of Traditional Chinese Medicine, between February 1st and April 25th, 2024. T2DM was diagnosed according to the criteria defined by the World Health Organization (WHO) in 1999 (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Patients were excluded based on the following criteria: 1) severe hepatic or renal insufficiency; 2) other forms of diabetes: such as type 1 diabetes mellitus or gestational diabetes; 3) malignancy; 4) cognitive impairment or psychiatric disorders; 5) conditions that impeded the completion of questionnaires. A total of 149 patients with T2DM, who provided complete clinical and dietary data, were included in this cross-sectional study. Informed written consent was obtained from all participants. This study protocol was approved by the Ethics Committee of Putuo Hospital, Shanghai University of Traditional Chinese Medicine (Approval No. PTEC-A-2024-40(S)-1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuestionnaires and evaluation of mineral intake\u003c/h3\u003e\n\u003cp\u003eThe participants\u0026rsquo; intake of 35 major food groups and beverages over the previous 72 hours was assessed using a previously validated food frequency questionnaire. Daily intake of energy and minerals was calculated using a food composition database published in 2002 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The Chinese Dietary Guideline (CDGs, 2013) was used to evaluate whether the participants\u0026rsquo; daily mineral intake met the recommended levels outlined by the Dietary Reference Intakes (DRI) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAnthropometric and biochemical measurements\u003c/h3\u003e\n\u003cp\u003eHeight and weight were measured with patients barefoot and wearing light clothing. Body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated by dividing weight in kilograms by height in meter squared (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Venous blood samples were collected after an overnight fast of at least 10 hours. Lipid profiles, including triglycerides, total cholesterol, LDL-C, and HDL-C were measured using Roche cobas 8000 fully automated biochemical analyzer.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables were presented as number (percentage). Differences in means were examined using t test (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e,\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), and differences in proportions were analyzed with the chi-square test. Differences between patients\u0026rsquo; daily mineral intake and DRI were calculated using Medcalc statistical software (version 20.0). Partial correlation analysis was conducted to evaluate the association between dietary mineral intake and serum lipid profiles adjusting for covariates including gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake, daily energy intake, metformin usage and lipid-lowing treatment. Multivariable linear regression models were used to examine the association between HDL-C and level of each daily mineral after adjusting for gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake and daily energy intake (Model 2), and further adjusted for HbA1c, proportion of energy from fat, metformin usage and lipid-lowing treatment on the basis of model 2 (Model 3). All the analyses were performed using SPSS version 26.0 IBM SPSS Statistics for Windows, Version 26.0. Armonk, N Y: IBM Corp.).\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\u003eClinical characteristics of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\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\u003eCurrent smoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCurrent drinking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e68.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e67.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e68.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDuration of diabetes (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e13.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBody weight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e70.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e69.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eγ-GT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e28.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e28.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e27.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFPG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePPG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e9.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTC(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBUN(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSCR(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e77.9\u0026thinsp;\u0026plusmn;\u0026thinsp;43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e87.4\u0026thinsp;\u0026plusmn;\u0026thinsp;44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;40.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUA(\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e336\u0026thinsp;\u0026plusmn;\u0026thinsp;113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e344\u0026thinsp;\u0026plusmn;\u0026thinsp;122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e326\u0026thinsp;\u0026plusmn;\u0026thinsp;103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGFR(mL/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e94.8\u0026thinsp;\u0026plusmn;\u0026thinsp;34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95.8\u0026thinsp;\u0026plusmn;\u0026thinsp;34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e93.7\u0026thinsp;\u0026plusmn;\u0026thinsp;35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLipid-lowering treatment (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStatin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFibrates (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMetformin (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c11\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eBMI, body mass index; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; γ-GT, γ-Glutamyl transpeptidase;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eFPG, fasting plasma glucose; PPG, postprandial plasma glucose; HbA1c, Glycosylated hemoglobin; TC, Total cholesterol;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eTG, Triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen, SCR, serum creatinine; UA, uric acid; GFR, glomerular filtration rate\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutrient intake of patients included.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDRIs\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\u003eEnergy intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy (kcal/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1626\u0026thinsp;\u0026plusmn;\u0026thinsp;346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1697\u0026thinsp;\u0026plusmn;\u0026thinsp;323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1546\u0026thinsp;\u0026plusmn;\u0026thinsp;356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2050\u003csup\u003e#\u003c/sup\u003e, 1700\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy intake from Carbohydrates (%/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy intake from Fat (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u0026ndash;30\u003csup\u003e#, \u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy intake from protein (%/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDaily mineral intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium(mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2315\u0026thinsp;\u0026plusmn;\u0026thinsp;1135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2411\u0026thinsp;\u0026plusmn;\u0026thinsp;1179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2206\u0026thinsp;\u0026plusmn;\u0026thinsp;1082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1400\u003csup\u003e#, \u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e451\u0026thinsp;\u0026plusmn;\u0026thinsp;310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e435\u0026thinsp;\u0026plusmn;\u0026thinsp;294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e468\u0026thinsp;\u0026plusmn;\u0026thinsp;369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1000\u003csup\u003e#, \u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIron (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003csup\u003e#, \u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1773\u0026thinsp;\u0026plusmn;\u0026thinsp;611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1828\u0026thinsp;\u0026plusmn;\u0026thinsp;595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1711\u0026thinsp;\u0026plusmn;\u0026thinsp;628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2000\u003csup\u003e*, \u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIodine (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215\u0026thinsp;\u0026plusmn;\u0026thinsp;267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231\u0026thinsp;\u0026plusmn;\u0026thinsp;279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196\u0026thinsp;\u0026plusmn;\u0026thinsp;252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120\u003csup\u003e#, \u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5\u003csup\u003e#\u003c/sup\u003e, 7.5\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelenium (mg/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.3\u0026thinsp;\u0026plusmn;\u0026thinsp;35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.5\u0026thinsp;\u0026plusmn;\u0026thinsp;52.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\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\u003eDietary Fiber intake\u003c/b\u003e (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u0026ndash;25\u003csup\u003e#, \u0026amp;\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\"\u003eLevels of daily macronutrient and mineral intake were compared with data of the 2023 revised edition of the Dietary Reference Intakes (DRIs) for Chinese Residents. \u003csup\u003e#\u003c/sup\u003eP\u0026lt;0.001 in men, \u003csup\u003e\u0026amp;\u003c/sup\u003eP\u0026lt;0.001 in women, *P\u0026lt;0.05 in men, \u003csup\u003e\u0026sect;\u003c/sup\u003e P\u0026lt;0.05 in women.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003eTable 3. Correlation between mineral intake and levels of lipid profiles\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSodium(mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eCalcium (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eIron (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePotassium (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eIodine (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eZinc (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSelenium (mg/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData were analyzed using partial correlation analysis, and adjusted for gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake, daily energy intake, metformin usage and lipid-lowing treatment.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4. Linear regression analysis of the correlation HDL-C with dietary mineral intake.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 181px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eIron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e4.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eIodine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-2.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e-2.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eZinc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eSelenium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Model 1. Univariable linear regression.\u003c/p\u003e\n\u003cp\u003eModel 2. Data were adjusted by gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake and daily energy intake.\u003c/p\u003e\n\u003cp\u003eModer 3. Further adjusted for HbA1c, proportion of energy from fat, metformin usage and lipid-lowing treatment on the basis of model 2.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of the patients\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the clinical characteristics of the 149 patients included in the study, comprising 79 males and 70 females. The mean age of participants was 68 years. The duration of diabetes was shorter in men compared to women, while levels of glycosylated hemoglobin A1c (HbA1c) and serum creatinine were significantly higher in men (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Other parameters, including BMI, alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transpeptidase, fasting blood glucose, postprandial blood glucose, TC, TG, LDL-C, HDL-C, serum urea nitrogen, uric acid, glomerular filtration rate, proportion of lipid lowing treatment and metformin usage were comparable between men and women.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatus of dietary mineral intake in the participants\u003c/h3\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the total energy intake was higher in men than in women (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the proportion of energy derived from carbohydrates, fats and proteins was similar between men and women. There were no significant sex differences in dietary mineral intake. However, compared to the DRIs, both men and women had significantly increased intakes of sodium, iron and iodine (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, sodium intake was 65.4% (2315 mg/d VS 1400 mg/d) higher than the DRI, iron intake was 30.8%(15.7 mg/d VS. 12 mg/d)higher, and iodine intake was 79.2% (215 mg/d VS. 120 mg/d) higher. In contrast, the intake of calcium, potassium, zinc, and dietary fiber was significantly lower than the DRI in both groups (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with calcium intake reduced by 54.9%༈451 mg/d VS 1000 mg/d༉, potassium by 11.35% (1773 mg/d VS. 2000 mg/d༉, zinc by 24.88%༈9.39 mg/d VS 12.5 mg/d༉, and dietary fiber by 53.5% (9.3 mg/d VS 20 mg/d). The daily intake of selenium was similar to the DRI.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCorrelation between mineral intake and lipid profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 displayed the results of partial correlation analysis, showing that the daily intake of calcium, iron, iodine, zinc and selenium was significantly associated with HDL-C levels (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Specifically, dietary calcium, iron, zinc and selenium intake showed a positive correlation with HDL-C (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05), while iodine intake was negatively associated with HDL-C (r = -181, \u003cem\u003eP\u003c/em\u003e=0.049). No significant associations were observed between daily mineral intake and TC,\u0026nbsp;TG,\u0026nbsp;and LDL-C. Additionally, no significant\u0026nbsp;correlations were found between HDL-C and the intake of sodium and\u0026nbsp;potassium.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Univariable and multivariable linear regression analysis indicated that dietary iron and zinc intake was significant related to serum HDL-C (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, Table 4). However, the association of dietary calcium, iodine and selenium with HDL-C became significant after adjusting for gender, age, course of diabetes, current smoking, drinking, BMI, dietary fiber intake and daily energy intake, and further adjustment HbA1c, proportion of energy from fat, metformin usage and lipid-lowing treatment (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, Model 2-3 in Table 4).\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThis study offers critical insights into the relationship between dietary mineral intake and lipid profiles in patients with T2DM. First, compared to the DRI for Chinese residents, sodium, iron and iodine intake were significantly higher in both men and women, while calcium, potassium, zinc, and dietary fiber intake were markedly lower. Second, we found significant association of daily intake of calcium, iron, iodine, zinc and selenium with serum HDL-C levels.\u003c/p\u003e \u003cp\u003eThe findings of elevated sodium and reduced potassium intake are consistent with dietary shifts seen in rapidly urbanizing regions of China, where diets have moved towards higher consumption of fats, proteins, and sodium, but lower potassium intake (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Similarly, studies in Europe have reported that a very low proportion of participants met the recommended intake for calcium and selenium among population adhering to the Mediterranean diet (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In our study, the intake of calcium and selenium was also insufficient, confirming the global prevalence of inadequate mineral intake in populations with diabetes. The longstanding policy of universal salt iodization in China has successfully increased iodine sufficiency (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e); however, it has led to excessive iodine intake in coastal regions (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), which our data also reflected.\u003c/p\u003e \u003cp\u003eThe association between mineral intake and lipid metabolism has been explored in previous research. Firstly, Zinc deficiency has been linked to dyslipidemia (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Clinical trials have demonstrated that zine supplementation significantly reduces serum TG, TC, and LDL-C, while raising HDL-C level in patients with T2DM (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).Our findings align with this, showing a positive association between dietary zinc intake and HDL-C after adjusting for confounders. Notably, zinc\u0026rsquo;s role in lipid homeostasis may be enhanced when combined with selenium supplementation, as seen in other studies (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Secondly, the relationship between iron intake and lipid profiles is more contentious. While some studies suggested that dietary iron intake is associated with adverse lipid profiles (e.g., high TG and low HDL-C), others have found no significant effect of iron supplementation on HDL-C levels (\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Our study observed a positive association between iron intake and HDL-C, warranting further investigation to confirm this link. Thirdly, meta-analysis showed that elevated dietary calcium intake was associated with lower blood TG, LDL-C and higher HDL-C concentrations (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), while a positive relationship between dietary calcium intake and HDL-C was also observed. Finally, the role of iodine in lipid metabolism is complex and has yielded inconsistent findings across different studies. Research from Korea suggests that dietary iodine is inversely associated with triglyceride and positively correlated with HDL-C in postmenopausal women (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In contrast, excessive iodine intake has been shown to negatively impact HDL-C and apoA1 levels in certain populations (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). In our study, we observed that excessive iodine intake was negatively associated with HDL-C, supporting previous findings of an inverted U-shaped relationship between iodine exposure and lipid abnormalities (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Further studies are needed to better understand the mechanisms underlying these associations.\u003c/p\u003e \u003cp\u003eA key strength of this study is its detailed assessment of dietary mineral intake and its comprehensive analysis of associations with serum lipid profiles in a sample of patients with T2DM. The use of a validated food frequency questionnaire ensured reliable data collection, and the application of multivariable linear regression models allowed us to control for potential confounders, such as age, gender, BMI, fiber intake and energy intake. However, several limitations must be acknowledged. First, the cross-sectional nature of the study precludes any causal inference between dietary mineral intake and lipid profiles. Additionally, the reliance on self-reported dietary intake data may introduce recall bias, as participants may not accurately remember or report their dietary consumption. Moreover, the study was conducted in a single hospital setting, which may limit the generalizability of the findings to broader populations, particularly those outside urban areas or in different regions of China. Multi-center longitudinal studies should be warranted to validate the causal relationships between dietary mineral balance and lipid homeostasis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights significant disparities in dietary mineral intake among patients with T2DM in China, with a clear divergence from DRI. Importantly, this study demonstrated significant associations between dietary mineral intake and serum HDL-C, indicating that these minerals may play a key role in lipid homeostasis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI, body mass index; ALT, Alanine aminotransferase; AST, Aspartate aminotransferase; \u0026gamma;-GT, \u0026gamma;-Glutamyl transpeptidase; FPG, fasting plasma glucose; PPG, postprandial plasma glucose; HbA1c, Glycosylated hemoglobin; TC, Total cholesterol; T2DM, type 2 diabetes mellitus; TG, Triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BUN, blood urea nitrogen, SCR, serum creatinine; UA, uric acid; GFR, glomerular filtration rate\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consents were collected from all participants, and this study was approved by the Ethics Committee of Putuo Hospital, Shanghai University of Traditional Chinese Medicine (Number of approval form of ethics committee: PTEC-A-2024-40(S)-1).\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of date\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study were available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Clinical Characteristic of Health System in Putuo District, Shanghai (2024tszk02), Research Project of Shanghai Municipal Health Care Commission (202240309), Technology Innovation Project of Putuo District Health System (ptkwws202003, ptkwws202302).\u003c/p\u003e\n\u003cp\u003eAuthors \u0026apos;contributions\u003c/p\u003e\n\u003cp\u003eW L, Y S, H Y and J L performed the statistical analysis and wrote the manuscript, D X, W Z and L X contributed to data collection, L W and T L participated in the design of this study and reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThanks to W L, Y S, H Y and J L for conducting statistical analysis and writing the manuscript, D X, W Z and L X for assisting in data collection, L W and T L for participating in the design of this study and reviewing the manuscript. Thanks for obtaining permission from all those mentioned.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi Y, Teng D, Shi X, Qin G, Qin Y, Quan H, et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study. \u003cem\u003eBmj\u003c/em\u003e (2020) 369:m997. doi: 10.1136/bmj.m997 \u003c/li\u003e\n\u003cli\u003ePan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. \u003cem\u003eDiabetes care\u003c/em\u003e (1997) 20:537-44. doi: 10.2337/diacare.20.4.537 \u003c/li\u003e\n\u003cli\u003eGong Q, Zhang P, Wang J, Ma J, An Y, Chen Y, et al. 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Geneva, Switzerland: World Health Organization [Available from: http://whqlibdoc.who.int/hq/1999/WHO_NCD_NCS_99.2.pdf.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLu J\u003c/strong\u003e, \u003cstrong\u003eGu Y\u003c/strong\u003e, \u003cstrong\u003eLiu H\u003c/strong\u003e, \u003cstrong\u003eWang L\u003c/strong\u003e, \u003cstrong\u003eLi W\u003c/strong\u003e, \u003cstrong\u003eLi W\u003c/strong\u003e, et al. \u003cstrong\u003eDaily Branched-Chain Amino Acid Intake and Risks of Obesity and Insulin Resistance in Children: A Cross-Sectional Study\u003c/strong\u003e. \u003cem\u003eObesity (Silver Spring, Md)\u003c/em\u003e (\u003cstrong\u003e2020\u003c/strong\u003e) \u003cstrong\u003e28\u003c/strong\u003e:\u003cstrong\u003e1310-16\u003c/strong\u003e. doi: 10.1002/oby.22834 \u003c/li\u003e\n\u003cli\u003e[2023 revised edition of the \u0026quot;Reference Intake of Dietary Nutrients for Chinese Residents\u0026quot;]. \u003cem\u003eYing Yang Xue Bao\u003c/em\u003e (2023) 45:521-24. doi: 10.13325/j.cnki.acta.nutr.sin.2023.06.001 \u003c/li\u003e\n\u003cli\u003eLu J, Hou X, Zhang L, Jiang F, Hu C, Bao Y, et al. 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Dietary calcium intake in relation to blood lipids and lipoproteins profiles: A systematic review and meta-analysis of epidemiologic studies. \u003cem\u003eNutr Metab Cardiovasc Dis\u003c/em\u003e (2022) 32:1609-26. doi: 10.1016/j.numecd.2022.03.018 \u003c/li\u003e\n\u003cli\u003ePark JK, Woo HW, Kim MK, Shin J, Lee YH, Shin DH, et al. Dietary iodine, seaweed consumption, and incidence risk of metabolic syndrome among postmenopausal women: a prospective analysis of the Korean Multi-Rural Communities Cohort Study (MRCohort). \u003cem\u003eEuropean journal of nutrition\u003c/em\u003e (2021) 60:135-46. doi: 10.1007/s00394-020-02225-0 \u003c/li\u003e\n\u003cli\u003eLiu M, Li SM, Li XW, Wang PH, Liang P, Li SH. [Exploratory study on the association between high iodine intake and lipid]. \u003cem\u003eZhonghua Liu Xing Bing Xue Za Zhi\u003c/em\u003e (2009) 30:699-701. \u003c/li\u003e\n\u003cli\u003eWang D, Wan S, Liu P, Meng F, Ren B, Qu M, et al. Associations between water iodine concentration and the prevalence of dyslipidemia in Chinese adults: A cross-sectional study. \u003cem\u003eEcotoxicol Environ Saf\u003c/em\u003e (2021) 208:111682. doi: 10.1016/j.ecoenv.2020.111682 \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":"dietary mineral intake, nutrient, HDL-C, Type 2 diabetes, dyslipidemia","lastPublishedDoi":"10.21203/rs.3.rs-6019693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6019693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWhile dietary interventions are critical for managing diabetes, there is limited research on the role of specific minerals in regulating lipid metabolism. This study aims to examine the correlation between dietary mineral intake and serum lipid profiles in patients with type 2 diabetes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDaily mineral intake was accessed using a validated dietary questionnaire administered to 149 subjects. Partial correlation and multivariable linear regression analysis were conducted to examine the relationship between daily mineral intake and serum lipid profiles.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAccording to the Dietary Nutrient Reference Intakes (DRI) for Chinese Residents, daily intake of calcium, zinc, potassium, and dietary fiber was significantly lower in both men and women (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, sodium, iron and iodine intake were elevated. Partial correlation analysis indicated that daily intake of calcium, iron, iodine, zinc and selenium was positively associated with serum high-density lipoprotein cholesterol (HDL-C) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas dietary iodine intake was negatively related to HDL-C (r= -0.181, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). Multivariable linear regression analysis showed that dietary intake of calcium, iron, iodine, zinc and selenium was significantly associated with HDL-C after adjusting for covariates (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, there existed not significant correlation of dietary mineral intake with total cholesterol, low-density lipoprotein cholesterol, or triglyceride.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe dietary mineral intake of patients with type 2 diabetes was largely suboptimal. Dietary calcium, iron, zinc and selenium intake were positively associated with serum HDL-C, suggesting a potential benefit for lipid homeostasis in this population.\u003c/p\u003e","manuscriptTitle":"Dietary mineral intake was correlated with seral HDL-C in patients with type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 06:16:02","doi":"10.21203/rs.3.rs-6019693/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":"5dda22e8-9c6d-48e3-8260-8ce61a5850fa","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-02T07:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 06:16:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6019693","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6019693","identity":"rs-6019693","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}