Effects of histidine and metformin on the risk of diabetic nephropathy and its influence pathway in a female population: A cross-sectional study in Chinese patients with type 2 diabetes

Effects of histidine and metformin on the risk of diabetic nephropathy and its influence pathway in a female population: A cross-sectional study in Chinese 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 Effects of histidine and metformin on the risk of diabetic nephropathy and its influence pathway in a female population: A cross-sectional study in Chinese patients with type 2 diabetes Wei-Ming Luo, Yuan-Yuan Ma, Hui-Ying Liu, Peng-Zhe Xie, Wei Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5764336/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 Objective This study explored the effect of histidine on the occurrence of diabetic nephropathy in different sex populations and its specific possible pathway, as well as the influence of metformin on the pathway. Methods This study retrieved 1031 patients with type 2 diabetes mellitus and performed a cross-sectional study at the First Affiliated Hospital of Liaoning Medical University in Jinzhou, Liaoning Province, China. We used stepwise logistic regression to analyze the association between histidine and diabetic nephropathy in the general population and in sex-stratified populations. Mediating effect analysis was used to explore the specific pathway of this relationship in the female population. Results The protective effect of histidine on diabetic nephropathy was influenced by sex and was significant in women (univariable: OR: 0.68 (95% CI: 0.5,0.93), multivariable: OR: 0.54 (95% CI: 0.38,0.78)). The specific pathway of its effect was partly through affecting tryptophan metabolism. Conclusions The protective effect of histidine against diabetic nephropathy in the female population was stronger than that in the general population and was negatively affected by metformin. This helps us pay more attention to the clinical nutritional and preventive value of histidine and tryptophan in female diabetic patients. Diabetic nephropathy Histidine Metformin Mediation effect Gender difference Figures Figure 1 Strengths and limitations of this study Strengths: 1. We analyzed the role of amino acids in the occurrence of complications in patients with type 2 diabetes by using metabolomics, which can provide ideas for the prevention and treatment of diseases from the perspective of metabolic pathways. 2. Compared to work already published in the field, our work pointed out gender differences in diabetic nephropathy protection and suggested differences in prevention and treatment according to sex. 3. We also provided a metabolic perspective on the effects of metformin use, providing clues for the development of clinical nutrition and preventive measures in people with different medication regimens for type 2 diabetes. limitations: 1. Due to the nature of the cross-sectional study, we cannot prove the existence of a causal relationship between His and metformin and the occurrence of DN. 2. We only have data and results from one center in Jinzhou, China, and future multicenter studies are needed to confirm our research. Introduction Diabetes mellitus is a group of metabolic disorders caused by insufficient absolute or relative secretion of insulin, among which type 2 diabetes mellitus (T2DM) is the most common. Diabetic nephropathy (DN) is one of the most common and serious complications in T2DM patients(1), as well as one of the main causes of end-stage renal disease(2, 3), accounting for 23% of end-stage renal disease patients at the end of 2017(4). The prevalence of diabetes is increasing rapidly worldwide, especially in developing countries(5). In the United States, the number of diabetic patients who started treatment for end-stage renal disease increased significantly from more than 40,000 in 2000 to more than 50,000 in 2014(6). In China, the number of diabetic and chronic kidney disease patients has reached 24.3 million(7). Albuminuria and glomerular filtration rate (GFR) are currently the most commonly used clinical biomarkers in T2DM patients with DN, but they still have limitations in the correct identification of DN. Therefore, there is an urgent need for novel biomarkers that can identify DN at an early stage and improve risk stratification. Amino acids, as essential substances for the formation of proteins needed by the human body, participate in a variety of metabolic processes in vivo, and their relationship with T2DM and its complications has received much attention(8, 9). Some scholars have found that due to reasons such as insulin resistance and reduced protein intake, plasma amino acid levels in DN patients will change significantly(10). Many studies have found that changes in amino acid metabolism are related to the occurrence of DN and the nutritional status of patients(11, 12). Some amino acids can also be used as predictive factors to predict the occurrence and development of DN(13, 14), suggesting that they are important in the early monitoring and prevention of DN. Many published studies have indicated that histidine (His) metabolism is related to the development of T2DM and its complications. One study showed that the gut flora of T2DM patients was more enriched in His metabolism than that of healthy controls(15). A study found that an increase in blood sugar was associated with a decrease in His and glutamine(16). Another study of 3,587 patients found that His was negatively associated with the risk of microangiopathy(17). Low levels of His have been linked to increased inflammation and oxidative stress in chronic kidney disease(18). Carnosine is thought to have a protective effect in DN, and l-His is an important component of carnosine(1). However, there are few related studies on the risk of DN in female patients with T2DM, and the specific pathway of its influence is still unclear. This research is carried out under the current background. MATERIALS AND METHODS Study Method and Population Our study population was from the First Affiliated Hospital of Liaoning Medical University (FAHLMU), a tertiary general hospital located in Jinzhou City, Liaoning Province, China. We could access the data for research purposes after January 1st, 2020. The inclusion criteria for the study population were as follows: 1) patients diagnosed with T2DM or treated with antidiabetic drugs and 2) complete information on DN. 3) Complete information of His, tryptophan (Trp). 4) The information on the use of metformin is completed. Exclusion criteria: 1) T2DM patients younger than 18 years old; 2) subjects lacking the study indicators, height, weight and blood pressure. 3) Patients with extreme outliers of His or Trp. A total of 1821 patients with T2DM were initially included in this study. According to the inclusion and exclusion criteria, a total of 1031 subjects were included in this study, including 188 DN patients in the case group and 843 T2DM patients in the control group. The authors could obtain detailed information from individual participants. The Ethics Committee for Clinical Research of FAHLMU approved the ethics of the study, and informed consent was waived due to the retrospective nature of the study, which is consistent with the Declaration of Helsinki. Data collection and clinical definitions Demographic and anthropometric information was retrieved from the subjects' electronic medical records, as well as messages of current clinical factors and diabetes complications. Demographic information included gender and age. Anthropometric height, weight, systolic blood pressure (SBP) and diastolic blood pressure (DBP) information were measured. Clinical parameters included glycated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), creatinine (Crea), and plasma His and Trp concentrations. The duration of DN in T2DM patients was recorded. In hospitals, anthropometric indicators were measured using standardized procedures. Participants are required to wear light-colored clothing and no shoes. Height and weight were accurately measured to 0.5 cm and 0.1 kg, respectively. Blood pressure was measured behind the right arm of the adult cuff with a standard mercury sphygmomanometer and in the appropriate position after 10 minutes of sitting rest. Age was obtained from the date of birth to the date of hospitalization or medical examination and was calculated annually. Body mass index (BMI) was calculated as the ratio of weight (kg) to height (m) and classified according to the overweight and obesity standards recommended by the National Health Commission of China(19). In this study, the diagnosis and classification of T2DM were based on the criteria published by the World Health Organization (WHO) or the population treated with antidiabetic drugs(20). The diagnostic criteria for DN were based on the standards of care for T2DM(21). According to the RCS curve, His and Trp were stratified according to 51 µmol/L and 46 µmol/L, respectively ( Fig S1 ). Laboratory Assay Details of the amino acid measurements were published previously(22). Briefly, dried blood spots were used in the metabolomic assay, which were prepared from capillary whole blood through 8-h fasting. We measured the metabolites by direct infusion mass spectrometry technology equipped with the AB Sciex 4000 QTrap system (AB Sciex, Framingham, MA, USA). High-purity water and acetonitrile were purchased from Thermo Fisher (Waltham, MA, USA) and were utilized as the dilution agent and mobile phase. 1-Butanol and acetyl chloride were obtained from Sigma‒Aldrich (St Louis, MO, USA). Isotope-labeled internal standard samples of amino acids (NSK-A) were purchased from Cambridge Isotope Laboratories (Tewksbury, MA, USA), while standard samples of His and Trp were purchased from Chrom Systems (Grafelfing, Germany). In brief, 8.5 mL of venous blood was drawn from each participant at 08:00 to 09.30 h in the morning after an 8-h fast. Laboratory tests were carried out at a specialized diagnostic laboratory. The level of lipid profiles was analyzed with an automatic biochemistry analyzer (Hitachi 7150, Tokyo, Japan). We also assayed the levels of HDL-C and LDL-C by selective solubilization. Statistical Analysis Continuous data are expressed as the mean ± standard deviation (SD), nonnormally distributed data are expressed as the median (interquartile range), and categorical variables are expressed as numbers (percentages). To analyze whether there were differences in various indicators between the case group and the control group in different sexes. For continuous variables, a t test or variance analysis was used for normally distributed variables. The rank sum test was used for nonnormal distributions, and the chi-square test was used for categorical variables. First, a binary logistic regression was performed in the general population to determine whether the relationship between His and DN in the study was significant, and then the same analysis was performed by gender stratification. The logistic regression model was used to obtain odds ratio (OR) values of His with DN and their 95% confidence intervals (95% CI). Traditional risk factors for DN are adjusted through structural adjustment: Multivariable model 1 adjusted for age, sex, and BMI. Multivariable model 2 adjusted SBP, DBP, LDL-C, HDL-C, TG, TC, HbA1c, and duration of DN based on model 1. Multivariable model 3 adjusted UA and Crea based on model 2. Finally, mediation effect analysis was conducted in the female population to explore the specific path and influencing factors of His on DN. We use multiple imputations to interpolate the missing values. All analyses were performed using R version 4.1.0. RESULT Description of Study Subjects The selection characteristics of DN were analyzed between the case group and control group in the total population and the population stratified by sex (Table 1 ). A total of 1031 T2DM patients were included in the study, with a mean age of 57.24 years old (sd: 13.82) and a mean BMI of 25.29 (sd: 3.85) in the total population. Table 1 Clinical and biochemical characteristics of participants according to sex and the occurrence of diabetic nephropathy. Variables Total subjects Male P Female P Non-DN DN Non-DN DN Mean/number (SD or %) Mean/number (SD or %) Mean/number (SD or %) Mean/number (SD or %) N 1031 452(82.5) 96(17.5) 391(81.0) 92(19.0) Age(years) 57.24 ± 13.82 54.8 ± 14.84 57.72 ± 14.33 0.079 59.16 ± 12.81 60.54 ± 9.8 0.333 Weight(kg) 70 (60, 80) 75 (67.75, 83) 75 (67.75, 84.25) 0.908 62 (56.4, 70) 65 (57.75, 75) 0.05 Height(cm) 167 (160, 172) 172 (170, 175) 172 (170, 175) 0.582 160 (156, 163) 160 (158, 165) 0.235 BMI(kg/m²) 25.29 ± 3.85 25.51 ± 3.84 25.56 ± 3.44 0.907 24.85 ± 3.82 25.85 ± 4.34 0.028 BMI < 18.5 27 (2.6) 15 (3.3) 1 (1.1) 9 (2.3) 2 (2.2) BMI ≥ 18.5and < 24.0 351 (34.1) 124 (27.4) 34 (35.4) 159 (40.7) 34 (37.0) BMI ≥ 24and < 28.0 433 (42.0) 210 (46.5) 37 (38.5) 154 (39.4) 32 (34.8) BMI ≥ 28.0 220 (21.3) 103 (22.8) 24 (25.0) 69 (17.6) 24 (26.0) SBP (mmHg) 140.39 ± 23.99 138.67 ± 22.43 142.1 ± 22.42 0.173 139.41 ± 24.56 151.21 ± 27.73 < 0.001 DBP (mmHg) 81 (74, 90) 82 (75.75, 90) 80 (71, 91.25) 0.311 80 (72, 89) 82.50 (74.00, 90.50) 0.103 HbA1C(%) 9.54 ± 2.41 9.45 ± 2.33 9.58 ± 2.18 0.631 9.63 ± 2.56 9.53 ± 2.40 0.734 Triglyceride (mmol/L) 1.69 (1.13, 2.39) 1.66 (1.1, 2.36) 1.62 (1.13, 2.21) 0.801 1.72 (1.13, 2.55) 1.80 (1.20, 2.48) 0.506 TC(mmol/L) 4.64 (3.86, 5.29) 4.36 (3.74, 5.06) 4.47 (3.81, 5.08) 0.483 4.77 (4.03, 5.49) 5.05 (4.20, 6.08) 0.035 HDL-C(mmol/L) 1.02 (0.85, 1.25) 0.94 (0.82, 1.19) 0.98 (0.81, 1.15) 0.519 1.07 (0.89, 1.28) 1.17 (1.01, 1.37) 0.002 LDL-C (mmol/L) 2.78 (2.19, 3.36) 2.70 (2.11, 3.19) 2.70 (2.03, 3.30) 0.762 2.87 (2.28, 3.4) 2.95 (2.37, 3.8) 0.076 His (µmol/L) 50.5 (35.28, 78.3) 52.54 (36.01, 82.3) 48.11 (34.56, 70.57) 0.273 52.01 (36.02, 79.64) 41.49 (31.97, 65.77) 0.005 < 51µmol/L 519 (50.3) 217 (48.0) 52 (54.2) 192 (49.1) 58 (63.0) ≥ 51µmol/L 512 (49.7) 235 (52.0) 44 (45.8) 199 (50.9) 34 (37.0) UA 311 (245.95, 381.5) 323.9 (255, 397.25) 360.5 (292, 424.58) 0.009 286 (227.5, 347) 308 (243.75, 363) 0.067 Crea 58.97 (49.02, 73.3) 65 (56, 76) 74.75 (55.9, 101.94) 0.001 51 (42, 60.74) 51.19 (43.68, 70.21) 0.103 Use of metformin 358 (34.7) 155 (34.3) 31 (32.3) 0.797 131 (33.5) 41 (44.6) 0.061 BMI, body mass index. SBP, systolic blood pressure. DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; His, Histidine; UA, uric acid; Crea, serum creatinine, DN, diabetic nephropathy. Data are mean ± standard deviation, median (IQR), or n (%). P values were derived from the t test for normally distributed variables, the Mann‒Whitney U test for skewed distributions, and the chi-square test (or Fisher’s test if appropriate) for categorical variables. P < 0.05 was defined as statistically significant. The total population was stratified according to sex. In the male population, the differences in UA and Crea were statistically significant between the case and control groups. The two indicators were all higher in the case group. In the female population, the differences in BMI, SBP, TC, HDL-C, and His between the two groups were statistically significant, and female DN patients had higher BMI, SBP, TC and HDL-C. Non-DN patients had higher His levels. Effect of Histidine on Diabetic Nephropathy in the General Population We performed univariate and multivariate logistic regression analyses between His and DN and showed the results in Table 2 . From the results of numerical His, the OR value of the single factor regression was 0.78 (95% CI: 0.65–0.95). After multifactor stepwise regression adjustment, the OR value was changed to 0.71 (95% CI: 0.77–0.88), and the results were all statistically significant. Table 2 Odds ratio of histidine for the risk of diabetic nephropathy. OR (95%CI) P Univariable model His, per µmol/L 0.78 (0.65,0.95) 0.009 < 51µmol/L reference ≥ 51µmol/L 0.67 (0.49,0.92) 0.013 Multivariable model1 His, per µmol/L 0.77 (0.63,0.94) 0.005 < 51µmol/L reference ≥ 51µmol/L 0.64 (0.47,0.89) 0.007 Multivariable model2 His, per µmol/L 0.71 (0.57,0.88) < 0.001 < 51µmol/L reference ≥ 51µmol/L 0.56 (0.4,0.79) < 0.001 Multivariable model3 His, per µmol/L 0.71 (0.57,0.88) < 0.001 < 51µmol/L reference ≥ 51µmol/L 0.56 (0.4,0.79) < 0.001 In different models, the categorical His divided into two groups by 51 µmol/L was used as a reference, and then, changes in numerical and categorical His, OR, odd ratio, His, and histidine were observed. Multivariable Model 1 was adjusted for age, sex, and body mass index. Multivariable Model 2 was adjusted for variables in Model 1 and concentrations of systolic blood pressure, diastolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, total cholesterol, and glycosylated hemoglobin. Multivariable Model 3 was adjusted for variables in Model 2 and concentrations of uric acid, serum creatinine and the duration of diabetic nephropathy. According to the categorical variables divided according to the RCS curve, the protective effect of His on DN was statistically significant, and its univariate and multivariate OR values were 0.67 (95% CI: 0.49–0.92) and 0.56 (95% CI: 0.4–0.79), respectively. Effects of Histidine on Diabetic Nephropathy in Different Genders Logistic regression analysis was conducted after the total population was stratified according to sex (Table 3 ). The protective effect of His on the occurrence of DN disappeared in the male population. His protective effect was increased in women compared with the general population (univariable: OR: 0.68 (95% CI: 0.5,0.93), multivariable: OR: 0.54 (95% CI: 0.38,0.78)). Table 3 Odds ratio of Histidine for the risk of diabetic nephropathy in different groups by sex. man woman OR (95%CI) P OR (95%CI) P Univariable model His, per µmol/L 0.87 (0.68,1.11) 0.236 0.68 (0.5,0.93) 0.008 < 51µmol/L reference reference ≥ 51µmol/L 0.78 (0.5,1.22) 0.273 0.57 (0.35,0.9) 0.015 Multivariable model1 His, per µmol/L 0.87 (0.68,1.11) 0.231 0.66 (0.48,0.91) 0.004 < 51µmol/L reference reference ≥ 51µmol/L 0.78 (0.5,1.22) 0.278 0.52 (0.32,0.83) 0.006 Multivariable model2 His, per µmol/L 0.85 (0.64,1.11) 0.218 0.54 (0.38,0.78) < 0.001 < 51µmol/L reference reference ≥ 51µmol/L 0.7 (0.43,1.13) 0.142 0.41 (0.24,0.68) < 0.001 Multivariable model3 His, per µmol/L 0.85 (0.64,1.11) 0.218 0.42 (0.25,0.7) < 0.001 < 51µmol/L reference reference ≥ 51µmol/L 0.7 (0.43,1.13) 0.142 0.41 (0.24,0.68) < 0.001 In different models, the categorical His divided into two groups by 51 µmol/L was used as a reference, and then, changes in numerical and categorical His, OR, odd ratio, His, and histidine were observed. Multivariable Model 1 was adjusted for age, sex, and body mass index. Multivariable Model 2 was adjusted for variables in Model 1 and concentrations of systolic blood pressure, diastolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, total cholesterol, and glycosylated hemoglobin. Multivariable Model 3 was adjusted for variables in Model 2 and concentrations of uric acid, serum creatinine and the duration of diabetic nephropathy. Effects of the use of metformin on diabetic nephropathy in the female population We further explored the effect of metformin use on DN in the female population ( Table S1 ). The use of metformin is a risk factor for the prevalence of DN in female patients (univariable: OR: 1.6 (95% CI: 1.01, 2.53), multivariable: OR: 1.93 (95% CI: 1.15, 3.22)). However, the exact path is unclear. The specific pathways and influencing factors of Histidine on Diabetic Nephropathy in the female population Then, we used the causal step approach(23) to analyze the mediating effect of His and the use of metformin on DN in the female population and drew a correlation diagram (Fig. 1 ). There was a positive correlation between His and Trp concentration (OR: 1.29, 95% CI: 1.18–1.41). His (OR: 0.89, 95% CI: 0.83–0.95) and Trp (OR: 0.91, 95% CI: 0.85–0.97) both exist as protective factors for the occurrence of DN, and part of His protective effect on DN is completed through Trp. Meanwhile, the protective effect of His on DN was affected by metformin. As seen from the results, metformin is a risk factor for DN, and its use is negatively correlated with the concentration of His (OR: 0.90, 95% CI: 0.82–0.99) (Table 4 ). At the same time, because we found that metformin could not directly affect Trp, it was not marked in the path diagram. Metformin can significantly affect the protective effect of His on DN. Since His and Trp are positively correlated, metformin can also indirectly affect the role of Trp as a protective factor by influencing His. Table 4 Mediation analysis of the relationship between metformin and DN by histidine and tryptophan. His Parameter estimate OR (95%CI) P Total effect a -0.05 0.42 (0.25,0.7) < 0.001 Direct effect path a’ -0.01 0.48 (0.28,0.82) 0.006 Path b 0.26 1.29(1.18, 1.41) < 0.001 Path c -0.10 0.49 (0.29,0.81) 0.005 Path d -0.11 0.90(0.82, 0.99) 0.030 Path e 0.09 1.93 (1.15,3.22) 0.013 Direct effect path e’ 0.08 1.8 (1.07,3.04) 0.027 Adjusted for age, sex, body mass index, systolic blood pressure, diastolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, total cholesterol, glycosylated hemoglobin, uric acid and serum creatinine. DN, diabetic nephropathy, His, histidine, Trp, tryptophan. Path a’ indicates the path from His to DN (Outcome) when controlled for Trp (Mediator). Path b indicates the path from His to Trp (Mediator). Path c indicates the path from Trp (mediator) to DN (outcome). Path d indicates the impact of metformin on His. Path e indicates the impact of metformin on DN (outcome). Path e’ indicates the impact of metformin on DN (Outcome) when controlled for His (Mediator). Sensitivity analysis Since we found that the use of metformin would affect His metabolism, we adjusted the influence of metformin on the relationship between His and DN according to the above results, and then we obtained model 4, which was included in the supplementary data (Table S2, Table S3) . After adjusting for the use of metformin, significant results in the general population and sex-stratified population remained unchanged, but their OR values changed slightly. Then, we changed the interpolation method of the missing values and performed the same analysis again. After random forest imputation of missing values (UA = 187, TG = 288, TC = 289, Crea = 147), the effects remained stable and significant in multivariable analyses ( Table S4, Table S5 ). DISCUSSION The development of metabolomics in various fields has attracted great attention, and metabolomics is an effective strategy to fully understand kidney disease and its mitochondrial and energy metabolism dynamics(24). His is the basic unit of protein and one of the essential amino acids, especially for children. At present, many studies have proven the relationship between His, T2DM and DN. Compared with healthy controls, plasma His was significantly lower in patients with T2DM and was negatively correlated with changes in urinary albumin excretion(25). Carnosine, composed of two amino acids, beta-alanine and l-His, has a protective effect on the kidney(26). Researchers have found that patients with chronic kidney disease have lower plasma His, accompanying persistent inflammation and higher mortality(18). At the same time, dietary supplements of His can reduce oxidation and inflammation, which is expected to treat kidney disease(27). All studies are consistent with our results, and His supplementation has a certain protective effect on the occurrence of DN. In addition, our study found that the relationship between His and DN is related to sex. Women with lower plasma His levels had a higher risk of developing DN, while His showed no significant protective effect in men. We believe that this phenomenon may be related to the significant activation of the mTOR signaling pathway by His supplementation. Some studies have shown that the mTOR pathway can integrate amino acid and insulin signals(28, 29). mTOR can sense the availability of nutrients, so it is activated under nutrient-rich conditions, especially high levels of amino acids(30). Additional studies have found that the addition of His can activate and regulate the mTORC1 pathway(31, 32), which can affect insulin secretion(33, 34). This is also consistent with our results. mTOR expression was higher in women, explaining the sex difference in the results(35, 36). Multiple studies have found that the progression of DN leads to changes in serum metabolites(37, 38). Amino acids, as metabolites, are expected to be more effective early biomarkers. Both His and Trp are aromatic and heterocyclic amino acids. Our study found that His not only directly affects the risk of DN in T2DM patients but also indirectly affects the risk of DN by affecting the concentration of Trp, which constitutes a mediating effect relationship between Trp and DN. Some scholars have proposed that His and Trp biosynthesis is one of the most thoroughly characterized central metabolic pathways(39, 40). Currently, many studies have linked His and Trp to explore their interactions by comparing their sequences, structure and functions(41, 42). The incidence and severity of DN were significantly correlated with Trp(43–45). Trp derivatives are considered another promising biomarker against DN progression(46). Metformin, a commonly used drug in diabetes, has a significant positive effect on the risk of DN(47, 48). We found that the role of metformin in human metabolism is complex, and metformin may play the opposite role in the intestinal pathway. Studies have shown that the effects of drugs on microorganisms in chronic diseases can confuse conclusions and affect microbial composition and protective effects(49, 50), which is consistent with our conclusion. Although we found that metformin did not change the sex difference in the effect of His on DN, its influence on metabolism should not be ignored. We believe that its negative effect on DN is produced by affecting the metabolism of amino acids. Our research has important guiding significance for clinical practice. (1) We proposed that supplementation with His could reduce the risk of DN in T2DM patients, suggesting that His could be used as a biomarker to assist in the early identification of DN risk in T2DM patients. (2) The protective effect of His was different between genders, and the OR value was lower in the female group than in the general population. (3) A possible pathway of His to DN in the female population was proposed, which provided a new idea for further clarifying the role of amino acids in DN. (4) Raising the risk effect of metformin on the development of DN gives us more perspective to look more carefully when evaluating the effect of metformin on T2DM and its complications. Our research also has a shortcoming. (1) Due to the nature of the cross-sectional study, we cannot prove the existence of a causal relationship between His, metformin and Trp and the occurrence of DN, which needs to be confirmed by more prospective cohort studies. We adjusted the duration of DN to exclude the influence of DN duration on the results. In conclusion, we found that His had a higher protective effect on the incidence of DN in the female population than in the general population and proposed a possible route of its influence. The negative effects of metformin on the protection of His were also noted. Future studies are needed to confirm our findings. Declarations Ethics approval and consent to participate The Ethics Committee for Clinical Research of FAHLMU approved the ethics of the study, and informed consent was waived due to the retrospective nature of the study, which is consistent with the Declaration of Helsinki. Competing interests The authors declare that they have no competing interests. Author Statement The authors thank all doctors, nurses and research staff at Liaoning Medical University in Jinzhou for their participation in this study. An earlier version of it has been submitted as a preprint according to the following link: https://www.researchsquare.com/article/rs-3099032/v1 . Funding This work was supported by the National Key Research and Development Program of China (2021YFA1301200, 2021YFA1301202) and the Special Fund of State Key Joint Laboratory of Environment Simulation and Pollution Control. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contribution Among the authors in the list, Q-Z conceived the project, did the supervision of all the paper. And Z-ZF helped to do the supervision of all the paper and gave some advice to all the manuscript. W-ML wrote the manuscript, analyzed the data and designed experiments. Y-YM and P-ZX did the investigatiton. H-YL reviewed the written article and did the visualization. W-Z collected the information and did the investigation of this manuscript. J-Z edited and optimized the pictures and tables. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request. Requests to access the datasets should be directed to [email protected] . References An earlier version of it has been submitted as a preprint according to https://doi.org/10.21203/rs.3.rs-3099032/v1 . Ahluwalia TS, Lindholm E, Groop LC. Common variants in CNDP1 and CNDP2, and risk of nephropathy in type 2 diabetes. Diabetologia. 2011;54(9):2295-302. Stel VS, van de Luijtgaarden MW, Wanner C, Jager KJ, on behalf of the European Renal Registry I. The 2008 ERA-EDTA Registry Annual Report-a precis. NDT Plus. 2011;4(1):1-13. Kurokawa K, Nangaku M, Saito A, Inagi R, Miyata T. Current issues and future perspectives of chronic renal failure. J Am Soc Nephrol. 2002;13 Suppl 1:S3-6. Kramer A, Boenink R, Noordzij M, Bosdriesz JR, Stel VS, Beltran P, et al. The ERA-EDTA Registry Annual Report 2017: a summary. Clin Kidney J. 2020;13(4):693-709. Samsu N. Diabetic Nephropathy: Challenges in Pathogenesis, Diagnosis, and Treatment. Biomed Res Int. 2021;2021:1497449. Burrows NR, Hora I, Geiss LS, Gregg EW, Albright A. Incidence of End-Stage Renal Disease Attributed to Diabetes Among Persons with Diagnosed Diabetes - United States and Puerto Rico, 2000-2014. MMWR Morb Mortal Wkly Rep. 2017;66(43):1165-70. Zhang L, Long J, Jiang W, Shi Y, He X, Zhou Z, et al. Trends in Chronic Kidney Disease in China. N Engl J Med. 2016;375(9):905-6. Guasch-Ferre M, Hruby A, Toledo E, Clish CB, Martinez-Gonzalez MA, Salas-Salvado J, et al. Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis. Diabetes Care. 2016;39(5):833-46. Duranton F, Lundin U, Gayrard N, Mischak H, Aparicio M, Mourad G, et al. Plasma and urinary amino acid metabolomic profiling in patients with different levels of kidney function. Clin J Am Soc Nephrol. 2014;9(1):37-45. Zhou C, Zhang Q, Lu L, Wang J, Liu D, Liu Z. Metabolomic Profiling of Amino Acids in Human Plasma Distinguishes Diabetic Kidney Disease From Type 2 Diabetes Mellitus. Front Med (Lausanne). 2021;8:765873. Zimmet PZ, Alberti KG. Epidemiology of Diabetes-Status of a Pandemic and Issues Around Metabolic Surgery. Diabetes Care. 2016;39(6):878-83. Gabbai FB. The role of renal response to amino acid infusion and oral protein load in normal kidneys and kidney with acute and chronic disease. Curr Opin Nephrol Hypertens. 2018;27(1):23-9. Lin HT, Cheng ML, Lo CJ, Lin G, Lin SF, Yeh JT, et al. (1)H Nuclear Magnetic Resonance (NMR)-Based Cerebrospinal Fluid and Plasma Metabolomic Analysis in Type 2 Diabetic Patients and Risk Prediction for Diabetic Microangiopathy. J Clin Med. 2019;8(6). Del Coco L, Vergara D, De Matteis S, Mensa E, Sabbatinelli J, Prattichizzo F, et al. NMR-Based Metabolomic Approach Tracks Potential Serum Biomarkers of Disease Progression in Patients with Type 2 Diabetes Mellitus. J Clin Med. 2019;8(5). Zhang L, Wang Z, Zhang X, Zhao L, Chu J, Li H, et al. Alterations of the Gut Microbiota in Patients with Diabetic Nephropathy. Microbiol Spectr. 2022;10(4):e0032422. Stancakova A, Civelek M, Saleem NK, Soininen P, Kangas AJ, Cederberg H, et al. Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 Finnish men. Diabetes. 2012;61(7):1895-902. Welsh P, Rankin N, Li Q, Mark PB, Wurtz P, Ala-Korpela M, et al. Circulating amino acids and the risk of macrovascular, microvascular and mortality outcomes in individuals with type 2 diabetes: results from the ADVANCE trial. Diabetologia. 2018;61(7):1581-91. Watanabe M, Suliman ME, Qureshi AR, Garcia-Lopez E, Barany P, Heimburger O, et al. Consequences of low plasma histidine in chronic kidney disease patients: associations with inflammation, oxidative stress, and mortality. Am J Clin Nutr. 2008;87(6):1860-6. Chen C, Lu FC, Department of Disease Control Ministry of Health PRC. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. 2004;17 Suppl:1-36. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med. 1998;15(7):539-53. Selby NM, Taal MW. An updated overview of diabetic nephropathy: Diagnosis, prognosis, treatment goals and latest guidelines. Diabetes Obes Metab. 2020;22 Suppl 1:3-15. Wang Q, Sun T, Cao Y, Gao P, Dong J, Fang Y, et al. A dried blood spot mass spectrometry metabolomic approach for rapid breast cancer detection. Onco Targets Ther. 2016;9:1389-98. MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593-614. Kalim S, Rhee EP. An overview of renal metabolomics. Kidney Int. 2017;91(1):61-9. Pena MJ, Lambers Heerspink HJ, Hellemons ME, Friedrich T, Dallmann G, Lajer M, et al. Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus. Diabet Med. 2014;31(9):1138-47. Kilis-Pstrusinska K. Carnosine and Kidney Diseases: What We Currently Know? Curr Med Chem. 2020;27(11):1764-81. Lee YT, Hsu CC, Lin MH, Liu KS, Yin MC. Histidine and carnosine delay diabetic deterioration in mice and protect human low density lipoprotein against oxidation and glycation. Eur J Pharmacol. 2005;513(1-2):145-50. Davis TA, Suryawan A, Orellana RA, Fiorotto ML, Burrin DG. Amino acids and insulin are regulators of muscle protein synthesis in neonatal pigs. Animal. 2010;4(11):1790-6. Proud CG. Regulation of protein synthesis by insulin. Biochem Soc Trans. 2006;34(Pt 2):213-6. Sadri H, Giallongo F, Hristov AN, Werner J, Lang CH, Parys C, et al. Effects of slow-release urea and rumen-protected methionine and histidine on mammalian target of rapamycin (mTOR) signaling and ubiquitin proteasome-related gene expression in skeletal muscle of dairy cows. J Dairy Sci. 2016;99(8):6702-13. Prizant RL, Barash I. Negative effects of the amino acids Lys, His, and Thr on S6K1 phosphorylation in mammary epithelial cells. J Cell Biochem. 2008;105(4):1038-47. Gao HN, Hu H, Zheng N, Wang JQ. Leucine and histidine independently regulate milk protein synthesis in bovine mammary epithelial cells via mTOR signaling pathway. J Zhejiang Univ Sci B. 2015;16(6):560-72. Lee MJ, Feliers D, Mariappan MM, Sataranatarajan K, Mahimainathan L, Musi N, et al. A role for AMP-activated protein kinase in diabetes-induced renal hypertrophy. Am J Physiol Renal Physiol. 2007;292(2):F617-27. Efeyan A, Zoncu R, Sabatini DM. Amino acids and mTORC1: from lysosomes to disease. Trends Mol Med. 2012;18(9):524-33. Baar EL, Carbajal KA, Ong IM, Lamming DW. Sex- and tissue-specific changes in mTOR signaling with age in C57BL/6J mice. Aging Cell. 2016;15(1):155-66. Tao Z, Zheng LD, Smith C, Luo J, Robinson A, Almeida FA, et al. Estradiol signaling mediates gender difference in visceral adiposity via autophagy. Cell Death Dis. 2018;9(3):309. Garibotto G, Sofia A, Saffioti S, Bonanni A, Mannucci I, Verzola D. Amino acid and protein metabolism in the human kidney and in patients with chronic kidney disease. Clin Nutr. 2010;29(4):424-33. Zhang F, Guo R, Cui W, Wang L, Xiao J, Shang J, et al. Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy. Ren Fail. 2021;43(1):980-92. Alifano P, Fani R, Lio P, Lazcano A, Bazzicalupo M, Carlomagno MS, et al. Histidine biosynthetic pathway and genes: structure, regulation, and evolution. Microbiol Rev. 1996;60(1):44-69. Fani R, Brilli M, Lio P. The origin and evolution of operons: the piecewise building of the proteobacterial histidine operon. J Mol Evol. 2005;60(3):378-90. Takeuchi H, Okada A, Miura T. Roles of the histidine and tryptophan side chains in the M2 proton channel from influenza A virus. FEBS Lett. 2003;552(1):35-8. Shirazi AN, Mozaffari S, Sherpa RT, Tiwari R, Parang K. Efficient Intracellular Delivery of Cell-Impermeable Cargo Molecules by Peptides Containing Tryptophan and Histidine. Molecules. 2018;23(7). Rhee EP, Souza A, Farrell L, Pollak MR, Lewis GD, Steele DJ, et al. Metabolite profiling identifies markers of uremia. J Am Soc Nephrol. 2010;21(6):1041-51. Wettersten HI, Weiss RH. Applications of metabolomics for kidney disease research: from biomarkers to therapeutic targets. Organogenesis. 2013;9(1):11-8. Chou CA, Lin CN, Chiu DT, Chen IW, Chen ST. Tryptophan as a surrogate prognostic marker for diabetic nephropathy. J Diabetes Investig. 2018;9(2):366-74. Hasegawa S, Inagi R. Harnessing Metabolomics to Describe the Pathophysiology Underlying Progression in Diabetic Kidney Disease. Curr Diab Rep. 2021;21(7):21. Holman RR, Paul SK, Bethel MA, Matthews DR, Neil HA. 10-year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577-89. Ren H, Shao Y, Wu C, Ma X, Lv C, Wang Q. Metformin alleviates oxidative stress and enhances autophagy in diabetic kidney disease via AMPK/SIRT1-FoxO1 pathway. Mol Cell Endocrinol. 2020;500:110628. Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015;528(7581):262-6. Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Manneras-Holm L, et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med. 2017;23(7):850-8. Additional Declarations No competing interests reported. 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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-5764336","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398800620,"identity":"5e9c1fd5-ebe6-44fe-8517-a7f745aaf942","order_by":0,"name":"Wei-Ming Luo","email":"","orcid":"","institution":"Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, China","correspondingAuthor":false,"prefix":"","firstName":"Wei-Ming","middleName":"","lastName":"Luo","suffix":""},{"id":398800622,"identity":"17a67bdc-414c-4acb-a530-b8c992b76d86","order_by":1,"name":"Yuan-Yuan Ma","email":"","orcid":"","institution":"Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, China","correspondingAuthor":false,"prefix":"","firstName":"Yuan-Yuan","middleName":"","lastName":"Ma","suffix":""},{"id":398800625,"identity":"9c59fc9d-25df-45bf-b5f3-9a20ed8466fe","order_by":2,"name":"Hui-Ying Liu","email":"","orcid":"","institution":"Center for Obesity and Metabolic Disease Research, School of Basic Medical Sciences, State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China","correspondingAuthor":false,"prefix":"","firstName":"Hui-Ying","middleName":"","lastName":"Liu","suffix":""},{"id":398800626,"identity":"fafe01d7-3a03-4081-a0e1-d2a29a431d03","order_by":3,"name":"Peng-Zhe Xie","email":"","orcid":"","institution":"Tianjin Medical University Cancer Institute and Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peng-Zhe","middleName":"","lastName":"Xie","suffix":""},{"id":398800627,"identity":"0481bb6e-c2a7-4082-a042-0398d542201b","order_by":4,"name":"Wei Zhang","email":"","orcid":"","institution":"Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, China","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":398800628,"identity":"eef4ef85-824e-415b-9eec-8089403d22b8","order_by":5,"name":"Jun Zheng","email":"","orcid":"","institution":"Tianjin Medical University General Hospital, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Zheng","suffix":""},{"id":398800629,"identity":"c6a12c7a-c978-441a-933f-c104f0a4f555","order_by":6,"name":"Zhong-Ze Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBAC9gYgwdgAZjM+gIgl4NfCcwChhdngAKla2CSI08J+9vDLnzts8uT9Dx+r/phzmIGfPceA4ecOPFp48tIsJM+kFRseOJZ24+C2wwySPW8MGHvP4NZiz5BjZmDYdjhxY2OPGViLwY0cA2bGNjy28L8xM0gEaWnm/1YA0mJPUItEjvGDg0At89l42BjAtkgQ1PLGjLGxLS1xAw+bscTZbek8EmeeFRzsxeuwHOOPP9tsEuf3H374oXKbtRx/e/LGBz/xaGEARQeIhEYjAw+IOIBXAzDSP4BI+QYCykbBKBgFo2DkAgC8y1ZOwIsMxAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, China","correspondingAuthor":true,"prefix":"","firstName":"Zhong-Ze","middleName":"","lastName":"Fang","suffix":""},{"id":398800630,"identity":"62e2109f-8fb9-403f-82c7-dd4b34313ad0","order_by":7,"name":"Qiang Zhang","email":"","orcid":"","institution":"Tianjin Medical University General Hospital, Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-01-04 15:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5764336/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5764336/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73516724,"identity":"67749caf-50f3-4a26-b085-f93499e99597","added_by":"auto","created_at":"2025-01-10 17:48:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54809,"visible":true,"origin":"","legend":"\u003cp\u003eMediating effect pathway between His, Trp, DN and metformin. His, histidine; Trp, tryptophan, DN, diabetic nephropathy.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5764336/v1/5d7960a60f8808388d041792.png"},{"id":73518820,"identity":"8b464a45-02da-48df-89a6-288af1ede037","added_by":"auto","created_at":"2025-01-10 18:04:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1096959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5764336/v1/3833cdd1-9bc6-46b1-b987-017270878857.pdf"},{"id":73517584,"identity":"34fa2f6e-5863-494d-ba58-f4d728b08e8b","added_by":"auto","created_at":"2025-01-10 17:56:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43235,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5764336/v1/a93a89da7c5b1fb02e33e46c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of histidine and metformin on the risk of diabetic nephropathy and its influence pathway in a female population: A cross-sectional study in Chinese patients with type 2 diabetes","fulltext":[{"header":"Strengths and limitations of this study","content":"\u003cp\u003eStrengths:\u003c/p\u003e\n\u003cp\u003e1. We analyzed the role of amino acids in the occurrence of complications in patients with type 2 diabetes by using metabolomics, which can provide ideas for the prevention and treatment of diseases from the perspective of metabolic pathways.\u003c/p\u003e\n\u003cp\u003e2. Compared to work already published in the field, our work pointed out gender differences in diabetic nephropathy protection and suggested differences in prevention and treatment according to sex.\u003c/p\u003e\n\u003cp\u003e3. We also provided a metabolic perspective on the effects of metformin use, providing clues for the development of clinical nutrition and preventive measures in people with different medication regimens for type 2 diabetes.\u003c/p\u003e\n\u003cp\u003elimitations:\u003c/p\u003e\n\u003cp\u003e1. Due to the nature of the cross-sectional study, we cannot prove the existence of a causal relationship between His and metformin and the occurrence of DN.\u003c/p\u003e\n\u003cp\u003e2. We only have data and results from one center in Jinzhou, China, and future multicenter studies are needed to confirm our research.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus is a group of metabolic disorders caused by insufficient absolute or relative secretion of insulin, among which type 2 diabetes mellitus (T2DM) is the most common. Diabetic nephropathy (DN) is one of the most common and serious complications in T2DM patients(1), as well as one of the main causes of end-stage renal disease(2, 3), accounting for 23% of end-stage renal disease patients at the end of 2017(4). The prevalence of diabetes is increasing rapidly worldwide, especially in developing countries(5). In the United States, the number of diabetic patients who started treatment for end-stage renal disease increased significantly from more than 40,000 in 2000 to more than 50,000 in 2014(6). In China, the number of diabetic and chronic kidney disease patients has reached 24.3\u0026nbsp;million(7). Albuminuria and glomerular filtration rate (GFR) are currently the most commonly used clinical biomarkers in T2DM patients with DN, but they still have limitations in the correct identification of DN. Therefore, there is an urgent need for novel biomarkers that can identify DN at an early stage and improve risk stratification.\u003c/p\u003e \u003cp\u003eAmino acids, as essential substances for the formation of proteins needed by the human body, participate in a variety of metabolic processes in vivo, and their relationship with T2DM and its complications has received much attention(8, 9). Some scholars have found that due to reasons such as insulin resistance and reduced protein intake, plasma amino acid levels in DN patients will change significantly(10). Many studies have found that changes in amino acid metabolism are related to the occurrence of DN and the nutritional status of patients(11, 12). Some amino acids can also be used as predictive factors to predict the occurrence and development of DN(13, 14), suggesting that they are important in the early monitoring and prevention of DN. Many published studies have indicated that histidine (His) metabolism is related to the development of T2DM and its complications. One study showed that the gut flora of T2DM patients was more enriched in His metabolism than that of healthy controls(15). A study found that an increase in blood sugar was associated with a decrease in His and glutamine(16). Another study of 3,587 patients found that His was negatively associated with the risk of microangiopathy(17). Low levels of His have been linked to increased inflammation and oxidative stress in chronic kidney disease(18). Carnosine is thought to have a protective effect in DN, and l-His is an important component of carnosine(1). However, there are few related studies on the risk of DN in female patients with T2DM, and the specific pathway of its influence is still unclear. This research is carried out under the current background.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Method and Population\u003c/h2\u003e \u003cp\u003eOur study population was from the First Affiliated Hospital of Liaoning Medical University (FAHLMU), a tertiary general hospital located in Jinzhou City, Liaoning Province, China. We could access the data for research purposes after January 1st, 2020. The inclusion criteria for the study population were as follows: 1) patients diagnosed with T2DM or treated with antidiabetic drugs and 2) complete information on DN. 3) Complete information of His, tryptophan (Trp). 4) The information on the use of metformin is completed. Exclusion criteria: 1) T2DM patients younger than 18 years old; 2) subjects lacking the study indicators, height, weight and blood pressure. 3) Patients with extreme outliers of His or Trp. A total of 1821 patients with T2DM were initially included in this study. According to the inclusion and exclusion criteria, a total of 1031 subjects were included in this study, including 188 DN patients in the case group and 843 T2DM patients in the control group.\u003c/p\u003e \u003cp\u003eThe authors could obtain detailed information from individual participants. The Ethics Committee for Clinical Research of FAHLMU approved the ethics of the study, and informed consent was waived due to the retrospective nature of the study, which is consistent with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection and clinical definitions\u003c/h3\u003e\n\u003cp\u003eDemographic and anthropometric information was retrieved from the subjects' electronic medical records, as well as messages of current clinical factors and diabetes complications. Demographic information included gender and age. Anthropometric height, weight, systolic blood pressure (SBP) and diastolic blood pressure (DBP) information were measured. Clinical parameters included glycated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), uric acid (UA), creatinine (Crea), and plasma His and Trp concentrations. The duration of DN in T2DM patients was recorded. In hospitals, anthropometric indicators were measured using standardized procedures. Participants are required to wear light-colored clothing and no shoes. Height and weight were accurately measured to 0.5 cm and 0.1 kg, respectively. Blood pressure was measured behind the right arm of the adult cuff with a standard mercury sphygmomanometer and in the appropriate position after 10 minutes of sitting rest. Age was obtained from the date of birth to the date of hospitalization or medical examination and was calculated annually. Body mass index (BMI) was calculated as the ratio of weight (kg) to height (m) and classified according to the overweight and obesity standards recommended by the National Health Commission of China(19). In this study, the diagnosis and classification of T2DM were based on the criteria published by the World Health Organization (WHO) or the population treated with antidiabetic drugs(20). The diagnostic criteria for DN were based on the standards of care for T2DM(21). According to the RCS curve, His and Trp were stratified according to 51 \u0026micro;mol/L and 46 \u0026micro;mol/L, respectively (\u003cb\u003eFig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eLaboratory Assay\u003c/h3\u003e\n\u003cp\u003eDetails of the amino acid measurements were published previously(22). Briefly, dried blood spots were used in the metabolomic assay, which were prepared from capillary whole blood through 8-h fasting. We measured the metabolites by direct infusion mass spectrometry technology equipped with the AB Sciex 4000 QTrap system (AB Sciex, Framingham, MA, USA). High-purity water and acetonitrile were purchased from Thermo Fisher (Waltham, MA, USA) and were utilized as the dilution agent and mobile phase. 1-Butanol and acetyl chloride were obtained from Sigma‒Aldrich (St Louis, MO, USA). Isotope-labeled internal standard samples of amino acids (NSK-A) were purchased from Cambridge Isotope Laboratories (Tewksbury, MA, USA), while standard samples of His and Trp were purchased from Chrom Systems (Grafelfing, Germany). In brief, 8.5 mL of venous blood was drawn from each participant at 08:00 to 09.30 h in the morning after an 8-h fast. Laboratory tests were carried out at a specialized diagnostic laboratory. The level of lipid profiles was analyzed with an automatic biochemistry analyzer (Hitachi 7150, Tokyo, Japan). We also assayed the levels of HDL-C and LDL-C by selective solubilization.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous data are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), nonnormally distributed data are expressed as the median (interquartile range), and categorical variables are expressed as numbers (percentages). To analyze whether there were differences in various indicators between the case group and the control group in different sexes. For continuous variables, a t test or variance analysis was used for normally distributed variables. The rank sum test was used for nonnormal distributions, and the chi-square test was used for categorical variables.\u003c/p\u003e \u003cp\u003eFirst, a binary logistic regression was performed in the general population to determine whether the relationship between His and DN in the study was significant, and then the same analysis was performed by gender stratification. The logistic regression model was used to obtain odds ratio (OR) values of His with DN and their 95% confidence intervals (95% CI). Traditional risk factors for DN are adjusted through structural adjustment: Multivariable model 1 adjusted for age, sex, and BMI. Multivariable model 2 adjusted SBP, DBP, LDL-C, HDL-C, TG, TC, HbA1c, and duration of DN based on model 1. Multivariable model 3 adjusted UA and Crea based on model 2. Finally, mediation effect analysis was conducted in the female population to explore the specific path and influencing factors of His on DN. We use multiple imputations to interpolate the missing values. All analyses were performed using R version 4.1.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULT","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDescription of Study Subjects\u003c/h2\u003e \u003cp\u003eThe selection characteristics of DN were analyzed between the case group and control group in the total population and the population stratified by sex (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 1031 T2DM patients were included in the study, with a mean age of 57.24 years old (sd: 13.82) and a mean BMI of 25.29 (sd: 3.85) in the total population.\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 and biochemical characteristics of participants according to sex and the occurrence of diabetic nephropathy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal subjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-DN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNon-DN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean/number\u003c/p\u003e \u003cp\u003e(SD or %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean/number\u003c/p\u003e \u003cp\u003e(SD or %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean/number\u003c/p\u003e \u003cp\u003e(SD or %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean/number\u003c/p\u003e \u003cp\u003e(SD or %)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e452(82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96(17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e391(81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92(19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.24\u0026thinsp;\u0026plusmn;\u0026thinsp;13.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.72\u0026thinsp;\u0026plusmn;\u0026thinsp;14.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.16\u0026thinsp;\u0026plusmn;\u0026thinsp;12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60.54\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (60, 80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (67.75, 83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (67.75, 84.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 (56.4, 70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65 (57.75, 75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (160, 172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (170, 175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172 (170, 175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160 (156, 163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e160 (158, 165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.51\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.85\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;18.5and\u0026thinsp;\u0026lt;\u0026thinsp;24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e159 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;24and\u0026thinsp;\u0026lt;\u0026thinsp;28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e433 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e154 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (26.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.39\u0026thinsp;\u0026plusmn;\u0026thinsp;23.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.67\u0026thinsp;\u0026plusmn;\u0026thinsp;22.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142.1\u0026thinsp;\u0026plusmn;\u0026thinsp;22.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139.41\u0026thinsp;\u0026plusmn;\u0026thinsp;24.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e151.21\u0026thinsp;\u0026plusmn;\u0026thinsp;27.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (74, 90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (75.75, 90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (71, 91.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80 (72, 89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82.50 (74.00, 90.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69 (1.13, 2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66 (1.1, 2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 (1.13, 2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.72 (1.13, 2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.80 (1.20, 2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.64 (3.86, 5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.36 (3.74, 5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.47 (3.81, 5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.77 (4.03, 5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.05 (4.20, 6.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.85, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.82, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.81, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (0.89, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17 (1.01, 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.78 (2.19, 3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70 (2.11, 3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.70 (2.03, 3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.87 (2.28, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.95 (2.37, 3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.5 (35.28, 78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.54 (36.01, 82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.11 (34.56, 70.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.01 (36.02, 79.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41.49 (31.97, 65.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e519 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (54.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e192 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e512 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311 (245.95, 381.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e323.9 (255, 397.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360.5 (292, 424.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e286 (227.5, 347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e308 (243.75, 363)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.97 (49.02, 73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (56, 76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.75 (55.9, 101.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51 (42, 60.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.19 (43.68, 70.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of metformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e358 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eBMI, body mass index. SBP, systolic blood pressure. DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; His, Histidine; UA, uric acid; Crea, serum creatinine, DN, diabetic nephropathy.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, median (IQR), or n (%). P values were derived from the t test for normally distributed variables, the Mann‒Whitney U test for skewed distributions, and the chi-square test (or Fisher\u0026rsquo;s test if appropriate) for categorical variables. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was defined as statistically significant.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe total population was stratified according to sex. In the male population, the differences in UA and Crea were statistically significant between the case and control groups. The two indicators were all higher in the case group. In the female population, the differences in BMI, SBP, TC, HDL-C, and His between the two groups were statistically significant, and female DN patients had higher BMI, SBP, TC and HDL-C. Non-DN patients had higher His levels.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffect of Histidine on Diabetic Nephropathy in the General Population\u003c/h3\u003e\n\u003cp\u003eWe performed univariate and multivariate logistic regression analyses between His and DN and showed the results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. From the results of numerical His, the OR value of the single factor regression was 0.78 (95% CI: 0.65\u0026ndash;0.95). After multifactor stepwise regression adjustment, the OR value was changed to 0.71 (95% CI: 0.77\u0026ndash;0.88), and the results were all statistically significant.\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\u003eOdds ratio of histidine for the risk of diabetic nephropathy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariable model\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis, per \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.65,0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.49,0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariable model1\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis, per \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.63,0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.47,0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariable model2\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis, per \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.57,0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.4,0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariable model3\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis, per \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.57,0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.4,0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eIn different models, the categorical His divided into two groups by 51 \u0026micro;mol/L was used as a reference, and then, changes in numerical and categorical His, OR, odd ratio, His, and histidine were observed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMultivariable Model 1 was adjusted for age, sex, and body mass index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMultivariable Model 2 was adjusted for variables in Model 1 and concentrations of systolic blood pressure, diastolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, total cholesterol, and glycosylated hemoglobin.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMultivariable Model 3 was adjusted for variables in Model 2 and concentrations of uric acid, serum creatinine and the duration of diabetic nephropathy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the categorical variables divided according to the RCS curve, the protective effect of His on DN was statistically significant, and its univariate and multivariate OR values were 0.67 (95% CI: 0.49\u0026ndash;0.92) and 0.56 (95% CI: 0.4\u0026ndash;0.79), respectively.\u003c/p\u003e\n\u003ch3\u003eEffects of Histidine on Diabetic Nephropathy in Different Genders\u003c/h3\u003e\n\u003cp\u003eLogistic regression analysis was conducted after the total population was stratified according to sex (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The protective effect of His on the occurrence of DN disappeared in the male population. His protective effect was increased in women compared with the general population (univariable: OR: 0.68 (95% CI: 0.5,0.93), multivariable: OR: 0.54 (95% CI: 0.38,0.78)).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdds ratio of Histidine for the risk of diabetic nephropathy in different groups by sex.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ewoman\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnivariable model\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis, per \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.68,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.5,0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.5,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57 (0.35,0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariable model1\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis, per \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.68,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.48,0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.5,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.32,0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariable model2\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis, per \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.64,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.38,0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.43,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.24,0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultivariable model3\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHis, per \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.64,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42 (0.25,0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51\u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.43,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.24,0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eIn different models, the categorical His divided into two groups by 51 \u0026micro;mol/L was used as a reference, and then, changes in numerical and categorical His, OR, odd ratio, His, and histidine were observed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMultivariable Model 1 was adjusted for age, sex, and body mass index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMultivariable Model 2 was adjusted for variables in Model 1 and concentrations of systolic blood pressure, diastolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, total cholesterol, and glycosylated hemoglobin.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eMultivariable Model 3 was adjusted for variables in Model 2 and concentrations of uric acid, serum creatinine and the duration of diabetic nephropathy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffects of the use of metformin on diabetic nephropathy in the female population\u003c/h2\u003e \u003cp\u003eWe further explored the effect of metformin use on DN in the female population (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The use of metformin is a risk factor for the prevalence of DN in female patients (univariable: OR: 1.6 (95% CI: 1.01, 2.53), multivariable: OR: 1.93 (95% CI: 1.15, 3.22)). However, the exact path is unclear.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe specific pathways and influencing factors of Histidine on Diabetic Nephropathy in the female population\u003c/h2\u003e \u003cp\u003eThen, we used the causal step approach(23) to analyze the mediating effect of His and the use of metformin on DN in the female population and drew a correlation diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was a positive correlation between His and Trp concentration (OR: 1.29, 95% CI: 1.18\u0026ndash;1.41). His (OR: 0.89, 95% CI: 0.83\u0026ndash;0.95) and Trp (OR: 0.91, 95% CI: 0.85\u0026ndash;0.97) both exist as protective factors for the occurrence of DN, and part of His protective effect on DN is completed through Trp. Meanwhile, the protective effect of His on DN was affected by metformin. As seen from the results, metformin is a risk factor for DN, and its use is negatively correlated with the concentration of His (OR: 0.90, 95% CI: 0.82\u0026ndash;0.99) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). At the same time, because we found that metformin could not directly affect Trp, it was not marked in the path diagram. Metformin can significantly affect the protective effect of His on DN. Since His and Trp are positively correlated, metformin can also indirectly affect the role of Trp as a protective factor by influencing His.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation analysis of the relationship between metformin and DN by histidine and tryptophan.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eHis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42 (0.25,0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect path a\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48 (0.28,0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29(1.18, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49 (0.29,0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90(0.82, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.93 (1.15,3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect path e\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8 (1.07,3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAdjusted for age, sex, body mass index, systolic blood pressure, diastolic blood pressure, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, total cholesterol, glycosylated hemoglobin, uric acid and serum creatinine. DN, diabetic nephropathy, His, histidine, Trp, tryptophan.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePath a\u0026rsquo; indicates the path from His to DN (Outcome) when controlled for Trp (Mediator).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePath b indicates the path from His to Trp (Mediator).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePath c indicates the path from Trp (mediator) to DN (outcome).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePath d indicates the impact of metformin on His.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePath e indicates the impact of metformin on DN (outcome).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePath e\u0026rsquo; indicates the impact of metformin on DN (Outcome) when controlled for His (Mediator).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eSince we found that the use of metformin would affect His metabolism, we adjusted the influence of metformin on the relationship between His and DN according to the above results, and then we obtained model 4, which was included in the supplementary data \u003cb\u003e(Table S2, Table S3)\u003c/b\u003e. After adjusting for the use of metformin, significant results in the general population and sex-stratified population remained unchanged, but their OR values changed slightly.\u003c/p\u003e \u003cp\u003eThen, we changed the interpolation method of the missing values and performed the same analysis again. After random forest imputation of missing values (UA\u0026thinsp;=\u0026thinsp;187, TG\u0026thinsp;=\u0026thinsp;288, TC\u0026thinsp;=\u0026thinsp;289, Crea\u0026thinsp;=\u0026thinsp;147), the effects remained stable and significant in multivariable analyses (\u003cb\u003eTable S4, Table S5\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe development of metabolomics in various fields has attracted great attention, and metabolomics is an effective strategy to fully understand kidney disease and its mitochondrial and energy metabolism dynamics(24). His is the basic unit of protein and one of the essential amino acids, especially for children. At present, many studies have proven the relationship between His, T2DM and DN. Compared with healthy controls, plasma His was significantly lower in patients with T2DM and was negatively correlated with changes in urinary albumin excretion(25). Carnosine, composed of two amino acids, beta-alanine and l-His, has a protective effect on the kidney(26). Researchers have found that patients with chronic kidney disease have lower plasma His, accompanying persistent inflammation and higher mortality(18). At the same time, dietary supplements of His can reduce oxidation and inflammation, which is expected to treat kidney disease(27). All studies are consistent with our results, and His supplementation has a certain protective effect on the occurrence of DN.\u003c/p\u003e \u003cp\u003eIn addition, our study found that the relationship between His and DN is related to sex. Women with lower plasma His levels had a higher risk of developing DN, while His showed no significant protective effect in men. We believe that this phenomenon may be related to the significant activation of the mTOR signaling pathway by His supplementation. Some studies have shown that the mTOR pathway can integrate amino acid and insulin signals(28, 29). mTOR can sense the availability of nutrients, so it is activated under nutrient-rich conditions, especially high levels of amino acids(30). Additional studies have found that the addition of His can activate and regulate the mTORC1 pathway(31, 32), which can affect insulin secretion(33, 34). This is also consistent with our results. mTOR expression was higher in women, explaining the sex difference in the results(35, 36).\u003c/p\u003e \u003cp\u003eMultiple studies have found that the progression of DN leads to changes in serum metabolites(37, 38). Amino acids, as metabolites, are expected to be more effective early biomarkers. Both His and Trp are aromatic and heterocyclic amino acids. Our study found that His not only directly affects the risk of DN in T2DM patients but also indirectly affects the risk of DN by affecting the concentration of Trp, which constitutes a mediating effect relationship between Trp and DN. Some scholars have proposed that His and Trp biosynthesis is one of the most thoroughly characterized central metabolic pathways(39, 40). Currently, many studies have linked His and Trp to explore their interactions by comparing their sequences, structure and functions(41, 42). The incidence and severity of DN were significantly correlated with Trp(43\u0026ndash;45). Trp derivatives are considered another promising biomarker against DN progression(46). Metformin, a commonly used drug in diabetes, has a significant positive effect on the risk of DN(47, 48). We found that the role of metformin in human metabolism is complex, and metformin may play the opposite role in the intestinal pathway. Studies have shown that the effects of drugs on microorganisms in chronic diseases can confuse conclusions and affect microbial composition and protective effects(49, 50), which is consistent with our conclusion. Although we found that metformin did not change the sex difference in the effect of His on DN, its influence on metabolism should not be ignored. We believe that its negative effect on DN is produced by affecting the metabolism of amino acids.\u003c/p\u003e \u003cp\u003eOur research has important guiding significance for clinical practice. (1) We proposed that supplementation with His could reduce the risk of DN in T2DM patients, suggesting that His could be used as a biomarker to assist in the early identification of DN risk in T2DM patients. (2) The protective effect of His was different between genders, and the OR value was lower in the female group than in the general population. (3) A possible pathway of His to DN in the female population was proposed, which provided a new idea for further clarifying the role of amino acids in DN. (4) Raising the risk effect of metformin on the development of DN gives us more perspective to look more carefully when evaluating the effect of metformin on T2DM and its complications. Our research also has a shortcoming. (1) Due to the nature of the cross-sectional study, we cannot prove the existence of a causal relationship between His, metformin and Trp and the occurrence of DN, which needs to be confirmed by more prospective cohort studies. We adjusted the duration of DN to exclude the influence of DN duration on the results.\u003c/p\u003e \u003cp\u003eIn conclusion, we found that His had a higher protective effect on the incidence of DN in the female population than in the general population and proposed a possible route of its influence. The negative effects of metformin on the protection of His were also noted. Future studies are needed to confirm our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eThe Ethics Committee for Clinical Research of FAHLMU approved the ethics of the study, and informed consent was waived due to the retrospective nature of the study, which is consistent with the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor Statement\u003c/h2\u003e \u003cp\u003e The authors thank all doctors, nurses and research staff at Liaoning Medical University in Jinzhou for their participation in this study. An earlier version of it has been submitted as a preprint according to the following link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchsquare.com/article/rs-3099032/v1\u003c/span\u003e\u003cspan address=\"https://www.researchsquare.com/article/rs-3099032/v1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2021YFA1301200, 2021YFA1301202) and the Special Fund of State Key Joint Laboratory of Environment Simulation and Pollution Control. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAmong the authors in the list, Q-Z conceived the project, did the supervision of all the paper. And Z-ZF helped to do the supervision of all the paper and gave some advice to all the manuscript. W-ML wrote the manuscript, analyzed the data and designed experiments. Y-YM and P-ZX did the investigatiton. H-YL reviewed the written article and did the visualization. W-Z collected the information and did the investigation of this manuscript. J-Z edited and optimized the pictures and tables.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author upon reasonable request. Requests to access the datasets should be directed to \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\[email protected].\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAn earlier version of it has been submitted as a preprint according to https://doi.org/10.21203/rs.3.rs-3099032/v1\u003cu\u003e.\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eAhluwalia TS, Lindholm E, Groop LC. Common variants in CNDP1 and CNDP2, and risk of nephropathy in type 2 diabetes. Diabetologia. 2011;54(9):2295-302.\u003c/li\u003e\n\u003cli\u003eStel VS, van de Luijtgaarden MW, Wanner C, Jager KJ, on behalf of the European Renal Registry I. The 2008 ERA-EDTA Registry Annual Report-a precis. NDT Plus. 2011;4(1):1-13.\u003c/li\u003e\n\u003cli\u003eKurokawa K, Nangaku M, Saito A, Inagi R, Miyata T. Current issues and future perspectives of chronic renal failure. 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Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis. Diabetes Care. 2016;39(5):833-46.\u003c/li\u003e\n\u003cli\u003eDuranton F, Lundin U, Gayrard N, Mischak H, Aparicio M, Mourad G, et al. Plasma and urinary amino acid metabolomic profiling in patients with different levels of kidney function. Clin J Am Soc Nephrol. 2014;9(1):37-45.\u003c/li\u003e\n\u003cli\u003eZhou C, Zhang Q, Lu L, Wang J, Liu D, Liu Z. Metabolomic Profiling of Amino Acids in Human Plasma Distinguishes Diabetic Kidney Disease From Type 2 Diabetes Mellitus. Front Med (Lausanne). 2021;8:765873.\u003c/li\u003e\n\u003cli\u003eZimmet PZ, Alberti KG. Epidemiology of Diabetes-Status of a Pandemic and Issues Around Metabolic Surgery. Diabetes Care. 2016;39(6):878-83.\u003c/li\u003e\n\u003cli\u003eGabbai FB. The role of renal response to amino acid infusion and oral protein load in normal kidneys and kidney with acute and chronic disease. 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N Engl J Med. 2008;359(15):1577-89.\u003c/li\u003e\n\u003cli\u003eRen H, Shao Y, Wu C, Ma X, Lv C, Wang Q. Metformin alleviates oxidative stress and enhances autophagy in diabetic kidney disease via AMPK/SIRT1-FoxO1 pathway. Mol Cell Endocrinol. 2020;500:110628.\u003c/li\u003e\n\u003cli\u003eForslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 2015;528(7581):262-6.\u003c/li\u003e\n\u003cli\u003eWu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Manneras-Holm L, et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med. 2017;23(7):850-8.\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":"Diabetic nephropathy, Histidine, Metformin, Mediation effect, Gender difference","lastPublishedDoi":"10.21203/rs.3.rs-5764336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5764336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study explored the effect of histidine on the occurrence of diabetic nephropathy in different sex populations and its specific possible pathway, as well as the influence of metformin on the pathway.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study retrieved 1031 patients with type 2 diabetes mellitus and performed a cross-sectional study at the First Affiliated Hospital of Liaoning Medical University in Jinzhou, Liaoning Province, China. We used stepwise logistic regression to analyze the association between histidine and diabetic nephropathy in the general population and in sex-stratified populations. Mediating effect analysis was used to explore the specific pathway of this relationship in the female population.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe protective effect of histidine on diabetic nephropathy was influenced by sex and was significant in women (univariable: OR: 0.68 (95% CI: 0.5,0.93), multivariable: OR: 0.54 (95% CI: 0.38,0.78)). The specific pathway of its effect was partly through affecting tryptophan metabolism.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe protective effect of histidine against diabetic nephropathy in the female population was stronger than that in the general population and was negatively affected by metformin. This helps us pay more attention to the clinical nutritional and preventive value of histidine and tryptophan in female diabetic patients.\u003c/p\u003e","manuscriptTitle":"Effects of histidine and metformin on the risk of diabetic nephropathy and its influence pathway in a female population: A cross-sectional study in Chinese patients with type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 17:47:57","doi":"10.21203/rs.3.rs-5764336/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":"3e3bbcd7-6576-44ef-a0f1-dddb1dc042d3","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-10T17:48:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 17:47:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5764336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5764336","identity":"rs-5764336","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}