Association between monocyte-lymphocyte ratio and diabetic peripheral neuropathy in the US populations: a population-based study

Association between monocyte-lymphocyte ratio and diabetic peripheral neuropathy in the US populations: a population-based study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between monocyte-lymphocyte ratio and diabetic peripheral neuropathy in the US populations: a population-based study Zirui Li, Yang Jian, Chengliang Deng, Zairong Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6637207/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background Inflammation has emerged as a significant contributor to the development and progression of diabetic peripheral neuropathy (DPN), and the monocyte-lymphocyte ratio (MLR) is a novel inflammatory marker in peripheral blood. However, research on the association between MLR and DPN is limited. This study aimed to explore the association between MLR and DPN in patients with diabetes. Methods Data from the 1999–2004 National Health and Nutrition Examination Survey on US populations with diabetes were analyzed. Peripheral blood tests and other essential variables were collected. MLR was calculated as the ratio of monocyte to lymphocyte count, both of which were obtained directly from laboratory data files. DPN was defined as participants experiencing numbness, loss of feeling, or painful sensations or tingling in their feet in the previous 3 months or having ≥ 1 insensate area based on monofilament testing. Results A total of 1,345 participants were included, with 56.5% (760 / 1,345) exhibiting DPN. Multivariate regression models revealed that the presence of DPN significantly increased by 10% with a 0.1 unit rise in MLR (adjusted OR: 1.1, 95% CI: 1-1.2, P = 0.046) after adjusting for all covariates. The restricted cubic spline analysis showed a non-linear curve ( P = 0.007). There was no significant interaction between MLR and stratification variables in the subgroup analysis ( P > 0.05). Conclusions Higher MLR levels are associated with DPN in the population of the United States. MLR shows promise as a potential biomarker for early identification of DPN. diabetic peripheral neuropathy monocyte-lymphocyte ratio diabetes NHANES inflammation Figures Figure 1 Figure 2 Figure 3 Introduction Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus, affecting a significant proportion of patients worldwide [ 1 – 3 ]. It is characterized by peripheral nerve damage, which leads to symptoms such as numbness, tingling, and pain in the extremities. DPN not only significantly impairs the quality of life for affected individuals but also places a considerable burden on healthcare systems [ 4 ]. According to the World Health Organization, the global prevalence of diabetes mellitus was estimated as approximately 9.3% in 2019, with projections suggesting a further increase in the coming years. Projections suggest that the global prevalence of diabetes will increase to 10.2% (578 million people) by 2030 and 10.9% (700 million people) by 2045 [ 5 ]. With the rising incidence of diabetes, the burden of DPN is also expected to escalate, making it a growing public health concern [ 6 ]. The pathogenesis of DPN is multifactorial, involving various mechanisms such as chronic hyperglycemia, oxidative stress, inflammation, and dyslipidemia [ 3 , 7 , 8 ]. Among these mechanisms, inflammation has emerged as a significant contributor to the development and progression of DPN [ 7 , 9 ]. Inflammatory responses activate immune cells, including monocytes and lymphocytes, which play a crucial role in the pathophysiology of DPN [ 10 , 11 ]. The monocyte-lymphocyte ratio (MLR) is a novel inflammatory marker in peripheral blood. Recently, several studies have shown that the MLR is associated with the occurrence and progression of diabetes complications, including diabetic nephropathy (DN), diabetic retinopathy (DR), and peripheral arterial disease (PAD) [ 12 – 15 ]. It is reasonable to assume that MLR may play a significant role in the onset and progression of DPN, given the mounting body of research emphasizing its importance in diabetes complications [ 12 – 15 ]. However, research on the relationship between MLR and DPN is limited. Understanding the relationship between MLR and DPN could provide valuable insights into the inflammatory mechanisms underlying the development and progression of this debilitating condition. Moreover, MLR has the potential to serve as a non-invasive biomarker for predicting the risk and severity of DPN, aiding in early detection and intervention. Therefore, this observational study aimed to explore the association between MLR and DPN in a population of patients with diabetes. Ultimately, the findings of this study may have significant implications for the early detection, prevention, and management of DPN in these patients. Methods Study design and participants Data on health and nutrition were gathered from Americans as part of the National Health and Nutrition Examination Survey (NHANES). Participants completed questionnaires on their histories and habits, in addition to undergoing physical and laboratory examinations. Researchers, decision-makers, and healthcare professionals utilize this data to better understand health issues, identify patterns and inequities, and promote nutrition and public health. For our analysis, we used open data from three NHANES cycles (1999–2000, 2001–2002, and 2003–2004). Further details on the data can be found on the NHANES website ( https://www.cdc.gov/nchs/nhanes/index.htm ). To participate in the survey, participants needed to submit to a blood test examination. In-person interviews conducted in participants' homes were also used to gather data on basic demographics and medical histories. A stratified multistage probability survey was used in the NHANES research to assess the health and nutritional status of non-institutionalized Americans [ 16 ]. A mobile examination center (MEC) conducted home visits, screenings, and laboratory tests as part of the NHANES to gather detailed demographic and health data. Details on the questionnaires and interviews are available on the NHANES website ( https://www.cdc.gov/nchs/nhanes/index.htm ). The NHANES research was approved by the National Center for Health Statistics (NCHS) Ethics Review Committee, and all participants provided written informed consent. No additional Institutional Review Board permission was required for the secondary analysis [ 17 ]. Study variables and outcomes In this study, the monocyte count to MLR was calculated using the values directly obtained from the laboratory data files of NHANES. A diagnosis of DPN required the satisfaction of two conditions: (1) a diagnosis of diabetes and (2) a diagnosis of PN. To assess PN, health technicians used a standard 5.07 Semmes-Weinstein nylon monofilament mounted on a plastic handle, which delivers approximately 10 grams of force, to test the sensation on the bottom of each participant’s feet at three specific sites (plantar surface of the first metatarsal head, fifth metatarsal head, and hallux) on each foot, totaling six sites [ 18 ]. The sites were tested in a non-sequential order to enhance the examinee’s ability to discrimination sensation. The monofilament was applied until it buckled and then held for one second. An absence of sensation was defined as two incorrect or indeterminable identifications or one incorrect and one indeterminable identification at a site. If a participant had at least one insensate area on either foot, they were considered to have PN [ 19 ]. A questionnaire was administered to gather information on the participants’ medical history and demographic characteristics, including a history of physician-diagnosed hypertension, sex, age, race/ethnicity, marital status, smoking status, and education level. Based on the marital status questionnaire, participants were categorized as either married/living with a partner or living alone. Smoking status was classified into three categories: never smoked (less than 100 cigarettes in a lifetime), former smoker (more than 100 cigarettes in a lifetime but currently not smoking), and current smoker (more than 100 cigarettes in a lifetime and currently smoking). Education level was grouped into three categories: less than 9 years, 9–12 years, and more than 12 years. Participants were also divided into normal weight, overweight, and obese groups based on their body mass index (BMI) categories: less than 25.0 kg/m 2 , 25.0-29.9 kg/m 2 , and more than 30.0 kg/m 2 , respectively. Other covariates included glucose levels (mmol/L), glycosylated hemoglobin (HbA1C, %), white blood cell count (WBC, ×10 9 /L), C-reactive protein (CRP, mg/dL), and total cholesterol levels (mmol/L). Statistical analysis Statistical software packages R 3.3.2 ( http://www.R-project.org , The R Foundation) and Free Statistics Software version 1.8 were used for all analyses. Demographic and clinical characteristics were expressed using means, standard deviations, and frequencies (percentages). Especially, CRP was expressed using median and interquartile range because it was not a normal distribution. Differences between continuous and categorical data were examined using independent and chi-squared tests, respectively. Binary logistic regression was used to examine the connection between MLR and DPN. Both single and multiple variable analyses were conducted. We presented four models for multivariate logistic regression: (1) Model 1: unadjusted; (2) Model 2: adjusted for sociodemographic variables (age, sex, marital status, race/ethnicity, education level); (3) Model 3: adjusted for sociodemographic variables and variables that reflect overall health status including BMI, smoking status, and hypertension; and (4) Model 4: adjusted for age, sex, marital status, race/ethnicity, education level, BMI, smoking status, hypertension, glucose, HbA1C, WBC, CRP, and total cholesterol. Restricted cubic spline (RCS) analysis was used to explore the dose-response relationship between MLR and the risk of DPN. RCS allows for flexible modeling of nonlinear relationships without predefining the shape of the association between continuous variables and the outcome. Visual representations of the spline curves were generated to examine the relationship between age, sex, marital status, race/ethnicity, education level, BMI, smoking status and hypertension, glucose, HbA1C, WBC, CRP, total cholesterol, and DPN risk. Subgroup analysis was conducted to examine the association between MLR and DPN, considering factors such as sex, age category (< 60 years, ≥ 60 years), race/ethnicity, smoking status, BMI, hypertension, and HbA1C category (< 6.5%, ≥ 6.5%). The multivariate logistic regression model was employed for this analysis. The logistic regression model's interaction test was performed to examine the odds ratios (ORs) between the subgroups that were studied. On average, less than 5% of the variables' data were missing, and these missing data were removed from the analysis. Results Study population and baseline characteristics This study collected data from three NHANES cycles, specifically from the years 1999–2000, 2001–2002, and 2003–2004. The initial pool of potential participants was identified as 31,126 individuals. From this pool, 21,156 participants under the age of 40 years were excluded, and 6,420 participants without diabetes were excluded. Additionally, 18,116 participants with missing data on DPN were excluded, as well as 272 participants with missing peripheral blood MLR. After further excluding 117 participants with missing covariate data (43 with missing marital status data; 36 with missing BMI data; 32 with missing total cholesterol data; and 6 with missing smoking, education, and hypertension data), a total of 1,345 participants remained and were included in the analysis. Figure 1 presents the inclusion and exclusion criteria and shows a flowchart outlining the participant selection process for the study. Table 1 summarizes the baseline characteristics of both patients with DPN and the non-diabetic peripheral neuropathy population (NDPN) in terms of demographics, socioeconomic factors, comorbidities, laboratory metrics, and baseline characteristics. Out of the 1,345 participants included, 760 individuals (56.5%) were identified as having DPN. A statistical analysis revealed significant differences in age, smoking status, hypertension, and glucose levels between the DPN and NDPN groups, with p-values less than 0.05 for these comparisons. Specifically, the DPN group had a significantly older age ( P < 0.001), a larger proportion of smoking population ( P = 0.007), a larger proportion of hypertension population ( P < 0.001), and higher glucose levels ( P = 0.032) compared to the NDPN group. Table 1 Baseline characteristics of the study participants. Variables Total No DPN DPN p n 1345 585 760 Sex, n (%) 0.18 male 720 (53.5) 301 (51.5) 419 (55.1) female 625 (46.5) 284 (48.5) 341 (44.9) Age (years) 64.2 ± 11.7 63.0 ± 11.7 65.2 ± 11.6 < 0.001 Race/ethnicity, n (%) 0.475 Non-Hispanic white 562 (41.8) 238 (40.7) 324 (42.6) Non-Hispanic black 292 (21.7) 122 (20.9) 170 (22.4) Mexican American 384 (28.6) 172 (29.4) 212 (27.9) Others 107 ( 8.0) 53 (9.1) 54 (7.1) Education level(years), n (%) 0.072 12 436 (32.4) 208 (35.6) 228 (30) Marriage status, n (%) 0.367 Married or living with a partner 837 (62.2) 372 (63.6) 465 (61.2) Living alone 508 (37.8) 213 (36.4) 295 (38.8) BMI, n (%) 0.843 Underweight/ normal (< 25.0kg/m 2 ) 201 (14.9) 91 (15.6) 110 (14.5) Overweight (25-29.9 kg/m 2 ) 497 (37.0) 213 (36.4) 284 (37.4) Obese (≥ 30 kg/m 2 ) 647 (48.1) 281 (48) 366 (48.2) Smoking status, n (%) 0.007 Never 603 (44.8) 289 (49.4) 314 (41.3) Current 219 (16.3) 94 (16.1) 125 (16.4) Former 523 (38.9) 202 (34.5) 321 (42.2) Hypertension, n (%) < 0.001 No 485 (36.1) 242 (41.4) 243 (32) Yes 860 (63.9) 343 (58.6) 517 (68) Laboratory Metrics Glucose (mmol/L) 8.2 ± 3.8 7.9 ± 3.4 8.4 ± 4.0 0.032 HbA1C (%) 7.4 ± 1.8 7.3 ± 1.7 7.5 ± 1.8 0.179 WBC (×10 9 /L) 7.5 ± 2.1 7.4 ± 2.1 7.5 ± 2.1 0.274 CRP (mg/L) 0.3 (0.1, 0.7) 0.3 (0.1, 0.7) 0.3 (0.1, 0.7) 0.755 Total cholesterol (mmol/L) 5.2 ± 1.1 5.3 ± 1.1 5.2 ± 1.1 0.095 BMI, body mass index; HbA1C, glycohemoglobin; WBC, white cell count; CRP, C-reaction protein; DPN, diabetic peripheral neuropathy. CRP exhibited a non-normality distribution, making the median and interquartile range more appropriate to describe central tendency. Univariate logistic regression between variables and the presence of DPN To determine the variables associated with DPN in the entire study population, a univariate logistic regression analysis was conducted. The results of the univariate analyses are presented in Table 2 . We found that race/ethnicity, marital status, BMI, HbA1C, WBC, CRP, and total cholesterol were not associated with DPN. Furthermore, we found that being female (OR = 0.86, 95% CI: 0.69–1.07) and having a higher education level (OR = 0.73, 95% CI: 0.55–0.97) were negatively associated with DPN. In contrast, the univariate analysis showed that age ≥ 60 years (OR = 1.31, 95% CI: 1.04–1.65), former smoker status (OR = 1.46, 95% CI: 1.15–1.86, P = 0.002), hypertension (OR = 1.5, 95% CI: 1.2–1.88), and glucose (OR = 1.03, 95% CI: 1-1.06) were positively correlated with DPN. Table 2 Univariate analysis for the presence of DPN. Variable OR_95CI P Sex,n(%) Male 1 Female 0.86 (0.69 ~ 1.07) 0.18 Age (years) ,n(%) =60 1.31 (1.04 ~ 1.65) 0.023 Race/ ethnicity, n(%) Non-Hispanic white 1 Non-Hispanic black 1.02 (0.77 ~ 1.36) 0.873 Mexican American 0.91 (0.7 ~ 1.18) 0.457 Others 0.75 (0.49 ~ 1.13) 0.17 Education level(years), n(%) 12 0.73 (0.55 ~ 0.97) 0.028 Marital status, n(%) Married or living with a partner 1 Living alone 1.11 (0.89 ~ 1.38) 0.367 BMI, n(%) Underweight/ normal (< 25.0kg/m 2 ) 1 Overweight (25-29.9 kg/m 2 ) 1.1 (0.79 ~ 1.53) 0.56 Obese (≥ 30 kg/m 2 ) 1.08 (0.78 ~ 1.48) 0.646 Smoking status, n(%) Never 1 Current 1.22 (0.9 ~ 1.67) 0.204 Former 1.46 (1.15 ~ 1.86) 0.002 Hypertension No 1 Yes 1.5 (1.2 ~ 1.88) < 0.001 Laboratory Metrics Glucose (mmol/L) 1.03 (1 ~ 1.06) 0.033 HbA1C (%) 1.04 (0.98 ~ 1.11) 0.18 WBC (×10 9 /L) 1.03 (0.98 ~ 1.08) 0.274 CRP (mg/L) 1.02 (0.94 ~ 1.11) 0.599 Total cholesterol (mmol/L) 0.92 (0.84 ~ 1.01) 0.095 BMI, body mass index; HbA1C, glycohemoglobin; WBC, white cell count; CRP, C-reaction protein; DPN, diabetic peripheral neuropathy. CRP exhibited a non-normality distribution, making the median and interquartile range more appropriate to describe central tendency. Multivariate logistic regression between MLR and the presence of DPN In this study, we constructed four models to analyze the independent effects of MLR on DPN. The effect sizes (OR) and 95% CIs are listed in Table 3 . In the unadjusted model (Model 1), MLR was found to be significantly associated with the occurrence of DPN (OR = 1.11, 95% CI: 1.03–1.21, P = 0.011). The model-based effect size indicates that for every 0.1 unit increase in MLR, the risk of DPN increases by 11%. In Model 2, after adjusting for sex, age, race/ethnicity, education level, and marital status, the presence of DPN increased by 9% for every 0.1 unit rise in MLR (OR = 1.09). After further adjusting for BMI, smoking status, and hypertension, the OR was 1.1 (95% CI: 1.01–1.2) in Model 3. In the fully adjusted model (Model 4) (adjusted for all covariates presented in Table 1 ), for each additional 0.1 unit increase in MLR, the risk of DPN increased by 11% (OR = 1.11, 95% CI: 1-1.2). Table 3 Association between MLR and the presence of DPN. Characters Model 1 Model 2 Model 3 Model 4 OR(%95CI) P OR(%95CI) P OR(%95CI) P OR(%95CI) P MLR*10 1.11 (1.03 ~ 1.21) 0.011 1.09 (1 ~ 1.19) 0.042 1.1 (1.01 ~ 1.2) 0.034 1.1 (1 ~ 1.2) 0.046 Trisections T1(< 0.232) 1.2 (0.92 ~ 1.56) 0.182 1.26 (0.96 ~ 1.66) 0.09 1.25 (0.96 ~ 1.65) 0.103 1.25 (0.9 ~ 1.72) 0.106 T2(0.233–0.313) 1(Ref) 1(Ref) 1(Ref) 1(Ref) T3(≥ 0.314) 1.52 (1.17 ~ 1.99) 0.002 1.47 (1.12 ~ 1.93) 0.005 1.47 (1.12 ~ 1.93) 0.006 1.47 (1.12 ~ 1.94) 0.006 Adjusted covariates: Model 1: unadjusted. Model 2: adjusted by sociodemographic variables (age, sex, marital status, race/ ethnicity, education). Model 3: adjusted by Model 2 + BMI, smoking status and hypertension; Model 4: adjusted by age, sex, marital status, race/ ethnicity, education level, BMI, smoking status and hypertension, glucose, HbA1C, WBC, CRP and total cholesterol. MLR, monocyte-lymphocyte ratio; DPN, diabetic peripheral neuropathy. For sensitivity analyses, we converted MLR from a continuous variable to a categorical variable. When analyzing MLR using trisection, a significant association was found between MLR and DPN after adjusting for potential confounders. Compared with participants with a middle MLR (Q2: 0.233–0.313), the adjusted OR values for MLR and DPN in the lower quartile (Q1: < 0.232) and upper quartile (Q3: ≥ 0.314) were 1.25 (95% CI: 0.95–1.65, P = 0.106) and 1.47 (95% CI: 1.12–1.94, P = 0.006), respectively. Furthermore, we observed a trend in the effect size across different MLR groups (0.289). The results of nonlinearity of MLR and DPN The results of the analysis on the nonlinearity of the relationship between MLR and DPN are presented in Fig. 2 . The smooth curve and the results of the Generalized Additive Model showed that the relationship between MLR and DPN was nonlinear ( P = 0.007), even after adjusting for sex, age, race/ethnicity, education level, marital status, BMI, smoking status, hypertension, glucose, HbA1C, WBC, CRP, and total cholesterol. We used both binary logistic regression and two-piece-wise binary logistic regression to fit the association and selected the best-fit model based on the p-value for the log likelihood ratio test. In the threshold analysis, the OR of DPN was 1.308 (95% CI: 1.075–1.591, P = 0.011) in participants with MLR ≥ 0.3. This means that the risk of DPN increases by 30.8% with every 0.1 unit rise in MLR (Table 4 ). However, there was no association between MLR and DPN when MLR was < 0.3 (Table 4 ). Table 4 Threshold effect analysis of the relationship of MLR with DPN. MLR Adjusted Model OR (95%CI) p -value < 0.3 0.922 (0.573 ~ 1.485) 0.622 ≥ 0.3 1.308 (1.075 ~ 1.591) 0.011 Likelihood Ratio test 0.019 MLR, monocyte-lymphocyte ratio; DPN, diabetic peripheral neuropathy; CI, confidence interval. Subgroup analysis We conducted subgroup analyses to examine the variables that may affect the association between MLR and the presence of DPN. We used sex, age (< 60, ≥ 60 years), race/ethnicity, smoking status, BMI category (< 25, 25-29.9, ≥ 30 kg/m 2 ), hypertension, and HbA1C category (< 6.5, ≥ 6.5%) as stratification variables to observe the trend of effect sizes in these subgroups (Fig. 3 ). The effect size of MLR on the occurrence of DPN remained consistent across all subgroups. There was no statistically significant interaction between MLR and sex ( P for interaction = 0.241), age ( P for interaction = 0.304), race/ethnicity ( P for interaction = 0.76), smoking status ( P for interaction = 0.406), BMI ( P for interaction = 0.264), hypertension ( P for interaction = 0.27), or HbA1C ( P for interaction = 0.944) in relation to the presence of DPN. Discussion Findings from this study show an association between MLR and DPN in a representative population of US adults with diabetes. This association remains after adjusting for sex, age, race/ethnicity, education level, marital status, BMI, smoking status, hypertension, glucose, HbA1C, WBC, CRP, and total cholesterol, demonstrating a robust relationship. To the best of our knowledge, our study is the first to demonstrate a J-shaped relationship between MLR and DPN, with an inflection point of approximately 0.3 ( P for non-linearity = 0.007). Subgroup analysis was conducted to better understand the trend of MLR and DPN in different populations. In this study, we stratified the analysis based on variables such as sex, age (< 60, ≥ 60 years), race/ethnicity, smoking status, BMI category (< 25, 25-29.9, ≥ 30 kg/m 2 ), hypertension, and HbA1C category ( 0.05, indicating no interaction between MLR and DPN with these variables. This suggests that MLR may serve as a useful biomarker for predicting DPN. We also considered additional potential confounding variables that could influence the association between MLR and DPN. However, even after adjusting for these variables, MLR remained strongly correlated with the occurrence of DPN. These findings highlight the potential therapeutic utility of MLR in identifying patients at higher risk of developing DPN. Early detection and treatment of individuals with a high MLR may help delay or even halt the progression of DPN, leading to improved patient outcomes and reduced burden of diabetic complications. This observational study aimed to investigate the association between MLR and DPN in individuals with diabetes. MLR, which reflects the balance between inflammatory monocytes and anti-inflammatory lymphocytes, has emerged as a potential biomarker for various inflammatory conditions [ 20 ]. Recently, some studies have shown that the higher MLR is associated with the occurrence and progression of diabetes complications, including DN, DR, and PAD [ 12 – 15 , 21 ]. Moreover, MLR has been widely studied in various inflammation-related disorders including cancer, tuberculosis and cardiovascular diseases, and has proven to be a reliable biomarker of systemic inflammation [ 22 – 24 ]. Therefore, understanding the relationship between MLR and DPN could provide valuable insights into the pathogenesis and progression of this debilitating diabetes complication. The findings of this study demonstrate a significant association between MLR and DPN. The MLR was found to be significantly higher in individuals with DPN compared to those without DPN, suggesting a potential role of inflammation in the development and progression of DPN. This observation is consistent with previous studies that have reported elevated MLR in other inflammatory conditions. For instance, Aksoy Sarac et al. [ 25 ] demonstrated higher MLR in patients with alopecia areata. In our study, we found that the association between MLR and DPN exhibited a J-shaped curve in RCS (Fig. 2 ). In the threshold analysis, the inflection point was approximately 0.3. Specifically, the risk of DPN increased with increasing MLR in those with MLR ≥ 0.3, whereas the risk of DPN no longer dropped with increasing MLR in those with MLR < 0.3. The current research status of MLR in the context of DPN is limited, with only a few studies investigating the association between MLR and diabetes complications. Wang et al. [ 26 ] found that MLR was significantly increased in type-2 diabetes participants with proliferative diabetic retinopathy. Yue et al. [ 13 ] demonstrated that MLR is a risk factor for DR and may be related to the pathophysiology and clinical aspects of DR. Gao et al. [ 21 ] explored the association between MLR, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio and the risk of non-healing ulceration in patients with type 2 diabetes. They found that after adjusting for confounding variables, MLR and NLR were significantly higher in type 2 diabetes patients with non-healing ulceration. This suggests that MLR may serve as a potential biomarker for identifying and monitoring disease progression or individuals at risk of developing diabetes complications, including DPN. In the future, consideration could be given to integrating MLR into screening protocols or managing diabetes complications. Despite these promising findings, it is important to acknowledge the limitations of this study. Firstly, its cross-sectional design does not allow for causal inferences to be made. Longitudinal studies are needed to determine whether MLR can predict the development or progression of DPN over time. Secondly, the study population consisted of the United States’ population, which may limit the generalizability of the findings to other populations. Further studies involving larger and more diverse populations are warranted to confirm the observed association. Finally, some important variables that increase the risk of neuropathy are not covered in this study (for example: duration, LDL cholesterol, etc). Additionally, the underlying mechanisms linking MLR to DPN remain unclear. It is possible that the imbalance between monocytes and lymphocytes reflects systemic inflammation, which contributes to nerve damage and the development of DPN. Alternatively, MLR might be a consequence of DPN rather than a cause, as chronic inflammation associated with DPN could lead to changes in the immune cell profile. Further research is needed to elucidate the underlying mechanisms and establish the causal relationship between MLR and DPN. Conclusions This study establishes a crucial connection between MLR and the risk of DPN, highlighting the potential of MLR as a significant predictor of DPN risk. This finding has important implications for risk stratification and management strategies for diabetic patients. Abbreviations DPN diabetic peripheral neuropathy MLR monocyte-lymphocyte ratio DN diabetic nephropathy DR diabetic retinopathy PAD peripheral arterial disease NHANES the National Health and Nutrition Examination Survey MEC mobile examination center NCHS the National Center for Health Statistics PN peripheral neuropathy BMI body mass index WBC white blood cell count HbA1C glycohemoglobin levels CRP C-reactive protein RCS restricted cubic spline ORs odds ratios NDPN non-diabetic peripheral neuropathy population NLR neutrophil-to-lymphocyte ratio Declarations Ethics approval and consent to participate The study was conducted according to the Declaration of Helsinki. The studies involving humans were approved by Ethics Committee of Affiliated Hospital of Zunyi Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements. Consent for publication Not Applicable Availability of data and material The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The data used to support the findings of this study are available from the corresponding author upon request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This study was supported by grants from Collaborative Innovation Center of Chinese Ministry of Education (Grant No. 2020-39), the National Natural Science Foundation of China (82360445), Shanghai Wang Zhengguo Trauma Medicine Development Foundation (SZYZ-TR-05), and Scientific Research and Talent Training Funds of Kweichow Moutai Hospital (Grant No.2022-13). Author Contributions CD and WZ: contributed to the conception or design of the work; LZ and JY: conducted the study, analyzed data and wrote the manuscript. All authors read and approved the final manuscript. Acknowledgments We thank Dr. Jie Liu (People’s Liberation Army of China General Hospital, Beijing, China) and Dr. Qilin Yang (The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China) for helping with this revision. References Pop-Busui R, Boulton AJ, Feldman EL, et al. Diabetic Neuropathy: A Position Statement by the American Diabetes Association. 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Biomolecules, 2022, 13(1): 39. Lin Y, Qu L, Wu J, et al. Identification of Adipogenesis Subgroups and Immune Infiltration Characteristics in Diabetic Peripheral Neuropathy. J Immunol Res, 2023, 2023: 3673094. Vieceli Dalla Sega F, Cimaglia P, Manfrini M, et al. Circulating Biomarkers of Endothelial Dysfunction and Inflammation in Predicting Clinical Outcomes in Diabetic Patients with Critical Limb Ischemia. Int J Mol Sci, 2022, 23(18): 10641. Yue S, Zhang J, Wu J, et al. Use of the Monocyte-to-Lymphocyte Ratio to Predict Diabetic Retinopathy. Int J Environ Res Public Health, 2015, 12(8): 10009-10019. Qiu C, Liu S, Li X, et al. Prognostic value of monocyte-to-lymphocyte ratio for 90-day all-cause mortality in type 2 diabetes mellitus patients with chronic kidney disease. Sci Rep, 2023, 13(1): 13136. Ning P, Yang F, Kang J, et al. Predictive value of novel inflammatory markers platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio in arterial stiffness in patients with diabetes: A propensity score-matched analysis. Front Endocrinol (Lausanne), 2022, 13: 1039700. Zipf G, Chiappa M, Porter KS, et al. National health and nutrition examination survey: plan and operations, 1999-2010. Vital Health Stat 1, 2013, (56): 1-37. US Department of Health & Human Services. Office of Extramural Research. Available Online: Http://Grants.Nih.Gov/Grants/Policy/Hs/Hs_policies.Htm (Accessed on 1 September 2023). National Center for Health Statistics: NHANES 1999–2000 Data Release (June 2002): Lower Extremity Disease Exami- Nation (LEX), MEC Examination [Article Online], 2003. Available from Http://www.Cdc.Gov/Nchs/Data/Nhanes/Ie.Pdf. Accessed 1 September 2023. Cai XY, Li WL, Ge SW, et al. Peripheral Neuropathy Associated with Higher Mortality in Population with Chronic Kidney Disease: National Health and Nutrition Examination Surveys. Kidney Dis (Basel). 2023,10(2):79-88. Liu J, Liu X, Li Y, et al. The association of neutrophil to lymphocyte ratio, mean platelet volume, and platelet distribution width with diabetic retinopathy and nephropathy: a meta-analysis. Biosci Rep, 2018, 38(3): BSR20180172. Gao H, Yi Y. Association of Monocyte to Lymphocyte, Neutrophil to Lymphocyte, and Platelet to Lymphocyte Ratios With Non-Healing Lower Extremity Ulcers in Patients With Type 2 Diabetes. Int J Low Extrem Wounds, 2023, 13: 1534734623 1197884. Mayito J, Meya DB, Miriam A, et al. Monocyte to Lymphocyte ratio is highly specific in diagnosing latent tuberculosis and declines significantly following tuberculosis preventive therapy: A cross-sectional and nested prospective observational study. PLoS One, 2023, 18(11): e0291834. Vakhshoori M, Nemati S, Sabouhi S, et al. Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: a systematic review and meta-analysis. J Int Med Res, 2023, 51(10): 3000605231204469. Pivatto Júnior F, Santos  BS, Englert EF, et al. Monocyte-to-lymphocyte ratio as predictor of cancer therapy-related cardiotoxicity in patients with breast cancer: a pilot cohort study. Breast Cancer Res Treat, 2023, 200(3): 355-362. Aksoy Sarac G, Acar O, Nayır T, et al. The Use of New Hematological Markers in the Diagnosis of Alopecia Areata. Dermatol Pract Concept, 2023, 13(2): e2023 118. Wang H, Guo Z, Xu Y. Association of monocyte-lymphocyte ratio and proliferative diabetic retinopathy in the U.S. population with type 2 diabetes. J Transl Med, 2022, 20(1): 219. 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-6637207","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475921809,"identity":"bccf0424-9483-4722-8f7d-1aa99d09e288","order_by":0,"name":"Zirui Li","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zirui","middleName":"","lastName":"Li","suffix":""},{"id":475921812,"identity":"6cb2694f-ddc0-4b7a-a81d-53fd6a56fc44","order_by":1,"name":"Yang Jian","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Jian","suffix":""},{"id":475921813,"identity":"62e18d12-32be-4ab2-b5b3-0bc5c0d40568","order_by":2,"name":"Chengliang Deng","email":"","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chengliang","middleName":"","lastName":"Deng","suffix":""},{"id":475921814,"identity":"648a8e98-fe21-4f49-b2cf-f5d2e95125b2","order_by":3,"name":"Zairong Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFACxgYIzcx84MCHH6RpYUs8OLOHNOt4jA9zsBGhTr79cNuHjztqE/vZeT4cZuBhkOcXO0DAWT2JzTNnnjmeOLOZd8PhAgsGw5mzE/BrYWZIbGbmbTuWu+EwUMsMHoYEg9sEtLDxP4Ro2X+Y58FhHjYitPBIgG2pyd3AzMNAnBYJiYfNjDPbDtTPOMxmAAxkCcJ+ke9Pf8zwsa3OmL//8OMPH37YyPNLE9ACBYfhthKlHATqiFY5CkbBKBgFIxAAAJWtRUVTgT0OAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Zunyi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zairong","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-05-11 02:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6637207/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6637207/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85845729,"identity":"e3f38fe3-33f5-4973-8cd6-b82b92f254eb","added_by":"auto","created_at":"2025-07-02 09:38:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":169336,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the screening and enrollment of participants. \u003c/strong\u003eNHANES, National and Nutrition Examination Survey; MLR, monocyte-lymphocyte ratio.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6637207/v1/a649cf74388598fcfbdfabaf.jpeg"},{"id":85844718,"identity":"18c1c519-cb69-4e80-85f3-b3a70bf311a3","added_by":"auto","created_at":"2025-07-02 09:30:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNonlinear dose-response relationship between MLR and DPN. \u003c/strong\u003eAdjustment factors included age, sex, marital status, race/ethnicity, education level, BMI, smoking status and hypertension, glucose, HbA1C, WBC, CRP and total cholesterol, only 95% of the data is displayed. The red line and pink area represent the estimated values and their corresponding 95% confidence intervals, respectively. MLR, monocyte-lymphocyte ratio; DPN, diabetic peripheral neuropathy; BMI, body mass index; HbA1C, glycohemoglobin; WBC, white blood cell count; CRP, C-reactive protein.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6637207/v1/8be9eea083bb234a8d7f6e67.jpeg"},{"id":85842570,"identity":"acbe6ce9-911b-4282-b7f6-40c5bc87caf0","added_by":"auto","created_at":"2025-07-02 09:22:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":282703,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect size of MLR on the presence of DPN in subgroups.\u003c/strong\u003eOR, odds ratio; CI, confidence interval; MLR, monocyte-lymphocyte ratio; DPN, diabetic peripheral neuropathy; BMI, body mass index; HbA1C, glycohemoglobin.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6637207/v1/c53e6ce4a40e70e8b4176d3e.jpeg"},{"id":85846839,"identity":"5bc98a15-7da2-4e56-aea1-d7c6ca8b3551","added_by":"auto","created_at":"2025-07-02 09:46:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1650991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6637207/v1/2355c478-db31-43da-8cdb-a21151daa11b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between monocyte-lymphocyte ratio and diabetic peripheral neuropathy in the US populations: a population-based study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus, affecting a significant proportion of patients worldwide [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is characterized by peripheral nerve damage, which leads to symptoms such as numbness, tingling, and pain in the extremities. DPN not only significantly impairs the quality of life for affected individuals but also places a considerable burden on healthcare systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the World Health Organization, the global prevalence of diabetes mellitus was estimated as approximately 9.3% in 2019, with projections suggesting a further increase in the coming years. Projections suggest that the global prevalence of diabetes will increase to 10.2% (578\u0026nbsp;million people) by 2030 and 10.9% (700\u0026nbsp;million people) by 2045 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. With the rising incidence of diabetes, the burden of DPN is also expected to escalate, making it a growing public health concern [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pathogenesis of DPN is multifactorial, involving various mechanisms such as chronic hyperglycemia, oxidative stress, inflammation, and dyslipidemia [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Among these mechanisms, inflammation has emerged as a significant contributor to the development and progression of DPN [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Inflammatory responses activate immune cells, including monocytes and lymphocytes, which play a crucial role in the pathophysiology of DPN [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe monocyte-lymphocyte ratio (MLR) is a novel inflammatory marker in peripheral blood. Recently, several studies have shown that the MLR is associated with the occurrence and progression of diabetes complications, including diabetic nephropathy (DN), diabetic retinopathy (DR), and peripheral arterial disease (PAD) [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It is reasonable to assume that MLR may play a significant role in the onset and progression of DPN, given the mounting body of research emphasizing its importance in diabetes complications [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, research on the relationship between MLR and DPN is limited. Understanding the relationship between MLR and DPN could provide valuable insights into the inflammatory mechanisms underlying the development and progression of this debilitating condition. Moreover, MLR has the potential to serve as a non-invasive biomarker for predicting the risk and severity of DPN, aiding in early detection and intervention.\u003c/p\u003e \u003cp\u003eTherefore, this observational study aimed to explore the association between MLR and DPN in a population of patients with diabetes. Ultimately, the findings of this study may have significant implications for the early detection, prevention, and management of DPN in these patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eData on health and nutrition were gathered from Americans as part of the National Health and Nutrition Examination Survey (NHANES). Participants completed questionnaires on their histories and habits, in addition to undergoing physical and laboratory examinations. Researchers, decision-makers, and healthcare professionals utilize this data to better understand health issues, identify patterns and inequities, and promote nutrition and public health.\u003c/p\u003e \u003cp\u003eFor our analysis, we used open data from three NHANES cycles (1999\u0026ndash;2000, 2001\u0026ndash;2002, and 2003\u0026ndash;2004). Further details on the data can be found on the NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To participate in the survey, participants needed to submit to a blood test examination. In-person interviews conducted in participants' homes were also used to gather data on basic demographics and medical histories.\u003c/p\u003e \u003cp\u003eA stratified multistage probability survey was used in the NHANES research to assess the health and nutritional status of non-institutionalized Americans [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A mobile examination center (MEC) conducted home visits, screenings, and laboratory tests as part of the NHANES to gather detailed demographic and health data. Details on the questionnaires and interviews are available on the NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The NHANES research was approved by the National Center for Health Statistics (NCHS) Ethics Review Committee, and all participants provided written informed consent. No additional Institutional Review Board permission was required for the secondary analysis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy variables and outcomes\u003c/h3\u003e\n\u003cp\u003eIn this study, the monocyte count to MLR was calculated using the values directly obtained from the laboratory data files of NHANES. A diagnosis of DPN required the satisfaction of two conditions: (1) a diagnosis of diabetes and (2) a diagnosis of PN. To assess PN, health technicians used a standard 5.07 Semmes-Weinstein nylon monofilament mounted on a plastic handle, which delivers approximately 10 grams of force, to test the sensation on the bottom of each participant\u0026rsquo;s feet at three specific sites (plantar surface of the first metatarsal head, fifth metatarsal head, and hallux) on each foot, totaling six sites [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The sites were tested in a non-sequential order to enhance the examinee\u0026rsquo;s ability to discrimination sensation. The monofilament was applied until it buckled and then held for one second. An absence of sensation was defined as two incorrect or indeterminable identifications or one incorrect and one indeterminable identification at a site. If a participant had at least one insensate area on either foot, they were considered to have PN [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA questionnaire was administered to gather information on the participants\u0026rsquo; medical history and demographic characteristics, including a history of physician-diagnosed hypertension, sex, age, race/ethnicity, marital status, smoking status, and education level. Based on the marital status questionnaire, participants were categorized as either married/living with a partner or living alone. Smoking status was classified into three categories: never smoked (less than 100 cigarettes in a lifetime), former smoker (more than 100 cigarettes in a lifetime but currently not smoking), and current smoker (more than 100 cigarettes in a lifetime and currently smoking). Education level was grouped into three categories: less than 9 years, 9\u0026ndash;12 years, and more than 12 years. Participants were also divided into normal weight, overweight, and obese groups based on their body mass index (BMI) categories: less than 25.0 kg/m\u003csup\u003e2\u003c/sup\u003e, 25.0-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e, and more than 30.0 kg/m\u003csup\u003e2\u003c/sup\u003e, respectively. Other covariates included glucose levels (mmol/L), glycosylated hemoglobin (HbA1C, %), white blood cell count (WBC, \u0026times;10\u003csup\u003e9\u003c/sup\u003e/L), C-reactive protein (CRP, mg/dL), and total cholesterol levels (mmol/L).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical software packages R 3.3.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) and Free Statistics Software version 1.8 were used for all analyses. Demographic and clinical characteristics were expressed using means, standard deviations, and frequencies (percentages). Especially, CRP was expressed using median and interquartile range because it was not a normal distribution. Differences between continuous and categorical data were examined using independent and chi-squared tests, respectively. Binary logistic regression was used to examine the connection between MLR and DPN. Both single and multiple variable analyses were conducted. We presented four models for multivariate logistic regression: (1) Model 1: unadjusted; (2) Model 2: adjusted for sociodemographic variables (age, sex, marital status, race/ethnicity, education level); (3) Model 3: adjusted for sociodemographic variables and variables that reflect overall health status including BMI, smoking status, and hypertension; and (4) Model 4: adjusted for age, sex, marital status, race/ethnicity, education level, BMI, smoking status, hypertension, glucose, HbA1C, WBC, CRP, and total cholesterol. Restricted cubic spline (RCS) analysis was used to explore the dose-response relationship between MLR and the risk of DPN. RCS allows for flexible modeling of nonlinear relationships without predefining the shape of the association between continuous variables and the outcome. Visual representations of the spline curves were generated to examine the relationship between age, sex, marital status, race/ethnicity, education level, BMI, smoking status and hypertension, glucose, HbA1C, WBC, CRP, total cholesterol, and DPN risk.\u003c/p\u003e \u003cp\u003eSubgroup analysis was conducted to examine the association between MLR and DPN, considering factors such as sex, age category (\u0026lt;\u0026thinsp;60 years, \u0026ge;\u0026thinsp;60 years), race/ethnicity, smoking status, BMI, hypertension, and HbA1C category (\u0026lt;\u0026thinsp;6.5%, \u0026ge;\u0026thinsp;6.5%). The multivariate logistic regression model was employed for this analysis. The logistic regression model's interaction test was performed to examine the odds ratios (ORs) between the subgroups that were studied. On average, less than 5% of the variables' data were missing, and these missing data were removed from the analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and baseline characteristics\u003c/h2\u003e \u003cp\u003eThis study collected data from three NHANES cycles, specifically from the years 1999\u0026ndash;2000, 2001\u0026ndash;2002, and 2003\u0026ndash;2004. The initial pool of potential participants was identified as 31,126 individuals. From this pool, 21,156 participants under the age of 40 years were excluded, and 6,420 participants without diabetes were excluded. Additionally, 18,116 participants with missing data on DPN were excluded, as well as 272 participants with missing peripheral blood MLR. After further excluding 117 participants with missing covariate data (43 with missing marital status data; 36 with missing BMI data; 32 with missing total cholesterol data; and 6 with missing smoking, education, and hypertension data), a total of 1,345 participants remained and were included in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the inclusion and exclusion criteria and shows a flowchart outlining the participant selection process for the study. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of both patients with DPN and the non-diabetic peripheral neuropathy population (NDPN) in terms of demographics, socioeconomic factors, comorbidities, laboratory metrics, and baseline characteristics. Out of the 1,345 participants included, 760 individuals (56.5%) were identified as having DPN. A statistical analysis revealed significant differences in age, smoking status, hypertension, and glucose levels between the DPN and NDPN groups, with p-values less than 0.05 for these comparisons. Specifically, the DPN group had a significantly older age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a larger proportion of smoking population (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), a larger proportion of hypertension population (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher glucose levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) compared to the NDPN group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study participants.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo DPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDPN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e760\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\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e720 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e301 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e419 (55.1)\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\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e625 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e284 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341 (44.9)\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\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\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\u003cb\u003eRace/ethnicity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e562 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e324 (42.6)\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\u003eNon-Hispanic black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (22.4)\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\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212 (27.9)\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\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 ( 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (7.1)\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\u003cb\u003eEducation level(years), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (28.2)\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\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e553 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e318 (41.8)\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\u0026gt;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e436 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e208 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228 (30)\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\u003cb\u003eMarriage status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e837 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e465 (61.2)\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\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e508 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295 (38.8)\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\u003cb\u003eBMI, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight/ normal (\u0026lt;\u0026thinsp;25.0kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (14.5)\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\u003eOverweight (25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e497 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e284 (37.4)\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\u003eObese (\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e647 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366 (48.2)\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\u003cb\u003eSmoking status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e603 (44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314 (41.3)\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\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (16.4)\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\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e523 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321 (42.2)\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\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242 (41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243 (32)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e860 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e343 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e517 (68)\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\u003cb\u003eLaboratory Metrics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\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\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3 (0.1, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3 (0.1, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (0.1, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBMI, body mass index; HbA1C, glycohemoglobin; WBC, white cell count; CRP, C-reaction protein; DPN, diabetic peripheral neuropathy. CRP exhibited a non-normality distribution, making the median and interquartile range more appropriate to describe central tendency.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate logistic regression between variables and the presence of DPN\u003c/h2\u003e \u003cp\u003eTo determine the variables associated with DPN in the entire study population, a univariate logistic regression analysis was conducted. The results of the univariate analyses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We found that race/ethnicity, marital status, BMI, HbA1C, WBC, CRP, and total cholesterol were not associated with DPN. Furthermore, we found that being female (OR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.69\u0026ndash;1.07) and having a higher education level (OR\u0026thinsp;=\u0026thinsp;0.73, 95% CI: 0.55\u0026ndash;0.97) were negatively associated with DPN. In contrast, the univariate analysis showed that age\u0026thinsp;\u0026ge;\u0026thinsp;60 years (OR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.04\u0026ndash;1.65), former smoker status (OR\u0026thinsp;=\u0026thinsp;1.46, 95% CI: 1.15\u0026ndash;1.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), hypertension (OR\u0026thinsp;=\u0026thinsp;1.5, 95% CI: 1.2\u0026ndash;1.88), and glucose (OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI: 1-1.06) were positively correlated with DPN.\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\u003eUnivariate analysis for the presence of DPN.\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\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR_95CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex,n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.69\u0026thinsp;~\u0026thinsp;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years) ,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u0026gt;=60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31 (1.04\u0026thinsp;~\u0026thinsp;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ ethnicity, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u003eNon-Hispanic black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.77\u0026thinsp;~\u0026thinsp;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.7\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.49\u0026thinsp;~\u0026thinsp;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level(years), n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.68\u0026thinsp;~\u0026thinsp;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.55\u0026thinsp;~\u0026thinsp;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried or living with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (0.89\u0026thinsp;~\u0026thinsp;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight/ normal (\u0026lt;\u0026thinsp;25.0kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u003eOverweight (25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.79\u0026thinsp;~\u0026thinsp;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.78\u0026thinsp;~\u0026thinsp;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22 (0.9\u0026thinsp;~\u0026thinsp;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46 (1.15\u0026thinsp;~\u0026thinsp;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (1.2\u0026thinsp;~\u0026thinsp;1.88)\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\u003cb\u003eLaboratory Metrics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1\u0026thinsp;~\u0026thinsp;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\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\u003e1.04 (0.98\u0026thinsp;~\u0026thinsp;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.98\u0026thinsp;~\u0026thinsp;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.94\u0026thinsp;~\u0026thinsp;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.84\u0026thinsp;~\u0026thinsp;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eBMI, body mass index; HbA1C, glycohemoglobin; WBC, white cell count; CRP, C-reaction protein; DPN, diabetic peripheral neuropathy. CRP exhibited a non-normality distribution, making the median and interquartile range more appropriate to describe central tendency.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariate logistic regression between MLR and the presence of DPN\u003c/h3\u003e\n\u003cp\u003eIn this study, we constructed four models to analyze the independent effects of MLR on DPN. The effect sizes (OR) and 95% CIs are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the unadjusted model (Model 1), MLR was found to be significantly associated with the occurrence of DPN (OR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1.03\u0026ndash;1.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). The model-based effect size indicates that for every 0.1 unit increase in MLR, the risk of DPN increases by 11%. In Model 2, after adjusting for sex, age, race/ethnicity, education level, and marital status, the presence of DPN increased by 9% for every 0.1 unit rise in MLR (OR\u0026thinsp;=\u0026thinsp;1.09). After further adjusting for BMI, smoking status, and hypertension, the OR was 1.1 (95% CI: 1.01\u0026ndash;1.2) in Model 3. In the fully adjusted model (Model 4) (adjusted for all covariates presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), for each additional 0.1 unit increase in MLR, the risk of DPN increased by 11% (OR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1-1.2).\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\u003eAssociation between MLR and the presence of DPN.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(%95CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(%95CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR(%95CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR(%95CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMLR*10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.03\u0026thinsp;~\u0026thinsp;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (1\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1 (1.01\u0026thinsp;~\u0026thinsp;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1 (1\u0026thinsp;~\u0026thinsp;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrisections\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT1(\u0026lt;\u0026thinsp;0.232)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.92\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (0.96\u0026thinsp;~\u0026thinsp;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25 (0.96\u0026thinsp;~\u0026thinsp;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25 (0.9\u0026thinsp;~\u0026thinsp;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2(0.233\u0026ndash;0.313)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT3(\u0026ge;\u0026thinsp;0.314)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52 (1.17\u0026thinsp;~\u0026thinsp;1.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47 (1.12\u0026thinsp;~\u0026thinsp;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.47 (1.12\u0026thinsp;~\u0026thinsp;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.47 (1.12\u0026thinsp;~\u0026thinsp;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAdjusted covariates: Model 1: unadjusted. Model 2: adjusted by sociodemographic variables (age, sex, marital status, race/ ethnicity, education). Model 3: adjusted by Model 2\u0026thinsp;+\u0026thinsp;BMI, smoking status and hypertension; Model 4: adjusted by age, sex, marital status, race/ ethnicity, education level, BMI, smoking status and hypertension, glucose, HbA1C, WBC, CRP and total cholesterol. MLR, monocyte-lymphocyte ratio; DPN, diabetic peripheral neuropathy.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor sensitivity analyses, we converted MLR from a continuous variable to a categorical variable. When analyzing MLR using trisection, a significant association was found between MLR and DPN after adjusting for potential confounders. Compared with participants with a middle MLR (Q2: 0.233\u0026ndash;0.313), the adjusted OR values for MLR and DPN in the lower quartile (Q1: \u0026lt; 0.232) and upper quartile (Q3: \u0026ge; 0.314) were 1.25 (95% CI: 0.95\u0026ndash;1.65, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.106) and 1.47 (95% CI: 1.12\u0026ndash;1.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), respectively. Furthermore, we observed a trend in the effect size across different MLR groups (0.289).\u003c/p\u003e\n\u003ch3\u003eThe results of nonlinearity of MLR and DPN\u003c/h3\u003e\n\u003cp\u003eThe results of the analysis on the nonlinearity of the relationship between MLR and DPN are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The smooth curve and the results of the Generalized Additive Model showed that the relationship between MLR and DPN was nonlinear (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), even after adjusting for sex, age, race/ethnicity, education level, marital status, BMI, smoking status, hypertension, glucose, HbA1C, WBC, CRP, and total cholesterol. We used both binary logistic regression and two-piece-wise binary logistic regression to fit the association and selected the best-fit model based on the p-value for the log likelihood ratio test. In the threshold analysis, the OR of DPN was 1.308 (95% CI: 1.075\u0026ndash;1.591, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) in participants with MLR\u0026thinsp;\u0026ge;\u0026thinsp;0.3. This means that the risk of DPN increases by 30.8% with every 0.1 unit rise in MLR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, there was no association between MLR and DPN when MLR was \u0026lt;\u0026thinsp;0.3 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eThreshold effect analysis of the relationship of MLR with DPN.\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAdjusted Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.922 (0.573\u0026thinsp;~\u0026thinsp;1.485)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.308 (1.075\u0026thinsp;~\u0026thinsp;1.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikelihood Ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMLR, monocyte-lymphocyte ratio; DPN, diabetic peripheral neuropathy; CI, confidence interval.\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\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eWe conducted subgroup analyses to examine the variables that may affect the association between MLR and the presence of DPN. We used sex, age (\u0026lt;\u0026thinsp;60, \u0026ge; 60 years), race/ethnicity, smoking status, BMI category (\u0026lt;\u0026thinsp;25, 25-29.9, \u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e), hypertension, and HbA1C category (\u0026lt;\u0026thinsp;6.5, \u0026ge; 6.5%) as stratification variables to observe the trend of effect sizes in these subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The effect size of MLR on the occurrence of DPN remained consistent across all subgroups. There was no statistically significant interaction between MLR and sex (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.241), age (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.304), race/ethnicity (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.76), smoking status (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.406), BMI (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.264), hypertension (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.27), or HbA1C (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.944) in relation to the presence of DPN.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFindings from this study show an association between MLR and DPN in a representative population of US adults with diabetes. This association remains after adjusting for sex, age, race/ethnicity, education level, marital status, BMI, smoking status, hypertension, glucose, HbA1C, WBC, CRP, and total cholesterol, demonstrating a robust relationship. To the best of our knowledge, our study is the first to demonstrate a J-shaped relationship between MLR and DPN, with an inflection point of approximately 0.3 (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003cp\u003eSubgroup analysis was conducted to better understand the trend of MLR and DPN in different populations. In this study, we stratified the analysis based on variables such as sex, age (\u0026lt;\u0026thinsp;60, \u0026ge; 60 years), race/ethnicity, smoking status, BMI category (\u0026lt;\u0026thinsp;25, 25-29.9, \u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e), hypertension, and HbA1C category (\u0026lt;\u0026thinsp;6.5, \u0026ge; 6.5%) to observe the effect sizes in these variables. We found that all values of \u003cem\u003eP\u003c/em\u003e for interaction were \u0026gt;\u0026thinsp;0.05, indicating no interaction between MLR and DPN with these variables. This suggests that MLR may serve as a useful biomarker for predicting DPN. We also considered additional potential confounding variables that could influence the association between MLR and DPN. However, even after adjusting for these variables, MLR remained strongly correlated with the occurrence of DPN. These findings highlight the potential therapeutic utility of MLR in identifying patients at higher risk of developing DPN. Early detection and treatment of individuals with a high MLR may help delay or even halt the progression of DPN, leading to improved patient outcomes and reduced burden of diabetic complications.\u003c/p\u003e \u003cp\u003eThis observational study aimed to investigate the association between MLR and DPN in individuals with diabetes. MLR, which reflects the balance between inflammatory monocytes and anti-inflammatory lymphocytes, has emerged as a potential biomarker for various inflammatory conditions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Recently, some studies have shown that the higher MLR is associated with the occurrence and progression of diabetes complications, including DN, DR, and PAD [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, MLR has been widely studied in various inflammation-related disorders including cancer, tuberculosis and cardiovascular diseases, and has proven to be a reliable biomarker of systemic inflammation [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, understanding the relationship between MLR and DPN could provide valuable insights into the pathogenesis and progression of this debilitating diabetes complication.\u003c/p\u003e \u003cp\u003eThe findings of this study demonstrate a significant association between MLR and DPN. The MLR was found to be significantly higher in individuals with DPN compared to those without DPN, suggesting a potential role of inflammation in the development and progression of DPN. This observation is consistent with previous studies that have reported elevated MLR in other inflammatory conditions. For instance, Aksoy Sarac et al. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] demonstrated higher MLR in patients with alopecia areata. In our study, we found that the association between MLR and DPN exhibited a J-shaped curve in RCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the threshold analysis, the inflection point was approximately 0.3. Specifically, the risk of DPN increased with increasing MLR in those with MLR\u0026thinsp;\u0026ge;\u0026thinsp;0.3, whereas the risk of DPN no longer dropped with increasing MLR in those with MLR\u0026thinsp;\u0026lt;\u0026thinsp;0.3. The current research status of MLR in the context of DPN is limited, with only a few studies investigating the association between MLR and diabetes complications. Wang et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] found that MLR was significantly increased in type-2 diabetes participants with proliferative diabetic retinopathy. Yue et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] demonstrated that MLR is a risk factor for DR and may be related to the pathophysiology and clinical aspects of DR. Gao et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] explored the association between MLR, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio and the risk of non-healing ulceration in patients with type 2 diabetes. They found that after adjusting for confounding variables, MLR and NLR were significantly higher in type 2 diabetes patients with non-healing ulceration. This suggests that MLR may serve as a potential biomarker for identifying and monitoring disease progression or individuals at risk of developing diabetes complications, including DPN. In the future, consideration could be given to integrating MLR into screening protocols or managing diabetes complications.\u003c/p\u003e \u003cp\u003eDespite these promising findings, it is important to acknowledge the limitations of this study. Firstly, its cross-sectional design does not allow for causal inferences to be made. Longitudinal studies are needed to determine whether MLR can predict the development or progression of DPN over time. Secondly, the study population consisted of the United States\u0026rsquo; population, which may limit the generalizability of the findings to other populations. Further studies involving larger and more diverse populations are warranted to confirm the observed association. Finally, some important variables that increase the risk of neuropathy are not covered in this study (for example: duration, LDL cholesterol, etc).\u003c/p\u003e \u003cp\u003eAdditionally, the underlying mechanisms linking MLR to DPN remain unclear. It is possible that the imbalance between monocytes and lymphocytes reflects systemic inflammation, which contributes to nerve damage and the development of DPN. Alternatively, MLR might be a consequence of DPN rather than a cause, as chronic inflammation associated with DPN could lead to changes in the immune cell profile. Further research is needed to elucidate the underlying mechanisms and establish the causal relationship between MLR and DPN.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study establishes a crucial connection between MLR and the risk of DPN, highlighting the potential of MLR as a significant predictor of DPN risk. This finding has important implications for risk stratification and management strategies for diabetic patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003ediabetic peripheral neuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eMLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003emonocyte-lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003ediabetic nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003ediabetic retinopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003ePAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003eperipheral arterial disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003ethe National Health and Nutrition Examination Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eMEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003emobile examination center\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNCHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003ethe National Center for Health Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003ePN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003eperipheral neuropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003ebody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003ewhite blood cell count\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eHbA1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003eglycohemoglobin levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eRCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003erestricted cubic spline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eORs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003eodds ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNDPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003enon-diabetic peripheral neuropathy population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 372px;\"\u003e\n \u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the Declaration of Helsinki. The studies involving humans were approved by Ethics Committee of Affiliated Hospital of Zunyi Medical University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants\u0026rsquo; legal guardians/next of kin in accordance with the national legislation and institutional requirements.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from Collaborative Innovation Center of Chinese Ministry of Education (Grant No. 2020-39), the National Natural Science Foundation of China (82360445), Shanghai Wang Zhengguo Trauma Medicine Development Foundation (SZYZ-TR-05), and Scientific Research and Talent Training Funds of Kweichow Moutai Hospital (Grant No.2022-13).\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eCD and WZ: contributed to the conception or design of the work; LZ and JY: conducted the study, analyzed data and wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Jie Liu (People\u0026rsquo;s Liberation Army of China General Hospital, Beijing, China) and Dr. Qilin Yang (The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China) for helping with this revision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePop-Busui R, Boulton AJ, Feldman EL, et al. Diabetic Neuropathy: A Position Statement by the American Diabetes Association. Diabetes Care, 2017, 40(1): 136-154.\u003c/li\u003e\n\u003cli\u003eDyck PJ, Davies JL, Wilson DM, et al. Risk factors for severity of diabetic polyneuropathy: intensive longitudinal assessment of the Rochester Diabetic Neuropathy Study cohort. Diabetes Care, 1999, 22(9): 1479-1486.\u003c/li\u003e\n\u003cli\u003eSelvarajah D, Kar D, Khunti K, et al. Diabetic peripheral neuropathy: advances in diagnosis and strategies for screening and early intervention. Lancet Diabetes Endocrinol, 2019, 7(12): 938-948.\u003c/li\u003e\n\u003cli\u003eBurgess J, Frank B, Marshall A, et al. Early Detection of Diabetic Peripheral Neuropathy: A Focus on Small Nerve Fibres. Diagnostics (Basel), 2021, 11(2) :165.\u003c/li\u003e\n\u003cli\u003eSaeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract, 2019, 157: 107843.\u003c/li\u003e\n\u003cli\u003eBraffett BH, Gubitosi-Klug RA, Albers JW, et al. Risk Factors for Diabetic Peripheral Neuropathy and Cardiovascular Autonomic Neuropathy in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) Study. Diabetes, 2020, 69(5): 1000-1010.\u003c/li\u003e\n\u003cli\u003eStino AM, Rumora AE, Kim B, et al. Evolving concepts on the role of dyslipidemia, bioenergetics, and inflammation in the pathogenesis and treatment of diabetic peripheral neuropathy. J Peripher Nerv Syst, 2020, 25(2): 76-84.\u003c/li\u003e\n\u003cli\u003eLee KA, Park TS, Jin HY. Non-glucose risk factors in the pathogenesis of diabetic peripheral neuropathy. Endocrine, 2020, 70(3): 465-478.\u003c/li\u003e\n\u003cli\u003eZhao B, Zhang Q, Liang X, et al. Quercetin reduces inflammation in a rat model of diabetic peripheral neuropathy by regulating the TLR4/MyD88/NF-\u0026kappa;B signalling pathway. Eur J Pharmacol, 2021, 912: 174607.\u003c/li\u003e\n\u003cli\u003eLi W, Guo J, Chen J, et al. Identification of Immune Infiltration and the Potential Biomarkers in Diabetic Peripheral Neuropathy through Bioinformatics and Machine Learning Methods. Biomolecules, 2022, 13(1): 39. \u003c/li\u003e\n\u003cli\u003eLin Y, Qu L, Wu J, et al. Identification of Adipogenesis Subgroups and Immune Infiltration Characteristics in Diabetic Peripheral Neuropathy. J Immunol Res, 2023, 2023: 3673094.\u003c/li\u003e\n\u003cli\u003eVieceli Dalla Sega F, Cimaglia P, Manfrini M, et al. Circulating Biomarkers of Endothelial Dysfunction and Inflammation in Predicting Clinical Outcomes in Diabetic Patients with Critical Limb Ischemia. Int J Mol Sci, 2022, 23(18): 10641.\u003c/li\u003e\n\u003cli\u003eYue S, Zhang J, Wu J, et al. Use of the Monocyte-to-Lymphocyte Ratio to Predict Diabetic Retinopathy. Int J Environ Res Public Health, 2015, 12(8): 10009-10019.\u003c/li\u003e\n\u003cli\u003eQiu C, Liu S, Li X, et al. Prognostic value of monocyte-to-lymphocyte ratio for 90-day all-cause mortality in type 2 diabetes mellitus patients with chronic kidney disease. Sci Rep, 2023, 13(1): 13136.\u003c/li\u003e\n\u003cli\u003eNing P, Yang F, Kang J, et al. Predictive value of novel inflammatory markers platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, and monocyte-to-lymphocyte ratio in arterial stiffness in patients with diabetes: A propensity score-matched analysis. Front Endocrinol (Lausanne), 2022, 13: 1039700.\u003c/li\u003e\n\u003cli\u003eZipf G, Chiappa M, Porter KS, et al. National health and nutrition examination survey: plan and operations, 1999-2010. Vital Health Stat 1, 2013, (56): 1-37. \u003c/li\u003e\n\u003cli\u003eUS Department of Health \u0026amp; Human Services. Office of Extramural Research. Available Online: Http://Grants.Nih.Gov/Grants/Policy/Hs/Hs_policies.Htm (Accessed on 1 September 2023). \u003c/li\u003e\n\u003cli\u003eNational Center for Health Statistics: NHANES 1999\u0026ndash;2000 Data Release (June 2002): Lower Extremity Disease Exami- Nation (LEX), MEC Examination [Article Online], 2003. Available from Http://www.Cdc.Gov/Nchs/Data/Nhanes/Ie.Pdf. Accessed 1 September 2023. \u003c/li\u003e\n\u003cli\u003eCai XY, Li WL, Ge SW, et al. Peripheral Neuropathy Associated with Higher Mortality in Population with Chronic Kidney Disease: National Health and Nutrition Examination Surveys. Kidney Dis (Basel). 2023,10(2):79-88. \u003c/li\u003e\n\u003cli\u003eLiu J, Liu X, Li Y, et al. The association of neutrophil to lymphocyte ratio, mean platelet volume, and platelet distribution width with diabetic retinopathy and nephropathy: a meta-analysis. Biosci Rep, 2018, 38(3): BSR20180172.\u003c/li\u003e\n\u003cli\u003eGao H, Yi Y. Association of Monocyte to Lymphocyte, Neutrophil to Lymphocyte, and Platelet to Lymphocyte Ratios With Non-Healing Lower Extremity Ulcers in Patients With Type 2 Diabetes. Int J Low Extrem Wounds, 2023, 13: 1534734623 1197884.\u003c/li\u003e\n\u003cli\u003eMayito J, Meya DB, Miriam A, et al. Monocyte to Lymphocyte ratio is highly specific in diagnosing latent tuberculosis and declines significantly following tuberculosis preventive therapy: A cross-sectional and nested prospective observational study. PLoS One, 2023, 18(11): e0291834.\u003c/li\u003e\n\u003cli\u003eVakhshoori M, Nemati S, Sabouhi S, et al. Prognostic impact of monocyte-to-lymphocyte ratio in coronary heart disease: a systematic review and meta-analysis. J Int Med Res, 2023, 51(10): 3000605231204469.\u003c/li\u003e\n\u003cli\u003ePivatto J\u0026uacute;nior F, Santos \u0026Acirc; BS, Englert EF, et al. Monocyte-to-lymphocyte ratio as predictor of cancer therapy-related cardiotoxicity in patients with breast cancer: a pilot cohort study. Breast Cancer Res Treat, 2023, 200(3): 355-362.\u003c/li\u003e\n\u003cli\u003eAksoy Sarac G, Acar O, Nayır T, et al. The Use of New Hematological Markers in the Diagnosis of Alopecia Areata. Dermatol Pract Concept, 2023, 13(2): e2023 118.\u003c/li\u003e\n\u003cli\u003eWang H, Guo Z, Xu Y. Association of monocyte-lymphocyte ratio and proliferative diabetic retinopathy in the U.S. population with type 2 diabetes. J Transl Med, 2022, 20(1): 219.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"diabetic peripheral neuropathy, monocyte-lymphocyte ratio, diabetes, NHANES, inflammation","lastPublishedDoi":"10.21203/rs.3.rs-6637207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6637207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInflammation has emerged as a significant contributor to the development and progression of diabetic peripheral neuropathy (DPN), and the monocyte-lymphocyte ratio (MLR) is a novel inflammatory marker in peripheral blood. However, research on the association between MLR and DPN is limited. This study aimed to explore the association between MLR and DPN in patients with diabetes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from the 1999\u0026ndash;2004 National Health and Nutrition Examination Survey on US populations with diabetes were analyzed. Peripheral blood tests and other essential variables were collected. MLR was calculated as the ratio of monocyte to lymphocyte count, both of which were obtained directly from laboratory data files. DPN was defined as participants experiencing numbness, loss of feeling, or painful sensations or tingling in their feet in the previous 3 months or having\u0026thinsp;\u0026ge;\u0026thinsp;1 insensate area based on monofilament testing.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,345 participants were included, with 56.5% (760 / 1,345) exhibiting DPN. Multivariate regression models revealed that the presence of DPN significantly increased by 10% with a 0.1 unit rise in MLR (adjusted OR: 1.1, 95% CI: 1-1.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046) after adjusting for all covariates. The restricted cubic spline analysis showed a non-linear curve (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). There was no significant interaction between MLR and stratification variables in the subgroup analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigher MLR levels are associated with DPN in the population of the United States. MLR shows promise as a potential biomarker for early identification of DPN.\u003c/p\u003e","manuscriptTitle":"Association between monocyte-lymphocyte ratio and diabetic peripheral neuropathy in the US populations: a population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-02 09:22:47","doi":"10.21203/rs.3.rs-6637207/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-19T17:44:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T14:56:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:56:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170558100900283605156022487729881308767","date":"2026-05-07T15:58:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101089801021761192494568975020120898068","date":"2026-05-06T05:51:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T17:58:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256863644553602614766304508131083899719","date":"2026-05-05T16:12:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265903486048189321836477541233580239706","date":"2026-05-05T11:32:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-06T11:22:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304997015913396408324407604627933388707","date":"2025-07-03T16:17:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246234835767028817512111653872036060531","date":"2025-06-27T03:14:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-24T18:17:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-19T01:02:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-26T15:37:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-24T01:25:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-05-24T01:24:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"385d9c4c-2a19-4bd6-b232-4aa96a25aa91","owner":[],"postedDate":"July 2nd, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-19T17:44:03+00:00","index":96,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-08T14:56:27+00:00","index":93,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T17:56:44+00:00","index":91,"fulltext":""},{"type":"reviewerAgreed","content":"170558100900283605156022487729881308767","date":"2026-05-07T15:58:15+00:00","index":90,"fulltext":""},{"type":"reviewerAgreed","content":"101089801021761192494568975020120898068","date":"2026-05-06T05:51:08+00:00","index":88,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T17:58:32+00:00","index":87,"fulltext":""},{"type":"reviewerAgreed","content":"256863644553602614766304508131083899719","date":"2026-05-05T16:12:52+00:00","index":85,"fulltext":""},{"type":"reviewerAgreed","content":"265903486048189321836477541233580239706","date":"2026-05-05T11:32:59+00:00","index":83,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-02T09:22:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-02 09:22:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6637207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6637207","identity":"rs-6637207","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}