Association of novel inflammatory markers with gestational diabetes mellitus in a representative U.S. sample: evidence from NHANES 2007-2018

Association of novel inflammatory markers with gestational diabetes mellitus in a representative U.S. sample: evidence from NHANES 2007-2018 | 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 of novel inflammatory markers with gestational diabetes mellitus in a representative U.S. sample: evidence from NHANES 2007-2018 Yanfen Chen, Genping Zeng, Xijing Lu, Tan Zeng, Yuxi Miao, Peiyin Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4055713/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Early detection of gestational diabetes mellitus (GDM) can lower the chance of occurrence. Recent years have seen a surge in research on novel inflammatory indicators, such as systemic immune inflammatory index (SII), lymphocyte-monocyte ratio (LMR), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR). Inflammation is linked to the pathophysiology of GDM and can be targeted for treatment. However, the relationship between GDM and these novel inflammatory markers is unclear. Methods We included participants with a diagnosis of GDM who were between the ages of 20 and 44, as well as complete blood counts from the US National Health and Nutrition Examination conducted between 2007 and 2018. SII, LMR, NLR, and PLR were among the novel inflammatory markers. First, we logarithmically transformed the exposure components to account for skewed distribution. We tested the relationship between GDM and novel inflammatory markers using a multiple logistic regression model and subgroup analyses to analyze the stability. And RCS curves were created to evaluate the non-linear connection. Results Following the inclusion of 3,722 women aged 20–44 years with GDM, multivariate logistic regression analysis revealed a positive correlation between log2-LMR and GDM (OR = 1.55, 95% CI = 1.20–2.01, p = 0.001), while negative correlations were observed between log2-SII, log2-PLR, and log2-NLR with GDM (OR = 0.84, 95% CI = 0.71–0.99, p = 0.04; OR = 0.73, 95% CI = 0.56–0.94, p = 0.01; OR = 0.65, 95% CI = 0.47–0.97, p = 0.03), and the correlation remained significant even after controlling for all confounders. Correlations were consistently shown by subgroup analyses. When the log2-LMR value was less than 1.79, the risk of GDM reduced with rising log2-LMR, and this tendency was reversed when larger than 1.79. Conclusions Elevated levels of new inflammatory markers are correlated with an increased risk of GDM and may offer clinicians with information to screen for GDM and identify GDM therapeutic targets. Further studies are required to investigate the causal relationship between the new inflammatory markers and GDM. Gestational Diabetes Mellitus (GDM) National Health and Nutrition Examination Survey (NHANES) novel inflammatory markers Systemic immune inflammatory index (SII) Neutrophil-lymphocyte ratio (NLR) Lymphocyte-monocyte ratio (LMR) Platelet-lymphocyte ratio (PLR) cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Gestational diabetes mellitus (GDM), a common complication of pregnancy, is an abnormality of glucose tolerance first detected during pregnancy in pregnant women with no previous history of diabetes [ 1 ] . Its incidence has been increasing globally and has reached 16.7% [ 2 ] , with a recurrence rate of 30–69%, posing a serious threat to maternal and fetal health and becoming a worldwide health problem. GDM can have serious adverse effects on maternal and fetal health [ 3 ] , such as postpartum hemorrhage, infections, preterm delivery, fetal abnormalities, and neonatal overweight, which are at much higher risk compared with that of normal pregnant women. Increasing researches suggest that maternal high-glucose environment can increase risk of insulin resistance in offspring [ 4 , 5 ] . Joseph conducted a demographic study and suggested that mothers with GDM would increase the risk of cardiovascular disease, obesity, and type 2 diabetes in their offspring [ 6 , 7 ] . Several studies have suggested that early diagnosis and timely treatment of GDM can significantly reduce the incidence of complications [ 8 , 9 ] . It is particularly important to construct GDM risk prediction models to screen high-risk populations so that early management can be achieved. Although several studies on early screening for GDM worldwide have been reported, large sample size studies are limited [ 10 ] . Due to the popularity of eugenics policies, the high prevalence, and adverse outcomes of GDM, researchers have been paying attention to the early prevention and screening of GDM, but the screening criteria for GDM have not yet been fully defined [ 11 ] . More convenient, effective and economical screening criteria are essential to open up avenues for GDM prevention. Recently, novel inflammatory markers play an increasingly important role in diseases of diagnosis and prediction, including systemic immune inflammatory index (SII), lymphocyte-monocyte ratio (LMR), neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) [ 12 , 13 ] . NLR as neutrophil count / lymphocyte count, PLR as platelet count / lymphocyte count, and LMR as lymphocyte count / monocyte count. And SII is an emerging marker that can reflect the immune and inflammatory response of human body. It was first defined in 2014 as a novel inflammatory biomarker combining platelet count (PC), neutrophil count (NC), and lymphocyte count (LC) with the formula PC*NC / LC [ 14 , 15 ] . Numerous studies have demonstrated the promising value of SII in assessing the risk and prognosis of cardiovascular, metabolic, and immune diseases. Increasing evidence has demonstrated that GDM is closely related to systemic immune inflammation with higher serum inflammatory cytokine levels in women with GDM [ 16 ] . Furthermore, high glucose-induced chronic low-grade inflammation and pro-inflammatory immune responses are closely associated with the pr o gression of GDM [ 17 ] , but the biological roles of the inflammatory factors have not yet been fully elucidated. Considering the relationship between the novel inflammatory markers and GDM is unclear, we conducted a cross-sectional study based on data from the National Health and Nutrition Examination Survey (NHANES) provided by the Centers for Disease Control and Prevention (CDC) to investigate the relationship between those inflammatory markers and GDM, with the expectation that it would provide some clinical data supporting for the selection of clinical screening markers of GDM, and to further determine their predictive value of GDM. 2 Materials and methods 2.1 Study design and population The National Health and Nutrition Examination Survey (NHANES) is a nationally representative, large-sample size study conducted by the National Centre for Health Statistics (NCHS), investigating the health and nutritional status of the U.S. population. A complex multistage sampling scheme has been used to recruit participants, and data has been collected through a two-part process of household interviews and health screenings that include demography, anthropometric measurements, diet and personal interview questionnaires. Human samples including blood and urine were also collected to complete laboratory tests. This cross-sectional study used data of six research cycles with 59,842 participants from the NHANES database: 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018. The flowchart for screening participants was shown in Fig. 1 . Among all participants from 2007–2018 NHANES, 7,283 female participants aged 20–44 years completed the Reproductive Health Questionnaire (RHQ) of the Personal Interview Health Questionnaire. Participants with missing data of GDM (n = 2,473), blood cell count data (n = 389) and covariates information (n = 699) were excluded in this study. A total number of 3,722 women was ultimately included in this study, representing 26,145,847 samples in the United States. 2.2 History of Gestational Diabetes Mellitus (GDM) In NHANES, history of GDM was identified through the Reproductive Health Questionnaire. Women who answered "yes" to the question RHQ162 "During pregnancy, were you ever told by a doctor or other health professional that you had gestational diabetes, excluding diabetes that have known before the pregnancy," were considered to possess history of GDM. Although there may be some bias in the self-reported GDM history to define disease diagnosis, a reliable diagnosis of GDM is challenging because of the lack of data on 75g OGTT or fasting glucose during pregnancy in the current NHANES database. Inspired by previous NHANES articles on GDM and other endocrine metabolic diseases, it seems reliable that we used questionnaire results as valid prevalence of GDM in this study [ 18 – 20 ] . Detailed information on the Reproductive Health Questionnaire can be found on the CDC's official website ( https://www.cdc.gov/ ). nchs/nhanes/). 2.3 Exposure variables Exposure factors for this study were the Systemic Immune Inflammatory Index (SII), Lymphocyte-monocyte ratio (LMR), Neutrophil-lymphocyte ratio (NLR) and Platelet-lymphocyte ratio (PLR), which were calculated by formulas based on the complete blood count reflecting the body's immune function. Blood specimens were collected at the time of individuals' participation in the interviews, generally at the investigation site or designated sampling points. Then laboratory tests were performed using Beckman Coulter DxH 800 instruments at the NHANES Screening Centre, with standardization or near-standardization collection and analytical testing process improving data accuracy. SII was calculated as platelet count * neutrophil count / lymphocyte count, PLR was platelet to lymphocyte ratio, NLR was neutrophil to lymphocyte ratio and LMR was lymphocyte to monocyte ratio. 2.4 Covariates Considering the clinical relevance, our study incorporated potential variables that may affect SII, LMR,NLR, PLR and GDM, including age (< 35, and ≥ 35 years), BMI ( 30 kg/m 2 ), race (Mexican American, Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other race), Poverty Income Ratio (PIR) (< 1.5, 1.5–3.5, and ≥ 3.5), marital status (married or living with partner, and living alone), education level (under high school, high school, and above high school), smoking status (yes, and no), alcohol consumption (never, former, mild, and heavy), age at menarche (< 15, ≥ 15 years), hypertension (yes, and no), hyperlipidemia (yes, and no), and diabetes (yes, and no) [ 18 , 19 , 21 , 22 ] . The marital status of living alone included divorced, widowed, separated, and never married. Smoking status was defined as "yes" if at least 100 cigarettes had been smoked in a lifetime and as "no" if less than 100 cigarettes had been smoked. According to a previous study, alcohol consumption status was categorized as “never” (had < 12 drinks in lifetime), “former ”(did not drink last year but drank ≥ 12 drinks in lifetime), “mild”(≤ 2 drinks per day for women or ≤ 3 drinks per day for men on average over the past year), or “heavy” (> 3 drinks per day for women or > 5 drinks per day for men on average over the past year) in our study [ 23 ] . Data on history of hypertension, hyperlipidemia, and diabetes mellitus was obtained from participants' self-reported health questionnaires [ 24 , 25 ] . 2.5 Statistical analysis All statistical analysis procedures need to use appropriate NHANES sampling weights to ensure reliability of results according to instructions on the NHANES database. Continuous variables in this study are expressed as weighted mean ± standard deviation, and categorical variables are expressed as weighted percentages. For the skewed distributions of SII, LMR, NLR and PLR among women aged 20–44 years, we performed log2 transformation of them before data analysis. The analyses were divided into four steps: the included participants were first divided into history of GDM and non-history of GDM groups, with continuous variables tested by weighted linear regression models and categorical variables tested by weighted chi-square models. In the second step, we used weighted multivariate logistic regression models to test the correlation between novel inflammatory markers including SII, LMR, NLR, PLR, and GDM in three models adjusting different covariates. Crude model was not adjusted for any covariate; minimally adjusted model was adjusted for age, BMI, and PIR; and fully adjusted model was adjusted for all covariates including age, BMI, race, PIR, marital status, education level, smoking status, alcohol consumption, age at menarche, hypertension, hyperlipidemia, and diabetes. In the third step, after controlling for confounding bias, restricted cubic spline analysis was performed by fitting the non-linear relation between these four inflammatory markers and GDM to visualize their non-linear association. Finally, subgroup analyses were used to test for potential heterogeneity among each strata classified by covariates. All analyses were done via R software (version 4.3.1) and were statistically significant at p < 0.05. 3 Results 3.1 Basic characteristics of the included participants The weighted baseline characteristics of the participants included in this study were shown in Table 1. The weighted prevalence of GDM was 12.39% in this study, women with history of GDM have significantly higher rates of participants aged > = 35 ( p = 30 ( p < 0.0001). Compared with Non-GDM women, more women of Mexican and other race were in the GDM group (P = 0.02). Moreover, 57.98% of women diagnosed with GDM were Non-Hispanic White, while 10.39% were Non-Hispanic Black and 6.28% were other Hispanic. No significant differences of PIR, education level, smoking status, alcohol consumption, age of menarche were observed between GDM group and non-GDM group. Notably, prevalence of hypertension, hyperlipidemia and diabetes mellitus were all significantly higher ( p < 0.05) among women with GDM. In addition, c onsidering the skewed distribution of novel inflammatory indicators among women included in this study as shown in Fig. 2 , SII, PLR, NLR, and LMR were log2-transformed before further analysis. We found that the GDM group possessed higher log2-LMR level (P < 0.05) compared with the non-GDM group, whereas no significant differences of log2-SII, log2-PLR, and log2-NLR were observed. 3.2 Association between GDM and novel inflammatory indicators Without adjusting any covariate in crude model, log2-LMR (OR = 1.52, 95% CI = 1.19–1.95, p = 0.001) had a significantly positive relationship with GDM, while log2-PLR (OR = 0.77, 95% CI = 0.60-1.00, p = 0.05) was significantly negatively associated with GDM. After adjusting for age, BMI, and PIR in minimally adjusted model, associations between GDM and log2-PLR (OR = 0.74, 95% CI = 0.57–0.96, p = 0.02) and log2-LMR (OR = 1.53, 95% CI = 1.20–1.96, p <0.001) were consistent with those in crude model. After adjusting for all covariates in fully adjusted model, log2-LMR (OR = 1.55, 95% CI = 1.20–2.01, p = 0.001), log2-NLR (OR = 0.65, 95% CI = 0.47–0.97, p = 0.03), log2-PLR (OR = 0.73, 95% CI = 0.56–0.94, p = 0.01) and log2-SII (OR = 0.84, 95% CI = 0.71–0.99, p = 0.04) were all significantly associated with GDM and met the statistical significance. The result suggested that each unit of increased log2-LMR score was associated with 55% increased risk of increased GDM. We then converted novel inflammatory markers from continuous variables to categorical ones (Q1-Q4, quartiles) for sensitivity analyses. Specific results were also shown in Table 2 and were broadly similar to those of the main analyses. Other details of the association between inflammation markers and GDM were listed in Table 2. Compared with the lowest log2-LMR quartile, participants in the highest log2-LMR quartile had 78% increased risk of GDM (OR = 1.78, 95% CI = 1.26–2.51, p = 0.001), and the third quartile had 42% increased risk of GDM (OR = 1.42, 95% CI = 1.01–2.01, p = 0.05).While compared with the first log2-SII quartile, participants in the forth log2-SII had 25% decreased risk of GDM (OR = 0.75, 95% CI = 0.53–1.07, p = 0.11), and in the third quartile had 12% decreased risk of GDM(OR = 0.88, 95% CI = 0.6–1.28, p = 0.49).But the log2-SII association did not meet the statistical significance. Furthermore, the log2-PLR had 33% decreased risk of GDM (OR = 0.67, 95% CI = 0.47–0.95, p = 0.03), the log2-NLR with 30% decreased risk (OR = 0.70, 95% CI = 0.49-1.00, p = 0.05). Furthermore, we also visualized potential non-linear relation of GDM and these inflammatory indexes via restricted cubic spline (RCS) plot, shown in Fig. 3 . After adjusting all covariates, RCS plot of log2-LMR exhibited strong U-shaped non-linear relationship with GDM, which was characterized by a substantial reduction of GDM risk with log2-LMR value increasement when it was less than 1.79, and increased thereafter ( p for non-linearity = 0.05). However, log2-SII, log2-PLR, and log2-NLR did not show non-linear relationship with GDM prevalence ( p for non-linearity > 0.05). 3.3 Subgroup analysis for the association between GDM and novel inflammatory markers. To determine whether the association between inflammatory indicators and GDM were stable across subgroups, we then performed subgroup analyses based on age, BMI, ethnicity, marital status, PIR, education level, smoking status, alcohol user, age of menarche and presence of hypertension, hyperlipidemia, and diabetes mellitus. Associations between GDM risk and indicators including NLR, PLR, and SII were generally consistent across all subgroups, which was not the case for LMR. More results of subgroup analysis in details for other inflammatory markers were shown in Fig. 4 . Taken together, GDM risk was consistently associated with lower NLR and PLR, while with higher SII and LMR, in most covariates strata. 4 Discussion In this study, the correlation between GDM and novel inflammatory markers were analyzed using the representative US NHANES database. The results showed that SII, LMR, NLR and PLR were significantly associated with GDM, and these associations became more significant after adjusting for all covariates. LMR was positively associated with the risk of GDM, with the risk of GDM in the highest quartile of LMR being 1.78 times higher than that in the lowest quartile of pregnant women, while NLR, PLR, and SII were negatively associated with GDM, and our subgroup analyses suggested the stability of these findings. To the best of our knowledge, this is the largest study to analyze the correlation between GDM and novel inflammatory markers up to now. SII, LMR, NLR, and PLR are novel inflammatory markers that have attracted much attention in recent years, and they are the combination of neutrophil counts, lymphocyte counts, platelet counts, and monocyte counts in the complete blood count, reflecting the state of immune inflammation better than a single source of blood counts. SII was initially used as a prognostic indicator of outcome in cancer patients, and is a novel marker of systemic immune inflammation, which has been widely used in the study of endocrine abnormalities, heart failure, myocardial infarction in the elderly, hepatic fibrosis, hyperlipidaemia, etc [ 13 – 15 , 22 , 26 ] . A prospective observational study in 2023 found that SII levels were higher in women with GDM than in healthy women, which is consistent with the findings of the present study, and also suggests that SII is useful in the prediction of GDM, and could be an adjunctive tool in the prediction of GDM [ 27 ] . A retrospective study of 467 pregnant women was conducted by Seval, which not only found that SII levels were higher in women with GDM than in healthy women, but also found that SII levels were significantly higher in the late than the early stages of pregnancy [ 28 ] . Wang concluded that NLR is a stable biomarker that reflects the systemic inflammatory response of the body, which is important for the diagnosis of cardiovascular and cerebrovascular diseases. Both Yildiz and Ning Han found that NLR levels were higher in patients with GDM than in healthy women [ 27 , 29 ] , which was consistent with our findings. NLRP3 and its effector molecules (Caspase-1, IL-1β, IL-18) were higher in women with GDM than in healthy women, potentially suggesting that GDM is a chronic inflammatory state [ 29 ] . It has also been shown that LMR and PLR can be used as prognostic indicators for cervical cancer patients with high accuracy, and the inclusion of LMR and PLR to routinely assess and monitor cancer conditions will better guide clinicians to adopt personalized treatments and improve patient prognosis [ 30 ] . Tumour development is largely influenced by the level of inflammation. Inflammatory markers can reflect the balance between tumour and inflammation in the body, and have also been proposed as predictors of tumour recurrence [ 30 , 31 ] . Taken together, SII, LMR, NLR, and PLR have all been suggested to be closely associated with a variety of inflammatory diseases and are crucial in assessing disease prognosis. The 75g oral glucose tolerance test has been used to diagnose gestational diabetes mellitus, which typically manifests between 24 and 28 weeks of pregnancy [ 32 ] . To lower the risk of GDM for both mother and fetus, it is imperative to look for indicators that can be utilized for early detection and screening of the disease [ 33 , 34 ] . Prior to the diagnosis of GDM, both mother and fetus may have been exposed to a high-glucose environment to different degrees. Numerous perinatal conditions, including gestational diabetes, pre-eclampsia, and preterm labor, have been linked to intestinal dysbiosis and inflammation [ 35 ] . According to certain research. Insulin resistance and malfunctioning β-cells in the pancreas are closely linked to the development of GDM. Furthermore, endolipin and lipocalin are involved in the pathophysiology of GDM and are connected to the metabolism of glucose. Pregnant women with GDM have higher levels of endolipin and lower levels of lipocalin than normal pregnant women, which are related to oxidative stress and inflammation, and prolonged inflammatory stimulation may lead to the expression of aberrant insulin signaling pathways. Some studies have also shown that GDM patients have increased expression of both Toll-like receptor 4 (TLR4) and inflammatory cytokines, and NLRP3 levels are abnormally elevated in diabetic rats [ 36 ] . Among them, IL-1 is an inflammatory factor closely related to GDM and insulin resistance. And soybean isolate protein could attenuate the TLR4/NF-κB inflammatory pathway and reduce blood glucose level in GDM patients [ 37 ] . TGF-β is one of the most important inflammatory cytokines related to pregnancy and plays an important role in hormone secretion and embryonic development during pregnancy [ 38 ] , and TGF-β induces GDM and pre-eclampsia. In GDM patients, the expression of IL-6, IL-1β, and TNF-α was elevated, which can inhibit the expression of GLUT-1, GLUT-4, GLUT-9, affect the insulin signaling pathway and promote the development of GDM [ 39 ] . Oxidative stress plays a prominent role in the pathological mechanisms of metabolic diseases, and the increase of glucose and lipids can promote reactive oxygen species production, which in turn affects insulin secretion signaling and promotes pancreatic β-cell apoptosis. And studies have shown that patients with GDM have elevated levels of oxidative stress, with significant elevations in thiobarbituric acid reactive substances, lipid hydroperoxide, malondialdehyde, and xanthine oxidase [ 40 ] . A prospective study has shown a positive correlation between the TyG index [ 41 ] , which is a composite of glycaemia and lipids, and GDM risk, as compared to a single glycaemic index. 2024 The TyG index is a more accurate measure of the body's inflammation than is glucose, Lipids better reflect the inflammatory state of the body, is a factor that has been studied in many cardiovascular diseases in recent years, and may be used as a predictor of type 2 diabetes mellitus [ 42 ] . Some studies have shown that leukocytes, neutrophils, lymphocytes, monocytes and NLR in early pregnancy are clinically relevant in identifying individuals at high risk of GDM. Suheyla Gorar concluded that leukocyte and platelet counts are positively associated with the risk of GDM. Our study also confirms previous findings that GDM is associated with inflammatory markers. Its specific pathological mechanism is related to oxidative stress, which can induce GDM progression by mediating inflammatory responses, stimulating NF-κβ signaling and producing inflammatory factors such as TNF-α [ 43 ] . It is worth mentioning that pro-inflammatory factors can in turn impair insulin signaling and damage pancreatic β-cell function. Pro-inflammatory diets can increase the risk of GDM and pre-eclampsia [ 44 ] , whereas multi-nutrient therapies have shown positive effects on improving endocrine abnormalities in GDM and may be associated with NF-κβ factors. Micro RNAs have an important role in the pathogenesis and therapeutic targets of GDM [ 45 ] . GDM patients have high levels of micro RNA-222 and micro RNA-17-5p, which are involved in insulin resistance and inflammatory signalling pathways to varying degrees. It has been suggested that micro RNA-17-5p/Mfn1/2/NF-κβ is a novel key target pathway for the treatment of GDM [ 46 ] , and micro RNAs can also be used to predict the risk of GDM and its complications. However, further studies on the predictive value of micro RNA for GDM are still needed [ 47 ] . It has to be admitted that our study has some limitations. Firstly, this study is a cross-sectional study, which cannot accurately derive the causal relationship between GDM and novel inflammatory markers. Second, the diagnosis of GDM is still dependent on participants' self-report due to the lack of test data for 75g OGTT in the NHANES database. However, researchers from NHANES told participants that their positive answers to RHQ162 should exclude diabetes known before pregnancy during the interview questionnaires. What’s more, previous studies have shown the validity of using this self-reported GDM as definition of diagnosis [ 18 – 20 , 48 ] . Third, although our study corrected for the interference of some common confounders, we could not exclude the interference of other confounders. 5.Conclusion In this study, we found that novel inflammatory markers were strongly associated with GDM in pregnant women in the U.S. and verified the non-linear relationship between them. SII, NLR, LMR, and PLR may help clinicians to optimize the diagnosis of GDM and to reduce the risk of maternal and fetal pregnancy complications, but further prospective studies are needed to confirm our findings. Declarations Ethical approval The survey protocol has been approved by the Institutional Review Board of the National Center for Health Statistics. All participants in the NHANES database have signed informed consent before enrollment. Author Contributions Yanfen Chen: statistical analysis, revising and graphing; Genping Zeng: investigation and writing original draft; Xijing Lu: information summary; Tan Zeng: analysing data; Yuxi Miao: methodology; Peiyin Li: collecting information; Songping Luo, Lei Zeng, Ruling Lu: revising and financial support. Funding support: National Natural Science Foundation of China (No.81804135); Luo Songping National Famous Elderly Chinese Medicine Experts Inheritance Workshop (National TCM Human Education Letter [2022] No.75); Luo Songping National Famous Traditional Chinese Medicine Practitioner Inheritance Workshop (National TCM Human Education Letter [2022] No.5); Special Research Fund for Obstetrics and Gynaecology of China Combined Traditional Chinese Medicine and Western Medicine, 2023 (No.FCK-ZSYTW-09); 2023 Guangdong Basic and Applied Basic Research Fund Enterprise Joint Fund Project (Public Health and Medicine and Healthcare) ; National TCM Inheritance and Innovation Centre "Youth Project" (No.2022QN21). Data Availability Statement Publicly available datasets were analyzed in this study. These data can be found here: https://www.cdc.gov/nchs/nhanes/. Acknowledgments We are grateful for the assistance of all participants in this study and efforts of all the staff members of NHANES. 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Wu W, Tan Q Y, Xi F F, et al . NLRP3 inflammasome activation in gestational diabetes mellitus placentas is associated with hydrogen sulfide synthetase deficiency[J]. Exp Ther Med ,2022,23(1):94. Wang S, Ma L, Ji J, et al . Protective effect of soy isolate protein against streptozotocin induced gestational diabetes mellitus via TLR4/MyD88/NF-κB signaling pathway[J]. Biomed Pharmacother ,2023,168:115688. Wen B, Liao H, Lin W, et al . The Role of TGF-β during Pregnancy and Pregnancy Complications[J]. Int J Mol Sci ,2023,24(23). He M, Guo X, Jia J, et al . Regulatory mechanisms underlying endoplasmic reticulum stress involvement in the development of gestational diabetes mellitus entail the CHOP-PPARα-NF-κB pathway[J]. Placenta ,2023,142:46-55. Saucedo R, Ortega-Camarillo C, Ferreira-Hermosillo A, et al . Role of Oxidative Stress and Inflammation in Gestational Diabetes Mellitus[J]. Antioxidants (Basel) ,2023,12(10). Li H, Miao C, Liu W, et al . First-Trimester Triglyceride-Glucose Index and Risk of Pregnancy-Related Complications: A Prospective Birth Cohort Study in Southeast China[J]. Diabetes Metab Syndr Obes ,2022,15:3705-3715. Tao L C, Xu J N, Wang T T, et al . Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations[J]. Cardiovasc Diabetol ,2022,21(1):68. Nguyen-Ngo C, Perkins A V, Lappas M. Selenium Prevents Inflammation in Human Placenta and Adipose Tissue In Vitro: Implications for Metabolic Diseases of Pregnancy Associated with Inflammation[J]. Nutrients ,2022,14(16). Hong L, Zhu L, Zhang J, et al . Association of dietary inflammatory index with risk of gestational diabetes mellitus and preeclampsia: a systematic review and meta-analysis[J]. Br J Nutr ,2024,131(1):54-62. Zhang H, Luan S, Xiao X, et al . Silenced microRNA-222 suppresses inflammatory response in gestational diabetes mellitus mice by promoting CXCR4[J]. Life Sci ,2021,266:118850. Li J, Wang Y, Wu T, et al . Baicalein suppresses high glucose-induced inflammation and apoptosis in trophoblasts by targeting the miRNA-17-5p-Mfn1/2-NF-κB pathway[J]. Placenta ,2022,121:126-136. Gu Z J, Song Q J, Gu W Q, et al . New approaches in the diagnosis and prognosis of gestational diabetes mellitus[J]. Eur Rev Med Pharmacol Sci ,2023,27(21):10583-10594. Schoenaker D, Mishra G D. Association Between Age at Menarche and Gestational Diabetes Mellitus: The Australian Longitudinal Study on Women's Health[J]. Am J Epidemiol ,2017,185(7):554-561. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files supplementarydeclaration.docx LMR.pdf NLR.pdf PLR.pdf SII.pdf Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4055713","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281236322,"identity":"0e9e8da8-7207-4795-8fb7-9ab3ba0739ec","order_by":0,"name":"Yanfen Chen","email":"","orcid":"","institution":"Foshan Clinical Medical School of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yanfen","middleName":"","lastName":"Chen","suffix":""},{"id":281236323,"identity":"1fc5e0b8-7f9e-465b-b078-b48b67173f2e","order_by":1,"name":"Genping Zeng","email":"","orcid":"","institution":"The First Clinical Medical School of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Genping","middleName":"","lastName":"Zeng","suffix":""},{"id":281236324,"identity":"8c32103a-81d2-4b84-8044-a76f35e59db3","order_by":2,"name":"Xijing Lu","email":"","orcid":"","institution":"The First Clinical Medical School of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xijing","middleName":"","lastName":"Lu","suffix":""},{"id":281236325,"identity":"7884b327-47b5-47eb-89d2-dc8cabaa6b7f","order_by":3,"name":"Tan Zeng","email":"","orcid":"","institution":"Yichun College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Tan","middleName":"","lastName":"Zeng","suffix":""},{"id":281236326,"identity":"b8aef713-f3b3-4eb1-aea2-6d9376bc8909","order_by":4,"name":"Yuxi Miao","email":"","orcid":"","institution":"The First Clinical Medical School of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuxi","middleName":"","lastName":"Miao","suffix":""},{"id":281236327,"identity":"e56616bc-75ee-477a-bd4a-17868a198e01","order_by":5,"name":"Peiyin Li","email":"","orcid":"","institution":"The First Clinical Medical School of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Peiyin","middleName":"","lastName":"Li","suffix":""},{"id":281236328,"identity":"33317cd0-eabd-4d21-a73a-c2a30f2c5a3c","order_by":6,"name":"Songping Luo","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Songping","middleName":"","lastName":"Luo","suffix":""},{"id":281236329,"identity":"fc78efdf-88f0-451b-8024-4a77080c01b5","order_by":7,"name":"Lei Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxmb+jw8+/mNj5mdvIFILc3uDseEMNj52yZ4DRGph7zlgJszDJsdvcCOBSC28MxLSGGfwmElLzny88QZDjU00QS2SMxKOPfggkWbML51WbMFwLC23gZAWwxmJ7YYzDI4lS87OMZNgbDhMWIv9jWQ2aZ6E//Ubbp4hUgtjzzGglgNszAY3eIjV0t7DbDizgY1ZsgfolwRi/MLYzMP44GMDKCoPb7zxocaGsBZkYCCRQIpyiBZSdYyCUTAKRsHIAAD8Rz8M8Ts7TwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zeng","suffix":""},{"id":281236330,"identity":"e1428a7f-f9b1-4ebe-8f93-c1813b2088bb","order_by":8,"name":"Ruling Lu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ruling","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-03-09 12:31:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4055713/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4055713/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53197767,"identity":"25e3cbf9-b500-4e5f-a72b-c3394985a7d3","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11816,"visible":true,"origin":"","legend":"\u003cp\u003eflow chart of participants inclusion and exclusion in current study.\u003c/p\u003e\n\u003cp\u003eAbbreviations: NHANES, National Health and Nutrition Examination Survey; GDM, gestational diabetes mellitus; BMI, body mass index; PIR, Poverty-income ratio.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/682754ca048f0113c75b9808.png"},{"id":53198935,"identity":"6558dfae-ced3-4114-9d9d-5b81334acf7c","added_by":"auto","created_at":"2024-03-21 18:50:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26759,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of novel inflammatory markers among the finally included participants.\u003c/p\u003e\n\u003cp\u003e(A): SII, Systemic immune-inflammation index; (B): LMR, Lymphocyte-Monocyte Ratio; (C): NLR, Neutrophil-Lymphocyte Ratio; (D): PLR, Platelet-Lymphocyte Ratio.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/5aa7164805244a2a315eab0d.png"},{"id":53197768,"identity":"0b0d7ab0-c616-4068-8bbe-fb96b64013c6","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43272,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationships between novel inflammatory markers with GDM.\u003c/p\u003e\n\u003cp\u003e(A): SII, Systemic immune-inflammation index; (B): LMR, Lymphocyte-Monocyte Ratio; (C): NLR, Neutrophil-Lymphocyte Ratio; (D): PLR, Platelet-Lymphocyte Ratio.\u003c/p\u003e\n\u003cp\u003eOR odds ratio; CI confidence interval. ORs (solid lines) and 95% confidence levels (shaded areas) were adjusted for age, BMI, PIR, race/ethnicity, marital status, education, smoking status, alcohol consumption,age of menarche, hypertension, hyperlipidemia and diabetes mellitus. Vertical dotted lines indicate the minimal threshold for the beneficial association with estimated OR = 1.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GDM, gestational diabetes mellitus; BMI, body mass index; PIR, poverty-income ratio.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/b844a4ef080a1a21faa3393e.png"},{"id":53197773,"identity":"b5ed7983-9209-4f50-80ec-f53436ea5503","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":150686,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between novel inflammatory markers and GDM. Abbreviations: LMR, Lymphocyte-Monocyte Ratio; SII, Systemic immune-inflammation index; PLR, Platelet-Lymphocyte Ratio; NLR, Neutrophil-Lymphocyte Ratio; BMI, body mass index; PIR, poverty-income ratio.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/db0fd689d09f9fbfbb341f97.png"},{"id":53199486,"identity":"ed5bb2f5-4315-475e-8d39-1d74031f6be8","added_by":"auto","created_at":"2024-03-21 18:58:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":507728,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/4f87e43a-1816-47c3-94b7-565a7471069d.pdf"},{"id":53197770,"identity":"0d2d5fd2-ea0e-4ee6-b382-45639dea8bb2","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10890,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydeclaration.docx","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/cc322952c9bd78b0b70d3384.docx"},{"id":53197771,"identity":"6bf56888-7bb3-425c-b09c-867683a90752","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7081,"visible":true,"origin":"","legend":"","description":"","filename":"LMR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/c88e207a0df76f977174fb6e.pdf"},{"id":53197774,"identity":"285502cb-a2cb-451d-8ffb-96c4d25455ef","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":7031,"visible":true,"origin":"","legend":"","description":"","filename":"NLR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/a4a423db5aed7a12a4c6f559.pdf"},{"id":53197777,"identity":"a056bd4b-e144-4e73-9bcd-28b175fbbedf","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":6999,"visible":true,"origin":"","legend":"","description":"","filename":"PLR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/7be9c6cc6b8a5ce1d61b1ee3.pdf"},{"id":53197776,"identity":"f1bbb413-1412-42cf-898c-aea03037a0e8","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":7004,"visible":true,"origin":"","legend":"","description":"","filename":"SII.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/eec04ca26e2316af10b2fd92.pdf"},{"id":53197775,"identity":"49ae8262-b0ec-48f0-9995-1d15916c7403","added_by":"auto","created_at":"2024-03-21 18:42:19","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":75514,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-4055713/v1/d5dba05bdc99e0a0c74eff99.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of novel inflammatory markers with gestational diabetes mellitus in a representative U.S. sample: evidence from NHANES 2007-2018","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM), a common complication of pregnancy, is an abnormality of glucose tolerance first detected during pregnancy in pregnant women with no previous history of diabetes\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Its incidence has been increasing globally and has reached 16.7%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, with a recurrence rate of 30\u0026ndash;69%, posing a serious threat to maternal and fetal health and becoming a worldwide health problem. GDM can have serious adverse effects on maternal and fetal health\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, such as postpartum hemorrhage, infections, preterm delivery, fetal abnormalities, and neonatal overweight, which are at much higher risk compared with that of normal pregnant women. Increasing researches suggest that maternal high-glucose environment can increase risk of insulin resistance in offspring\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Joseph conducted a demographic study and suggested that mothers with GDM would increase the risk of cardiovascular disease, obesity, and type 2 diabetes in their offspring\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Several studies have suggested that early diagnosis and timely treatment of GDM can significantly reduce the incidence of complications\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. It is particularly important to construct GDM risk prediction models to screen high-risk populations so that early management can be achieved. Although several studies on early screening for GDM worldwide have been reported, large sample size studies are limited\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Due to the popularity of eugenics policies, the high prevalence, and adverse outcomes of GDM, researchers have been paying attention to the early prevention and screening of GDM, but the screening criteria for GDM have not yet been fully defined\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. More convenient, effective and economical screening criteria are essential to open up avenues for GDM prevention.\u003c/p\u003e \u003cp\u003eRecently, novel inflammatory markers play an increasingly important role in diseases of diagnosis and prediction, including systemic immune inflammatory index (SII), lymphocyte-monocyte ratio (LMR), neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. NLR as neutrophil count / lymphocyte count, PLR as platelet count / lymphocyte count, and LMR as lymphocyte count / monocyte count. And SII is an emerging marker that can reflect the immune and inflammatory response of human body. It was first defined in 2014 as a novel inflammatory biomarker combining platelet count (PC), neutrophil count (NC), and lymphocyte count (LC) with the formula PC*NC / LC\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have demonstrated the promising value of SII in assessing the risk and prognosis of cardiovascular, metabolic, and immune diseases. Increasing evidence has demonstrated that GDM is closely related to systemic immune inflammation with higher serum inflammatory cytokine levels in women with GDM\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Furthermore, high glucose-induced chronic low-grade inflammation and pro-inflammatory immune responses are closely associated with the pr\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eo\u003c/span\u003egression of GDM\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, but the biological roles of the inflammatory factors have not yet been fully elucidated.\u003c/p\u003e \u003cp\u003e Considering the relationship between the novel inflammatory markers and GDM is unclear, we conducted a cross-sectional study based on data from the National Health and Nutrition Examination Survey (NHANES) provided by the Centers for Disease Control and Prevention (CDC) to investigate the relationship between those inflammatory markers and GDM, with the expectation that it would provide some clinical data supporting for the selection of clinical screening markers of GDM, and to further determine their predictive value of GDM.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a nationally representative, large-sample size study conducted by the National Centre for Health Statistics (NCHS), investigating the health and nutritional status of the U.S. population. A complex multistage sampling scheme has been used to recruit participants, and data has been collected through a two-part process of household interviews and health screenings that include demography, anthropometric measurements, diet and personal interview questionnaires. Human samples including blood and urine were also collected to complete laboratory tests.\u003c/p\u003e \u003cp\u003eThis cross-sectional study used data of six research cycles with 59,842 participants from the NHANES database: 2007\u0026ndash;2008, 2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, 2015\u0026ndash;2016, and 2017\u0026ndash;2018. The flowchart for screening participants was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among all participants from 2007\u0026ndash;2018 NHANES, 7,283 female participants aged 20\u0026ndash;44 years completed the Reproductive Health Questionnaire (RHQ) of the Personal Interview Health Questionnaire. Participants with missing data of GDM (n\u0026thinsp;=\u0026thinsp;2,473), blood cell count data (n\u0026thinsp;=\u0026thinsp;389) and covariates information (n\u0026thinsp;=\u0026thinsp;699) were excluded in this study. A total number of 3,722 women was ultimately included in this study, representing 26,145,847 samples in the United States.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 History of Gestational Diabetes Mellitus (GDM)\u003c/h2\u003e \u003cp\u003eIn NHANES, history of GDM was identified through the Reproductive Health Questionnaire. Women who answered \"yes\" to the question RHQ162 \"During pregnancy, were you ever told by a doctor or other health professional that you had gestational diabetes, excluding diabetes that have known before the pregnancy,\" were considered to possess history of GDM. Although there may be some bias in the self-reported GDM history to define disease diagnosis, a reliable diagnosis of GDM is challenging because of the lack of data on 75g OGTT or fasting glucose during pregnancy in the current NHANES database. Inspired by previous NHANES articles on GDM and other endocrine metabolic diseases, it seems reliable that we used questionnaire results as valid prevalence of GDM in this study\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Detailed information on the Reproductive Health Questionnaire can be found on the CDC's official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). nchs/nhanes/).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Exposure variables\u003c/h2\u003e \u003cp\u003eExposure factors for this study were the Systemic Immune Inflammatory Index (SII), Lymphocyte-monocyte ratio (LMR), Neutrophil-lymphocyte ratio (NLR) and Platelet-lymphocyte ratio (PLR), which were calculated by formulas based on the complete blood count reflecting the body's immune function. Blood specimens were collected at the time of individuals' participation in the interviews, generally at the investigation site or designated sampling points. Then laboratory tests were performed using Beckman Coulter DxH 800 instruments at the NHANES Screening Centre, with standardization or near-standardization collection and analytical testing process improving data accuracy. SII was calculated as platelet count * neutrophil count / lymphocyte count, PLR was platelet to lymphocyte ratio, NLR was neutrophil to lymphocyte ratio and LMR was lymphocyte to monocyte ratio.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eConsidering the clinical relevance, our study incorporated potential variables that may affect SII, LMR,NLR, PLR and GDM, including age (\u0026lt;\u0026thinsp;35, and \u0026ge;\u0026thinsp;35 years), BMI (\u0026lt;\u0026thinsp;25, 25\u0026ndash;30, and \u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e), race (Mexican American, Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other race), Poverty Income Ratio (PIR) (\u0026lt;\u0026thinsp;1.5, 1.5\u0026ndash;3.5, and \u0026ge;\u0026thinsp;3.5), marital status (married or living with partner, and living alone), education level (under high school, high school, and above high school), smoking status (yes, and no), alcohol consumption (never, former, mild, and heavy), age at menarche (\u0026lt;\u0026thinsp;15, \u0026ge;\u0026thinsp;15 years), hypertension (yes, and no), hyperlipidemia (yes, and no), and diabetes (yes, and no)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The marital status of living alone included divorced, widowed, separated, and never married. Smoking status was defined as \"yes\" if at least 100 cigarettes had been smoked in a lifetime and as \"no\" if less than 100 cigarettes had been smoked. According to a previous study, alcohol consumption status was categorized as \u0026ldquo;never\u0026rdquo; (had\u0026thinsp;\u0026lt;\u0026thinsp;12 drinks in lifetime), \u0026ldquo;former \u0026rdquo;(did not drink last year but drank\u0026thinsp;\u0026ge;\u0026thinsp;12 drinks in lifetime), \u0026ldquo;mild\u0026rdquo;(\u0026le;\u0026thinsp;2 drinks per day for women or \u0026le;\u0026thinsp;3 drinks per day for men on average over the past year), or \u0026ldquo;heavy\u0026rdquo; (\u0026gt;\u0026thinsp;3 drinks per day for women or \u0026gt;\u0026thinsp;5 drinks per day for men on average over the past year) in our study\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Data on history of hypertension, hyperlipidemia, and diabetes mellitus was obtained from participants' self-reported health questionnaires\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analysis procedures need to use appropriate NHANES sampling weights to ensure reliability of results according to instructions on the NHANES database. Continuous variables in this study are expressed as weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical variables are expressed as weighted percentages. For the skewed distributions of SII, LMR, NLR and PLR among women aged 20\u0026ndash;44 years, we performed log2 transformation of them before data analysis.\u003c/p\u003e \u003cp\u003eThe analyses were divided into four steps: the included participants were first divided into history of GDM and non-history of GDM groups, with continuous variables tested by weighted linear regression models and categorical variables tested by weighted chi-square models. In the second step, we used weighted multivariate logistic regression models to test the correlation between novel inflammatory markers including SII, LMR, NLR, PLR, and GDM in three models adjusting different covariates. Crude model was not adjusted for any covariate; minimally adjusted model was adjusted for age, BMI, and PIR; and fully adjusted model was adjusted for all covariates including age, BMI, race, PIR, marital status, education level, smoking status, alcohol consumption, age at menarche, hypertension, hyperlipidemia, and diabetes. In the third step, after controlling for confounding bias, restricted cubic spline analysis was performed by fitting the non-linear relation between these four inflammatory markers and GDM to visualize their non-linear association. Finally, subgroup analyses were used to test for potential heterogeneity among each strata classified by covariates. All analyses were done via R software (version 4.3.1) and were statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Basic characteristics of the included participants\u003c/h2\u003e \u003cp\u003eThe weighted baseline characteristics of the participants included in this study were shown in Table\u0026nbsp;1. The weighted prevalence of GDM was 12.39% in this study, women with history of GDM have significantly higher rates of participants aged\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;35 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BMI\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;30 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Compared with Non-GDM women, more women of Mexican and other race were in the GDM group (P\u0026thinsp;=\u0026thinsp;0.02). Moreover, 57.98% of women diagnosed with GDM were Non-Hispanic White, while 10.39% were Non-Hispanic Black and 6.28% were other Hispanic. No significant differences of PIR, education level, smoking status, alcohol consumption, age of menarche were observed between GDM group and non-GDM group. Notably, prevalence of hypertension, hyperlipidemia and diabetes mellitus were all significantly higher (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among women with GDM.\u003c/p\u003e \u003cp\u003eIn addition, \u003cb\u003ec\u003c/b\u003eonsidering the skewed distribution of novel inflammatory indicators among women included in this study as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, SII, PLR, NLR, and LMR were log2-transformed before further analysis. We found that the GDM group possessed higher log2-LMR level (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared with the non-GDM group, whereas no significant differences of log2-SII, log2-PLR, and log2-NLR were observed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between GDM and novel inflammatory indicators\u003c/h2\u003e \u003cp\u003eWithout adjusting any covariate in crude model, log2-LMR (OR\u0026thinsp;=\u0026thinsp;1.52, 95% CI\u0026thinsp;=\u0026thinsp;1.19\u0026ndash;1.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) had a significantly positive relationship with GDM, while log2-PLR (OR\u0026thinsp;=\u0026thinsp;0.77, 95% CI\u0026thinsp;=\u0026thinsp;0.60-1.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) was significantly negatively associated with GDM. After adjusting for age, BMI, and PIR in minimally adjusted model, associations between GDM and log2-PLR (OR\u0026thinsp;=\u0026thinsp;0.74, 95% CI\u0026thinsp;=\u0026thinsp;0.57\u0026ndash;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and log2-LMR (OR\u0026thinsp;=\u0026thinsp;1.53, 95% CI\u0026thinsp;=\u0026thinsp;1.20\u0026ndash;1.96, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) were consistent with those in crude model. After adjusting for all covariates in fully adjusted model, log2-LMR (OR\u0026thinsp;=\u0026thinsp;1.55, 95% CI\u0026thinsp;=\u0026thinsp;1.20\u0026ndash;2.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), log2-NLR (OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI\u0026thinsp;=\u0026thinsp;0.47\u0026ndash;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), log2-PLR (OR\u0026thinsp;=\u0026thinsp;0.73, 95% CI\u0026thinsp;=\u0026thinsp;0.56\u0026ndash;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and log2-SII (OR\u0026thinsp;=\u0026thinsp;0.84, 95% CI\u0026thinsp;=\u0026thinsp;0.71\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) were all significantly associated with GDM and met the statistical significance. The result suggested that each unit of increased log2-LMR score was associated with 55% increased risk of increased GDM.\u003c/p\u003e \u003cp\u003eWe then converted novel inflammatory markers from continuous variables to categorical ones (Q1-Q4, quartiles) for sensitivity analyses. Specific results were also shown in Table\u0026nbsp;2 and were broadly similar to those of the main analyses. Other details of the association between inflammation markers and GDM were listed in Table\u0026nbsp;2. Compared with the lowest log2-LMR quartile, participants in the highest log2-LMR quartile had 78% increased risk of GDM (OR\u0026thinsp;=\u0026thinsp;1.78, 95% CI\u0026thinsp;=\u0026thinsp;1.26\u0026ndash;2.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and the third quartile had 42% increased risk of GDM (OR\u0026thinsp;=\u0026thinsp;1.42, 95% CI\u0026thinsp;=\u0026thinsp;1.01\u0026ndash;2.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05).While compared with the first log2-SII quartile, participants in the forth log2-SII had 25% decreased risk of GDM (OR\u0026thinsp;=\u0026thinsp;0.75, 95% CI\u0026thinsp;=\u0026thinsp;0.53\u0026ndash;1.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11), and in the third quartile had 12% decreased risk of GDM(OR\u0026thinsp;=\u0026thinsp;0.88, 95% CI\u0026thinsp;=\u0026thinsp;0.6\u0026ndash;1.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49).But the log2-SII association did not meet the statistical significance. Furthermore, the log2-PLR had 33% decreased risk of GDM (OR\u0026thinsp;=\u0026thinsp;0.67, 95% CI\u0026thinsp;=\u0026thinsp;0.47\u0026ndash;0.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), the log2-NLR with 30% decreased risk (OR\u0026thinsp;=\u0026thinsp;0.70, 95% CI\u0026thinsp;=\u0026thinsp;0.49-1.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFurthermore, we also visualized potential non-linear relation of GDM and these inflammatory indexes via restricted cubic spline (RCS) plot, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. After adjusting all covariates, RCS plot of log2-LMR exhibited strong U-shaped non-linear relationship with GDM, which was characterized by a substantial reduction of GDM risk with log2-LMR value increasement when it was less than 1.79, and increased thereafter (\u003cem\u003ep\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.05). However, log2-SII, log2-PLR, and log2-NLR did not show non-linear relationship with GDM prevalence (\u003cem\u003ep\u003c/em\u003e for non-linearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subgroup analysis for the association between GDM and novel inflammatory markers.\u003c/h2\u003e \u003cp\u003eTo determine whether the association between inflammatory indicators and GDM were stable across subgroups, we then performed subgroup analyses based on age, BMI, ethnicity, marital status, PIR, education level, smoking status, alcohol user, age of menarche and presence of hypertension, hyperlipidemia, and diabetes mellitus.\u003c/p\u003e \u003cp\u003eAssociations between GDM risk and indicators including NLR, PLR, and SII were generally consistent across all subgroups, which was not the case for LMR. More results of subgroup analysis in details for other inflammatory markers were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Taken together, GDM risk was consistently associated with lower NLR and PLR, while with higher SII and LMR, in most covariates strata.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, the correlation between GDM and novel inflammatory markers were analyzed using the representative US NHANES database. The results showed that SII, LMR, NLR and PLR were significantly associated with GDM, and these associations became more significant after adjusting for all covariates. LMR was positively associated with the risk of GDM, with the risk of GDM in the highest quartile of LMR being 1.78 times higher than that in the lowest quartile of pregnant women, while NLR, PLR, and SII were negatively associated with GDM, and our subgroup analyses suggested the stability of these findings.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the largest study to analyze the correlation between GDM and novel inflammatory markers up to now. SII, LMR, NLR, and PLR are novel inflammatory markers that have attracted much attention in recent years, and they are the combination of neutrophil counts, lymphocyte counts, platelet counts, and monocyte counts in the complete blood count, reflecting the state of immune inflammation better than a single source of blood counts. SII was initially used as a prognostic indicator of outcome in cancer patients, and is a novel marker of systemic immune inflammation, which has been widely used in the study of endocrine abnormalities, heart failure, myocardial infarction in the elderly, hepatic fibrosis, hyperlipidaemia, etc\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. A prospective observational study in 2023 found that SII levels were higher in women with GDM than in healthy women, which is consistent with the findings of the present study, and also suggests that SII is useful in the prediction of GDM, and could be an adjunctive tool in the prediction of GDM\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. A retrospective study of 467 pregnant women was conducted by Seval, which not only found that SII levels were higher in women with GDM than in healthy women, but also found that SII levels were significantly higher in the late than the early stages of pregnancy\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Wang concluded that NLR is a stable biomarker that reflects the systemic inflammatory response of the body, which is important for the diagnosis of cardiovascular and cerebrovascular diseases. Both Yildiz and Ning Han found that NLR levels were higher in patients with GDM than in healthy women\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, which was consistent with our findings. NLRP3 and its effector molecules (Caspase-1, IL-1β, IL-18) were higher in women with GDM than in healthy women, potentially suggesting that GDM is a chronic inflammatory state\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. It has also been shown that LMR and PLR can be used as prognostic indicators for cervical cancer patients with high accuracy, and the inclusion of LMR and PLR to routinely assess and monitor cancer conditions will better guide clinicians to adopt personalized treatments and improve patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Tumour development is largely influenced by the level of inflammation. Inflammatory markers can reflect the balance between tumour and inflammation in the body, and have also been proposed as predictors of tumour recurrence\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Taken together, SII, LMR, NLR, and PLR have all been suggested to be closely associated with a variety of inflammatory diseases and are crucial in assessing disease prognosis.\u003c/p\u003e \u003cp\u003eThe 75g oral glucose tolerance test has been used to diagnose gestational diabetes mellitus, which typically manifests between 24 and 28 weeks of pregnancy\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. To lower the risk of GDM for both mother and fetus, it is imperative to look for indicators that can be utilized for early detection and screening of the disease\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Prior to the diagnosis of GDM, both mother and fetus may have been exposed to a high-glucose environment to different degrees. Numerous perinatal conditions, including gestational diabetes, pre-eclampsia, and preterm labor, have been linked to intestinal dysbiosis and inflammation\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. According to certain research. Insulin resistance and malfunctioning β-cells in the pancreas are closely linked to the development of GDM. Furthermore, endolipin and lipocalin are involved in the pathophysiology of GDM and are connected to the metabolism of glucose. Pregnant women with GDM have higher levels of endolipin and lower levels of lipocalin than normal pregnant women, which are related to oxidative stress and inflammation, and prolonged inflammatory stimulation may lead to the expression of aberrant insulin signaling pathways. Some studies have also shown that GDM patients have increased expression of both Toll-like receptor 4 (TLR4) and inflammatory cytokines, and NLRP3 levels are abnormally elevated in diabetic rats\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Among them, IL-1 is an inflammatory factor closely related to GDM and insulin resistance. And soybean isolate protein could attenuate the TLR4/NF-κB inflammatory pathway and reduce blood glucose level in GDM patients\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. TGF-β is one of the most important inflammatory cytokines related to pregnancy and plays an important role in hormone secretion and embryonic development during pregnancy\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, and TGF-β induces GDM and pre-eclampsia. In GDM patients, the expression of IL-6, IL-1β, and TNF-α was elevated, which can inhibit the expression of GLUT-1, GLUT-4, GLUT-9, affect the insulin signaling pathway and promote the development of GDM\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Oxidative stress plays a prominent role in the pathological mechanisms of metabolic diseases, and the increase of glucose and lipids can promote reactive oxygen species production, which in turn affects insulin secretion signaling and promotes pancreatic β-cell apoptosis. And studies have shown that patients with GDM have elevated levels of oxidative stress, with significant elevations in thiobarbituric acid reactive substances, lipid hydroperoxide, malondialdehyde, and xanthine oxidase\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. A prospective study has shown a positive correlation between the TyG index\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e, which is a composite of glycaemia and lipids, and GDM risk, as compared to a single glycaemic index. 2024 The TyG index is a more accurate measure of the body's inflammation than is glucose, Lipids better reflect the inflammatory state of the body, is a factor that has been studied in many cardiovascular diseases in recent years, and may be used as a predictor of type 2 diabetes mellitus\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSome studies have shown that leukocytes, neutrophils, lymphocytes, monocytes and NLR in early pregnancy are clinically relevant in identifying individuals at high risk of GDM. Suheyla Gorar concluded that leukocyte and platelet counts are positively associated with the risk of GDM. Our study also confirms previous findings that GDM is associated with inflammatory markers. Its specific pathological mechanism is related to oxidative stress, which can induce GDM progression by mediating inflammatory responses, stimulating NF-κβ signaling and producing inflammatory factors such as TNF-α\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. It is worth mentioning that pro-inflammatory factors can in turn impair insulin signaling and damage pancreatic β-cell function. Pro-inflammatory diets can increase the risk of GDM and pre-eclampsia\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, whereas multi-nutrient therapies have shown positive effects on improving endocrine abnormalities in GDM and may be associated with NF-κβ factors. Micro RNAs have an important role in the pathogenesis and therapeutic targets of GDM\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. GDM patients have high levels of micro RNA-222 and micro RNA-17-5p, which are involved in insulin resistance and inflammatory signalling pathways to varying degrees. It has been suggested that micro RNA-17-5p/Mfn1/2/NF-κβ is a novel key target pathway for the treatment of GDM\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, and micro RNAs can also be used to predict the risk of GDM and its complications. However, further studies on the predictive value of micro RNA for GDM are still needed\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt has to be admitted that our study has some limitations. Firstly, this study is a cross-sectional study, which cannot accurately derive the causal relationship between GDM and novel inflammatory markers. Second, the diagnosis of GDM is still dependent on participants' self-report due to the lack of test data for 75g OGTT in the NHANES database. However, researchers from NHANES told participants that their positive answers to RHQ162 should exclude diabetes known before pregnancy during the interview questionnaires. What\u0026rsquo;s more, previous studies have shown the validity of using this self-reported GDM as definition of diagnosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Third, although our study corrected for the interference of some common confounders, we could not exclude the interference of other confounders.\u003c/p\u003e"},{"header":"5.Conclusion","content":"\u003cp\u003eIn this study, we found that novel inflammatory markers were strongly associated with GDM in pregnant women in the U.S. and verified the non-linear relationship between them. SII, NLR, LMR, and PLR may help clinicians to optimize the diagnosis of GDM and to reduce the risk of maternal and fetal pregnancy complications, but further prospective studies are needed to confirm our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eThe survey protocol has been approved by the Institutional Review Board of the National Center for Health Statistics. All participants in the NHANES database have signed informed consent before enrollment.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eYanfen Chen: statistical analysis, revising and graphing; Genping Zeng: investigation and writing original draft; Xijing Lu: information summary; Tan Zeng: analysing data;\u0026nbsp;Yuxi Miao: methodology; Peiyin Li: collecting information; Songping Luo,\u0026nbsp;Lei Zeng,\u0026nbsp;Ruling Lu: revising\u0026nbsp;and financial support.\u003c/p\u003e\n\u003cp\u003eFunding support:\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (No.81804135); Luo Songping National Famous Elderly Chinese Medicine Experts Inheritance Workshop (National TCM Human Education Letter [2022] No.75); Luo Songping National Famous Traditional Chinese Medicine Practitioner Inheritance Workshop (National TCM Human Education Letter [2022] No.5); Special Research Fund for Obstetrics and Gynaecology of China Combined Traditional Chinese Medicine and Western Medicine, 2023 (No.FCK-ZSYTW-09); 2023 Guangdong Basic and Applied Basic Research Fund Enterprise Joint Fund Project\u0026nbsp;(Public Health and Medicine and Healthcare) ;\u0026nbsp;National TCM Inheritance and Innovation Centre \u0026quot;Youth Project\u0026quot; (No.2022QN21).\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. These data can be found here: https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe are grateful for the assistance of all participants in this study and efforts of all the staff\u003c/p\u003e\n\u003cp\u003emembers of NHANES.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eConsent to Publish declaration: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCarter E B, Thayer S M, Paul R, \u003cem\u003eet al\u003c/em\u003e. Diabetes Group Prenatal Care: A Systematic Review and Meta-analysis[J].\u003cem\u003eObstet Gynecol\u003c/em\u003e,2023.\u003c/li\u003e\n\u003cli\u003eScheuer C M, Jensen D M, McIntyre H D, \u003cem\u003eet al\u003c/em\u003e. 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New approaches in the diagnosis and prognosis of gestational diabetes mellitus[J].\u003cem\u003eEur Rev Med Pharmacol Sci\u003c/em\u003e,2023,27(21):10583-10594.\u003c/li\u003e\n\u003cli\u003eSchoenaker D, Mishra G D. Association Between Age at Menarche and Gestational Diabetes Mellitus: The Australian Longitudinal Study on Women\u0026apos;s Health[J].\u003cem\u003eAm J Epidemiol\u003c/em\u003e,2017,185(7):554-561.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gestational Diabetes Mellitus (GDM), National Health and Nutrition Examination Survey (NHANES), novel inflammatory markers, Systemic immune inflammatory index (SII), Neutrophil-lymphocyte ratio (NLR), Lymphocyte-monocyte ratio (LMR), Platelet-lymphocyte ratio (PLR), cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-4055713/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4055713/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEarly detection of gestational diabetes mellitus (GDM) can lower the chance of occurrence. Recent years have seen a surge in research on novel inflammatory indicators, such as systemic immune inflammatory index (SII), lymphocyte-monocyte ratio (LMR), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR). Inflammation is linked to the pathophysiology of GDM and can be targeted for treatment. However, the relationship between GDM and these novel inflammatory markers is unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe included participants with a diagnosis of GDM who were between the ages of 20 and 44, as well as complete blood counts from the US National Health and Nutrition Examination conducted between 2007 and 2018. SII, LMR, NLR, and PLR were among the novel inflammatory markers. First, we logarithmically transformed the exposure components to account for skewed distribution. We tested the relationship between GDM and novel inflammatory markers using a multiple logistic regression model and subgroup analyses to analyze the stability. And RCS curves were created to evaluate the non-linear connection.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFollowing the inclusion of 3,722 women aged 20\u0026ndash;44 years with GDM, multivariate logistic regression analysis revealed a positive correlation between log2-LMR and GDM (OR\u0026thinsp;=\u0026thinsp;1.55, 95% CI\u0026thinsp;=\u0026thinsp;1.20\u0026ndash;2.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), while negative correlations were observed between log2-SII, log2-PLR, and log2-NLR with GDM (OR\u0026thinsp;=\u0026thinsp;0.84, 95% CI\u0026thinsp;=\u0026thinsp;0.71\u0026ndash;0.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; OR\u0026thinsp;=\u0026thinsp;0.73, 95% CI\u0026thinsp;=\u0026thinsp;0.56\u0026ndash;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI\u0026thinsp;=\u0026thinsp;0.47\u0026ndash;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), and the correlation remained significant even after controlling for all confounders. Correlations were consistently shown by subgroup analyses. When the log2-LMR value was less than 1.79, the risk of GDM reduced with rising log2-LMR, and this tendency was reversed when larger than 1.79.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated levels of new inflammatory markers are correlated with an increased risk of GDM and may offer clinicians with information to screen for GDM and identify GDM therapeutic targets. Further studies are required to investigate the causal relationship between the new inflammatory markers and GDM.\u003c/p\u003e","manuscriptTitle":"Association of novel inflammatory markers with gestational diabetes mellitus in a representative U.S. sample: evidence from NHANES 2007-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 18:42:14","doi":"10.21203/rs.3.rs-4055713/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2f8f8dc-9672-4fa8-9d3c-64e13bcf38f8","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-21T18:42:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 18:42:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4055713","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4055713","identity":"rs-4055713","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}