Introduction

Type 2 diabetes mellitus (T2DM), one of the most serious and prevalent chronic diseases, can lead to a variety of complications such as cardiovascular diseases, nephropathy, neuropathy, retinopathy, lower limb amputations, and reduced life expectancy, significantly contributing to increased mortality rates in T2DM patients [1,2,3]. The prevalence of T2DM is rising with the rapid development of the global economy and lifestyle changes. Epidemiological data indicate that, as of 2021, the global prevalence of T2DM among individuals aged 20–79 years was estimated at 10.5% (536.6 million people), with projections suggesting that over 1.31 billion people worldwide could be affected by 2050, with similar rates observed in both genders [4].

The primary clinical criterion for diagnosing T2DM is elevated venous blood glucose levels. T2DM comprises 90% of all cases of diabetes mellitus [5]and is associated with several pathogenic factors, including genetic predispositions, immunological factors, environmental influences, insufficient physical activity, and poor lifestyle choices [6,7,8]. The pathogenesis primarily involves the relative insufficiency of insulin secretion by pancreatic β-cells and the insensitivity of tissues and organs to insulin, which triggers insulin resistance (IR). This leads to a compensatory increase in insulin secretion, ultimately causing pancreatic β-cell damage and failure [9, 10].

The role of inflammation in the development of T2DM and associated metabolic disorders has garnered significant attention [11, 12]. In recent years, the NLR has been increasingly studied as a composite biomarker that better reflects the systemic inflammatory state compared to individual biomarkers, being cost-effective and easy to detect [13,14,15]. NLR has been reported as a reliable inflammatory marker in type 2 DM [16] and other inflammatory conditions including gastrointestinal diseases [17], cardiac conditions [18], thyroiditis [19], thyroid conditions [20], irritable bowel disease [21], and Covid-19 infection [22]. Hence, studying the association between T2DM and NLR is reasonable. However, previous studies have been constrained primarily by their small sample sizes, leaving the relationship between NLR and T2DM ambiguous. Consequently, this study aims to elucidate the potential relationship between NLR and T2DM using a large dataset from the NHANES, seeking to uncover new insights.

Methods

Participant selection and process

The NHANES is a population-based, cross-sectional survey conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of adults and children in the United States. The research team includes professional health investigators, medical technicians, and physicians. The NHANES database, updated biennially, comprises demographic, dietary, examination, laboratory, and questionnaire data. All participant data collection was conducted with informed consent and approved by an ethical review board. We used data from 2007 to 2016 to select participants. We initially screened 50,588 participants, with the specific exclusion criteria as follows: Exclude participants < 20 years old (n = 21,387); Exclude participants lacking education data (n = 39); Exclude participants missing neutrophil and lymphocyte counts (n = 2,508); Exclude participants missing diabetes data (n = 608); Exclude participants lacking other important covariate data (n = 16,143). A total of 9,903 eligible participants were included (Fig. 1).

Fig. 1
figure 1

Participants and flowcharts

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Detection and definition of NLR

Venous blood was collected in the morning after an overnight fast at the Mobile Examination Centre, and a Beckman Coulter DxH 800 instrument was utilized to perform a complete blood count on the specimen. NLR was determined by dividing the count of neutrophils by that of lymphocytes [23].

Definition of T2DM, hypertension

Diagnosis of T2DM was established based on: (1) self-report of T2DM, (2) fasting blood glucose ≥ 7.0 mmol/L, (3) presence of T2DM symptoms with random blood glucose ≥ 11.1 mmol/L, (4) glycosylated hemoglobin A1c (HbA1c) ≥ 6.5% [24]. Diagnostic criteria for hypertension: NHANES participants were surveyed by healthcare professionals both at home and at the Mobile Examination Center. In the questionnaire, participants were asked, “Have you ever been told you have high blood pressure?” Response options were “Yes” or “No.” Participants answering “Yes” were classified as having hypertension, and those answering “No” were classified as not having hypertension.

Covariates

The covariates comprised demographic, anthropometric, and laboratory measures. The covariates were specified as follows: age groups (20–39, 40–59, and ≥ 60 years), sex (male and female), racial categories (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, and other races), education levels (less than high school, high school, or higher), and BMI, classified into three categories: normal, overweight, and obese (< 25 kg/m2, 25–29.9 kg/m2, and ≥ 30 kg/m2). Smoking status was categorized as Never, Former, or Current. Participants were queried regarding whether they had ever smoked 100 cigarettes in their lifetime and if they were currently smoking to distinguish between current and former smokers. Participants were classified as never smokers if they had consumed fewer than 100 cigarettes in their lifetime. Participants were classified as ex-smokers if they were not current smokers but had consumed 100 cigarettes in the past. The activity was defined as any moderate-intensity exercise, fitness, or recreational activity leading to a slight increase in breathing or heart rate-such as brisk walking, bicycling, swimming, or volleyball for at least ten consecutive minutes weekly. Drinkers were defined as individuals who consumed at least 12 alcoholic beverages annually. Additionally, we included hypertension, poverty income ratio (PIR), Total cholesterol (TCHO), triglyceride (TG), Low-density lipoprotein cholesterol (LDL-C), High-density lipoprotein cholesterol (HDL-C), HbA1c. All covariates were sourced from the NHANES database.

Statistical analysis

DecisionLinnc1.0 software was employed for data analysis [25]. DecisionLinnc1.0 is a platform that integrates multiple programming language environments and enables data processing, data analysis, and machine learning through a visual interface. We refer to the “WTMEC2YR” weighting variable and multiply the 2-year MEC weights by one-fifth to derive 10-year weights. Categorical variables are expressed as percentages, Continuous variables are first tested for normality. Data following a normal distribution are represented by Mean ± Standard Deviation, while data not following a normal distribution are often described by the median and interquartile range to depict the central tendency and dispersion. Weighted logistic regression was employed across three distinct models to examine the relationship between NLR and T2DM. Model 1 was not adjusted for covariates. In Model 2, adjustments were made for age, sex, and race. Model 3 included adjustments for sex, age, race, education level, BMI, smoking status, recreational activities, alcohol drinker, hypertension, PIR, TCHO, TG, LDL-C, HDL-C, and HbA1c. Subgroup analyses were also conducted. Furthermore, RCS was utilized to explore potential non-linear relationships between NLR and T2DM risk. P < 0.05 was considered statistically significant.

Results

The characteristics of the participants

A total of 9,903 participants with complete data were included in this analysis (Fig. 1). Of these, 8,623 were non-T2DM, and 1,280 were T2DM. Compared to the normoglycemic group, the diabetic group was older (P < 0.001), better educated (P < 0.001), exhibited a higher obesity rate (P < 0.001), and a greater prevalence of individuals who lacked regular exercise and alcohol drinker (P < 0.001). The differences in PIR, TCHO, TG, LDL-C, HDL-C, and HbA1c between the two groups were statistically significant (P < 0.001). Detailed information can be found in Table 1.

Table 1 The characteristics of the study participants
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The relationship between NLR and T2DM

As shown in Table 2, a significant correlation was identified between NLR and T2DM. Covariates were not adjusted for in Model 1, while Model 2 was adjusted for age, sex, and race; Model 3 included adjustments for all covariates. In conclusion, analyses revealed that in Model 3, NLR remained positively associated with T2DM (OR:1.14,95%CI:1.05–1.24, P = 0.003). Subsequently, quartile analysis of NLR was conducted, using Q1 as a reference, and the OR for Q4 was significantly higher than that for Q1 (OR: 1.86, 95% CI: 1.58–2.21, P < 0.001). Following complete adjustment for all covariates, Patients in the highest quartile of NLR have a risk of develo** the disease that is more than one time higher than those in the lowest quartile (OR: 1.56, 95% CI: 1.19–2.06, P = 0.002).

Table 2 The association between NLR levels and prevalence of T2DM by logistic regression analyses
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Subgroup analyses

To ascertain the robustness of the association between NLR and T2DM across various population subgroups, subgroup analyses were conducted following Model 3. Table 3 demonstrates that the interaction effect between NLR and T2DM was statistically significant concerning race and hypertension (P < 0.05); in contrast, no significant interactions were observed for age, sex, education level, BMI, smoking status, recreational activities, and alcohol drinker (P > 0.05).

Table 3 The results of subgroup analyses
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Non-linear association between NLR and T2DM

RCS was employed to demonstrate better the relationship between NLR and T2DM (Fig. 2); a strong non-linear correlation was observed between NLR and T2DM,

We conducted a threshold effect analysis and found an inflection point. The inflection points of models 1, 2, and 3 were generally consistent. After adjusting covariates according to Model 3, the inflection point was 2.27. Observations indicate that when NLR is below the inflection point, the risk of T2DM is lower, when NLR exceeds the inflection point, the risk increases rapidly.

Fig. 2
figure 2

The association between NLR and T2DM. RCS shows a non-linear relationship between NLR and T2DM. The fitted regression line is a solid black line; the black dashed line indicates the position where the OR is equal to 1; the shaded area indicates the 95% CI; NLR, neutrophil-to-lymphocyte ratio

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Discussion

In this cross-sectional study, we utilized the NHANES database to analyze relevant data from adult participants in the United States. We explored the relationship between NLR and the risk of T2DM, and we concluded: NLR levels in T2DM patients were significantly higher than those in non-T2DM patients. There was a significant positive correlation between NLR and the risk of T2DM, and this relationship persisted even after adjusting for multiple confounding factors. RCS analysis showed a significant nonlinear relationship between NLR and T2DM, with an inflection point at 2.27. The subgroup analyses revealed a significant interaction effect between NLR and T2DM concerning race and hypertension (P for interaction < 0.05). In contrast, no significant interactions were found for age, sex, education level, BMI, smoking status, recreational activities, and alcohol drinker (P for interaction > 0.05).

T2DM represents a prevalent endocrine system disorder characterized by multiple metabolic disturbances that induce a state of chronic hyperglycemia [11, 24]. Evidence suggests that low-grade inflammation plays a crucial role in the pathogenesis of T2DM [26], and HbA1c, a commonly used laboratory marker for diagnosing T2DM, reflects the average blood glucose levels of the human body over three months, facilitating the monitoring of these levels [27]. However, HbA1c does not assess changes in the body’s inflammatory state. The NLR can effectively recognize such changes [28]. In clinical practice, blood counts can be easily tested, offering rapid and cost-effective results. NLR, as a ratio of neutrophils to lymphocytes, provides greater accuracy than a single measurement and effectively reflects the systemic inflammatory response [29]. Neutrophils and T-lymphocytes are pivotal in the development and progression of diabetes. It has been reported that hyperglycemia affects the number and function of circulating neutrophils. However, neutrophils in T1DM and T2DM patients exhibit different characteristics, with increased neutrophil counts observed only in T2DM patients [30]. It has been demonstrated that in patients with T2DM, the expression of activation markers on neutrophil membranes differs from that in healthy controls. This is evidenced by a decreased expression of the adhesion molecule LFA-3, increased levels of activation markers such as CD11B and CD66B, and increased adhesion of neutrophils to endothelial cells, leading to systemic inflammation and endothelial damage [

Conclusion

Our findings indicate a significant positive correlation between neutrophil-to-lymphocyte ratio levels and the risk of T2DM. However, current results cannot determine a causal relationship between the two, further prospective studies are needed to confirm their relationship.