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Dietary inflammatory index as a predictor of prediabetes in women with previous gestational diabetes mellitus

Diabetology & Metabolic Syndrome volume 16, Article number: 265 (2024) Cite this article

Abstract

Introduction

Gestational diabetes mellitus (GDM) is associated with an increased risk of developing type 2 diabetes mellitus (T2DM). The inflammatory potential of diet is crucial in GDM development. This study compares dietary inflammatory indices (DII) in females with and without a history of GDM and constructs a predictive model for prediabetes risk.

Methods

Cross-sectional data from NHANES cycles (2011–2014) were analyzed using the DII. Independent t tests, chi-square test, and Mann-Whitney U test examined DII scores in relation to GDM history. Multivariate logistic regression assessed DII’s association with prediabetes in females with GDM history. Restricted cubic spline (RCS) and LASSO regression modeled non-linear relationships and predicted prediabetes risk.

Results

971 female participants were included. Those with GDM history had lower DII scores (1.62 (0.58, 2.93) vs. 2.05 (0.91, 2.93)). Higher DII scores in females with GDM were linked to prediabetes, remaining significant after adjusting for confounders. RCS analysis found no non-linear correlation (non-linear p = 0.617). The prediabetes model for GDM history had strong predictive performance (AUC = 88.6%, 95% CI: 79.9-97.4%).

Conclusion

Females with GDM history show lower DII levels, potentially reflecting improved diet and health awareness. Higher DII scores correlate with increased prediabetes risk in this group, emphasizing diet’s role in diabetes risk. Further studies are needed to confirm these findings.

Introduction

Gestational diabetes mellitus (GDM), defined as “glucose intolerance of variable severity that develops or is first detected during pregnancy,” represents the most common complication of pregnancy [1]. Importantly, the global incidence of GDM displays significant geographic variability, attributed to diverse population characteristics and regional differences in diagnostic criteria. According to the International Association of Diabetes Study Groups in Pregnancy (IADPSG), prevalence ranges from a modest 6.6% in Japan and Nepal to a striking 45.3% in the UAE [2]. Despite regional constraints and varying diagnostic standards, the prevalence of GDM exhibits a global upward trend, closely linked to rising rates of obesity among females of childbearing age and the increasing background prevalence of type 2 diabetes mellitus globally [3].

The onset of GDM is influenced by multiple risk factors. Studies identify advanced maternal age, family history of diabetes mellitus, prior GDM history, obesity, delivery of a macrosomic baby (birth weight > 4500 g), polycystic ovary syndrome, prescribed medications (e.g., glucocorticosteroids, antipsychotics), and dietary factors [4]. Notably, GDM significantly increases the risk of developing type 2 diabetes mellitus (T2DM) postpartum. Vounzoulaki et al. report a nearly tenfold higher risk of T2DM in females with previous GDM compared to those with normal glucose levels. This risk remains significant across follow-up periods, with cumulative incidences in GDM history groups reaching 9.22–16.15%, markedly higher than the control group’s 1.90% during the longest follow-up [5].

The Dietary Inflammatory Index (DII) serves as an objective tool to assess the overall inflammatory potential of diets, comprising 45 dietary components. It quantifies dietary anti-inflammatory and pro-inflammatory effects, indirectly reflecting their impact on blood cytokine concentrations and assessing dietary patterns’ inflammatory propensity [6, 7]. Studies demonstrate a strong association between lower DII values (indicating higher anti-inflammatory potential) and reduced risk of type 2 diabetes [8,9,10]. This effect may stem from improved insulin sensitivity under an anti-inflammatory dietary regime [11,12,13]. Conversely, elevated DII values correlate positively with GDM risk among females of childbearing age [14]. These findings underscore the potential for dietary modifications to prevent or manage T2DM in females with a history of GDM, emphasizing the role of anti-inflammatory diets in improving postnatal outcomes and societal dietary practices.

As previously noted, GDM significantly increases the risk of future prediabetes in mothers, along with short- and long-term complications for both mothers and neonates [15, 16]. However, while current research has focused on managing and preventing GDM and its associated health issues [17], there is limited research on the dietary habits of females with a history of GDM and their impact on subsequent diabetes risk, especially regarding inflammatory diets in females of childbearing age, whether they have a history of GDM or not.

Therefore, this study aims to utilize data from the 2011–2014 National Health and Nutrition Examination Survey (NHANES) to compare dietary inflammatory indices between females of childbearing age with and without a history of GDM. We introduce the Dietary Inflammatory Index (DII) and employ multivariate weighted logistic regression modeling and Restricted Cubic Spline (RCS) analyses. Our objective is to assess the association between DII levels and prediabetes risk in these females. Additionally, we develop a prediction model to quantify the prediabetes risk among females with a history of GDM, aiming to inform personalized and evidence-based dietary intervention strategies.

Materials and methods

Study design and the participants

The NHANES, conducted by the National Center for Health Statistics (NCHS) of the United States, is a cross-sectional survey aimed at gathering health and nutrition information regarding American adults and children. All NHANES data are publicly accessible and can be downloaded freely from NHANES website. Participants in NHANES provide written informed consent, and the entire process is approved by the Institutional Review Board of the Centers for Disease Control and Prevention. Data on gestational diabetes mellitus (GDM) history in NHANES were first collected from 2007 to 2008, with subsequent research focusing primarily on the period from 2007 to 2010. To explore this issue further, we incorporated cross-sectional data from two consecutive NHANES cycles (2011–2014), involving 10,072 female participants. We compared Dietary Inflammatory Index (DII) scores between reproductive-age females with and without a history of GDM. Additionally, we conducted a comprehensive investigation to study the association between DII and prediabetes in female participants with a history of GDM, and developed a nomogram model specifically to predict prediabetes for this subgroup. The Institutional Review Board of Fujian Maternity and Child Health Hospital determined that this study does not involve oversight of human subject research.

Exclusion criteria were applied as follows: (1) female participants aged outside the range of 20 to 44 years (n = 7,555); (2) female participants who had never given birth to a live infant (n = 1,197); (3) female participants reporting a doctor-diagnosed diabetes (n = 58) or borderline diabetes managed with oral hypoglycemic agents (n = 3), and those reporting previous critical GDM (n = 20), totaling 81 participants excluded; (4) female participants with missing DII data (n = 197) or GDM information (n = 11), resulting in 207 exclusions; and (5) female participants with missing covariate and other variable data (n = 61). After manual data filtering, a final cohort of 971 female participants without diabetes mellitus was selected for subsequent analysis. The detailed recruitment process of study participants is illustrated in Fig. 1.

Assessment of the dietary inflammation index (DII)

In this study, we utilized data from 28 food parameters sourced from 11 countries worldwide, including alcohol, caffeine, carbohydrates, fats, proteins, and vitamins A, B1, B2, B6, B12, C, D, E, folate, β-carotene, cholesterol, energy (kcal), fiber, iron, magnesium, selenium, zinc, saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, N3 and N6 fatty acids, and niacin, to calculate the Dietary Inflammatory Index (DII). All data underwent standardized processing.

From numerous inflammation markers, we selected six representative markers to evaluate the impact of food parameters: pro-inflammatory markers (IL-1β, IL-6, TNF-α, C-reactive protein (CRP)) and anti-inflammatory markers (IL-4 and IL-10). Each food parameter was scored based on its weighted association with these six inflammation markers and its role in either promoting or inhibiting inflammation. A food parameter received a score of + 1 if it significantly increased IL-1β, IL-6, TNF-α, or CRP levels, or decreased IL-4 and IL-10 levels; conversely, it received a score of -1 otherwise. Z-scores were calculated based on participants’ exposure levels to each food parameter. Standardized estimates of dietary intake were converted to centiles for each DII component [18]. These centiles were then multiplied by the respective component-specific inflammation effect scores to derive the total DII score for each individual. In this study, DII was treated as a continuous variable, and participants were categorized into three groups: Low, Medium, and High.

History of gestational diabetes mellitus

Female participants reported their history of gestational diabetes mellitus (GDM) in the reproductive health questionnaire. They were asked, “During your pregnancy, did a doctor or other health professional ever tell you that you had diabetes, sugar diabetes, or gestational diabetes?” Female participants who answered “yes” to this question were categorized as having a history of GDM.

Prediabetes

Female participants who reported being informed by a doctor or other health professional that they had any of the following conditions—prediabetes, impaired fasting glucose, impaired glucose tolerance, borderline diabetes, or that their blood sugar was higher than normal but not high enough to be called diabetes or sugar diabetes—were classified as having prediabetes.

Fig. 1
figure 1

Flowchart of the study participants

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Covariates

In this study, we identified the following variables as potential confounding factors: age, race, BMI, birth history of macrosomia, age of menarche, marital status, education levels, smoking, drinking, hypertension, minutes of sedentary activity, thyroid problems, and mean platelet volume. Age, race/ethnicity, and education levels were gathered from demographic questionnaires. Age of menarche, birth history of macrosomia, prediabetes, health risk for diabetes, alcohol consumption, and smoking status were obtained from questionnaire data. The time point for age and BMI data in this study was independent of pregnancy status and based on cross-sectional attributes collected from the NHANES database. Race/ethnicity was categorized as non-Hispanic white, non-Hispanic black, Mexican American, other Hispanic, and others. Education level was classified into three categories: less than high school, high school, and more than high school. Additionally, information on arthritis, gout, heart failure, coronary heart disease, angina pectoris, heart attack, stroke, emphysema, thyroid problems, chronic bronchitis, and liver disease was obtained from responses to the medical conditions questionnaire provided by female participants. This section provided self- and proxy-reported personal interview data covering a wide range of health conditions and medical histories for all participants. Furthermore, participants’ complete blood count (CBC) and red blood cell (RBC) folate information were obtained from Laboratory Data. The CBC parameters were derived using Beckman Coulter methodology, which includes counting and sizing with an automatic diluting and mixing device for sample processing, and hemoglobinometry using a single-beam photometer. The Beckman Coulter DxH 800 instrument at the NHANES mobile examination center (MEC) was used to analyze blood specimens and provide a distribution of blood cells for all participants. CBC information collected included neutrophil count, platelet count, basophil count, lymphocyte count, eosinophil count, monocyte count, and white blood cell (WBC) count. RBC folate measurements encompassed Whole Blood Folate and Serum Total Folate. Whole-blood folate was assessed using a microbiologic assay, while serum folate forms were measured using isotope-dilution high-performance liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS).

Statistical analysis

Due to the complex sampling design utilized in NHANES surveys, we incorporated sample weights corresponding to different study periods in our analytical methods to generate accurate estimates of health-related statistical data. We compared baseline characteristics, including CBC components and DII scores among female participants across different groups based on independent t tests, chi-square test, and Mann-Whitney U test.

For analyzing the association between DII and prediabetes in female participants with a history of GDM, we used a multivariable logistic regression model. Specifically, we employed three models: an unadjusted model and two adjusted models (Model 1 and Model 2). Model 1 adjusted for age, race, and BMI, while Model 2 further adjusted for additional factors such as birth history of macrosomia, age of menarche, marital status, education level, smoking, drinking, hypertension, minutes of sedentary activity, thyroid problems, and mean platelet volume.

To explore nonlinear relationships, DII was categorized into three groups—Low, Medium, and High—using equal intervals: <0.89 for Low, [0.89–2.38) for Medium, and ≥ 2.38 for High. Additionally, we employed restricted cubic spline (RCS) regression with 3 knots (at the 10th, 50th, and 90th percentiles) to further investigate the nonlinear relationship between DII and prediabetes in female participants with a history of GDM.

Subgroup analyses were conducted based on various demographic and health-related factors to assess significant interactions with the association between DII and prediabetes in this population.

Given the significantly increased risk of type 2 diabetes in females with a history of GDM, our objective was to develop a predictive nomogram model for predicting prediabetes in these participants. To identify the most critical dietary factors associated with prediabetes and mitigate collinearity among variables, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. In the LASSO model, we utilized cross-validation to evaluate model performance and select optimal parameters. This involved dividing the dataset into 10 subsets for training and testing the model multiple times, thereby assessing its performance across different settings. The lambda value, which minimizes bias during cross-validation, was selected with consideration of model stability and generalizability to new data. Additionally, a prediction nomogram model was developed based on key variables, and its discriminatory ability in predicting prediabetes in female participants with a history of GDM was validated using receiver operating characteristic (ROC) curves.

All statistical analyses were performed using R software version 4.1.6 (http://www.R-project.org, The R Foundation, Vienna, Austria), and statistical significance was determined by a two-tailed P-value < 0.05.

Baseline characteristics of study participants

This study included a total of 971 female participants from NHANES (2011–2014), with a weighted average age of 33.96 years. Among all participants, the weighted median DII score was 1.96 (0.88, 2.93), and 12.98% reported a history of GDM. Compared to females without a history of GDM, those with GDM tended to be older, from other racial backgrounds, and had higher rates of prediabetes. They also had an earlier age at menarche, a history of macrosomia at birth, and increased health risks related to diabetes (all P < 0.05). Detailed baseline characteristics of all female participants categorized by history of GDM are provided in Table 1.

Specifically, females with a history of GDM were on average 2 years older (36.00 (31.00, 41.00) vs. 34.00 (29.00, 40.00)) and experienced menarche earlier (12.00 (11.00, 13.00) vs. 12.00 (12.00, 14.00)) compared to those without such history. Furthermore, females with a history of GDM were more likely to have had macrosomic births (19.8% vs. 12.5%), report prediabetes (12.7% vs. 3.3%), and indicate health risks associated with diabetes (40.5% vs. 16.4%) (Table 1).

It is noteworthy that female participants with a history of GDM had a significantly lower DII score compared to those without (1.62 (0.58, 2.93) vs. 2.05 (0.91, 2.93)) (Table 1). Further comparisons of individual components of the DII between the two groups revealed that females with a history of GDM had lower inflammatory scores in Fiber (0.27 (-0.18, 0.59) vs. 0.38 (-0.00, 0.59)), Magnesium (0.11 (-0.09, 0.27) vs. 0.16 (-0.00, 0.29)), N3 fatty acids (-0.15 (-0.23, -0.03) vs. -0.11 (-0.23, 0.01)), and Selenium (-0.12 (-0.17, -0.06) vs. -0.11 (-0.17, -0.02)) (all P < 0.05, Table S1). Finally, we compared all components of the complete blood cell count between female participants with and without a history of GDM, finding that those with a history of GDM had a lower Hematocrit (38.35 (36.40, 40.20) vs. 38.60 (36.50, 40.70)) (P < 0.05, Table S2).

Table 1 Baseline characteristics of participants grouped by with or without a history of GDM
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Characteristics of female participants with a history of GDM grouped by Prediabetes Status

Based on the findings presented in Table 1, female participants with a history of GDM exhibited a higher prevalence of prediabetes. To further explore this association, we analyzed the characteristics of these females stratified by prediabetes status. Out of 126 female participants with a history of GDM, 16 were diagnosed with prediabetes. Compared to those without prediabetes, females with prediabetes were more likely to have hypertension (37.5% vs. 13.6%) and thyroid disorders (25.0% vs. 5.5%). Additionally, they reported shorter sedentary activity times (240.00 (150.00, 360.00) vs. 390.00 (240.00, 540.00) minutes) (all P < 0.05, Table S3).

It is noteworthy that females with prediabetes had higher Dietary Inflammatory Index (DII) scores compared to those without prediabetes (2.75 (1.27, 3.42) vs. 1.59 (0.44, 2.81)). Specifically, females with prediabetes exhibited higher scores in Vitamin B6 (0.03 ± 0.15 vs. -0.09 ± 0.16) and Vitamin E (0.38 (0.05, 0.42) vs. 0.23 (-0.05, 0.38)) (all P < 0.05, Table 2).

Furthermore, we conducted a comparative analysis of complete blood count (CBC) components among female participants with a history of GDM stratified by prediabetes status. The results revealed that females with prediabetes had higher mean platelet volume (8.40 (8.00, 9.00) vs. 8.05 (7.15, 8.55)) (P < 0.05, Table 3).

Table 2 Comparison of dietary intake of each DII component between female participants with a history of GDM grouped by prediabetes status
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Table 3 Comparison of each component of complete blood cell count (CBC) between female participants with a history of GDM grouped by prediabetes status
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Association between DII and prediabetes in female participants with a history of GDM

We conducted a multivariable logistic regression analysis to explore the association between DII and prediabetes in female participants with a history of GDM. We observed that higher DII scores were correlated with prediabetes in this cohort. When analyzed as a continuous variable, DII showed a positive correlation with prediabetes. In the unadjusted logistic regression model, the odds ratio (OR) was 1.68 (95% confidence interval [CI]: 1.09–2.59). After adjusting for confounding factors, including age, race, BMI, history of macrosomia, age at menarche, marital status, education level, smoking, drinking, hypertension, sedentary activity time, thyroid issues, and mean platelet volume, the fully adjusted Model 2 indicated that DII remained significantly associated with prediabetes (OR: 1.97; 95% CI: 1.03–3.77) (Table 4). When considered as categorical variables, individuals with Medium DII (OR: 28.67; 95% CI: 1.34-614.39) and High DII (OR: 36.40; 95% CI: 1.71-772.83) showed a significantly higher risk of prediabetes compared to those with Low DII (Table 4).

Furthermore, RCS analysis was employed to explore the nonlinear relationship between DII and prediabetes among female participants with a history of GDM. The RCS curve indicated no significant nonlinear negative association between DII and GDM history (p for nonlinear = 0.617) (Fig. 2). We also conducted stratified analyses to assess whether the association between DII and prediabetes remained consistent across different subgroups among female participants with a history of GDM. However, no statistically significant differences were observed across subgroups stratified by macrosomia history (Yes, No), age (Below 35, Above 35), race (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, Other Race), education level (Less than high school, High school, More than high school), marital status (Married, Single, Living with partner), smoking (No, Yes), drinking (No, Yes), and hypertension (No, Yes) (all p for interaction > 0.05) (Fig. 3).

Table 4 Multivariable weighted logistic regression model revealed the relationship between DII and prediabetes in female participants with a history of GDM
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Fig. 2
figure 2

The RCS analysis on the association between DII and prediabetes in female participants with a history of GDM. RCS, restricted cubic spline; DII, Dietary Inflammatory Index; GDM, gestational diabetes mellitus; OR, odds ratio; CI, confidence interval

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Fig. 3
figure 3

Subgroup analysis of the association between DII and prediabetes in female participants with a history of GDM. Each stratification was adjusted for age, race, BMI, birth history of macrosomia, age of menarche, marital status, education levels, smoking, drinking, hypertension, minutes sedentary activity, and mean platelet volume. OR, odds ratio; ; CI, confidence interval; BMI, body mass index; DII, Dietary Inflammation Index; Inf: Infinity

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Identification of Key Prediabetes-related dietary factors in female participants with a history of GDM

We aimed to develop a predictive nomogram model using 10-fold cross-validation of LASSO regression, incorporating all 28 dietary components and four covariates (hypertension, minutes of sedentary activity, thyroid issues, and mean platelet volume) to pinpoint dietary factors most closely associated with prediabetes in female participants with a history of gestational diabetes mellitus (GDM) (Fig. 4). Through LASSO regression analysis, we identified hypertension, sedentary activity duration, thyroid issues, mean platelet volume, vitamin B6, β-Carotene, polyunsaturated fatty acids, saturated fat, and vitamin C as the nine predictive variables in the nomogram model. Subsequently, we validated the robust predictive performance of this model using ROC curve analysis, yielding an area under the curve (AUC) of 88.6% (79.9–97.4%) (Fig. 5).

Fig. 4
figure 4

The LASSO penalized regression analysis for identifying key related factors. (A) The coefficient shrinkage process of all 28 dietary components and four covariates (hypertension, minutes sedentary activity, thyroid problem, and mean platelet volume), we represent the changes in coefficients of different features under various levels of shrinkage by drawing lines of different colors. (B) A 10-fold cross-validation of the LASSO regression model. LASSO, least absolute shrinkage and selection operator

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Fig. 5
figure 5

Establishment and validation of a risk prediction model for prediabetes in female participants with a history of GDM. (A) A nomogram model based on hypertension, minutes sedentary activity, thyroid problem, and mean platelet volume, and 5 key related dietary factors (vitamin B6, β-Carotene, polyunsaturated fatty acids, saturated fat, and vitamin C) identified by LASSO regression analysis. (B) ROC curve for evaluating the predictive power for predicting prediabetes in female participants with a history of GDM of the nomogram model. LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic. * P value < 0.05

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Discussion

In this study, we investigated the relationship between DII and the risk of GDM using data from the nationally representative NHANES (2011–2014). In contrast to findings from previous analysis of the 2007–2010 NHANES data, our study revealed that lower DII scores were associated with an increased likelihood of GDM occurrence. Previous studies had shown a strong association between higher DII scores and elevated GDM risk [19]. Diet is widely recognized as a significant factor influencing inflammation and oxidative stress in vivo, which in turn affects inflammatory responses across various diseases [20]. The onset of GDM has been linked to pancreatic β-cell dysfunction, delayed β-cell response to blood glucose levels, and insulin resistance exacerbated by placental hormone release [21]. Dietary control and modification have been demonstrated to partially prevent and improve GDM outcomes [22, 23]. Research indicates that pro-inflammatory diets can induce insulin resistance by elevating circulating levels of inflammatory factors, thereby increasing GDM risk [24]. Conversely, plant-based anti-inflammatory diets and low-fat plant-based diets have been shown to mitigate GDM development by reducing insulin resistance, inflammation levels, and improving β-cell dysfunction [25, 26].

We attribute this outcome to several factors. First, individuals with a history of GDM may exhibit heightened awareness of healthy dietary intake due to their recognition of the disease’s long-term implications. This phenomenon aligns with the ‘health belief model’ in psychology, which posits that individuals, upon receiving disease-related information (such as risk and susceptibility), tend to evaluate their health status and consider behavioral changes. During the survey period from 2011 to 2014, those with a history of GDM likely realized the benefits of an anti-inflammatory diet in mitigating later health complications associated with the disease, prompting dietary improvements. This aligns with the decision-making processes outlined in the health belief model [27]. Consequently, this psychological shift has led to the implementation of effective intervention programs across various disease areas [28, 29].

Secondly, changes in population demographics and improvements in diet quality have also played a role. Recent studies indicate a global trend towards enhanced diet quality, driven by factors such as educational attainment, urbanization, and increased consumer demand for healthier diets and chronic disease management [30,31,32]. While these improvements vary across regions, our study sample, consistent with previous research, suggests that females of childbearing age with a history of GDM may have contributed to reduced DII scores from 2011 to 2014 through proactive dietary adjustments towards foods with lower inflammatory potential.

We found that the presence or absence of prediabetes among females of childbearing age with a history of GDM correlates with DII levels. Specifically, patients with prediabetes exhibited significantly higher DII levels compared to those without prediabetes. This difference remained statistically significant between the high and low quartiles of DII after adjusting for modeling factors. These findings support a positive correlation between prediabetes prevalence and inflammatory potential as assessed by DII, consistent with prior research [11, 33, 34]. This suggests that among individuals with a history of GDM, insufficient focus on maintaining an anti-inflammatory diet may increase the risk of developing prediabetes or diabetes. Moreover, higher DII levels may be associated with elevated all-cause, cardiovascular, and gastrointestinal cancer mortality rates [35].

Our cross-sectional study from 2011 to 2014 further underscores the association of DII with diabetes and related conditions, highlighting variability in dietary patterns within the population. Additionally, we developed a risk prediction model for prediabetes among female participants with a history of GDM. Using LASSO regression analyses, we identified nine predictor variables: hypertension, sedentary activity duration, thyroid disorders, mean platelet volume, vitamin B6, beta-carotene, polyunsaturated fatty acids, saturated fat, and vitamin C. These dietary factors were pivotal in predicting prediabetes. Our study demonstrated robust predictive modeling through column-line graphs and ROC curve analysis, which aligns with previous predictive and machine learning models for diabetes risk in GDM based on demographic, clinical, and biomarker data [36,37,38].

By focusing on dietary components, our study complements existing prediction models, showing high efficacy in predicting prediabetes. This approach encourages further exploration of diverse prediction methods, offering new insights for clinical strategies aimed at preventing prediabetes following GDM.

Our study exhibits several strengths and limitations. The NHANES dataset, with its extensive sample size and complex multi-stage probability sampling design, provided robust statistical power and reliable results. Specifically, our analysis from 2011 to 2014 revealed a novel finding: a negative correlation between GDM and the DII. This differs from previous research, suggesting a potential protective effect of GDM on inflammatory potential. Future studies should further explore this correlation, emphasizing the variability in dietary structures among populations. As economic and social developments promote dietary initiatives, leveraging dietary modifications could effectively manage diseases and prevent associated risks long-term.

However, our study has limitations. Firstly, due to the cross-sectional nature of NHANES, we could not establish a causal relationship between DII and GDM, necessitating prospective investigations. Secondly, NHANES data primarily reflects the United States, potentially limiting the generalizability of our findings to regions with differing GDM incidences and diagnostic criteria. Lastly, GDM is a multifaceted condition influenced by genetics and lifestyle factors. Despite including numerous covariates in our model, residual and unmeasured covariates may still confound our results.

In conclusion, our study suggests avenues for future research, particularly examining the impacts of dietary and psychological changes on DII scores. Such investigations may hold therapeutic potential in attenuating disease progression and preventing GDM onset.

Conclusion

Our study revealed that females with a history of GDM tend to exhibit lower DII levels compared to their counterparts without GDM, possibly attributable to heightened self-protection awareness and improved global dietary practices. However, further comprehensive cross-sectional studies and causal analyses are warranted to delve deeper into these associations. Notably, among females with a history of GDM, those at risk of prediabetes demonstrate a propensity towards consuming more inflammatory foods, underscoring the positive association between DII and diabetes onset. Additionally, we developed a predictive model for prediabetes risk among female participants with GDM history, yielding promising predictive capabilities.

Data availability

Publicly available datasets were analyzed in this study. All the raw data used in this study are derived from the public NHANES data portal (https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx (accessed on 7 August 2024)).

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Acknowledgements

We acknowledge the NHANES database for providing their platforms and contributors for uploading their meaningful datasets.

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Authors and Affiliations

  1. Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian Province, China

    Yanhong Xu

  2. Fujian Clinical Research Center for Maternal-Fetal Medicine, Fuzhou, Fujian Province, China

    Yanhong Xu

  3. The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China

    Zhiying Yao, Jiayi Lin, Nan Wei & Ling Yao

  4. Xiangya Hospital, Central South University, Changsha, Hunan Province, China

    Zhiying Yao

  5. National Key Obstetric Clinical Specialty Construction Institution of China, Fuzhou, Fujian Province, China

    Yanhong Xu

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Contributions

Yanhong Xu: writing—original draft preparation, Conceptualization and methodology, data curation; Zhiying Yao: writing—review and editing, formal analysis; Jiayi Lin: writing—review and editing, validation; Ling Yao: writing—review and editing, formal analysis; Nan Wei: writing—review and editing, formal analysis. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yanhong Xu.

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The NCHS Ethics Review Board protects the rights and welfare of NHANES participants. The NHANES protocol complies with the U.S. Department of Health and Human Services Policy for the Protection of Human Research Subjects. NCHS IRB/ERC Protocol No.: 2011–2014. Ethical review and approval were waived for this study as it solely used publicly available data for research and publication.

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Xu, Y., Yao, Z., Lin, J. et al. Dietary inflammatory index as a predictor of prediabetes in women with previous gestational diabetes mellitus. Diabetol Metab Syndr 16, 265 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-024-01486-7

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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