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Mediating effect of TyG index on the association between glucose-lipid metabolism-related dietary pattern and T2DM: a propensity score-matched analysis

BMC Endocrine Disorders volume 25, Article number: 114 (2025) Cite this article

Abstract

Objective

This study aimed to investigate the association of dietary patterns (DPs) with risk of T2DM, emphasizing the intermediary role of HOMA or TyG indices among a Chinese adult population.

Research design and methods

Directed acyclic graphs combined with propensity score matching were used to minimize confounding, resulting in 1330 subjects for final analysis. Principal component analysis and reduced rank regression, applied to eleven food groups. Multivariable logistic regression and restricted cubic spline regression models were used to assess associations between there DPs with prevalent T2DM, as well as insulin resistance and β-cell function (HOMA-TyG). Mediation analyses were conducted to evaluate whether the HOMA-TyG index mediated the relationship between DPs and T2DM.

Results

The DP1, characterized by high intakes of poultry, meat, and preserved foods, was associated with elevated body mass index, triglycerides, and hemoglobin A1c. Both PCA-DP1(ORQ4VsQ1 = 2.15, 95% CI: 1.53–3.03) and RRR-DP1 (ORQ4VsQ1 = 1.69, 95% CI: 1.82–3.58) were significantly positively correlated with T2DM. RRR-DP1 additionally demonstrated a dose-dependent relationship with HOMA-insulin resistance and TyG. Furthermore, the TyG index mediated approximately 19.51% of the relationship between RRR-DP1 and T2DM.

Conclusions

These findings indicate that glucose-lipid metabolism-related dietary pattern, notably high in animal fat, exacerbates insulin resistance and heightens T2DM risk. Tailoring dietary interventions to modify this pattern may be an effective strategy for preventing and managing T2DM.

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Introduction

Over the past several decades, dramatic socio-economic transitions and rapid urbanization in China have coincided with a marked rise in the prevalence of type 2 diabetes mellitus (T2DM) [1]. It is characterized by impaired insulin secretion and suboptimal peripheral tissue response to insulin, ultimately affecting energy and glucose homeostasis [2]. Research has shown that T2DM development is multifactorial, involving genetic, epigenetic, lifestyle-related, and environmental factors [1]. According to the 2019 Global Burden of Diseases, dietary factor contributed to 29.7% of the disability-adjusted life-year of diabetes [3]. Risk factors include higher intakes of unprocessed and processed meat, and low intakes of fresh fruit and vegetables [4, 5].

Emerging evidence underscores that focusing on overall dietary patterns (DPs), rather than individual foods or nutrients, may offer more practical insights for dietary interventions in the general population [6, 7]. Multiple analytical approaches have been used to identify these patterns, with a priori (dietary quality indices) and posteriori (factor analysis or principal component analysis [PCA]) methods being the most common [8]. Hybrid approaches, such as reduced-rank-regression (RRR), can further elucidate causal pathways by combining both dietary data and intermediate biomarkers [9]. While RRR has been applied to examine DPs and various metabolic pathways, including glucose homeostasis [10], inflammatory biomarkers [11], and blood lipids profiles [12], evidence linking glucose-lipid metabolism-related dietary patterns to T2DM, especially in Chinese adults, remains scarce. To bridge this gap, we used three indicators of glucose metabolism, lipid metabolism, and adiposity measures in our RRR analysis, specially selecting HbA1c and fasting C-peptide over fasting glucose and fasting insulin to better capture longer-term glycemic control and endogenous insulin secretion [13].

Insulin resistance (IR), hyperinsulinemia and islet beta cell dysfunction are crucial in T2DM onset and progression [14], and dietary factors, particularly those rich in energy and macronutrient intakes, are implicated in both hyperinsulinemia and IR [15]. Although the homeostatic model assessment (HOMA) is widely used to evaluate β-cell function and IR [16], the triglyceride glucose (TyG) index has shown superior performance for predicting IR and has been proposed as a viable alternative marker [17]. Elevated TyG levels have been associated with T2DM and may serve as an early indicator of high-risk individuals [18]. Although previous studies have linked DPs to HOMA-IR [19] and suggest that diets high in saturated fat exacerbate IR [20] while fiber intake improves glycemic control [21], investigations into the roles of DPs and TyG in T2DM are limited, and few have explored whether IR mediates the relationship between DPs and T2DM prevalence.

To address these gaps, we employed PCA and RRR to identify dietary patterns associated with glucose-lipid metabolism among Chinese adults. We then evaluated the association of these patterns with T2DM prevalence, with a particular focus on the mediating roles of HOMA and TyG indices.

Research design and methods

Inclusion and exclusion criteria

This community-based observational, cross-sectional study was conducted from June 2019 to December 2020 in Jiangsu Province, eastern China. A total of 2230 subjects, including 793 diabetes and 1437 non-diabetes were enrolled. All participants underwent a health examination, sample collection and a standardized face-to-face questionnaire including general information, self-reported medical history and the use of medications, family history of diseases (hypertension, diabetes, etc.), lifestyle (cigarette smoking, alcohol drinking, etc.), dietary behavior, and so on. All physical examinations and interviews were performed by the trained healthcare staff. This study was approved by the Institutional Review Board of Changzhou Centers for Disease Control and Prevention (Changzhou CDC Ethics [2019] 01), and all the participants signed an informed consent form before participation.

Clinical and biochemical data

T2DM was defined according to the criteria of American Diabetes Association (ADA) [2], based on at least one of the following: a self-reported physician diagnosis, current use of hypoglycemic medications or insulin, fasting blood glucose (FBG) ≥ 7.0 mmol/L or HAb1c ≥ 6.5% validated at least twice in different periods. Body mass index (BMI) was calculated by weight in kilograms divided by the square of height in meters. It was divided into three levels: normal weight (18.5 ≤ BMI < 25.0), overweight (25.0 ≤ BMI < 28.0) and obesity (BMI ≥ 28.0) [22]. The planned structured exercise was defined as engaging in (1) ≥ 20 min of strenuous exercise (e.g., running, climbing, cycling) for ≥ 3 days during the past week, (2) ≥ 30 min of regular exercise a little more challenging than usual (e.g., swimming, doubles tennis, volleyball) for ≥ 5 days during the past week, or (3) ≥ 30 min of walking faster than 5 km/h for ≥ 5 days during the past week [23]. In our study, Participants were categorized into three groups according to exercise frequency: never, activity but not sufficient (< 3 day/week of planned), sufficient (≥ 3 day/week of planned). Smoking status was defined as having smoked at least 100 cigarettes in one’s lifetime. Drinking status was defined consuming alcohol at least once per month. Blood pressure was measured three times by trained nurses using a standard mercury sphygmomanometer according to a protocol from American Heart Association (AHA) [24]. Hypertension was defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg or history use of antihypertensive medications.

Plasma lipid concentrations [total cholesterol (TC), total triglycerides (TG), low-density lipoprotein cholesterol (LDLC) and high-density lipoprotein cholesterol (HDLC)] were measured using AU680 Chemistry Analyzers (Beckman-Coulter, Brea, CA, USA). High TC (≥ 6.22 mmol/L), High TG (≥ 2.26 mmol/L), low HDLC (< 1.04 mmol/L) and High LDLC (≥ 4.14 mmol/L) were defined according to the Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults (revised 2016) [25]. Fasting insulin and C-peptide concentrations were measured using iFlash 3000 immunoanalyzers (YHLO Biotech, Shenzhen, China). The triglyceride-glucose (TyG) index was calculated based on the formula: TyG index = log [[fasting TC (mg/dL) * fasting blood glucose (mg/dL)]/2] [26]. The homeostasis model assessment of β-cell function (HOMA-%β), HOMA of insulin resistance (HOMA-IR) was obtained [16]. HOMA-IR was calculated as [FBG (mmol/L) * fasting insulin (IU/mL)]/22.5. HOMA-%β was calculated as [20*fasting insulin (IU/mL)/[FBG (mmol/L)-3.5]] %. Glucose and triglyceride values were converted from mmol/L to mg/dL and multiplied by 18.020 and 88.545, respectively.

Selection of covariates and propensity score matching

Directed acyclic graphs (DAGs), created using the DAGitty v3.0 (http://dagitty.net/), were utilized to identify the minimal sufficient set of confounders for this study [27]. DAGs have been shown to outperform traditional variable selection methods, such as step-wise regression procedures, by explicitly modeling causal relationships [28]. For this analysis, T2DM was designated as the outcome and dietary patterns as the exposure. The minimal sufficient adjustment set included age, gender, household income, education, occupation, drinking, smoking, physical exercise, family history of diabetes, obesity, and metabolic syndrome (Figure S1). Subsequently, covariate balancing generalized propensity score were computed, and a 1:1 nearest neighbor matching algorithm with a caliper of 0.02 was used to create matched case-control pairs. Standardized mean differences (SMD) were evaluated before and after matching to ensure balance of covariates. Following propensity score matching (PSM), 1330 participants remained for subsequent analyses.

Dietary analysis

Dietary intake over the preceding 12 months was assessed using a food frequency questionnaire (FFQ) adapted from a previous study and from the National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention [29, 30]. Eleven major food items of the local area were included: rice, wheat, poultry, meat, fish, eggs, fresh vegetables, fresh fruits, soybeans, dairy products, and preserved food. Consumption frequencies were recorded as never/rarely, 1–3 days/week, 4–6 days/week, or daily, corresponding to 0, 2, 5, or 7 days/week, respectively. The estimated weekly intake for each food group was calculated as the average consumption per day multiplied by the frequency category. A subset of 197 participants completed repeat FFQs within one year, demonstrating satisfactory reproducibility.

PCA was used to derive dietary patterns based on these 11 food groups. Patterns were identified according to screen plot, eigenvalues > 1, interpretability and the amount of variance explained. Factor loading with values above 0.25 or lower than − 0.25 indicated prominent types. The RRR was performed to identify glucose-lipid metabolism-related dietary patterns, using 11 food groups as predictors and the the following biomarker responses: HbA1c, C-peptide, TC, TG, HDLC, LDLC, BMI. Absolute factor loading > 0.25 characterized each dietary pattern, and corresponding factor scores were derived from weighted combinations of standardized food group intakes. Spearman’s correlation was used to relate these factors to continuous variables, while point-biserial correlation was employed for binary variables.

Statistical analysis

All statistical analyses were conducted on software Stata version 15.0 and R version 4.0.5. The Shapiro–Wilk test was conducted to assess the normality of continuous variables. Normally distributed data are expressed as mean ± standard deviation (SD), whereas non-normally distributed data are presented as median (25th-75th percentile). Categorical variables are expressed as percentages (%). The chi-square test, Fisher’s exact test, Mantel-Haenszel method, and ANOVA were employed to compare group differences where appropriate.

Restricted cubic spline regression (RCS) models with three knots placed at the 1th, 50th and 99th percentiles were conducted to explore potential non-linear dose-response relationship between dietary patterns and adverse outcomes (insulin resistance (HOMA-IR, TyG index) or islet β cell function (HOMA-%β) or T2DM). Each dietary pattern score was also divided into quantiles, with the fourth quantile (Q4) signifying the highest consumption. Multiple logistic regression and generalized linear models were modeled to obtain effective parameters (odds ratios (OR) or beta coefficients (β) and 95% confidence intervals (CI)) and applied to assess the relationships between prevalent T2DM and dietary patterns. Three models were specified: model 1 was crude without any covariables, model 2 was adjusted for age, gender, education, occupation, household income, physical exercise, drinking, smoking, and model 3 further adjusted for family history of diabetes, obesity, metabolic syndrome. Covariates in models were derived from the primary DAG analysis. Mediation analyses were performed to assess the indirect effects of DPs on the risk of T2DM through HOMA-IR, HOMA-%β, or TyG index, while controlling for covariates. The bootstrap method with 5000 samples was used to estimate indirect and total effects, and the mediation ratio was calculated as (indirect effect/total effect) *100. Cochran-Armitage trend tests were employed for statistical trend analyses. All reported P-values were two-sided and values 0.05 were considered statistically significant.

Sensitivity and subgroup analyses

Missing data were imputed using multiple imputation (“mice” package, https://cran.r-project.org/web/packages/mice/index.html). Additional models were fitted for comparison with the DAG approach, including full models containing all variables and models with another statistical variable selection method (least absolute shrinkage and selection operator (LASSO) regression) [31]. Covariates were gender, marriage status, physical activity, family history of diabetes, BMI, dyslipidemia. Subgroup analysis was further performed according to age, gender, and BMI categories.

Results

Characteristics of dietary patterns

A total of 2230 (49.98%, males) participants provided dietary data, with ages ranged from 20 to 92 years (mean 53.73 years). After propensity score matching, the balance between case and control groups improved substantially, as indicated by SMDs below 10% for characteristics including age, gender, education status, occupation, smoking, drinking and family history of diabetes (Fig. 1; Table 1).

Fig. 1
figure 1

Flow chart for screening subjects with inclusion and exclusion criteria

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Table 1 Descriptive characters of participants in a community-based case-control study before and after matching
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As Fig. 2 and Table S1 depicts, three glucose-lipid metabolism-related dietary patterns were identified using PCA and RRR methods. The first dietary pattern (DP 1) was characterized by high intakes of animal fat foods (poultry, meat) and preserved foods. The second dietary pattern (DP 2) was marked by higher consumption of rice and wheat, whereas the third dietary pattern (DP 3) featured greater intakes of fish, egg, soybeans, and dairy products. Correlations between factors and the response variables estimated, estimated via PCA and RRR, was presented in Figure S2. Notably, DP 1 showed a significant positive association with elevated TG, BMI, and HbA1c.

Fig. 2
figure 2

Spider-web diagram of factor loadings for selected food groups for the dietary pattern identified using principal component analysis (A) and reduced rank regression (B). PCA, principal component analysis; RRR, reduced rank regression; DP, dietary pattern

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Relationship of different dietary patterns with T2DM risk

Restricted cubic spline regression revealed linear dose-response associations of PCA-DP1 and RRR-DP1 with risk of T2DM (Fig. 3). In the fully adjusted models, individuals in the highest quartile of these patterns had a markedly increased T2DM prevalence compared with those in the lowest quartile, whereas RRR-DP2 was negatively associated with T2DM (Table 2). Adjusting for additional confounders in model 3, DP1 continued to demonstrate a significant positive association with T2DM for both PCA [ORQ4VsQ1 = 2.15, 95% CI: 1.53–3.03, P− trend < 0.01] and RRR [ORQ4VsQ1 = 1.69, 95% CI: 1.82–3.58, P− trend < 0.01]. Conversely, RRR-DP2 maintained a significant negative association with T2DM [ORQ4VsQ1 = 0.65, 95% CI: 0.64–1.23, P− trend = 0.021], though PCA-DP2 did not reach statistical significance [ORQ4VsQ1 = 0.89, 95% CI: 0.64–1.23, P− trend = 0.581]. Neither PCA-DP3 nor RRR-DP3 showed significant associations with T2DM (Table 2).

Fig. 3
figure 3

Dose-response association between dietary patterns (DP) of each method (principal component analysis and reduced rank regression) and prevalent T2DM (N = 1330). (A ~ C) PCA-derived DP 1 (panel A), PCA-derived DP 2 (panel B), PCA-derived DP 3 (panel C). (D ~ F) RRR-derived DP 1 (panel D), RRR-derived DP 2 (panel E), RRR-derived DP 3 (panel F). T2DM, type 2 diabetes mellitus. Odds ratios (OR) and 95% confidence interval derived from restricted cubic splines regression, with knots placed at the 1th, 50th and 99th percentiles of the distribution of DP scores. Models were adjusted for age, gender, education status, household income, physical exercise, drinking, smoking, family of diabetes, obesity, metabolic disorders

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Table 2 Odds ratios (OR) and 95% confidence interval (CI) of prevalent T2DM associated with each score increase in DP
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Sensitivity analyses using LASSO-based confounder selection supported these results, reinforcing the robust positive association of DP1 with T2DM [PCA: ORQ4VsQ1 = 2.03, 95% CI: 1.47–2.83, P-trend < 0.001; RRR: ORQ4VsQ1 = 2.38, 95% CI: 1.72–3.29, P-trend < 0.001] (Table S2).

Relationship of dietary patterns and HOMA-TyG index

As shown in Figure S3 and Fig. 4, we also observed the dose-response association of PCA-DPs or RRR-DPs with the HOMA-TyG index. RRR-DP1 had a significant linear association with both HOMA-IR and the TyG index (PHOMA−IR = 0.042, PTyG−index = 0.001, respectively), and PCA-DP1. Each one-unit increment of RRR-DP1 score was linked to 1.12 units higher HOMA-IR (β = 1.12, P < 0.001, model 3) and 0.14 units higher TyG (β = 0.14, P < 0.001, model 3) (Table 3). Likewise, PCA-DP1 was positively associated with TyG [βQ4VsQ1 = 0.13, 95%CI: 0.04–0.21, P− trend = 0.006]. Again, Model 2 showed similar trends, and LASSO-based sensitivity analyses corroborated the positive link of DP1 with TyG [ORQ4VsQ1 = 0.12, 95% CI: 0.04–0.20, P− trend < 0.001] and RRR [ORQ4VsQ1 = 0.15, 95% CI: 0.07–0.22, P− trend < 0.001] (Table S2).

Fig. 4
figure 4

Dose-response association between reduced rank regression (RRR)-derived dietary patterns (RRR-derived DP) and the homeostasis model assessment of insulin resistance (A ~ C) [HOMA-IR, n = 1330. DP 1 (panel A), DP 2 (panel B), DP 3 (panel C)], triglyceride-glucose index (D ~ F) [TyG index, n = 1330. DP 1 (panel D), DP 2 (panel E), DP 3 (panel F)], and homeostasis model assessment of β cell function (G ~ I) [HOMA-%β, n = 1330. DP 1 (panel G), DP 2 (panel H), DP 3 (panel I)]

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Table 3 Beta coefficients (β) and 95% confidence interval (CI) of HOMA-TyG index associated with each score increase in DP1
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Subgroup analysis

To confirm the association between DP1 and prevalent T2DM, subgroup analyses by gender, age, and BMI (Fig. 5) showed younger participants, and those with higher BMI were more prone to T2DM. Among males under 60 years of age, elevated RRR-DP1 scores were positively associated with T2DM prevalence and higher TyG (Table S3, S4).

Fig. 5
figure 5

The association between DPs on the risk of T2DM (A, B) and TyG index (C, D) by age, gender, and BMI category. OR, odds ratios; CI, confidence interval; β, estimated coefficients. ORs or β were adjusted for age, gender, education status, household income, physical exercise, drinking, smoking, family of diabetes, obesity, metabolic disorders. DPs, dietary patterns; T2DM, type 2 diabetes mellitus; TyG index, triglyceride-glucose index. BMI, body mass index. Normal (18.5 ≤ BMI < 25.0), overweight (25.0 ≤ BMI < 28.0) and obesity ((BMI ≥ 28.0)

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Mediation by TyG in the relationship between dietary patterns and T2DM risk

Finally, RRR-DP1 was significantly associated with T2DM after full adjustment, and mediation analysis indicated that the TyG index accounted for 19.51% of this relationship (Table 4, Tables S5, S6). Sensitivity analyses with LASSO-based models yielded comparable findings, underlining the mediating role of the TyG index in the link between dietary patterns and T2DM (Table S7).

Table 4 Mediation effects of TyG index on the association of DP with prevalent T2DM
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Discussion

In this study, we applied both PCA and RRR within a propensity-score–matched case-control design to evaluate how different DPs influence T2DM risk, insulin resistance, and β-cell function. Our results reveal that a “glucose–lipid metabolism–related” DP1, distinguished by high intakes of poultry, meat, and preserved foods, is strongly associated with T2DM prevalence—an effect that appears mediated in part by elevated HOMA-IR and the TyG index.

Dietary patterns represents a complex interplay of foods and nutrients that jointly influence metabolic health [32]. By combining PCA, which aggregates food group correlations, and RRR, which integrates disease-specific biomarkers, we identified three distinct DPs. Consistent with prior epidemiological and interventional research [33,34,35], DP1 was characterized by significant consumption of processed or preserved meat and minimal fruit or vegetable intake—factors that have been repeatedly linked to adverse metabolic outcomes, including hypertriglyceridemia and postprandial hyperglycemia [36], contributing to IR and β-cell dysfunction [37].

Conversely, RRR-DP2—encompassing rice, wheat, and dairy—displayed an inverse association with T2DM, underscoring how biomarker-driven methods (RRR) can highlight potentially protective dietary components, such as high-fiber carbohydrates and certain dairy products [38, 39]. Although a “high-carbohydrate” diet is often viewed as a risk factor for T2DM [40], not all carbohydrate sources carry equal metabolic risks [41]. For instance, whole grains or minimally processed wheat and rice with higher fiber content can moderate postprandial hyperglycemia and reduce IR through improved glycemic control and altered gut microbiota composition [42]. Moreover, cultural and regional factors—including portion sizes, cooking methods, and the combined intake of vegetables or lean protein—may collectively influence the net metabolic effect of carbohydrate-rich foods [43]. Thus, while RRR-DP2 appears high in carbohydrates, its potential protective effect may reflect the quality and context of these carbohydrates, rather than the simple quantity of total carbohydrates. Future research assessing refined versus unrefined grain intake could provide additional insights into these protective trends.

Although randomized controlled trials (RCTs) note that carbohydrate-reduced, high-protein diets improve postprandial glycemia [44], our DP3, characterized by fish, eggs, and soybeans, showed no significant correlation with T2DM. One plausible explanation is that co-consumption of refined sugars and processed meats may have dampened the inherent metabolic benefits of fish and soy [36, 45], emphasizing the importance of overall dietary context in shaping glycemic and lipid responses [46, 47].

Taken together, these patterns illustrate the multifaceted nature of diet–disease relationships. The robust linkage between DP1 and T2DM risk, in conjunction with its positive relationship to HOMA-IR and TyG, highlights IR as an early and critical driver of glucose dysregulation [48, 49]. Diets rich in saturated fats and processed foods may accelerate IR through increased circulating triglycerides, postprandial hyperglycemia, and chronic low-grade inflammation, ultimately burdening β-cell function [37]. Conversely, monounsaturated and polyunsaturated fats (MUFAs and PUFAs) could counteract IR by modifying cellular membrane composition and reducing inflammatory responses [50]. Our subgroup analyses further indicate that younger and obese individuals may be more susceptible, echoing findings in large cohort studies [9]. Coupled with evidence of TyG mediating role, these results support the hypothesis that dysregulated glucose–lipid metabolism, fostered by certain dietary behaviors, predisposes to T2DM. Interestingly, in some sensitivity models, we observed instances of a negative proportion mediated, which can occur when the indirect effect (via TyG) and direct effect of the dietary pattern move in opposite directions—a phenomenon known as competitive mediation [51, 52]. Such negative mediation proportions reflect the complexity of metabolic pathways and indicate that certain dietary components may partially mitigate or offset the diet’s overall impact on T2DM. Reporting both direct and indirect effect estimates provides a clearer interpretation of these opposing forces [53].

In this context, adhering to a healthy lifestyle—including diets rich in fiber, unsaturated fats, whole grains, fruits, and vegetables—can effectively reduce the incidence and severity of metabolic syndrome [48, 54]. Plant-oriented diets appear to ameliorate IR by improving lipid profiles and attenuating systemic inflammation [55]. Although our study focused on T2DM specifically, the observed protective effect of RRR-DP2 points to broader metabolic benefits and highlights the need for integrated lifestyle interventions that target IR before hyperglycemia becomes manifest.

A major strength of this study is our comprehensive approach to confounder management. Directed acyclic graphs (DAGs) and propensity score matching (PSM) facilitated rigorous adjustments, enhancing the validity of comparisons between diabetic and non-diabetic participants. Furthermore, mediation analyses clarified the potential mechanistic role of TyG in connecting these “glucose–lipid metabolism–related” diets to T2DM risk. Nevertheless, certain limitations warrant acknowledgment. First, the cross-sectional nature of this analysis precludes causal inferences. Second, reliance on self-reported food frequency questionnaires introduces possible recall bias, and our participants from eastern China may not represent other dietary and genetic backgrounds. Additional large-scale, longitudinal, or interventional studies are needed to replicate these findings and substantiate their broader applicability.

Conclusions

In summary, our results indicate that both PCA-DP1 and RRR-DP1 are positively associated with insulin resistance (HOMA-IR, TyG index) and heightened T2DM risk in Han Chinese population, whereas RRR-DP2 demonstrates a protective association against T2DM. Furthermore, our study provides evidence that the TyG index partially mediates the link between the glucose–lipid metabolism–related RRR-DP1 and T2DM, underscoring the pivotal role of dysregulated lipid–glucose pathways in the onset of type 2 diabetes.

Data availability

Availability of data and materialsThe datasets generated and analyzed during the current study are not publicly available due to privacy and ethical considerations but can be available from the corresponding author upon reasonable request.

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Acknowledgements

The authors gratefully acknowledge Director Junmin Ji from the Changzhou Center for Disease Control and Prevention for his valuable support in management and storage of the collected samples. The authors are very grateful to thank the staff and participants of Hongmei Community Health Service Centers of Changzhou as well as Xixiashu Community Health Service Centers of Changzhou for their contributions. In addition, the authors wish to thank undergraduate students and volunteers who participated in this project.

Funding

This work was supported by the National Natural Science Foundation of China [81803198, 81903353], National Natural Science Foundation of Jiangsu Province [BK20180187], China Postdoctoral Science Foundation (2021M691335), and the Key Program of the National Natural Science Foundation of China (81830023).

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Author notes

  1. ShuShu Li, Rong Xia and Xing Gong these authors were equally first authors.

Authors and Affiliations

  1. Department of Endocrinology and Metabolism, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China

    ShuShu Li, Hechun Liu, Zhangyao Su, Yucheng Liang & Tao Yang

  2. Changzhou Center for Disease Control and Prevention, 203 Taishan Road, Changzhou, 213022, PR China

    ShuShu Li & Huibin Dong

  3. Key Lab of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, PR China

    Rong Xia, Xing Gong, Chao Wang & Shoulin Wang

  4. Department of Respiratory & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, PR China

    Rong Xia

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Contributions

Shushu Li: Conceptualization, Software, Writing, Funding acquisition. Rong Xia: Formal analysis, Methodology, Writing. Xing Gong: Methodology, Software, Writing. Chao Wang: Data curation, Validation, Funding acquisition. Huibin Dong: Formal analysis, Investigation. Zhangyao Su: Data curation, Investigation. Yucheng Liang: Data curation, Investigation. Hechun Liu: Validation. Shoulin Wang: Conceptualization, Supervision, Funding acquisition. Tao Yang: Conceptualization, Supervision, Funding acquisition.

Corresponding authors

Correspondence to Shoulin Wang or Tao Yang.

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Ethical approval was granted from the Institutional Review Board of Changzhou Centers for Disease Control and Prevention (Changzhou CDC Ethics [2019] 01).

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Li, S., Xia, R., Gong, X. et al. Mediating effect of TyG index on the association between glucose-lipid metabolism-related dietary pattern and T2DM: a propensity score-matched analysis. BMC Endocr Disord 25, 114 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-025-01892-6

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Keywords

BMC Endocrine Disorders

ISSN: 1472-6823

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