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Introduction

Somatic symptom disorder (SSD) refers to a psychiatric condition marked by an intense preoccupation with bodily symptoms.¹ These symptoms cause significant distress or disruption to daily life, evident through exaggerated and maladaptive cognitive, emotional, and behavioral reactions. For instance, an individual with SSD might exhibit chronic pain, gastrointestinal disturbances, or fatigue, alongside anxiety, catastrophizing thoughts, and avoidance behaviors.² Estimates of the prevalence of SSD in the general population range from 6.7% to 17.4%, with an average frequency of 12.9%.¹ Despite SSD’s elevated prevalence and strong association with detrimental functional consequences, such as diminished quality of life and increased healthcare utilization, research on interventions for its treatment and management remains sparse.³ As a result, healthcare providers continue to face challenges in providing adequate treatment for SSD.

Pharmacotherapy is one of the important approaches in the treatment of SSD. Pharmacological treatment for SSD includes non-psychotropic medications (such as β-blockers, non-steroidal anti-inflammatory drugs, and muscle relaxants for alleviating somatic symptoms), psychotropic medications, and herbal remedies (such as St. John’s wort). Psychotropic medications have been proven effective in treating some somatic symptoms, including selective serotonin reuptake inhibitors (SSRI) (eg, escitalopram, fluoxetine), serotonin and norepinephrine reuptake inhibitors (SNRI) (eg, venlafaxine), atypical antidepressants (eg, mirtazapine), and tricyclic antidepressants (eg, amitriptyline).⁴ However, a significant number of individuals suffering from SSD do not attain a treatment response, which is characterized by a reduction in severity exceeding 50%, following treatment with antidepressant monotherapy administered at adequate doses and for an adequate duration.⁵ Additionally, antidepressants mainly improve mood and relieve somatic symptoms by regulating neurotransmitters such as 5-hydroxytryptamine (5-HT) and norepinephrine.^5,6 However, patients with SSD may have more extensive neurotransmitter imbalances, including the dopamine (DA) system.⁷ Antipsychotic medications can regulate DA.⁸ Typical antipsychotic medications include chlorpromazine, trifluoperazine, and pimozide. These drugs, among the earliest developed in the field of antipsychotic medications, primarily exert their effects by targeting DA D2 receptors but may also interact with other receptors.⁹ In the 1990s, new antipsychotic medications are developed, known as second-generation or “atypical” antipsychotic medications, such as quetiapine, aripiprazole, and risperidone.¹⁰ Risperidone, classified as an atypical antipsychotic, exhibits robust antagonistic effects on DA D2, 5-HT2A, 5-HT2C, 5-HT1D, α1-, and α2-adrenergic receptors. It serves as an effective augmenting therapy for SSRI-resistant major depressive disorder. Furthermore, risperidone has the capability to counteract the SSRI-induced suppression of norepinephrine activity through its 5-HT2A antagonism.⁵ Paliperidone, which is the principal metabolite of risperidone, has demonstrated notable therapeutic benefits when used as an adjunctive treatment alongside citalopram in patients diagnosed with somatoform disorder.⁵ Compared with other second-generation antipsychotic medications, risperidone has relatively fewer adverse reactions.⁵ However, currently, antipsychotic medications are usually not used as monotherapy for SSD but are part of combination therapy, combined with other treatment methods to improve therapeutic efficacy.⁵

In recent years, the combined application of neuromodulation techniques and pharmacotherapy has provided new ideas for the treatment of SSD.¹¹ Repetitive transcranial magnetic stimulation (rTMS), a form of noninvasive brain stimulation, has the capability to modulate the neural excitability of specific brain regions, which has been applied in the treatment of various neurological and psychiatric disorders.¹² It has few side effects and is considered a promising intervention for improving symptoms in patients with SSD.¹³ rTMS has good efficacy in modulating targeted neural activity and further alleviating symptoms, so an increasing number of studies are focusing on examining the effects of rTMS on negative symptoms and cognitive deficits in patients with SSD.¹³ Xue Li et al report that rTMS combined with family intervention and risperidone has a synergistic effect in the treatment of schizophrenia, especially in improving positive symptoms, negative symptoms, and cognitive function.¹⁴ Additionally, data from Rui Li et al show that rTMS combined with risperidone treatment may affect the brain-gut-microbiota axis by regulating the gut microbiota in patients with chronic schizophrenia, thereby participating in the therapeutic effect.¹¹ Given this, this study aimed to explore the application effect of rTMS combined with low-dose antipsychotic medication (risperidone) in SSD.

It is worth noting that patients with SSD often have comorbid symptoms such as anxiety and depression and may have central nervous system dysfunction.^2,15 Neurotransmitter imbalance may be one of the mechanisms leading to symptoms and requires further study. It is well known that DA receptors are widely expressed in the body and function in both peripheral and central nervous systems.¹⁶ Their dysfunction is associated with anxiety and obsessive-compulsive symptoms.¹⁷ Furthermore, high levels of γ-aminobutyric acid (GABA) in the medial prefrontal cortex play an important pathophysiological role in the generation of somatic symptom disorders.¹⁸ Meanwhile, 5-HT abnormalities are also considered a key biological cause of somatic symptoms.⁵ On the other hand, data from Bumhee et al showed that patients with SSD have higher levels of interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) compared with healthy controls, and hypersensitive C-reactive protein (hs-CRP) and IL-6 have a complete mediating inhibitory effect on the relationship between the functional connectivity strength of the default mode network and depression levels.¹⁹ Against this background, we included studies on neurotransmitters and inflammatory markers to comprehensively evaluate the biological effects of rTMS combined with risperidone on SSD. By analyzing changes in neurotransmitters such as DA, GABA, 5-HT, and inflammatory markers, we can reveal the potential mechanisms of combination therapy and provide a scientific basis for the treatment of SSD.

Materials and Methods

Ethics Statement

All experimental procedures were approved by the Medical Ethics Committee of The First Affiliated Hospital of Harbin Medical University, and patients or their families provided informed consent and signed an informed consent form. This study adhered to the Declaration of Helsinki.

Subjects and Grouping

Ninety patients with SSD admitted to The First Affiliated Hospital of Harbin Medical University from May 2023 to May 2024 were selected as the study subjects. Inclusion criteria: Patients who met the diagnostic criteria for SSD in the International Classification of Diseases, 10th Edition²⁰ and the Chinese Classification and Diagnostic Criteria for Mental Disorders, 3rd Edition, and had a Patient Health Questionnaire-15 score ≥ 5; patients aged 18–75 years; patients who had not received antipsychotic medication or rTMS within the past 6 months; patients on the 17-Item Hamilton Rating Scale for Depression (HAMD)²¹ score ≥ 17 points before treatment. Exclusion criteria: Patients with severe physical diseases (including stroke, malignant tumors, chronic obstructive pulmonary disease, end-stage renal disease, liver cirrhosis, myocardial infarction, etc) or brain organic diseases; patients with a history of schizophrenia, mania, and depression associated with various types of organic lesions; patients with epilepsy, acute-phase cerebrovascular accidents, intracranial infections, and intracranial presence of metal or other foreign bodies; patients with suicidal behavior or suicidal ideation; patients with allergies or sensitivities to all the medications used in the study; and women who were pregnant or breastfeeding.

Depending on the treatment regimen, the enrolled patients were divided into the medication and combination groups, with 45 cases each. The medication group was treated with low-dose antipsychotic medication, while the combination group received low-dose antipsychotic medication combined with rTMS.

Treatments

Patients in the medication group were treated with low-dose antipsychotic medication, specifically risperidone (Janssen Pharmaceutical Co., Ltd., Xi ‘an, China; State Drug Administration H20010309), administered orally at an initial dose of 0.5 mg twice daily, with the dose adjustable to 1.5 mg within one week, for a treatment cycle of 8 weeks. The combination group received low-dose antipsychotic medication combined with rTMS. Specific methods: The antipsychotic medication regimen was identical to that of the medication group. YRDCCY-1 transcranial magnetic stimulator (Yi Ruide Medical Equipment New Technology Co., Ltd., Wuhan, China) was used for the treatment, with an output pulse frequency range of 0–100 Hz and a peak stimulation intensity range of 1.5–6 Tesla. Specific operations: The patient was laid on the magnetic therapy bed, relaxed systemically, with the “8”-shaped coil placed on the right dorsolateral prefrontal cortex. The treatment was stimulated with an intensity of 1Hz frequency and 80% motor threshold, delivering 800 pulses per session, lasting 20 minutes per session, once daily for 5 consecutive days, followed by a 2-day break. This cycle was continued for 8 weeks.

Observation Parameters

General data: General demographic information such as gender, age, years of education, ethnicity, marital occupation, and place of residence of the study subjects were collected through a retrospective case system.

Anxiety and depression: The Hamilton Anxiety Rating Scale (HAMA)²² was used to assess patients’ psychological states. The scale includes 14 items, such as anxious mood, fear, and cognitive function, scored on a 0–4 scale (0 = none, 1 = mild, 2 = moderate, 3 = severe, 4 = very severe), with a maximum score of 56. The HAMD-17 scale was used to assess the depressive symptoms in both groups of patients. This scale consists of 17 items covering aspects such as depressed mood, guilt, and suicidal thoughts, and is divided into five factors: anxiety somatization, weight, cognitive disturbance, retardation, and sleep disturbances. Each question has a different scoring standard, and the corresponding score is selected based on the patient’s response. The total score is then calculated.

Clinical efficacy: HAMD-17 was utilized to evaluate the treatment response. A reduction rate of ≥ 75% before and after treatment was considered clinical remission, 25–74% was considered effective, and < 25% was considered ineffective. The total effective rate was calculated as [(number of clinical remissions + effective cases) / total number of cases × 100%]. The reduction rate was calculated as [(pre-treatment HAMD-17 score – post-treatment HAMD-17 score) / pre-treatment HAMD-17 score × 100%].

Quality of life: The Medical Outcomes Study 36-Item Short Form Health Survey (SF-36) (SF-36)²³ was used for assessment. The survey included four functional domains: physical, social, psychological, and daily living. Each domain is scored out of 100, with higher scores indicating better quality of life for the patient.

Adverse reactions: The occurrence of adverse reactions including fatigue, drowsiness, anorexia, nausea, xerostomia, and dizziness, were compared.

Neurotransmitters levels: Before treatment (admission day 2) and after treatment (8 weeks of treatment), 5 mL of cubital vein blood was collected with an EDTA blood collection tube. Blood samples should be processed within 2 hours; otherwise, they must be stored at −80°C, with strict avoidance of multiple freeze–thaw cycles. The levels of GABA, 5-HT, and DA were measured using the enzyme-linked immunosorbent assay (ELISA).²⁴ ELISA kits for GABA (CB10292-Hu), 5-HT (CB10030-Hu), and DA (CB10524-Hu) were purchased from Coibo Biotechnology (Shanghai, China).

Inflammatory factor levels: Levels of CRP, IL-1β, and IL-10 were measured using ELISA. ELISA kits for CRP (CB10116-Hu), IL-1β (CB10347-Hu), and IL-10 (CB13566-Hu) were available from Coibo.

Statistical Processing

Data were processed using SPSS 26.0 software. The normality of measurement data was tested using the Shapiro–Wilk method, and all data conformed to a normal distribution. Additionally, the measurement data were expressed as mean ± standard deviation (Mean ± SD). Comparisons between groups used independent sample t-tests, and within-group comparisons used paired sample t-tests. Numeration data were expressed as [number of cases (n)], and analyzed using chi-square tests, with Yates’ correction applied to small sample sizes. A P-value < 0.05 was considered statistically significant.

Results

General Data

Baseline data differences between the two groups were analyzed, revealing no statistically significant differences in age, gender, disease duration, marital status, ethnicity, residence, or education level (P > 0.05), indicating comparability (Table 1).

HAMA and HAMD Scores

There was no notable difference in HAMA and HAMD scores between the two groups before treatment (P > 0.05). After treatment, HAMA and HAMD scores in both groups decreased compared to pre-treatment levels (P < 0.05). Post-treatment improvements in HAMA and HAMD scores were better in the combination group than in the medication group (Table 2).

Table 2 Comparison of HAMA and HAMD Scores Before and After Treatment in the Two Groups

SF-36 Scores

There was no significant difference in SF-36 scores between the two groups before treatment (P > 0.05). After treatment, SF-36 dimension scores in both groups increased (P < 0.05), with the combination group’s scores higher than the control group’s (P < 0.05) (Table 3).

Table 3 Comparison of SF-36 Scores Before and After Treatment Between the Two Groups

Clinical Efficacy

The total effective rate in the medication group was 84.44% (38/45), while the total effective rate in the combination group was 97.78% (44/45). According to the chi-square test, the total effective rate in the combination group was higher than that in the medication group (P = 0.026) (Table 4).

Table 4 Comparison of Clinical Efficacy Between the Two Groups

Occurrence of Adverse Reactions

The medication group and the combination group showed no significant difference in the incidence of adverse reactions such as fatigue, drowsiness, anorexia, xerostomia, nausea, and dizziness (all P > 0.05) (Table 5).

Table 5 Comparison of the Incidence of Adverse Reactions Between the Two Groups

Neurotransmitters Levels

There was no significant difference in GABA, 5-HT, and DA levels between the two groups before treatment (P > 0.05). After treatment, GABA and 5-HT levels in both groups increased compared to pre-treatment levels, while DA levels decreased, with the combination group’s levels higher/lower than the medication group’s (Table 6).

Table 6 Comparison of Neurotransmitters Levels Between the Two Groups Before and After Treatment

Inflammatory Factor Levels

There was no remarkable difference between the pre-treatment levels of CRP, IL-1β, and IL-10 between the two groups (P > 0.05). After treatment, CRP and IL-1β levels in both groups decreased compared to pre-treatment levels, while IL-10 levels increased, with the combination group’s levels lower/higher than the medication group’s (Table 7).

Table 7 Comparison of Inflammatory Factor Levels Between the Two Groups Before and After Treatment

Discussion

SSD is common in primary health care institutions, and diagnosis and treatment are often challenging.¹ Therefore, this study evaluated the therapeutic effects of rTMS combined with low-dose antipsychotic medication in patients with SSD. The results showed that rTMS combined with low-dose antipsychotic medication has significant advantages in improving anxiety and depression, enhancing quality of life, and regulating neurotransmitter levels, and inflammatory factors in the treatment of SSD.

Specifically, the improvement in HAMA and HAMD scores in the combination group was better than that in the medication group, indicating that rTMS combined with low-dose antipsychotic medication is more effective in improving anxiety and depressive symptoms. Additionally, the SF-36 scores in the combination group were higher than those in the medication group, indicating that combination therapy is more effective in improving quality of life. Meanwhile, the total effective rate in the combination group was higher than that in the medication group, and there was no significant difference in the incidence of adverse reactions between the two groups, revealing that rTMS combined with low-dose antipsychotic medication is superior to monotherapy in overall efficacy and does not increase the risk of adverse reactions. Studies have reported that rTMS leads to a greater reduction in depressive symptoms than medication, which is further reflected in higher rates of response and remission. Additionally, rTMS results in a more substantial decrease in anxiety and anhedonia symptoms compared to switching antidepressants.²⁵ Furthermore, in the study of Ren et al, intermittent theta burst-rTMS (iTBS-rTMS) demonstrates a favorable therapeutic effect in patients with methamphetamine use disorder, with improvements in both depression and impulsivity. These enhancements are strongly associated with the therapeutic efficacy of iTBS-rTMS.²⁶

In addition, this study highlighted that the levels of GABA and 5-HT in the combination group were higher than those in the medication group, and the level of DA was lower than that in the medication group, indicating that rTMS combined with low-dose antipsychotic medication is more effective in regulating neurotransmitter levels. According to previous results, the serum levels of 5-HT in both the acupuncture + rTMS combination group and the rTMS-only group are found to be higher post-treatment compared to their pre-treatment levels.²⁷ In the study by Feng et al, after rTMS treatment, the GABA levels are notably elevated.²⁸ A study reveals that antipsychotic medications such as risperidone may regulate N-methyl-D-aspartate receptors through the glycogen synthase kinase 3β-β-catenin signaling pathway and/or the activation of cyclic adenosine monophosphate response element-binding protein 1, and the regulation of GABAₐR may also be related to these signaling pathways.²⁹ Thomas et al’s study shows that risperidone affects motor activity and neural activity in mice, especially having a significant impact on neural oscillations in the prefrontal cortex and hippocampus, by blocking DA receptor D₂R and regulating the activity of serotonin receptors (5-HT₁AR and 5-HT₂AR).³⁰ Based on the results of this study, there is a synergistic mechanism in neurotransmitter regulation when risperidone is combined with rTMS. Risperidone, as a D2 receptor antagonist, can block excessive DA activity in the mesolimbic system to alleviate positive symptoms,¹⁴ while rTMS activates glutamatergic neurons in the prefrontal cortex through high-frequency stimulation, promoting DA release and enhancing prefrontal-striatal circuit function. The combined treatment may partially counteract the inhibitory effect of risperidone on DA, thereby optimizing DA metabolic balance. Additionally, risperidone indirectly increases synaptic 5-HT levels by blocking 5-HT2A receptors, and rTMS stimulates the dorsolateral prefrontal cortex to increase 5-HT release into the limbic system. The synergy of the two may increase 5-HT concentrations in relevant brain regions, thereby improving anxiety and depressive symptoms. On the other hand, rTMS can enhance GABAergic interneuron activity to inhibit prefrontal cortex hyperactivity, and risperidone can indirectly regulate DA-GABA interactions to enhance GABAergic inhibition. The combined treatment can more effectively reduce prefrontal cortex excitability and reduce anxiety and obsessive-compulsive symptoms.

Meanwhile, this study also observed that the levels of CRP and IL-1β in the combination group were lower than those in the medication group, and the level of IL-10 was higher than that in the medication group, indicating that rTMS combined with low-dose antipsychotic medication is more effective in regulating inflammatory marker levels. In line with the results of the present study, in the study of Yang et al, deep rTMS reduced microglial activation at the lesion sites and normalized cytokine levels (IL-1β, IL-6, and IL-10) in regions affected by cuprizone.³¹ Based on the results of this study, rTMS may activate the vagus nerve-cholinergic anti-inflammatory pathway, inhibit microglial activation, and reduce the release of pro-inflammatory cytokines (such as IL-1β). Although risperidone does not directly have anti-inflammatory effects, it may inhibit microglial M1 polarization by regulating the DA signaling pathway. When combined, the two may synergistically reduce peripheral blood CRP and IL-1β levels and increase the level of the anti-inflammatory cytokine IL-10.

Limitation

This study preliminarily revealed the efficacy of rTMS combined with risperidone in the treatment of SSD in improving anxiety and depression, enhancing quality of life, regulating neurotransmitter levels, and inflammatory factors. However, this study also has some limitations. For example, we did not perform sample size calculation, and the sample size was small, which may affect the generalizability of the findings. Additionally, the 8-week observation period in this study may not be sufficient to assess the long-term efficacy and potential delayed effects of rTMS combined with risperidone treatment. The lack of long-term follow-up may not allow for the assessment of symptom recurrence rates and drug dependence after treatment.

Conclusion

In conclusion, this study confirms that rTMS combined with low-dose antipsychotic medication is superior to monotherapy in improving anxiety and depression, enhancing the quality of life, regulating neurotransmitter levels, and inflammatory factors in the treatment of somatic symptom disorder, with fewer side effects and significant clinical efficacy. This study provides a new direction for the treatment of SSD, and it is hoped that the treatment regimen can be further optimized to benefit more patients in the future. Importantly, future research should further verify the mechanisms and long-term safety of combination therapy by expanding the sample size, extending the follow-up period, and controlling confounding factors (such as treatment adherence, social support systems, etc). Additionally, the treatment of SSD requires multidisciplinary collaboration, including psychiatry, neurology, and psychology. The multidisciplinary collaborative treatment model can integrate the advantageous resources of various disciplines to provide patients with more personalized and comprehensive treatment plans and improve the overall treatment level of SSD.

Ethical Statement

All experimental procedures were approved by the Medical Ethics Committee of The First Affiliated Hospital of Harbin Medical University, and informed consent was signed by patients’ families.

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Funding

No funds, grants, or other support was received.

Disclosure

The authors declare that they have no conflicts of interest for this work.

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Introduction

The prevalence of diabetes, particularly type 2 diabetes (T2D), is rising globally, posing a significant public health challenge due to its various acute and chronic complications.¹ Among these complications, cardiovascular diseases (CVDs) stand out as the leading cause of death in patients with T2D.² Vascular dysfunctions, including arterial stiffness (AS) and impaired vasodilation, can emerge before the onset of severe CVDs symptoms.³ Therefore, early assessment of AS is particularly important in the management of T2D. The brachial-ankle pulse wave velocity (baPWV) is a simple, effective and non-invasive method for evaluating AS,⁴ and can independently predict cardiovascular risk, providing important evidence for assessing the development of CVDs in individuals.⁵

Insulin resistance (IR) is considered a significant factor to AS and the progression of CVDs.⁶ While the euglycemic hyperinsulinemic clamp (EHC) is considered the gold standard for assessing IR,⁷ its invasive nature, time consuming and requirement for hospitalization limit its practical applicability. The homeostasis model assessment index (HOMA-IR) offers a simpler approach to assessing IR.⁸ However, it presents specific challenges for patients undergoing insulin therapy. Recently, a growing number of non-insulin-based IR surrogate markers have been proposed, including the triglyceride-glucose (TyG) index, triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-c) ratio, and metabolic score for insulin resistance (METS-IR).^9–11 These markers have been associated with various metabolic diseases. One of our previous studies examined their relationship with nonalcoholic fatty liver disease (NAFLD) in patients with T2D, highlighting their clinical relevance in this context.¹² Building on this foundation, our current study shifts the focus toward AS, a distinct yet critical cardiovascular complication in T2D. A more recent development is the natural log transformation of the glucose disposal rate (log_e GDR), a non-insulin-based model to assess insulin sensitivity (IS) in T2D patients.¹³ The log_e GDR is calculated based on body mass index (BMI), triglycerides (TG), the urinary albumin to creatinine ratio (UACR) and γ-glutamyl transferase (GGT), and it has been validated and demonstrated a strong association with CVDs and mortality rates.¹³ Despite these advances, no studies to date have specifically investigated the relationship between the log_e GDR and AS in T2D. Based on the groundwork laid by our earlier research, this study aims to explore the novel association between log_e GDR and AS, offering fresh insights into the complex interplay between IS and vascular health.

The prevalence of non-obese T2D is gradually increasing, particularly in Asian countries.^14,15 Although CVDs and other conditions have traditionally been associated with obesity and being overweight, recent evidence suggests that non-obese T2D patients may have higher all-cause and cardiovascular mortality rates.^16,17 This could be attributed to factors such as increased visceral fat, impaired IS, and heightened inflammatory responses despite a normal BMI.¹⁸ Furthermore, recent studies have reported non-obese T2D patients have a comparable or even higher prevalence of AS compared to their obese counterparts¹⁸ Due to significant differences in metabolic characteristics between obese and non-obese diabetic patients, particularly regarding IS.¹⁹ And research exploring the relationship between IR surrogate markers and AS in non-obese patients with T2D remains limited. Therefore, we aim to analyze the relationship between the log_e GDR and AS in this population.

Methods

Study Design and Population

We retrospectively reviewed the medical records of patients aged ≥ 18 years with T2D from the Department Endocrinology of Linyi People’s Hospital, from January 2020 to March 2023. The exclusion criteria were (1) subjects missing key anthropometric measurements (height and weight); (2) subjects who had severe liver and kidney dysfunctions; (3) subjects with a history of angina, myocardial infarction and cerebrovascular accident; (4) subjects who had not undergone the baPWV tests or whose clinical data were incomplete, including GGT, TG and UACR; (5) subjects with BMI ≥ 24 kg/m². Ultimately, a total of 790 non-obese patients with T2D were eventually included (Figure 1).

It is important to note that a portion of the participants included in the current study were also part of our previous work, which primarily investigated the association between IR markers and NAFLD in the overall T2D population. In terms of exclusion criteria, the previous study mainly excluded confounding factors that could affect the diagnosis and analysis of fatty liver disease.¹² For the overlapping populations, we further compared differences in baseline characteristics and various IR indices between the two studies, and the results did not show significant differences, suggesting that the results of this study are relatively stable and have some replication.

Demographic Information

The sex, age, diabetes duration and self-reported current cigarette smoking and drinking status were collected.

Physical Examinations

According to unified standards, the height, weight, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured and collected. The bioelectrical impedance analysis (Omron DUALSCAN HDS-2000, Kyoto, Japan) was used to measure the visceral fat area (VFA) and subcutaneous fat area (SFA).

Each participant’s baPWV was measured using the automated system BP-203RPE III (Omron Healthcare Co., Japan) by trained technicians. The device simultaneously recorded pulse waveforms from the brachial and tibial arteries and automatically calculated baPWV values. Before measurement, participants were required to rest in a supine position for at least 5 minutes to ensure hemodynamic stability. Subsequently, appropriately sized cuffs were placed on both upper arms and ankles, and the device was operated according to standard protocols to obtain waveform signals and compute baPWV values.²⁰ To enhance measurement accuracy, this study analyzed data in cases where there was a significant difference between left and right baPWV values and assessed each side’s baPWV separately. AS was defined as baPWV ≥1800 cm/s.

Laboratory Measurements

Following an overnight fast, blood samples were collected and analyzed in the morning for alanine aminotransferase (ALT), aspartate aminotransferase (AST), GGT, TG, HDL-c, total cholesterol (TC), low density lipoprotein-cholesterol (LDL-c), serum creatinine (Scr), uric acid (UA), Cystatin C (Cys C), hemoglobin (Hb), fasting blood glucose (FBG) and glycosylated haemoglobin (HbA1c), fasting insulin (FINS) and UACR. A comprehensive overview of the tools and methods utilized in this research is available in our earlier publication.¹² Non-obese was defined as BMI < 24 kg/m².

Parameter Calculation

1. BMI = weight (kg) / height² (m²);
2. eGFR = 175 * Scr (mg/dL) ^−1.234 * age ^−0.179 * (0.79, if female);²¹
3. HOMA-IR = FBG (mmol/L) * FINS (µU/mL)/22.5;⁸
4. TyG index = ln [TG (mg/dL) × FBG (mg/dL)/2];¹⁰
5. TG/HDL-c ratio = TG (mmol/L)/HDL-c (mmol/L);¹¹
6. METS-IR = ln [(2*FBG (mg/dL)) + TG (mg/dL)] *BMI)/(Ln [HDL-c (mg/dL)]);⁹
7. Log_e GDR = 5.3505–0.3697 * log_e (GGT, IU/L) – 0.2591 * log_e (TG, mg/dL) – 0.1169 * log_e (UACR, mg/g) – (0.0279*BMI, kg/m²).¹³

Statistical Analysis

Statistical analysis was performed using SPSS 26.0 (SPSS Inc, Chicago, USA) and R (version 4.3.2). Data were presented as means ± SD for normally distributed variables and as medians (interquartile ranges) for non-normally distributed variables. Independent-Samples T test and Mann–Whitney U-test were used for comparisons of normally and abnormally distributed continuous variables between two groups, respectively. Categorical variables were presented as percentage (%) and were compared by Chi-square test. For normally distributed data, an Analysis of Variance (ANOVA) and Student-Newman-Keuls tests were used for multiple and pairwise comparisons between the log_e GDR tertiles groups, while the Kruskal–Wallis one-way ANOVA test was used for abnormally distributed data. Pearson correlation and multiple linear stepwise regression analyses were used to evaluate the independent correlations of baPWV. Univariate logistic regression analysis and directed acyclic graphs (DAG) were used to guide the selection of covariates for AS. The DAG was constructed using the dagitty package. And the identified minimal adjustment set includes age, BMI, diabetes duration, FBG, TG, HOMA-IR, METS-IR, TG/HDL-c ratio and TyG index. Logistic regression analysis was used to analyze the independent correlates of AS. Net reclassification improvement (NRI) analysis was performed using the survIDINRI package in R to assess the incremental predictive value of log_e GDR compared with other IR markers for identifying AS. Statistical differences were defined by P-value (two-tailed) less than 0.05.

Results

Clinical and Biochemical Characteristics

The clinical and biochemical characteristics of the participants are shown in Table 1. A total of 790 non-obese patients with T2D were enrolled in our study. The subjects were divided into two groups including non-AS group (baPWV < 1800cm/s) and AS group (baPWV ≥ 1800cm/s). Compared with the non-AS group, the age, diabetes duration, VFA, SFA, SBP, DBP, AST, GGT, UA, Scr, UACR and Cys C were increased in AS group, but the HbA1c, eGFR, Hb and log_e GDR were markedly reduced (all P < 0.05). There were no obvious differences in BMI, TC, LDL-c, HDL-c, TG, FBG, FINS, ALT, HOMA-IR, TG/HDL-c ratio, TyG index, METS-IR and the percentages of males, smoking and drinking between the two groups (all P > 0.05).

Table 1 Clinical and Biochemical Characteristics by Presence of AS

Then, according to tertiles of log_e GDR, the participants were divided into three groups: T1 (0.25–1.98), T2 (1.98–2.28) and T3 (2.28–3.12) (Table 2). As the log_e GDR tertiles increased, the age, diabetes duration, BMI, VFA, SFA, SBP, DBP, TC, LDL-c, TG, FINS, HbA1c, ALT, AST, GGT, UA, Scr, UACR, Cys C, HOMA-IR, TyG index, TG/HDL-c ratio, METS-IR, baPWV, the percentages of smoking, drinking and AS were gradually decreased, while the HDL-c, Hb and eGFR were gradually elevated (all P < 0.05). The FBG and the percentages of males were no significant different between the three groups (both P > 0.05).

Table 2 Comparison of Variables According to the Tertiles of Loge GDR

Correlation Between baPWV or AS and Each Variable by Univariate Analysis

As shown in Table 3, a Pearson correlation analysis was performed to analyze the association between baPWV and each variable. The results displayed that the baPWV was positively related to age, diabetes duration, VFA, SFA, SBP, DBP, TG, FINS, GGT, UA, UACR, Cys C and TG/HDL-c ratio, while negatively to the Hb, HbA1c, eGFR and log_e GDR (all P < 0.05). BMI, TC, LDL-c, HDL-c, FBG, ALT, AST, HOMA-IR, TyG index and METS-IR were not correlated with baPWV (all P > 0.05).

Table 3 The Correlation Between baPWV or AS and Different Variables by Univariate Analysis

Moreover, univariate regression analysis was conducted to identify the factors associated with AS. The results showed that AS was positively related to the age, diabetes duration, VFA, SFA, SBP, DBP, FINS, AST, UA, UACR, Cys C, and negatively to the Hb, HbA1c, eGFR and log_e GDR (all P < 0.05). No significant relationships existed between AS and BMI, TC, LDL-c, TG, HDL-c, FBG, ALT, GGT, HOMA-IR, TyG index, TG/HDL-c ratio, METS-IR and the percentages of males, smoking and drinking (all P > 0.05).

Independent Variables of baPWV by Multiple Linear Stepwise Regression Analysis

The covariates for multivariate linear regression analysis were determined based on the results of Pearson correlation analysis and previous literature reports. A multiple linear stepwise regression analysis was conducted to analyze the independent correlations of baPWV (Table 4). The age, diabetes duration, VFA, SFA, SBP, DBP, TG, FINS, GGT, UA, UACR, Cys C, TG/HDL-c ratio, Hb, HbA1c, eGFR and log_e GDR were set as the dependent variables based on the results of Pearson correlation analysis, and the results displayed that the age, SBP and log_e GDR fit a regression model (all P < 0.05).

Table 4 Multivariate Linear Regression Analysis with baPWV as the Dependent Variable

Independent Correlations of AS by Logistic Regression Analysis

Finally, AS was served as the dependent variable, and based on the results of univariate logistic regression analysis, the DAG diagram (Figure 2), and previous literature, the following variables were included as independent variables: age, diabetes duration, VFA, SFA, SBP, DBP, FINS, HbA1c, AST, UA, eGFR, UACR, Cys C, Hb, BMI, FBG, TG, HOMA-IR, TG/HDL-c ratio, TyG index, METS-IR, log_e GDR and the percentages of smoking and drinking. A logistic regression analysis was performed to analyze the independent correlates of AS (Table 5), and the results found that after adjusting for the other variables, the log_e GDR (OR: 0.286, 95.0% CI for OR: 0.110–0.743), age (OR: 1.196, 95.0% CI for OR: 1.138–1.258), SBP (OR: 1.053, 95.0% CI for OR: 1.031–1.075) and FBG (OR: 0.886, 95.0% CI for OR: 0.792–0.990) were independently related to AS.

Table 5 The Independent Variables for AS

Figure 2 The DAG of identifying confounding variables.

Predictive Value of IR Markers for AS

To assess the incremental predictive value of various IR markers for AS, NRI analysis was performed based on logistic regression models (Table 6). All models were adjusted for potential confounders, including age, diabetes duration, VFA, SFA, SBP, DBP, FINS, HbA1c, AST, UA, eGFR, UACR, Cys C, Hb, BMI, FBG, TG, smoking and drinking. Building upon the base model without any IR marker, integrating log_e GDR yielded a modest improvement in the model’s ability to reclassify patients with AS (NRI:0.043, 95% CI 0.009–0.079, P = 0.011). In contrast, building upon the base model, integrating other IR markers such as HOMA-IR (NRI:0.007, P = 0.697), TyG index (NRI:0.011, P = 0.356), TG/HDL-c ratio (NRI:0.006, P = 0.317), and METS-IR (NRI: −0.004, P = 0.568) did not significantly improve the predictive performance.

Table 6 Analysis of the NRI for Predicting AS

Discussion

This cross-sectional study of non-obese patients with T2D revealed a significant negative association between the log_e GDR and both baPWV and AS. Increased log_e GDR tertiles corresponded with a significant reduction in baPWV and AS incidence. Furthermore, after adjusting for confounding factors, the log_e GDR was independently associated with baPWV and AS.

IR is common among diabetic patients, leading to endothelial dysfunction and inflammatory responses that contribute to AS and atherosclerosis.²² Although the EHC is considered the gold standard for assessing IS, its complexity, time consuming, and requirement for specialized personnel limit its use in large-scale clinical studies. HOMA-IR is a commonly used and simpler indicator of IR, but it relies on FINS. Previous studies have shown that fluctuations in insulin levels can be significantly influenced by an individual’s glucose tolerance and the effects of treatment. Therefore, FINS levels may not be entirely accurate for patients with T2D undergoing treatment.^23,24 Recently, an increasing number of studies have explored the close association between non-insulin-based IR surrogate indicators and AS across various populations. For instance, a study in a healthy Japanese cohort found a significant correlation between the METS-IR and AS.²⁵ A study involving 1895 participants showed a close correlation between the TyG index and the TG/HDL-c ratio with AS in hypertensive patients, while no such relationship was observed in patients with prehypertension.²⁶ Furthermore, research on patients with T2D had indicated that the TyG index was independently and more strongly associated with the prevalence of increased AS compared to HOMA-IR.²⁰ The relationship between non-insulin-based IR surrogate indicators with AS had also been validated in lean postmenopausal women, Chinese non-hypertensive and older subjects.^27–29

The log_e GDR is a newly developed model for assessing IS in T2D, and it has been validated as a reliable EHC-based surrogate capable of capturing the variability of IS in patients with T2D well.¹³ The inclusion of metabolic components (GGT, UACR, BMI and TG) allows log_e GDR to reflect a more comprehensive metabolic profile and potentially capturing a broader range of pathogenic mechanisms. In our study, we found that it was closely associated with IR markers as well. As the tertiles of log_e GDR increased, significant reductions were observed in other IR markers, suggesting a consistent relationship between log_e GDR and IS. Notably, we found that the log_e GDR was independently related to baPWV and AS. This relationship remains important even after adjusting for other confounding factors including IR markers (HOMA-IR, TG/HDL-c ratio, TyG index, and METS-IR).

The mechanisms potentially linking log_e GDR to AS are likely multifactorial and may involve several key pathways. The components included in the calculation of log_e GDR, including GGT, UACR, BMI and TG, may have been suggested as part of circadian syndrome.³⁰ Recent studies indicate that circadian syndrome may be a better predictor of CVDs risk than metabolic syndrome,³⁰ suggesting that log_e GDR might reflect a disruption in circadian rhythms, potentially influencing cardiovascular health. GGT is a key marker of oxidative stress, promoting endothelial dysfunction by reducing nitric oxide bioavailability and increasing vascular inflammation, both of which contribute to arterial stiffening. TG facilitates lipid accumulation in the vascular wall, leading to foam cell formation and atherosclerosis progression. Elevated TG levels are also associated with increased production of small, dense LDL particles, which enhance oxidative stress and vascular inflammation. UACR reflects endothelial dysfunction and vascular damage, as albuminuria is linked to increased vascular permeability and low-grade inflammation, both contributing to arterial remodeling. Additionally, BMI, particularly in the context of visceral adiposity, is associated with chronic low-grade inflammation and activation of the renin-angiotensin-aldosterone system, further promoting vascular stiffness. These components effectively represent the key metabolic pathways leading to AS, supporting the close relationship between log_e GDR and AS.^31–33

Additionally, AS is a degenerative vascular process that increases with age.³⁴ High SBP levels may damage endothelial function, leading to progressively stiffer arteries.³⁵ Be consistent with the above findings, we found a strong relationship between age and SBP with AS in non-obese patients with T2D. This underscores the importance of managing SBP as a modifiable risk factor for AS, particularly in this population. Interestingly, we observed a negative correlation between AS and FBG, which was inconsistent with most studies that suggested elevated FBG was a significant risk factor for AS.³⁶ The multifaceted influencing factors of AS may help explain this phenomenon. As mentioned earlier, the average age in the AS group was significantly higher than that in the non-AS group, and some studies have suggested that older diabetic patients tend to have better blood glucose control.³⁷

The relationship between the novel IS index log_e GDR and AS has not been extensively studied in the context of non-obese T2D. Our study is the first to observe a strong association between log_e GDR and AS in non-obese patients with T2D, highlighting its potential clinical significance. Although non-obese individuals with T2D may have normal body weight, they can still exhibit significant vascular changes. Since log_e GDR incorporates metabolic parameters including BMI, TG, UACR and GGT, it may reflect a broader metabolic disorder amenable to intervention than other IR markers. Importantly, logₑ GDR demonstrated the highest NRI among the evaluated IR indicators, indicating relatively better discriminatory capacity for AS. However, the overall improvement in risk prediction was modest, suggesting that its incremental value in risk stratification may be limited. Therefore, while logₑ GDR shows potential as a complementary tool for early identification of cardiovascular risk in non-obese T2D patients, its clinical utility should be interpreted with caution. Further prospective studies with larger, diverse cohorts are needed to confirm these findings and to clarify the role of logₑ GDR in improving cardiovascular risk prediction models.

Several limitations of this study should be acknowledged. First, as with all cross-sectional studies, we cannot establish causality between log_e GDR and AS. Longitudinal studies are essential to determine the temporal relationship and causal pathways between these variables. Second, using BMI < 24 kg/m² to define “non-obese” may not perfectly capture individuals with increased visceral adiposity, which is a key driver of metabolic dysfunction. Future studies could consider including measures such as waist circumference or waist-to-hip ratio, which provide more direct insight into visceral fat distribution. Lastly, this study is single-center and based on a small sample size, which may limit the generalizability of the results. Future prospective multi-center studies involving larger populations are needed to confirm these findings and further investigate the underlying mechanisms.

Conclusion

In conclusion, the log_e GDR, as a new simple index of IS, is independently associated with AS in non-obese patients with T2D. Its inclusion in existing risk models modestly improved the identification of arterial stiffness. The potential utility of log_e GDR in cardiovascular risk assessment warrants further investigation and validation in future studies.

Ethics Approval and Consent to Participate

The study was approved by the Human Ethics Committee of the Linyi People’s Hospital. All procedures were performed in accordance with ethical standards laid out in the Declaration of Helsinki. Informed consent was obtained from the patients.

Acknowledgments

Shuwei Shi is currently Department of Endocrinology, Linyi People’s Hospital Affiliated to Shandong Second Medical University, Linyi, China. This study was conducted while she was affiliated with the School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, China. Baolan Ji and Guanqi Gao are co-corresponding authors for this study.

Funding

This study was supported by grants from the Postdoctoral Program of Affiliated Hospital of Jining Medical University (JYFY322152).

Disclosure

All authors declare that they have no competing interests in this study.

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