Association Between Uncontrolled Type 2 Diabetes mellitus and Concentrated Growth Factor Composition: A Cross-Sectional Study

ABSTRACT

Objective:

Concentrated Growth Factor (CGF) is a third-generation autologous platelet concentrate utilized for its high density of bioactive molecules. However, the influence of chronic hyperglycemia on the specific growth factor composition of CGF remains underexplored. This study aimed to quantify and compare the concentrations of IGF-1, VEGF-A, and TGF-β1 in CGF derived from individuals with uncontrolled type 2 diabetes mellitus (T2DM) versus non-diabetic controls.

Methods:

A cross-sectional study was conducted involving 80 participants: 40 with uncontrolled T2DM (HbA1c > 7%) and 40 non-diabetic controls (HbA1c < 6%). CGF was isolated via a standardized variable-speed centrifugation protocol (Medifuge MF200). Growth factor levels were quantified using validated, high-sensitivity enzyme-linked immunosorbent assays (CV < 10%). Data were analyzed using independent t-tests and Pearson’s correlation (P < 0.05), adjusting for age and BMI.

Results:

CGF from uncontrolled diabetics exhibited significantly lower mean concentrations of all measured factors compared to controls: IGF-1 (0.42 ± 0.15 vs. 1.58 ± 0.06 ng/mL; P< 0.001), VEGF-A (0.11 ± 0.02 vs. 0.21 ± 0.03 ng/mL; P< 0.001), and TGF-β1 (2.52 ± 0.36 vs. 2.76 ± 0.15 ng/mL;P < 0.001). The largest reduction was observed in IGF-1 (~72%), followed by VEGF-A (~46.7%) and TGF-β1 (~8.7%). Correlation analysis indicated that these reductions were significantly associated with hyperglycemic status (HbA1c) rather than demographic variables such as age or BMI.

Conclusion:

Uncontrolled hyperglycemia is associated with a significant quantitative depletion of key regenerative growth factors within CGF, particularly IGF-1 and VEGF-A. These findings suggest that the intrinsic regenerative potential of autologous CGF may be compromised in diabetic patients, indicating a need for optimized glycemic control or adjunctive strategies in clinical regenerative procedures.

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder marked by persistent hyperglycemia resulting from impaired insulin secretion, insulin action, or both. It is a major global health concern, affecting over 500 million people worldwide, and the number is projected to rise to 643 million by 2030 and 783 million by 2045.[1,2,3] One of the principal complications of T2DM is delayed wound healing, primarily due to prolonged inflammation, oxidative stress, and endothelial dysfunction.[4,5]

Concentrated growth factor (CGF), obtained through variable-speed centrifugation, represents a third-generation autologous platelet concentrate. This process yields a dense fibrin network enriched with leukocytes, CD34⁺ stem cells, and key regenerative molecules such as insulin-like growth factor-1 (IGF-1), vascular endothelial growth factor-A (VEGF-A), and transforming growth factor-β1 (TGF-β1).[6,7] CGF is more stable and provides a sustained release of growth factors compared with earlier platelet-derived products such as platelet-rich plasma (PRP) and platelet-rich fibrin (PRF).[4,8]

These growth factors play distinct roles in healing: IGF-1 promotes fibroblast proliferation and matrix synthesis, VEGF-A induces angiogenesis, and TGF-β1 modulates immune responses and tissue remodeling.[9,10,11] T2DM has been reported to be associated with altered platelet function and biochemical composition, which may correspond to reduced growth factor release and diminished regenerative potential.[12,13,14] CGF and advanced PRF have demonstrated higher concentrations of growth factors, including TGF-β1, VEGF, and platelet derived growth factor -BB (PDGF-BB), compared with PRP and plasma rich in growth factors, facilitating superior cellular activity in vitro and improved clinical healing outcomes.[3,7,8,15]

Despite its clinical promise, evidence on how uncontrolled hyperglycemia affects CGF composition remains limited. This study compares IGF-1, VEGF-A, and TGF-β1 levels in CGF from uncontrolled diabetics and non-diabetic controls to explore the association between hyperglycemia and CGF regenerative efficacy.

MATERIALS AND METHODS

STUDY DESIGN AND PARTICIPANTS

A cross-sectional design was adopted to compare two groups. Based on prior data by Rudresh et al.,[2] a moderate effect size (Cohen’s d = 0.6) and 80% statistical power indicated a sample size of 72; hence, 80 participants were recruited to account for attrition. Participants were aged 30–60 years, with uncontrolled T2DM defined as hemoglobin A1c (HbA1c) > 7% and non-diabetics as HbA1c < 6%. Diabetic participants were identified based on HbA1c > 7%, consistent with criteria for uncontrolled diabetes. Specific details regarding duration of diabetes, type of anti-diabetic medication, or presence of complications such as neuropathy, nephropathy, or retinopathy were not collected in this study. Exclusion criteria included systemic diseases unrelated to diabetes that could influence platelet function or inflammatory status (such as hepatic, renal, or autoimmune disorders), smoking, pregnancy, and the use of anticoagulant or anti-inflammatory medications. Participants were selected based on HbA1c criteria alone; common metabolic comorbidities frequently associated with diabetes, such as hypertension or dyslipidemia, were not specifically screened for or excluded. As this was a non-interventional, cross-sectional observational study, it was not registered in a clinical trial registry. All participants provided informed consent, and institutional ethical approval was obtained.

CONCENTRATED GROWTH FACTOR PREPARATION

Venous blood (10 mL) was collected in plain tubes and centrifuged using the Medifuge MF200 centrifuge with a fixed sequence: acceleration (30 s), 2700 rpm (2 min), 2400 rpm (4 min), 2700 rpm (4 min), 3000 rpm (3 min), followed by deceleration (36 s). This protocol was selected as it is the most widely validated method for generating CGF, providing consistent fibrin architecture and growth factor retention across samples.[6,7] The process yielded three distinct layers: upper serum, middle fibrin-rich CGF, and lower red cell layer. The CGF was gently separated, compressed with sterile gauze, and stored at −80°C for analysis.[3] Samples were stored in a single freeze cycle, with no repeat thawing before assay, to avoid degradation of growth factors. All enzyme linked immunosorbent assay (ELISA) measurements were performed in a single analytical batch to minimize inter-assay variability.

GROWTH FACTOR QUANTIFICATION

CGF samples were lyophilized at −50°C, reconstituted in phosphate-buffered saline, and shaken for 14 days to ensure complete dissolution. Growth factors were quantified using ELISA kits (Elabscience, China): IGF-1 (E‑CL‑H0085), VEGF-A (E‑EL‑H0111), and TGF‑β1 (E‑EL‑H1657). Optical density (OD) readings were obtained using a Merilyzer EIA Quant reader, and concentrations were calculated from standard curve equations: IGF‑1 = 0.921 × OD + 0.257; VEGF‑A = 2.272 × OD + 0.069; TGF‑β1 = 0.660 × OD + 0.366.

ELISA ASSAY VALIDATION

All ELISA assays were performed using commercial kits (Elabscience, China) for IGF-1 (E-CL-H0085), VEGF-A (E-EL-H0111), and TGF-β1 (E-EL-H1657), following the manufacturer’s protocols. Each sample was analyzed in duplicate, and mean OD values were used for final concentration calculations. The detection ranges and sensitivities (limit of detection [LOD]) were 0.156–10 ng/mL (LOD: 0.094 ng/mL) for IGF-1, 0.078–5 ng/mL (LOD: 0.045 ng/mL) for VEGF-A, and 0.313–20 ng/mL (LOD: 0.188 ng/mL) for TGF-β1. Intra-assay and inter-assay coefficients of variation (CV%) were less than 8% and 10%, respectively, indicating high assay precision. Standard curves were generated using a four-parameter logistic (4-PL) model, and curve fitting was performed using Microsoft Excel and SPSS software. Calibration curves demonstrated R² values greater than 0.98 for all assays, confirming strong linearity and reproducibility. All analyses were performed by a single investigator to minimize operator variability.

STATISTICAL ANALYSIS

Data were analyzed using Python, and Microsoft Power BI. Normality was assessed via the Shapiro–Wilk test. Independent t‑tests compared groups, while Pearson’s correlation evaluated associations between growth factors, age, and body mass index (BMI). Significance was set at P < 0.05.

RESULTS

DEMOGRAPHIC ANALYSIS

The diabetic group (n = 40) had a mean BMI of 29.92 ± 2.90 kg/m² and a mean age of 53.90 ± 4.81 years, while the non-diabetic control group (n = 40) had a BMI of 28.16 ± 4.83 kg/m² and a mean age of 52.00 ± 5.70 years. There were no significant differences between groups in age or BMI (P = 0.1112 and 0.0518, respectively), suggesting demographic factors are unlikely to account for differences in growth factor levels [Table 1].

Table 1.

Comparison of demographic characteristics between diabetic and non-diabetic control groups

Groups	Diabetic (mean ± SD)	Non-diabetic (control) (mean ± SD)	Independent-sample t-test P value
BMI	29.9150 ± 2.8979	28.1550 ± 4.8346	0.0518
Age	53.9000 ± 4.8134	52.0000 ± 5.6975	0.1112

BMI = body mass index, SD = standard deviation

Data are expressed as mean ± SD. Independent-sample t-test was used for group comparison. Exact P values are reported for each parameter: age (P = 0.1112) and BMI (P = 0.0518). A P value < 0.05 was considered statistically significant. There were no statistically significant differences in age or BMI between diabetic and non-diabetic participants

GROWTH FACTOR CONCENTRATION ANALYSIS

IGF-1, VEGF-A, and TGF-β1 levels in CGF were measured by ELISA. In the diabetic group, IGF-1 showed weak negative correlations with age (r = –0.18, P = 0.25) and BMI (r = –0.22, P = 0.15). VEGF-A also exhibited mild negative correlations with age (r = –0.21, P = 0.17) and BMI (r = –0.19, P = 0.22), while TGF-β1 showed minimal correlations (r = –0.05 and –0.07, P > 0.50). These correlations were weak and statistically non-significant, indicating that age and BMI were not major contributors to variations in growth factor levels in either group [Table 2].

Table 2.

Comparison of concentrated growth factor (CGF) concentrations between diabetic and non-diabetic control groups

CGF	Non-diabetic group	Diabetic group	Independent t-test P value
IGF-1 (ng/mL)	1.58 ± 0.06	0.42 ± 0.15	<0.001*
VEGF-A (ng/mL)	0.21 ± 0.03	0.11 ± 0.02	<0.001*
TGF-β1 (ng/mL)	2.76 ± 0.15	2.52 ± 0.36	0.0003*

IGF-1 = insulin-like growth factor-1, SD = standard deviation, TGF-β1 = transforming growth factor-β1, VEGF-A = vascular endothelial growth factor-A

*less than 0.001

Data are expressed as mean ± SD. Independent-sample t-test was used for group comparison. Exact P values are reported for each growth factor: IGF-1 (P < 0.001), VEGF-A (P < 0.001), and TGF-β1 (P = 0.0003). A P value < 0.05 was considered statistically significant. All three growth factors showed significantly lower mean concentrations in the diabetic group compared with non-diabetic controls

GENDER-BASED DIFFERENCES

Gender-based subgroup analyses were exploratory and intended to identify potential patterns rather than infer definitive group differences. Within-group comparisons revealed slightly higher IGF-1 and TGF-β1 in diabetic females versus males, but differences were not statistically significant (P > 0.05). No gender differences were observed in controls.

STANDARD CURVES AND STATISTICAL ANALYSIS

Growth factor concentrations were calculated using ELISA OD values and standard curve equations: IGF-1 (ng/mL = 0.921 × OD + 0.257), VEGF-A (ng/mL = 2.272 × OD + 0.069), and TGF-β1 (ng/mL = 0.660 × OD + 0.366). Standard curves were fitted using a 4-PL regression model, and the coefficient of determination (R²) exceeded 0.98 for all analytes, confirming strong linearity and reliability. Curve generation and regression fitting were performed using Microsoft Excel (Microsoft Corporation, USA) and IBM SPSS Statistics (version 25). Independent t-tests were used to compare group means for each growth factor (IGF-1, VEGF-A, and TGF-β1). Given the exploratory nature of this study and the biological interdependence of these markers, no formal correction for multiple comparisons was applied. Statistical significance was set at P < 0.05. Independent t-tests showed that all three growth factors were significantly lower in the diabetic group compared with non-diabetic controls: IGF-1 (0.42 ± 0.15 vs. 1.58 ± 0.06 ng/mL; P < 0.001), VEGF-A (0.11 ± 0.02 vs. 0.21 ± 0.03 ng/mL; P < 0.001), and TGF-β1 (2.52 ± 0.36 vs. 2.76 ± 0.15 ng/mL; P = 0.0003) [Figure 1].

Figure 1.

Comparison of insulin-like growth factor-1 (IGF-1), vascular endothelial growth factor-A (VEGF-A), and transforming growth factor-β1 (TGF-β1) concentrations in concentrated growth factor between uncontrolled diabetic and non-diabetic control groups. Values represent mean ± standard deviation (n = 40 per group). Independent-sample t-test results: IGF-1 (P < 0.001), VEGF-A (P < 0.001), and TGF-β1 (P = 0.0003). Analyses were performed using Microsoft Excel and IBM SPSS Statistics v25

RESULTS INTERPRETATION

IGF-1 showed the largest reduction (~72%) in T2DM, followed by VEGF-A (~46.7%) and TGF-β1 (~8.7%). These findings indicate that uncontrolled diabetes is associated with lower concentrations of key growth factors in CGF, potentially limiting its regenerative potential, independent of age, BMI, or gender.

DISCUSSION

This study shows that T2DM lowers IGF-1, VEGF-A, and TGF-β1 levels in CGF. The reduction was greatest for IGF-1 (~72%), followed by VEGF-A (~46.7%) and TGF-β1 (~8.7%). In this study, the T2DM and control groups had comparable mean age and BMI, with no statistically significant differences observed. Therefore, the reductions in IGF-1 and VEGF-A levels in CGF are unlikely to be influenced by demographic confounders. It appears that our findings are similar to earlier reports that showed lower levels of growth factors in PRF samples from people with diabetes.[2,3]

Importantly, the present study extends this knowledge by quantifying these alterations specifically in CGF, a third-generation platelet concentrate characterized by a denser fibrin matrix and more sustained growth factor release than earlier formulations such as PRF and PRP.[4,9] CGF from healthy individuals contains higher levels of VEGF, TGF-β1, and PDGF-BB, which promote the proliferation of periodontal and other mesenchymal cells involved in tissue regeneration.[8,10]

In our study, these factors were reduced in diabetic participants, indicating that hyperglycemia may be associated with reduced regenerative potential of CGF. IGF-1 was especially low in diabetics (0.42 ± 0.15 ng/mL) compared with non-diabetics (1.58 ± 0.06 ng/mL), consistent with Nijenhuis-Noort et al., who reported an inverse relationship between IGF-1 and blood glucose in newly diagnosed type 2 diabetes.[14,16] Overall, these findings suggest that poor glycemic control may be associated with alterations in anabolic signaling pathways needed for tissue repair. VEGF-A, which is essential for angiogenesis, was also markedly lower in diabetic individuals. Chen et al.[16] showed that glycemic control influences VEGF-related vascular responses, supporting the observation that chronic hyperglycemia is associated with lower VEGF-A levels, which may hinder new vessel formation and tissue repair. In line with this, Kizilaslan et al.[6] reported that in a diabetic rat model, CGF application improved bone healing but was associated with impaired vascular responses, supporting the reduced angiogenic potential of CGF under hyperglycemic conditions.

Our results showed TGF-β1 levels were marginally lower in diabetics (2.52 ± 0.36 ng/mL) compared to non-diabetics (2.76 ± 0.15 ng/mL). Although the reduction is not very large, it is still important for clinical outcomes. Aging and higher body fat, both common in people with diabetes, can impair platelet function and reduce growth factor production.[17,18]

The current study did not find significant differences in these parameters between the diabetic and control groups, suggesting that the observed reduction in growth factor concentrations in the diabetic group is directly related to hyperglycemia rather than confounding demographic factors. In addition, an imbalance between connective tissue growth factor and TGF-β1 may disrupt tissue repair by altering matrix remodeling in diabetic conditions.[19]

Although CGF itself does not release fibroblast growth factor-21 (FGF-21), emerging evidence suggests that systemic factors such as FGF-21 can enhance tissue repair in diabetes by improving metabolic and inflammatory balance. This indicates that combining metabolic modulation with CGF-based regenerative therapy may further optimize healing outcomes in diabetic patients.[20]

Given the weak and non-significant correlations observed with age and BMI, the lower levels of IGF-1 and VEGF-A in diabetics appear more closely associated with hyperglycemia rather than demographic differences.

Demographically, our study observed a negative correlation between growth factor concentrations and both age and BMI in the diabetic cohort, supporting the findings of McCarthy et al.[17] and Gawaz et al.[18] Interestingly, our data did not show any significant differences in growth factor levels between males and females. This supports earlier findings suggesting that metabolic status plays a more important role than gender in determining the content of platelet-derived growth factors.[21,22]

Previous studies have also reported reduced growth factor levels in PRF or CGF among individuals with diabetes, which is consistent with our findings.[2,3,6] Our study adds to existing work by showing these differences specifically in CGF, an advanced platelet concentrate with a stronger fibrin structure and slower, sustained growth factor release.[4]

While CGF has shown clear therapeutic benefits, such as a dense fibrin matrix and controlled release of growth factors, our findings suggest that these advantages may be reduced in diabetic patients. Nevertheless, adjunctive strategies have shown promise. Sustained-release CGF has been shown to improve bone formation and support chronic wound healing, particularly when blood vessel integrity is preserved.[5,8] Its effectiveness in diabetic wound care is further supported by comparative reviews on platelet concentrates like CGF.[3] Although such approaches were not investigated in the present study, they represent promising avenues for future translational research.

Clinically, these findings highlight the need to consider individual metabolic status when tailoring regenerative protocols to patient profiles. As emphasized by Azad et al.,[23] the principles of precision medicine, which integrate individual metabolic characteristics with therapeutic strategies, may help improve regenerative outcomes in the future. Furthermore, platelet dysfunction is a well-known concern in diabetes,[12] and emerging experimental work, such as that of Zhang et al.,[24] suggests that thermosensitive hydrogels loaded with CGF could enhance bone repair in diabetic models. Although these adjunctive strategies were not part of the present study, they represent promising avenues for future translational research aimed at improving the clinical performance of CGF in metabolically compromised patients.

STRENGTHS AND LIMITATIONS

This study demonstrates that T2DM is associated with lower levels of key growth factors in CGF-IGF-1, VEGF-A, and TGF-β1, which are important for tissue repair. Strengths include a well-defined diabetic cohort, standardized CGF preparation and measurement, and analysis accounting for age, BMI, and gender.

Limitations include its single-center design, modest sample size, and lack of functional assays to assess the direct influence of growth factor variations on healing or cell proliferation. The study also did not record detailed clinical variables such as duration of diabetes, type of anti-diabetic medication, or presence of complications (neuropathy, nephropathy, or retinopathy), all of which may influence platelet function and growth factor release. Additionally, common metabolic comorbidities such as hypertension or dyslipidemia were not specifically assessed, which may have contributed to variability in growth factor levels. These factors may limit the generalizability of the findings.

Despite these limitations, the study provides important insights into how metabolic status is associated with CGF composition. Future research should include larger, multi-center cohorts, assess both functional and clinical outcomes, and incorporate additional biomarkers, patient characteristics, and medication profiles to better understand CGF’s regenerative potential in T2DM.

CONCLUSION

This study demonstrates that T2DM is associated with lower IGF-1 and VEGF-A concentrations in CGF, independent of age and BMI, as both demographic factors were similar between the groups. The minimal and statistically non-significant difference observed for TGF-β1 after adjusting for covariates further supports that hyperglycemia, rather than demographic differences, may be associated with reduced regenerative potential of CGF. While the current findings are limited to biochemical associations, they highlight the need for further functional and clinical studies to assess how glycemic control may influence the regenerative performance of CGF in vivo. Emerging approaches such as precision medicine and adjunctive biomaterial strategies, including thermosensitive hydrogels, warrant exploration in future research to optimize CGF-based regenerative therapies for diabetic patients.

CONFLICTS OF INTEREST

There are no conflicts of interest.

ETHICAL POLICY AND INSTITUTIONAL REVIEW BOARD STATEMENT

Ethics approval or waiver has been obtained with their letter number MRDC/IEC/2024/121 dated August 27, 2024.

AUTHOR CONTRIBUTIONS

Not applicable.

DECLARATION OF PATIENT CONSENT

Patient consent obtained.

DATA AVAILABILITY STATEMENT

Not applicable.

LIST OF ABBREVIATIONS

• A-PRF Advanced Platelet-Rich Fibrin

• ANOVA Analysis of Variance

• BMI Body Mass Index

• CGF Concentrated Growth Factor

• CTGF Connective Tissue Growth Factor

• CV Coefficient of Variation

• DM Diabetes Mellitus

• ELISA Enzyme-Linked Immunosorbent Assay

• FGF-21 Fibroblast Growth Factor-21

• HbA1c Glycated HaemoglobinI

• GF-1 Insulin-Like Growth Factor-1

• LOD Limit of Detection

• OD Optical Density

• PBS Phosphate-Buffered Saline

• PDGF Platelet-Derived Growth Factor

• PRF Platelet-Rich Fibrin

• PRGF Plasma Rich in Growth Factors

• PRP Platelet-Rich Plasma

• R² Coefficient of Determination

• SD Standard Deviation

• T2DM Type 2 Diabetes Mellitus

• TGF-β1 Transforming Growth Factor-Beta1

• TNF-α Tumour Necrosis Factor-Alpha

• VEGF-A Vascular Endothelial Growth Factor-A

Funding Statement

Nil.