Evaluation of the association between insulin resistance and the development of periodontitis in individuals with varying levels of dietary sugar intake – A pilot study

Abstract

Aim:

The aim of this study was to evaluate insulin resistance (IR) and compare it with the periodontal status of individuals using homeostasis model assessment insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), and periodontal inflamed surface area (PISA) index in individuals with variable sugar intake.

Materials and Methods:

The study comprised of 20 patients aged 20–25 years who were not previously diagnosed with diabetes. The participants were categorized into two groups–low sugar intake (n = 10) and high sugar intake (n = 10), depending on their glycemic loads and were evaluated for IR as determined by HOMA-IR and insulin sensitivity as measured by QUICKI. The development of periodontal disease was estimated by means of PISA. Statistical significance was assessed by Mann–Whitney U and Spearman’s correlation tests.

Results:

The mean HOMA-IR of the low sugar group was 1.60, and the high sugar intake group was 2.52, and the mean QUICKI in the low sugar group was 0.355, and the high sugar group was 0.33, with a highly significant P = 0.001. There was a significant difference between the mean PISA values of the low-sugar group (54.02) and the high-sugar group (329.44). The lower QUICKI and the higher HOMA-IR values in the high sugar intake group were indicators of IR.

Conclusion:

The results of this study indicate that there is a potential systemic link between IR and periodontitis, and it is plausible that dietary sugars have an impact on this association. This study contributes to understanding the interplay between metabolic health and oral conditions, emphasizing the importance of dietary considerations in maintaining both insulin sensitivity and periodontal health.

INTRODUCTION

In recent years, the intricate connections between systemic health and oral well-being have garnered increasing attention within the realm of medical research. Among these interdependencies, the relationship between insulin resistance (IR), sugar intake, and the development of periodontitis has emerged as a pivotal area of investigation. Periodontitis, a chronic inflammatory condition affecting the supporting structures of the teeth, is not only a significant oral health concern but has also been associated with various systemic conditions, including diabetes.

Diabetes mellitus (DM) is a worldwide health crisis that is increasing at an enormous rate. The International Diabetes Federation reports that the global prevalence of DM in 2021 was 537 million, and this number is projected to reach 700 million by 2045. Impaired glucose homeostasis leads to the development of type 2 DM (T2DM). The glucose homeostasis model assessment (HOMA) initially presented by Matthews et al.[1] is used to estimate IR. T2DM is regarded as a nonautoimmune disease that is caused by a variety of intriguing variables, including inflammatory pathways, acquired or environmental factors, and genetics. In essence, periodontitis is a biofilm-induced disease that is initiated and exacerbated by several bacterial species found in dental plaque. It has been established that periodontitis has consequences that extend beyond the oral cavity and adversely impact the patients’ overall health.[2] The field of periodontal medicine is an emerging discipline that deals with numerous associations between systemic disorders and periodontitis.

The rising prevalence of IR is often linked to excessive sugar consumption and adds a layer of complexity to our understanding of the intricate web of health factors contributing to periodontal health. Periodontitis is recognized as the sixth complication of DM.[3] In light of this, the present study aims to evaluate the relationship between IR and periodontitis, with a specific focus on individuals with varying levels of sugar intake. This study is designed to provide a comprehensive assessment of the impact of dietary sugars on IR and their subsequent influence on the development and progression of periodontitis. Several studies on the assessment of metabolic disorders and periodontitis have been published worldwide, but relatively little is known about the correlation between HOMA-IR and QUICKI in individuals with periodontitis.[4,5]

The aim of this study is to assess and quantify IR in study participants utilizing established indices such as homeostasis model assessment of insulin resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI), and integrate periodontal inflamed surface area (PISA) as a novel parameter to comprehensively evaluate the extent and distribution of periodontal inflammation.

MATERIALS AND METHODS

This double-blinded pilot study involved 20 participants, who were selected from the outpatient department of periodontology at our institution. The study participants were stratified into two groups based on sugar intake levels. The intake of sugar was assessed according to the World Health Organization (WHO) criteria (2015), which defines low sugar intake (Group 1) as consumption of added sugars <10% of daily intake and high sugar intake (Group 2) as more than 10% of daily intake.

This study included subjects aged 20–25 years, with no history of being diagnosed with diabetes. Individuals undergoing orthodontic and periodontal treatment were excluded from the study. Subjects with systemic conditions that could independently influence IR were also excluded from the study. Pregnant individuals were excluded due to the hormonal changes during pregnancy that could impact metabolic parameters. In addition, subjects taking corticosteroids and anti-inflammatory drugs were excluded. To maintain homogeneity in the study population, smokers and alcoholics were also excluded, as these lifestyle factors could introduce additional variability in the outcomes. Before participation, all the selected individuals provided written informed consent after a detailed explanation of the study objectives and procedures. Ethical approval for the study was obtained from the University Ethics Committee (Ref no: IHEC-II/0465/23).

Homeostasis model assessment IR evaluation and QUICKI was used to determine the blood glucose and insulin concentrations. Venous blood sample (5 mL) was collected in the morning through the median cubital vein by venipuncture after an overnight fast of 8–12 h. Blood was collected in tubes that contained sodium fluoride as a glycolytic inhibitor and ethylenediamine tetra-acetic acid (EDTA) as an anticoagulant for glucose analysis (BD Vacutainer™ Fluoride/EDTA; Becton Dickinson India Pvt Ltd) and in tubes that contained sodium heparin as an anticoagulant for insulin analysis (BD Vacutainer™ Plus; Becton Dickinson, India Pvt Ltd). To extract plasma, the blood samples were centrifuged at 1800 g for 15 min at 4°C as soon as they were collected. The plasma samples were utilized for the estimation of glucose and insulin levels.

IR was evaluated by the homeostasis model assessment insulin resistance (HOMA-IR) index calculated by the formula:

HOMA-IR = fasting glucose (mmol/L) × fasting insulin (μUI/mL)/22.5

Insulin sensitivity was evaluated by the QUICKI derived using the inverse of the sum of the logarithms of the fasting insulin and fasting glucose:[6]

1/[log (fasting insulin μU/mL) + log (fasting glucose mg/dL)]

Probing depth and bleeding on probing were evaluated, and the PISA score was computed using a spreadsheet that was downloaded from Nesse’s publication (www.parsprototo.info).[7]

Statistical analysis was performed using IBM SPSS (IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY, USA: IBM Corp. Released, 2022). Mean and standard deviation were used to summarize the data. Statistical significance of associations was determined with Mann–Whitney U and Chi-square test.

RESULTS

Twenty individuals (12 male and 8 female) participated in the study. The mean age and proportions of male and female participants in both groups were comparable, with no statistically significant differences in gender distribution between the low and high sugar intake groups (Chi-square test, P = 0.650). The results comparing physiological parameters between the two groups based on sugar intake levels were analyzed using the Mann–Whitney U test.

The homeostasis model assessment IR evaluation showed that individuals with high sugar intake (Group 2) had a significantly higher mean HOMA-IR of 2.526 ± 0.46 compared to those with low sugar intake (Group 1) of 1.604 ± 0.235 [Figure 1]. The low sugar intake group (n = 10) had a significantly lower mean rank of 5.65 compared to the high sugar intake group (n = 10) with a mean rank of 15.35. The mean rank and sum of ranks were markedly higher in the high sugar intake (Group 2), suggesting a potential impact of sugar intake on HOMA-IR levels. The results indicate a significant difference in HOMA-IR between the low-sugar intake and high-sugar intake groups.

Figure 1.

Comparison of mean HOMA-IR between low and high sugar intake groups

The quantitative insulin sensitivity check test revealed that individuals with low sugar intake (Group 1) had a higher mean QUICKI 0.3550 ± 0.008 compared to those with high sugar intake (Group 2) (0.3330 ± 0.009), suggesting a potential association between lower sugar intake and improved insulin sensitivity [Figure 2]. The results reveal a significant difference in QUICKI between the low sugar intake (Group 1), with a mean rank of 15.15, and the high sugar intake Group 2, with a mean rank of 5.85. The low sugar intake group exhibited higher mean rank and sum of ranks, suggesting a potential association between lower sugar intake and improved QUICKI values.

Figure 2.

Mean quantitative insulin sensitivity check index in subjects with low versus high dietary sugar intake

The PISA index showed that the low sugar intake group had a significantly lower mean rank of 5.60 compared to the high sugar intake group with a mean rank of 15.40. The results demonstrated a significant difference in mean PISA between the low sugar intake (54.020 ± 26.78) and high sugar intake (329.440 ± 172.09) groups, suggesting a potential impact of sugar intake on pancreatic insulin secretion and the development of periodontitis. The results revealed a significant disparity in PISA between the low sugar intake and high sugar intake groups [Figure 3]. Higher mean rank and sum of ranks in the high sugar intake group suggest a potential correlation between increased sugar intake and elevated PISA levels. The high PISA score in the group with high sugar consumption suggests a higher risk of periodontitis development due to the enhanced sensitivity of the periodontal tissues to increasing dietary sugars.

Figure 3.

Comparison of mean periodontal inflamed surface area index in Group 1 and Group 2

The Mann–Whitney U test indicated a highly statistically significant difference (P < 0.001) in all parameters between the two study groups [Table 1]. Thereby, the results suggest strong evidence of significant differences in HOMA-IR, QUICKI, and PISA between individuals with low and high sugar intake [Table 2]. The mean HOMA-IR values were higher for Group 1, and the QUICKI values were lower for Group 1 when compared to Group 2. This suggests that there is an inversely correlated association between insulin sensitivity and IR. The findings imply a potential impact of sugar intake on these physiological parameters, highlighting the importance of dietary considerations in the context of metabolic health and periodontal tissue health.

Table 1.

Comparison of homeostasis model assessment insulin resistance, quantitative insulin sensitivity check index, and Periodontal inflamed surface area between subjects with low and high dietary sugar intake

	Group	n	Mean rank	Sum of ranks	P
HOMA IR	Low sugar intake	10	5.65	56.50	<0.001
	High sugar intake	10	15.35	153.50
QUICKI	Low sugar intake	10	15.15	151.50	<0.001
	High sugar intake	10	5.85	58.50
PISA	Low sugar intake	10	5.60	56.00	<0.001
	High sugar intake	10	15.40	154.00

QUICKI – Quantitative insulin sensitivity check index; PISA – Periodontal inflamed surface area; HOMA-IR – Homeostasis model assessment for insulin resistance; n – number of subjects in the group

Table 2.

Comparison of mean, standard deviation, and standard error of homeostasis model assessment insulin resistance, quantitative insulin sensitivity check index, and periodontal inflamed surface area among subjects with low versus high dietary sugar intake

Parameter	Group	n	Mean±SD	SEM
HOMA IR	Low sugar intake	10	1.604000±0.2350035	0.0743146
	High sugar intake	10	2.526000±0.4636378	0.1466151
QUICKI	Low sugar intake	10	0.3550±0.00850	0.00269
	High sugar intake	10	0.3330±0.00949	0.00300
PISA	Low sugar intake	10	54.020±26.7800	8.4686
	High sugar intake	10	329.440±172.0893	54.4194

QUICKI – Quantitative insulin sensitivity check index; PISA – Periodontal inflamed surface area; HOMA IR – Homeostasis model assessment for insulin resistance; SD – Standard deviation; SEM – Standard error of mean; n – number of subjects in the group

DISCUSSION

The current study aimed to assess IR and examine its relationship with periodontal health by utilizing the homeostasis model assessment of insulin resistance (HOMA-IR), the QUICKI, and the PISA index in individuals with varying levels of sugar consumption. This pilot study dealt with systemically healthy individuals to assess the impact of dietary sugar intake as a precursor for predicting the onset of diabetes and the progression of periodontitis in healthy individuals. The results demonstrate an inverse relationship between IR and insulin sensitivity, indicating that increased sugar consumption may have a detrimental impact on both metabolic function and periodontal health.

HOMA-IR is a test used to predict the risk of individuals of developing diabetes. This study utilized HOMA-IR and PISA to correlate the dietary sugar intake with the development of diabetes and periodontitis in healthy individuals. The study findings revealed a significant difference in IR as measured by HOMA-IR between individuals with low and high sugar intake. The high sugar intake group exhibited a substantially higher mean HOMA-IR of 2.526 and also had a high PISA mean score of 329.4 compared to the low sugar intake group. This result was consistent with that of Genco et al., who in 2005 found that overweight, nondiabetic individuals with high sugar intake had high IR with a significantly increased odds ratio (1.48) for severe periodontal disease compared to nondiabetic individuals with low IR.[8] In addition, a study conducted in 2010 by Benguigui et al. found that participants with high HOMA-IR had nearly 4 times higher odds of severe periodontitis compared to those with low HOMA-IR (OR ≈ 3.8–3.97).[9] The study findings were also supported by Lang et al. in 2025, who analyzed data from 1588 participants in the National Health and Nutrition Examination Survey to investigate the association between IR indicators and periodontitis, wherein HOMA-IR was significantly associated with increased odds ratio of 1.00 (P = 0.0028), indicating that greater IR corresponds to higher periodontitis risk.[10]

QUICKI is used to assess insulin sensitivity, and this parameter was utilized in the study to corroborate HOMA-IR values and to confirm its usefulness for predicting the onset of diabetes. Furthermore, the QUICKI results contrasted with the HOMA-IR findings, indicating that individuals with low sugar intake had improved insulin sensitivity (0.3550 ± 0.008) compared to those with high sugar intake (0.3330 ± 0.009). The findings of the current study were supported by a 2017 study by Pulido-Moran et al., which showed tissue insulin sensitivity and IR, demonstrated by lower QUICKI values and higher HOMA-IR values in the periodontitis group (0.01< P < 0.05) compared to the nonperiodontitis group.[11] The lower QUICKI in the high sugar intake group aligns with the concept that excessive sugar consumption may lead to decreased insulin sensitivity, a crucial factor in the pathogenesis of IR and related periodontal tissue inflammation.

PISA is a measure of the inflammatory burden caused by periodontitis and the surface area of the bleeding pocket epithelium.[12] The examination of PISA further elucidated the impact of sugar intake on periodontal tissue health. Individuals with high sugar intake exhibited a significantly higher mean PISA compared to their low sugar intake counterparts (P < 0.001). Nesse et al., in 2008, stated that PISA contributes as a dynamic source for the progression of poor metabolic control in T2DM subjects with severe periodontitis. Nesse et al. have calculated that an increase of PISA by 333 mm² was associated with a 1% point augmentation of HbA1c, independent of other factors.[7] Impaired glucose homeostasis and increased IR result from chronic subclinical systemic inflammation, which is manifested in the early stage of T2DM.[13] Colombo et al., in 2012, stated in their study that impaired insulin sensitivity and insulin signaling in periodontitis are probably related to the plasmatic rise of tumor necrosis factor- alpha and other inflammatory cytokines.[14] These findings were supported by a systematic review by Woelber et al., in 2023, who analyzed 9 randomized controlled trials and found that restriction of free sugars led to a statistically significant reduction in gingival inflammation, with a standardized mean difference of 0.92 (P < 0.004), and concluded that restricting free sugar intake is beneficial in reducing gingival inflammation, and supports dietary sugar reduction as a modifiable factor for improving periodontal health.[15]

The present study has the following limitations: A limited sample size and an unequal gender distribution in both groups. In addition, the study participants were divided into two groups according to the WHO definition of added sugar intake; no particular measuring scales were employed to categorize sugar intake. Furthermore, the patients’ evaluation of their sugar intake was conducted without any reference to specific dietary charts.

This study adds a novel dimension by exploring the relationship between insulin sensitivity and periodontitis within the context of varying sugar intake levels. This study is the first of its kind in observing the association between IR (HOMA-IR), insulin sensitivity (QUICKI), and dietary sugar intake, thereby suggesting a potential link between dietary sugars and the development of periodontitis by utilizing the PISA index. Chronic inflammation, a common factor in both IR and periodontal disease, may serve as a plausible bridge connecting these seemingly disparate health conditions.

CONCLUSION

The current study provides compelling evidence supporting a potential association between IR, insulin sensitivity, and sugar intake, with implications for the development of periodontitis. This association suggests that systemic factors related to IR may contribute to the pathogenesis or progression of periodontitis. The observed associations highlight the importance of dietary considerations in managing both metabolic health and periodontal well-being. Ultimately, a deeper understanding of this interplay holds the promise for evidence-based practice aimed at improving both oral and systemic health outcomes. Future research endeavors should focus on elucidating the mechanistic pathways linking sugar intake, IR, insulin sensitivity, and periodontitis to develop targeted preventive strategies and therapeutic interventions.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.