Usman Muhammad Tukur^1*, Shamsuddeen Idris Musa^2*, Zainab Aliyu Adam², Bishir Usman³, Muhammad Adam Abubakar ⁴, Ni’imatullahi Kabir Isah⁵, Rabi Rabi’u Abubakar⁶, Sani Rabi’u⁷

¹School of Metallurgy and Environment, Central South University, Changsha, China

²General Studies, Emirates College of Health, Kano, Nigeria

³Department of Pure and Industrial Chemistry, Bayero University Kano, Kano State, Nigeria

⁴Department of Actuarial Science, Federal University Dutse, Jigawa State, Nigeria

⁵Kano Agricultural Supply Company, Kano, Nigeria

⁶Science Laboratory Technology, Federal Polytechnic, Kazaure, Nigeria

⁷General Studies, School of Health Technology, Daura, Nigeria

Corresponding Author: Usman Muhammad Tukur & Shamsuddeen Idris Musa, School of Metallurgy and Environment, Central South University, Changsha, China & General Studies, Emirates College of Health, Kano, Nigeria

Abstract
Many years ago, periodontal disease precisely posed significant oral health challenges, resulting in complications such as tooth mobility and tooth loss. This condition demonstrates a strong association with several systemic diseases, particularly diabetes mellitus. The relationship is substantial as periodontal disease can enhance insulin resistance, thereby complicating diabetes management. This study examined the treatment response rates in patients with simultaneous periodontal disease and diabetes, while also investigating the statistical correlation between these conditions. To derive meaningful insights, the research utilized secondary data spanning a decade (2015-2024) from the dental clinic in Kano State. The research tested two hypotheses, Null Hypothesis (H0): Patients with concurrent diabetic and periodontal diseases do not exhibit accelerated treatment response. Alternative Hypothesis (H1): Patients with periodontal disease demonstrate rapid treatment response. Statistical analysis, conducted using SPSS software, employed correlation analysis and t-tests to examine the relationship between periodontal and diabetic treatments (variables X1 and X2). The results revealed a robust correlation coefficient (r) of 95.7% between the variables. Additionally, t-test results demonstrated statistical significance, confirming a strong linear relationship between the treatment modalities for both conditions. Based on these findings, the study highlights the interconnected nature of periodontal and diabetic treatments. The research recommends that healthcare providers implement specialized treatment protocols for diabetic patients with periodontal complications. This integrated approach is essential for optimizing treatment outcomes and effectively managing the complex interplay between these conditions.

Keywords: Oral health, Periodontal, Diabetic, Treatments, Healthcare, Analysis

1. Introduction
Periodontal disease, commonly referred to as gum disease, represents a chronic inflammatory condition that poses a significant threat to oral health. This progressive infection targets the gums and can lead to devastating effects on both soft tissue and the underlying bone structure that provides essential support for teeth [1-2]. The condition stands as one of the two most critical challenges to oral health worldwide and serves as the primary factor in tooth loss among adults [3]. The development of periodontal disease is intricately linked to inadequate oral hygiene practices, which create favorable conditions for plaque accumulation. This plaque manifests as a transparent, adhesive biofilm containing numerous bacteria that adheres to the tooth surface [4]. When left undisturbed, this bacterial film triggers an inflammatory response in the surrounding gum tissues, initiating the disease process [5].

Figure 1: Early stage of periodontal disease.

Periodontitis represents the severe progression of untreated gingivitis, marking a critical escalation in periodontal disease where inflammation extends beyond superficial gum tissue to infiltrate the deeper structures supporting the teeth. This irreversible stage is characterized by the destruction of the alveolar bone (the jawbone that houses tooth sockets), periodontal ligaments (connective tissues anchoring teeth to bone), and cementum (the protective outer layer of tooth roots). As the infection advances, the gums begin to detach from the teeth, forming pathological spaces known as periodontal pockets. These pockets become reservoirs for harmful bacteria, plaque, and tartar, accelerating tissue degradation and creating a self-sustaining cycle of inflammation and damage. The transition from gingivitis to periodontitis occurs when persistent bacterial toxins and the body’s hyperactive immune response overwhelm the gingiva’s ability to heal. Immune cells release inflammatory mediators, such as cytokines and enzymes like collagenase, which inadvertently break down connective tissues and bone. As the alveolar bone resorbs and gum tissue recedes, teeth lose their structural support, leading to mobility, shifting, or misalignment. Deepening pockets—often exceeding 4 millimeters in depth—harbor anaerobic bacteria that thrive in low- oxygen environments, exacerbating infection [13].

Figure 2: Untreated periodontal disease.

Advanced Periodontitis: In the most severe stage, the supporting structures of the teeth are severely damaged, leading to significant tooth loss. Treatment at this stage may involve surgical procedures and more intensive dental care [14].

Figure 3: Severe periodontal disease.

The current global statistics from 2025 indicate that, periodontal disease affects more than one billion people worldwide, with severe cases found in 743 million individuals, representing 11% of the global population [15-17]. The impact is especially noticeable in developing nations, where inadequate access to dental healthcare services worsens the problem [18]. The widespread occurrence of periodontal disease across age groups has elevated it to a major public health issue. Risk factors encompass lifestyle choices (smoking), oral hygiene practices, medical conditions (diabetes), medication use, age-related changes, genetic factors, and stress levels [19-21]. Scientific evidence strongly links periodontal disease to broader health issues, including heart disease, diabetes, and pregnancy-related complications [22]. The severity of this connection is highlighted by the fact that Type 2 diabetic patients with advanced periodontal disease have a mortality risk more than three times higher than those with minimal or no periodontitis [23-24]. Importantly, managing periodontal health has been shown to enhance glycemic control in Type 2 diabetes patients [25-27].

Figure 4: Oral Health Management Strategy.

Although oral health research has made considerable strides, critical gaps persist in developing effective global treatment strategies [28- 30]. Key challenges include complex healthcare referral pathways, prohibitive treatment expenses, systemic disease complications, and limited comprehension of how periodontal and diabetic treatments interact in patients with both conditions [31-34].

Here, the study implemented a dual analytical approach, combining treatment response analysis in patients with concurrent periodontal disease and diabetes with statistical correlation studies using secondary data. Three main objectives guided the research: examining the connection between diabetes and periodontal conditions, assessing their linear relationship, and evaluating treatment response speeds in affected patients. The research outcomes suggest  this  methodology  could  lead  to  improved  treatment optimization and better management of the complex interactions between these conditions, advancing future oral health interventions.

2.Methodology
The data used in this research work is secondary data from 2015 to 2024, which was collected from the Dental clinic in Kano State.

2.1 Hypothesis
Ho: patients with diabetic and periodontal diseases do not respond to treatment quickly.

H₁: patients with periodontal disease only respond to treatment quickly.

2.2 Statistical Tools Used in the Analysis
2.2.1 Correlation
Let us consider two variables x and y. The correlation R is given by equation 1 below.

By analogy, the deviation from the mean  are often called the first moments about the mean, while  and are called the second moments. A convenient name for  is, therefore, product-moment. From this analogy; that is the reason why r is known as the product-moment correlation coefficient. To ease computations, for the following compact equation 2 were used.

2.3. To Test for the Significance of Correlation
A test for the statistical significance of correlation can be carried out using T-test. Thus, we proceed,
Hypothesis:
H_o: Patients with diabetic and periodontal diseases do not respond to the treatment quickly.

H₁: Patients with periodontal diseases only respond to the treatment quickly.

Level of significance  = 0.05 

Test statistic using equation 3 below:

Where , and  are the Decision Criterion, 

Reject the null hypothesis, if  

We can reject the null hypothesis, if 

3. Results and Discussion
The research findings of this investigation are illustrated in the subsequent tables and figures, revealing the strong interrelationship between treatments for periodontal disease and diabetes.

Table 1: Patients with periodontal and diabetic diseases over the years.

Table 2: Treatments of patients with periodontal and diabetic diseases over years.

Table 1 and figure 5 presents longitudinal raw data on patients registered for diagnosis in the clinic with Periodontal Disease (X1) and Diabetic Disease (Y2), highlighting that a significant number of individuals have experienced complications from both conditions over an extended period (10 years).

Figure 5: Number of cases over ten (10) years.

Figure 6: Treatments over ten (10) years

Table 2 and figure 6, in contrast, illustrates the subset of these patients who experienced combined treatment (X2: periodontal treatment, Y2: diabetic treatment) and subsequently achieved improved health outcomes. The data in Table 2 is derived from the original patient cohort documented in Table 1, emphasizing the effectiveness of dual-treatment interventions for those with comorbid diagnoses. 

Table 3: Descriptive Statistics.

Correlation Results for Periodontal, Diabetic Disease and Periodontal, Diabetic Treatment:

Set 1 Range = Sheet1!$A$2:$C$12

Set 2 Range = Sheet1!$D$2:$F$12

The header indicates that this is a correlation, with Range 1 variables (X₁, Y₁) (Periodontal Disease and     Diabetic disease) located at A2:C12, to be correlated with Range 2 variables X₂, Y₁ (Periodontal Treatment and Diabetic Treatment) located at D2:F12. The descriptive statistics table gives the mean, standard deviation, standard error and sample sizes for each of the Set 1 variables (X_1, Y₁) and Set 2 variables (X₂, Y₂). See table 3 above.

Table 4: Simple Correlation.

The correlation gives the simple correlation coefficients (r) for each of the X₁^­ and Y₁ variable. R is >= -1 and <= +1 for any variable correlated with any other variable. In this analysis, r is 0.282 for the simple correlation of X₁ with Y₁ meaning that there is a Week positive correlation between the two variables i.e., Periodontal Disease and Diabetic Disease (X₁, Y₁). See table 4 above.

Table 5: Paired Samples Correlations.

The correlation gives the simple correlation coefficients (r) for each of the X₂^­ and Y₂ variables. R is >= -1 and <= +1 for any variable correlated with any other variable. In this analysis, r is 0.577 for the simple correlation of X₂ with Y₂ meaning that there is a positive correlation between the two variables i.e. Periodontal Treatment and Diabetic Treatment (X₂, Y₂). See table 5 above.

Table 6: Paired Samples Correlations.

The correlation gives the simple correlation coefficients (r) for each of the X₁^­ and X₂ variable. R is >= -1 and <= +1 for any variable correlated with any other variable. In this analysis, r is 0.957 for the simple correlation of X₁ with X₂ meaning that there is a strong positive correlation between the two variables i.e. Periodontal Disease and Diabetic Treatment (X₁, X₂). See table 6 above.

Table 7: Paired Samples Correlations.

The correlation gives the simple correlation coefficients (r) for each of the Y₁^­ and Y₂ variable. R is >= -1 and <= +1 for any variable correlated with any other variable. In this analysis, r is 0.957 for the simple correlation of Y₁ with Y₂ meaning that there is a strong positive correlation between the two variables i. e Diabetic Disease and Diabetic Treatment (Y₁, Y₂). See table 7.

Computation: By using SPSS, we have the following output:

Table 8: Paired Sample Test

T-TEST PAIRS=Treatment Periodontal WITH Treatment Diabetic (PAIRED)/CRITERIA=CI

(.050)/MISSING=LISTWISE.

Since the sig value 0.00 < α = 0.05, we reject H_o and conclude that the treatment patient with periodontal differ significantly with patient with both periodontal and diabetic.

Figure 7: Oral Health Management Strategy.

4. Conclusion
Conclusively, this study highlights the critical need for accurate diagnosis of periodontal disease in diabetic patients by analyzing clinical traits observed during treatment, while emphasizing the interdependence of periodontal and diabetes management. Findings reveal that diagnosing periodontal disease in diabetic populations requires meticulous care and tailored treatment protocols. Statistical analyses, correlation and t-test over multiple years demonstrated a strong relationship between periodontal disease (X1) and its treatment (X2), with a correlation coefficient confirming their simultaneous delays in treatment efficacy observed in a Kano dental clinic. The correlation strength was statistically significant (95.7%), validating a near-absolute association between these variables. The study advocates for policymakers, researchers, and healthcare; providers to leverage these insights for advancing interdisciplinary research, refining clinical guidelines, and raising public awareness about the bidirectional risks of periodontal disease and diabetes. Targeted health education is recommended to mitigate societal and medical impacts, emphasizing preventive care and early intervention for at-risk populations.

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