ORIGINAL RESEARCH

https://doi.org/10.5005/jp-journals-10019-1409
International Journal of Prosthodontics and Restorative Dentistry
Volume 13 | Issue 2 | Year 2023

Correlation between Implant Stability and Alkaline Phosphatase in Controlled Diabetics: A Clinical Study

Sheezan Akhtar1, Pankaj Dhawan2, Neha Jain3https://orcid.org/0000-0001-6239-5480, Anjana Goyal4, Harsimran Kaur5, Piyush Tandan6

1-3,5,6Department of Prosthodontics, Manav Rachna Dental College, Faridabad, Haryana, India

4Department of Biochemistry, Manav Rachna Dental College, Faridabad, Haryana, India

Corresponding Author: Neha Jain, Department of Prosthodontics, Manav Rachna Dental College, Faridabad, Haryana, India, Phone: +91 9818095343, e-mail: nejamaverick@gmail.com

Received on: 17 May 2023; Accepted on: 10 June 2023; Published on: 28 June 2023

ABSTRACT

Purpose: The purpose of the present study was to find the correlation between gingival crevicular fluid (GCF)/peri-implant crevicular fluid (PICF) alkaline phosphatase (ALP) levels and implant stability in controlled diabetic patients.

Materials and methods: In this study, 30 controlled diabetic patients needing implants in the mandibular posterior region were divided into two groups, that is, group I—patients with normal serum ALP and group II—patients with raised serum ALP. Each individual was rehabilitated with one implant following a nonfunctional immediate loading protocol. Implant stability in each individual was assessed by measuring implant stability quotient (ISQ) values. GCF/PICF samples were collected to evaluate the level of ALP. Both parameters were evaluated at baseline, 1, and 3 months postimplant placement, and the resulting values were compared using analysis of variance (ANOVA) and post hoc Bonferroni test within the group, and unpaired t-test was used for comparison between the two groups. The correlation between the two parameters was determined using Pearson’s correlation coefficient (r) test.

Results: In group I patients with normal serum ALP, the mean ALP in GCF/PICF and ISQ values at baseline was 685.19 IU/L and 72.53; at 1 month, were 608.08 IU/L and 68.73; at 3 months, were 766.26 IU/L and 79.33, respectively. In group II patients with raised serum ALP, the mean ALP in GCF/PICF and ISQ values at baseline were 1042.75 IU/L and 72.40; at 1 month, were 932.75 IU/L and 65.07; at 3 months, were 1111.73 IU/L and 76.80, respectively. A significant difference was seen in GCF/PICF ALP values between the two groups, whereas nonsignificant difference was observed in ISQ values between group I and group II.

Conclusion: No direct correlation was found between implant stability and GCF/PICF ALP in patients with normal serum ALP, and a negative correlation (with an increase in GCF/PICF ALP values, there was a corresponding decrease in the ISQ values) was found in patients with raised serum ALP at different time intervals.

How to cite this article: Akhtar S, Dhawan P, Jain N, et al. Correlation between Implant Stability and Alkaline Phosphatase in Controlled Diabetics: A Clinical Study. Int J Prosthodont Restor Dent 2023;13(2):81-87.

Source of support: Nil

Conflict of interest: None

Keywords: Alkaline phosphatase, Biomarker, Dental implants, Diabetes, Implant stability

INTRODUCTION

Today replacing missing teeth using dental implants is a preferred treatment option with a high degree of success and improved oral health-related quality of life.1 Its longevity is highly dependent on the bony osseointegration around the implant and underlying systemic diseases such as diabetes mellitus that impair the bone healing process.2 Hyperglycemia has always been regarded as a relative contraindication to oral implants due to inhibition of osteoblastic activity which can be evaluated by various biomarkers, such as ALP. Additionally, alteration in bone remodeling processes leads to deficient mineralization and reduction in bone-implant contact and affects osseointegration in diabetics.3-5

Implant stability is a measurement of the clinical immobility of an implant and an indirect indicator of osseointegration. There are various methods to assess implant stability, such as cutting torque resistance, insertion torque measurement, reverse torque, periotest, and resonance frequency analysis (RFA).6,7 RFA is a noninvasive diagnostic technique that works on the idea of resonance frequency, according to which the stronger the bone-implant contact, the higher the frequency that repeatedly vibrates onto the implant. In RFA, the frequency unit of Hertz is directly transformed to the implant stability quotient (ISQ).6,8

The osteogenic process during the healing period results in matrix mineralization which can be indicated by several biomarkers. ALP is a significant biomarker found in the cells of mineralized tissue and for the production of bone and bone turnover.9 Raised ALP is a consequence of increased osteoblast activity and suggests that active bone growth may be taking place.10 Correlation between ISQ values and ALP levels can act as prognostic indicators for possible implant failure and can provide significant clinical guidelines for implant loading in diabetics with good glycemic control.

The purpose of this clinical study was to establish a relationship between implant stability and the level of the bone biomarker, that is, ALP activity in GCF/PICF in controlled diabetic patients during the 3-month healing period. The null hypothesis of this study was that there was no correlation between implant stability and ALP in GCF/PICF at different time intervals in controlled diabetic patients with normal and raised serum ALP levels.

MATERIALS AND METHODS

This prospective study was conducted in the Department of Prosthodontics Crown, Bridge and Department of Biochemistry, Manav Rachna Dental College, Faridabad, India, to evaluate the correlation between GCF/PICF ALP and implant stability in controlled diabetic patients [glycated hemoglobin (HbA1c) level—7–8%]. Clinical assessment of stability was made using RFA measuring ISQ levels, and GCF/PICF ALP level was evaluated by collecting GCF/PICF samples at baseline, 1-, and 3-month intervals. The data collection forms, informed consent form, and study protocol was approved by the Ethical Committee (MRDC/IES/2020/11).

Patients with the chief complaint of missing mandibular posterior teeth were examined and selected for implant treatment after taking a detailed dental and medical history and thorough investigations. The nature of the study was explained to all included subjects, and the patients who were ready to undergo the treatment were made to sign the consent form. The estimation of the sample size was done using the nMaster 2.0 software (Department of Biostatistics, Christian Medical College, Vellore, India). The power of the study was taken to be 80% with a confidence interval of 95%, and a total of 33 patients were included in the study considering the dropouts. A total of 30 patients were recruited following the below-mentioned inclusion and exclusion criteria. To ensure the double blinding and to remove the bias, patients were not informed in which group they will be kept, and the samples were collected by another operator.

Patients aged between 25 and 55 years and having one or more than one missing mandibular posterior teeth were included in the study. Patients with existing healthy contralateral posterior teeth with no systemic diseases other than diabetes (HbA1c levels between 7 and 8%) were only included. Patients with HbA1c levels above 8% were excluded from the study. Patients suffering from bone disorders or systemic illnesses that impair bone metabolism, patients with a history of smoking, and poor oral hygiene were excluded. Individuals who had undergone cancer treatments and were on immunosuppressive medications or anticoagulants three months before the study and patients having inadequate vertical interarch space for the prosthesis were also excluded.

A total of 30 subjects were divided into two groups (n = 15 subjects each) on the basis of serum ALP level, as group I had controlled diabetic participants with normal ALP levels (serum ALP level—20–120 IU/L). Group II had controlled diabetic participants with raised ALP levels (serum ALP level—>120 IU/L).11

Each individual was rehabilitated by at least one implant (Neodent, Straumann, Curitiba, Brazil) in the mandibular posterior region following an immediate nonfunctional loading protocol. Complete clinical, radiographical, and serological examination was carried out before the implant placement. Implant size was determined on the basis of the cone-beam computed tomography systems analysis. Osteotomy preparation was performed under local anesthesia (2% lignocaine with 1:80000 concentrations of adrenaline), and the implant was placed at the crestal level.

Implant stability was measured using an ISQ instrument (Osstell®, Goteborg, Sweden), and if the resulting ISQ value was greater than 70, placement of healing abutment was done over the implant and left till the completion of the study period. A Smartpeg mount (Osstell, Goteborg, Sweden) was used to screw a Smartpeg to the implant. After removing the Smartpeg mount, the RFA evaluation was done using the transducer mentor probe attached to the portable ISQ instrument. The magnetic tip of the Smartpeg was held 2–3 mm away from the transducer mentor probe until the device emitted a bleeping sound, and the ISQ value was obtained (Fig. 1). Implant stability was later assessed during the healing period at 1- and 3-month intervals.6

Figs 1A and B: Implant stability assessment. A Placement of probe intraorally B. Instrument showing ISQ value

The level of ALP in GCF/PICF was evaluated during the osseointegration period. The selected test site was air-dried and isolated to avoid salivary contamination. 10–20 µL GCF was collected immediately after the surgical procedure from the adjacent tooth, and 10–20 µL PICF was collected from the implant site after 1- and 3-month time intervals with a microcapillary pipette by placing it at the entrance of the gingival sulcus, bloodlessly touching the gingival margin to restrict the bleeding from the gingiva. The collected GCF/PICF samples were swiftly transferred to labeled airtight Eppendorf tubes, which were stored at –80°C until the time of assay (Fig. 2).12 The quantitative determination of ALP in GCF/PICF was done by using a commercially available ALP kit (Erba Diagnostic Mannheim GmbH, Mannheim, Germany) using a digital spectrophotometer machine (Systonic, Panchkula, India) (Fig. 3).13

Fig. 2: Gingival crevicular fluid/peri-implant crevicular fluid (GCF/PICF) sample collection from the patient

Fig. 3: Absorbance of ALP using spectrophotometer machine

The recorded ISQ values and calculated ALP activity at baseline, 1, and 3 months were statistically analyzed using Statistical Package for the Social Sciences (SPSS) software (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, New York, United States of America: IBM Corp.) and MedCalc software (Acacialaan 22, Ostend, Belgium). ANOVA and post hoc Bonferroni test were done within the group, and an unpaired t-test was used to compare the parameters between the two groups. The resultant values were correlated within the groups using Pearson’s correlation coefficient (r) test.

RESULTS

The mean ISQ values at three different time intervals in both groups are presented in Figure 4. The mean ISQ values were compared at baseline, 1, and 3 months using the repeated measures of ANOVA (Table 1). There was a noticeable difference found in the mean value of ISQ at baseline, 1, and 3 months, and the difference was statistically significant (p = 0.001). The inter-interval comparison of mean ISQ values in both the groups done using the post hoc Bonferroni test (Table 2) showed that the mean ISQ values decreased significantly from baseline to 1 month and then increased significantly to 3 months (p = 0.001).

Fig. 4: Graph showing the mean difference of ISQ values at day 0, 1st and 3rd month; ISQ values from day 0 to 3rd month in groups I and II

Table 1: Repeated measures ANOVA to compare the mean ISQ of groups I and II at different time intervals
Group ISQ Mean Standard deviation F-value p-value
Group I Baseline 72.53 1.55 42.402 0.001*
1 month 68.73 2.69
3 months 79.33 1.72
Group II Baseline 72.40 1.30 19.174 0.001*
1 month 65.07 1.10
3 months 76.80 1.26

*Indicates p < 0.05 as statistically significant

Table 2: Post hoc Bonferroni test for interinterval comparison of ISQ values in both the groups
Group Time interval Mean difference p-value
Group I Baseline 1 month 3.80 0.001*
Baseline 3 months −6.80 0.001*
1 month 3 months −10.60 0.001*
Group II Baseline 1 month 7.33 0.001*
Baseline 3 months −4.40 0.001*
1 month 3 months −11.73 0.001*

*Indicates p < 0.05 as statistically significant

The mean ALP levels in GCF/PICF at three different time intervals in both groups were presented in Figure 5. The mean GCF/PICF ALP was compared at baseline, 1, and 3 months using the repeated measures of ANOVA (Table 3). There was a significant difference in mean ALP at baseline, 1, and 3 months (p = 0.001). The interinterval comparison of mean GCF/PICF ALP in both the groups using the post hoc Bonferroni test (Table 4) showed that the mean ALP decreased significantly from baseline to 1 month and then increased significantly to 3 months (p = 0.001).

Fig. 5: Graph showing the mean difference of GCF/PICF ALP at day 0, 1st, and 3rd month; ALP values from day 0 to 3rd month in groups I and II

Table 3: Repeated measures ANOVA to compare mean GCF/PICF ALP values of groups I and II at different time intervals
Group GCF/PICF ALP Mean Standard deviation F-value p-value
Group I Baseline 685.19 72.57 13.942 0.001*
1 month 608.08 85.86
3 months 766.26 70.52
Group II Baseline 1042.75 58.78 22.865 0.001*
1 month 932.75 66.79
3 months 1111.73 68.43

*Indicates p < 0.05 as statistically significant

Table 4: Post hoc Bonferroni test for inter-interval comparison of GCF/PICF ALP values in both the groups
Group Time interval Mean difference p-value
Group I Baseline 1 month 77.11 0.001*
Baseline 3 months −81.07 0.001*
1 month 3 months −158.18 0.001*
Group II Baseline 1 month 110.01 0.001*
Baseline 3 months −68.98 0.001*
1 month 3 months −178.99 0.001*

*Indicates p < 0.05 as statistically significant

Intergroup comparison of ISQ values was done using an unpaired t-test (Table 5), and it showed that the mean ISQ values at 1 and 3 months, were significantly higher among group II as compared with group I. ISQ values from baseline to 1 month were significantly more among group II. The mean ISQ values at baseline to 3 months were significantly more among group I as compared to group II (Fig. 4).

Table 5: Intergroup comparison of ISQ values using unpaired t-test
ISQ Group I Group II Mean difference t-test value p-value
Mean Standard deviation Mean Standard deviation
Baseline 72.53 1.55 72.40 1.30 0.13 0.255 0.800
1 month 68.73 2.69 65.07 1.10 3.67 4.894 0.001*
3 months 79.33 1.72 76.80 1.26 2.53 4.599 0.001*
Baseline to 1 month 3.80 2.96 7.33 1.35 −3.53 −4.213 0.001*
Baseline to 3 months 6.80 2.11 4.40 1.88 2.40 3.286 0.003*
1–3 months 10.60 2.72 11.73 1.71 −1.13 −1.366 0.183

*Indicates p < 0.05 as statistically significant

Intergroup analysis of ALP levels in GCF/PICF using an unpaired t-test (Table 6) showed significantly more values among group II compared to group I at baseline, 1, and 3 months (Fig. 5).

Table 6: Intergroup comparison of GCF/PICF ALP values using unpaired t-test
GCF/PICF ALP Group I Group II Mean difference t-test value p-value
Mean Standard deviation Mean Standard deviation
Baseline 685.19 72.57 1042.75 58.78 −357.56 −14.828 0.001*
1 month 608.08 85.86 932.75 66.79 −324.66 −11.560 0.001*
3 months 766.26 70.52 1111.73 68.43 −345.47 −13.616 0.001*
Baseline to 1 month 77.11 42.65 110.01 69.43 −32.90 −1.564 0.129
Baseline to 3 months 81.07 40.25 68.98 53.40 12.09 0.700 0.490
1–3 months 158.18 62.84 178.99 74.64 −20.81 −0.826 0.416

*Indicates p < 0.05 as statistically significant

There was no significant correlation between ISQ values and GCF/PICF ALP at baseline, 1, and 3-month intervals in group I (Table 7). There was a significantly negative correlation of GCF/PICF ALP with ISQ values at 1- and 3-month intervals in group II (Table 8).

Table 7: Pearson’s correlation coefficient test to correlate GCF/PICF ALP and ISQ in group I
GCF/PICF ALP Pearson correlation (r) and p-value ISQ
Baseline 1 month 3 months
Baseline r −0.375 – –
p-value 0.169 – –
1 month r – −0.059 –
p-value – 0.834 –
3 months r – – −0.504
p-value – – 0.056
Table 8: Pearson’s correlation coefficient test to correlate GCF/PICF ALP and ISQ in group II
GCF/PICF ALP Pearson correlation (r) and p-value ISQ
Baseline 1 month 3 months
Baseline r 0.435 – –
p-value 0.105 – –
1 month r – −0.349 –
p-value – 0.021* –
3 months r – – −0.346
p-value – – 0.039*

Level of significance at p < 0.05; *indicates statistically significant

DISCUSSION

Implant stability has always been considered a benchmark for the success of implant therapy. It is a measure of the absence of any micro movement and successful osseointegration of the implant with the surrounding bone, which is adversely affected by many local as well as systemic conditions such as diabetes mellitus.5

Prolonged exposure to a diabetic environment affects bone metabolism and compromises bone microarchitecture. These changes affect the implant stability and bone ALP level during osseous healing. Oates et al.14 assessed implant success in diabetic patients. The authors concluded that as the HbA1c level increases, the rate of implant failure increases, making it important to assess the clinical success of implants in diabetic patients having good glycemic control with HbA1c levels within the range of 7–8%, as was done in the present study.

During osseointegration, the osteoclastic activity causes the bone to resorb, followed by progenitor cells directly differentiating into osteoblasts and then intramembranous ossification around the implant surface. The early immune-inflammatory response, angiogenesis, and osteogenesis are the three overlapping stages of osseointegration. The woven bone, which is formed at the end of 4 weeks on the peri-implant interface, is entirely swapped with mature lamellar bone by 12 weeks.16,17 This explains the decrease in the implant stability at 1 month from the value determined at baseline (ISQ > 70), and by 3 months, the stability values were favorably nearly equivalent or increased to those obtained at baseline.

A similar result was seen in the study conducted by Baishi et al.,18 in which the implant stability was measured in diabetic patients on the day of implant placement and at five postsurgical examinations, that is, 1, 2, 3, 6, and 30 months. The values of the ISQ varied from 67.5 to 83. In the first 30 days, the implant stability fell by 12.7% and then continued to improve after the 1st month, reaching a value that was the same as the initial measurement acquired at the time of implant placement. The baseline mean value of the ISQ was 77.0, with a standard deviation of 1.32.

Alkaline phosphatase (ALP) is a lysosomal enzyme produced by various cells such as polymorphonuclear leukocytes, osteoblasts, and fibroblasts present within the area of the periodontium and gingival crevice during the inflammation by host tissue injury.19 During diseases or destructions of periodontal ligaments, the cells become damaged, due to edema or destruction of a cellular membrane, as a result of which there is an increased release of cells into the GCF, leading to an increased level of ALP. During the early phase of healing, low ALP activity most likely implies a steady-state bone condition with the bone-implant interface.20 Since ALP is thought to be involved in the first step of mineralization, high ALP activity from 1 to 3 months of healing could indicate primary mineralization followed by calcification of peri-implant bone over the few weeks with continued bone-tissue remodeling in the interface.21 Hence, a change in the activity and concentration of ALP in GCF denotes the beginning and progression of the healing process.

In a study conducted by Abdulhameed et al.,22 significant changes were seen in ALP levels after 2, 4, 12, and 24 weeks of implant placement in which a maximum increase was noted during the 2nd week following surgery and drop was observed after 2nd week during the healing process in the periodontium. ALP levels were restored to the preoperative level 24 weeks after implant placement.

The trend seen was in line with the result of the study conducted by Tirachaimongkol et al.23 stating that during the healing period of implant, the level of ALP decreased from 1 to 4 weeks, and a subsequent increase was seen at 6, 8, 10, and 12 weeks. The formation of a clot and neovascularization begins within 24 hours. The recruitment of mesenchymal stem cells leads to soft callus formation occurring 7–9 days postimplant placement. Replacement of soft callus by hard callus (semirigid structure) occurs within 14 days. As the hard callus formation progresses, the woven bone replaces the calcified cartilage during the 3–4 weeks. By 12 weeks, at the peri-implant interface, woven bone is completely replaced by mature lamellar bone in direct contact with the implant surface.15,16

The relationship between implant stability and GCF/PICF ALP levels in group I patients did not show a direct correlation. However, in group II patients, a negative correlation was observed due to the fact that the patients had raised serum ALP leading to increased GCF/PICF ALP levels as well. ISQ values were not increased proportionally to serum ALP. Hence, it leads to a negative correlation between the two parameters in group II. The ISQ values were observed to be nearly similar in both groups, irrespective of the serum ALP level.

Within the limitations of the study, in group I patients, since no significant correlation was found between GCF/PICF ALP values and ISQ values, the null hypothesis was accepted, whereas in group II patients, since there was a negative correlation between GCF/PICF ALP values and ISQ values at 1st and 3rd-month intervals the null hypothesis was rejected and the alternative hypothesis was accepted. Future research can be carried out to compare the ALP level and ISQ values in diabetic and nondiabetic patients.

Keeping in view the limitations of the study since no definite correlation could be drawn between the two factors. Hence a better understanding of the success of implants in controlled diabetics can be achieved by increasing the sample size, evaluating long-term results as well as evaluating other biomarkers. However, good implant stability was seen in both groups, 3 months postimplant placement indicating a good prognosis in diabetic patients with good glycemic control.

CONCLUSION

Within the limitations of the study, it can be concluded that in controlled diabetics patients with normal or raised serum ALP, the mean ISQ value and GCF/PICF ALP values decreased significantly from baseline to 1 month and then increased significantly up to 3 months. It was found that there was no significant correlation of GCF ALP with ISQ at different time intervals in group I, whereas a significant negative correlation was found in group II.

ORCID

Neha Jain https://orcid.org/0000-0001-6239-5480

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