Description

·      a biology research paper with a minimum of 20 pages.  ·      The research should include a thesis question in the introduction for “what factors influences the success or failure of dental implants?” (doesn’t have to be worded exactly like that) and be sure to explain in the introduction the definition of implants and the importance of this topic. ·      A dentist should take in account these factors before providing them to his or her patients to make sure there the right candidate. ·        I am not looking for a book report but rather a research to prove that these factors really do influence implants.    ·      Be sure to provide Sources from an academic database with images or graphs, attached are examples of several resources that may be used.  ·      Examples of such factors may include smoking, proper food diet, oral hygiene, lack of jawbone tissue, diabetes, hyperthyroidism, implants size, gum disease, force of inclusion, cement vs. screwed implants.  ·      Please do not plagiarize. ·      If you have any questions, please feel free to ask me. ·      Thank you!

Smoking, Radiotherapy, Diabetes and Osteoporosis as
Risk Factors for Dental Implant Failure: A Meta-Analysis
Hui Chen1, Nizhou Liu2, Xinchen Xu1, Xinhua Qu3*, Eryi Lu2*
1 College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2 Department of Prosthodontics, Shanghai Ninth People’s Hospital, College
of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3 Department of Orthopaedics, Shanghai Key Laboratory of Orthopaedic Implant,
Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Abstract
Background: There are conflicting reports as to the association between smoking, radiotherapy, diabetes and osteoporosis
and the risk of dental implant failure. We undertook a meta-analysis to evaluate the association between smoking,
radiotherapy, diabetes and osteoporosis and the risk of dental implant failure.
Methods: A comprehensive research on MEDLINE and EMBASE, up to January 2013, was conducted to identify potential
studies. References of relevant studies were also searched. Screening, data extraction and quality assessment were
conducted independently and in duplicate. A random-effects meta-analysis was used to pool estimates of relative risks (RRs)
with 95% confidence intervals (CIs).
Results: A total of 51 studies were identified in this meta-analysis, with more than 40,000 dental implants placed under riskthreatening
conditions. The pooled RRs showed a direct association between smoking (n = 33; RR = 1.92; 95% CI, 1.67–2.21)
and radiotherapy (n = 16; RR = 2.28; 95% CI, 1.49–3.51) and the risk of dental implant failure, whereas no inverse impact of
diabetes (n = 5; RR = 0.90; 95% CI, 0.62–1.32) on the risk of dental implant failure was found. The influence of osteoporosis on
the risk of dental implant failure was direct but not significant (n = 4; RR = 1.09; 95% CI, 0.79–1.52). The subgroup analysis
indicated no influence of study design, geographical location, length of follow-up, sample size, or mean age of recruited
patients.
Conclusions: Smoking and radiotherapy were associated with an increased risk of dental implant failure. The relationship
between diabetes and osteoporosis and the risk of implant failure warrant further study.
Citation: Chen H, Liu N, Xu X, Qu X, Lu E (2013) Smoking, Radiotherapy, Diabetes and Osteoporosis as Risk Factors for Dental Implant Failure: A Meta-
Analysis. PLoS ONE 8(8): e71955. doi:10.1371/journal.pone.0071955
Editor: Hamid Reza Baradaran, Iran University of Medical Sciences, Iran (Islamic Republic of)
Received March 22, 2013; Accepted July 3, 2013; Published August 5, 2013
Copyright: 2013 Chen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Opening Project of Shanghai Key Laboratory of Orthopaedic Implant (KFT2012003), and grants from the Laboratory of
Animal Research from the Science and Technology Commission of Shanghai Municipality (Project No. 11140902000). The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: xinhua_qu@126.com (XQ); lueryi222@126.com (EL)
Introduction
Dental osseointegrated implants are generally considered as
effective and predictable restorations for the replacement of
missing teeth. However, although highly desirable outcomes and
the long-term survival of dental implant treatments are well
documented in numerous studies [1–4], implant failures still occur
for various reasons. Therefore, the risks associated with dental
implant failure have become a frequently discussed topic in recent
dental research.
A variety of conditions, including implant design (length, shape
or surface texture), patient-related medical risk factors (systemic
diseases or habits, such as smoking,), and surgery-related factors
(surgeon’s experience or surgical design) have been considered to
influence the outcome for implant restoration [5–7]. With the
dramatic advancements in materials science and surgical techniques,
increasing attention is focused on patient-related conditions
as risk factors for dental implant failure [8].
According to research by Buser and colleagues’, patients
exposed to with irradiation (radiotherapy) before or after
implantation, or patients with severe diabetes or heavy smoking
habits have significantly increased risks of dental implant failure
[9]. It has been suggested that such conditions could impair
implant survivability by increasing the susceptibility of the patient
to other diseases or by interfering with the tissue healing process
[1]. Moreover, osteoporosis, with its high prevalence in the aged
population, is also considered a relative contraindication for dental
implant therapy [10,11]; the alveolar ridge atrophy and low bone
mineral density, caused by osteoporosis may impair bone quality
and quantity at implant sites [12,13]. While a number of studies
have assessed the influence of smoking, radiotherapy, diabetes and
osteoporosis on implant failure, the results have been inconsistent.
Since life expectancy is expected to increase with the advent of
better therapies and targeted medicine, an increasing number of
patients who smoke or previously smoked, who received radiotherapy
for head and neck cancer treatment, or who present with
diabetes or osteoporosis may require dental implant treatment.
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The aim of the present study was, therefore, to provide a
comprehensive and critical meta-analysis of clinical studies
published in international peer-reviewed literature concerning
these four factors of high prevalence and/or of high risks, in order
to draw evidence-based conclusions as to the influence of these
factors on the outcome of dental implant treatment.
Methods
Search Strategy
We performed a systematic literature search of MEDLINE and
EMBASE database up to January 2013. All searches were
performed using medical subject heading (MeSH) or free text
words. We combined search terms for outcomes (survival, success,
osseointegration, failure, removal, replacement and loss), risk
factors (1, smoking, smoker or tobacco; 2, irradiation, radiotherapy
or head and neck cancer; 3, diabetes, diabetic, diabetes
mellitus or hyperglycemia; 4, osteoporosis, osteopenia, low bone
mineral density or bone loss) and key subjects (dental implant or
oral implant). Reference lists of identified articles and relevant
papers known to reviewers were also searched. Emails were sent to
the authors of identified studies for additional information, where
necessary. Studies were limited to English publications. Considering
the study by Mish and his colleagues, we referred implant
removal or implant loss to ‘‘implant failure’’ [14].
Selection Criteria
Three reviewers (H Chen, N Liu and X Xu) conducted the
search independently. Titles and abstracts were screened for
subject relevance. Studies that could not be definitely excluded
based on abstract information were also selected for full text
screening. Two reviewers examined the full text of all relevant
studies for inclusion possibility (smoking: N Liu and X Xu;
radiotherapy: H Chen and X Xu; diabetes and osteoporosis: H
Chen and N Liu). Where there was a disagreement for study
inclusion, a discussion was held with a third reviewer (X Qu) to
reach a consensus.
Studies were eligible for inclusion if they met the following
criteria: (1) human study; (2) observational study; (3) studies
focusing on the influence of smoking and/or radiotherapy and/or
diabetes and/or osteoporosis on dental implant failure; (4) studies
providing outcome data for dental implant failure or relevant data
that could be calculated by the reviewers; (5) studies providing data
for both a non-risk (control) group and a risk (study) group; (6)
studies published in English. Exclusion criteria were agreed as
follows: (1) animal study; (2) in vitro or laboratory study; (3) review
or case report; (4) studies providing craniofacial implant data for
which dental implant data could not be extracted; (5) studies
providing patient-related data (to be specific, survival/failure rate
that was calculated at the patient-level); 6) studies without data on
non-smoking, non-irradiation, non-diabetic or non-osteoporotic
groups.
Data Extraction and Quality Assessment
Two reviewers (H Chen and N Liu) independently extracted
data using a structured form. The following information was
extracted from each included study: year of publication, country,
first author’s family name, study design, follow-up period,
characteristics of subjects (number of patients, gender and age),
information relevant to risk factors, characteristics of the dental
implants (number and placement position) and data on dental
implant failure.
The methodological quality of the included studies was
independently and appraised twice by two reviewers (H Chen
and X Xu) using elements of McMaster Quality Assessment Scale
of Harms (McHarm) [15]. The criteria of the quality assessment
are presented in Table 1. Any discrepancy that occurred during
data extraction and quality assessment was resolved by consensus
or discussion with another reviewer (X Qu).
Statistical Analysis
We evaluated dental implant failure for any reason attributable
to the implant as our outcome measure of interest. Relative risk
(RR) was used as the common measure of association across
Table 1. Criteria of Quality Assessment (a Modified McHarm checklist).
ITEMS YES NO/Not sure
1 Were the harms PRE-DEFINED using standardized or precise definitions? 1 0
(In present study, we defined ‘‘harms’’ as the totality of adverse consequences of an implant surgery)
2 Were SERIOUS events precisely defined? 1 0
(In present study, we defined complications that didn’t lead to IMPLANT LOSS or IMPLANT REMOVAL
as SERIOUS events, e.g. sensitivity on function, radiographic bone loss #4 mm or 1/2 of the implant
body, probing depth #7 mm, etc. [14])
3 Were SEVER events precisely defined? 1 0
(In present study, we defined IIMPLANT LOSS as SERIOUS events)
4 Did the study specify the TRAINING or BACKGROUND of who ascertained the harms? 1 0
5 Did the study specify the TIMING and FREQUENCY of collection of the harms? 1 0
6 Did the author(s) use STANDARD scale(s) or checklist(s) for harms collection? 1 0
7 Was the NUMBER of participants that withdrew or were lost to follow-up specified for
each study group?
1 0
8 Was the TOTAL NUMBER of participants affected by harms specified for each study arm? 1 0
9 Did the author(s) specify the NUMBER for each TYPE of harmful event for each study group? 1 0
10 Did the author(s) specify the type of analyses undertaken for harms data? 1 0
A Total of 10 Points
doi:10.1371/journal.pone.0071955.t001
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studies. The RRs and 95% confidence intervals (CIs) were
extracted or calculated from each study, and then we pooled the
overall RRs using the inverse of corresponding variances as
weights. For the meta-analysis, a random-effects model was
considered [16]. Heterogeneity between studies was tested through
the Cochran Q and I2 statistics (I2 values of 25, 50, and 75% are
considered as low, moderate, and high, respectively [17]).
Subgroup analyses were used to identify associations between
the risk of dental implant failure and other relevant study
characteristics (mean age, geographical location, design of study,
sample size and length of follow-up) as possible sources of
heterogeneity. Publication bias was measured using Begg’s and
Egger’s regression tests and visualization of funnel plots [18]. The
stability of the study was also detected by sensitivity analysis,
through re-meta-analysis with one involved study excluded each
time. All statistical analyses were performed with Review Manager
5.01 (The Cochrane Collaboration, Copenhagen, Denmark) and
Stata version 11 (StataCorp, College Station, TX).
Results
Literature Search
The literature search yielded a total of 3,735 primary studies, of
which 3,472 were excluded after title screening. An additional 65
studies were included after checking the references of relevant
reviews and studies. Finally, 328 studies were included for full-text
assessment, of which 277 were excluded for one of the following
reasons: (1) studies focusing on irrelevant outcome assessment
(n =144), such as bone loss or primary stability; (2) studies without
a non-risk group (n= 56); (3) studies only providing patient-related
data (n= 21); (4) studies where data related to implant failure could
not be calculated (n= 53); and (5) studies where the reported data
were represented in another included in our analysis (n = 3) [19–
21]. As a result, 51 studies met the inclusion criteria for metaanalysis,
with 33 studies for smoking [22–54], 16 for radiotherapy
[31,44,55–68], five for diabetes [31,44,47,48,69] and four for
osteoporosis [44,70–72], respectively. Of note, four studies
involved more than one risk factor and were included in more
than one group [31,44,47,48]. A flow diagram of the study
selection process is presented in Figure 1.
Study Characteristics and Quality Assessment
The detailed characteristics of the included studies and the
results of the quality assessment are summarized in Tables 2–5.
The number of implants in each study ranged from 56 [34] to
5,843 [49]. The earliest study was published in 1993 [22], and the
latest in 2012 [53,54,67–69]. In terms of study design, 23 studies
enrolled patients prospectively [24,25,27–29,32–34,37–40,42,46,
47,55,58,60–62,64,66,69], whereas 28 were retrospective database
reviews [22,23,26,30,31,35,36,41,43–45,48–54,56,57,59,63,65,67,
68,70–72]. By geographic location, 18 studies were conducted in
the United States [24,26,30–33,35,36,38–40,42,45,50,53,57,
65,71], 24 in Europe [23,27,28,29,37,43,44,49,51,52,54–56,58–
64,66–68,72] and nine in other regions [22,25,34,41,46–
48,69,70]. The overall study quality averaged 8.2 (range, 5–10)
on a scale of 1 to 10.
Smoking
The multivariable-adjusted RRs in each study and the pooled
RRs of dental implant failure for smoking versus non-smoking
patients are presented in Figure 2, Table 2 and Table 6 (33 studies;
35,118 implants). In the pooled analysis, smoking was associated
with higher risk of dental implant failure (RR = 1.92; 95% CI,
1.67–2.21). There was moderate heterogeneity among the studies
(P = 0.03, I2 =35%). Stratifying by study design, the pooled RRs
for prospective studies and retrospective studies were 1.34 (95%
CI, 0.90–2.00) and 2.01 (95% CI, 1.75–2.30). Stratifying by
geographical location, the summary RRs were 1.59 (95% CI,
1.27–1.98) for studies conducted in the United States, 2.27 (95%
CI, 1.62–3.20) for Europe and 2.23 (95% CI, 1.77–2.81) for other
regions. With regard to the mean age of patients, the pooled RRs
for ,55-year-old and $55-year-old patients were 2.15 (95% CI,
1.87–2.47) and 1.67 (95% CI, 1.13–2.47), respectively. A subgroup
Figure 1. Flow Diagram of Screened and Included Papers.
doi:10.1371/journal.pone.0071955.g001
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analysis indicated no influence of study design, geographical
location, length of follow-up, sample size or mean patient age.
Radiotherapy
Figure 3 shows the association between radiotherapy and risk of
dental implant failure from a collection of 16 studies and 5,246
implants. A pooled analysis indicated a direct association between
radiotherapy and the risk of dental implant failure (RR = 2.28;
95% CI, 1.49–3.51). The heterogeneity among the studies was
high (P,0.0001, I2 = 70%). As far as geographical location was
concerned, the summary RRs were 1.46 (95% CI, 0.12–17.3) for
studies performed in the United States and 2.29 (95% CI, 1.45–
3.63) for Europe. Stratifying by length of follow-up, the pooled
RRs for ,5-year and $5-year duration were 1.76 (95% CI, 1.20–
2.59) and 1.62 (95% CI, 0.85–3.11), respectively. According to the
mean age of the patients involved, the pooled RRs for ,55-yearold
and $55-year-old patients were 1.95 (95% CI, 1.11–3.42) and
1.40 (95% CI, 0.33–5.97). In the subgroup analysis, study design,
geographical location, length of follow-up, sample size and mean
patient age, had no influence on the risk of dental implant failure
(Table 6).
Table 2. Study Characteristics (SMOKING).
Author (Year) Country Study Follow-up Patient Characteristics Smoking Implant Characteristics QS
Mean Age CON/STY F CON/STY
Position
(Mand./Max.) FC/FS
Bain , 1993 Canada Retro 37.88 m 54.7 yr NA/NA 311 NA 1,804/390 1,115/1,079 86/44 8
De Bruyn, 1994 Belgium Retro NA (20–80 yr) 91/26 66 NA 338/114 208/244 5/10 8
Gorman, 1994 USA Prospec NA NA 228/82 NA NA 142/646 NA 47/42 7
Bain, 1996 Canada Prospec NA NA NA/NA NA NA 176/47 NA 10/9 8
Minsk, 1996 USA Retro 6 yr NA NA/NA NA 20 per day 570/157 358/369 52/17 9
Lindquist, 1997 Sweden Prospec 10 yr (33–64 yr) 24/21 32 NA 139/125 Mandible 3/0 8
De Bruyn, 1999 Belgium Prospec 7 yrs NA 13/10 NA 13.2 per day 32/30 Maxilla 9/6 10
Grunder, 1999 Switzerland Prospec 34.4 m 58615 yr 55/19 34 NA 164/55 NA 3/0 9
Jones, 1999 USA Retro 58 m 50 yr 44/19 40 NA 217/126 204/147 5/11 8
Keller, 1999 USA Retro 12 yr (15–73 yr) 26/28 NA NA 143/105 Grafted maxilla sinus 26/7 10
Lambert, 2000 USA Prospec 3 yr NA NA/NA NA NA 1,928/959 1616/1271 115/85 8
Olson, 2000 USA Prospec 38615 m 56612 yr NA/NA 1 NA 65/51 Grafted maxillary sinus 1/2 7
Wallace, 2000 USA Retro 4 yr NA 39/17 27 NA 115/72 NA 8/12 7
Schwartz-Ara, 1999 Israel Prospec 5 yr 47 yr NA/NA 27 NA 50/6 39/17 5/1 7
Geurs, 2001 USA Retro 3.261.3 yr NA NA/NA NA NA 267/62 Grafted maxilla sinus 13/7 6
Widmark, 20001 Sweden Prospec (3–5 yr) NA 25/11 NA $half a
pack a day
131/67 Local: 120/Grafted: 101 14/26 10
Kumar, 2002 USA Prospec NA NA 389/72 NA NA 914/269 357/826 8/15 5
Van Steenberghe, 2002 Belgium Prospec NA 50614 yr NA/NA 243 NA 1,107/156 NA 19/8 7
Karoussis, 2003 Switzerland Prospec 10 yr NA 41/12 NA NA 84/28 NA 3/2 10
DeLuca, 2006 Canada Retro 59.8 m 49.3 yr 285/104 283 NA 1,045/4,94 NA 32/26 9
Peleg, 2006 USA Prospec 69 m NA 505/226 453 NA 1,505/627 Maxilla sinus grafting 28/16 7
Mundt, 2006 Germany Retro 88.2 m 54.1 yr NA/NA 94 NA 294/363 296/367 6/30 8
Alsaadi, 2008 Belgium Retro 2 yr NA 351/61 240 NA 1,291/223 698/816 80/21 8
Balshe, 2008 USA Retro 5 yr 49.4 yr 1299/119 861 17.767
per day
3,841/766 2,633/1974 188/77 7
Levin, 2008 Israel Prospec 6.14 yr 45 yr 54/10 40 NA 54/10 NA 3/1 7
Tawil, 2008 Lebanon Prospec 42.4 m NA 50/40 33 NA 254/245 NA 2/5 9
Anner, 2010 Isreal Retro 31628 m 52612 yrs 412/63 299 NA 1,400/226 NA 56/21 7
Cavalcanti, 2011 Italy Retro 5 yr 50 yrs 1019/458 1025 NA 3,882/1,961 NA 112/107 9
Conrad, 2011 USA Retro 35.7 m 55.3 yr NA/NA 168 NA 446/48 Maxilla 28/6 8
Rodriguez, 2011 Spain Retro $6 m 53613 yr 182/113 188 NA 644/389 NA 18/14 9
Vandeweghe, 2011 Belgium Retro 22 m 54613.4 yr 288/41 43 NA 608/104 NA 7/5 9
Lin, 2012 USA Retro 12 m 59.6 yr 47/28 186 NA 93/62 Grafted maxiila sinus 12/13 9
Vervaeke, 2012 Belgium Retro 3167.2 m 56612 yr 235/60 168 NA 244/849 458/648 11/8 10
CON = control group, that is non-smoking group;STY = study group, that is smoking group; F = Female; Mand. =mandible; Max. = maxilla; Retro = retrospective study;
Prospec = prospective study; yr = year; m= month; NA = not available; Local = local bone; Grafted = grafted bone; FC = failure implant number of Control Group;
FS = failure implant number of Study Group; QS = quality assessment score.
doi:10.1371/journal.pone.0071955.t002
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Diabetes and Osteoporosis
Five studies were included to analyze on dental implant failure
with regard to diabetes (6,774 implants). The results of the pooled
analysis are shown in Figure 4. The pooled RR for patients with
diabetes versus patients without diabetes was 0.90 (95% CI, 0.62–
1.32), indicating no association between diabetes and the risk of
dental implant failure. We found high heterogeneity across the
studies (P = 0.07, I2 = 58%).
Four studies were concerned with the association between
osteoporosis and dental implant failure, with a collection of 3,070
implants. In the pooled analysis, the association between
osteoporosis and the risk of dental implant failure was direct but
not significant (RR = 1.09; 95% CI, 0.79–1.52), with high
heterogeneity across the studies (P = 0.14, I2 =46%). (Figure 5)
Since limited studies focusing on diabetes and osteoporosis met
our inclusion criteria, and insufficient data could be extracted from
the included studies, no subgroup analysis was performed to
further investigate the association between diabetes and osteoporosis
and risk of dental implant failure.
Publication Bias and Sensitivity Analysis
Publication bias was determined by visualization of funnel plot,
Begg’s test, and Egger’s regression test. With the exception of
radiotherapy (Begg’s test: P = 0.47; Egger’s test: P =0.02), there
was no evidence of publication bias for smoking (Begg’s test:
Table 3. Study Characteristics (RADIOTHERAPY).
Author (Year) Country Study Follow-up Patient Characteristics Radiotherapy Implant Characteristics QS
Mean Age CON/STY F Time Dose (Gy) CON/STY Position (Mand./Max.) FC/FS
Esser, 1997 Germany Prospec NA (37–79 yr) NA/NA 9 BP 60 66/152 Mandible 7/33 7
Werkmeister,
1999
Germany Retro 3 yrs 55 yr 17/12 6 BP 54 79/30 Local: 64/Grafted: 45 19/8 7
Keller, 1999 USA Retro 12 yrs (15–73 yr) 52/2 NA NA 55 and 61 237/11 Grafted maxilla 33/0 10
Shaw, 2005 UK Retro 3.5 yr 58 yr 43/34 32 BP 40–66 192/172 Local: 238/Grafted: 126 25/31 9
Yerit, 2006 Austria Prospec 5.463.2 yr 58614 yr NA/NA 15 BP 50 162/154 Local: 238/Grafted: 78 15/29 9
Schepers, 2006 Netherlands Retro up to 23 m 66.11 yr 27/21 19 AP 60–68 78/61 NA 0/2 8
Landes, 2006 Germany Prospec 36 m 63 yr 11/19 8 BP 57 42/72 NA 0/1 8
Nelson, 2007 Germany Prospec 10.3 yr 59 yr NA/29 30 BP up to 72 311/124 281/154 4/7 7
Alsaadi, 2008 Belgium Retro 2 yr NA 410/2 240 NA NA 1,499/15 698/816 98/3 8
Schoen, 2008 Netherlands Prospec 12 m 62611 yr 16/19 15 AP 60.167.7 64/76 Local bone 2/2 9
Klein, 2009 Germany Retro 5 yr 58.4 yr 16/27 12 BP ,50 or
$50
74/116 Local: 62/Grafted: 128 12/13 8
Cuesta-Gil, 2009 Spain Prospec / 52 yr 32/79 31 Mixed 50–60 311/395 Local: 454/Grafted: 252 6/75 9
Salinas, 2010 USA Retro 41.1 NA 18/26 19 Mixed . 60 116/90 Local: 105/Flap: 114 8/23 10
Linsen, 2012 Germany Prospec 48634.3 m 56616 yr 32/34 23 BP 36 or 60 135/127 213/49 6/8 10
Jacobsen, 2012 Switzerland Retro 67 m 52.4 yr NA/NA 16 AP NA 93/47 Local: 41/Flap: 99 14/14 9
Fenlon, 2012 UK Retro / NA 29/12 NA AP 66 110/35 Grafted bone 3/15 8
CON = control group,that is non-radiotherapy group; STY = study group, that is radiotherapy group; F = Female; BP = before placement; AP = after placement;
Mand. =mandible; Max. =maxilla; Retro = retrospective study; Prospec = prospective study; yr = year; m= month; NA = not available,; Local = local bone; Grafted = grafted
bone; FC = failure implant number of Control Group; FS = failure implant number of Study Group; QS = quality assessment score.
doi:10.1371/journal.pone.0071955.t003
Table 4. Study Characteristics (DIABETES).
Author (Year) Country Study Follow-up Patient Characteristics
Diabetes
Type Implant Characteristics QS
Mean Age CON/STY F CON/STY
Position (Mand./
Max.) FC/FS
Keller, 1999 USA Prosp 12 yrs (15–73 yr) 52/2 NA NA 237/11 Grafted maxilla 0/0 10
Morris, 2000 New Zealand Prosp 36 m NA 408/255 NA II 2632/255 Mixed 180/20 7
Tawil, 2008 Lebanon Retro 42.4 m 62.15 yr 45/45 33F II 244/255 Mixed 2/7 9
Alsaadi, 2008 Belgium Retro 2 yr NA 402/10 240 I:1 II:9 1,480/34 698/816 202/0 8
Anner, 2010 Isreal Prosp 31628 m 52612 yr 426/49 299 NA 1,449/177 Mixed 72/5 7
CON = control group, that is non-diabetes group; STY = study group, that is diabetes group; F = Female; Mand. =mandible; Max. =maxilla; Retro = retrospective study;
Prospec = prospective study; yr = year; m= month; NA = not available; Local = local bone; Grafted = grafted bone; FC = failure implant number of Control Group;
FS = failure implant number of Study Group; QS = quality assessment score.
doi:10.1371/journal.pone.0071955.t004
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Table 5. Study Characteristics (OSTEOPOROSIS).
Author (Year) Country Study Follow-up Patient Characteristics Implant Characteristics QS
Mean Age CON/STY F CON/STY
Position (Mand./
Max.) FC/FS
Amorim,2007 Brazil Retro 9 m 58.2 yr 20/19 39 43/39 Mandible 0/1 8
Alsaadi,2008 Belgium Retro 2 yr NA 393/19 240 1,446/68 698/816 92/9 8
Holahan,2008 USA Retro 5.4 yr 6369 yr 564/192 746 306/340 378/268 17/20 7
Dvorak,2011 Austria Retro 664 yr $45 yr 115/62 117 543/258 396/432 17/20 7
CON = control group, that is non-osteoporosis group; STY = study group, that is osteoporosis group; F = Female; Mand. =mandible; Max. = maxilla; Retro = retrospective
study; Prospec = prospective study; yr = year; m= month; NA = not available; Local = local bone; Grafted = grafted bone; FC = failure implant number of Control Group;
FS = failure implant number of Study Group; QS = quality assessment score.
doi:10.1371/journal.pone.0071955.t005
Figure 2. Forest plot of studies with dental implant failure risk for smoking versus non-smoking patients. The combined Relative risks
(RR) and 95% confidence intervals (CIs) were calculated using the random-effects model.
doi:10.1371/journal.pone.0071955.g002
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P = 0.49; Egger’s test: P =0.94), diabetes (Begg’s test: P = 0.33;
Egger’s test: P = 0.23) or osteoporosis (Begg’s test: P = 0.17; Egger’s
test: P = 0.34). Sensitivity analysis showed that excluding any one
study from the pooled analysis did not vary the results
substantially. (See Figure S1 for funnel plots of smoking,
radiotherapy, diabetes and osteoporosis risk factor)
Discussion
Principle Findings
After reviewing numerous studies assessing the potential risk
factors for dental implant failure, this meta-analysis supports the
view that smoking and radiotherapy are associated with a higher
risk of dental implant failure. Our findings suggest that individuals
who smoke, or who have undergone radiotherapy before or after
implantation, might suffer an approximately 35 or 70% higher risk
of dental implant failure, respectively, as compared with nonsmokers
or those who have not been exposed to radiotherapy. We
found no significant inverse impact of diabetes on the risk of dental
implant failure, whereas osteoporosis showed a direct but not
significant association. However, because of the limited number of
studies focusing on diabetes and osteoporosis, these results should
be interpreted carefully and verified by further studies. The
findings of this meta-analysis, may offer clinical dentists with
additional insights into the prognosis of dental implant treatment
and may help in the establishment of potential treatment plans.
Implications
The outcome of this meta-analysis indicated that individuals
who smoke were more likely to suffer from dental implant failure.
This finding is consistent with a previous meta-analysis performed
in 2006, with an elevated OR of 2.17 (95% CI, 1.67–2.83)
indicating the inverse impact of smoking on implant osseointegra-
Table 6. Subgroup analysis to investigate differences between studies included in meta-analysis.
Subgroup No. of Studies RR (95% CI) I2 (%) P value
P value for heterogeneity
between subgroups
Smoking
Design of Study
Prospective 15 1.34(0.90,2.00) 67 ,0.0001 0.06
Retrospective 18 2.01(1.75,2.30) 14 0.29
Geographical Location
United States 13 1.59 (1.27,1.98) 46 0.04 0.08
Europe 13 2.18 (1.56,3.05) 56 0.007
Other Regions 7 2.23 (1.77,2.81) 0 0.90
Length of Follow-up (years)
$5 11 1.72 (1.37,2.15) 28 0.18 0.32
,5 17 1.98 (1.68,2.33) 14 0.29
Sample Size (implant)
,500 16 2.25 (1.64,3.08) 25 0.17 0.23
$500 17 1.81 (1.56,2.11) 40 0.05
Age (years)
,55 11 2.15 (1.87,2.47) 0 0.67 0.23
$55 6 1.67 (1.13,2.47) 0 0.54
Radiotherapy
Design of Study
Prospective 6 2.02 (1.37,2.97) 0 0.73 0.58
Retrospective 10 2.50 (1.32,4.75) 81 ,0.00001
Geographical Location
United States 2 1.46 (0.12,17.16) 69 0.07 0.72
Europe 14 2.29 (1.45,3.63) 71 ,0.0001
Length of Follow-up (years)
$5 5 1.62 (0.85,3.11) 62 0.03 0.83
,5 8 1.76 (1.20,2.59) 20 0.27
Sample Size (implant)
,250 10 2.14 (1.27,3.60) 64 0.003 0.56
$250 6 2.74 (1.43,5.25) 76 0.001
Age (years)
,60 8 1.95 (1.11,3.42) 78 ,0.0001 0.68
$60 3 1.40 (0.33,5.97) 0 0.69
doi:10.1371/journal.pone.0071955.t006
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PLOS ONE | www.plosone.org 7 August 2013 | Volume 8 | Issue 8 | e71955
tion [73]. Although the underlying mechanism is still not
completely understood, researchers previously posited that smoking
impaired the wound healing processes involved with implant/
tissue integration [27]. Others suggested that smokers treated with
implants had an increased risk of postoperative complications,
such as infection and peri-implantitis [53]. Bain and colleagues
recommended that patients commence a smoking cessation
protocol at least one week before and at least two months after
dental implant surgery to assure dental implant osseointegration
[22]; however, others have demonstrated that pre-operative
smoking cessation, especially short-term cessation, bears no
significant effect on reducing the risk of dental implant failure [74].
The present meta-analysis indicates that radiotherapy was
strongly associated with increased risk of dental implant failure. A
former review of animal and human studies reached a similar
conclusion that implants placed in irradiated bone experienced 2–
3 times higher rates of failure [75]. Moreover, implants placed in
irradiated maxilla were reported to have a higher failure rate
compared with those in irradiated mandible [76]. Bone responds
to irradiation with various cellular, vascular, and metabolic
alterations occurring at different sites in the irradiated bone and
adjacent tissues [77]. Several plausible mechanisms to explain how
bone responds to irradiation have been proposed, including
altered osteoblast and osteoclast function during bone repair and
remodeling, the formation and the subsequent breakdown of
hypoxic-hypocellular and hypovascular tissues, and a decreased
rate of tissue perfusion and tissue fibrosis [77–79]. Such responses
were previously believed to be highly variable and partly related to
the administered dose of radiation [77]. Researchers suggested
that a fractionated dose would be better tolerated than a single
exposure at the same level of intensity [80]. Furthermore,
adjunctive treatment with the use of hyperbaric oxygen (HBO)
was expected to increase the regenerative capacity of tissue
damaged after radiotherapy; however, no strong evidence was
found to support the use of HBO to decrease dental implant
failure for radiotherapy-exposed patients [81].
Figure 3. Forest plot of studies with dental implant failure risk for patients with radiotherapy versus non-smoking. The combined
Relative risks (RR) and 95% confidence intervals (CIs) were calculated using the random-effects model.
doi:10.1371/journal.pone.0071955.g003
Figure 4. Forest plot of studies with dental implant failure risk for patients with diabetes versus non-diabetes. The combined Relative
risks (RR) and 95% confidence intervals (CIs) were calculated using the random-effects model.
doi:10.1371/journal.pone.0071955.g004
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Diabetes and osteoporosis are both highly prevalent disorders
among elderly patients [10,11,82]. After reviewing the published
literature, we found a lack of high quality and single-risk-factor
focused studies with regard to the effects of diabetes or
osteoporosis on dental implant survival. The present meta-analysis
revealed no direct impact of diabetes or osteoporosis on the risk of
dental implant failure, although both were reported to affect
wound healing in oral tissues [1]. Clinical dentists are advised to
avoid dental implant treatment in poorly controlled diabetic
patients, and studies indicate that the long-term use of bisphosphonates
by osteoporotic patients may cause osteonecrosis of the
jaw [83]. Unfortunately, data was insufficient yet to give an
explicit explanation of its effect on risk of dental implant failure.
Diabetes and osteoporosis can be well controlled by drug
intervention; yet since, none of the studies included a discussion
as to the different level of severity of diabetes or osteoporosis in
these patients and on the risk of dental implant failure, this limited
our ability to further assess the risk of these two factors in the
present meta-analysis.
Strength and Limitations
To our knowledge, this study is the most comprehensive metaanalysis
to estimate the association of smoking, radiotherapy,
diabetes, and osteoporosis with dental implant failure. We were
able to include a substantial total number of subjects (more than
40,000 dental implants placed under risk-threatening conditions),
which significantly increased the statistical power of our analysis.
We made sure to minimize the bias by means of study procedure.
Not only did we search MEDLINE and EMBASE databases to
identify potential studies, but also we manually examined all
reference lists from relevant studies. The McHarm quality
assessment tool was used to evaluate each of the included studies
to ensure sufficient study quality (mean score of 8.2 out of 10).
Publication bias was also absent, as determined by visualization of
funnel plot, Begg’s test and Egger’s test.
Despite the above strengths and advantages, this meta-analysis
has several limitations. First, the present study was subject to
confounding factors that could be inherent in the included studies
and it is difficult to completely rule out the possibility that other
risk factors were responsible for the observed associations. Second,
heterogeneity might have been introduced by methodological
differences among the studies. Many of the I2 estimates calculated
in this meta-analysis were judged as high. While we were able to
perform subgroup analyses on studies of smoking and radiotherapy,
which indicated no influence on the study design, geographical
location, length of follow-up, sample size and mean patient
age, the diabetes and osteoporosis implant failure data were
insufficient for a stratified analysis. Although these issues might
have reduced the strength of the conclusions drawn in this metaanalysis,
visual inspection of the forest plots suggests that there is
considerable consistency in the RRs across the studies. Third, the
search was limited to English studies and only performed with the
use of two electronic databases, mainly because of the limited work
force for the present research; this might have introduced a
selection bias to the results.
Suggestion for Future Studies
On the basis of this meta-analysis, several questions should be
answered in future studies. First, what is the compound effect of
multiple risk factors on dental implant failure? For instance, what
is the risk of dental implant failure for smokers with diabetes, or
smokers with osteoporosis? To answer this question, several welldesigned
cohort studies with adequate control for confounding
factors should be considered. Second, could different severity levels
of the four risk factors, such as the severity of the disease or the
frequency of smoking, have an effect on dental implant failure? An
investigation that specifically focuses on the quantity of smoking,
the overall irradiation dose, and/or the severity of diabetes and
osteoporosis may offer insight into this question. Third, could the
application of smoking cessation or HBO treatment as an adjunct
to radiotherapy decrease the risk of dental implant failure? Future
studies, including randomized controlled trials, concerning the
topics are needed to gain a better understanding of the underlying
relationship among these risk factors.
Conclusions
The present study investigated the influence of smoking,
radiotherapy, diabetes and osteoporosis on dental implant failure,
and may provide clinical dentists with additional insight for dental
implant treatment prognosis and treatment strategies. We found
that, smoking and radiotherapy are associated with a higher risk of
dental implant failure while diabetes has no significant inverse
impact on the risk of dental implant failure. The association between
osteoporosis and the risk of dental implant failure was direct but not
significant. However, because of the lack of high quality and
individual risk-isolated studies with respect to diabetes and
osteoporosis, additional, well-designed studies, with adequate control
for confounding factors, are required in future investigations.
Supporting Information
Checklist S1 PRISMA Checklist. (PDF)
(PDF)
Figure 5. Forest plot of studies with dental implant failure risk for patients with osteoporosis versus non-osteoporosis. The
combined Relative risks (RR) and 95% confidence intervals (CIs) were calculated using the random-effects model.
doi:10.1371/journal.pone.0071955.g005
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Figure S1 Funnel Plot of Smoking, Radiotherapy,
Diabetes and Osteoporosis. (PDF)
(PDF)
Acknowledgments
We would like to express our sincere gratitude for Professor Zhiyuan
Zhang’s consultation on head and neck cancer radiotherapy, Professor
Tingting Tang and Dr. An Qin’s advice on osteoporosis, and Professor
Yinli Lu’s suggestions on diabetes. Special thanks to Chao Ji, Pengfei Wen
and Li Ni for their generous help in literature searching.
Author Contributions
Conceived and designed the experiments: HC XQ EL. Performed the
experiments: HC NL XX. Analyzed the data: HC NL XX XQ.
Contributed reagents/materials/analysis tools: HC XQ. Wrote the paper:
HX XQ EL.
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© 2017 Journal of International Society of Preventive and Community Dentistry | Published by Wolters Kluwer – Medknow 351
Aims and Objectives: Dental implants have emerged as new treatment modality
for the majority of patients and are expected to play a significant role in oral
rehabilitation in the future. The present study was conducted to assess various
factors affecting the survival rate of dental implants.
Materials and Methods: The present retrospective study was conducted in the
Department of Prosthodontics. In this study, 5200 patients with dental implants
which were placed during June 2008–April 2015 were included. Exclusion criteria
were patients with hormonal imbalance, patients with chronic infectious disease,
patients receiving immunosuppressive therapy, pregnant women, drug and alcohol
addicts, and patients with severe periodontal diseases. Parameters such as name,
age, gender, length of implant, diameter of implant, location of implant, and bone
quality were recorded. Data were tabulated and statistically evaluated with IBM
SPSS Statistics for Windows, Version 20.0., IBM Corp., Armonk, NY, USA.
Results: Out of 5200 patients, 2800 were males and 2400 females. Maximum
implants failures (55) were seen in age group above 60 years of age (males – 550,
females –700). Age group <40 years (males – 750, females – 550) showed 20
failed implants. Age group 41–60 years (males – 1500, females – 1150) showed
45 failed implants. The difference was nonsignificant (P = 0.21). Maximum
implant failure was seen in implants with length >11.5 mm (40/700) followed by
implants with <10 mm (20/1650) and 10–11.5 mm (60/2850). The difference was
significant (P < 0.05). Maximum implants failure (30/1000) was seen in implants
with diameter <3.75 mm followed by implants with diameter >4.5 mm (16/1600)
and implants with diameter 3.75–4.5 mm (50/2600). The Chi‑square test showed
significant results (P < 0.05). Mandibular posterior showed 3.3% implants failure,
maxillary posterior revealed 2.2%, maxillary anterior showed 2.1%, and mandibular
anterior showed 1% failure rate; this difference was significant (P < 0.05). Type I
bone showed 0.3% implant failure, Type II showed 1.95%, Type III showed 3%,
and Type IV revealed 0.8% failure rate; this difference was significant (P < 0.05).
Conclusion: Age, length of implant, diameter of implant, bone quality, and
region of implant are factors determining the survival rate of implants. We found
that implant above 11.5 mm length, and with diameter <3.75 mm, placed in the
mandibular posterior region, in Type III bone showed maximum failures.
Keywords: Bone quality, failure, implant, length of implant, success
Factors Affecting the Survival Rate of Dental Implants: A Retrospective
Study
Sonal Raikar1, Pratim Talukdar2, Sarala Kumari3, Sangram Kumar Panda4, Vinni Mary Oommen5, Arvind Prasad6
Address for correspondence: Dr. Sonal Raikar,
Department of Prosthodontics and Crown and Bridge,
DY Patil Dental School, DY Patil Knowledge City,
Lohegaon, Pune, Maharashtra, India.
E‑mail: drsonal_k@yahoo.co.in
Introduction
In ancient times, either removable or fixed partial
dentures were the treatment modalities for the
missing teeth. Dental implants have emerged as new
treatment modality for the majority of patients and are
1Department of
Prosthodontics, DY Patil
Dental School, Pune,
Maharashtra, 2Department
of Prosthodontics, Daswani
Dental College, Kota,
Rajasthan, 3Department of
Prosthodontics, Mallareddy
Institute of Dental
Sciences, Hyderabad,
Telangana, 4Department
of Prosthodontics, Faculty
of Dental Sciences,
Siksha ‘O’ Anusandhan
University, Bhubaneswar,
Odisha, 5Department of
Prosthodontics, Al-Azhar
Dental College, Thodupuzha,
Idukki, 6Department of
Prosthodontics, MES Dental
College, Perinthalmanna,
Kerala, India
Abstract
Original Article
Access this article online
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Website: www.jispcd.org
DOI: 10.4103/jispcd.JISPCD_380_17
How to cite this article: Raikar S, Talukdar P, Kumari S, Panda SK,
Oommen VM, Prasad A. Factors Affecting the Survival Rate of Dental
Implants: A Retrospective Study. J Int Soc Prevent Communit Dent
2017;7:351-5.
This is an open access article distributed under the terms of the Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak,
and build upon the work non-commercially, as long as the author is credited and the new
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For reprints contact: reprints@medknow.com
Received : 23-10-17.
Accepted : 01-12-17.
Published : 29-12-17.
Raikar, et al.: Survival rate of dental implants
352 Journal of International Society of Preventive and Community Dentistry ¦ Volume 7 ¦ Issue 6 ¦ November-December 2017
expected to play a significant role in oral rehabilitation
in the future.
A dental implant is a surgical component that interfaces
with the bone of the jaw or skull to support a dental
prosthesis such as a crown, bridge, denture, facial
prosthesis or to act as an orthodontic anchor. 90%–95%
has been reported as the success rate of implants over the
10 years.[1] Although it has become the treatment of choice
for most of the dentists, still, the complications arising
from dental implant placement are the biggest challenge.
Among various complications, bleeding from implant
site, infection, and pain are early complications of
implant. Dental implant failure is quite common. Lack
of osseointegration during early healing, infection
of the peri‑implant tissues, and breakage are the
reasons for implants failure. There are few indications
and contraindications for implant placements. The
contraindications of implant placement are patients
with epilepsy, children and adolescents, patients having
endocarditis, history of osteoradionecrosis, smokers, and
diabetic patients. Absolute contraindications are patients
with history of myocardial infarction, cerebrovascular
accident, patients with history of bleeding, history of
heart transplant, immune suppression, active treatment of
malignancy, drug abusers, and psychiatric illness.[2]
There are many related factors affecting implant
failure. First, group of factors are host related, second,
related to implant placement site‑related factors,
third, related to surgery‑related factors and fourth are
implant fixture‑related factors and fifth are implant
prosthesis‑related factor. Age and gender of the patient,
smoking habits, systemic disease, and oral hygiene are
host‑related factors. Position in arch, quality, and quantity
of bone are implant placement site‑related factors. Initial
stability, angulations and direction of implant and the
skillfulness of an operator come under surgery‑related
factors. Surface roughness, length and diameter of
dental implant, macrostructure and microstructure of an
implant fixture are implant fixture‑related factors. Type
of prosthesis, retention method, and occlusal scheme
are implant prosthesis‑related factors.[3] Albrektsson
et al. concluded that factors such as design and surface
of implant, condition of implant placement site, surgery
technique, and occlusal loading affect osteointegration.[4]
The present study was conducted in the Department of
Prosthodontics to assess various factors affecting survival
rate of dental implants.
Materials and Methods
This retrospective study was conducted in the
Department of Prosthodontics DY Patil Dental College,
Pune. In this study, all the dental implants which were
placed during June 2008–April 2015 which met the
inclusion criteria were enrolled. The study was carried
out by two trained persons by radiographic and clinical
examination of patients with dental implants at follow‑up
visits based on survival rate of dental implant according
to implant length, diameter (<3.75–11.5 mm), and bone
quality. Sample size of 5200 was selected form total
7010 treated cases at 95% confidence level and 0.69 of
confidence interval. In 5200 patients, 2800 were males
and 2400 were females with age range of >41 years
to <60 years. Informed consent was obtained from all the
participating individuals. Ethical approval was obtained
from Institutional Ethical Committee (ethical committee
letter Ref No‑DYP: 242A/2015). Exclusion criteria were
patients with hormonal imbalance, patients with chronic
infectious disease, patients receiving immunosuppressive
therapy, pregnant women, drug and alcohol addicts, and
patients with severe periodontal diseases.
Parameters such as name, age, gender, length of implant,
diameter of implant, location of implant, and bone
quality were recorded. Survival rate of implants was
evaluated based on length, diameter, location (maxilla or
mandible), and bone quality (Type‑I, II, II, IV).
Results thus obtained were subjected to statistical
analysis. P < 0.05 was considered significant. Data
were statistically evaluated with IBM SPSS Statistics
for Windows, Version 20.0., IBM Corp., Armonk, NY,
USA using Chi‑square test at significance of 0.05.
Results
Table 1 shows that out of 5200 patients, 2800 were male
and 2400 were females. Table 1 shows that maximum
implants failures (55) were seen in age group above
60 years of age (males – 550, females – 700). Age
group <40 years (males – 750, females – 550) showed 20
failed implants. Age group 41–60 years (males – 1500,
females – 1150) showed 45 failed implants. The
Chi‑square test indicates nonsignificant P value [Table 1].
Graph 1 shows that maximum implants failure was
seen in implants with length >11.5 mm (40/700)
followed by implants with <10 mm (20/1650) and
10–11.5 mm (60/2850) and the difference was
significant (P < 0.05).
Table 1: Total number of failed implants
Age group (years) Male Female Failed implants P
<40 750 550 20 0.21
41‑60 1500 1150 45
>60 550 700 55
Total 2800 2400 120
P>0.05 nonsignificant
Raikar, et al.: Survival rate of dental implants
Journal of International Society of Preventive and Community Dentistry ¦ Volume 7 ¦ Issue 6 ¦ November-December 2017 353
Graph 2 shows that maximum implants failure (30/1000)
was seen in implants with diameter <3.75 mm followed
by implants with diameter >4.5 mm (16/1600) and
implants with diameter 3.75–4.5 mm (50/2600). The
Chi‑square test showed significant results (P < 0.05).
Graph 3 shows that mandibular posterior had 3.3%
implants failure, maxillary posterior revealed 2.2%,
maxillary anterior showed 2.1%, and mandibular
anterior showed 1% failure rate. The difference was
significant (P < 0.05). Graph 4 shows that Type I bone
showed 0.3% implant failure, Type II showed 1.95%,
Type III showed 3%, and Type IV revealed 0.8% failure
rate. The difference was significant (P < 0.05).
Discussion
Recent advancements in the field of dentistry have
revolutionarized the use of dental implants. Thus, missing
teeth can be well managed. Nowadays, there is increase in
demand for dental implant. However, failures in implants
are also common. Failure rates are early failure and late
failure. Early failure is one that failed osseointegration
within several weeks to months. Bone necrosis, bacterial
infection, surgical trauma, inadequate initial stability, and
early occlusal loading can result into early failure. Late
failure is failure that turns up after functional loading of
several period of time. It takes place because of infection
and excessive loading.[5] The present study was to assess
various factors affecting survival rate of dental implants.
We found that maximum implants failures (55) were seen
in age group above 60 years of age. Age group <40 years
showed 20 failed implants. Age group 41–60 years showed
45 failed implants. It has been seen that when patients age
increases, failure rate had a tendency of increment.
We found that maximum implants failure was seen in
implants with length >11.5 mm followed by implants
with <10 mm and 10–11.5 mm. This is similar to the
results of Albrektsson et al.[4] However, Esposito revealed
that maximum failures were seen in implants with length
between 10 and 11.5 mm.[6] Misch in his study showed
that implants <10 mm had lower success rates (7%–25%)
than longer 10 mm implants.[7]
In the present study, maximum implants failure was
seen in implants with diameter <3.75 mm followed by
implants with diameter >4.5 mm and implants with
diameter 3.75–4.5 mm. This is in agreement with the
results of Shirota et al.[8]
In the present study, mandibular posterior showed 3.3%
implants failure, maxillary posterior revealed 2.2%,
maxillary anterior showed 2.1%, and mandibular anterior
showed 1% failure rate.
We observed that Type I bone showed 0.3% implant
failure, Type II showed 1.95%, Type III showed 3%, and
Type IV revealed 0.8% failure rate. Type I is the best
bone with maximum implant survival rate.
Graph 1: Survival rate according to implant length
Graph 3: Survival rate according to bone quality
Graph 2: Survival rate according to implant diameter
Graph 4: Survival rate according to bone quality
Raikar, et al.: Survival rate of dental implants
354 Journal of International Society of Preventive and Community Dentistry ¦ Volume 7 ¦ Issue 6 ¦ November-December 2017
Renouard in 2006 conducted a structured review
based on Medline and hand search database during
1990–2005 period studies (53 studies) to evaluate the
relationship between implant survival rates and their
length and diameter. Published studies relevant to
following factors were recorded: (i) implant length and
diameter, (ii) implant survival rates, and (iii) criteria
for implant failure which were placed in healed
sites. He concluded that increased implant failure
was associated with shorter and wider implant due to
poor bone density and operator skill; however, short
or wide implant may be considered in unfavorable
site such as lesser bone density.[9] Borie et al. in the
review article concluded that length, diameter, and
connection of each implant have a degree of influence
in bone biomechanics. They also stated that despite
the influence of diameters and lengths of implant,
peri‑implant bone stress and strain should remain
within the physiological limits to avoid a pathological
overload, bone resorption, and consequent risk to the
long‑term success of implant prosthetic.[10]
Arsalanloo et al. stated that shorter implants can be used
adjunct to longer one in case of bone grafting and wider
implant used for scarce bone.[11] Busenlechner et al.
stated that smoking and periodontal conditions double
the implant failure rate.[12] Bataineh and Al‑Dakes
suggested that increase in implant length improves
implant stability even with poor bone quality.[13] Yeşildal
et al. suggested increase implant diameter over length
for success.[14] Abraham et al. found lower compressive
and tensile stresses in the peri‑implant bone in the
RP model compared to the NP model.[15] Topkaya
et al. concluded that implant length and diameter are
important in its success. They also stated that loss
of neck alveolar bone has decreased success rate.[16]
Wang et al. stated that adequate soft and hard tissues
are needed for implant healing.[17] Narrow implant
diameter has greater stress and higher failure rate than
larger implant diameter.[18] Shigehara et al. done a
study to evaluate long‑term outcome of immediately
loaded full‑arch, fixed, one‑piece prostheses supported
by dental implants and suggested immediate implant
for edentulous jaws.[19] French et al. observed longer
survival rate in tissue‑ and bone‑level implants than
tapered effect implants.[20]
It has been observed from our study that higher failure
rate is associated with smaller or wider diameter implants
but higher success can be found with increased length.
The success also depends on operators’ skill and available
bone height and quality. Hence, careful selection of case
and absence of systemic conditions help in improving the
survival rate of implants.
Limitation of our study was that it was restricted to
particular geographic location and patients reporting to
particular hospital were only included.
Further long‑term clinical studies are required to evaluate
the various risk factors and implant diameter length on
its success on different populations.
Conclusion
Age, length of implant, diameter of implant, bone
quality, and region of implant are factors determining the
survival rate of implants. We found that implant above
11.5 mm, implant with diameter <3.75 mm, implant
placed in mandibular posterior region, implant placed in
Type III bone showed maximum failures.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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© 2017. This work is published under
https://creativecommons.org/licenses/by-nc-sa/4.0/ (the “License”).
Notwithstanding the ProQuest Terms and Conditions, you may use this content
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dentistry journal
Article
Influence of Diabetes on Implant Failure and
Peri-Implant Diseases: A Retrospective Study
Alice Alberti 1,2,* , Paolo Morandi 1,2, Beatrice Zotti 1,2, Francesco Tironi 1,2, Luca Francetti 1,2 ,
Silvio Taschieri 1,2,3 and Stefano Corbella 1,2,3
1 Department of Biomedical, Surgical and Dental Sciences, Universitaà degli Studi di Milano, 20122 Milan,
Italy; paolo.morandi@unimi.it (P.M.); zotti.beatrice@gmail.com (B.Z.); francesco.tironi@unimi.it (F.T.);
luca.francetti@unimi.it (L.F.); silvio.taschieri@unimi.it (S.T.); stefano.corbella@unimi.it (S.C.)
2 IRCCS Galeazzi Orthopedic Institute, 20161 Milan, Italy
3 Department of Oral Surgery, Institute of Dentistry, I.M. Sechenov First Moscow State Medical University,
119146 Moscow, Russia
* Correspondence: alice.alberti@unimi.it
Received: 9 June 2020; Accepted: 1 July 2020; Published: 4 July 2020

Abstract: Diabetes is an important modifying factor of periodontitis, but its association with
peri-implant diseases has not been fully explored and the existing literature reports controversial
results. The aim of this retrospective study was to evaluate the influence of diabetes on peri-implantitis
and implant failure. Smoking status, history of periodontal disease, presence of diabetes, diabetes
type, therapy and glycaemia levels were collected in a total of 204 subjects treated with 929 implants,
with a mean follow-up time of 5.7 3.82 years after loading. Odds ratio (OR) for diabetes as a direct
cause of peri-implantitis and implant failure were calculated, adjusted for smoking status and history
of periodontitis. Nineteen patients were diabetic and most of them presented a good control of the
disease at the time of surgery. The overall patient-level prevalence of peri-implantitis was 11.3%.
Among diabetic patients, one developed peri-implantitis, whereas one experienced multiple implant
failures. The calculated ORs, adjusted for smoking status and periodontitis, were not statistically
significant. The results revealed no association between diabetes and peri-implantitis or implant
failure coherently with the existing scientific literature. The actual influence of hyperglycemia on
implant failure is still uncertain and new studies with larger cohorts of patients are needed.
Keywords: dental implants; diabetes; hyperglycemia; implant failure; peri-implantitis
1. Introduction
In the last few decades, the scientific evidence on biofilm-related inflammatory peri-implant
diseases has substantially increased. Peri-implantitis, which leads to progressive marginal bone
loss around implants, represents the main cause of late implant failure. A new definition has been
settled for peri-implantitis in theWorldWorkshop on Periodontology (WWP) in 2017 [1], where the
diagnosis of peri-implantitis required: (a) the presence of bleeding and/or suppuration on gentle
probing; (b) an increased probing depth compared to previous examinations (in the absence of previous
examination: PD 6 mm); (c) further bone loss as compared to the initial bone remodeling (in the
absence of previous examination: bone levels 3 mm apical of the most coronal portion of the
intraosseous part of the implant). The lack of a univocal definition in the past led to controversial
results, with reported prevalence rates ranging from 1.1% to 85% on the implant level [2] and from
0 to 39.7% on the patient level [3]. History of periodontitis and low hygiene levels are proven risk
factors for peri-implantitis, while the role of other factors, including smoking status and diabetes,
is still unclear. Diabetes mellitus (DM) comprises a group of metabolic disorders characterized by
Dent. J. 2020, 8, 70; doi:10.3390/dj8030070 www.mdpi.com/journal/dentistry
Dent. J. 2020, 8, 70 2 of 8
hyperglycemia, which is due to the impairment of insulin secretion and/or action. The incidence of
DM and its prevalence has been increasing significantly over the last few decades. The International
Diabetes Federation estimated 451 million cases of diabetes in 2017, which represent a global prevalence
of 8.4% and are expected to rise [4]. While diabetes has been proven as an important risk factor for
periodontitis, its association with peri-implant diseases has not been fully explored, and the existing
literature reports controversial results. Some hypotheses of how diabetes could interfere with implant
success have been formulated, and these include: the suppression of osteoblastic dierentiation,
proliferation and activity, deficits in the healing process, and the alteration of the immune response [5].
The primary aim of this retrospective study was to evaluate the relationship between the presence
of diabetes and the occurrence of biological complications at the implant site, namely the development of
peri-implantitis and of post-operative complications; the secondary aim was to evaluate its association
with implant survival rate.
2. Materials and Methods
The clinical records of all subjects treated with implants during the period between 1 January 2005,
and 31 December 2018 in the Dental Clinic of the IRCCS Istituto Ortopedico Galeazzi (Milan, Italy) were
screened. The following inclusion criteria were adopted for clinical record selection: (a) 18-year-old or
older patients at the time of intervention; (b) patients who gave their written informed consent for the
use of their clinical records for research purposes; (c) patients whose implants present complete clinical
and radiographical records, including at least one radiograph per year and a report of complications.
Patients lost at follow-ups were excluded from the study.
2.1. Outcomes
The primary outcome was the correlation between the presence of diabetes and the development
of peri-implantitis. The secondary outcomes were: patient—and implant-level cumulative prevalence
of peri-implantitis, patient—and implant-level cumulative implant survival rate, and prevalence of
post-operative complications that occurred immediately after the surgical intervention.
2.2. Data Collection
The following parameters were collected: gender; age at the time of surgery; ASA score; presence
of systemic diseases, smoking status, history of periodontal disease, and presence of diabetes; in case
of diabetes, diabetes type, diabetes therapy at surgery, glycaemia levels, and glycated hemoglobin
(HbA1c) before surgery, glycosuria and leukocyte formula before surgery were also registered; implant
type and characteristics (width, length); prosthesis type (fixed partial dentures, full arch fixed dentures,
full arch removable dentures); date of diagnosis of peri-implantitis; date of implant loss/removal.
The diagnosis of diabetes was formulated according to the American Diabetes Association guidelines [6].
Peri-implantitis was defined as the presence of bleeding and/or suppuration on gentle probing, together
with at least 2 mm bone resorption, evaluated through the comparison of baseline and follow-up
periapical radiographs [7]. All the clinical and radiographic records were re-analyzed to verify the
diagnosis of peri-implantitis according to the most recent definition [1].
2.3. Statistical Analysis
The Shapiro–Wilk tests served to evaluate the normality of the distribution of the variables
considered. Descriptive statistics were provided by means of mean values and standard deviations for
normally distributed variables.
The cumulative survival rate was calculated by means of survival tables. The absolute patient-level
prevalence of peri-implantitis was calculated for diabetic and non-diabetic subjects. Correlation between
baseline parameters and outcomes was provided through the use of logistic regression. Odds ratio
(OR) for diabetes as a direct cause of peri-implantitis and implant failure were calculated, adjusted for
Dent. J. 2020, 8, 70 3 of 8
smoking status, history of periodontitis, gender, age, ASA score, presence of systemic diseases, implant
type and characteristics, and prosthesis type) on survival curves. The level of significance was p < 0.05.
3. Results
A total of 204 patients and 929 implants were included. A wide range of implant systems were
used, but all of them presented an internal connection with a polygonal design. Among all included
subjects, 90 were males and 114 were females, the mean age at the time of surgery was 57.3 13.7 years,
127 had a history of periodontitis, 50 were smokers, and 18 were former smokers. The mean follow-up
time was 5.7 3.82 years, varying from 3 months to 15 years after loading. Nineteen patients were
diabetic, and most of them demonstrated a good control of the disease. Two subjects presented type 1
DM and were being treated with insulin, while seventeen presented type 2 DM; the details of their
therapy is specified in Table 1, together with the descriptive analysis of diabetes-related parameters.
Among the diabetic patients, seven received a full-arch implant-supported prosthesis, three received
an overdenture prosthesis, six fixed partial dentures, and seven were treated with multiple single
crowns; three patients were treated with both fixed partial dentures and single crowns, and one with
both an overdenture and a fixed partial prosthesis.
Table 1. Clinical parameters of diabetic patients (n = 19). Continuous variables are reported as
mean standard deviation (minimum; maximum). Discrete variables are reported as number of cases.
Parameter Values Before Surgery
Diabetes type Type 1: 2
Type 2: 17
Diabetes therapy
Diet only: 5
Metformin: 7
Insulin: 3
Sulfonylureas (glimepiride): 1
Metformin + sulfonylureas (glimepiride, glibenclamide): 3
Metformin + pioglitazone + glicazide: 1
Glycemia at surgery (mg/dL) 127.63 25.67 (91; 155)
Glycated haemoglobin at surgery (%) 6.40 0.36 (5.9; 8.0)
Glycosuria (mg/dL) 0 0 (0; 0)
Lymphocytes (109/L) 2.48 0.72 (1.47; 3.43)
Neutrophiles (109/L) 4.19 1.30 (2.40; 6.21)
A total of 23 cases of peri-implantitis (patient-level) were registered, representing an overall
prevalence of 11.3%. Only one diabetic patient (type 2) developed peri-implantitis (5.3%) whereas one
subject with type 1 diabetes experienced multiple implant failures due to a failure of osseointegration.
Figures 1 and 2 show radiographic evidence of peri-implantitis in one diabetic (Figure 1) and one
non-diabetic patient (Figure 2). Figures 1 and 2 show radiographic evidence of peri-implantitis in
one non-diabetic (Figure 1) and one diabetic patient (Figure 2). A clinical image of the same diabetic
patient is represented in Figure 3.
Dent. J. 2020, 8, 70 4 of 8
Dent. J. 2020, 8, x FOR PEER REVIEW 4 of 8
Figure 1. Periapical radiograph of one case of peri-implantitis in a non-diabetic subject.
Figure 2. Periapical radiograph belonging to the only diabetic patient who developed peri-implantitis.
Figure 3. Clinical photograph of the same diabetic patient, showing suppuration and exposure of the
implant threads.
Patient-level cumulative implant survival rate was 95.42% 10 years after surgery, which was
96.51% and 94.74%, respectively, for diabetic and non-diabetic patients, without any significant
difference. None of the diabetic patients experienced post-operative complications.
The OR for diabetes as a cause of peri-implantitis, adjusted for smoking status and history of
periodontitis, was not statistically significant (OR = 0.47 (95% C.I. 0.06–3.76)). Similarly, the
Dent. J. 2020, 8, x FOR PEER REVIEW 4 of 8
Figure 1. Periapical radiograph of one case of peri-implantitis in a non-diabetic subject.
Figure 2. Periapical radiograph belonging to the only diabetic patient who developed peri-implantitis.
Figure 3. Clinical photograph of the same diabetic patient, showing suppuration and exposure of the
implant threads.
Patient-level cumulative implant survival rate was 95.42% 10 years after surgery, which was
96.51% and 94.74%, respectively, for diabetic and non-diabetic patients, without any significant
difference. None of the diabetic patients experienced post-operative complications.
The OR for diabetes as a cause of peri-implantitis, adjusted for smoking status and history of
periodontitis, was not statistically significant (OR = 0.47 (95% C.I. 0.06–3.76)). Similarly, the
Figure 2. Periapical radiograph belonging to the only diabetic patient who developed peri-implantitis.
Dent. J. 2020, 8, x FOR PEER REVIEW 4 of 8
Figure 1. Periapical radiograph of one case of peri-implantitis in a non-diabetic subject.
Figure 2. Periapical radiograph belonging to the only diabetic patient who developed peri-implantitis.
Figure 3. Clinical photograph of the same diabetic patient, showing suppuration and exposure of the
implant threads.
Patient-level cumulative implant survival rate was 95.42% 10 years after surgery, which was
96.51% and 94.74%, respectively, for diabetic and non-diabetic patients, without any significant
difference. None of the diabetic patients experienced post-operative complications.
The OR for diabetes as a cause of peri-implantitis, adjusted for smoking status and history of
periodontitis, was not statistically significant (OR = 0.47 (95% C.I. 0.06–3.76)). Similarly, the
Figure 3. Clinical photograph of the same diabetic patient, showing suppuration and exposure of the
implant threads.
Dent. J. 2020, 8, 70 5 of 8
Patient-level cumulative implant survival rate was 95.42% 10 years after surgery, which was 96.51%
and 94.74%, respectively, for diabetic and non-diabetic patients, without any significant dierence.
None of the diabetic patients experienced post-operative complications.
The OR for diabetes as a cause of peri-implantitis, adjusted for smoking status and history of
periodontitis, was not statistically significant (OR = 0.47 (95% C.I. 0.06–3.76)). Similarly, the association
between diabetes and implant failure, adjusted for the same proven risk factor, resulted not significative
(OR = 1.23 (95% C.I. 0.11–13.30)). The results were adjusted for gender, age, ASA score, presence of
systemic diseases, implant type and characteristics, and prosthesis type: none of the above-mentioned
parameters were found to influence the development of peri-implantitis.
The small number of cases among diabetic patients prevented us from considering diabetes type
and therapy for statistical analysis.
4. Discussion
The association between diabetes and the status of peri-implant tissues has been explored
extensively in literature, with heterogenous and controversial results [8–10]. The results of our study
revealed no association between diabetes and the occurrence of peri-implantitis, finding an insignificant
OR in the examined cohort. Such results are coherent with those presented by Renvert et al. [11] who
did not find a significant OR between a history of type 2 diabetes and peri-implantitis in a cohort of
270 subjects followed-up over time. It must be noted that in the above-mentioned paper, the authors
adopted a definition of peri-implantitis that was dierent from ours, and that could, hypothetically,
be the cause of finding a higher prevalence of peri-implantitis. Conversely, Ferreira et al. [12] observed
that diabetic patients were more susceptible to develop peri-implantitis, reporting a peri-implantitis
prevalence of 24% in diabetic patients and 7% in non-diabetic patients. It must be noted, however, that
these results refer to diabetic patients regardless of their glycemic control. In fact, the authors found
a higher risk of peri-implantitis, with an adjusted OR of 1.9, for subjects with uncontrolled diabetes,
even though the latter was not clearly defined. Daubert et al. [13] also reported a relative risk of 4.1 for
peri-implantitis in diabetic patients; however, their study included only five diabetic patients, which
could have influenced the statistical analysis in both excess and defect. A meta-analysis published
by Monje et al. in 2017 [10] calculated that both the OR and RR (risk ratio) for peri-implantitis were
statistically higher in hyperglycemia than in normoglycemia; however, such meta-analysis could
not evaluate the impact of smoking and glycemic level because of the lack of information from the
included studies.
A relationship between the level of metabolic control of diabetes and peri-implantitis has
been suggested in the literature. Venza et al. [14] found that some clinical parameters, including
PD and radiographic bone loss, were significantly higher (p < 0.05) in poorly-controlled diabetic
patients (HBA1c 8%), as compared to well-controlled diabetic patients (HBA1c < 8%). The authors
thus suggested that a poor glycemic control may be involved in the modulation of periodontal
destruction and could have a correlation with the severity of peri-implantitis. On the other hand,
Gomez-Moreno et al. [15] found that higher HBA1c levels led to higher bone loss over 3 years after
implant placement, although this association was not statistically significant. The association between
elevated HbA1c levels and increased marginal bone loss had a statistically significant result in a
dierent prospective study by Aguilar-Salvatierra et al. [16]. However, in a recent meta-analysis of
seven prospective studies [17], Lagunov et al. observed that PD, BOP, and marginal bone loss showed
a significantly higher increase in type 2 DM patients as compared to healthy patients, after a period of
12 months, independently from the level of glycemic control.
Our study only included one poorly-controlled patient, undergoing multiple implant failures.
Therefore, no further analysis was possible regarding the development of peri-implantitis. In addition,
the patient presented HbA1c 8.0, but lower than 9%, which is considered as “moderately-controlled
diabetes” in some studies.
Dent. J. 2020, 8, 70 6 of 8
Our study did not reveal any statistically significant association between diabetes and
post-operative complications, but this could also be due to the small size of the DM group.
As for implant survival rates, the present study did not find any association with diabetes. A recent
review published by Oliveira-Neto et al. in 2019 [5] came to the same conclusion, reporting that
diabetes did not aect implant survival rate in two meta-analyses of high methodological quality [18,19].
Chrcanovic et al. [18] analyzed a total of 604 subjects (49 diabetic, 555 non-diabetic) and reported
an RR of 1.07, while Moraschini and Barboza [19] analyzed a total of 2334 subjects (802 diabetic,
1532 non-diabetic), with an RR of 1.43 and 3.65 for type 1 and type 2 DM, respectively; it must be noted
that in all the included studies diabetes was under control at the time of the surgery.
A weakness of the present study is the wide range of follow-up times, which was almost 6 years on
average, representing a medium-term follow-up, but reached a minimum of 3 months after functional
loading. Such short follow-up time still allows the evaluation of post-operative and short-term
complications, but cannot account for a long-term analysis. However, all the diabetic patients but one
presented a follow-up of more than 1 year, reaching a maximum of 13 years.
Another limitation of the study is that more than one systemic disease can be found in the same
patient, resulting in a confounding factor. It must be noted that the only patient undergoing multiple
early failures also had cardiovascular disease.
One of the strengths of our study is the fact that glycemia levels and glycated hemoglobin at surgery
were recorded, which could allow the detection of the association between the level of compensation of
the disease and the occurrence of early complications. Although the small number of diabetic patients
prevented us from performing a specific and “powerful” statistical analysis, the registration and
analysis of these parameters are fundamental for further meta-analysis of similar studies. Moreover,
it should be noted that the only patient with poor glycemic control (HbA1c 8.0%) experienced multiple
implant failure with a lack of osseointegration. Interestingly, some previous studies reported higher
rates of early implant failures in diabetic patients [20,21], and one prospective study by Ghiraldini et al.
observed that hyperglycemia negatively aected the implant osseointegration [22]. However, a recent
meta-analysis by Shi et al. [23] found no significative association between diabetes and implant failure
in patients with both good and poor metabolic control.
A possible limitation of our study could be that glycemia levels and HbA1c were not registered at
follow-up visits, preventing us from disclosing a possible association between glycaemia levels
in diabetic patients and long-term complications. Even though glycemia levels and HbA1c
measured immediately before surgery could be of great relevance in relations to early complications,
the subsequent changes in the level of metabolic compensation could be not easily controlled over
the years and may influence the onset of long-term biological complications. Hence, further studies
analyzing the association between glycemia levels before and after surgery, and the occurrence of
biological complications and implant failure, are needed.
It should be underlined that the definition of peri-implantitis proposed by Heitz-Mayfield et al.
in 2014 was used in this study, but all the included records were re-analyzed during data collection
and the cases of peri-implantitis were confirmed in light of the new definition settled in the WWP in
2017 [1].
5. Conclusions
The actual influence of DM and hyperglycemia on peri-implantitis and implant failure is still
uncertain and new studies on larger cohorts of patients are needed also taking into consideration
further parameters, such as HbA1c baseline and follow-up values, baseline and follow-up diabetic
therapy, and duration of diabetes. Future studies are needed to investigate the relationship between
the long-term changes in glycemia and HbA1c levels and the health status of peri-implant tissues.
Monitoring the main parameters of glycemic control is desirable not only for research purposes, but
also for clinicians, since poor metabolic control may lead to complications such as increased risk of
Dent. J. 2020, 8, 70 7 of 8
infections. Within the limitations of the present study, our results confirm that implant therapy in
diabetic patients with good glycemic control should be considered a safe and viable treatment option.
Author Contributions: Conceptualization, A.A., P.M. and S.C.; methodology, S.C., L.F. and S.T.; validation, S.C.,
L.F. and S.T.; formal analysis, S.C.; investigation, A.A., P.M., B.Z. and F.T.; data curation, A.A., P.M., B.Z. and F.T.;
writing—original draft preparation, A.A., P.M., B.Z. and F.T.; writing—review and editing, S.C.; supervision, S.C.,
L.F. and S.T. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
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