Table 1.

Subgingival microbiome affected by diabetes mellitus.

References	Study design	Population	Test for microor ganisms' detection	Key microbiome	Glycemic parameters	Periodontal parameters	Diabetes type	Outcomes in diabetic groups	Conclusions
Matsha et al. [52]	Case-control study	128	16S rDNA sequencing	Firmicutes, Proteobacteria, Bacteroidetes, Fusobacteria, Actinobacteria, Spirochaetes, Synergistetes, Chloroflexi, and Tenericutes	Plasma glucose and HbA1c	BOP, PD	2	↑Firmicutes, ↑Proteobacteria, ↑Bacteroidetes, ↑Fusobacteria, ↑Actinobacteria.(DM with gingival bleeding)↑Bacteroidetes ↓Actinobacteria	Oral microbiota changed in different glycemic statuses and stages of periodontal disease
Joaquim et al. [71]	Case-control study	100	Quantitative real-time PCR	Porphyromonas gingivalis,Tannerella forsythia, Treponema denticola, Eubacteriumnodat, Parvimonas Micra, Fusobacterium nucleatum ssp., and Prevotella intermedia	HbA1c, fasting plasma glucose	Visible plaque accumulation, BOP, marginal bleeding, PD and CAL	2	There were no differences between groups	Subgingival levels and bacterial prevalence are not significantly different in chronic periodontitis presenting DM, smokers, or smokers with DM.
Demmer et al. [70]	Cohort study	152	Human Oral Microbe Identification Microarray	Actinobacteria and Proteobacteria	HbA1c, fasting plasma glucose	BOP, PD, and CAL	2	18 taxa associated with inflammation. 22 taxa associated with insulin resistance	Inflammation: ↓Actinobacteria ↓Proteobacteria ↑Firmicutes and TM7
Saeb et al. [59]	Cross-sectional case-control	44	16S rRNA profiling	-	Fasting plasma glucose	PPDand CAL	2	↓Phylogenetic diversity	Pre-diabetic subgingival microbiota associated withreduced phylogenetic diversity
Long et al. [62]	Case-control study	294	16S rRNA gene sequencing	Actinomyces and Atopobium	History of diabetes	-	2	↓Actinobacteria phylum	The oral microbiome may play an important role in the diabetes etiology
Babaev et al. [72]	Cross-sectional	74	16s rRNA sequencing	P. gingivalis, T. forsythia, A. actinomycetemcomitans, T. denticola, P. intermedia, F. nucleatum/periodontium, and P. endodontalis	-	-	2	↑Porphiromonadaceae, Fusobacteriaceae (Combined pathology) ↓Sphingobacteriaceae bacteria (Periodontitis)	The metagenomic analysis confirmed the microbiota pathogenic role in combined pathology
Ganesan et al. [61]	Cross-sectional	100	16S rRNA gene sequencing	Fusobacterium, Parvimonas, Peptostreptococcus, Gemella, Streptococcus, Leptotrichia, Filifactor, Veillonella, TM7 and Terrahemophilus	HbA1c	CAL, PD, Mean gingival index	2	Diabetic microbiomes exhibited significant clustering based on HbA1c levels	Diabetics and diabetic smokerswere microbially heterogeneous and enriched for facultative species
Bachtiar et al. [73]	Cross-sectional	12	16S rRNA amplicon sequencing	Phorphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Aggregatibacter, Fusobacterium, and Veillonella	-	-	2	↑Tannerella forsythia in subgingival biofilms (DP group of the red-complex bacteria. ↓Aggregatibacter	Classic periodontopathogens diversity increased in the subgingival niche of periodontitis subjects with diabetes
Shi et al. [27]	Cohort Study	32	Metagenomic shotgun sequencing	Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, Fusobacterium nucleatum, Campylobacter rectus, Prevotella intermedia, Prevotella nigrescens	HbA1c	Gingival index and recession, attachment level, pocket depth and bleeding on probing	2	Butanoate metabolism pathway was enriched in the periodontal healthy state in Type 2 DM, but not in No Diabetes.	Type 2 DM patients are more susceptible to shifts in the subgingival microbiome toward dysbiosis in developing periodontitis.
Farina et al. [68]	Case-control study	12	High-resolution whole metagenomic shotgun sequencing	-	HbA1c	Bleeding on probing	2	↑Anaerolineaceae bacterium oral taxon 439 in diabetic subjects with moderate to severe periodontitis vs patients without history of periodontitis.	The presence of type 2 Diabetes Mellitus and/or periodontitis were associated with a tendency of the subgingival microbiome to decrease in richness and diversity.

rDNA, recombinant deoxyribonucleic acid; HbA1c, hemoglobin A1c; BOP, bleeding on probing; IL-6, interleukin 6; IL-17, interleukin 7; RANKL, receptor activator of NF-κB ligand; DM, diabetes mellitus; PD, probing depth; CAL, clinical attachment loss.